From 4c8da722464b9e8a46a71086ef9418470c4515f0 Mon Sep 17 00:00:00 2001 From: elisno Date: Mon, 1 Jul 2024 15:12:30 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@7a801c5ee1e11be3732a7ea01725de8aca8d147d --- master/.buildinfo | 2 +- .../cleanlab/benchmarking/index.doctree | Bin 3248 -> 3248 bytes .../benchmarking/noise_generation.doctree | Bin 81345 -> 81345 bytes .../.doctrees/cleanlab/classification.doctree | Bin 290603 -> 290603 bytes master/.doctrees/cleanlab/count.doctree | Bin 283717 -> 283717 bytes .../.doctrees/cleanlab/data_valuation.doctree | Bin 26578 -> 26578 bytes .../cleanlab/datalab/datalab.doctree | Bin 174487 -> 174487 bytes .../guide/_templates/issue_types_tip.doctree | Bin 4354 -> 4354 bytes .../guide/custom_issue_manager.doctree | Bin 31452 -> 31452 bytes .../guide/generating_cluster_ids.doctree | Bin 6318 -> 6318 bytes .../cleanlab/datalab/guide/index.doctree | Bin 12087 -> 12087 bytes .../guide/issue_type_description.doctree | Bin 250944 -> 250944 bytes .../cleanlab/datalab/guide/table.doctree | Bin 63584 -> 63584 bytes .../.doctrees/cleanlab/datalab/index.doctree | Bin 5445 -> 5445 bytes .../cleanlab/datalab/internal/data.doctree | Bin 105136 -> 105136 bytes .../datalab/internal/data_issues.doctree | Bin 77301 -> 77301 bytes .../cleanlab/datalab/internal/factory.doctree | Bin 64553 -> 64553 bytes .../cleanlab/datalab/internal/index.doctree | Bin 4573 -> 4573 bytes .../datalab/internal/issue_finder.doctree | Bin 46989 -> 46989 bytes .../_notices/not_registered.doctree | Bin 3440 -> 3440 bytes .../issue_manager/data_valuation.doctree | Bin 79832 -> 79832 bytes .../internal/issue_manager/duplicate.doctree | Bin 75245 -> 75245 bytes .../internal/issue_manager/imbalance.doctree | Bin 68346 -> 68346 bytes .../internal/issue_manager/index.doctree | Bin 5282 -> 5282 bytes .../issue_manager/issue_manager.doctree | Bin 80662 -> 80662 bytes .../internal/issue_manager/label.doctree | Bin 88614 -> 88614 bytes .../issue_manager/multilabel/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/multilabel/label.doctree | Bin 79258 -> 79258 bytes .../internal/issue_manager/noniid.doctree | Bin 90556 -> 90556 bytes .../internal/issue_manager/null.doctree | Bin 68181 -> 68181 bytes .../internal/issue_manager/outlier.doctree | Bin 78825 -> 78825 bytes .../issue_manager/regression/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/regression/label.doctree | Bin 108542 -> 108542 bytes .../underperforming_group.doctree | Bin 114895 -> 114895 bytes .../datalab/internal/model_outputs.doctree | Bin 78458 -> 78458 bytes .../cleanlab/datalab/internal/report.doctree | Bin 34190 -> 34190 bytes .../cleanlab/datalab/internal/task.doctree | Bin 57819 -> 57819 bytes .../datalab/optional_dependencies.doctree | Bin 3451 -> 3451 bytes master/.doctrees/cleanlab/dataset.doctree | Bin 100920 -> 100920 bytes .../cleanlab/experimental/cifar_cnn.doctree | Bin 407995 -> 407995 bytes .../cleanlab/experimental/coteaching.doctree | Bin 48525 -> 48525 bytes .../cleanlab/experimental/index.doctree | Bin 5365 -> 5365 bytes .../experimental/label_issues_batched.doctree | Bin 158466 -> 158466 bytes .../experimental/mnist_pytorch.doctree | Bin 555175 -> 555175 bytes .../experimental/span_classification.doctree | Bin 34890 -> 34890 bytes master/.doctrees/cleanlab/filter.doctree | Bin 94218 -> 94218 bytes .../.doctrees/cleanlab/internal/index.doctree | Bin 4532 -> 4532 bytes .../internal/label_quality_utils.doctree | Bin 19410 -> 19410 bytes .../cleanlab/internal/latent_algebra.doctree | Bin 85348 -> 85348 bytes .../internal/multiannotator_utils.doctree | Bin 46750 -> 46750 bytes .../internal/multilabel_scorer.doctree | Bin 183513 -> 183513 bytes .../internal/multilabel_utils.doctree | Bin 34042 -> 34042 bytes .../cleanlab/internal/neighbor/index.doctree | Bin 6725 -> 6725 bytes .../internal/neighbor/knn_graph.doctree | Bin 111899 -> 111899 bytes .../cleanlab/internal/neighbor/metric.doctree | Bin 38404 -> 38404 bytes .../cleanlab/internal/neighbor/search.doctree | Bin 32456 -> 32456 bytes .../cleanlab/internal/outlier.doctree | Bin 29778 -> 29778 bytes .../token_classification_utils.doctree | Bin 69171 -> 69171 bytes .../.doctrees/cleanlab/internal/util.doctree | Bin 212686 -> 212686 bytes .../cleanlab/internal/validation.doctree | Bin 41565 -> 41565 bytes .../cleanlab/models/fasttext.doctree | Bin 2465 -> 2465 bytes .../.doctrees/cleanlab/models/index.doctree | Bin 5009 -> 5009 bytes .../.doctrees/cleanlab/models/keras.doctree | Bin 106237 -> 106237 bytes .../.doctrees/cleanlab/multiannotator.doctree | Bin 165197 -> 165197 bytes .../multilabel_classification/dataset.doctree | Bin 67275 -> 67275 bytes .../multilabel_classification/filter.doctree | Bin 86794 -> 86794 bytes .../multilabel_classification/index.doctree | Bin 4916 -> 4916 bytes .../multilabel_classification/rank.doctree | Bin 47085 -> 47085 bytes .../cleanlab/object_detection/filter.doctree | Bin 38032 -> 38032 bytes .../cleanlab/object_detection/index.doctree | Bin 3852 -> 3852 bytes .../cleanlab/object_detection/rank.doctree | Bin 149811 -> 149811 bytes .../cleanlab/object_detection/summary.doctree | Bin 166920 -> 166920 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 98688 -> 98688 bytes master/.doctrees/cleanlab/rank.doctree | Bin 113711 -> 113711 bytes .../cleanlab/regression/index.doctree | Bin 3738 -> 3738 bytes .../cleanlab/regression/learn.doctree | Bin 222189 -> 222189 bytes .../cleanlab/regression/rank.doctree | Bin 19815 -> 19815 bytes .../cleanlab/segmentation/filter.doctree | Bin 28604 -> 28604 bytes .../cleanlab/segmentation/index.doctree | Bin 3788 -> 3788 bytes .../cleanlab/segmentation/rank.doctree | Bin 51266 -> 51266 bytes .../cleanlab/segmentation/summary.doctree | Bin 69021 -> 69021 bytes .../token_classification/filter.doctree | Bin 27210 -> 27210 bytes .../token_classification/index.doctree | Bin 3934 -> 3934 bytes .../token_classification/rank.doctree | Bin 60167 -> 60167 bytes .../token_classification/summary.doctree | Bin 79564 -> 79564 bytes master/.doctrees/environment.pickle | Bin 16374777 -> 16374755 bytes master/.doctrees/index.doctree | Bin 42636 -> 42636 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28116 -> 28116 bytes .../tutorials/clean_learning/tabular.ipynb | 130 +- .../tutorials/clean_learning/text.ipynb | 1758 ++++---- .../nbsphinx/tutorials/datalab/audio.ipynb | 1324 +++--- .../tutorials/datalab/datalab_advanced.ipynb | 286 +- .../datalab/datalab_quickstart.ipynb | 178 +- .../nbsphinx/tutorials/datalab/image.ipynb | 4000 ++++++++--------- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 172 +- .../tutorials/datalab/workflows.ipynb | 1186 +++-- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 584 +-- .../nbsphinx/tutorials/indepth_overview.ipynb | 210 +- .../nbsphinx/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- .../nbsphinx/tutorials/object_detection.ipynb | 186 +- .../nbsphinx/tutorials/outliers.ipynb | 574 ++- .../nbsphinx/tutorials/regression.ipynb | 202 +- .../nbsphinx/tutorials/segmentation.ipynb | 1044 ++--- .../tutorials/token_classification.ipynb | 166 +- .../tutorials/clean_learning/index.doctree | Bin 3019 -> 3019 bytes .../tutorials/clean_learning/tabular.doctree | Bin 60765 -> 60765 bytes .../tutorials/clean_learning/text.doctree | Bin 230171 -> 230173 bytes .../.doctrees/tutorials/datalab/audio.doctree | Bin 333645 -> 333649 bytes .../datalab/datalab_advanced.doctree | Bin 203507 -> 203507 bytes .../datalab/datalab_quickstart.doctree | Bin 142186 -> 142186 bytes .../.doctrees/tutorials/datalab/image.doctree | Bin 514366 -> 514366 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3367 -> 3367 bytes .../tutorials/datalab/tabular.doctree | Bin 120657 -> 120657 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 149929 -> 149929 bytes .../tutorials/datalab/workflows.doctree | Bin 402435 -> 402441 bytes .../tutorials/dataset_health.doctree | Bin 325916 -> 325916 bytes master/.doctrees/tutorials/faq.doctree | Bin 199351 -> 199353 bytes .../tutorials/indepth_overview.doctree | Bin 220325 -> 220325 bytes master/.doctrees/tutorials/index.doctree | Bin 3139 -> 3139 bytes .../tutorials/multiannotator.doctree | Bin 137334 -> 137334 bytes .../multilabel_classification.doctree | Bin 64488 -> 64488 bytes .../tutorials/object_detection.doctree | Bin 140181 -> 140181 bytes master/.doctrees/tutorials/outliers.doctree | Bin 104189 -> 104189 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 17310 -> 17310 bytes master/.doctrees/tutorials/regression.doctree | Bin 106940 -> 106940 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 1994473 -> 1994473 bytes .../tutorials/token_classification.doctree | Bin 176679 -> 176643 bytes .../tutorials/clean_learning/tabular.ipynb | 2 +- .../tutorials/clean_learning/text.ipynb | 2 +- master/_sources/tutorials/datalab/audio.ipynb | 2 +- .../tutorials/datalab/datalab_advanced.ipynb | 2 +- .../datalab/datalab_quickstart.ipynb | 42 +- .../_sources/tutorials/datalab/tabular.ipynb | 2 +- master/_sources/tutorials/datalab/text.ipynb | 2 +- .../_sources/tutorials/dataset_health.ipynb | 2 +- .../_sources/tutorials/indepth_overview.ipynb | 2 +- .../_sources/tutorials/multiannotator.ipynb | 2 +- .../tutorials/multilabel_classification.ipynb | 2 +- .../_sources/tutorials/object_detection.ipynb | 2 +- master/_sources/tutorials/outliers.ipynb | 2 +- master/_sources/tutorials/regression.ipynb | 2 +- master/_sources/tutorials/segmentation.ipynb | 2 +- .../tutorials/token_classification.ipynb | 2 +- master/searchindex.js | 2 +- master/tutorials/clean_learning/tabular.ipynb | 130 +- master/tutorials/clean_learning/text.html | 18 +- master/tutorials/clean_learning/text.ipynb | 1758 ++++---- master/tutorials/datalab/audio.html | 2 +- master/tutorials/datalab/audio.ipynb | 1324 +++--- .../tutorials/datalab/datalab_advanced.html | 4 +- .../tutorials/datalab/datalab_advanced.ipynb | 286 +- .../datalab/datalab_quickstart.ipynb | 178 +- master/tutorials/datalab/image.html | 58 +- master/tutorials/datalab/image.ipynb | 4000 ++++++++--------- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 2 +- master/tutorials/datalab/text.ipynb | 172 +- master/tutorials/datalab/workflows.html | 416 +- master/tutorials/datalab/workflows.ipynb | 1186 +++-- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 6 +- master/tutorials/faq.ipynb | 584 +-- master/tutorials/indepth_overview.ipynb | 210 +- master/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 98 +- master/tutorials/object_detection.ipynb | 186 +- master/tutorials/outliers.html | 6 +- master/tutorials/outliers.ipynb | 574 ++- master/tutorials/regression.ipynb | 202 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 1044 ++--- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 166 +- versioning.js | 2 +- 177 files changed, 12902 insertions(+), 12550 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index 58a9beebf..458c93dd1 100644 --- a/master/.buildinfo +++ b/master/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 79a0ee5757b393a76301f094f1add0ae +config: 1765f9fb54b06ed20817275083d0b26b tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index f8f194bdf103f2deee27748813bb2a8623556c38..93d5f99460c48bfa6c14eff88268949651abe628 100644 GIT binary patch delta 117 zcmdlWxj}M6IHO^5RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Yc~Xk8Nt%(x<_^XHPBOG{axZ5D0F_lFGXMYp delta 117 zcmdlWxj}M6IHO^PMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Yv1M{fYD!|t<_^XHPBOG{axZ5D03Qh=p8x;= diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index ad176bd5f293cdb9db113a09652377f76a6e61db..fdc1c61a59781a7ecdce1df46a1e0b6a22624be2 100644 GIT binary patch delta 1464 zcmX^3o8{ndmJRWYhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!eVFtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= Opm$ zrY6Rg$tkHRi7A^WFtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= O0+Y7e%sbbEqx{bk+kf{z;0qDPaLmSaRyg4}1b3T5poRiu3Rl=9c zbTc&Hn~Z5Y&4I;-9Mw)O6##Q-)!b?7-@Gu_yApx5Z+_U}n!8s`nEQI|60DtFA4Lv> z)#up=3nK@?(o(L%?xRIhRT8tosVP}tEvMZf^gvsF@Y!W6%vsfie zewoce0GZ^FJb|G`9t#7|Kih-e?P6bGvUd;bhRIC@>^MxW*vqD1a{mF=0e4(J4Zj$c#8`Y*BL#rWO{#u`+&!fHEEj$&#L*W z?RW(aG1JIxwC3f7;5BWP;UMX95_y$aIjHXn|DFP7pv7yvcPA$HiJ5G!iLPayMH>$| zWhP|P9pfo<<}n{Tf!WlFK^x1U&@#V{PQ<3e)fgX6Lu;5{=MHh_7mt7jBn?_q0rA#> z%n?zil1Xn>WlT-fMMxJXZKNuf?Fm?sEa)h*8UIgINOL(#E%H^QOO1HCPtk;)s-{yo zUl~UpiIxpXo3qHWu#$*{{c1H$c$A4|u$h)a4Mhz_xrsS6kZi$0AU(+d&l3tRWN?l0 a^VACttadla^rsKSFSdTtN&e`MIe!DBNH8t{ delta 4197 zcmbuC%}Z2K7{)p0-tnuMMia*rAqu50Nyl-_$%F)>=tF0;2F*;J4>KJm%Yv*$qDB#d z!4*ymAz_df8dx`ZTeNW%En5_n(;!$1g+qZLiHpunskZaXAMiZC_j&Jo-gE9$ENd#3 zHPgcEU6pNZ-Qg>3B|eAG;p%i$lsQVBWu2Aw&I-TZS?O}xeGY$lhqK(_@&|%}j(|SG zJ^(Up_oriGyh*icF6%aZMH%xXj{fJtyf+|uoxbXgE0|PU^=4}mOD4U)MM;4y8ejNg zHc08!gZ=GT*V}dPodr|kR552(?b^U_NLL4|0rX$JW*xSIczs~F_k7}7IVZD;tAr~tXx{n}O{ID|K0yfidsHK>uC^s>OMv^T!2&5+&;CWKPg$!<` a!UFYz1*_doGX3d8iHnV&bdo>%qqg6R0{cM# diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index 81cf26975d452b91b67cf08029a83984ae4bb002..1104d7f4dd08d21d253dba8d7475b338a0e6e37d 100644 GIT binary patch delta 3571 zcmX@QLh$Gc!3~~_hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!f^74c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL3O)@>C4 delta 3571 zcmX@QLh$Gc!3~~_h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A__74c{!F*1<%ktqPjlEM#iktQ$M42)VYd zv0g!L2;Xt|!c2PPY-V)*Lat+LJ$94p*aN<8WH=U_e$NHYA=lPVVdu%U)iZhvxmsJ} zzmS`f7?T>vjU3KYTXMCYOHU!!ue-8ekrz4kn?(w>cu7ycz$9V6d0pMSAhNXTPxe!n zoE{s+D7yL9#oL_Zo3UB%j+H&>+NTFTX0(`YAjQbO{f0E-EixQ{WP*|!;}K)h-2ebn8myXJ%@`$MV)j5 KwgbCfElL0uF+dvt diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index 9251754fda1d9d17e4de594694e6e5e481ead78a..e8a6af59b023745420fbfc224399dad4ae89b462 100644 GIT binary patch delta 477 zcmca~p7GLo#tqSohRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!HM7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aMpm$ zrY6Rg$tkHRi7AtN7;liFt5MKl^HruDOyp^u{ExMPES-Uy*j-u3(>aMasVis$tEmwjZz5D`a+y`~WaR z3DvO$+nKzL8!gmWut`S383X8Syp1*uW$WJ7zg`OTA zK(bnBU`$7?f$=zMy_%Rst_fi9^$+ThC2BRiZ zzz%($M;V4kX2~UR;-+97{LeR%2EVpdz-~(4Mf{V?B)Ew->|9itD*|V5ZjD?7<$UiilG%ZI z&Haa|gX@$|AdgsaTGk7UY^8Tg@DBFy=n!v*U$Lk_=&9U+5Tz~j>tT!8o8 iq_fh-rDpB8fS*2~5n$iAfP0Z$TmK`!oy*g-js633np9T+ delta 4228 zcmbuC-Aj{E9LG6l&D@suWH07LWC^|9_%gSiMM#rEY8z^8WRT3~*-JWcNNTc@UXUat zL-sHp2*hQW#42KkI6>G=QH{)8yOQjx8@-Y4i|9Q07wqdF@cDgy-{12)XJ>lBsu!#; zeWboy4hCcKo?xwNSM9E--PvHTb2LQd`lvG$a>y=6y=o6NDvn0GD-@1|l`y+WegK%J zbTzX@)9I4pjRvYMnj|e{?FZ<5qLXPh>lO%AS)C-C0aWQ~Wvjc7l|krvma6%HG^})W zvb7^E$06%1K#AdMZKLIEM(IZS%_meg741Ow$w2RQM<~>^%5L{QK&_AZdQt0izl>UA zSAD4U+>jTw9vvPAtSq1SiyGAtH(=C^b#UIgYXa7RWW%g&bQ!ssNVbQ)8~=%9HPBNN z*O9CS>Yt3FR(~>qTCb!ssP&ot7`3iFG@G^{;=w9-$rd$xSdbFlo z89VfO0eQTTtcS((3Tlq$%h{#E?@}=H(#l6GhH6VI602EV;TW#BJ|{LlmL|S!SOG2y z1ZqIGI!`Ww6W0Z6;eWo7EcmsJ0%l$MF5;h9CV@?~VdkRBd<8gzv+LvnDCc{Bk=zc< ztM5NdEj&u;H1dcQr)8tS_*QzS3h!VKpE^KGz%R%}gipJ0xmmAv(^B4llGbiw24*g* z4D5v3@Ny|YbJ)%9)djnc4|LE9?C5%vPb+y0Y|MBJ+UAGh;ohyTM5_o*-g7foh iH)uvGUaGs30)BFz#(;g}0vpm$ TrY6Rg$tkHRi7A^K8KWx!+@cjP diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index b0fc759b0ad6abc57ef047eee30e649f40835d89..e52cad49f484948afbb5aa68d0f69e04e710b123 100644 GIT binary patch delta 62 zcmZ2yxXy4xHlty3RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<~fW);sB4<6P^G7 delta 62 zcmZ2yxXy4xHltyNMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<~fW);s9!664U?y diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index f2a0535ac0b16b3c495adbbf5e0be42c8aec034e..d422e090d52c584eb6252a07164dd0f960ea1ed3 100644 GIT binary patch delta 62 zcmdlUw>@q{G^1g1RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<{rk?x&WrI6qx`3 delta 62 zcmdlUw>@q{G^1gLMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<{rk?x&VPa6VCtu diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index dd8836aa5b2c0259c4cb5e73d7226ea482ce005b..2b90d0ad8b2cd1f5e54c5615ce05c74280c48554 100644 GIT binary patch delta 81 zcmX@Gg8#q@{tc;&M#)vlNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( j$*JZ^DaIygMi%;$V+E9(cQLl_VgzBP?YkJ6+b#hBapm$ jrY6Rg$tkHRi7EP%V+E9(cQLl_VgzBP?YkJ6+b#hBKh+v) diff --git a/master/.doctrees/cleanlab/datalab/guide/table.doctree b/master/.doctrees/cleanlab/datalab/guide/table.doctree index 8f6c583f4ebd40589f7e0cd0550830cb4e820208..342a628ce41b2b010587263defc39b6327ce4133 100644 GIT binary patch delta 64 zcmaFxf%(A)<_)J=4U?;qlgbJzEcH{EKLoQjFU|g UO-+m~lT%Vt5>qz+VNLl20Q=__u>b%7 diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index 07b0c63136ac81a7b1820d1123153f46d453bf01..fe525169401a594f27e0d1dde56e91cd6298dd0b 100644 GIT binary patch delta 117 zcmX@AbyRDEFQZ{{RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Yc~Xk8Nt%(x<~qjhf@Ek*6TZ&~0L!2ycK`qY delta 117 zcmX@AbyRDEFQZ|GMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Yv1M{fYD!|t<~qjhf@Ek*6TZ&~098~Y;{X5v diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index 2698fc2f59a936c2b53dee856efa374c00be11ca..d118cebaaf35b2cb7c5faea9f6cab748b266daa1 100644 GIT binary patch delta 5702 zcmbtYZD?C%6y|ihX}6@eX-a8>bLmF9ZmXK6Hf_3fw6iViSY1QQ!dSPoO_or#>%MI0 zx@Mi|CdypvUggSktUvZgm|8h(?~hF!=r2Kt;1Cs5P*BEdMJb3M!}sP6>F?)-`}g@c z=RD`$!(F)HT2Rj}3a6%0$?UPo)=)Z=3P+op8XF?%k&(lZWOFK#4mUNlWTGvpWHOa* zNgjT(DbpAgle7q+%Io*?Pet>Hpn_U=-=s3(Gmc}*!d+Rxm9A^6VR#kyx%Y$Sm3kHL zDBWKKt5@@5-XUDQnm78M1=ZPIDON$ok8_8gF=&=AKY9eKH_Pt_dazovd}ZT7RCPXh z2vxmS*9NqS%j@64@|@tdhIKAzrU`K|TEa`sUaUz_IQx3VY7GO?*13B?%D7qxb$j%JD$U)ZVQ_tbim*b2B&fFrQ+;}DLzZwhih;chPwCg z{P1}+1wZ&ad^3~MfxkOegeJgN-#)%oLtV^f*N6uvzX7|b3=|mNitpju6A?5KissU3 z7r#7}Ljz$O3C!59gU7|6Gk&qYt4lPT8N$orz|11pSDrifbup+sch2D7Ut5I+LcvA; zJCiTWUI%Bvx8LXuVdnY5r?>nVR5-@tAliG+V|~;~8~Cg5|3VY=kfTCetuy%4kEQTS z-SOh^Jn{1|I0xaqXL8N`7qPoyK$y!WNB-Cc28qq(87#tmVz|bzUTURLp8B_X4Kz@P zqpFy&+ok}Qua?k+sUffM6_!;+JsKQ+8z^AqJLwnsoSz1_ECnf}Q-s2oS=0TrLvE_0yBZ;tXG1iq<3!=TG8d*CY9e2|R;o>D z*T7p(@1$igp-SF{msnpuMFScf3%e;GRm%I(SlC7sNl@l@;Q_assYqtgZ2xa!v6o1* z5|8zFkT%QfF={~r_Rx+;p6aJ9B^uMI=_S*;7@|5kGDuM!I|ZiGVcIEA9HJgPw1#X? zQ9+Kg{Sfy5;j&(j#b5Dtl@NXjhr V6=;`@uTanfC33G+|6{*h{T~Jl{{R30 delta 5702 zcmbtYZERCz6wVpkZqV+o>#}T(NC)W{I<@OcM++kxZVMw!)@-364BBnont@|{xn;o0 zAP7c-Z18HXL5Tao9~0D>6M27#kwkx)nAAjK)I?1*(Gcd0W{L5G_}*S(_WOC0`}g@c z=RD`$lRN)`YhFD&C_EBNrw@%COos*|gOPYP($pAfh&E9vT=Dle7S!%IkOYPepTxpn_WWUZ*nQGmc@(!d+Rxm9CpBVR#jHyZ3_Tm3kF# zE8SZJt5@?=-ZZXW&Fg)KKy`LkiWQLYW8CUz3_8Kr9zTrLJHhV;IF%-UaURGb|!#b>Dpa19Pax?>m5 z^9qPcq7 z#V?)8p@FcC1g7oR!PDZ;X}?(8-Yy!>r17#iFuefwm1i$}T?{JEUNHFgS685cP;im| z&g4rox4>EOo!7cTn0dbR>4$y{DjefU5beG1u|8^}b^O)$f1wF_#8DyMsx$cHkEQTS z-SOh^Jo9ruI0xaqZ*t9p^VnU{E6gR6qkrrHgT&_2G#24*(O+X&FUDzvPyJiH3L2=x zQB_RXZBu~DS4wEy)R0&B3d^dZP7RLkbri7jZS;$L-cNm4GM#29rl35shLYGomywLE zrTrRCq;m2Ywj0h|7K4=4DMI1Ptf?N_CO6d4J&lmcvmqMMaiZ{EnF~`6HIc7fE!C#9 zXyC2qw$l=rP$h4}ORO)SrCtq=`JEJyD&;+BENml+Bq;Mc@POORR3tNKw*NP=&_$$K ziO2doNE_v?1Z_qG_Q;M$p6sC~N;IZZ(?zCrIYf1GppRlYb_z_V!?ax)xH3lA&CxCLwTAWaaVnR&BQ%WX#UZh;m%_y$XHV#?z%n*D zGfqeGWmv{!UqteqNu8}Iu=%Wif+~iiQ#xDWFv%@vXd}KA1rnEBxJaY;LO3LrA}O=f VSD-~UzDz+6l*rvu{g3^2<$p~1x>^7L diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index 3702b821cba652d6efa668ea7f81de15bea01646..424432460971baaa99109eef670b10e66d82f357 100644 GIT binary patch delta 2705 zcmex*o8{|mmJOkdhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!gf7@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEqpm$ zrY6Rg$tkHRi7A`g7@5e>HrY`zX0s@BKNsmXg0xOHP=X_z|&4K&}naR_>dA=|+ z3t3vzCp*XpZ}ydlCeP;d%?>gb$gp{{qhb;}Sq{$G?4apMUexAnR@3fNAkWsxYTA60 z<9$5H(hUye$-WZ2o8x_tvyyFu#O7ez*4z!NE|ncciQiRnWy#EBz{r!>{ITE!CAMc4|0mP$o63vGw^(Ge zTxAR;NnWdd85yCrnX!2{8QLZ@HaBlBXjdYmL<4Fq*lgTW&rL@1W^B%wyr7MH@`+pO zn`>v7krUcbBQ}T6nnX_Jwee8l=I*7Z$?-Qx>yK3(;-vd~^TtCP$VnO-57liJILRYO zx{Z?s{}oJrf0JkO$y@))*S)#)4wC>`+G{8Cec;(__|}=6fEN4*(XRJt6B&tYv*5qy zoMbo}n6ReH@Gy#QU&g_Bmz)v=Xux)$0c2M9vD58^7)2*X%o5&iBEqpm$ zrY6Rg$tkHRi7A`w7+aV~*Eac~VZr1D;sTpDv-k*+t-YB{omR5Ko3Be8VkS@fW-D1+ z7P7SVY&KCcBv0$a&FboIWW*0JpeJtD*Stwy98KJON+*z9Tkq;!Cd1av4-L1llNGv^ zo9(T7GRV?exH)&R3mbV_C+9Ba-JG!MlQJ3F1s{}c{&`M`oFoC%I{D{0$<4Pfv+|H` i^Jc*Ze_2V_Hd*jN-{!N=428+i3d!;Oo7erSW&{9*I%*sM diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index 8ebea8705c65eeaa2b77357751e570200ed08aee..eb71b5ae0ff9e40578881ff3ef86a1666b394d5c 100644 GIT binary patch delta 122 zcmcbsd{=pcKciuCRdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN fc~Xk8Nt%(x<_5;O%tj<@)1Ul+U21bCYY7hklFug2 delta 122 zcmcbsd{=pcKciuWMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV fv1M{fYD!|t<_5;O%tj<@)1Ul+U21bCYY7hk6rUwJ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 747a5c4f05f74bdbc038c813a4e4ec484cd95383..0798f67a1abf0e54d75d6aaffd9807fa70bc2ea0 100644 GIT binary patch delta 1125 zcmeBu&(!;#X+t=pVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;Uc?MoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8~14uW2&? delta 1125 zcmeBu&(!;#X+t=pVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?+YMoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8}}!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<}#*VTmYSN6omi) delta 62 zcmew$^+9TbE0bY{MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<}#*VTmX0f6T1Ka diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree index 38b783c3c404f63db360903676d6d8358e4695c8..3db389d8560ca69ae6952ad1e66908233cbfdf05 100644 GIT binary patch delta 2628 zcmccdp5?}SmJQL2hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!HM81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<0P0y?9smFU delta 2628 zcmccdp5?}SmJQL2h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7AtN81IsyOVOxqvjlSk=EeSN$Vg3-Ax21Swh8Sd$ImeBo5INoZJ_qe_0e^d z*!(K?6&WE8_xHg>JxXl9ll+Jh+gGPAB_q-{H|CtgL%khskx0fV046iwPC{q&sKx!}-B%q-z64{LIb&mX&dk zsnu=+6B&NpT)0b=oC>CJ*Q(8lhtfF7aIB(H!Q_TF(vu%t5ZpZF%mi|Z7pM`NA6(c@ zUfm3Gf!gM{`;9#0d0=zH8zoILv;yOJ`Z^Itw#mvZ!rNQL7+;VX+mjcVv2M3fU`!=5 Mp>5ArXUr7<07VcxFaQ7m diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index b8a8c7c510b72bd333f19b727ed24cf768e621e1..70f69ff7cfc9d76db6c56a97bd6c10d6efd1c229 100644 GIT binary patch delta 2632 zcmaERn&s_jmJNZ7w#ik=No55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi%-h`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OOK;TlfF~ delta 2632 zcmaERn&s_jmJNZ7wiy=j@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7EOi`N_rl#rdU0$*Gek+8b{^$T*kLkYrlB_WVQ+7B zUB4u^0EEejzP`gh!hj(*WA1fOL+BK%8kZbz~FW$|O^QV$)J4pNPWoG2s?w`gs z`NKM%$tJNu)2E9ws&2O0c!<0NkUcs6y!2!pf1b_z_e>z8r~#%SxDk{0?_u36esB$W z^>6m(`16Z+$cjyn3)D6rzVk_gEbaZ%<9Qfaw^y+-zU3g#0AMa+-L50R7)EA#nBFVJ V$g$l^oKcFr04UiGY`Z;S1OTl;JDval diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 2a21d1ac5ee746500c409fbb1533bc5a210dfde0..8d557366dd7374f89acf910387672a4ddebf6bf0 100644 GIT binary patch delta 2563 zcmex0mF3q|mJNZ7w#ik=No55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi%-h`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0qpm$ zrY6Rg$tkHRi7EOi`N_rl#rdU0$*GeYcNuR!$T*kLkYrHY?fMl{eoO@Zlm~`{cE<9FyNm|0Cao&1+?SDRY4E*O+9CFjcgK+oFf4nov4FiyNPrtY1`X8iSDkz;2 z+fzgCk!kzp2jQiZ*lZbfjZB*-gZ%wDb{@Hr)}UvwIWD<|oy;WDprM@8+3xF64&+#E8|+N}S|+qT6WmoZf6U3bbFFVoA2`dMcYQ z%p)@h+a@nqBe*$wMIU*^M%(5EYc}wb?(fZwy9CJ425fdr+#G!3iU#R6LRu`qpnCC9 lh}?z)D7`0q!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<}OAFVE}?06I1{I delta 62 zcmZ3axkz(EB%@)5MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<}OAFVE|mI5{du- diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree index 4bc1c37340b912da88ec878f18bbd60826bc1310..b6f8788fd4d001a80cb08d9cebe0a1ff6e925281 100644 GIT binary patch delta 2506 zcmbRCjb++5mJN}NhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!g97+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NSd$Ty$> delta 2506 zcmbRCjb++5mJN}Nh8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A`A7+;W~ZL;Hvn#uAk4WwHL)Y&)yo*bO zl{_OhcL;1?CsXSu2_5o6taI}XDK<6oY@IyAF`7J`o7Xy>qb#6VH~V@tkm+S$RD73y=}Yn?+L!$P0(A%?@cF$@4$NX8x=cGHnJsJTT_~CAPoM*Q3Pt--SCUv3+7` z4Y`q~;KsfAa@8qLvXV{rWIbQ5&B?7CJVvJW&Fc<)B(Hkwn!N6Sz~-#u+T{7W zYjeOwQSxfXuFVQJZOOECvR*FFW|ha1b=>{x}+?!**Ns_65J5c|2 SJ+kZuwWRpBFE(Z@W(NS(rx*|b diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 783bdda499d1da215fb0e2a0e390ad7f7a038005..a0b665047737dcbbf230b64bfe743cb2a9e15c76 100644 GIT binary patch delta 3034 zcmZ3sg>~5$)(xJFhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!f^7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>jENal(F delta 3034 zcmZ3sg>~5$)(xJFh8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A__7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>jpU%%i0 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/index.doctree index c43bcc718082b7ff22fb9f14f8bcf81658b9a99d..ee855fc5b8635f7742ad5d77aa3e541913f554fe 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr#5|RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<`%|>JOG>z6m|dr delta 62 zcmaDV^HgR-Fr#6HMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<`%|>JOFl_6RZFL diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/multilabel/label.doctree index cf8d0c8b5d189124a7cb099bcf1756ee11519b6b..a85095784748cca80a4f75b5fa439fa2c576d1a6 100644 GIT binary patch delta 2706 zcmbRBnq}5&mJObahRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!f^7(bGsZSukRrs)Ta8TlvcvJ{eTD^UB)$pS{go2yw5vXHG^e6uXK z7%O?&H~$sr;vi4!bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+pm$ zrY6Rg$tkHRi7A__7(bGsZSukRrs)Ta8TlvcvJ{eTD^UB)$pS{go2yw5vXHG^e6uXK z7%O?&H~$sr;vi4!bfARxdV)IhX z26DYEU?j1bUFRYb*^Zakyi31_OxrgL81XVwVDo>|6=d2BOy&}sJ1yQ*;_oP%4oYl4 zVz-tO+k>4dDG9oDZuOLC=l8xtp5u3HzT&r=ytKY|b8Sc{c_F@c^MSDE#$9ZwdUHqX0<+f-Y?o* zw|y&lQC&2-ZoAHAracRJ$#OhMyY%Gs*SR<6oNDDE--ylYuQO?qrG3`+8b-z+3aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~vEH<|+UH delta 3021 zcmdmUn03!#)(zf_h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A_F7_XC|ZL;9Jyy-XM8TltOvJ{eTD@gm~1zf_LRaoz`kf(j~03aH-A6LxWvSD`dbR;cIN9C65ngRH`- zX|h9`;O2zWo5|Jw;1ciV%U8CN>v)iMkGrqPwcTMk&*qQMn#s#9K)(aE8^7h@Bs1s~ z>e(hw_{zPR`FkOG9>|#P$HJ(w)s(S;ykaI}J5c{xUb37Iwp)7pdLhPq9tsSY4m5y! z`(1g)Z5m{m(lq_QJ0sh6A5X^H>i~xuZz)AoB diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index 917a2c1aa6d4f5f76eaf858c9d99600870d28499..e7b3c7287eef8d13057bdb1c7b4e196c771f8f6b 100644 GIT binary patch delta 2688 zcmbuA%PT}-7{(jsj5CulB3aNlg~mo>E@y6K5XF>YgCv(Z$4p_PW?V9INmCXYjgKtI zWrMOX%AKXB#Gt_Eqhi<=;Ge^z^f3 zZ4Mn})Tv;63L;~4t;zSs#uDtbB2+DWjJUFM^uQO&r?AS5uFWfHP+&%)Z4R|Y#S_%ZyLSL9J$f3`!JaSPS-|+ay^Jbr zM$n$4b+u^V$p#;a&Nbd4flrg;%-dolTXPq3hktSMw#1NA{xcWTYTGd$yqHQm{HfSm zJE1L7daw1&r(*t;lBx8b2%5lwv9&%ubr0+!&z&+fA*`pfp+g<8lnVaf$R_8s*#P2( zrA#V{KO?WbVps69<#*(pQt*xSEDCSVpmqh{-#*BIx<{!n5e9A?ohHu@AA`vN delta 2688 zcmbuA-7AA(7{(iW=e5j4azMO==0vl-duLN-vYM6R1WC+3EFUFhq9z|nInXo@?SO_8 zlmnYv;z-Jg6Gtc_?RZXvBl&vs7r6EZ{Q6zjb3ga<+^i~ZR+Vw5!Z=`YyN5;w-FmO+ z6|Di$Y!quGW58kvn0-FUVwDVD(P#2VCeiBi_xnA5J!{M~u`L}(hl(!F@Nb?a>xNh~ zJD*On910kpg~%}7s&kYO%4kPhg4GQ%yTH*{p^JyiGS1UzX$5v#A+ihKc<=%yv^KDM zT{e_GOkC9^df+S7vsh(DxBB%AC@>>QTtcl2(iv*yt$To#l65VaV9yuZB4GU8UPkqe z6KKy#M-v)&rrCv}ODzvb;Pb>3v$dC!*w%~O;a{9=?P27U|IFpI(RqpoFQ>|GPdfIF z9%zf?-m$*rbj+XLL^{2v13GYEEHrQr5FYM1fD-J>k1`vR53gTRfW^TcH_WSnC0F%=76 xtI2tlLgpQLT%q^7Vc<=p$fHUFh8A8pa-|aJjQd`BI(REmf%fMVG#fT diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index 772d4e2a48b0cb1bb41d7cf776f0ba4d92ecaef9..8f67c3bafbfed1723df6f762b4627c20fc144832 100644 GIT binary patch delta 2967 zcmaF)oaNpF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D64N%J@ delta 2967 zcmaF)oaNpm$ zrY6Rg$tkHRi7A`w7_X6`ZF-|2WBp_nmO|2PoGi#SeX}j=ViwZ1ZWiQv!%Dig$&5;! zo3{%{3X-mMGo#WZGJ*$aW6$IOm0hG;2-V3w`9g@j)mipF)snYcgb<^!mX{76B6Y%zX@GWMlm%R zqFs4&Npv|mAq~@hB=#AZj^CV+IEzf1H*ZKL*H&PVDR0hC*CW$ru=cXd-DLV1teq>j zmD~suC==KmTI9)2Mj{18*aSzm$pr>1o8zkdILI`@-%Mb0QL{8Bnc5q=MK?$EvU8EA zeY5C{Cu9cQ=Jj*+$f;yN!6vXqVDsX|+sO>|%>ru@$Ox{@3%3@N7fI7L-`*!ehK=AN zVS*$7<}D|x$+21SW6NaAE4-UOUaTgkD1>R(yEcoLjO3yCv43;NlMEixwQg4Yn5{{= vHgIt9d$MkKmu4h4RRVJ)>-J{~jIYT|)sy7|*thqoF&-i#Lv05(k29D6cQmAW diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index d2e38f0e20962a8d030f2187454f877a7f5beb1f..da661e78f60d1febd9e490e8ea7d6505f64bffae 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr#5|RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<`%|>JOG>z6m|dr delta 62 zcmaDV^HgR-Fr#6HMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<`%|>JOFl_6RZFL diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree index 9c51a1827c73fe6b2b5d7a535b2c19201056e70a..e82ef29d795de828b8ca97b36ce4511d6fe7cd62 100644 GIT binary patch delta 3483 zcmbuC-%C?r7{_^QXIgU;Tj|6SB#4O0v$bDc6kZJ!%3&gwgt@b=#ps6%M_Aa<1yX9^ z%h*LVR;v&Swt8?cMCe7)mE>-sn?)n&qMO(V>Y^B(<4xY~yFcLbe4p?4d7k%hs_Xjd zx<1#-tjeU~i(a{Qv^x+~tZt{>COZOSV}6IvsW<{wyX*?OU5d}A1YAD<5qr?)rb$)- zFwW#@-l>b#E~t@<1~d7k?>dZrWtiUzkd@F&p6)Z!jph+VxDv|K%FZbyD-GS-_Gb&! zbe^P+dlG;$IYSq^&YNmSiODYzpGW`a{No+RZ zojqJf2kJyDG=BR>y*UglWWj+_fl~wUBx)HH!g?BhkZA<*a~U&zep*Fw*<&P;jcfze zHC%ctD0X2Qz^U+BAJ_#(s2*pb8u>FtlSm_{{ zzvYZjd>Y=7C^7@h{akFqODq>zc|U}w#Qg*85IVU+xIv_3oL{}%C>}f62|d*N49~Xg z7OThDSL|>%_@v59BI;q!Tks~5m0>oB<6_qt_F)IcC%;UvPO*HEMZpk+THpz`zB8J5 cd(s<6&2R8x{1%&NgWPMsM$KaBAsgEM51mbKQvd(} delta 3483 zcmbuC(MwZd7{>W(XIgU;Tj|6SB#4O0vmH0Ki^8jcLOD#tl34DnH5(Z&+(m^AT_B|v zevDmIW3>vQVAhNKAR;e{t|WI8-7FeG7v020P#4AM9B=Y=&;Eel`@GNdzVCN9)eU2H z!?@DN>|wXxe{o{m-yKi_iYKJF9E$98gxvO!OI4k2kJBDd)SfYCkK$2-=YwNGy2>g5 z#+f|BI}P#L1$A=CWTl|=-GI?AO$*xrvJ!s5GyP_|-aLv3SHgLEvwIrJN=Ns%{n-XJ zohPZ|t^}Y=&eC|-1xxKHG5I;-lgUqcq|Zcm98zoTs@h`c_$h$YMoXD~3zCvPo1`y} z*wxX$gC%sVZ-?hmRo7_)b^&?nI=hUjuK25{*Q7d!sv1JC0adDmkE2)KIII5hxIqMkt^Vx*D#*+vjQm$lO8Csh=ey=D@*=nha_ z$E8;bCJ^6u!_3R;5?y)!5cw?V@o6Qfygq~)(9-9APsclwMP{P;pG!@6iPa)2AB6CEi}^J|wI#UmFxVT5|0<++x< zV(l3FiXHAIpVD|q#JucT3*JPkGQx&%Tq}E{7{~dkXIoR(+(^x`qJ<@N*|E*7$k7EOnMrgdDr!61NJua>XI@}?VHX;) zd7#t9VHAWC$<{-hcWaQN=*@%(N=8ADK}8Y-y&IiP-}KV&`2#-B=Xt)r=l44&8`fsS z+C(Sgg+amF-#xHvhd&_j&SqPa)$aFre0FcMVE6Mjt0Ula2wtz?cX)j}ZGk4Ic%S_M zsF*t<_HiaUUMe8cMLYzF)AUudnS;<7VwDnxJ`;~vIT|ugR6*tfu6y2~$HFSmX3^E4 zCvHoZ7H;4SM5bw+pT|PW(10_9jhvxhyYnSb!x{SGilA2CzKx}zb+~mBwbmUv2v{lR zcAycZI=TU)7;|T6)3FQaoP#IUqSnai8>m%(b`Fhc?)(#=VM+>@%fKzF?feWio0}G+ zBkRO*-asW?=QN1DSi(Y{zUxrZOZfNxy+CAQYzf~wGz!`&I^tJj{u}Palef^N(fw-d ziJKR}xs`LbP+i2T#@-(7QDaY!)u^$5yu+)pSKM2v#$F$P16|hQ!j@C^(E{{IR`A)D za+-X)pu;Zf*a0J5dl6rO(PDjDHPxmYL7XSJbDI&eC&bxM6`Au+=pQOq#O0PU_R6$;z0B9J9$-5qg`1J@u4lKgnWv?f+nGT& z^DJ4h{H_b%g2e(!7rO#3sB*b9lJPLp%d6TLgNnF=a_T7i0KF{=>pI0a12VlkC@J1G zQoF!(vLV18LK*&7%bPc@XT@Y)MIuMe(ZUjX*>TRc$k7W%GLz_)s94*X!$n4^wMvEU!d_^^ z=7CNxj-nuxNVXp0+^s>5qMHd3l#GHPgNh^wx*MHMZ@Tn*{(#T(d7kg@`TfqxhRd_z z@p&Rza}Y9X8%;6b^AO%@gDmD zP%&3T9N>&}s8m3t^LPjnr|GL^JqMvv#Hu6=eJ1X=a5QLoPz{+2xb}IY4hyS5n?z@$ zj<^lI&4{45q)hA!Dd*vJ|BwJl!)HJqU@&IoGt?Oa<5T8G-lQEUC)J%E*B zuB~W9sjhQ?QH;4VwC><3bj~Y>SEAO)vFoT+cVY&OX~z8oXqb}1`7&^e>H?pkW^+((jJ+~0UN7^ttPj{uN#UmCJFD3(Z01Sn3#wc$jbzxzbn>ze#-Jjupqx6uK0t4a!a9#KPLE9Q3QCH1 zh14l9t*rO6hfs$9Rk_^HmNy~2C?}D>&opu{&bqJ#EbjPF=AyAT1ksg``xwVO z_Cbzca(KzVD+ZyoF7gb7tRR<0$9t7+;TW7<8Y?z#1-q8t4#QL>)3N+S_Y_oKfFFpt z9*X+i)pen$zmD^SqMoq62z_9y62u5>>pYz_i_WYt>Bz8I#h&DGP@NQ}V$AnK#I@o? zC=0)t@dAU_vmvt(xN7dq9>kX2$Vs~#nAt|&mop2Pl`7~MzsbwMGFeH3MQYmNN#GBK zA~tg+4;GDq>TERIKvgoDRlKuAr38W*a8{A_XgHNkA7{JGg_xHql-zn`92r1{55wMd z^s+W9qt8%P=?MP*`Bg~)=Q+}f8d85f$t#at1TxD3XCIQ+fU}wNYMuj%lIbdPG)K`d zm(*yPPr3gTg+Rl0p;lU?XJy6$`k4x~Z!Y-!Ldz#KT5RlKpug2IiS_n@%Rvy?|# zO3=g6c$MD#I3c;!+YTC#7`XUK1@bM&%qx(}9KfBtH#LF~#F6yaKLd5zQaR%8@Gx z!Vf--B+;Bm$H6A0BpZN0g9w z-z-#tHiCGAzfB=P6(X%eCb$$KfL|PTzvLhXse?uFSW0Z#ria{I;~0qXeb5AWoRXy#y(!!lWATA^-WL L^_Lrk6=A;t5e#+l delta 4920 zcmb_gTS!v@80O^6xz$$9YGD^b1iQJ}a+{+PAuy6sksg|gwdqWoVQgKfXswjeq?Nxk zyHRhwlv96lAZjW_NWH8eOv|u@vLvDwgmgRGk)X%E^{~h9^8e@izjN61glu|3*1k)~ z*lR8;tKMH#rn4DrhAfA{xZa@8T<#R}`TSY+w$x%~8+d2#6kt}Ypac9S&w^#LhWd+?w9%K! z9|}#_%r)F!^aNCAy|n^VCB0R_n@SXNAebSSo7DT`sdDllJ1RVgd5J>K&HILt0VMb! z>|H~zj%HhWb?%zl$nT$Dl@xNFA+TC=S#6h%dtJliF9Mm=`cvYhfh3kFqKw-Ua z4i8-@K@Ugem;3XRh4f~B8)!ge;Nn&%@;(zo#LgRY698pz;8?UXuG~6}jP4Db;Kz6B zfXuNsUCk$4D911V5$-qe{Ss O^yia0Sfv-{&-x9ql{jSp diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index ab8be1e7901f79a4ef710377b3f486de20b1950d..9d27c0c8dc69b201ee6ff8e684cdebbb3cd4b80c 100644 GIT binary patch delta 1062 zcmeC{X6ox^+ThJ-m|T^dR8~-7sh^sbY-nL-Vq{>NnwXeknq-!2nrdibV4h}So}84F zoNAtwVr-ITWU;x1@je^r+9q%044wRtV?OCRH*e(pMTR@U78>%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je|e5 z<}THtC;EkFN)G^w@m7={=y>C56p62))s8^L&hm$IA;3r?3sWQ4P-}J+w(ehCM6G|a zDYVzdrQ4|W%lH}8`t&MneLQgm31K@wPZcH1k8UKoyeEp=`3}%4`)=(Z({1O=yf(c7 z7)hYJGt;Q`r_m3fEEoE*rIfjIXAtePad#9b_FzP{dzXK+qy6aVprW2cwW&z;C%f9KJVxG@qOR-u;MjKitkS(!-;4 z&{)Js{_6?iQa`=(<^ZvwS^S~pEh09wIsSE}p%Jj^;RpOA(Ax%~6Vw|zc22S${@&yq zvzKboOTGWK(hR_2yhY_FI^JN9ABnG_l`lcX&hh7)Ucg8q3lq2Qq1MCzZ2f(E0k!^5 zM9^OM2Ggi@clZWseKi7GUyR;GLRd>ZrJ~~F=VPG;=ZWH4st)wZu7^j+bZecir^hxXsT%hZUEtT>G0&D6Dhje^fS?E4~tGdOLeRQ`wD@AFFP xUz@mVd;B65*hhfbr^~}WUaLTMad8E{urq`tP~g&jtJne;zJc#tlBNC4wiC*F!65(u diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index d1324c8b7f6f584516eb713437fd0339068982d3..746d4e4a97a8d4b865b42519bcffc71e6ae53f5c 100644 GIT binary patch delta 62 zcmew@^;>E~3zK1TRdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x=B-S}xB#(B6y*Q_ delta 62 zcmew@^;>E~3zK1nMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t=B-S}xB!dT6dM2l diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index c0ec22fb23c77f5e9998bd01f070924f03c719c4..2b54887bdad366f3748b2bd5af0390fd77fde477 100644 GIT binary patch delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$KEj1b delta 1253 zcmdlng>Apm$ zrY6Rg$tkHRi7Auo7!Q-KYkGhzqtWIiOb^+})I0eFX9Ia!jVFH)FxdQuYXU2o`Zs?N z2xcNv@8k_)rkn2w7m%xe^9HfqWJD4uxQ)Sfvq;vHYd2WGjLa^I?9Z2TrO19?#pf*K zhW!Tt+09$k1cfQl?{0O_icI~}*K0FMZf-5L7oo@i$1cZu@(c*t&dS9oMTYmc1Cvji rBzcw^Z?`gMJWhtC;50iOs9AV>g*W3#8}h6-0u~hf+c$SGwlV?${bzN7 diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index c8015823d0b9787feb843d10d3a27a2f157e45fc..616800c85ee41f6c84b0467b7f23b0d69ec58096 100644 GIT binary patch delta 13101 zcmbuG`*VzE6vyYhm&7L7Y_9I44PoMz6*fXk64BIXgdju-kz0vNf+mPdr42z#u;Ecd zizj665yV)*MInq|IxapWwT8uqD} z)Vk2%Z_Q2BY{!i?3S^OJ4QuOeeHh>`?v5pITdZ$qEEI>2?P0(%4GY5-)^>wCBpq){ z2v)qBFSQl+Dx&7gWUoxLP8{rM+iDK?h2^6GC8fM)UnHY#Ol*Z8q@Q8qu?a}dlrqD% zBRLakF}rlz zQ0kv^;9RTnl2GdR^CqLYo-W8mrRNtHqtZu96VY7jJ{*ElPcIvfQp=0uQ0kUd&roXk z>H#R#u(l6My}WJ`O8uoY2BjX{7>!a(K7E2xgSTX$)J~t9Q0dRx7opNyJEx(!KG>Cy zQfY4>Dz$wDQ!5U@)cFVDoipOwdbGmVc@SEr!-wJ7RCaVLO1*PpJt}Q;`V1;9sDMwp z8E03aRO`9R=(-vz;T~>(p$4UvUfh7@T2cKRrB1rM1*M8BcTsB4^=T+Iq%Iz%djGx` zr6%2kPr5$0H>1?-dr@evaSx86)NOx{La8P7=TPd?C-5`kAOBXP)Z`a%H^nqQ1*u#` zGzKg&SE%$4%1v@7KUAxE&`MO>%}6~_?IRQ2N3~1*DFUr?Vjxwb+@q~26}dfB7G+#p}Dnz+myU-?7J28}2BNuh{S}pHMV^HoqGaW*;KlY(rXq`9F{*Az<@lzU?c#iHC(Z_{X0`}Htdhib16C-jwo zt40v|JjFGmC>Pj&BIaGnVR?xg0*G0Pkq-rS==;2G5(IUMC!~=Ph}bDMX3?9Vcp_I% zqbOLsVmjmv?)Wjo@4hk-XIV0 zWi9;*RKU@>(jdVIiiPln1t}Nc2DSt%+kmgE&hcN_Pcb!Vp?wx*2ONm> zx&xP>f0VKYSpml`8v2$Jhl<7dsfs2#jF2tV@g1cAZezO*OPf!SY3LXx(jh?9cG3So z4~jCBb7(Xyy{^J1qN5@S3lwjT&Qn&RF*pj#(q=&*I^Y~NjW1DzOEC!P;LZ6~TOpH__HDEAs?3< zt|qBIu9U$|NCyvIGES{+=}I8t<6PA!o-l=oRypc@SFBiCjqiv#q&PzmC2D!|4Oq^6 zf~yd8Of#_8ZuJUqAK^Ut?G8dZ_;Jx*^%^=Bgu`a>5!E0bm#dAz=(KX?^vbK@uDaRcYFvS$cD3fivB8=@@aw@QRg!US>giH6(J5Lh z(Q&uxBOb2SQh0f?W=3Bd9HnAu_&m(jki13oI@yt7!CC;zxVF0#l8_-e4LRJZ%?Jg9 zW7*xe(Bsx#o34D|l=eeA5HJ3!lf{tNBU8{~uylBWK5RG_H|Mst#FY{lgmm!cW%sp4 z_$|dgWY|c0clhvej4A05z}1R=!T>G^w>0WO@LGK5t8ai`4jQ}5*yYQ?Rv0fGO5u^X zD^mAu@&KG|(T|`f4@1u2`yW3H BR|)_C delta 13101 zcmbuG`E!kD6vyYhOSqAno7KLwAxvzETsKX$Bt%oAgdjx8B3mUwEJejqX%VypH#}-+ zQDsmgb=w{hu}n;=lQv~EQ4>p~i(R#`RG8><@9m8In(zGszTeL|=Q+=L-uHR$Ep2FD z+R(l@js{N;PfpIBm6aTvYECsrWSYZ5%^{Z1%A7k1g6HMVpPQCp&F}xJ^)QtWXt9e_j_ec2?SWsJhJ|4Z?zp)XB%N%F z4NyE`yv@_6QV18AbpOFC&nT$W*ACFSMUzm*MTAnu>m7ZH#fJz@Ok3(~<|8O`;EnhhqrIr-Lpw#VapQ6;z zb%Ri}Jy~e3_xCP9 zsdT^}mD;|7sTGG|>ib9Foipa!2DHN0cnDgiW5?jxRD65~O1*t@6DsXceg>81Rlp}* z&e^pn)mnK8T~|XD+{2yE*P_&-3!BkgD{7vh)TB$>QL4Cn2c>qqmW5IS>qntf&)*NA z)c70lNoT&b4W(w^jYM;exnG7-cm6#Nr4}|+qSPmk;b+7@{;ff&3D4nf8rt*(q;fUU z1hB+hq0&DnH@+45pjyqH)}Y$nM(T%ZADZYMs$J$wVQ8J>{HY4%9&b-6$nBAQY3GtI zv>N4JeTCMe`5L-YKFaOYleVJTsX??3xu~<(YE?g)fN~dx&{0(TqnY-ibvA|3P=L$% z;dBON)(@f?0MmYn#J3027bv%P6pcW+rf9OD-0Qk1C-gKS1p0+>k1=z`c`wQI_xEa5aV_s;L+_OPrOHHpR%+bL^g`>*OxJ ztfOCn3OG7f8pIkwu?TrYt(!Garw3Go_5e$-K5+M4Zlpu#<`q(GVBZ&X4z5F zbsH{0M5MAFSpml`8u6wQgNntu>53-0jFBzW^=%~&ZezO*OPf!SY3LXx(!o#E_0<1A z4_2frm1s09y{^C~qN5_Qixf|eny;)uV{jCfrOhILbig@kI;v0+F2x|EgC{SrDyMs( zft)&_7@BR9RwYU}+HWkqdcor9XO#VDF?iEiCBzdf0Y_g{BGGe_)jEYkK(Z+GQQgIj zcr{RDd#igu7nUwjz|UZu4+&#?s2$L`D5Thn8w1rtox#}c4sN1VmY^V)<=V;^+X)7x?vHvnt8Bw)S%=ny5sr zo#?tx^%4)(X^FflK?^}&8yux#Y4|MI)sQ?z)JEBn!2y~d%eZ#A6q1l3ISo0~s^tWM z!LjV_o9Jyc?_F<3f0Mjtkui<^5(Tjoj$3_?11^2&Q! z6a1E9A2Mtty$^i&IL4H8@Z%aqKWP9LgxebRZtz-s=dEvsUk;l3$k^q}!44QN9g5(Q zxHnw)ZuS72ZP827lZU0(Ewm@uD^mA?yFiMKTpXq6^#vP19Gt0Ffm&{yrEeICirXC1 z^U$IRDK@h2DZLVX{Nv>FdI3BIq}ZM3RO?&N0l=$o>OpYhhu+ig13RAMp6E}JoTV;- zFFezm;0vvOUT&V8;_fyIU2&EU9f7|ZI6f$d*bI3iSJT<8z`f<&YX64{%fFdi@%|5x Cn6XCy diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index d41acb8828796b400c33fd3a027799cb574be185..5506b1927661f365d2ac8a7c27efaa8ce1582474 100644 GIT binary patch delta 1676 zcmeDE&D8swX+tohZE{s|QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk%fLresZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm90KT0t8UO$Q delta 1676 zcmeDE&D8swX+tohZH7gBd{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SVv2rBesZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$sZ)tH%}AZ$V7qmPhwSM+P?XN#A!-wPLpvW z(`K;0gXPvyV*47!UP^3#q8v_%?a$O4DY5;MMlvO~KhU;hAv4KL5ai#y#$Y7}1=?R& zGE0)F-62$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm9057l_%m4rY diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index c9e8b2a3bb5abb74c99b5ab699381ae3afe07744..b0e958f950dabb219fa7f8079dd93ce4c6b7ea79 100644 GIT binary patch delta 117 zcmeyW`Big6IHO^5RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Yc~Xk8Nt%(x<_<<{9 delta 117 zcmeyW`Big6IHO^PMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Yv1M{fYD!|t<_<t*=JFFaEE(KgTQ-(a@hH44E--KSO0KeRZ z3siW$e%}o2b|mmUfGds!xe9D{Bse__uN?^*bC4!HZ>=rtcn(#oK4@kSLs5gr|@ewhi_g}Z9dN%%Rk3_ikeKNQ==(xt`VO)j=zs6OxW=)HcKhdjfru)@oOUcwuA`P$Ynn9k>Y3s`bSAMXqC zUjI`N=i`0X9MF=P$vgQ%KWoEqz83X6LG=Fq&sb`nZ$BLEfl~9lHGS+Pgih(5iZYb1{xAbAi>W{x7Avd#+Qla+SsoLTsxzT|U=nG$7z8>%Vzev`UUf%tNp zE~4;y{hk@xZA;*JKv!%DCd#zgmf-X(y|yK&&yfl|Z_O=idrlQ#$eV(Doc>C7PT`oK z3^;csaic4*VRMh8zogB^R5klbb!NP(mDEp#az>Ck)p|;OT7? znfO5-@$wrv!IjJj!!X&2?PHGR|BzEWuG2v@zF{c;qZ8d&hb%k4Me?y*6>^J}O;Qcq zhO96&M>kmSAb#$V&LQh(a3!%#k;-1{p~i!mVap?uEYe4$x4=DM3WOne0vtSp)%2R| K67IP4tLr~g>)u37)6&6 z6SXYkEIlcju!t3u*5}mhq*|%b))26mkSK-*u~Lcm9Cy_p|pt z`<%1q%uG#pXiazMk#rVrv)i&)t$20vq})7PcuZ7egvFYhm6c=7j}*?Zbau|uk$Dj@jxDSYP*nw|IsIje0h99DTrYpeC9N2fJ1YGHoE=_EK)R3*ad!Fs z6seXg?@IqO5*&DlQw}JU2QGJ31$P26M^*4zhiRqaoHBeemakhLHgYbOvs-TYQ46+M z1_FHY=-cXF@dgpaG z;76N(gHoq$osH%?_17<<)Y$FNVC3rI_#J5|b;izzXs-X_vry{QilZV$MR2qA*9F@L$Bp=On+p#xLYTfZ5R9gB0Og(l2rk0$7 zcTWE4`)Gx=v&m?gq;v4uq}Crssf!!SQ0ZGu7g4FX8NTVvU+h7t=`EMhb^Yo~xQ9(` zttfR@dkvcF&1?TfspZ!Xq0}3lT>zDbE@o?->u%Xm?A|-G0IWWmR&<|4sjLUS>TZAi zdz3o8Hx|wHslFzZy1zdIq|zbAz5=lnr7(0%qzH}OM7e)4GPI*=P7Y$o>x`H&X^Dx= zMa!ILW(JfS6wJ(MsXhx~yHRb(2o{QJJ4Ug7R9ilpMWJ;r8q3;IZtr-u7<^#bRn84Bi zrt9*fz9fb`KI>Z74U;aEP$>oVbPvAgi!rbmWVo#h~XEg*p)yC9e#y9;hDpR)oc^! zP(;;h*f3?_&)_6hU=8qfgKOn%?{8s?fZK^zjh5!}McY{re^}104m@bxfP}$W6&3I2 zj3c$yLl&r6kNQ0-|pALttQ0tlN; z@bZ6qo*DT&jjR<+f`q}UKtSwjT>j@mv4X+ZSTfu|U7c(wa9X;1Ux^!{% z|JcKB0?9wt8u@X~%zWx*C6(TO!0rt~z1ABgFB+mq9cUwvaCi=G!6cJ(2HnQABT#Dd z9eCm-ig+K4coB^alhP5f-nMNeqHjh`lB{sswpgVbz%b20L$`i>D|BW=u(UP2sZ+od$h&XRT`jjD^wa-|C3s3RfXi0n(D zdzlJLrAx^Drbm2!t8@@emkY5U4c;#ODrn%FlXoAG@;Gs3;%l>{R4S{Meg*_W!p1rd zz`NRoe%B;JEYeMWzpL`+3pboUTRjLDWcJJ|qE7DXyH1YC(ONsE6Cd7fX;(=td zfOwuZLB0%X>HRQy&R{@Xyj-47j-Sgzkz-94M^BNDjsVI0c82W3*UgtZKm!s6$4r-=os#dtW2f#A`;pv^Zg8$U9Q;)FMHb-yS#aUt&6j3LX0kQQ(LYAlLqs!}EWNVP z+areyvLV9?I*^EAo(76t;72VCws&!9K~8KwLJy z+;r05MZ@+P8a)o_JFhLZhVfw@#N|z~pBsA68&U6hE*wTtdyAoAI2Z_7dJLs-MT9tz z5_%1H(V@VdMQy*qc>IFm@T?ZX#fSO~N;mvjKtD#-geVg{J4S>tOPni|PV6=Y^NmB$%Ams_uZv6>VxWOdfevMSiB?uJ)1+ z75Q%4EuM8p92+~h;|)q4P$L~l=k(>?G|-3MHC ze73ADqpn9PGMJJOuLQ0NSD7+*b*sv*^QuA%P1?s`+TWX;3e+y5kBfOqmPBQ}fvL|$CsEY3;|I=s9C;%BeVZ0F4L3;V uDIs1vDer=o06YMGA4cYNEjrwTFi3>)p+^y%c*-j;3W^| z0+HUAG=DKL)! delta 15985 zcmbuG`&U%g701`PckaC~2#mu+e39ZKN^2so;n7A7Vho^3Q6z$h2#hEwBBaI^qv$eX zqLyWxr6)xj7O{fT`kb1bR4X;w8Uhv*62;IUR;p68ik28Dk)F9zo0T8uJAc6U``LS+ zea_i)W~Qz;q^>vQXa);k7Mq=2xUwL7a&Ba9WL$n^bVOvBB_cmIJU==w&k`GF3D1qp zi^{P?MaJb>msoSGwykUcP}Kyc+kNFQ119D5OFVpSm$ed1Zmah7vv+wc1?fUQ%--Yu zBcxi6ysLfBN^syocG<5;c3p0-3G4=Bwwl0oHq&awK4oMamakJDI(i~T1!eZCoHa;OJEW(qlTfNHe;q)z#}}MO ziT35t=5^$GeC0tPSerwtmReD1!0JIX*M>C(D7Ce0CQ3c`I-F~3c^XP>-B^O=diM=D z;Ky5jg;J+)n}g;$^_Q=p)M-1Q!N}3W2|LqK>dak_&|Lq?XQR|>RgoyQVecxG`ek)I zO8raCT$FnDH(#SvYu#%ob#uKhDxG$y5|#e^XaSn*_Tz7&)P@rQsI>e8n0ovqOf5YP z@0@}&56}v0=TguzN$26SNo_oWQWrN@pwhQnE}>F$D}2+Lzu1dXGup18>-yD~a1Wb0 z+EME6&N?*LTi5@CQY&v9MyWTudjKjAS` z?@;QD{%L5g&knSp)B}T=Ae9a?_7#YwXoaC;B1LHI7Rvpzk)a(`cWMYjUT4INNlQ&^ z9$MynGc%yvfIwzOOZ8b0+ksmy|M=clnWlsiA2y@+a)GTA;<%NH@^a{-B& zst;wJ$z!X4y~leuutI*QnEBDwr7XrBhahTP&XQ0E5;6P~6+03Lq9d=er`&VcxQ1;8 z9g3-TEgPY@{tQlH1=a#zH#k-<@BOVT8@Qc#%@}DO&)&fT_@hd8&Gn#l0}=-3R8)L` zGmg|+4_%0TsdW!4_5$(zse{aex74vF(13)2eHbX_Z=Gk}JU>mEN%d#g0-$T03m|MZ z!^{8e1!m;GZf5OZ5+n>>^9RI^#^rx15-S*Zou$AH)YHv|1E;04_l4M-cHd^z$j;MS z@DF|L7LfcCt&yML%*>~5QPSw0hwT0k)N6xL@}Oag)P*(z35OTp7ECrtXVGm;JN=~& zZ`TtiQN#ye#Mv}1RLVfadfRrCioOY+ELq^TZL>%>fnmtG;b+83ZvbDlI(z==OOh`w zoFzSm@xtL7IPryP(mP1q^&KO`M%t7qy@EK*DU;GEH%HooG^#GHSRz#cM;!_ACS+d< z-7+dFmo6jsn;!9nZPFn$T`t5vG<1jbivZU*C+|Hd<#Xc9#Mk9WX;e`w{TK*@gpFl9 zfOoVD{kBDV8|f6i*M!)UKKVk5MIC5MyVL;W?A+r`SEZ>wXyTRsl9J#nO^E$z)k7&S z48-&FiSiXtOYeuubB6-rl9loTvi(FJjvQ;cIA)4`Y!pc5cQR!!zJ7t+1saerIBvqk zR*EW;hX8q#$a^I+z3TNUtdPe8P_BzdyeX#!1L^8pbJ+pen-A7P#nul7VbF*iYC8RB zoWYYCPRsY;72KpOa&p=`1{fjDfu zx#^U_gGTH(G`k(r|Gc)=8zzLh5tk=T``pln-iUh7bKww7oo$AukzgPc)@LY(DY!sQAuzv+qh+)+H85&XSd*#tC!9)WP#1)KyYj;T9& zUKh8`Q+ro{WG=*hyx=1>5i=;W4bJkR!n0}5)qgE}1-+f*SCH9;K{!i8ELc(PykpK6> z@Z-%X+HmLB#bqyR9{`K-eLoU@GmNfRmPBPefvL|yCsEXO6I^FLi9Ct^wq1*!jvHj~ u)F6*tlz&l61Remt3nS}>78CA97$m~@(5(ngKI@Sm4MmVn;;-A^n*R$|FJvMB diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index 3a2a39bcbd7f43c43239aa5693d021c4fd767869..379380c6bc867c4c02a54162a1910effcdb7c429 100644 GIT binary patch delta 976 zcmX>#f$7u)rVXi#hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!f=G4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7YO_48 delta 976 zcmX>#f$7u)rVXi#h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A_>G4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7Wa=;t diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index bd94dc731677bb72e693e02ab6accc82fb6fd73e..23bbeeea073e9f84829ecd6243586019fc66f65e 100644 GIT binary patch delta 1139 zcmeBrz}oeIb%QsfVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;UX1#sg&N+Nf@@c`j2o2bo$sxeJ)d((1A~kH3VKJgt+@Nl9$Z7u`!v zC~Z`C0c+nWzJQ55+c%$+>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3Oek&@ delta 1139 zcmeBrz}oeIb%QsfVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?$i#sg&N+Nf@@c`j2o2bo$sxeJ)d((1A~kH3VKJgt+@Nl9$Z7u`!v zC~Z`C0c+nWzJQ55+c%$+>LXJt*yfkAB4h>`SbLY^H!^~4^G5XroMidec(R>=_~tW) z^U2e0IQ>F6qu}Oy){K&5Sr6486q>S)eEqDG8zh9c8}KlCkeANEjsP1n9cT#a_U|H$ r2c*e&j^On7HjK*KeGM7)xhXJZJJ1w0L$VAonB2+Dzr8h&v6T@3pUhw3 diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index b9c543a56eb21073e253af5567edb352fa1121c6..29b06e5defd86f9078e711592f7bdcd750ce5b97 100644 GIT binary patch delta 117 zcmdm@yhV9~KciuCRdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Yc~Xk8Nt%(x<_5-YW-_$duvYK@0I1U>eE>k+FfPQJRHenptwPsfDGfVUlsONusHV Yv1M{fYD!|t<_5-YW-_$duvYK@05XRn=>Px# diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 05d2f469469532f69f743a3d4715b5f7b2df3265..db0ed1b0f1cab913193d2843c51cda5bfa1ed258 100644 GIT binary patch delta 480 zcmcaKo$=Ci#to^AhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!f=F*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?pm$ zrY6Rg$tkHRi7A_>F*1>%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi5j5+&&X delta 1690 zcmaDdi}lGY)(z2&h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7AtN7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi5|oDb0e diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index f0adef583b7292eaf520195f5faa13febed8886d..d07add36e441c77f2a1038c33fcca71f38911049 100644 GIT binary patch delta 1932 zcmbRDmTBHwrVZ(ghRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!fAFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$pm$ zrY6Rg$tkHRi7A_BFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$H5nc2GRtG#rMVdESR76BXi6|9BSzS~YQP%m*Q0i{a`U5`C^L)SOdtNv%f`%7C z!!;*a>TYqnntaU#nVxF5EkD!j#`Jj0ecee1_87Dz!8Qj9pBrS@Dg10hjnTfZ}K7@5$;kV{CV|6S1&HM_i zW`%!RunKh@UDyq}URb{vB>7b978Fg+K8=>@%!!MFrb6MV1?AX8)NoT#tr>EyVWuJ- zPcBKu>J8y+@j4?LD(%DrNQH8UZspdp!`LoVxVN$mt6Je5o9+R##RColfXVAY;cHxD zC_3i3jf|wze_S3^Mt;zngp8%rAK+VjgGh%p^yiMfNWB_b*c3!vgUu@7%ANk@0LHYi zo;}bdq@i6(KU(Lq1G~_`{zKWQtM5ny>U#0`N?@Dww7~v2;9zn3y}YPn4v4Z;&upG} z#b&^q+3s!w8}0gv(R_FJ+-Vrfe2?^e`G5~wUT+95d+sZL~0rzI)j#}IN{pMVWGCJJq| zbRJ;ZyN0(%=1%Uaa Rz{4Od#$G}dLjDdH(%(ABc|-sJ delta 5860 zcmb`L-AmI^9L70EXUpl_O7tqwqUdA>jIV&~MUS=;pD|;u# ziGB=83hAO3usYx|D$=^Cq#`0BN<^t3%Ic!Jh_cRahEjKX)*tYBp6B~L-}AzG5i-08 z8Lqg=;!XK3mw#)GE8AoD*qzn(yd1mLkyD*-tIqR!9r;d&&13iGx*fT8r?<*i<*v%E zs`EBxH`X<6^ZHoOmBsFoep|#UlC0pVF<%IVs_hS`iFN84Fr1%>yR5@dHegQW?-H88 z;9-2!SWYo_K42cfoh`g}>PgU92Dk9`)CI9n@D^q*Gx63Lhp?_I{Kl*uSltSLGp7Qp zS>d1Nu0UN!=JkNCoog0?B%f^Eh@xrs(`cz~M?wrV6$;OAmSYo9!%c;?X2`XMnF@70 ztvC&K4eeI$p>-}fuoDd&IAlj%>yI>|uIG<02evuS4DL?=4i+|nh(OPf(_yxGCeKDk2eXY_g z(MCx;U@oM>Q7f>vLDyQ=ku^!Y$|Z00QShq^$hjnpSNClp@oKN1=){R)qQph1q8Aq{ z2sAdb7vouyL~kY8Y>G0=x+r+jRZDV^$H;gYQXrXqHp{lwkyprV@<%CM7#qpSD59jR zQeZ4f<#y5s>=5~`3$IPO=xrf7Bi>en>S`i-wQYz@6WfoGIOJsdlVwawOb?P>Kp^$% zB3dIBoh2sV>I}nb$33DKgO|xE;J_GHND>)cSO zP;l3g6$3%gR zmdplB`%0-NK5|Z#T(fxKqG>ZDhqZH7Y}rRsfNH~RVk^y@7P+q$dIMXMmnDW&>IBRu S1RjQH5%v$@Hs}X+u1tVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;UW*j6ccHRmc;zS%7&RITix7PA+KXpB%zkNS4J_n+w=3u#&EIb0N=b z^4wd$nTLNq6X`Zio+wqnIa;Wh99uzJ`$YW7wY69LG8tMoPn5buhBmN2h2>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGnqt9-Zs delta 1199 zcmey>$@Hs}X+u1tVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?$Rj6ccHRmc;zS%7&RITix7PA+KXpB%zkNS4J_n+w=3u#&EIb0N=b z^4wd$nTLNq6X`Zio+wqnIa;Wh99uzJ`$YW7wY69LG8tMoPn5buhBmN2h2>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGl_d2H|i diff --git a/master/.doctrees/cleanlab/internal/neighbor/index.doctree b/master/.doctrees/cleanlab/internal/neighbor/index.doctree index f5522ac1dd26438315468c2140aecfdb998ab156..93d06d82b08554d212b7e4bdd627e69aff4c78a0 100644 GIT binary patch delta 122 zcmX?Va@1slKciuCRdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN fc~Xk8Nt%(x<_5;oJVqpI)1Ry$Aia48-&$?}l;tL| delta 122 zcmX?Va@1slKciuWMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV fv1M{fYD!|t<_5;oJVqpI)1Ry$Aia48-&$?}7PTcE diff --git a/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree b/master/.doctrees/cleanlab/internal/neighbor/knn_graph.doctree index 2f12fe85351d864f588a1a296cf4ca7585633862..9cf249042f08575f505e58407e840be6a9cb44c5 100644 GIT binary patch delta 1928 zcmbRJif#5Qwhe)dw#ik=No55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi%-h`N_rl#rdU0$*Ge+x~gs7!dS~_NU||Ntx=QrFgK8=bMtYQBo^|t zPCm{ey!kk%D|yWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~x%SZm0kN delta 1928 zcmbRJif#5Qwhe)dwiy=j@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7EOi`N_rl#rdU0$*Ge+x~gs7!dS~_NU||Ntx=QrFgK8=bMtYQBo^|t zPCm{ey!kk%D|yWXiQvnh3jok}kSuGpNQeu+{S+}B*rOkr#o>%F1W6=w{dQ4kKm#4WKo-XxNh zY>#kn_P6TdBujhl=5iNLGW@^!qpPGY={8Px!8Ao_hi9FZa2{_Q~h xKiNsw2eNnZc0Msi^*Hi0*KB`O!Wc?sh}Ud?QpU()L%Pk-dXIm5(`Lq*i~x-&R{Q_} diff --git a/master/.doctrees/cleanlab/internal/neighbor/metric.doctree b/master/.doctrees/cleanlab/internal/neighbor/metric.doctree index 6e52bb737eab171dd672ae3a06c583462bd3fe4d..d2ed6395908c7687af1b57488369f0bbf1dcd1c9 100644 GIT binary patch delta 1023 zcmZo!!_=~dX@fVTVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;Ud3#${w^n|wheWOCIM{>j^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ0H!EQfB*mh delta 1023 zcmZo!!_=~dX@fVTVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?+k#${w^n|wheWOCIM{>j^z3rV*WqC*KEsC~12 Q?lv_tYzF0`swrKJ0Pn><761SM diff --git a/master/.doctrees/cleanlab/internal/neighbor/search.doctree b/master/.doctrees/cleanlab/internal/neighbor/search.doctree index d2bd05dd32c935fe717d69ba260910e2313f82ba..b2b04c9cea78f2fe3b62ebd6bb4883b080d21332 100644 GIT binary patch delta 527 zcmX@{m+{13#tq(#hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!fE7&nliZE`I071Ax6Y|B}?`8ta!3+Y-n+j2^?lCBMCW76i=d=>0u kYW*hWCr*~u)%*mT?RlQ>d08k>QO#lD@ delta 527 zcmX@{m+{13#tq(#h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A_F7&nliZE`I071Ax6Y|B}?`8ta!3+Y-n+j2^?lCBMCW76i=d=>0u kYW*hWCr*~u)%*mT?RlQ>d0B!4_GXMYp diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index 34d68eecf3f72de4c645731fe9e673a7bc68cb4b..53b3e68c73d7755061b63a4745fda7792a314279 100644 GIT binary patch delta 731 zcmccgg7MM|#tpuVhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!In81IpxD_+!P^F^jPg=ATrGpm$ zrY6Rg$tkHRi7Auo81IpxD_+!P^F^jPg=ATrGw#ik=No55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi%-h`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5df=zB7Oh> delta 1705 zcmdlyhh_5|mJP*>wiy=j@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7EOi`N_rl#rdU0$*GeArSvA>W4vofvME5FS)0R{7ci2ib^25uM*hhQ zSPRM0-nID*n;sikTKhKdr*V^+tfWxB*2Ua*aC^Oh|NWZJ&DVA~S|@@(Gx;kh}Pwod-=TyXMiH=)hXUWzD@ NWqa0kU=cr$5dat+3}yfT diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 07a8993829c1d0480014885a96574b706a6f80e3..bed48e4a177a1acca051ad3456fc47ed00e09851 100644 GIT binary patch delta 7878 zcmbuE|4-C)9LM*5pLa*x#oh7!yAJKz7$x7oIO8s761#~KlNkwg-%tn+QRhyInNdtP z$AnMxo?l3(;+S91eB)lyXtfMyZZImTjjVvS)(;VEnH^iOK6jXZz>`1V`FuQIulM`) z`n-2HwrCt%G~Os-S?*T1>)>90(Ry#4JF751H#;-W>vVeZT>0)iZ&q$*L0w^i+vRe5 z3tXO@+`8;S#m^=H)wH!(4s#vHq#}^7F)4O!3nZ_X4qb@_k~YcA_^n8nrmamfDRC=S zuTSbze!{3e*}i%+O07$)M5%AA+W}DJv~>uODbrS;EJ`0nr60b~jY>x{TT$uHIhRoB z+5#^seSSkbdZwit1wfS+TNoeDg{WjO#b z)ZL5Q_o38V70|aLIH#v~Ah#$K_?w;EfLdjF)f8Iicy%p6422!5b)wWUR~JfM^6WyX z3%ff|YHC9^K$S-tAE3lXK081R9n5zv)-*DdeL8xQm?)}fKnIQI#H_i z$fp2RTG|IuqNigwKvb_ogRDDRs)gQyK3UfJDoV{d@o*JLz1q`*QGIesbf6v9o?bwy zy}i{a_2jwRD0TjP14>=!yNpsd^}|%hm41}keXR(swc*BJsPtdif=Z19-(mYu4MHui zYL>;rr^2A2tCY1P@98nAQahrRO{1~Vkld!cITb0jGxgZESeDQKHem+onQIKv9?(65 z%N2V-OLrNoj>SC0SqC%H*?9IVc6PRqflv*!q)B66tX@eB+a}h#q8O?{BJFdq7#$en z#9h8hNzE~n;Wd_x?F*au-$d-0#RyK%D$P0t!`+-G@h&g7ahxb{p5i3Omtw^=f$E?6W`K8=Q{#j;1XCs&*Xd-q6)T^C3 z#|+rww*~7-7Ok_)Dt>&-{=sWXrkVh&TtN!mt#rO_CH?40=g*3J_gS=1`=O^l7>@$C zAT@{c=fJxvRBaq3nfV3KK)LQU(Mz$s3|zh;eRvA@qr#M#q0by&Non14ev(vbmrtQZs3z3;0WUUSA!uV4KZYJ)=lf=Qo8=d*_;a2|36*Z4uYc#C01=*Ak!sZlW>I4(qspwiLoHdOkv?E)%& z>bW{p`t+s_^h`@P3xFyu_6R^0KGT~oI8o`@t^3eAhhEx&QqPsP15{b=oJEP>me~Me zxVz_f>_w?JDxhygXiiV=L~c42K&-$s<${nmbzdcM_%QZK(fh*B5ZyHKk2 z(5C=ZS~`YMqPKGoKvb`TU)CQk)k1H1e5)^Go6_Kfklg`d1j{jcUhkiD@BB?NVU?(t-?V0rQArZbIgR!MkqzdMC=Z#S37;4 z8L-7~57m<@+UA&5{P>XlgV&TwH9=OniWIsVnSA|f`q7)opA>iQvRI+^Lr;G&9s_Pc zYL4U^z`H6`Z33m3`8m))xmstU=i_-9xO~I<$TZ%H4(pry^`hCvwX~ea3(+DTn_S<- zk4ItGN(q^KR>lo9R>}{6dn;r@s!2*D9Kt{qukxcf-#}G0ya&}!9EhfXhev>;p@j57 zFF%qD)}M7SRXp3z<0#`@K8+Tkno!pVyx4$+pv~R<2zr2B@0;nxQ{0O#gQR}$2esm` z$;}i!$aUZ_k?L|0)GEa%5@>jsKL9s#SfBSLzYg+4uQ-eFP4O6NyThBn^AEdF z!#&;(#1QUTUP+Bp{41abq>3j1sQRnT$_rNfxxizDO1IG0zw=Lk2#>8uwQ2;jD1C$K z$FY>h)7(RT75%iRCX}?y*P)NlOp7L2+%jpvcLu5YkHPF>DNU2KGK%g*O%~WjQeBQF OLQAZe)c=FO*Z&8kp0HQ| diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree index 80ad3302c5e1573853bb2b7da44876fb15e6a570..5809c2fd23c14311564844299eb5215c981bfb6d 100644 GIT binary patch delta 1783 zcmcb6gz4@PrVYW2w#ik=No55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi%-h`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_Vpm$ zrY6Rg$tkHRi7EOi`N_rl#rdU0$*GeKQ)MTwW!z^-vMFGl3BtOYpE0#DlCOR8{ssJ# zzp)gOZ9>RqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_V!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x=6XgY4ghq#6Au6Y delta 62 zcmZ1|yij<9AERN0MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t=6XgY4ggO{5=8(2 diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 9a73ff20c57b67c718effdb163232c7856aa9691..05899cab30ad1bf505199aa2a72f3286c7257dd0 100644 GIT binary patch delta 117 zcmbQJK2d#xH=|*4RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Yc~Xk8Nt%(x<{HMuY-DH);Be#y0FUt`F#rGn delta 117 zcmbQJK2d#xH=|*OMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Yv1M{fYD!|t<{HMuY-DH);Be#y02!qsod5s; diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index 2f2941cfcb7c7cad52f1a54ac2adc12df5a30354..4da14c163d182d0b3404084e4e79f4546b789024 100644 GIT binary patch delta 4131 zcmbuC-%Ha`7{+3(IVQx=s@YBWr9o6c=9!3(VobOwv)q7c9Pk!4cSCb9-8=7m@x zoZwi9jzGG~1_wGy!=P&S zGLrUsSXa?{4NgisAHjaIV2`5Yev)V@V~Y(B3(#^S`{NqgDJYrKqUV^i2G78cAJ4tVIH_u_swvWck^z zr+o$-FtMgfXnhY1u&b|I5c@l~R)hz0zdNTzBA8b8(L@3+ve=heJw0*{ dG^8$Lrpx2-0{=|(To^6rQ3f6^{@QZ|e*sXQO$Y!0 delta 4131 zcmbuC-AmI^7{__am%U}?R2G#^YBWr9+s~~nCU~LcK(}BKT@=H;7FklpFBTZ2ldvD4nDxI*DbxHHTWaI0|l zGLmj{vq15BElx^(k6}Mqu*FbvFG)7**`o5O04+DLKhA-@67sU9?|X1^mTWFp?Ow{# zZ9mn!mx+t=GpD}-SF4|WxG;AZcPi1wf+(ezq{g+xlF&0vd;)vL!h%^65yV7@j{PGr zuv5tx@|yES7+A-hv$7P9L60m&&E2E26q#WvOHn;CAxm+2G%8CGc@UPR=p75nQd}qP zxS`yBoh8)g1{3L{Z*U{VQsbwKa8j~5T|(NZi7b8Aljl<@oC{l>u0sN_k*8T)WO>=I zXMN>3U}8y^(uOX$$$DNlBldT0tq2e1dUsxfL@=)Gqsb&(V(~9^$i>KvD?_*#@si0k zH93*_ii8(x_VC+mF-q>C=c+(MvYUl$@y8Ob{r^2w?D+3i-0bamu@8QeqgP+?NftE$0-+l6_9bt4SnHeueN{1_mUDQ~c9f{RE3E_x6} zme0p(;S=)Qo!)Cs0x}If+-QL(QipddfDFNW+hca;A+ldROUR+4#QKkl=kc+6K*zK6Z5H7>Gu*|&>9X)a% d)TBORqRXT30{={OTo^6rPzG);{@Swze*yjE8i)V@ diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index cd2ede27df72da9899319f716d826dda02252c09..baf8f4b5198c5fefd1558f397763ce019455d48e 100644 GIT binary patch delta 1709 zcmX@x#dWreYeO`nVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;UX5#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y+kRAF%)c delta 1709 zcmX@x#dWreYeO`nVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?$m#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$xjHnj3&CexoPb`Qw)=Q@Y!WH=Ta zr|+HbkZWtH+ahvpJ?QzEoJ8oToCr$#tedTa=82IJ%uxO8n~O4k>?BYBWCsJT>D_M` zMYc<+Fy0U(-;nKnW{gfrr0d_#IFpf^i*$X{f%+1*&t1$I;Ypt6=*jW6T-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y-~#37G%@ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index 85800673174fb1d4cb33be476d3195a70de6bde7..b0ccfba5439eccb7146a5cd867719b3e1876e75c 100644 GIT binary patch delta 1200 zcmX@z%W}GxWrHuHVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;UdZMnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgXcQev|+J delta 1200 zcmX@z%W}GxWrHuHVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?+^MnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgS3iZp;7x diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index cb6faa0d758de9adb0c026c31a189930ba3500d1..10b2857ad8f90601709d0f0ec53c767943923814 100644 GIT binary patch delta 751 zcmeBL#@e-vb%QsfVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;Ud3Mm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuF=a!P7SV#?+kMm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuFa4M diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree index ead52101e36c42d1286d922a752a9190b46896cb..3f1ceeb80cdb8d3471a0284ab90d46544c582302 100644 GIT binary patch delta 139 zcmdm@wnc42A){S#RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN uc~Xk8Nt%&`eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6J(ge-3W delta 139 zcmdm@wnc42A){S}MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV uv1M{fYD!{?eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6LD+bY`t diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index 67211d7f8ee6c02891bbb0887638b8997a4cd9ea..34785960bfda2c4ed7217303610cdb35f3a4f06b 100644 GIT binary patch delta 760 zcmaF+p6Ts-rVZ|lhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!eZ7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~v_r~zX delta 760 zcmaF+p6Ts-rVZ|lh8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7A^a7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~v+!Npm$ zrY6Rg$tkHRi7At77&nrk%Yi3o@&=|wyg_K&uExjm7G*oP+_T`nwD&6VP;}vV49kkm|~h_mTa18XkuWVW?`P3l$4xm Xo|Iy2l4fMFc>-fEIokY~_i+ONiyg_K&uExo5g(tGml?00WSnGdnPzNeWNct+lxAU=W|o|6YGG+=m}H!6l4xpT XY?+*rnv$5Zc>-fEIokY~_i+ON6-Xll diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 40cd13419bd1c44000b26d70822ad14928b46577..67206445b30bc73af6e62babece406536182893b 100644 GIT binary patch delta 1704 zcmdlyiF5NL&JFI2hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!GR7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0GFa3T>t<8 delta 1704 zcmdlyiF5NL&JFI2h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7AsS7~hhiYoVau<|j;cOyp^uKGmIxfAR*_Lb9}1Z9c>Hk)159^_z3~ znaObU=7oZP$l?61IWQWvMb58^@)NKxmvd>&m~tY zqk0OtT1B+hk*l>(cRefViEwkEu@Nun+9n4Y=WJGRH1H%{>*mI;iR9###;&x@DA z8E2B|i0K6iOyb*tfl)0$o&z8T)U9On$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0Pw#EMF0Q* diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index 955a3e6893ebd02e6c3673f2847aae216c5e3d2f..b84b669d064f27f04027772caafd294599dfc7df 100644 GIT binary patch delta 2429 zcmbW&%_~Gv7zXgp^*xA~g}5ePQB2H?`M697!z8gVuCmhHJ9knnY#7N`nS~P4RaaS9 zjLb@@7pG(=B|BRS(}<<=v9MCE`~~y;0l(*Y&wDzZ%>&Ekf#qz3In^aqS(sbwXw#yq zQ}Vi94v!WNM?8vG^=MAF!xxo&s-mcxPl>drMMN=*N1C9W_p|ig&CvPGZ#w*qVshKuPx6OOzT7&*)KG zo9@xp)8-YlHDx~rTN9E4l=SA(P@3n>L+Oq522e`-16j~>i_>GzHPEQ1{c#%ZAI^bV zXQ_QCW&vq%{BR7SGEFBF#TeD6ll63d5o*IYZNI)0VpQLLttRW|P7B73QIUx}U}IC* zRmuMW0}X7l0-WF|$m)!sjg|6=E_Q=`3oR$M!lNmk$oPgDrxSp$1VA^$ZRAW(bpIOD(j!pGliA=JwI)ccJq25naXTo<(Qt)Qqxl&< zYDeom+IrTuineAPCtzz*?gAyfxn@yX+u+M+m|T^dR8~-7sh^sbY-nL-Vq{>NnwXeknq-!2nrdibV4h}So}84F zoNAtwVr-ITWHGsp@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WQP%jRqV delta 1486 zcmZo@W@~6>+u+M+m|+ngpOu#xub*U`WNevcY-VI^U}}_RVVGu?oNQ`gX=<2coNSV4 zYGQ1eoRXT7m@>JJ@h};>KByUOUc$7Bg*>g(SJX1{Pd>n2NS1cj&C5Ah$+OvY@^TKL z%_h9OtYq2jGTA|jb@Of^0e14VZ+4K1ASaGKsDW(e*eoiuii>ocH-At&L7uH{oAot! z_mghpWT=BDr|%3VQ}=?CvYUN&wUHOiE|V9WRN9=m|0a3fhG@TeFqw&L$182Vd?b|; z+s_}1A=CEF3r>o#k?HUWd$~3nUpPQUA_GS`NPE%kc0=-PpC-o0y1kf}(UeT8_WP{KGS>v diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index ac01cbf21bf64d3da396c51d351832b3fe319191..4a594f1147f7dde5e5bb0155cb0d58d9f8c0ec6b 100644 GIT binary patch delta 2066 zcmZ4ggKhl}whiu#hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!GR7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D>>7Wp delta 2066 zcmZ4ggKhl}whiu#h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7AsS7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D-H>K% diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index 6b223a590d0c7f495092dde89ee87a49d6849736..765ecbec120311c1d4348638833fae4dba4ce369 100644 GIT binary patch delta 121 zcmbOwJ4<#$Fr#5|RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN cc~Xk8Nt%(x<`%|wGPF&ez$m&oklB+P03v}UTL1t6 delta 121 zcmbOwJ4<#$Fr#6HMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV cv1M{fYD!|t<`%|wGPF&ez$m&oklB+P0HxL=#{d8T diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index 7ec629df192b679669198aeaaa98806f80e6131b..ef2d2f862e5f95e8120ad214366c4a33dae541a1 100644 GIT binary patch delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8tei1*jPfZBw7?o~T}bERjFmXrc){vH5qmHL ztt0JXVk*djEV}G;L10lRLqZbs(#>FcE5hi#g7yq_c|D(h!0-FkT6^ua_Fjx-F2*t+ zG$~I1X}_!TVjyAT%V}h9($@()o52 zogMLFClwO27lNqk+!&$gy3122+8t>HP+^Py1Tfi1A#wMH1x4#_egIJ6y$w5B?z{#Z zQ+b}ccNp!W@ck30>zPMq0axLCoPkZ#kXWF1fMcpewNGxLd0v^`iMn<^3!|=sFUn9? z&&(L=s>kA}t7)zabzSqS0(Jc{50CANcs911>&3#GDLb?*>V@{TrOh}>4-YDFWFbqg ziB@`)aF%gXuGF(y-ruIAQ+B)318mm+6l}dpVuSG*tN?mx){H(|Xl|){o@( SEN7eX#O1guC;1zXru_z}=p!)z delta 4106 zcmbuC-AmI^7{~dY-*mOqxmCoEyr?V;tlXUQh4Ly5w7{Bgy^!17nr}F3Y7|{4Vi#th z^^;DqSOsN}MK33PAh0MDNl0Qhy&0^#B8=`U=xor-?eFU!@cDk9=Q+=Lo^zJwGMDBu zA9xjKYpK`U9&Ym%G`pHzWg%B_q08Ye43#=V#esmkw9M^nb_I(3?jl!NAb2e34~kLc z8-VGySOu?XQmy)`%@&biI+SPJ`k%&^{2mDRh{U#EDv0`=@iY*fEV0`_w7RMlE4N3# z)MwX6sx0EbUM=0YJ3rxL`}orWHCI?XE4LdLkjIsbp#>t_1n)0d0ij{hIW)mQl+Jgd z=)jb(U^Sm;>6Lsx=7C~JHUzDS+ zzL_!9RiBHauBQ1O)OFpfO4Rko0z9@Wl3YA*9=U=YVAH162V(rC1tso0&W)ujkYk?X+4BH5_BfC-XpPaiEbvIx-IB;UH< zO~xyhd?WXgIm9}EGf%=^KHbYSTHnp=z`#u@kW}cf0I@&MQuolcel`iLaGr#@gst>` zkTqoj_J6(?Tj}FPHsDx>LOz*i*if}`1(I0|fZ5m6OS>9GCh_0IxlG(jpEjyKw0w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7pm$ zrY6Rg$tkHRi7Au&7;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7pm$ zrY6Rg$tkHRi7At77*`sRtV@4#Ah*oq9%h5dk!<{vH!<~)X~gCpW`8C!^+K($C11Pl qjdq#FcIHO^5RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN fc~Xk8Nt%(x<_^YMMkA87=}(@(BDUF=xt<#UcYP)! delta 122 zcmX>jdq#FcIHO^PMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV fv1M{fYD!|t<_^YMMkA87=}(@(BDUF=xt<#U`936% diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index 46e686501127eb90157e1a52bb40bbe7e78b30ae..6d2c855763cff94e6ecbd1893a5bd00e5ae968be 100644 GIT binary patch delta 707 zcmX>!f%(t`<_+$QhRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!GR7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-A-UZ`c delta 707 zcmX>!f%(t`<_+$Qh8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7AsS7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-A`=iikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20Yz{}4*&oF delta 1026 zcmbO`n`Q27mJPm)h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7Auo7+;aDYx+fQM*qz(m>ikN(;Gc`D{CQnT74$_bMa69ASk@~D4QZH zWoC$P?&R4-MxcSb4|9k3=6if?WCjD+jLjbe=TqngiOt3$6_mIE7@iWF{l%ZMkQ<)$ zQi7Wg$?WE*L_fFgPcL%y+X;$po>mdeONsvKwmNTe_2-KUY;L>c!A^<(=v%Iar0d@f IOp9k20qGSwZvX%Q diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index 6227a1f33377ae08d33e49628c7ce8f7e8c06b7c..8efca260579d51a0d1f7972cdd59f620572d0dcd 100644 GIT binary patch delta 483 zcmX?gh4IuC#tq(#hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!H682^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dem0oiP9a delta 483 zcmX?gh4IuC#tq(#h8Y&|@mYD9@%l-|Nye6G#%4yw2Bt=77KUkN$;qY`mZpYD#>pm$ zrY6Rg$tkHRi7At782^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dZ_umf-*Z diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index bb9d89d005d60f8fa0461f257a99881c6b4a43b1..06c65cfa08a77d2bb41950b1d4a3a55dc800dae6 100644 GIT binary patch delta 122 zcmca7cTa9ZI-_B7RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN fc~Xk8Nt%(x<{6CZ8I4HRrayTfi`?ev%*VL_q%|jF delta 122 zcmca7cTa9ZI-_BRMSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV fv1M{fYD!|t<{6CZ8I4HRrayTfi`?ev%*VL_CIuzW diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 9221aaee672f2d74086aa91726b99c0ea86018f1..99c8ff64c67015413f91880a6092a4f892bba03f 100644 GIT binary patch delta 699 zcmZp_#@v35d4oHnVRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x za;kY!im^$Wk;UW+#-C*9y2u{6`8U&27P7QnBSV)Wf79kD&S|WqYu&8KKbwhkZIc!G z+cp;qEhJa#X;BRhvTV)YJX!7pIWeiopTGIHf@m)3wr+OpTS1PEj(s_s+a|O`kZ$AT Ni|nbJ7rabo1OVMo;4J_E delta 699 zcmZp_#@v35d4oHnVTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?$S#-C*9y2u{6`8U&27P7QnBSV)Wf79kD&S|WqYu&8KKbwhkZIc!G z+cp;qEhJa#X;BRhvTV)YJX!7pIWeiopTGIHf@m)3wr+OpTS1PEj(s_s+a|O`kZ$AT Ni|nbJ7rabo1OPM5*0lft diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 3953f084d232f99fb63e702e1df91b33b7a72349..de82676b1980e09ee6191b5e57d04822786ff453 100644 GIT binary patch delta 1014 zcmX@}mgUS_mJPm)hRIdQNo55Uminn_$%YnYCPoIPsfmdxrb%YWrm2P|2IgrN=E+G( z$*JZ^DaIygMi!gv7@5e?wvlNK6X~{1-pCX`-QSdvXYx5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVcpm$ zrY6Rg$tkHRi7A`w7@5e?wvlNK6X~{1-pCX`-QSdvXYx5JwS%vfu4Z5LVWXV{<-9Z1I!4C&2NNSDR9MRUeVcy%Ty15Fk8BqaBj(dGDNu{O24qx*E+#w@Oztn%Vd8zVbHu%Uv0lGAyOb z;Ec>~Vp7vm;wo2+FBe-OH95I!g)S9SDx}86$5u+KTq&hXmz2~>UAmT!Pb*h>+QqIa z|3&%rxD$3&@3fv3EA>py%(pswO4?T{8yn|}t5792u1Z`5S7a>uHzP;cC3!$<*Bnw*SKUi= zRkN1_Nq1ZOBso?-sY^IW+InA(m~w!A+2tIF(Ma&Ti93Ls0o)m$GW_*2O1?z2jbGZi z5?2!Jjf^XenX1CS?WpCosScf5Nj|%@17vg`XyI#>ia@$Km6K%PZ+N}Du4Fv_yaVdBloix07;58k zKyr&RvAioWmN%i}nM4cc0@~PSE~3h5C9S+GT7jx=dTMxhZI^gHw`3hYFWJY}iPrOD zQscNNkQ`DvN{~noZrq-P#OYz(OfA&BzhGMo{8^I7TcA4ss*fi*?6KA}m-J z{Hjh~?2DzbsldsHS>gQ4?q#UV$WO!mzxa%q-oRg;};-^KH zCcedwPA$*L`NXm{`GiCp7`6|v877LYvhfkitb=YQOiI1lnYep{j*WWV^A!-J@p zIaXeiiro@h9Puy2Ix0{Pt`ltsme^)SR71iD9fbfB{8HO3>R%3Ctz#hHqf3ZCs*oI+1u}d7o8BC5fy}(=BJcw3M>~A?8pyl z6b!XXz6>1vlc;z!=qNd1xI&lsfViG3qN|(pfES6aeJE7S`61~Q zrCEz*|M!0rmEFT%UyHRV(q9qk?$$SidQ7O0bN_c8pF2XR20tnYwUkhOQ@ zf>7J%H6rI$A}VX5a>p8hJV#C@S7<^`<`F8hcY8uDBh>6AKM?8^p~8v;iYL@sSyyuI z7ed`RIe<`q66(p;frOe)s5(~%6KV;jvXlqqLkPKnob1_Kpw1I&pJOOFcaKoR#tb9W z8A7eAHi}T62&F2MMW|-zc5i`NOQ`UCp0xajP;;6ALY*blhODgF zgnUoPM*laLP#XyqjgU%5K`*eKu(?}RR2pG3AK|@5e+vHY966h?b$}C zJ%lPeAy8Wg_5GCQH}G)vkx;w-Ot^`ulY~ldAAB2AR|&PXBTLxf_IPS|Cu@2{mfD>=`~clTgcl`JGTN2z7dKrx*C#K|*aja)MBE2{oqsz?b;k zYeLP5w7!bXLgacvPN{=PL>(a1(eH*3>K38W2UK{C&mAVziNO~LwT4h{mt?-h=XMjy zc~YQO5UP5oE${HTS%f;i;(JLPqV^K1x2d-@&c;_7(12UaXAdZ+#AE-|8Kw9a^E`aL z1zNu9W0!13Zu(<>)hsukH$RFu%ueU!>wEA+m$~`tv)l5cCwO=-sKoah)SWLs-OOKI zS5lgrenf2VZ<{fw2VZeW8~&dS-2!sc@x+0o4@FB#|K%7De~)RB^$@}&00urNtZ94WRN77uEzHYs_2n3umXLWTN2 z%1<3$9vv6qBkB*&DY(BlpB|?@nxNctJe8oT91p@Uf@)c&AT<37eV_~PU^(XEY-}w)KC2pE z0aYZ#+AwPs$=O)7PxUW3xAD_PSL9|BX1`s4nKgop@9hEn=h3tIF?@M`<}f!^$p_M? z%a{dxiE*|6rEorPjErACwH}h?H`3&MW(VAae>JumzijHaNPBqN!`j<8wjWOXg0V*w zlD4>=F(3D<0(q{`Kq7q+ITbzA%rDDp%BN1S@e^`t@!1o+lAP|mJF^LYWl0cUG+s}% zxOG%z)broul7xx%NY67SRzSWI&1bAC$9G-c z``F@nFI>qX~gkaJW!Ukt}@0`>Wm8&$lM*YoA3CnDFvvoCh7SmG5KM`ATd zFSR5&^^o)xbzPGOCa3gD9xxzvKs0go&HVM5M&f5lOX33j)KAa>`UKe`7`vvJ;-Jv7NNDn(*^yUgS=pkT&v~xgq?VS!#|Kujo41%lkk@ z6t5<96`riWToq0tyyBc>Nlp~c&9#AA%WG@+YqQ(&n?VxN!k@_FjQsUE29Pvuvz(8c zi^8#>-1NEe=-hO%hsE=3ylKMs(}&o|K*3MMotcqQTcc)6_~-Loz*^NFgdJ%E87jQ% zVi7BN!pE%O(Pz>8D5L^B>0=ed@HZ#eLEilY9e;L183HQbtmU)U84%zZeiShB&nJZO zc?*MZM_fAbvyKa0+!x(d7!t!PHX~mp*L)&!JfGm;hb-_aamIi;Pd(5NnQBmc|Fh|_Z=UbBj?BG+4%}{F;pP9@vL&;PxoEccYC?w6$_mf`Z2p z)eFUGtyLD_%1I04hP65n?f9y~9Tvl*+(b5V#TFHvs# zFK)iVkT&S-esXrhujM5G_8waK%;|tSR#UW;X!X^V> zcS9-(`STlc(Lnz#3Ty|`4bFbBZvNh6M?Q>Oev1YLA78LW@BwV?U?eHdi!cM_N#vZI zUBk%VS*GKk<{Oc)S4cEI%@5%Z=Bv5;hz%n;QNuX;(Tjf!9o%s~XzMWkukG1<#?Ij&=JNv;KYrUlK5N_GvI?=C%klko z4xO3wFn|x*HW1Vbni0T{*fEeFyES`e(-{i>{f>d)!#@uKkiKvhgpm2HJ(*y^4~ql% zzY8;GenJ=Gis4#X=*gkNKf5#e{<}s4jRX-RcV~u`kLw&8ozbToQYTK4FoPoygIz)P z`n6`h%;7X#(DI*u3s^dSFNegy*%5B_Gj7Fy-sk1D`^KYsoG0P>k)Yq{f=uvC{!qb3 z9vlL$$xu&`2Qrr~{hi~zzi07F_Gg1?s8W(M0_<&7Fo^dY&H}v*5LJu>$)#Z?zx?1R ze#znSm_}HG`K3i!U~>0@0Hp6VeAy#UxpI8)u_3@7zg5M1k7j^E#X}XZKlXs!KT~3eWJDeHLA3r;SKYwN<==A)8iXU@g0Eh}e>hy-Cq{ssf#~eBQv>;yXNQ9ihtZi6=j_}|zWMn$?kM?x5&mCE z6CSYmd6qd~U~-pUsYsu{<5Rrjyo$ebzO4dhoas0D+81hYi}SND4B)>0<}2t|uSq!H z@oosnewroEZ*r*vhp<*%o~-GspeD0Z~v7MnuaMR6A%nx{aO#xi}Y+C-^>$6|qHwKxWKlQKu z5&a_=cmH%fBrz8*}_Q1jBL%Gn?Xf||8C z6^xGOTB^XamqYe(`-*Kx23&TB)x!p3szj70} z^Fo(!NGtBL5SFk@2ks7S&^C$tg+V|Vm&RR{5wXCKZk(Ra`(%L$J-HbQ24x|v5#U7S z<_ZY#Y|!v4gY1PII_It6-X#Eu55* z^~p64FX1}*>K##8G)zREpO3UdE> zmzx|Q6vsc~?$O$;w_G`T*>4}XZL~H=Dmg&ix2s(8g0hnqBq^k|@gb5oL2R1q0g|ko z8)TG{sNiF>BrH&Hf$zK$!3FC0Bm<>< zN_|BCt>hYQAZa8yz~It&80ZX6elIBn_O%POzo9{{cDNXAYi%VdP$+}`=gRs6fEx#Nq|zQ-F{25 zpCU}VBRNWoeeOxrl!wU=CF5C4jAa2x7AIK#L^AyAl>rezX&GS>OnNC9%Yab(mBhy2 z836U%6eJxQr~;)woAe-Ie@Y;zvRWDnH6JBiETDv|hXIZYm(CT`4a%9N*SK@Jj#+Dr^UmgrF&?Dd1IxcDQ9KJOD9ks4ontn+viG8(Y94* zNawN`S@alyJVuKnGjZm2nZ9byJm#%HVDWBgcaZQ#9R}ykk@A8TLCwX|$qcT8urgrp zCDJD}w0bU=c4LJZutM5ZP#1Xci*y<*jOAD9TFTAEwd~y7SSK}5ZWNoOfR&pX`O=L7 z%q+;xjB2a&HI*r3hjc0{Gu3xWcTkvs-7MTdWN);zBXeDkz0zN4F=C%|1=ZD+{nB}? z?3oTq%dpJC#0hU7mQJFwWgU~wW#wV}acK_A&`x-`e^T0;@{oE)I)jymxo4%p%zCJQ zL7eAaUShXJ^dHhy%sdRaCS64JQ1Yg9A*&wJZb^S*w#A`4(m|AmX7|}`@zVoo5i<`D z|CFwzJS=`9ox`e!Kb}hSnQamCQaXh40AI7~;qe>kQD!}~e<$ie`+?mjzyBz0!f+cC zPk1fBqLMNd_@=r-4=c%J84e*S-06`$paa*I5;De_s|7^Z8|hG(Tv}E{ixnef=#~Z< zE2o#0X=%E$IbL>%HkeXT_AAXA8da0E4-wjKZYaB{5{fHY$aVL+eRDAPI^51CfnqYYb<+rR>?xhWq!{q)YW@Nbh z9Br#L$!F5G&?f(pCF?{1(+ci#$$z5OYgtNOJy_s+alE{pO(+I7mw%Fg=U2Mxpt+TN z8_SIf)KRlpHLj{aXU|~tcyNE+ba?FtIVU603AVJCqdlcRN-facS-zi95P}c;rpWtK zt-VZ>`vdT3clk)7Djn?5TP~-T>(^gis~Ey4fxXsMq&O>Dc@l~CWy+O;MDY3ux#9l_ zv=TgZ1}osyALV|Zm;Wq^b$uKaOACAF$U~@Dlcve-Eb)(R1Y&Cqo&pPI%ge}xf%DA* zdD-HCg~0X8mT#wGpH*r-XXt18%*CLf61uK z=3v7bnFb`Z8j3b{8zc2F_JF*nNf2$rb$Khs+4c);2nc&5cLUe9pJnjiEm0=pefb1B zmTY_|Urz<;_f&q24y4UK$?FFR;k8nw$fiM8*`RnzYb&`G=~lKxq8T{(My>_B8V?Ty zt^;xn$USFj4i;2b91sK_T0^mp*`@@##&5Mk;P2lm{6q5M1`6~{h)fof8!3LE)_m7Y zVP$xbho|voz!~*tFi2>lkb$lJhRg(&TPt!{wSl1^f{Nq>`E%PUq=5`zWP*G?a>#5r zxr<^wi%0qqzJ<5DD?%mc2Iq!q`Y@>MsmKUKL~_0{4CV|{^k5kR{n$|e%pPKci-sxy zi+BGA#slw-P~4`Ly*x@WnL2Xj7=?_w_vEpPDvYYA2dhm|JYw+>#AH1M#`-;(b>~nY@5|mY+I_e*!Ish z%CofXiB4kMDV@c(m6FA_@4F}u(zd62h;66!6x&wmEw=sGN0~?4t{o({?KMPfYZxlF zy)aCw{X2l`imb zvQiJKj8nD-AybrgkYG|7gi`kJ=&cb_x2JAqX`z%f2fxBHv_p9~X@(L#nsv zdL{mJ$kKtM+4|}u&O-ex3W&<51_Ytl|O*;3@U#Bbup^iQiUIOsLC@whY%j2 zz{w?5Iuls7C!qnjUsJ_$2L$iqfk$hp-m$E@`;AgXgX|34r!rEVR@wbrOS4SDu%~L00FcXZcF#>teP807z3ts zRThJbAgoR>qNmElGS!ibkMvgMNdz87WT?io@-V%hDxD=t2oDp7s0LFWw8K;^a}9y9 z4OjUXG6#q*{)={>Ia>8Is|s>{RCQuD!;rC}3jP_V8pX;(-~`o0xnRv#Q&fFfVV+M_ z^%cSdWXw?IeLW1UJyX?LDloEfwyMk5Bj7OU1lG+{1;e#-RHJBASDUB0MqwitsaS3_ z5>IK7r;4HSO<$%;W96>wmeJuxT>?pm%YNm=}Bg$mtj!=nv&wpzvVjDWCcTBAZs zb^^0v1G56*tc|KSl$-fmRMQzHg*yvW!LB`>la*$~c2mwzT4-WxTDQSVVj;R&~ zgPr>32KefdsuaVJhJ&XjsQz0mHJo!*^-K^K&bg^V&$P%qn0#AxO3)>&dry@>r^ID{ zsKiJ_0~$r`iHK(91r!1Ee~k zHZ(q#;0MMjBL*aNxN8NKe+e+d^!fo?l|)-$+jjwau%Uy>0=qT~ zs8K9zXm1^`QJ91wle_=(vE!Dxr98 zYQQTKo2SKq4Gr#TAQCk`Q1FS-`2jjcpP*n;7~rLxRoX2I^m&gc(9L}T2I>>ag8>Jr zr`$Lk@F%VP<3zwJYQ`C70zOl)Wmf`HsGUvs0vam?;Rikm*so?4KKE~x2_)1Hu)w1q z1G=|ThoqJg93jk$7+X$S zTMNDk?yU->5qNr2V7Y+ebO2EY6RCDpzXiUagH7Pcz-lxY+ujJ=L4C~oK-A;Ahk?IS zjU9g!h?YV`yOW;;TBz@CeG%A+<+cbKQ)9rMPeH-pa zP(-LuyRu1;-+9Y73;Kh4i?dyj-%Ve)5Av(`aEBnjdM9)eiw!ylm7-=F-8CqHafuZJ zkdp<14-M~mV0cE*v~VHvcm5pIf_Ys3f;cxP$jGu0nHUViKeX|M_JOW^TcK`SWy*gZk5sUaU7586a~ zT6b17WR(j+18JP>z7|xO7FG9x(Bm`W3hVw1T0)D-&qPaCd?8vo_*GB^ireB{&_>qi znbqlTG(grgZ&@8&$1Kd*_dW;F5hL0X9K~u|f{d&h3|2-2yWuc>Py`3;o?tuJ@FUj% zE^-HNq;^S;46aKxu%%q^HI|7KSzs`TIW^FPHzW3g`zi$&P*Y6&CioaNZ}&RE``FD} zw^6X)yni+aH7d(Hr$sus?bw zKL`7xckei{m_0!()|?dFh$76*4erirbntwO$_LPBX@PmOgGXouv)={5)a;3?gTomV zB@CVf8;zSGP`*C+BON?-`J#!_w*Nmd@xa}piL34vO>90Ktfn@od`dJ&%0@sSe4<#BBuEGk2?rngG||JI?}F#iQLL;SJwxl4$D{M*zm#2+i;dW866rBlz4aH@`r_<9BCqlWimtND};&dk5M6kEDLNutO}-%4vD7%uO1iT&tdyd2=V8zt#U&A zIqb095Pv4ok`M9U5sjK1GL{K4(?Pcox~wv>6T2UE1j-z<@H2 zAHj?ZA%j^`Lv*JYJdfAO@Ygyt;6g1`XHfQMi4Ih4tX6|N7em^7J?N|^_sk&ST1ZLw z>!pwchS@*_0KKk;Fzm*}*F(gBUIv`I9a=E(R>%T&F`CZWDpkRaw?m{1GpS#c${_kt zhyqr77?Qz|V~Qvcp{xl3mzy{Y@Y%bNv1%e99II5%mkY&mA!?L4VX19L8sqa$U-j&s8<|1tmR#$h@v+J`Y zwyG0sX|A5ay#yQEw{HM$T#SeWu!Xuf9*p*aHN6)E$F)=kFa$l^+gcsV^4JDlmptpR zIuW$$u0~tydG@XKae<+YX5ChY16N4hFu1g{I!B816Pu!5@%6d}6wCqpc2(bInYSW;i#Ibjtob1{Brf9 zAarzXR1Z*~;|!=itt|%oPUH_P53xiCAn#YWj;LqLkS@2KP_x|d79HTpN%eJxA?J$+ zrJhkIVxtzFQ!irF3UX@){CHk%V2I2wjI6$-PQi?*uc#RwpM1foe^tG^SQ=S$UEK&X z^5;!v(-hoRzbFF@Z|NM2C;8uF)6H`=m~1)I{Gc%R`4?CD^5*jKSIkHkO4ly+?{P*cU!kg!ab_ zBnN~t1Sv!qi&{!<%)rg?P=-7Y4-9>w!xVa) zF(CWG982gr%s^FpD9e`@2m=!wp=X&H_~Z)x1v9YH8@iTlH0TRo`$E+?ig^He ztwV!>CCySE6pqxXz@LesYH<7)vjX%=v-E)xZ9`eU=Y!*wLNAQ5G=y8PW0c{Lt@H(eZnU<^@XS zU`?gwf*Bo+ZcXI~bWHe8bB1NjLF~JzndXk#&;IO04Qo2l7$77yQ15dopgCFN#f(-@ z)zrk9@W`H;qgbFuy)}JsM!l`SW(BTK8mf7Qj|thDt@s!)PO}$ZcVv<#lVQCtSX47z z6AXGeM?}N-Q#GvN(-7uvEV005eM%1|pb0!kHPlqz?jzjFA?wCtN!gW*pPV@K8^}XTnKMOU5x9 zNr-`a3=9o{8D}(`5GM3q(Coo}cjuz!cpy6F-_~qI=^adcr0LHZFd$nUss-}zyI2MO zWp6Y=BLBX3nr+PdV_=y6LF2-qFp1NWkZC8?*1$9f(C)&a(<1nA|i5efg_$I4V&)4_RLb{OA@;ZysO%lGNJbKP3=+i-6ee4m1o+qQlZ%YmG%{FF!-JJ9VgUg{jH6n2s=M& z$7zMyR%+dHdRd@Ohu(xGU?26m`f}R9th;9AHVNN)8dgEKrx?*fSE*xa6 zOxMEYGj-*pLh-?D-3(fsJYN?r5o(Vv)J4*wYO$^+Ew)>#OQyxc%XJ$#q3xuVI!J3< zuGTH4VDfdkDzr9XgRT!Pp4cQ_mb+Ouhayxd&>g1@%-eKFDZ*bnbzNy~&)vF?w0Lo^ zZagjaE7J9##Yg)^+%*S9b_O2Nd;04(st8x z-DX-G@>1;D{aUw>)}DN$TR@AW-s0)Qu^)6(Xn!aE(UqdLDo!sNOsYrk zU6TId6?*jHAyNzv)cX}XDOleaJ;xm1QwJZY^=OkVX;3*#zl(OQ4A(cqUE}u+LGQtO z^qQzeuY@CvdcS{cHS5u9=H$ACR(;ri8pBgIy;UWg{N&U7eXdq1z2E2VM(X`O=Zh9G zlVU{?Hk8x*?cK0~z7AD`8?LRWuSUE5yOMrAEpGotj~;LnX8Kjt*FdcyG7{Q;M5;>G z)cf7@QXReDJ$>Kmf1nMf*4O*JI-#N7?~^f2^vEZl?@8CekOX}SZ9BS!-X9FFT8Wo! zZlm`H!-)2JzxS2xB=(lsS?uk0vUu6D6#Y7io06vY8C_ZzipFHs|t`sh;>ff8Gj4v}@xcSnh!&olK&w3s|x-^t&E7!Y0^p_fpbJpT#XWW%doIv9_>^iTcc;v~@~ z#wnsrTIPxzES)ZjZkQ>`aDEmReNQF=TQmoYPTZ>b0{u6Xm3fOq(G^R?%ML6R6}@5w zRy2`d%xcj`->ngQOJ6Tu7P(PW^s9VP(aWHy=x5u+-hS9AUS{1TD*EystY~7^VMThh z*MfAsX}>6@|^k_JP)(Efsd37LS$*+d+%7qr%YU4RTqZ=rHu^8!5)d zhM_s06uXoQJ7o}x^%{g-3KWXbEyMhAAJ#g|6Dri!?;3_a*hMbe*)6Ozn%yw#e`SRE zBebA@*cRHwh=F0PseW$_6J=XEBCHQZ@MMQwp$MT9!@RUumk)DL|Ls37ERD8(xG-#~ zM(Az9dJ!yfW7uEREU5)y=+ha5oyuE9G5_8chW3Au+TV7BB~V;%QCJqWMgGaK;Z(Mo zH^j@hTVZ$UWp8hXO{ZPVeGt~0vSWH2HifbSKZ=(P{U@voZSa^gJP8sy{xigIjjH;R z#;}Rv&es`6(Av@l!!25CGaAsYRKo9etARt`+Q7roF}v7ckJDg7zb=Bc{-mPe3N_Z{ z+6MH+A5z<;o&kMWgcMW0GoVl3kzzsv!(rMsvZbLdH4ER~;MZ?pCj;6UN!m{BYKWy^ zC({h*izcKtr@H}tgqjpHdl^PjO)3T%?ocs5WE9N>TmR$Tj%QUC0}DQ5)wh zFkGd$!xk9^YXw|xBi0TXR({`P_&~u2sUB2y!(%BA z4ZjcXMvHsfhWq`uW{2>V6zpx+aO7tj8lxN`uv_>Y|J2hv{GnRl_x*@)^a>ndedy@$ z0W?}Z<%ZX%xUJ`f>uG8+G%x%K*k)5moMw}nx z%Q_D>;`|sFYYj8v{1_K^j4(PS$V4PRem2U8^J82;e6$hg$GDh1);NXMI>#GvevE6w zCmC^mjEm=Tj5w-saqbkc>w39H9GSIa&KYq&jEn6r7*Re< zm4q&7K~Y2h5Pfp*HPH!zZyIq@j4vE=TXcfTcST1uJ`f%8^&?SVJ)VjY@W*rUviUDX zm+SgQbUDL&vA3iTVsA%2iI?SkHm;*QG?tkBmi$w0LY6%F6Vi=OW%BE0K%l7|8ksP^ zS2QM+6bniRF$R+ocF>uUDbTSn6V8lryBbCl%8aRTwwp{iDaQ53Tqb|;YkVeu@NX$; zil!I7E^YD$e{7V=AN<+TVka+SP5$7|FK6-x|L_VXfAE+6#^ewF%&I28qHk9hFI!gA zrfn^-n)kck2Drv*u)@2SAN(Wbi$_h3f< zY-$QW|7fbr@)!bN4A8}B2~2>a|beZWkR2c)C+m_}g9KKy24d8C5@0RmJ(U6G0Dr=7ka z*!G*8j8;knpZ1#qL5+u|@<4ge^n}f01r#{>km)G1avz*9Iar=M5j9NcdRGe^Z%pOD z#WSYQtU?f!_jYqS*nSrMaKNF9CMVRNH@#tb%8S@)30x0M!Ep9fQxEJY#B0KD2wubA zV)YsU6|B08Hf?XbZR*Boc*-1t`N%lZP)1@??*_q|4^6IOF#<3Qm~zOJ4!b@wbz}4* zDpZFTCJ&=4QK4@9Wio&=HgiSL?v3dkn?F%z5ERp^pu~YRHZ3#K^C zby=RIW50rBN|=W#sW*WM5q@{NTh?3`yHn{{Gt0wk0$n=J+>=?x_rUA~F(=F#Siij4 z#X1VWN0rTqC|F>l>gI=NF#zLhn*YYfnA+we`1qlYc@sXat7l%1kBQ%zPvK*11M^ON ze9}-jpVwG8pYXl;Ag(tzGoSLJrOvzXp9(7cv)^n@>lnCoCB<42fF8PTy5Z#J>) z;~)bCQXIo;bvUhQ335wb3kD0^7W$sj_jAm(aF#P=zIiF@$mfT2!0QXli!fT&V)K&X zqn%h{{*HMzMofhOvcdHAFXpB!yUvKZ{^^E(PV;i(&~i|oZ=TQW>I7zG8v5`g+K^k0 zQ-a!i%<&*&qM{0Xyv5v(<%Q>sx6B>!Zyxo9wf30%1^DATbFV+XUmY^n!|`4DsF~&a zaR|Q)Y;eq6mqloF@c@WId!HQn`m3|%4D9<|FPRtOqx712B#ZAO(5DPu#XU0*7GBEx z;f{G2cF%M7&CA$;M!`}K&9_-Jg18t3w?8tIm0ILe^YX7}03wXrEQ&+dC4tzomU#Hf zYx6=z?g@edUi-_umu2%5;-NK2!(j}p+R1|UG~WRQomm0^B9D2==EQG zmazaMfrB$sOT#~bWwMle2Queb@)%_V-IuEzaNm5(a+XyfVr8Td$7PmU24Pn6RxkxG z0vZcHue7vgRBbn)xrY*oDh(`pu;-379rjyqS%b_)vI$UW9jeamV70)`n=R-C^mkx& zfrVw|jo2hTso_y*S;`PFz>nYrP0w5Ef?6JFIXG{-B?ajYelE0BmZM|keHIyZx$3`L z)Pd;O*Akm_nlbLP~ZI4Zk>XC@vOso5syJ@z1A(P+?XP) zJsC&lZnGL)3jhydtn`Lc(@1MWY@ONBR))|ZBNs-52=PiWSPJ~tt9bHCRvl)k_NYVy zEL+8T77v4x8dmhu+&g%*rj>*=U)%aEOJ0Tixf{&*j@h5P0d%nzbh>9XfRle#mV+(J ztYu;ChSohHY}V`so{zK!f&<1#oA4%Oy>`|xEIxT;)w_c=jnVP_z_)xFI5yF`5o@th z7b|)b^c{ScVnuI)zJmw)SSKscvGpLUl;xfr(I(>mn8@(y(bl8IVb}r_##+(CmUl39 zf)%~``3{zwWUYXC>^{xv!xQ1;S=Mh^Jq>Kgw+4WddDb8}YQD7rV?=lR0t0S$bXfp; zBUuU7qmDJM0qf7AuY}8YAO{bfYzc*u?N$?3xplYoEH=sWeb$-9QGGbjn5;@z z<*;>z-(f)aLspuGX^va_V<#PR%36=L7uflN6>SWp3fXtj`W>@E5Rw^g|HHZh^OStu z%9@3C0_w^446yw@t4S1k&RxII`46od8QqWqD<50iVIw8Iur9_%lKf@;9mmbVx7OeA zangG$WK5ttZaHC#;J9nTuCwYA+hT?>?#qU*sZ;L-fpfJ~$zWVUnOKkzZBxO^Qk$B^ zL@$OpN`Zt}TLU;jVOt~NuEB}{w$b>wHOQ8$K*xqU8@h9K4ekrGHOEJT(Y8m5j^{15 z)y3)&pdOu|mcz#KRkAO9cXZm;BYr@z*S3m5ts)RWK*DXm0Y2L7_VKm}LFl-Ww{^s{Y_{!3d>l7lxc+ROZ91mAEw^>V z?TR+o+G5(aMQFEZo6zoYq0lbtH`^YT2jYH-8iHJ?Rl$dcZS&vD*$;(wD<9eF;`5K5*&=Yg z_Kocf%Uvx&M5nzeSYOd*fan_yF*xk8h+wG1t_OKOn+0m+cBbzO{TCg70YV8kD(&c& z~fJ%jCNMz{v!)b=r0(k)v1SoU$kY3~6)8toj|2@5VBD`TeSaFj%*Yx3A&03Gb0N-qJK0460x+&TT{; z?~0azQMu?P_NEo>qZt+={NABfXsv2b<+yEdbTzwK3Hps2(E!}4YhS}K#`q^$^a|Sd z$OB&0vlmLa*>Gb6dmRZnzHVeM!!jJZ^X-Xn?f3S52n6&?u(Qnn7y}>-HC*2usS0ru z*T&urb2X=(eHNx;JK5*5a)*(fAT!C%G8rS*(&3b3dkMq^*pg~r!k~GK0T6~T1#7z5 ze`Xm62`6!V?emm~6JcoRgN6ppQM(1Ihu9lq{r@nODZU7U4dx;JXAQGAX1PV^M{ok? zK6@zSN7{h215RFIPiOQ>0bu8H`xR`rIjig}ciymQ1*`3YaU2g>YahxmVnc*6 zJ(zkFJhbLkrTEljZ2R?797t0v$)awV%iJt>4?x7O>e+^}&vA=FEmCKH7^g-VB99f!ogu zazrzXsQ`)F6Lji-*9x;Wj&>}$gNOhSf((3aq%{E7_g%WdP`v}~I2U3Q<{2Ctuvwa$ z9o1CaUGh#O{MGH)%+jTQ=yrkxMd{M;sn_ur$K8c@BOFs?+y`OK+EK=_fx(x*Kxgy! z!a%pDW#d42lp~7)3Qee^@R!EbpyV2-8YY!>oMRF8zc9069W1{ii9pr~519JJIb2vt z(3V04Bt2GbmXu*1NfiX|0r zx7m3V+b_rFypHYG%i$cwqGdGPW`GS-V=|$%q_Z81J_$l@-G~aHQWcaS){GK#31Xbb zur5>MosY5HEh;;IW{F(_pVU2~H25gy=)BB^wFZW5bG~A7^gGTeV9I%?3Lf9%^oD@y zQnLXnjyt84C(9`()8irvtAYLAWBT7y6W7)YO1_z<#6*1*~~otr5SiaXBjU(W+Xq77!rG|_cyV7&*< z)3p1NkDM&`nFvhLV`l|s9?m~=9-};Le(Bu(^(p{_hlJP8@+_m%wBfF)XzPs2Ej+%= z{M)$|eNPIbEz2&a!9gAa#0C2z5b%&~Ehr+d!K{a2nl2oqG#p9+ykPH3P zB2i5lbuqa)5$a-E4SbPjfX=m_a?{A*V%Z&uu^_ViFqVOqFmlrB3I^Gi%UGbx;TkV+ z1Gl+c+tJsXkO#q^Jgx|q`zQoF?*-aTlVtPqP!i<|qu?c@Mevs~E;CC~O~A8<-PM8Y zBJ>yvZD|V^{tzbaZBQ$*i|TD$Okap2UDVv{4hDG}qAU<){z-B$ys5-l z<1loe^6R4?B1Q*M2xFG(1l8`_Y?qZK%O_$ZuSCDleAqD+W=s%$X67W(JCY{5`mlH< z8pC@EONd*uT`ifG5Eub3A)sQRE0u~pVzH}G7|vm%6|O6^w$f@>FP5MuOnzBLEZ^u_ z#MKVNX+PhaBVp-V3GGu)(@`-jU* z8x-Ag;moBD$uM$H!DU>a4v<1KDg90 zW*>YK19ih^k-Z_DJD0g1bn1uky2CxcRI@eCZYm_s>P;E1P$i9!@yLxn_-rP2yd~R=;%yKH%sz}00c|+ZtZ4T z;)npD?c4=|xz7LKW?5;8NPRoH4>Oufkj2>9eO6%QW3qei*H5b0_yW?r6gShVM+8`x z<~|{?GNp%`W$}fPK&^;20kCXOcR8jZ_q00~y85|q5Z49T!Ay=a4WR&p;wEtqhq#%( zJ@{V~FwA{PP|oO)?m`ATiEz-B%Xm$=%AJlWLl;xm7F%LBm`xu%V6J0+Pm;>I5?$yW23%uU}xo0D4nM0l^D*svvF1 z8+V~FfB*5`eMP7{_{rUuB_asJwKlp^;Wu*629_m)AE6Xn7w#D>@KwR;Nmdbitn_;B zD@kclq-UF8qaI~F1wtt{#^V*vcCF~?D_nc0if64*T2j;FAXx!CRnK!#kaK$jPa{Sb z%mpWp2nz!XSoCM9matMYkC#Pf#8*O(T~mUrl@UGR?|1JDakT+?alCx04vWr(QgNQ@M=M`L!LyS9O*$X#vk&qjL!&f z0{DE=Lti6pk?le6Nfm`>I+HB@+8Ht zYr1C^KTZMYaY`ZVd&HxcA?1uY;rWe`Cx|@a7PDT^#SXabjAscpQOENhmfYSioddqP z;9**4d_e%0Jv%TfEw6g^Gb#j!hy%X3<~dkAtxUV=*@IbWamRC@c&yy@7=mLc$vNz;n(=TZ|B}_DL}E71^ro_i z`C^=RAw&;^^6)50Hmb#s@eV;L5?u4Mmu2S{M2H#q`Yw~aXscHttTWl$n%U{eGHm@! z?>%HnfxXqUy-YiAh>svRx*VH4&x`iA6~b2wy=dQ8A>6yvi}rmL!gVXXofu6!an?SU zTG;lbUGEJFr5uAgg@)1}lWW{_aJa!3v@MxEE~(D};vg zUbGpk5MI9^1k#F2UbGpk5MI9FMVrA2;SYDcXfs$LH2vvCo52cU{bydZ8LSY-zVeP` zw2hFZLzJbX-*Ri&%hUj5ee6>TbdvdswQc~pyY2;$m+#?HxocYA%GC5YHv!pHJL$G@Ok_|!1Ilw{bhu*a+f)%Ry&|FXiKUViiQTu}0 zzWuoQfp3Lo3+oHbnl}`hnHm$Rp{c2F4$C`({}G#InIZx}(+sBfn{|{PBUmw$g>VII0`uRp-rs@tXW@rRA_NUq&5LQi{j7>6T;%dTrmqS6wNec>pXED%J;*%A$MX2^3(%&yz7Ndv z0ukot1wNGg7lBH7#bo8=Vjt5A^ouZEm-*IVR`e^`BZ0`YVwEqhIP+@ zeZ5%jCKItTVk69WE1wghr&?=@llP$SeRL@i37$VIlLE{9?weRVEY}&I6=t3E{Z1XYV(u0%6$S`QS=sT)JmLr+Z`|G|ihP9J0XrL4iEW%p82$lurznCT+#8!>u z=>4K1_^4C_T67}gl!^Gv>}n{8O2Mh+B0MaQ5RsYCclYocN_%TWRAPC0h!G&#UN{#k zDYAY9TAvod_l+aa`m_k%YZlRwQLt`!Cm?+kJLWmOr0TL`(iS2H(8_!DIcLN2A6WoJKk>Kuyf#MV`PJzK)S~NI?0>LT8 z3q{((@66oUP5ZvT?;kyR|LErq>E7o)N9N3&nLFps4TdE2C>{Gva17efz*bdBakw&2 z+7rCBH26tHu&T2c0(;OWtAk&mValdGfD)N)!n@K>8-wd1LOBI3FR&xVM{7kz@#tN_ zYY-C%>pq~8=IsuKS|h*E_4|WYJCYSc>WOUq$dO=dwoc*Or>I)D%L+qAk+Z2X*jlO+ z(u#aO5sWzp1t{~S%-r+GV0i6xvoGuxzCRuO0HG(2#iz!{&IX^y^e%=#Atm>8CNVwh z`;ozygW=8Z${_)C%g@0#;p^y(-ICiVD{^*_OncCvJHf3nb1xeLne;1suQqT*NOcN1 zd9kFm9gl*yVlwA{piV)moh8j$CrLjcuY)leGv~prcjmWXRI*%-F!=Z2y~0}QAA&Kd zayi!1kHM%cnITY+=e_QBu|HkPu|&;(!X8E)PNNm|H0+XsytYQen8i!b31=XgSuF z$PiTSSB}s|69S)5=We9eVofh}^Gp{~8IypNv-veHiyXwhA(7i4 z9U7+?PxI14;3yc=5ABl~0_V(lGbuYH3NxbtNi(F*K|g$9Lp$0e#2b@WGQCkqPd^EQ zn>vKRNit4wOs5byNyeMGU2G5X?`He!?d~D3FuRxRnb!Q%mlW9baw0G5Tb$|Xfg$dW zq}KlWe)RcQAqiMF;3=s4h>&Gcf29XThrk&%F2~HVwr0EWAzQ^?Lnel-6XM^U9CAtA zc49`zL(Ie#q+3c3IxFYVYV&M%Px!{BK<9-aMMz8ak+PxDt3xc9YzUb3kq%p}UmpU8 zt+-0+Z4QCMR=oLgTL>Js;?0{oLwX|}zt7)tqb3JKmSb`w&XgavI(0q-4qI_fxmQBq zuoZ9azZC+9t$1_C!w@)Z#hWFML*TF#Z)Q9Rfx}k3`PHisIBdn6f4mKW!&bbx>thHU zw&KmlpF_l~m!5Z!5R%Ue|xgT8T$IpDl%*TDe;imn0d+OA|J8jZ%3?j z{xUdX#rb!wAsc~2{Dyc1%A^tx(zbj`9UJVxda_*zSlU)D>)}X(Mnf;Y7bdGK^zEmX zSZlvwhWWrF%%k{Ba3t- zOYawXlMV&4n+U-;8Gw|AX5~bB3af6od#enUbK4Mz$M-UC64XN` z_Q6|kmkks;c(haYN@(Vn{VdXetNYMx2W1C@n#UZLEf>w0qq4Tnk`~vVl%+V4Hl5n} z&`;-N)iH77KiEM}?L#(vCCAK*0E$SPZpqhWFGP1uzAf8=>k~`pfif8?iw7c0Xv2H5 zrS@dT7ae?v(?c2NOFZ$%i0 zLG1{TqjukHG+x${!-5VU=C=o-X@H6qG^MT_wJc!ca=pHMtKhOI1mkVQ1{Exq>oDWG z0N^hri!I@Ck@7*9FwdaK^oKGxa$s@Y?o=Kp?~F*|D8P)xY=!a1e{Lq{d$#N8PP1HB zNgAsa1@K2cR;!xIYhj(30`_MyD0&vj8)D{moa4(5a(CjX3WZ~Tee3wrg3fZIy)>&@ z*;PJNjMkc-a!1UBxcmu`38C)Pp;&%e@-SM~M-B-9b9{QRpZs^Mvye?A<>K7qgQ4;h z2n(|rZf7Y?-#0;?fJxIi02J!Vws-Cxz57*pBpH=PmX&n>#2LB1WF_l(o*Khc2{(qoT4S zd49*+o0hmJ8ek$y@3D%mRPUyEiRt9*W&$?{k+LF1E0W=%z+}CE@)h;+R9tc3qL6n! ziW3;!01N?`Y7`FdqxLfy5i(t_kWML*(*DgVlUB79!8D_mVvdvD8vY^&iLR-DbD4TY z01d3CfP!sI{d0pAn1svvKo43JqClNk(KS4nR zuiD89#1xy@0C{}G8P9Q^$=;bQ20un8*sUwAoRTYUCT*RnGiUT47IW)z0 zXBL476d-Y>1HM!o5rEk(6>BR@a9=A0Dis6Gk0))YQc08AD~3wr1v%Ln+ZD%?(m&zU zY+hFds`@5p!x3usP(TL64OrGou@R>q!KQt0#dD-P2?TjR#X`a6@&K&)1y;jBiVqcI zQ|~Lqdco%QunOv?-3Y}^#QcYXrIcb=SwBGmd!c-U88}IC26K1{(1wy3GZZ{43m{O? zvmc!_TLHVR9Cp(j+Y^Qm#UAA7GnDi?0cV%W3vZrYZhOM36$;ocUBWz)LA0yeq4dt^WwTMS!jJP zHT<-qg}s>0(8XsJ1B8`>FDOvCUO7B}EA{0?1>|~MyJxN_AlKu~4c8Qf2rp!V>`=1# zkpjvVZK>l-civL`g6vx5FlluD9YuYsjfl%#>x}NxLxoQziErKKidRA}^F)|i=y>y|j|!*?#GCCtDZWGZ34^23i+@Ri!j~i#3B?U_Ra!e-rkhfR$!xhAQ9U;K#9q| ztgs$bHb~hHtsx*L$KFif<{&t$t87K)4^?84XD$R7NkP9Jri^lu6mo5p@_VGEC|C;h z##_0W-;PtRu$PE@H&Ka6Val;0Co54&RXM`xsmjBG&5{{P%!`xdSjt&S)aFzRt`1(;f4bo6ru(J7(-cCncC&RD8MZJw1Q zgnp|WC)gZbsl+7aD={0a<&^BUDp6@1Agq$M zoF`Ewyx_{6sAw;?r*EWh_9;<2X+UHny?Ri2Lm27E5#=^qwAn}-AFBX|xF3`|1qb_6 zcn)(;D>2(=6!g$uIK}VhvA(sDj=rcw)$xJHLs3}U0m`S>m18i=(!sE^Qf^@Y=ffq-n~JL7};r{Eusc!Ln+mq?0Vvo3t7Ni9%d+`4kcn z5*kj-iLx;Iq<(0bH0aYCiqPJe*(`sO`KZ#J!d~DuCyDkoZRiNew`u3NP*he}jte`0 zKS}+R)<_P8gph?U`avK17s614J8b#q_u#~0Co0bjJ%@2!m>PGHawOMAp?XY?#1VLo zx^nCUb^v*E^`cPN0pv~D@=(|T<7>g)Ir*{A*LF8s(RJL&uRBJ42lae0r^hbkMQ!j?j+?EDApJ9;15h!%Gi` z^7n4L(&a}(!!R)xR6m_gI};jUM^@@IndIf!P)x#N+x?qeXN24Am(88X=lk$!6weO* zTuHH`!i(fra;-!L?GCO&4W*$nN4p;h9DUTnuf6DoOQA)Mc5kK9r@Us-h@V0$mYLT1 z^v|~aO$N)$&`~7$W+h@cqA00?w!Y*%e@NVc6#K6JGPyif_ z@GSy2Zxi37iH^CFWJ(ooGk9uyl8DxbuDU{m2!&Fdt%m6&gUFdxXKsP;RWBy~8lQKza^4 z8QRu243)Oq5Xhp=VcsP7O_+E~RaPx5UGgM4yhd0%C(e_;4h%bPCpCYp69%uQGWK+6 zy)X!jyqVG<%wOErO%`@rask>rEUW=0=KO=*XmUF$46_mqDBEl=sOH<#$e6GbLhqI0 z!;a!Q$zHlMAq@4JD$vY*?@47C!#IrPg5yDd1hH;4UzD*Er zb`i5eQ_hRS4C@;PIXCy+E(5}j;cQmUD~QSmg+2No@N)So?7HCf+pvoB3Zz>`gkdsr zHWZhO7f3sd3cDtF<&VWW=idRH$A@)5hjj2Scj*fr-%kts1v8E0;+49=zOaDKBq!j|hj9mp65)aO=$OFLgMiy!@}~xNt~$d2>rr zIHbJ1*~=6TDKBp>&IpH;mp8}fgf~Z~5{rrI``ga!LxAFY9}Nn_laMN0OjTcoQ|!$| zwI#SRg^E74kg;ksozW#c7h;0cyeFkganu2Ppd`tPYrK`i#iejy*n*@kLdjyXNJ%5!-BP086H61^vn&Q zp=-h)Ia~E9+Z28PtxtwYw`~o-EcE%!j__eZiSj++a9~05?!y&OqWMI4O_F^)ybvij z1%O9Ff0-x4M_0q)jH1KugcrFHjn*fV$e)E{%G=l$UWobS!MIo0w(pG!~5unGguCXbP=dz zopTr;7XkI4IENXD5f`x>S|>-Kl6B5uhB2a=a88;U@e`KAl=KMP=i!+x#%D#u2@dCT zBg(KGHsnV>8n^_|wd@ysIayIKnMfeIfrDH3|X7l(6 z6J}1y*ibN6vLF&PJ>qJG)beRY#9L%X7H0lqc0?t?#+P7wv%q>b4;|VB!kYyV&qZwc zZE*$JoLL&Nzhb(X{cXfk>!`CT;&g>9)qYLHa7+TmUFP7Xh{c$-fA0MUkKL{!9f01p zI|BEeRu*M?9F35ch@_g>+7l5&FpCbHQkf~jpRTwVu^WxBG$$gEHY@eega4He^{Oraym@trn1u=BYo*O z=g3+1R)XQKk>8*Ra=4vUB5O%qo<8u5gfeTKCiS*8C;CQ0r5pa&48O?VF>l~-6}MRU z+>fg2N6xd6zZo3)12%9H21fwSnJ!jFx(Nw(hudgRiL^CascjO}ii*s@EE#bL3bH}{ zjZ!14I}7!b^pr?A^w};VfNso=%s?B013%1*JSD{!de|HZt4>_yQwk%uiKeAd4rN4xclM3wg9x`OBV5vcFwO!WgI zVYQPZ)*2iMtDU_0=a5L$XWAI}+>w!Dv2*L_$i~R1F_1!C@~6mY)zus#-LlB}$T&anZ$#y_Um7(r3FPVWE?A>Ukg%7CL#e z>_j9ibn@o4b2b-Pe%|H+T`oq#LMQ*hqU({c(8-&%Z%4vHCvV=k7YPfUyjl5|NLc9P z&EK9z_QNc6a?e@XBI550ojZSvL~X7JM{$;FnD_i6vOCfc8|K=nDoZ8I!yHr{tJzWM zZJFyeRG1fst&2w9v@Ag7f|-&mqpCJ^a4nTR#(CMzq)J^CW+ejdEg~f*&_U0O6myJ0z}084@%=BRYjQC z{vR+aG^+K2S6i)WQN?-f(y35i|N9SKqvBM{1ut#l|3Y7rlT@hsEu7bk?GLKZI%%pJ zj%2NUwja&UQVkL8e#%kxz-o=b5`cB4zvik?BejhkhatnhRQZzwOI2RyD&4F|Hht$6|}s6)^Sg%O0P6ixkAkq))`*5RAGvX{D-$>v{s>Brr)pmAtYX{X=!ota&RhW{UHf##msAEbt1ja6q*6VI_x(z*5OR%=EiW^9) z6swkFMno$wZ_?BM2GE9gke5u8g@mF!9Pp3qI} zjZ)P`3ut(K+n4?_UUeBwhar&bN_A!0bCT+#oy2L_6jeB8wH}n-k1SoPav{@_)FbJ& zIjZFjQZSl6Uj?h*yf+P5sG5)S3y$qUk1tZ8mY*3r3hd-aW%0)ck+U<*!$*z-ky^0_pY*=ukEL?*S@o zn`*KU?S~y0(I|-48aablKu_;hO%jjqv`@7fr;-Kq`hL|}WDgxiCihSm(VfRs-H{0W z$&xcF4>E0-DuOtlR{e$(qCW+cEYZ9FI;%3G**x8+a-mNzsp=y;QGdb__FYlwB|1cY zRpm=&Oi=~Ut9rn(p-u( zd1B>Q`b@P!_|&wQs6fWB31GX>E3Z_j*9FTFy1!A?!5G4ZKmfs?g|t5L2O|6RC}#h) z8`7?5K=Gis$POLbsbh7~8kM#~+qUhsMY?v{4(e!COvl)mc11<)I>Z#UkBaW7i6tDa zmz`S7P9Uto(mao7&pxWYL%6IJFp#$RQ?(tEW3LRKIrY^mkx&%4?ZqU0z+=we0vhS* zqOL^UUDOkV3#_WFMua{qPETZRp)R!<(062_bQLQHJ zSwp=Ki7=2Xbyd628$oIX(tRZavigjwJE>DoeN~#cRH?7dLQ}f!t#%6wy`{re1~&K?BJqmog_hJW4%9GB~ZNQy<5GL4@o`UiMXckQZq{4`yfe zMV5HnsJ2ebwxp_&6Eg{$0=5z3*8gDVK{jWry;*`7N5Zqzr3gFxH{O&Ap`89gL^f%{ zb|6=6ov<}7P$MU7!W;k{NDrCSTSUAZ-AIj`x(OQqAS%*vof+KA{7K->zh#+I1h2nZ z;RUs#2GZVb)TkF|{y{E~+O<<#=XDD^sFCx!j-;HIXD2mwhR0#5(Nk*Y# zCS$^&)z52_4QHT#XAV$b=3$Vv#FVP_Ky_6tFBUL7NuBmEX{-^22WBKV_pgeo$_VgAeuHyZL+i5MrJ0r z$t0z-)tJQ$+nO~gaeY*op8s0C%7K)f&hw@d=Bc4<5^wJPM(v3Ro3jrMu1VS~SI3b2 zMQX%rA^*7FnQmIF&a5blATocM`m28*YZ(;^qBuEnjgSGWFumF;P-9+x^2BPjgV6Pj z@6?YFoxqA`p!r+WUfv`nKhKZEZUuKOBWhRTy_tYx{uJRXk5aNXjn_@Xpa__YoF+P`rL^^6J5swen>?7=r z`a6j=dGt>Gbw%{ZkSQQnsX)gt5G%hxdg>1~lvriTC#^oI=T(#%6FQKt{;dAFLf-A^ ztZ8r0y_-&O(Rf!#R>1Nk9!H?CV7!}Vv2dPWJT!}h^AKOnbHw6nxv@(>sjJZ1)ivRW z;pm?z#NS!7kPZ*fKov{YwP}-Dn*GADL+WXk2*y`JG_WbnJIoQK=B}IFIkIeQ=S=ds zdTn=7(95Gb@r}`75B}3qxYwQ>Tv^~vo5XAWLI_1H1)6-)&7=t;m&OM)CIxl7`Vfy% z)xAlZA2m*-I7t)t@BNH}QhBoGD8g7aMwqQqkftFyH%lXy624E-bamkRArToG%n)n^ z45Ym>HCqtL69t6rzGOkD*^kz2XEhgD-a*p~r`YCnQAbS{($vjKlYSb1I@XI@SVtloMrbM`ULd_w zq9GMy6ENDwX8PC)vNMs%kR-)d%H)FQCL z=&BVOd&Dr#*-&7!QrPF-S`A|UWD_ck%GYUuU ztNk`j=L%sB+pc*au=?(-pt-i~(i}v&vd!SZdo|4jn;H8x(<>xd=>g3rv@35Iq=7mz z$2HqAxi{|&FHdW{=(@8Sf<~xy84#{s(PW_p`Tc=n+GsJ@yC0S+dmX~lB-*5M9{CRMPceYv*~L& zR#WO+BMSBPn{t>ROAF&k#2Jk{gR4W4{liZ7fl!IKx*IUq<`5%=Y>!fShjX z;ZD68MNM|LyUgmNXA;AaAV=acwS$a!Hjl!lr34-TH3*wWA$DKr`4&<3@M5d0oj1MT zCQ1kEv8)-|E=q-otAG|uT6O8*LQZ#xLM#)Jkt3rzO2_&fIz@Gd*A0OUDd`?H4KYXp zR!_R2M-;_K!f$ry9W`Il2-(m#Y9f}y<$h7VXb$5CMtv(dBn*j~jOEbbt0>g#pj;0* z!=qp-#%*!FBx=OJ=Rg7G1Y&?E>Sa+)$?lLKUwVH+RC-ksu-(p&>K8>FLacO906SMz za&Ye*QF~#tjQRSlT~YPjrKZn`sO9zky>!fLqez?8BWjSvPSNn)z1OXL$%~kLXENwX zR8^95sfGL8(fyrCK~Yp)I{!&jw`!6cHSD5YFvEfEaTFdGtd_W6(nZ6!Be+BhW209H zi7e^S%b+$dlhdJLbWvrgIiO`Ufp2KCzXrCCUN3ID(INUz@q@?3(JtaPyJ6AoUBzwV zqF0OCBBn;m;M<7|q2rwBX+Bt`)oGv&APHSM)}&L{M5`qGk{WxXedlhB3ZS3&M$bVj zh(qrh+|h&9IUGIHPa=ExYIGYZs?eyL(LorO&!)Zq3c6YdM% zQGZ3F<}wVL0J;9|3`54CeG>F9i&J@#Wxup zGqkG^pfJMKCvB=eawl$?+D8Zg<7Tay_RZ3+z}SM_Oy-%jn6*oOZ}rAnv37aWqIGhx zTT1RV(qihlb1?wzD8^smW(#@IP+N%vw6XG;-dx+0@%hwBi^-#HkQ5+Okh-Dymla=?ix!MzmF^Je6N5U3p zF$sE+_B!I(%zS<5&;!~+M&R5bE#`O=$M8I&J&yFBK6KCtZEHs0 z+K+hc*F2@Ygf{=Iv)T@f!242+>q%OSzo11Wk6eA5FG(8kx{A>Ny>nID2rc}z>)IYn z`1G52;V0bE?qwKhceJChKHT-L_B6wYd7#Cd{^NS=|4@4i-4A|$to?!!$o*A|IdR1Z z*#E8}cm6cir<h73A*}&7PYR- zHBk9aZ5KwM-zR)b-1S*|7VQg4d!3mP_{vd-j){Ambf?fJP`K)PF#_}5bjz_maI1>$ z8Ja*(FWo3c;FXVVHI_g^Ro(As6I`mM8_ozctf5H9yCiQ|N5ju28wo~aIqJ3bJM#rbq5n8+x4$|rFFh6P* zr$a3Dh$&LMj%QP&l5~i_?)j>#0V_1;5G1|P&WZhz^k!t86&W@sSfknJ=efrU+M;-2~=;X z>&FO8ZH*riF0|31ij{Kq`R#OT8G-j5beI|%k|TH0;Ya1&U35zsf#%)u;b3JC-Dz|@ zs#>hu#|T{LquYQTlj`-;p|&Ww`c@3konQpI57uqL5;!Pfl>?AcSN>ULv=e=<$?2u+~hEZuZQz;O<~ z1LV)u4P_Xo=HbV~XY+OSVGW6RFVZ27AWGw5tHrt_=#F@Fsnidqe5*q&Z_?f?bg2Cs z-Un~2lZ?=9BYr5{wnc%q*e%yxlg47+ly9`6NOE(7V%K5u>ADI_q?bofq zV$3+8o6UGMIIP3GIm8Xp_=pq|5{}~s#*!1dD6~NupVT#CLaaNjLoVI-p^wk#9-zBJ zaVg9zfWR*obm;ZVuuD4BCOJ33=PSCVj6l<$@cHt*>$<~e4gB@9WP-xm_*`jbnQjFW zLU&I$4C~L0@9VBGjPOVJl&Ra}3ZzVLp6L2B0$rc!mSKh1_*|Ebd6foSp%L+Li3w!K z*5A2Si&+o8Pc8(T83qI&}vOweV7dGGtpk|+RYZ;KEeGD>U3801!F>F(X{Nfyg z>~M`%gQCz%et!6YD_!Uwqh+RMyIHFrdd48*S0e&wmC2BZlqz(jPfV#JYz`Ir$2g+N zefnMJL4T~lv149?H3B&8o6+Ad}r^Br~P7#YSDtw>(5KGB z3+b^l#gmxlrM0K-^I~csb=)P}a&Oq>>lNZH2WugsQ661q;>lt)zA&ZYg?5Msv zrZ!UX9APpwtp&RSmoA%PT$v~J{XXU*D_uh%A0=xe7$A>sY z0LW^LB6ts2di;SWZSgAROJol)6IHF$DkLt+xjmWpAqI-w`^E;*Yae4U8}Ee28vyhn zzuU*wrPU56errdj*vG=#N+k~7Wb>-}uH?m$#JWWPIp(6>((S&nrL3dew~KXQW_acp zt6*1um)J+lSwB~aEyK!1+f<7kiR{m20yq26el=oEj0UY8`wZh}GnKI|$s$u~dosRW ztS=cB37Y+(erz$rjFZP=B02@kZ&A+DGqw>~+9cJ3G*5+6-7_YpcBQFNu>lyli~_4F zeHRn^3v>NDNwI@4-|yl`<}X1xo5~V^)#d6I`bDGISKtIBqiO6IM3-cc6d%!u_Sp6gSGQR5U84Z>9d zFq>)umnG|Cb1|km|6^gT#W*fY&YSLDT^ zYT{WDe93|0wDr`{9Cwgu>{yGqt4u?STE%T;S96=VGwd2z6qky5N!j`sXJVL_=0T>+ zO^YDr=ZQ{4*+1^(zgM8-o*+2K>qq?t#+5RCJ{&61-alNT-Mb|2HluAB73YUJc4nhZ zpG}EtikxGb2`35&?qu`kw3T%D^tfHMDk3!9zdjE2eL9;w1Q5K4`C6PGDJxAQwDYF8 z=gjU+_DTwj+%GBc?t!>6robbI;xemZDI2cAv(B!E?sD=<+*>3H1(c&MK*i}!+%rb$ zx4UsWF)0z(!Dj#T+4R8MI3?!YK`TNaS!$o*Os9Q_WA6;n_kYBNLd4(RApRlqMK|a8 zXV_el0KdwlmL}bePIHgXMLV7qW+Aoniof9otZvqfm$MM*927s14No)b$3J9-AE=67 z$o^h6I({1a`?s<26ESlFNjGU3mB~&*G%xGH;wUsqL(2yTLCEmzGT*K zYmVQ=<}Njx#_y>N*Q-VGr#;}hqIdiqCdat`@t>Jd#tn*Z?E}|`lj8eTOr91~O`c@; z<&2ut^XvHOhyp-T(Bh&nEE>Bw{t*+U&eHfdjJn^Z`04I&UAZ^@7be2=gYlm1y5vaw z9%j1c=i)n8Lqx?FehOWvuVru3Hw+Ot+^^;3H$e+wF&Rg z@Jladm`UEggi3VMri5DP&<`;6$id4Q1L?sn313%1j=H4eUdC=Z{XoJZv^;=NjUG9g z@R;?E^~V!lv*BgQsf0h+bwO#uQ1rz2E2qpERDLI62OyE#4-&>;g#;jf`p3hBNzBE@ z{gUt&?fP}9XAYwMeoNq6aXG&yV78WduLPi~wAZ_YYRsR;eN3o?_4@4|iOsN1G{+;+ zi}Z9$^P?X2i7S-ANpxbdHfluTZ8mlEosd`ylhree(9Mey^U(=8-0VfC zuS`75=oPL>)UmnC#7&9w&>d6%IMbbO+MBo)-7x{dmz+3}=uN!xv|el(lCR6(Kbpvw z8@C@%JjERFj)F`D($o@I%cJ#KCSKs&|k9(ynG9d<5B zH@`@{gVq_`>_%(9P29>fQ~Ew}FS;`n>$2d~uuGBwraq*#M-t{lI*$|pfk0iN~{raS*2%od- zlT=7bvyz<2xW~CwX@)V$86$`l2J=o0+&f4*S18L#^1vj3fKWmoT9VFS!ZyFTeWRoz zOk&G#9@sRg5@tt$-yGRIsSy2SP3xp{XqmURO=^LD@_`OX0ce>gbxv|ZKiSYd3H4es z*Y<&)N#2MjQ@Gg+>6t}z#h|3+n6)%`Xc18=l0E37(MiA8!S(KjWXS5I+I0BJq(Mke zX-EO#2@Q@~5Kq|2z$-nr(pA1=U>o7An4*0SK zXo`PEa8qG&Cn(hf6p5@!@(P4$t$=~Fq-krX@*a z#UF2ueB?xL4Nq>4$zV9bqN&L~9eYvHm)7j+Lo>J50R&qT@Va8A}>nJ)!qes0j3OA1=1ETeq$|d~X-CF$*IEhD+ z=omdFvtiGrC9!%(sGr0PJx8zi7jCsLTVGo^#jkn#8&C*~{orbWexGQrwCLee3;eGu z8|p*EZ+~vA|1AFcw5k5SXx?e2{}Gx=;XZV43;jAVnha{K|6Vi`i)?>=-(J68{Pjd9 zeYo&Zr|$Z;;t>OT=|71djO(NSOFZK1e)>r9*GmKReZ_B^4$;3C&9=k!o5T<5j?}a7 z?l6J&j@J8$AKV>hqyBn=jryZW`cvW&N2chXh}-s0*Z(M*^JeKEi{{KZ`pcp@aGrjL zXeKYv*AS2Byjb5|G#@O}zY{-rw!+4@_ZoeW_`$Mu`d8w?-)^uOVEQI~toXsTt$I6w z@MN3*jQH&jJM`BOftjA)r`P#Nk*rp!ey9XY)?UzeNBAUw4Wmac>Mc?TBEi@7pGDOB!pksN{PnhE{!nEl6oIW(HV9i0Vx39mn0)| z5)4z2FpQUueo8dN39L+m0W(py%BZ8~jfRZ^Yet%3Ooe#8N;jkkth8*y<_ck5$T1uc zSj!6xn3cM687o_C`fAk3u(3kCZhv9;EO@>6(tznS<-7`;8)iw4Pu{jR?5Ys2$!!g( znB|mmHtX9PpwKuAx@37L1LnkWIhLx6!2>ga9m)kL(KI?A9#OY!njnFsAY z$p9Y}UAi+2swhvjl5aQ7a8{5v&BAv%_5cD8a3RJ0Je^lhoNW*id^6WDUo=y{G5jh* zt6_;@EM|^qHL4>mUTJ7z$1OpBUS+5yJlAzyg?gtW0oZDDujV?_=Iae^g7?5p28%Gl zPumRZD#klyyqUOQ0H|dfLfJnp5`1jXrN{(o2^VcXV@)7`0}E` zU5GI7n&B8)1oLA0LvjFh}i2=Y4mWECS>lB zM$`!%hDZVNsF)_sjy4_@TJNkg?n9FN+e!|Fm7D|ah z8u8#QZHzso#i;>b7!Ns+m&=-ZlguxTs2N@x;{dv{nejL#sRbefs837d0egwV-PT4# zB{&K&dob#=Yi|0IsG5RTx9MXP%XR8b#tlLvFS;71BDDJt)Ih53j?>M5z-;Sj94xey z+}k+5BD^R_I{?$c7$w?furb_${5q$pAK5j?=u5n&8w2RMp~meP2jD^iVhz%!O9y8f zKEfC;DW47+WegV0jbn|SvF4>qCmSKHVszwlC(pPZ?0>@BBJa8pRbZEMxNy@Jq+68XeR9^e$J!&;ZkDLBXpwUJWe+P4|(rsuKFTu4%5|Fu#Fm8Ma&e8f-!p;N^Ol zB{$6y9JHY(Om@g&#)g?tHF){WbyPMEM>Hr7Gk-9RgyU4EU(mML7-Q?2f${jR*&)G% zs>I9na4Xr?EgBk4=ukc1WWt9@Rho$ke|VpP_sQ%m(;Wemm}}~Vb?p{;CfK3lfnuE5 zgr4S>T1@!iyj>&HDj`Zh6O?yy<92Oo!W8DEa5Jn>a~0HYWx~{<;*amw+Jq|0%Ws}q zWZEKBP|yLdf+ZbIsLH&Y!?iA^?SjMN?xyAc-lJ&m9;Wx`_z=(=xJzdEzAt*b*wNqg z6FT3TG|;q9P-r^Dv<9I8^;f2Rv<6heO(NeXZ%gozA!C#YRhpMO!O=0M^Fjry#+z3E zdlg8NxK9&IsOr3&!?ek!8-l}-X{L2p4p*j|@Du6|vtZgGd1JFVrVUsQJLguw9ZmD0 zJ4qaJ7Md{IhFqN+7Mbp#yM%tJ>4V^q^{oj}Bv$f=jVnx#(ETEPjR`(>$OH7p@9_O1 zcfF~tfZ4whKVsh6WQq~PMacIi%nO&?Dqn3gq3ZVXn_uj(^^hC8OqgwV4imq}^aXOE zwuBlE+pO~DD1O*XJ8r_1@TD*zttrf~?-brCJkOZ!3a79yHTA>xbJ=+l>{xLZO}}hH zY%)mQWX=^+FTuv|ClltRF}G{lbz9)bzlk17>4IA(R54%9VZ|qM-1TQwrvLEZnTYl~Pc( zeMfqylu&qazeL2YEnX>zB>@UBjYPb8=$C?7*Z_nH)Zm}8PZ05_nSu#h9Hv99l(hmT zs7?x|pK+KzbyMnNcDCT=u2dyUncyhxY@G>B@xpW%hC=|yg_cC5T#?jEJ4K~3NZJiv4OVPlk&3=FmGQCrWYOkQ_69H+w3OVWg9UtH)>awl7ZG+ z+1Z#XG{Ewqt)Hc=7oydAl`;==vXdKhAr?W`k6*#7xsOy6Nx6v_wxB}CiUGIbQ9 z-V6mCF_|KngHkamHXwAPO$Vnsh|#BeN$Nn1?{JB_PfR_7RR@8az3IhiseU35fA~6e zXf;GYXG{pI@=Kx0?_M+=K=7yCwxrIhDUHQd&!jd+n24~*Mqk`bttU)f?{NjZ7;vi4 zlTT7j7zgGtmKFS^Q1JIDMloC#KoE%yz41rt2XwS&m3}Ey`c10L6-@T~rD3+(xKw~u zo1Uwd)=h-Soa$+d#mN0JAgzjM`qxhDAj0X?I%&tHzpidyKdp*n;{K6oaLSOaG|{o@ zwCc5`=EnTAv*NE)&1sLZdV|oB<}TF&);3Oih;VGKeL(3aJSn(knspxK(>hIosgBmR zG_4uQDS&#ji+aaIP`h4fA($Dh6=oOh);sNr&}QNAv=c&a%96C@qIq*<8XWcHI$k$A zEz<{~2mUlx9hgFOUW+BreB+KRLqm&( z)yGmLtRgUliog?UFL(a->6~w6!C*sjnjvVACxptpN*MZaObd< z_ARd!OrTcq0@|)a`atw*$3IZr$@qThb;zRp`1z!Ezw}z9&0K{q32G8ABQyJ?Uy__? zK;QJWVzQ+ADt(S6e?LAmhqRkZF^+K1JTRZX)`8OL?wSOSKLvbfjYD&g|)N{>)b+{lObOAq$~3> zeiZ^PE3gT8;){$3A)s^nj2lA0*PSxlMboQW#xFv^%DpQh;6E!E?;Mgb0zC!eWrCrC zv5Y<)kuh63f5@Wm<7L9$hxcV{5TirM$qYFB%;$J@&Sw09eFTNd z#Hi|Nylb!Adyr%Jcx6IihGA;DT{EY1c6si%omrPIQ)Xc=}vU5(X zs4;<}#?JKnTA5zLmo5cmx?$}B1&WjD^5D#Ri1{y{PL)?U?kUUMqLj?1D$KOUm~ZCY z%rEH9wwX0CY}n0E>nw2`z1=SJreJbrTBg<0j?c)fCQSKkR_1Hre=onzd?T7~=Vmq# z0Z+L!^Q~}2)ym9sVw&u=F*5|S)WMmRtW5MI2Nxxl(43>0moT$eD~uoAeJWF&BBA5Y zW`>BNdH?0iuPu3TDAAcTc%CWEji}$@#1(dA_p{9Z`V?D_Cz%%| z@38!uXxC2#%p-S>OF}!$I%EG)bV8!yLzAjn2gj?Kn z&8m!0t4KCpB+jOvt7Wyv27AH_CjVA5Ypc-Hj-agL=(QzQGnqim@x(01q17D{B}gaxnx7 z6rYOdlkip6P@&WxhG$I^%}%4T28ibKv00NubK=CTF`^kbHEW<~u9}gxQZ!q9oi$v9 zyq1<{!!OO_;JdvR{v4 z_Fuap?TQ8z4~mQI(6OC5Ru`>NX*;xS+g@9wYp3m?j#kBVjE!kmRMf6ROi}x&=#H9L z3V3UzRW4H5GaGSgi=Ow)&cf^k!F^+CsZaI@$tt9cALj9d!>q5CU5fEIezT@Vb|1lE zd(G_W2-oK@A8TdjqV@1+kd4ELdfBtF9Db;ueHE>TFG8}#qBwC>WG}*UXrjzsgVw|L z@NDb*#$8m|m;)i)76;VXhtYb7iO!xO{Nb7|8*?s)bCAVm{~^U55|x;ZdE1S{j7iGg ziB{)pL$>uu!KIY!l~|KTre&W%b6B654fPgTIzx(cvJr3GNnQL{ZuYmBu@nMaryY&5 zXNyV^Yno&~$9M*VncFDag>0PR>`W7zWe*eM%E^}22u7Q=$!;Z~93g`Gd3Tisq@|*?Tc7)-%Us zccg)%vrPzo6mFJRmWIQ#)3OVY)@?~%*2{LG-_OaGBS^4mi-bU$HFF9e?q{+GQu}$? zPi@9XQTM-%PiY(H|5xV+ceMi+kk?ct>U*gzp(vwVH4YL)4sI* zwo>z)t71y~qA2Gm#)?)IJ*M@#=EP&RC9MdOl1ci~9FbZ-uUDzE9nnw9sZGq)?7YbS zuLm`w$0p|N78;x}*=B=DQ*$D#OEx&UGACWwpzb=G{AKHH@=x4olRsy3&U&FM7i#S3eml5EuGMAh)wKQgey!Y#LIpoG$Q_GG zPNCY_K>9W;H>(}ejVip|!|Ck95h5;6_woLl36=VrmApwVDwTGl{tJ{L(n?wb9;Joio|&Xbm z@=yht`7n0?qQAjtP>tk2$~}*{_kFI4mORc~k9n)BH{49%W@j>0HQ0p&zRvZg>wnAr z8qu>UU~O{(ZG?Qv#hej?+ndeZ>EJ?}e9lEx(q;f;^IVfFY%Am~u?HsS9P=>2+Q#Ig zQ(hV-g|Pu#aLxOR5h<;bw+UxjNhLlWd3!LE6(EvI#`|>%pe?H9eT{gth}Nx__Yk#} z@;+BhUYhf2Na6Q#WM0oeP=s9r`>M2Qex439JcBf93YZR+>}Jt7O#lS0q0RC-I>2>S z%e)pYaDCAs?}Qy(X{Wpu>M#(%AiRcKQ8W;kvPMei6g*4$ObY{_ZNv zw_rBIm{3IZ&c%uBcgXXlmtykw!{a4It=8vb{qOIX9aHiTGf@>8`CK)xGV^CLQAg+J z?_$@aM)_V$23V6-sJN)cLK?b<-J_y?rrHy3%leGuMD{7`{q|i z1QuddQ8}&$oiHfB*by*449)jtoZUy|zj21E{lxrr>{%OU=TBzt5kEKo82o+5qWlHS zN()y?BKoh(-+_8F;J@P~$=jB{!*03sZL*y0`71CToyGsnODwKbzaxJXLPxWWYkf)0 zgxXLY_HKa-jogzTYG=2cHs6~cT!|cR*DsSS9@5j9oIR4its?Zu(iV^WNxbTdwj^}> zFdwo(^@S@Lk@U`&3_6zI9)WDRlHY=Cu2JAjdmYb@#RLT_LJ(Q`%)S*Fa4{ctnf9W0 znF!$ZrF~B3|LkD5nG~GOKZld5FFkxNzZGJxL4iP$Gd!DxN;2Hn8NX)VV>nC(;7mUNq>8&uBqwR2SZ(>d;s3zscJLCn;ok3qdkp*XP z0&%oJxD-s z7>Ri;7L?VTmfS6PV&gpGNx|=E&VW>x)_Y#CpK-qPs$eeTIOs#cZxF<{cQps1A)C7v1kjtI=3z)9wxEyH=J&vpmc^Q1 zv+G5@S+GILInIjXWN1_LWrNIL5pP ztpY%IW>mE^&CM#qb;bhoQ>KDpE6gPxaQ(E^oQg?2&wFc(%LWh)fkM1IbU znb`o=5_+uEdt4mW~+3S!P>?lGbk;`BM25i&9b?nK#{P;jd>{rXV;{fOU3|6xVsKWiloV!OUV9 zS+>Y>((a=LR=>kPUSTy~)8Tg#c&sl&IgT8Jte$U9n; zf8lS8fGnW!C{_dC)-0ThnP~J5Eu1c8{O7S6Ml2G_`?n)DtLm6SF?bm!6;^fzb~!T&zrm~zFI!MJh)$bdi21e@g_~zHFX4Mf zSxX8p0zq1PWuZ5_Mt)aV6DHyCW_{sP%+@yqVl7eqKi0kiI;x|6|7>LAff$fL65J)S zF*HzI1IZ?vtZR~uy9@4CAVZO0MT#{P$lwqt(&Ex0h2pL)6qo-y_s-sae7|%0oeuwV z;N02C%rnpX&O3JR%sVc@;CRt7I2b?NF=`u&@;-yH#qD;Fy1=3*3k;0UM@H2{cz??- zc~Eubv2-8WThM*%x-lx3K`j_E--XVCw`k?lQ4;dwyUO@(`1qSqMtd;l zV5{gKiFv=ai#{fT=Rt?)H6%XWJvx}gFK-xq8`=b()Hr%DqoKv4>Z{l7v7u$OmK3i& z1skt_>m1E=9I0C|2y_o7|cG zOid23vhcG5(XUt<8p(_xvl!1j6g`%ROW<+vrLB3T*TJ=m$UwRX>SlS%Cr2EL{It^lTO@ z;WF!iW5TQGVg>>jGEXHfefNjx!wfB*E4$yP=!-1137NBTolT5^ z#rU|)N5nBt!LCs6a+Mc8?ivFdY|i3Q4PqV>Ba3Vtv!DF7ZV_{W7~z?gF_{kV{Hk+I zL-_k@c~A_?js+;#jM5)ncdYFdLyVap`pjR&R5R!w320TlV(jpi{xJgy^$&w%V57)c zY%?OJBM~7mHpYYe?w1g=mBk4eSIV<@(SB8r3nlojIY!B-ax)?*D06nUJ^F1%geUe+ zi*bUb39p2RoS4pJosv|<*FbD}OdB%(mXGCYVA%K=*gp^b^^_PH5vSSAm=-KmulKg1Lg&hv20 z&%}x}+hY!sDrs96V@G5NI2f~;q`P%IW;4s1Aa0zfxH!@UcRm+0lBHS%1Oy0T+~n7o zcceGr)@vOv#<-9g>3TKBmPiqKE2faJ-hM9zGmMlN^kmJb-$_{cA|?)0$20rsUCg=q zz@*w)`hl#x7d4U^2t$#d^fZevFwlYw%R5OcM4}J;7qS(vuDc^i7wK|_e$BF%$P?|$ zyz4+V^gm`m(GHTv;*EjQ{wyjZnGs|bqdljj-e`eD%5uOMP|EmW?tF-}GaThi=;ekQ zbd$17H34fpuIMgxViadQj_f7f2?r?&2j@Og7I!=x2l-c0rklyQ%+2~svnYoZ1Etgd zeYIkMQL&77T=99srFpdYcTp_GBMzqLC}~rM$s@^}h@Qx$V*EZ{n!wWC5CjYeJ*<(; zs<#&$rj}md?FzRuN_WCxV5naFSF*GjLkC0n#o_8~=~i0~9lgntdZ5f`duqo<|c{`*b=Sw;*8G%E)V-|TL- z$pK{+B-O`{&Pd^EiCeAw-0-n;(nK3R*!QBe#F`JDx-30LgBz|%nWitB++&-}$ynlq z9Hy3nhY)u-u7BdDR3Z{R#n!i_fnw2SUbd&!{sCzHLn-qSZ0>YXX5QTfc+w*&lk+X0 znt+_^%luK(r%WRn2Qc_AX#)iJe|qAmXVQ(l+KJ=*?($)7T5F1e!FDEn+4x zuvP3MqJfaMv9+uOs@uh0WNZZn>>L;?WqGAq*E2Q~CA}!`j01YaY8b|M3JawKK3g4eL)=yCo(qUYHcjM;NOf>^syVs z)5jRQ#2%hI(qh}#!1G2{Y#hrp3(QiEo|eS6M2)w`cE$~hV}~-xh6oG>n7hE47x3Ze zSRXqeCY~G%=e0NE<+FJ~i|54ls|UaLEsqT*#9yq6EnzwK!x3+~CAKkcur+o!D`&(Z z6yw-UE*9dE2V=*PnmKrsC%pJLUo*YV@-<`s3tuz$F7Y+9@iJdCCBO4E)BalQMpAPZ zZ^!NjP`v1Q>>W~LWB-b+50;PTzKT6g{;vBF+m^|jrT~lp*dF^fjGIODvC%i~BH=r!dE98i-?43+n*26*j9bt0 zs)w(@hOy4*+QB%lD!pBUn=BjTD6d7ew- zMv-TKMI0Om+>9Hm;vDqwY*iQ6f@KmZ6p2BeAJ`3&YNRoGlRnf7Cme|r6V~wAZ2@8X z{@1wEMC&Jhi#ti4)z{+ok=jt-iGwc;n{m*+xZULW?0#GbN#FWe9K2K9jQ{#04vsr+ z#z&vWEhF)1Z{y%S%VwPODbAJU2pdSt&5rIPak{z>3UiY&45t4v7Y!)9>wyQk%a}eN zkM@$qpjuBE!)9F!dH30%C9~CTXq1nPWlR#alx5>}jb*Q_c$m$8vLuF~jf2_aFUzpz zVS2WZef{rYFu>elSVbZ2e3=y03y^i-XRGVm%Yvy|o^_Tny#N6~41i2$iY#n`ai?I} zw^Xq=LS*RQYlM>we+rc``HQj;@j~kEGBLvM9XzqAr!2yOt0Y`AKxSmt_CE0)cf4Vk zEX0uqdmbkOUL7RA#03t$L)%3N@eQ3g(jN%O$_ zl4RF;lfXZu$+n94*uo6iE*k8YB^yW+ROiVaI`at@jFC;Hv7r-XS{ht4RaVX@Yc_hX zl6m0EU(4$8ves6~0vJTaASzsRz{$3Aku1cP4~muxvK?L_$ToMSY!gizyH@swR=;M8 zAo2EXvPm?-sNJ%)EORkSH|CF)9+a&|Hyug`p?Qa7EZ(dCUgTdB#n;Dk56hTdNo5_D zwLs5A@qy^7G~OR|`dP;EYS)4iiMt<_?YBlF5=y~Q2F&GkDL}s@BEFDCVUTYyn$`|JHSWF@?}l8|@hr`x&KO_FzohA>iJxX8T7=FR;`cC!0YGP1 z#DIL2xNma&W-BQBbqZ7Ea2P|<;#;zeijY}{Hk)H4I5a0doMj5lA-sNWZi0RD;+Rq^IDe=@5)bOqGEg7qeFAQ64i?0Xv zg57t;!;)qZHtvt_N1p4B#CwzH!4vV|h-wkG{v{q>E-b>wevQ9I#N2Q>emcuQ^_jhY zCwjuav)y<*{>Z<#*8f-oEG@g+6m<`fs!_kY@hsPH%x)m>gd$(Y_W&=N@k~C*1O#xU z881fh598-Dh-5+0@XdMeqxfuFG-FnKH`M3P_yb=6m;~#$c&39Sgc1hTcvCH0+z zCH4u=zKqQS$AoE=%|w@k`Cnu#w_Ow7dC!l z`wK#J9hlID#%2#oI6_4mG9m$P+2f@28kz7t4gNAJ;dh$$M_B@V%UECWy4#@=139$R#3$SB83b279`FN_j!?to~b_01C-xJNMC$ERg>&uyLAO5>V5g3%S zM&g5axyya1l{{=9Z%F0$@Rg6FoYypy+tS!QZRE{q>>nNEV_EjSSt#@nrdHmlb2s@u zG<#FgKor_t&N3en-gC^Ewb&N5=^6YS{hOTh6lf z1C&&LvUsSEe1CmWHJThPXW3&7K)Lu@jQk->H^ODkjFT5z0fC3{a+bHP97Y?toaLM^ z;xdP-9FDqHqx&W~%cY2%5Y3Y07g!c`T;}Q&`B_3B zJ6*npVFJiu?9GrTvV1w?G9S&6gWLUTl#(xJnVoYOYYXHDmKd*APP4b-k<*T9SQ@<$9Bz(4>_2UH`MAK1$t zvRUrOY=5q`a`4DpjplBXSFvjU4dV9jZEBfN7)-i?{S zpGQ(Wy2s2yez*#%J#g4I-ax_ReVD6)$3BX$34uyq1&dEX4Ig1r?? zX{H!M2)MLVFs~JcwNeacc|8N{W68B#jd70vg^qLv+^PK60Sa(Cu}ba({w)*>7*|2D z*eXI{08E_NLjmsFt8rK_1-Ngo#;+n3;J&>YukE7%_wChK+gIUe3(wR+inioAZ@8iZ z!%KGjN38e||Y>2XP%B8$Z;0VEFRCMfz5COgzT^C?=M`CvWI{DXmKzQv^ILekwz zQ3NqCMo?H^G&4hChhC%)^}*}26t5jXfK6R>UU)}|g6WnL0TYLpE6PA0xOTL{m(;_# z@d~)yp&HvxQFJCx$qdC`y9g@}`9E zuTmUgsRjWP17lT<$Ge_OByJDBk?7Tlq?1e zXQ7qLEOb$Xzmj%-x0)%_S!@^Jboh2lWfkF8+ExkY*{kt@_DZhvv+JnLBHW7tl+B4u zi$ata$lvqAlp)M!t8%$hAGhzJB+Z;xx}vvI%)*_sSPt&SSIR9!05m{(kr2!pq?}5K ze;CI9K0iXa%N2glRVkaX5Qly$5t@<*04~&epcBJsTV1|xLXC+O;MWbS@hn1 z0aP=?WR^f;fSg2Bes75q+)z~G_bZgm$keJy`+MFikCJX_%10$^3aj24t=dX{I#{V{$a9A^|2xxG#my4B z+Nmy)crORlRr37Hkx#eEnNO#1<& zMc>g$rDqn&ud53DDw{+ecx$lgE$OHOZw7fGahS@TUn%?%rmAHY3D6r7kwSZ@!0}i$ z{@9Zj>1ZS`!n{5zmVq6CzDD7+fvUdvtsXwUfS&HKN^h~!qPWTyCJq9FRmN;xF zz<-yD=`^$ei~v}Sef9zj@SIzx>d(THQ^^LmJB^U%zSiEj>6)6rgHltI6U}_ zs)7*v`J5`A1&Ld2y>q+cf}$RPDOdcd^1!<2488~5p z`;pL@;HGZOtfEgIaO;m>L$#fy8Q`uy>f$egLjY%wPKLv>zE@NAaYE6hg&NM|R^uBj z)ns0R*R@u^Af~*uqdJZBbFP7E7naSJFziI4Q=#gH=$Fv8!T8rOwJXc8A;93&2(_c2 ziK*Sy1?-w=uJFVY`l$b2@-ObEUdqxaI8**a;OYjc;Yb6#8;nwOOa8D?YVg!ojeEwb z$%uz1%hlXGE?KF*&2k0+O5*oDVc7Tdl|9-rXrV94Ol{Nw4Nq6I>{|JID_2da6FL)V zZ$O0|i|DDIQyFR_!-yQXOg$LaWU86I0+Y-LGKulx<*Wo2 zOjCc)(quUTFK4K~BLtStW+(9UYxP)W4frE9Z0Na(=6$1P+3Cm$@%sWb(~cr8^AC&F zs|kUGr7Sjp0mF`^qkw!W^kJvk9-myJ?&1mrxOK$5@6;aX)OIyJ1Qm(Zof)lLg#jHV z05@^Q9(4&zCkzy2;Ji@n$uH0r{HX3^PkG|Y$JFtRbuPnzSw@I;KdbJ}NNf~3Hd*b8 z9WIi=hSwc#ds)roa2({Ma1(y#HTL?NnrU4~knpKH>N|E8PDh`pPq6F_=QsgU103{P zolKOq=&iavqq2q|f3c<^_vT=0s^%uKcFx$o`}CQy&9>IKv%EtAu0t@C@B`EYKI5Q) z_s%!*Iw#F~8+gjN-ov(z@ky);1#);h;`WfRVIUDb4r z80!XV+KEs=MUpH2W{~EzEpk8aG7$YRLc{PK9D`_ZNT;W_s~g%b)v)ZkJ-u1e9q)_P ztP^oCxKyU;W6cLo$~Ei#`QWq?jfWe9+6X!VbXVjvT{DdhU^qH5Uqf$295F+aX2Z*S z{AGUg20p569!?d55%iR8+1`e8Xny-Dp&f&#F%?XxiFvr3CL=#6joO!01$x{174-S87 z)-rRDztAkC9PD0c81`*p5M=?&D9EY6&HkOV zKhWTf&RQSZkGh0tf1L$xz``@j<;v=do&l@KvdWpAx3s%fnI8h7riy-Wr2AENz> zWl8fHq8)nT?<~gOkI<%4`8q~h^HFkKKN_&$xFmz9;1;-i7 zwLW;0PRrW(cvu9m_PB#ln@!noOtP@YW6T2meyLi2rm>sQkFtRx9%XCWF`G1pusBzH zjW=oZI>(}by#-pP`)31BYNPRx3hiuuK7oQKS`hsvX}i)cDS3*PVP=5{Hkz_k>xvVn zY8TNthUZKz%N*lxtiV~?UtIYqMZ+c9ZnUlFS86BF2J&{bwvJ9ej&0B~O*AZ}u}r%m zFMZ2*rB-`{&M|6tYFYN80#+&hxJ$d9#bUV3aeKAnX)QjkW9JZZK+80~a~!4})M_b* zkB7BPZ%&CGdi|tjI=sbkSbtQzl5)_U(6TJZ0A>lp90QBaXkXHj?SIiSIPk!yf6*o~ zluX)RDZYI{n@@{R{f(t~9%j>J?L?NbJn*L0fRA5i8pbhXeo5yN-S27xnaV$@jX|9s zYMt8;e2p*50r~ z&n7&#N0EiL-uUOnI`Fy1ElN=UHvL9?O?Y|uKDR*CzDkrFv+4tCWs9ZL|fac&P?5v{0?kplfQeFXaR`s&)T zOfa}2x~#upkB1D=b!IXoQj-J$758cHi0cp2{l?pQZIrGZ!@Du^THx_YxbONECHj-wah=ilQ&a@C4leJB|&i zH$%rTh9DsGH>w|wo2fIg3@QHwllHalsTI#FZJut~mq?43R_P}Gf1q^!8{Ha8Y1Seg zi^GWjlJU%9-KPH!yn>hMexba)ztyo!2LFqfVWo~~R`6diC2ItIN!ICDUI6_UV9t7- z{{Mr%CVa2kNcGk42OZ0#@xORY-mEk6jk^}>!dd!Q4lvW!&>Us{rn5tx#3McM?cF+^ z54vm)@xqD=y3x#n6Icwe0dV;a>3=re(#iQ2k9{BM4vP5L=Z|%9j(kx5TKC3=4;nw{ z;GF`OO(xP8(rnl5^*d;`^^W@1K74|kE%o(W_@H%=9+n1Nwr?c*0b)M*M_2t=8auv+ zet<0>d!>&Mo7GSMJ&g?cO=d z$000P53?06_|~jnxY2Ul@acE|5Lp)Ro2xP`WI9Iw_Xc+D}JZnL=$uu87!JVWo_6% zV`n%BvCW+gRW$Y|H^U5COG%!F(KPn0w~%&56T@K|Yie%jLrHFLXRxTdqJzPr?mnFi z-Kp8l3^lxI&1?P}rC}G#jGJpEnq!8>2>h-##_x589~?NPqi~(U1697!`QT@%hH7T@ z6Kw30X)t*4L}!jS2%4X0Xy(kvqB(}G)X0?c4TxHy?GnQ^8vAmEp$CoayVd}gs&S_0 zyF_`5*2jBOD-DP(k6G|y1;tl zO+yu>Dt&B7q&9H;PeX5N1O9Ifqj|ZvZZZZ?4g4uKS~PIj-e^(hVn<^!P0-fO7)%W= z)x+pSVJ9^*wx_WNe2fWHvz`5oiUWbrV-&EFRvGkK$>3&B-)!x_d841K%kkkN$7=NiSxK^g3c56(0; zXE8Sm49iPC$XkJ27aQsP{LgvDS+=NVpsyR6v(R{yO{NMAfYB_*ZDHewyDT++vf_#K zT4CJ4NQ6k{hj)By9PnjqPOdgSqijyCHLm?4HUrli%f5`wobQciD4YBrjNg6{n|qs$ zRV-6TsHutQ=`nlQ!t?zI8$4{Mv4K50mhbC@3lA7&4Dw+>Tqovg9Pd769B$1Ao18MH z+VH_|&Ka#}aQy}285*2<#i+376TH7|yia3aJ|KM~v15GTsnN%Q4?cNq{MnyPU5Pl# z+qAMCFQQ{RQ!ayu2t-UofYTmFb~L?kT@+c+1Vt|ct;uX1dy=gmTUa{FUtpS@(xxU#S)%|Q*kMhdg2ciy+rR~wx zqo#ZYJThWn7%}`lElV*mEiUfiK}Sr_3nwM9ry$X`X_2=0?ClXF}(ip;bDo+H0`aZ zL=#OQmnJ@<31qUw3L1M)kyuJ&^VEqBG}}yFq6-}3BXl+-C001{!5%q@Pwi>2G|`s^ zQ^qBhQG_=W5|2}a^HUOs(IP@-Cc=3O4s0??Jm>lEEenAr&FIvz8%;#!;tV;F`dg>y0Z5l5R1Mk}c84DW%QPvvo#ql-|bP6|E{vazp1P`oyAz0aEUL zzBig%p0o(vpH}INyH_L`#G*@R+4!WJUm^gizvr$!deYRaMZ+g1v26IOn~>BFomx~1 zi^p_P+$!cK|%h2;*fVqp3%2}-MGnlZd$AuDnYB@C0FHsT^F$`tS z&bF_5VFqsmM~4o_->yuma6xNZwDiK^o0H%IFH#-&_O2wDo^!#z`;uUK&IRj!OcK-J zg(FGVT>03a&L_38<%6OtN${GMON*{1!Rn3+uD+4<4IH5*xFeq>{c6t#!`~!Ln>7EjUewRq}iPTqr^E=JN!H)xy~BRTIuG74rU~?d{F)jumNtGmAsr~nqz_S zKv6M8b{M9(wT@8XoVTv_W0QSZI;+nxUGRfR$@|?oS@G_L$#4-15gSimoD3JSaKY|N zlHr9u7yPs|xv)7OoOB==CM;a6@?dhQm=8WUn%u>g54OLb44;X(1b;n9UTVt+S3OIH zZxLK<*XPOeXzaDu$#AC~7poVU_tBul(Ol>#y2pRmf8lA~!ceiqvN2fpDF&OIZ( zoX+c<(A6zP4bjq`=K8otBXh7wbPs2Fo8fYnue}2XqJ_JPTBAly&0p5;{F|F?(Zia@T3Ye^wy-M zVcBRi^wwO^Zmb!4YcBZnIP*taK6r78`Lqom+&R+>9XFR?)@<`DF&~^i-+YF*SzNNn z3|%yrpxzR*)}9ZJUTxk_v&DQTlxDroTtj1jTyK8v#HT&7O+ZlWG|#0RM0MsO%3qq=Ji~7v`#Zyq)Z^ zPAOqD{t@UiSbI*STjG@d_4r!q)*!`Fqppoo*3fD^?44pUrToSz7E_AwP5DG+9@HWQ zzS45$VQecPgtt$D*Araq%MK~8sj5B&rM#s25r?P1EyP@cvk@tIG(mi%5Zk&>$`l7a zLER7mckZy1jWk>Ih?KM-KEdhHDHa=EH8y1mEu#0Nl$9*|2`yFNgDYpJd`lG{u`Hz> zLjxr}AE$U91Yo=wIjl^HraDjCB1jflE9m?U7IeO5yP)%wor2Cw>I9vmLjr>Nh@kVf zKMOh!JT2%v{8vG{wii;;sLsd!PGak?4sgdKuceHmI=^#Yz}@~((0SqGlzgi5PyVSE zou6x#T1<7WZk<~DB|5jqi@T(1-T6lRx^pUR#F2ecyMKxH?GeB>Bo>eQN2R)SvX%2F-1pPP_svHdVbs>SvP=u$1VuTK;ZLX%T1w*SJM zYO(zfS*aG=w<$=q*#7ClREzD$RS2;yN2mTp+f@6Bsg~Y%&*aodT08aT2&LVan`&th z%c@c>EyDjNp>|&UoSIB)C;epVQ0CgnxRcrpUA{HS1E*g|UC8&d*zam;3*L^{@GsAqi z_t3N$^v)yg25a8IwPilI)GO^R%Rs`H-YAXbH6r94!>leU{>HQ|<*mdg)t4U*C_98vo#GB3O!JFP#1Sz`cO(vho9 zT(>N(SyMjvye{pQm=E4Nk_O8)(goseKd0@X!HLJxUfc4qv(BW!XFV=~?p)dsmT?=@ zAw?Zp+-ZvTUP%|D&Rx5CiQXwQ{&I}9omx$xTS z+dX|Xv-T{AE*SMnA5S&Xseigfd&xu6E!sOWGJU!uFONwfXe3l6Xyl~&LmM_QU+TKZuc`!YQp&hl~De$Etv)!FHz7*vLv6{R1v;dS(` zEPXtKj_{_+^j|5>`4a`DoST+Dg~n#g5$e0&yz~>4d`l$6zMY>gVQDfWucT{GWv?`M zv|m=QF@Cfx{W0wco~%!A$sh|75b@UU)2k?dMXey!{GI98i=n>-++4m%+nq@_*z*dn zyp+C_fg}bnSg3FokZ?<&R&_tUn&rx6uF(X%On1aTzD~Dk$WyygFXJNvHNLmuz*0Oi zR@(3+n>EUS0|p%5yWSZ)X>fDn4EO-a#pd{CjI-y1_N_B49l~#IGGIl=C79AK16Fig za9D?oH4XTnxKD9^3q^#dW?1^N$7vbc zXc4s;8Qyk0*tL8i_PfFiXBvB`LO}Rtv_Nv;*bKP;og-;GJ_GjKbHVj9GQMT_(t}6O z%{b}6SB9}VgXvxzfEbJCFV8sT%2O%Ylws+k2W=K;zuqF~c4uuyXR5EayE0(^Cnv#+ zx(s-Ezy*IgkkN+fKy)Vg^-pHN#~?02?Kwd+lYbR-+w+2;MY~HGdWukTU7$1M zratqI=yCZh+ha5TUw z6ZYcPrupEW)|p>1w*vx;0M-t-7iW&8irwR!X{m%6u9+4C?@>RK8hEXHCis-$lsT|b zrlk)0_-0z_prL=Jr4B~4&a~8ls(t1mYH=YFp#nvJJ7S=gv>8MQRMYS?I^W4AhIOm8qd!neIEI-sw5$}UH#$^7) z(3t*4xs;H}uo$-F4?y2*G8>?#E?;?~j2nfvxRD}rv^8jFfjX06O)9`*fW62tdjAdZ zAu+S3Ef8r|mf(fHOU{IE;mb39a9LVrh!sR{&d4lfSAGBjcze7)GjlV;H!XoAk8(LX z^G_SmT70q~^XeCg+@d0L+?UY7O-5%{I{}lO(=z)o`2P4@E(CHlL~jx@Rp@DlEMInI zM{zG*(6wdfZSeMvS(`vtC?zn9(Y!FUojnX4m_;}lUKf&;PfVzJc-9xR92t&}$oiA9 zy6~)SSqBM+*q&JouRQ*iWoECejtuim0#G|Jt1amVx!oH*VzLStm0C40s}JrLm$j@O zSl$&=Ru>0&x@2bEwuk5Q!mOU;88kWzfxP(Vu~`?PRpW0bXU!tdkeOMfj_jr-!R;4h z-2;>7rD(G%i{;v9i#jEE&YG-)u0SguXLTTAjNg}4OBz)7V_6N|;2Cp;7vs^@tVdta z)B{n|=~=cYBr?A-PQRP=Mr;wF^hp-W883?fJnm}m=UFbIwOf6%UlY}PIApsJS(>?L zo9y5@$vb;tJ$9A*AsBJ|@yd4DQ>;bbko~G@XmO`(=23Lrdf4K1KGI%|4s_1e{d->SsAN)-CtlPg`#uoE6N0mCtl&8{Bpa^X`3A>@XP>u&XP3zAARBn<`etiM z{Ez*!`!ZO*P%O7eMTP9UWP5g<(Iq!6ll<2g>vj2=g%MqhCVgOds3a&L#HiI8LUf_} z5M!VuAj}jVrq}88#xR{BSYiqaCvbAUpR9kdlMP-mID4NM)N2@-&9YMg(DZo5sO&!& z`X@p~k3FQ>yFfkYR9rU8`%n(UIzHQg)1Ujg)r?Xjw9uHM#!+k{GAf+xl z=ig&s5MrAMOk{^48!m$S2Gt~G&tibdO3oh6tcK{+>^hmaJ6ZZXtP0x7gxa?R* zV8V@jBXFDe-7|0)}n zipFdo{M04Kn$d17=)Vqi#ufE*d|iR;V!xbW(5K;tEprOUZ+&pi!-nu2F(hX;5$5`^ zoLVC6{Am7n``8?~u=gA6uE@DXgny;UnM%@))#sch@zUfRPC2X7bGDNnUz?pXoaK-L zD5DcnPsnM=omjVemwk#)(VSYsv+zA8b6C&{N={YM1;Xl95=}4ab^K-fp zf#i#Fsu*jp6CSZV=UXQWP5X^GzcCtPCj>~~H6^#67;9^Cni7Izw&si{#O-(G{6v1A zsmnP|I>My~_!^4)DQ7MjOCO!eu_9*t^g@o3q#O2o&RB+KhDEn?7K1`~m2bYEXJ#&@yfNDvLT#kD4cDWq&gf2Yw^^)98tkkP~Z+5^By5<@MzJ^}8 zgIM_zG}Od1*98ygliQrsy?HRt*)59aJSdjuyilHdp1~G~R=DZhbJ03}tM#)J9fDAI zLoV|S^UCQ$C-m%bg)RDY>y8-1!LBVVZDsbUTrcd9m>Vw=t;71H+$Xl83p~Sb(sNli zgCmZdrY2X6S7zikVXBs+g?d!+Q*M0}`F@HGPR`Dq2>y3kmAPS2er~IdEJG$i+_5~D zW$X3qW_hE~k|$Q}@r@O^dw3#f)9T!b3_Lijna{2K@WnN`y;;{Do-1y8ICnRb zm4W_N0|JAz7%Y@H$Rbrg?p*Hg;Od72bAHW*cfVZl#r@o&;4+28j{GCH1Ngil!RfDZ zy=f4C$bCnH2W;}-t1y?~YjNIOmhQ)*eScz1DwNza-=o{KCG2Y?z)Qu~T=J5^Z4D4d z2@UdC9Jv8ZE}qdakLgY;E^}?;ydr8Sk$!nBZtOXjLjSyb3^sxx^Br3Jq?UOs2MW02 z_q58}&#+knZ)%spJ1aO&JOIM10`4{5{l;Fu?84^5)jZBjKdZw)htbuoD>CfxF`kHrNjr-!D-yk*RFVM@w_J7+k1_|wd;hfb+^ z$C&j{ou0Rja!ANxx5-7>c`sQe(xAjxw4{xVJ&Ncd_P~r(n!MqK53H;cOuWik{Kx2QxdFN?x)Aqb&G^pR1NBsMdBL1q&8^9|X z5Bo9iE7;#mVwauFJ4(}*oz9y_)3&%EOl$tUnCC;MOTJg~hEjQouL}tV-ONj%)1@sB zgxIl<@;tx=9Vue<>%0|wg01rW_VxJ4s(Sfw&kpzZhVJ=F8XWJLKb+;9HRwFLs^=AZ ztZtq^$(FCb_igf9FpiuY7Ru1uy5Mac@>8hzk}mn+E1je48kj$pYGQdv{w*3rUGu#d zXMoW-AToakW&WvOKGRK=L<*Zz-lw|@6dubmYxC#1`TeNXHAVS{XzZ&6`PNi6 zr)7f9ZY>vdcH-N75pA>AHVQghUnA(O><2+-L$(MyYhRl`lWOPZ-2(24z4?~aLv~$0 z?84yG*W^GxY&hbA5kKeGr zJsku7eoEZ4LBVgl7HS(65O<|SghJnfWehfi05=Y4I^1c9GU5uvcu$Lh?X-bSYgf>S zrEB;M(HW2ERPdS-mjxDpuLzE=CafTY*3POP1(xP9rdI(Rr{@v`_bc$C!T7-i6)d8O z;tF!%P)30VhN}tsS@SjVAiCfV!&CqRST;40aqan~% z=DH^Tjt`tsRIJzrZ_6rZ#yDe$Miv+|I-65)fR}w;ZozeyH_-4hU?|Eg5__NxYX^Iy z4!cKrpkX?3B%V}W!1OZlGeCcQZ(P9x5xfDb>F(i)?@lO)cNAT~y=NDUW-&ht_8hcw zE>!*Pc?C=tP*`BPplhXKG2TAE;3n;PHY_NZPAy@`k^&prJ!+N}^kgy8-l>KCQTM6h zukq^D1#%Xv;Sd(h5;wxL*B8LOlw9-iuPLaY%)bAjKt&DHZ(D(hW&Y<@SvVQ@+*{DO z55vk7G8dqXr*>lOV^wIGCREuL-lzJUWmkBwA4tp3+L0| z*KG^I84nj5)4uQ&ZF(VrLfRKWLfXX=A+0)8NE_KhNc*X$kakU^kk;I%@JEWY+l~ zS*@V!Ygo|rglg$r#(2l%)0f4N?9Lw}trT*e?G=Ya@b z%|Bwu3!RV`I@K0@7FEnE^~AAf3s-@!*=1>N*yTbY+^NI`$6qR33qEW~Y~=5SsSJY~ zvXnCv^}Jrl^s1f`?Sy`RQs@nLCYt$;pcpoS9^uKMfp-d--Y@bj!UBB|KTXBN&6z^&s*iYi(JsD4n=mTvr{QtCqd5k zw&+k~WFU8ac#$uvA5+u`Ei@;)qIm&D)MH^`$0GF?;cajx{(w3av78P;|CiRZFCf#4 zcdB(hun5+WmA9A7{`~iH;bnV@-m`S>kPrIx z=}Gp;Z%eToS`|>FLR&8u!S}N@?BCDojuhqb2jG(q6s6jLT7EfPw39(90AjjU3-XoW zUyc@4SwY#UCyH3U9dQ`=WYJV>(Ovx2*`luj3;+5{(R_v}5CSl@u)m9UYMFSsNLe4~ zaoc|`J}P3mt7n{l7ZM=&2*uwLG zPw^2EJf}4&&a{K)hZg+rL#>Jnz>smzj>Qq)@GR_Ce2%0WHlX+y@@z4vcq#e&k0Xjx z2yQ@3aSY2lUSK`|HO(#Yz~Ix!-<~Q0CCZCE(9#XXJ~-1*{0<60smaAG8$$(r;xmbN zNVCFR{Ivs64$UpDBPw`ZRD6Mma=Nm37m1%SrT9D%(KN02CJ@4d=M^6#e{Z;`xIa;m zYDMvIl5fMB;zta7$%o5C&B+d8>eYMR?oM;svDa=o7^ZofmF)viKCsT3+<9 zSkK?5p*`d3H0k5vAnz_g#4a8 zw&V};d+el=bVqpp`86-?!MVJ&xm6{n9U#8{Qa;{$IUj#yMM)~jAMzcqt&eLWeI_^@;7K^9B5z{H?Xk5ZNuKL_CNUpjza0u^|c!q=f|l4v(HyisW#t6&zu236av z?eWjvrJq<9EQA{dsJ9GLe#B+YXjZz!8QotQ5Lk0YbuJASb28$&l2UBT z2NhjQ-#G9=Q{U42UVKoKPzr8ui9Yae>QX;DKIoWO3dftd*h@*JaJ-odPBxdq;p2eV z7Q9sM~X$Y{2HpmoKogD6?{7q?kC``LFlm+wqWj?1J?(AJ-1ZDAm<;u@3us< zekcpa@Vf0cmS$x^7+JN5sa28u7%oiE+MK5nSb@=}IH72;?R7_J%ZTv57#rBnVK zrST^ea%sQ%eC)zUrQjKyOJI6ZdcPGP z)OeP4y|D9uGPv)I(@eLhvP>%CE@{~gD&jlwjf8ZUsN!5%543lCnHWda zmfc`!yPv@@98KrCZNg(Hdb+c?J@(&Owi!I0WK_7Jn|sSx7Hbwp93Vfw$JCXnzC^hH zfJ*(bY#TUSBm6=Rm$A6K`Eg((j%bwRq-6mByA*YsBjyU~Z8SE1zHG#)IEBhJT*^r>> zby*6p1AOUi*-_q?>L`4ATju`Yj1vjsbqLkGddesnI+bmoH<-a_o) z#^tR%_}I$M5!=-0`!L@^w_`^=0MnsVcUQFSpc8$fR;h&A3e$5U$S<6f|pA`4O5edA?8w zk_F{ePQ0#uTPeh@T3vpO*4NlAg0^F81u)xf<$q9Z+Z`y+0aryt+f$DS1KgVNfQ17+KM|Iof+!>W16vEAEN-U<+f#F-I;~n^|#J%tyY?uQ1#3!JP7nXEvf; zsM)~^@Q*a9q7z;@wxW&IXECl5DmqzWFr-PPFvn+9Jh9<({~jyGQ#!KCjiTG(4M?!y%att~$m?5vpJAlik2FJ zga-QC`jvN_`CyrE@Tn#(Ds@;92`Rb1ttH279t`N$Hhu7usefZ&cF8!GS8*xN~! zPGTNclu>y}#0MW`R<`yL?c>#uFtu|3zgGilW^<__s##cB4;?=WUkFR5Ri=sd;Rn+y z{cJ^B(Wdd6{pM8s2_7=PuI$RlbPHBALbg6SarUkQ zF5Wu&@OWg^V~ihaw|k){s_izW6*{$RVKd~ibD=+4xMN{Uw4mFV7HIE|g~QS1Zev`L zXZNiQ(D9uML;qDaEb2K%Btn6`#xz2~dl$M{b!>|2_Ac~BxxL3UB0ogEfP+WxF}|og zdaO6f+Ou$wOOiG{(Wp>oCa1Sil%YTTfJdjuG4Wr7vPm<#IjZSBW-}UbU||Ea>cGPK zXjh*x-pF_W*!1Z(2EHbJHAakZ-9jJqZJ#lX(doK{JsmhIgb9lJYD{;=ytt_En2BNj zp+UhRfd*r^R;P=QXaj=7_2FTm5&9s5DMT*`2}heUXHRbWKN6Wx-LTn>@U_x0iyi%g z!EZ}=h`}g{2-8aRp}|^RSfE6r4+#u5==9+dG`iQfk7b=5hfn+e&8s=|HmXjMxNiPwPRK|`kVB|(BLphSa3i{m?S(TTpJQ-(gSp0h%PYHWFi`z z;{VTSr~;#F$DDL*Y77nx2n-4Wy@duyB&GnJDLgzVz^DgLJ0U^A`d~Es-K6EE z_Bhaecto(lV2Uv40zxFlfDm0sSYU)PJSZ&47!nX}#G)hXGi?0B^?`=q2wj-O5Ev#2 z4$_7P2kOG0(hQ*?CViM5iw4cxEAk661O`e>28lK(*d&o?BXmJTB}Su38yK!PRt=ix z4cBdtTP5=Q=R)C%yLmxP|3{GlM1}w~W5GCo|3H1HNe4|2*ab_%fxko_5o!w58T0|+ zp+*S~+=Mc0{7eDC`oPf8fRIpV`1**5h>-B`z=&WFSQn_(p-pQiH^9$|&^nP{xHe25 zA_+DDfF#VQiwHDD zodZ((bjeXRFDvWVv^m&Tuu%d!3ycVo1ehe@p}Np8i9}~KL>TmeT0HXQ+)Nw4|Iv?S z-5=|PXRR4GSyb`Q^%5M=9LgLN6|WvPqHn*U>i=Pl!JnrFQ~oDqg*R7rL|ZDhz z(|;~d8-da{j~ns7N<`YqaRJDA%Qz?hFlhhAps{QtRh=p}2W1p5DT!FsM`RqY<<;~yMo z2nv9{L~96zDhbm<#e@Nu2pBr`hJXORVfhD#7_68X|)=D%*RAKt(`U(6$E#{x>GU)Tv|3hP1OZ)N%ZT^Eu5oq+wv8_*< z*S^zivcs?r0@DMEsNh(8Rl5NiP4tL@F)I4 z7jU6)hp2HkxI~4}BWR2oH;ijE%J+Y&YMN$(Rp<5m-t)cR_jb-1xx1^no~nAbd%5oW zGOS#VZB+Ao)h^~N-?odvwsB2NG+$FZg~ig4lgr}=Jf7A8lVLP3J}H zpJyMJRg#+(?gF8R^PXq-vK(L6d;?w1)(zJ&*zl>NXIi%Jo35uuyH_1+r=B-W10~Nj z7{WP*wC&oOSwQ{c%}i_>7=+`q&UFfgZ`vq?n#qrR&vW#gqvQ%jm;F6HbLG4nGHERf z_ueKotzq}>CHu%B+EbkM((GYb#d6S24ByV>6hq_XF(4f!&vVc_MO!RdHH)R=(M|MA zKF9XJI_4@W8e%c0x&_yF*(V&;v;2TF?`N{HZt8|j=jM4Qo|4n~jj0*zE37oOxbTD7 z1A>K@u3}y$x9o{+d(BZ$(-K>ax@F-Yv4{B2*Jtmuzg{ulP~;4SSLm};7%GoeMBnf{ z*U{8M!4}&xVqnnsx%oUKRh3IS|1%g#o{n1R6!MzJy6@WYj^Oi!soIX>X{y2E>YzRGk8b4{+y!4M zw6(!{zxldMoy|_~SPXeq7}Zrg%gbwCvB(B*=PWZ&&qdctYc<>u>QfpGm0m@>nwc{) zn=7#0no7~40H=||sxnb7EKCO5WI-$hnH7yFjJoSGl~!!M zXWo#tJ!fBwDp=J0g06UI6)ww%$F8E&8@BBfRm>sx_e)q`!c`-sVKHN4v&OtlXjb8R zgM=kM>ONa z6ss`QDx#_8CHGQc*DWh$Xqvm8o&55OrkrY{PFym0c;a)33lG0(pJ;nia#T?*<`{2Q z&aevwX@~eMr3@_HbmL`o{H?R5bjo7ODEWNe)^ny}m~u1lc~q&4c)6TgjCMli+ix>< z1&jd?J1$qWSW;0&(Z^7G9F@(-DTHq|Hqlbr_r&9S&Y4`F(sZbV9VmV{bWX3VW$Vmi zW?tU$F%PvI>xM7Y-MnJ4>j&>WHXjpB-iBhimdf}vG}H#>pMieqGAa!gnb0PUv@p-s za*TJUSoCoUXy|CVqnn;y)QgT{s&b>LjV!gawuSo9EVVwR>F`;O&oVcd87PaEbaPG-gQHk9Jyx?|>YCZ_WcE*K)0eO3$L`NZ z9#k)O{7!s5o7lJ-dqiG3bWZoW9R3+kw~V|TcR3!038c8Ht)Ve0(M*rlPjo^Le~IPb zWl~M)*1>=18bya2_H%8cRL9{ajW=vlY*o|%lxdfTDLT^Q7&#NNxFZr9oiJd5TAxd9 zFeg?VHRn573_@D)SjrS$pzS-@i_(k0&_+efiy4#cRjU+4}&y!B8u-NvwQUV@K6B18o)?+fW=laSSGvRV}FbvQa?YE*=%wx9Z3^rPW)ef7* zato@bU^QU5MAI#c!mS(FIhaNaOms=cUxDG^%4G>F4=>h$vM!0AOB(jlTZRYMHGLZ+ zO0|8iVHp8i2fAcRv%a|NboRBBW^rpAjqx^R>YScg3@iMx_?=lg4O8R({5&o%+|sye zeK*=u!;#90P*fewa7|1Q^i7xTkZGo{eRwvmyLWNjZ5<;p%M`W?y~se;aY0f^9h(sM zi{mIR9+|csJ=Voinh)oknb>m9j=YeQelTaxtj#PlxNj>|1kr|FT)0K2Xe$c0t3_sT zxW5f|a8pX-TS4E%!|kGl6wM+oBePhrg1!&VThI1Z%;jCBz^ufPlrwNp$eqR1d|nW9 zHFA~Ir!=aBJ6j!AY(j%lcXGt{&YrW+SgpaS*w!q~z!Qv8j_=$+qs^hGOa4cd6wdAmwR96hh&~5_f3A+nUiBLNf?yv&Q5t-(oIq??)ZGxz`EpzYVLH?sU4bL z6>Itx`*-|q)!kop_gCHhmBE3kyPp6<)!kop_kVdfrt0n|!c}$m|9hOF>h7<)`>XE$ zs=L1&XQ;aS+s8_(?*6K~zv}MaO-!ch?oSLcRNehmcYiX5xa#h&y89EO4OMr4vS!#XC29P(mUR{K3L==ap!eJl z=FQ>n*Zj>5KA88LbG6LhgEQ~>O~i4v>1yq*Vl0;h49&JaPM z^#UjwHK(d3F6`bT!?d1#QMwUjmU!X0pVtuTArw?LJ*Abst#O50o%=)Ne7ZqciL4kV-l8WlVQK|Nik!+9bL%$R(X`v|lqw@oN` zXtSD4Zq*i$OuwoRZ1V#)etw1Nq05qir&K49S&`R)# zI+!F407DTxUxdO0@k6#wA+M_-sXzzjfPpB5f}s&&C)ycocwXihq_k{{YrXm3sy7o` zE#$?DQ417tOn!cE20Qb`(iO$_i?qdf(h5M#*~&t&8tGK zPifc@H9wmF?JNl%WV4Wn1XGW&HvS@~Vn?#4Ylm{y(7t|FL*YdO?14$Cv zgNFhzOk$Q}YchLLxNBw)Y|D&u$RyAKrGwGPfojaD3R!zOVBz_K6HIvdMKa?6IxrMj z^iA+!MKa<*i-CzJN6^<9pn-bDEaq2on|W94KWF~&B*i5)=sO|Gd4HWL)S!fESh_}1 zr%YY5c^cbwJfG|kpd22le5UQjvy9c0oCk1>V6BXz3vkpdNR%5rY3OQ8n_g4 zX~0mB2FQbW_;u+YbS$Lcr z=^>;CdWC`(Y`bS3aP5>9rQ1^_fi1q!Nz`Xr%M zmFW-Q@Cq)uM(X{bO`?67d=_@wBFVmaItB#@+zQE4Cg^xuE20rhVJJokasY&rI;P#e zlrku-U2!ZauPnqjpZo6YLG{T`W;oFCAY9B|1{-+a#8-NR2E1{R9NbzdO-rS#Fhi6B_|-S zk-|JMSpJwyo+uV|^bsFJg;d8}0sJX%N+ogEVhhd%@15IxRi;j=(ZKr104FCo2L_Kc zcbnA|BS9hpNjV-UBjuY&=SgZ-SH!5t=1;CiTTN;d05BTj7_vRoT)c&`ROiXvW05Fm z+<@nSI3!s=m&==u!g>wF7j56l7v#-@t(3`go{uHqO9V7|2Otx@IGxO5j{}jPcL4+% zd1-gb%pXtBW2#|OR7@_?juM+C!{7^{)+M(ZrIX$D?EJll17sz+ky(NnqLA+Ek>ut$ zR4<@;FPJ2=p=CCJ%%WF8G=tYx=?#ftb zqGBJIz8lO?o7lvh#8yqQ;n`VgYO8HYsQttfTjrBCpzNY9x;|+Fu1xu7lA|`d)KhSI z=|vKe7i(#@KdSDJs{5nr{;0Y?s_u{fv)mtcggN>CU+2&2 zk|{-)lOb=;Kj16Zl$B_cOZ%SISM1pKT9>&uelVZEi-Q|K`0X`&WD8W8^9w{hh;kUj zvWXQd_~!}rnnh(FTh{8((9*7;qE!@z0O>+>8jOeL5MiUPf??tV2P%Q9 zk_*I<^pvIz(YeEdb#^U$1kEmQFDjt4M6kaH)HWztGK7b%}CQ zr18%JpgaM?rmo-r=GqFvw-es-6lr|-H$QF+bSg`jVGQ`tBb%GJjf?8^Uf(a;Uh5GY_=kmu=h*SUY!IzC zS+l^W5MZP%5kWGG`Xf{Wii(=2oD>%|pZu?dfA3!J=KP}X=YYJ)nq!5WW3wkZvJ?_= zM9(*)656R}g(b9yc3pH=_j*s&sRqcdLd_Ec_^L}0B;cC{Ax+ArI9_zc)U(1X-qC;2 z&E2yFE(LJkxBV

HYUs6=5*@o z5O;);=H=#lY;dV7d<<&j^d4Zphcz(Bs(B_shM3YF!~50QxRNt z$9zh=CY=-a#@RC#U4BMqqX9Urc)HW#{^G1Hi)zJTn-_KO2pmH6{mY{L#HZtL?R za!pq;an#~|V%g?J&X@io;1@?_+8y4j?OJu>ynPqfiH%zpU7UEz{$g8c(KGE%skp7} zt&4t%DhzWmR3EIQ~*&o=M4c5(kq#bI82be)|qexjJ!Ve$AcJ!gM$M90O?RUEFk zAKv=m;>&M zO9&q;7;q#;e*D@~&kC=2`1r-8qv{=xVsf5DA5&79!dtRnC=gEi67CGEZNp1FE4<>4 zxr^`ku`oI{jt>6v;o`G;iofiEIK9KA`5%Z=&qw_9hnFroO5Fb8;?cpaw_f^}iQ?!E zmyW*U_Qnf3iv9XJto_?wRM7HY+XT~`ej>wm<(D$^Q!h^fjY{lxgQ8as>A&)Vg? zK0j{k+^IV=R!sir^4g~5OZ$tB(;K^X>JCgxEZTle^p*0{#!j7N0X?z)oQj{hl%s#)d zzHgm{V~4aOgRCgnXh7)YLEOt+EK~{rn@>-J)1Z!=1D8|;JW*7Qbnyw|2?FqoHi^RT z3S2~SYD*m|Fl#EhwA>>dQYZio=7G{ts}cSTF?qwf5W5H`O>)CtUJMnD$EUSCd5+je zOdUF>r?^WrN)9Al(Nxt3O-t?V0;TX#?5Xw%Iu6y+jB8wyc7;NX|4pL?C|NoHW_TV6 z+9&&rOtYOCE_F^`gM$W$KFCXajS%cm;L-tjgRT$7(9UAPX-$io3ua5NTw=>%G6AV% z`#05y`<6HEU5Al^RYDdO_3zoWFsUqP22|j@q{$V78MB&k%~?{#v+%xcok1Eq}1W$r9`)g@*BfM zGVr#brO_ck6H^7)k$DRMh80+RT#6?*lYtPqzbVuzOJM^YW{i-xHmgU9AEY+qy#cg0Ek(gNk6_Gw?{9>5hpMM0<>1NwWa**mdV%`0J`QR! zX{hHc@RZjE8!Zts03@{>VTIEg-iZ}H{)xu@$5evx$=jeeEePt!&2Salg_cKyj(~NU z2h-vdo)mA4?4GuSmz4f#m$sQQINRYC?c~?yw;}Sy!bMG;hJ5K`iOi_x(QSk^1e{0< zfL8|OGmaja90KtLt|XhJ^>{PD3?sv=2s|3%15)q6c9S(hiE$D>HKQ102G6-FlPwVP zA*BJ=0;O}w%mch`QZAc(NI1bfE6yk*H!7(`U;KC<@vZul7KsYUTE~4yi{4r87+L{r z3{nADGXUgMme~f4kH5*vlgK71%Cb4rf|tj`YXYT}OdKMW#e-slyo;RUIx)mvMwuX| zfX=~XQ2Hdnzz>rvQlA|2Gtayu1N6P(ob{REiH!>N@SC*MCfi13Pq~@xlPwgzgJ#DIg->Dv zOa*Wo5RKB|&>;PYT|;`yOg6sgiKE>tx#ch|jx74*hKc;yVpvY>J65TjUs{fm%B<*l z%u%v!DBn+QX`EqUlb#x_zC<)ng%m2ui765LZBs0{gS>W=CS_Q*%PC8FYpR@Lb4p7F zWz!yPXWk~6Jb9%^uDebS2(dwybyp5rMe&(#Lj4O{6*)|705*sYrF?hPW6`DtTa>|aNs>3|>eW8lm}v5rG6*s#LHbOSO?mypCdj%zVt0WUsxb>AeZ=wUFB|v14GD*i{uw z8IkQKB_-LD-d08GX5urE_6)b-{&hB?1ly*j1X+w4(Rr6-A9i;15Zu061e*v+Lh5)c zWDiOfBsL6A0%f70b@(nxP|%5l6b{i8(^RTbU>p%d0goXA6-3Z+vyoj+qE|kqfx?@g z)U+X{|4^KeMSV?dw&uB6igmjpP$RIb= zg%b`0gl&?y>oRCjX2}cJEHdB4nq^IcBjU9ztB64j6N@_dK=#PV0zgq9C##5EKmuZ1 z?LMVlh{QllBsRIIrD@-Ya!qb$bp{sC{RgggN4BDb`#l~HnA`Ut!hH5NoqAd1E5JT zg;7&S!kj|LkCJU7do#IFP5l0drsIbvHY(gHLK8=Sy{ShQ5%cjFS07B zU6a^qR6il5(U@dy>2fqh%c^jDNUuv#27AbYmVPN&M22SrK}KRaBvzqH1Ct?|VshxW zVs<5Wkl&u^B5_nwKy?=~-;`HYPLIx}Jy=;&%u>>WRnozDVavhw1cewF187}wIiiHb zQ;hm6K7>MKrOGM{xV-?G5eEmk1r*TsV(L)P2~@2@^^{aLoXN-lN|C@Ngk=m%C<*Ew)|f9#4&bQq;FJv6dFzr}6;IEYqfzlF zvCVkzdi>{2BN{Y|5a$ohDU)Br^HYSe83Pk8R7l0pb8&X!iN;Vgc#$CkG@R4Sdma;L zC2I!DI00NLf>GB4BR(u`)V6mecZ}1{vPv0{?bOLa@(U}I^VTIll*njH#z5c;DH!$8 zC~QOlFxA6fj7uAU0H|4NJ4J5AsEmXSD=d9PouxcJSms^Pz;>l58vZ%Qv}0EXt`D?q z7Jr>3m?AFFM!`s_;n|~9g OMdnvZ+q!u2$)-v5Da}$;PSVQ%=&7cjIMJaCqjC{* z0UOXzs3nC3g97Uk3f8&9aBAUlL0u&69d>Ul0N_A*{#%fSClr2hnUbh>j^xpXfCyqW z6i>#zi(gb$0nkw{ajeQRC1uXS}iYzG7SMfC?Db4O{npg4q>T+$V~ z1w99$B=xpfE(4+#)E`J`HQL6K8&zpm>WGt;HSODlDc=CB1YqspI&jX!-7NqI1#ZLnmlk(hpYW19_@(l_lYcBBl;c64j7qbI$ro$6uoGetJoesz>u zVqbQsC59CyJ19@>KFK)GHiSl^{!{YH69LAI@^K&&EJE$1!UwJ_LSO7r3dJ}o>@Q+4 z>~g@vP&&C!G%;*8%3raM`SfQ@GN0V4nis$N5bs7>v!VSp>?6VK!h@NuyYeNic6=(6=&E=aUN4Gf&xF3BEe6HSDWe*+#ZD)<*E|9;wX!Un#}V_CN^1o z(xaLVlyR`JQ6kzifn&08K0s{n;qBG%Cx0iSX2L=34Zhq6xfJN67F z8l^y@+pkYmX_*|!U20vkXy$(fZkB?%v%P)(L&f^8Q5Zr0xd zI6}ZAZp>*HB@`rL>cpB8W)BMb{a4)BbP*;C@6U)GG8J$N5P_E4$-5c%DaME*QSmP=01B_ z5ifs$7eQKK1T|3!rH)8pGDRhF%GSumq1}|pCNg3jZen%GEsMvWY&xNy>T5}DhKa-R z7KYvdq}Fp`fiJRwvD#B~ln^r2K&1yTs(>`y!yF`(%wmC(B!?(fR|^ErL1Zd`CvU}3 z6>eaJ=K;T(MfP<#7IPlOt8hk_ z8=Ey3Zo^Cnye^cOAvFb@Q!X~YLU#BZepw7)37^8LiKS^4u$fUO*#~z9^)Tcx)qLz_ z)&=~Y&<5jl#92-}1w)NuPXmMcF>|hGGA1`2ZC)uY$2J(7*FbUc)~5bl2>dsYG!)J= zv>y}!5DC0{jB)9Sp$r@-Xoe5y$4IGdQ9HLSg zgNzc7GRaPnB?fwvbS6tys=}>|dxShU7u$>-UY03qE66B|z}2TT9XUMeakeKln-ev| znh(P5kSFF&z{@0#;1cX`dD(;{!F>SFp#10q>t=KsPAVMULEB~#(AcXyS$|4F+ee@E zt;k`Mm3VR73e?xaXDZpbErMDy(m+50PflE)Dy2QKerPj=OcUFat{Ph`y}5bsu{o>4 z1Y>=r1fYqBUsib}Aj$opY?G}|%Zzhr!}Ts+$Bu#|3ThuKIOkGWmF94T+C zT%W2w<;f^IIUc*(H;dFH4Xcf!Gf`v(7d$Bi3`>_8=2cEEbT48p7-_JUH+; zmRupx7TrAQAX5J$oPZy#Vp8xpUf?R4K!;okt|)ZCP^P`mkosp!$?&iVG}K zgO%iCS&~n>`CuL=9R}SFT?0r*E_nKvg#WTQL4YqYNQ#7E4O!$K|2H^B9Qj})Fh#~S zv1hSnjjy0Jg4A79(os!&Wg#;0B4`O4JrDHA_67(G*1F{9m=GuU& zEG}21iZ1o4@h-qD&r(B(Ji;Z0$->DEhZv)@c2^j4_KBij%&s1I@_4_RgGXJu4~cx6 zY{ac<#6Fy6F%b}WE4_Gs)WdbnLw{g&@=8+9XgQ-{SUlwfD1Vz~O@x>~v zw|HV@$c{?DvY=(Oen(|Ilk-91`lWhuC$)15rVPt=YGKMt^W^lp{qtFaK-nffR9qg| z;Em&YB4r*Z0KO$z>X>)0>BNr6W{92hRJFxdNIY4xic5Scu_MO=OjTrx`9SJ+=H*jK z*8*gQ5{Svg3}vgO#IEL3-UnX;fQjmjBUY$X0TEKC5-WY;1_mAmEQEuN3aMfT|xh8Hq zyd#>2ZYD9sQo~}^!HZ80KaTCNY;|&jYJ@OKX;9##iT1AH*g(|JW_8Cc#8PGfDFO*) zN=ka_G|%WaX9l+~)jy&lY_0gQ*;_$*5ljblL%6YERRE=k`mhU9nBOCm42%+mf`oj7 zK1TTqj1YddiEl+a?%WK=PyQ zjDWGS02QUA2CF%1BJ~v%BC16k#6+A6ahdo1z8TI(MAX9~ z*;y|B#Jm9pOgh!kPV)FXr2{Rl|Gj;kg#TiUFi?pn0K>xSlAshFpN+&W+j&dDCc2=g~dwwA=cX*bVOMrAv#GUc_&3K;d`c}ieplOHN(YeD0~gNV~4uQJNO zfGg!jq`9=;{~yRov!r14?q@u4(;OM+Nt zflxc|hsZqkj}p%>Fr1)q03oO>`k>cIj`66VKyXp!v{;o$j+AD#DA=6RY=x)3j%67~ z8*xPdj7&538p#tRna?n!*q46WEHy=-y|VfOvxejqt%#wclkozM1?V?yF6>?)G^ozc z3d8i2UPvrcN#z9`7@HDH4}X4W!mOBmO$OC1sZl)8_cp0%jaKYngtwvLAR|Fym1QOo zXvBiK3TVyXBB6+z8-P=-QCFnp+d_WQ?gix_iI16n8qMMz-dNx~-A@m4cXP|u$ z@W)aGJ&b~aJ=0d?KDpI!cBMAzzABDALbvw$(K!bz%0Y@U_LOrqHhoqxy+Ny-Yh=dD zx*uTELVO7T4oGvP#)0#em89&nXFW{L#xBn{wzT9PYgaZVWt6tF_C zY7U7{mKW-a&39KbG^-h!)eKFUJzUMutY&CdGc^CLs#bYho0pkc&Csl7XjU^c%c$VA zM8?qXSk2JH=_s8EIGw5)n$--=9 zG^-h!)eOyQhGsQGlhn#;hGsQGv*+w-+{nwm(Jix8%=q=5 z-w^q+m#tOvzsP$(o&NLOf#g4{nf)H>$M)@-VtklnAsX^J>ar+UBtKU`Oo2E>q&F_1rHmS4ej>dxOz7+e&q#w zh`yQEJI@_=*k$}J4aOa|?A#vWTMvwi+c<69-Mu;n+mBt=waZs*Dd0H{{k~Xv_p$-v zvqLT#CH`{oMT5izcQ4yZym8mEeZ}&-mSx2;cQ30K_a1yvomh70Mf;1McP|?v1{`w7 zAo1kg%dD^P+st7X4HPHbyX-BoV*Ev8#l`EFjTPUXa8Xv&$p4%(;iB3OxWn|2y5^Rc!6{dQI@fn^!FMGfr^qcdmRpBUX=})hoF8TUV~{5PkKR z&KXBs;$QiQ*nY~DPgclXg9WEuIa89db9891-_$E_FMs%_du304FONJb{#3Z~{){sh zIk?2X>QGHwZ>$_0yf3b@=Zh!ARio!l9=SZ2JaWY=hNx{`IePAtlU4*%PHMSOok<}~ zwRrm3(|)F$o}cz3Wo-RkC!W6Rcj|7w({}TnzMJoi-F#BnezGCofm%B?mTNZ=Y`+RY;HB<5MjoD{f-MT5e+FQts}&%gWDq zFv*z`|np*RrVj{8D;Tw8}5Tw-)|#%F4>OW<_r;JhF6UOE$iT z`&O3kxpq}~BJ9}Ia>UMy!T}&B!lbLY{l{08huS-<%7g0Z&E=ss^Xkf*w4<90fBLtp zEBhCine4^zh;vQlo!ZfzhDUyOP5DljT~of(MO(^uI{wb&d9oqSkpxsw;RmOJ@Rt(D#5MAIYuskpv8J)XY4d{@J6XgMN! z!OdG+#_xQSR5F(%Z|c^T(eeA=enaI=feDf~#qrMKcZcbZ))1M@=e{mt@5UF z(M^Rv{ppS6n_7KidAs@i#>%EB*u03Dx8voOed1eLa8t|IciyThCOpjXOI~Ri6Cc0- zm6or^;*V}_IX)KG-`sLYEM{(Lnb4!WO;97h@7*nfn_n&8@VHmY_p<$#UGHW3qb$|R z8Xo(U)h+e$^)6h!%k|Hdd`rvUDBse=+sn7K;`Z__o&Wb; zZb=JYQq;^bca*!W{>_%7?`GQD?Jg6(pbOUW&eFPqX)y@1@WC<$Ia*e#?ZoLq5H?<@|OQ zMuja>w#cg1S7e#C*PnM|JM2!)-<_7fi;^Gf`2VirSFNu$^QUTkRjn`SAc|Cjs`V8w z;8p9Z%|9cm))#A~s9Ik;)vT)ZRkgn4@*t|#mwXFV>njQ}LC*1ijP-Rw@cR2LKknE~ zhYAkT562XzecbXN-OC>p@zj3vYQ^Ipx4c&QWzB})>@Bu_(sE+um)map^l4o!Eq-vu%DI{EiucY~dDw0)G0I;_yH*RUvwb}aHv>rz^rFc^BPK`af|2Grs;>1O@ zfqTcwtNYh`d0lZe$mb#FfNcsA9E#LJHVy$dEcB4%F8XZYZ_>{SulTn|S57{f0{1SA zUa+)4mn)g3DZvfZn{7Z}1rJ_K13UH6O%&^c3dE#*Ka>QLs|XS+NFVYl#qeDi&egcO zI!yI&H$ohc%R@m8MHE%)VVcfC=Lx?w)zvR=3|V+KK`52n@=Zfm9bXTWA*oTQm5vsQ zICj{oZdt$RnAG_%!lvjOd03QmkMig_>ia`;?V3@`G1o}}(r$QqJx%9%VHBd`y6eFb zVtEjr6b1D0wZVE{vR*-z0+CcvhqN575vrU+>dUX;yeUvNJmv^VX*yzH%O*GKM&yi8 zKTy)2V((_2H|fBu=ocYalpGNblvc8?`+#8TeND1I7z>bg8wwTH8Bh>JLuBGf_H<|` zOj)8e=1aCLE^26&U*OG9*PK3q$jgUp9TEYYaAg z!BU2%u`5$!%1bNLdBgmd{AlnjjS}@2G*)pXNI-@46Puw`Kt%Q`L&` zfebNNK_#s>9STnYEJm(mx1_t_XI9!M<%;~?a$pyvLOmqY4rJkQqQUoOD+So0+veGe znfjDwO>y(kRmWvhnvDnm{$tpx?pe)aa6-*%DlqZ5n(F2A7Q8cZ6jCnVigxrN+0_onw@W^OCv-S}8aj!ZV9>fMLKA3o`;_bsN^*&yys# z%u+eDSDIH_7bex28(Ut2w15g8&00J(y1r>c!@h#mMij za;rO9bw{i2XrZ#zZn9N(v|TpR>W)_3(RQb6{GYU=1!rBc>a*I+BM}t$(0$kR>L@@- zcM_|W*ZT%{-nr_N&R-EPEpgw`W#;x;zly)B`MZX{Yx%n_=(YZOW3<@tTR#S9Pk-(D zj$auC{KPxgcN4*+1-*l3wqO6td+M;!ASCqAq|gctNQ%*}V6Bp5%VyyuM za*w@LBaMr8ZU71MGz9VG7m#ikLXM*iwo6d=PWho2s*Uk^Gr53*S( z@d~;I9U(pv1w4l|n71ySeM+a{==_q*)IueOwbXE>+Y2V?0yM;)E^5BLX~^0zKiWdq zx8cF&A|y(X?1w^MG*npkP^-;YmxsajhD?1*>z){KwX{f*8iqSNB>hvmF6kor)ZYYO zo61c#C`yH54;!vsgvQm%k-K3s&OECCm1#lKRr*j5GAkOVL3^4wv4RQ9p>UlE32IV@ zwKJMi25UQN&6GD+*5|V+&m%T$Y>tfP;TV-XV4;Xr3%j#jaB_Ih=w)azRb7mK`Nctz z-=NNhMNfnG58E1=Vb_&hsQ9x?xaWM|5*rR{#`Xy(AfC7a^w3t$F(Fy@O@_X%F}o!T z9aQ%*!)9W`CMJiRd-ch!7T_6{{I%i4vrH9N7?hOojvEG~uWDXrvuvs***BShoB4 zbZUZ(J!CRWZnzMxBK64)7b%Y}DK^_?Tb*v^Z~|xzb|wFx&3Xt?s|IH^>ja&x!^tfv zdnKW`&WbOm8|z8Jz9UwgxMWZenb0XcW$o&DN*SK*CMZtWq}P`VP3YH}24z#8DVlQI z=gzjp@z1U6Ro(omn_qSFOV-q?n_uFVTiyJsn;-t;|Br2c)2>+6GdSs(n|{$P^Fe5K zuRZam;a}PG9d_jFgM#x;y{YeDt~TPKnFHo7Kj)@k`8hWqe6U#k`s&ehJFLDr=&<@0 zyH)(z&(12Y)TxUiWn|`K)syX9OKWviM;)-ah0jCyQTo>+Qch zU34t19X0?+)e64RNlbrtey;%W&Q4#ovwyg5W*@O` z(t=(=heLw>&m3ax$jN9$u;2+lqw~iF28S6#|k3SJS z(Os;1f_Hh}3&Hkb^_C9mfQ0Mbt*G8{12W7ie*gv99)4SxP6^#9&>uK*Uya2?81gj2Xp% zA5 zdfqh6f+hoa*m^Q%sAQWqWwOaF+hTZW-HG*pc#>MpiAAM#>k-I=_*NVl8nN==O|+J1M5a+@fezx35I}J zDNq>PaR$x`+dw1Qa19VlLc=*l` zE^%8;HSq~@dwIpkVTf8dHWe^6RxYNV@B7?*03gW|K)^IQFjyD?C6fB5;JCXQpef^0 z-SzC`_vUKS0DX0OGcnxcCkv_PPZq~Lx9+>unO2=?)tQ#h{qLP=#Flymg^$_XqwS;O{~H z9^&th{5{OyBm6ze-(&ne&fgRKJ;~ow{QZf)jr={$-!uF@%inYS{h7b#gS)@^g6Y=* zJM`FS@nPq|ZvrFKEgNrj4o$#!4dFH{o}W!VM~zgLjW@q=UXS{`%N}hN@;Q^;4aB7b z@=moaN6Qo2%{wt3H1(_y4?5=V7natE%fySLgSR%l@XLfpO7?jQ0x~k-KOSdW2L1fHzZH0)0N0BK!xJ6ei{A}U8`jnHLD3Dj%>&4bz zi$^zc?ct3t(#&UXy)ZghFQoX;CdB-*7w2>o-+cqI&s8tJ-LIZVpY7p-)*ThSijb7) z=OHo!(WTotTPf~pSO2iwUk>VlC5GOt_ z#WPCW$o5SyUfnV4IXt(P1D^+$3{B3}i<+&11T$?f=eZIi)&_0#0g>hl3NZuQGl)^( zUP5E?RWy=Z-j4V7C`N%VoNxpYLF#rHl&{8h?zuS9(W_KCx&q&qdIQ@gZaS_$rD--_ z8K*e^xJ})%rc=}k!~+Y&zYMA2I)<*>20%VwhDEOprf3yS^j}IU7XUPx1f&$t0!9ol zumCnrxO)rYZqtQy@EaP4M!%>7RV@0dqvk9$O$Q7pAssJ{Q>3&iVR2erN}H43zww7M zHaI3*kO=7_aU!2-rTaD^Tb)-40#+%aa0e^=*(irvA*bX`0)+))7>=i-8oF*y2M}xl zg zm~p;<%n{f(h;)qzbWSro!*o~`3weU_Xz>_2f%?5A7+yBxkysPpTSE01k62-rVW|Z0 z4ZUa?B6A2+Hl_+qY*o|5`mUQM)~7V9SIjOI*tzPMEYO%=hDp5KE9OjavyRJ#zZ0g8 ze(xxSoSN4hml+p?HrVK8zA?}*48v2DViImLzm zW9h1a!PM-M8#3ACma&DNZ&I_#jVfaD`!~)J3$EDIJ*mwgsZFB#D<9KtZc}I74#a`f+HOmaoMrAudoH6Muo0 zXVe4F$f?rxfVYO-&|wC>clP}9Y)fvsAWmJo>4bVEx!IU_?TfXWdb2lrK3DMm2NU;dBRs+a}?|BGbdknE?wt6D{W{ycxBC2Tdv* zrl*0U@HLYdyu(|HFsEq^hC4vkPi|0#VpXx@;pXmH&qS{vn4g!49;|DG$!$xwG?~=` zz$x0t>Rj#}_z;CZ9o!z#_^eP*_aXlDwOjt`poFD2slc zSbpeoTDxvOPLAY;qn#|dUE)7ssQsNsHjULxJX}VDQMs8(R}4@N+mqQTfZ|yN-6H!~ z(bBy_5#3cHcj7#P6ipKytIgGs(oOARb}3_1-*vL3yuF?9mb6FPIoS-4ailXM!R;G{ z;*#&f%9B$Vis&zDluWVy>^X8fa6C=R6>SaplAl9)WZe%venlLATI4N^lt*y?s3>_8 zSc!vc6l+6aOzX_!B5yk%T{PO?QrcI-{VhvgMq=BUPB)Db3m47qDP|tGscV+}C5_ol zh6Nf^(FB`<2aA5iO0e^)7i(XVgBm|)9+#(KFa%tkJh_5xf$stDlH2K;%UUR(KB*NT zW=MO}{Tzxlu3lb&MW$3SK%oKYlG184WK$Ytb(0Rp=_hSEtd6X<92%;F(wSomay--z zIYL<(3cemI92UxVLhv-#caV zwg0v5e&E#2r&e}1?R{Hzw=M2GZSyfHeXgFq`FK%3{-R$0cRu^JA$832{~a?t5PiS1 zx#$0SzyJ5A`PpK}4`j<}bKMpP`I~$H-~0mRPCs)qe}4(4pSfkq%*;LFi8r>~nOPyW zeX(VvxbE*;P8M&xw`DK!@cyMbam1Tj#*0h3m-Z6Zyt(BR@#hTEXXCo|66;SYbrH^6 z9DU~fE#pKWr4NR2bm6@(cNWtdYr2d5-rjPQ=KA^fcM?mc^o(eLr`qtE0i?``>>xNJ`j^!&$`Z;Lm2BE56Pwn1X)Ke);bHGKU1 zA3X0_!;!ej?~0)#k#3pJz0`cbDWAyO-S6gXjhOR+e3-ZR_|6AgP7=5D=e+gLvZv zmDiL#(X^OP&&vU@p!Z$y;;q=Wrx=vsySW4T?uk8*A0#fxl#UYFPiU~)?b}9*Jv(qT z_=u0c>QFjeO!)$7M~BjB;-vSHF6dbLjyP>FzwP~IpBmA<6Te;CMPB}%(Y?im@}pkE zyM3zu<^JM}PNnfp-;?K_{JlQ%O&=v1WjmWXm(CE6_Tj*jxtIHipLU_ef6C$Y=`UWc z74ON9*L}=+znk~+AaUj%eC#Y|$dh}NP82_vL*T>Yf39C$;H^5r+bJWM<k9HFo3K4>8Y1Ti{rapICuMZz*$>Fl|Zsy7FTIINWt54}i@m1-Gr zNA*QIeMo7k(BvQ(wrA;ALY0@lXV22v;siNt?%c3S7qf?QsX^WN`1a7!WU*Oxt~;!B zsd!PooGtZhYQ(<7Ii*(KSHhm9;P znYp6V*S>b<52knf+L`Z5>8Db9OG>k(^thB3N@=B(u8`6{q;!Lngp>j)Jt3tBrF5&5 zo|e*8Qfigb6;gUrN*_z<6Dd6;rOq>EBK}p1{bnG2BBiX9TBS5rN_R>rC#5A)I!a2f zO6glt`b`zerF5^98m080lvYUT4WyZ8-YmsW<(GF$>24`K zBBkr3^k*q;lG4jkdQ?j9O6fx>{Yy%XQtEa#QXr*4Qo3DAdr9d}Qc|RJt&|Rx(x0Vt zjFgtol;TNJTqUJ~l%AH-3@KeIrSqk9y_7DN(%+@DL`sXLbhVUPq;#v4R!ixADLpTx zC#3W@DQ%L{vr_t-ls=cz`)7BX$>T`zYd_)3+oaS>O6#Pwrik?O-g4-X}grpmC{X8x=2c2Na+$OJtw7^mrL=VQoLSDYoxSRN}Hwh zfRz3yrHxX0T}mY>y)C7`OX(vieJrILq}1sgq|2mKBcoloQ7R4i{w}!`q$zw{t%E*uXf~{jqy*h-VK-^C(EM^7; zhaFhj6df0R2EEinOd0pLF~R9->67@&)m@@5_Z3rzW=4p2hG)7459w{c309k>hce=~ zPrp1(YOceYozgeOKc9U$YWqh{=_eWav7b2f*q3_*|H_r_i$71(T6>70-!2UoH?Qc} zBiM0p>EZZD!zr&sN7f!z+7f?v;K^H~@0t%Q?GX$gUwSq^utVG0Bj|N_>9hEA-*1=p z72EGUw??cN+xi9m*GqqFyX?~V5@O?roomF#2hZJu3eBzEgSkhQ*2m{8pTJFhaRT~y z{n)>E7cV{AaSw59zn8lQSA4U4dtnPx&-+=wVD&MjzeGO_t~j>TyF>J#ZPyqslo6df ziH+ZTb&p{Cai!YTqsWJR?8$6UQCSgDpM3D;nIMJsdrGM$=rFYuL`QT~w-VKAoO=^yj5V;{)}_ zZ0#A`@{1D8!QqKhuX?$sSh~6QUcr`MmY%Hqa{iP-W1{!|+4-fl?XN4c%hcXt`J)WA zFD@wEUU4{}**D|Q%a3`br?_Ch)~?Nq`wkRSU+dphymTbb)O$|p*7%g|xl+%d^y|{= z@n?C1f*Y(A+c)*^7u~?1d9vff`(594TTij@$E^&B-hD=hsT~GFxjpEm?!kcA{18PVrz=Gge!BUG_)*-Lwf(|7Q3 z{cpBSmL_Y1n7X9&&CC;G@-3ykf+v=g24^x)1b@A(bb=sO zmj6C{RjES41#<*e|$kb?MYt9C>T$+*rK+wo)My#p-pXuEFR# zN=xHI&#o;!9*Z1$Bo^o2ReB;8&$y@b_ed0I$*B5-- zJs9_N>2J{?vHkf{KXL!Ht2zhAJu9bV*xrukN-xDC$5}AKZ=ZXy^lB_VzFDqm@>{Xb zRz~%t@BgifxOkuCHNosHa`K1g@7F4)Oek^^Ln#!;yj)ryi=|iO@D0CR_iE{_NEAPO zlSf?mMrm-+`daC?@$p~2(e?%cj$haz`qdY2mnOz1oceCLlm5D0?yuoxr~RWWe)@h{ zobzF6ZhS)B$EAy6@zzgD=f&cMQ@>zirj^koMKP{p>tmURg6^GK>F4m9UR_$B=9`zgwEin2nm%Hj zhd!I{bmLkZcf^DJk#4O&&B%}af=NAEQ3IqX*mvQk7z|1 zkM@i=)IB-+#5I`1qWcYl2|E zRxSMb05S8lm%E86Jz57fzcQqYxNq{Fdx(RM4!Q^W*wzK{kI@|J#rn5~>>+l%KBR{@ z?e!sD#X^G>d2mB33wGGR1xLNoBY5k;))~=V1*lOQ<3nf``-toTd;ULcU3Wkg$Mes_ zLYHC%DNjYQiyC{wUQobZQ0(n#6kF^<5@UQ;Y>XzN*rI4uV$`*3j3tR0jJ>{oXY9m8 zqp|%yJ9mJ5^ZVz`&d%)Y?(FRB?B3nJKpOZ*kh?%hy%SVZhCZ@xO08q@qPWQ5k`xjJ z!9UcuG|;;TL6t=LMwacGGy!Qvby9xu_)mk&h+54o{~D-mBb)BBarhTR-?y-IFuc@U zc4)L^f&qx(gtIsukG<1hH1Q!#w zw$LXSqd&IP=4|n>J&YMS_VLaT+R@SyxxbU;n#}ky+#W*4XI9MZ@@31=`Wj_Dsd}xl z9->BP^u7Rt@pW)H8g&Bmrf00>l%yTMW2r&LFUwXDN%6Y4nh+QM8sfl>|A_W18z|Dd zSyaj0vW*qodbWqS*aKQX_Qe(-W|J}TQAs)xRMwr|OhshWthePSa0x?S%T_mV=XM~J ziY-=4P_3=O?&58N7<9$p8z&n?^2eJ?fWe!BPe^ka(KBUTO(T>lYG8 z$Jz$Ci;zKbMi!(}e9e#m8doXILsT7N!SbPz>tc>&-?xN`s9~1B^~|LA4sUvzXbq#( z@X+$Ss1p`-FR^@tWvGFA7J}R0bKDuD(57|nn%f|ClqJh>P|#@UJzfq56Ha%7DQygx zejnnD5N>0o1+1pQ>uezcAqWOG75elA)`wwJxP> z2`x?oCMv;{9qQ+F}RiFwH2-O;SE4+f1}= z-AoG>Q7KU*&$hI2(}bsHYNe~BT@MRL8yVp)qEjrlb-o&(TUzUUTua8Hu#bgpt_9&N zx9sRq3s+r*lKsPdDcX!`T7PBv?L%@>dRQ|pSX*hfVAt zVa8h1b}zQn(yA0-g3Dq4=uAtswDj;ON=ZW%8Ox;-Z+l>6;e&S1Ragn4;qK%vQp{Xs zNj69{kY>L|`!XhkRMV6;hNRM%0@>AY03KeGQVwDsEm$L!#*|-bSgVTEZ!G?98qOV0 z>Sv7Dzs~Yp=P_@D(qtaZ>0&G)MrT+oAIgn)gyYn4Z#ms4PHmJLVd3aUEXBJUaWF39 zQ~xb;NF;^F3bj_H_!w(hF=%UU5rHDuJaK94U#o7-+F{XAwhNT0NbduzB}JuO(s$=E zA{@0;p{0GQcv6cS+r7lFEa^mLGd!#zs^uCK3^7oz6RD2(dFbkU} zR%h})ONv3Ff%L>f+REf|5n`aQ?9(&RzN#Yrpat7;W`bs`QflY&l_+bOu?S_9x0VvO z4rAUhS-@mk=$x`7#qTdyTbnyGPDo|2j9PA5MffJ-LOI>8em*Ie2PX57a23&8Ek+T1 z`u`~ybsq&!EQYm=I%`1`#RbvvII@;B78BUR#jSr(A(7W6yjif*o*Z(AlBB#J2Ljs*dsOcKr_yeY1WHJqX*RKSywSLMAmORZ(K z9MySJ+o9o1wpFT7RMYf8y9H6NCIZ%yNA{<qB%dNj0h#{=jxi z1X{6`%`ft_xD^LF5*HXy`27o?V14EXj91E7V`=>aZGEP8LDnq8eeq9_b-ICi-GX<8 z1`{YcM^|2Ah;{oZ?x8jXUai69m-pqXi36S1Is90 zsd7OD3eSpG>2bjg*VipyYds>R)?_}hm(5sNsi9`OTdG(Q0Wbv)pxXyZdREPfgFCJn z-n*JsbJ|7trKl*YL2n2s-6(a5f+vO#FcWsxv0}+#Y3#AMiT+`cy74~wIJYu^%KExd z?OoUs4^vP$P9Dbl->%@r9;$J3O2QxgZ;SmD=MHUw7Is>yQ@jVNb$W!*(q z?`XvW%fis0+H`Sx7~qKjUiPSp+;w8C_6r;AF&8w8$VzY#RRIbh;X6g}dQ` zY=67EN@HR?zhL~V%3&X@G}U@Yn`Iv9C+K*8tC4cnDCIQ2(65&jGxR6I4CSZS$NIns zkFV92o2b54L}EY@`{!=NDCcDE67HE`U89rP2SIjmH;rAkM<4=3$GCW|3pLW9wpD6q z&TZykIqFQ#ZCX7_*^g)F9KP)uDh)|1Ej`-BU#j4#o{Vao!>z}2DVTSZ(!5FpW!5j1l~5*D9qb`a7#pv^5nJ89%2=Rgp9{FB3k$Dn)m#5{!j40`D)#)hB(N zRJzs-y|NEi@>5B2f=jYkInmn6jlSvWO#0x_f4?a(&|>}c#p%oxIh}z9(%1lOTXUvX ztxD@xhq!Ao(YC2poU$+({-_qKj2CiZgSq zH?&NF_FyhcxO{W zu_KQixxGJdsbZHLHExXDMWke67m=?@x9_F7bF*Zqme#*|&Hr76+W#QOB~L)cfa*=O zI5c&i^myEoF~WLCZ!N<9s)R0M_dB{qs91W)ic>YNfZZZf25qSkEPg&>oot|(O?T^4 z&hER7??8l3KVH=G2^;9<-)YZ zLAtmZ*R1{Yq;kXBOHX#bZSA8c_uaL|X-TqfwpJ6-Hfyj>8sm@y%N0rzMOUxcfKI%u zr7vMk6ghvmufg}M!`$eF!!|*_%QKixgzFDiCO@zux&#_X{NM@aTmckcD=LU;wXN+z zUQMky?R;citn=``{O}9-Yui4>Uj#j|UeGCc%H~Z>$r=?^Ry5C%9t?6r17^L@ojmYVz0Gr-3LeFYxiCOVwWUU7Nr8u{IOk^}@|=-y&WPuzvW|;&>UxW&^=xoL|0S0ZF%4`u5;X0_J52cx zb^26Au)^(N!OFtMHk^Y44J7p0kH&pcw;WYGi70SbQyUf%rr$NRmmeTq*%I`T|Q{0 zs6H5przaH;5!cCvg`SxzcDBKzFcY87`p4Ry=p+o^3!RiR&xU`qI8@I|bm%56ocZ}7 zc;Z+HwVtR~M0DsOCm#zn_OjvG1~c&^Zq$qk!z8#QSQC>VAAhoQTJaQP`scQX@>^=X zUC&*NOU&y-ZkNl7k%MgTL@XoqQ^@$`VLcy_G{km9&-7~t<&=#_!|wfAub6gNJZqR# zGM9mgAsikd=R}?iku*xqBgVP2i%T^07Re?#W}GQIE6~|LK4iKWRKGCUq8j2f+|M2$ zz8+^gtdr5rO2lN>{U*h!`YSlyT}ifsI++a=sxB*eO49hIb}w;k;{Q)BOuv~MdeOR) zsNuKCwk%DAK+my{<+kN%hN5fL_ZG*d<*7$DLmZwVClYh!YxjM#`hIk5Dcr4X*8fv) zM(0Mw$lr)6bLQA^NXw=98F;Fj-Ir{;p;u$?`b8<}SH+VqZ--ZR`|SUb!#u`rxpf78 zkvm8@Q?$L)y8?w5cGIf3(8gYdQqt>}rl-T|`_k+;jXWs47PQmDTtK&JE9-lT_=UEc zdeiXa-kbbpp=n%r$x<607f-71qZOXNI9DSA%LsN0vO5CD0ocKD&#(-6)7_f6#pAxJ zA4YQ)V>wVME52)cu1OM4#%wFJKBM+sFX8ykwp%aAFG0Xv zDLO9Sk`Y;1d9z8@D=zM_jlqMr2<*CZUnGvDCzZ4Z(G+zG_53dEr)&tQ=^HB^eakn| zAD)4SjM{}qcQT;veO7vBpolr%LMOL>ZH<32zoOI=O@5IshAD`6%Fv)8%}XLGx>tlO zbDDdK&gX21U75^b&%1JR%p_0x;^uf0=YkEvsV4V@HCJv_i<*?xySWG9wE!=%AzQA7 z%#&x0BIS1*0!Xc(d=*JHC~Zb`GqTo?cBjL4U^$mBOXFk~-lTAwv*M#GQ+9N;{$fPY zYjW{o9-J288QOIlVk=E-UO%}^t;5k+@bX>{nszI%;fS~OhTEBBL#zwGXH@Kzp&ul_3j%mx3Aj(K= zU4(uZn;IbM{GBJ`-E&(ZU50EwMO#~ji%u_PIKzUa>!4JnWspex*XGnSQE?QF|Dm-f z)x6hIUxU-%6zGshd1q5~_FTvByD!zAD?++e0k?`Jlj~unqw+EoX2IFjIJZ{r+8TDZ zupLVr^T1C=r3JV4p~Y?uJjBzYc7z^G$o0_`X}xDFAMw`1jzyU%Z1ybniC?QgZ9vO; z+Y2ZMst*<6zIKEJ|5GNlY%4F($j`2iraY-(4V5APimgg(9%+1l9VaHTpukAs(1??R zO4^I)YRzw)u~DnSVn=Cv?#Slph2rf>2>sTwmAg0|WJgrU?Z~ffdVHa+*oh^hx7dKR{DZCG{TNmUy`NG5^T0C zCG~9OPfNzM@}d@f>wAdgO8<{qYfc1t;WLDBn=0GkO}GMV7`!Q_uDvF;nBMBc1?_58 zJEAk@k-gh`qPksglU$4h79+g8#AJT45hAehsBK4ZCNl-jvTAB{ftg!RXT_n3R@?rD z_D3H`rxR1#>oRLQqBX|3|6dMt@3Q=f~kK(^V!GYYlnBS zBMRis=h>nd5SQ17d^6;N86`Rv7UR0w5gDRPD7_zPFH0FmT6$4@(bfTCaraz9(VBsJ zico6Xj)iHxv9&wd_MpZcJ?&AsJ=SA(bjK?uP?YXt$1z-9Tg1h_b_8^cW9i1^`mSS; zc$bhjMKrhYUB^Hg^hog)#S(M1N*wRT6i3@G9`5Kayaw43qHBWlHHd=3b4XrKv8D1R zUz0st_z%mg0?RP&^u5%vG)-ECm8)%}h zvf_`&6lmEzQ@{5#Ujuipo(keMlOx|U}9;@;G!ba`Q%}XTp%M^ZP zclR2*2bu=K;+Jn^gU?h&ukkiUA~ma_M|4T+>{*75qTG7fhV@~1#+v(e*AN;Pju$b~ zHrQtyWHNxF{3n)E$CnJX8j~Qdy9P@ zPDeK5BwRlHqZD7f$7F03SA#BO;>h68Ryk9c%ziCulN~(u;wlS}G15%oU~YYgR71Bi-UQC_B4UsIQ@j)0 z2azm3Su;_Kp?VNYd+iGh5)Bp2_uB*9WKvs-lJ44zQT)^R(6pC|H{n<+K>TpPj<{BG z$g?zWdbqu7F^awuUtRom$bMa?@FJA>i@lh*c*KsUW6aJb`sA41UB`1Z;7Lgr<4cOD zAMGx^ctHwft>{`x4FAbK*+AXPp!N8|E}TZ5hTh8uStG>d)Ap$b!)5aO!;b&(t7b?g;>{Ygr=%PBj3pcuDXU^Pe3nMErKa z9xQnb59|$u@gmkV9U$G zpdxU!T#H&~UBe2LDFrkjVb-mw2Fd5nLe(McVBa=ERZ$ntYjzxhadfmk=iudn~FH(A=LO3-mf7w+RAGh?K;^PwdAeL`zFa;>B9% z`?wws&$OFe!^OC=%zi((XJ*v^&{eUucR|)a{deePXMi(*t0afoQO9BBi`8e=sX7jw z5oO-V8Nj9UDYm$tW$BZydJX$(Ixu}7Pr}=C^5y9?H%Fr3DZhh9pSwA-3{OSfLJoL_ zQfDy1`8&<(MI5nd=k=+*u83p20T`dd;hY-&nX+p3Dk0+C9Y1RX;~(}CloQpfIE6Ot z|dz3@!&bnjAPMhOSz#=;r!c>F=>m{N{d@=w;cNq0**W*Q`l_J%p|0dG{F zlJq#ZkB8V%#sMeFl=v?&B3VV%a+LFVA9n)9OZ*hC5gvCc#$~(S+7fQ!5aQ;W0ZzCKXsPj5|gcx1XfxR=50pfin z2No8Y#9gL%bKX1Bfp1P`-3y|Wv3=di@1n~~46P!E@7iQI0DK8snFdsYAS2$@;*Iz( z)nwg311aBnGK%$!6bouN;5C`T%WG0C2TorZ$9E`rUpDz(9dWR>)Pz=C4&or6ej${V ze9xV}Y~&0Om+LyPdNUboApB|ay^554zMt+BUG*GTaWpbM0;~yFg?7MZ>u-?jexvnox?2Jrc0UUqivLrVbpt zFb{ux5fkh1qXGT;!@j;o%v7PdY#Ng<6v0OkOs+Afzc(2NC%Ds-j!<1#v}3Q%le)t0 zBc8T&JlApmbNDI(Wr_*EHV(x4QjjRt&hc8O^H&qn?X2=25KN)T3GO1kg9FFvTq6$_ z8XZnXU)7s_`x4|aoun$6%%3?(;gsmB`ibG4rLLwpy?;IHJEl;1uA*KBPd&Vzkscpt#8FgK4T! z)*VOfOZqwRXh(92l>T`Q$)1dc(MPNw3Li-opOEfvxbOfj*+yYzIyP zn8Ghu!#{eG?Tdlc>D^?ga%{2#TRtYklU1Vg=7Gh;_|F`80Z(QMWWLqu91uZp2?{Sd z$X_h^(t*J1zk0BZjS@jQO@P&OMMYqvh5BV){oS{L#!TI7-`x4Y= zA^cMFY&hCe3$d!?h$)NYsuG=q=R5cUKY?~l)F18KS>nKg2vmQ(@-Wj8gcte-6``Cy zgFI;LEgY6_NR>X6d3XcKJcFEr{Iy-k%H{Ft#7^qbQ$r2j1FccBxM>cD*!r z4afHoB5{KQJCQt`fJacxhl9Oo+{wZ2WNCq}o!aEUu?O?;#v3h)5NEf@S;?8~osn5h zA0bBrmX}c2qiQ|5q(CwfoZ#7PWij6g3LL3}5x3iD9c;1nUw|$mlZOWE*{LKny1P#e{ zWYPGQPCrrZqGODK-n~Fq_!b1;OR_RwedV2gN%je7pzyxpzyl7MBq>}By5_)%!zYAL zFlT^sI3?e3bTFLJK6`k01I7=8UwTiu1c4^_r4IsHR&^bHq!APxd!n^TL>|P?o<-lf{ z$=KZah<8dJJ1>IgHs6b)u@B(lhWsH%nmGscwEJvr{=lKjm5d_ckz+I7!!bfpp>Y8;X!&@U#I}2-MfHCi*L3pyWoSb9D+k^km+S(yz1K)0{;ixFoIC!#!>A`S z3{E)gusdjOeiU}Ikh25*bq5V^Ti7|(Ad~pavxpPNmNH3l{Sk-VozHaAF;6FgCMF%9 zh(?11t6Dsv2=(lX2mVEUoc9g5x&t3vkJD@r#{qa<0Kpug{J5qA)hGa{8cC0szYYKPbNl+ z-^w`QCAbX7CH=;Bt3t>O6otz=p+2UtcDFU0{4r&h!OGt>)QPC<&2=b;uNbKmj%J^| z0cW*5+?i$AAzB-qFuL&JuuNP!GJ+P3)VBls$~!0FOYV`_5Aeyw!mN7;d$&z4Nyf%F zVVV-*#FSz(A5Aeiz9hc1UOL+c9|AW4Pp?W&ELzONJ~yhe^DiCe=T*^FomjM(&N1!z zmd@}GzSdq_-3i0eS|I&ga!n_Uf^qh9-$gm`;z=G(z$?(8_^G8RyD7freiQ{CT)nOn z!5Z`M^0Kpq)A+w%bGNDI#Kw$y@|z()=_Y*A9Ux2%3DcWR!3 zYSJ40ry9I zhEt2T`gZ4O8z*KW&_IFH)oliv(x8B=+|dC;oYPWYgG+aGVhskWZR^B$ot$4AXsQR^ zpTm_ga2=h&ecMlaA%dE8fl=HMkGe>s`1Km%7j}qh9GITm)q|LB@zUUdYVSjdr(K;d zbR5Hqr_DE~V}-ZP)csP!9?}G#kA!#SOJ%})LDh^_$()V^r*y0N{5ST#sime>ed z!__wB$vgy=MR(H=$es^D_il@Chsy3%X_4#iGln}mr1jO`(DoUD-T~8>mrna;)zxP6 zby8rpGQ5%qLUI0#2aupPYRws zs~YX@+Zo?s%j*Uzj&mN?$-D=BJKp(9$GIA?hl;3)&bbC!W^jb2U2}Gmm+?eV_sOUw z(G4E%_+)2u^w|v_uGptegdCi-Pj$}FlVhhlx9Z7=nNA!qFvGoB&h>hd=17y`+{hGX zqL!qz&z#wYBVxqoPIm*~xG$Wc5-ytS43==~ml}S0o)eGppDc#TaW~L>id(N6>T|QR z3clH24?~SwC^gQ>!;755^<59qjUs1vPEgCcIf*NwuPoK^8%(|cIOzz`Sg(9=~*%v|k^(=x@KHPWJ( zKIV;cgz#MJ?5&d~raRZ@$aii$?tWS_1sfR1p%Q431ijy7j3#Ngf8?yQY(bQfaq z!oHI_;N-5I&ZT-XcDEA|Fy~hI-ub3UKecH%UYb8jAX;+qOi2Apu>OAAiMo^)2xlk-nWl{7tu z=!CCqx^~7Hi?_k_AgRMyXFa^9cLG7u(x08*>dE%MIuGi}!1J>0%y9LBbEBR#XFEse zNyG1QSS15hyYH+b_FQrzU_WVw56Zn0YJ6I%?t<1`MRmY%()P2ng^0M0Suats=7y8B zRI6JUP0o9K8_tV( z%D86GLo+7Vf*@B5gG>g9Tfr`bGn~ZaouwxqhPt2+&P@$>!E8C%zMKoXFk34;@n}exh)cI@h-d{#<|tHxq9dfsoh<(^&}dDO%yYn z>*Z>%^H%7aH%O@cxt_a!fNPbW>_5nb;F^ooAL7c;llO+o_E+wJS;M!%tr_8prKO$p zwb*;4tENGsffTjK)lhsf%GFQ9#naKQC^shIsjF;IxubB_xI!*~x{QNq*E*xGY6r); z>KG&%DBg~D!I5$D`w6aNoGc-}nB@9V&+RqEbzDzIPIYDJ$@|k>r}gBC87}N-Sb}Yq z>xrIgo8z+R$%`p2yPo{%bJs0BxqGhbH$Ay@o-{g^&~(0Qo`KX==zHx2u5V~iy58-a z1+v>fOHh|luKHrsB3AR1i5}l#wO4kKFS4ng2)pK{O zc0JH@w|?U~pyy6VcRkZ{r)0=^!@^!{ldEQ{T<>&-riWY?^kms1t^}Q7{&5%fu@)682w(g6-H)(^ zM-Q|mgu+f@Kz57llP*q+vAX&xPKc%zdzTfGM2ze>Z7igg9|4JGrC*S<*nx-e`y>a0i1J1qi&V?^eI2mV9Ff-nq#Wd!t z8ow>1#M0vP`ZO+6Sc$?#E9Wtd`xQ|Z>B*Nx6|{8VWNfH#pW_@lF;Cw{&G%HWpaK0b ztbFOEV4dJhgq2ak>?2D{Tm%m8E*pCq!AHC$mD8 zIeM~1xbogWUuPg-#$}Z4>F;u=tN128U>;On)&~^Z-I7W>@plCUivi<_rIiL^MMVV` z#yF32y-2AziHoU~l}&oGdR5sU&b?ku!G3^~?`kL;4Se}!jDc^yj48_3UdH(GotH5_ zeBot`8{c*r6Tnwp#%T9k#<=q(mngR$ci+Vn=IbqEYVh5bV38MFB2!*!856`eTE-OP z>nvl6@LiTDCoi%Di;^s%c84W+hFLP7H4N3iUZ zpeSGYh9_=i`0hqvX%h3`TN`6M=<7O4^G5Op*a0g| zo!meAXfT`f0NmhMthC`HQ)m1PXM>hm855CpQT|-Q&v4U03aG0@xos&-)(Pe7M4DQA zx#2EN{0wJ1g-G0}1d;O2R3P2gO5}H1TUROPwy`k%cr^Co>e4c#Y+I!wxM5ZPxbQQa zM_lGV`xRM#<7c?(dkX(pDed-0Vfr)3RI05UH|BU#6dd>&&R2NKtX%pV!rsR4`BKwL| z*2_|`$*Ir7mC9Pm%}Ql0Nlcmot1gvwCbOhxNM+^aWA6?bK9bl$rhRnyL(J_%EZJ1} zL)28X0OnBu^D2P(6u=5$fMkX8YTQ&)kph@|0nD=imh4>s^(}z;6~F=tV8shy(n#{F zm)4SxNpq2y^m3_eFA|gHlWMXh?=8ec>6lVYjslpg0Hzke{wRpyie4#H`mR*dg96y2 z0@#xRSdM_N&M)|^0P*hv*oy+#%L14*b16ldJvvbUlZKy9lZG!b>H1RT3`qU}E#9su zW76rRno1VHN*BO<2WXtq6Q-K{3Sa>RuxFS;vO+npP+33C&K!iGn5v#HVupm;NYOzNV6uQc$Z1UKll z2I@6eS{ky%)aFXIp_slpHsICqFX|DkRDzV5(Mos2IefLFv?(EPMwcr`?;G&=Rc?NS zmE##jwp0c*B&P7?=vV;vXqa3%fTe5T9}UQ*0hvKqSO=S2bfl&7B`#rW)k=vqSZG-* zrCc$&K!76$>&0NxYC6$M3Gq<0yw~KCj3=pXYk))ad26MkhxG5@KT4N@8BS4X8>PF4 z^sLDIm!`Er=3zS71_icJKwDrpDWR>>$3tJOpVQ&C=;BJeaIOq72%6Xq8-%@NZKs47 zvgu7b)UZ_(c9pucSIQYO#Q63~dqXklpHamnxZS~~6?CctL}!X(9TEPA$*aqyrk*b= zjN7jfTbepTMW)4w#fF=f)A3G9rE=0Uqx>!S&A}X7$^s)0GnyJg1;b6(sA!B*&7e}9 z7_7~gX=scx)9{w8G0>3~O_(mhlMXihK*c(P;{dhjtdy&cXp^hB;!_@@6{by zcknZuV_1}X-(Qw{Oc}AL>II#TRT>+%lY1OAzlN&BDN)6=fXL*6=N@djK_laoKAmMa zE9ISNgP9>?>AZzE#~3?8JTai zOc@{plL0coLk-CI7?^Ys09FmG(!ie@koTU0CgWoO86N{k4-FvWV*p204axu+s0@$+ zj%z?(ZVpTa$N)c5#jZ*h4*JZzKQ#09a6a!2&Ad4@^WM3yGY5xep5bQR z6`DEpGxMI%%v(Y;?+DEt_L+G zGxILb%$qzj&nz=<@yxu#Gs_KLGOCvo%FNTp%=5;~v&763!_0d-GjHw8yt6a&#?H)P zjG1?JX5Q49+3?LA!I;^|&Ag#A^M20EMr>y5HS=};W)9$!&1|Y>UboC_sPoxQ=kq>p zK3nO0Hq!aLhnvqPYGwOGaAz*2iE;{e|GR7?P-Uf_I0 z=3ZFQ9?`j8N?U+{-tftfD4{nN#z(ZHH{A9k`l~lw*&}M-N9hLebswcOz`Z`4*`zNH zULMi%zMN_8t8@icyB}k7`YGLwk1$52i!<=EK$^^C{0pJ!ZVJ$45ndlvHl3qC`zcMZ zUe-uZ+5k*Uz;b(zzDrPA0X$8BFFQw7`z!qczUr^^1i0H@=?zeC02ZioG-(K9-i2$pB$HxQkAM!g3r9RM;kaDO1vY7Am9VUW@Z zv?GI{nrHNG5Ep1S81+4)RfD0ZXLNfo`u&XR3_+8g(flFI`KtzehAPz!r>W6U)b64_ zLm|vXn}$MPF8XIEH0z@F!<4=N8-^({0FQ?$Jpft_SK@u-;gG5IbZn(3O{b+}lt_wB z!yWawR5-{(*O`l+4Ciu~T0Gd`UIND?O{fl**XB6;)Nv%&qwVQhLo6NluY?ZQqY*_dcaOMYj$JqbK7P z4+Ka5jE8zG)Hz8R4zMRl83s^#f>O_Lkh)C3;H;*x6ELW&>F@+Jc{P=qsEh(go(S4& zeBgk%<~4awQlA*&Rs!aEJEt}?f z4Ncp8os2V;I%J%x)CaZqRAmCdFH@CC0L`W;la0*V%>v$^@uTVYiZ!p7C+YWT%1Fd; zji#f_N!mGGnFwHlaszCrzq-^ixd{@_y2q%Rd8@Rd$MPY=@I zK#C-Xm9_IY5aV&7xy_KIOBh?fL>XG0i#wX4cvGBgXhXifIZ$m%{VSVUTjJ8NljbY9 z|5jeSxOf!|`(IkJ6vO*36-`w>1(=-*ll+(dP36pq%U}=x(vxM%G|HZ@6rnN8(dvKc z)^a6@j?Y6z+zQS(wnCYtanxUlVf>eNti-4sre9ZL#12!#RhWi{DQy)d<>9n6^ye^* zPD9DVbR$g}?Q>W*@zO9faqI}Pu7?8n21CA+@c2e(;V|tamQ;2%2IVl#TaEd5m>#Z% z77kPF8peKDgRwbGL0_ZlZPf5f#)M1Rb z(b;sE-!`hW4x_V;zFMaY2DrZtExAb^>*1Mh(x~;Ypqq4MJu13MEiz!eHz_Mandoy< zwlZc3DzYr0+Fzmjx!2oyxNBp0lNM}%iQc3q8(2ucjWDU3bao@m_a-&iq>R;sEMEa3 zHCAataC2Q%P`x*!gEuK@Ga7o6u5X6u1I>@67e=hXRI4EJ;T`CH{P zfKFRE^Xyi5r{~mmo00->W*cK|GGQOj=}4wB#Ya8@z}HnxyOqVjre-lH^1ZUiXQgb=g5Mx@=0(cdql}^@i`0p1QbJIEm8AnZwy+aVa4puvY&_`yT4H9^6L8O%J)Qf?n+DGiTc zc=uB8BUpd;Qu+}rse9?c5iIR{smW2$vZ>!uEbZC!^-(NZ*>wLXmiBD=_!xuaV~A(6 z>E~lub+ak>IErV}$m0kCvgwE8OnY-2flM~FIiYm%$(9N!>|F?R7$4W4fP;x{VDrw6 zFtW*Z0ySQx_a`9yDs}o1jk-#yKSID&y78mZ#CTN-_@x2_G^tWZ_h1960@chSgX*3{ zER{i1Pom8kBu=7e2E9Lt$RmTg{DcN&P})zBoY{~1Jr8T9%L z*VOJT*R<#?W7p4e6_G!4o2CHxu+WJK5PCI{emtYppp;6u-0A(#@E>T*FI;`+U%1@b zUzGM*0nZUA@X1I@`56WH&ZH!V;;yxD_DIm~j4ni}7PPRyjY<9x`Pr{rdG@au=L|BQ z0?#9Y%Air_VP(iXuOtA3U4ZRm(3}e}HfZnybJqS1AzTK{{S5|% z%->)p85EVxw3KYt$AxUJq0B`@&G@qGqS8U@Lf@~^%zsfR`UhCtzGH)awTx1>A z`ki|}>33`qGU({BTKLiA;*Q4J~HUH@7+S$!#tkeFsC5NnhXL%*S^)v&CJEXeKSb z3+-ppox2#=OsZkQh-T7UixTUTDfLp;7spn2ijm>A(tvOi3qB0AV$sZ`%2uvunw6`) zVC5PjZQP_}4P3Xe=t_1NEDKu|3}Id&l%YZxu14|CRc|K!X6Hs39V}p)1FkNUe%G)n zPNknF;Lk`1m{C=04PRh#(Fu#sq?=BRP9`;WaWhuBV04-Ew+ri9CiPLU?q3Ynbi3nMlO?nxX0D{{K2#lf3Pl&{s9}|=Km0b+8e|brrMX}B*rBl z-jm1YnqMXjywB?0b6<(mS{uAF%S*Kfq2Tlm2?ZCEGoO z;bzi;hpe`n4>1EWDdG_i%#=q;J77m1VRmGa=VSQGOzQa;;Bp;_a_g;nLjaI9*`oj2h{8t z2Kxame}=W<0sZ+5wB_Xg7aZquivJ6ZUrsCkQknr=`U_KWIhFn!PGC7r`dg`C*hm}x zMu99k@i*$rqM+x%u2IA1N`mh-X=)9Agt{*L1oe8VxNLZ4JTCld5En?zi>dBleEb}( zy+*BHpz>?9@ddhfjUK#EIvBb1yvry(9%p-6klF@M!}VFDQn!C#0V*B%2eU|};xCnf zKB}y7Jx=IM-&twkzY1;zJz}q!YmX{jd8rKWWv0tGu``{t>&;5SWdnrE21@0#E_|i* ziDcF$POw&W<+kBHQhFQEIQ)5}t1uL@;V;vY z*D$`z^ap?sms9my4jmjPebvPJaKs1yQJ^J#CT zCNj$w(ITAf1A7KhJU-LUYtLJ1qpANano@ejmzzZF8X+6 zp6DMWxocz*8aNc2rlfbc>JOi~hv{+d50qUqgaq04asl1b{n*)ouW_il}pa zS-|kd5cpT?BKW&${4q*_Z{B$c662p^B_-x!UlJBXQYT#><57gDf5t?h*>!_Kx+S;&=KJ!+a1Dxgl7l16b>$ zwg+(epn2=4uCLk>V2UpnJLoG}(u%2Vk=L%6+Qo+}T(|)ZXuOdI1*-T~=*oZ+`tYoy zTN{KdTa*k?QbbA0Ic|`Ixp;7M;rXqXoz+Aans8Ab0s7F)i`7df3E+vzuEy< zn*g<$CT{Zg5I1xWJ#|xY#p{B5p?Ni*4^Ufy)i036^whvNfn3F%K(!NSk;S>EaoCQNYL-Cvoitkm=S!eoCzUM8v{5D1YDW3U#q_&;43fwQGi$xiYw1M}EP!!&T93|h65 ziUh$Rc2fT!7{pH6seyMvX!tt{4aV@jqlv-l0Dz0ZsOTMqmsNWMOf8EV-qBBG)%eJF zvc-o=;}K4W;G+6W3oQ#&YcbBX@o%xgMageHW?Fm*)OUqegs7td-iD|n0EUICgN)3V z9f@*F@GMF5%tNc^iFrk?P*nYjnuejwE7}yMHaE(rOr~dT!IZBogM7ZEReg&L;c9=w zElLbmM;LEO=3qQ{GJV@Ew}N@{$&hFsz3;u)%IRO=c3pwUKX;W5oOLT!)n-YUG- zW2#yX^XM^sQ4XqkOgGA5+#cg|J;qYXqreMVUmi2=1wAj1nqN@23Q+J1%B+BLFUTzd ze&huuMlj3X2n^Z_dJ~}z0~lOU?F(?YBBX4mTNPog+bO6L#%?w!c2A!*l4&NYSHCVcPstJHuJZ_w--jGeEc_B3M6#ouqYz!S45Jgw7)`yct-P8*G% zsAWxP{3lvk6P>+4yJ});U7%Mr)!K$TRIL^=?@^~(n8f$!>so3@qkNEO%Eq%h)BVA@ z@{SG3ledcsMM0IjsB;v=?xL@wP-d4b<24CPYr`Zw_|sH-->^NY;7YA7B~ zoCRpcj}9tEeDu+%hNBv4HS&{#p4Iuufo#xvJW0r>UY3v9HR>iR_c0_rqZSgJrU4S* zR9u3Wv_=9834mq?rJTEXcAhWC_XrL@H94y#^%7o_%BMc~Igk390_s81Nvb8mA?hN* zQ5qw`DKbm&FKq>A#>Zu8J>5Q#vq_$DX|-&VwNwC}U(1*;^Qbq;P#=<>Q@8{xshI?V z5+vA5(4SFoWPvqSM-~v^V;10D1XvPya9FO1gb;9#j ztA(d%OU&bZD$h`jdYv{%Nw?{^1pDc(1efS_6ZJjfqMRA`N|0YO^%Km&+0E2u0Ee3) z24yUmo;8Dq{)^(8!&Ux8Yn!Xn07|w{X96s1q0R#+6Rj=-*dDDe258t)tpI=mVKk$q z+T2~A%rK6-#pzN@wLG2^K5q$^@rXLKf;WFe8(YCeK1yq?4h1r-HN5{LI?-Ak2~fEW z*0D#lw2e9lAg2vy_Gzn{fc?~#Gb7rm!+|Ym$JoPm%+kHRTCQNtOWLco@SJd2j9Q$2 zZ?Bd@`j7T-;^(M(2RO)cG^+#FjdS#42e`g-0;rLjXSO zgl3LK3;GWELg5w%>QmZbMF zYB-)!hIfV^+(c74tL5-SsPPIlgigEAysTK&hrVsE;=iUlhk8)P&;Sp-b54JE#*+Jt zs&>Kp@QkK+QTzB}C-af1)MM=jZ&Rpkd`AtQG;r+TA;ZT`T8T2XVxih+v{(ZVW3k>m zqc(92(&G@eKclyCSbU#Rk9Z99Gx|OrqwtJMc2)ZUBz0BC0Nm*cg+HVA-I#M7KnT}d z+7}K{{-DF|!v;?tjEYy9PLp83ze3q{(aQq$Vbg4!yfA2SB;?eeJ~h%s9s-4=B(a^^%RwWA-o8(_j>eh zR72B+8eSjK_m@?KbbA-N)5U(OC#n5azd|cb>uF$u8cIF-Arx6p%lfH9-DM$DbQ!WI zs1+zIK@HGE)&u}=1C6aB9ZA4qnohS9)QJGG{ncJx=~{L7YIxD{V)sf@P9km&Lq!o_ zcr;#(q9y|nTcp#{0bFKw64YES5u1y2+K~ip&q{>tr_+y#u$FWRABe$Erx^p)M1Y$E zVf*RSc#t|6V66t;4Z@^Lrv57KGEM71Kl9;|lqkgYKpTNuc8WJMVoJ_N2K zot{oalP(WYyJ=)oH2$#c*SkjQG8DU-bQ(Vidx+ggYvlQD3=|Nvp#+IRY6I#pO!Wg# zpJ6b7bUHpvod8gKID@5=p{z5*5qqSkjX;c;PUmFK{1FH)(#dy~;&?RTs&pFrDQsc(XzuOK zPtn^PE&qwkA7FwPNvEAAbrL|iF|6oaQ(;Mm#vtlRC-1T9rvUY*X;~lBow0DUQ1djk zGL`!TvoD?Ie4-`;y!!-hHk}4eS8LGbajd>S$FW=tdKFqS9`1`rz8p;(SSrHJ#G@=L z!2nmIQAuhEq%q1h05HHc0Wh{9^dd

    dDeL?;)NL>fB7W!n>2AVxYgz<2zV$zw+k zpJ*ICY0?xNu<^*3rCk#+9eDV|$TflG!v@MyiHVq^YzVCYU<1+rGyK0##A?IC-{yZt zK4B7O6_0$K{~7e7len_0li)RYyem-k$#9uG=#^;VWL6wTz6r80+|rIQ(y;&-S_v?! z^#L%TW$522++2)mb!1^cYXM*^<%nY#CBO(a2dFWXF$^9q9GR+?MHWV|H2?;$G6hfL zER3Mc!njEb!;pQPHeD@)B!;a5HJy$Xj>k@#HHIyMew~iSuve@LP;UmBeVQiBz{cb> z?VW)tPt*9B(53%Oj1E&m@h>c@C(@l}Vh%HTB!bxMGu4Iw{DzGb??mj`hW{9d!xCtgyQWaGyQ`jT2NhFA+3 zQn936TC7$<`pse}&_ZLEV2)eJxS$Z$9FGM zL|eRo(mqvf7A*H3SRW?V@{(L0qsL7iK5^XOu`5j*X z>J9+QN_9Jcd6l{wpi!Ed1@ItE{T^T~F-wQl>UY4NYhdFV^;=*azg9N`Jo%cjRo|#v zfi+vJevX~ciM4QZCn+FZT@J80UEKiCZk?I|aBm$7oTMe|)l7hf8BDvCp>6~=dxN^g z=cMe@@rUTs@rQKrTeTLYtW*2Zql;=Wu$C$RVfpWo&1x%??y_h#WiAZvf_e$5_Ztbsez7J5f8=h<_~EskSaEU$Hjzr475W z=Ev?rjIf2~?t*XMLiSxS+AWm08!`4#TDuzyI%h`G>)mh}TPQ3G>SRh;vS%?@`1gpZ zkJ8TX(c`1kX%AX(l+-=Y=~4RX2XyEtRoSbq0yw@`C4k=h)YSkv`?$d3{b=h^swNox zBA8|H0d2Y-dfZ+sVOHQEirzqq{wT$<3sg`f?wb!B|n`h?Ee)2T;yOT5)>~i-PCSnFe;j?1n)Y*vHCUbZPWDf5@Kb?&jY+CqE`uAMKWay4Rk1E4gF&88jej#E? zD*62&T@Pg81A2R4x9;6Kb!s=m+DY{;qKmJfr57@1U&fH0-WuCclS9aGf^XLyNyo)$dCz z;=X7Oe1JxJonk(e*n)>R!Rr+I2#seYReOZH&T(4vD56p7<7jgjV^8LjF*ateunxV# zdiU2~yPdHHPaJ1a?Pa|p& zZ)_OVdPDSAMCVxY2}@;sy)q#Q4S$(CDP?+zzBz-syhO<}DDA6=eGqqFMeK!G^moKz zh_HVmPC|sfj<^63{f(#`eiLyNv59X*)PEOo1hLfbBlbhwc^`2IV%Z0Yg?)_JiCBhz zBMz8Pa02-Yn!p>><0CpmjV%$uNL~CXVi!d7&k@=GuaC<=qwo1C_zOCUpXz>z=mBx) z3m#nkRPrl2k)L*a#m@x(nT2gWZnQ@N6$ucrE5QC0u`*HZVG#SH+Cv~pN3(x}IATQg z==LbYE=IRUKs1QKM(9Ee$=)zf#H~QF(ke)-+z+zTLj?{MpgD9jmcllpe;4Q zmUQW4I!kL}!q{0#7RwHU7-Gc3Saw^)D#fMA(7et(Nc4vrz>Fk~mPt)0bAzG)m`$Jq$kH);1>SwTr z8aoAkgPj7unaWz1!43)O+Pz(0ejHv*Wir~`pu8@lJpdwmCfjoBX9DsZnqcxg&m~PT-2$;ZS)|sdXR%wGoZaUmXZQK0W@~1Znr)C(YGZ3wG~>mTI-6t} zp3P1ju-K1QG%gx`JkIfhT{7)?(-9Kpmq9gl*nl2ky*hOb>xSQcW?&-s$RF&X_<{LF zemf&&$z|t9xKS>99K^$1IO*xsGPmu6xSQJ^4bdTwJs9Fq9(yLlFL~|p5P|vZ$q=*h z*%Ki$huX6tc8A(iAZp}C4Nj*c`O&7Om;v}5dwl~t6^$rh|A6q?0;sNVN>kAO1!8tV zTrqs~QwUcfoca_(=5Tse2v-PSmlU@9KwK(pcZVonM4-QjXyzzt4>X^gBn!mv+sOi{ zW)ZtN#VTgUgl^JeqBN$M-4(I7#iYcr;&u_G86U}1vIiLJ11rJ$z)HsYg34%h_=u{qoO0Hm#glnIW6%qy;3`s| zgR01xKdmBX*QBbP-La~6Z>W^0CMxTyVa$-vfpWHh@tiFvptxO^?p3#=$7$2LPuq?y zdw2Q;T_K!CR7W=qr{~qBrd!lND-Wl`HS8|tYoa^tksSxRm%+yFqIK-zF}rnd*|u-H z)|`1^P4ta$3aTY#52}Uk8BTUBu~?@zM(A+bQCncvI#TPi>xj=mbw%{6iZ8hR; zUG(R0>Qc|Em(YGFJG37SuWN@!FUaL(sV@c1sgHYGI3;T!7j1F_+@qwy4#7WO@-)N< z{)C1%#FczPpN#0=2=_L`8rfNmN(L(5*v`Wr{WL}kpGJ!sW2B!(PGh?!MA0Vp&k!S; z*tH-oHo=KZqaT~vDT49kP891%{~)Tp#7;iFYIq2h-dZDsew>0wJ9*1YN-LV$=@35F z6uH(@zGij}2wyX}x}M%Og98FHQ0eBdeubtqx64CZZjO_`LRDLc*x$nL6$5na%VF4EgB zXz~}SLszkXzpG?u)(zw2MS9jvtb}zJ_bzrvZ@x&)d)S#F;Ceb*(!;J7{F&>QzY<#V z4*Mx2=!qwZ&s4f68vJKk)e}AUGo|l^9`c#SLc|yKdl~UxH$IHUlVaZbiT`6Hw6|Rl z|Jd8p+fGV@d)wJl{$=hBg9?}*HM)g$>d>)GcZ@uu)D8dr%iYH=2hp<+O4>v>`k+BL8+{%K!%+FfPQmaO3>5N@u96UW21#t@AXu4C&LF!mM7qI%^Xa$2 zXruG#>0o<6Ecw=_ecKpOCZwd$XN^l_l`*gws!$N90 z0jKqlhEBGV)0qi&=9u{6kDs&?6~vQvD-2xg2V?2~iMUD+slh}%>2bQ1@&HWXK&3nX zPbIr-Gk~Y^hm>;?s_r38n}lZ`$&mjDGUUhd<`eA-x|2WxsI;1F^S_T#{!an-Y2ReK za7v!m4G*#vG|jku>)w6(gbl!p#C?iC#V(HjGFP2qhbHH5kf4^=s0HGQP5$Thl4eZ7 zIJ$;<&axBH+bJ-=h7wQ36~(`$r{d9b4IMKg$245EHT3H=JPxlRdm5^14ON&fV%c<5 z%NqJJ9it^aN17qx#0)&pt)Uz<@r=BNt0A$Cvr25wnq}9Zr!(=yx`y7&wA-YV#J*3E z$Olw+jLu=bTI1(T@v1hyNt!qZ1Ml5g82-P|*IBUsg~AA(<_ldVT=g$hd^U!LFSL9% zo(sQF;2d;}FVuMsMs3MF3@;gR=h~GZ8q7r(y+k+W;sNavb)JW(woCM39-i+m(eU}W z#ebmJ^X&={#TQ`Q`aoM2V9fkLWf$TUv4GDaI~yHZh~BV)-Ym3p7&IzaK13Q>5nn_t z!T56Ww~m?dZ+LqVI@k>gTZ~7_8}xFqoecla4qk$5cZ8}e!Asr|8odN#%Mp6H1b0b? z7B9uH-*73O79H{}#m!fcg@4}1T88fYoXRi5c|NE0%aH3irT87=$8(zaJDyse)92qY zq&}z4e@N`+A1LN6rCDy5f@roJBl251y&R|XmMZ^=5%ewX{u56^Z>iu4j0SIM?Ftxs zOBusay>DquIBpnkDb`B6f9kh<@lI^VcL03A^$F_Tw`~_ZL{gSYHzofBRx)T05bd3^hwKGA$ zt+Z5kD@M+PG-In>GTuSc`O+4WK3bEep2JG-=d$D5%a2>_BGAh3w=+W2_oMnw(?UNk z_G!B6NB=ubMYqAwEgHBD?=!cIp}fXW?A~D=9 zrc>Kt#-`jm(16#{m>swZYw6$)^pv%fX(vXWwKRSwG}qE!JJGDJQlnic@G8yOg*J1Q zV(!KedXvuV#yirG-FT+EN#VP3yB5?B4Z6Y}yF|RL#<3>Gv9|PTw~bkbdu>cbs!=UD zM!Bu@b`R`r#atiM`~|wO7e5kQpd|ayS})M3eRztyK%e*Fa^M@Q{dSq)U3}@LtPG+A zWqeub?tVLdz%F{Z-_8P&^Z-WKT~z%5hOS*S^{}0fP93nTBAomn9>8|d!h>kxyXe&s zy9CudgdZ7pQOTorN*Z|xeQ+1eKV+BqPXU34rGR9I@f;_XGhoY5DGcU&1Hp0)s(S=I zU(EkVdym+m2%`)x80NDE%%pZlQIqp1&k4H>1s}s>z&y%w3@v>gO+8`fruoN^e;%DS zDA{rJop}^`62DpeavW`c9_>GlCj-gf1_F7?K|nnNy***aML5PuTms3%MxkDWHl9Qu z-$$=b!jXMc@)Y`)#In%1Q+9{oEnJb|$AYNFf?;K-*lBdsE!5#OI@cE3ciQf26w)4m z!kuC1sreZ^4s4-2XY6$llg{EL7kU>^kqyq-rT=5?_BovOa>_Z;$wT?i+v$Rrb75PG zhZtJ}&!d#P^yQ1)5|bwEoW@{&nsfnGCsxM>e4;8B(K0{L9~Utee4>9ZVmSIleJ|PJ z5a}-4n}a{G(c5=|Xx&x25XHY@XGaQh6`(Fxq}HoNcS7mV72L=rM+@qA6$LD(6<6`h zd5(tOv`bU}Yq%SnqdC_wR-B`k*U<3KQRpr7)PC1-XFEsduG_02hTO2{1fSy)pS%g8 z`^V>Gr;0bxNySVj2xKipX>MT{k^GGykhKDxy@k#z`Rh{W+jh2KJnToY_B9ALMh@IY z-@Hv3?x2s|rg3*Ls@yXKCqXoMWdBTk5<7{f$HzuXU+-fBu`c(}hHlgT zN9ax)?!oojl=3mIdFlHY>u%G6`}Q1&&yVdA)cXOd@iv`#fEFa>l%W0((O9IE!k8js zr$QJ-Wu;V)&|;*#_7E`M2m*GyLBM1wI{MfygfQ&(fan%sZwh!i>zUn%ezDPfp3({% z)%TR*JNA5tH4fU!Q)(V*{~7$0PdZ!tU|RRrqQmCDNjq5EAWulsIaL#8a=D!%I4!uDQo}s_r-~sgv)p(0X`7?CpUyPL3 z-rBhkj`0rtSWclA1RO0zQQzafBHreqdhc;*#N{${;yvE=#P9lKeL(vb=X24v4;U-t z3{ud&5BME)CcXTC)+x>xOCI;Tk0$V-HIvVBb$dCR#UIh)Q0_;&8U$P}1@ZI0xciE? zd1&*$cux_RTLj#rNuTh}Rt*P)yL^HRf+nNar~QnlpSv{wGw%L(>BVQXuDjIyi@g{E zBf=_(m0#^4-*=Ol4V?l3cV~`ws^NIesSl+*0ZuIlUw{(^0jo`@bW|rN!d6tLda(FD z+zO^zd*`O0Yf+uLINMy&oaPYYqB%8;!q-N@6gtt#MnTb?@<_pm@jJw;=*~KbB{7^% z5U8V+lqJx~8}x03^bB+g|EH8=flg`Y2M0-E&4Qet5kr|J>0uCj7Q3xzPOuY-FpQU= zpb#gm$&rIDI z@DI#5PK6*Y%zD#@_Gd&zEQ{lm2^KO0I|9e8xF{`!#C2*SL*uy4Xo&l9ov{#|<2i#M zhGcefQ{wne3zKKyz+md`UtEH=#&;?q`Ad9fIK;37&TkM2vSK_gn$T&3@VtahZ-{t_ zoCYTAKA)R4JP~T~6-6d;sza>Fg6hNI$JX*@bMn!%#7=gEcVFfR5F5V+2z zAW&ab%$ewQ{UMW+3n{p+1!+PiCm+JNxIfbSOp*~-xG*)z>|_MTW$tKl>t)K3MOs#k zEYj?7nYe~=g}DlFajVd&tWFAqaUC;L%xt2J3)lz<*Q+4y%I0K67#A&+5@&ZZ81s7N zT4tBr1G772poJ@!lOAPvk|T`E=UzGYqUEEFIh?E}zh1eZoK7x-^vZ?hbdn%Hu3Q?L zozuyIFfLy~dYcm+@)hA4a>K^uYeX?~Iqea~H9P=;%h!r#Mmu~>^BOxp z(UZzfXM}S$aq7{^pPhaPXQ<*FhwwFZ>Qbqy&On5>RdtR*46o*#hp1cK*$9!Lg;SJD z)xgDqts2hRfY<)cHJ!KtkiXP&qCwKo+RihG&UKup5Ifp99cWKo)bwlr;CfCX)|gTs zRsEW3hQY5{4bZq=&uZx45?{bG!=i@HFGx$-2xFz3##jiP!3YRAKM4YUuYiEdEh&Ff z42RkO7+_~?Qjw}p_qIUIGN?txt&uLVVqC{iWBDi zgfPyE)_vGz^|tT^1Xy^|-^I&*vLQcPZaL;Cc^*JYhr^T*!n==b($n@1^c2WJ?> z`VLN0%J&j=5x=AJD?rR{cqTmE5%-g$w6dGilMZ)6S3OF(ER=MVZgj_R^2WlTd6b&= zaGKEG&N%a<6xqWmMYX#)MG!9C6SvD%U2u0kO5=NC0Lal*bWmgqy4lsq1CC-AL7)Ub z1dP|FP2DkINeS#2j8BAs-TDwPSrr1-<_16Dn!g+nLbcM~E>XhA0^r%#Mc^5J~NbZN>l%qaw~S`Gu6MLUP#j&Pa;tfut8IaTQu^VJmhS9rdfn*ZwbggE;v9s*WV&C#gg@gtp92tOO? zghO;3YfRB>RV2?C!o8^TXLuWyddwKh`af6YBdE7c;qBpEqP}xL2)NLjg0D%${}=j>5h`L zYBJiZ*d1kZ%=(YQk5iq7!L#^M_H7wLzA8J5(D|u&f|x~braG;HB&A$cbCG9F!%#nq zZccOhnJitArDyWT*=hK6JVnf+ebc>^!Hq-k$;-7+?BE#~90#GdQ4M7Yab+|Kt?z6E%4NIws~bU%$-fU#rUJf|eW zFkXoY&KIwctt-%}`Dm_^qp?x$g8#I07ERqi+Rbh z8T`Z59V1xu<9VQCQRpY|VphG_lAK7vvv>lMDEN|4K(gFE&t#6&LQ<$omh=KrDR2u- z%itM%!4nWsZUzsC7n!`6bs>i&XHgKPxPWX5O4h()m9UUQNwZp_@`Hj!bp+&A@V35y zybAtqCLmNnpB4fND0pr_Aq9V$x%3V7xgPNlg5_uN`BSjILmcZ)UJdO%J# z%!^ssvBWcyJ21e~5Ln>_3;F^0EG9Bp>d;+L>FNQo(A|q!FYifmPX#=!3l@4SX#7Gz zUj<(b=&#`ZU!pNkL1EuVA%m3ozX%wjpxIXeJ_U`Vi08u1_~Ih zU|O(%vC{wK>^Fx98LuigVhWh3faj6nd?zzF%vG2i3tt0FV<0oVZez|Dz)TNF4U!kL zCM1yLxe745O#0sfC1Wr}3>FqE*po=WG6nTa8Os%1HsIU&W|GCsHe)~6m~s!ml<{+v zDPjBX}@5QW?*!u%cUQ*D=fU6417y~yv;PXu0_K0WXo&wj%Lk0`j zi)f9cm8I%1*Z&XsGxm4(P*OJgaOSJv$sTKp7Mal62D1#TCZrNHe1a}>B;V7>yk3oKILc7de|Oc(H3e<*Rg zzzPL!7g(jh?E-5RxLsg_0=ElnR^YaHzXG?x?=S!>Ut4aA->oWci{GcfZSe;cxGnyO z0=LBQ#e2?MGq=s==TO~KIl z0%|DOyudtv)lzbFp{UeRkZFm4dJ2**6VO0Gv)=_YQn2k00ZkMvST3NM0-nl>>S&Rd zpMSnR`_`;-ho7;AVexdRwDW*eM|&@31+0pK zCbHO0@|~j6#{*JmKaMSBpQ3Mz&K3qTn9X46QPCgl0ns1o#jGy7C3%>FQTqjqQ1IM` z8L}`rQi*GFG=pzn)W&&0%9!BAtkTEDz$68m&j^^J;Fq%krYY!tPQVNXJ}IN!c_Fh@ zW#a__vlZmLC}6IFLze{1SHMfk<4hJR*nCyMVg(;>30SJ&)NS+r^ScsWYaT|HGdRFC z-2SeBa1Y4YukvD6j(d{4MnTTU0@f*bX}|^rk0V55lL37HE8qy(qAD>W1^5;G@`dpfL03D z8PJBoPOcnYL=Gn#<^eg`4qnWP-B6M{Dd^ZFs@#7&E8%JDu+UY(uLg8iP!Rh%K%=Ju z-sl0Kw}Qd#1@u+G)7hcXUqO?O0tPbhiRUpaA%j$OjIy!fPl#gMh_G)Rl((71WZ@JJ4~R^ znF@FUJpd_4K1{$I1GxXi87^d=s%#q}V1a^hzY18SfG5?%>JkO+$^OpZIyZ*TM&nNp z$k~T`F>4r9mSd|pISQ8dM1~6tz8#z{dqDKBdNHf-LMidOf(FY4+*EK4fS-SF zD;fKzsN7YMZH0jQ3hZzJ4;3U{CE&4w!v@$2daMzRNCmvT2a0{lz$d4Yev^>rsxr=i zzZCEi1<3PC0q^?(@DBrBp8(*k2c$aQdoio}Zqfg!;KX4DKI@Z`YDYxni-OMvL<#lE zn0Hh(qAI9%Oh9x6pA869u=j*$1S=SONIv*?|l$bF=1kXrVC}z$Zk`eyFDJiYMvA z6l6{!V1$CN=>&{a!28X>t6s&Q*bknfEfyS4Gd_^Qc$zJfY}O;8ZcKu*9xLB-vd7DLPa49Jz_1dEMTz$UMd5{ zELG5^s({}WM6V`bxq?*H1*}lew1$9{3V44U81=1Ia=ez1wF-EJ4XCVF(7ujIAC;#3()^6H5RfDN5Rc80`4h@7%Skx|G;0Nj5ZB?+d8$`b`&40xts+6>Wnp@4S>g4LG_c<~T`zZFbcDB!h%2a5!}6_Anp zYKJ94-mA*zr2;+*puhGz%YElr@%3imiLtndY&t7_IH?!&l2b64&ieJYh<<7hh<;ix zW>vx#MDR7e0^XAdAftki9Rf1v=W$w0PBbK|szf_2D%ll`xFR5@f=t&0H@s>t#t)LR##0a3U0{3K#F_7glYCjT<5+0DVFXhFoKO!W#jDmZ% zfU*iwMG7bn;4>xzzX++QD$&0RsH|YH0aX-aj3PhUR#PxKKtK%zO`{2@rJzA{0d*84 z3&j74aI*E3Y!4LBK*6~n0gV*I4-wEr!3P7HDHs|{G+HPa7+XLq1-us%ifxmh-_xZ| zpT!l@PE|I<6VP74>i7aWDmb1{fTiG5A^}|#WJ@Zbn}WG11oTia)0a|6FC{5c3+SU@ zPkI6U6y!(Wg7X6uEXge37X_)a3K*<_cY8vXp$f+36fjKMzf=G(9|M&Us`4bafRPG< zLk0Y%Abw#1V-!R$E?}I3)g=W?P~emiFiC-LN;x4@l*}tHVA}scMFBGu@LDu*b(Vsq zKMR`MB{*hT?QOdkgl_698oZ*yMSW~O7s+P!UgDmcX|mq zr7E9#3pk_TbRPld6!5A$INu8js`M9dNkQxZ0t~Y@kl{s5)h$a!W;pPg4f34QwGzx2w$gpqVmEcQpQU!W*uH2 z$$u*d$C4T-^tFPC_<#%Gt%6wV1iV+!7vF?I<0FHsTqqU?V(=LN?T?B3AGj!3!9{?0 z5tU=>IhnV9K!z9|kmMjQW);~k$sr0{{n!kyuz|VwIUWY$c|Z&#K+I>-dB-xOC-Ty* z+nDGNkVFA5G6axJLCfO;QYZ*IEg+Qw-pCAD(kO7fNY5aG-8y>J+w3fW}QhR26`&+l}jh2w~|%q1@u+G3o*fDe+5|0iNP-na9Obn2{v*A7~%mb z)aS*#Q_{yO$oGSQ@d~Qt6);gj=KKOCD>z$3z*Ggi z8ypIqt{_@@0KBKnRO0$In*l}~vsDc8%=3WwxxkBAr>cm_MGAIS6|h9Xu4)37Dc}v` zVDS$R_;}wrCM%fi;X-)_8h}+EkV4mZG3%sBUdLcQ>py5B`WrnU`kTF&6{ndbZ&gsg zrGRY=UP%Av)uvG5P9ZQU7Iu3v>rI#>?^STGgMj@C9(NXSP{G%30uC!U-BZ9(1-zRb z%Jdyq;(Bq40d6v~qaCZ9^?AF=&h=Q?x0kIVPv0XqM1-zyg z%8jQ0dmu7M$N={=Y*DjYKoSo~6H4aAtOWaflAb~dFYpBmsT2%3C?Ji3wuc0yQ@~4n zAxj1Y(T)koq`>teD+Anov1{T9(a2Gd-~X71UqAdeJr4uiC-G0Blae9S1EOERi+Ra~ z8LVOb)<(aW2SoozFJ}4Ain)?fzGN7CSx9MB$#O-&PYN~~P)!MLXLH3&hDk%uR zCE#ZT6R_vCvn+#tZ`bE7+Muz(57OXCn$7#NY#;dIa`w1Q==n zekYVNhG}|&+>$;*LA+uDMl#sI7BW>7FxmrRV5}GOk|!|0^J^3fi>YymlRY5%Q%#J2 zr(B0UA(1}aOP6gR0cJAT$`)?>n1jign>m2@Ms#v zO4D4xQV)p!?}+)nOaGJ8pL2$xCc{b(NQTv3%u3fv?5$Ohvx9*33RZOzuu;LY-U2o& z$UPJQ&mUWrJjVeaskH`6b~10QbD^-0!}O7)#TvkSq2x_@m*8of?Z&+k^yf;2aU@f zkTba|})DIPqRDq8d--EBo zmGG{k04Wu?^^=AH-pa86AT}a}MtToOg=X|(R*H&}oLPaZpN#?D(W6+OYKulr0G|-C zkW16QRFU*N4DiB@=khuNLOme*1-zJ-T$q8)`n+KxJS^q`(f`qlS<~uCa!CNp)8P!f zH7YWcW|g%JcvD4yvK|l%<-M47xUD2tRPaM*0hJZ-#;Gt+g~7K!x>WaoSgh&uqSi5N zt%?k_8Qf$S2Ae^*o(CjD125(!H)indAl=LZqTj-cS;K~l{Z*z#PX>3%cC~!TSuE6zd zmIBwaISO3Q<|}YLTcn_<&$zaf$xN=X!d-&7#{OV{p9Z5?v37~CD?A`oxYCPRP5qL* zM!~7=0@f?IwL`!r1?hGQ*vh~sWkfS&Y*!Vx&|M1LV)iOjRTdoa^x&apjW-A$)nAi9Fiw*&+!z)FwoSWE?O5pftu zbMtj*F6}M8s<=iHF_6<8TUO34DT8zD=ihI{Y6=gCpQ*f7AjQx&Wj$)4v_;1-%&fm=*I1#U3~6u89{R^XOWjDcLIeGPna_9d9$m)s~; z=O6*4Js{`ylNYmoe=ly8S8({FfJzGH{wtu0f`gv~R9BGutAJVp9Kg%^pXHPgOn}FhT(q!Q{p;O2INy#ux?VlZ(c91@nx? zBn7TpQyCm(Ka1F$#b?b>71t3`;5sr_f$PWu1+F8D6}XNpV<0PDCM_eLEmzQ@f`D)X zy!XFVnqDrAq_0)bC98l93L0h;uvx)t1N;iw<`az_3L2O~cPsd%kZA0aS{FxG787z% zRhAlXL_x6s7L0oh87ZmI(D>g2Bz-MhUs4uF2PUVrbdDlxHS@s0sfnW*LqVUaXlb?EWQ`>k`pnIaqd_H zaVx0@L_ayle3G8DvZSZ-(ye+`1f*49?r2s91zDO12pz1r)fR7gpewTa1BR6f9WE<(5!T(L6zw_JA3}y_kjdOIg3X0sQ-i zdkd_jDn(m}N)-jAjLGT>+?G&FfqUWWDsbIupx|U@G1^#xJn7;2yO|Q#^Og!+N7^WG z9SKw5TJ6XH|G&e(YUfHZojo9D-_?s*U3*J%4-fdPY~6(P_K20SyMTTQW*ab2L6|Wz zSV40GdF6Nv9tOv^AqJmdO#d={ZN0Std}h>in{Pc;ahq>L1#a_gqClE2{)M5y zZN04&xUILX0=Md$0@vaY2Ct?4uRkhAhN+5c421Ww(AlELp~SK-?Pd#jIAVC3%v9^y>sn zRnUHe0N)HHXEq8U1#A5R<|??jL%;$B(RT@0tRTT|0m~FL-6vqVf)@say8z>Vx`U#! zT2-zYuuehaL!z-!!B6JwwUe;HUz(0!}D! zE8w&Ow*t;7a4X=V0=ELL7{KGdTLITq#jSu_3fu~~tH7;*2Mq90P!uc0L#d+29+38c z{Zrj!w}m}X@Y8Y8f9_MV@uZNK3cj8e@Q;Fb2E0|!;Jj#jP*B5wPYO<55{<74u)Htd zFQQ@@U2z=0zg!j)!z0%9s{(=*oHrnrf>sYiBd&rvw*(|mP{n}63g+GsjbsX@8Q@E) zB*8sVNu!{VDJH#wo%cl}lY&}CBddZ0<}&0^;9i$p3fzj%>jJcYx8n1wid*r86u1>% zRDoOZKPqr5zLWyD;(t=$R(yE{rrv#4B_(dfS5e?re02qG#n)2cR(xFrZpAlH;8uKN z1#ZPRGl1K_Tk$Pb#jW@@3fzhhQ{Yy7M+I)hcV>X^FQZud&6Vip0qGDuyqK5V+s8zH zk9hG$E^a>$NQMDk%>B8-8l+(8Q_&x)pyM+E!xfZzEnuXA?SBgxt)TF0bN?BqWVgvP zQNeuU))WO1225ArIzCH*>)9Lyu4nTVxSlO4#=pPI*}JYSRh7cVkv|l;o~=;edbUb| z>)Bcc5BM5jMQQHr8$2LYy2*=Ki$0j=udPZZf07E?uHdw>uuDNm6fwD1!7Kv~D2Q#c z99GcTfMW^{8*q|=?^{RDs0zCV&(AYB!KacimXv#m0lt;R33eB7RkOIB-B93qc3Xk# z**yjDOrF0UDsf$lU?AmwNGruedO)i1sTafleI@yY0@u%13ckh@jn@j^8Su^k?tjyg zi^@k;Nt;B#X9eSoMwH^7Td#~;(G<9T1~QQP`P^1)gfN)J6&BV~Ky1w-#p3-Vo)Wj* zgbLhplPGY@P0nC7TP+?cR#SOE>L-mCvleEQ4h8v5 z8Mzd=h2~Y@7L#9rTTCGZZYf0-xTW}hRN@v>iosq!x1(Sx@pyazzDk zjebf9W1x)DsHz&S#Tp8p_>4+zC9aWr4CLPrXDUlE4Lu-UH1=ZNKVzCHaQ_+8QbE2l zVxWxz_a7Qz417`smImkU)=^d5LOUyPjdWAsX6ea5+834zXB&MyAb$4qVph4Tk~~mB z$65@0)?g)AIh-x{6ufC5V1xp!`pp`n6ijI#3O6R=jn z4Kw6zP>{`du~~uZmS2HubcX`h=xz_hwOp(FgwV#1PH|bgygAlDKqr8BX2eM&mKlLh z<2elzK3JSGVxtj@j96gAJ0o~?LulfAaTf25xMKvSPcWQq#AALZLlmFIvp8VH4kLKI zCcxcB95N!(h>u3>G-8Dj*Nwnr3C{P%h#5xsjo56&StI_8jy3_+%O<)QV&;=P0a5r5 z_;-WP8!_F8a3h`=G0g~`Sb_{Zfdt};5fMi4qy|9#WFCS)Vu#>w(;>FG=fB59=No|u z4V<{p2>!SfvHM2wCz^=yhm;WfIUxjp&j-QZ+(Gb%a}fMJ9K;bL_|r7R_=7Wuw??dV z&!0bO0_9JLAov>{2>$*Bg1=&cxMc)?L4p{6Hv+*QdqCVYVyhASc?BSUY5>9ibBEyn zfkSLDg8v1K82-cRW6A%Ggy4UvLGXWQAo$0Bh`UDc&)x)w)WKCd{D3fPo z#};t$zdGsY_!lQu$hz;@CuVSA2)3`?gE| zjV{I3Mgfr-XMRS0|92=!fzbb*#O804vQW2x$OPE#2s!hFZ22}RBV7-Oj7_PdMh2Vo zj1)gAOyg@lam|!QgAznyBb2C-v9K9Tv#61|LUy?6uW4%3$lM{jzKv#>?mHkZ@BE&A z-5-(=HHokO#57;|DHDF6^UpWs;u_xLaq1Z@vT(>TH;P?MqM^W(8m%2YGGpTWEknKQ zb@aOwDj7X85q2AaR_>59-zQD#xHXtdI`A#dKzE|!gb#nmk`yTLzXDEwmqb-!L?$)` zWT0U&koCxS=}8I{ZCSwcZU1*k^o-SyD+>sOkNBohs^bJ9nwgpgMuvo(`i>fX03M@YoC(ew%PdsX+~d#+c={+6eq ze@|Q!Hy?hF7oIWr_V>6^Ow=yE7?lfPs({8Zd;C4y6*DrvFX$&c?hNKbH9idF!}~Hg zbmh=(4!z;S4;(Gdhmw2<q50^Nh2Zw6%p$;F4 z@Sz+Z`tcz?9}4mz9S()8XPhM?#~bm%$A>a}_=^+ba43KeKl9-Xv%kyuzs8De6cEVw zqX}8*@C^AfX+kEBrKB?#?GWq$dX6^4iOd|xcc=;3Y3f}&JM{QQH6bT9)tZ!3 zHVK7FGH=UJY*raJGHW2;uO_6X`(Lp?`pc+J2y*egYeEJZ{&+zK~^ z*b>LE(&^n$Un_r{C{g;%pm_IUr1U3i61n+8C4TO;{wRTig#0zU&=qWEV_5#WUMNK) zFOX{=|X6Xt&6Pni4Z*o0yH2yKnRW-w+SneNyH#)Ph7`xg_+@Vgi4f(>5` zdxf1|Oej6Jcrl^ahrG}t?CD}yN^It0o!naHY5P{-1^wfS|xx<4O8LOBOYkp{9ngEt6wh(zin8H z4uD9gES6lhX8SwcdsZ)XI)3%AHeqREvs7}%%WkNzHRwb@ls>ef?G&PWTG#|i z5}Fd`g$}gmkbm2==U+19;>~P=>Gcrsp)0^oJsr% z;=M?aKMB80Hhe_Boc>M=U+hcd;}=4! zE|!ur%hX-bP#r8LXV^@v5obc5H1q|l!Ws4xmUc6t6&hNJ)!htxj#b=Ds2)~plTcsl zBNn^sGh;&fwq3&fD>lApl_AMy_F_2}Uo*?Ly}_z$)@Fa2OE1R6kHs&kR++$P_+;O| z{?Ut{Tc*3rq!bnsGi#8gNbiLnV(BpJvOjtGzhcBs&#$poj$9xT3d-w-`dSyU92j4q zJtZ^wTh;rk-}>6qS+^LL|1m2AJ;u5}CiEK?{4t>@H@r|~Eb(L5P^|4^LR0Q~q0Cs! z$C~cX7USiCV(s}Y+4_jZc-&=tunLa}4ae#`Ce*4$bT>50@8N>4Y` z*NOtuIFp|)RLkWbRQhGJ_(`5{iERiP5?YRTTI-4bSK8A0g>~NHrPr*|&;jqwCNvPgCzwzMybGJq;)`ymueB04u|55Y{vtH30=WG)`Xtp4cCO8;oaVZ>f>eKitzg; zy$WvSLm6o6xZ|015#C?~H=*e7yii(r!&new8t=yXLA2#jp&zTDFuO-&Mm?myXC`uPEl(M@Qaxf#@ zq+0zrT2J@4xhyE1x{^?b~w}x{* z$LwD-arhU^@-?BlSG`cc4KFkUGki_z7fkJSb6`%d32n#JT@$MK$P1-=?1fHa+OA1$ z73qaeJc-W1(Q(K8E^*FNm=9}a(q6_ySQCoO#vy;jd~ed1kM)7Ia$rWQ^}*k>&zpSNV%=a;s0PRl z|LS>fy3`0b;!pA9&A~$jo^wk14oH#E;Z9yCu#Xq2ggKlhwftBP`Qxp7TYPNox16&e zCS#guoJ%na(}c!h`lSiI#k5NkdWD~xOsHPC7aEG+WDI+Rsg>4S|J`;8YGiTR6$ z<;TQDYmUEL^7k+1)xO3y#>WYAX9wm==7s8{@It-QdZC0_y-=*|Ug%*?FBB8g1g&fS zHJjgWP8*wl!0c_^z!W_UE@cW2Oy{3)@%^-H0jK@x{(ZkWXDt3L(~4|{9Jc>XmJj)g zrC-aW0;ac_iE)`RuT4UItqx&gWq9(z+5Giee!x!%SN)}Xf9Rht)>h8b0`tqPt^WPX fJ|r%h`2v$T^FSn&=nps4*Sb8Nt@Q2NCeQx^K$QTa delta 382455 zcmb@vcU%-l{5SsW^#ks>!g0MH3MkDU8yFk0VvAit1sf_VDpt^FY$)ieV;6f@tYhyz z_HOJAdy74q_?w-bTa)ke{PD~1&Ff|Oe0FEv^QlvIX7}#HNAl0;U;gsAZk0NA?9sb> z$CyrKJC*$*t!#xdWn;^iNvjl>R-tp}@|AulAJ?gD=W;3K%a#41bC=XEDP5*q?V|8k z+Ej}E3Tz z?hi4Uy)w9eevJR$4YRl1#{VuZThfC>`>#r}`#d&q{&}{EZBo+3#&wJXYs@`NZ0E>m z_F$mvU0lk=PKk_R*LMv7v#Z%nY=xLec3)%(wqL4+JrM0=Jm5`-7Cu&8 zq7Cq5cw*T2c6P0%b9!n@@AQ;TF?|MPrgu;6$DZ|uhXo5))4Hc;rXouPXAa2h)319< zdjFX2y}P7lP0MwIbJcpe+2t`|Y{wW6*q`Bv1q)wiJJ~zY79>{$dnZlD!n6pGurW%* zOanPv2fNtYrF~31yD&K%B*`L-Y{Rr5HmaMGak2Y1S``FX%6=^60Vxk#*x2NSGBz$b z1f0#+$=Kv58~aCEX~xOkU1ebl)8tHPa4s>TG^-AeU>CKI0QoPn&7xc@8+Xmh4(c4q zL3l;ErGU0sQ7Lv>=`xH3#BG0M(N4=%3;K2LGoW`Sn;ag=cIjZ^%n~8RVz7NmmS&T^ zR<>+%IWY2cWGs@S6gYdz8OWaT8JS4%rd#z$c1nt!jVtM94|a@Hs|3t`DZP8L1?@w? z>)CdLcv`LzY#1XlA|f2P52kwA+mUi)m~tS$bV)hmVYT57pxOIK!k+DFVGEKgvC>jy z*a@3d?BQ@N`*f9oWukn@$u{<0l%8>cyPoPEu&}SE5^!gD^q`>MSQ~N2eTQsR(%$Fjc;TAaKgS-m5P16X;)JrINFx~K*$@Kqs@c74ek2MQqz4L$K=9O4 z-OO2*vpEXX4L2WoP&MMa%gGKL3c<-i$RJwI_w2_MFDvV;m!%Bo(!I|#koO|n!mjZJ zvZ^Q(^7Gvxk1cFm2UIP_jqTg5u9_&(~?uOc-c#qftpNE*L&XkRi`K&o;AVk|X&DDx0kP9@70%LxAhm zBMDL$JB^JgY3KbE*TIMqTh5dK+AB#V*dzofgpvhlTduXRw@Qbx=ewHNgmyMYitO?u zN(3E_JJ#K;6k}&oV$2|8%Shho-(!vBBd0|ydov}FeHI;zB5Ok^${!(O7%RB@Y@QVf z#wQLtrL7g`IXgAQ1`48QnZWJnSvHXLB-<*=<&zN=_JR$WkG*Vm49bb`y+&F}UpA&= z1XB&v2#cZsJ#oRK?&8_6Y+!4&>H|4n%n43S9=cZw<$0D8s z*_hpMm}fs@F3TK-VNPPM+kKupgt-T*68PQ$%TgG4~O3nab*z+l@K5i|78t+&5za zzV|!kO3bQ>xeJ)Pu!iTRV=n!CZG7*p5P#B341dGb(w%)P|ix~7dVHv@Cq zQ+bZX+$K#+eD4|NB09Cf+zZUvPPW6`Ld<>J*&cJpF?VlrGUh%ZE_;If@FJewh;Lqs z>4b0ojk%f1KA2mAxsR1HFn1Gk%GNyh7v=)G^~d)h=6*7d$J{>5Nvlo3+-A%*PtC>L zT#n0@H=D$>`|-{9Z+Pwu=4Q>BjEnw;xt*eX%w54;_5&7kTQJw}`ZUb#!kqCN&ppMQ zDiPp&D=~M_I}>vsFqb`Q70;f-?CXSC_~s(a{ar8{bJuxp^#aUo!(3Lx3e3I1TtIc6 zJBzt`t$1z@<_edu!Xj#sUx4@n@2JCxSCe<9^Lzjx&1wO?g8dz99)9$-N#%- z{ctf?@)qXUc3vsRO~u@wBW7dn3Fdq=8_2kOr!nUMV)^@Q#QuTV$r})hxQCd#*fte& zk1#iMW@!a?Z$0KV1YgEn0p^yh?Gnh{dylzaukhRr%ndv|Cy2Xu2Xo!;o(ti)FPQs9 z^1<-l%2w>3$joAM`j=rYv9ISE+2{S;Y}OK=lI#2PriJHrWwiwj*j1UovbyOOwrEL7 zcK_Vc?8T`TcJ!cbY--`pOj$N=whf$}S>4F$R{Gd2<2>U09y{2!i<+}_2H9}63M*US zYQGF>#O7sL*k1;9XG2!HmH9omVFWe9b{Oqr?`O4T?-sE zgU#%lp+B-$R%+O9m0tG#>(wX^j+yqq5 z#^+RKYv-5Yt4iin-`;K1iVr^4Ok`m82>6kiL<)r**6JL(KG_^e{@FSZ!H5m!WjnN`*ItOnC+1>Ju;*7yqR<>}q8JX{Ib_D3gZk&FAxrs3nW~kwwb@~_tn5S36VoSvjq4NGq?swGfqf_@jqL4N25xO9WjoCc zVS{J4U{BAAMK?G9j~;?|Fqdgu=aRa`!uFp~f$JrZ8r)QYQ^U)~4KM*~6?-73`F?iy zx=N^j;dD8}-kxq`69ycbMzY{&-AVf)#Bn?47L=9Nv8ww?nnA zb3GC6v6JR;UGg@BRU)lX{5j$PG^iC|;bxPWWnBe@JKy$VZ)im}nF)yzB`w*E>)&MPa+ zB4rQaZa4|2!k$$&cKw>NZ1kEa)Fw9%;@WhtZ7&RI4`w$a&WL5x=EkzpwRTpvS;NH> z7dBHDmy~l8Ca&5W6qtk7BL!ynKt;r5kIr+Jznz6aiR{qQM$Ac8Y;Hh8i-m zFE^Qx7m#GLRd~qd@?btT!%OtEO+ITdM8w}k;k(F%5{v3&gk8Bn!nI7E5pl@ ze@x4Ij%9<_Vg$bGcn&gN0UPz(OK{e*O~HCk_F?}#H5hc1KT)uAPWA!0a#U>MsSGyo zR90Y_jcW=p~RYDF?IA~#35sqM7}3_@M;`y}~)M`h!l8#fKVHVh&giuB;|hQUE>&V%Xy9S=*nsold+3JsAgT{g)deU|+9W;ew>w^OtT?z))?o0Kna z$-usxvRd}jyNmz5Z4PAWro0bFL_A2^=T5XB6aTEwgKT>A^|~0tO~1g*rG$fJM9eE5 z50sHIX|ynHWlU=Z3j+_!nd^kypkyE+2L}pd4Hff%7aUa3G8Ef9*qX3F#~hd9CczLB zlS(W3A~UmuNYm2JY@~%b>0tU1J2rMRDgPY?q7ZpNjCI+|d?qs8Daow+?_j)znnWuJd(m`f|_!^7?MwernDL~}BvIaP#Dl>&hS}B2{4C+jXU|SPOSJq@m zfJ12=W)<&YxUW95SHj0veiCz;ZxRk_&iu}&CG6Cec|Z!ZOlDS6Cz?d@$U1R%nLcM_tS^WBqdLdd&LxytJxo!GNjspFU)pwHCHS;O2W5`RP=_3 zlNKo2O|F(xiQWg&S*|z zp6oDNw4LF}@VO$9h$pMe7cC-JUoH@RBv(Hy5(+4nif)(W%YHs7nnAz{u8Wj%{_2i< zq9X)h$^+3!LiR2asfi4eo`}ZLIF{1|AYGhb=?l@&|85M303swq(EFF@d!SC;J&8f3WSum z6kAD}lx;0;N3IsM7q1}+(LPl?Ov-~j?ICVOz?$_IeY)%z??DSkwmhnapLhrhNF{&tF80J=SbNq z)5NoBoGe5PKoO(mGPA?<9TI(&+&R>zl)%Eh;%*@6y*dQWnI&dAy_eg*O*?yiz=c7RIt#yp~9FbuGO#ch-pwL>k#fF`$*E`X=!P z9%crl7e=v7{Eq0P+9}SX6{gxQ@lFDx+)E=3ME*u=IXu>N-!EQG$gqRrWyDrDj)>>b zif1||j-(ldaS+}=A)ZKd%RVKZO)JBW)8bs3t{uzp=&ZOGksHu;rV;tDkQGWv&@&9& zS5A$RXvuVCb2-UzQebig$!aods8?0eTE&;${FCIaf+v?XmF(ojgZ+M%93@xlb(EAL zV3X1$=LuLwH%UFx+HXB1QG`_XlAy&gmf=co$qm<(!*?oh= zMabEkBvlFdXp7_nQR*d>{6?;x*da+J#!A>D(UPm3_DLM1;VDNXm5F3GPDqr5{BT;L zB-J$MCFh71e_WCH*L1e)l8prH_D#t}QufeY$)7}qGxsIcl)P^C7m^PI!S`0;A0F?# zlMEu&x_y$Y4dBa`5KHe81d~+ynviC>bUi71EkJsJTwN0+%^>ndX{BCLASFcVUt*RD zm0lucwI=CwQWn~zqiANGC}EnzJuc~J;=N|!(rQ7x)C;zj{OZ6aUl-L5@9i>Mo6~XwhcV}sTVzocg zr2Ygv*-bi}n5s=Lsf>88Z$D{5F_ck*LMvKyVr33yN#(pU@b)mNp}44W@X{G1gL$K* z{z#YpDkyV%te{MOa<33k z@mdw$mNusxUI}9SfrKCs@=WT6hwe##B!U_rNyn4)*ziQUo?PwwQhJJXnvMUG)(zxS zX}Lm{LlUf#LH3edt>~8Zu+mK$8iTX%rCPA3{?Gt`RMmj|OQt4ZUNzZKUgyEpW$UOt ziJ_|>NKnDUwPpV9cVVIoZTR3Geo{SI8{)%Hjb&DfT{>>aZVdLcdKd(fewInVw!VX= zgGw!AxwO{cpdf-O#qj#)x0HzkD8TSw_F)g4r2&{Z$Oab-mI0c;{uhh~77df#C!YO%glrNCgsDnxYo0I#KqFCPI*qwcy-**>uvK{5VaPP2@R0L)L-BUyZr4Iy6&8Yy$Lf ztrnPnL#~7IOJw(l(}oLzsRvJc)DRE&r3;Kgs-9Pri*zYW6iDWwkK7nS3TG8{R@F`}Ak|MN;-md!g*)jzZarDMH!L zo#e+z+4J3nvQv5rWh?g*%6{!FFCb;t4iL(w4-(261`B1c43V!PWv7i4$~OB&DEoD^ zP2!0?8hs0;KXTiwDE@ft$wqF_TuIU?fpL2-yWPhU%rvZ)^Vv&*0EeD zdvt|RcKj;&K2o;L2BEBZlTh}+W})opt#UL6;$_$)ms94_&sz`4V<}PzqF{HB*v$V; zKAS{gm2+}8A$whxPoxwIKccP$Q5Kcb6)S-Tyb==V%^yMnJ$onrL=xy-kfJP+w~|i5 zCy*9?)GPc+QD9K`6R49>(UKVaghNr5a?gR^E`gIuDs(1h2fr2dC|*JHyn{Qk15YL> zKGCeP`;JgVgPcN@6S$x<5~kNwpdF%&5^gQ5SzDoz@MM>t6liw_U!C7jag30InkqWe z?3jLUvQ-BK#byye04?~3(7ie;W(Q!wfT@Qfn<7XMR(lxMQ(>YR>~Q7ZdMOG-ybQxK z6ys=RnA%s-gC@nUU5~*d-LsNMITz2*LjLQe42oaX^MjX4g+gUS9BEff^3+n==9$a zxHM@G*3D4_!L_p#BS=sPQzHy-S+ zKhz2`PQ*(=mR+d=FX|6ffjq)mV8SWI{2(x4yW0TYUQ>iqbZK1j#Di)F64Y?kO~or- zT{!Em0_|YoaWLh+;v8>FShGlxM25sAPZe5H_Q(rG0^gB9<68wfdxWdKdZ$3=UND*T zNwHJKlQx;MQz%d7*_3TbwJ&a^SjS%tE~jj+XrWxnkI(RSN9AJ;ufo*~rQeJN{gi$) zjvAnJ%J>2shbp%Q^5o#L%K8ePyp*SWYod#`7?8YKp#-nP%gbQJmCEt~d~l51q|{Lc z$3GE8Q0XHwR@^J7_HCb_+TDXn1Bnp%G37B5H+N1bpOUNB&nQ&tmFWWG=yQPY|s|4)U9bB_g{{Hp90%$wn-pnyu0nHU57i1Mxafa64m-$MhU zND!NB0e)ZKu?P5leZUpqH^^gOfZrhdO9reZ)v}`k-l4aNxOwR4ssTfZ#We8&DI}am z{S@#g36V)H0)8hJ%jyt-c2ID&hRFf@2Gzeez$z;OTJQl1idF=Wq&&Ydpp3HE4ZzG@0lyFvD-Hy_ zA^lIl*?_7fH(TBb*hvD-`&cmMrzZi2(K*Rl9vwXMOfcr87XcO$z}wygw5NGig1TAs zp5R}BLE!8(MG3h4bHG#*l#P^uZ73F`T*v)S#LDWxvm`35y1tIfF;2jdReX9kA1@n=(yg{HpfXg-xyiOv<*(%T< zs_$9{BJ1L)J<&E0tv)e1zP&&ub_@(BUK`mZP)WJy!d+GXnBVT92Zm+@P6_3+f7h>p zO{u$k6uJa)c5a}N=HxN@hDegY<;YmIdx77TUcc@K0iv zW9tGn#IR4c2A-k_9L|l8MDX;Uz-0t}%)Y=D#GB7f2W})St-C0Av+|X|Op+^mZv|E& zq@pMg?V({8u6r7|h>$6-1b0_>Be*;0ZD4tV+w@c5hT>#U)Rz39c4mGoFZvcpx{YW{ zP-$xCqP&cno*yI!1-XGTuskft4TtCh!x&)q1ld91C?*jua0hK5j!7vMRFjxsOPQcs zG~+6A!9WmmF4M#vN$d*`Rt(xod@E6DFu*YKbc#Ahu>1^IKh^ynae4vT&b^5^jWu>zShULfNq z2Gt`7v-5+xQG5OM7KIO>F4F=FW(Ez@^1govf{5=stO^RHFcjZ|VrNW{3PTTRn;^NG`f{Ux{7hG&U5u_$QsB}(nN9U`8JJ#G5+|l$wPy>=<>mLWrA$D#1 zGU!b4hRdW-RVet0Q-{=TrNis~-u3$NBw?IdC^X`=EkRx~YD{#B4sc^vl|NU;c31gxrF~CTC^5(A43(ND6_5?;g1>Ji1cFnof;HgIZw;g{GD|gA z#t(1TN2mg57T$hX1xy{ODn}GvHCE-H&Gs9w^3P_Q=c@d(*&+EV|7^AytMWf7Ej?2; zhB)WIY}I1Ykqum|(h=Qftx%1l(H#w*(GWeoomzrf%RfWj8R@7Drd?4Dq?s$C2g%@d zIh_Q|e~}#kG}DVD;8KDj1w{QS(t#@V)oSqIs_N%5|M`Q1F<{DzY~ze{hXcgdR|mk= z*HlU3V#9#+o2uExM~Uz7Pz%T1QlVEN<4-4RLFPTxy#F0kfKWqcY!fTshWje9^nV3Z z4`QFGB(UleRR;6F0|*h|DiydfHpKv6eNv54V>7@pa`jv(PnJ=s(L{_h4h#rZmkZ#@ z#%A?3a`gwRI!nP{J>XTN1M|4-j4*YMh$l;itK&(vL1okr32v`SYBUbwvd5~a+v|(3 z4~hsU*wREjnOO!lv~HaU?pzHk1z=NkaneBn3L6v!!m-WNN{WsI?r))vrP(Ax^-^Y> zPveq2Csy&3n_p}?i883Gq~ROgD3ePTPSmr-;Q1k3^ZbWz`@84Ec8 zuy>T2yl|8EA`5+9^mnxewED%QhMIJBt_n%V_(UOKdAeE;lAow8@YY~;H4(aO8>Svc zDKyun8oGz8y)+YIPHKn{+DVyZ&^AtegF$~kk5_Nz{;r#-Uc>!uJBk0R$WxaJMSQuX z>SuxIuWN(4zYP7I2G!@a#Sq_#;(=zvOb7t{=7#Gx^-Kw}<@PgbnuqH`0K7P>zD==S z1_D6p1$76`sfRA97f@OSg|!2Iy{tA+Waf84R$Wtf<^)mSP*ZHxe8;JKQ@yuX7Flpx zU5^vw>0N5qY`w33Q!E)KJXH7MWcc(*y{I@|Df>k2EtXeaJyR!eg2cX5)9h0TVbBGbb8Ra$!IVQ)AJ|A6Jb_w(tdQVtoB(%2gXhuZLLcZb1)ru& z$UZRF61I0!={kAH&N*P&s%G zr7xR+rD^C3%n33{B>Y@8xHZil5)!07JY7HdCZ!DZ0jjJ4tJ4?*Y|top8qJ!QlK~X8 z2o3_4G)q~qd$>*ko^}XUgVQU`GLW8T=?%kL2Ge{Rh#SZW^!yl0BdBd1yq@A&JABgx zeOMfY3O9#^X?=sKHvSPv2bhr=ydW4&95thZuX7X3=VO9(M)ddF+~9_(=x;-j<_((4 z!FajmiW&Vix;2%;(BGswnhP{*4s3w4IcO)!C@W)u`xh;p}=BCx#`)QVO*OLcpUU7euax~kxzsj+i{an4@CTg;1 z9xG$5G*dM}Al*4E8h*~x(B@Emh+yVWM{R!c!(dopwuZJlt`9f_u%lTiGh8=U^Ugrm zBi9G7pQ8_n^x2}3!SGF*qtwaF!9WCaJ(OCIYx+QN+*ry2_JwN4z>HHG3D?Wx%o%@H z(~PpaMjCR!-7|w#Fyn$|Bf^B=v^}`0IURuhZo048fM#|u^_iw0b*|*7gS9}~ zu#=USU-DiPD9G>oq}fg_KL-qZe9^eL1enNZaiX*mYpZiSP-^#Z3G;KX_BYBUczu9c zkb-1mumog{@LEC6JI!cV+o%-@^0%{SpHYW~fDTXEv}v5MWj$Dltv+o@jt!5{9_QTg zN2K-$_jh|~Z6cTCqB!kn%Gr7Y;LN*e1_g{g0Nk4V19@?!dj+i?WYpEF!5Wdd3HWB# zmIh&!v{59?0KShOGEA$a{ex2B?@i00w~BTMnkm5f)wDlwf3L>#nl7rL9m@q`WNmE~ zY7PHR(mKKQ$v^1%w{jBeA?4b({g-m$2HMhoeyq+EaUyi>Xt6 zv|qWrIWbV%i?Z`>05^}&c0hs4OILEVmUc|c$37RLfdEz+V-0&#e7;Zp5IDbR}}?7$SX zT?dw|)^4Wg?YTqh+}=V-cw~(h^*MKzL}+1Ap*Bs#lkGQYHwW;fXO9-mQ*nWE*R(!T zp!;>Ln~-yEYMT*+Z+EpPRa{vey!lEyTFh7L_g4Fs6d3qP`-$PNX8)-zO%Qf{)sEHj zSDUMKOG&i=oesTKiyQu`*VUDh0%qMUEBbvC{Nc(W<#qdtp}iWS-@QTGMzN-7E$sV) zZjzKIgX4ARZCrfyT7u3O$N;{%+6{EnAE!dbR)eJBxY?r(Dc$~wZZf(0^_i}SkZ)h=(AfiAcH?W^WS{6m_O5)=B@(#UFS^O3y)%F7!pT(yqZb?|)}xm=aeL)tdi2F2Ooj&N{f36;voJqxbv!r}FwAi5c8*Z3TT* z((IoV_3H__;|D$36UV~zt)j1vN<-u%)QjOuCFAw}@Vxe;-XEU6+WI!6Kwe$FKdO^{ z()(jFrh(oclR-&>=|(oy`;*~qbD`SipY{G^7}i?vkG`n(LTgzah1Tw;2-TK!)~_Qn zbWYRzohs^~_dB&px?qusz4e`Cyh7hH^{xE|hsZnV3#C}m*ID{xLZ%GWxAzyp4um&{ z=|#jRuSW|$DLYQ^$<>L1PmGfVpES!CBv?FEP~9+H(Bbk7LG}5w1Vbmy6I7qGKu}$_ zNT_ynv0&(B%LGIJvPv-Yr!_)rJ=P1=N^KBYd%H<6^b#nv_G-J(TAN)$HR~S1(7*2! z3_bXeVCaoU1T{s+_4kOO-=EQ^NqF0)U(}QOK@fr`gm?*AlMQi@`0Y0*B#o4PGCyRo zhHq`&dI7A%hL8`$EvZ{W&^I@*WRSPPGi^izJxrT*rXEnTN@wM8gSEwp24iOX3!`wZU)Sfc6G-ni7}I>tcu{U}w_| z=u;^8YHl|JI-`NftaQV0VoBKm!vmt`mmI@qQcXJA5Kin{J>TGW_iom(hxj;mp5Z3J z9kReMP|M>o8wBed+GzMnz|L(k_(#$i(6E_Ur|mAoHiF>WW2i=wq2F%?G?T+ZRy}1n zMi2~V4GKa=pEvji;F}i=A4%DVmkj=4vc^@zMN+oJbpx7^VldN913J!*$tSlA=zSAR z*1K<*N|b-`(vVB4O?YESBBp-!#gIvCmhsJij>+R{Z55%X7~o9kriDKQgsvo3f2$4c zNvzYy8oG^?t>g>sOc2(T3|&GBq{M~pBH~1s4Xs2P9#lRwhuA?;Gc=aS@KeK3^b4Z6 z;r%T`{qY;$Hgq`w``9JaA7{$0p%48-Pp{A?YQEa%VWH@4I*c%QWN3eqEq~>Q)+M+t z=7j3W)M9W!=nEplpmm`Mq={oNG=}tYir+%F5QR>k4t+-oT(}x~m1w-{Zs@vNV8Cjv zea5KqR#3lDb|gGm!uUJQn%%$bQFFhPv^C0zK5z8$u2u_A#2L|C1(T}sMl@H!!7)Od;%sNTYe<|4S7wY3pV<1m@u)`;fEnEb1~5zUt{ zIV0JK1{X|fIt#exx(MRT>S{zY6kMQQPeGjNy##SWGK6ZE`Vw)_*9iI>(fk-!>p0Me z=Es;!7-B^8V@&QGW^{^}ll=Vn)d(Y+ALHvoM;g)m7?U|;jFZV#=QtypALFZ`6OCwo zjLA#6MwHcmjsr6<1nc8U?rk1@HBHKO@3Y46Wz#zX?w3>eW=3K#ifwlSPs zEj3SY*w6*W-^kULi;Txe+1O=*VVABjqQu7t%xWY0Og1KeEHt9|F($Pejc9&+5~BHW z5-E6KixCZs`0fg5L=)nZ-0ax_uWvJcMxXiN;_Kl)A-=*73GsF6s1RR4CxrM)JSm7f z^R(dGxN}A{7v@z4YpxnqaQb;8nh<07dl!soxW(k6OGY#y#$@X&Ml>NNRzg*p5_yBK z3qiU6mJkI&ca3OPj4KYhFGNA5hsM+7s`0T9iSM2X2J8M(NP+9Gg=%yE5Mr*&dm-iw zpM}ee{5+% zf}Cj5h!*}4Yw~CRrZOgf_75#@@@Ie44<>*1XH_xz4Sm0wP;E)P$#3Xiel+=0KDmy` zpYoRyh1TlU7pf^53ay=OZ1N}f$Yw%oCtC=uRcs|x`_$UxH#BS~82aaAVrU)A?_{b@ zl)v4@lqqKp{o?@N(R35_0lu6=VF7S*FH?q+IRw8AG_4Y$zpDnD8dG%V?A)Ptpqx0& z2x2dw1L||^G+tm?xT6QOG}KrYRjJ=bApyJc7rS-%d6S zN6*$c8AeVq(R}F^!>pZZdQL4vh3TdVM22&KMh1vv*h>+caO8Z`ufzkU#im~;1t0C71x^35o8<@$JU$17e|)A z3r)X^d8j9wiwEW0YT8HPVSv=?0DJE;eG!9|16>9bG8Bhf4&IgkLMB0#It=^kH6-zM$MfHv;j-ptNISAaA znH3QIP)3LZhtl+`X0#E##AJclVsm5C7u}YcDf^;kfNKPzMpCK6$uFiF-cy*H@jW)I z6J$=K^#VkQ6^kQ|75x3wlm^pv=C0IHRhafr4^co_X|>=is{*l}TWY?CLID+B9HqWv zrNhn+b4{9UbuO%6WC`Y=AP6_eg6%OFmdUaVO`bNU1KD#*$-zqf$vM$bV#(mc0?yx7!m zmN|jU(B{uIv$Q>>AJqYG&oeI|fO!keG@pL@mzWNCW|6rL^(fBE6=P`(->oz^qB*gK zrTMZD8SPsA;IdG<$vl@jJ21?$G{yqZIo>jG_!jd~n)j_Y+%vc1et4-5OxS1ctK|EE zto{Ce;O%j9Ez%Dpo;3HOa4Bk_6HGj1u1TXQs@xxwu9#DC^B^JGHm zZ<%}1#6LoRNvBbBkpIl=;a}%#^T0fW1nI*^W{RadKyqNfNErUae4o-z%^?zLHt|)J zmu8w@b^R_#_A7HLrAe_WK_E8DQVym>9#z;H?kl{WRyaus-b>iyCOESjxgw zk!1n3G=3N<+G4lVhmRzdt2FuMZewW^tmG zvt`rkdp%WoosCWfCnvk*vyZBxF^iCbU`A z8gy%pl+0*q=}oC5MxYF(p~4WOXF#=<7CF4}vjy$Jaf2af*_tMoAaY+C%>)$H`vq2M zZy7?GKhlvVLEw~R%O8}9kP6Ld)UlQd=5bTEOWiEFG(pb{w4}qo`&-UaL;*yVYf!w$ z1pc)tV1{=_TJj~#R_Mfn+!G{Z&9NU ztAq{L!rl`t=zJ6=-{)CORMYH#V56m9k);EuzsM2-N&yQ^haiXwj!jREfKP#C68er3 zk`;`bW63X$?x5RJg##X(YgtOQEG24o;Ilh&v1I^}t8f`jN^^07eUb*hEw{8Nj^z`9 zrpTfIl@l#`ux!itEtDoVHdA?6}!dkM}y*vDHHLbbv^V+f&1n z(6X2!u>{32j1x4vY^eznJmNBN&JGJYG=qaNc#nnqb{`j_i3ct3(6`4pvf*LN-{`w( z9NF%S#Y)I=R|RtOUCSY=XFdPajHcW7JF^4-h4u7DmPfq!pvwyj%}daJz%&S7TD}&? z%pC!Uk?7%oHW*qe1!hI}`BddQzeLN;XjjtApx7M*^A?jsj(u`Ds z!dpgIw~#okCAKarM#iVX0*RICHFiN5ct>vCPNZ2CV5NDj;5(Kk$a=Rpo=k(w)m91V zb#`g3TZ?6zaJ{v0u^a`XLaon;Z6eIp4aJfs-(r1R9NUm6uWh$ZCh`2hVdcIghC&SN_f`qSs zy7kXD8>JPkIx!wdVCl-%OQdtwRkxzknz+-r5pVVPAB$^P%hSve{rQmyGwKvCKN0~# z(1P|wR&*@-X(cJxvcwt%Yy4#0rz(c`v4I1?QDZ3^|GH|eR@M+=lwNJERMT=nXD%)f z_C22lj_F`Uzgvv0TDOxGom0Z3qKkD8`gk+fL0{=@9ge=J$C0T6tZ%9I4*cUBMD3m; zN}L~QJz1QN02>H>6@82q?Qdc628_3&bFP^DaiTSh2%R;>8bZeOc{8l#ix;hho6zuo zw!j()N6fV*7N_S(1OgVg-_B(L=pAu6SkuF!0F5`Iw`6wc9Gd@mt<+{~BGfIgUSRm- z__M(J27P*ybIsQkRvXP)MFagS zO~etU_F50|PTP9OI;Pkz2?ZLHRSqkku%hF6c>dY#xRuO5HK(opNPM&BtmTT;7VLP% z+L+k>;8km#;@Jp+ncR`iS7*oA#>TWc3*K3*N1Yu~j>6}_>5$vK~`%ZsBqfdVC?g>{rR^w1KM z#{z9RGM;R$v!Mgm`09laTMa^n8Eu7P{%VoMHk+b(h?)qPKxs5+Hx=-fJXsi&H4#s7|cQ>)w1 z91KVAmKrvypO(d@%=e-nl^C9Ad+y}HhIF%)0yrrUZGGOn+!Dj`P!!vTd0*Ba^f9H`XKjzDU5{|Iu_`sI0AR4lt_KA^n+0m6cB;=){ks*oUT#N+ zE3rMQ2H4TTd`u1vvZHYclQq%#2A(TD!Ip{Ht;y(P^kiyF%V&C1!= zFzfiQ7u4UjhXZ!gZ-eI2mp7b#$a3ie+pmSpU?Ay#OuVozn5b#P==yIBtQ=cXot zdo}HADEc)2&~F6$@{0oChg$X>Vz75rsuu1~w3iX_q@=#xLNNpb_tc_rP?%<~0M|CO zALPNn$Rzs|3PA-dfUwkXeG@xMT`XvgpY1J)petM1(SdP%HKDzo>gm$I+Hr!cWIN60 zY5gdz_$Lb6Qtapl2ROOF;Z!@#u^SE$B2yO8bgh1AeuQy->~rMca{n~cz4S)ii{_-= z0@Z`;=!GpTU5~+3Nhve{66YZ2WDl{|r+L!*FBm6q9<&ETc7*+PAn(#?6YMVH-NTdZ zRS2nN?Q@G|6i#NE$I4jRq^0&Q_^~q3eCR8IaIU=rFBd4W(7uQ^&Jk)loV3VJ^^H|d zE{G5kNyM#OVW)XC>Q^rvI#$_RkxpUkT6=ezVZjg9XvRT1dMDLbN)I<~vNJ(A6ydSG z_9@i;3(~aF)`Rv~1s^0eezPxD^5pxA_TLzuJa^eHCsFj|uDyqxzuM&q8drI;=`(v1 zDNk;GZ9gvK$;lt>M@WIx&vtY;5qB-7FLv}?4U@OO+BcKdmdG6FwRwDXb)dsR(Y5pa zCDNxR7#i%ba9{nBg6%z;N5hR8M+us_kl*az0m)~?Tn#&Qg~56Unh$WD0W2^$(0U6C zpJH~DR)Am5wQ#`IZpUVtX8nVT6C}wZBH&A};{&qSjTTz?KFl#f0(4hYqrm$}M{!0` z=M1(;2U1@~l?9=t9oZC_b~ni}g8RV*H7L2psfNi>j!QJk{u^dStb^uv| z!d?kYb*N}MI$SxRn`4iNm*H9u$6ngV$WC{dsb$FSU zL5>?lhA)F1hyS|_5Wzg7Ho?IWj=QA!Eh8OA{(JKPYtiW!$9$SG;>|qA4t^i|+c?Kz z(oH6u90%aIJVz*%F}TW8raJBrV~m>a*iT~&4yFQdP;D?GV;q3C*^cu>hHrCe zWPk`}IJF5<7CO*#FWh1OxWuuO)&#GYI@;1KQIYIRplhvTF)stWxZbgY!Q#LL8ywYX z4%H*bS)g#(Eg49*d0CjS(-A_zQ+EsCv3ngQX|_`Q;2Q9{gjWjA$RfwXy(feg9-I_f zSby3PPus$bE2!7cJ`^p5r>{946X}oMaG-ZaI8VX3w;bq{1tz=P5n7D7CtUq_-yx=L z9DIA@(8DV)9c^evEo`8SuZ|#4P*~am{%YqM2UmV{+?4QR@tm)Y*OdLnEWp9e$S$FR zy+zKJ{D=-OOP#g(ZUmMNaE_;$-E$(tY>g9rJAs=ofSJKgJ#Ym%Eifg-nM&HZVsuJq z=2zGr2s{Mrt58w~_nMt2i9hGsoM(s=hdG?RX^f5L7a5>1FD46$OFCQ8*cBt>*9GXFJKnicL($jxVPasb zCe92Z-nACa6a}9R^*cBp$$7G7niHKQz&WDo=0qzVOkV8n^pXOKzE1QFBYbt-0Owjl z)*0e-@KYr?AlrGBsNLZgXA|1c0=LIHWiWfZvyeDGA=l~8Ud3cVvNL(k1=LNT?oJ9^ zKYMJT_X1}xcw~mtOB(xgrgI!M?5(yQCD@l7g+5lgz?n}FgBLk>1cGMirbM7!=UhhN z#qSHOgkVkDcc^DU8pYXDOxNa>4-*J$II;%t)M74tV*M^Asn_ zl0TehiX}?YJ7-y%{tE?eA-~Yb`qQ~hf<%e?MjIyEwmfuwqIAtRctq@az?ovT%yp8^ z6x(33!u5?>fW$!8IZgnJnqGi2!LFUu0`%9pj&K50Hn@)bcMCw|N+r$g2-TYn(Apvo zPII_IkN|L{%XNZ5e=|L<@-#~egqQ~k-k=jtcxfRj&BGd_dDwd~t_Yf0H-_zfC^{6N z>HbByr3w#wqMGX>C+&iG*HP|o*BX2?Woq*LpS4_S>gM7L&@rMLja^$gyRc1N=eQP9 zoAby~Kf9h%CLOYm3w`eK?<+ntM3etyDe^o1>Bs>oF0^6u(Pe=PI=P}KdzbI%=-^*w zunaCubrs2w#~I`K$QUz`kA$R2t`U?G(34*`+?(lYMzd}}A=?vh z>jo&B?@HxVO<(9bg}PZ-d6|owO2Lw=T*D}5LOsFvmBP{ut|c@p1uP(fiGd*pT^XEU zV~)D|D$w7LXIvlU=8`on_v4HokmNcP;v%Ev1R|e2pU=ck2#iizQ@9JNC&J}#)#pueo3#j2y-}KLQ zKnsQ2%e65$z)h7MSmc}$5BWs^zqV+jq#XBGXEhVIItjGNX{f0$lBl_@N3HSB? zKIj1ixE59pci*GQA*2rAwn3^wSFAfmfozPs;K#9UJt+LM}gU^S;L2jy#YyKMr3~^s0ju|=Jy_>=@0v>c_yQ$s?6cDgpvQh36M54Q+iz!jg zukM31F)K&}a?pmwT!rQ})=kA{=2iET} zAppIFC4=CN8*PbTO;zvRXomok*FU?_4gn^Q{pIdMnHcFHVS_6b{vh=f(oFf01u;T6 zTo>v=pB}*Cl(%}&<^U#_dp&4#0Fw(!dC=wnCc8&@(B=RpV`DsMa{!ZFDtOT5045() z_MpuHOfHJ|pv?hHo~z|Sn**5Kk?5&M9i#0NQIlvvU-8{uQCk1Tli5xkNFB`2ihp>ln`&nlQl# zvB%LHjQE&Un@OH4QgCf(2?OYsixhPgcr36#>uD?E<-9%JL-Ub1e=BMj0z4yWhJQbT z6C`Z)XyAxho=?%V21Dx7>|Xed9m+q4M!OdmCA?MGGoFoK5+(uAGBU{stS|)_1;@F(^IVKof2LdL|Y%r39iX0;NEw6 zl+vlkuy zz_7;F-dfbK!r>48x$n}B0_?REuZ=Q5=Ydxz75W)ng3G~x?%pVV>Hr(`_ST}9Z$YH~ zcsZ|X+bnMj3RU6V;oj;}aJqC!11$H8cMw0lg1dh8QmqRS+I;??*Yt5-bkGZXq2EL= zS{!1s=_GG$>OjV8x%Jb%=%=%|2^U;7(@XWR8wU*nBP+T|bG&Fphs6^w@S+tRCeJPQ zq7@w`_bm4|q;&U$83$cz{>Ybhy*DtJpDF&h+e>DO9rk#sPLy*hAm|WSdA}DO1H)qO zJnTg)JxoTO_M(*@Cc`d!(Mk`KudWFBw)L79ExYj5r+2()rH9EL54~umhsp4#UbND~ zWXo4xw9><5!dveEO84>W0*KZH!xhZ${GLbl*WTfvy~J0%1yZcqCxh`)pOPYjz(RvB z9qfi-PLSQy*8zmfe0uP@sgGu{f}lo#JI})+U`wTMi5L_f>ZS!jD&Gl;!4PBR{1xVa zi`BkU6u}1(f>I4ixVneJeO}++B3_&`C43Z{9h{YZ1xfeAWUySg?+2O*lz;$wH}z>j zLANjy$i5%e9u`LW>hQLL@@U_636>MSE#reyp4?f2*4~-p| z465#X%c}=9HGEqM!j;-WwRLrcYAHW))n4D|rh^gneJ@G1@J7DzG%q~wEcVRsi3zV*2=~7Hr=U4)yhoAks9; zq)l$3*|7n>gQVY@JJ`3XSa#MA^`Re^#8!Gf!ne9uSY2{_R4?1`R;pP7JywU2k?_$N z-y6Pn08b_qBgHttvtQAJgub~xn$O>oSQ21tc|NqUh)u;z_0c>5_^v3HE1&gIouvF9 zFimFo(3TOFD`J+fklKZUq8n%XzEBPxC_)98U+4MIY68nux8Q$6uCoh$RLkH0VXrPr zeCQ`7v0UEe#Yi7P(Pb-qaTH^}5MN}E?b~`^I?bbOtln8g2|0J#&j}N@`_@p;iwX|- zjNIuy19<%^vNMc4?3+N1<-Fjt!R)iX1GKh*udn#t$+^jq0e-&i3!$7ijsOTJ1fIR; z(~-H#+ebe1$~uYw9X$KQw~|ayCOq?@pUdIm0=9eML!S=Ar1O<;H6JbT`WqkmkRQIP z{X`J7@Wp4}DpGdE7Xe|&Ujjn4Zvw(+CaeLG;b(c+K#GMRA2cd`m=&lRc0!ZjK_x=q zZdF(!`dl7oCyOSGW{HYnA%aD}L5USyVF>F>EuJR-MKI9BmNLWF*0BBPGt8W1M_gfp z$qX{d7e=*g#gUG|W2=-3f{qelXzhx{{SqFA)~=Ym9vSwEHsV0?^b(;E?P+>wwlk4< zUHRjR`>Tglq}j>j5Fk3PIGY%%O5HHD(8X#g8-$^SE+#)T4r@TEVb^~gSxjgdHjZZh z5(|lrEG{6DX?lgB1ud3r)POLwpvB~n;bCY&i^*EqVg5xx;K(qvpv70?$AzHr7POe0K0B-=bu{*ligfY2mbVJRsLl)HxQlHa)gL}x9`=S> zkk}1jWY_ZVbzvULu09>2WpE6`T;SK{<+g{>tQ7sPHbugQJHk3hmtp0MQ-X8J!? z{09$)(XRL*`cY!4FDox>T4EG9U8^M84s(_00KOd!v(wCHnwFTwFY$wqqZmS3Jw6%s zo)!qN)efHyJNN&w_TAA<9NpW;-Nv|s0UK=7EXzffWJ~T=ahHo^hZd?ay_;S_F%Wt$ z_CP`pEuq&7gcfQ-HNE#5Is_6R0lwMUk;t3x_fPI7=j3RRwa-4c%$>QjGj~yaj#7c* z^9{Y6DT@t>CDqB0a}9loca_ip*l@MsHGBH?v>OdEr>3Z`=91!~2A&Xnt6>||gw2XT zCO)Pwkq#OWS`%otUrIFwFYY(ohf22pj)_XP1&z^UTjNAkeLR!<nW6LqS~-+thtd;h|ayY&%bR=>j)#q=?OVhofOHj#+K#-lcI(jOUp0}c2bUu`xwdCq4c~}ImA5*rRS~6q1($)dfuuW9={2t=dH@&`}d*ryj3|| z{KP^&{Nx~Oi8SN4q%NNWYsly+t8!>w zOEwJ&_YLt5lKuUZRa%IM*{PoF7c^$s?0fV*dctx+J-KYPFnqF}cqdXucRiPblS;-6 za#Ag-qas$8i=6F#L)g98>cJtc?3kdASqUfQMgdJ(0`2T>kr}DQ z*+iWw!@SPKnv(^Sn@8_gA?Nh2ZDC24Y!yb<8SP{}$um27FiCL_YfZYCWSBfkK!Jw2 zvS>#tT4S^9DPr7aOels}q5YFZ)(7J!6lDe>EoFR%X{)BPLk^TvgBCK(zCZyDDB3TG zKm_z+dRq9F=%dbEWbv3RiGs`p*B-K-RI@O!x9m7|9?0k`e){#7?GpZdYJhAmy%TgB zDt@0ZT>Nw?6aRj2B#k)q@AbyX9#d_A=OkGsCMN!r7B-T62$TnsW+k$rFn^wGnhzI{ z#C@c5IZPvQ--b@!TZyA;CRl{MmGyEwX`v z{1>*#?40T6rQJfnxCP(sl~oF#{r1T=3ZJqAvfjeq=N^%LA?WStNtqneHe4OFzqkpv0<1~qBc5v?ZS=;L3oN&80Ija4JW`Q43+9qfKe3z zVJy@2gN0SYHVb{Hkxv-1?+9RWc zjM4RahG8m|FhdfC9i0^60S={Mbn<=+D*J@d$@?u>-!IG=wc}CHDmAJzDN{Eb0QZK5 z9Yz?H(CJ)fF>&5CJ}e27I5Py6I3Jo4Mib{-5IG}^CeF9ueR&v7oNvK{xnXTFU4S9e zjF*=*%vu~a88b<=!bHQj8^dVgd<&{V7)_jS!SFp{G;zKK0}h9=X_x+&Fq*dB0+*v< zG;zKKNvFeT;(QB!JRe3A=UXuNN*GO?Z^6o&VRWsOy2jnGWURYE*`u)b7_Bg0rG{ra z(tCj0tFU)i4kW}e%!TZ=S9XW6cVRM2Y@y2S2{c`6NKYf~wPceP4)S@3vBQ=@&MGZ) zf)Yo$ha*v*xA;JrtGodwEc6~D?+QA1`3p=ADqKwH#WbHSk+&vUo^nhMN>RQ7Z!h_I z2PO*n!&iP7qZ$fBD2zYQWA>;gc>)nKEle(+C?u8rn^z&NYs(u#R&9B?6WQrkSe?WM z%IP6Wojd@7>d8;h7tT}VS2UEb#1f{r^MsO6IqGO3(;P*h57C(u42qQB!Q`q|!I>TV zkQSbDOs2z7r~%|~Gd9v}Pogw(7dRFxpDAjI^oo;Xjw4xN{Ymkg#%}N+UXH55@EIl~ zNb3guljMZ+s*@^5z46EDZ>$Q#ES(&c2ia&b6q!*@kCF+tB+t_2n3I_{JQCs;=my;~ z<*~@HAa*83t4of21E&>{C&#SE{Ee3Iy1W(n^25l0!~3|>A|J&m?Jtrq$96vY&b#%kYN{tW3(T%NFga=Oi)u{k#Y zYkrRP#US}RX>96!C11_i+!!WNH|<8quOa3=K(!mrhn3ai<@C^|pd1)DQGOgV3#Vv9 z$@J-R`tG0wbpuF9P)I+RI7?2Cg)-Q6<+eKvA@U!Qqt8&%>u>{KQg%S$DAun$w#0Ua zmrLdJm?gW}q2+RV%#t;yuC(3v%O7mt)?F>%h@miYw|p9VCzDugUIFqWj^vvzh1J1h zr#uZ2hJhk}Jk2n`uO{_l?bxhvUUL>{PgnR(EaKqDb)OJlPcmTXO zBd0qxnNp9Rm(!h^thwf*ya-Kt{R25IZ@MAG8Ma-Q|BCEULx~k81Loe6(>Emu1|hDu zt<$)V_vOA^#dV*_Uvh=ieXVy=Cm(y*TOq@&a<#dB9Yqt9+{{h_*^p4@;Ya4*R zwt*ftVu-`s!mYh5+dUlhP6%p_j$)EJ+&?@9lcX{PmZS~~45x=N8MEJl!s#AW*8Hh{ zxK$ZzW#QGZwuN3%;e4V4T4lJkJAPD!(`}^eMh9cU8)4CfK=b!=$c+-i={aNuJ3cu) z1r56=_69xH*{WWEcn;Yv%?7K<2(NIndnUX*x-Ze0!?C+6Y3!m0Aial$yOIuHl(q-@ z^62j|jz985cq!?1JR+DLp$x}Vk)!}$Lr9}=J4X@{)!c`un}uVx^awCS|8;l}ocl7o zDyF|M1l^EuS6I+C{0|>;-oFJsZ`?QB&Os!$b3iyI_p-uzf^1NDJEV%g21+apdoiIG zGyAkAbBBgw5@#j^HIf`=4-1c>9xqtx;;3-UNjL!usHIR_z!(u`<~L)*m)eU&ewYxB zNnk!>MNbMxrBt60j!g;Q$Js2L9*%jY`57yGW;p60(`N*Ac{m*+nUTEagkzrXe8!qL zH~bg0<1tIc&kNth**Gr@$JCek%x3zcaMW|J&j=CUg^%TI_ALv?q~o8l3@gHq^5{@! zRX8TI5U`+Zb@($5Gh%%><{a>6N_HE=QOO%cSS~&|o(SQGc`Pp3iS6NQ;q|U?)Uz*2 zWG!6S8-9fwY5)Fk%;{eS)8wE84hcVpZ{r;7kK#F$9}CAk_5vzEd+rp!pThdqS{QvM z9939{-hR4JQ-?q3dU84ZYs_Nw$ej@rNyzpFePI6GaMV6RN@NVNz9^tN(4&Mp%p)%b zQ~W8s8oc{6d}LL`B8h+iO1Fqo#3KSY)qp*17o=^LSi z>B%p_5qXGgk1%amVa3-9kWR7)AJTdV%@-0{giR)j(1<8vNtQ*zqxumy#UUTA$RkiE z`Pls|`e&X%x8`kf63JiGM2rwU96BdNpt8cxxO5xdMX|R+t<(sb6$+ytywgQ|K$wd# zx3%=6&u4qxj-=0Lvm;Jo9_3MbUBoQOtx<#ylO-_(mL=8rGD0Xq8qvZRrnZdODMn~$ z(Kdo+Nle!44iQaIvI-9nuoDw---x&oR2kE%#WbQ`B+o7B!V92W6gWZBj|BH)~vQVfjx2zygZ(7ODC%WYN9|whOi^EIAMni3+kb2Lqae zCD_?jh-dCzoQS~WDz;~SM87f0os`|Z??ya2^m8Yrj&g5O;Ns>&2JL8A74(%6GDmtZ zdd^QH{L~xPoQ)`Pw43#}6MrKvMM%p{OP}4Z2E8svp!OgXzK9q_Qm;i|YFM&sTiuP| z`$M-}k9hAuDx2$k$llu#j}Rk=00q{{UT9`yA6TN?;Pnv@?J;s zO{OycNLm0%a48sGE3%!F*nAZfdB~1fhUC3u7c|Re*6=kr8>9bC{PM`3sgqktq_j7Qi_4%a6RlIkdM# z{*2{Nt0)qcb2APn8bz+;9L6<`#O&>4Fsd&jpYk9a-6|6ECUpjLrFG;h4s*RE60;qd z!Axo&iAu8R#S6ruHmo?2qHE+99>rhx#P$^q$ewg=D2+UWS*!WXi@^-*8%eWo)`zsOJNIj`S_N#_*=8%9K8a&sX>eHJeWI*f{>xd+o%!5FM_ z{vFVHTx17yNT)@UcoCpYQzL)H%qN+6wjw~{PS-ngBKKiZAckeD1O$tgMmE5t?+k{P z-jTtM^^x~o$j*NH>SV<_>u7&_TVxU+85KJt?a{s_6!#GiUe?+jIm#33`L>!h#QR8q zH%S^X&|7S!tgoL4e;to}X~!f0`^v~UocF3Tk*E}yY36eso4MyA>C!KoE)2LBN%P=E z^$otz^m1ezex^n1*bw$^j?1?rJ(1SqXF(mFM;2r9U@9d)3!3yglIFoozzu&y(x-*2 zIpVKKngQ=XCD+r(_q%@7aB#=VAkxe zh@xpQYigBI)=Az6RTNEw*|(~MD4GVd=7y9gng+9GFJlx3!%-UU?dEDwN}rBQE@+0AG|aT>r+ z{i05A)h-$o)sO4G;jpNCycswuYAd(X?Fms;F`FT&?%CaHligFI8sUPC2mrUIM$tD4 zFi%)JBdWYQ7OZ$#Q~-J1GcN!lRz^K=7PTo`7qthiO#u-$Z;U#}wfW7~s9{`%VLwJ` zxe9yjlb}M&;iy1jIuuoelo}`ieX9}EUiOiw(f&A`Q84^gREaxzlR32xDeYb}f`mPd z+Ks^a+cM1C`?W}iXHnY`C_iN=CX?x}qJrW5^QcRhA<>F}Swx}N?q|yupne_Iz|rn8 zG<_R|dYL*!Itne{M-^cPFnaMCb{rK}*eg2t+C3JJ+SaYE@N%I4P}Njmp4Ez|!uiv4 zwG^F@s$D~#HdF+Y!~lgi7ytg=gkh{HP&`HUgQHMUsQ69L{=`O#+gJjfnak}CSvcqzP{5ebEB2aN4*j~$l zp3YHxl1AnAw~A*%0J*b3B9-Hd6uYHSnf0B5*5INe$#TV2Nd{`aQZXEpOffgvyH2qH zvzjklTro0Qto|%6{A}K#sD{b;Sy1u*`GzkUaX>-eHe;&;tvRe1f(E>4RMdc_XB0co z2#azRLD2lB;ss_kiwV`{#Eq(iz5_N8%uf{6-KeoyiJ`%76#dX4hGDW7!QOkTxKE=m z9Q&j|RY77Dr#VLZ!C2?$nSv`ac2RE8-=NtsxNTLVYt#M?=e(k6DYD0~!bkiZ>=#Wd z%shs;>e0>~NSBAe`q67_T(32Z{uvt~2}KHo1>w=|0@oj+M21fbZu=AElarjh1M70{SrwEs8!M zd>R@>Pw+yjmnbjY@MH(+p|X9n$;Ijv(cPmHh19HTsd%s0KGF2F^<((6Z#1eZ7E=$q z7du$58xTzo)<1@-gQMxe`p58ONOU)B5=WyFUxeH^I=Tt6_YZ{i)1sdsr$__ET6BZU zqv?_Q$8cj#G(A%P7}m^>rbp@@!#9hgzemRFfq&b6Q}})KL8NCgm}q#qBbpwme+;en zMAIYnkD>B#G(A%P7=At}8hPd^(a2w(iKa*DAH$T((ez0DWAL~UO^?(+hNE|)>5=-! z@cv;mJyQP|?mUV98nbbMIm4os3THBWLU4dsG<(}`(WnO-0v7cEXQ2XJ59s-4ba$kx z*HqXkdEM+t2W3ZpyQkuomCLo1nC-jPl^P$o8K88<%pB}el*KT(w$dKsmBPiOYF#BJ z{ioNC66gCp>M0*OlF}7jeMm={60;scu_nT$Fy$kp1}4Jr2qk?#E90P2D8EI-ImTgJ zw6X*fe%ZxK)k=C6Qs7{J@ESSQtbv+zNmhE3UCU|*lD!&bDwfrDtrGRpI>u^Lf=x4; zWQ=Bj!jxgcF1@ZhIcZk968Wc^exz%;vL~#_P|kE9JGykO4h1>NA>3q_a+N)B%3(L~ zgqL|r1JZ8Yfl|~tF}p!sWi*HvPs&=hKvxbS(txzf!+ z^ywF^l$aVE6l)@6wNaur8!(wZbyEh4Y>Hc`?CfA;2jy4XXsMl*i?Bwcu=Qa<7bR-R zPap;qfgVR>9ezzuC8orsRlfRAwN$wTGeTJroWL?bIYndx&ju-9V_gVF3{&1j#=!1` zUhD<#WlDQ{v4hncrL2n;3vP~6UPm*b7kiQ$;VLKSIZ^rEPNXwzvN8&@My=YR>_-+Y zqG4oeifSZWELSe!5oFq2CEd@#d|=2tBl4di(AT{F^D7GRRf^S zugYjg77$6(M^=uNPnB!9OHX})3Ss*c)r?KJTT8dXV5yV$mx*qDTN z?K`w@+nzAEUUn+Jjh=?JRNn8NzE}Q$a9Jy05VZVDxkbb(ucum$gaXQKCnoEnJj(wT z&`3{Ll?!;ds>X8{SYAbi5DzG9SIiukUN;}Md#a}L+c)!3eUDSgd^qW=I*W+{l*0%i zss%#NTB==0gn?v{o5~fg1gqpo_Z1PyisLkK38|;LAWmGW)>q}ADc$fiMFIMeV~u&B66arg@>JIO zR}-@eGyk%p2Ek7j)dn8pS~@Ez zeJO6S3Y7`|omvpswNueIMzT5YybdbN%4F#_Dw+0T9JwV0hMQsMEP(^VVY)D5+?#WqY0%<->^#~EwKBMe9uk1!z zjZmExCq=U1R)T%YRH%s?gFfMV(;4W*ZL;VP&~T!v6uE}QR!D;(W2VYzXSY~f*n*#s6II z4C@xCvZb;JCUY075XF21DnLbXa-@pxxLk#(^wfr^=xABli!ehT_$)hi3;>T?CwFKdZ_l;1G)A z34MN1r8!Z_x1La~6`ZTXDV0q4oN`9>8+BtCc~13-eO^#Cr`m?AS5)-r>|%I#UDZwa zcZWNw&BFBozpA1U%a}IJF*fARD;47Pi=RQF9n~(x^PM#phxZoYn0GO6(#2cvrQLBvC1v z7xCOr3*9BUs}~6Va>-M@M(~$$e(H~iWzWwtSAJAgg*r9WQHVL*|Duo@&gywEJU~sK zdvn2?)>iKpyxG5=dY!=bP^g+dbX^Ra!quOfT;l%>X0D55~Up;xwg113|U7k@z~RBBgPZB{q; zqbyi=2yDkpMP_zTkH9IFp?bo19o1Yz8o4-=x)FJ%4*^%9&Vdc&y7%dVQF$Pay|5ynG{u-|y zCurd7NovgM3p2sV$?BgF8UT9nUE$u#XQ-E9wf}UcdOPCYLKo-_+2!iK0)d?bPrzZW zx&dOOB!$=1jmY|XH$BPT4QhX=T%i71=n9R$Q)8ZP{tsFZEMKa&#|#4hfcbKTda$6M zGvBK*>Hj~_!d9tM{{vDr)~fpmq$aLce~0iI8!4cApCte_+N>TV1xPSp5DeR*z9*oy z+xA}=aPxNcUW{AYq!URs+^KFZkSg1)#^jCvK&#xN{wT%Oy`bcfdNU>+W<*{bQ+vaz z6KaA+sC|wiTsW`JK@8?VIV2+8w*-Q2s(tA!1XOp_KRD6Ps}I%ZX?KTBPt_lU?>D?u z565imWs12Le$$uqlDRs;(ZAHE&~9Ueae>57>R5qgFUJ@sgsOz#sw7H}m_T?@CFTZ( zkbnRbVJqgzz4i9qgu2VkS8gwZ=D_==KLY5zi?dWW=jQ z3^t=A6rLVEKF^b%;SM z=#Y^kV>*hPMjblEba!HGNLlxossCOy0jnph=@A1MN!Y~>y<_Hz8X;@?#!SF+IM*-6 z56xlRz?knihom7fldv2*d=-N_H^B6eJ3OX8=Wwbl29XK_g*l8Fpj3-vnvor$!G3Ue zd`xCFyNzOEuA3k86Jn(TDBQ_vqKo@%joIlSHm`4wsqY~+eGkVhsfW{X3@KhQq83@; z6zf7pylU-7p2rn9lR=MSs*&8YEj=nm_je}dl9;+M_fbqYf2$<5>|$LpLjhH&ctfB! zY^W01pVh|F_YbhnJTE?WDVM1*Gj?%R@mq&4VoR!s%>k`q34JGp(D8%X$FAm=UFi_} z7yrS-(pXo1nccA1_HO*Lv9T-oWr``WGFS22j^(jaeX(i_X`l%pNnJVy!jzSDPx;M307a)F^KJNucHzuJq)3nm+XOoM5&+a?Qa&gwp>f zNc#}yOwCpj)2bw!IA&>G#$7~Tu)(X2qR7lNIb1MBgs7L&vKRckdO<~ z-1du$n%k1NT)eF5!S&MQnl!zPzpmNIp&H-P^pWCqU2kh>!NB5-cD^M0o@SyHsQ&jg z*ClD__lKIsT&$LlHIs2%K>0*tu*Y31dR?{WHX1`-YNq0#!178Xm(-7H|E|g9+8Fc} zs|``274I~AC0VA*Uz#qQ%hw;Jh1~6*G$$l+3Afi;IG0I|T4czDVkd2=B=gAKw7ocu z@7=W$M*9|Uy{h)9B#-Ust)=g%WitgkKP^Fe>`gyyp(Gk-{k6k6jdr!P`Zxwbo92QKm(PG?9yoT>G;mE`1_x9p{WfI|*kVk&B&D zdtXu~o~YJZbB0t6)?-C3gS6UP-0Qj~XnRQ!YiOd@npVzC(MpKF-dxh`a;<%I%gFf_^Y+j2J6l1w1JYGrq4$0 zIL@WY7HLkCzEyjhL&a^^_LI^V3U+AeE4^43|8lo>wiKx8d$hAStJr9POGj3|>%d}aC$h@QNA<2vHYR_}1ln2-( z6TBa4rLEz;ex#*)BA7%&o@!@Ek!bBRt;q=o0Q%S3-8|BGy+tQ0FYM#mf%%SU8`#5C$0}Az)ZqX2upZCsj*kqXzigk^bLT5>`cg@@WWr8|xmP{0BS zib;pW^teiAkw;)|+;UFobV1x7PU5$h8^x{hz_BBXy2j;@z_PUVFtlCVX6{mk&T%!6 zJ_;1D7jkAOkVzy7edDUa{N8Z^SfvRlKvBMP5!Lf_NZb+no+-hOKM#$|^g(KhcD_`i zYkt!kmQIc9hjdaXCUJ+5swMQ=9Dr})&WHmCd^`0!Z>>uq+k>_kt{frRuyWl zh^vFux)o&-7{8ATrQ40@6FV5PP6)wF^`ke%?dQCkZjGyp?4Gc9M_e=RrSd&!FC`Qv z9gh7J7bQt)Cmy8D0?#9Hg-B@xizbqSr)fU$>O|aXuBmmWD) zDF9A4ruQb}>c#t!VbN6gKi7|^?~P-9Iwve1J5GqUrO+PmEId9F>0Kg$1WT&w-(@m!u#`~Z(wnJD=FS6^6fq$eJVWY5-*poPzvP(%X`LG=M$P^ z{o+gfB`UN!@mife3=ET{6AT3B8S!%hMekm>IzCyFv7g^gFNLf7;^*=1GU2eq?m_{p zLe!D?FiG9T<88V-nfEGvJajo7Um(e*6v_v3F2q}hF6S%pH>3<*WZs+j8l==c<9i5w z9N*uK8G@{S9glr#D)b7VpyT;GN%Kei0zM15`!2pJZ`%D8-+_nnfuG{(ySZ6(E3i*+ zt131-dnSD12!p&6YSt0IT^yBgf^K{lCORXd6V6D|1{8|oNA{FvtOiF*!d|Z8b1f4N za}}>?ov?&Ai;EL>@us3AA=XzSd1s=Zli^7wS7a!N53Gbiq;=jP`2h|5ARPs^c z_n|gXP7D`BS^u26$fz>GTbO6H9hKlzMe1$g>EwiFQtjYOD2fMJzdmCb44;;;y|!dl zyH_W4lFo{vcoWOTgzDsGWd?!H>k^)EKk2m7rd-`_o1eVelTgW(d*-Kv_-azb&|jqY zKCzm1#Utkv-bg?KigdtLGd{RU{ieJVC*D_QZSOQbcr6Mv>?%i z6a*&u!8JqTU&zHEV#5Q9_kue_Z%g9$^gXjeei7L$@rNp+1P@9QcY2D=&Ak&(ak(n` zCqC!nQ^lY}v#zm;UO~KvNQ1#7ir<%#7|tLb^8-nxr;yOb~3S=ZoM`R*zdcD*^KZ4! z6W4Pc{qZ8vIwP+0M`8`Gt_-K7Gh9bI!>O0uS0 zt@5SGNN%=Bs!LAP$m|bKnlg@m4P7nyOLaB;<}{T62=}QP`n1%g>|aEB(m;6QZ;zI zJINry21!6s{^Ffz=b@zAd{|p|G|4)}UsajZ9XqdIZyX`4#`tp;z*2-gNDq+0Q9NPVvgCc-BTH5$hw??Tx$Bb0S4GZvD0U*zKg_NQ>vkq@#JZ6c#g`mD zoa{rq^EKYWy7Y0*_4R>dy8g^M(!oQ?{CPdhK9W4kTMF$Sx02mSw0+JPNVu9D%X|Lt zo5|Si6@nB%L9GwlvhO56z|ND|y3P9M$+xg7Wy_%yz#ZzmN#4l2`pvh=tFe8I0hVfW zXuSj16ur27NqTvve1$ZD6|V-2@=AHnEj`6IMajMLM6Hx3=uv|{ssw#VN=IzW6^_#j zr{RMkQ=U@GAA71lBVcDRR#mLoG8wiPzr+UDH(J8-& zpyO6Dc@g%Y77SUIGE|DA3LvEO!IWCCdPT|%WO~8`)3;2i2Isb<43 z-7h8HHHGd6Q@SB%Z%hfjy3w(ueS=F(k)7Q&*q}+B>umQyWVR(a)t~g5o9aP=T)McBULUVkCze@1xR8sxxo4ISNdPtZC{}2{H?qK~UB#HPeB?U1*UyN&*~EI7cUj zt-EOgUxRWkQbXlkFy0#+nz+SqK z;$O%1(R~o{X7|%Yi{G6cpzACCuGtXXTd~!4xNe>J*SaHh6UElf(Yos5UvH1KGJQ4P z%JjiR-BA&F|76`G@rvEkbiatLIWu(+#ny~+-8r!}aE@-P*h>9YS4%|hyg=7oY~5R| z`$PQe)1_8^y;tgj#lJ3IrF$vf?YlKr?M_>#ix>ZTaih*oy!O#%-Er}EKX27tloX6# z^hot0K~Z`aDA=vr?jr`V+LgMY>`vtS)4J}+9?0UW72X4WIit&EA&E4+tQ-5^V79rU ztJi?};LK0DgJSsV?4-XchQr3*`oZFNH>&IBh~aR2ZG9)E&8;E&MA4yg8|bq|1uvE9 z(OaDUM_O#RV<6MB_4El`u0C8WLAOYupUHY5Nz>}LO3|AYSPkdn^lO>Oq&!KFt%&kJ zWYzF7S)U+cX6yB<{~MmC3|5}gGxT5oH<&Lo^=Tr{43mDnR7{|TdMUy!G3&7fUH+lU zDuq^6Hfp3_E0v<)OKP~$SpP}n`TR@0r0yrU5rnkRA7I^wylJD~{@+lX)K;I4TtX5j zmY*Gbb$k6iF@P=Uq~Gx0&{KBNdrF$ITIp3IAFt~JNosF>b%^S%r_Y8X+BB|CF7>7T z{)MwH06zB9-xh84a?pR%J=rZ?A$^G6hXF&!VS0ZtHclC(50@_1Nxj}3dQ8-B=E7u8 zu?o{}s{Vv1jB)0F(1(Q^5qcvRQrgeUdBucTdQqTnD)e*3R{A&k$6{pHFVv60&R?xv zvMwo|(Z`isnVKN(Whal8>8~+^!PVvZ+F~DYTP3;cv-@~Li`9B}QH+7>^o62@E^XGY zk|IXMc6|uho%>Z45^~tdo9)OaD}K~p5v3qI_05b)R16!3N~1C-pl- zLB2er_Yegccu{{)iXepcATMHhsIN~RwG5&4!+Yfogy}c*AMM2+wDOkK=_+sQlSR|i zc_6t5c>%p;6VavMiT+1%<~916K3^2;)^GZSlICDctm2FJszdUh`r0m{U3%CXHi#lb zyBMSnEM9b5*S+c_F|uP-80BgR6@}Se)zDrPLE&q7FMgL$!=Ml+{eyxGVd7sm)He*1 zB;alpT?yL$mzy6v3O9H!2NI%C`jG4|4Y;W(r$V8`XM@X{8xG-;-hU$bwleIYTYd$0 zx7!$S1+e~!DQj!Ukf8EdqHIPxLUA7hU(8f>GOXbmeBRYC1@8@?k%K_fU6Km_4z;1Q%FvKE*N!oCmST2TG|7-H(uNOH4an2cq6!Gbs>jHZ^WPXe$&-OO zgW%7ZhRvd#U~jpN_V05H%O%jxAo5Q&>06rXeNlxx8)#?=>lYgSuw%-Hor?`CxMTEN zYQSY?OrBhryUbufS`TO(Y=gX3h@mY>b|XV;vhaID9i(e}A2jev@q>RbH1HF{&DvcC z+yX!-p?EFe?QX+OtW*qO!cT^=+&Uo#urVGeR5jfA%;h+D*g$tjvItyy#4r!9;uf&$ zr~z3do^#1QVd%)Y?5Q;1%JXt4m(vF1>j^l(@biYgoQwS>ta(IN$-itkE6GNuuh}Ae z%bU{NcKIy>vQj+POZZ)zUe?~1rk8gQ49L>2oXg}#hLPMxK~JUWrSmfbtrX9?f##(F zS1F#eYWB)-lSAdaF_cT`QPX}mAPYuvEf@d(kjS!R}z<3b&VCA%iIRW#W>f6 z=?#s@y7AmPGsBFu)U_C-B8&@hG{iA!Or#N6u$^-WQQEldS4-pK7b}s=+BjQp4oZ~P zn>!>Kk;UV=UaqIwde9dJqon9R*JzX$ag`ZH;QsP93+uL`znF52w0Jyoz2rP2?ra#> zP|JK{EuK%0wHPH$!Yd1n(q<6t8X1>!u>zV(^Wv_}jL72gTrVSA7-w)U^;;VkN$K7F z+8B|=<2jcpCB_Y$i@Ad|y)5i#L>7C~fU;MAnYyR3;8I(i-WkM>ZQ`Tq#8@8@`e_ZBPz3@}&*(rc7F($r@!u){f_T zIqeYnK|4wc|OL%#}v^ zwini?-~S-3ALXq!w&hT}*Ge0(udg%4@c}z@lM%O_HxxoP8-pZG!k=%ob*3xZjkp~b zT+fL=8j)q=Ie>nj%}}ooNE@p&4jGZP<2e_fUyR>!OYb@=&1t-j8)@x$=708;M%>YB z&PsO5NS`4xsLXRl?2{d~Qd)lANZ%mFpsHUo;tpMNRvDLVfv4aacKinYb>mb?t~}xv zou7#ky}e_^?L+4hrQ9hFu<^kDNqMprDU9 z-u>!n$WY0}Xhom9<@IRUMshd_`!js7q zQmRPv7AofYL9Q&Vha>sxg0eask4W>z1`0uHps4r7fdx9nq&FZzumXd;9;4Q z(H^#KNsGot3_*23q3(#I6%_xNMvIU#5%YJY`Qx_Ka=Xqyl;%OQp@D(~<)pb2#lf^k z5{v^B?q|`bVZiaUcigGVPNmUu^^9<%vuR0EIx~5iDh(X%O_JJ{V! z%aWwWnKEg%TB>771CLSIutQmyn-2|Y|nD1bW& z`Vi+#s?|%k*0TShZuHOIMWXRHKF?;rd zsL1qN5(G4m%bNmuk#jd~dR?TbK!eF>&Zt^SISBi^w$p5Q)y|Q1ri)b8@ zJt!T!bn#gUw0eClXgxUn1CPnw%hCr*@WE(S!X9Yd`QwsxNm%vzGgH%P9ZQQw=?m{? zr+?)yVeApORR>zVey0>}Kv6aYiaoa4@$@E06T{xy>4n@=>OI7F4GNP4M;@gcC3Og) zNIlRZ^-m;ehNAGK53c-~{!Y?pB$TEHTADtEo)i)G_3W;mfqfjyyq)6Jfs_6j-FP(4 zt(ief)3dnyJ|KgZrf1C>buwsade)p0l5t4J~F84soCkiJ;MTA5zcB;&pWZwHFhk2_eyRvFg$pl_QDIj(pm59zd=d?!h% zjZlvsXg&H-9-_*MhG)~ftI3| zOC|3Nw5ok`9xqodu#q3V$VOhfB;zC}@4qyo7OsZL-^tUm@k#JzZAN{`8xxO=z&(C|CW2k&pjJJ0(@~TPb zBjj`Od6wKvlHGDAb>3y%lZd$h8vdD)>?)G%zEQyGUh6Y9=j2&sg0tWDe$Bdt_$PLiFt0MqgyoLiDWpvPmW_M9-RKEiz|er!vak znR%@IJfY?1zlC-kG6y0Tg@8h7_~{Xd7d@@T$4Fmf=qg%T#)Duw9e!C-eX z(s4oJG~#+7vo_Ip_x2;fO%r8g{JzYyBIh3aGigP7M!)2ACap-%ny+qT(u(w~dG$^v ztw_(BPwr>ZiuA0x@>OOZq^MB(XXbdM^AdV(5zw0R8g8lkZdumhX}5dUW_vq;zMff6 zd5`Yxoka`GGs2sFvuJ^N*6d#`i%wcu^V@(dT40_v_lITC0`si7A~K5>m}ku`(OLI+ zpIfELnuruxpq)v|(z2?Mo#(P@ks%io`#?us7V?a1=-==X7bI4NYZ+Oz-aI3$$<5-M zieXuP7OgkWzFlm#iF~+m7OgkWzIAS&b%l%ks#BH+Z+dsjqV?w4532M=i%erh{NKvW zZyS;|0y*=tBFW&%h^$%S$r+eFD~pz#XC(K`%W8csDpq5j&>HhgA@Oe3CcIz_DFCMansuA^ zpplQWXt8;wvwknKXt8X^E2r$1VrL@{t7J=BF`!U2;H`T$eZ@WFHqSHr z6S7~hr#BE%GrPU$CZs$t8@uI!O-LzJHA3smJHw{h+1}ju&IV_@BYOrTSnipe3QHPh z*F#QctRUW`r+Y?q=qbzI5H6}tS(I&$HLLf-Y$a@Kn;nQ{B>>Qhh{A z*|Z8hv+JSh*|Z8hTXT6fGy5g~_QmXMT85te;7vs~Ekn*~maZ-)x_)W%tvk;szI>6r23J*(?xzs!EYzI`T6g{= zAAG!Aa?txTIGzvqx#n!=zHrSgrwUewtmo3Q@w4EQe@=U(4p}FoFMXq8{uY=+i_bH? zZ4J&jBxwOkC_YbU@%eeMHacemkD#TR99#u@zIY;la)L7xa%k~+#`TOo#~JJ5?D@#F zmquqYJtN1F;_^nPS&1@YNAUtpI7OMOjov@B4LD}-2bq5{Qp?yiM7{G9u= zZaLP|__ccGlp*tF>rFf%wpY$DY&a88fKq+JLsHULIYYUUe;%GQl{Y($${E0$&&K4? zSK~0%PneMNHE#w@$r8MrP!QxT#x%+ec5+|>Z?$@JK_`h>}n>Jm0c4-q`5>pbB&@o1< zj!|mV9phCUwe8wz;uAEgl9+a}Z8fnm3GLcj5p%ZPfWSyH(CT;Fs-l3p5zn)uo~ag>|pOX1Qq+_X}XUbaM;tb5VBC{5Vo zNX*=OR3`L>Oa>4aYnsme<)YSvJu=3iWbvjy#kfXdl1cZGtxO|CQK5OUsWo44`K5!Y1~w(4tmBD!09_;Z>}_&^vTmj) z_DlfseGj^?g%!YA+tYMdf+!+BQ2diT19{%pG?#OqJJ?j0H;)cC?L@AG3-K_Xtb2CP z2?9o&G9{5^D8GGeI^-rA^2Ah=S%Oc*lNa?&uCS@x6efw5fI^x#cM2dL$4vvlevYX< zpGtf+-}DA+emd2p75j!d$C5Qam}-%TT}6H*zrPA;r$@*l!YH)P5 z>B}ngA8j|)s0ImOYKJwpm9&K{9PHd4w*F|sZj2E&4m0_iz^vV-J3Rdvx7Rd<=f4dP znjCqDOFV3_M!cuO=M2|I1=Wy&)q?4p_|#?9RWTf_5QxxaGdoT-+3qlP&3-5;5o&HHX`b?#dJgDWw)b9nP> zP3{KX%#6!j&zH|#Qf z>bX?0Bf5#Xb%@2^&YSF>J*YVxoRGVNt8@A!n?WW{$&Icl8sx~b+)Qqex~pu;xVhS< zj0tOP%E(=xyP7M`6>Psfyp8|X7kcl=wLbe$@65fy6<6;-u9Tu*-h_E6RoqXKbq1dv z=Jw_eI`WB4A74Go{lpz~l5?J(>*K0>p4CCW^~hVy)tK*!ye)@mo^=^{4-k#E|_hsJ7|B&Xi z=6MqwC~U3PdCUG2SfGBeRzQ4SlDF|cK@>bI4LY{Z`;NjwmCkwc|IV)(5_6-$&TzAP zUibg58!%8;(q>SXFQA}T-cvyvb`9*Sfu(QWQfDf|o56X_oakr4h&*=}`q_MJUaasr zZAxAibw#)^BX76xyNWq^fo}A3_p1WseFj`W8Tz%k06NBoW0_+>8HGOdn5o^Utx5k zPfzj}+KXgvIOdN>VlguBo$@mrqyU_D%cna>m||{L&0i=5%GWdhM;9DGIvH2JO8~U= z&!6p%Lx>^8zul_=-RkAftBONVkr$TyT4KOG7oFcTi1c+;SBGW=`C6p8RX`CPT$pyj zu~~kosAx!Pp5Mel{B}vJ{5)5&`Kd!b*on=e*Yqb ze4O2@78G?U7+!@*MkaJFpzphI%?|+Iegzkh9rPbJaE4KX3I;feCUF~9;J`(wGpgV& zx3BMnf^VyE^UW&wio2VsqF@XEf%p7^N!*gFm)VpcTUD^!6=ziCKLRw#-(0ZOZl3r~ z$lNUjOEIwNdH!Vm!a_IDZ7mpui1xF5h#v_|szYl@-!{8K^p6D*c6Rfi#m<6;E_Oe` z$o&O&c2aoFTw9IADI2#Z5nG1&k~PZ4Zlv<{JwGz&U_pBXZsGZYmSlY`vorKMR1lAe zHCBXRvh1mSYck+WfnU<(o!BRF?Fq%}3w@3hTy?PfiI`6moWx1h7xtYjXzl#(F9u2= z#hKn)nDCN07iec%bg95P!Ms{<0%Jak`4yOM6pZ$uK5*hm!ErnKIqzA)Lnrz<=5@go zd;013N5Mwn`$KOFdI`VNI-AwP-@CY*&k5h_Jj{17Bb3!bK$dCM*<@XSdHugv3!#a& z*fYq@Ru6p1IlXIJ@!jmq2SbczPm){P+y;SstHfMHEHbn8#q4#=W8Eace1p5TH?bs{ z{l$!WYnZu(Gg<#q?L+ECn|Db745IH!H2Y(MFx3j96a@Wa%}4A-HkLT^JV|U;$D8dj zyv zn0$qHj+XE)*s{c3G{E`@3W?K0OL(BNycJ2pi+Iaz-n^r;)a8V$XIu7gi`>hxtmDmP zxfXhQkxASv-?E%Hz08&b5AoYCTUxg8Z-ZM~e&^Z=EwT9XTjY1PRB}ZrN-f@8C+7Z^ zJj`@o&NxB>GecHqi+tclTKK+8q@$l_z^GI}z5k&c{3}N*>Vt3i~W? z5FjrPTH3=m`z@#rPA`6oxToE73lXPT2M=3{Tq=gpL^kZCYaZ&`q)uELaM088HAXz2kR zTUveLa_hp;XqE46_nvv z5U%zwbgv?^`)fquIW#)~5eAMf93rx_A6wWF!>({v;ZV}%bt69rn_L(!s*TK4lRK6oJAzJ21cNTv0d)6MpDaVi;LGzwijT<2E##q&in|CfgSjV%MqYEAl3upX$e}KX(g+HKgzv+6RyMt(kleY@j{QKLB z){MPV=!zMvss6f8$H<~KtWT<0NC6bH{Z!{Ucrx5X!0BHB6WnzWMT2ZxV zC62~8DE27wLb}Zm2=Fetz2Qf8@e&ZbC0*W?cIeZsbRDqd@ z^o}T+CXRF-^@_|qlJ<}lEk+QbSOm_86?H`@1L(y;ybCW<;BW7yEJ7`zFz$b-i|E@R z*kJ#irf3;f1>SK*sR$Ji#=$?aXg25YV{#E@34p=KQj5l-IrKLa(RYI~J-DRfZBd$0 zv=A9sZ@rlLhyGd#^Lvct?PsQw1SoT8$$RhSB3SgWEPn6#1NbR}heMZR#i zr06LJyWOMc83GLK`W78`7x~;6UepWooQCmHp1kGy|5*F#_^5KW?RP3u_ZDaiw75fM zYB(&erPG;CJFab~?k>3778(|JSqfWVX>cekZp9W~WN~L z(iu^cXcn25%!|s$(|(G&Nx>3B;R;KR79O}L>I~bf;SMXJcwDr{&ruHStR73(M_r+L zmk(op2nuit_C?R^gM;zIZBbh&$dY--7Z_{YW@pp|8iko)U|>HK<%00u=AH2715v+z z4<%O&SlN{GAx0=ye7G*^-gj|8fZ&KgEnaxQnWz++A|^+bF#)+vt9<5W+53rv2I4 z3U~0>=s7##94RfLebF2)OA?kGG@ zAtGrI99-W(;XIDpbAfOv*It)L2&3*>8Pz2V-SCMRVJ>G#kz5FO zr!YqMQwrhA7bf~lCG1YwxqVR!QyI<1gfBurOA*33DMATlW(a9st(dV8VAW%GJs->z zE~FSrSr3aGBlN}g`NH2hTF;Aw^Jwi#un=Iew1$@m^JqE-A|VDS?m-qOj~BvLR>q8i ziDrvhPZsXwQZGyqR&mkU(}Xi=+b*s~LU${!Q$cn}$PMFG^M!Jn86;yV!1TrZMMAhi zlaXNAQehR>YE`R*wp_GetuTh7)neWzVLvBRHz?aHB(Z{$Bvi-*B1Wt$*I2w+ZGxDTw zjmDhq!fT^na~AEnKYAHg?B((3Wt2|?v8V^{jtdSr8$F!Hgvm5apxkZJl2BK)r3T}->wayd`1oR@o(hj0B9&aaoUwRD z!A3n$B*yClMg3^BMhYV+%tyQGMBZqwKvYCWp@bbv&xeRQT7y>jT}1onfX3pot|A9Y zS;pd?Jw)3rfWU`dqBXPxq(6vUsR=acC(0lMDh7xad^-UQ22s!qp-^rt&KV}kA$32D zq^TP*7~P{p{uEPHQaBzxk&5{EL#!x{rn?~s7!bNyf(T3t?x_@A0B^$HdJ$YVuo>S@ z6a`ZBDTG@LUYIG`Ok9&WWb-yd{=c${ zy}MbogAiSgMRMXE7CS{J$?wy_Y%C01l(^Bq&_S4wNw;M6gMoA)>Mr)9fn&tWkKC zmAEg>P{kBpWFx*}!E%^lFQ!?nVqk7Mh*i`a{&p5$<~ZDN6H^?>$H0MVKLx|xxte!~xyJDvDkP1A@o_I^!W;xZNP}M7CYx{gL8> zJVqyYL6mr=H5=_A5pUsAZR5lzxzu|~F`P{y`orthVt5t7M2ocIg|?|7 zt2a>$2k04DHqJC_V^W1U#Dz_*TPkkG$zruioJ4bWijl?tSFtayw^_WCRsh5x6k_oP zCsvA{`^958L!LWoroQgD*^tA}m<`$BoY|0XE}0Fv>x$Wslm9Rqvh#KELeA*-Z;LlN zv3jU_A^x4U^s0)##cWH$%U_Fka#=G!iW_ixn#YS7L$9a$Flz5`iHWpgq(tLfV?0n# zq1Xox^NeXr+5Dx*A7Bx{+Tup_V`gw_+tn!MFvodL)0hlS5}(#F!@1PL_A&Em7MNIz z*B3jY>-%H8DjSQNqDTFp?#EqY9&tYSq<2gwnpgk-H#7$5`R z&lbyK;J_vmZKsHNt6`%dwJ{zvGgdPL48m=scV|vLzUXZ#bW3rEV)(YK$=e(@nS5zo zOe`mZV}%yG{@{k9d?Ym#LhzbPGVxkp$!iN1W>aHH0xirg zKS`P;3)8)sxyR` zl+Y}V!8$_=R4s}fhB_RPP%I~#%6EbdDmR@`WSxY2c-J45`~;`Im!!bM`$@_AZ*SCJ zBV#}wNa0Ukp}61<=OysX@(&j2Ubx3q$!ZFx`Wg@cpbZ{%U9yRl75BR(sRw7-L2R6I zpZFOQ4Sq=cj6`c5OW;mBCeitsWWF66-Tqdxkz)Sx6&L54&yxAyE08l>02tdEu3aDk zB=BQzw`HS>z}OGX=xn$OyvvGr3T=puz5ne9|KaA!op4E-PMEGocInyN-BywMSR0ft zjg@>mGUmx*^C|R&pxhudr476%-FYk44LK=eDcTLH)yL+ut4n=Vu~UeGPU~WKQHTLR zr)k81a^<*BV(cahsQXP)?9y+qn+O44ElY`QPBTD4VGY`3j27U~tk`gxNg;#q=7q5# zZj=+Nwgx$vHxU(uv0i-MDNME?lJNGpSW6P#pAxIJfpF-o*hS>`s_-p*3q7;9i3kr>w#2BvD}ql!#2k@r!)@>Ub!~b2HS3lwF4i)?mJ?m z2zCA5*uErOdnneMg!@m#!lh)Vu;sZ}xUlsUKK6U;bt2ljE3s2)MyjuTd=0YPJZu13 ze>;|Dz3a;Zz@pW0KKS~bSZf+v&#WiyfO@`)?FM(Y#!}rxXGVZ~0pU?B_F*i|O0Ee- z#WvpEk76^ec}r2yU$JK>8juMs0Uv!4OLbg_AYjmomnE^d#Fee94xDc*SWK0CeQy3lm{Cgl#oi+$s$u4phL zAVA=w{M7<4e7I5E2O<_T09^8qI}VK!uWlK)p9f)T>$r_p5H{%$_Y|55b_^m*H`T zNoYMHZZ`?{N5wrM|Nm7I2d`C^;xTb?@LF{#4wl6oB;k8`Tr~-+lyN>J-$7km9Z|uY zgt+=N+p)f=AU!Y611oaka=%Lj7+|Uiu_h&P8%TI&R2*zdS&A(tumYW)$O<%aavZ!z zUW&UNrdCd(39`_aGdnCZbgBWPfMwj%!9ls@xYxbqN*`3HLk}M|Hl&q>teU z_GNkDniFxhCP7f_$vBV7m$5!IXX4%y!XGZi!MpmU_{e28oO6|RM#;6fT9WQ_BW^u3 zMtu0sxO*i3#s{o``H$i*+r#hZ*KE0_Z`ttKJGOlF2ey3XC$@YOo^&=*2)33sq_^>_ z*zRbCi_`^|x=IV^ZES);S*ry;c!#^x$D|+pu%5I&k<+q~bQa+|r->9MpiA-kR?;By z`$juy1*hi?lmV z?JlL-AxjD)D9lIu^h(&s)=NtB1O+I`>?G_!Z|PoF-eojVDBVaWOE$h9Eu~tR1{T>k zJw{q+0R$e#N@J?2!X0O^gj6TCn?SOc-Da< z=S$}h0{0h6XGa(>eAzec00(kyPDb@2Q!@kiPRs%I_={4}O zO8SUG0~iRvrvJ;xX(N5zgEvVVQ~RHDjdUy_Fl&pHW_uQ+z>{00RQFLag~#uZstAF5 zyQCD?{jfgVc#rf2h4$IPHG8E=gh12%QkvBzMhNKv=^xYt{x~F+5dvL~($54kk4bsd zKG5K#bUGoh`V_qr-Z?GpLQUYnIqA=YK*j~>GTJU+>qRNe9zh&P|Ymo{Z{3WyS=L zePllp0_BZl6qf_CPPnhJjJ_32Y9bp&2skyDQ9mmTZ6O;*vnm4`5R-4J`QmN?G7aer zm{UG)0%UNsbEVV=-w?>=Qa%NVu|SZ!Pj#xjrN%k*CslF~Di??dI}I!mYTq{0QOJ zzpb3<25s8QGYI#B0C`g))BF%QyjQx6bHd~y)DB!e=B_Jl+f7ayJganBFFBuvJJSLg zxSK!7e<1Dfb@DgF z1~(R1dtpU_{5%c&uQ2&|XOdh7eG>M`mhUChujb1~@FDC}ChtIWFCQbXrlEf0jJz*W z&X;>t?vQ)nwUgvV7n9!G&IL15JoFGq43HCu%I__bdl0@KmdTrv&~LRI-gR8YovY-% zNLXAg?@hwpzsmoh*tLcj{!ZPh9kJgwc?>OGL=X@l@R7?QxfdFGR&Im3?2tR5Ya=6_ zP%8Yy@KXkwW__}%R(_MFBVgzzN8j|q-UsAVFRZ^Hz{j9Ym&5dr!sqQ*u8V?|Ox29#>6RvalEa3p%bSIY z&E%(@g#uo;!yL|%{he;DVCD;*Z4{SCx|f~e8VUclXY;LeWb?_K*?cWs6@QZd+q)~| zG@@@9rs#*#{1gsoTT4F={Gq;rOtu)=QU8VtZe$5=q}Wg4JBXlTKwm+639n`fMgupR zD~=J$>szuSDOreOm{GmRh96NS1M?(Atz7xJy7{IsT;n(NbwdB zS#L>4)_^XJO(6Ij2LXHz;B0w>KwEg7y&RJ`|Ji7;5n;S(T|2Fqm9oK?>0c5 zds=$qqJxSMaA~~Zn8NM5;P9Z+iZVj%$XP`!4HEOTrJ8it2}M3o@KM=c3J<*Vl46JX zscG6BMK2ncH3MS+CR+#a-badH8j6hW7JX3oU|=`i+!Oe|QFzi!4hb9vIPx9|xA;uQ z38af9%JRgotdv=1PNnurcM47z;C>@?#DbngQ-Swfl4Qut&cF`^h9-` z%KGSBXzO77dzjLhX2dYV;N%FUy;&2JyDIajHSxKr%oC66t^D%Hzo4&jF-@ajy8!}M zJ5UKn1YnggQpr5>heav*M19@GN;25tiBcsqk4uy*Z`16rhnme)e-_CDeVEP1nno>_O8V4>>4#L&xN~#yaq%eZQaPeH0 zl4hA50J7QrUGuY*wbtm_5{Vm1EmYEME(fe)yuU~(qG)Ke<;&9X0|t! zeQBBDx;x4{HY};5Pn0KY>3Z!b1UU6@&>LkUN2=+^bL8G`?x4I>ck6+Z|N%t=MRStLjfKD)Ulx{~lp{ zQ9u(FA4!|*J@6o372N8;v?05Os{d?5qZ_O0eUDte7-|{F$uz0C3XYdE;tgq|f-h<_ z(T43*aOV&cJ=$L7$c}ONUZ5(QW{w65{W9WuqgUaoJxFwOK_ir&+`t>zzggynK1QgP zQ@GT~NL3^Jw6h9cQ`C_|>vt(JDk580Bh`$%d?ahv-5WpXp>n41@QOw&8Z!6dRZdm$ zv1Wj(4Ubocrwmm6Va=Pz-iEhvxQgP%ItDpgfe}*6-OyH%ie?+{sZFY`c#l}M7R(AT zxLBg<4K6^UC#9+?KQ=n0Naf*1p+ka>0Nok+OjXg0djK#D9h#%!z9Tt&nkognlE{1f zN7Z;bMhswM{JcW7hm{oVMbuar$VGB6ZRUTMjE!;B0+oOj52Gb2dzv-{3KKIQ;J{s0 ztCDTOznGVbJE~Rj(6bW72{x&czg=+{fZ^T^20@8clfNAS0fG$YZBgCivrc8)t%6&@ z7+2HmS6#Jbqmd_6eYog~-&LJyCLE+9l>7&sF;l6e%3PYkkx_xidsRAD{j85Fn)4zI%*jtGsv|p0VGU26%yGD8six-O zW366FGtmYPvFP9pwP01Dy_(XFvB~{7DMS!d-b?L_s(m#-;*NdPSIi>$4Oah6^Dy)k zrVV=H=g7yu3|A*}61EqbB&-pc1>7fA_onLo3I7=%R5o~uLj9-7(a=4)8m_rms`kMf zG-}#r$O0n(w#DuA>P(LR`UDeyJj!Smpl`C;k7@`f0z_It6^}C2ZKz$FL0FKjzHV}D z^d`%sg57y)s!#L=o>U9*;4<}0)5HP=k2hg9o}li`bx?_u)D%+;M9|Ts&1z>HKUqDW zn{0SaSJO;3zF-B;P@i*V=N9!Bsk?A(M!P~ij%z6IR;kHq9hveTTc@U)ahPhOnSn!D zZrSi^jrtHb$92%p!^z2j-ntzv61PJ*S@Y?ei^s>YO^B zqGrRhh+w$l&b@=!Vsv#Xi;a8>(A9ua04y0mmQXP$^ zH-IlBe0r!3rEEwn0`je=F2{YJfH}BH0jmGl)L%Y%rgo=T6vBb8)HBF{%zTaN{0BAF z*)IT5;<(T1^V|s5+frkiW7M7kTukkO0{&g!Nq!qqFQ|=i z%yf-`#)JO>lk%hHsRhd`WwvI?_ehHuS8B%pFHk!FlV&wXX~ujF&1=bj$as2zX2bsi zuizz`a~v=4WiR%oau0{?(1T5Z;sV6BE`1?V4uSydYC|AM~8{i0dV>8tNX z4b439AG{`R(iqsrU4u2@G<_=rm~O3WiqbD@Y)}XO2oHRFr$*z0+OKcrg=H5sBdG-^ zuoz$i?AT6c!!3=JZSlC#BMscD&h$zz9&2Lk*{Jl5=B*VQ)qm8$asrb)ypLf=d=2FLY)AqM!Q?K?mr)Kok z{=%gO4mKkk9I7p_W%G^@oAch5Xa{h(-Idx{j^t{Mb_+*6BVG&F(lQ*v616ZjVWRJh z+WGKFWzr9?D$tG|`aS&$DjyF+L-*@iTYUVWwu2XIE0?R-J7<8!0NayO_w>73 zla=)MwSzgSYoBVJIAvXXseQ>Q;MNOzQ64LD!XYyXm32w=G%C z|0LJ#pqXMbtweQ9=ZoMnRA2l-quXf5C>@1sbRMYut;PpGOV%xd zSe;q(<8@6O*;G`a+srwce2xxr_OM>0yUwM)TBhs9rS|z*2j6jE{LQ<{4EA)Lu9|CX z)mwBmoQCrD>hfu3#Y8O_s9V~z9+>~TZqoOtpgmr6QP-Mlau;rxS+D<7SIJQoJ=Vo@ zK5+amT`$fD{NCwCvT|?Upby|Q@E2ci(!fDmy-A%5?Dd6QhSqNSV9w!^J@gJ7?1TpT zwp{8yAAKCB*^Ykt`P7~>W39#;**azW;MC^&=C-Wov}vK|Jm+kCeK%^g5o|oKlYR$B zG(lh%wR@<3C8wajyXoQ6HH1DAIL?Qi1Zo<+CHgEqP_)swg|S)R;l$; zOV$}0>hK0z@6>^wl}~`W(p%#rh)7O1e>cCrX!9BHk4J9^PDXVfFyjbDy6T za-OQExvPMgx-U3p@J9Yq47RI0r|D^)zIg}q22?sr&qsFhU{Ab%y1prmrp{GvZis2DctfBh~h_crM(X=aRIpz-MGF)(>!cOK}Ek#N&feOo&S|9GRnSGW!r{souuGRbFu8D@ZfXJ%)VV0pj#cCW&kb&D)V6Y`h z-&bbHht`g}jx|iDbclE~eEwZnRdQ-!hfe+}aK&hf;hLL?>g0t6stczWT{6Itp9PLs zzQoYq0eG&cG8`tn(>59A*P~M?)8G1`+9QT4-fs3IA?@vp+n{Sl4Y?GEWE{XS4m76f z{x2DHIP!#{GgYS#g%K3yqp1P#tkU6(fo9VZ?~-8(yN6`bSwof;YTsq68*;vA*!%6e zz<_m}s^MHR%)%8{4Y2mYR9|u3K(h}Ws-J}|ZW<=hERsUuTr@ly6sgQl6yj+Q3`LE= zKbWqqv2FZEn(2%Q(;3$}#^+fx!eNPfd}9YTI=)dn%yfvz_(PlcS{pWcA~4<$=20Z| zlOW!gH3@DT5xeJ z6q88e%ed5gviM>yHAfk5#}%8diHDEcGkMn~#FxPwpVZbZEB-0WV3}xfd?PNJG$tPQ z4Kt~4$HgD#5YA7EAIen`GCdwnVK5mcqWH6(-|krmG-*W63-2~Usi))lXu4OT2ky8o z{-W7L4UfjprRu;;z!-qHa)iUr$B*R{eBye%GbjDy2l3aq3|F7VH{_!BFXLBnBJX`2 zU&|Gn#Y-^R#hRaBvdbLX1e0AhJ0#?D2;E&1Y&Zn5M}mY)-CaMyWS1d62{$9}=!{dYC;d!LcQs4gb+!xgk+b!W`Z+b`3owH{k#Uta&0{ajmU2 zveRe#;qttMTNLAC#f_EC(WgnpP0_QpdT*54%GMdJ%ujGbXUF@9(Yyc=vpDaKW|bz) zN0SGtoN@QE1P!0}2rU_#@aOkXN3Of!r1TSiqZ$nxpFp#HuXbEQ8&o&H*c&aLmCzU! z%r5rE(!;0XAw!wy|Q5ZpdO@0?n}V55VmC38fUyhrWUdt9+af!SVt*qH7?`i_9xiSdoTEb^8y6R@aPfl#=h-V4E+p^K{w+TCWY?S|=L<8pY z-SN2(3G2DkS)UR%u+h!p#DyG|wPoT5t}=JK#Njm0{aV;9(#Et?)qO)Ex|&` zr{irg3`xkIRcjaj#L*6w?=9VNUAsgFPOFbPCJyGbIv_alH!ihNSYlUBQ^r1toGNSj zCz>?rIw)~GS8DaJ#Mhi6s-qKsr1@~}SBhwipke!=zch(+&|Yo!0MyRDxGnmiO{AFx zGbbdUdKul2|B}1*=vmu>U}U3DO#F7|#Y^;wZD}k6Ap; zXN{n26jJR>gCXLqp$&eKlvr!YTZ=ZQCGPoNw0hV-BXKFs1bk)MiBbkPlPM9*5ca_65&fFYjNeX#0m=t+rCI#L4Mc0NgQPh zp^#^+BVluU;{1ApW9HKk6=*-61|3J(hGY#c^r0X{gnsc{EQYhV!8 zxu*D`11*d+O#%Q)*kRwYl~D+{g#702hT=OKH_?eR7T0w$zMzz3EG`H(F127ev=24X z++PjyjKx`D#;Ft>Fcg+pvenP+W~BMF2vhx|?#2%kUW=H*3ws%Nb1F#fW2D)2!NBb3 zYg|vE0+xgSK;veP!=1rKnwc)cA!w-a88wFsk;dOR4(o*UdUzjg^ru>BNHL~;nT|D- z8y7nA7OFwD8RozvV6S^9OmJOf zg)-dvxG|p3X%wwF4$0*|8GZ1dXN*+)OTI$4sXRY~k01PQyvWf%d&MZ`l-1==;~Xya z);(i?PN=SrjZ_=!nQH4QUNu5hosxaA$4g_Si^)xPStb=xdjD`_!n9>Xxy4WF=fc{j zOT8qMEu9-A!FmB>)`Q+jCMU}EO#&xkQX?8Aedc5x*enTNpE9ZX)@Fq8wn?zez@)xv zm-L2H(dVF~SDaq>;Ysk-UM9nth@>1YLu^lTYRle9lkC_GwS&#LvxX+Y;{sDmI6NsO zh>e~anPl?bm7|j;aaHu1khFqkqaUOC#ZB(`qV=7Uz46?cNt4W~k64n_hN6X%&Tlv} zVe%Q(+pS0va;i`H)huAo8lw8@7-*^QaMA)!*410hs!!T(R((;eS@r0E8NqnStoqhR z%&HGOWmbLo?`9=iUr0*fR6qI;b86)EBv}7qlzivD8F%YLv+DC7C*^Xg|Lm7+QvKN` z$%UNil`WHNzDM=8ctNLRwY#Ymzv-CFwc?(=le>P8{%sKe*C+Z%{UVdyP>G@l6m>h9 z=AAl0$AJEh^ZLSj$(h`k^YKCQ9}KuVe*8Qcwtq9$s(q2HVkee3=)GBKpFbq;0;P5F zaK|StQoJ~k|K_Je(5xN+sbL7_!Pj{E*j)q9Hu<-!bWi!NxqU@sl`ZPI@~$;H_G^h9 zN`KO!HFESwF;Yy8dksm6M(;gRZqk+=R8!)Ei@j3b(F`eU?F~|BRtTZ&DE0wC=94nQ z4t4q?-wo|*ma>ITl1VtQMM?)sAtqt()+rY)SPqu$QWn#4P_$2>ddm;Wn1m}krEKRo zXo6^UItk<86q<(=#Blf=nsS)qa4{l<=1GNt3FwlNMs16@?kSfz4t;y2ETXl=qTVS~ zpZj72(DhB(#&PI0Acf)>H>=Lk1F39*frE!7>yG;2DHNv)ag)fD#xz4EBmFyJ%1ciA z&*GHTv=-1vQif76>d2x+3YY^FCit(9@V_c_rh~C zQ~FW3H3qOn?HP08+9fGX{MqP>+LT*-HhS++3Ou)wE)Z`ylCq17jz5<2#+pr?aXJOw z)-f40XHy2#%&tHkB4lg_OFFx*rrMy6ol-q;^3@cj3z4lJs{bg(9o5yO_+Y2UDd`ka z5|95#I#Ck@buBof2bp|;8cxI2($(#y0^h-6VD{*kDNnM9Vq)xSG1u)3W zDhO4WRq#-mTEj`WQJuP#ixz9m83OgGi@4MqhSc_4ZH-e>4|1umQd41a#pvirx;eTq zGc}4rN4QBr>V7L$J?~3W$5N;VZzxawoufHtyjdw{r=(8eQqwBT2JSmM^#n)0IWnid zo0BS_X)PnJrm9eR4`>s6B`&`B(UR21Tqp3PDz!O6qxyc4@2Yu~B~SG*hGap2g7w%m z*b>jwRy8ecImKHXJaSgrNjugU`h{s!AHe~L(Rj|%v^r;&O2LLSQ>Q#|lbQCLU(LGR zUX#|5)7QHlY4oez5DwwAq|)YtEWAGx2w`Zo6MF zYtiOXnwCQ-x?!d>_)jyPcel)1#COs#S8>@rbB2x&(thGnFFZ^;!Bvc(rx|FLdKvGp zR;Dz>yWggDV-=42TcpGOSu)Iaw@m+mx*ZT$1h6)^EkAuUr`TPN=_Vsgb51uocsJK{ z&cSQk)5Ex|0~(~8Y|y(=y2%Fh{nAY~7~V47WCKOp^aGs7g$T?wScjTx_%$ruzOq;! zygoYpFhxT`48o(|vGBN_vO|ByUPfFaCCwW2oda z#TzUy1_1u759@MjlIHn-2%Yz2l$3e2E(6+oLt(*qHO5Gt+l+K`M~;toy2GpO4D z0~8yP@fWpi_H@aZO<0J#XV9!zd=Y1QkBs&-lSRfu0tRHf0S{r{<#mhB$fH&*Ai3i% zF&Rr-z~QbMGCJ8o=#-vu+ZMtX`5E0w7&I~ifzbHK=!}b`X)K?Z0Y`VX;*jYX#rCxR z6)-2m8n>I9an~$JtCbm4*LE;UW#DG5&iKt4h{fWJc0`b|dopTBOX_+oqrMx2(N|eP z9$m|LMBBEJ-_#5~3h9~ai&O7ryyctBQ2Zo=>PQvCo?x!>ev#qC+q&5&^9@nDhh3%< zk)erurojfn3Er9WTJUM2gjT25sdWh8q)fkvT=#dd!pjC_?%{({bt5uqHW>h#7Eg=Hd`{CNF@-%u znL9u|s4gaxX8n@Eu#C<0qg1UH*GMz734wNs%&Bx?GIm^qAE4Z`HiTA~K8v`m^&lCi_U^h^h80{1eR_Je-S$z1yF z!V|yN;s?2zbf>6D;h6=QFNp@m6=zn_5;#zjIgqAj2KIf;`;YPBF_~g}V8U!bbeNv` zl(Kd8#jvIMiY<87(7Bmub`b7dmf6w?!dumuvNjO*x|Dg9r2E~<9AOP%#{JA#(rF!i zoLL`qf&YA(S&xM8o@XY~O#4Bu9?11grW<~CKIVSNaP56+rNgt;*^tA+?WN67we zE6#%N>Mp|WvaDM~_}8ke$t2%sZPqE0E=tT|l(Q-|Yb)vT)tOntXwD0OGCCmTxUBlj z(NG+gm!%+GFJtAOOR{2U=2HX{1LhMV@UW>_%Lw7We#~l5Lccj#U5G%^`B{~e*6V3L}C&9WKlP1|HM)Z;p_)T;#98)&KH-JP>%n7QhDWDlg}id}kV#}RWI2eCBW zB3YUP#VpNv((LmT<|m3^W^gZf)$GRTXD8YPp{}}Y>Z#<_Q~3_)+2b;69O?0FPzq=Glo3){tK$MSg9Seuaj#F}@6W%*NTHtqIp#1Pz5eb)}JPRsVDvdGbV zEh;;l?TYo8*^@1KNAUBU?2z^}BP2oEwlte&ll06cIZ3A_IjS(g%4rL>+CipT=g;AfuwJ;$~i&ukK*UhEWDeg_9G6XK#ARRJ-SR;ME}?W zT1>{*opKUw!4iY&<RRzHX8n^8>R8s8lFMC1_^&^Tuqo$qGje7~G~6kdRV zz#UR|e)F6qv~}-lk+YX#n*!d@CZ{cFvsc>ZtfQ^FW5*l;Mcu@cvawB2&U7L?66Da# z?HP=FAvtGgI#8ytDgp`v0YTTC&9oYr)-9(QH34zYoI`|w-w*Tz3i{;Spm06b1fTom zz$ZRoNE?{5l2!vd2IV+XYanH4&O$=KF_PX16(e$1QxjMlowJM(7#@>Da|tTrN6RHS zd#O#}AZ5Fsv2uD7)G2a)qb3lc&Z#6CsMF@GrS$=Sea;eU6BrV5iU@(5MtTi&NX|J% zt$~H9Ico`lK^gQ;I6pJz70vV)97v27wX(8B5#9J6SWuXA+ZCuV&z~J9=NzJ#ACRWa zU^?T;6*=3jV8rh~C+7kg`TghS+$W*^{2W(%2z^)Pz^B@e;Jd4G>PWa{O%6Qk9l_() z=KMiI{kj|@49>W2GmLK#ZpArwNw{Qd&IS?+x97|xGmP7{IWeF?-0im{x`QGX1{eM!RcopW2zJkAl$IG|_lHZ$MPeRGRwkq{s` zp8Q+Qe8fqe zn@DV&7oW@6T$q>}MQph;J@*yCU67Oef`TTlEy`W!2DX6R{wpqC%;zh0~_jmI9?%Z4tBFOv2tRl`XWfie&d9E92EOqNyMa-{e z6_K)$RYaFxSw;BNl?el7(exz~BTmS6?00#KIgsxS+yoW^0XZ7>)h~FeM%A;Dh0K6FF z-zCQjrNrdf;$6-1wwhgjN}Id}G)?p?OlLg2L*5%gc|c&E4bgmLSYBTeR(8u{TsX5w z9vtXCf}8ctYeB++gYu?RxG>_y!dxk2xR|%D~SF7Q~~?{;i2sR1G=&Qf7+e>|D2xe z|Kob+pC#ox4P^iSWDxs*G=%+s?6CY}X&Y%fC+sh|ZdPUrN(T zDu1&n#FB*kEfgM%!Lt6uo|?4$pNXQJa`FR7*rPCi2?ZexCRy$NU8an$U`=_Zk~QV= zIr*Q7DUVm@|3H|z)v&6p!>lR`wz8`1vOPbZsB+of{0|O< zAq8`<46OA&`u4J9-5J%jhKp#*W*2*6@tORUR=k_|=)3vPDSD{?NG^csP(X7K;Qv8u z%6E|I!Cs7XKCl3uln$YfAq6zYJWZ-%(CSqdwYS6Su!4sa-IH0o{_KUHf?fqToq!ng z7~5`m0itL$3?>Fkz5pa_r77@sg|KmEfyx2GvXX*BJO~Gr6{OfeSTm0OT{fYh4hqW?)a|u^w+n$7hbZf-~&bXji4NK z-BS~6QR80=-O$Q_0tMQ9u>fAitfqe%Q+uc&huxc-u&*H53e~=Q1XcanlqCO20mE;uSBomAI=o~2w3>{a|LrKW-kc9l;-_E?EQoBR|@2= zK#yr?7atY8r0_~Jt}{-0TF}-J7*@V7P=dzstIq{fNjS!;(36Be*%lhf|E;_VcY&kg zeLjWI3a{ZQ4GYt4ApF>j{e7TCVICiTcW+-9;SFJa-$J;s>>3{0zwjIhn++^nO#c7d z@WLd58xUO>O|!5F8tISxvx_`1Tvp>}%P9iIOA9^F;&p{SI9*rx9x6b|iG?&T^UV0< zG9320nq|hqAMJp0Xm(*OQNf#n!V5%{Q{{y_Ncyx%h3AQghAD-A0wFwTb|K8Mui^Uh z3;PijDV7xuBjwhuE__6@ulTFBwuX0+y+*-xb0NhH2bp^`wD?$&Epn@AF>l%xP# z?=M_q!E)Gru#nk8ywKK`ji$UU9L!3H55F&5V$G%&@rvMl zAu&A`S{5DRQrp`V9pj>QF6In3T#NQ|xVU~%i33}#Z?hse&B!1$Xi)@*y_o31mPLVF z2D_jlICaRRo(?V=%i$(On2UAkVlMWeYtbf-@A|$)R5#dyIfo+U_Pbtq_uwL1S5}o* zq(u!VR7qMW3x;~^?dgas6h(h>QjIqjnKZjNv&f{`57|YZ*p`GH3X6tw(WKI%7M$2i zMi<@SQY$7DDePIjSX7vmac-7b8DlDo;3jWIapJ}13~iR0GhA3!B;#=Vt}!dwd2P`{ zt_u6=qBfkx`qUI1=29DPFQWQnGoy^*#|vH1c&|j?nter5KC9jI!$tR~wMzi;?PEn- zELl<4*A+=Qp?rQf>p1uivyP`43x{)1=gZYe%c$T0TTeDGF=i;|^Y}C-F_`Vk#RmBw>xeS+;#f@#)RQvd1 zI6zCZgfAr&L!Zh-CmM_4cq)ZgJx=EGV%@H9QHYH>-gdw z6s95GjKQ0!PSBm|Q0#}{-uo0DZw^kLQapl>Mx?@p(9A8i?JA0?*GQP=_2p`N<_3MH zdF`oyZ`}Skt5`)L>hoQ9o1+;UOTsa%>s_X4Stf)LmGh|@7AcS6u6?yNdX|&zhI%h8 zrg+Fh3=IOqw509Jir3Nf{f6~5^V65@C3i{bKI zCc17zu@y~=H}M?I+-yI#rg%9`(>J61xhUd&yx16J@EqXl22T*s%9l{GM>pWvPB6@rkLoU zRwdW?Y_xO75(O7sBPf|la}v-bQ*h<@Qn)g{Ps#Z2(F}uv0ELe}y)b%V$NnWgcC2Q) zM3$s;BJL2CY~w^6uPj;4nYpXB1a2E*MC_Ybk^(!xiDvd?mzeZ5Ew_Znr7j*>GTnyF z&~su5ocL$*+DoJ(xeV^JN`AIwQ;*LtNu_xK&nO96{e9lOFSqca$D2yN zt8=PY+{p$x))eYdyUisu-&Q7FCj(Nwtt1rf-de)Pku@bZX&UnX1x0h5+601uPg85q z)tTKT2Pt|16Al6#Dbqhi*On;0S1cAO`L~iS7C^1V!ICxKOAVJFD)IT>p%s0!WC5Y| z*YT1S-%E?p*oc!Qf6(-l|Ec@F_~_Y^pK1DouTW6Q?}Z|4IA8L@6(nR<-cs+CSlROK z;jCvRmuw&$_ND}O5Z=Q_-j!Uof^g;M61dR+9?s>J!iE0#a0|=Qk0f6+hf=t({~mtg zSX%A~VVXDlyR&a;hzI;m?^w!|>lwh7dm31}gXFIYE!(5n1|#W;y>qh*K_RF2d$$yn;oOvtQN2q$d z@;Be^)3}F4c_?7uT-Wkfju56aDt}`Ip`mH{Wg7^4w=H*dfKV7x?rII8u5&rK&kEeG zclmFY5I*Z$ZcFmN9auh?gnfsVZzlQP3(Fsp|KE)%w;<^cq~#x&-^y~>%(??zy6fN8~uQB@2WmGdH+YYfE+ve3n7rKmUj{Lfg8j8A- zd`-Jqt?e;DP4w$y^m*8Ar9qRl^ys)t_ho#%{B_a4;{ zjomk|DQfq_C||U+_b4w^v3FiKbf))c_irzY3;T>37v>)xsMYEq< z*N5uE1GK>cHH-MKxfn$Ggi(|2{X?`mU3i!vJXkHz2*Pz5wE?I_1Vuy`G{HgPK^hkE zUvn{thbjnSa8OvdP8S*+st*blga)a@BMbrq`Jo4W7y<<>;=kr%5Q7(u+G+0}t_{^` z0z<>X!Zlh!s79dH>jY}GUM&a?4Gb3O12{eYTP_B%?&nd1)$SFeL%25t>V{HhBp?^^B#Sp*ka{#lrd@I0KYJ8Sa7f)JR&qaEKHzR z2WbpiL6AWk6c7{yVHoD+%}KNJ3pEI|>R?THKnTbctkVL4P`yE|18)h_hXmnXpQbM2 z`Dw#-5y1vMU>Sl0I?!l%U`Rknh#@S{pp6iOBgo~6tKUq#!1Mo?Y6H;c8Dob1TeXPy z>zn{If94o_KXA0*FpVHI0Pdd=gldC;YPdEeSOXG;h6L-;jYFfM>i3m0t43h~fne~U zfN+6cpbZUC2ded&@bECbE?g5FfNpG_<%P>1UZ~b!XttLf+K*uK3uKQgof(0 zg1|s+kUl_wdv&ec#rxNa)fSb3jYIx>ooXu-h^EaQ8+v{ijPvZ{g-N`!|85*ZQ>b%bWUO-N@IHNqDF4MVL%+`bFFF%6mBS#k z`QvCW|9_E6fNJl~ZrfM~{;v*<2-5|HfUPv@0CjLsNGLRsu%HmF8uQX;gXrL~T0>BH zh%PuRR1l~S3x^w(BJ@Ea;p#v^SfCD_syD_H?}1N%fg#%9Fnt*3p3nsZgR=#O2IxZtFsy`!hiVNG5ikfCf^`B=N`tW`_Y{PO z1#3bAH2NTDSkU=IfFOa|01#UjrqMxBDSdj9_+lpeWt`9JKv2bdI9 z+V{Opk|aq$;`D$4<4~cytI7m!Fzq6!>#A#_x`JU zvbM^0>=aNUEIX&zmc{%iYPRbanB$sH*>1U5=$2jZts>hk%fD0L6}$Y(MNweb6)nNm zG+=r6Y1hgDXBTaoJyJZ82=qR?ru8ufY7#9Njd1OEDEgZMliOsrZIzn<^V|!QvlwFiWxgylXhBQL#+a zxKcUGX$ESpoh$e{Q_(Nvs5WOSj;D(Acbzvdm~wFawV9gap2Hid)ss7RqY3u=H>T~n zr&2M!7$r`Tt=}~YdQMk#Tk)h4B*%!~Mm`3paLo=CIDIJRY zpPQ;?o#YP1vd`wJ;2hADOw(S7V3w_LK!bIwqly>M^2vENU2S1Lmrip_jI;@&npJ@ z>IP<-!n2cGNM7SDyNYk3|64To*+>eGT{+Vj$m znpLzM&GJ<($L?FiROfcAyzS|3F(1r%cb>GulKT_(*y(C*Vt1@;Vv>e7>}z(OU9w=K zLgmyYmgMML7=%NxL43zi6dUgYwikC%P%WG=YQa{TUK9Oe)i7UB zoq~>bivr2ISg&&Sn#L{MfM)-djoyI_dHBNyP&c(YHq9}CObw%=JJ{Sf>oI%%&z3O`HlTTW42o%1z-U`T4v(hd2fnzg;wOp;)}LoU7Z0Vxcfuc|TGSvuMehTF`Z0DHc&T zTpLRaM*#{ydSk%}A~%Q6u+G~>j2cFWW?_Z0{D*jl3M$pH1spW8{mj&NVw2o~G>-arYG+8zW3{O;ZoUkeMT0zOn6)gjcFYlwoVf&gab+HNQ6R zY51zeLLX^{II;|@V5qqwJ62KE&4Sc1@G_#u(~qc4=@s*R>LNv)IDUHftdA0(H;M{QK!-WQ&g<9) zx8!+DGP4+5x$|^pmf{xtf~r|Mu9+O#zlkH()Nl+G3Jf)|Oj&|=N?F68^iWq+Z2v-$ z{aI6S&bcasS$YwfbJ6UIG^>=J#etX2RI|w)=i~~ZirK%JHmHVop&N$f8LpiAdBtMW z!CUS*rswiHuJooc5ZPf_>v2!x;LX{p^kONfv5Ja1Trd^sW*ZPJeXHR*-U-jeVo>Xh z9FswAGEF^$(Vg>L%Z|3Lg>sn~n%J?J^MKH5Q~LEH!|V88Pv3PfrB1EbwG7`k@fn&- zax@;6JyVepiX2usUa-h?4;?l`6FuR<5!&z715R>atJqidH%jt1f$D1XY)P)n#9G*;ifmVZ5N~vM)zE zsxJGg%f9Neue$8F5h$p-?5i&OZ6VzMf9bNf#Ox#IcMd-J-Sk5`Wfn(yH)l?nUi-cC zZj=Kq?JS;J`+Bz-Z_J#|-(UEf5xg<;!l@craA(|oVf&{tuZxNGGY`!Iwg-ZyB7Ro@QhF6ONUWNaPVfKyqFp8YqPuwb(E4FDg@R=(1>hF1$-po<1v#?_2o;bxLJkK+F>m`s5M>5}7o1<3d0(am2$V}^iUA~9 z0>6r&Jw4sdYXuEhwjXQ>>Q@pCPwGw+&m7$d;4Ha2VISONh<_bfzuR83uOrzLW)`Z*;B0zo6Z4@^b172>E)QLzA=Xk<-9LGG0Ip|uXG zC67bRYu!5eIcd5K!5#hd%>LPw8xoo4SB#LSb`?-Mq%IYiMmfOM79c#Pm=E4SEfg4x zVTz2edJf=Q#wj>B6;yeSU@v%l3xEdk@OVm*#FNh(WMU{xB1SiEdvF-!Z-HU)9Km>K znNlgeD&ods{ZZMJUZW{B#;Na_1+!TIb?Fu*<`3{Q&|wwmK6wY=oGmW~UJzv^Gr?tg z>VY z!k)TDtx4`r%pv?OesV_rE?HNXm;n=pX@_XLMf{gX2=c}<3LcpIV3<UOl;7BVX+l2o;kE{0p@l*R}pg-O^rdtDQ_misiDmmw z-z7wk)Z}?DzDw332=yE|CemLdXh{Wp1zddjZ-BFAb@E$yBwis;E*&=wSQo^5&e2tq zl~s?hBU!);V8h9PWLU5hfFx9PwU`I=Zs)+91mv8&lc`PaFQ;$CG0g+U|o`pcBY;uS30-w^MTohDs&!(9pfPtxK1%+JR zHazfEATP-wa)4&T>t5i}7=9p%b+Th5f>ffb7<@8y1Mni32f!0tcZ7)cZOgM=61xBt z0_SIM1!HKDjiAUkX2d|vlnx`f3qMWjP`XzQ@uH~Ty%j#cs1Fqzu3| zppuk_<+Q*=vS~LJ0*6B+R-x!>t_3g=bWVg_1nceqt0jXBK0kRwxuX;=jR zsCuLVszoiHU#yfG@H3JXcmWnTEn8J{WKXdF0`mq4Ei-v)l6w?K95h`^>o1yKN8ia_ zr7D$s6<|}e9s{L-A!Mm!9pnp!0wNp8C>Zb9w2-)Bsi>p*uny$W+5wxB!UWWeB|9fr z$6EHP6F8+t za_h38OV9*UI+bZzS~jsu0C97kV-=ww0UItW5)V%IG%QyDmN_1tLCy>Fvoy1B0671| z4nt)AI{0EV3R3tSR(PPAOcezLtP`An-wHs`*n#97kryW?o{gak0Mz3bF3B>ijKL9E znX{SPr6G8yJQ;bk zKrhz+RIr-7505-E%vW?^vC`nw`^H_miu

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'cancel_transfer', 'getting_spare_card', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'lost_or_stolen_phone'}
+Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}

    Let’s print the first example in the train set.

    @@ -880,43 +880,43 @@

    2. Load and format the text dataset
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    @@ -1213,7 +1213,7 @@

    4. Train a more robust model from noisy labels -{"state": {"7c73348968774ea2bfc42fc202242f70": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6f4ca3b8054843dca836efad3447e526": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c8d93fe968f24d46b815055f71fc9486": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7c73348968774ea2bfc42fc202242f70", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6f4ca3b8054843dca836efad3447e526", "tabbable": null, "tooltip": null, "value": 391.0}}, "48337b46095146c2b808c53bea2653e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "570178b2084e4c65a1aeb887ed8b304b": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0b437892039948e8abb66dc411c2e04f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_48337b46095146c2b808c53bea2653e1", "placeholder": "\u200b", "style": "IPY_MODEL_570178b2084e4c65a1aeb887ed8b304b", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "bc553302eb144ee1affc907e042d6c5c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "45ccc4a60a034745b926bb540d0e915a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "22f06b02a2e546d5b45f1f56ce9f0644": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_bc553302eb144ee1affc907e042d6c5c", "placeholder": "\u200b", "style": "IPY_MODEL_45ccc4a60a034745b926bb540d0e915a", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200764.3kB/s]"}}, "8a4fde9243d3412685287673ee2eb1b4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "81cceb7356c54fe6917cce0213b9538e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0b437892039948e8abb66dc411c2e04f", "IPY_MODEL_c8d93fe968f24d46b815055f71fc9486", "IPY_MODEL_22f06b02a2e546d5b45f1f56ce9f0644"], "layout": "IPY_MODEL_8a4fde9243d3412685287673ee2eb1b4", "tabbable": null, "tooltip": null}}, "8d71fa197d2545bba0a3256f24725cae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2b8cf285d37641e78f819e258a1471d5": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "f04b2d469897481ea059b74c219803ca": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8d71fa197d2545bba0a3256f24725cae", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2b8cf285d37641e78f819e258a1471d5", "tabbable": null, "tooltip": null, "value": 2211.0}}, "0654f51f54c744d98029a5ea15c37e7c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3fe3546160e64f05886cf998dbf3d44d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4873b50be2dc4a2b99b71c0258d7bd8a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0654f51f54c744d98029a5ea15c37e7c", "placeholder": "\u200b", "style": "IPY_MODEL_3fe3546160e64f05886cf998dbf3d44d", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "58f565f2791d42f3bec1f5f720b8cd85": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a8ddb64ad7f44965bb03d2cdb1a58784": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "29ee7f74a6bf4c2a822d2a7326b68385": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_58f565f2791d42f3bec1f5f720b8cd85", "placeholder": "\u200b", "style": "IPY_MODEL_a8ddb64ad7f44965bb03d2cdb1a58784", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007374kB/s]"}}, "087e012a31e74812b07d928968bc3c5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "251f05f2d534421ea834728e6e80c34a": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_4873b50be2dc4a2b99b71c0258d7bd8a", "IPY_MODEL_f04b2d469897481ea059b74c219803ca", "IPY_MODEL_29ee7f74a6bf4c2a822d2a7326b68385"], "layout": "IPY_MODEL_087e012a31e74812b07d928968bc3c5e", "tabbable": null, "tooltip": null}}, "c884cea631794039964063852dcfcb74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c04125b9dc5c46b298392e2e44751ffe": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b30eb88d1f0a44c2a212fbce52fe66f6": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c884cea631794039964063852dcfcb74", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c04125b9dc5c46b298392e2e44751ffe", "tabbable": null, "tooltip": null, "value": 665.0}}, "a1733c6fe59a40a9973bb2adf659b2f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "39a6f9b8d9474c6896d33cd8e0822b9f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8fabaa55f275451ab3cef6d7deb821c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_a1733c6fe59a40a9973bb2adf659b2f9", "placeholder": "\u200b", "style": "IPY_MODEL_39a6f9b8d9474c6896d33cd8e0822b9f", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "d0aa47a8a00543e6ad72c388f9080719": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3580f78a814e469999540909f5d7c17d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d6b4a4277cca46f3965f64c58a0ce1c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d0aa47a8a00543e6ad72c388f9080719", "placeholder": "\u200b", "style": "IPY_MODEL_3580f78a814e469999540909f5d7c17d", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u200798.0kB/s]"}}, "751cbc2ed7634303a0e36ef0a4409514": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5cdd87483a4b48dbaf9539299fb3282b": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_8fabaa55f275451ab3cef6d7deb821c7", "IPY_MODEL_b30eb88d1f0a44c2a212fbce52fe66f6", "IPY_MODEL_d6b4a4277cca46f3965f64c58a0ce1c5"], "layout": "IPY_MODEL_751cbc2ed7634303a0e36ef0a4409514", "tabbable": null, "tooltip": null}}, "f35cae2ff4de4ac09ee0eef4156fb86a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "43d7d96a652f4111bce4b6480fcb9957": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "41cfe9c4d1cd4fea9b7df6ff9928999b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f35cae2ff4de4ac09ee0eef4156fb86a", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_43d7d96a652f4111bce4b6480fcb9957", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "08e9ec5f60494f048e5d58bb8dfa2782": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c1c1bc732ed24cb9ac28092022314462": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0a9075cf3c864a86b308b079f5ee7873": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_08e9ec5f60494f048e5d58bb8dfa2782", "placeholder": "\u200b", "style": "IPY_MODEL_c1c1bc732ed24cb9ac28092022314462", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "866c3e4cfe8c447384b363599afff30d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7b40009d218e4df1bf1bf7ab2887b25f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "91e70d4791c84851a1b91fa5e7c57cd6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_866c3e4cfe8c447384b363599afff30d", "placeholder": "\u200b", "style": "IPY_MODEL_7b40009d218e4df1bf1bf7ab2887b25f", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007167MB/s]"}}, "4710be403bdb45689713c7fb0b2c8b36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3278dd636e23462a89f4f6e23e81cf14": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0a9075cf3c864a86b308b079f5ee7873", "IPY_MODEL_41cfe9c4d1cd4fea9b7df6ff9928999b", "IPY_MODEL_91e70d4791c84851a1b91fa5e7c57cd6"], "layout": "IPY_MODEL_4710be403bdb45689713c7fb0b2c8b36", "tabbable": null, "tooltip": null}}, "9ac89da8e95c4b04ae98998044928144": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a088ce5c5e4a4a50a697175e3012eeb2": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "0c7f0a99520d4d0f86d594a5fb2f6cab": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9ac89da8e95c4b04ae98998044928144", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a088ce5c5e4a4a50a697175e3012eeb2", "tabbable": null, "tooltip": null, "value": 466062.0}}, "4148422cf74744c5bf382eb313e0a314": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0f56503d7ab04c1eaaf800c590ddc4de": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "af29b9c29f12466fbbc40d495ae3db5c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4148422cf74744c5bf382eb313e0a314", "placeholder": "\u200b", "style": "IPY_MODEL_0f56503d7ab04c1eaaf800c590ddc4de", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "f2a5b3c6a48140f7bfb68bf54f205281": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6b43fcc00af140d0ab061913b16a022a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "561c63a2b65b4116a28fb3b4ac4f32e6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f2a5b3c6a48140f7bfb68bf54f205281", "placeholder": "\u200b", "style": "IPY_MODEL_6b43fcc00af140d0ab061913b16a022a", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u20074.45MB/s]"}}, "805d33b8fa22471abfeee9fa61e9e2de": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8c6db16778bc46b4aed4aaea436134e0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_af29b9c29f12466fbbc40d495ae3db5c", "IPY_MODEL_0c7f0a99520d4d0f86d594a5fb2f6cab", "IPY_MODEL_561c63a2b65b4116a28fb3b4ac4f32e6"], "layout": "IPY_MODEL_805d33b8fa22471abfeee9fa61e9e2de", "tabbable": null, "tooltip": null}}, "e5c784eab8a94900a4d3a70982295f74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3c521633f0bb427da41e208cdb5b96c6": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "fcf75ab6cf814290a0f372321afcf3ea": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e5c784eab8a94900a4d3a70982295f74", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3c521633f0bb427da41e208cdb5b96c6", "tabbable": null, "tooltip": null, "value": 48.0}}, "c75bd3098c8a48acbb95bca0290d883d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "155e84efd692454399d4426bb5713e35": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3a2ff520b6424d049b8b15606b60599d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c75bd3098c8a48acbb95bca0290d883d", "placeholder": "\u200b", "style": "IPY_MODEL_155e84efd692454399d4426bb5713e35", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "0b35ffe60d7c4b9799b5fce1f288989c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ebdd4dfe6aec4e448aa148abafca1b7d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "66f4157cf10d4672b4b214e5bf794928": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0b35ffe60d7c4b9799b5fce1f288989c", "placeholder": "\u200b", "style": "IPY_MODEL_ebdd4dfe6aec4e448aa148abafca1b7d", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20078.09kB/s]"}}, "dd2a4c676cf043739927dfa7c9d87d4a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7f0fe51d36484535bc14d531e03fabdf": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_3a2ff520b6424d049b8b15606b60599d", "IPY_MODEL_fcf75ab6cf814290a0f372321afcf3ea", "IPY_MODEL_66f4157cf10d4672b4b214e5bf794928"], "layout": "IPY_MODEL_dd2a4c676cf043739927dfa7c9d87d4a", "tabbable": null, "tooltip": null}}, "d50bce29eb5246ab873d30e7b2f1931a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e1a796714b5e4b1386e7c5fc265f5a18": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "924e964b896b445da36a28a097c512a8": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d50bce29eb5246ab873d30e7b2f1931a", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e1a796714b5e4b1386e7c5fc265f5a18", "tabbable": null, "tooltip": null, "value": 231508.0}}, "fa4457b0ed8c4544b0ca7b52eecbb8b3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0d6f6129381940e38ea8824966649246": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "9e8b313266ba4a21bb5d1e6a8e8a20bd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fa4457b0ed8c4544b0ca7b52eecbb8b3", "placeholder": "\u200b", "style": "IPY_MODEL_0d6f6129381940e38ea8824966649246", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "ce23779e7d4543509b65346ec8ed319e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c59e2d5352f74a18a4f0864015d5680e": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e94459cf036f48d2a8288e79274ed9a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ce23779e7d4543509b65346ec8ed319e", "placeholder": "\u200b", "style": "IPY_MODEL_c59e2d5352f74a18a4f0864015d5680e", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200730.1MB/s]"}}, "7ff82688f10e4abb980a8344fbc698fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aa428e0260a64e6da94c0ea83d11772e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_9e8b313266ba4a21bb5d1e6a8e8a20bd", "IPY_MODEL_924e964b896b445da36a28a097c512a8", "IPY_MODEL_e94459cf036f48d2a8288e79274ed9a6"], "layout": "IPY_MODEL_7ff82688f10e4abb980a8344fbc698fe", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"fe702e6a2dbf409c9c44ca8eb27dffcc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "20de08960d534ff89e4d9748aae1b593": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "33222d3a75f94b0badb9b80898204763": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fe702e6a2dbf409c9c44ca8eb27dffcc", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_20de08960d534ff89e4d9748aae1b593", "tabbable": null, "tooltip": null, "value": 391.0}}, "f893ddf9db054e05b5719e8272e508e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8c1d39b74d17432a831b7e63d65fc7c0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2d114fd4a23f44a9b28eb2eefa18ce30": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f893ddf9db054e05b5719e8272e508e6", "placeholder": "\u200b", "style": "IPY_MODEL_8c1d39b74d17432a831b7e63d65fc7c0", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "1c6fb4c64d13487194c96f1bdc086e42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f03c166056b94c999588193b57b1ab4f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "48a7c543fbdc47eb91f1afe3d775a0f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1c6fb4c64d13487194c96f1bdc086e42", "placeholder": "\u200b", "style": "IPY_MODEL_f03c166056b94c999588193b57b1ab4f", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200762.0kB/s]"}}, "02e3e418c19240f486b6744bed9dc1a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "62351de8abb94a038c8769c2df5c458f": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2d114fd4a23f44a9b28eb2eefa18ce30", "IPY_MODEL_33222d3a75f94b0badb9b80898204763", "IPY_MODEL_48a7c543fbdc47eb91f1afe3d775a0f8"], "layout": "IPY_MODEL_02e3e418c19240f486b6744bed9dc1a5", "tabbable": null, "tooltip": null}}, "91b179d4d8f345959726ca7ba76ac7a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "04d9fd81f1a64f5d864276ca3169c4ae": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "48ac4d127e6141eb8fa1f6bf4d15e4c2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_91b179d4d8f345959726ca7ba76ac7a9", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_04d9fd81f1a64f5d864276ca3169c4ae", "tabbable": null, "tooltip": null, "value": 2211.0}}, "7ffce27927fb488788c78c75defb22e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c2a16ddd367b4238aa5afd5a22f28d8c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f599421afd5149bfa8a6ee654777c7c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7ffce27927fb488788c78c75defb22e0", "placeholder": "\u200b", "style": "IPY_MODEL_c2a16ddd367b4238aa5afd5a22f28d8c", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "31d200dead6d4d7a9d475999bb9cd002": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "35dadc5a2f124430a20eb48108c03ab1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "ae97d0f75f4740cbb7ce3ea7ed2d44aa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_31d200dead6d4d7a9d475999bb9cd002", "placeholder": "\u200b", "style": "IPY_MODEL_35dadc5a2f124430a20eb48108c03ab1", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007407kB/s]"}}, "0cbf3b1097fc425e989d58c0735c519d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "340785de497a4c63ab7144c513dbc840": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_f599421afd5149bfa8a6ee654777c7c1", "IPY_MODEL_48ac4d127e6141eb8fa1f6bf4d15e4c2", "IPY_MODEL_ae97d0f75f4740cbb7ce3ea7ed2d44aa"], "layout": "IPY_MODEL_0cbf3b1097fc425e989d58c0735c519d", "tabbable": null, "tooltip": null}}, "3e413d091d0d4b6482bc9ed11d5d236c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0b359b2ad56c46998822be17f96e77fb": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e20b0fbd46bf4b59bc4da8f83218e600": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3e413d091d0d4b6482bc9ed11d5d236c", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0b359b2ad56c46998822be17f96e77fb", "tabbable": null, "tooltip": null, "value": 665.0}}, "68e32e4abb734a84a60e14567ee5ab16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7443c554c2da4d4790de7a725d038932": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "a8d4e5776925443ebb839809dfaca832": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_68e32e4abb734a84a60e14567ee5ab16", "placeholder": "\u200b", "style": "IPY_MODEL_7443c554c2da4d4790de7a725d038932", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "06823c4e7990422188c080cab19edf7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc9418d15a01426f91adaea86b81b6eb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "aa7b347bc9c145b09318d5248c15af77": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_06823c4e7990422188c080cab19edf7b", "placeholder": "\u200b", "style": "IPY_MODEL_fc9418d15a01426f91adaea86b81b6eb", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u200798.3kB/s]"}}, "5b140acae5da41218fdb0bba821dd741": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "690a2c7ac41a426d9ea764ad3d62a191": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_a8d4e5776925443ebb839809dfaca832", "IPY_MODEL_e20b0fbd46bf4b59bc4da8f83218e600", "IPY_MODEL_aa7b347bc9c145b09318d5248c15af77"], "layout": "IPY_MODEL_5b140acae5da41218fdb0bba821dd741", "tabbable": null, "tooltip": null}}, "8658abefb5a143a0ae1d78b86bb5e6a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fa52b986ca5d4e199fbbe62d54662389": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "9a7533d991e54982b696f43606dbe2b5": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8658abefb5a143a0ae1d78b86bb5e6a7", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fa52b986ca5d4e199fbbe62d54662389", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "438118108888448cb339b8dc2230aa72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edc251780b214e988fe3e98369e79de3": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b7d8f867996c41a0b6bc2bffd9f7965d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_438118108888448cb339b8dc2230aa72", "placeholder": "\u200b", "style": "IPY_MODEL_edc251780b214e988fe3e98369e79de3", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "9d38a50f62054962a5ac76122754b6df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3aafc982b22a428aa27c5cd1250efa05": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1f3ed30be4ca4e5b97f396f778f155f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9d38a50f62054962a5ac76122754b6df", "placeholder": "\u200b", "style": "IPY_MODEL_3aafc982b22a428aa27c5cd1250efa05", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:01<00:00,\u200728.1MB/s]"}}, "e5831f2ec42f4838ab828634c0bd28fd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "91c85984c43b4bb6ac43cf0e512599f1": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b7d8f867996c41a0b6bc2bffd9f7965d", "IPY_MODEL_9a7533d991e54982b696f43606dbe2b5", "IPY_MODEL_1f3ed30be4ca4e5b97f396f778f155f5"], "layout": "IPY_MODEL_e5831f2ec42f4838ab828634c0bd28fd", "tabbable": null, "tooltip": null}}, "09f87f9e986947dda9b2e8c0bc235a7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7430ccc73950450389eb7ded2db99afc": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "bdbdcb9609f647b6ac21adad814ab8dd": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_09f87f9e986947dda9b2e8c0bc235a7b", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7430ccc73950450389eb7ded2db99afc", "tabbable": null, "tooltip": null, "value": 466062.0}}, "673865438c8e4b0296bbe6ca3c060cb9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ab8d06a6112542ae917846ddae4c46ac": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b729c768ffe84c23ab3835b3f3f8051b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_673865438c8e4b0296bbe6ca3c060cb9", "placeholder": "\u200b", "style": "IPY_MODEL_ab8d06a6112542ae917846ddae4c46ac", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "ca4b9735d66b414697862773b6ae1163": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b49778737bd8479f946ac4060638f1d9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "704c7b4ef65041a89cd5d3fc1f81e95f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ca4b9735d66b414697862773b6ae1163", "placeholder": "\u200b", "style": "IPY_MODEL_b49778737bd8479f946ac4060638f1d9", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u200715.0MB/s]"}}, "57162eef07c144edbbf29b08ca208753": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a6a71af506bb4925baad0f1c7f46552e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b729c768ffe84c23ab3835b3f3f8051b", "IPY_MODEL_bdbdcb9609f647b6ac21adad814ab8dd", "IPY_MODEL_704c7b4ef65041a89cd5d3fc1f81e95f"], "layout": "IPY_MODEL_57162eef07c144edbbf29b08ca208753", "tabbable": null, "tooltip": null}}, "13325f0cbdac42e292d5ec55dd02d83e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f650bcae88e3498c9713ed7fad85c313": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "8e023743450746ba98fb9cff5b8ab3ca": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_13325f0cbdac42e292d5ec55dd02d83e", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f650bcae88e3498c9713ed7fad85c313", "tabbable": null, "tooltip": null, "value": 48.0}}, "c9974b364cf9469292e4c0a172a720dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9facb5b56860415d974458a4f8a98e99": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "73bb6421a1694f5abed56acfff8df5c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c9974b364cf9469292e4c0a172a720dd", "placeholder": "\u200b", "style": "IPY_MODEL_9facb5b56860415d974458a4f8a98e99", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "c7cd56b89e03457e8bfa240f778aeac4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5e5e6673b88a48b6a1e0b40d0eb77931": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3123835078454cf9b22abb8a471265a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c7cd56b89e03457e8bfa240f778aeac4", "placeholder": "\u200b", "style": "IPY_MODEL_5e5e6673b88a48b6a1e0b40d0eb77931", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20077.41kB/s]"}}, "69cbdc81324248878408dbfed717680f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fce6374730574858a51fc6bb15b16ff0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_73bb6421a1694f5abed56acfff8df5c8", "IPY_MODEL_8e023743450746ba98fb9cff5b8ab3ca", "IPY_MODEL_3123835078454cf9b22abb8a471265a0"], "layout": "IPY_MODEL_69cbdc81324248878408dbfed717680f", "tabbable": null, "tooltip": null}}, "fbfcfc4949a741f88d46ad1b0042769e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "14034de7bb7744e19694d7e8a08ca368": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "9ac47759986e4465a5e745617e40364c": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fbfcfc4949a741f88d46ad1b0042769e", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_14034de7bb7744e19694d7e8a08ca368", "tabbable": null, "tooltip": null, "value": 231508.0}}, "98055291c2ee452694ecc19e51ecaead": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "58ef1824a5c949b8a654f8a0277b7b97": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "079f3f1e033f4bbca8478c880a927afc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_98055291c2ee452694ecc19e51ecaead", "placeholder": "\u200b", "style": "IPY_MODEL_58ef1824a5c949b8a654f8a0277b7b97", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "72404aea60be4c0aaa808f8f70065420": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b8348d0d4075416fa30cf5f27f5fd6fd": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7f9ca0d6a1fb49489d44ed52e9e5b6aa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_72404aea60be4c0aaa808f8f70065420", "placeholder": "\u200b", "style": "IPY_MODEL_b8348d0d4075416fa30cf5f27f5fd6fd", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200732.8MB/s]"}}, "624e82a0b0ec439696797f4a87dcb861": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3101229ea6044f0bf8820ecad5523c3": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_079f3f1e033f4bbca8478c880a927afc", "IPY_MODEL_9ac47759986e4465a5e745617e40364c", "IPY_MODEL_7f9ca0d6a1fb49489d44ed52e9e5b6aa"], "layout": "IPY_MODEL_624e82a0b0ec439696797f4a87dcb861", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/clean_learning/text.ipynb b/master/tutorials/clean_learning/text.ipynb index abdb62899..e5a2ac8fa 100644 --- a/master/tutorials/clean_learning/text.ipynb +++ b/master/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:10.483142Z", - "iopub.status.busy": "2024-06-28T15:32:10.482709Z", - "iopub.status.idle": "2024-06-28T15:32:13.547102Z", - "shell.execute_reply": "2024-06-28T15:32:13.546477Z" + "iopub.execute_input": "2024-07-01T15:01:48.389395Z", + "iopub.status.busy": "2024-07-01T15:01:48.389202Z", + "iopub.status.idle": "2024-07-01T15:01:51.596566Z", + "shell.execute_reply": "2024-07-01T15:01:51.595964Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.549911Z", - "iopub.status.busy": "2024-06-28T15:32:13.549439Z", - "iopub.status.idle": "2024-06-28T15:32:13.552758Z", - "shell.execute_reply": "2024-06-28T15:32:13.552287Z" + "iopub.execute_input": "2024-07-01T15:01:51.599757Z", + "iopub.status.busy": "2024-07-01T15:01:51.599136Z", + "iopub.status.idle": "2024-07-01T15:01:51.603065Z", + "shell.execute_reply": "2024-07-01T15:01:51.602415Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.554848Z", - "iopub.status.busy": "2024-06-28T15:32:13.554437Z", - "iopub.status.idle": "2024-06-28T15:32:13.557452Z", - "shell.execute_reply": "2024-06-28T15:32:13.557018Z" + "iopub.execute_input": "2024-07-01T15:01:51.605582Z", + "iopub.status.busy": "2024-07-01T15:01:51.605171Z", + "iopub.status.idle": "2024-07-01T15:01:51.608781Z", + "shell.execute_reply": "2024-07-01T15:01:51.608196Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.559516Z", - "iopub.status.busy": "2024-06-28T15:32:13.559185Z", - "iopub.status.idle": "2024-06-28T15:32:13.584970Z", - "shell.execute_reply": "2024-06-28T15:32:13.584357Z" + "iopub.execute_input": "2024-07-01T15:01:51.611405Z", + "iopub.status.busy": "2024-07-01T15:01:51.610984Z", + "iopub.status.idle": "2024-07-01T15:01:51.666636Z", + "shell.execute_reply": "2024-07-01T15:01:51.666058Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.587334Z", - "iopub.status.busy": "2024-06-28T15:32:13.586930Z", - "iopub.status.idle": "2024-06-28T15:32:13.590797Z", - "shell.execute_reply": "2024-06-28T15:32:13.590247Z" + "iopub.execute_input": "2024-07-01T15:01:51.668846Z", + "iopub.status.busy": "2024-07-01T15:01:51.668483Z", + "iopub.status.idle": "2024-07-01T15:01:51.672233Z", + "shell.execute_reply": "2024-07-01T15:01:51.671774Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.593121Z", - "iopub.status.busy": "2024-06-28T15:32:13.592769Z", - "iopub.status.idle": "2024-06-28T15:32:13.596104Z", - "shell.execute_reply": "2024-06-28T15:32:13.595568Z" + "iopub.execute_input": "2024-07-01T15:01:51.674498Z", + "iopub.status.busy": "2024-07-01T15:01:51.674053Z", + "iopub.status.idle": "2024-07-01T15:01:51.677796Z", + "shell.execute_reply": "2024-07-01T15:01:51.677326Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'cancel_transfer', 'getting_spare_card', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'lost_or_stolen_phone'}\n" + "Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.598094Z", - "iopub.status.busy": "2024-06-28T15:32:13.597802Z", - "iopub.status.idle": "2024-06-28T15:32:13.600928Z", - "shell.execute_reply": "2024-06-28T15:32:13.600341Z" + "iopub.execute_input": "2024-07-01T15:01:51.679875Z", + "iopub.status.busy": "2024-07-01T15:01:51.679530Z", + "iopub.status.idle": "2024-07-01T15:01:51.682840Z", + "shell.execute_reply": "2024-07-01T15:01:51.682369Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.602926Z", - "iopub.status.busy": "2024-06-28T15:32:13.602603Z", - "iopub.status.idle": "2024-06-28T15:32:13.605952Z", - "shell.execute_reply": "2024-06-28T15:32:13.605418Z" + "iopub.execute_input": "2024-07-01T15:01:51.684949Z", + "iopub.status.busy": "2024-07-01T15:01:51.684614Z", + "iopub.status.idle": "2024-07-01T15:01:51.687925Z", + "shell.execute_reply": "2024-07-01T15:01:51.687477Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.607995Z", - "iopub.status.busy": "2024-06-28T15:32:13.607672Z", - "iopub.status.idle": "2024-06-28T15:32:18.588947Z", - "shell.execute_reply": "2024-06-28T15:32:18.588363Z" + "iopub.execute_input": "2024-07-01T15:01:51.690015Z", + "iopub.status.busy": "2024-07-01T15:01:51.689695Z", + "iopub.status.idle": "2024-07-01T15:01:58.269951Z", + "shell.execute_reply": "2024-07-01T15:01:58.269375Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81cceb7356c54fe6917cce0213b9538e", + "model_id": "62351de8abb94a038c8769c2df5c458f", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "251f05f2d534421ea834728e6e80c34a", + "model_id": "340785de497a4c63ab7144c513dbc840", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5cdd87483a4b48dbaf9539299fb3282b", + "model_id": "690a2c7ac41a426d9ea764ad3d62a191", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3278dd636e23462a89f4f6e23e81cf14", + "model_id": "91c85984c43b4bb6ac43cf0e512599f1", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c6db16778bc46b4aed4aaea436134e0", + "model_id": "a6a71af506bb4925baad0f1c7f46552e", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f0fe51d36484535bc14d531e03fabdf", + "model_id": "fce6374730574858a51fc6bb15b16ff0", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa428e0260a64e6da94c0ea83d11772e", + "model_id": "e3101229ea6044f0bf8820ecad5523c3", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.591766Z", - "iopub.status.busy": "2024-06-28T15:32:18.591358Z", - "iopub.status.idle": "2024-06-28T15:32:18.594443Z", - "shell.execute_reply": "2024-06-28T15:32:18.593945Z" + "iopub.execute_input": "2024-07-01T15:01:58.272720Z", + "iopub.status.busy": "2024-07-01T15:01:58.272512Z", + "iopub.status.idle": "2024-07-01T15:01:58.275361Z", + "shell.execute_reply": "2024-07-01T15:01:58.274859Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.596515Z", - "iopub.status.busy": "2024-06-28T15:32:18.596181Z", - "iopub.status.idle": "2024-06-28T15:32:18.598873Z", - "shell.execute_reply": "2024-06-28T15:32:18.598422Z" + "iopub.execute_input": "2024-07-01T15:01:58.277576Z", + "iopub.status.busy": "2024-07-01T15:01:58.277231Z", + "iopub.status.idle": "2024-07-01T15:01:58.279889Z", + "shell.execute_reply": "2024-07-01T15:01:58.279457Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.600840Z", - "iopub.status.busy": "2024-06-28T15:32:18.600538Z", - "iopub.status.idle": "2024-06-28T15:32:21.461322Z", - "shell.execute_reply": "2024-06-28T15:32:21.460567Z" + "iopub.execute_input": "2024-07-01T15:01:58.281927Z", + "iopub.status.busy": "2024-07-01T15:01:58.281583Z", + "iopub.status.idle": "2024-07-01T15:02:01.122494Z", + "shell.execute_reply": "2024-07-01T15:02:01.121708Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.464638Z", - "iopub.status.busy": "2024-06-28T15:32:21.463834Z", - "iopub.status.idle": "2024-06-28T15:32:21.471562Z", - "shell.execute_reply": "2024-06-28T15:32:21.471019Z" + "iopub.execute_input": "2024-07-01T15:02:01.125497Z", + "iopub.status.busy": "2024-07-01T15:02:01.124853Z", + "iopub.status.idle": "2024-07-01T15:02:01.132982Z", + "shell.execute_reply": "2024-07-01T15:02:01.132483Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.473714Z", - "iopub.status.busy": "2024-06-28T15:32:21.473378Z", - "iopub.status.idle": "2024-06-28T15:32:21.477300Z", - "shell.execute_reply": "2024-06-28T15:32:21.476829Z" + "iopub.execute_input": "2024-07-01T15:02:01.135273Z", + "iopub.status.busy": "2024-07-01T15:02:01.134876Z", + "iopub.status.idle": "2024-07-01T15:02:01.138920Z", + "shell.execute_reply": "2024-07-01T15:02:01.138424Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.479264Z", - "iopub.status.busy": "2024-06-28T15:32:21.478937Z", - "iopub.status.idle": "2024-06-28T15:32:21.482305Z", - "shell.execute_reply": "2024-06-28T15:32:21.481845Z" + "iopub.execute_input": "2024-07-01T15:02:01.140950Z", + "iopub.status.busy": "2024-07-01T15:02:01.140631Z", + "iopub.status.idle": "2024-07-01T15:02:01.143747Z", + "shell.execute_reply": "2024-07-01T15:02:01.143216Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.484315Z", - "iopub.status.busy": "2024-06-28T15:32:21.483999Z", - "iopub.status.idle": "2024-06-28T15:32:21.487149Z", - "shell.execute_reply": "2024-06-28T15:32:21.486605Z" + "iopub.execute_input": "2024-07-01T15:02:01.145826Z", + "iopub.status.busy": "2024-07-01T15:02:01.145406Z", + "iopub.status.idle": "2024-07-01T15:02:01.148540Z", + "shell.execute_reply": "2024-07-01T15:02:01.148071Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.489496Z", - "iopub.status.busy": "2024-06-28T15:32:21.489087Z", - "iopub.status.idle": "2024-06-28T15:32:21.497056Z", - "shell.execute_reply": "2024-06-28T15:32:21.496469Z" + "iopub.execute_input": "2024-07-01T15:02:01.150516Z", + "iopub.status.busy": "2024-07-01T15:02:01.150212Z", + "iopub.status.idle": "2024-07-01T15:02:01.157258Z", + "shell.execute_reply": "2024-07-01T15:02:01.156695Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.499326Z", - "iopub.status.busy": "2024-06-28T15:32:21.499002Z", - "iopub.status.idle": "2024-06-28T15:32:21.732976Z", - "shell.execute_reply": "2024-06-28T15:32:21.732384Z" + "iopub.execute_input": "2024-07-01T15:02:01.159339Z", + "iopub.status.busy": "2024-07-01T15:02:01.159040Z", + "iopub.status.idle": "2024-07-01T15:02:01.408184Z", + "shell.execute_reply": "2024-07-01T15:02:01.407652Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.735585Z", - "iopub.status.busy": "2024-06-28T15:32:21.735168Z", - "iopub.status.idle": "2024-06-28T15:32:21.922637Z", - "shell.execute_reply": "2024-06-28T15:32:21.922053Z" + "iopub.execute_input": "2024-07-01T15:02:01.410863Z", + "iopub.status.busy": "2024-07-01T15:02:01.410467Z", + "iopub.status.idle": "2024-07-01T15:02:01.588794Z", + "shell.execute_reply": "2024-07-01T15:02:01.588210Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.925255Z", - "iopub.status.busy": "2024-06-28T15:32:21.924857Z", - "iopub.status.idle": "2024-06-28T15:32:21.928930Z", - "shell.execute_reply": "2024-06-28T15:32:21.928386Z" + "iopub.execute_input": "2024-07-01T15:02:01.592672Z", + "iopub.status.busy": "2024-07-01T15:02:01.591557Z", + "iopub.status.idle": "2024-07-01T15:02:01.597151Z", + "shell.execute_reply": "2024-07-01T15:02:01.596544Z" }, "nbsphinx": "hidden" }, @@ -1105,7 +1105,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0654f51f54c744d98029a5ea15c37e7c": { + "02e3e418c19240f486b6744bed9dc1a5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1158,7 +1158,23 @@ "width": null } }, - "087e012a31e74812b07d928968bc3c5e": { + "04d9fd81f1a64f5d864276ca3169c4ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "06823c4e7990422188c080cab19edf7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1211,7 +1227,30 @@ "width": null } }, - "08e9ec5f60494f048e5d58bb8dfa2782": { + "079f3f1e033f4bbca8478c880a927afc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_98055291c2ee452694ecc19e51ecaead", + "placeholder": "​", + "style": "IPY_MODEL_58ef1824a5c949b8a654f8a0277b7b97", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "09f87f9e986947dda9b2e8c0bc235a7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1264,30 +1303,23 @@ "width": null } }, - "0a9075cf3c864a86b308b079f5ee7873": { + "0b359b2ad56c46998822be17f96e77fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_08e9ec5f60494f048e5d58bb8dfa2782", - "placeholder": "​", - "style": "IPY_MODEL_c1c1bc732ed24cb9ac28092022314462", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0b35ffe60d7c4b9799b5fce1f288989c": { + "0cbf3b1097fc425e989d58c0735c519d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1340,110 +1372,168 @@ "width": null } }, - "0b437892039948e8abb66dc411c2e04f": { - "model_module": "@jupyter-widgets/controls", + "13325f0cbdac42e292d5ec55dd02d83e": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_48337b46095146c2b808c53bea2653e1", - "placeholder": "​", - "style": "IPY_MODEL_570178b2084e4c65a1aeb887ed8b304b", - "tabbable": null, - "tooltip": null, - "value": ".gitattributes: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0c7f0a99520d4d0f86d594a5fb2f6cab": { + "14034de7bb7744e19694d7e8a08ca368": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9ac89da8e95c4b04ae98998044928144", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a088ce5c5e4a4a50a697175e3012eeb2", - "tabbable": null, - "tooltip": null, - "value": 466062.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0d6f6129381940e38ea8824966649246": { - "model_module": "@jupyter-widgets/controls", + "1c6fb4c64d13487194c96f1bdc086e42": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0f56503d7ab04c1eaaf800c590ddc4de": { + "1f3ed30be4ca4e5b97f396f778f155f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9d38a50f62054962a5ac76122754b6df", + "placeholder": "​", + "style": "IPY_MODEL_3aafc982b22a428aa27c5cd1250efa05", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:01<00:00, 28.1MB/s]" } }, - "155e84efd692454399d4426bb5713e35": { + "20de08960d534ff89e4d9748aae1b593": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "22f06b02a2e546d5b45f1f56ce9f0644": { + "2d114fd4a23f44a9b28eb2eefa18ce30": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1458,39 +1548,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bc553302eb144ee1affc907e042d6c5c", + "layout": "IPY_MODEL_f893ddf9db054e05b5719e8272e508e6", "placeholder": "​", - "style": "IPY_MODEL_45ccc4a60a034745b926bb540d0e915a", + "style": "IPY_MODEL_8c1d39b74d17432a831b7e63d65fc7c0", "tabbable": null, "tooltip": null, - "value": " 391/391 [00:00<00:00, 64.3kB/s]" - } - }, - "251f05f2d534421ea834728e6e80c34a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4873b50be2dc4a2b99b71c0258d7bd8a", - "IPY_MODEL_f04b2d469897481ea059b74c219803ca", - "IPY_MODEL_29ee7f74a6bf4c2a822d2a7326b68385" - ], - "layout": "IPY_MODEL_087e012a31e74812b07d928968bc3c5e", - "tabbable": null, - "tooltip": null + "value": ".gitattributes: 100%" } }, - "29ee7f74a6bf4c2a822d2a7326b68385": { + "3123835078454cf9b22abb8a471265a0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1505,31 +1571,94 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_58f565f2791d42f3bec1f5f720b8cd85", + "layout": "IPY_MODEL_c7cd56b89e03457e8bfa240f778aeac4", "placeholder": "​", - "style": "IPY_MODEL_a8ddb64ad7f44965bb03d2cdb1a58784", + "style": "IPY_MODEL_5e5e6673b88a48b6a1e0b40d0eb77931", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 374kB/s]" + "value": " 48.0/48.0 [00:00<00:00, 7.41kB/s]" + } + }, + "31d200dead6d4d7a9d475999bb9cd002": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "2b8cf285d37641e78f819e258a1471d5": { + "33222d3a75f94b0badb9b80898204763": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fe702e6a2dbf409c9c44ca8eb27dffcc", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_20de08960d534ff89e4d9748aae1b593", + "tabbable": null, + "tooltip": null, + "value": 391.0 } }, - "3278dd636e23462a89f4f6e23e81cf14": { + "340785de497a4c63ab7144c513dbc840": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1544,16 +1673,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0a9075cf3c864a86b308b079f5ee7873", - "IPY_MODEL_41cfe9c4d1cd4fea9b7df6ff9928999b", - "IPY_MODEL_91e70d4791c84851a1b91fa5e7c57cd6" + "IPY_MODEL_f599421afd5149bfa8a6ee654777c7c1", + "IPY_MODEL_48ac4d127e6141eb8fa1f6bf4d15e4c2", + "IPY_MODEL_ae97d0f75f4740cbb7ce3ea7ed2d44aa" ], - "layout": "IPY_MODEL_4710be403bdb45689713c7fb0b2c8b36", + "layout": "IPY_MODEL_0cbf3b1097fc425e989d58c0735c519d", "tabbable": null, "tooltip": null } }, - "3580f78a814e469999540909f5d7c17d": { + "35dadc5a2f124430a20eb48108c03ab1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1571,7 +1700,7 @@ "text_color": null } }, - "39a6f9b8d9474c6896d33cd8e0822b9f": { + "3aafc982b22a428aa27c5cd1250efa05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1589,64 +1718,60 @@ "text_color": null } }, - "3a2ff520b6424d049b8b15606b60599d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c75bd3098c8a48acbb95bca0290d883d", - "placeholder": "​", - "style": "IPY_MODEL_155e84efd692454399d4426bb5713e35", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "3c521633f0bb427da41e208cdb5b96c6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3fe3546160e64f05886cf998dbf3d44d": { - "model_module": "@jupyter-widgets/controls", + "3e413d091d0d4b6482bc9ed11d5d236c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "4148422cf74744c5bf382eb313e0a314": { + "438118108888448cb339b8dc2230aa72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,67 +1824,56 @@ "width": null } }, - "41cfe9c4d1cd4fea9b7df6ff9928999b": { + "48a7c543fbdc47eb91f1afe3d775a0f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f35cae2ff4de4ac09ee0eef4156fb86a", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_43d7d96a652f4111bce4b6480fcb9957", + "layout": "IPY_MODEL_1c6fb4c64d13487194c96f1bdc086e42", + "placeholder": "​", + "style": "IPY_MODEL_f03c166056b94c999588193b57b1ab4f", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 391/391 [00:00<00:00, 62.0kB/s]" } }, - "43d7d96a652f4111bce4b6480fcb9957": { + "48ac4d127e6141eb8fa1f6bf4d15e4c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "45ccc4a60a034745b926bb540d0e915a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_91b179d4d8f345959726ca7ba76ac7a9", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_04d9fd81f1a64f5d864276ca3169c4ae", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "4710be403bdb45689713c7fb0b2c8b36": { + "57162eef07c144edbbf29b08ca208753": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1812,7 +1926,25 @@ "width": null } }, - "48337b46095146c2b808c53bea2653e1": { + "58ef1824a5c949b8a654f8a0277b7b97": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5b140acae5da41218fdb0bba821dd741": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1865,71 +1997,49 @@ "width": null } }, - "4873b50be2dc4a2b99b71c0258d7bd8a": { + "5e5e6673b88a48b6a1e0b40d0eb77931": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0654f51f54c744d98029a5ea15c37e7c", - "placeholder": "​", - "style": "IPY_MODEL_3fe3546160e64f05886cf998dbf3d44d", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "561c63a2b65b4116a28fb3b4ac4f32e6": { + "62351de8abb94a038c8769c2df5c458f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f2a5b3c6a48140f7bfb68bf54f205281", - "placeholder": "​", - "style": "IPY_MODEL_6b43fcc00af140d0ab061913b16a022a", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 4.45MB/s]" - } - }, - "570178b2084e4c65a1aeb887ed8b304b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2d114fd4a23f44a9b28eb2eefa18ce30", + "IPY_MODEL_33222d3a75f94b0badb9b80898204763", + "IPY_MODEL_48a7c543fbdc47eb91f1afe3d775a0f8" + ], + "layout": "IPY_MODEL_02e3e418c19240f486b6744bed9dc1a5", + "tabbable": null, + "tooltip": null } }, - "58f565f2791d42f3bec1f5f720b8cd85": { + "624e82a0b0ec439696797f4a87dcb861": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1982,88 +2092,7 @@ "width": null } }, - "5cdd87483a4b48dbaf9539299fb3282b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8fabaa55f275451ab3cef6d7deb821c7", - "IPY_MODEL_b30eb88d1f0a44c2a212fbce52fe66f6", - "IPY_MODEL_d6b4a4277cca46f3965f64c58a0ce1c5" - ], - "layout": "IPY_MODEL_751cbc2ed7634303a0e36ef0a4409514", - "tabbable": null, - "tooltip": null - } - }, - "66f4157cf10d4672b4b214e5bf794928": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0b35ffe60d7c4b9799b5fce1f288989c", - "placeholder": "​", - "style": "IPY_MODEL_ebdd4dfe6aec4e448aa148abafca1b7d", - "tabbable": null, - "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.09kB/s]" - } - }, - "6b43fcc00af140d0ab061913b16a022a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6f4ca3b8054843dca836efad3447e526": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "751cbc2ed7634303a0e36ef0a4409514": { + "673865438c8e4b0296bbe6ca3c060cb9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2116,25 +2145,7 @@ "width": null } }, - "7b40009d218e4df1bf1bf7ab2887b25f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7c73348968774ea2bfc42fc202242f70": { + "68e32e4abb734a84a60e14567ee5ab16": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2187,7 +2198,7 @@ "width": null } }, - "7f0fe51d36484535bc14d531e03fabdf": { + "690a2c7ac41a426d9ea764ad3d62a191": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2202,16 +2213,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3a2ff520b6424d049b8b15606b60599d", - "IPY_MODEL_fcf75ab6cf814290a0f372321afcf3ea", - "IPY_MODEL_66f4157cf10d4672b4b214e5bf794928" + "IPY_MODEL_a8d4e5776925443ebb839809dfaca832", + "IPY_MODEL_e20b0fbd46bf4b59bc4da8f83218e600", + "IPY_MODEL_aa7b347bc9c145b09318d5248c15af77" ], - "layout": "IPY_MODEL_dd2a4c676cf043739927dfa7c9d87d4a", + "layout": "IPY_MODEL_5b140acae5da41218fdb0bba821dd741", "tabbable": null, "tooltip": null } }, - "7ff82688f10e4abb980a8344fbc698fe": { + "69cbdc81324248878408dbfed717680f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2264,7 +2275,30 @@ "width": null } }, - "805d33b8fa22471abfeee9fa61e9e2de": { + "704c7b4ef65041a89cd5d3fc1f81e95f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca4b9735d66b414697862773b6ae1163", + "placeholder": "​", + "style": "IPY_MODEL_b49778737bd8479f946ac4060638f1d9", + "tabbable": null, + "tooltip": null, + "value": " 466k/466k [00:00<00:00, 15.0MB/s]" + } + }, + "72404aea60be4c0aaa808f8f70065420": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2317,31 +2351,87 @@ "width": null } }, - "81cceb7356c54fe6917cce0213b9538e": { + "73bb6421a1694f5abed56acfff8df5c8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0b437892039948e8abb66dc411c2e04f", - "IPY_MODEL_c8d93fe968f24d46b815055f71fc9486", - "IPY_MODEL_22f06b02a2e546d5b45f1f56ce9f0644" - ], - "layout": "IPY_MODEL_8a4fde9243d3412685287673ee2eb1b4", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c9974b364cf9469292e4c0a172a720dd", + "placeholder": "​", + "style": "IPY_MODEL_9facb5b56860415d974458a4f8a98e99", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "tokenizer_config.json: 100%" } }, - "866c3e4cfe8c447384b363599afff30d": { + "7430ccc73950450389eb7ded2db99afc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7443c554c2da4d4790de7a725d038932": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7f9ca0d6a1fb49489d44ed52e9e5b6aa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_72404aea60be4c0aaa808f8f70065420", + "placeholder": "​", + "style": "IPY_MODEL_b8348d0d4075416fa30cf5f27f5fd6fd", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 32.8MB/s]" + } + }, + "7ffce27927fb488788c78c75defb22e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2394,7 +2484,7 @@ "width": null } }, - "8a4fde9243d3412685287673ee2eb1b4": { + "8658abefb5a143a0ae1d78b86bb5e6a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2447,31 +2537,51 @@ "width": null } }, - "8c6db16778bc46b4aed4aaea436134e0": { + "8c1d39b74d17432a831b7e63d65fc7c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8e023743450746ba98fb9cff5b8ab3ca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_af29b9c29f12466fbbc40d495ae3db5c", - "IPY_MODEL_0c7f0a99520d4d0f86d594a5fb2f6cab", - "IPY_MODEL_561c63a2b65b4116a28fb3b4ac4f32e6" - ], - "layout": "IPY_MODEL_805d33b8fa22471abfeee9fa61e9e2de", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_13325f0cbdac42e292d5ec55dd02d83e", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f650bcae88e3498c9713ed7fad85c313", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 48.0 } }, - "8d71fa197d2545bba0a3256f24725cae": { + "91b179d4d8f345959726ca7ba76ac7a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2519,84 +2629,36 @@ "overflow": null, "padding": null, "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8fabaa55f275451ab3cef6d7deb821c7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a1733c6fe59a40a9973bb2adf659b2f9", - "placeholder": "​", - "style": "IPY_MODEL_39a6f9b8d9474c6896d33cd8e0822b9f", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" - } - }, - "91e70d4791c84851a1b91fa5e7c57cd6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_866c3e4cfe8c447384b363599afff30d", - "placeholder": "​", - "style": "IPY_MODEL_7b40009d218e4df1bf1bf7ab2887b25f", - "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 167MB/s]" + "top": null, + "visibility": null, + "width": null } }, - "924e964b896b445da36a28a097c512a8": { + "91c85984c43b4bb6ac43cf0e512599f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d50bce29eb5246ab873d30e7b2f1931a", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1a796714b5e4b1386e7c5fc265f5a18", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b7d8f867996c41a0b6bc2bffd9f7965d", + "IPY_MODEL_9a7533d991e54982b696f43606dbe2b5", + "IPY_MODEL_1f3ed30be4ca4e5b97f396f778f155f5" + ], + "layout": "IPY_MODEL_e5831f2ec42f4838ab828634c0bd28fd", "tabbable": null, - "tooltip": null, - "value": 231508.0 + "tooltip": null } }, - "9ac89da8e95c4b04ae98998044928144": { + "98055291c2ee452694ecc19e51ecaead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2649,46 +2711,59 @@ "width": null } }, - "9e8b313266ba4a21bb5d1e6a8e8a20bd": { + "9a7533d991e54982b696f43606dbe2b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fa4457b0ed8c4544b0ca7b52eecbb8b3", - "placeholder": "​", - "style": "IPY_MODEL_0d6f6129381940e38ea8824966649246", + "layout": "IPY_MODEL_8658abefb5a143a0ae1d78b86bb5e6a7", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fa52b986ca5d4e199fbbe62d54662389", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" + "value": 54245363.0 } }, - "a088ce5c5e4a4a50a697175e3012eeb2": { + "9ac47759986e4465a5e745617e40364c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fbfcfc4949a741f88d46ad1b0042769e", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_14034de7bb7744e19694d7e8a08ca368", + "tabbable": null, + "tooltip": null, + "value": 231508.0 } }, - "a1733c6fe59a40a9973bb2adf659b2f9": { + "9d38a50f62054962a5ac76122754b6df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2741,7 +2816,7 @@ "width": null } }, - "a8ddb64ad7f44965bb03d2cdb1a58784": { + "9facb5b56860415d974458a4f8a98e99": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2759,7 +2834,7 @@ "text_color": null } }, - "aa428e0260a64e6da94c0ea83d11772e": { + "a6a71af506bb4925baad0f1c7f46552e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2774,16 +2849,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_9e8b313266ba4a21bb5d1e6a8e8a20bd", - "IPY_MODEL_924e964b896b445da36a28a097c512a8", - "IPY_MODEL_e94459cf036f48d2a8288e79274ed9a6" + "IPY_MODEL_b729c768ffe84c23ab3835b3f3f8051b", + "IPY_MODEL_bdbdcb9609f647b6ac21adad814ab8dd", + "IPY_MODEL_704c7b4ef65041a89cd5d3fc1f81e95f" ], - "layout": "IPY_MODEL_7ff82688f10e4abb980a8344fbc698fe", + "layout": "IPY_MODEL_57162eef07c144edbbf29b08ca208753", "tabbable": null, "tooltip": null } }, - "af29b9c29f12466fbbc40d495ae3db5c": { + "a8d4e5776925443ebb839809dfaca832": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2798,128 +2873,79 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4148422cf74744c5bf382eb313e0a314", + "layout": "IPY_MODEL_68e32e4abb734a84a60e14567ee5ab16", "placeholder": "​", - "style": "IPY_MODEL_0f56503d7ab04c1eaaf800c590ddc4de", + "style": "IPY_MODEL_7443c554c2da4d4790de7a725d038932", "tabbable": null, "tooltip": null, - "value": "tokenizer.json: 100%" + "value": "config.json: 100%" } }, - "b30eb88d1f0a44c2a212fbce52fe66f6": { + "aa7b347bc9c145b09318d5248c15af77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c884cea631794039964063852dcfcb74", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c04125b9dc5c46b298392e2e44751ffe", + "layout": "IPY_MODEL_06823c4e7990422188c080cab19edf7b", + "placeholder": "​", + "style": "IPY_MODEL_fc9418d15a01426f91adaea86b81b6eb", "tabbable": null, "tooltip": null, - "value": 665.0 - } - }, - "bc553302eb144ee1affc907e042d6c5c": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": " 665/665 [00:00<00:00, 98.3kB/s]" } }, - "c04125b9dc5c46b298392e2e44751ffe": { + "ab8d06a6112542ae917846ddae4c46ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c1c1bc732ed24cb9ac28092022314462": { + "ae97d0f75f4740cbb7ce3ea7ed2d44aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31d200dead6d4d7a9d475999bb9cd002", + "placeholder": "​", + "style": "IPY_MODEL_35dadc5a2f124430a20eb48108c03ab1", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 407kB/s]" } }, - "c59e2d5352f74a18a4f0864015d5680e": { + "b49778737bd8479f946ac4060638f1d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2937,113 +2963,71 @@ "text_color": null } }, - "c75bd3098c8a48acbb95bca0290d883d": { - "model_module": "@jupyter-widgets/base", + "b729c768ffe84c23ab3835b3f3f8051b": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_673865438c8e4b0296bbe6ca3c060cb9", + "placeholder": "​", + "style": "IPY_MODEL_ab8d06a6112542ae917846ddae4c46ac", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" } }, - "c884cea631794039964063852dcfcb74": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "b7d8f867996c41a0b6bc2bffd9f7965d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_438118108888448cb339b8dc2230aa72", + "placeholder": "​", + "style": "IPY_MODEL_edc251780b214e988fe3e98369e79de3", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "b8348d0d4075416fa30cf5f27f5fd6fd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c8d93fe968f24d46b815055f71fc9486": { + "bdbdcb9609f647b6ac21adad814ab8dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3059,17 +3043,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7c73348968774ea2bfc42fc202242f70", - "max": 391.0, + "layout": "IPY_MODEL_09f87f9e986947dda9b2e8c0bc235a7b", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6f4ca3b8054843dca836efad3447e526", + "style": "IPY_MODEL_7430ccc73950450389eb7ded2db99afc", "tabbable": null, "tooltip": null, - "value": 391.0 + "value": 466062.0 + } + }, + "c2a16ddd367b4238aa5afd5a22f28d8c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ce23779e7d4543509b65346ec8ed319e": { + "c7cd56b89e03457e8bfa240f778aeac4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3122,7 +3124,7 @@ "width": null } }, - "d0aa47a8a00543e6ad72c388f9080719": { + "c9974b364cf9469292e4c0a172a720dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3175,7 +3177,7 @@ "width": null } }, - "d50bce29eb5246ab873d30e7b2f1931a": { + "ca4b9735d66b414697862773b6ae1163": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3228,99 +3230,57 @@ "width": null } }, - "d6b4a4277cca46f3965f64c58a0ce1c5": { + "e20b0fbd46bf4b59bc4da8f83218e600": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d0aa47a8a00543e6ad72c388f9080719", - "placeholder": "​", - "style": "IPY_MODEL_3580f78a814e469999540909f5d7c17d", + "layout": "IPY_MODEL_3e413d091d0d4b6482bc9ed11d5d236c", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0b359b2ad56c46998822be17f96e77fb", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 98.0kB/s]" - } - }, - "dd2a4c676cf043739927dfa7c9d87d4a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": 665.0 } }, - "e1a796714b5e4b1386e7c5fc265f5a18": { + "e3101229ea6044f0bf8820ecad5523c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_079f3f1e033f4bbca8478c880a927afc", + "IPY_MODEL_9ac47759986e4465a5e745617e40364c", + "IPY_MODEL_7f9ca0d6a1fb49489d44ed52e9e5b6aa" + ], + "layout": "IPY_MODEL_624e82a0b0ec439696797f4a87dcb861", + "tabbable": null, + "tooltip": null } }, - "e5c784eab8a94900a4d3a70982295f74": { + "e5831f2ec42f4838ab828634c0bd28fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3373,30 +3333,25 @@ "width": null } }, - "e94459cf036f48d2a8288e79274ed9a6": { + "edc251780b214e988fe3e98369e79de3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce23779e7d4543509b65346ec8ed319e", - "placeholder": "​", - "style": "IPY_MODEL_c59e2d5352f74a18a4f0864015d5680e", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 30.1MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ebdd4dfe6aec4e448aa148abafca1b7d": { + "f03c166056b94c999588193b57b1ab4f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3414,33 +3369,46 @@ "text_color": null } }, - "f04b2d469897481ea059b74c219803ca": { + "f599421afd5149bfa8a6ee654777c7c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d71fa197d2545bba0a3256f24725cae", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2b8cf285d37641e78f819e258a1471d5", + "layout": "IPY_MODEL_7ffce27927fb488788c78c75defb22e0", + "placeholder": "​", + "style": "IPY_MODEL_c2a16ddd367b4238aa5afd5a22f28d8c", "tabbable": null, "tooltip": null, - "value": 2211.0 + "value": "README.md: 100%" + } + }, + "f650bcae88e3498c9713ed7fad85c313": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f2a5b3c6a48140f7bfb68bf54f205281": { + "f893ddf9db054e05b5719e8272e508e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3493,7 +3461,23 @@ "width": null } }, - "f35cae2ff4de4ac09ee0eef4156fb86a": { + "fa52b986ca5d4e199fbbe62d54662389": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fbfcfc4949a741f88d46ad1b0042769e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3546,7 +3530,49 @@ "width": null } }, - "fa4457b0ed8c4544b0ca7b52eecbb8b3": { + "fc9418d15a01426f91adaea86b81b6eb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fce6374730574858a51fc6bb15b16ff0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_73bb6421a1694f5abed56acfff8df5c8", + "IPY_MODEL_8e023743450746ba98fb9cff5b8ab3ca", + "IPY_MODEL_3123835078454cf9b22abb8a471265a0" + ], + "layout": "IPY_MODEL_69cbdc81324248878408dbfed717680f", + "tabbable": null, + "tooltip": null + } + }, + "fe702e6a2dbf409c9c44ca8eb27dffcc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3598,32 +3624,6 @@ "visibility": null, "width": null } - }, - "fcf75ab6cf814290a0f372321afcf3ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e5c784eab8a94900a4d3a70982295f74", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3c521633f0bb427da41e208cdb5b96c6", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index c68c2498f..5ce92a112 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1347,7 +1347,7 @@

    5. Use cleanlab to find label issues -{"state": {"f715cd04eb304470889617b12390a482": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8bdfb71cc05c4bac9e99957e74de9c77": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "1db3e87999ce494283770c1a2f49302e": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f715cd04eb304470889617b12390a482", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8bdfb71cc05c4bac9e99957e74de9c77", "tabbable": null, "tooltip": null, "value": 2041.0}}, "9d23f4f000134f42aa3aa92e38319d3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c2ba3bd1eaf1447681bbf99a54df9974": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b69c454713fc43798c96786b37d49252": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9d23f4f000134f42aa3aa92e38319d3e", "placeholder": "\u200b", "style": "IPY_MODEL_c2ba3bd1eaf1447681bbf99a54df9974", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "c6d20ede22124fc6ba4a66d669a06d2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c4148b51f19748b4af9b5af3134378e7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "c8bd4367ddb84b3b8e0e3a5262065f23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c6d20ede22124fc6ba4a66d669a06d2a", "placeholder": "\u200b", "style": "IPY_MODEL_c4148b51f19748b4af9b5af3134378e7", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007453kB/s]"}}, "1c9cc6a7f35641d69ddc253c76d83c9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1f40455e27c443f1a07b5cb09a22204f": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b69c454713fc43798c96786b37d49252", "IPY_MODEL_1db3e87999ce494283770c1a2f49302e", "IPY_MODEL_c8bd4367ddb84b3b8e0e3a5262065f23"], "layout": "IPY_MODEL_1c9cc6a7f35641d69ddc253c76d83c9c", "tabbable": null, "tooltip": null}}, "d032e929b7b1431e9c3ecd0fd4616630": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f1ec22b3aa7b41f3b4e8a8039ca0e7b7": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "64e55746d02f4001819ff517c805ec2a": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d032e929b7b1431e9c3ecd0fd4616630", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f1ec22b3aa7b41f3b4e8a8039ca0e7b7", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "9ee6c4ddb70d429894715ea2b81f09ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2686f2dc96554990b324ec629db035e9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cc2b727fb77747d48eb6de89948fc805": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9ee6c4ddb70d429894715ea2b81f09ec", "placeholder": "\u200b", "style": "IPY_MODEL_2686f2dc96554990b324ec629db035e9", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "d3e7c58589c6499a8302f0f3976e6bfb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d5b79d09ca894d9db33c61c119111efd": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8a9362716caa416894d6eb38ae7c4593": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d3e7c58589c6499a8302f0f3976e6bfb", "placeholder": "\u200b", "style": "IPY_MODEL_d5b79d09ca894d9db33c61c119111efd", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u2007223MB/s]"}}, "fa4eedb31c004046bba082cc5ad03e73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "52657ce9f99944db9bf445c23ceced00": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_cc2b727fb77747d48eb6de89948fc805", "IPY_MODEL_64e55746d02f4001819ff517c805ec2a", "IPY_MODEL_8a9362716caa416894d6eb38ae7c4593"], "layout": "IPY_MODEL_fa4eedb31c004046bba082cc5ad03e73", "tabbable": null, "tooltip": null}}, "5119fd3b9a554a0285d69241b4238840": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3ea0082e65c1445c8dfc684c31820e38": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "401cca213830476aaf131b5754a45e4d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5119fd3b9a554a0285d69241b4238840", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3ea0082e65c1445c8dfc684c31820e38", "tabbable": null, "tooltip": null, "value": 3201.0}}, "7e85c622ae8344ee942c12255338f02a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "21bc7d8902984f4e9224cd00d4fdf617": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "287434e88ed84e0d9ccb4a05565006c8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7e85c622ae8344ee942c12255338f02a", "placeholder": "\u200b", "style": "IPY_MODEL_21bc7d8902984f4e9224cd00d4fdf617", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "3a10a4645a7e48a590198cd1cf366613": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b3f5fd4d072b4db99ee74accb42cf952": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fa9ead685eee4696b3fa292b908dfc7c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3a10a4645a7e48a590198cd1cf366613", "placeholder": "\u200b", "style": "IPY_MODEL_b3f5fd4d072b4db99ee74accb42cf952", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007770kB/s]"}}, "b269424bfa5045f7b4b658254b88dc06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2156682227184679bb5ab7dea5aa2597": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_287434e88ed84e0d9ccb4a05565006c8", "IPY_MODEL_401cca213830476aaf131b5754a45e4d", "IPY_MODEL_fa9ead685eee4696b3fa292b908dfc7c"], "layout": "IPY_MODEL_b269424bfa5045f7b4b658254b88dc06", "tabbable": null, "tooltip": null}}, "6bd2d975a792427da49ecc4c18a16a8c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cb0244a6576f4fcb8a82691d4c44cb1e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "be9e767c57dc49cdbe293ab91d54433c": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_6bd2d975a792427da49ecc4c18a16a8c", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_cb0244a6576f4fcb8a82691d4c44cb1e", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "8ca4f7884da04fb096679c29f1741f78": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c6bb2423370b4480ba7f39186635762d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cffcc92eae50405295d02fa1d657d816": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8ca4f7884da04fb096679c29f1741f78", "placeholder": "\u200b", "style": "IPY_MODEL_c6bb2423370b4480ba7f39186635762d", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "b1a615b70ccc4afe8f75b0b212ec32dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "309b9b81d6a14292a59bbdd1e3761b22": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "19f26f6b8d5449568a114a9b47d7f057": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b1a615b70ccc4afe8f75b0b212ec32dd", "placeholder": "\u200b", "style": "IPY_MODEL_309b9b81d6a14292a59bbdd1e3761b22", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u2007103MB/s]"}}, "95bd0f790b244919af5670fbb6054c52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "22472e38039e465a92f6cb2af7431848": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_cffcc92eae50405295d02fa1d657d816", "IPY_MODEL_be9e767c57dc49cdbe293ab91d54433c", "IPY_MODEL_19f26f6b8d5449568a114a9b47d7f057"], "layout": "IPY_MODEL_95bd0f790b244919af5670fbb6054c52", "tabbable": null, "tooltip": null}}, "13ae702bd5d84279b8e63a3da792d5a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2c74f436fa81418cb31a0552913a960b": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "6ea6bf34c19f4af8aaa9da65d1baf4df": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_13ae702bd5d84279b8e63a3da792d5a9", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2c74f436fa81418cb31a0552913a960b", "tabbable": null, "tooltip": null, "value": 128619.0}}, "3ca8fc03589e42e4a31d4782f3e9fcf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "eff038df27584d5cb2d841dbeac70bfe": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "5ca0848a6ec84d39bb230c4a4572cac1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3ca8fc03589e42e4a31d4782f3e9fcf9", "placeholder": "\u200b", "style": "IPY_MODEL_eff038df27584d5cb2d841dbeac70bfe", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "86e85c6c49f343e2ac9a4a4f8a700987": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "622242b975d64c2cb5a8827d70f41c55": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "798ff95456a14a879b0ee75ddb28b8a2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_86e85c6c49f343e2ac9a4a4f8a700987", "placeholder": "\u200b", "style": "IPY_MODEL_622242b975d64c2cb5a8827d70f41c55", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20072.24MB/s]"}}, "fb0c05ec8fbf40339f9afc2d5752cce3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1535038e095c43858d008484de0216eb": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_5ca0848a6ec84d39bb230c4a4572cac1", "IPY_MODEL_6ea6bf34c19f4af8aaa9da65d1baf4df", "IPY_MODEL_798ff95456a14a879b0ee75ddb28b8a2"], "layout": "IPY_MODEL_fb0c05ec8fbf40339f9afc2d5752cce3", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"4748994f76ed4bc388496a39e510d54d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fcffd70673164f8aadd97b82906cc76e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "0a99e571562d43a7aa5ecd2abda2aa2d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4748994f76ed4bc388496a39e510d54d", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fcffd70673164f8aadd97b82906cc76e", "tabbable": null, "tooltip": null, "value": 2041.0}}, "d8487dd8ed3e42a38371219c3c729283": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c3d1ad19115840c0bd02e22d3f39fbd9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4fe53b9dff9148ffbaee866c5033bba3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d8487dd8ed3e42a38371219c3c729283", "placeholder": "\u200b", "style": "IPY_MODEL_c3d1ad19115840c0bd02e22d3f39fbd9", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "f64888652c004082a1b036270e2702e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "78b6d5bc6077499094dfc9bd718f4db7": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6884e940a8554f6589d336edaee67f33": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f64888652c004082a1b036270e2702e0", "placeholder": "\u200b", "style": "IPY_MODEL_78b6d5bc6077499094dfc9bd718f4db7", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007481kB/s]"}}, "dfeb5c97c0174fe3b02f601346925b4a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cf3a1744d9c64353a34451f3e0dc96ef": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_4fe53b9dff9148ffbaee866c5033bba3", "IPY_MODEL_0a99e571562d43a7aa5ecd2abda2aa2d", "IPY_MODEL_6884e940a8554f6589d336edaee67f33"], "layout": "IPY_MODEL_dfeb5c97c0174fe3b02f601346925b4a", "tabbable": null, "tooltip": null}}, "7687a1cfa1264a529fcedffaa6a4e467": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5a64304d36024180b26f4f79387324dc": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "f07baddc31e847c595d90dd42ca379fb": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7687a1cfa1264a529fcedffaa6a4e467", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5a64304d36024180b26f4f79387324dc", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "8458463007ef42c0a82ddb9da20103ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1a72151665fc4b27b30d0d9a8c74e578": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7f01730dbf1446ca92cdfd900a358c1a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8458463007ef42c0a82ddb9da20103ba", "placeholder": "\u200b", "style": "IPY_MODEL_1a72151665fc4b27b30d0d9a8c74e578", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "f34dc10446834ea5b31b833757faa688": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ca1c4425f72b4b4a83a222e1429dbfed": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3b887681781b4f25abe1998059b0ae53": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f34dc10446834ea5b31b833757faa688", "placeholder": "\u200b", "style": "IPY_MODEL_ca1c4425f72b4b4a83a222e1429dbfed", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200794.6MB/s]"}}, "fc74c0118a094bc3a970ec592d969b3d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2d6758bb73d44586a7740d66e26f26cf": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_7f01730dbf1446ca92cdfd900a358c1a", "IPY_MODEL_f07baddc31e847c595d90dd42ca379fb", "IPY_MODEL_3b887681781b4f25abe1998059b0ae53"], "layout": "IPY_MODEL_fc74c0118a094bc3a970ec592d969b3d", "tabbable": null, "tooltip": null}}, "d0a6b2ff14e14058a38164d872d62aca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "208e173931a84902991001290bbcd8e8": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b6a2cd8ff5374f089fad080858e3fd96": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d0a6b2ff14e14058a38164d872d62aca", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_208e173931a84902991001290bbcd8e8", "tabbable": null, "tooltip": null, "value": 3201.0}}, "32de4a18c7f147a8be35c654b53457aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b5cb5977357e4fab995dcae8e2643177": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b2e03ad67f4e4dd08bf9d4b1f4fb712d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_32de4a18c7f147a8be35c654b53457aa", "placeholder": "\u200b", "style": "IPY_MODEL_b5cb5977357e4fab995dcae8e2643177", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "169802f2389c4c4e8717f6593ecaccaf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "692bb6d688714e569e4dfa9584bc0ad0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "17cad868ac7f43f3a8289ad5c1dbdd25": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_169802f2389c4c4e8717f6593ecaccaf", "placeholder": "\u200b", "style": "IPY_MODEL_692bb6d688714e569e4dfa9584bc0ad0", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007778kB/s]"}}, "4284b301638341a3b41f91a5b5daab48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "11d84ef51dce4f7aaa879bf5e2b72f38": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b2e03ad67f4e4dd08bf9d4b1f4fb712d", "IPY_MODEL_b6a2cd8ff5374f089fad080858e3fd96", "IPY_MODEL_17cad868ac7f43f3a8289ad5c1dbdd25"], "layout": "IPY_MODEL_4284b301638341a3b41f91a5b5daab48", "tabbable": null, "tooltip": null}}, "e6590d45e9154268897f1c416b514bc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "00fc7e046c8f4c308d5d222a635bb52c": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e0eec76f9c4b41dab7f14303a673e770": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e6590d45e9154268897f1c416b514bc9", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_00fc7e046c8f4c308d5d222a635bb52c", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "4c63aa921de543f9a38a79a37dbf15a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ab62c5cec180440ebb1975bcb91646f5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "9583f3f76f304e7abc2f1a387e1d3df1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4c63aa921de543f9a38a79a37dbf15a6", "placeholder": "\u200b", "style": "IPY_MODEL_ab62c5cec180440ebb1975bcb91646f5", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "c1fc1902ebdc421e8d0afcb0d1027f63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ea4a743f2c1443eab20e9c8135c3321d": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f2bf833e4ea64394b10f33d310c0ee23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c1fc1902ebdc421e8d0afcb0d1027f63", "placeholder": "\u200b", "style": "IPY_MODEL_ea4a743f2c1443eab20e9c8135c3321d", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200793.5MB/s]"}}, "2d9a8d5f92fe4b3aaec557c0461f8829": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "299a08f2fa054f7bbe1daaa618adeb92": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_9583f3f76f304e7abc2f1a387e1d3df1", "IPY_MODEL_e0eec76f9c4b41dab7f14303a673e770", "IPY_MODEL_f2bf833e4ea64394b10f33d310c0ee23"], "layout": "IPY_MODEL_2d9a8d5f92fe4b3aaec557c0461f8829", "tabbable": null, "tooltip": null}}, "59b0ce30e35142928e041a6b8d2dc230": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dff35f7ef39b489d8db92f12e36f0799": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "cf3af27e08194934863770ad9095424f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_59b0ce30e35142928e041a6b8d2dc230", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_dff35f7ef39b489d8db92f12e36f0799", "tabbable": null, "tooltip": null, "value": 128619.0}}, "868c56c897d2493bb075baa99c580d21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6cd92f1c94e145be925eb36473830cbb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "67737bdcaa0b4c8aad9b8db3c9fbeb51": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_868c56c897d2493bb075baa99c580d21", "placeholder": "\u200b", "style": "IPY_MODEL_6cd92f1c94e145be925eb36473830cbb", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "8034cbf813a3405d8b1b6885128dbb55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "70432221e66e4c1b925252a9aabda6f1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "391af44b9e68466688f4ac70187a5ea4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8034cbf813a3405d8b1b6885128dbb55", "placeholder": "\u200b", "style": "IPY_MODEL_70432221e66e4c1b925252a9aabda6f1", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u200710.6MB/s]"}}, "aef29a94b0394011a06ba738f227041b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "999d991d513f43d38a94a4e22e5eae99": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_67737bdcaa0b4c8aad9b8db3c9fbeb51", "IPY_MODEL_cf3af27e08194934863770ad9095424f", "IPY_MODEL_391af44b9e68466688f4ac70187a5ea4"], "layout": "IPY_MODEL_aef29a94b0394011a06ba738f227041b", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/audio.ipynb b/master/tutorials/datalab/audio.ipynb index ef95879eb..2f2d2a40d 100644 --- a/master/tutorials/datalab/audio.ipynb +++ b/master/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:25.530965Z", - "iopub.status.busy": "2024-06-28T15:32:25.530540Z", - "iopub.status.idle": "2024-06-28T15:32:31.009406Z", - "shell.execute_reply": "2024-06-28T15:32:31.008863Z" + "iopub.execute_input": "2024-07-01T15:02:05.352362Z", + "iopub.status.busy": "2024-07-01T15:02:05.351840Z", + "iopub.status.idle": "2024-07-01T15:02:11.364535Z", + "shell.execute_reply": "2024-07-01T15:02:11.364016Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.012191Z", - "iopub.status.busy": "2024-06-28T15:32:31.011789Z", - "iopub.status.idle": "2024-06-28T15:32:31.015685Z", - "shell.execute_reply": "2024-06-28T15:32:31.015243Z" + "iopub.execute_input": "2024-07-01T15:02:11.367286Z", + "iopub.status.busy": "2024-07-01T15:02:11.366756Z", + "iopub.status.idle": "2024-07-01T15:02:11.369937Z", + "shell.execute_reply": "2024-07-01T15:02:11.369499Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.017841Z", - "iopub.status.busy": "2024-06-28T15:32:31.017501Z", - "iopub.status.idle": "2024-06-28T15:32:31.022379Z", - "shell.execute_reply": "2024-06-28T15:32:31.021949Z" + "iopub.execute_input": "2024-07-01T15:02:11.372033Z", + "iopub.status.busy": "2024-07-01T15:02:11.371712Z", + "iopub.status.idle": "2024-07-01T15:02:11.376772Z", + "shell.execute_reply": "2024-07-01T15:02:11.376263Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.024337Z", - "iopub.status.busy": "2024-06-28T15:32:31.024159Z", - "iopub.status.idle": "2024-06-28T15:32:32.833612Z", - "shell.execute_reply": "2024-06-28T15:32:32.832843Z" + "iopub.execute_input": "2024-07-01T15:02:11.378959Z", + "iopub.status.busy": "2024-07-01T15:02:11.378766Z", + "iopub.status.idle": "2024-07-01T15:02:12.901153Z", + "shell.execute_reply": "2024-07-01T15:02:12.900530Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.837171Z", - "iopub.status.busy": "2024-06-28T15:32:32.836718Z", - "iopub.status.idle": "2024-06-28T15:32:32.848336Z", - "shell.execute_reply": "2024-06-28T15:32:32.847757Z" + "iopub.execute_input": "2024-07-01T15:02:12.904092Z", + "iopub.status.busy": "2024-07-01T15:02:12.903651Z", + "iopub.status.idle": "2024-07-01T15:02:12.914311Z", + "shell.execute_reply": "2024-07-01T15:02:12.913807Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.850867Z", - "iopub.status.busy": "2024-06-28T15:32:32.850504Z", - "iopub.status.idle": "2024-06-28T15:32:32.856225Z", - "shell.execute_reply": "2024-06-28T15:32:32.855744Z" + "iopub.execute_input": "2024-07-01T15:02:12.916523Z", + "iopub.status.busy": "2024-07-01T15:02:12.916188Z", + "iopub.status.idle": "2024-07-01T15:02:12.921874Z", + "shell.execute_reply": "2024-07-01T15:02:12.921422Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.858524Z", - "iopub.status.busy": "2024-06-28T15:32:32.858072Z", - "iopub.status.idle": "2024-06-28T15:32:33.339585Z", - "shell.execute_reply": "2024-06-28T15:32:33.339035Z" + "iopub.execute_input": "2024-07-01T15:02:12.923867Z", + "iopub.status.busy": "2024-07-01T15:02:12.923684Z", + "iopub.status.idle": "2024-07-01T15:02:13.374643Z", + "shell.execute_reply": "2024-07-01T15:02:13.374029Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:33.342120Z", - "iopub.status.busy": "2024-06-28T15:32:33.341633Z", - "iopub.status.idle": "2024-06-28T15:32:34.307317Z", - "shell.execute_reply": "2024-06-28T15:32:34.306830Z" + "iopub.execute_input": "2024-07-01T15:02:13.376868Z", + "iopub.status.busy": "2024-07-01T15:02:13.376659Z", + "iopub.status.idle": "2024-07-01T15:02:14.191014Z", + "shell.execute_reply": "2024-07-01T15:02:14.190519Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.309737Z", - "iopub.status.busy": "2024-06-28T15:32:34.309513Z", - "iopub.status.idle": "2024-06-28T15:32:34.327930Z", - "shell.execute_reply": "2024-06-28T15:32:34.327428Z" + "iopub.execute_input": "2024-07-01T15:02:14.193499Z", + "iopub.status.busy": "2024-07-01T15:02:14.193141Z", + "iopub.status.idle": "2024-07-01T15:02:14.211506Z", + "shell.execute_reply": "2024-07-01T15:02:14.210918Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.329961Z", - "iopub.status.busy": "2024-06-28T15:32:34.329778Z", - "iopub.status.idle": "2024-06-28T15:32:34.332922Z", - "shell.execute_reply": "2024-06-28T15:32:34.332483Z" + "iopub.execute_input": "2024-07-01T15:02:14.213644Z", + "iopub.status.busy": "2024-07-01T15:02:14.213459Z", + "iopub.status.idle": "2024-07-01T15:02:14.216713Z", + "shell.execute_reply": "2024-07-01T15:02:14.216187Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.334863Z", - "iopub.status.busy": "2024-06-28T15:32:34.334688Z", - "iopub.status.idle": "2024-06-28T15:32:50.142203Z", - "shell.execute_reply": "2024-06-28T15:32:50.141641Z" + "iopub.execute_input": "2024-07-01T15:02:14.218727Z", + "iopub.status.busy": "2024-07-01T15:02:14.218427Z", + "iopub.status.idle": "2024-07-01T15:02:28.819416Z", + "shell.execute_reply": "2024-07-01T15:02:28.818802Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.144906Z", - "iopub.status.busy": "2024-06-28T15:32:50.144703Z", - "iopub.status.idle": "2024-06-28T15:32:50.148671Z", - "shell.execute_reply": "2024-06-28T15:32:50.148141Z" + "iopub.execute_input": "2024-07-01T15:02:28.822324Z", + "iopub.status.busy": "2024-07-01T15:02:28.821907Z", + "iopub.status.idle": "2024-07-01T15:02:28.825931Z", + "shell.execute_reply": "2024-07-01T15:02:28.825366Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.150805Z", - "iopub.status.busy": "2024-06-28T15:32:50.150473Z", - "iopub.status.idle": "2024-06-28T15:32:50.858162Z", - "shell.execute_reply": "2024-06-28T15:32:50.857571Z" + "iopub.execute_input": "2024-07-01T15:02:28.827995Z", + "iopub.status.busy": "2024-07-01T15:02:28.827681Z", + "iopub.status.idle": "2024-07-01T15:02:29.521211Z", + "shell.execute_reply": "2024-07-01T15:02:29.520635Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.861090Z", - "iopub.status.busy": "2024-06-28T15:32:50.860665Z", - "iopub.status.idle": "2024-06-28T15:32:50.865818Z", - "shell.execute_reply": "2024-06-28T15:32:50.865293Z" + "iopub.execute_input": "2024-07-01T15:02:29.524947Z", + "iopub.status.busy": "2024-07-01T15:02:29.524000Z", + "iopub.status.idle": "2024-07-01T15:02:29.530833Z", + "shell.execute_reply": "2024-07-01T15:02:29.530342Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.868281Z", - "iopub.status.busy": "2024-06-28T15:32:50.867893Z", - "iopub.status.idle": "2024-06-28T15:32:50.969315Z", - "shell.execute_reply": "2024-06-28T15:32:50.968655Z" + "iopub.execute_input": "2024-07-01T15:02:29.534459Z", + "iopub.status.busy": "2024-07-01T15:02:29.533509Z", + "iopub.status.idle": "2024-07-01T15:02:29.635169Z", + "shell.execute_reply": "2024-07-01T15:02:29.634583Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.971877Z", - "iopub.status.busy": "2024-06-28T15:32:50.971633Z", - "iopub.status.idle": "2024-06-28T15:32:50.985705Z", - "shell.execute_reply": "2024-06-28T15:32:50.985220Z" + "iopub.execute_input": "2024-07-01T15:02:29.637589Z", + "iopub.status.busy": "2024-07-01T15:02:29.637281Z", + "iopub.status.idle": "2024-07-01T15:02:29.650412Z", + "shell.execute_reply": "2024-07-01T15:02:29.649911Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.987837Z", - "iopub.status.busy": "2024-06-28T15:32:50.987508Z", - "iopub.status.idle": "2024-06-28T15:32:50.995477Z", - "shell.execute_reply": "2024-06-28T15:32:50.995054Z" + "iopub.execute_input": "2024-07-01T15:02:29.652492Z", + "iopub.status.busy": "2024-07-01T15:02:29.652304Z", + "iopub.status.idle": "2024-07-01T15:02:29.660528Z", + "shell.execute_reply": "2024-07-01T15:02:29.660066Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.997717Z", - "iopub.status.busy": "2024-06-28T15:32:50.997278Z", - "iopub.status.idle": "2024-06-28T15:32:51.001400Z", - "shell.execute_reply": "2024-06-28T15:32:51.000850Z" + "iopub.execute_input": "2024-07-01T15:02:29.662724Z", + "iopub.status.busy": "2024-07-01T15:02:29.662297Z", + "iopub.status.idle": "2024-07-01T15:02:29.666626Z", + "shell.execute_reply": "2024-07-01T15:02:29.666160Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.003434Z", - "iopub.status.busy": "2024-06-28T15:32:51.003117Z", - "iopub.status.idle": "2024-06-28T15:32:51.008898Z", - "shell.execute_reply": "2024-06-28T15:32:51.008334Z" + "iopub.execute_input": "2024-07-01T15:02:29.668442Z", + "iopub.status.busy": "2024-07-01T15:02:29.668268Z", + "iopub.status.idle": "2024-07-01T15:02:29.673924Z", + "shell.execute_reply": "2024-07-01T15:02:29.673445Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.011016Z", - "iopub.status.busy": "2024-06-28T15:32:51.010696Z", - "iopub.status.idle": "2024-06-28T15:32:51.125754Z", - "shell.execute_reply": "2024-06-28T15:32:51.125163Z" + "iopub.execute_input": "2024-07-01T15:02:29.675826Z", + "iopub.status.busy": "2024-07-01T15:02:29.675650Z", + "iopub.status.idle": "2024-07-01T15:02:29.788802Z", + "shell.execute_reply": "2024-07-01T15:02:29.788252Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.127912Z", - "iopub.status.busy": "2024-06-28T15:32:51.127668Z", - "iopub.status.idle": "2024-06-28T15:32:51.234154Z", - "shell.execute_reply": "2024-06-28T15:32:51.233644Z" + "iopub.execute_input": "2024-07-01T15:02:29.791024Z", + "iopub.status.busy": "2024-07-01T15:02:29.790694Z", + "iopub.status.idle": "2024-07-01T15:02:29.899192Z", + "shell.execute_reply": "2024-07-01T15:02:29.898612Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.236252Z", - "iopub.status.busy": "2024-06-28T15:32:51.236020Z", - "iopub.status.idle": "2024-06-28T15:32:51.341460Z", - "shell.execute_reply": "2024-06-28T15:32:51.340903Z" + "iopub.execute_input": "2024-07-01T15:02:29.901288Z", + "iopub.status.busy": "2024-07-01T15:02:29.901099Z", + "iopub.status.idle": "2024-07-01T15:02:30.006069Z", + "shell.execute_reply": "2024-07-01T15:02:30.005505Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.343704Z", - "iopub.status.busy": "2024-06-28T15:32:51.343370Z", - "iopub.status.idle": "2024-06-28T15:32:51.451072Z", - "shell.execute_reply": "2024-06-28T15:32:51.450482Z" + "iopub.execute_input": "2024-07-01T15:02:30.008275Z", + "iopub.status.busy": "2024-07-01T15:02:30.007928Z", + "iopub.status.idle": "2024-07-01T15:02:30.113221Z", + "shell.execute_reply": "2024-07-01T15:02:30.112655Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.453664Z", - "iopub.status.busy": "2024-06-28T15:32:51.453217Z", - "iopub.status.idle": "2024-06-28T15:32:51.456589Z", - "shell.execute_reply": "2024-06-28T15:32:51.456096Z" + "iopub.execute_input": "2024-07-01T15:02:30.115374Z", + "iopub.status.busy": "2024-07-01T15:02:30.115188Z", + "iopub.status.idle": "2024-07-01T15:02:30.118487Z", + "shell.execute_reply": "2024-07-01T15:02:30.117941Z" }, "nbsphinx": "hidden" }, @@ -1392,107 +1392,73 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "13ae702bd5d84279b8e63a3da792d5a9": { - "model_module": "@jupyter-widgets/base", + "00fc7e046c8f4c308d5d222a635bb52c": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "1535038e095c43858d008484de0216eb": { + "0a99e571562d43a7aa5ecd2abda2aa2d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_5ca0848a6ec84d39bb230c4a4572cac1", - "IPY_MODEL_6ea6bf34c19f4af8aaa9da65d1baf4df", - "IPY_MODEL_798ff95456a14a879b0ee75ddb28b8a2" - ], - "layout": "IPY_MODEL_fb0c05ec8fbf40339f9afc2d5752cce3", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4748994f76ed4bc388496a39e510d54d", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fcffd70673164f8aadd97b82906cc76e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 2041.0 } }, - "19f26f6b8d5449568a114a9b47d7f057": { + "11d84ef51dce4f7aaa879bf5e2b72f38": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1a615b70ccc4afe8f75b0b212ec32dd", - "placeholder": "​", - "style": "IPY_MODEL_309b9b81d6a14292a59bbdd1e3761b22", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b2e03ad67f4e4dd08bf9d4b1f4fb712d", + "IPY_MODEL_b6a2cd8ff5374f089fad080858e3fd96", + "IPY_MODEL_17cad868ac7f43f3a8289ad5c1dbdd25" + ], + "layout": "IPY_MODEL_4284b301638341a3b41f91a5b5daab48", "tabbable": null, - "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 103MB/s]" + "tooltip": null } }, - "1c9cc6a7f35641d69ddc253c76d83c9c": { + "169802f2389c4c4e8717f6593ecaccaf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,99 +1511,64 @@ "width": null } }, - "1db3e87999ce494283770c1a2f49302e": { + "17cad868ac7f43f3a8289ad5c1dbdd25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f715cd04eb304470889617b12390a482", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8bdfb71cc05c4bac9e99957e74de9c77", + "layout": "IPY_MODEL_169802f2389c4c4e8717f6593ecaccaf", + "placeholder": "​", + "style": "IPY_MODEL_692bb6d688714e569e4dfa9584bc0ad0", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": " 3.20k/3.20k [00:00<00:00, 778kB/s]" } }, - "1f40455e27c443f1a07b5cb09a22204f": { + "1a72151665fc4b27b30d0d9a8c74e578": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b69c454713fc43798c96786b37d49252", - "IPY_MODEL_1db3e87999ce494283770c1a2f49302e", - "IPY_MODEL_c8bd4367ddb84b3b8e0e3a5262065f23" - ], - "layout": "IPY_MODEL_1c9cc6a7f35641d69ddc253c76d83c9c", - "tabbable": null, - "tooltip": null - } - }, - "2156682227184679bb5ab7dea5aa2597": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_287434e88ed84e0d9ccb4a05565006c8", - "IPY_MODEL_401cca213830476aaf131b5754a45e4d", - "IPY_MODEL_fa9ead685eee4696b3fa292b908dfc7c" - ], - "layout": "IPY_MODEL_b269424bfa5045f7b4b658254b88dc06", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "21bc7d8902984f4e9224cd00d4fdf617": { + "208e173931a84902991001290bbcd8e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "22472e38039e465a92f6cb2af7431848": { + "299a08f2fa054f7bbe1daaa618adeb92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1652,91 +1583,40 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_cffcc92eae50405295d02fa1d657d816", - "IPY_MODEL_be9e767c57dc49cdbe293ab91d54433c", - "IPY_MODEL_19f26f6b8d5449568a114a9b47d7f057" + "IPY_MODEL_9583f3f76f304e7abc2f1a387e1d3df1", + "IPY_MODEL_e0eec76f9c4b41dab7f14303a673e770", + "IPY_MODEL_f2bf833e4ea64394b10f33d310c0ee23" ], - "layout": "IPY_MODEL_95bd0f790b244919af5670fbb6054c52", + "layout": "IPY_MODEL_2d9a8d5f92fe4b3aaec557c0461f8829", "tabbable": null, "tooltip": null } }, - "2686f2dc96554990b324ec629db035e9": { + "2d6758bb73d44586a7740d66e26f26cf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "287434e88ed84e0d9ccb4a05565006c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7e85c622ae8344ee942c12255338f02a", - "placeholder": "​", - "style": "IPY_MODEL_21bc7d8902984f4e9224cd00d4fdf617", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7f01730dbf1446ca92cdfd900a358c1a", + "IPY_MODEL_f07baddc31e847c595d90dd42ca379fb", + "IPY_MODEL_3b887681781b4f25abe1998059b0ae53" + ], + "layout": "IPY_MODEL_fc74c0118a094bc3a970ec592d969b3d", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "2c74f436fa81418cb31a0552913a960b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "309b9b81d6a14292a59bbdd1e3761b22": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "3a10a4645a7e48a590198cd1cf366613": { + "2d9a8d5f92fe4b3aaec557c0461f8829": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1789,7 +1669,7 @@ "width": null } }, - "3ca8fc03589e42e4a31d4782f3e9fcf9": { + "32de4a18c7f147a8be35c654b53457aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1842,49 +1722,53 @@ "width": null } }, - "3ea0082e65c1445c8dfc684c31820e38": { + "391af44b9e68466688f4ac70187a5ea4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8034cbf813a3405d8b1b6885128dbb55", + "placeholder": "​", + "style": "IPY_MODEL_70432221e66e4c1b925252a9aabda6f1", + "tabbable": null, + "tooltip": null, + "value": " 129k/129k [00:00<00:00, 10.6MB/s]" } }, - "401cca213830476aaf131b5754a45e4d": { + "3b887681781b4f25abe1998059b0ae53": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5119fd3b9a554a0285d69241b4238840", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3ea0082e65c1445c8dfc684c31820e38", + "layout": "IPY_MODEL_f34dc10446834ea5b31b833757faa688", + "placeholder": "​", + "style": "IPY_MODEL_ca1c4425f72b4b4a83a222e1429dbfed", "tabbable": null, "tooltip": null, - "value": 3201.0 + "value": " 16.9M/16.9M [00:00<00:00, 94.6MB/s]" } }, - "5119fd3b9a554a0285d69241b4238840": { + "4284b301638341a3b41f91a5b5daab48": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1937,98 +1821,7 @@ "width": null } }, - "52657ce9f99944db9bf445c23ceced00": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cc2b727fb77747d48eb6de89948fc805", - "IPY_MODEL_64e55746d02f4001819ff517c805ec2a", - "IPY_MODEL_8a9362716caa416894d6eb38ae7c4593" - ], - "layout": "IPY_MODEL_fa4eedb31c004046bba082cc5ad03e73", - "tabbable": null, - "tooltip": null - } - }, - "5ca0848a6ec84d39bb230c4a4572cac1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3ca8fc03589e42e4a31d4782f3e9fcf9", - "placeholder": "​", - "style": "IPY_MODEL_eff038df27584d5cb2d841dbeac70bfe", - "tabbable": null, - "tooltip": null, - "value": "label_encoder.txt: 100%" - } - }, - "622242b975d64c2cb5a8827d70f41c55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "64e55746d02f4001819ff517c805ec2a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d032e929b7b1431e9c3ecd0fd4616630", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f1ec22b3aa7b41f3b4e8a8039ca0e7b7", - "tabbable": null, - "tooltip": null, - "value": 16887676.0 - } - }, - "6bd2d975a792427da49ecc4c18a16a8c": { + "4748994f76ed4bc388496a39e510d54d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2081,56 +1874,7 @@ "width": null } }, - "6ea6bf34c19f4af8aaa9da65d1baf4df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_13ae702bd5d84279b8e63a3da792d5a9", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2c74f436fa81418cb31a0552913a960b", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "798ff95456a14a879b0ee75ddb28b8a2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_86e85c6c49f343e2ac9a4a4f8a700987", - "placeholder": "​", - "style": "IPY_MODEL_622242b975d64c2cb5a8827d70f41c55", - "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 2.24MB/s]" - } - }, - "7e85c622ae8344ee942c12255338f02a": { + "4c63aa921de543f9a38a79a37dbf15a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2183,7 +1927,30 @@ "width": null } }, - "86e85c6c49f343e2ac9a4a4f8a700987": { + "4fe53b9dff9148ffbaee866c5033bba3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8487dd8ed3e42a38371219c3c729283", + "placeholder": "​", + "style": "IPY_MODEL_c3d1ad19115840c0bd02e22d3f39fbd9", + "tabbable": null, + "tooltip": null, + "value": "hyperparams.yaml: 100%" + } + }, + "59b0ce30e35142928e041a6b8d2dc230": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2236,7 +2003,46 @@ "width": null } }, - "8a9362716caa416894d6eb38ae7c4593": { + "5a64304d36024180b26f4f79387324dc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "67737bdcaa0b4c8aad9b8db3c9fbeb51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_868c56c897d2493bb075baa99c580d21", + "placeholder": "​", + "style": "IPY_MODEL_6cd92f1c94e145be925eb36473830cbb", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" + } + }, + "6884e940a8554f6589d336edaee67f33": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2251,31 +2057,69 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d3e7c58589c6499a8302f0f3976e6bfb", + "layout": "IPY_MODEL_f64888652c004082a1b036270e2702e0", "placeholder": "​", - "style": "IPY_MODEL_d5b79d09ca894d9db33c61c119111efd", + "style": "IPY_MODEL_78b6d5bc6077499094dfc9bd718f4db7", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 223MB/s]" + "value": " 2.04k/2.04k [00:00<00:00, 481kB/s]" } }, - "8bdfb71cc05c4bac9e99957e74de9c77": { + "692bb6d688714e569e4dfa9584bc0ad0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6cd92f1c94e145be925eb36473830cbb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "70432221e66e4c1b925252a9aabda6f1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8ca4f7884da04fb096679c29f1741f78": { + "7687a1cfa1264a529fcedffaa6a4e467": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2328,7 +2172,48 @@ "width": null } }, - "95bd0f790b244919af5670fbb6054c52": { + "78b6d5bc6077499094dfc9bd718f4db7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7f01730dbf1446ca92cdfd900a358c1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8458463007ef42c0a82ddb9da20103ba", + "placeholder": "​", + "style": "IPY_MODEL_1a72151665fc4b27b30d0d9a8c74e578", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "8034cbf813a3405d8b1b6885128dbb55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2381,7 +2266,7 @@ "width": null } }, - "9d23f4f000134f42aa3aa92e38319d3e": { + "8458463007ef42c0a82ddb9da20103ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2434,7 +2319,7 @@ "width": null } }, - "9ee6c4ddb70d429894715ea2b81f09ec": { + "868c56c897d2493bb075baa99c580d21": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2487,7 +2372,72 @@ "width": null } }, - "b1a615b70ccc4afe8f75b0b212ec32dd": { + "9583f3f76f304e7abc2f1a387e1d3df1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4c63aa921de543f9a38a79a37dbf15a6", + "placeholder": "​", + "style": "IPY_MODEL_ab62c5cec180440ebb1975bcb91646f5", + "tabbable": null, + "tooltip": null, + "value": "classifier.ckpt: 100%" + } + }, + "999d991d513f43d38a94a4e22e5eae99": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_67737bdcaa0b4c8aad9b8db3c9fbeb51", + "IPY_MODEL_cf3af27e08194934863770ad9095424f", + "IPY_MODEL_391af44b9e68466688f4ac70187a5ea4" + ], + "layout": "IPY_MODEL_aef29a94b0394011a06ba738f227041b", + "tabbable": null, + "tooltip": null + } + }, + "ab62c5cec180440ebb1975bcb91646f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "aef29a94b0394011a06ba738f227041b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2540,7 +2490,74 @@ "width": null } }, - "b269424bfa5045f7b4b658254b88dc06": { + "b2e03ad67f4e4dd08bf9d4b1f4fb712d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_32de4a18c7f147a8be35c654b53457aa", + "placeholder": "​", + "style": "IPY_MODEL_b5cb5977357e4fab995dcae8e2643177", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "b5cb5977357e4fab995dcae8e2643177": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "b6a2cd8ff5374f089fad080858e3fd96": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0a6b2ff14e14058a38164d872d62aca", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_208e173931a84902991001290bbcd8e8", + "tabbable": null, + "tooltip": null, + "value": 3201.0 + } + }, + "c1fc1902ebdc421e8d0afcb0d1027f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2593,7 +2610,7 @@ "width": null } }, - "b3f5fd4d072b4db99ee74accb42cf952": { + "c3d1ad19115840c0bd02e22d3f39fbd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2611,56 +2628,7 @@ "text_color": null } }, - "b69c454713fc43798c96786b37d49252": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9d23f4f000134f42aa3aa92e38319d3e", - "placeholder": "​", - "style": "IPY_MODEL_c2ba3bd1eaf1447681bbf99a54df9974", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "be9e767c57dc49cdbe293ab91d54433c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6bd2d975a792427da49ecc4c18a16a8c", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cb0244a6576f4fcb8a82691d4c44cb1e", - "tabbable": null, - "tooltip": null, - "value": 15856877.0 - } - }, - "c2ba3bd1eaf1447681bbf99a54df9974": { + "ca1c4425f72b4b4a83a222e1429dbfed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2678,43 +2646,57 @@ "text_color": null } }, - "c4148b51f19748b4af9b5af3134378e7": { + "cf3a1744d9c64353a34451f3e0dc96ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4fe53b9dff9148ffbaee866c5033bba3", + "IPY_MODEL_0a99e571562d43a7aa5ecd2abda2aa2d", + "IPY_MODEL_6884e940a8554f6589d336edaee67f33" + ], + "layout": "IPY_MODEL_dfeb5c97c0174fe3b02f601346925b4a", + "tabbable": null, + "tooltip": null } }, - "c6bb2423370b4480ba7f39186635762d": { + "cf3af27e08194934863770ad9095424f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_59b0ce30e35142928e041a6b8d2dc230", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dff35f7ef39b489d8db92f12e36f0799", + "tabbable": null, + "tooltip": null, + "value": 128619.0 } }, - "c6d20ede22124fc6ba4a66d669a06d2a": { + "d0a6b2ff14e14058a38164d872d62aca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2767,92 +2749,60 @@ "width": null } }, - "c8bd4367ddb84b3b8e0e3a5262065f23": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c6d20ede22124fc6ba4a66d669a06d2a", - "placeholder": "​", - "style": "IPY_MODEL_c4148b51f19748b4af9b5af3134378e7", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 453kB/s]" - } - }, - "cb0244a6576f4fcb8a82691d4c44cb1e": { - "model_module": "@jupyter-widgets/controls", + "d8487dd8ed3e42a38371219c3c729283": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "cc2b727fb77747d48eb6de89948fc805": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9ee6c4ddb70d429894715ea2b81f09ec", - "placeholder": "​", - "style": "IPY_MODEL_2686f2dc96554990b324ec629db035e9", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "cffcc92eae50405295d02fa1d657d816": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8ca4f7884da04fb096679c29f1741f78", - "placeholder": "​", - "style": "IPY_MODEL_c6bb2423370b4480ba7f39186635762d", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d032e929b7b1431e9c3ecd0fd4616630": { + "dfeb5c97c0174fe3b02f601346925b4a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2905,7 +2855,49 @@ "width": null } }, - "d3e7c58589c6499a8302f0f3976e6bfb": { + "dff35f7ef39b489d8db92f12e36f0799": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e0eec76f9c4b41dab7f14303a673e770": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e6590d45e9154268897f1c416b514bc9", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_00fc7e046c8f4c308d5d222a635bb52c", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 + } + }, + "e6590d45e9154268897f1c416b514bc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2958,7 +2950,7 @@ "width": null } }, - "d5b79d09ca894d9db33c61c119111efd": { + "ea4a743f2c1443eab20e9c8135c3321d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2976,41 +2968,56 @@ "text_color": null } }, - "eff038df27584d5cb2d841dbeac70bfe": { + "f07baddc31e847c595d90dd42ca379fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7687a1cfa1264a529fcedffaa6a4e467", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a64304d36024180b26f4f79387324dc", + "tabbable": null, + "tooltip": null, + "value": 16887676.0 } }, - "f1ec22b3aa7b41f3b4e8a8039ca0e7b7": { + "f2bf833e4ea64394b10f33d310c0ee23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c1fc1902ebdc421e8d0afcb0d1027f63", + "placeholder": "​", + "style": "IPY_MODEL_ea4a743f2c1443eab20e9c8135c3321d", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 93.5MB/s]" } }, - "f715cd04eb304470889617b12390a482": { + "f34dc10446834ea5b31b833757faa688": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3063,7 +3070,7 @@ "width": null } }, - "fa4eedb31c004046bba082cc5ad03e73": { + "f64888652c004082a1b036270e2702e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3116,30 +3123,7 @@ "width": null } }, - "fa9ead685eee4696b3fa292b908dfc7c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3a10a4645a7e48a590198cd1cf366613", - "placeholder": "​", - "style": "IPY_MODEL_b3f5fd4d072b4db99ee74accb42cf952", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 770kB/s]" - } - }, - "fb0c05ec8fbf40339f9afc2d5752cce3": { + "fc74c0118a094bc3a970ec592d969b3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3191,6 +3175,22 @@ "visibility": null, "width": null } + }, + "fcffd70673164f8aadd97b82906cc76e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index 26e60506d..c68bc59f2 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1291,7 +1291,7 @@

    Functionality 3: Save and load Datalab objects

    -
    +
    @@ -1566,7 +1566,7 @@

    Functionality 4: Adding a custom IssueManager -{"state": {"84e1f61107d940b7977e4fff0fb8f2bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "905e6b0f80c7424ca91d5ba60216672e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "8fc926f029414a8aaaf8ecab225fc0fe": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_84e1f61107d940b7977e4fff0fb8f2bd", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_905e6b0f80c7424ca91d5ba60216672e", "tabbable": null, "tooltip": null, "value": 132.0}}, "0a6708021b7f4b4288ce7905c15eab15": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4d70992f11b84bf9a36040fe5ef16e1c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "40c3bb8a23164577a0c0611033e6d54c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0a6708021b7f4b4288ce7905c15eab15", "placeholder": "\u200b", "style": "IPY_MODEL_4d70992f11b84bf9a36040fe5ef16e1c", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "954e9713147d4bdb88dfcbb4b36fab1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b93d0824edb848df8dfe762bda5a4c34": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "fd7cae3437574fd3875f05820537adde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_954e9713147d4bdb88dfcbb4b36fab1d", "placeholder": "\u200b", "style": "IPY_MODEL_b93d0824edb848df8dfe762bda5a4c34", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200712955.99\u2007examples/s]"}}, "84e74dbfaf7b43918c3fef375f4c2636": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c5cc8030a1864cbc888bc657ba9d1871": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_40c3bb8a23164577a0c0611033e6d54c", "IPY_MODEL_8fc926f029414a8aaaf8ecab225fc0fe", "IPY_MODEL_fd7cae3437574fd3875f05820537adde"], "layout": "IPY_MODEL_84e74dbfaf7b43918c3fef375f4c2636", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"0dc84865474c4bf7a312e538bc8f4a74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bd9368c5e9b842ca818c69f779cd5276": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "39e0d0ff92854bb5b45f8340a9c5c5eb": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0dc84865474c4bf7a312e538bc8f4a74", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_bd9368c5e9b842ca818c69f779cd5276", "tabbable": null, "tooltip": null, "value": 132.0}}, "746118eed9d445c9a37e681cbacd9674": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d57ca1f4799d45229ae2f7c720c262f5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "979503323663435eae635a194817476f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_746118eed9d445c9a37e681cbacd9674", "placeholder": "\u200b", "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "af633ab6f6924af0b3f4ac3691d76422": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bcb7b5d047f845978925e2ef6da3385e": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "cac3b162735445c0915d9ecfed155f4c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_af633ab6f6924af0b3f4ac3691d76422", "placeholder": "\u200b", "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200711804.36\u2007examples/s]"}}, "890c2c0c04564f5da3221229c05800df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d16dede2bb2e40b282d000f989523e41": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_979503323663435eae635a194817476f", "IPY_MODEL_39e0d0ff92854bb5b45f8340a9c5c5eb", "IPY_MODEL_cac3b162735445c0915d9ecfed155f4c"], "layout": "IPY_MODEL_890c2c0c04564f5da3221229c05800df", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index fe9f08e9b..1e7141136 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:55.137126Z", - "iopub.status.busy": "2024-06-28T15:32:55.136940Z", - "iopub.status.idle": "2024-06-28T15:32:56.356852Z", - "shell.execute_reply": "2024-06-28T15:32:56.356217Z" + "iopub.execute_input": "2024-07-01T15:02:34.100510Z", + "iopub.status.busy": "2024-07-01T15:02:34.100309Z", + "iopub.status.idle": "2024-07-01T15:02:35.344393Z", + "shell.execute_reply": "2024-07-01T15:02:35.343853Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.359787Z", - "iopub.status.busy": "2024-06-28T15:32:56.359107Z", - "iopub.status.idle": "2024-06-28T15:32:56.362371Z", - "shell.execute_reply": "2024-06-28T15:32:56.361926Z" + "iopub.execute_input": "2024-07-01T15:02:35.347246Z", + "iopub.status.busy": "2024-07-01T15:02:35.346746Z", + "iopub.status.idle": "2024-07-01T15:02:35.349837Z", + "shell.execute_reply": "2024-07-01T15:02:35.349391Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.364580Z", - "iopub.status.busy": "2024-06-28T15:32:56.364380Z", - "iopub.status.idle": "2024-06-28T15:32:56.373022Z", - "shell.execute_reply": "2024-06-28T15:32:56.372568Z" + "iopub.execute_input": "2024-07-01T15:02:35.352173Z", + "iopub.status.busy": "2024-07-01T15:02:35.351845Z", + "iopub.status.idle": "2024-07-01T15:02:35.361048Z", + "shell.execute_reply": "2024-07-01T15:02:35.360394Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.374888Z", - "iopub.status.busy": "2024-06-28T15:32:56.374711Z", - "iopub.status.idle": "2024-06-28T15:32:56.379461Z", - "shell.execute_reply": "2024-06-28T15:32:56.379052Z" + "iopub.execute_input": "2024-07-01T15:02:35.363704Z", + "iopub.status.busy": "2024-07-01T15:02:35.363238Z", + "iopub.status.idle": "2024-07-01T15:02:35.368807Z", + "shell.execute_reply": "2024-07-01T15:02:35.368166Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.381515Z", - "iopub.status.busy": "2024-06-28T15:32:56.381337Z", - "iopub.status.idle": "2024-06-28T15:32:56.567465Z", - "shell.execute_reply": "2024-06-28T15:32:56.566945Z" + "iopub.execute_input": "2024-07-01T15:02:35.371407Z", + "iopub.status.busy": "2024-07-01T15:02:35.370950Z", + "iopub.status.idle": "2024-07-01T15:02:35.579618Z", + "shell.execute_reply": "2024-07-01T15:02:35.578902Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.570098Z", - "iopub.status.busy": "2024-06-28T15:32:56.569740Z", - "iopub.status.idle": "2024-06-28T15:32:56.946795Z", - "shell.execute_reply": "2024-06-28T15:32:56.946221Z" + "iopub.execute_input": "2024-07-01T15:02:35.582660Z", + "iopub.status.busy": "2024-07-01T15:02:35.582270Z", + "iopub.status.idle": "2024-07-01T15:02:35.982874Z", + "shell.execute_reply": "2024-07-01T15:02:35.982240Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.949083Z", - "iopub.status.busy": "2024-06-28T15:32:56.948728Z", - "iopub.status.idle": "2024-06-28T15:32:56.972968Z", - "shell.execute_reply": "2024-06-28T15:32:56.972339Z" + "iopub.execute_input": "2024-07-01T15:02:35.985209Z", + "iopub.status.busy": "2024-07-01T15:02:35.984882Z", + "iopub.status.idle": "2024-07-01T15:02:36.009092Z", + "shell.execute_reply": "2024-07-01T15:02:36.008586Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.975923Z", - "iopub.status.busy": "2024-06-28T15:32:56.975533Z", - "iopub.status.idle": "2024-06-28T15:32:56.987983Z", - "shell.execute_reply": "2024-06-28T15:32:56.987424Z" + "iopub.execute_input": "2024-07-01T15:02:36.011743Z", + "iopub.status.busy": "2024-07-01T15:02:36.011310Z", + "iopub.status.idle": "2024-07-01T15:02:36.023407Z", + "shell.execute_reply": "2024-07-01T15:02:36.022817Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.990765Z", - "iopub.status.busy": "2024-06-28T15:32:56.990359Z", - "iopub.status.idle": "2024-06-28T15:32:59.165996Z", - "shell.execute_reply": "2024-06-28T15:32:59.165438Z" + "iopub.execute_input": "2024-07-01T15:02:36.025951Z", + "iopub.status.busy": "2024-07-01T15:02:36.025605Z", + "iopub.status.idle": "2024-07-01T15:02:38.178872Z", + "shell.execute_reply": "2024-07-01T15:02:38.178173Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.168493Z", - "iopub.status.busy": "2024-06-28T15:32:59.167970Z", - "iopub.status.idle": "2024-06-28T15:32:59.190456Z", - "shell.execute_reply": "2024-06-28T15:32:59.189877Z" + "iopub.execute_input": "2024-07-01T15:02:38.181613Z", + "iopub.status.busy": "2024-07-01T15:02:38.181161Z", + "iopub.status.idle": "2024-07-01T15:02:38.204407Z", + "shell.execute_reply": "2024-07-01T15:02:38.203790Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.192911Z", - "iopub.status.busy": "2024-06-28T15:32:59.192503Z", - "iopub.status.idle": "2024-06-28T15:32:59.210971Z", - "shell.execute_reply": "2024-06-28T15:32:59.210474Z" + "iopub.execute_input": "2024-07-01T15:02:38.207031Z", + "iopub.status.busy": "2024-07-01T15:02:38.206574Z", + "iopub.status.idle": "2024-07-01T15:02:38.225655Z", + "shell.execute_reply": "2024-07-01T15:02:38.225020Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.213153Z", - "iopub.status.busy": "2024-06-28T15:32:59.212814Z", - "iopub.status.idle": "2024-06-28T15:32:59.227073Z", - "shell.execute_reply": "2024-06-28T15:32:59.226626Z" + "iopub.execute_input": "2024-07-01T15:02:38.228105Z", + "iopub.status.busy": "2024-07-01T15:02:38.227775Z", + "iopub.status.idle": "2024-07-01T15:02:38.243503Z", + "shell.execute_reply": "2024-07-01T15:02:38.242861Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.229388Z", - "iopub.status.busy": "2024-06-28T15:32:59.229043Z", - "iopub.status.idle": "2024-06-28T15:32:59.248480Z", - "shell.execute_reply": "2024-06-28T15:32:59.247931Z" + "iopub.execute_input": "2024-07-01T15:02:38.245863Z", + "iopub.status.busy": "2024-07-01T15:02:38.245474Z", + "iopub.status.idle": "2024-07-01T15:02:38.267061Z", + "shell.execute_reply": "2024-07-01T15:02:38.266454Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c5cc8030a1864cbc888bc657ba9d1871", + "model_id": "d16dede2bb2e40b282d000f989523e41", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.250632Z", - "iopub.status.busy": "2024-06-28T15:32:59.250452Z", - "iopub.status.idle": "2024-06-28T15:32:59.266154Z", - "shell.execute_reply": "2024-06-28T15:32:59.265602Z" + "iopub.execute_input": "2024-07-01T15:02:38.269410Z", + "iopub.status.busy": "2024-07-01T15:02:38.269038Z", + "iopub.status.idle": "2024-07-01T15:02:38.286735Z", + "shell.execute_reply": "2024-07-01T15:02:38.286110Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.268379Z", - "iopub.status.busy": "2024-06-28T15:32:59.267966Z", - "iopub.status.idle": "2024-06-28T15:32:59.274040Z", - "shell.execute_reply": "2024-06-28T15:32:59.273500Z" + "iopub.execute_input": "2024-07-01T15:02:38.289163Z", + "iopub.status.busy": "2024-07-01T15:02:38.288765Z", + "iopub.status.idle": "2024-07-01T15:02:38.295067Z", + "shell.execute_reply": "2024-07-01T15:02:38.294503Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.276184Z", - "iopub.status.busy": "2024-06-28T15:32:59.275830Z", - "iopub.status.idle": "2024-06-28T15:32:59.294259Z", - "shell.execute_reply": "2024-06-28T15:32:59.293677Z" + "iopub.execute_input": "2024-07-01T15:02:38.297383Z", + "iopub.status.busy": "2024-07-01T15:02:38.296989Z", + "iopub.status.idle": "2024-07-01T15:02:38.317743Z", + "shell.execute_reply": "2024-07-01T15:02:38.317117Z" } }, "outputs": [ @@ -1447,7 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a6708021b7f4b4288ce7905c15eab15": { + "0dc84865474c4bf7a312e538bc8f4a74": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,48 +1500,33 @@ "width": null } }, - "40c3bb8a23164577a0c0611033e6d54c": { + "39e0d0ff92854bb5b45f8340a9c5c5eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0a6708021b7f4b4288ce7905c15eab15", - "placeholder": "​", - "style": "IPY_MODEL_4d70992f11b84bf9a36040fe5ef16e1c", + "layout": "IPY_MODEL_0dc84865474c4bf7a312e538bc8f4a74", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bd9368c5e9b842ca818c69f779cd5276", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } - }, - "4d70992f11b84bf9a36040fe5ef16e1c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 132.0 } }, - "84e1f61107d940b7977e4fff0fb8f2bd": { + "746118eed9d445c9a37e681cbacd9674": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1594,7 +1579,7 @@ "width": null } }, - "84e74dbfaf7b43918c3fef375f4c2636": { + "890c2c0c04564f5da3221229c05800df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1647,49 +1632,30 @@ "width": null } }, - "8fc926f029414a8aaaf8ecab225fc0fe": { + "979503323663435eae635a194817476f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_84e1f61107d940b7977e4fff0fb8f2bd", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_905e6b0f80c7424ca91d5ba60216672e", + "layout": "IPY_MODEL_746118eed9d445c9a37e681cbacd9674", + "placeholder": "​", + "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5", "tabbable": null, "tooltip": null, - "value": 132.0 - } - }, - "905e6b0f80c7424ca91d5ba60216672e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": "Saving the dataset (1/1 shards): 100%" } }, - "954e9713147d4bdb88dfcbb4b36fab1d": { + "af633ab6f6924af0b3f4ac3691d76422": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1742,7 +1708,7 @@ "width": null } }, - "b93d0824edb848df8dfe762bda5a4c34": { + "bcb7b5d047f845978925e2ef6da3385e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1760,31 +1726,23 @@ "text_color": null } }, - "c5cc8030a1864cbc888bc657ba9d1871": { + "bd9368c5e9b842ca818c69f779cd5276": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_40c3bb8a23164577a0c0611033e6d54c", - "IPY_MODEL_8fc926f029414a8aaaf8ecab225fc0fe", - "IPY_MODEL_fd7cae3437574fd3875f05820537adde" - ], - "layout": "IPY_MODEL_84e74dbfaf7b43918c3fef375f4c2636", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "fd7cae3437574fd3875f05820537adde": { + "cac3b162735445c0915d9ecfed155f4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1799,12 +1757,54 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_954e9713147d4bdb88dfcbb4b36fab1d", + "layout": "IPY_MODEL_af633ab6f6924af0b3f4ac3691d76422", "placeholder": "​", - "style": "IPY_MODEL_b93d0824edb848df8dfe762bda5a4c34", + "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 12955.99 examples/s]" + "value": " 132/132 [00:00<00:00, 11804.36 examples/s]" + } + }, + "d16dede2bb2e40b282d000f989523e41": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_979503323663435eae635a194817476f", + "IPY_MODEL_39e0d0ff92854bb5b45f8340a9c5c5eb", + "IPY_MODEL_cac3b162735445c0915d9ecfed155f4c" + ], + "layout": "IPY_MODEL_890c2c0c04564f5da3221229c05800df", + "tabbable": null, + "tooltip": null + } + }, + "d57ca1f4799d45229ae2f7c720c262f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 0ab929cc5..e8c4bda9d 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:02.265971Z", - "iopub.status.busy": "2024-06-28T15:33:02.265601Z", - "iopub.status.idle": "2024-06-28T15:33:03.540244Z", - "shell.execute_reply": "2024-06-28T15:33:03.539709Z" + "iopub.execute_input": "2024-07-01T15:02:41.409044Z", + "iopub.status.busy": "2024-07-01T15:02:41.408875Z", + "iopub.status.idle": "2024-07-01T15:02:42.611044Z", + "shell.execute_reply": "2024-07-01T15:02:42.610498Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.543066Z", - "iopub.status.busy": "2024-06-28T15:33:03.542566Z", - "iopub.status.idle": "2024-06-28T15:33:03.545721Z", - "shell.execute_reply": "2024-06-28T15:33:03.545256Z" + "iopub.execute_input": "2024-07-01T15:02:42.613616Z", + "iopub.status.busy": "2024-07-01T15:02:42.613300Z", + "iopub.status.idle": "2024-07-01T15:02:42.616526Z", + "shell.execute_reply": "2024-07-01T15:02:42.616068Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.548125Z", - "iopub.status.busy": "2024-06-28T15:33:03.547790Z", - "iopub.status.idle": "2024-06-28T15:33:03.557073Z", - "shell.execute_reply": "2024-06-28T15:33:03.556489Z" + "iopub.execute_input": "2024-07-01T15:02:42.618748Z", + "iopub.status.busy": "2024-07-01T15:02:42.618428Z", + "iopub.status.idle": "2024-07-01T15:02:42.627446Z", + "shell.execute_reply": "2024-07-01T15:02:42.626999Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.559397Z", - "iopub.status.busy": "2024-06-28T15:33:03.559034Z", - "iopub.status.idle": "2024-06-28T15:33:03.564198Z", - "shell.execute_reply": "2024-06-28T15:33:03.563684Z" + "iopub.execute_input": "2024-07-01T15:02:42.629537Z", + "iopub.status.busy": "2024-07-01T15:02:42.629203Z", + "iopub.status.idle": "2024-07-01T15:02:42.633941Z", + "shell.execute_reply": "2024-07-01T15:02:42.633516Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.566643Z", - "iopub.status.busy": "2024-06-28T15:33:03.566263Z", - "iopub.status.idle": "2024-06-28T15:33:03.754952Z", - "shell.execute_reply": "2024-06-28T15:33:03.754321Z" + "iopub.execute_input": "2024-07-01T15:02:42.636177Z", + "iopub.status.busy": "2024-07-01T15:02:42.635851Z", + "iopub.status.idle": "2024-07-01T15:02:42.823356Z", + "shell.execute_reply": "2024-07-01T15:02:42.822807Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.757588Z", - "iopub.status.busy": "2024-06-28T15:33:03.757353Z", - "iopub.status.idle": "2024-06-28T15:33:04.137542Z", - "shell.execute_reply": "2024-06-28T15:33:04.136949Z" + "iopub.execute_input": "2024-07-01T15:02:42.826055Z", + "iopub.status.busy": "2024-07-01T15:02:42.825690Z", + "iopub.status.idle": "2024-07-01T15:02:43.206067Z", + "shell.execute_reply": "2024-07-01T15:02:43.205474Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.139670Z", - "iopub.status.busy": "2024-06-28T15:33:04.139477Z", - "iopub.status.idle": "2024-06-28T15:33:04.142221Z", - "shell.execute_reply": "2024-06-28T15:33:04.141788Z" + "iopub.execute_input": "2024-07-01T15:02:43.208532Z", + "iopub.status.busy": "2024-07-01T15:02:43.208145Z", + "iopub.status.idle": "2024-07-01T15:02:43.211102Z", + "shell.execute_reply": "2024-07-01T15:02:43.210626Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.144228Z", - "iopub.status.busy": "2024-06-28T15:33:04.144051Z", - "iopub.status.idle": "2024-06-28T15:33:04.182999Z", - "shell.execute_reply": "2024-06-28T15:33:04.182501Z" + "iopub.execute_input": "2024-07-01T15:02:43.213282Z", + "iopub.status.busy": "2024-07-01T15:02:43.212936Z", + "iopub.status.idle": "2024-07-01T15:02:43.248404Z", + "shell.execute_reply": "2024-07-01T15:02:43.247768Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.185443Z", - "iopub.status.busy": "2024-06-28T15:33:04.185256Z", - "iopub.status.idle": "2024-06-28T15:33:06.400028Z", - "shell.execute_reply": "2024-06-28T15:33:06.399335Z" + "iopub.execute_input": "2024-07-01T15:02:43.251350Z", + "iopub.status.busy": "2024-07-01T15:02:43.250964Z", + "iopub.status.idle": "2024-07-01T15:02:45.296650Z", + "shell.execute_reply": "2024-07-01T15:02:45.296009Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.402739Z", - "iopub.status.busy": "2024-06-28T15:33:06.402145Z", - "iopub.status.idle": "2024-06-28T15:33:06.421740Z", - "shell.execute_reply": "2024-06-28T15:33:06.421232Z" + "iopub.execute_input": "2024-07-01T15:02:45.298975Z", + "iopub.status.busy": "2024-07-01T15:02:45.298607Z", + "iopub.status.idle": "2024-07-01T15:02:45.317301Z", + "shell.execute_reply": "2024-07-01T15:02:45.316762Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.423971Z", - "iopub.status.busy": "2024-06-28T15:33:06.423624Z", - "iopub.status.idle": "2024-06-28T15:33:06.430447Z", - "shell.execute_reply": "2024-06-28T15:33:06.429995Z" + "iopub.execute_input": "2024-07-01T15:02:45.319610Z", + "iopub.status.busy": "2024-07-01T15:02:45.319291Z", + "iopub.status.idle": "2024-07-01T15:02:45.325606Z", + "shell.execute_reply": "2024-07-01T15:02:45.325097Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.432560Z", - "iopub.status.busy": "2024-06-28T15:33:06.432209Z", - "iopub.status.idle": "2024-06-28T15:33:06.438239Z", - "shell.execute_reply": "2024-06-28T15:33:06.437699Z" + "iopub.execute_input": "2024-07-01T15:02:45.327815Z", + "iopub.status.busy": "2024-07-01T15:02:45.327438Z", + "iopub.status.idle": "2024-07-01T15:02:45.333038Z", + "shell.execute_reply": "2024-07-01T15:02:45.332566Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.440265Z", - "iopub.status.busy": "2024-06-28T15:33:06.439954Z", - "iopub.status.idle": "2024-06-28T15:33:06.450509Z", - "shell.execute_reply": "2024-06-28T15:33:06.450061Z" + "iopub.execute_input": "2024-07-01T15:02:45.335093Z", + "iopub.status.busy": "2024-07-01T15:02:45.334787Z", + "iopub.status.idle": "2024-07-01T15:02:45.345460Z", + "shell.execute_reply": "2024-07-01T15:02:45.344912Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.452688Z", - "iopub.status.busy": "2024-06-28T15:33:06.452339Z", - "iopub.status.idle": "2024-06-28T15:33:06.461491Z", - "shell.execute_reply": "2024-06-28T15:33:06.461029Z" + "iopub.execute_input": "2024-07-01T15:02:45.347438Z", + "iopub.status.busy": "2024-07-01T15:02:45.347138Z", + "iopub.status.idle": "2024-07-01T15:02:45.356126Z", + "shell.execute_reply": "2024-07-01T15:02:45.355581Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.463689Z", - "iopub.status.busy": "2024-06-28T15:33:06.463348Z", - "iopub.status.idle": "2024-06-28T15:33:06.470367Z", - "shell.execute_reply": "2024-06-28T15:33:06.469791Z" + "iopub.execute_input": "2024-07-01T15:02:45.358103Z", + "iopub.status.busy": "2024-07-01T15:02:45.357792Z", + "iopub.status.idle": "2024-07-01T15:02:45.364571Z", + "shell.execute_reply": "2024-07-01T15:02:45.364114Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.472356Z", - "iopub.status.busy": "2024-06-28T15:33:06.472176Z", - "iopub.status.idle": "2024-06-28T15:33:06.482081Z", - "shell.execute_reply": "2024-06-28T15:33:06.481598Z" + "iopub.execute_input": "2024-07-01T15:02:45.366559Z", + "iopub.status.busy": "2024-07-01T15:02:45.366255Z", + "iopub.status.idle": "2024-07-01T15:02:45.375353Z", + "shell.execute_reply": "2024-07-01T15:02:45.374817Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.484231Z", - "iopub.status.busy": "2024-06-28T15:33:06.483891Z", - "iopub.status.idle": "2024-06-28T15:33:06.495536Z", - "shell.execute_reply": "2024-06-28T15:33:06.495097Z" + "iopub.execute_input": "2024-07-01T15:02:45.377315Z", + "iopub.status.busy": "2024-07-01T15:02:45.377010Z", + "iopub.status.idle": "2024-07-01T15:02:45.392963Z", + "shell.execute_reply": "2024-07-01T15:02:45.392390Z" }, "nbsphinx": "hidden" }, @@ -1565,9 +1565,11 @@ "# Note: This cell is only for docs.cleanlab.ai, if running on local Jupyter or Colab, please ignore it.\n", "from sklearn.metrics import roc_auc_score\n", "\n", - "issue_results = lab.get_issues(\"label\")\n", - "outlier_results = lab.get_issues(\"outlier\")\n", - "duplicate_results = lab.get_issues(\"near_duplicate\")\n", + "def precision_at_k(predicted_indices, true_indices, k):\n", + " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / k\n", + "\n", + "def recall_at_k(predicted_indices, true_indices, k):\n", + " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / len(true_indices)\n", "\n", "def jaccard_similarity(l1, l2):\n", " s1 = set(l1)\n", @@ -1578,26 +1580,40 @@ " return 0\n", " return len(intersect_set) / len(union_set)\n", "\n", - "identified_label_issues_indices = issue_results[issue_results[\"is_label_issue\"] == True].index.tolist()\n", + "label_issues = lab.get_issues(\"label\")\n", + "predicted_label_issues_indices = (\n", + " label_issues.query(\"is_label_issue\").sort_values(\"label_score\").index.to_list()\n", + ")\n", + "predicted_label_issues_indices_by_score = (\n", + " label_issues.sort_values(\"label_score\").index.to_list()\n", + ")\n", "label_issue_indices = np.where(y_train_idx != noisy_labels_idx)[0]\n", "\n", - "label_quality_scores = issue_results[\"label_score\"].tolist()\n", + "label_quality_scores = label_issues[\"label_score\"].tolist()\n", "Z = (y_train_idx == noisy_labels_idx).astype(float).tolist()\n", "\n", - "identified_outlier_issues_indices = outlier_results[outlier_results[\"is_outlier_issue\"] == True].index.to_list()\n", + "predicted_outlier_issues_indices = (\n", + " lab.get_issues(\"outlier\").query(\"is_outlier_issue\").index.to_list()\n", + ")\n", "outlier_issue_indices = list(range(125, 130+1))\n", "exact_duplicate_idx = [index for index, elem in enumerate(X_train) if (elem == X_duplicate).all()][0]\n", "if exact_duplicate_idx >= 125: # if the random index selected to create a duplicate >= 125, then the last point is also an outlier\n", " outlier_issue_indices.append(131)\n", - " \n", - "identified_duplicate_issues_indices = duplicate_results[duplicate_results[\"is_near_duplicate_issue\"] == True].index.tolist()\n", - "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", + "predicted_duplicate_issues_indices = (\n", + " lab.get_issues(\"near_duplicate\").query(\"is_near_duplicate_issue\").index.tolist()\n", + ")\n", + "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", - "assert jaccard_similarity(identified_label_issues_indices, label_issue_indices) > 0.4\n", + "k = len(label_issue_indices)\n", + "assert precision_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert recall_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert precision_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", + "assert recall_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", "assert roc_auc_score(Z, label_quality_scores) > 0.9\n", - "assert jaccard_similarity(identified_outlier_issues_indices, outlier_issue_indices) > 0.9\n", - "assert jaccard_similarity(identified_duplicate_issues_indices, duplicate_issue_indices) > 0.9" + "\n", + "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", + "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] } ], diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 1f4e67fd5..a1388c4c1 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -727,49 +727,49 @@

    2. Fetch and normalize the Fashion-MNIST dataset

    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    [3]:
@@ -824,7 +824,7 @@ 
    2. Fetch and normalize the Fashion-MNIST dataset
    -
    +

    Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.

    @@ -1073,8 +1073,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 1 ...
-epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.070
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.893
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439
 Computing feature embeddings ...

    @@ -1082,7 +1082,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1096,7 +1096,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1105,8 +1105,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 2 ...
-epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.975
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.917
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491
 Computing feature embeddings ...

    @@ -1114,7 +1114,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1128,7 +1128,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1137,8 +1137,8 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
     
 Training on fold: 3 ...
-epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.951
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.061
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490
 Computing feature embeddings ...

    @@ -1146,7 +1146,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1160,7 +1160,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings

    -
    +
    @@ -1222,7 +1222,7 @@

    7. Use cleanlab to find issues

    -
    +
    @@ -2059,35 +2059,35 @@

    Low information images - low_information_score is_low_information_issue + low_information_score 53050 - 0.067975 True + 0.067975 40875 - 0.089929 True + 0.089929 9594 - 0.092601 True + 0.092601 34825 - 0.107744 True + 0.107744 37530 - 0.108516 True + 0.108516 @@ -2115,7 +2115,7 @@

    Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

    diff --git a/master/tutorials/datalab/image.ipynb b/master/tutorials/datalab/image.ipynb index 9bd5eb804..03d847503 100644 --- a/master/tutorials/datalab/image.ipynb +++ b/master/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:09.403235Z", - "iopub.status.busy": "2024-06-28T15:33:09.402738Z", - "iopub.status.idle": "2024-06-28T15:33:12.466652Z", - "shell.execute_reply": "2024-06-28T15:33:12.466079Z" + "iopub.execute_input": "2024-07-01T15:02:48.074971Z", + "iopub.status.busy": "2024-07-01T15:02:48.074723Z", + "iopub.status.idle": "2024-07-01T15:02:51.342353Z", + "shell.execute_reply": "2024-07-01T15:02:51.341605Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:12.469241Z", - "iopub.status.busy": "2024-06-28T15:33:12.468934Z", - "iopub.status.idle": "2024-06-28T15:33:12.472447Z", - "shell.execute_reply": "2024-06-28T15:33:12.472019Z" + "iopub.execute_input": "2024-07-01T15:02:51.345614Z", + "iopub.status.busy": "2024-07-01T15:02:51.345060Z", + "iopub.status.idle": "2024-07-01T15:02:51.349168Z", + "shell.execute_reply": "2024-07-01T15:02:51.348682Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:12.474380Z", - "iopub.status.busy": "2024-06-28T15:33:12.474201Z", - "iopub.status.idle": "2024-06-28T15:33:24.977088Z", - "shell.execute_reply": "2024-06-28T15:33:24.976474Z" + "iopub.execute_input": "2024-07-01T15:02:51.351438Z", + "iopub.status.busy": "2024-07-01T15:02:51.351054Z", + "iopub.status.idle": "2024-07-01T15:03:02.526470Z", + "shell.execute_reply": "2024-07-01T15:03:02.525870Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd367ed6b3e145b9ba56c440d63f6948", + "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "999579ced4a04955b6fe76b06613510d", + "model_id": "a9efee99388e4bd987cba82e4c249be5", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9611c5ddf09444219b0832f81930fdd7", + "model_id": "b13b21c3b7544706aacfbba4f3504a8b", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2bbd780010f04629a57e9a48d6241a4e", + "model_id": "dcfb76cdced842fd810c0329fa0f1c7f", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21ffc7623d52499f96e098345ad1b94d", + "model_id": "0febc72cf36d4d939a7991cbb880240e", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c14226990a04e87aa10a393e2a0203a", + "model_id": "6296fc9f1a3947edb989ab3a35afbefe", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "519660b9e9fd424bbb188e3f3d9d3b89", + "model_id": "bc98754b340343f594559442ba450aa4", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "510e84f543554f8d8f0f21ce00483d7d", + "model_id": "d60f32b2907d4a288385a30c717ef39d", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:24.979342Z", - "iopub.status.busy": "2024-06-28T15:33:24.979046Z", - "iopub.status.idle": "2024-06-28T15:33:24.983576Z", - "shell.execute_reply": "2024-06-28T15:33:24.983103Z" + "iopub.execute_input": "2024-07-01T15:03:02.528967Z", + "iopub.status.busy": "2024-07-01T15:03:02.528621Z", + "iopub.status.idle": "2024-07-01T15:03:02.532647Z", + "shell.execute_reply": "2024-07-01T15:03:02.532080Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:24.985815Z", - "iopub.status.busy": "2024-06-28T15:33:24.985393Z", - "iopub.status.idle": "2024-06-28T15:33:36.598392Z", - "shell.execute_reply": "2024-06-28T15:33:36.597792Z" + "iopub.execute_input": "2024-07-01T15:03:02.534910Z", + "iopub.status.busy": "2024-07-01T15:03:02.534585Z", + "iopub.status.idle": "2024-07-01T15:03:13.866603Z", + "shell.execute_reply": "2024-07-01T15:03:13.865937Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "748b7bbe2c8345c6a22623d9f52f46cf", + "model_id": "70b6c17f51c948158afefdd56830a23f", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:36.601110Z", - "iopub.status.busy": "2024-06-28T15:33:36.600777Z", - "iopub.status.idle": "2024-06-28T15:33:55.067117Z", - "shell.execute_reply": "2024-06-28T15:33:55.066477Z" + "iopub.execute_input": "2024-07-01T15:03:13.869049Z", + "iopub.status.busy": "2024-07-01T15:03:13.868821Z", + "iopub.status.idle": "2024-07-01T15:03:31.582919Z", + "shell.execute_reply": "2024-07-01T15:03:31.582298Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.070279Z", - "iopub.status.busy": "2024-06-28T15:33:55.069803Z", - "iopub.status.idle": "2024-06-28T15:33:55.075612Z", - "shell.execute_reply": "2024-06-28T15:33:55.075060Z" + "iopub.execute_input": "2024-07-01T15:03:31.585953Z", + "iopub.status.busy": "2024-07-01T15:03:31.585389Z", + "iopub.status.idle": "2024-07-01T15:03:31.591279Z", + "shell.execute_reply": "2024-07-01T15:03:31.590830Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.078062Z", - "iopub.status.busy": "2024-06-28T15:33:55.077664Z", - "iopub.status.idle": "2024-06-28T15:33:55.082536Z", - "shell.execute_reply": "2024-06-28T15:33:55.081922Z" + "iopub.execute_input": "2024-07-01T15:03:31.593306Z", + "iopub.status.busy": "2024-07-01T15:03:31.592981Z", + "iopub.status.idle": "2024-07-01T15:03:31.596855Z", + "shell.execute_reply": "2024-07-01T15:03:31.596450Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.085223Z", - "iopub.status.busy": "2024-06-28T15:33:55.084873Z", - "iopub.status.idle": "2024-06-28T15:33:55.094273Z", - "shell.execute_reply": "2024-06-28T15:33:55.093706Z" + "iopub.execute_input": "2024-07-01T15:03:31.598838Z", + "iopub.status.busy": "2024-07-01T15:03:31.598577Z", + "iopub.status.idle": "2024-07-01T15:03:31.607398Z", + "shell.execute_reply": "2024-07-01T15:03:31.606925Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.096457Z", - "iopub.status.busy": "2024-06-28T15:33:55.096269Z", - "iopub.status.idle": "2024-06-28T15:33:55.123566Z", - "shell.execute_reply": "2024-06-28T15:33:55.123064Z" + "iopub.execute_input": "2024-07-01T15:03:31.609325Z", + "iopub.status.busy": "2024-07-01T15:03:31.609007Z", + "iopub.status.idle": "2024-07-01T15:03:31.635278Z", + "shell.execute_reply": "2024-07-01T15:03:31.634840Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.126192Z", - "iopub.status.busy": "2024-06-28T15:33:55.125844Z", - "iopub.status.idle": "2024-06-28T15:34:29.556539Z", - "shell.execute_reply": "2024-06-28T15:34:29.555882Z" + "iopub.execute_input": "2024-07-01T15:03:31.637322Z", + "iopub.status.busy": "2024-07-01T15:03:31.636996Z", + "iopub.status.idle": "2024-07-01T15:04:03.652341Z", + "shell.execute_reply": "2024-07-01T15:04:03.651742Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.070\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.893\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01d874da00234ef1aa34287815d37d45", + "model_id": "b69aa5fb137444eb962d31f239578d65", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02ee74aebbc04f9a82b5829341e501a6", + "model_id": "6ca247bf72f54f03aabdd5d72546025f", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.975\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.917\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddb26404cd4644d3a9efa8efd7af9104", + "model_id": "d6465626e3264fa58f44ddccd18cfef2", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c60874cc394b428786de11432b5ba1ce", + "model_id": "3fa46dee97a14f9594eb60312b03e045", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.951\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.061\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca1506e7d3f846adb2f7487be4ad5f1d", + "model_id": "76cd9d157bf74d6e93db6f5727c6f900", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff1c46ea9c474d1bb4645c7cbbf298b0", + "model_id": "9717f3b4aaae491d9cb2e07d49a003a5", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:29.558914Z", - "iopub.status.busy": "2024-06-28T15:34:29.558674Z", - "iopub.status.idle": "2024-06-28T15:34:29.573241Z", - "shell.execute_reply": "2024-06-28T15:34:29.572642Z" + "iopub.execute_input": "2024-07-01T15:04:03.654962Z", + "iopub.status.busy": "2024-07-01T15:04:03.654720Z", + "iopub.status.idle": "2024-07-01T15:04:03.668632Z", + "shell.execute_reply": "2024-07-01T15:04:03.668209Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:29.575486Z", - "iopub.status.busy": "2024-06-28T15:34:29.575301Z", - "iopub.status.idle": "2024-06-28T15:34:30.062525Z", - "shell.execute_reply": "2024-06-28T15:34:30.061951Z" + "iopub.execute_input": "2024-07-01T15:04:03.670732Z", + "iopub.status.busy": "2024-07-01T15:04:03.670344Z", + "iopub.status.idle": "2024-07-01T15:04:04.150524Z", + "shell.execute_reply": "2024-07-01T15:04:04.149791Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:30.065567Z", - "iopub.status.busy": "2024-06-28T15:34:30.065103Z", - "iopub.status.idle": "2024-06-28T15:36:09.855028Z", - "shell.execute_reply": "2024-06-28T15:36:09.854365Z" + "iopub.execute_input": "2024-07-01T15:04:04.153028Z", + "iopub.status.busy": "2024-07-01T15:04:04.152825Z", + "iopub.status.idle": "2024-07-01T15:05:40.110641Z", + "shell.execute_reply": "2024-07-01T15:05:40.110011Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f55b4eb540fd4f368229bcf7012adf9f", + "model_id": "8b242b3757014ca08c0be26603c856e5", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:09.857655Z", - "iopub.status.busy": "2024-06-28T15:36:09.857109Z", - "iopub.status.idle": "2024-06-28T15:36:10.330566Z", - "shell.execute_reply": "2024-06-28T15:36:10.330003Z" + "iopub.execute_input": "2024-07-01T15:05:40.113143Z", + "iopub.status.busy": "2024-07-01T15:05:40.112512Z", + "iopub.status.idle": "2024-07-01T15:05:40.560298Z", + "shell.execute_reply": "2024-07-01T15:05:40.559714Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.333473Z", - "iopub.status.busy": "2024-06-28T15:36:10.333080Z", - "iopub.status.idle": "2024-06-28T15:36:10.397225Z", - "shell.execute_reply": "2024-06-28T15:36:10.396605Z" + "iopub.execute_input": "2024-07-01T15:05:40.563315Z", + "iopub.status.busy": "2024-07-01T15:05:40.562801Z", + "iopub.status.idle": "2024-07-01T15:05:40.624738Z", + "shell.execute_reply": "2024-07-01T15:05:40.624116Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.399332Z", - "iopub.status.busy": "2024-06-28T15:36:10.399151Z", - "iopub.status.idle": "2024-06-28T15:36:10.408208Z", - "shell.execute_reply": "2024-06-28T15:36:10.407701Z" + "iopub.execute_input": "2024-07-01T15:05:40.627071Z", + "iopub.status.busy": "2024-07-01T15:05:40.626639Z", + "iopub.status.idle": "2024-07-01T15:05:40.635299Z", + "shell.execute_reply": "2024-07-01T15:05:40.634756Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.410606Z", - "iopub.status.busy": "2024-06-28T15:36:10.410094Z", - "iopub.status.idle": "2024-06-28T15:36:10.415260Z", - "shell.execute_reply": "2024-06-28T15:36:10.414712Z" + "iopub.execute_input": "2024-07-01T15:05:40.637389Z", + "iopub.status.busy": "2024-07-01T15:05:40.636989Z", + "iopub.status.idle": "2024-07-01T15:05:40.641711Z", + "shell.execute_reply": "2024-07-01T15:05:40.641175Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.417475Z", - "iopub.status.busy": "2024-06-28T15:36:10.417052Z", - "iopub.status.idle": "2024-06-28T15:36:10.958110Z", - "shell.execute_reply": "2024-06-28T15:36:10.957499Z" + "iopub.execute_input": "2024-07-01T15:05:40.643683Z", + "iopub.status.busy": "2024-07-01T15:05:40.643498Z", + "iopub.status.idle": "2024-07-01T15:05:41.152016Z", + "shell.execute_reply": "2024-07-01T15:05:41.151428Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.960727Z", - "iopub.status.busy": "2024-06-28T15:36:10.960231Z", - "iopub.status.idle": "2024-06-28T15:36:10.969176Z", - "shell.execute_reply": "2024-06-28T15:36:10.968687Z" + "iopub.execute_input": "2024-07-01T15:05:41.154341Z", + "iopub.status.busy": "2024-07-01T15:05:41.154029Z", + "iopub.status.idle": "2024-07-01T15:05:41.162706Z", + "shell.execute_reply": "2024-07-01T15:05:41.162252Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.971544Z", - "iopub.status.busy": "2024-06-28T15:36:10.971137Z", - "iopub.status.idle": "2024-06-28T15:36:10.978595Z", - "shell.execute_reply": "2024-06-28T15:36:10.978143Z" + "iopub.execute_input": "2024-07-01T15:05:41.164766Z", + "iopub.status.busy": "2024-07-01T15:05:41.164446Z", + "iopub.status.idle": "2024-07-01T15:05:41.171486Z", + "shell.execute_reply": "2024-07-01T15:05:41.171059Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.980809Z", - "iopub.status.busy": "2024-06-28T15:36:10.980353Z", - "iopub.status.idle": "2024-06-28T15:36:11.774826Z", - "shell.execute_reply": "2024-06-28T15:36:11.774218Z" + "iopub.execute_input": "2024-07-01T15:05:41.173399Z", + "iopub.status.busy": "2024-07-01T15:05:41.173075Z", + "iopub.status.idle": "2024-07-01T15:05:41.934946Z", + "shell.execute_reply": "2024-07-01T15:05:41.934291Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.777464Z", - "iopub.status.busy": "2024-06-28T15:36:11.777117Z", - "iopub.status.idle": "2024-06-28T15:36:11.793779Z", - "shell.execute_reply": "2024-06-28T15:36:11.793195Z" + "iopub.execute_input": "2024-07-01T15:05:41.937509Z", + "iopub.status.busy": "2024-07-01T15:05:41.937076Z", + "iopub.status.idle": "2024-07-01T15:05:41.952809Z", + "shell.execute_reply": "2024-07-01T15:05:41.952240Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.796111Z", - "iopub.status.busy": "2024-06-28T15:36:11.795755Z", - "iopub.status.idle": "2024-06-28T15:36:11.801625Z", - "shell.execute_reply": "2024-06-28T15:36:11.801150Z" + "iopub.execute_input": "2024-07-01T15:05:41.954986Z", + "iopub.status.busy": "2024-07-01T15:05:41.954646Z", + "iopub.status.idle": "2024-07-01T15:05:41.960097Z", + "shell.execute_reply": "2024-07-01T15:05:41.959674Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.803713Z", - "iopub.status.busy": "2024-06-28T15:36:11.803391Z", - "iopub.status.idle": "2024-06-28T15:36:12.281855Z", - "shell.execute_reply": "2024-06-28T15:36:12.281284Z" + "iopub.execute_input": "2024-07-01T15:05:41.961941Z", + "iopub.status.busy": "2024-07-01T15:05:41.961770Z", + "iopub.status.idle": "2024-07-01T15:05:42.348365Z", + "shell.execute_reply": "2024-07-01T15:05:42.347794Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.285071Z", - "iopub.status.busy": "2024-06-28T15:36:12.284635Z", - "iopub.status.idle": "2024-06-28T15:36:12.295272Z", - "shell.execute_reply": "2024-06-28T15:36:12.294767Z" + "iopub.execute_input": "2024-07-01T15:05:42.350754Z", + "iopub.status.busy": "2024-07-01T15:05:42.350573Z", + "iopub.status.idle": "2024-07-01T15:05:42.359462Z", + "shell.execute_reply": "2024-07-01T15:05:42.358866Z" } }, "outputs": [ @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.297976Z", - "iopub.status.busy": "2024-06-28T15:36:12.297776Z", - "iopub.status.idle": "2024-06-28T15:36:12.304804Z", - "shell.execute_reply": "2024-06-28T15:36:12.304249Z" + "iopub.execute_input": "2024-07-01T15:05:42.361756Z", + "iopub.status.busy": "2024-07-01T15:05:42.361579Z", + "iopub.status.idle": "2024-07-01T15:05:42.366530Z", + "shell.execute_reply": "2024-07-01T15:05:42.365855Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.307029Z", - "iopub.status.busy": "2024-06-28T15:36:12.306836Z", - "iopub.status.idle": "2024-06-28T15:36:12.512134Z", - "shell.execute_reply": "2024-06-28T15:36:12.511544Z" + "iopub.execute_input": "2024-07-01T15:05:42.368583Z", + "iopub.status.busy": "2024-07-01T15:05:42.368393Z", + "iopub.status.idle": "2024-07-01T15:05:42.547146Z", + "shell.execute_reply": "2024-07-01T15:05:42.546558Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.514678Z", - "iopub.status.busy": "2024-06-28T15:36:12.514470Z", - "iopub.status.idle": "2024-06-28T15:36:12.523352Z", - "shell.execute_reply": "2024-06-28T15:36:12.522769Z" + "iopub.execute_input": "2024-07-01T15:05:42.549711Z", + "iopub.status.busy": "2024-07-01T15:05:42.549286Z", + "iopub.status.idle": "2024-07-01T15:05:42.557834Z", + "shell.execute_reply": "2024-07-01T15:05:42.557185Z" } }, "outputs": [ @@ -2446,47 +2446,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "

    " ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.525904Z", - "iopub.status.busy": "2024-06-28T15:36:12.525428Z", - "iopub.status.idle": "2024-06-28T15:36:12.722284Z", - "shell.execute_reply": "2024-06-28T15:36:12.721692Z" + "iopub.execute_input": "2024-07-01T15:05:42.559948Z", + "iopub.status.busy": "2024-07-01T15:05:42.559775Z", + "iopub.status.idle": "2024-07-01T15:05:42.735572Z", + "shell.execute_reply": "2024-07-01T15:05:42.734991Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.724319Z", - "iopub.status.busy": "2024-06-28T15:36:12.724140Z", - "iopub.status.idle": "2024-06-28T15:36:12.728868Z", - "shell.execute_reply": "2024-06-28T15:36:12.728378Z" + "iopub.execute_input": "2024-07-01T15:05:42.737707Z", + "iopub.status.busy": "2024-07-01T15:05:42.737529Z", + "iopub.status.idle": "2024-07-01T15:05:42.742129Z", + "shell.execute_reply": "2024-07-01T15:05:42.741571Z" }, "nbsphinx": "hidden" }, @@ -2590,165 +2590,66 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "00e493ba9c68410a9bfb07d35f58c61a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "013d72ca9aa146a593784e72f356edd6": { + "01c1fefd1d1549a1ac3d1168746bc81e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c91e1c0bc2da4f678e94080291cc61d8", - "placeholder": "​", - "style": "IPY_MODEL_629afeba7adb4995975ea1d03301cd3f", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "01815763b36f44db9e29147b04f45a49": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "01d874da00234ef1aa34287815d37d45": { + "0423a125c3324adc9f2433bebe77773b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_06700ad7253445b29d4575f934ab5afe", - "IPY_MODEL_9fdacce60b084ad6b7edc2e06ad6b7d5", - "IPY_MODEL_343f48933d79402992e30514270b02e5" - ], - "layout": "IPY_MODEL_e4991d19e073408f9cef1ae11eb71019", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "02ee74aebbc04f9a82b5829341e501a6": { + "070af112be824f3a9dfc9d11f6c99bdb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ceddd53d284e47e080fd5b52a11b902d", - "IPY_MODEL_115578eb2f074173835af6ef40b58192", - "IPY_MODEL_33f1930405e34e288f0f52c5e451dac0" - ], - "layout": "IPY_MODEL_169122b3abf3499a96cd266c100d3ebc", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_268de63ed1994c07842b4b562dfac3f6", + "placeholder": "​", + "style": "IPY_MODEL_2a1a89abfab4432f93e32ce870b637ab", "tabbable": null, - "tooltip": null - } - }, - "061c5f364286484db172fb4804eaca0d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": "100%" } }, - "06700ad7253445b29d4575f934ab5afe": { + "08550632bef14f9a894de4b50f4b0628": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2763,15 +2664,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_40fdb275580d43d9b74fe903a7a8104d", + "layout": "IPY_MODEL_d78ce21ee1fc44e8b680bda13d37d943", "placeholder": "​", - "style": "IPY_MODEL_5c2bfa97829b4fdea2882b9050f5562d", + "style": "IPY_MODEL_abecf5a0521e45f78a788d98cc335e5c", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4.42M/4.42M [00:00<00:00, 77.0MB/s]" } }, - "06f943bb034d473babe64569cf34ef3f": { + "0abf4ae675284a88873e14026b73ab9e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2789,7 +2690,7 @@ "text_color": null } }, - "0a5ef6cfa8714c0ab6b0ef41dfb1a925": { + "0cfffd1a2e16481ba0fb26b13818b9d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2842,53 +2743,49 @@ "width": null } }, - "0c7b3a4a480e4a4f8a7a93dcc0cc643a": { + "0febc72cf36d4d939a7991cbb880240e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aca6b827bc144b7e84332be12838c12a", - "placeholder": "​", - "style": "IPY_MODEL_64f5f6e8c1bb4d1ea5c7e2269014d6b6", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b374b826cf5a4a668332dc70f8c2fe4a", + "IPY_MODEL_213628c3350547b8801807bf6755bac5", + "IPY_MODEL_08550632bef14f9a894de4b50f4b0628" + ], + "layout": "IPY_MODEL_c87df7230ab74cf3b4ce36554b4f64b5", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:07<00:00, 8621.86 examples/s]" + "tooltip": null } }, - "100b4c65b0f44c9b8b9c7cb4afbc38ac": { + "109113d68eb84c2eace6f4deda6b040b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_97c2e55e3c57419c81374350ea34b432", - "placeholder": "​", - "style": "IPY_MODEL_061c5f364286484db172fb4804eaca0d", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1011f6c6efe84551a4d233008b8b790f": { + "11703dfe59744973b7ee5db1b50a7304": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2941,33 +2838,48 @@ "width": null } }, - "115578eb2f074173835af6ef40b58192": { + "126a880302694496ab5834d71ecffa75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d8139f444e324e12818613912a49a6f9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_95453138f12b4a908bb147d1409d6b93", + "layout": "IPY_MODEL_6bb8ca43d39f43d0933376cc22b61a88", + "placeholder": "​", + "style": "IPY_MODEL_ef22307e229c427e9f2c45041e108373", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Generating test split: 100%" + } + }, + "160c29ae9eb04803b9e5fc42229024de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "14620fb3ef91409c93ca2866a6b245cd": { + "182fe610a03c4f0c8820d62efd2942a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,7 +2932,33 @@ "width": null } }, - "15d24ab1c32544e9b1dad6314ed3918f": { + "197981c8f9c84c00abea85184c72fcd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ac933a814c124345b0730704f11cfbc8", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a940901f95f4ae58045f253ec9f48b7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "19d83e638f7a404ba0eb4fdd71302435": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3073,7 +3011,7 @@ "width": null } }, - "15fc493178c94958a5bd63dc73acf625": { + "19dfb010e30849f38c848ea70dda5645": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3091,7 +3029,23 @@ "text_color": null } }, - "1640d13bd96d405ba01bbe9f22029432": { + "1a2b07452f87444f8ac98839eeafdd7b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1ab8a64cb49340769b8aa2cc1774732d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3144,16 +3098,100 @@ "width": null } }, - "169122b3abf3499a96cd266c100d3ebc": { - "model_module": "@jupyter-widgets/base", + "1ba2c9f296904da78195fe3e45321059": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_833e5dbaba7546f582547cdf7571c108", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_21904a4bfca34e3295a0b17083f1cdb9", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "1c8e1b8efcbf4cd6ad8ccd71c8886907": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "213628c3350547b8801807bf6755bac5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c71cb776520442f0baba7a63fdbf2324", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a1990d1ffd9240a7a4bf2438eaf81754", + "tabbable": null, + "tooltip": null, + "value": 4422102.0 + } + }, + "21904a4bfca34e3295a0b17083f1cdb9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2529da9ccb564a7d98cf91d4e2f454fc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, @@ -3197,7 +3235,7 @@ "width": null } }, - "1903503bad43448ea1ac70783f0fb30d": { + "255e06fe46cb4c0eaa40336d22beaf70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3250,7 +3288,41 @@ "width": null } }, - "1a95c58232ad42d683b9174f9f853649": { + "25aea1e677d84e4da5e2f1a580b847bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "266601a55125440a88a8a16e00cebd66": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "268de63ed1994c07842b4b562dfac3f6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3303,7 +3375,7 @@ "width": null } }, - "1ad9b1cdbd544af3a314df0c82ec6565": { + "295b6321cc6f4b1bbfa02a28f8581b1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3356,53 +3428,7 @@ "width": null } }, - "1bc6bb27d1ce4ccd8dd7cfe252d16d39": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aea72aba96c34f99adbd16416576dbed", - "placeholder": "​", - "style": "IPY_MODEL_c27e7863116b4bf48eab88e0869c69a4", - "tabbable": null, - "tooltip": null, - "value": "Downloading builder script: 100%" - } - }, - "1c04a881488944cfb2a78e4629f5d219": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1903503bad43448ea1ac70783f0fb30d", - "placeholder": "​", - "style": "IPY_MODEL_4385afbe20eb4363b22e1ff9d6288fff", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 7615.20 examples/s]" - } - }, - "1d383e62e8714383b5a6879ad3316e61": { + "299c0f0403e1447fb83af15e00e8f19f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3455,57 +3481,41 @@ "width": null } }, - "216f04617573408697842aba4967a052": { + "2a1a89abfab4432f93e32ce870b637ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f054cf08430c477d9eab2ccc80e48b99", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_355b9a91686843b0a583fca9630ed656", - "tabbable": null, - "tooltip": null, - "value": 4.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "21ffc7623d52499f96e098345ad1b94d": { + "2a940901f95f4ae58045f253ec9f48b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_013d72ca9aa146a593784e72f356edd6", - "IPY_MODEL_c7ec958a864d4b8b9aeb4c7b4f1055df", - "IPY_MODEL_4c86867b8903466fa9e6d4e388aa0434" - ], - "layout": "IPY_MODEL_41de0713ce794701a08845a6d7031022", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "2279b7989e5940fc9318addab8b1ab9e": { + "2cb677caec644d3ab2a8b6a70208559e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3558,7 +3568,23 @@ "width": null } }, - "238898e5c2874588924e33ad1a5a14be": { + "2d09848b13f94c7a9483a0063af7658b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2f77ed4c555c4365a30f1c760cb59e74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3576,7 +3602,7 @@ "text_color": null } }, - "24f4a95a06be4e01a6b70cd2cd3bccda": { + "348018e48d954c87b48ff2a6e7470304": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3592,23 +3618,30 @@ "description_width": "" } }, - "25c58f4aec6b4f41be25042769d63c52": { + "367462867871411f81ad39c2100a77d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_93b948080aae40babe81f47f38dc60fa", + "placeholder": "​", + "style": "IPY_MODEL_3f77fa09c365457f893db8dde97d6a1a", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:07<00:00, 8559.65 examples/s]" } }, - "25d73f1cd6f74f7f8eb5013af3fc4aea": { + "3773b260a0514063a4cb27c7bcfcdb72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3661,7 +3694,53 @@ "width": null } }, - "2ad862ff6f0f4f3a8db6362836d85b81": { + "3abd2a96be1649ed9f2a7594cfdf3bbd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f15ed2b240814bb897b777075c726c92", + "placeholder": "​", + "style": "IPY_MODEL_956d09bfdd86417caf76bb337c098a81", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "3ddd27d196364f1db80581facbadf18f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_79d53959ea3648699bfb173209305675", + "placeholder": "​", + "style": "IPY_MODEL_160c29ae9eb04803b9e5fc42229024de", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "3ec84e0ec4d94afd8f15a91eb757624e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3714,7 +3793,51 @@ "width": null } }, - "2bbd780010f04629a57e9a48d6241a4e": { + "3edb1a47c3314c0abfc78f23d62ebe68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cfffd1a2e16481ba0fb26b13818b9d3", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_70ca2521a7cf454c8f41cc8665bd8572", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "3f77fa09c365457f893db8dde97d6a1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3fa46dee97a14f9594eb60312b03e045": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3729,16 +3852,34 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_46c6a150015345b7a2500e89263d7aa2", - "IPY_MODEL_c979207c10fd4e0f91eb87b8a8cb5bdb", - "IPY_MODEL_75d1007bec0746c98aeda8aef0434706" + "IPY_MODEL_a50bac222b154e1e8f1af99c2d2f7cb5", + "IPY_MODEL_f306b412103a4a999f13f32ecb0841fe", + "IPY_MODEL_838e5992cb0f492aa79d9e4411b13437" ], - "layout": "IPY_MODEL_15d24ab1c32544e9b1dad6314ed3918f", + "layout": "IPY_MODEL_1ab8a64cb49340769b8aa2cc1774732d", "tabbable": null, "tooltip": null } }, - "30926f7236cb4acb82089cc6bc32bca9": { + "424a8de7240d4c088b2b2dbadf1113ab": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4367f895c508411fb72fbf123bfd2662": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3791,23 +3932,43 @@ "width": null } }, - "32020bd3922749389ef4e7b04e878ded": { + "445bd193a0b443b68d88db83f68b1135": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4751ad8ff2014ae4aefb61464aa0b996": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "3235e856e34143f1b59787649ca7badc": { + "4a97191b1a1643199e46244e6611352a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3860,102 +4021,25 @@ "width": null } }, - "33f1930405e34e288f0f52c5e451dac0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3235e856e34143f1b59787649ca7badc", - "placeholder": "​", - "style": "IPY_MODEL_4b0f4e804c4747d3b5b564a82e7c932f", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.82it/s]" - } - }, - "340d530d31fa467c9418a8767d1a0eb2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a0156d3f29e94c14a8be31866b4025fa", - "placeholder": "​", - "style": "IPY_MODEL_3ff0016903f24827855530023693263e", - "tabbable": null, - "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.48MB/s]" - } - }, - "340d8d367bbf4aae8a133ab9a2dfda9d": { + "4c9f6ca0676a4e6bbace6bdaf151e92c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4cb59733ebbd4647b7d2611b71086dfc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_01815763b36f44db9e29147b04f45a49", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "343f48933d79402992e30514270b02e5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1011f6c6efe84551a4d233008b8b790f", - "placeholder": "​", - "style": "IPY_MODEL_84a10c91f7e44b49a6ec73367bf116ef", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 57.41it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "353d9eda4aec4d30805d245cde63c51c": { + "50042e9a2b5145a780c7c3d799e26aad": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4008,80 +4092,7 @@ "width": null } }, - "355b9a91686843b0a583fca9630ed656": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "35d76f48e70949a588e1ac466e50a6f8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3727b7633ef444e593e7a339abd041df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3e483d0ac53644caa94492a8d78843c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1a95c58232ad42d683b9174f9f853649", - "placeholder": "​", - "style": "IPY_MODEL_f8d8dfbdc92f4599abc3232a34342853", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.35it/s]" - } - }, - "3e8bab513b2544f2be136ee1c48ca51c": { + "5594cd23fb504b0dbdef0ecfe202453c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4134,23 +4145,33 @@ "width": null } }, - "3f77865e92e04447b8130e58472c9196": { + "5695fa5956b04b45a058d58f6cb27b12": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_87900fda6f274f88ab3a99c82a5a9940", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1a2b07452f87444f8ac98839eeafdd7b", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "3ff0016903f24827855530023693263e": { + "57fa64edffdb4d37a90dbea656009ab1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4168,60 +4189,39 @@ "text_color": null } }, - "40fdb275580d43d9b74fe903a7a8104d": { - "model_module": "@jupyter-widgets/base", + "58ba69b1748d4a2bb5d07f10349efa22": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "40ff8e1e3db2418ca7a02ad19a7f7df5": { + "5b07f8803c514642801639b40c1aa34b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5c0a9c7370bb48c09729cb766ab1781a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4274,7 +4274,97 @@ "width": null } }, - "4140430b2ab443be9abe907b644904c1": { + "5d069f3fc0fd47028eaaf3380b16e427": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_97c91dea2ffc40f1830438b9a24225ce", + "placeholder": "​", + "style": "IPY_MODEL_635f8889b38545dca62037898e9e98c2", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 71.33it/s]" + } + }, + "5d1a794999184d7c86308a072ca269b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5fa84089fac429dba7a0f14fcadf7b8", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2d09848b13f94c7a9483a0063af7658b", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "5f29c5efd23943e1a08518f859506345": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a24112b64ebe4079b08768fea3888272", + "placeholder": "​", + "style": "IPY_MODEL_19dfb010e30849f38c848ea70dda5645", + "tabbable": null, + "tooltip": null, + "value": " 29.5k/29.5k [00:00<00:00, 4.28MB/s]" + } + }, + "61b5f9d9099a4161a69f1c58aaa1f54e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "61c7ca03d1b84ecdbe6bfb8765d33dd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4289,15 +4379,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_85c842922c6248a8a088b6edfa110613", + "layout": "IPY_MODEL_8c7ec74595b64ca194e8d3ba1c2a16fe", "placeholder": "​", - "style": "IPY_MODEL_589db42ac06b44af8e8131d6bc810558", + "style": "IPY_MODEL_d627f7cbc1534c10a92cc7fd0f874ca6", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 60000/60000 [00:36<00:00, 1693.23it/s]" } }, - "41642d27417146338cd059871f247264": { + "624903d3a14d4864906b939e0507936f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4313,17 +4403,98 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1d383e62e8714383b5a6879ad3316e61", - "max": 5148.0, + "layout": "IPY_MODEL_3773b260a0514063a4cb27c7bcfcdb72", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f2567d63206c480fbbdf4639222fd3db", + "style": "IPY_MODEL_1c8e1b8efcbf4cd6ad8ccd71c8886907", "tabbable": null, "tooltip": null, - "value": 5148.0 + "value": 60000.0 + } + }, + "6296fc9f1a3947edb989ab3a35afbefe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f247cc3e079b4ad2aa902a3150c5d9ec", + "IPY_MODEL_783884a15ffc4bceb98545ac1564a7f5", + "IPY_MODEL_fc671361cddb4f3e9903ef6aae4d3083" + ], + "layout": "IPY_MODEL_66aef97a11f842d89fcec4deed40e16d", + "tabbable": null, + "tooltip": null + } + }, + "62c6658ea89d458aa1198b576f9eee56": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "635f8889b38545dca62037898e9e98c2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "64e2e67bdc244016895a62ad062fa077": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_953f69d720d74dc0824fc0ac9a4ffa3b", + "placeholder": "​", + "style": "IPY_MODEL_a38689b69ffd44938ebabe7dc9710b39", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "41de0713ce794701a08845a6d7031022": { + "66aef97a11f842d89fcec4deed40e16d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4376,7 +4547,7 @@ "width": null } }, - "4275cd122298472195f82b5dbef8d87f": { + "6930211e1e8a42d88c3294247388155e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4429,71 +4600,7 @@ "width": null } }, - "4385afbe20eb4363b22e1ff9d6288fff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4642b85d59724c77be6fd2f457c1494f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_741e5deed4024afbbef7823b6cb2006f", - "placeholder": "​", - "style": "IPY_MODEL_3727b7633ef444e593e7a339abd041df", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:39<00:00, 1495.96it/s]" - } - }, - "46c6a150015345b7a2500e89263d7aa2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bffce29067514a888fdf51ef1678bd16", - "placeholder": "​", - "style": "IPY_MODEL_cd680fba706c428f9bec61692bff4a29", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "4a641d26d813428dba2094e759ca0888": { + "69835f01f4d24840900be4eafa6cd376": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4546,25 +4653,7 @@ "width": null } }, - "4b0f4e804c4747d3b5b564a82e7c932f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4b84205d415c45babd7ebcdf93517d66": { + "6bb8ca43d39f43d0933376cc22b61a88": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4617,7 +4706,7 @@ "width": null } }, - "4c86867b8903466fa9e6d4e388aa0434": { + "6c1c35f1bae449bb91822d760764fd52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4632,15 +4721,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70ff80ef934e4944b298fa9da7d63c45", + "layout": "IPY_MODEL_8205e7ba3ebf40938d66cce708243de9", "placeholder": "​", - "style": "IPY_MODEL_845381ac8fa94650bcf7163b99d2b1d0", + "style": "IPY_MODEL_bc15b56e48ef468eb9e5db2eba40dd06", "tabbable": null, "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 87.8MB/s]" + "value": "Map (num_proc=4): 100%" } }, - "4cb59733ebbd4647b7d2611b71086dfc": { + "6c4353aa625f407c8370f78a1dc830c8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4693,49 +4782,7 @@ "width": null } }, - "4cc0b478adc941c3aaea012fcc71511b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "510e84f543554f8d8f0f21ce00483d7d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7b479247b91c4946888088deb8e56a6a", - "IPY_MODEL_c89e8fe224e14e5bbd1d416d2fbe1b95", - "IPY_MODEL_84f3a655756d4b028c767227ead18ba1" - ], - "layout": "IPY_MODEL_a0db81988b184e339aae03632c9b28a8", - "tabbable": null, - "tooltip": null - } - }, - "519660b9e9fd424bbb188e3f3d9d3b89": { + "6ca247bf72f54f03aabdd5d72546025f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4750,68 +4797,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e94068c8564545f282e3a4980638365a", - "IPY_MODEL_c1c5ae1b9dcc420581edd6264c6a2820", - "IPY_MODEL_0c7b3a4a480e4a4f8a7a93dcc0cc643a" + "IPY_MODEL_f035123f0a8341818f2529edc8258078", + "IPY_MODEL_5d1a794999184d7c86308a072ca269b6", + "IPY_MODEL_a774929f38e2462193ebeff227330d56" ], - "layout": "IPY_MODEL_2279b7989e5940fc9318addab8b1ab9e", + "layout": "IPY_MODEL_d460961168ad48bf8a3a86fe06603769", "tabbable": null, "tooltip": null } }, - "532e8f8731f84440bad7ccc71cd28a56": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "539affff4ad04832be7355dbe98a7d01": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "53b4cbc606f94f22a4b554b335600c1d": { - "model_module": "@jupyter-widgets/controls", + "6cb79bd433b44c4c8cd62d84839582bc": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "55b634610a934f5c9801e4876af40a56": { + "6d4b2efa08f448b0a99ccda6ee415ef7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4827,17 +4875,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bb21fd6b378748a49c86c19e35f55f0b", - "max": 60000.0, + "layout": "IPY_MODEL_6cb79bd433b44c4c8cd62d84839582bc", + "max": 4833.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_35d76f48e70949a588e1ac466e50a6f8", + "style": "IPY_MODEL_58ba69b1748d4a2bb5d07f10349efa22", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 4833.0 } }, - "562d8ef38a2d4bd3b8848f2756739a5c": { + "6d5821fefb5648caac6867e9f7a4c67b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4890,7 +4938,63 @@ "width": null } }, - "58084819006a400d9cdfa62c0fd73d0b": { + "6de62af38a2f43a9846f333eda1224f2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "70b6c17f51c948158afefdd56830a23f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6c1c35f1bae449bb91822d760764fd52", + "IPY_MODEL_766722799c914e01bd6ba03a4860a722", + "IPY_MODEL_81491c5e50a34db7ab3810ebfafe7b12" + ], + "layout": "IPY_MODEL_2529da9ccb564a7d98cf91d4e2f454fc", + "tabbable": null, + "tooltip": null + } + }, + "70ca2521a7cf454c8f41cc8665bd8572": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "72cf872836144d31ac5f693e9157dde9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4943,7 +5047,7 @@ "width": null } }, - "582d80487b6f4f07bfdddee7d7276b6d": { + "732da230ecd142e79d60e75c14f8749a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4959,7 +5063,7 @@ "description_width": "" } }, - "589db42ac06b44af8e8131d6bc810558": { + "7662e69c3e7b45a1bc4d55996a7060cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4977,7 +5081,7 @@ "text_color": null } }, - "58ea1a14f16441f7ac9aec12aa6bcf0a": { + "766722799c914e01bd6ba03a4860a722": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4993,17 +5097,85 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c623cd95c65f4227bc6a142428b2e2d0", - "max": 40.0, + "layout": "IPY_MODEL_88f5a8e733a449bf9d33c407463abc0b", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b79a550c01b84942a32015f5d9879e3a", + "style": "IPY_MODEL_ad6f123f6f11456097bebc2f41cfafd7", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 + } + }, + "76cd9d157bf74d6e93db6f5727c6f900": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7f5b0df354d74d42b00745784e61cbb5", + "IPY_MODEL_d844173c6e344ebcbb4c2988636ce3a8", + "IPY_MODEL_f68300c0882c4adca9ac2341e102684c" + ], + "layout": "IPY_MODEL_ddea9d00c6b648c2ab8a73e6395f71ba", + "tabbable": null, + "tooltip": null + } + }, + "783884a15ffc4bceb98545ac1564a7f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c5cfb97d21914845a48cbeea88f4d542", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cb7c5ad137844450a4dd8bb2cd5b55d5", + "tabbable": null, + "tooltip": null, + "value": 5148.0 + } + }, + "786e4946ac9d4815956096e2294ce1a9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5a99a0960d9346219c1f9d5f53b6a66c": { + "78c28ce7e495439eb9b5618da236dfff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5018,15 +5190,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f6039d93a76046e5a47332266efbcad2", + "layout": "IPY_MODEL_6930211e1e8a42d88c3294247388155e", "placeholder": "​", - "style": "IPY_MODEL_78ff9f12373143beb1480d97598c1fd8", + "style": "IPY_MODEL_d5b89a2622784b2684e5d0e4a1d8e288", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.46it/s]" + "value": "Downloading builder script: 100%" } }, - "5ad29a8b409a4c558b64b79f12934c9d": { + "79d53959ea3648699bfb173209305675": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5079,30 +5251,7 @@ "width": null } }, - "5af3e59cee874976bc838323e7706739": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5ad29a8b409a4c558b64b79f12934c9d", - "placeholder": "​", - "style": "IPY_MODEL_c19e17cb683a43bd955fbf81102c4165", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.81it/s]" - } - }, - "5c2bfa97829b4fdea2882b9050f5562d": { + "7a5e963277ae42569b466604816aee6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5120,31 +5269,30 @@ "text_color": null } }, - "5fea0aa6ab6c432499d18388d7215fb9": { + "7ec457de82a94296bda5ce8043891a1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_100b4c65b0f44c9b8b9c7cb4afbc38ac", - "IPY_MODEL_216f04617573408697842aba4967a052", - "IPY_MODEL_60acea659f564db7a12ee0db15590f10" - ], - "layout": "IPY_MODEL_25d73f1cd6f74f7f8eb5013af3fc4aea", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a1f03e62934145f48df36ed07ef54135", + "placeholder": "​", + "style": "IPY_MODEL_57fa64edffdb4d37a90dbea656009ab1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.45MB/s]" } }, - "60acea659f564db7a12ee0db15590f10": { + "7f5b0df354d74d42b00745784e61cbb5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5159,69 +5307,117 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1ad9b1cdbd544af3a314df0c82ec6565", + "layout": "IPY_MODEL_b56fcd9aca4c49c0b688460c48f52150", "placeholder": "​", - "style": "IPY_MODEL_238898e5c2874588924e33ad1a5a14be", + "style": "IPY_MODEL_0423a125c3324adc9f2433bebe77773b", "tabbable": null, "tooltip": null, - "value": " 4/4 [00:00<00:00, 1235.44it/s]" + "value": "100%" } }, - "629afeba7adb4995975ea1d03301cd3f": { + "81491c5e50a34db7ab3810ebfafe7b12": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c0717785491f4d29856b706b3233759a", + "placeholder": "​", + "style": "IPY_MODEL_c9c1491278bb49a0a831ab5dea82a8c5", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 7815.27 examples/s]" } }, - "63e9e74faee2419a8226b92ac225e31b": { - "model_module": "@jupyter-widgets/controls", + "8205e7ba3ebf40938d66cce708243de9": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "64f5f6e8c1bb4d1ea5c7e2269014d6b6": { + "8224804dbbd04b29bd71274520c5718d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_299c0f0403e1447fb83af15e00e8f19f", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8824a37abc7b47a7b37e0eb6c3e6cc11", + "tabbable": null, + "tooltip": null, + "value": 29515.0 } }, - "65e63b482d884a54ba1811f393e142f6": { + "8239b72ad4ca43fb92f0bc981abfd95b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5274,23 +5470,60 @@ "width": null } }, - "6ad9be8c9330405c950fd1e4bf6f2bcc": { - "model_module": "@jupyter-widgets/controls", + "833e5dbaba7546f582547cdf7571c108": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6fc748b471b64f71a9cf9f308a0c6f58": { + "838e5992cb0f492aa79d9e4411b13437": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5305,15 +5538,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dbf46341abf942659d8c32c85a6689a7", + "layout": "IPY_MODEL_c07a9548b6234526a02e4a5ca84bc144", "placeholder": "​", - "style": "IPY_MODEL_06f943bb034d473babe64569cf34ef3f", + "style": "IPY_MODEL_266601a55125440a88a8a16e00cebd66", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 63.32it/s]" } }, - "70a57a24141f4298ad66d387b76ae643": { + "8767f058e5b649bfb0c2d444b49f3f9d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5366,7 +5599,7 @@ "width": null } }, - "70ff80ef934e4944b298fa9da7d63c45": { + "87900fda6f274f88ab3a99c82a5a9940": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5419,25 +5652,46 @@ "width": null } }, - "7156e47ae30c49029e1051be60fedd8a": { + "8824a37abc7b47a7b37e0eb6c3e6cc11": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" + } + }, + "88c76968e9da4cb6ae77b6883ae64280": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_69835f01f4d24840900be4eafa6cd376", + "placeholder": "​", + "style": "IPY_MODEL_4c9f6ca0676a4e6bbace6bdaf151e92c", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" } }, - "741e5deed4024afbbef7823b6cb2006f": { + "88f5a8e733a449bf9d33c407463abc0b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5490,70 +5744,7 @@ "width": null } }, - "748b7bbe2c8345c6a22623d9f52f46cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c1e14573e8a14d2da05856b01902bfe6", - "IPY_MODEL_55b634610a934f5c9801e4876af40a56", - "IPY_MODEL_1c04a881488944cfb2a78e4629f5d219" - ], - "layout": "IPY_MODEL_30926f7236cb4acb82089cc6bc32bca9", - "tabbable": null, - "tooltip": null - } - }, - "759ff835cd044874ad69b6424e9c4e1e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "75d1007bec0746c98aeda8aef0434706": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_af66ef66990540bba700a8b18d8ab6ec", - "placeholder": "​", - "style": "IPY_MODEL_f8c664df72d6466aaadd0af74042c5d5", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.16MB/s]" - } - }, - "7611501b359946e6a8b64ae0a49434e6": { + "8a368087070540ac84cfd57d04c80102": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5606,25 +5797,31 @@ "width": null } }, - "76dcd467640d4c13a2c0ac067cfdd034": { + "8b242b3757014ca08c0be26603c856e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_070af112be824f3a9dfc9d11f6c99bdb", + "IPY_MODEL_624903d3a14d4864906b939e0507936f", + "IPY_MODEL_61c7ca03d1b84ecdbe6bfb8765d33dd4" + ], + "layout": "IPY_MODEL_b6006aab30524546a2357c0b60b3422c", + "tabbable": null, + "tooltip": null } }, - "78e0941cdb4f489cb776e7f12e9dbd86": { + "8b62a322205f4dbd9b437035f7710286": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5639,15 +5836,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b88f2d209bbb49ab913ba214e4fa2da2", + "layout": "IPY_MODEL_bd4ca863b31b4a6087a17d6cfa52ba96", "placeholder": "​", - "style": "IPY_MODEL_15fc493178c94958a5bd63dc73acf625", + "style": "IPY_MODEL_8be92883d1f447b7b27692d27d470559", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": "Computing checksums: 100%" } }, - "78ff9f12373143beb1480d97598c1fd8": { + "8be92883d1f447b7b27692d27d470559": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5665,7 +5862,7 @@ "text_color": null } }, - "791e454bb7974023b43014ebc8f1c796": { + "8c7ec74595b64ca194e8d3ba1c2a16fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5718,25 +5915,7 @@ "width": null } }, - "79907f8846974be690f37ae0b061520b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7b479247b91c4946888088deb8e56a6a": { + "8e4fdf510840497e9b8908d892a617e3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5751,82 +5930,147 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0a5ef6cfa8714c0ab6b0ef41dfb1a925", + "layout": "IPY_MODEL_ef43f60fb2ce4517935a3a3d655e6e4c", "placeholder": "​", - "style": "IPY_MODEL_63e9e74faee2419a8226b92ac225e31b", + "style": "IPY_MODEL_445bd193a0b443b68d88db83f68b1135", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": "Downloading readme: 100%" } }, - "7bf3c6eec63c4a8bbd6deaacc2e60042": { + "8fd06c99c9a747d8b7932274f22d7a1f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d4f0191a9824520b481222f74ff8de2", - "placeholder": "​", - "style": "IPY_MODEL_4cc0b478adc941c3aaea012fcc71511b", + "layout": "IPY_MODEL_8a368087070540ac84cfd57d04c80102", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_25aea1e677d84e4da5e2f1a580b847bb", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 4.0 } }, - "7f4c2fe39ada4e6582a507e9acd7d649": { - "model_module": "@jupyter-widgets/controls", + "93b948080aae40babe81f47f38dc60fa": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_00e493ba9c68410a9bfb07d35f58c61a", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_759ff835cd044874ad69b6424e9c4e1e", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "845381ac8fa94650bcf7163b99d2b1d0": { - "model_module": "@jupyter-widgets/controls", + "953f69d720d74dc0824fc0ac9a4ffa3b": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "84a10c91f7e44b49a6ec73367bf116ef": { + "956d09bfdd86417caf76bb337c098a81": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5844,30 +6088,31 @@ "text_color": null } }, - "84f3a655756d4b028c767227ead18ba1": { + "9717f3b4aaae491d9cb2e07d49a003a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7611501b359946e6a8b64ae0a49434e6", - "placeholder": "​", - "style": "IPY_MODEL_532e8f8731f84440bad7ccc71cd28a56", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ab1b471e80f9406fabb0d7e0b174084c", + "IPY_MODEL_197981c8f9c84c00abea85184c72fcd5", + "IPY_MODEL_5d069f3fc0fd47028eaaf3380b16e427" + ], + "layout": "IPY_MODEL_19d83e638f7a404ba0eb4fdd71302435", "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8790.83 examples/s]" + "tooltip": null } }, - "85c842922c6248a8a088b6edfa110613": { + "97c91dea2ffc40f1830438b9a24225ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5920,31 +6165,25 @@ "width": null } }, - "8c14226990a04e87aa10a393e2a0203a": { + "98e13251741f42169572ab3c91ade8e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_eaaa22f94db549fb8e47e6917c661949", - "IPY_MODEL_41642d27417146338cd059871f247264", - "IPY_MODEL_b2d82e9c2f994037b0e0975750778a83" - ], - "layout": "IPY_MODEL_fde3ef7840f440e18047964aceeecefe", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8d4f0191a9824520b481222f74ff8de2": { + "9a36f96ef3224e05847983090297222a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5997,7 +6236,30 @@ "width": null } }, - "8e73f65890fc4fefb308aaf8321b0e77": { + "9f1afcead928423ab651a3cfb6bc7cc4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cfc5b02015154a29bae32de6ea0acd51", + "placeholder": "​", + "style": "IPY_MODEL_7a5e963277ae42569b466604816aee6f", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 59.96it/s]" + } + }, + "9fda0321906e4bb7a73fc697ca19c8e3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6050,25 +6312,7 @@ "width": null } }, - "8f3b67fe29a34428a9dcc77323065aa5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "922abaecac1c46f4bb9ba4ff7faeace7": { + "a145248aa276456f95640b1b4a24d4bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6086,7 +6330,7 @@ "text_color": null } }, - "95453138f12b4a908bb147d1409d6b93": { + "a1990d1ffd9240a7a4bf2438eaf81754": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -6102,57 +6346,7 @@ "description_width": "" } }, - "9599396e66b4465ea0e42e641e6a2557": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c99f3cc6eec14c488e7ae6623bdcc6fc", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25c58f4aec6b4f41be25042769d63c52", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "9611c5ddf09444219b0832f81930fdd7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d6e3dfd49a9341cbbcee99cd75b5cc7d", - "IPY_MODEL_f91439725ffb4a859b9e13acd25e5e3c", - "IPY_MODEL_d03921811ef44790b73875b7c7becb5c" - ], - "layout": "IPY_MODEL_353d9eda4aec4d30805d245cde63c51c", - "tabbable": null, - "tooltip": null - } - }, - "97c2e55e3c57419c81374350ea34b432": { + "a1f03e62934145f48df36ed07ef54135": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6205,57 +6399,7 @@ "width": null } }, - "999579ced4a04955b6fe76b06613510d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_78e0941cdb4f489cb776e7f12e9dbd86", - "IPY_MODEL_b26b9f4f9e9c4d70851630b1b5296441", - "IPY_MODEL_340d530d31fa467c9418a8767d1a0eb2" - ], - "layout": "IPY_MODEL_8e73f65890fc4fefb308aaf8321b0e77", - "tabbable": null, - "tooltip": null - } - }, - "9fdacce60b084ad6b7edc2e06ad6b7d5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_562d8ef38a2d4bd3b8848f2756739a5c", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_24f4a95a06be4e01a6b70cd2cd3bccda", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a0156d3f29e94c14a8be31866b4025fa": { + "a24112b64ebe4079b08768fea3888272": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6308,7 +6452,66 @@ "width": null } }, - "a0db81988b184e339aae03632c9b28a8": { + "a31c56a1f6f544c1846467d596dd7b15": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a38689b69ffd44938ebabe7dc9710b39": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a50bac222b154e1e8f1af99c2d2f7cb5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5c0a9c7370bb48c09729cb766ab1781a", + "placeholder": "​", + "style": "IPY_MODEL_2f77ed4c555c4365a30f1c760cb59e74", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a6307f65c4f14a07ad202a2748b4132f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6361,7 +6564,78 @@ "width": null } }, - "a43e1212e20e49bfb45d1ca51918abcd": { + "a774929f38e2462193ebeff227330d56": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_11703dfe59744973b7ee5db1b50a7304", + "placeholder": "​", + "style": "IPY_MODEL_786e4946ac9d4815956096e2294ce1a9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.06it/s]" + } + }, + "a7cf6a20863544c78418cc502f39b678": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8b62a322205f4dbd9b437035f7710286", + "IPY_MODEL_8fd06c99c9a747d8b7932274f22d7a1f", + "IPY_MODEL_db1fd46972524542bbb7ec0eeef4b2ba" + ], + "layout": "IPY_MODEL_182fe610a03c4f0c8820d62efd2942a9", + "tabbable": null, + "tooltip": null + } + }, + "a9efee99388e4bd987cba82e4c249be5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8e4fdf510840497e9b8908d892a617e3", + "IPY_MODEL_de7773b079344e19b9f86e5643e773ed", + "IPY_MODEL_7ec457de82a94296bda5ce8043891a1e" + ], + "layout": "IPY_MODEL_a6307f65c4f14a07ad202a2748b4132f", + "tabbable": null, + "tooltip": null + } + }, + "aa609ade2290479691002af80a845d3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6414,7 +6688,48 @@ "width": null } }, - "a598e468e4a04cbb8a19460650aeb41f": { + "ab1b471e80f9406fabb0d7e0b174084c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_255e06fe46cb4c0eaa40336d22beaf70", + "placeholder": "​", + "style": "IPY_MODEL_98e13251741f42169572ab3c91ade8e4", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "abecf5a0521e45f78a788d98cc335e5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ac339d14f5334e5fbce36888ade893b6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6467,7 +6782,7 @@ "width": null } }, - "a63043994e1446838f64bbd78dc7bb13": { + "ac933a814c124345b0730704f11cfbc8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6520,101 +6835,73 @@ "width": null } }, - "ac993184217a4a069874329815a7ded0": { + "ad6f123f6f11456097bebc2f41cfafd7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4275cd122298472195f82b5dbef8d87f", - "placeholder": "​", - "style": "IPY_MODEL_922abaecac1c46f4bb9ba4ff7faeace7", - "tabbable": null, - "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 607kB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "aca6b827bc144b7e84332be12838c12a": { - "model_module": "@jupyter-widgets/base", + "aeda1e802b974623a7d922ff793e1002": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f4f96f825f2043db8c6d5179d06cd01c", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_348018e48d954c87b48ff2a6e7470304", + "tabbable": null, + "tooltip": null, + "value": 10000.0 } }, - "ad646ab90327403e8f0b8e3c738d3955": { + "b13b21c3b7544706aacfbba4f3504a8b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3abd2a96be1649ed9f2a7594cfdf3bbd", + "IPY_MODEL_d7462fe7497245929fb556be574c9b66", + "IPY_MODEL_e7d291738cf84330bb7fbd4947e558a1" + ], + "layout": "IPY_MODEL_295b6321cc6f4b1bbfa02a28f8581b1d", + "tabbable": null, + "tooltip": null } }, - "ad73b70f57d9434e853c8286c209c9d0": { + "b374b826cf5a4a668332dc70f8c2fe4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6629,15 +6916,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70a57a24141f4298ad66d387b76ae643", + "layout": "IPY_MODEL_f679da4310d84ae0a1679faa5659f6c7", "placeholder": "​", - "style": "IPY_MODEL_7156e47ae30c49029e1051be60fedd8a", + "style": "IPY_MODEL_0abf4ae675284a88873e14026b73ab9e", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Downloading data: 100%" } }, - "aea72aba96c34f99adbd16416576dbed": { + "b56fcd9aca4c49c0b688460c48f52150": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6690,7 +6977,7 @@ "width": null } }, - "af66ef66990540bba700a8b18d8ab6ec": { + "b6006aab30524546a2357c0b60b3422c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6743,7 +7030,31 @@ "width": null } }, - "b1ad97237b2c4011b851a00293f85c32": { + "b69aa5fb137444eb962d31f239578d65": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_64e2e67bdc244016895a62ad062fa077", + "IPY_MODEL_1ba2c9f296904da78195fe3e45321059", + "IPY_MODEL_9f1afcead928423ab651a3cfb6bc7cc4" + ], + "layout": "IPY_MODEL_ba0143519d7f4309b1af7b6461fd8a90", + "tabbable": null, + "tooltip": null + } + }, + "ba0143519d7f4309b1af7b6461fd8a90": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6796,90 +7107,72 @@ "width": null } }, - "b1b972e9e7f64377ad85c5aa38aa9c79": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b26b9f4f9e9c4d70851630b1b5296441": { + "ba30ea8e68274773937367758a4abf1b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_58084819006a400d9cdfa62c0fd73d0b", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3f77865e92e04447b8130e58472c9196", + "layout": "IPY_MODEL_72cf872836144d31ac5f693e9157dde9", + "placeholder": "​", + "style": "IPY_MODEL_e7eedd97596a479c9a69c82d61e53877", "tabbable": null, "tooltip": null, - "value": 8845.0 + "value": " 40/40 [00:00<00:00, 64.53it/s]" } }, - "b2d82e9c2f994037b0e0975750778a83": { + "bc15b56e48ef468eb9e5db2eba40dd06": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1ad97237b2c4011b851a00293f85c32", - "placeholder": "​", - "style": "IPY_MODEL_53b4cbc606f94f22a4b554b335600c1d", - "tabbable": null, - "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 804kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b41cff4d7b16460e8fbb1e24113ab070": { + "bc98754b340343f594559442ba450aa4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_88c76968e9da4cb6ae77b6883ae64280", + "IPY_MODEL_3edb1a47c3314c0abfc78f23d62ebe68", + "IPY_MODEL_367462867871411f81ad39c2100a77d8" + ], + "layout": "IPY_MODEL_9a36f96ef3224e05847983090297222a", + "tabbable": null, + "tooltip": null } }, - "b52f0a3b663249bdbad4dfd86a3f26b8": { + "bd4ca863b31b4a6087a17d6cfa52ba96": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6932,49 +7225,31 @@ "width": null } }, - "b79a550c01b84942a32015f5d9879e3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "b85d9be3c4ee46e38ce7887f5d591f31": { + "bd4e5e775e0d4b5d90568b686f8fd56f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4b84205d415c45babd7ebcdf93517d66", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_32020bd3922749389ef4e7b04e878ded", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_78c28ce7e495439eb9b5618da236dfff", + "IPY_MODEL_6d4b2efa08f448b0a99ccda6ee415ef7", + "IPY_MODEL_ee2470d66aed4014820acd898b666713" + ], + "layout": "IPY_MODEL_ccb6f4531ee843d4ad0080744e6078d2", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null } }, - "b88f2d209bbb49ab913ba214e4fa2da2": { + "c0717785491f4d29856b706b3233759a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7027,7 +7302,7 @@ "width": null } }, - "bb21fd6b378748a49c86c19e35f55f0b": { + "c07a9548b6234526a02e4a5ca84bc144": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7080,30 +7355,7 @@ "width": null } }, - "be037b5bc2ca4ee996763a4581d3e262": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_40ff8e1e3db2418ca7a02ad19a7f7df5", - "placeholder": "​", - "style": "IPY_MODEL_b1b972e9e7f64377ad85c5aa38aa9c79", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.07it/s]" - } - }, - "bffce29067514a888fdf51ef1678bd16": { + "c5cfb97d21914845a48cbeea88f4d542": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7156,108 +7408,7 @@ "width": null } }, - "c19e17cb683a43bd955fbf81102c4165": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c1c5ae1b9dcc420581edd6264c6a2820": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_791e454bb7974023b43014ebc8f1c796", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_dea3b0f1e22b49aebd2a6671c1773443", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "c1e14573e8a14d2da05856b01902bfe6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_14620fb3ef91409c93ca2866a6b245cd", - "placeholder": "​", - "style": "IPY_MODEL_8f3b67fe29a34428a9dcc77323065aa5", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "c2009fa1dcb54044b14d94c615567a79": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "c27e7863116b4bf48eab88e0869c69a4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c3c185ce8c54435f908584bfc3d5436e": { + "c71cb776520442f0baba7a63fdbf2324": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7310,7 +7461,7 @@ "width": null } }, - "c4cca31f1d014a0a962d3f164bca3b23": { + "c8467f56948a44689852152948675255": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7325,39 +7476,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4a641d26d813428dba2094e759ca0888", + "layout": "IPY_MODEL_5594cd23fb504b0dbdef0ecfe202453c", "placeholder": "​", - "style": "IPY_MODEL_e466b9881b0840f9935a01d3cc542f83", + "style": "IPY_MODEL_109113d68eb84c2eace6f4deda6b040b", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "c60874cc394b428786de11432b5ba1ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c4cca31f1d014a0a962d3f164bca3b23", - "IPY_MODEL_e5c369a269084adb8bbcc7348e10c651", - "IPY_MODEL_be037b5bc2ca4ee996763a4581d3e262" - ], - "layout": "IPY_MODEL_1640d13bd96d405ba01bbe9f22029432", - "tabbable": null, - "tooltip": null + "value": " 10000/10000 [00:01<00:00, 8360.91 examples/s]" } }, - "c623cd95c65f4227bc6a142428b2e2d0": { + "c87df7230ab74cf3b4ce36554b4f64b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7410,59 +7537,41 @@ "width": null } }, - "c7ec958a864d4b8b9aeb4c7b4f1055df": { + "c9c1491278bb49a0a831ab5dea82a8c5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c3c185ce8c54435f908584bfc3d5436e", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_582d80487b6f4f07bfdddee7d7276b6d", - "tabbable": null, - "tooltip": null, - "value": 4422102.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c89e8fe224e14e5bbd1d416d2fbe1b95": { + "cb7c5ad137844450a4dd8bb2cd5b55d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fb73e42bc42c43d581643273c2c12573", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c2009fa1dcb54044b14d94c615567a79", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c8ba7020086d47b98cce4fb5096a67b6": { + "ccb6f4531ee843d4ad0080744e6078d2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7515,7 +7624,7 @@ "width": null } }, - "c91e1c0bc2da4f678e94080291cc61d8": { + "cf6f623149dc4d96afc8c831b7926d0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7559,42 +7668,16 @@ "min_width": null, "object_fit": null, "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "c979207c10fd4e0f91eb87b8a8cb5bdb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c9c85c81e35e4c79900acebf8ed10e55", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6ad9be8c9330405c950fd1e4bf6f2bcc", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c99f3cc6eec14c488e7ae6623bdcc6fc": { + "cfc5b02015154a29bae32de6ea0acd51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7647,7 +7730,7 @@ "width": null } }, - "c9c85c81e35e4c79900acebf8ed10e55": { + "d460961168ad48bf8a3a86fe06603769": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7700,7 +7783,25 @@ "width": null } }, - "ca1506e7d3f846adb2f7487be4ad5f1d": { + "d5b89a2622784b2684e5d0e4a1d8e288": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d60f32b2907d4a288385a30c717ef39d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7715,16 +7816,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4140430b2ab443be9abe907b644904c1", - "IPY_MODEL_340d8d367bbf4aae8a133ab9a2dfda9d", - "IPY_MODEL_3e483d0ac53644caa94492a8d78843c1" + "IPY_MODEL_126a880302694496ab5834d71ecffa75", + "IPY_MODEL_aeda1e802b974623a7d922ff793e1002", + "IPY_MODEL_c8467f56948a44689852152948675255" ], - "layout": "IPY_MODEL_65e63b482d884a54ba1811f393e142f6", + "layout": "IPY_MODEL_50042e9a2b5145a780c7c3d799e26aad", "tabbable": null, "tooltip": null } }, - "cc0ffca4aee04bd8946bbf80a09e90bf": { + "d627f7cbc1534c10a92cc7fd0f874ca6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7742,25 +7843,57 @@ "text_color": null } }, - "cd680fba706c428f9bec61692bff4a29": { + "d6465626e3264fa58f44ddccd18cfef2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3ddd27d196364f1db80581facbadf18f", + "IPY_MODEL_5695fa5956b04b45a058d58f6cb27b12", + "IPY_MODEL_ba30ea8e68274773937367758a4abf1b" + ], + "layout": "IPY_MODEL_6c4353aa625f407c8370f78a1dc830c8", + "tabbable": null, + "tooltip": null + } + }, + "d7462fe7497245929fb556be574c9b66": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3ec84e0ec4d94afd8f15a91eb757624e", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6de62af38a2f43a9846f333eda1224f2", + "tabbable": null, + "tooltip": null, + "value": 26421880.0 } }, - "ce9ec70d05804fdba79263567a590fc9": { + "d78ce21ee1fc44e8b680bda13d37d943": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7813,53 +7946,33 @@ "width": null } }, - "ceddd53d284e47e080fd5b52a11b902d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2ad862ff6f0f4f3a8db6362836d85b81", - "placeholder": "​", - "style": "IPY_MODEL_76dcd467640d4c13a2c0ac067cfdd034", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "d03921811ef44790b73875b7c7becb5c": { + "d844173c6e344ebcbb4c2988636ce3a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a43e1212e20e49bfb45d1ca51918abcd", - "placeholder": "​", - "style": "IPY_MODEL_cc0ffca4aee04bd8946bbf80a09e90bf", + "layout": "IPY_MODEL_aa609ade2290479691002af80a845d3d", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_732da230ecd142e79d60e75c14f8749a", "tabbable": null, "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 114MB/s]" + "value": 40.0 } }, - "d6e3dfd49a9341cbbcee99cd75b5cc7d": { + "db1fd46972524542bbb7ec0eeef4b2ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7874,121 +7987,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dec4f3552fbb46b28ac7bc2f8095a209", + "layout": "IPY_MODEL_2cb677caec644d3ab2a8b6a70208559e", "placeholder": "​", - "style": "IPY_MODEL_79907f8846974be690f37ae0b061520b", + "style": "IPY_MODEL_7662e69c3e7b45a1bc4d55996a7060cc", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "d8139f444e324e12818613912a49a6f9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d8402ea2970a4161873c85143f7bb387": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": " 4/4 [00:00<00:00, 1226.49it/s]" } }, - "dbf46341abf942659d8c32c85a6689a7": { + "dcea0f344da44aa4bd657710ee8edf49": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8041,31 +8048,7 @@ "width": null } }, - "dd367ed6b3e145b9ba56c440d63f6948": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1bc6bb27d1ce4ccd8dd7cfe252d16d39", - "IPY_MODEL_7f4c2fe39ada4e6582a507e9acd7d649", - "IPY_MODEL_ac993184217a4a069874329815a7ded0" - ], - "layout": "IPY_MODEL_e9687d9dff8b454ca67f60b37790aeed", - "tabbable": null, - "tooltip": null - } - }, - "ddb26404cd4644d3a9efa8efd7af9104": { + "dcfb76cdced842fd810c0329fa0f1c7f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8080,32 +8063,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ad73b70f57d9434e853c8286c209c9d0", - "IPY_MODEL_b85d9be3c4ee46e38ce7887f5d591f31", - "IPY_MODEL_5a99a0960d9346219c1f9d5f53b6a66c" + "IPY_MODEL_ff86e317738e43de99250a83cfb7ad7f", + "IPY_MODEL_8224804dbbd04b29bd71274520c5718d", + "IPY_MODEL_5f29c5efd23943e1a08518f859506345" ], - "layout": "IPY_MODEL_d8402ea2970a4161873c85143f7bb387", + "layout": "IPY_MODEL_4a97191b1a1643199e46244e6611352a", "tabbable": null, "tooltip": null } }, - "dea3b0f1e22b49aebd2a6671c1773443": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "dec4f3552fbb46b28ac7bc2f8095a209": { + "ddea9d00c6b648c2ab8a73e6395f71ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8158,25 +8125,33 @@ "width": null } }, - "e466b9881b0840f9935a01d3cc542f83": { + "de7773b079344e19b9f86e5643e773ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9fda0321906e4bb7a73fc697ca19c8e3", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5b07f8803c514642801639b40c1aa34b", + "tabbable": null, + "tooltip": null, + "value": 8845.0 } }, - "e4991d19e073408f9cef1ae11eb71019": { + "df8581440a0d42198080a4cd32ee9a4a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8229,56 +8204,7 @@ "width": null } }, - "e5c369a269084adb8bbcc7348e10c651": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce9ec70d05804fdba79263567a590fc9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b41cff4d7b16460e8fbb1e24113ab070", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "e94068c8564545f282e3a4980638365a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a63043994e1446838f64bbd78dc7bb13", - "placeholder": "​", - "style": "IPY_MODEL_ad646ab90327403e8f0b8e3c738d3955", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "e9687d9dff8b454ca67f60b37790aeed": { + "e5fa84089fac429dba7a0f14fcadf7b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8331,7 +8257,7 @@ "width": null } }, - "eaaa22f94db549fb8e47e6917c661949": { + "e7d291738cf84330bb7fbd4947e558a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8346,15 +8272,74 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b52f0a3b663249bdbad4dfd86a3f26b8", + "layout": "IPY_MODEL_df8581440a0d42198080a4cd32ee9a4a", "placeholder": "​", - "style": "IPY_MODEL_f1e89ef58c7c4a19a40750e69fa7d630", + "style": "IPY_MODEL_4751ad8ff2014ae4aefb61464aa0b996", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 26.4M/26.4M [00:00<00:00, 105MB/s]" + } + }, + "e7eedd97596a479c9a69c82d61e53877": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee2470d66aed4014820acd898b666713": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dcea0f344da44aa4bd657710ee8edf49", + "placeholder": "​", + "style": "IPY_MODEL_61b5f9d9099a4161a69f1c58aaa1f54e", + "tabbable": null, + "tooltip": null, + "value": " 4.83k/4.83k [00:00<00:00, 527kB/s]" + } + }, + "ef22307e229c427e9f2c45041e108373": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f054cf08430c477d9eab2ccc80e48b99": { + "ef43f60fb2ce4517935a3a3d655e6e4c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8407,65 +8392,30 @@ "width": null } }, - "f1e89ef58c7c4a19a40750e69fa7d630": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f2567d63206c480fbbdf4639222fd3db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "f55b4eb540fd4f368229bcf7012adf9f": { + "f035123f0a8341818f2529edc8258078": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_6fc748b471b64f71a9cf9f308a0c6f58", - "IPY_MODEL_9599396e66b4465ea0e42e641e6a2557", - "IPY_MODEL_4642b85d59724c77be6fd2f457c1494f" - ], - "layout": "IPY_MODEL_a598e468e4a04cbb8a19460650aeb41f", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6d5821fefb5648caac6867e9f7a4c67b", + "placeholder": "​", + "style": "IPY_MODEL_a145248aa276456f95640b1b4a24d4bf", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "f6039d93a76046e5a47332266efbcad2": { + "f15ed2b240814bb897b777075c726c92": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8518,43 +8468,30 @@ "width": null } }, - "f8c664df72d6466aaadd0af74042c5d5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f8d8dfbdc92f4599abc3232a34342853": { + "f247cc3e079b4ad2aa902a3150c5d9ec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8239b72ad4ca43fb92f0bc981abfd95b", + "placeholder": "​", + "style": "IPY_MODEL_fed701653a71451196013b62e55b30ef", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" } }, - "f91439725ffb4a859b9e13acd25e5e3c": { + "f306b412103a4a999f13f32ecb0841fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8570,17 +8507,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c8ba7020086d47b98cce4fb5096a67b6", - "max": 26421880.0, + "layout": "IPY_MODEL_4367f895c508411fb72fbf123bfd2662", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_539affff4ad04832be7355dbe98a7d01", + "style": "IPY_MODEL_62c6658ea89d458aa1198b576f9eee56", "tabbable": null, "tooltip": null, - "value": 26421880.0 + "value": 40.0 } }, - "fb73e42bc42c43d581643273c2c12573": { + "f4f96f825f2043db8c6d5179d06cd01c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8633,7 +8570,7 @@ "width": null } }, - "fde3ef7840f440e18047964aceeecefe": { + "f679da4310d84ae0a1679faa5659f6c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8686,28 +8623,91 @@ "width": null } }, - "ff1c46ea9c474d1bb4645c7cbbf298b0": { + "f68300c0882c4adca9ac2341e102684c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7bf3c6eec63c4a8bbd6deaacc2e60042", - "IPY_MODEL_58ea1a14f16441f7ac9aec12aa6bcf0a", - "IPY_MODEL_5af3e59cee874976bc838323e7706739" - ], - "layout": "IPY_MODEL_3e8bab513b2544f2be136ee1c48ca51c", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ac339d14f5334e5fbce36888ade893b6", + "placeholder": "​", + "style": "IPY_MODEL_424a8de7240d4c088b2b2dbadf1113ab", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.31it/s]" + } + }, + "fc671361cddb4f3e9903ef6aae4d3083": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cf6f623149dc4d96afc8c831b7926d0a", + "placeholder": "​", + "style": "IPY_MODEL_a31c56a1f6f544c1846467d596dd7b15", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 866kB/s]" + } + }, + "fed701653a71451196013b62e55b30ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ff86e317738e43de99250a83cfb7ad7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8767f058e5b649bfb0c2d444b49f3f9d", + "placeholder": "​", + "style": "IPY_MODEL_01c1fefd1d1549a1ac3d1168746bc81e", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" } } }, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index f67c6a971..452755a26 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:16.757940Z", - "iopub.status.busy": "2024-06-28T15:36:16.757462Z", - "iopub.status.idle": "2024-06-28T15:36:17.958795Z", - "shell.execute_reply": "2024-06-28T15:36:17.958249Z" + "iopub.execute_input": "2024-07-01T15:05:46.317874Z", + "iopub.status.busy": "2024-07-01T15:05:46.317719Z", + "iopub.status.idle": "2024-07-01T15:05:47.417876Z", + "shell.execute_reply": "2024-07-01T15:05:47.417402Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:17.961629Z", - "iopub.status.busy": "2024-06-28T15:36:17.961160Z", - "iopub.status.idle": "2024-06-28T15:36:17.980033Z", - "shell.execute_reply": "2024-06-28T15:36:17.979543Z" + "iopub.execute_input": "2024-07-01T15:05:47.420276Z", + "iopub.status.busy": "2024-07-01T15:05:47.420000Z", + "iopub.status.idle": "2024-07-01T15:05:47.437670Z", + "shell.execute_reply": "2024-07-01T15:05:47.437224Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:17.982722Z", - "iopub.status.busy": "2024-06-28T15:36:17.982219Z", - "iopub.status.idle": "2024-06-28T15:36:18.009820Z", - "shell.execute_reply": "2024-06-28T15:36:18.009219Z" + "iopub.execute_input": "2024-07-01T15:05:47.439750Z", + "iopub.status.busy": "2024-07-01T15:05:47.439498Z", + "iopub.status.idle": "2024-07-01T15:05:47.478024Z", + "shell.execute_reply": "2024-07-01T15:05:47.477526Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.012110Z", - "iopub.status.busy": "2024-06-28T15:36:18.011825Z", - "iopub.status.idle": "2024-06-28T15:36:18.015545Z", - "shell.execute_reply": "2024-06-28T15:36:18.015080Z" + "iopub.execute_input": "2024-07-01T15:05:47.480262Z", + "iopub.status.busy": "2024-07-01T15:05:47.479916Z", + "iopub.status.idle": "2024-07-01T15:05:47.483182Z", + "shell.execute_reply": "2024-07-01T15:05:47.482737Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.017747Z", - "iopub.status.busy": "2024-06-28T15:36:18.017308Z", - "iopub.status.idle": "2024-06-28T15:36:18.025346Z", - "shell.execute_reply": "2024-06-28T15:36:18.024913Z" + "iopub.execute_input": "2024-07-01T15:05:47.485314Z", + "iopub.status.busy": "2024-07-01T15:05:47.484937Z", + "iopub.status.idle": "2024-07-01T15:05:47.492797Z", + "shell.execute_reply": "2024-07-01T15:05:47.492370Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.027654Z", - "iopub.status.busy": "2024-06-28T15:36:18.027210Z", - "iopub.status.idle": "2024-06-28T15:36:18.029982Z", - "shell.execute_reply": "2024-06-28T15:36:18.029524Z" + "iopub.execute_input": "2024-07-01T15:05:47.494826Z", + "iopub.status.busy": "2024-07-01T15:05:47.494542Z", + "iopub.status.idle": "2024-07-01T15:05:47.497119Z", + "shell.execute_reply": "2024-07-01T15:05:47.496587Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.031982Z", - "iopub.status.busy": "2024-06-28T15:36:18.031712Z", - "iopub.status.idle": "2024-06-28T15:36:21.013378Z", - "shell.execute_reply": "2024-06-28T15:36:21.012714Z" + "iopub.execute_input": "2024-07-01T15:05:47.499036Z", + "iopub.status.busy": "2024-07-01T15:05:47.498842Z", + "iopub.status.idle": "2024-07-01T15:05:50.430868Z", + "shell.execute_reply": "2024-07-01T15:05:50.430331Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:21.016269Z", - "iopub.status.busy": "2024-06-28T15:36:21.015842Z", - "iopub.status.idle": "2024-06-28T15:36:21.025749Z", - "shell.execute_reply": "2024-06-28T15:36:21.025286Z" + "iopub.execute_input": "2024-07-01T15:05:50.433520Z", + "iopub.status.busy": "2024-07-01T15:05:50.433131Z", + "iopub.status.idle": "2024-07-01T15:05:50.442780Z", + "shell.execute_reply": "2024-07-01T15:05:50.442322Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:21.028066Z", - "iopub.status.busy": "2024-06-28T15:36:21.027704Z", - "iopub.status.idle": "2024-06-28T15:36:23.179292Z", - "shell.execute_reply": "2024-06-28T15:36:23.178620Z" + "iopub.execute_input": "2024-07-01T15:05:50.444757Z", + "iopub.status.busy": "2024-07-01T15:05:50.444440Z", + "iopub.status.idle": "2024-07-01T15:05:52.320323Z", + "shell.execute_reply": "2024-07-01T15:05:52.319680Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.181990Z", - "iopub.status.busy": "2024-06-28T15:36:23.181404Z", - "iopub.status.idle": "2024-06-28T15:36:23.201549Z", - "shell.execute_reply": "2024-06-28T15:36:23.201017Z" + "iopub.execute_input": "2024-07-01T15:05:52.322868Z", + "iopub.status.busy": "2024-07-01T15:05:52.322271Z", + "iopub.status.idle": "2024-07-01T15:05:52.341011Z", + "shell.execute_reply": "2024-07-01T15:05:52.340483Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.203946Z", - "iopub.status.busy": "2024-06-28T15:36:23.203567Z", - "iopub.status.idle": "2024-06-28T15:36:23.212158Z", - "shell.execute_reply": "2024-06-28T15:36:23.211658Z" + "iopub.execute_input": "2024-07-01T15:05:52.343025Z", + "iopub.status.busy": "2024-07-01T15:05:52.342731Z", + "iopub.status.idle": "2024-07-01T15:05:52.350595Z", + "shell.execute_reply": "2024-07-01T15:05:52.350103Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.214394Z", - "iopub.status.busy": "2024-06-28T15:36:23.214040Z", - "iopub.status.idle": "2024-06-28T15:36:23.223997Z", - "shell.execute_reply": "2024-06-28T15:36:23.223443Z" + "iopub.execute_input": "2024-07-01T15:05:52.352704Z", + "iopub.status.busy": "2024-07-01T15:05:52.352276Z", + "iopub.status.idle": "2024-07-01T15:05:52.361059Z", + "shell.execute_reply": "2024-07-01T15:05:52.360522Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.226314Z", - "iopub.status.busy": "2024-06-28T15:36:23.225937Z", - "iopub.status.idle": "2024-06-28T15:36:23.234783Z", - "shell.execute_reply": "2024-06-28T15:36:23.234277Z" + "iopub.execute_input": "2024-07-01T15:05:52.363255Z", + "iopub.status.busy": "2024-07-01T15:05:52.362931Z", + "iopub.status.idle": "2024-07-01T15:05:52.370565Z", + "shell.execute_reply": "2024-07-01T15:05:52.370092Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.236939Z", - "iopub.status.busy": "2024-06-28T15:36:23.236749Z", - "iopub.status.idle": "2024-06-28T15:36:23.246131Z", - "shell.execute_reply": "2024-06-28T15:36:23.245660Z" + "iopub.execute_input": "2024-07-01T15:05:52.372696Z", + "iopub.status.busy": "2024-07-01T15:05:52.372359Z", + "iopub.status.idle": "2024-07-01T15:05:52.380928Z", + "shell.execute_reply": "2024-07-01T15:05:52.380440Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.248212Z", - "iopub.status.busy": "2024-06-28T15:36:23.248028Z", - "iopub.status.idle": "2024-06-28T15:36:23.255901Z", - "shell.execute_reply": "2024-06-28T15:36:23.255343Z" + "iopub.execute_input": "2024-07-01T15:05:52.382940Z", + "iopub.status.busy": "2024-07-01T15:05:52.382568Z", + "iopub.status.idle": "2024-07-01T15:05:52.389986Z", + "shell.execute_reply": "2024-07-01T15:05:52.389445Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.258126Z", - "iopub.status.busy": "2024-06-28T15:36:23.257798Z", - "iopub.status.idle": "2024-06-28T15:36:23.265482Z", - "shell.execute_reply": "2024-06-28T15:36:23.264904Z" + "iopub.execute_input": "2024-07-01T15:05:52.392057Z", + "iopub.status.busy": "2024-07-01T15:05:52.391736Z", + "iopub.status.idle": "2024-07-01T15:05:52.398743Z", + "shell.execute_reply": "2024-07-01T15:05:52.398311Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.267847Z", - "iopub.status.busy": "2024-06-28T15:36:23.267498Z", - "iopub.status.idle": "2024-06-28T15:36:23.276176Z", - "shell.execute_reply": "2024-06-28T15:36:23.275581Z" + "iopub.execute_input": "2024-07-01T15:05:52.400864Z", + "iopub.status.busy": "2024-07-01T15:05:52.400548Z", + "iopub.status.idle": "2024-07-01T15:05:52.408413Z", + "shell.execute_reply": "2024-07-01T15:05:52.407979Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 6f6318e65..cc4207eec 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -791,7 +791,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer'}
+Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}

    Let’s view the i-th example in the dataset:

    diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 475827b74..94ec2b5de 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:26.272789Z", - "iopub.status.busy": "2024-06-28T15:36:26.272608Z", - "iopub.status.idle": "2024-06-28T15:36:29.104013Z", - "shell.execute_reply": "2024-06-28T15:36:29.103502Z" + "iopub.execute_input": "2024-07-01T15:05:55.109624Z", + "iopub.status.busy": "2024-07-01T15:05:55.109456Z", + "iopub.status.idle": "2024-07-01T15:05:57.756143Z", + "shell.execute_reply": "2024-07-01T15:05:57.755510Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.106890Z", - "iopub.status.busy": "2024-06-28T15:36:29.106333Z", - "iopub.status.idle": "2024-06-28T15:36:29.109810Z", - "shell.execute_reply": "2024-06-28T15:36:29.109260Z" + "iopub.execute_input": "2024-07-01T15:05:57.758704Z", + "iopub.status.busy": "2024-07-01T15:05:57.758362Z", + "iopub.status.idle": "2024-07-01T15:05:57.761689Z", + "shell.execute_reply": "2024-07-01T15:05:57.761157Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.112003Z", - "iopub.status.busy": "2024-06-28T15:36:29.111592Z", - "iopub.status.idle": "2024-06-28T15:36:29.114674Z", - "shell.execute_reply": "2024-06-28T15:36:29.114249Z" + "iopub.execute_input": "2024-07-01T15:05:57.763881Z", + "iopub.status.busy": "2024-07-01T15:05:57.763378Z", + "iopub.status.idle": "2024-07-01T15:05:57.766675Z", + "shell.execute_reply": "2024-07-01T15:05:57.766123Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.116836Z", - "iopub.status.busy": "2024-06-28T15:36:29.116485Z", - "iopub.status.idle": "2024-06-28T15:36:29.142651Z", - "shell.execute_reply": "2024-06-28T15:36:29.142099Z" + "iopub.execute_input": "2024-07-01T15:05:57.768614Z", + "iopub.status.busy": "2024-07-01T15:05:57.768315Z", + "iopub.status.idle": "2024-07-01T15:05:57.808437Z", + "shell.execute_reply": "2024-07-01T15:05:57.807887Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.144877Z", - "iopub.status.busy": "2024-06-28T15:36:29.144489Z", - "iopub.status.idle": "2024-06-28T15:36:29.148574Z", - "shell.execute_reply": "2024-06-28T15:36:29.148059Z" + "iopub.execute_input": "2024-07-01T15:05:57.810722Z", + "iopub.status.busy": "2024-07-01T15:05:57.810309Z", + "iopub.status.idle": "2024-07-01T15:05:57.814281Z", + "shell.execute_reply": "2024-07-01T15:05:57.813706Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.150742Z", - "iopub.status.busy": "2024-06-28T15:36:29.150368Z", - "iopub.status.idle": "2024-06-28T15:36:29.153564Z", - "shell.execute_reply": "2024-06-28T15:36:29.152998Z" + "iopub.execute_input": "2024-07-01T15:05:57.816292Z", + "iopub.status.busy": "2024-07-01T15:05:57.816001Z", + "iopub.status.idle": "2024-07-01T15:05:57.819153Z", + "shell.execute_reply": "2024-07-01T15:05:57.818607Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.155686Z", - "iopub.status.busy": "2024-06-28T15:36:29.155353Z", - "iopub.status.idle": "2024-06-28T15:36:33.062605Z", - "shell.execute_reply": "2024-06-28T15:36:33.062024Z" + "iopub.execute_input": "2024-07-01T15:05:57.821168Z", + "iopub.status.busy": "2024-07-01T15:05:57.820783Z", + "iopub.status.idle": "2024-07-01T15:06:01.454864Z", + "shell.execute_reply": "2024-07-01T15:06:01.454218Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.065550Z", - "iopub.status.busy": "2024-06-28T15:36:33.065329Z", - "iopub.status.idle": "2024-06-28T15:36:33.965085Z", - "shell.execute_reply": "2024-06-28T15:36:33.964485Z" + "iopub.execute_input": "2024-07-01T15:06:01.457576Z", + "iopub.status.busy": "2024-07-01T15:06:01.457191Z", + "iopub.status.idle": "2024-07-01T15:06:02.359759Z", + "shell.execute_reply": "2024-07-01T15:06:02.359194Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.968066Z", - "iopub.status.busy": "2024-06-28T15:36:33.967676Z", - "iopub.status.idle": "2024-06-28T15:36:33.970602Z", - "shell.execute_reply": "2024-06-28T15:36:33.970111Z" + "iopub.execute_input": "2024-07-01T15:06:02.362504Z", + "iopub.status.busy": "2024-07-01T15:06:02.362099Z", + "iopub.status.idle": "2024-07-01T15:06:02.365173Z", + "shell.execute_reply": "2024-07-01T15:06:02.364692Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.972948Z", - "iopub.status.busy": "2024-06-28T15:36:33.972596Z", - "iopub.status.idle": "2024-06-28T15:36:36.086496Z", - "shell.execute_reply": "2024-06-28T15:36:36.085881Z" + "iopub.execute_input": "2024-07-01T15:06:02.368303Z", + "iopub.status.busy": "2024-07-01T15:06:02.367393Z", + "iopub.status.idle": "2024-07-01T15:06:04.354878Z", + "shell.execute_reply": "2024-07-01T15:06:04.354255Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.089588Z", - "iopub.status.busy": "2024-06-28T15:36:36.088943Z", - "iopub.status.idle": "2024-06-28T15:36:36.113521Z", - "shell.execute_reply": "2024-06-28T15:36:36.112998Z" + "iopub.execute_input": "2024-07-01T15:06:04.359326Z", + "iopub.status.busy": "2024-07-01T15:06:04.358175Z", + "iopub.status.idle": "2024-07-01T15:06:04.383863Z", + "shell.execute_reply": "2024-07-01T15:06:04.383356Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.116126Z", - "iopub.status.busy": "2024-06-28T15:36:36.115727Z", - "iopub.status.idle": "2024-06-28T15:36:36.125325Z", - "shell.execute_reply": "2024-06-28T15:36:36.124905Z" + "iopub.execute_input": "2024-07-01T15:06:04.387331Z", + "iopub.status.busy": "2024-07-01T15:06:04.386438Z", + "iopub.status.idle": "2024-07-01T15:06:04.396138Z", + "shell.execute_reply": "2024-07-01T15:06:04.395755Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.127442Z", - "iopub.status.busy": "2024-06-28T15:36:36.127118Z", - "iopub.status.idle": "2024-06-28T15:36:36.131240Z", - "shell.execute_reply": "2024-06-28T15:36:36.130840Z" + "iopub.execute_input": "2024-07-01T15:06:04.398058Z", + "iopub.status.busy": "2024-07-01T15:06:04.397776Z", + "iopub.status.idle": "2024-07-01T15:06:04.401475Z", + "shell.execute_reply": "2024-07-01T15:06:04.401092Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.133167Z", - "iopub.status.busy": "2024-06-28T15:36:36.132866Z", - "iopub.status.idle": "2024-06-28T15:36:36.138900Z", - "shell.execute_reply": "2024-06-28T15:36:36.138496Z" + "iopub.execute_input": "2024-07-01T15:06:04.403322Z", + "iopub.status.busy": "2024-07-01T15:06:04.403036Z", + "iopub.status.idle": "2024-07-01T15:06:04.408720Z", + "shell.execute_reply": "2024-07-01T15:06:04.408332Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.140862Z", - "iopub.status.busy": "2024-06-28T15:36:36.140553Z", - "iopub.status.idle": "2024-06-28T15:36:36.146597Z", - "shell.execute_reply": "2024-06-28T15:36:36.146207Z" + "iopub.execute_input": "2024-07-01T15:06:04.410591Z", + "iopub.status.busy": "2024-07-01T15:06:04.410423Z", + "iopub.status.idle": "2024-07-01T15:06:04.416683Z", + "shell.execute_reply": "2024-07-01T15:06:04.416154Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.148465Z", - "iopub.status.busy": "2024-06-28T15:36:36.148170Z", - "iopub.status.idle": "2024-06-28T15:36:36.153742Z", - "shell.execute_reply": "2024-06-28T15:36:36.153299Z" + "iopub.execute_input": "2024-07-01T15:06:04.418724Z", + "iopub.status.busy": "2024-07-01T15:06:04.418385Z", + "iopub.status.idle": "2024-07-01T15:06:04.424043Z", + "shell.execute_reply": "2024-07-01T15:06:04.423521Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.155782Z", - "iopub.status.busy": "2024-06-28T15:36:36.155479Z", - "iopub.status.idle": "2024-06-28T15:36:36.164116Z", - "shell.execute_reply": "2024-06-28T15:36:36.163656Z" + "iopub.execute_input": "2024-07-01T15:06:04.426089Z", + "iopub.status.busy": "2024-07-01T15:06:04.425788Z", + "iopub.status.idle": "2024-07-01T15:06:04.434068Z", + "shell.execute_reply": "2024-07-01T15:06:04.433526Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.166281Z", - "iopub.status.busy": "2024-06-28T15:36:36.165967Z", - "iopub.status.idle": "2024-06-28T15:36:36.171549Z", - "shell.execute_reply": "2024-06-28T15:36:36.171073Z" + "iopub.execute_input": "2024-07-01T15:06:04.435974Z", + "iopub.status.busy": "2024-07-01T15:06:04.435800Z", + "iopub.status.idle": "2024-07-01T15:06:04.441070Z", + "shell.execute_reply": "2024-07-01T15:06:04.440586Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.173603Z", - "iopub.status.busy": "2024-06-28T15:36:36.173270Z", - "iopub.status.idle": "2024-06-28T15:36:36.179096Z", - "shell.execute_reply": "2024-06-28T15:36:36.178548Z" + "iopub.execute_input": "2024-07-01T15:06:04.443100Z", + "iopub.status.busy": "2024-07-01T15:06:04.442719Z", + "iopub.status.idle": "2024-07-01T15:06:04.447928Z", + "shell.execute_reply": "2024-07-01T15:06:04.447468Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.181331Z", - "iopub.status.busy": "2024-06-28T15:36:36.181023Z", - "iopub.status.idle": "2024-06-28T15:36:36.184720Z", - "shell.execute_reply": "2024-06-28T15:36:36.184156Z" + "iopub.execute_input": "2024-07-01T15:06:04.450005Z", + "iopub.status.busy": "2024-07-01T15:06:04.449608Z", + "iopub.status.idle": "2024-07-01T15:06:04.453217Z", + "shell.execute_reply": "2024-07-01T15:06:04.452674Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.186917Z", - "iopub.status.busy": "2024-06-28T15:36:36.186527Z", - "iopub.status.idle": "2024-06-28T15:36:36.192238Z", - "shell.execute_reply": "2024-06-28T15:36:36.191670Z" + "iopub.execute_input": "2024-07-01T15:06:04.455383Z", + "iopub.status.busy": "2024-07-01T15:06:04.455056Z", + "iopub.status.idle": "2024-07-01T15:06:04.460142Z", + "shell.execute_reply": "2024-07-01T15:06:04.459596Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/workflows.html b/master/tutorials/datalab/workflows.html index 2fc9df52b..4720c4579 100644 --- a/master/tutorials/datalab/workflows.html +++ b/master/tutorials/datalab/workflows.html @@ -879,7 +879,7 @@

    4. Identify Data Issues Using Datalab - +
    - - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
     AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
    8nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
    @@ -3542,7 +3542,7 @@

    1. Load the dataset 
    -100%|██████████| 170498071/170498071 [00:02<00:00, 83450279.11it/s]
+100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]
    @@ -3694,7 +3694,7 @@

    5. Finding image-specific property scores

    -
    +
    @@ -3711,7 +3711,7 @@

    5. Finding image-specific property scores

    -
    +
    @@ -3874,7 +3874,7 @@

    Vision-specific property scores in the transformed dataset -{"state": {"02bec9b1c6084729a3637a206e447d9d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "be287562794f4a518cf017c87c9bb835": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "03f6b9d07c1443d598ec9f33572e18d2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_02bec9b1c6084729a3637a206e447d9d", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_be287562794f4a518cf017c87c9bb835", "tabbable": null, "tooltip": null, "value": 200.0}}, "d8d1b1a783f6402da95cc6eb9774aab6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5419a131323c4d419c12f8229ecd6339": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "14edf79b75964399b033d0bf68999408": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d8d1b1a783f6402da95cc6eb9774aab6", "placeholder": "\u200b", "style": "IPY_MODEL_5419a131323c4d419c12f8229ecd6339", "tabbable": null, "tooltip": null, "value": "100%"}}, "55dd8a5d604642df9c9d62f2c24684df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "98ce6e6b751848419553bed6e9d74379": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "dbcbd0cc37774c279102b607f1d2f129": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_55dd8a5d604642df9c9d62f2c24684df", "placeholder": "\u200b", "style": "IPY_MODEL_98ce6e6b751848419553bed6e9d74379", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007746.44it/s]"}}, "acb8a5d64588422b98aa99e642774766": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "451c772a56324a01bb8a79944d07ccb9": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_14edf79b75964399b033d0bf68999408", "IPY_MODEL_03f6b9d07c1443d598ec9f33572e18d2", "IPY_MODEL_dbcbd0cc37774c279102b607f1d2f129"], "layout": "IPY_MODEL_acb8a5d64588422b98aa99e642774766", "tabbable": null, "tooltip": null}}, "0e34b193c4d34137bbcad321b8cd1de8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e84e667dd53a4c65b4378b383e66c2f2": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e3a97118610a4a8bafdaf9ffac12612d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0e34b193c4d34137bbcad321b8cd1de8", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e84e667dd53a4c65b4378b383e66c2f2", "tabbable": null, "tooltip": null, "value": 200.0}}, "4360dc3380b14f92b9552eda6a8e20ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "87bd60144c07417a8293887d0866a96a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7fbb77d6ea2e46569c100c09757f2a52": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4360dc3380b14f92b9552eda6a8e20ca", "placeholder": "\u200b", "style": "IPY_MODEL_87bd60144c07417a8293887d0866a96a", "tabbable": null, "tooltip": null, "value": "100%"}}, "0d838fb608934bffb704a7530b16efe0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "32e466d1f0be4c2b871e95c442638fe2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "e7d739ec00864423a21b98a67fe17219": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0d838fb608934bffb704a7530b16efe0", "placeholder": "\u200b", "style": "IPY_MODEL_32e466d1f0be4c2b871e95c442638fe2", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007831.61it/s]"}}, "7c9fe377569340acb6e777bce2670127": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "329aa1acbe77409c816dff4173888262": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_7fbb77d6ea2e46569c100c09757f2a52", "IPY_MODEL_e3a97118610a4a8bafdaf9ffac12612d", "IPY_MODEL_e7d739ec00864423a21b98a67fe17219"], "layout": "IPY_MODEL_7c9fe377569340acb6e777bce2670127", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"5daa3a2aa6bb4c9f84ea2674afd64ead": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc145740c00f4075a0ddec1338af6a17": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "bd44e36d29ce400580eb9c39706e9764": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5daa3a2aa6bb4c9f84ea2674afd64ead", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fc145740c00f4075a0ddec1338af6a17", "tabbable": null, "tooltip": null, "value": 200.0}}, "ef85a24fdc2e4cf783b12b95dc654a54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7d1e93d6b2384e198a1aab9f26b5c1fa": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b80a9a6f3cc8460ba1eacd1eebdd1695": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ef85a24fdc2e4cf783b12b95dc654a54", "placeholder": "\u200b", "style": "IPY_MODEL_7d1e93d6b2384e198a1aab9f26b5c1fa", "tabbable": null, "tooltip": null, "value": "100%"}}, "981dd7e875cd448284efead3ec98c9ab": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5c30ade94053466c8c64d7b0abffbc55": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6681d426ed2e406a80e7d1e331ba04a8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_981dd7e875cd448284efead3ec98c9ab", "placeholder": "\u200b", "style": "IPY_MODEL_5c30ade94053466c8c64d7b0abffbc55", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007784.17it/s]"}}, "11db28fa3e49460cb17bc5b1cbe02474": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1fa7dd59335f4faa818bcf4e966c7c70": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b80a9a6f3cc8460ba1eacd1eebdd1695", "IPY_MODEL_bd44e36d29ce400580eb9c39706e9764", "IPY_MODEL_6681d426ed2e406a80e7d1e331ba04a8"], "layout": "IPY_MODEL_11db28fa3e49460cb17bc5b1cbe02474", "tabbable": null, "tooltip": null}}, "277e9c9f4f2942e8ba4a67175d4da165": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a1e2e6018ec643d38a6a7f43c30c3d0b": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "5fe8b6850a5246f4896ff6995970d3c4": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_277e9c9f4f2942e8ba4a67175d4da165", "max": 200.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a1e2e6018ec643d38a6a7f43c30c3d0b", "tabbable": null, "tooltip": null, "value": 200.0}}, "2b75c32350ca4a21bcc6c23534605260": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7055ef739a1f4669a5783ae634c9e715": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "6cc234a88c8e4669af66844757fac7f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2b75c32350ca4a21bcc6c23534605260", "placeholder": "\u200b", "style": "IPY_MODEL_7055ef739a1f4669a5783ae634c9e715", "tabbable": null, "tooltip": null, "value": "100%"}}, "0e8630e122434af98c6e33cb01b60aa5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f812040db2d642d08a03d0f598afe058": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "7df90a685b4747759b5a5df590246cc1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0e8630e122434af98c6e33cb01b60aa5", "placeholder": "\u200b", "style": "IPY_MODEL_f812040db2d642d08a03d0f598afe058", "tabbable": null, "tooltip": null, "value": "\u2007200/200\u2007[00:00<00:00,\u2007847.08it/s]"}}, "ba235d6a06cb4c11a73df384a365fbe8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bc30a28b7d6044ff9e4159420fbd7a37": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_6cc234a88c8e4669af66844757fac7f7", "IPY_MODEL_5fe8b6850a5246f4896ff6995970d3c4", "IPY_MODEL_7df90a685b4747759b5a5df590246cc1"], "layout": "IPY_MODEL_ba235d6a06cb4c11a73df384a365fbe8", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/workflows.ipynb b/master/tutorials/datalab/workflows.ipynb index a40cddd8f..71ffac131 100644 --- a/master/tutorials/datalab/workflows.ipynb +++ b/master/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.361660Z", - "iopub.status.busy": "2024-06-28T15:36:40.361112Z", - "iopub.status.idle": "2024-06-28T15:36:40.809139Z", - "shell.execute_reply": "2024-06-28T15:36:40.808612Z" + "iopub.execute_input": "2024-07-01T15:06:07.601006Z", + "iopub.status.busy": "2024-07-01T15:06:07.600505Z", + "iopub.status.idle": "2024-07-01T15:06:08.023065Z", + "shell.execute_reply": "2024-07-01T15:06:08.022566Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.811963Z", - "iopub.status.busy": "2024-06-28T15:36:40.811488Z", - "iopub.status.idle": "2024-06-28T15:36:40.944953Z", - "shell.execute_reply": "2024-06-28T15:36:40.944353Z" + "iopub.execute_input": "2024-07-01T15:06:08.025689Z", + "iopub.status.busy": "2024-07-01T15:06:08.025283Z", + "iopub.status.idle": "2024-07-01T15:06:08.152849Z", + "shell.execute_reply": "2024-07-01T15:06:08.152350Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.947272Z", - "iopub.status.busy": "2024-06-28T15:36:40.946935Z", - "iopub.status.idle": "2024-06-28T15:36:40.971280Z", - "shell.execute_reply": "2024-06-28T15:36:40.970561Z" + "iopub.execute_input": "2024-07-01T15:06:08.155131Z", + "iopub.status.busy": "2024-07-01T15:06:08.154741Z", + "iopub.status.idle": "2024-07-01T15:06:08.177601Z", + "shell.execute_reply": "2024-07-01T15:06:08.177069Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.974379Z", - "iopub.status.busy": "2024-06-28T15:36:40.974119Z", - "iopub.status.idle": "2024-06-28T15:36:43.938511Z", - "shell.execute_reply": "2024-06-28T15:36:43.937823Z" + "iopub.execute_input": "2024-07-01T15:06:08.180286Z", + "iopub.status.busy": "2024-07-01T15:06:08.179872Z", + "iopub.status.idle": "2024-07-01T15:06:10.839277Z", + "shell.execute_reply": "2024-07-01T15:06:10.838727Z" } }, "outputs": [ @@ -280,7 +280,7 @@ " \n", " 2\n", " outlier\n", - " 0.356959\n", + " 0.356958\n", " 362\n", " \n", " \n", @@ -315,7 +315,7 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356959 362\n", + "2 outlier 0.356958 362\n", "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:43.941207Z", - "iopub.status.busy": "2024-06-28T15:36:43.940593Z", - "iopub.status.idle": "2024-06-28T15:36:52.261673Z", - "shell.execute_reply": "2024-06-28T15:36:52.261071Z" + "iopub.execute_input": "2024-07-01T15:06:10.841884Z", + "iopub.status.busy": "2024-07-01T15:06:10.841353Z", + "iopub.status.idle": "2024-07-01T15:06:18.620342Z", + "shell.execute_reply": "2024-07-01T15:06:18.619784Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:52.264189Z", - "iopub.status.busy": "2024-06-28T15:36:52.263727Z", - "iopub.status.idle": "2024-06-28T15:36:52.413280Z", - "shell.execute_reply": "2024-06-28T15:36:52.412618Z" + "iopub.execute_input": "2024-07-01T15:06:18.622535Z", + "iopub.status.busy": "2024-07-01T15:06:18.622344Z", + "iopub.status.idle": "2024-07-01T15:06:18.765943Z", + "shell.execute_reply": "2024-07-01T15:06:18.765367Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:52.416001Z", - "iopub.status.busy": "2024-06-28T15:36:52.415579Z", - "iopub.status.idle": "2024-06-28T15:36:53.780189Z", - "shell.execute_reply": "2024-06-28T15:36:53.779592Z" + "iopub.execute_input": "2024-07-01T15:06:18.768372Z", + "iopub.status.busy": "2024-07-01T15:06:18.768184Z", + "iopub.status.idle": "2024-07-01T15:06:20.089155Z", + "shell.execute_reply": "2024-07-01T15:06:20.088659Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:53.782813Z", - "iopub.status.busy": "2024-06-28T15:36:53.782410Z", - "iopub.status.idle": "2024-06-28T15:36:54.243902Z", - "shell.execute_reply": "2024-06-28T15:36:54.243296Z" + "iopub.execute_input": "2024-07-01T15:06:20.091428Z", + "iopub.status.busy": "2024-07-01T15:06:20.091067Z", + "iopub.status.idle": "2024-07-01T15:06:20.540474Z", + "shell.execute_reply": "2024-07-01T15:06:20.539777Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.246679Z", - "iopub.status.busy": "2024-06-28T15:36:54.246127Z", - "iopub.status.idle": "2024-06-28T15:36:54.255761Z", - "shell.execute_reply": "2024-06-28T15:36:54.255221Z" + "iopub.execute_input": "2024-07-01T15:06:20.543076Z", + "iopub.status.busy": "2024-07-01T15:06:20.542728Z", + "iopub.status.idle": "2024-07-01T15:06:20.551688Z", + "shell.execute_reply": "2024-07-01T15:06:20.551243Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.258144Z", - "iopub.status.busy": "2024-06-28T15:36:54.257783Z", - "iopub.status.idle": "2024-06-28T15:36:54.282979Z", - "shell.execute_reply": "2024-06-28T15:36:54.282497Z" + "iopub.execute_input": "2024-07-01T15:06:20.553781Z", + "iopub.status.busy": "2024-07-01T15:06:20.553342Z", + "iopub.status.idle": "2024-07-01T15:06:20.571023Z", + "shell.execute_reply": "2024-07-01T15:06:20.570476Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.285367Z", - "iopub.status.busy": "2024-06-28T15:36:54.285016Z", - "iopub.status.idle": "2024-06-28T15:36:54.507945Z", - "shell.execute_reply": "2024-06-28T15:36:54.507290Z" + "iopub.execute_input": "2024-07-01T15:06:20.573201Z", + "iopub.status.busy": "2024-07-01T15:06:20.572911Z", + "iopub.status.idle": "2024-07-01T15:06:20.803461Z", + "shell.execute_reply": "2024-07-01T15:06:20.802856Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.510812Z", - "iopub.status.busy": "2024-06-28T15:36:54.510405Z", - "iopub.status.idle": "2024-06-28T15:36:54.530061Z", - "shell.execute_reply": "2024-06-28T15:36:54.529521Z" + "iopub.execute_input": "2024-07-01T15:06:20.806333Z", + "iopub.status.busy": "2024-07-01T15:06:20.805945Z", + "iopub.status.idle": "2024-07-01T15:06:20.825160Z", + "shell.execute_reply": "2024-07-01T15:06:20.824612Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.532229Z", - "iopub.status.busy": "2024-06-28T15:36:54.532035Z", - "iopub.status.idle": "2024-06-28T15:36:54.700650Z", - "shell.execute_reply": "2024-06-28T15:36:54.699991Z" + "iopub.execute_input": "2024-07-01T15:06:20.827381Z", + "iopub.status.busy": "2024-07-01T15:06:20.826963Z", + "iopub.status.idle": "2024-07-01T15:06:20.993329Z", + "shell.execute_reply": "2024-07-01T15:06:20.992779Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.704472Z", - "iopub.status.busy": "2024-06-28T15:36:54.703989Z", - "iopub.status.idle": "2024-06-28T15:36:54.716459Z", - "shell.execute_reply": "2024-06-28T15:36:54.715941Z" + "iopub.execute_input": "2024-07-01T15:06:20.995655Z", + "iopub.status.busy": "2024-07-01T15:06:20.995315Z", + "iopub.status.idle": "2024-07-01T15:06:21.005096Z", + "shell.execute_reply": "2024-07-01T15:06:21.004627Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.718610Z", - "iopub.status.busy": "2024-06-28T15:36:54.718408Z", - "iopub.status.idle": "2024-06-28T15:36:54.728637Z", - "shell.execute_reply": "2024-06-28T15:36:54.728151Z" + "iopub.execute_input": "2024-07-01T15:06:21.007134Z", + "iopub.status.busy": "2024-07-01T15:06:21.006797Z", + "iopub.status.idle": "2024-07-01T15:06:21.015972Z", + "shell.execute_reply": "2024-07-01T15:06:21.015502Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.730717Z", - "iopub.status.busy": "2024-06-28T15:36:54.730400Z", - "iopub.status.idle": "2024-06-28T15:36:54.768982Z", - "shell.execute_reply": "2024-06-28T15:36:54.768461Z" + "iopub.execute_input": "2024-07-01T15:06:21.017811Z", + "iopub.status.busy": "2024-07-01T15:06:21.017637Z", + "iopub.status.idle": "2024-07-01T15:06:21.046279Z", + "shell.execute_reply": "2024-07-01T15:06:21.045826Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.771243Z", - "iopub.status.busy": "2024-06-28T15:36:54.771054Z", - "iopub.status.idle": "2024-06-28T15:36:54.774061Z", - "shell.execute_reply": "2024-06-28T15:36:54.773608Z" + "iopub.execute_input": "2024-07-01T15:06:21.048363Z", + "iopub.status.busy": "2024-07-01T15:06:21.048043Z", + "iopub.status.idle": "2024-07-01T15:06:21.050539Z", + "shell.execute_reply": "2024-07-01T15:06:21.050117Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.776076Z", - "iopub.status.busy": "2024-06-28T15:36:54.775901Z", - "iopub.status.idle": "2024-06-28T15:36:54.795979Z", - "shell.execute_reply": "2024-06-28T15:36:54.795395Z" + "iopub.execute_input": "2024-07-01T15:06:21.052583Z", + "iopub.status.busy": "2024-07-01T15:06:21.052269Z", + "iopub.status.idle": "2024-07-01T15:06:21.070692Z", + "shell.execute_reply": "2024-07-01T15:06:21.070162Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.798272Z", - "iopub.status.busy": "2024-06-28T15:36:54.797955Z", - "iopub.status.idle": "2024-06-28T15:36:54.802433Z", - "shell.execute_reply": "2024-06-28T15:36:54.801877Z" + "iopub.execute_input": "2024-07-01T15:06:21.072780Z", + "iopub.status.busy": "2024-07-01T15:06:21.072455Z", + "iopub.status.idle": "2024-07-01T15:06:21.076730Z", + "shell.execute_reply": "2024-07-01T15:06:21.076273Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.804497Z", - "iopub.status.busy": "2024-06-28T15:36:54.804166Z", - "iopub.status.idle": "2024-06-28T15:36:54.833324Z", - "shell.execute_reply": "2024-06-28T15:36:54.832732Z" + "iopub.execute_input": "2024-07-01T15:06:21.078798Z", + "iopub.status.busy": "2024-07-01T15:06:21.078478Z", + "iopub.status.idle": "2024-07-01T15:06:21.105961Z", + "shell.execute_reply": "2024-07-01T15:06:21.105424Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.835614Z", - "iopub.status.busy": "2024-06-28T15:36:54.835280Z", - "iopub.status.idle": "2024-06-28T15:36:55.211393Z", - "shell.execute_reply": "2024-06-28T15:36:55.210819Z" + "iopub.execute_input": "2024-07-01T15:06:21.108003Z", + "iopub.status.busy": "2024-07-01T15:06:21.107678Z", + "iopub.status.idle": "2024-07-01T15:06:21.447286Z", + "shell.execute_reply": "2024-07-01T15:06:21.446728Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.213606Z", - "iopub.status.busy": "2024-06-28T15:36:55.213255Z", - "iopub.status.idle": "2024-06-28T15:36:55.216591Z", - "shell.execute_reply": "2024-06-28T15:36:55.216098Z" + "iopub.execute_input": "2024-07-01T15:06:21.449527Z", + "iopub.status.busy": "2024-07-01T15:06:21.449200Z", + "iopub.status.idle": "2024-07-01T15:06:21.452276Z", + "shell.execute_reply": "2024-07-01T15:06:21.451757Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.218693Z", - "iopub.status.busy": "2024-06-28T15:36:55.218365Z", - "iopub.status.idle": "2024-06-28T15:36:55.232267Z", - "shell.execute_reply": "2024-06-28T15:36:55.231672Z" + "iopub.execute_input": "2024-07-01T15:06:21.454363Z", + "iopub.status.busy": "2024-07-01T15:06:21.454023Z", + "iopub.status.idle": "2024-07-01T15:06:21.466646Z", + "shell.execute_reply": "2024-07-01T15:06:21.466203Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.234822Z", - "iopub.status.busy": "2024-06-28T15:36:55.234454Z", - "iopub.status.idle": "2024-06-28T15:36:55.248860Z", - "shell.execute_reply": "2024-06-28T15:36:55.248321Z" + "iopub.execute_input": "2024-07-01T15:06:21.468583Z", + "iopub.status.busy": "2024-07-01T15:06:21.468405Z", + "iopub.status.idle": "2024-07-01T15:06:21.481924Z", + "shell.execute_reply": "2024-07-01T15:06:21.481439Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.251109Z", - "iopub.status.busy": "2024-06-28T15:36:55.250759Z", - "iopub.status.idle": "2024-06-28T15:36:55.261210Z", - "shell.execute_reply": "2024-06-28T15:36:55.260768Z" + "iopub.execute_input": "2024-07-01T15:06:21.484047Z", + "iopub.status.busy": "2024-07-01T15:06:21.483628Z", + "iopub.status.idle": "2024-07-01T15:06:21.493274Z", + "shell.execute_reply": "2024-07-01T15:06:21.492857Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.263582Z", - "iopub.status.busy": "2024-06-28T15:36:55.263219Z", - "iopub.status.idle": "2024-06-28T15:36:55.273514Z", - "shell.execute_reply": "2024-06-28T15:36:55.272922Z" + "iopub.execute_input": "2024-07-01T15:06:21.495372Z", + "iopub.status.busy": "2024-07-01T15:06:21.495048Z", + "iopub.status.idle": "2024-07-01T15:06:21.504141Z", + "shell.execute_reply": "2024-07-01T15:06:21.503595Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.275723Z", - "iopub.status.busy": "2024-06-28T15:36:55.275369Z", - "iopub.status.idle": "2024-06-28T15:36:55.279072Z", - "shell.execute_reply": "2024-06-28T15:36:55.278640Z" + "iopub.execute_input": "2024-07-01T15:06:21.506165Z", + "iopub.status.busy": "2024-07-01T15:06:21.505846Z", + "iopub.status.idle": "2024-07-01T15:06:21.509379Z", + "shell.execute_reply": "2024-07-01T15:06:21.508840Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.281212Z", - "iopub.status.busy": "2024-06-28T15:36:55.280883Z", - "iopub.status.idle": "2024-06-28T15:36:55.334857Z", - "shell.execute_reply": "2024-06-28T15:36:55.334288Z" + "iopub.execute_input": "2024-07-01T15:06:21.511428Z", + "iopub.status.busy": "2024-07-01T15:06:21.511040Z", + "iopub.status.idle": "2024-07-01T15:06:21.560989Z", + "shell.execute_reply": "2024-07-01T15:06:21.560473Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
    \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
     AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
    8nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
    \n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.337385Z", - "iopub.status.busy": "2024-06-28T15:36:55.336901Z", - "iopub.status.idle": "2024-06-28T15:36:55.342582Z", - "shell.execute_reply": "2024-06-28T15:36:55.342159Z" + "iopub.execute_input": "2024-07-01T15:06:21.563215Z", + "iopub.status.busy": "2024-07-01T15:06:21.562809Z", + "iopub.status.idle": "2024-07-01T15:06:21.568340Z", + "shell.execute_reply": "2024-07-01T15:06:21.567916Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.344705Z", - "iopub.status.busy": "2024-06-28T15:36:55.344399Z", - "iopub.status.idle": "2024-06-28T15:36:55.356329Z", - "shell.execute_reply": "2024-06-28T15:36:55.355779Z" + "iopub.execute_input": "2024-07-01T15:06:21.570390Z", + "iopub.status.busy": "2024-07-01T15:06:21.570048Z", + "iopub.status.idle": "2024-07-01T15:06:21.580186Z", + "shell.execute_reply": "2024-07-01T15:06:21.579714Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.358690Z", - "iopub.status.busy": "2024-06-28T15:36:55.358150Z", - "iopub.status.idle": "2024-06-28T15:36:55.575548Z", - "shell.execute_reply": "2024-06-28T15:36:55.574952Z" + "iopub.execute_input": "2024-07-01T15:06:21.582185Z", + "iopub.status.busy": "2024-07-01T15:06:21.581851Z", + "iopub.status.idle": "2024-07-01T15:06:21.792455Z", + "shell.execute_reply": "2024-07-01T15:06:21.791912Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.577840Z", - "iopub.status.busy": "2024-06-28T15:36:55.577511Z", - "iopub.status.idle": "2024-06-28T15:36:55.585510Z", - "shell.execute_reply": "2024-06-28T15:36:55.584968Z" + "iopub.execute_input": "2024-07-01T15:06:21.794636Z", + "iopub.status.busy": "2024-07-01T15:06:21.794453Z", + "iopub.status.idle": "2024-07-01T15:06:21.802233Z", + "shell.execute_reply": "2024-07-01T15:06:21.801679Z" }, "nbsphinx": "hidden" }, @@ -3743,10 +3743,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.587717Z", - "iopub.status.busy": "2024-06-28T15:36:55.587380Z", - "iopub.status.idle": "2024-06-28T15:37:01.427411Z", - "shell.execute_reply": "2024-06-28T15:37:01.426755Z" + "iopub.execute_input": "2024-07-01T15:06:21.804166Z", + "iopub.status.busy": "2024-07-01T15:06:21.803993Z", + "iopub.status.idle": "2024-07-01T15:06:27.357122Z", + "shell.execute_reply": "2024-07-01T15:06:27.356570Z" } }, "outputs": [ @@ -3770,7 +3770,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1966080/170498071 [00:00<00:08, 19658053.51it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8269545.10it/s]" ] }, { @@ -3778,7 +3778,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9469952/170498071 [00:00<00:03, 52230874.01it/s]" + " 6%|▌ | 10387456/170498071 [00:00<00:02, 56842742.05it/s]" ] }, { @@ -3786,7 +3786,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17760256/170498071 [00:00<00:02, 66158713.83it/s]" + " 13%|█▎ | 21495808/170498071 [00:00<00:01, 80950295.23it/s]" ] }, { @@ -3794,7 +3794,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25395200/170498071 [00:00<00:02, 70095628.99it/s]" + " 19%|█▉ | 32440320/170498071 [00:00<00:01, 91992107.79it/s]" ] }, { @@ -3802,7 +3802,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33718272/170498071 [00:00<00:01, 74822583.06it/s]" + " 26%|██▌ | 43483136/170498071 [00:00<00:01, 98507606.82it/s]" ] }, { @@ -3810,7 +3810,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 42041344/170498071 [00:00<00:01, 77646889.37it/s]" + " 32%|███▏ | 54427648/170498071 [00:00<00:01, 102169111.94it/s]" ] }, { @@ -3818,7 +3818,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50823168/170498071 [00:00<00:01, 80964433.57it/s]" + " 38%|███▊ | 64716800/170498071 [00:00<00:01, 102261059.68it/s]" ] }, { @@ -3826,7 +3826,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 59080704/170498071 [00:00<00:01, 81434669.67it/s]" + " 44%|████▍ | 75759616/170498071 [00:00<00:00, 104794429.63it/s]" ] }, { @@ -3834,7 +3834,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 67862528/170498071 [00:00<00:01, 83414756.69it/s]" + " 51%|█████ | 86573056/170498071 [00:00<00:00, 105786259.21it/s]" ] }, { @@ -3842,7 +3842,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76218368/170498071 [00:01<00:01, 79826592.63it/s]" + " 57%|█████▋ | 97779712/170498071 [00:01<00:00, 107658431.70it/s]" ] }, { @@ -3850,7 +3850,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 84246528/170498071 [00:01<00:01, 76490866.06it/s]" + " 64%|██████▎ | 108560384/170498071 [00:01<00:00, 107631887.95it/s]" ] }, { @@ -3858,7 +3858,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 91947008/170498071 [00:01<00:01, 76082662.08it/s]" + " 70%|███████ | 119635968/170498071 [00:01<00:00, 108510039.53it/s]" ] }, { @@ -3866,7 +3866,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100368384/170498071 [00:01<00:00, 78428938.84it/s]" + " 77%|███████▋ | 130514944/170498071 [00:01<00:00, 108504773.07it/s]" ] }, { @@ -3874,7 +3874,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 108888064/170498071 [00:01<00:00, 80391787.54it/s]" + " 83%|████████▎ | 141557760/170498071 [00:01<00:00, 109047203.13it/s]" ] }, { @@ -3882,7 +3882,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 117735424/170498071 [00:01<00:00, 82707343.94it/s]" + " 89%|████████▉ | 152567808/170498071 [00:01<00:00, 109231770.68it/s]" ] }, { @@ -3890,7 +3890,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 126189568/170498071 [00:01<00:00, 83202299.90it/s]" + " 96%|█████████▌| 163512320/170498071 [00:01<00:00, 109164928.11it/s]" ] }, { @@ -3898,39 +3898,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 137068544/170498071 [00:01<00:00, 90724902.11it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 148504576/170498071 [00:01<00:00, 97755628.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 94%|█████████▎| 159744000/170498071 [00:01<00:00, 101954468.01it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169967616/170498071 [00:02<00:00, 101064380.25it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 83450279.11it/s] " + "100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]" ] }, { @@ -4004,10 +3972,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.430525Z", - "iopub.status.busy": "2024-06-28T15:37:01.429953Z", - "iopub.status.idle": "2024-06-28T15:37:01.498795Z", - "shell.execute_reply": "2024-06-28T15:37:01.498158Z" + "iopub.execute_input": "2024-07-01T15:06:27.360097Z", + "iopub.status.busy": "2024-07-01T15:06:27.359363Z", + "iopub.status.idle": "2024-07-01T15:06:27.426972Z", + "shell.execute_reply": "2024-07-01T15:06:27.426512Z" } }, "outputs": [], @@ -4029,10 +3997,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.501417Z", - "iopub.status.busy": "2024-06-28T15:37:01.501047Z", - "iopub.status.idle": "2024-06-28T15:37:01.542795Z", - "shell.execute_reply": "2024-06-28T15:37:01.542166Z" + "iopub.execute_input": "2024-07-01T15:06:27.429234Z", + "iopub.status.busy": "2024-07-01T15:06:27.429051Z", + "iopub.status.idle": "2024-07-01T15:06:27.469917Z", + "shell.execute_reply": "2024-07-01T15:06:27.469464Z" } }, "outputs": [], @@ -4066,10 +4034,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.545456Z", - "iopub.status.busy": "2024-06-28T15:37:01.545096Z", - "iopub.status.idle": "2024-06-28T15:37:03.018066Z", - "shell.execute_reply": "2024-06-28T15:37:03.017446Z" + "iopub.execute_input": "2024-07-01T15:06:27.472017Z", + "iopub.status.busy": "2024-07-01T15:06:27.471718Z", + "iopub.status.idle": "2024-07-01T15:06:28.881795Z", + "shell.execute_reply": "2024-07-01T15:06:28.881232Z" } }, "outputs": [ @@ -4143,10 +4111,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:03.020409Z", - "iopub.status.busy": "2024-06-28T15:37:03.020049Z", - "iopub.status.idle": "2024-06-28T15:37:03.815261Z", - "shell.execute_reply": "2024-06-28T15:37:03.814627Z" + "iopub.execute_input": "2024-07-01T15:06:28.884131Z", + "iopub.status.busy": "2024-07-01T15:06:28.883805Z", + "iopub.status.idle": "2024-07-01T15:06:29.648363Z", + "shell.execute_reply": "2024-07-01T15:06:29.647874Z" } }, "outputs": [ @@ -4161,7 +4129,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "451c772a56324a01bb8a79944d07ccb9", + "model_id": "1fa7dd59335f4faa818bcf4e966c7c70", "version_major": 2, "version_minor": 0 }, @@ -4185,7 +4153,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "329aa1acbe77409c816dff4173888262", + "model_id": "bc30a28b7d6044ff9e4159420fbd7a37", "version_major": 2, "version_minor": 0 }, @@ -4435,7 +4403,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02bec9b1c6084729a3637a206e447d9d": { + "0e8630e122434af98c6e33cb01b60aa5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4488,33 +4456,7 @@ "width": null } }, - "03f6b9d07c1443d598ec9f33572e18d2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_02bec9b1c6084729a3637a206e447d9d", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_be287562794f4a518cf017c87c9bb835", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "0d838fb608934bffb704a7530b16efe0": { + "11db28fa3e49460cb17bc5b1cbe02474": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4567,7 +4509,31 @@ "width": null } }, - "0e34b193c4d34137bbcad321b8cd1de8": { + "1fa7dd59335f4faa818bcf4e966c7c70": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b80a9a6f3cc8460ba1eacd1eebdd1695", + "IPY_MODEL_bd44e36d29ce400580eb9c39706e9764", + "IPY_MODEL_6681d426ed2e406a80e7d1e331ba04a8" + ], + "layout": "IPY_MODEL_11db28fa3e49460cb17bc5b1cbe02474", + "tabbable": null, + "tooltip": null + } + }, + "277e9c9f4f2942e8ba4a67175d4da165": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4620,72 +4586,7 @@ "width": null } }, - "14edf79b75964399b033d0bf68999408": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d8d1b1a783f6402da95cc6eb9774aab6", - "placeholder": "​", - "style": "IPY_MODEL_5419a131323c4d419c12f8229ecd6339", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "329aa1acbe77409c816dff4173888262": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7fbb77d6ea2e46569c100c09757f2a52", - "IPY_MODEL_e3a97118610a4a8bafdaf9ffac12612d", - "IPY_MODEL_e7d739ec00864423a21b98a67fe17219" - ], - "layout": "IPY_MODEL_7c9fe377569340acb6e777bce2670127", - "tabbable": null, - "tooltip": null - } - }, - "32e466d1f0be4c2b871e95c442638fe2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4360dc3380b14f92b9552eda6a8e20ca": { + "2b75c32350ca4a21bcc6c23534605260": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4738,31 +4639,7 @@ "width": null } }, - "451c772a56324a01bb8a79944d07ccb9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_14edf79b75964399b033d0bf68999408", - "IPY_MODEL_03f6b9d07c1443d598ec9f33572e18d2", - "IPY_MODEL_dbcbd0cc37774c279102b607f1d2f129" - ], - "layout": "IPY_MODEL_acb8a5d64588422b98aa99e642774766", - "tabbable": null, - "tooltip": null - } - }, - "5419a131323c4d419c12f8229ecd6339": { + "5c30ade94053466c8c64d7b0abffbc55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4780,7 +4657,7 @@ "text_color": null } }, - "55dd8a5d604642df9c9d62f2c24684df": { + "5daa3a2aa6bb4c9f84ea2674afd64ead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4833,60 +4710,33 @@ "width": null } }, - "7c9fe377569340acb6e777bce2670127": { - "model_module": "@jupyter-widgets/base", + "5fe8b6850a5246f4896ff6995970d3c4": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_277e9c9f4f2942e8ba4a67175d4da165", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a1e2e6018ec643d38a6a7f43c30c3d0b", + "tabbable": null, + "tooltip": null, + "value": 200.0 } }, - "7fbb77d6ea2e46569c100c09757f2a52": { + "6681d426ed2e406a80e7d1e331ba04a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4901,15 +4751,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4360dc3380b14f92b9552eda6a8e20ca", + "layout": "IPY_MODEL_981dd7e875cd448284efead3ec98c9ab", "placeholder": "​", - "style": "IPY_MODEL_87bd60144c07417a8293887d0866a96a", + "style": "IPY_MODEL_5c30ade94053466c8c64d7b0abffbc55", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 784.17it/s]" + } + }, + "6cc234a88c8e4669af66844757fac7f7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b75c32350ca4a21bcc6c23534605260", + "placeholder": "​", + "style": "IPY_MODEL_7055ef739a1f4669a5783ae634c9e715", "tabbable": null, "tooltip": null, "value": "100%" } }, - "87bd60144c07417a8293887d0866a96a": { + "7055ef739a1f4669a5783ae634c9e715": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4927,7 +4800,7 @@ "text_color": null } }, - "98ce6e6b751848419553bed6e9d74379": { + "7d1e93d6b2384e198a1aab9f26b5c1fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4945,7 +4818,30 @@ "text_color": null } }, - "acb8a5d64588422b98aa99e642774766": { + "7df90a685b4747759b5a5df590246cc1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0e8630e122434af98c6e33cb01b60aa5", + "placeholder": "​", + "style": "IPY_MODEL_f812040db2d642d08a03d0f598afe058", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 847.08it/s]" + } + }, + "981dd7e875cd448284efead3ec98c9ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4998,7 +4894,7 @@ "width": null } }, - "be287562794f4a518cf017c87c9bb835": { + "a1e2e6018ec643d38a6a7f43c30c3d0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5014,7 +4910,30 @@ "description_width": "" } }, - "d8d1b1a783f6402da95cc6eb9774aab6": { + "b80a9a6f3cc8460ba1eacd1eebdd1695": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ef85a24fdc2e4cf783b12b95dc654a54", + "placeholder": "​", + "style": "IPY_MODEL_7d1e93d6b2384e198a1aab9f26b5c1fa", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "ba235d6a06cb4c11a73df384a365fbe8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5067,30 +4986,31 @@ "width": null } }, - "dbcbd0cc37774c279102b607f1d2f129": { + "bc30a28b7d6044ff9e4159420fbd7a37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_55dd8a5d604642df9c9d62f2c24684df", - "placeholder": "​", - "style": "IPY_MODEL_98ce6e6b751848419553bed6e9d74379", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6cc234a88c8e4669af66844757fac7f7", + "IPY_MODEL_5fe8b6850a5246f4896ff6995970d3c4", + "IPY_MODEL_7df90a685b4747759b5a5df590246cc1" + ], + "layout": "IPY_MODEL_ba235d6a06cb4c11a73df384a365fbe8", "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 746.44it/s]" + "tooltip": null } }, - "e3a97118610a4a8bafdaf9ffac12612d": { + "bd44e36d29ce400580eb9c39706e9764": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5106,40 +5026,88 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0e34b193c4d34137bbcad321b8cd1de8", + "layout": "IPY_MODEL_5daa3a2aa6bb4c9f84ea2674afd64ead", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e84e667dd53a4c65b4378b383e66c2f2", + "style": "IPY_MODEL_fc145740c00f4075a0ddec1338af6a17", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "e7d739ec00864423a21b98a67fe17219": { + "ef85a24fdc2e4cf783b12b95dc654a54": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f812040db2d642d08a03d0f598afe058": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0d838fb608934bffb704a7530b16efe0", - "placeholder": "​", - "style": "IPY_MODEL_32e466d1f0be4c2b871e95c442638fe2", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 831.61it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e84e667dd53a4c65b4378b383e66c2f2": { + "fc145740c00f4075a0ddec1338af6a17": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 3194f04c5..2c0bb8a06 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:08.113610Z", - "iopub.status.busy": "2024-06-28T15:37:08.113258Z", - "iopub.status.idle": "2024-06-28T15:37:09.278340Z", - "shell.execute_reply": "2024-06-28T15:37:09.277671Z" + "iopub.execute_input": "2024-07-01T15:06:34.327040Z", + "iopub.status.busy": "2024-07-01T15:06:34.326556Z", + "iopub.status.idle": "2024-07-01T15:06:35.430619Z", + "shell.execute_reply": "2024-07-01T15:06:35.430106Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.280904Z", - "iopub.status.busy": "2024-06-28T15:37:09.280564Z", - "iopub.status.idle": "2024-06-28T15:37:09.283499Z", - "shell.execute_reply": "2024-06-28T15:37:09.282980Z" + "iopub.execute_input": "2024-07-01T15:06:35.433295Z", + "iopub.status.busy": "2024-07-01T15:06:35.432846Z", + "iopub.status.idle": "2024-07-01T15:06:35.435562Z", + "shell.execute_reply": "2024-07-01T15:06:35.435139Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.285782Z", - "iopub.status.busy": "2024-06-28T15:37:09.285368Z", - "iopub.status.idle": "2024-06-28T15:37:09.297090Z", - "shell.execute_reply": "2024-06-28T15:37:09.296514Z" + "iopub.execute_input": "2024-07-01T15:06:35.437647Z", + "iopub.status.busy": "2024-07-01T15:06:35.437334Z", + "iopub.status.idle": "2024-07-01T15:06:35.448830Z", + "shell.execute_reply": "2024-07-01T15:06:35.448366Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.299174Z", - "iopub.status.busy": "2024-06-28T15:37:09.298899Z", - "iopub.status.idle": "2024-06-28T15:37:13.501981Z", - "shell.execute_reply": "2024-06-28T15:37:13.501494Z" + "iopub.execute_input": "2024-07-01T15:06:35.450965Z", + "iopub.status.busy": "2024-07-01T15:06:35.450626Z", + "iopub.status.idle": "2024-07-01T15:06:39.722894Z", + "shell.execute_reply": "2024-07-01T15:06:39.722312Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index aa4b29d76..c761e1541 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -831,13 +831,13 @@

    How can I find label issues in big datasets with limited memory?

    -
    +
    -
    +
    @@ -1702,7 +1702,7 @@

    Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

    Professional support and services are also available from our ML experts, learn more by emailing: team@cleanlab.ai

    diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index cc0a526c8..86b92fc2a 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:15.853544Z", - "iopub.status.busy": "2024-06-28T15:37:15.853352Z", - "iopub.status.idle": "2024-06-28T15:37:17.049498Z", - "shell.execute_reply": "2024-06-28T15:37:17.048960Z" + "iopub.execute_input": "2024-07-01T15:06:41.824444Z", + "iopub.status.busy": "2024-07-01T15:06:41.824251Z", + "iopub.status.idle": "2024-07-01T15:06:42.924912Z", + "shell.execute_reply": "2024-07-01T15:06:42.924299Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:17.052466Z", - "iopub.status.busy": "2024-06-28T15:37:17.051871Z", - "iopub.status.idle": "2024-06-28T15:37:17.055531Z", - "shell.execute_reply": "2024-06-28T15:37:17.054963Z" + "iopub.execute_input": "2024-07-01T15:06:42.927716Z", + "iopub.status.busy": "2024-07-01T15:06:42.927441Z", + "iopub.status.idle": "2024-07-01T15:06:42.930770Z", + "shell.execute_reply": "2024-07-01T15:06:42.930234Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:17.057853Z", - "iopub.status.busy": "2024-06-28T15:37:17.057415Z", - "iopub.status.idle": "2024-06-28T15:37:20.475550Z", - "shell.execute_reply": "2024-06-28T15:37:20.474905Z" + "iopub.execute_input": "2024-07-01T15:06:42.932829Z", + "iopub.status.busy": "2024-07-01T15:06:42.932448Z", + "iopub.status.idle": "2024-07-01T15:06:46.102573Z", + "shell.execute_reply": "2024-07-01T15:06:46.101934Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.478721Z", - "iopub.status.busy": "2024-06-28T15:37:20.478099Z", - "iopub.status.idle": "2024-06-28T15:37:20.519804Z", - "shell.execute_reply": "2024-06-28T15:37:20.519187Z" + "iopub.execute_input": "2024-07-01T15:06:46.105551Z", + "iopub.status.busy": "2024-07-01T15:06:46.104970Z", + "iopub.status.idle": "2024-07-01T15:06:46.139232Z", + "shell.execute_reply": "2024-07-01T15:06:46.138546Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.522898Z", - "iopub.status.busy": "2024-06-28T15:37:20.522445Z", - "iopub.status.idle": "2024-06-28T15:37:20.573083Z", - "shell.execute_reply": "2024-06-28T15:37:20.572396Z" + "iopub.execute_input": "2024-07-01T15:06:46.141703Z", + "iopub.status.busy": "2024-07-01T15:06:46.141464Z", + "iopub.status.idle": "2024-07-01T15:06:46.166417Z", + "shell.execute_reply": "2024-07-01T15:06:46.165807Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.576361Z", - "iopub.status.busy": "2024-06-28T15:37:20.575827Z", - "iopub.status.idle": "2024-06-28T15:37:20.579483Z", - "shell.execute_reply": "2024-06-28T15:37:20.578971Z" + "iopub.execute_input": "2024-07-01T15:06:46.168919Z", + "iopub.status.busy": "2024-07-01T15:06:46.168681Z", + "iopub.status.idle": "2024-07-01T15:06:46.171591Z", + "shell.execute_reply": "2024-07-01T15:06:46.171158Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.581933Z", - "iopub.status.busy": "2024-06-28T15:37:20.581534Z", - "iopub.status.idle": "2024-06-28T15:37:20.584738Z", - "shell.execute_reply": "2024-06-28T15:37:20.584114Z" + "iopub.execute_input": "2024-07-01T15:06:46.173661Z", + "iopub.status.busy": "2024-07-01T15:06:46.173228Z", + "iopub.status.idle": "2024-07-01T15:06:46.175926Z", + "shell.execute_reply": "2024-07-01T15:06:46.175394Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.587053Z", - "iopub.status.busy": "2024-06-28T15:37:20.586691Z", - "iopub.status.idle": "2024-06-28T15:37:20.615756Z", - "shell.execute_reply": "2024-06-28T15:37:20.615148Z" + "iopub.execute_input": "2024-07-01T15:06:46.178276Z", + "iopub.status.busy": "2024-07-01T15:06:46.177828Z", + "iopub.status.idle": "2024-07-01T15:06:46.201962Z", + "shell.execute_reply": "2024-07-01T15:06:46.201387Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9cb302a654554b6dba5964ac8c805ba8", + "model_id": "4fd339b3d01f445392d6c990fdab5a89", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10f5bddcb2454f329c7a54207b2bf28e", + "model_id": "0e05780aa0694da2b04037e49f9ac6f9", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.619003Z", - "iopub.status.busy": "2024-06-28T15:37:20.618607Z", - "iopub.status.idle": "2024-06-28T15:37:20.625885Z", - "shell.execute_reply": "2024-06-28T15:37:20.625314Z" + "iopub.execute_input": "2024-07-01T15:06:46.208658Z", + "iopub.status.busy": "2024-07-01T15:06:46.208178Z", + "iopub.status.idle": "2024-07-01T15:06:46.214827Z", + "shell.execute_reply": "2024-07-01T15:06:46.214300Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.628461Z", - "iopub.status.busy": "2024-06-28T15:37:20.628012Z", - "iopub.status.idle": "2024-06-28T15:37:20.631873Z", - "shell.execute_reply": "2024-06-28T15:37:20.631318Z" + "iopub.execute_input": "2024-07-01T15:06:46.216919Z", + "iopub.status.busy": "2024-07-01T15:06:46.216531Z", + "iopub.status.idle": "2024-07-01T15:06:46.220056Z", + "shell.execute_reply": "2024-07-01T15:06:46.219618Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.634235Z", - "iopub.status.busy": "2024-06-28T15:37:20.633791Z", - "iopub.status.idle": "2024-06-28T15:37:20.640374Z", - "shell.execute_reply": "2024-06-28T15:37:20.639910Z" + "iopub.execute_input": "2024-07-01T15:06:46.221958Z", + "iopub.status.busy": "2024-07-01T15:06:46.221652Z", + "iopub.status.idle": "2024-07-01T15:06:46.228007Z", + "shell.execute_reply": "2024-07-01T15:06:46.227489Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.642498Z", - "iopub.status.busy": "2024-06-28T15:37:20.642312Z", - "iopub.status.idle": "2024-06-28T15:37:20.691414Z", - "shell.execute_reply": "2024-06-28T15:37:20.690793Z" + "iopub.execute_input": "2024-07-01T15:06:46.229956Z", + "iopub.status.busy": "2024-07-01T15:06:46.229562Z", + "iopub.status.idle": "2024-07-01T15:06:46.262926Z", + "shell.execute_reply": "2024-07-01T15:06:46.262323Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.694197Z", - "iopub.status.busy": "2024-06-28T15:37:20.693811Z", - "iopub.status.idle": "2024-06-28T15:37:20.737170Z", - "shell.execute_reply": "2024-06-28T15:37:20.736554Z" + "iopub.execute_input": "2024-07-01T15:06:46.265438Z", + "iopub.status.busy": "2024-07-01T15:06:46.265079Z", + "iopub.status.idle": "2024-07-01T15:06:46.292805Z", + "shell.execute_reply": "2024-07-01T15:06:46.292125Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.740010Z", - "iopub.status.busy": "2024-06-28T15:37:20.739710Z", - "iopub.status.idle": "2024-06-28T15:37:20.866813Z", - "shell.execute_reply": "2024-06-28T15:37:20.866236Z" + "iopub.execute_input": "2024-07-01T15:06:46.295480Z", + "iopub.status.busy": "2024-07-01T15:06:46.295249Z", + "iopub.status.idle": "2024-07-01T15:06:46.413036Z", + "shell.execute_reply": "2024-07-01T15:06:46.412421Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.869660Z", - "iopub.status.busy": "2024-06-28T15:37:20.868914Z", - "iopub.status.idle": "2024-06-28T15:37:23.945616Z", - "shell.execute_reply": "2024-06-28T15:37:23.944963Z" + "iopub.execute_input": "2024-07-01T15:06:46.415958Z", + "iopub.status.busy": "2024-07-01T15:06:46.415232Z", + "iopub.status.idle": "2024-07-01T15:06:49.386585Z", + "shell.execute_reply": "2024-07-01T15:06:49.386024Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:23.948221Z", - "iopub.status.busy": "2024-06-28T15:37:23.947849Z", - "iopub.status.idle": "2024-06-28T15:37:24.007400Z", - "shell.execute_reply": "2024-06-28T15:37:24.006793Z" + "iopub.execute_input": "2024-07-01T15:06:49.389098Z", + "iopub.status.busy": "2024-07-01T15:06:49.388711Z", + "iopub.status.idle": "2024-07-01T15:06:49.445289Z", + "shell.execute_reply": "2024-07-01T15:06:49.444750Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.009682Z", - "iopub.status.busy": "2024-06-28T15:37:24.009342Z", - "iopub.status.idle": "2024-06-28T15:37:24.050753Z", - "shell.execute_reply": "2024-06-28T15:37:24.050188Z" + "iopub.execute_input": "2024-07-01T15:06:49.447480Z", + "iopub.status.busy": "2024-07-01T15:06:49.447130Z", + "iopub.status.idle": "2024-07-01T15:06:49.487292Z", + "shell.execute_reply": "2024-07-01T15:06:49.486835Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "08a301fa", + "id": "6cb95977", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "d17b4a6d", + "id": "7616eae0", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "467b92b3", + "id": "c8e20eef", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "3793609e", + "id": "5c7c2dee", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.053187Z", - "iopub.status.busy": "2024-06-28T15:37:24.052862Z", - "iopub.status.idle": "2024-06-28T15:37:24.060784Z", - "shell.execute_reply": "2024-06-28T15:37:24.060277Z" + "iopub.execute_input": "2024-07-01T15:06:49.489589Z", + "iopub.status.busy": "2024-07-01T15:06:49.489256Z", + "iopub.status.idle": "2024-07-01T15:06:49.496732Z", + "shell.execute_reply": "2024-07-01T15:06:49.496311Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "b5be9b33", + "id": "02fbab1c", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "beda2e53", + "id": "80ce97c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.062996Z", - "iopub.status.busy": "2024-06-28T15:37:24.062565Z", - "iopub.status.idle": "2024-06-28T15:37:24.084314Z", - "shell.execute_reply": "2024-06-28T15:37:24.083768Z" + "iopub.execute_input": "2024-07-01T15:06:49.498789Z", + "iopub.status.busy": "2024-07-01T15:06:49.498386Z", + "iopub.status.idle": "2024-07-01T15:06:49.516684Z", + "shell.execute_reply": "2024-07-01T15:06:49.516122Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "d0ec4783", + "id": "f382c568", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.086577Z", - "iopub.status.busy": "2024-06-28T15:37:24.086173Z", - "iopub.status.idle": "2024-06-28T15:37:24.089622Z", - "shell.execute_reply": "2024-06-28T15:37:24.089095Z" + "iopub.execute_input": "2024-07-01T15:06:49.518671Z", + "iopub.status.busy": "2024-07-01T15:06:49.518371Z", + "iopub.status.idle": "2024-07-01T15:06:49.521416Z", + "shell.execute_reply": "2024-07-01T15:06:49.520910Z" } }, "outputs": [ @@ -1622,7 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a7b959af14b4aa68731b0af4b7e7ea2": { + "0111e2bffb134a42896020ea74d7e0c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,56 +1675,49 @@ "width": null } }, - "0df3eeea26e74ea597b01f7334be29d8": { + "08c89a132fac493d9d7986ef465e2458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_15f2ae0eeaf24047857abbeb37fad136", - "placeholder": "​", - "style": "IPY_MODEL_27cc3de215a9489c88ed634cc42daeac", + "layout": "IPY_MODEL_852d38b7e7464bfb919e2fb9d8164e6e", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_091b3aa8f4304d839459836b00997219", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: " + "value": 50.0 } }, - "0fc3523ecaed4cdcb1fa6d452d860686": { + "091b3aa8f4304d839459836b00997219": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f62440c0ecd8491b8ae0a9a2541dfa01", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f30a962c2cdd422483e4d9516c8e0f05", - "tabbable": null, - "tooltip": null, - "value": 50.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "10f5bddcb2454f329c7a54207b2bf28e": { + "0e05780aa0694da2b04037e49f9ac6f9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1739,16 +1732,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_555a49f5dd334422994f7c81fd0d7f54", - "IPY_MODEL_e9d99b127d7348f0aafad88df30a6f0b", - "IPY_MODEL_dc618dccc3d74be5b2b53edece0ec7e8" + "IPY_MODEL_ac61742632624d8e99ae7cb5f710a566", + "IPY_MODEL_faad99f698554ee5a74327e7ca036115", + "IPY_MODEL_ddebd597ac7544839e8b864a9d0ee839" ], - "layout": "IPY_MODEL_3f5ca8079afc4b2cb1683c22361efd61", + "layout": "IPY_MODEL_70ea901211b24a5e83c22f533f32e36f", "tabbable": null, "tooltip": null } }, - "15f2ae0eeaf24047857abbeb37fad136": { + "1447fe2269d345b3ab6d777d2314a43f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1801,25 +1794,7 @@ "width": null } }, - "27cc3de215a9489c88ed634cc42daeac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3f5ca8079afc4b2cb1683c22361efd61": { + "1def5cc260bc404184c09a073ccd4bd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1872,23 +1847,7 @@ "width": null } }, - "4ba67748833f4377b63cdafd5f5d997c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4cf14dfdb0b34b0d8465da1f8b07606c": { + "40e84f876df34c24a0156c5fdf89b318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1906,66 +1865,84 @@ "text_color": null } }, - "5296ebff86d244ff8e6b3474a6249a0a": { - "model_module": "@jupyter-widgets/controls", + "460a43df0d3b417d8996679fabe135e8": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "555a49f5dd334422994f7c81fd0d7f54": { + "4fd339b3d01f445392d6c990fdab5a89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_99f33331931743bfa9147fd8a8656809", - "placeholder": "​", - "style": "IPY_MODEL_4cf14dfdb0b34b0d8465da1f8b07606c", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_be7586025bf846d68f14028779700b35", + "IPY_MODEL_08c89a132fac493d9d7986ef465e2458", + "IPY_MODEL_664279e521a34f979477f41a406cb5c1" + ], + "layout": "IPY_MODEL_1447fe2269d345b3ab6d777d2314a43f", "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "7a59431350034e848cce8fea99c3e2fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "8de3b33011fa44cfa55abf7791a9482d": { + "60b07213fb4d4ba79fddd321add6003b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2018,7 +1995,7 @@ "width": null } }, - "99f33331931743bfa9147fd8a8656809": { + "651b9c830e68446881c37217e971cf46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2071,54 +2048,66 @@ "width": null } }, - "9cb302a654554b6dba5964ac8c805ba8": { + "664279e521a34f979477f41a406cb5c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0df3eeea26e74ea597b01f7334be29d8", - "IPY_MODEL_0fc3523ecaed4cdcb1fa6d452d860686", - "IPY_MODEL_bbe7d6f1caa5408ea1c6baac6048752d" - ], - "layout": "IPY_MODEL_0a7b959af14b4aa68731b0af4b7e7ea2", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_60b07213fb4d4ba79fddd321add6003b", + "placeholder": "​", + "style": "IPY_MODEL_40e84f876df34c24a0156c5fdf89b318", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1061876.00it/s]" } }, - "bbe7d6f1caa5408ea1c6baac6048752d": { + "6887dcd8b8cd4f4d89261e3c68e959a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8de3b33011fa44cfa55abf7791a9482d", - "placeholder": "​", - "style": "IPY_MODEL_5296ebff86d244ff8e6b3474a6249a0a", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1028519.86it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6957d92bb0cf4c47bfffb9903128dcf5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ca982934b4914b838423f2e8743bf780": { + "70ea901211b24a5e83c22f533f32e36f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2171,7 +2160,23 @@ "width": null } }, - "cc94bc38ba4a43e8a19bb8b38b129392": { + "762d2f4c294343aaac239f8510aee6d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "852d38b7e7464bfb919e2fb9d8164e6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2224,7 +2229,7 @@ "width": null } }, - "dc618dccc3d74be5b2b53edece0ec7e8": { + "ac61742632624d8e99ae7cb5f710a566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2239,107 +2244,102 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cc94bc38ba4a43e8a19bb8b38b129392", + "layout": "IPY_MODEL_651b9c830e68446881c37217e971cf46", "placeholder": "​", - "style": "IPY_MODEL_7a59431350034e848cce8fea99c3e2fd", + "style": "IPY_MODEL_6887dcd8b8cd4f4d89261e3c68e959a9", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 979954.67it/s]" + "value": "number of examples processed for checking labels: " } }, - "e9d99b127d7348f0aafad88df30a6f0b": { + "be7586025bf846d68f14028779700b35": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ca982934b4914b838423f2e8743bf780", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4ba67748833f4377b63cdafd5f5d997c", + "layout": "IPY_MODEL_0111e2bffb134a42896020ea74d7e0c2", + "placeholder": "​", + "style": "IPY_MODEL_dc2d26ad64ec4759a2171fea5ff47da6", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for estimating thresholds: " } }, - "f30a962c2cdd422483e4d9516c8e0f05": { + "dc2d26ad64ec4759a2171fea5ff47da6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f62440c0ecd8491b8ae0a9a2541dfa01": { - "model_module": "@jupyter-widgets/base", + "ddebd597ac7544839e8b864a9d0ee839": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1def5cc260bc404184c09a073ccd4bd2", + "placeholder": "​", + "style": "IPY_MODEL_6957d92bb0cf4c47bfffb9903128dcf5", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1591222.73it/s]" + } + }, + "faad99f698554ee5a74327e7ca036115": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_460a43df0d3b417d8996679fabe135e8", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_762d2f4c294343aaac239f8510aee6d2", + "tabbable": null, + "tooltip": null, + "value": 50.0 } } }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index da76b3390..4c0344124 100644 --- a/master/tutorials/indepth_overview.ipynb +++ b/master/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:27.714360Z", - "iopub.status.busy": "2024-06-28T15:37:27.713879Z", - "iopub.status.idle": "2024-06-28T15:37:28.959982Z", - "shell.execute_reply": "2024-06-28T15:37:28.959396Z" + "iopub.execute_input": "2024-07-01T15:06:52.670679Z", + "iopub.status.busy": "2024-07-01T15:06:52.670319Z", + "iopub.status.idle": "2024-07-01T15:06:53.828911Z", + "shell.execute_reply": "2024-07-01T15:06:53.828417Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:28.962815Z", - "iopub.status.busy": "2024-06-28T15:37:28.962469Z", - "iopub.status.idle": "2024-06-28T15:37:29.160428Z", - "shell.execute_reply": "2024-06-28T15:37:29.159774Z" + "iopub.execute_input": "2024-07-01T15:06:53.831480Z", + "iopub.status.busy": "2024-07-01T15:06:53.831210Z", + "iopub.status.idle": "2024-07-01T15:06:54.013309Z", + "shell.execute_reply": "2024-07-01T15:06:54.012774Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.163692Z", - "iopub.status.busy": "2024-06-28T15:37:29.163188Z", - "iopub.status.idle": "2024-06-28T15:37:29.176473Z", - "shell.execute_reply": "2024-06-28T15:37:29.175820Z" + "iopub.execute_input": "2024-07-01T15:06:54.015638Z", + "iopub.status.busy": "2024-07-01T15:06:54.015446Z", + "iopub.status.idle": "2024-07-01T15:06:54.026660Z", + "shell.execute_reply": "2024-07-01T15:06:54.026227Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.178819Z", - "iopub.status.busy": "2024-06-28T15:37:29.178582Z", - "iopub.status.idle": "2024-06-28T15:37:29.391572Z", - "shell.execute_reply": "2024-06-28T15:37:29.390996Z" + "iopub.execute_input": "2024-07-01T15:06:54.028614Z", + "iopub.status.busy": "2024-07-01T15:06:54.028276Z", + "iopub.status.idle": "2024-07-01T15:06:54.258453Z", + "shell.execute_reply": "2024-07-01T15:06:54.257871Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.393993Z", - "iopub.status.busy": "2024-06-28T15:37:29.393620Z", - "iopub.status.idle": "2024-06-28T15:37:29.420116Z", - "shell.execute_reply": "2024-06-28T15:37:29.419615Z" + "iopub.execute_input": "2024-07-01T15:06:54.260736Z", + "iopub.status.busy": "2024-07-01T15:06:54.260424Z", + "iopub.status.idle": "2024-07-01T15:06:54.286726Z", + "shell.execute_reply": "2024-07-01T15:06:54.286174Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.422535Z", - "iopub.status.busy": "2024-06-28T15:37:29.422200Z", - "iopub.status.idle": "2024-06-28T15:37:31.677275Z", - "shell.execute_reply": "2024-06-28T15:37:31.676561Z" + "iopub.execute_input": "2024-07-01T15:06:54.288988Z", + "iopub.status.busy": "2024-07-01T15:06:54.288686Z", + "iopub.status.idle": "2024-07-01T15:06:56.272596Z", + "shell.execute_reply": "2024-07-01T15:06:56.271981Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:31.679930Z", - "iopub.status.busy": "2024-06-28T15:37:31.679401Z", - "iopub.status.idle": "2024-06-28T15:37:31.700730Z", - "shell.execute_reply": "2024-06-28T15:37:31.700165Z" + "iopub.execute_input": "2024-07-01T15:06:56.275147Z", + "iopub.status.busy": "2024-07-01T15:06:56.274650Z", + "iopub.status.idle": "2024-07-01T15:06:56.292694Z", + "shell.execute_reply": "2024-07-01T15:06:56.292260Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:31.702898Z", - "iopub.status.busy": "2024-06-28T15:37:31.702688Z", - "iopub.status.idle": "2024-06-28T15:37:33.227201Z", - "shell.execute_reply": "2024-06-28T15:37:33.226576Z" + "iopub.execute_input": "2024-07-01T15:06:56.294630Z", + "iopub.status.busy": "2024-07-01T15:06:56.294450Z", + "iopub.status.idle": "2024-07-01T15:06:57.712997Z", + "shell.execute_reply": "2024-07-01T15:06:57.712387Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.230165Z", - "iopub.status.busy": "2024-06-28T15:37:33.229351Z", - "iopub.status.idle": "2024-06-28T15:37:33.243878Z", - "shell.execute_reply": "2024-06-28T15:37:33.243283Z" + "iopub.execute_input": "2024-07-01T15:06:57.715656Z", + "iopub.status.busy": "2024-07-01T15:06:57.715048Z", + "iopub.status.idle": "2024-07-01T15:06:57.728518Z", + "shell.execute_reply": "2024-07-01T15:06:57.728061Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.246084Z", - "iopub.status.busy": "2024-06-28T15:37:33.245762Z", - "iopub.status.idle": "2024-06-28T15:37:33.325997Z", - "shell.execute_reply": "2024-06-28T15:37:33.325374Z" + "iopub.execute_input": "2024-07-01T15:06:57.730397Z", + "iopub.status.busy": "2024-07-01T15:06:57.730225Z", + "iopub.status.idle": "2024-07-01T15:06:57.801036Z", + "shell.execute_reply": "2024-07-01T15:06:57.800476Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.328410Z", - "iopub.status.busy": "2024-06-28T15:37:33.328174Z", - "iopub.status.idle": "2024-06-28T15:37:33.543623Z", - "shell.execute_reply": "2024-06-28T15:37:33.543031Z" + "iopub.execute_input": "2024-07-01T15:06:57.803338Z", + "iopub.status.busy": "2024-07-01T15:06:57.802990Z", + "iopub.status.idle": "2024-07-01T15:06:58.016553Z", + "shell.execute_reply": "2024-07-01T15:06:58.016010Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.545784Z", - "iopub.status.busy": "2024-06-28T15:37:33.545592Z", - "iopub.status.idle": "2024-06-28T15:37:33.562693Z", - "shell.execute_reply": "2024-06-28T15:37:33.562099Z" + "iopub.execute_input": "2024-07-01T15:06:58.018803Z", + "iopub.status.busy": "2024-07-01T15:06:58.018381Z", + "iopub.status.idle": "2024-07-01T15:06:58.034965Z", + "shell.execute_reply": "2024-07-01T15:06:58.034432Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.564886Z", - "iopub.status.busy": "2024-06-28T15:37:33.564699Z", - "iopub.status.idle": "2024-06-28T15:37:33.574692Z", - "shell.execute_reply": "2024-06-28T15:37:33.574212Z" + "iopub.execute_input": "2024-07-01T15:06:58.037248Z", + "iopub.status.busy": "2024-07-01T15:06:58.036814Z", + "iopub.status.idle": "2024-07-01T15:06:58.046245Z", + "shell.execute_reply": "2024-07-01T15:06:58.045792Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.576695Z", - "iopub.status.busy": "2024-06-28T15:37:33.576495Z", - "iopub.status.idle": "2024-06-28T15:37:33.663696Z", - "shell.execute_reply": "2024-06-28T15:37:33.663087Z" + "iopub.execute_input": "2024-07-01T15:06:58.048367Z", + "iopub.status.busy": "2024-07-01T15:06:58.048051Z", + "iopub.status.idle": "2024-07-01T15:06:58.130967Z", + "shell.execute_reply": "2024-07-01T15:06:58.130377Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.666354Z", - "iopub.status.busy": "2024-06-28T15:37:33.665907Z", - "iopub.status.idle": "2024-06-28T15:37:33.801045Z", - "shell.execute_reply": "2024-06-28T15:37:33.800289Z" + "iopub.execute_input": "2024-07-01T15:06:58.133536Z", + "iopub.status.busy": "2024-07-01T15:06:58.133071Z", + "iopub.status.idle": "2024-07-01T15:06:58.249759Z", + "shell.execute_reply": "2024-07-01T15:06:58.249159Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.803432Z", - "iopub.status.busy": "2024-06-28T15:37:33.803187Z", - "iopub.status.idle": "2024-06-28T15:37:33.806941Z", - "shell.execute_reply": "2024-06-28T15:37:33.806446Z" + "iopub.execute_input": "2024-07-01T15:06:58.252274Z", + "iopub.status.busy": "2024-07-01T15:06:58.251852Z", + "iopub.status.idle": "2024-07-01T15:06:58.255729Z", + "shell.execute_reply": "2024-07-01T15:06:58.255184Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.808893Z", - "iopub.status.busy": "2024-06-28T15:37:33.808719Z", - "iopub.status.idle": "2024-06-28T15:37:33.812775Z", - "shell.execute_reply": "2024-06-28T15:37:33.812256Z" + "iopub.execute_input": "2024-07-01T15:06:58.257572Z", + "iopub.status.busy": "2024-07-01T15:06:58.257399Z", + "iopub.status.idle": "2024-07-01T15:06:58.261023Z", + "shell.execute_reply": "2024-07-01T15:06:58.260496Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.814864Z", - "iopub.status.busy": "2024-06-28T15:37:33.814431Z", - "iopub.status.idle": "2024-06-28T15:37:33.851921Z", - "shell.execute_reply": "2024-06-28T15:37:33.851323Z" + "iopub.execute_input": "2024-07-01T15:06:58.263055Z", + "iopub.status.busy": "2024-07-01T15:06:58.262734Z", + "iopub.status.idle": "2024-07-01T15:06:58.298627Z", + "shell.execute_reply": "2024-07-01T15:06:58.298174Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.854343Z", - "iopub.status.busy": "2024-06-28T15:37:33.853885Z", - "iopub.status.idle": "2024-06-28T15:37:33.900346Z", - "shell.execute_reply": "2024-06-28T15:37:33.899841Z" + "iopub.execute_input": "2024-07-01T15:06:58.300556Z", + "iopub.status.busy": "2024-07-01T15:06:58.300384Z", + "iopub.status.idle": "2024-07-01T15:06:58.341152Z", + "shell.execute_reply": "2024-07-01T15:06:58.340674Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.902629Z", - "iopub.status.busy": "2024-06-28T15:37:33.902277Z", - "iopub.status.idle": "2024-06-28T15:37:34.010055Z", - "shell.execute_reply": "2024-06-28T15:37:34.009418Z" + "iopub.execute_input": "2024-07-01T15:06:58.343232Z", + "iopub.status.busy": "2024-07-01T15:06:58.343056Z", + "iopub.status.idle": "2024-07-01T15:06:58.437535Z", + "shell.execute_reply": "2024-07-01T15:06:58.436855Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.012994Z", - "iopub.status.busy": "2024-06-28T15:37:34.012514Z", - "iopub.status.idle": "2024-06-28T15:37:34.121092Z", - "shell.execute_reply": "2024-06-28T15:37:34.120466Z" + "iopub.execute_input": "2024-07-01T15:06:58.440127Z", + "iopub.status.busy": "2024-07-01T15:06:58.439842Z", + "iopub.status.idle": "2024-07-01T15:06:58.527589Z", + "shell.execute_reply": "2024-07-01T15:06:58.526960Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.123432Z", - "iopub.status.busy": "2024-06-28T15:37:34.123168Z", - "iopub.status.idle": "2024-06-28T15:37:34.337858Z", - "shell.execute_reply": "2024-06-28T15:37:34.337368Z" + "iopub.execute_input": "2024-07-01T15:06:58.529972Z", + "iopub.status.busy": "2024-07-01T15:06:58.529737Z", + "iopub.status.idle": "2024-07-01T15:06:58.741167Z", + "shell.execute_reply": "2024-07-01T15:06:58.740717Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.340126Z", - "iopub.status.busy": "2024-06-28T15:37:34.339793Z", - "iopub.status.idle": "2024-06-28T15:37:34.547731Z", - "shell.execute_reply": "2024-06-28T15:37:34.547093Z" + "iopub.execute_input": "2024-07-01T15:06:58.743495Z", + "iopub.status.busy": "2024-07-01T15:06:58.743153Z", + "iopub.status.idle": "2024-07-01T15:06:58.920954Z", + "shell.execute_reply": "2024-07-01T15:06:58.920411Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.550178Z", - "iopub.status.busy": "2024-06-28T15:37:34.549981Z", - "iopub.status.idle": "2024-06-28T15:37:34.556248Z", - "shell.execute_reply": "2024-06-28T15:37:34.555797Z" + "iopub.execute_input": "2024-07-01T15:06:58.923453Z", + "iopub.status.busy": "2024-07-01T15:06:58.923009Z", + "iopub.status.idle": "2024-07-01T15:06:58.928872Z", + "shell.execute_reply": "2024-07-01T15:06:58.928426Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.558418Z", - "iopub.status.busy": "2024-06-28T15:37:34.558088Z", - "iopub.status.idle": "2024-06-28T15:37:34.773972Z", - "shell.execute_reply": "2024-06-28T15:37:34.773468Z" + "iopub.execute_input": "2024-07-01T15:06:58.930892Z", + "iopub.status.busy": "2024-07-01T15:06:58.930502Z", + "iopub.status.idle": "2024-07-01T15:06:59.148406Z", + "shell.execute_reply": "2024-07-01T15:06:59.147826Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.776118Z", - "iopub.status.busy": "2024-06-28T15:37:34.775925Z", - "iopub.status.idle": "2024-06-28T15:37:35.877501Z", - "shell.execute_reply": "2024-06-28T15:37:35.877002Z" + "iopub.execute_input": "2024-07-01T15:06:59.150754Z", + "iopub.status.busy": "2024-07-01T15:06:59.150391Z", + "iopub.status.idle": "2024-07-01T15:07:00.213417Z", + "shell.execute_reply": "2024-07-01T15:07:00.212813Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 8af61e177..b426f5b7a 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:39.503284Z", - "iopub.status.busy": "2024-06-28T15:37:39.503099Z", - "iopub.status.idle": "2024-06-28T15:37:40.687920Z", - "shell.execute_reply": "2024-06-28T15:37:40.687390Z" + "iopub.execute_input": "2024-07-01T15:07:03.695403Z", + "iopub.status.busy": "2024-07-01T15:07:03.695236Z", + "iopub.status.idle": "2024-07-01T15:07:04.786480Z", + "shell.execute_reply": "2024-07-01T15:07:04.785971Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.690877Z", - "iopub.status.busy": "2024-06-28T15:37:40.690286Z", - "iopub.status.idle": "2024-06-28T15:37:40.693591Z", - "shell.execute_reply": "2024-06-28T15:37:40.693053Z" + "iopub.execute_input": "2024-07-01T15:07:04.789244Z", + "iopub.status.busy": "2024-07-01T15:07:04.788665Z", + "iopub.status.idle": "2024-07-01T15:07:04.791892Z", + "shell.execute_reply": "2024-07-01T15:07:04.791444Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.695825Z", - "iopub.status.busy": "2024-06-28T15:37:40.695507Z", - "iopub.status.idle": "2024-06-28T15:37:40.703252Z", - "shell.execute_reply": "2024-06-28T15:37:40.702722Z" + "iopub.execute_input": "2024-07-01T15:07:04.793920Z", + "iopub.status.busy": "2024-07-01T15:07:04.793593Z", + "iopub.status.idle": "2024-07-01T15:07:04.801265Z", + "shell.execute_reply": "2024-07-01T15:07:04.800810Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.705494Z", - "iopub.status.busy": "2024-06-28T15:37:40.705178Z", - "iopub.status.idle": "2024-06-28T15:37:40.752328Z", - "shell.execute_reply": "2024-06-28T15:37:40.751695Z" + "iopub.execute_input": "2024-07-01T15:07:04.803286Z", + "iopub.status.busy": "2024-07-01T15:07:04.802900Z", + "iopub.status.idle": "2024-07-01T15:07:04.850047Z", + "shell.execute_reply": "2024-07-01T15:07:04.849570Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.755037Z", - "iopub.status.busy": "2024-06-28T15:37:40.754588Z", - "iopub.status.idle": "2024-06-28T15:37:40.771776Z", - "shell.execute_reply": "2024-06-28T15:37:40.771317Z" + "iopub.execute_input": "2024-07-01T15:07:04.852247Z", + "iopub.status.busy": "2024-07-01T15:07:04.852061Z", + "iopub.status.idle": "2024-07-01T15:07:04.869485Z", + "shell.execute_reply": "2024-07-01T15:07:04.869018Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.774143Z", - "iopub.status.busy": "2024-06-28T15:37:40.773746Z", - "iopub.status.idle": "2024-06-28T15:37:40.777708Z", - "shell.execute_reply": "2024-06-28T15:37:40.777230Z" + "iopub.execute_input": "2024-07-01T15:07:04.871391Z", + "iopub.status.busy": "2024-07-01T15:07:04.871213Z", + "iopub.status.idle": "2024-07-01T15:07:04.875222Z", + "shell.execute_reply": "2024-07-01T15:07:04.874787Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.779858Z", - "iopub.status.busy": "2024-06-28T15:37:40.779536Z", - "iopub.status.idle": "2024-06-28T15:37:40.792878Z", - "shell.execute_reply": "2024-06-28T15:37:40.792415Z" + "iopub.execute_input": "2024-07-01T15:07:04.877103Z", + "iopub.status.busy": "2024-07-01T15:07:04.876935Z", + "iopub.status.idle": "2024-07-01T15:07:04.890567Z", + "shell.execute_reply": "2024-07-01T15:07:04.890109Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.794938Z", - "iopub.status.busy": "2024-06-28T15:37:40.794619Z", - "iopub.status.idle": "2024-06-28T15:37:40.821318Z", - "shell.execute_reply": "2024-06-28T15:37:40.820726Z" + "iopub.execute_input": "2024-07-01T15:07:04.892340Z", + "iopub.status.busy": "2024-07-01T15:07:04.892165Z", + "iopub.status.idle": "2024-07-01T15:07:04.917921Z", + "shell.execute_reply": "2024-07-01T15:07:04.917510Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.823690Z", - "iopub.status.busy": "2024-06-28T15:37:40.823369Z", - "iopub.status.idle": "2024-06-28T15:37:42.858786Z", - "shell.execute_reply": "2024-06-28T15:37:42.858257Z" + "iopub.execute_input": "2024-07-01T15:07:04.919909Z", + "iopub.status.busy": "2024-07-01T15:07:04.919740Z", + "iopub.status.idle": "2024-07-01T15:07:06.770405Z", + "shell.execute_reply": "2024-07-01T15:07:06.769771Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.861902Z", - "iopub.status.busy": "2024-06-28T15:37:42.861153Z", - "iopub.status.idle": "2024-06-28T15:37:42.868329Z", - "shell.execute_reply": "2024-06-28T15:37:42.867757Z" + "iopub.execute_input": "2024-07-01T15:07:06.773094Z", + "iopub.status.busy": "2024-07-01T15:07:06.772569Z", + "iopub.status.idle": "2024-07-01T15:07:06.779189Z", + "shell.execute_reply": "2024-07-01T15:07:06.778660Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.870554Z", - "iopub.status.busy": "2024-06-28T15:37:42.870224Z", - "iopub.status.idle": "2024-06-28T15:37:42.882956Z", - "shell.execute_reply": "2024-06-28T15:37:42.882441Z" + "iopub.execute_input": "2024-07-01T15:07:06.781060Z", + "iopub.status.busy": "2024-07-01T15:07:06.780797Z", + "iopub.status.idle": "2024-07-01T15:07:06.793132Z", + "shell.execute_reply": "2024-07-01T15:07:06.792613Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.885139Z", - "iopub.status.busy": "2024-06-28T15:37:42.884799Z", - "iopub.status.idle": "2024-06-28T15:37:42.891477Z", - "shell.execute_reply": "2024-06-28T15:37:42.890929Z" + "iopub.execute_input": "2024-07-01T15:07:06.795311Z", + "iopub.status.busy": "2024-07-01T15:07:06.794896Z", + "iopub.status.idle": "2024-07-01T15:07:06.801219Z", + "shell.execute_reply": "2024-07-01T15:07:06.800801Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.893662Z", - "iopub.status.busy": "2024-06-28T15:37:42.893329Z", - "iopub.status.idle": "2024-06-28T15:37:42.895941Z", - "shell.execute_reply": "2024-06-28T15:37:42.895509Z" + "iopub.execute_input": "2024-07-01T15:07:06.803175Z", + "iopub.status.busy": "2024-07-01T15:07:06.802994Z", + "iopub.status.idle": "2024-07-01T15:07:06.805670Z", + "shell.execute_reply": "2024-07-01T15:07:06.805234Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.898071Z", - "iopub.status.busy": "2024-06-28T15:37:42.897574Z", - "iopub.status.idle": "2024-06-28T15:37:42.901102Z", - "shell.execute_reply": "2024-06-28T15:37:42.900664Z" + "iopub.execute_input": "2024-07-01T15:07:06.807492Z", + "iopub.status.busy": "2024-07-01T15:07:06.807328Z", + "iopub.status.idle": "2024-07-01T15:07:06.810895Z", + "shell.execute_reply": "2024-07-01T15:07:06.810453Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.903184Z", - "iopub.status.busy": "2024-06-28T15:37:42.902860Z", - "iopub.status.idle": "2024-06-28T15:37:42.905499Z", - "shell.execute_reply": "2024-06-28T15:37:42.905046Z" + "iopub.execute_input": "2024-07-01T15:07:06.812899Z", + "iopub.status.busy": "2024-07-01T15:07:06.812510Z", + "iopub.status.idle": "2024-07-01T15:07:06.815130Z", + "shell.execute_reply": "2024-07-01T15:07:06.814702Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.907596Z", - "iopub.status.busy": "2024-06-28T15:37:42.907270Z", - "iopub.status.idle": "2024-06-28T15:37:42.911435Z", - "shell.execute_reply": "2024-06-28T15:37:42.910901Z" + "iopub.execute_input": "2024-07-01T15:07:06.817057Z", + "iopub.status.busy": "2024-07-01T15:07:06.816734Z", + "iopub.status.idle": "2024-07-01T15:07:06.820893Z", + "shell.execute_reply": "2024-07-01T15:07:06.820448Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.913391Z", - "iopub.status.busy": "2024-06-28T15:37:42.913217Z", - "iopub.status.idle": "2024-06-28T15:37:42.941833Z", - "shell.execute_reply": "2024-06-28T15:37:42.941368Z" + "iopub.execute_input": "2024-07-01T15:07:06.822875Z", + "iopub.status.busy": "2024-07-01T15:07:06.822704Z", + "iopub.status.idle": "2024-07-01T15:07:06.851357Z", + "shell.execute_reply": "2024-07-01T15:07:06.850916Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.944033Z", - "iopub.status.busy": "2024-06-28T15:37:42.943856Z", - "iopub.status.idle": "2024-06-28T15:37:42.948433Z", - "shell.execute_reply": "2024-06-28T15:37:42.948007Z" + "iopub.execute_input": "2024-07-01T15:07:06.853186Z", + "iopub.status.busy": "2024-07-01T15:07:06.853017Z", + "iopub.status.idle": "2024-07-01T15:07:06.857526Z", + "shell.execute_reply": "2024-07-01T15:07:06.857095Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index fa739c4ac..f9fccade5 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:46.062655Z", - "iopub.status.busy": "2024-06-28T15:37:46.062439Z", - "iopub.status.idle": "2024-06-28T15:37:47.278130Z", - "shell.execute_reply": "2024-06-28T15:37:47.277493Z" + "iopub.execute_input": "2024-07-01T15:07:09.805934Z", + "iopub.status.busy": "2024-07-01T15:07:09.805760Z", + "iopub.status.idle": "2024-07-01T15:07:10.951874Z", + "shell.execute_reply": "2024-07-01T15:07:10.951332Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.281006Z", - "iopub.status.busy": "2024-06-28T15:37:47.280461Z", - "iopub.status.idle": "2024-06-28T15:37:47.487135Z", - "shell.execute_reply": "2024-06-28T15:37:47.486629Z" + "iopub.execute_input": "2024-07-01T15:07:10.954229Z", + "iopub.status.busy": "2024-07-01T15:07:10.953974Z", + "iopub.status.idle": "2024-07-01T15:07:11.145898Z", + "shell.execute_reply": "2024-07-01T15:07:11.145328Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.490066Z", - "iopub.status.busy": "2024-06-28T15:37:47.489490Z", - "iopub.status.idle": "2024-06-28T15:37:47.503413Z", - "shell.execute_reply": "2024-06-28T15:37:47.502810Z" + "iopub.execute_input": "2024-07-01T15:07:11.148952Z", + "iopub.status.busy": "2024-07-01T15:07:11.148446Z", + "iopub.status.idle": "2024-07-01T15:07:11.162195Z", + "shell.execute_reply": "2024-07-01T15:07:11.161701Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.505906Z", - "iopub.status.busy": "2024-06-28T15:37:47.505486Z", - "iopub.status.idle": "2024-06-28T15:37:50.206513Z", - "shell.execute_reply": "2024-06-28T15:37:50.205911Z" + "iopub.execute_input": "2024-07-01T15:07:11.164347Z", + "iopub.status.busy": "2024-07-01T15:07:11.163932Z", + "iopub.status.idle": "2024-07-01T15:07:13.785011Z", + "shell.execute_reply": "2024-07-01T15:07:13.784429Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:50.208774Z", - "iopub.status.busy": "2024-06-28T15:37:50.208544Z", - "iopub.status.idle": "2024-06-28T15:37:51.579425Z", - "shell.execute_reply": "2024-06-28T15:37:51.578910Z" + "iopub.execute_input": "2024-07-01T15:07:13.787263Z", + "iopub.status.busy": "2024-07-01T15:07:13.787077Z", + "iopub.status.idle": "2024-07-01T15:07:15.129078Z", + "shell.execute_reply": "2024-07-01T15:07:15.128523Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:51.581852Z", - "iopub.status.busy": "2024-06-28T15:37:51.581666Z", - "iopub.status.idle": "2024-06-28T15:37:51.586007Z", - "shell.execute_reply": "2024-06-28T15:37:51.585531Z" + "iopub.execute_input": "2024-07-01T15:07:15.131446Z", + "iopub.status.busy": "2024-07-01T15:07:15.131256Z", + "iopub.status.idle": "2024-07-01T15:07:15.135063Z", + "shell.execute_reply": "2024-07-01T15:07:15.134547Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:51.587856Z", - "iopub.status.busy": "2024-06-28T15:37:51.587684Z", - "iopub.status.idle": "2024-06-28T15:37:53.735997Z", - "shell.execute_reply": "2024-06-28T15:37:53.735310Z" + "iopub.execute_input": "2024-07-01T15:07:15.137004Z", + "iopub.status.busy": "2024-07-01T15:07:15.136825Z", + "iopub.status.idle": "2024-07-01T15:07:17.134343Z", + "shell.execute_reply": "2024-07-01T15:07:17.133735Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:53.738554Z", - "iopub.status.busy": "2024-06-28T15:37:53.738136Z", - "iopub.status.idle": "2024-06-28T15:37:53.746671Z", - "shell.execute_reply": "2024-06-28T15:37:53.746192Z" + "iopub.execute_input": "2024-07-01T15:07:17.136852Z", + "iopub.status.busy": "2024-07-01T15:07:17.136398Z", + "iopub.status.idle": "2024-07-01T15:07:17.144283Z", + "shell.execute_reply": "2024-07-01T15:07:17.143851Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:53.748672Z", - "iopub.status.busy": "2024-06-28T15:37:53.748467Z", - "iopub.status.idle": "2024-06-28T15:37:56.383626Z", - "shell.execute_reply": "2024-06-28T15:37:56.383027Z" + "iopub.execute_input": "2024-07-01T15:07:17.146377Z", + "iopub.status.busy": "2024-07-01T15:07:17.146026Z", + "iopub.status.idle": "2024-07-01T15:07:19.687541Z", + "shell.execute_reply": "2024-07-01T15:07:19.686980Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.385815Z", - "iopub.status.busy": "2024-06-28T15:37:56.385620Z", - "iopub.status.idle": "2024-06-28T15:37:56.389671Z", - "shell.execute_reply": "2024-06-28T15:37:56.389205Z" + "iopub.execute_input": "2024-07-01T15:07:19.689886Z", + "iopub.status.busy": "2024-07-01T15:07:19.689435Z", + "iopub.status.idle": "2024-07-01T15:07:19.692913Z", + "shell.execute_reply": "2024-07-01T15:07:19.692502Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.391657Z", - "iopub.status.busy": "2024-06-28T15:37:56.391481Z", - "iopub.status.idle": "2024-06-28T15:37:56.395227Z", - "shell.execute_reply": "2024-06-28T15:37:56.394761Z" + "iopub.execute_input": "2024-07-01T15:07:19.694737Z", + "iopub.status.busy": "2024-07-01T15:07:19.694568Z", + "iopub.status.idle": "2024-07-01T15:07:19.698060Z", + "shell.execute_reply": "2024-07-01T15:07:19.697520Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.397415Z", - "iopub.status.busy": "2024-06-28T15:37:56.397086Z", - "iopub.status.idle": "2024-06-28T15:37:56.400385Z", - "shell.execute_reply": "2024-06-28T15:37:56.399921Z" + "iopub.execute_input": "2024-07-01T15:07:19.700087Z", + "iopub.status.busy": "2024-07-01T15:07:19.699691Z", + "iopub.status.idle": "2024-07-01T15:07:19.702785Z", + "shell.execute_reply": "2024-07-01T15:07:19.702302Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 19c4a295c..013f8cdd9 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:59.018700Z", - "iopub.status.busy": "2024-06-28T15:37:59.018517Z", - "iopub.status.idle": "2024-06-28T15:38:00.265105Z", - "shell.execute_reply": "2024-06-28T15:38:00.264576Z" + "iopub.execute_input": "2024-07-01T15:07:22.044165Z", + "iopub.status.busy": "2024-07-01T15:07:22.043990Z", + "iopub.status.idle": "2024-07-01T15:07:23.190619Z", + "shell.execute_reply": "2024-07-01T15:07:23.190110Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:00.267828Z", - "iopub.status.busy": "2024-06-28T15:38:00.267338Z", - "iopub.status.idle": "2024-06-28T15:38:01.587223Z", - "shell.execute_reply": "2024-06-28T15:38:01.586436Z" + "iopub.execute_input": "2024-07-01T15:07:23.192992Z", + "iopub.status.busy": "2024-07-01T15:07:23.192742Z", + "iopub.status.idle": "2024-07-01T15:07:24.642885Z", + "shell.execute_reply": "2024-07-01T15:07:24.642213Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.589871Z", - "iopub.status.busy": "2024-06-28T15:38:01.589663Z", - "iopub.status.idle": "2024-06-28T15:38:01.592876Z", - "shell.execute_reply": "2024-06-28T15:38:01.592418Z" + "iopub.execute_input": "2024-07-01T15:07:24.645453Z", + "iopub.status.busy": "2024-07-01T15:07:24.645208Z", + "iopub.status.idle": "2024-07-01T15:07:24.648320Z", + "shell.execute_reply": "2024-07-01T15:07:24.647888Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.594798Z", - "iopub.status.busy": "2024-06-28T15:38:01.594621Z", - "iopub.status.idle": "2024-06-28T15:38:01.600889Z", - "shell.execute_reply": "2024-06-28T15:38:01.600425Z" + "iopub.execute_input": "2024-07-01T15:07:24.650220Z", + "iopub.status.busy": "2024-07-01T15:07:24.650036Z", + "iopub.status.idle": "2024-07-01T15:07:24.656028Z", + "shell.execute_reply": "2024-07-01T15:07:24.655606Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.602887Z", - "iopub.status.busy": "2024-06-28T15:38:01.602700Z", - "iopub.status.idle": "2024-06-28T15:38:02.098288Z", - "shell.execute_reply": "2024-06-28T15:38:02.097701Z" + "iopub.execute_input": "2024-07-01T15:07:24.657894Z", + "iopub.status.busy": "2024-07-01T15:07:24.657721Z", + "iopub.status.idle": "2024-07-01T15:07:25.141231Z", + "shell.execute_reply": "2024-07-01T15:07:25.140651Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.101009Z", - "iopub.status.busy": "2024-06-28T15:38:02.100809Z", - "iopub.status.idle": "2024-06-28T15:38:02.106483Z", - "shell.execute_reply": "2024-06-28T15:38:02.106007Z" + "iopub.execute_input": "2024-07-01T15:07:25.144290Z", + "iopub.status.busy": "2024-07-01T15:07:25.143822Z", + "iopub.status.idle": "2024-07-01T15:07:25.149165Z", + "shell.execute_reply": "2024-07-01T15:07:25.148739Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.108382Z", - "iopub.status.busy": "2024-06-28T15:38:02.108204Z", - "iopub.status.idle": "2024-06-28T15:38:02.112634Z", - "shell.execute_reply": "2024-06-28T15:38:02.112164Z" + "iopub.execute_input": "2024-07-01T15:07:25.151192Z", + "iopub.status.busy": "2024-07-01T15:07:25.150870Z", + "iopub.status.idle": "2024-07-01T15:07:25.154548Z", + "shell.execute_reply": "2024-07-01T15:07:25.154108Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.114751Z", - "iopub.status.busy": "2024-06-28T15:38:02.114411Z", - "iopub.status.idle": "2024-06-28T15:38:03.057670Z", - "shell.execute_reply": "2024-06-28T15:38:03.056990Z" + "iopub.execute_input": "2024-07-01T15:07:25.156514Z", + "iopub.status.busy": "2024-07-01T15:07:25.156335Z", + "iopub.status.idle": "2024-07-01T15:07:26.038062Z", + "shell.execute_reply": "2024-07-01T15:07:26.037425Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.060460Z", - "iopub.status.busy": "2024-06-28T15:38:03.059841Z", - "iopub.status.idle": "2024-06-28T15:38:03.286800Z", - "shell.execute_reply": "2024-06-28T15:38:03.286263Z" + "iopub.execute_input": "2024-07-01T15:07:26.040299Z", + "iopub.status.busy": "2024-07-01T15:07:26.040059Z", + "iopub.status.idle": "2024-07-01T15:07:26.281733Z", + "shell.execute_reply": "2024-07-01T15:07:26.281238Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.289126Z", - "iopub.status.busy": "2024-06-28T15:38:03.288767Z", - "iopub.status.idle": "2024-06-28T15:38:03.293054Z", - "shell.execute_reply": "2024-06-28T15:38:03.292569Z" + "iopub.execute_input": "2024-07-01T15:07:26.284005Z", + "iopub.status.busy": "2024-07-01T15:07:26.283674Z", + "iopub.status.idle": "2024-07-01T15:07:26.287739Z", + "shell.execute_reply": "2024-07-01T15:07:26.287303Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.295148Z", - "iopub.status.busy": "2024-06-28T15:38:03.294835Z", - "iopub.status.idle": "2024-06-28T15:38:03.761009Z", - "shell.execute_reply": "2024-06-28T15:38:03.760370Z" + "iopub.execute_input": "2024-07-01T15:07:26.289717Z", + "iopub.status.busy": "2024-07-01T15:07:26.289415Z", + "iopub.status.idle": "2024-07-01T15:07:26.747330Z", + "shell.execute_reply": "2024-07-01T15:07:26.746844Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.764257Z", - "iopub.status.busy": "2024-06-28T15:38:03.763873Z", - "iopub.status.idle": "2024-06-28T15:38:04.100471Z", - "shell.execute_reply": "2024-06-28T15:38:04.099895Z" + "iopub.execute_input": "2024-07-01T15:07:26.749504Z", + "iopub.status.busy": "2024-07-01T15:07:26.749157Z", + "iopub.status.idle": "2024-07-01T15:07:27.049969Z", + "shell.execute_reply": "2024-07-01T15:07:27.049390Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.103987Z", - "iopub.status.busy": "2024-06-28T15:38:04.103475Z", - "iopub.status.idle": "2024-06-28T15:38:04.445171Z", - "shell.execute_reply": "2024-06-28T15:38:04.444553Z" + "iopub.execute_input": "2024-07-01T15:07:27.052016Z", + "iopub.status.busy": "2024-07-01T15:07:27.051834Z", + "iopub.status.idle": "2024-07-01T15:07:27.386953Z", + "shell.execute_reply": "2024-07-01T15:07:27.386354Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.448311Z", - "iopub.status.busy": "2024-06-28T15:38:04.447953Z", - "iopub.status.idle": "2024-06-28T15:38:04.862739Z", - "shell.execute_reply": "2024-06-28T15:38:04.862176Z" + "iopub.execute_input": "2024-07-01T15:07:27.390094Z", + "iopub.status.busy": "2024-07-01T15:07:27.389720Z", + "iopub.status.idle": "2024-07-01T15:07:27.826810Z", + "shell.execute_reply": "2024-07-01T15:07:27.826201Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.867087Z", - "iopub.status.busy": "2024-06-28T15:38:04.866704Z", - "iopub.status.idle": "2024-06-28T15:38:05.319876Z", - "shell.execute_reply": "2024-06-28T15:38:05.319267Z" + "iopub.execute_input": "2024-07-01T15:07:27.830888Z", + "iopub.status.busy": "2024-07-01T15:07:27.830547Z", + "iopub.status.idle": "2024-07-01T15:07:28.275927Z", + "shell.execute_reply": "2024-07-01T15:07:28.275306Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.322789Z", - "iopub.status.busy": "2024-06-28T15:38:05.322420Z", - "iopub.status.idle": "2024-06-28T15:38:05.539025Z", - "shell.execute_reply": "2024-06-28T15:38:05.538465Z" + "iopub.execute_input": "2024-07-01T15:07:28.278580Z", + "iopub.status.busy": "2024-07-01T15:07:28.278386Z", + "iopub.status.idle": "2024-07-01T15:07:28.478171Z", + "shell.execute_reply": "2024-07-01T15:07:28.477537Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.541213Z", - "iopub.status.busy": "2024-06-28T15:38:05.541026Z", - "iopub.status.idle": "2024-06-28T15:38:05.743328Z", - "shell.execute_reply": "2024-06-28T15:38:05.742791Z" + "iopub.execute_input": "2024-07-01T15:07:28.481029Z", + "iopub.status.busy": "2024-07-01T15:07:28.480514Z", + "iopub.status.idle": "2024-07-01T15:07:28.679630Z", + "shell.execute_reply": "2024-07-01T15:07:28.679032Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.745675Z", - "iopub.status.busy": "2024-06-28T15:38:05.745487Z", - "iopub.status.idle": "2024-06-28T15:38:05.748456Z", - "shell.execute_reply": "2024-06-28T15:38:05.748001Z" + "iopub.execute_input": "2024-07-01T15:07:28.681815Z", + "iopub.status.busy": "2024-07-01T15:07:28.681633Z", + "iopub.status.idle": "2024-07-01T15:07:28.684760Z", + "shell.execute_reply": "2024-07-01T15:07:28.684215Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.750297Z", - "iopub.status.busy": "2024-06-28T15:38:05.750127Z", - "iopub.status.idle": "2024-06-28T15:38:06.726754Z", - "shell.execute_reply": "2024-06-28T15:38:06.726181Z" + "iopub.execute_input": "2024-07-01T15:07:28.686736Z", + "iopub.status.busy": "2024-07-01T15:07:28.686404Z", + "iopub.status.idle": "2024-07-01T15:07:29.599883Z", + "shell.execute_reply": "2024-07-01T15:07:29.599378Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:06.729839Z", - "iopub.status.busy": "2024-06-28T15:38:06.729460Z", - "iopub.status.idle": "2024-06-28T15:38:06.872733Z", - "shell.execute_reply": "2024-06-28T15:38:06.872148Z" + "iopub.execute_input": "2024-07-01T15:07:29.602491Z", + "iopub.status.busy": "2024-07-01T15:07:29.602156Z", + "iopub.status.idle": "2024-07-01T15:07:29.724845Z", + "shell.execute_reply": "2024-07-01T15:07:29.724400Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:06.875073Z", - "iopub.status.busy": "2024-06-28T15:38:06.874723Z", - "iopub.status.idle": "2024-06-28T15:38:07.017945Z", - "shell.execute_reply": "2024-06-28T15:38:07.017419Z" + "iopub.execute_input": "2024-07-01T15:07:29.727049Z", + "iopub.status.busy": "2024-07-01T15:07:29.726723Z", + "iopub.status.idle": "2024-07-01T15:07:29.857120Z", + "shell.execute_reply": "2024-07-01T15:07:29.856610Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:07.020497Z", - "iopub.status.busy": "2024-06-28T15:38:07.020139Z", - "iopub.status.idle": "2024-06-28T15:38:07.772705Z", - "shell.execute_reply": "2024-06-28T15:38:07.772059Z" + "iopub.execute_input": "2024-07-01T15:07:29.859645Z", + "iopub.status.busy": "2024-07-01T15:07:29.859295Z", + "iopub.status.idle": "2024-07-01T15:07:30.599850Z", + "shell.execute_reply": "2024-07-01T15:07:30.599307Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:07.775031Z", - "iopub.status.busy": "2024-06-28T15:38:07.774718Z", - "iopub.status.idle": "2024-06-28T15:38:07.778431Z", - "shell.execute_reply": "2024-06-28T15:38:07.777967Z" + "iopub.execute_input": "2024-07-01T15:07:30.602022Z", + "iopub.status.busy": "2024-07-01T15:07:30.601697Z", + "iopub.status.idle": "2024-07-01T15:07:30.605345Z", + "shell.execute_reply": "2024-07-01T15:07:30.604899Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 544303c3f..5a70f9aea 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -780,7 +780,7 @@

    2. Pre-process the Cifar10 dataset 
    -100%|██████████| 170498071/170498071 [00:01<00:00, 95344245.43it/s]
+100%|██████████| 170498071/170498071 [00:04<00:00, 39742139.91it/s]

    @@ -910,7 +910,7 @@

    3. Use cleanlab and feature embeddings to find outliers in the data

    -
    +
    @@ -1124,7 +1124,7 @@

    4. Use cleanlab and here.

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index 6169cfa22..de1ca9206 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:10.079179Z", - "iopub.status.busy": "2024-06-28T15:38:10.078994Z", - "iopub.status.idle": "2024-06-28T15:38:12.976778Z", - "shell.execute_reply": "2024-06-28T15:38:12.976174Z" + "iopub.execute_input": "2024-07-01T15:07:32.630513Z", + "iopub.status.busy": "2024-07-01T15:07:32.630022Z", + "iopub.status.idle": "2024-07-01T15:07:35.339624Z", + "shell.execute_reply": "2024-07-01T15:07:35.338990Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:12.979625Z", - "iopub.status.busy": "2024-06-28T15:38:12.979083Z", - "iopub.status.idle": "2024-06-28T15:38:13.326543Z", - "shell.execute_reply": "2024-06-28T15:38:13.326026Z" + "iopub.execute_input": "2024-07-01T15:07:35.342314Z", + "iopub.status.busy": "2024-07-01T15:07:35.341942Z", + "iopub.status.idle": "2024-07-01T15:07:35.677074Z", + "shell.execute_reply": "2024-07-01T15:07:35.676543Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:13.329227Z", - "iopub.status.busy": "2024-06-28T15:38:13.328804Z", - "iopub.status.idle": "2024-06-28T15:38:13.332930Z", - "shell.execute_reply": "2024-06-28T15:38:13.332475Z" + "iopub.execute_input": "2024-07-01T15:07:35.679709Z", + "iopub.status.busy": "2024-07-01T15:07:35.679377Z", + "iopub.status.idle": "2024-07-01T15:07:35.683566Z", + "shell.execute_reply": "2024-07-01T15:07:35.683132Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:13.335012Z", - "iopub.status.busy": "2024-06-28T15:38:13.334691Z", - "iopub.status.idle": "2024-06-28T15:38:17.730900Z", - "shell.execute_reply": "2024-06-28T15:38:17.730361Z" + "iopub.execute_input": "2024-07-01T15:07:35.685686Z", + "iopub.status.busy": "2024-07-01T15:07:35.685365Z", + "iopub.status.idle": "2024-07-01T15:07:42.517988Z", + "shell.execute_reply": "2024-07-01T15:07:42.517428Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 851968/170498071 [00:00<00:20, 8336065.33it/s]" + " 0%| | 786432/170498071 [00:00<00:21, 7820176.68it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10977280/170498071 [00:00<00:02, 62438830.70it/s]" + " 3%|▎ | 4980736/170498071 [00:00<00:05, 27792797.45it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 20873216/170498071 [00:00<00:01, 79009100.57it/s]" + " 6%|▋ | 10944512/170498071 [00:00<00:03, 42298222.53it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 30736384/170498071 [00:00<00:01, 86709726.78it/s]" + " 10%|▉ | 16449536/170498071 [00:00<00:03, 47151386.97it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 40763392/170498071 [00:00<00:01, 91578171.64it/s]" + " 12%|█▏ | 21168128/170498071 [00:00<00:03, 41939697.62it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50528256/170498071 [00:00<00:01, 93630346.09it/s]" + " 15%|█▍ | 25460736/170498071 [00:00<00:04, 36250418.01it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 60653568/170498071 [00:00<00:01, 96035464.51it/s]" + " 17%|█▋ | 29261824/170498071 [00:00<00:04, 31518178.43it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70418432/170498071 [00:00<00:01, 96491443.11it/s]" + " 19%|█▉ | 32604160/170498071 [00:00<00:04, 29639430.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 80543744/170498071 [00:00<00:00, 97924676.51it/s]" + " 21%|██ | 35684352/170498071 [00:01<00:04, 28240309.80it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 90341376/170498071 [00:01<00:00, 97866529.70it/s]" + " 23%|██▎ | 38600704/170498071 [00:01<00:04, 27686377.59it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100368384/170498071 [00:01<00:00, 98505652.49it/s]" + " 24%|██▍ | 41451520/170498071 [00:01<00:04, 27880418.29it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 110362624/170498071 [00:01<00:00, 98885459.16it/s]" + " 26%|██▌ | 44302336/170498071 [00:01<00:04, 27339381.04it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 120258560/170498071 [00:01<00:00, 98548885.72it/s]" + " 28%|██▊ | 47153152/170498071 [00:01<00:04, 27588406.47it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 130187264/170498071 [00:01<00:00, 98667276.16it/s]" + " 29%|██▉ | 49938432/170498071 [00:01<00:04, 27519027.46it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141361152/170498071 [00:01<00:00, 102538275.84it/s]" + " 31%|███ | 52723712/170498071 [00:01<00:04, 26308629.52it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 152797184/170498071 [00:01<00:00, 105995144.12it/s]" + " 32%|███▏ | 55377920/170498071 [00:01<00:04, 25864377.41it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 163414016/170498071 [00:01<00:00, 104278726.49it/s]" + " 35%|███▍ | 58884096/170498071 [00:01<00:03, 28360977.09it/s]" ] }, { @@ -388,7 +388,183 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 95344245.43it/s] " + " 37%|███▋ | 62717952/170498071 [00:02<00:03, 31182568.80it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 39%|███▉ | 66846720/170498071 [00:02<00:03, 34093308.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 42%|████▏ | 71303168/170498071 [00:02<00:02, 37046963.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 45%|████▍ | 76513280/170498071 [00:02<00:02, 41418183.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 47%|████▋ | 80871424/170498071 [00:02<00:02, 42002402.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 50%|█████ | 85721088/170498071 [00:02<00:01, 43883828.77it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 53%|█████▎ | 90144768/170498071 [00:02<00:01, 43748354.63it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|█████▌ | 94633984/170498071 [00:02<00:01, 43921848.70it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 58%|█████▊ | 99057664/170498071 [00:02<00:01, 43385313.39it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 61%|██████ | 104136704/170498071 [00:02<00:01, 45560088.81it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 64%|██████▍ | 108724224/170498071 [00:03<00:01, 43798020.18it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 113573888/170498071 [00:03<00:01, 45129830.46it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 70%|██████▉ | 118554624/170498071 [00:03<00:01, 46326838.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 72%|███████▏ | 123207680/170498071 [00:03<00:01, 46221454.72it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 75%|███████▍ | 127860736/170498071 [00:03<00:00, 45331471.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 78%|███████▊ | 132775936/170498071 [00:03<00:00, 46431225.49it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 81%|████████ | 137756672/170498071 [00:03<00:00, 47136533.89it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|████████▍ | 142966784/170498071 [00:03<00:00, 48566305.03it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 147849216/170498071 [00:03<00:00, 47837503.41it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|█████████ | 153649152/170498071 [00:03<00:00, 50699005.24it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▍| 160006144/170498071 [00:04<00:00, 54457105.34it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 166297600/170498071 [00:04<00:00, 56798743.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:04<00:00, 39742139.91it/s]" ] }, { @@ -506,10 +682,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:17.733413Z", - "iopub.status.busy": "2024-06-28T15:38:17.732871Z", - "iopub.status.idle": "2024-06-28T15:38:17.738007Z", - "shell.execute_reply": "2024-06-28T15:38:17.737546Z" + "iopub.execute_input": "2024-07-01T15:07:42.520241Z", + "iopub.status.busy": "2024-07-01T15:07:42.519920Z", + "iopub.status.idle": "2024-07-01T15:07:42.524659Z", + "shell.execute_reply": "2024-07-01T15:07:42.524205Z" }, "nbsphinx": "hidden" }, @@ -560,10 +736,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:17.740340Z", - "iopub.status.busy": "2024-06-28T15:38:17.739963Z", - "iopub.status.idle": "2024-06-28T15:38:18.297381Z", - "shell.execute_reply": "2024-06-28T15:38:18.296749Z" + "iopub.execute_input": "2024-07-01T15:07:42.526805Z", + "iopub.status.busy": "2024-07-01T15:07:42.526366Z", + "iopub.status.idle": "2024-07-01T15:07:43.068980Z", + "shell.execute_reply": "2024-07-01T15:07:43.068376Z" } }, "outputs": [ @@ -596,10 +772,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.299866Z", - "iopub.status.busy": "2024-06-28T15:38:18.299513Z", - "iopub.status.idle": "2024-06-28T15:38:18.793592Z", - "shell.execute_reply": "2024-06-28T15:38:18.793002Z" + "iopub.execute_input": "2024-07-01T15:07:43.071278Z", + "iopub.status.busy": "2024-07-01T15:07:43.070855Z", + "iopub.status.idle": "2024-07-01T15:07:43.585255Z", + "shell.execute_reply": "2024-07-01T15:07:43.584665Z" } }, "outputs": [ @@ -637,10 +813,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.795690Z", - "iopub.status.busy": "2024-06-28T15:38:18.795502Z", - "iopub.status.idle": "2024-06-28T15:38:18.799031Z", - "shell.execute_reply": "2024-06-28T15:38:18.798587Z" + "iopub.execute_input": "2024-07-01T15:07:43.587754Z", + "iopub.status.busy": "2024-07-01T15:07:43.587325Z", + "iopub.status.idle": "2024-07-01T15:07:43.591527Z", + "shell.execute_reply": "2024-07-01T15:07:43.591015Z" } }, "outputs": [], @@ -663,17 +839,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.801250Z", - "iopub.status.busy": "2024-06-28T15:38:18.800914Z", - "iopub.status.idle": "2024-06-28T15:38:31.916783Z", - "shell.execute_reply": "2024-06-28T15:38:31.916167Z" + "iopub.execute_input": "2024-07-01T15:07:43.593739Z", + "iopub.status.busy": "2024-07-01T15:07:43.593377Z", + "iopub.status.idle": "2024-07-01T15:07:56.035606Z", + "shell.execute_reply": "2024-07-01T15:07:56.035038Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "098cb2eeb14f4be9ad0d532925ef1c3a", + "model_id": "6e0af51d1d7c41f6b28e94e107a2e2dd", "version_major": 2, "version_minor": 0 }, @@ -732,10 +908,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:31.919440Z", - "iopub.status.busy": "2024-06-28T15:38:31.918961Z", - "iopub.status.idle": "2024-06-28T15:38:34.027699Z", - "shell.execute_reply": "2024-06-28T15:38:34.027090Z" + "iopub.execute_input": "2024-07-01T15:07:56.037942Z", + "iopub.status.busy": "2024-07-01T15:07:56.037579Z", + "iopub.status.idle": "2024-07-01T15:07:58.157290Z", + "shell.execute_reply": "2024-07-01T15:07:58.156719Z" } }, "outputs": [ @@ -779,10 +955,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.030317Z", - "iopub.status.busy": "2024-06-28T15:38:34.029979Z", - "iopub.status.idle": "2024-06-28T15:38:34.271466Z", - "shell.execute_reply": "2024-06-28T15:38:34.270826Z" + "iopub.execute_input": "2024-07-01T15:07:58.160021Z", + "iopub.status.busy": "2024-07-01T15:07:58.159723Z", + "iopub.status.idle": "2024-07-01T15:07:58.413397Z", + "shell.execute_reply": "2024-07-01T15:07:58.412322Z" } }, "outputs": [ @@ -818,10 +994,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.273998Z", - "iopub.status.busy": "2024-06-28T15:38:34.273648Z", - "iopub.status.idle": "2024-06-28T15:38:34.933909Z", - "shell.execute_reply": "2024-06-28T15:38:34.933303Z" + "iopub.execute_input": "2024-07-01T15:07:58.415968Z", + "iopub.status.busy": "2024-07-01T15:07:58.415746Z", + "iopub.status.idle": "2024-07-01T15:07:59.087448Z", + "shell.execute_reply": "2024-07-01T15:07:59.086838Z" } }, "outputs": [ @@ -871,10 +1047,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.936344Z", - "iopub.status.busy": "2024-06-28T15:38:34.936010Z", - "iopub.status.idle": "2024-06-28T15:38:35.228743Z", - "shell.execute_reply": "2024-06-28T15:38:35.228088Z" + "iopub.execute_input": "2024-07-01T15:07:59.090441Z", + "iopub.status.busy": "2024-07-01T15:07:59.090110Z", + "iopub.status.idle": "2024-07-01T15:07:59.428255Z", + "shell.execute_reply": "2024-07-01T15:07:59.427685Z" } }, "outputs": [ @@ -922,10 +1098,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.231241Z", - "iopub.status.busy": "2024-06-28T15:38:35.230869Z", - "iopub.status.idle": "2024-06-28T15:38:35.477957Z", - "shell.execute_reply": "2024-06-28T15:38:35.477300Z" + "iopub.execute_input": "2024-07-01T15:07:59.430501Z", + "iopub.status.busy": "2024-07-01T15:07:59.430156Z", + "iopub.status.idle": "2024-07-01T15:07:59.674251Z", + "shell.execute_reply": "2024-07-01T15:07:59.673618Z" } }, "outputs": [ @@ -981,10 +1157,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.480716Z", - "iopub.status.busy": "2024-06-28T15:38:35.480496Z", - "iopub.status.idle": "2024-06-28T15:38:35.566978Z", - "shell.execute_reply": "2024-06-28T15:38:35.566454Z" + "iopub.execute_input": "2024-07-01T15:07:59.676994Z", + "iopub.status.busy": "2024-07-01T15:07:59.676767Z", + "iopub.status.idle": "2024-07-01T15:07:59.764794Z", + "shell.execute_reply": "2024-07-01T15:07:59.764299Z" } }, "outputs": [], @@ -1005,10 +1181,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.569419Z", - "iopub.status.busy": "2024-06-28T15:38:35.569233Z", - "iopub.status.idle": "2024-06-28T15:38:46.264047Z", - "shell.execute_reply": "2024-06-28T15:38:46.263378Z" + "iopub.execute_input": "2024-07-01T15:07:59.767244Z", + "iopub.status.busy": "2024-07-01T15:07:59.766860Z", + "iopub.status.idle": "2024-07-01T15:08:10.069096Z", + "shell.execute_reply": "2024-07-01T15:08:10.068139Z" } }, "outputs": [ @@ -1045,10 +1221,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:46.266718Z", - "iopub.status.busy": "2024-06-28T15:38:46.266270Z", - "iopub.status.idle": "2024-06-28T15:38:48.609210Z", - "shell.execute_reply": "2024-06-28T15:38:48.608559Z" + "iopub.execute_input": "2024-07-01T15:08:10.071585Z", + "iopub.status.busy": "2024-07-01T15:08:10.071346Z", + "iopub.status.idle": "2024-07-01T15:08:12.388556Z", + "shell.execute_reply": "2024-07-01T15:08:12.388050Z" } }, "outputs": [ @@ -1079,10 +1255,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.612144Z", - "iopub.status.busy": "2024-06-28T15:38:48.611517Z", - "iopub.status.idle": "2024-06-28T15:38:48.819480Z", - "shell.execute_reply": "2024-06-28T15:38:48.818936Z" + "iopub.execute_input": "2024-07-01T15:08:12.391205Z", + "iopub.status.busy": "2024-07-01T15:08:12.390795Z", + "iopub.status.idle": "2024-07-01T15:08:12.593036Z", + "shell.execute_reply": "2024-07-01T15:08:12.592540Z" } }, "outputs": [], @@ -1096,10 +1272,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.822006Z", - "iopub.status.busy": "2024-06-28T15:38:48.821667Z", - "iopub.status.idle": "2024-06-28T15:38:48.824895Z", - "shell.execute_reply": "2024-06-28T15:38:48.824452Z" + "iopub.execute_input": "2024-07-01T15:08:12.595509Z", + "iopub.status.busy": "2024-07-01T15:08:12.595162Z", + "iopub.status.idle": "2024-07-01T15:08:12.598275Z", + "shell.execute_reply": "2024-07-01T15:08:12.597815Z" } }, "outputs": [], @@ -1121,10 +1297,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.826934Z", - "iopub.status.busy": "2024-06-28T15:38:48.826600Z", - "iopub.status.idle": "2024-06-28T15:38:48.834980Z", - "shell.execute_reply": "2024-06-28T15:38:48.834539Z" + "iopub.execute_input": "2024-07-01T15:08:12.600173Z", + "iopub.status.busy": "2024-07-01T15:08:12.599856Z", + "iopub.status.idle": "2024-07-01T15:08:12.608170Z", + "shell.execute_reply": "2024-07-01T15:08:12.607771Z" }, "nbsphinx": "hidden" }, @@ -1169,31 +1345,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "098cb2eeb14f4be9ad0d532925ef1c3a": { + "2727be2db0d2497f9fb52dc8697095a2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ceaa8379ba32458a89a926eadac8b9f7", - "IPY_MODEL_b7832f8930e04ecb82387de27ccda85c", - "IPY_MODEL_34e4202f5faf45f4bbdd37ca23b87fa2" - ], - "layout": "IPY_MODEL_3c8bf8f7c6de41b08aca8334534c44d5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c995655b1f814ffb9d408cda2ffbc566", + "placeholder": "​", + "style": "IPY_MODEL_f100e8e508354e3a998f09e41481fe4a", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 311MB/s]" } }, - "27730e8449934c789cd27668388c81a3": { + "56a99359b74d41799a5e62b4bf03f499": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1209,7 +1384,31 @@ "description_width": "" } }, - "2d2e5be95459464d9398638bc99b5ac9": { + "6e0af51d1d7c41f6b28e94e107a2e2dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bbb9fac4ce02410c9a7cf006ad612d8a", + "IPY_MODEL_beaa49c5ac66403896b3c555d2a06c91", + "IPY_MODEL_2727be2db0d2497f9fb52dc8697095a2" + ], + "layout": "IPY_MODEL_b4c1215d79fe4c049a90606906d2692c", + "tabbable": null, + "tooltip": null + } + }, + "847a911faef949b68edb66c688cd3c4b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,30 +1461,7 @@ "width": null } }, - "34e4202f5faf45f4bbdd37ca23b87fa2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5b4e0dce4be640c58c64a1d670de2e81", - "placeholder": "​", - "style": "IPY_MODEL_7a728423519d43daae91c1085fdb4228", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 143MB/s]" - } - }, - "3c8bf8f7c6de41b08aca8334534c44d5": { + "b4c1215d79fe4c049a90606906d2692c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1338,7 +1514,56 @@ "width": null } }, - "5b4e0dce4be640c58c64a1d670de2e81": { + "bbb9fac4ce02410c9a7cf006ad612d8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_847a911faef949b68edb66c688cd3c4b", + "placeholder": "​", + "style": "IPY_MODEL_e010df9fbdef472aa0c2e3f8a393bb55", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "beaa49c5ac66403896b3c555d2a06c91": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ce770c3a1d5d42a98a4d28d04bc1c7d7", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_56a99359b74d41799a5e62b4bf03f499", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "c995655b1f814ffb9d408cda2ffbc566": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1391,51 +1616,7 @@ "width": null } }, - "7a728423519d43daae91c1085fdb4228": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b7832f8930e04ecb82387de27ccda85c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2d2e5be95459464d9398638bc99b5ac9", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_27730e8449934c789cd27668388c81a3", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "bf29183e5e5f4369a7a8bb198f9346b3": { + "ce770c3a1d5d42a98a4d28d04bc1c7d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1488,30 +1669,25 @@ "width": null } }, - "ceaa8379ba32458a89a926eadac8b9f7": { + "e010df9fbdef472aa0c2e3f8a393bb55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bf29183e5e5f4369a7a8bb198f9346b3", - "placeholder": "​", - "style": "IPY_MODEL_f5a57583e54c48e280d535fd0d90277f", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f5a57583e54c48e280d535fd0d90277f": { + "f100e8e508354e3a998f09e41481fe4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 8d011fee3..46926446f 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:52.976102Z", - "iopub.status.busy": "2024-06-28T15:38:52.975670Z", - "iopub.status.idle": "2024-06-28T15:38:54.204177Z", - "shell.execute_reply": "2024-06-28T15:38:54.203645Z" + "iopub.execute_input": "2024-07-01T15:08:16.815662Z", + "iopub.status.busy": "2024-07-01T15:08:16.815212Z", + "iopub.status.idle": "2024-07-01T15:08:18.061264Z", + "shell.execute_reply": "2024-07-01T15:08:18.060688Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.207000Z", - "iopub.status.busy": "2024-06-28T15:38:54.206429Z", - "iopub.status.idle": "2024-06-28T15:38:54.225068Z", - "shell.execute_reply": "2024-06-28T15:38:54.224435Z" + "iopub.execute_input": "2024-07-01T15:08:18.064012Z", + "iopub.status.busy": "2024-07-01T15:08:18.063557Z", + "iopub.status.idle": "2024-07-01T15:08:18.081205Z", + "shell.execute_reply": "2024-07-01T15:08:18.080744Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.227793Z", - "iopub.status.busy": "2024-06-28T15:38:54.227339Z", - "iopub.status.idle": "2024-06-28T15:38:54.230442Z", - "shell.execute_reply": "2024-06-28T15:38:54.230003Z" + "iopub.execute_input": "2024-07-01T15:08:18.083492Z", + "iopub.status.busy": "2024-07-01T15:08:18.083103Z", + "iopub.status.idle": "2024-07-01T15:08:18.086376Z", + "shell.execute_reply": "2024-07-01T15:08:18.085828Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.232620Z", - "iopub.status.busy": "2024-06-28T15:38:54.232254Z", - "iopub.status.idle": "2024-06-28T15:38:54.268547Z", - "shell.execute_reply": "2024-06-28T15:38:54.268041Z" + "iopub.execute_input": "2024-07-01T15:08:18.088481Z", + "iopub.status.busy": "2024-07-01T15:08:18.088156Z", + "iopub.status.idle": "2024-07-01T15:08:18.174903Z", + "shell.execute_reply": "2024-07-01T15:08:18.174413Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.270959Z", - "iopub.status.busy": "2024-06-28T15:38:54.270511Z", - "iopub.status.idle": "2024-06-28T15:38:54.454589Z", - "shell.execute_reply": "2024-06-28T15:38:54.453953Z" + "iopub.execute_input": "2024-07-01T15:08:18.177216Z", + "iopub.status.busy": "2024-07-01T15:08:18.176851Z", + "iopub.status.idle": "2024-07-01T15:08:18.363421Z", + "shell.execute_reply": "2024-07-01T15:08:18.362759Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.457298Z", - "iopub.status.busy": "2024-06-28T15:38:54.456939Z", - "iopub.status.idle": "2024-06-28T15:38:54.703303Z", - "shell.execute_reply": "2024-06-28T15:38:54.702725Z" + "iopub.execute_input": "2024-07-01T15:08:18.366181Z", + "iopub.status.busy": "2024-07-01T15:08:18.365737Z", + "iopub.status.idle": "2024-07-01T15:08:18.613007Z", + "shell.execute_reply": "2024-07-01T15:08:18.612399Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.705458Z", - "iopub.status.busy": "2024-06-28T15:38:54.705190Z", - "iopub.status.idle": "2024-06-28T15:38:54.709668Z", - "shell.execute_reply": "2024-06-28T15:38:54.709113Z" + "iopub.execute_input": "2024-07-01T15:08:18.615413Z", + "iopub.status.busy": "2024-07-01T15:08:18.615032Z", + "iopub.status.idle": "2024-07-01T15:08:18.619703Z", + "shell.execute_reply": "2024-07-01T15:08:18.619083Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.711751Z", - "iopub.status.busy": "2024-06-28T15:38:54.711415Z", - "iopub.status.idle": "2024-06-28T15:38:54.717104Z", - "shell.execute_reply": "2024-06-28T15:38:54.716684Z" + "iopub.execute_input": "2024-07-01T15:08:18.621922Z", + "iopub.status.busy": "2024-07-01T15:08:18.621693Z", + "iopub.status.idle": "2024-07-01T15:08:18.629203Z", + "shell.execute_reply": "2024-07-01T15:08:18.628679Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.719115Z", - "iopub.status.busy": "2024-06-28T15:38:54.718793Z", - "iopub.status.idle": "2024-06-28T15:38:54.721262Z", - "shell.execute_reply": "2024-06-28T15:38:54.720825Z" + "iopub.execute_input": "2024-07-01T15:08:18.631920Z", + "iopub.status.busy": "2024-07-01T15:08:18.631513Z", + "iopub.status.idle": "2024-07-01T15:08:18.634527Z", + "shell.execute_reply": "2024-07-01T15:08:18.633964Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.723261Z", - "iopub.status.busy": "2024-06-28T15:38:54.722946Z", - "iopub.status.idle": "2024-06-28T15:39:03.562442Z", - "shell.execute_reply": "2024-06-28T15:39:03.561823Z" + "iopub.execute_input": "2024-07-01T15:08:18.636916Z", + "iopub.status.busy": "2024-07-01T15:08:18.636464Z", + "iopub.status.idle": "2024-07-01T15:08:27.681165Z", + "shell.execute_reply": "2024-07-01T15:08:27.680590Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.565404Z", - "iopub.status.busy": "2024-06-28T15:39:03.564765Z", - "iopub.status.idle": "2024-06-28T15:39:03.572338Z", - "shell.execute_reply": "2024-06-28T15:39:03.571880Z" + "iopub.execute_input": "2024-07-01T15:08:27.683915Z", + "iopub.status.busy": "2024-07-01T15:08:27.683447Z", + "iopub.status.idle": "2024-07-01T15:08:27.691061Z", + "shell.execute_reply": "2024-07-01T15:08:27.690544Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.574497Z", - "iopub.status.busy": "2024-06-28T15:39:03.574149Z", - "iopub.status.idle": "2024-06-28T15:39:03.577840Z", - "shell.execute_reply": "2024-06-28T15:39:03.577381Z" + "iopub.execute_input": "2024-07-01T15:08:27.693260Z", + "iopub.status.busy": "2024-07-01T15:08:27.692915Z", + "iopub.status.idle": "2024-07-01T15:08:27.696508Z", + "shell.execute_reply": "2024-07-01T15:08:27.696074Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.579710Z", - "iopub.status.busy": "2024-06-28T15:39:03.579448Z", - "iopub.status.idle": "2024-06-28T15:39:03.582873Z", - "shell.execute_reply": "2024-06-28T15:39:03.582423Z" + "iopub.execute_input": "2024-07-01T15:08:27.698591Z", + "iopub.status.busy": "2024-07-01T15:08:27.698265Z", + "iopub.status.idle": "2024-07-01T15:08:27.701394Z", + "shell.execute_reply": "2024-07-01T15:08:27.700844Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.584887Z", - "iopub.status.busy": "2024-06-28T15:39:03.584561Z", - "iopub.status.idle": "2024-06-28T15:39:03.587419Z", - "shell.execute_reply": "2024-06-28T15:39:03.586979Z" + "iopub.execute_input": "2024-07-01T15:08:27.703468Z", + "iopub.status.busy": "2024-07-01T15:08:27.703137Z", + "iopub.status.idle": "2024-07-01T15:08:27.706217Z", + "shell.execute_reply": "2024-07-01T15:08:27.705750Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.589440Z", - "iopub.status.busy": "2024-06-28T15:39:03.589127Z", - "iopub.status.idle": "2024-06-28T15:39:03.597072Z", - "shell.execute_reply": "2024-06-28T15:39:03.596559Z" + "iopub.execute_input": "2024-07-01T15:08:27.708232Z", + "iopub.status.busy": "2024-07-01T15:08:27.707899Z", + "iopub.status.idle": "2024-07-01T15:08:27.715999Z", + "shell.execute_reply": "2024-07-01T15:08:27.715525Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.599177Z", - "iopub.status.busy": "2024-06-28T15:39:03.598840Z", - "iopub.status.idle": "2024-06-28T15:39:03.601388Z", - "shell.execute_reply": "2024-06-28T15:39:03.600963Z" + "iopub.execute_input": "2024-07-01T15:08:27.718018Z", + "iopub.status.busy": "2024-07-01T15:08:27.717680Z", + "iopub.status.idle": "2024-07-01T15:08:27.720242Z", + "shell.execute_reply": "2024-07-01T15:08:27.719798Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.603502Z", - "iopub.status.busy": "2024-06-28T15:39:03.603179Z", - "iopub.status.idle": "2024-06-28T15:39:03.723060Z", - "shell.execute_reply": "2024-06-28T15:39:03.722461Z" + "iopub.execute_input": "2024-07-01T15:08:27.722277Z", + "iopub.status.busy": "2024-07-01T15:08:27.721939Z", + "iopub.status.idle": "2024-07-01T15:08:27.850295Z", + "shell.execute_reply": "2024-07-01T15:08:27.849694Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.725504Z", - "iopub.status.busy": "2024-06-28T15:39:03.725028Z", - "iopub.status.idle": "2024-06-28T15:39:03.827464Z", - "shell.execute_reply": "2024-06-28T15:39:03.826856Z" + "iopub.execute_input": "2024-07-01T15:08:27.852484Z", + "iopub.status.busy": "2024-07-01T15:08:27.852300Z", + "iopub.status.idle": "2024-07-01T15:08:27.955847Z", + "shell.execute_reply": "2024-07-01T15:08:27.955257Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.829919Z", - "iopub.status.busy": "2024-06-28T15:39:03.829592Z", - "iopub.status.idle": "2024-06-28T15:39:04.313676Z", - "shell.execute_reply": "2024-06-28T15:39:04.313059Z" + "iopub.execute_input": "2024-07-01T15:08:27.958252Z", + "iopub.status.busy": "2024-07-01T15:08:27.957880Z", + "iopub.status.idle": "2024-07-01T15:08:28.451750Z", + "shell.execute_reply": "2024-07-01T15:08:28.451203Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.316205Z", - "iopub.status.busy": "2024-06-28T15:39:04.316016Z", - "iopub.status.idle": "2024-06-28T15:39:04.389000Z", - "shell.execute_reply": "2024-06-28T15:39:04.388436Z" + "iopub.execute_input": "2024-07-01T15:08:28.454335Z", + "iopub.status.busy": "2024-07-01T15:08:28.454151Z", + "iopub.status.idle": "2024-07-01T15:08:28.527356Z", + "shell.execute_reply": "2024-07-01T15:08:28.526736Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.391365Z", - "iopub.status.busy": "2024-06-28T15:39:04.390985Z", - "iopub.status.idle": "2024-06-28T15:39:04.399547Z", - "shell.execute_reply": "2024-06-28T15:39:04.399087Z" + "iopub.execute_input": "2024-07-01T15:08:28.529697Z", + "iopub.status.busy": "2024-07-01T15:08:28.529341Z", + "iopub.status.idle": "2024-07-01T15:08:28.538428Z", + "shell.execute_reply": "2024-07-01T15:08:28.537958Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.401759Z", - "iopub.status.busy": "2024-06-28T15:39:04.401238Z", - "iopub.status.idle": "2024-06-28T15:39:04.404123Z", - "shell.execute_reply": "2024-06-28T15:39:04.403592Z" + "iopub.execute_input": "2024-07-01T15:08:28.540454Z", + "iopub.status.busy": "2024-07-01T15:08:28.540269Z", + "iopub.status.idle": "2024-07-01T15:08:28.542883Z", + "shell.execute_reply": "2024-07-01T15:08:28.542447Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.406104Z", - "iopub.status.busy": "2024-06-28T15:39:04.405929Z", - "iopub.status.idle": "2024-06-28T15:39:10.119031Z", - "shell.execute_reply": "2024-06-28T15:39:10.118402Z" + "iopub.execute_input": "2024-07-01T15:08:28.544877Z", + "iopub.status.busy": "2024-07-01T15:08:28.544701Z", + "iopub.status.idle": "2024-07-01T15:08:33.972038Z", + "shell.execute_reply": "2024-07-01T15:08:33.971430Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:10.121458Z", - "iopub.status.busy": "2024-06-28T15:39:10.121040Z", - "iopub.status.idle": "2024-06-28T15:39:10.130156Z", - "shell.execute_reply": "2024-06-28T15:39:10.129695Z" + "iopub.execute_input": "2024-07-01T15:08:33.974153Z", + "iopub.status.busy": "2024-07-01T15:08:33.973956Z", + "iopub.status.idle": "2024-07-01T15:08:33.982773Z", + "shell.execute_reply": "2024-07-01T15:08:33.982320Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:10.132289Z", - "iopub.status.busy": "2024-06-28T15:39:10.131950Z", - "iopub.status.idle": "2024-06-28T15:39:10.201214Z", - "shell.execute_reply": "2024-06-28T15:39:10.200720Z" + "iopub.execute_input": "2024-07-01T15:08:33.984722Z", + "iopub.status.busy": "2024-07-01T15:08:33.984546Z", + "iopub.status.idle": "2024-07-01T15:08:34.049986Z", + "shell.execute_reply": "2024-07-01T15:08:34.049478Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index 9540f56cf..27c621cb8 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -800,13 +800,13 @@

    3. Use cleanlab to find label issues

    -
    +
    -
    +
    @@ -891,7 +891,7 @@

    Classes which are commonly mislabeled overall

    -
    +
    @@ -1140,7 +1140,7 @@

    Get label quality scores

    -
    +

    Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

    @@ -1196,7 +1196,7 @@

    Get label quality scores -{"state": {"1c1b2410e3cc48bb8d4e3d5e81387088": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fe128ed3c0eb4ce7a6a8220516bcf5e8": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "6a21cb9490224017b4f63b3077027978": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1c1b2410e3cc48bb8d4e3d5e81387088", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fe128ed3c0eb4ce7a6a8220516bcf5e8", "tabbable": null, "tooltip": null, "value": 30.0}}, "2d212f1218b849149ab1dbecbab9a1e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ae4a8a8bae08447f943f64b763e74911": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b1c27a9f01dd463ca9687d81a5be93d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2d212f1218b849149ab1dbecbab9a1e1", "placeholder": "\u200b", "style": "IPY_MODEL_ae4a8a8bae08447f943f64b763e74911", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "22cb6a36455147f5b1bb1c434d6a9e1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d108010f360b474c8406b52ef77971ad": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "f2261b979563426081a86553acc28756": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_22cb6a36455147f5b1bb1c434d6a9e1b", "placeholder": "\u200b", "style": "IPY_MODEL_d108010f360b474c8406b52ef77971ad", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007776.76it/s]"}}, "5d4cd4bc65ce473890303c09f2290548": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0cdc6dee74c040a69b3494b8aab06e7a": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_b1c27a9f01dd463ca9687d81a5be93d6", "IPY_MODEL_6a21cb9490224017b4f63b3077027978", "IPY_MODEL_f2261b979563426081a86553acc28756"], "layout": "IPY_MODEL_5d4cd4bc65ce473890303c09f2290548", "tabbable": null, "tooltip": null}}, "0f957598d78841ffbd5ae356a219a0cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5d0717a9ba494648b412c84882ae83be": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ef6b1b176d644c0ab075adf2a0a6236f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_0f957598d78841ffbd5ae356a219a0cf", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5d0717a9ba494648b412c84882ae83be", "tabbable": null, "tooltip": null, "value": 30.0}}, "458bea55f4b24c5ab5cc1e7f3aa00697": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b0e946ecf2904b21b2a0ea7e127f9d7f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "4635042d0e4f49e3bf6aa1ac992c08e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_458bea55f4b24c5ab5cc1e7f3aa00697", "placeholder": "\u200b", "style": "IPY_MODEL_b0e946ecf2904b21b2a0ea7e127f9d7f", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "73a809c622b348c8affd0c44c2e1a8fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4b8c4cb8ba7a4ff4896c57ad28d3eca1": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "27597bf2a1be432eb70769222d89dc7d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_73a809c622b348c8affd0c44c2e1a8fe", "placeholder": "\u200b", "style": "IPY_MODEL_4b8c4cb8ba7a4ff4896c57ad28d3eca1", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.38it/s]"}}, "9fd930ccf17f479a9a9231dd8ae6d86e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b11ab931e8144b04919807741d6347a6": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_4635042d0e4f49e3bf6aa1ac992c08e3", "IPY_MODEL_ef6b1b176d644c0ab075adf2a0a6236f", "IPY_MODEL_27597bf2a1be432eb70769222d89dc7d"], "layout": "IPY_MODEL_9fd930ccf17f479a9a9231dd8ae6d86e", "tabbable": null, "tooltip": null}}, "4daa6952ccf640d5bb471ac97fca937a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b5eda668ac604134af3aae92fff71d06": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "061c85291828496fbf52c0bd1e246e72": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4daa6952ccf640d5bb471ac97fca937a", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b5eda668ac604134af3aae92fff71d06", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "70fa5a11ae724ba4825380abd56877bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "29d91937de334b2391a824c0115c189f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "53366ac2a1b242d29a5e4f1f927c7fb4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_70fa5a11ae724ba4825380abd56877bf", "placeholder": "\u200b", "style": "IPY_MODEL_29d91937de334b2391a824c0115c189f", "tabbable": null, "tooltip": null, "value": "100%"}}, "5f7ffcb3dfdc431ba05c649dcf5c676b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ed3903b8ba3b47e89556f14ce1662a29": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "d3fece7453394289bcb1e8b87a24bfcc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5f7ffcb3dfdc431ba05c649dcf5c676b", "placeholder": "\u200b", "style": "IPY_MODEL_ed3903b8ba3b47e89556f14ce1662a29", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007149866.37it/s]"}}, "7144445ec5d64b2aa78a1ed6ab26adf1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f3c4b5bc26264823bfb0f6b3ace17d05": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_53366ac2a1b242d29a5e4f1f927c7fb4", "IPY_MODEL_061c85291828496fbf52c0bd1e246e72", "IPY_MODEL_d3fece7453394289bcb1e8b87a24bfcc"], "layout": "IPY_MODEL_7144445ec5d64b2aa78a1ed6ab26adf1", "tabbable": null, "tooltip": null}}, "591d5a7b1f9b4ea8a0347e83195b4778": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a63d2edb73c64989b5e20ae6c7095773": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "18001b1ae0dc4bf8bfcf7cf650b5842b": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_591d5a7b1f9b4ea8a0347e83195b4778", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a63d2edb73c64989b5e20ae6c7095773", "tabbable": null, "tooltip": null, "value": 30.0}}, "2816574fe4494f77a0d739edadb1cb21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "13acb4b4c3314dbb8349d08e4f86c0a9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "740679addecf42bda4edfb03c3cd2918": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2816574fe4494f77a0d739edadb1cb21", "placeholder": "\u200b", "style": "IPY_MODEL_13acb4b4c3314dbb8349d08e4f86c0a9", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "4eca08c04d174cabacd8a567f0036242": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "96271a40a7334ed09a07a521c01f08ad": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "85558cbc107944af8618db036067ddc1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4eca08c04d174cabacd8a567f0036242", "placeholder": "\u200b", "style": "IPY_MODEL_96271a40a7334ed09a07a521c01f08ad", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200719.97it/s]"}}, "703c0ea26e484c1badc31d97206f4521": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "db5e31d22ec845c096d00b1290219c66": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_740679addecf42bda4edfb03c3cd2918", "IPY_MODEL_18001b1ae0dc4bf8bfcf7cf650b5842b", "IPY_MODEL_85558cbc107944af8618db036067ddc1"], "layout": "IPY_MODEL_703c0ea26e484c1badc31d97206f4521", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"b8a70293c3224c66a3f27168557b9ba8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ec84cd07627b443a8c8ce0e1f099c72c": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "754ca065e07f4e7eb99baf4c7cf19ce4": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b8a70293c3224c66a3f27168557b9ba8", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ec84cd07627b443a8c8ce0e1f099c72c", "tabbable": null, "tooltip": null, "value": 30.0}}, "58f2dea7d21a44d288433745eff48fb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a6f5a4d370df4f5ab60b6aa6323dfe0c": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "0d422e6ef3524fe18196d3c8b173b4b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_58f2dea7d21a44d288433745eff48fb5", "placeholder": "\u200b", "style": "IPY_MODEL_a6f5a4d370df4f5ab60b6aa6323dfe0c", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "d7d60f679eea43e79193e07ed20fc5aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2d90c5dd6d1848b38f0aa931986b4799": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "120e14c159a940bebfa008af4846e1af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d7d60f679eea43e79193e07ed20fc5aa", "placeholder": "\u200b", "style": "IPY_MODEL_2d90c5dd6d1848b38f0aa931986b4799", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007768.06it/s]"}}, "3889edbb1fe4425ebf7afefb82fe5802": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e93b88c996c44feeb3673439eaaea41d": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0d422e6ef3524fe18196d3c8b173b4b6", "IPY_MODEL_754ca065e07f4e7eb99baf4c7cf19ce4", "IPY_MODEL_120e14c159a940bebfa008af4846e1af"], "layout": "IPY_MODEL_3889edbb1fe4425ebf7afefb82fe5802", "tabbable": null, "tooltip": null}}, "8dfb6569b11b4e579ab5f1679787b922": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f781332402924b45849cdca8f6978ed3": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "722e544a67304c41a49646191b43f64e": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8dfb6569b11b4e579ab5f1679787b922", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f781332402924b45849cdca8f6978ed3", "tabbable": null, "tooltip": null, "value": 30.0}}, "1d6b185c42f944568fbf955268ac9154": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "880d91c28adf4495881e409632e6f2c0": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2535e62f9cf1494fb6cc24a0ac2907b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_1d6b185c42f944568fbf955268ac9154", "placeholder": "\u200b", "style": "IPY_MODEL_880d91c28adf4495881e409632e6f2c0", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "f5afe4dbdebe45929344b9d66f6e48a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5ab028ba9f524306aa0c67b32b1e5513": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "aa1f2aad145f4f339c1cd410fe718472": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f5afe4dbdebe45929344b9d66f6e48a8", "placeholder": "\u200b", "style": "IPY_MODEL_5ab028ba9f524306aa0c67b32b1e5513", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.33it/s]"}}, "a4188823d8964bafbc43b207d512faeb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cae016dc953549ce807817682c42dc87": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2535e62f9cf1494fb6cc24a0ac2907b2", "IPY_MODEL_722e544a67304c41a49646191b43f64e", "IPY_MODEL_aa1f2aad145f4f339c1cd410fe718472"], "layout": "IPY_MODEL_a4188823d8964bafbc43b207d512faeb", "tabbable": null, "tooltip": null}}, "88577bae8f0943e6a91fd0f7eb9523ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0ce80ae00bf44c64b8d09bc5ac656e27": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "a7e45bb52c5c49fd91aef928ec848223": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_88577bae8f0943e6a91fd0f7eb9523ad", "max": 4997683.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0ce80ae00bf44c64b8d09bc5ac656e27", "tabbable": null, "tooltip": null, "value": 4997683.0}}, "b8bda17c986d4f7fa6a7b2aaf2a0be0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3090d513b8a48f380607cc86cd1895f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "9b08851d974b4b75b2758e2517483e68": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b8bda17c986d4f7fa6a7b2aaf2a0be0a", "placeholder": "\u200b", "style": "IPY_MODEL_e3090d513b8a48f380607cc86cd1895f", "tabbable": null, "tooltip": null, "value": "100%"}}, "b53c3a56cde9432497a763625dc6e49a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a513f46efc83419b8f4e22c3b2c064d2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8df97aa7051e4cac814f7092dcecc401": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b53c3a56cde9432497a763625dc6e49a", "placeholder": "\u200b", "style": "IPY_MODEL_a513f46efc83419b8f4e22c3b2c064d2", "tabbable": null, "tooltip": null, "value": "\u20074997683/4997683\u2007[00:32<00:00,\u2007156404.46it/s]"}}, "998f6677b81d4e86955affe5f63bdf7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "731093637bac464aa707d2bcbb8b8fa8": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_9b08851d974b4b75b2758e2517483e68", "IPY_MODEL_a7e45bb52c5c49fd91aef928ec848223", "IPY_MODEL_8df97aa7051e4cac814f7092dcecc401"], "layout": "IPY_MODEL_998f6677b81d4e86955affe5f63bdf7b", "tabbable": null, "tooltip": null}}, "4bb2732aa35d440a84e89690b8a0f610": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a88b229f72f34e9a8c5e66a993f56a21": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "1a7a00c51cf841f9960806391ff7b8c0": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_4bb2732aa35d440a84e89690b8a0f610", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a88b229f72f34e9a8c5e66a993f56a21", "tabbable": null, "tooltip": null, "value": 30.0}}, "7e82ec8863a94f5d8e6065f6c467c1ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "470506ccc55441d4a3335d76966e3c9a": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "645ee85b81c0443e9b9b94ea8c6e87fd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7e82ec8863a94f5d8e6065f6c467c1ba", "placeholder": "\u200b", "style": "IPY_MODEL_470506ccc55441d4a3335d76966e3c9a", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "f6a973b86f594108bb655affcc8de322": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "620edc866142421db8ff2794fa79153f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "742abd1e12e941f0ade6da7a2b14d0d0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_f6a973b86f594108bb655affcc8de322", "placeholder": "\u200b", "style": "IPY_MODEL_620edc866142421db8ff2794fa79153f", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200722.07it/s]"}}, "0fba3cd87f5e4a30858fae2027c96f2f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "94d7b260381b4b7da5eca25e123790e6": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_645ee85b81c0443e9b9b94ea8c6e87fd", "IPY_MODEL_1a7a00c51cf841f9960806391ff7b8c0", "IPY_MODEL_742abd1e12e941f0ade6da7a2b14d0d0"], "layout": "IPY_MODEL_0fba3cd87f5e4a30858fae2027c96f2f", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 1b5222eab..3b1c6435d 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:13.402150Z", - "iopub.status.busy": "2024-06-28T15:39:13.401954Z", - "iopub.status.idle": "2024-06-28T15:39:14.646183Z", - "shell.execute_reply": "2024-06-28T15:39:14.645441Z" + "iopub.execute_input": "2024-07-01T15:08:37.513049Z", + "iopub.status.busy": "2024-07-01T15:08:37.512824Z", + "iopub.status.idle": "2024-07-01T15:08:39.014630Z", + "shell.execute_reply": "2024-07-01T15:08:39.013915Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:14.649149Z", - "iopub.status.busy": "2024-06-28T15:39:14.648692Z", - "iopub.status.idle": "2024-06-28T15:39:50.257887Z", - "shell.execute_reply": "2024-06-28T15:39:50.257232Z" + "iopub.execute_input": "2024-07-01T15:08:39.017371Z", + "iopub.status.busy": "2024-07-01T15:08:39.017127Z", + "iopub.status.idle": "2024-07-01T15:09:39.584116Z", + "shell.execute_reply": "2024-07-01T15:09:39.583459Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:50.260572Z", - "iopub.status.busy": "2024-06-28T15:39:50.260205Z", - "iopub.status.idle": "2024-06-28T15:39:51.486151Z", - "shell.execute_reply": "2024-06-28T15:39:51.485480Z" + "iopub.execute_input": "2024-07-01T15:09:39.586690Z", + "iopub.status.busy": "2024-07-01T15:09:39.586340Z", + "iopub.status.idle": "2024-07-01T15:09:40.720146Z", + "shell.execute_reply": "2024-07-01T15:09:40.719576Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.489186Z", - "iopub.status.busy": "2024-06-28T15:39:51.488560Z", - "iopub.status.idle": "2024-06-28T15:39:51.492413Z", - "shell.execute_reply": "2024-06-28T15:39:51.491891Z" + "iopub.execute_input": "2024-07-01T15:09:40.722658Z", + "iopub.status.busy": "2024-07-01T15:09:40.722386Z", + "iopub.status.idle": "2024-07-01T15:09:40.725657Z", + "shell.execute_reply": "2024-07-01T15:09:40.725218Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.494943Z", - "iopub.status.busy": "2024-06-28T15:39:51.494455Z", - "iopub.status.idle": "2024-06-28T15:39:51.498706Z", - "shell.execute_reply": "2024-06-28T15:39:51.498166Z" + "iopub.execute_input": "2024-07-01T15:09:40.727650Z", + "iopub.status.busy": "2024-07-01T15:09:40.727470Z", + "iopub.status.idle": "2024-07-01T15:09:40.731254Z", + "shell.execute_reply": "2024-07-01T15:09:40.730747Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.501145Z", - "iopub.status.busy": "2024-06-28T15:39:51.500786Z", - "iopub.status.idle": "2024-06-28T15:39:51.504649Z", - "shell.execute_reply": "2024-06-28T15:39:51.504101Z" + "iopub.execute_input": "2024-07-01T15:09:40.733340Z", + "iopub.status.busy": "2024-07-01T15:09:40.733016Z", + "iopub.status.idle": "2024-07-01T15:09:40.736638Z", + "shell.execute_reply": "2024-07-01T15:09:40.736162Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.506798Z", - "iopub.status.busy": "2024-06-28T15:39:51.506520Z", - "iopub.status.idle": "2024-06-28T15:39:51.509682Z", - "shell.execute_reply": "2024-06-28T15:39:51.509112Z" + "iopub.execute_input": "2024-07-01T15:09:40.738709Z", + "iopub.status.busy": "2024-07-01T15:09:40.738283Z", + "iopub.status.idle": "2024-07-01T15:09:40.741139Z", + "shell.execute_reply": "2024-07-01T15:09:40.740716Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.511926Z", - "iopub.status.busy": "2024-06-28T15:39:51.511586Z", - "iopub.status.idle": "2024-06-28T15:40:25.616678Z", - "shell.execute_reply": "2024-06-28T15:40:25.616008Z" + "iopub.execute_input": "2024-07-01T15:09:40.743089Z", + "iopub.status.busy": "2024-07-01T15:09:40.742768Z", + "iopub.status.idle": "2024-07-01T15:10:14.851046Z", + "shell.execute_reply": "2024-07-01T15:10:14.850360Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0cdc6dee74c040a69b3494b8aab06e7a", + "model_id": "e93b88c996c44feeb3673439eaaea41d", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b11ab931e8144b04919807741d6347a6", + "model_id": "cae016dc953549ce807817682c42dc87", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:25.619646Z", - "iopub.status.busy": "2024-06-28T15:40:25.619138Z", - "iopub.status.idle": "2024-06-28T15:40:26.311249Z", - "shell.execute_reply": "2024-06-28T15:40:26.310687Z" + "iopub.execute_input": "2024-07-01T15:10:14.853695Z", + "iopub.status.busy": "2024-07-01T15:10:14.853439Z", + "iopub.status.idle": "2024-07-01T15:10:15.523116Z", + "shell.execute_reply": "2024-07-01T15:10:15.522614Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:26.313705Z", - "iopub.status.busy": "2024-06-28T15:40:26.313226Z", - "iopub.status.idle": "2024-06-28T15:40:29.212189Z", - "shell.execute_reply": "2024-06-28T15:40:29.211650Z" + "iopub.execute_input": "2024-07-01T15:10:15.525482Z", + "iopub.status.busy": "2024-07-01T15:10:15.525022Z", + "iopub.status.idle": "2024-07-01T15:10:18.415066Z", + "shell.execute_reply": "2024-07-01T15:10:18.414464Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:29.214460Z", - "iopub.status.busy": "2024-06-28T15:40:29.214101Z", - "iopub.status.idle": "2024-06-28T15:41:02.133359Z", - "shell.execute_reply": "2024-06-28T15:41:02.132825Z" + "iopub.execute_input": "2024-07-01T15:10:18.417219Z", + "iopub.status.busy": "2024-07-01T15:10:18.417035Z", + "iopub.status.idle": "2024-07-01T15:10:50.808150Z", + "shell.execute_reply": "2024-07-01T15:10:50.807678Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3c4b5bc26264823bfb0f6b3ace17d05", + "model_id": "731093637bac464aa707d2bcbb8b8fa8", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:02.135440Z", - "iopub.status.busy": "2024-06-28T15:41:02.135225Z", - "iopub.status.idle": "2024-06-28T15:41:17.058471Z", - "shell.execute_reply": "2024-06-28T15:41:17.057815Z" + "iopub.execute_input": "2024-07-01T15:10:50.810430Z", + "iopub.status.busy": "2024-07-01T15:10:50.810008Z", + "iopub.status.idle": "2024-07-01T15:11:05.045003Z", + "shell.execute_reply": "2024-07-01T15:11:05.044449Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:17.061274Z", - "iopub.status.busy": "2024-06-28T15:41:17.060907Z", - "iopub.status.idle": "2024-06-28T15:41:20.968204Z", - "shell.execute_reply": "2024-06-28T15:41:20.967621Z" + "iopub.execute_input": "2024-07-01T15:11:05.047402Z", + "iopub.status.busy": "2024-07-01T15:11:05.047124Z", + "iopub.status.idle": "2024-07-01T15:11:08.765062Z", + "shell.execute_reply": "2024-07-01T15:11:08.764520Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:20.970611Z", - "iopub.status.busy": "2024-06-28T15:41:20.970161Z", - "iopub.status.idle": "2024-06-28T15:41:22.495041Z", - "shell.execute_reply": "2024-06-28T15:41:22.494478Z" + "iopub.execute_input": "2024-07-01T15:11:08.767118Z", + "iopub.status.busy": "2024-07-01T15:11:08.766928Z", + "iopub.status.idle": "2024-07-01T15:11:10.139794Z", + "shell.execute_reply": "2024-07-01T15:11:10.139251Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db5e31d22ec845c096d00b1290219c66", + "model_id": "94d7b260381b4b7da5eca25e123790e6", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:22.497895Z", - "iopub.status.busy": "2024-06-28T15:41:22.497483Z", - "iopub.status.idle": "2024-06-28T15:41:22.530031Z", - "shell.execute_reply": "2024-06-28T15:41:22.529445Z" + "iopub.execute_input": "2024-07-01T15:11:10.142331Z", + "iopub.status.busy": "2024-07-01T15:11:10.141916Z", + "iopub.status.idle": "2024-07-01T15:11:10.170330Z", + "shell.execute_reply": "2024-07-01T15:11:10.169827Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:22.532973Z", - "iopub.status.busy": "2024-06-28T15:41:22.532548Z", - "iopub.status.idle": "2024-06-28T15:41:28.764066Z", - "shell.execute_reply": "2024-06-28T15:41:28.763451Z" + "iopub.execute_input": "2024-07-01T15:11:10.172647Z", + "iopub.status.busy": "2024-07-01T15:11:10.172452Z", + "iopub.status.idle": "2024-07-01T15:11:16.278293Z", + "shell.execute_reply": "2024-07-01T15:11:16.277810Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:28.766438Z", - "iopub.status.busy": "2024-06-28T15:41:28.766065Z", - "iopub.status.idle": "2024-06-28T15:41:28.822918Z", - "shell.execute_reply": "2024-06-28T15:41:28.822414Z" + "iopub.execute_input": "2024-07-01T15:11:16.280555Z", + "iopub.status.busy": "2024-07-01T15:11:16.280188Z", + "iopub.status.idle": "2024-07-01T15:11:16.335969Z", + "shell.execute_reply": "2024-07-01T15:11:16.335497Z" }, "nbsphinx": "hidden" }, @@ -1038,57 +1038,46 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "061c85291828496fbf52c0bd1e246e72": { + "0ce80ae00bf44c64b8d09bc5ac656e27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4daa6952ccf640d5bb471ac97fca937a", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b5eda668ac604134af3aae92fff71d06", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0cdc6dee74c040a69b3494b8aab06e7a": { + "0d422e6ef3524fe18196d3c8b173b4b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b1c27a9f01dd463ca9687d81a5be93d6", - "IPY_MODEL_6a21cb9490224017b4f63b3077027978", - "IPY_MODEL_f2261b979563426081a86553acc28756" - ], - "layout": "IPY_MODEL_5d4cd4bc65ce473890303c09f2290548", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_58f2dea7d21a44d288433745eff48fb5", + "placeholder": "​", + "style": "IPY_MODEL_a6f5a4d370df4f5ab60b6aa6323dfe0c", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "0f957598d78841ffbd5ae356a219a0cf": { + "0fba3cd87f5e4a30858fae2027c96f2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1141,25 +1130,30 @@ "width": null } }, - "13acb4b4c3314dbb8349d08e4f86c0a9": { + "120e14c159a940bebfa008af4846e1af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d7d60f679eea43e79193e07ed20fc5aa", + "placeholder": "​", + "style": "IPY_MODEL_2d90c5dd6d1848b38f0aa931986b4799", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 768.06it/s]" } }, - "18001b1ae0dc4bf8bfcf7cf650b5842b": { + "1a7a00c51cf841f9960806391ff7b8c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1175,70 +1169,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_591d5a7b1f9b4ea8a0347e83195b4778", + "layout": "IPY_MODEL_4bb2732aa35d440a84e89690b8a0f610", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a63d2edb73c64989b5e20ae6c7095773", + "style": "IPY_MODEL_a88b229f72f34e9a8c5e66a993f56a21", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "1c1b2410e3cc48bb8d4e3d5e81387088": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "22cb6a36455147f5b1bb1c434d6a9e1b": { + "1d6b185c42f944568fbf955268ac9154": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1291,7 +1232,7 @@ "width": null } }, - "27597bf2a1be432eb70769222d89dc7d": { + "2535e62f9cf1494fb6cc24a0ac2907b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1306,15 +1247,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_73a809c622b348c8affd0c44c2e1a8fe", + "layout": "IPY_MODEL_1d6b185c42f944568fbf955268ac9154", "placeholder": "​", - "style": "IPY_MODEL_4b8c4cb8ba7a4ff4896c57ad28d3eca1", + "style": "IPY_MODEL_880d91c28adf4495881e409632e6f2c0", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.38it/s]" + "value": "number of examples processed for checking labels: 100%" + } + }, + "2d90c5dd6d1848b38f0aa931986b4799": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2816574fe4494f77a0d739edadb1cb21": { + "3889edbb1fe4425ebf7afefb82fe5802": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1367,7 +1326,7 @@ "width": null } }, - "29d91937de334b2391a824c0115c189f": { + "470506ccc55441d4a3335d76966e3c9a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1385,7 +1344,7 @@ "text_color": null } }, - "2d212f1218b849149ab1dbecbab9a1e1": { + "4bb2732aa35d440a84e89690b8a0f610": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1438,7 +1397,7 @@ "width": null } }, - "458bea55f4b24c5ab5cc1e7f3aa00697": { + "58f2dea7d21a44d288433745eff48fb5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1491,7 +1450,43 @@ "width": null } }, - "4635042d0e4f49e3bf6aa1ac992c08e3": { + "5ab028ba9f524306aa0c67b32b1e5513": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "620edc866142421db8ff2794fa79153f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "645ee85b81c0443e9b9b94ea8c6e87fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1506,33 +1501,114 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_458bea55f4b24c5ab5cc1e7f3aa00697", + "layout": "IPY_MODEL_7e82ec8863a94f5d8e6065f6c467c1ba", "placeholder": "​", - "style": "IPY_MODEL_b0e946ecf2904b21b2a0ea7e127f9d7f", + "style": "IPY_MODEL_470506ccc55441d4a3335d76966e3c9a", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": "images processed using softmin: 100%" } }, - "4b8c4cb8ba7a4ff4896c57ad28d3eca1": { + "722e544a67304c41a49646191b43f64e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8dfb6569b11b4e579ab5f1679787b922", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f781332402924b45849cdca8f6978ed3", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "731093637bac464aa707d2bcbb8b8fa8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9b08851d974b4b75b2758e2517483e68", + "IPY_MODEL_a7e45bb52c5c49fd91aef928ec848223", + "IPY_MODEL_8df97aa7051e4cac814f7092dcecc401" + ], + "layout": "IPY_MODEL_998f6677b81d4e86955affe5f63bdf7b", + "tabbable": null, + "tooltip": null + } + }, + "742abd1e12e941f0ade6da7a2b14d0d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f6a973b86f594108bb655affcc8de322", + "placeholder": "​", + "style": "IPY_MODEL_620edc866142421db8ff2794fa79153f", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 22.07it/s]" + } + }, + "754ca065e07f4e7eb99baf4c7cf19ce4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8a70293c3224c66a3f27168557b9ba8", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ec84cd07627b443a8c8ce0e1f099c72c", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "4daa6952ccf640d5bb471ac97fca937a": { + "7e82ec8863a94f5d8e6065f6c467c1ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1585,23 +1661,41 @@ "width": null } }, - "4eca08c04d174cabacd8a567f0036242": { - "model_module": "@jupyter-widgets/base", + "880d91c28adf4495881e409632e6f2c0": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "88577bae8f0943e6a91fd0f7eb9523ad": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, "border_right": null, "border_top": null, "bottom": null, @@ -1638,7 +1732,7 @@ "width": null } }, - "53366ac2a1b242d29a5e4f1f927c7fb4": { + "8df97aa7051e4cac814f7092dcecc401": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1653,15 +1747,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70fa5a11ae724ba4825380abd56877bf", + "layout": "IPY_MODEL_b53c3a56cde9432497a763625dc6e49a", "placeholder": "​", - "style": "IPY_MODEL_29d91937de334b2391a824c0115c189f", + "style": "IPY_MODEL_a513f46efc83419b8f4e22c3b2c064d2", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4997683/4997683 [00:32<00:00, 156404.46it/s]" } }, - "591d5a7b1f9b4ea8a0347e83195b4778": { + "8dfb6569b11b4e579ab5f1679787b922": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1714,23 +1808,31 @@ "width": null } }, - "5d0717a9ba494648b412c84882ae83be": { + "94d7b260381b4b7da5eca25e123790e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_645ee85b81c0443e9b9b94ea8c6e87fd", + "IPY_MODEL_1a7a00c51cf841f9960806391ff7b8c0", + "IPY_MODEL_742abd1e12e941f0ade6da7a2b14d0d0" + ], + "layout": "IPY_MODEL_0fba3cd87f5e4a30858fae2027c96f2f", + "tabbable": null, + "tooltip": null } }, - "5d4cd4bc65ce473890303c09f2290548": { + "998f6677b81d4e86955affe5f63bdf7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1783,7 +1885,30 @@ "width": null } }, - "5f7ffcb3dfdc431ba05c649dcf5c676b": { + "9b08851d974b4b75b2758e2517483e68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8bda17c986d4f7fa6a7b2aaf2a0be0a", + "placeholder": "​", + "style": "IPY_MODEL_e3090d513b8a48f380607cc86cd1895f", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a4188823d8964bafbc43b207d512faeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1836,7 +1961,43 @@ "width": null } }, - "6a21cb9490224017b4f63b3077027978": { + "a513f46efc83419b8f4e22c3b2c064d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a6f5a4d370df4f5ab60b6aa6323dfe0c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a7e45bb52c5c49fd91aef928ec848223": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1852,17 +2013,56 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1c1b2410e3cc48bb8d4e3d5e81387088", - "max": 30.0, + "layout": "IPY_MODEL_88577bae8f0943e6a91fd0f7eb9523ad", + "max": 4997683.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_fe128ed3c0eb4ce7a6a8220516bcf5e8", + "style": "IPY_MODEL_0ce80ae00bf44c64b8d09bc5ac656e27", "tabbable": null, "tooltip": null, - "value": 30.0 + "value": 4997683.0 } }, - "703c0ea26e484c1badc31d97206f4521": { + "a88b229f72f34e9a8c5e66a993f56a21": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "aa1f2aad145f4f339c1cd410fe718472": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f5afe4dbdebe45929344b9d66f6e48a8", + "placeholder": "​", + "style": "IPY_MODEL_5ab028ba9f524306aa0c67b32b1e5513", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.33it/s]" + } + }, + "b53c3a56cde9432497a763625dc6e49a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1915,7 +2115,7 @@ "width": null } }, - "70fa5a11ae724ba4825380abd56877bf": { + "b8a70293c3224c66a3f27168557b9ba8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1968,7 +2168,7 @@ "width": null } }, - "7144445ec5d64b2aa78a1ed6ab26adf1": { + "b8bda17c986d4f7fa6a7b2aaf2a0be0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2021,7 +2221,31 @@ "width": null } }, - "73a809c622b348c8affd0c44c2e1a8fe": { + "cae016dc953549ce807817682c42dc87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2535e62f9cf1494fb6cc24a0ac2907b2", + "IPY_MODEL_722e544a67304c41a49646191b43f64e", + "IPY_MODEL_aa1f2aad145f4f339c1cd410fe718472" + ], + "layout": "IPY_MODEL_a4188823d8964bafbc43b207d512faeb", + "tabbable": null, + "tooltip": null + } + }, + "d7d60f679eea43e79193e07ed20fc5aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2074,71 +2298,65 @@ "width": null } }, - "740679addecf42bda4edfb03c3cd2918": { + "e3090d513b8a48f380607cc86cd1895f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2816574fe4494f77a0d739edadb1cb21", - "placeholder": "​", - "style": "IPY_MODEL_13acb4b4c3314dbb8349d08e4f86c0a9", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85558cbc107944af8618db036067ddc1": { + "e93b88c996c44feeb3673439eaaea41d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4eca08c04d174cabacd8a567f0036242", - "placeholder": "​", - "style": "IPY_MODEL_96271a40a7334ed09a07a521c01f08ad", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0d422e6ef3524fe18196d3c8b173b4b6", + "IPY_MODEL_754ca065e07f4e7eb99baf4c7cf19ce4", + "IPY_MODEL_120e14c159a940bebfa008af4846e1af" + ], + "layout": "IPY_MODEL_3889edbb1fe4425ebf7afefb82fe5802", "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 19.97it/s]" + "tooltip": null } }, - "96271a40a7334ed09a07a521c01f08ad": { + "ec84cd07627b443a8c8ce0e1f099c72c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "9fd930ccf17f479a9a9231dd8ae6d86e": { + "f5afe4dbdebe45929344b9d66f6e48a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2191,278 +2409,60 @@ "width": null } }, - "a63d2edb73c64989b5e20ae6c7095773": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ae4a8a8bae08447f943f64b763e74911": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b0e946ecf2904b21b2a0ea7e127f9d7f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b11ab931e8144b04919807741d6347a6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4635042d0e4f49e3bf6aa1ac992c08e3", - "IPY_MODEL_ef6b1b176d644c0ab075adf2a0a6236f", - "IPY_MODEL_27597bf2a1be432eb70769222d89dc7d" - ], - "layout": "IPY_MODEL_9fd930ccf17f479a9a9231dd8ae6d86e", - "tabbable": null, - "tooltip": null - } - }, - "b1c27a9f01dd463ca9687d81a5be93d6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2d212f1218b849149ab1dbecbab9a1e1", - "placeholder": "​", - "style": "IPY_MODEL_ae4a8a8bae08447f943f64b763e74911", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "b5eda668ac604134af3aae92fff71d06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "d108010f360b474c8406b52ef77971ad": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d3fece7453394289bcb1e8b87a24bfcc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5f7ffcb3dfdc431ba05c649dcf5c676b", - "placeholder": "​", - "style": "IPY_MODEL_ed3903b8ba3b47e89556f14ce1662a29", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 149866.37it/s]" - } - }, - "db5e31d22ec845c096d00b1290219c66": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_740679addecf42bda4edfb03c3cd2918", - "IPY_MODEL_18001b1ae0dc4bf8bfcf7cf650b5842b", - "IPY_MODEL_85558cbc107944af8618db036067ddc1" - ], - "layout": "IPY_MODEL_703c0ea26e484c1badc31d97206f4521", - "tabbable": null, - "tooltip": null - } - }, - "ed3903b8ba3b47e89556f14ce1662a29": { - "model_module": "@jupyter-widgets/controls", + "f6a973b86f594108bb655affcc8de322": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ef6b1b176d644c0ab075adf2a0a6236f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0f957598d78841ffbd5ae356a219a0cf", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5d0717a9ba494648b412c84882ae83be", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "f2261b979563426081a86553acc28756": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_22cb6a36455147f5b1bb1c434d6a9e1b", - "placeholder": "​", - "style": "IPY_MODEL_d108010f360b474c8406b52ef77971ad", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 776.76it/s]" - } - }, - "f3c4b5bc26264823bfb0f6b3ace17d05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_53366ac2a1b242d29a5e4f1f927c7fb4", - "IPY_MODEL_061c85291828496fbf52c0bd1e246e72", - "IPY_MODEL_d3fece7453394289bcb1e8b87a24bfcc" - ], - "layout": "IPY_MODEL_7144445ec5d64b2aa78a1ed6ab26adf1", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "fe128ed3c0eb4ce7a6a8220516bcf5e8": { + "f781332402924b45849cdca8f6978ed3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 0428cc503..b1e0ee939 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -710,16 +710,16 @@

    1. Install required dependencies and download data 
    ---2024-06-28 15:41:31--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 185.93.1.246, 2400:52e0:1a00::718:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.
+--2024-07-01 15:11:18--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 169.150.236.98, 2400:52e0:1a00::871:1
+Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  4.92MB/s    in 0.2s
+conll2003.zip       100%[===================>] 959.94K  5.71MB/s    in 0.2s
 
-2024-06-28 15:41:31 (4.92 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -727,16 +727,16 @@ 
    1. Install required dependencies and download data

    diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index 153b10754..8650ebc00 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:31.234194Z", - "iopub.status.busy": "2024-06-28T15:41:31.234011Z", - "iopub.status.idle": "2024-06-28T15:41:32.436580Z", - "shell.execute_reply": "2024-06-28T15:41:32.435971Z" + "iopub.execute_input": "2024-07-01T15:11:18.503218Z", + "iopub.status.busy": "2024-07-01T15:11:18.502735Z", + "iopub.status.idle": "2024-07-01T15:11:19.975527Z", + "shell.execute_reply": "2024-07-01T15:11:19.974839Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-28 15:41:31-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-01 15:11:18-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.246, 2400:52e0:1a00::718:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "169.150.236.98, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n", + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 4.92MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n", "\r\n", - "2024-06-28 15:41:31 (4.92 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,15 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-28 15:41:31-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.117.65, 52.217.130.65, 52.217.92.116, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.117.65|:443... connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-07-01 15:11:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.28.244, 3.5.24.72, 52.217.13.252, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.28.244|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -160,7 +168,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 48%[========> ] 7.93M 37.9MB/s " + "pred_probs.npz 35%[======> ] 5.78M 28.9MB/s " ] }, { @@ -168,9 +176,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 60.6MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 52.3MB/s in 0.3s \r\n", "\r\n", - "2024-06-28 15:41:32 (60.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-01 15:11:19 (52.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -187,10 +195,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:32.439449Z", - "iopub.status.busy": "2024-06-28T15:41:32.439055Z", - "iopub.status.idle": "2024-06-28T15:41:33.746197Z", - "shell.execute_reply": "2024-06-28T15:41:33.745549Z" + "iopub.execute_input": "2024-07-01T15:11:19.978352Z", + "iopub.status.busy": "2024-07-01T15:11:19.977882Z", + "iopub.status.idle": "2024-07-01T15:11:21.215995Z", + "shell.execute_reply": "2024-07-01T15:11:21.215505Z" }, "nbsphinx": "hidden" }, @@ -201,7 +209,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -227,10 +235,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.749019Z", - "iopub.status.busy": "2024-06-28T15:41:33.748505Z", - "iopub.status.idle": "2024-06-28T15:41:33.751942Z", - "shell.execute_reply": "2024-06-28T15:41:33.751495Z" + "iopub.execute_input": "2024-07-01T15:11:21.218513Z", + "iopub.status.busy": "2024-07-01T15:11:21.218132Z", + "iopub.status.idle": "2024-07-01T15:11:21.221470Z", + "shell.execute_reply": "2024-07-01T15:11:21.221045Z" } }, "outputs": [], @@ -280,10 +288,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.754199Z", - "iopub.status.busy": "2024-06-28T15:41:33.753868Z", - "iopub.status.idle": "2024-06-28T15:41:33.756996Z", - "shell.execute_reply": "2024-06-28T15:41:33.756443Z" + "iopub.execute_input": "2024-07-01T15:11:21.223694Z", + "iopub.status.busy": "2024-07-01T15:11:21.223257Z", + "iopub.status.idle": "2024-07-01T15:11:21.226332Z", + "shell.execute_reply": "2024-07-01T15:11:21.225848Z" }, "nbsphinx": "hidden" }, @@ -301,10 +309,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.759117Z", - "iopub.status.busy": "2024-06-28T15:41:33.758809Z", - "iopub.status.idle": "2024-06-28T15:41:42.901434Z", - "shell.execute_reply": "2024-06-28T15:41:42.900715Z" + "iopub.execute_input": "2024-07-01T15:11:21.228084Z", + "iopub.status.busy": "2024-07-01T15:11:21.227917Z", + "iopub.status.idle": "2024-07-01T15:11:30.310755Z", + "shell.execute_reply": "2024-07-01T15:11:30.310211Z" } }, "outputs": [], @@ -378,10 +386,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:42.904438Z", - "iopub.status.busy": "2024-06-28T15:41:42.904121Z", - "iopub.status.idle": "2024-06-28T15:41:42.910537Z", - "shell.execute_reply": "2024-06-28T15:41:42.909908Z" + "iopub.execute_input": "2024-07-01T15:11:30.313310Z", + "iopub.status.busy": "2024-07-01T15:11:30.313004Z", + "iopub.status.idle": "2024-07-01T15:11:30.318459Z", + "shell.execute_reply": "2024-07-01T15:11:30.318009Z" }, "nbsphinx": "hidden" }, @@ -421,10 +429,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:42.912924Z", - "iopub.status.busy": "2024-06-28T15:41:42.912455Z", - "iopub.status.idle": "2024-06-28T15:41:43.306159Z", - "shell.execute_reply": "2024-06-28T15:41:43.305530Z" + "iopub.execute_input": "2024-07-01T15:11:30.320517Z", + "iopub.status.busy": "2024-07-01T15:11:30.320198Z", + "iopub.status.idle": "2024-07-01T15:11:30.659248Z", + "shell.execute_reply": "2024-07-01T15:11:30.658770Z" } }, "outputs": [], @@ -461,10 +469,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:43.308933Z", - "iopub.status.busy": "2024-06-28T15:41:43.308556Z", - "iopub.status.idle": "2024-06-28T15:41:43.313173Z", - "shell.execute_reply": "2024-06-28T15:41:43.312627Z" + "iopub.execute_input": "2024-07-01T15:11:30.661698Z", + "iopub.status.busy": "2024-07-01T15:11:30.661301Z", + "iopub.status.idle": "2024-07-01T15:11:30.665925Z", + "shell.execute_reply": "2024-07-01T15:11:30.665448Z" } }, "outputs": [ @@ -536,10 +544,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:43.315174Z", - "iopub.status.busy": "2024-06-28T15:41:43.314861Z", - "iopub.status.idle": "2024-06-28T15:41:45.999753Z", - "shell.execute_reply": "2024-06-28T15:41:45.998935Z" + "iopub.execute_input": "2024-07-01T15:11:30.667958Z", + "iopub.status.busy": "2024-07-01T15:11:30.667632Z", + "iopub.status.idle": "2024-07-01T15:11:33.481219Z", + "shell.execute_reply": "2024-07-01T15:11:33.480521Z" } }, "outputs": [], @@ -561,10 +569,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.003076Z", - "iopub.status.busy": "2024-06-28T15:41:46.002321Z", - "iopub.status.idle": "2024-06-28T15:41:46.006640Z", - "shell.execute_reply": "2024-06-28T15:41:46.006164Z" + "iopub.execute_input": "2024-07-01T15:11:33.484626Z", + "iopub.status.busy": "2024-07-01T15:11:33.483787Z", + "iopub.status.idle": "2024-07-01T15:11:33.488254Z", + "shell.execute_reply": "2024-07-01T15:11:33.487491Z" } }, "outputs": [ @@ -600,10 +608,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.008550Z", - "iopub.status.busy": "2024-06-28T15:41:46.008360Z", - "iopub.status.idle": "2024-06-28T15:41:46.013890Z", - "shell.execute_reply": "2024-06-28T15:41:46.013387Z" + "iopub.execute_input": "2024-07-01T15:11:33.490509Z", + "iopub.status.busy": "2024-07-01T15:11:33.490170Z", + "iopub.status.idle": "2024-07-01T15:11:33.496148Z", + "shell.execute_reply": "2024-07-01T15:11:33.495591Z" } }, "outputs": [ @@ -781,10 +789,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.015898Z", - "iopub.status.busy": "2024-06-28T15:41:46.015719Z", - "iopub.status.idle": "2024-06-28T15:41:46.043665Z", - "shell.execute_reply": "2024-06-28T15:41:46.043105Z" + "iopub.execute_input": "2024-07-01T15:11:33.498276Z", + "iopub.status.busy": "2024-07-01T15:11:33.497940Z", + "iopub.status.idle": "2024-07-01T15:11:33.525403Z", + "shell.execute_reply": "2024-07-01T15:11:33.524817Z" } }, "outputs": [ @@ -886,10 +894,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.045840Z", - "iopub.status.busy": "2024-06-28T15:41:46.045642Z", - "iopub.status.idle": "2024-06-28T15:41:46.050758Z", - "shell.execute_reply": "2024-06-28T15:41:46.050230Z" + "iopub.execute_input": "2024-07-01T15:11:33.527747Z", + "iopub.status.busy": "2024-07-01T15:11:33.527322Z", + "iopub.status.idle": "2024-07-01T15:11:33.532159Z", + "shell.execute_reply": "2024-07-01T15:11:33.531610Z" } }, "outputs": [ @@ -963,10 +971,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.052700Z", - "iopub.status.busy": "2024-06-28T15:41:46.052506Z", - "iopub.status.idle": "2024-06-28T15:41:47.506317Z", - "shell.execute_reply": "2024-06-28T15:41:47.505719Z" + "iopub.execute_input": "2024-07-01T15:11:33.534578Z", + "iopub.status.busy": "2024-07-01T15:11:33.534006Z", + "iopub.status.idle": "2024-07-01T15:11:34.915561Z", + "shell.execute_reply": "2024-07-01T15:11:34.914971Z" } }, "outputs": [ @@ -1138,10 +1146,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:47.508579Z", - "iopub.status.busy": "2024-06-28T15:41:47.508376Z", - "iopub.status.idle": "2024-06-28T15:41:47.512426Z", - "shell.execute_reply": "2024-06-28T15:41:47.511988Z" + "iopub.execute_input": "2024-07-01T15:11:34.917877Z", + "iopub.status.busy": "2024-07-01T15:11:34.917550Z", + "iopub.status.idle": "2024-07-01T15:11:34.921657Z", + "shell.execute_reply": "2024-07-01T15:11:34.921191Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 3a20b9544..30bcb95e3 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.6", - commit_hash: "0a675a1c4bd93cec9a874c1dbd565866d1f77dbe", + commit_hash: "7a801c5ee1e11be3732a7ea01725de8aca8d147d", }; \ No newline at end of file

    WrwQsqUyY;IxnJ11xZexs`H}iya>w;RGk+;JGtt- zsC1T8oflQ-Mb&vxbzXd@-=ylis5&pI&Woz^qS9YdbzZdidj9vF7viEL=XVdze5HPB zCvo5rdREL{`-*W;W-qZ z(a7}1^C|0VMOEpNJ9b64cC!wzP41Yo88QmD8%fr6N?2i#A}F9;xU3?@+6Ya_3Mu%i zi5e2+E{aWilbk(eW+TxboPgx#`xGbwM}fahryh_j+v)lE($rwn?*wRz9Cc4g{kEz4 zLwL+Es5C`&4;|i3rv?8KsbOg+d_`C^s(lj`rSmDWK%Ep&Bw!8* zw3^2{I>9Mnvgkgic0m9O?y9KqD{9<`v>1KfMl=)%TKzysX3$M-q}>R<2EK zAbFtrZQvv+&(I6uB+oVUm-8v{#$>1tujh}&Z->k}tUCLuv#&b)zP(NSch5c}+P1GA zHEY~>%S^Qlb+%sG8BDZ@bfwHMjVuXhXfTRD5LOwjY5*^hr8Dw42%e25DTPnLgR_7AOEm%`H&G6s9$ zsj@6^(WcTbP6K3}9XrGY*GI7H7= zawj@tYO7&)y*Mx!-g(X)U9-SZRfiZGptGE;HBQjY)HPY{RtHk&3iZ*eX4a-OGDLWZ zckM9eCTf=xEH|hdrYht!gH*G@u@V(D-2zA=ph(drrnL$<`mrQ_1-sjb2@ zo?Vb<%&R~mlp>g2P&oizibWOFkWO)3ps`fX32zu|RZ=Tad(#brg3dpj^Qk`ed3Ni8iasapF?YCFxBJ{$v2M*A z^SheV1^?bK=Z5xKMU@rsL9r+ltVg*7#Ee>MP!yda&u&hP-)_##+LT85Xrz0u%o)|I zmb%~)dF4?94`d7wNUex6XHifaXbVu2=((n}63^VSdSEc^<2fI7X-{}uKGhw>tZ(KF zZx4-te5&Kb0pHBoXWPei7d+VRw=bEyL#9=O0ph*P+-~6++C1bbnYp9CqiI0XA+667 zUBuSM{2f<9gSueD*s}B7S~+A2 zYV?#B?mTy&Z6AxCa?o>E9&<&D0b<;Uxm`NQQ#7@oyMK8kPx;O^hl(3_oBMpl;g$<% zdBVT^_OXCttJ~L7XDzQ#rpwiRFmg5z5C9JqL_v(Wl#q`Xg|t>d?~!wl=~e6dDkYbx z_3BU#l7*B){j3WHwhi?Ishb)-)6`bsGd*PV+|og{K3fqv4pa>8Phzq+ljJW;IhAHjEogdE~5!fB1IJO->@7!66BYV>H=5A8lrvu_IdNwTF zUpRk896F{U*I#ryr6DV}Ja%yx@tTjc-?Z!M#IdI|^y{qfW--I_O@FweMx5~DsRPAg zz7{W>($GhA{lgV~!|$3-ZRjg@JGJsFanz|a`(x|xuI0Oql+jX5ky(B54x-l|uIM38 z&{>Rof6BmMlGpI-%>NCSvP;nOw1x}Yi)Cjt^lMjJD~@m)26XJ2*Ok23)Twy~G5)NE zj-vgU4THq6bGXZK=QMN{&bxo@Cq}(=MJMsVM4rYsr{CPM9pI`X_PcyXG5Vz|I*Qrn z@b$g2srJrg{iE-OpT~D6P2#%?&bm1M`D^2s?;zeilZW{~(*4<2)BVvC8XgnxZ@Frq zSUIU-@c*$}zqYu1n7D9KgZV$xcRw+@yNi{W zb+|xvKNLV&P1}p0yAiSw2be>xdaA@z!4%-U38WNH3`Ks?<_heI50^`9r*v(LVWMGd zTGt_V_de0kEem;$p;4xjVyWb9k)`31o<+70j!LL@yx4=~Xk?n1RO?qM^a$>aI*RzZ zC|*yN7Ku?69;_Om^Ac`r07<9tH6`v{Tv|T77#inw1ADZD7pC+YIj~YXCF4WFb`N;4 zp||M%cGJ#TAf7yG!2A-mZ(yh7DcDCH$D$`yWJ3VY>>G^lHeD8@mbZ+nlSPgI`zezpHGuu!=(OX z{+20fKWf@_?}B1f6e*R&JotKDMiJ$;ape{aN}-bjhnv*)T3$|yby$JlO@24J)f#J< zGAP@$PJUXhBE4?coU*2WP4Z2NVdvJzl}ai4kGoW*^e?5INxlHnPT6D4L4lxVsS@W8 z={{^wg*-VLaPE*(2rQd&rhs5UvjTaJD*&TS7A5->CKxGIxSjJ<4OZZ>0qBbw8nX?B zL`Nyz7RBN_8pbxwZ|pEQq1&hg@L_i{!oE-Te$Y^_N63j z;BaR_f#wL>B@31WfZGLFn+)A{{DKx__E^NMA+rz@Tm{+>VwtQelDW|LD6{R6HYK@N zBwJHdq%@^d@l3BqEv3__1TfQU_ZsSq!dHRjN4gBgv27MTCI+=qa}dAyamiSnK!lwRrRn&X+&|ME->!9($M-AYLk>;$Hz#4buUX1{C*J2!F6v6O2$iF{MPg>}aKsNw(kZ zO^K2g#{IsY!rI9%r!6)NHyiD>21!&=eFpAM&3i-Ec*p`Sg0eNR1;}I2kczL<3T7bBjhrM4|vjC1G3zN%& zAj$X1f-=d7usopf4jf7ZYFeeAq;y>nd#-CZDhv5sQn#Y_^C0A(T-VSu>r!%>99Mi5 z(oO*i4(1)+4LJAhLq7}mjZ9(b%ToDdFSp?kn6j=kFy4nFW7R)g+3UwC? z#Uj^`sfOWBju>V9sZPFqhjG?ohNp>tR4BsYu{_wZ7;PFDzE2pA$e#tvL-l1re)Q43Y7o2`{R7~6!GQl8Y-+^&=x3iFSLk$+6yHAxQ;eUX3*@fHJ5L9O65 zc`ly2AoF}Zi6@xoRuNth3;H;+oE$69#->tj8*UYi_f5VQ)5@XXx~4#{Bh6+{VU|HE z!zO2wslp~vS;;NA*L+^w{B^^)n&fW9rUziQJN%o5?%5)*oD?z)TnPHe#W2Zz%rlr^ z03okE&cceqek2hRgJwCwcMVknw}lc+UMQ!are&Ohj31G#4N@b?jlk7yfPF)kg|3f{ z4j*g0>ZkR3fBSm!;gdUza*(4*9(`sMnw7*t!0f}+5%14y1f=nc z#*;b>v3-wGi&;i+Baf>I?mpHlcw-!wO|?u7MA;3=*=1XjccDN+B6TPTZ<8gKFL(rL zLftT|`<_kg8Z|OXk7Dyp=$JPWZ(HPNvHf^H+odcy0+|CjS~4#V!&N6OKjh{k!RvUq z`5;&|RfuaiPNE@qPZg2^$>fyrD5?U3YG9-w=HO^DQ9{tqD1{G{!UDpPBSjGFtZ6(2kdnfU(wq{Uf;l(w9&uB!!(C$o_ zA8jmBoCG=`8?O+VOb7J10_RJpG8(}$z+n!sS-Im!aa?QVbaE7jeul}Bz9mKFo9uc z!Q{)*sK>gCK@$^l?By&Uyf2lNzU6hm^B2R}>o(jhSK<3g=a@GXWss zf|6{1F*RCBw;?rJZAz~pQ=cJ@`$gl3J(Nl&OsHmcj~sHM14O?)mDL;m5o#7to4^rM zWcF&fhLCHX7@Uo$d?Gf?lsDAIe~~gObFF=3{B3Wl@3Al;TKu?(lbz8XrVlN1*&D{pCCN}5|Uf9B^wY>aPmZ zDJifX5mZ29@tDNYf;Z@Qv!Ij{Y1S+aio?v{GFX))oo`f(f_=z> zi4dAWsGN~;=;}If1K@lR=2SR3;Cq%Nr?`PBz(gG%Tavvg+;*PS7het3c@(j#P3f6{ zuPnQh(&<9Eg$;U8)Q;5ib-CPW=ak`l8tj~ubwX!Gp~b~X=^tXxI=6`L!}enDO&(%L zs5l`6V*QqjqXenaCrX71|D0UTV(4DVn`vWPr3}Y5>Sf9U3!R9Pj3t|LQvz1&SYw1N z(ZhFIlyF(xRji8WP6Q&+LR3t%NHOZwV+mUrgO%Y1ff!X&q#g)+4v1N55gAM+>L6SW zBYCVfGLCHze2GiJ4#1h1dJx?cv6j#Y6TRRiImuGGE{I9@HjQJ2NbZ*6JBsLaUsLxi zbh&6htkGbFa+0GMO4U%$$59BRBnV3n)nE~z08+(8XN#pI5#SIuC#rr?9zds5N|;D5 z)y)E|WQ7nwX00c{fDM@(sLQF^=+A=lCoksOvlKad@uEeKrYwRs9UMFgyOr($k zF2W*9O{$cR#K@~$NC-@;em-7;!iYv8GV)|oLzDWAYw@J@3o6tQSG+2A+qa;pb!Oo^P_xvk zq5c?EyC4Q;SHsN5jpMq+P1s1`r;@x*X^@}uKbqEJrlq{FHie&vYb1?NrS)b8P(11R z+0u0%QXnPYlNC}Rv76y(qEZF4kLV}ZWhVL_V^OE519Z+U6|x*$k7!h?iys3!8zr4BEir}0$s{5s+346+cm5k6ygBoL+r={za~LG(z#2e z3vl%K$z#VNCZ(+NgQu046Xp*N6^seV>g-X?q7V(zh&VmD=X_+KC3hP66`65LmXb7h zT~t{7SUFJIaets#!RpCXV6#OFVC?8c(f1*QQ$diRhM^t9-|Vmgy7*LC6l8oM!l{Ek z)7jY25i!iDP=u}wl^;<;wm}@r0Z4Bk`6)ezzMMRz`jR>gr`vn?H14)9KFm5(|BmTq zv#k;um=a4P7nx(Z~zq@ntci)E;iIcZ+`OIYCGMMhiRJ{ZYj@B4ZZoD zR;}q*n|xEY8F4)mZB^lpBrJ$N0yP}kSAh~WI3GdNP}3}`v!|m5NJ=;=;t*m&cQj$! zBl=kYi3T@$T$N4YMKQT$NSkmlPDxW?`y@6)fg5H-JXmC$tR}fz@ywHWL{d71oAutN zeY04B8WtmpGHzQc0a{d_K|^F#P`4=G;`uhI&`A6fmn1qR8YDgj#wN7SI@L7sA4V}U zNNEAmvH($SDMx)RR9-f@B3059+>u+UTcOEJ?ltm!r*s-^Y&Q>^w`-OTWXTt4QF3_% z$a%VkVxXeSTT^29y{rX;3M+m5C?fbiG>x1d-$Hr7!Rl8ybnp&IuPb#%@$q9FP>=~P zk%Y@ax2}OmllJApZ>C+7N$WbgleC^gAO6>?n)-<~d(7KmU)JF|P5F+=M*S=DhA=^? z6NQb&R<00^B<_!663=y1SSbZ98rKy~d0%ZjA}PbOP3x3LXtnHXHOcqWG^KIRY`%bz zOx6qeMKaR|)Go1US#7BRcc@J%tu?a785Lux@RbKF0PN0_2v1o`%+>JK6~RJ8i+rR) z0tKcbAGE5VsUW6Z8oN+=(FYu8 z-GF81ejBP>lioP&QK z1u~Bhg!ztR6k2y+%9av)!=>ku+#N97_*{Jw=CDUCS^Ub-f!@QlF3T}(^`nw{C1dM4 z-c-~!jkGg3x(R@2RM7&b8}M4B$(G!;AG#ffCUz=%k7*3q)wdIGwrd(zXVm4?3abrY zDtUXX;!0@7`^2EZBZF)8r~<91u}eTn0W5R15S)Isx;`^G@g=oQ;z${sZ8o%&XO<5) zv_kE){x!)rB{EYQ_sCM=kD^-WAD*mai%kI1fNy|_%J&x6JCiAJdCDzg#9^$#7;j<# zk%TAr59W_mX{@3C2tnw!>9Vo1$m`Tz&g1k$LFyZ9v=^a17F>yiHh?=*)DgoRgo{n;BB&{% zP%YVkVO>OwOn?(8W4PK4&{s?+!qOUb1(}%fl-x4B>4r=eOl@NKaGYq_+ zcFnTMLQzd5fta|g5nUt_fl`2~LG5JQjaI**D1sDA#hW8R!=T{^P-0)A4^yAl5?lJ? z#6x?ez9VNKcZ8rP=kw&o$C^T*4LhBH7KKwow@6B#PME4WB(cj?Lq*K2K6;m?;d^U! zrdBbWY<{Q{NB%}~ZjxS4n-F!%sBGgH zf#E#qk%f+tFp`#dPsK1oEsZ}yUD9uXF-3hkVDcp7;1U6cO^y&w?pV)zeyVir0}^4w z!;^0SQIei<;!4FZGY0b|)|pRSbP>i{UPiPD`p9~+L~Dqen5Kccl4s@BD=}6n-IEu= zWUC>VkkGY6im75rzNu>snknIAh9IW{OPNfs&?$4=?UgH3%r6R1 zGJ7Z~%#*@sC+D4~$W{tsuSnJIsPtpxc}vdrh$2F(GD|>NCB7L@hK%h)#~pX_n6a`p z8m zwQO3Sp%3-F{}2J>itvDg%wfpMa<3@83Y#Tv8^cHIUL_T#(IaQE?y;v*78lPBURDsd zY^ZshYBm-Cux|)A!n*oJoD9$<5XT3TC6Qw6RPyA6xMP4JmB&O_p!bacDZsoY!x-h`B5@n(NfR%qX2|E?jt-1gmnmjX@rPvD~Kx6CwJeUgsh(?aT(K0@k;bxNI{ub5nb z${8|1nO(YtqK0Hb(3I3Hm$}*+z8dKcjR0`Tsdtf%6e)?xucvLf-;^QQMx{)CZk!uG ze#cd7`e)PbOcuU~_#POPy5sVsA*>-OnutwU@|^co1gI(@J1xs4sE~Y~k}I-G1XcZQ zls5v0?7!eeB>Y!uSCQu@0ElAW;VmHw9yN_TAIaT=+zE{`to_8k zx4J$|QV($jGIl7(0_c~wEAvHUV3g4b95CcsVin{F&Q;xo z4^s*71POD(sSaWvF9|*!m@i1gj%rcYBzG7pSlN{RB455#5^G3m$Nfn%2<$u*aL-Fu zK6j5oh?eIBD-R9HiSQc0Pg7(Xg6?H+zzYoal;wnYj0#3hCKGTf3W&3FdtiHVP_nR+ zU>R#rm;mD>{37#oQu-73Z`X*wCb_$)lD0Bt-p+fOl@1O(w3r=vHZO@~1n~-5oTo`} zgiW~&4o=B1m6}n!cj3xOdL3=-sgyC;#=oEZtjL>*17uL2B;HhLenkLb%Aw;hW%LkS z!o}-hmlT+5td340+PNaEfn4L*jTmJz=>u~G)GW>@xSS-0I`_)hq2>2Ei8z{+?gYU+2sSHE~ z0We~4^91VGQ>FbZ;YZYE0?0?20q$`OF3PuC@Rg8ZUx{EWP{u)WX&EYm8ekw=78{66 z%s{dA^AVj&ZE~kJ84jSSQhN>Iiwh2DLLVd(A807r6UbWBZV5u-HAn&kMJYr+#HFDf z40aTQ5^W>&O#!gvoyj;86bop@6)r?n5jdKp)oW0G%K$K3@QGOB6y;@Gxd3OQELWA> z@x-pvwVISJTU_@2543hY5fz;(tuYJ^89v|-2qk$9Xg7od37wEh zR?LZ77sP3V(h-qSd6$YSQL+*A0zv4b`O0*lC}V z9#>7;+(xEoHEFY&w3(7Z8B~)t+vbW^lQzLHrQ|mTgj0c=SCclYNt@NA&7&J(cuz?d zttM@LM!e}*VvzoN| z9V|1|q|LvQ(_T&5{8r9rHEFYUcr|IWnzUI>+Dyt6ttM?&Fr_Nt@NA&2l2@ z)}+l~&G1VP>5}6d5{}S!n6XTgDJ}Zk|%9Z z&PnFt!vsT;0pvWc7DE;K|&jgS&js zv(DhG{QW&p4wzrxE%S18#xq9UwS9Xr`-A$y>^t@;G_Q5}X^sPfz zWWHzT@|L=YaUI|2B7T4S`8@*vsQE)r5ii|6zecRy_kx{8cE1Y_7kl0_e}EWz_xxQ% z&Hfh*6ZhOX-xROzcR^nIA7{+G{m;DcA~BtBR<|5h>K!TJ5gvp=NQ=?~5yF82Hp(knl@U`KKyIMDpV z3kHg(@1MV0&**Z>S0l##=z{&e?~iBP_27K|E(`8@@bbUch>tQuhKi-HUfL&^_R{6q z?>nBNQ98T(8$E+|?_A#CWvoCPamAe-qW`~}5uoaiq&R~5?eg(HIh zhy_Nyc>2<-M$9;7_`=|r;a9$#6F2AhXdiQBU?0^wvzSFkxoxY9la@+VWU-{kS3BOUd(Q)`)tA-PPqiv&I_+6WJ`Zn5y-?eF% zlkK`y+_89jW^-M~@a9ELee;+$&&I60?)b8eZG6|JU3iIY+J)b>X%}8%t9Cq_@XIz0 z!|QI%lm87F8r=dyTUfwh2KqX)6m#P!|=N{?aXbo3%_gA&e}%1@VmI( zaa#Nux+Zayd$PkyNR$XFaSyZJY<*fhJ`VAJg8{o`-%d$=rS9%(+%q9Pv(b ze=%iFb4T&WoaUjGM=}9N7CzEEyzE=-tmpbkpE>&5@J#_wi^Dgl(pL)9ahg&H)rE>9x%WB9Oun1U*nW#D(^Wbu599`wa;=n2VK@& zv-PCZGHHm8>>QtO$z{zysko2#@8CYdAMSX0`Q;vPd3k!wzP$X1p1!>NB5r=Jd{Jj! z(R^^l`R==m^G$rd@|N?_)va8})eT-yzOhjY%BRb|P=2pZE-1g(r7tv(*?K3Gb(8Nk zJj-7emf!1v|7!JGuKbYh7yhe!%^Utzo&~e6Z2r;K(-oqN+He&Y^~sgZ*~%l~BMXo0 z(_FsXlbS1MMM2Jr{^I1-%_HLHy1BXh%nrP&-2arT$~QTFP5GJCEUKIkMl>O6*Ea7K zpKIBo^01q|wsJ@r(b}=_YOebDtIOkV*uTpU?TM?)mr-*~`7%ztrhFM!UQ@n|FRrP) z3^TfnE$cbAe{J(l@wLplws~|cPAFB*A-ECcIu^D!t}9=QeO=`Ro6#Hy8~yXT=DoK* zW;1%9;gLbtS55>|gcE@yEo1gi*O#yP#p}y2^xh4XFVu=&=Wqj27ajjp}1@|vlnEyo~7D#nWyU2fRiTwES6CoC>s^YX>zJ!rv8 z<&i#SN%^HIFE{@g->(TYh3o~Wvwr}_v3PpdcEAi{i(Ci{<6J+4xSY3yrsyaql+QwmM@to5ly+yXL z3w?V<`9g1guRL6Tc}Mes@o-&uNBP3uxubkx&unfzJeB9Tr;&GX1vlQ=>H+4W2N)iD z=g#uQ^}4HkacA7sd}!R)op+Vz&cgSbM_1fqUhZ-`3jgl%c)9rQ)-U>*1st!vn=AP0 z?(!|&|3Rzg7w<4(tNHh|y3Txbo#CN%_q1GR)4k1qZe!?Fm_%j6uxbQG269_9ej6st zwyce985^x^jo8%qwxv-u0$Z%Gsu5T<0;@(KG+dPmtR|{PVCcRSRU@!!1V*0hsu37( znb2uU=S|fJtQvs?c+{#9C<6+jY6RkCl~!acBk;)Jy$_quY~MBKLv85gA-@!v!!8^s z{`_gPTKVOw-DeIE&wSeadY5ubasOw{zia&w$4bW;-Nh;YX}-DQK=$)ybL+=o{O8TL z?AfWOmifUCejs{xovH?h?s}EqLp&=Mj0pafyXpx=e0+8Alm6P)TG+Lwp35r`G1sYL z+%>`Y9k+c<74N@3XCO=#S6w1{Wv|*>j6LP5Q9Ee+MFq7hPxAh?LF0EF7!uvY*{5DL zLmYg4Fsko1PM`|)tvNf0CEs;8xc#)NChR5lIp)d{!B3m6n$}+I+;r85;FC+Q5`$`C zN28j!X6IqGrHD67FD7(CaAUX%G`(=%+1t&zFq_g!47=%ufx(%#Uv>2kwK`k^lBTps z&40KoNqz~{Fn2^MAOCob$L%*o#)KEqDp%Rj}2TwRm(o`^a zfPLtBh=IV#tK0Eva8i+%b63*lcpYy5w?Inx-x43cZu7Lh>xRTjAG%Ll*uE zYW2ehWWWm(GmnRi$wMXIGej)#COFi$mn>VrQ8c*49P0B6NEBi^F4(JGUlM}nE!c!C zi+cDloKU{ew0MI`RNOwb&piJMT(DF=$!fkd!>LzUFAufzJVs zaU1&vW60>9# zs1u7&0y6BNV1R>4lEmp6>?;l&>0ZReRYQ&~q!5OrC4?`eKw*DKqTycC`PR{b@v9pa zW`=q?G_R^;*@UMJauFN0BbXO_o52hd-$nKDEUQiF(-e0OUUYaixldnmM?}*M{$s^; zblp^7vGXlnVm_~d+Jd1Req<-EaRiMY1DN^mgzTNLNkT$s8ZH!11sKC3wwQ>N+T#*Q!ybe7TJe zeGo<%V&t)l##eW<>TVVitXFrlHnX$3o5ee5bvLW-X4_IV+RT^#>D}z2BjK(+Ek#F=0viB}(!Kr_J=c4;M{13e^>K&4S(12 zcU>@e<@MT#%$w0A{PFec+kbDcboas=U4m6xuCKql)^dtE6s`pb;o)d3z)CA^EZZ?1 zQ!=8OkrkWTDzswvx#@;KbgNZielEH$6kxeL-0d24ujCYHkZ5W}Uo*YvjH#{U8D})! zc|*{6=Zzn|3TV52;o`4{wp;fTF{ys$Az2^3&m8J0rXq}qDDJig1ulGTu!;FLyt|Si zK4Rq3LvCC+W?kqtVF`uk5mrC7;Fz95?i~eVTDTSA6xQ>Q_&GL|W)8QY`ZmLGs{bqjKYPY4(YwkJB4<;(kS5(V3PPMG^Y8SBB?K- z@`EN4qZ}`Y7S$QwGQ8=AOj_UKnN#6}OX(TI#1dQXY3v~OUb{G}X%KE(b!H_IoMIbT zT9)mrxgze20<_#vHQPQ!lAgiS4>>T0Ce9bJvb4H?FfJ^Pp7@&FwnlUE@N82!EalO; zTM4;S?rAG$aMqF3pVCre%E3af<2}%QO>$uByk;}V{AM2S7p~U=js(-xbzhP{7a?23 zD3<)`u4Ng)fxAqDb`kFgo*Y$^W?Rnm729T{n{eDRX9~>A^M}{BOtzG6m1wf1bSoxJ zS`$w;$>PXAH~`?;<;rczXYAwcF|m71CFT`p{Hi&~i;D`QvxudNRqWcTZ$c{0av&#~ zB>7~B=Pd5bWpc5;;E?ih$Em)nxCWk@E^xe#TikSGrY5=5ka8C?)5Jcbd${Ps#V2GP z*j1qb#^I2Ir&Q7{OaC9<2_7kQmN6NtWJ=HBVT0fT#z?$VjsrhB$)%P9!?qvIpUD1q z9Z2RO&}4BjSS9i-*LmG@J%?4vF}&ExmeOrlKBXqPS7A??W{UUjY3w9t*-+iW4V%x^ z6)LH@bwBUerUEss@8pUaD=~bmj;84a)$`p#w7ieLW$N)AvMD=B1wnW6tIFqEv%95? z&tmJ=DNoOotczZAWB+X0o%Mcxsx+d1T;1t9)Ff^_)t&A;cG>Dq*IG%a?sV0iuDa7z zC?)@Q?R16+4Q%hQCBN??8dl668H~FAHq7hAW8UZ={O$N9Etd7^$1b`5dt25khb`+M zmX3L&PcZYuCB1gcJSB!bG`a7Lo6cDh+;q-O`|c|ap0#wuj9yD`3VJQQ*;p^`ePii} z;H+6o$Nf^gdGk#pf>+;IdbJ^bbYzc1#Iv=_uKB(OK9_M`=7Yc(zUU5&+WmuKFbg6 zA=WNlJ|b9AyZrKD;{9weBKYGEm)pCFqkqV^9Zp+*VT~v}hj{CZ<->On7tKKY^6$&n z_ZGcNh?ynJ^Sg={{*HL!bIWh*B6ja{`-s3SEl1g$Q_Ht6_X~P-6?gaJyaQ^bSUQz& z$Lta8)H$;-n4|^!e&3rBi}OK`pxu6fdAc~szp8sTD=%$?%Wk{`JnRshFPX-dv{4E>PC6D7q%X?Vfx*d6JQd@;SpvyiE&fP@} z9Kp%W!7Da(6TJr`jvldMukFPxPtf}%xfKKcl+|1f3xg0TcI>Y76ypoIK-BTy#;U2Dky(0gL8J&Lh07=|nj_ z>AY}lGcPbXTa3w+t}U@=X~oa8XlIVVSi)|8JpFO5VCh>anjwI8s&`!s@+j%VAZ4%)#6sFKGf zwQLJFj_u-oUb+T0_TXnLT`Tyq07VELU{&dC6zL}4uZdW4) zb-&}L@2M*Wy|%w&NS#>t>0Z5q{rA1&?EYfTvKH-b!x4A%{vNu@4C*-r!;Zgm$SY#l zUYEw2A~l!#eUEcRwV^JW`$nG`|32g{{_f`Q9{%p-?>_!k@^?Rf5AgROfB)d`A^slb z?-Bm~$={>=J;vYT{5`?nll(o!-_!i9;_n&$p5^a3{+{RW1^)gO{QHm>ck*f#;xnx4 zgf|=olug0HNsC7mBwG={q|u_Zq_ql_mi0HiNZ_ids&)~VuxEmoDk`|REj*A00VTpK zh7FiYB1BVKiDzzEJutBDdXdsbhs|F-BKUmGi+^k{UR;BCdFjPRyNOS(K>XXA^4n_# zqVn#G{j)@h@N?#i4#3d@krJHJMFse#ZabitTt$rEZqCfwlt$#EN-*iJ+pk{#7ja(c z#Sy`t4Xf$qxiyGS2`T3OMURu_ub$ps%y|}Z=z`Vn_NjG?c~1w1sCkZ=%Y*qUxC#g? z4b=otW+A@wlvV+E{`&!<%e!>;>&InhKYg4Y`aLbh_ntyr@vId6H)(MEzogiqfp5QF zv-;}xS#$^jhAJRA0$J!!rknG?tnw~UD3fl#;h4ro7^kf(Ijo! zNO95OYr4p-77VMZsb*1zB?)I0RTp$^k&$JqUZh9OzOEjX!vifS@q!*;U!o7V>A}ku zje^dQw$$L6J&7(b+CYlh77$;8mxNJ>CZgA(`%2|mccZXi6#q)=8bwOY+R42Nyw7S& zIB%GE@AqrE)@XR&3qX+x5X&e@z6f+$b#i*n1wrTqwMzZO4mBYi} z6u~3P*q@<_BkpbN7erwOU(*Tz^9&0hHUK__dm-uq!qRahu({P8CH4#&{FA9_TF>f3 zjk}7KSI_JtzWi+U@R3HL&ZrFMK?mTt#8C=)bYGC$mZB?+E{m(Qlzc7%f@V|Jnn*pY z-LUwtnaRoTt4)Y5Wu&$V|D`;+LQ6oee>VB1OwoJa#@(}uZ()&GrfUEKR)Jo+dE3+T z@|p<3IuX7(#A(-nAy*lNgqozWW)^d*#v2CZZhNL)lo6Je2)f6b1^U}404`=>W-T`I zwo7Q66`J5p0BHK1Ol?Znel+d!$=!Mt(+)eSNR(R|8CuRE<_zRq^$cIPZAB?q5#)08 z$XQG~#p5142cu1)F^i6^c(%d1!W48X?J%ZWULmHgcv!_2ac#d4;{7d4bBII-?>{v2 z-VA94i9K_|Sp?BjIt}eGg6Kz!%Pwce89Jda)?JXje zzIYxX%EXR+VShGHCAyo~sa!$~IoW!WH?U^#o@NE^-10H>fx!E!;pd3$Nz0afhrp{> z0AdkjCN^R!r|qyI6e2RlQr?l?+89e2oPO)pDUVG?6~t{{P3=!yH|eg#m80up)OikT zxnSir9sowRn=i6dl0ae^KI=Mp0wFE!Q1)U_+y#tPKn|*&v*jrEEY0_QR?J4O9~0aOv)gEc*} zh61X?Mpg0*jfgwCN*)Zl&FaC;?JQaTQ&+vfi?c7+1i@O=rke4nBuE{uGtg33_$)my)txepuOgD zdC!pv9xQKU;-Go3Sr@eeOK~e?RI<(~skD~wKWk=Ya>@&86FE;Am~G&FZt`=J93+!-o=%t2+5~qDrAf?=JAp7GI1+8xRv}uUMc2rhE-x!Qu+>6%EN0?x(>aIkwQsY zS>DL`q<~3aiD82(A?MBId0y|4Ai}Ix^Gzy@A<~ZAFRU1w ztQ^&~4D$AHd&Y05SC1v=9?51_r23BgeLZSq@>^=77^aNQHb|A^hX)At^LDhI`@kyqr$5rF)+s0zmc>90Fcr!XpZ#ci9ahG7X57%6NFf;}3qY-^5>g2Xx(gZ_q%wy_Le=npvyn_sOaI=&Bete(f)7 z&3v7u*Qs?lF88V>Xs7H$>`2*1#e2WcK89+Frw?75>sHZ9sAJatR4f>~_75F$bryMG z$FJRShgK)9-7P#Y?iXwSvwf=r;{6lW{zQ!Z+1eBK%xiU;YSgLx6(B5W`KT+EA1#dr z1@ZLH)*ickON;Oa@*1A#P@u8M9NwmTt#-HYz=WgLjuyWhJ zj$M0Vn*l)2M~jt5t~JEXC#^j!Whh*9+}abW&!HfA4r3z>>jfvT{ma0eSs(sero|b( z4mh!vRLwdo_tV?!e3wt}b>-k6-(7dC=<*pq7&5O^Cw6&{1F!#w zj~BkT&K1YKi?r%DrOsmF)up~-&&?eDZ5MtvYx6o=JTH%Of&pTWEgabMlXcPAo8_l{ z7#YWh|Qak4jVeGw|Mg-ezD}^ zbq9zuKc>l$PdTva<8{Z2-^m-@GJnGk!v2H#PJ zd>p(TAOB=+=qnzTAMeZX@vQc{cNgYo^s%rbA4i_Mp`Td(8At75d>r^6p5srxrpvIS zYyZQ6Lw4lA&)(g8s2K4%KYOhkA7_8Q&dppIy}&O2HDx+ok~ZFeL5hWa@NUx#O(I3b`t;Y#L=mP`0jx1=y5yw6zcx^ zQeW}-b{x1=UP@i((otfreEIb^y);Cu?974N26KSN*iV>U_}GVeAV&9QMBgkwHpsVq z;(4bI6M9#!>`Zy%XKi2VBW~)-(Use`G~T5fAD@%AabdU8N#a*>azt{;{lvra<19Hnw?F4)MI5&SA4h)7Z|~lrG(~J? zRyOTl>LF(RbxLosaX_h)nAVpTyA9z7qnchmOsM_%I9g7VhJK~9g_}hxJ^%6mG4tQA z4;H;%c)7bctv^jRhGVRM>5pQioZ1r}e)$;jz>XX^c4+jAQNkU-$4UM8_~eH#XT@vs z<4bb7zxT<@zZ6#uHe?4jv~K%LXO38{wtO^ zXD}bHWNi{J4lW%b+6~~hKOIuKL~MG8@7}6i7T?i;p|m^jL%zG>AFrGw*2s^FT$lE% zDg8yPm$z!X{>mt^bCv_o$z^x!r?31}JS#s=k}Kv7+ut!J8l@N3^4+a+JzmxM)q)r{ zjE{?CzYBJF^+55E{CKzQXi%rM2a1WWbIlWmmyVcxX@?*D;PlfbcKN~SFG%S;DLpBr zsZzRAO0%W3L`s)S=^82BAf<<;6iDehDg8rA4N`hWN&a3)Mv9B151XaVf2l(u-1hO-i>*>BC=lnf!y( z@0Q{Z&gRRvq_n-1u9wmvDSaZP5mI_aN;xStOX)x<{aZ?hOKF9aj+fH=QaV*itE6t3kkCBPl&ErGH6j6Vl|<7fbOy z`Q^1z`a(*#N~!a?NRLRVpOn6o(r_ugBBha1dRa>QNol>54wceeDg8o9Z%WCL(o<5J zD5Vdi^d~93Dy90c;p{eQuxiolmx63dizI&Gbl#!qNh+a=_>~_gMpX)*Ay-QD5d?(ia{MDSeFkUYdk1T+(qoY*X5X-}yZF^%rJ=$4eM`^AUv50= zI)7$_q=;4}~)NyiQ{59-Md2AC?}851)KkX?M!toZDNB z`|GAY!Pp;{9*&P}*l&IO!&M)5>>Z3hsPs&HWODPy?$;iENU2?Pm7Jn~B#O-!#up$~ z9dquG;PIc8uV4N~@SCAcd%xCAoH_TU-oe|4mmZ4GIR4d*+{n1n*A+d;)-~d$58HPU zD@VQDCm42A>x-W9Mf-tlfiHIresXl_&5ZcXZR>fj@2uNFEIo#2`Sl92MdjNMh8$b^ zZAO0X6I^p#>7z&#vlnmdCLWvEc|@@O_|nSw_|g+gTjJwW&M5Upf9*UV`1i^3O^3g~ z-7a-&7mA|ie!TROQyLt9e(w)tK;Cs?si(O7gja`)i8prME*RsN*2kyYGG={zc;voI zQ3Y=7+*xqAYjDx2rN`qo6V7aDv#{gRLBZvx%O@W8%f);iHyStlrEa3@9^Goh`oieW z!98b{&di9_e|u>{Wcl1ak$azb^-J-+51Ul_O-6o3qm(y1=-9V=h*2lK+9!D7*QIkZ z!tVU)gwRHM@*IA5c$Zgq4y<$K84-I2!Flq@NKuSFi1#;t%xk^G_(45;h!G=8eS&}d zwzMKX%qZJg9PqCmeZv|YIQ(|oewxO=+EZN7zUOvg^q`GhgEyv>Zu?e~i+lAH_q?>jc4Fe~8@dK>UQoI# zZnEW9bQ)gmme+dp6O-TQ(OFEHyS{7i*|gFvaT_KV)>SEsi#5v^~+j>Q3?aiawqdyls;gVK|11@;ScH*@cU#bxk zPnXr&MQJ;+_puu$NQ?G7G5ePr;y1s1UTMdS{M;wlaA|2mERLUFIy4sVx~w!Y7WcnG zE;(V_2NsmxkHv?slnr*hcSAwcp1DC68+&c&BxXwW{*kLnhcw-{VP~N)Dm^TY5zNz# zi#8o8dTm}eSWLURbVx>sVJEHMDcEv#X%Kwq;=sFEg=?;r<2l%JEzgYa-nqW?4jg^7B9KAf0V9>f3x(VQYjWWye1awA1!T)#Xmh> z`ZN+n_8DIP>?cc?#fJ`fx~0E>L##t#*K7ZUX}0kfuXGH|=SmCWrf)u9+7OEzXFHHh zoA!OBL-4@ra#y#nmFr%3l3p7b=Cz-eI*Qr5F6 zl@`Y0BQKX8jm73yJXy;)iv9TFVs9r$mTCNgp4x!!oUG$#Jl z!JEr(>ZvVqFAf_V_d!{F<--ccNJNO}sPQe)0N&8TqkKaA#&c!%2!__x9@_%lsqw zsQr2-Lio+69oIj@H;;B&|7Av;-M}`};^{eDY*^5;&D_#u{dpPrxsO=5>&78X4-e=U z{Hojf>*DXmA6DuXDBG`R9S_?~{yod_yKisk-BdrIZ*WG>^^BYFz?0u>7$UzD3wy2a z8T`HX`mybz1FIZ%nDHal4+?7gu3sLvURtw$x1d-5^$dsb*W>Scsaw!}!1@mHkH(hR zruH7Rep=g;VC@a;9(*)p{SDDC1J1Nv+(@28aFU^7!~e(DRR>0OJpWz@#ED@^2!s&a zf|p{!B}j0GAVGo!cWZ&>&|n#yAVrF$v}AFI77r~HKYtI3ra+-Vf1jQAF4FJspPQYX zS(}}mot3v28ste!3kBs(D`7$ha}hF2LY7Po5*13C8f)pqtWu_PI+LudKxLonxD*K` z?LuxJHwWpn#O89QYI=!B#-rL96-+STT*mAzkj~L!rb3i36q8j(B@>j8DKFcC@~H-# zw#cio;G+XDN6F~bxX{YoZ( zF}H^4xK6gzGnJNU@>2UU!38O-K8){%TBh3n$+sxox&oyxS;vGC#1+cU*D?JjR5Xoyniyn?e2%T$qwPLIP;P6-R(r)6sNOC&Nk3CcD%At06uVDe&Vy z2_2(!Ho^9CKBrDY86lUlG9P9vNT-5A+(c+M(^Z`(@+MT0d17ye_=~6>rjFc(0FrGK zXyXqCYFz%|Xt@DfknZ#`wWLE8VZPZxY}(f}${=wVvtp=KR1^PV;&VUKng2xMFQ0_J zJB=z`%$<4{v3QDN15BrNo_&9r%27g%qTzHc)Yn~`+S?5>!F`hXim<_^lURCmff}?M zYU)c*yE)e;=|fGahJ9krFljx!j$`KI4dJQ7p(p!JJN>w4!=;`84-oJYA80{xb~?tw zy3iuj?x#>6O0xO8X!Ojal{QzP7-RABls>GO2eoeu{~~dk>?}?hmXd>as9j?j3>Nx%1y-r=}}VrhO1p@-EYFTH&L2n<{99OAR4tNEl`O8&*~msNX&alnV!6 zdrJAcc-|bMY?lgAGPUAGNRq2Id#2(}k@?E%#$(9=DbrS8Fm1<{$L1xUbG-Y-)Xwlu zb0>!#G|e^u$!KJ*OP28^B4}}`a_;0i2_otxOj|({Uk+g|08j54;7Mu|URt&@FQsoW z7ZT@=Vj>6aW1@-Em!gy(OZX=biEtAT^BqY-@>pv4ONpE+IOr7g8#H$pt=LkObhU&J zom>;)Mi8v0IB*7v4TfVFCs}1`veh)-_56Qxi2MsVuy{NEin$FHFM82bUsl}QTB~?@ zO{(daAvZ*W983FUQ?k|5?;3hWCaL1A80%)obScNEd!_|+{&x)T*jG*;rRpE12!q4} zXvj@?9LGhoTc&(2T1=nYCOcvp-7&#=>KTG115_gq?8J9= z(^)-XC8;Z^T|^~PHb+ly%E(PlYElaGk{l(sUP8!+#DVix}nLT&iFEkgYH&V&^@g4luCfkAcom}`w1GMQJh1xI7Rh{!0LNI>1_EOtYyYh zOlBa8xR~Lfa1fLNP zv-0yYWA`hIk#kB&RkGNWV8p0ke5IwWmJ33sfTYe11f z=2XKU!Wv}8YGsa_L+`!^A!ZD#K!@%?v@B|#j$sw(2q396X~oR&l^MNW+T4*lPP7k` zBA1b(TDTe0DFcLpusUOlmUG6^)yZ;@1ePvK+2VUp!3-yh^N(2va%#=ewY1qT zw6Yo6%Q6%i-fo3TLP>{wIL zoNwsbBU3@~xP#O%rf{0n&t*zbQg=*8EaZZnrHV5-W1y+L@at+0b)mUGL(lkVeP?%b zJ6d{7*E12^!wmNicmT=uAI)x14yVppliTZ_<~15wZ0v0==Av=JsVtWqT`p9c+{*Wp zE$1v?jWIZzm8+x~%eels!&hRTi$lX(=R)JNrV zdQ@+RNE^VpX|B0sOzlvv5T%$a=A87Z#5?mPHt>i2)hm@zN z4&`v%yBL)3M@e~SSAg@KUCmrr>`RcNUZxQ<$C|O?Of3&%iX-(tlu*;@CZ|r2;}m!R zY0HmIN_pC}8hdT*BP^d}Uaye_#%UQBjg#lDq!tyzH96j&CN-Gzz!)k|e#^?&(BxQr zrWrd%OyMCldv*C*qR;Fs_N7bB%_-8Of}5ECtr1r53V<yROO-=~2|+GQsLb=2zyjoMjue#Ar(#_JyYYt}$;LSSN2O*C z@A$khx7O>hW-$g0#D{IsVUhf2Zpj~#~Y>@o8|L-GYIJ^6Od zffKMi#V8l-oigJzgM)vaF=N|_gRRe-JL|#JU(K<4F#M8vq#oRS z#XLw05|Z_&*lT7lT57{enRg6UT{qv?3ZOTCfSlx7C4ffEtn4m6-H-!-3jw8{o?YCZ z<{o-5{f@bd9;|)e+)WRrJT$k{f@u~rmX4f3SDP~kb?8s0{;i_xb16j*Mdr!-$UM-6 zDwtt6a4{)_Ui}3H-VYIJ+W>u^nBmz14x zjY3?opYZCfG}O6x%F}3HC}3D7hJfKYRqbf1M3z4y%hP&)i@)gdK^jF)z~@nspUeyO z@-U5Hoy?@smtkFYzcCk2KCBd{86P6u1d4$vI{&Qt$%YXchKoLud_BODSDXs8yf+wdvFx5{6!FEf+J!9b z=uH{yW9%wq!EO$M7#;G^6lXNkVS#;{)jE}rB(cYc?su(LnC_>-e{LOY z!OSERC(G?m5d7%=$Eu#9ZBfey0}Wb@RS|Bk1<=J}mX=g~jX6TpEN+Q6$e_PS4YR*vOIxs1x>C(c{1%amB0xdEF{ktBjPnSD*{kLu z?v%A)Q%5TTho*8v6E}KJC`HA~@)lSjrf|!+h?pO%;UZ(Y)l>LZvK-RM*9W3**qeob zidTmxu)mrIZTJ%tYtbqetT_I^vMq-o2+pTuRSOQ{mQ8WOr?xWu5H2A zP_^n_qI69QhL4O(t8J;`LV*oUIk)3$Zhfdblzdao;~U7+cN_X|!Z((A1{w4x7FGPn z>ZL@zdKQ>Cj^u;43Jol8G+abRS>Ecw+Knw_ppbnS;J7>3k6!G9mwKwHWi*Zmj$&BL zvs2NgxdmtAjAOqV+lGmjV9}?gWxXEh+uc%{eBEoiiLYB*;223xd9*^2ldXZGbQ=p6 zo-$5gCpuG4vt*~GvTd+3ZNKO>lRrV+6 zhl2mrNw7bqMZ=z!TRB+i+N#tPQ>@M?AIxLP>jYF zrc|Y-@2U;Zfx)qsRGo^`;L`NvJhr>9Jw@W(E+7vYU^$?ZGn>gx*F;N*XgA0L`^go? z4s~ftcv;(rmblb*qv$(7dWx}wb7vX$uBNN#J5-JXP9r5jkVJ^+8}Do>Wr>3l_SMW! zk$Gyl(kIti?jm7?1zt&3LeX}V<&}<0kx+V2Emtuu!Sbgbi7w#Cf;2i14cIwW8X`{a zq`1;G)SP)k>4vo)MSCKN&*}tqA#KQ7R|Jq$_o@{}~Au|#v7e_XnC(HUv{aU`_ z)O2aMvI|OSQzt)d>ekpzq|cPbjwz#EtVPJO9}NuaRx2OvZ&k~kLc%f3p3Tl(O(utR ztmR9{7gs~RwP1EZ7NHbnwP4>JS%{3eS$dNt(zNd_Cao-yk!bP2d7D&nDX8R?wLBo`w0(47bh>s&XODlMs{ms2BI8rPha z^rfU#kjm16s71@w7R+T#oqgPbs~wYS6(Ya&wLC=3+N=?a$)E_uZmCtCmNr1GlGa<~ zQN3(7OB=28qo;dnd5fftPHhvTQ!Lofl1u^`?Ci`eDbma9ikCRP&4T5eR{98>w5;1> zDJl+bcQ#sJu>P&nb7*h6lAkO$6jyQVM+n=`~&H$49li#EE^* za>UY9%WItxxtEa{xzlJjj4a%n@f0@?SUUJo=U?ErId4AaqkU&#CXnL7MIRj(IIr}0$5Lda;`8N{VL5WaEK}no$Pzh5>BIUDq&RXGE7RXi_)Yp z`PAPQ$ap}5f|Oq6QC>35Y~UflgApys0v?aQi^hrB-b*yNZgJ6Z*0A(jN(6lw*}#p~ zThXnXZdkVK8OwW76mk{iCbf(Tq?G8W0(^;uaEZlJ9REXVA=dzF6)&;rmeeT5aeR@T z5QkfSa8@hak$uBt*&c!Rl+xMZ9%r-rQq1G1qGYZW<))Rh|ACw*IU%oKofYz>*gHxA zs$UP%cxJNT;E#FuKo#RK#Xv*%!4T24%w#T@K*C2|Flc_0}mjav6ZH%NsVyU zrO1)Tk$g^5v9vXWQWiD}5hov6u*6~tbO2j(zcwmNXY1|>5P$q-Ny^SBlA?+>aT9x< zTCk8|cD_Z_Z@$dvSR+r0^ljoJEmKPc>(35#wBnc;cujMS6#g+C} zDyc*TYV6KTy!F7NzZCT;)TFTJ`(BQrtTdFQGzz0ij~cm)86TznV+xW-(uSvv(B01) zxl?RkSg>`Ur60j$*x@2FVbH-9R*0QT#8CaLlli2Xtz5=K_@<*=U_y_(THz~kYM3>g z)IL;#sC@pW-Z`cndp>KL&V$?{Db=Lp6&81^v%Y9~qO&Ed z3aiuDRjev(g{m?qizCY1sEF7YWW_ATkxrwFeP65MN3&W&2vm~sfAYTx{pd=ZGxN3) zR@gD-kxDC|w3-It$X*I6)ec)Zy{z@F&Lk}W!2&u$5m{XU85o9FRzB6`EbIg%8efEQQ4P)vfR{xpLah zlTI!qj?}a&Iz?`ah-0;_upP|l6mI3@T`ltQbR(*KV}(m2sRHv!xX$Rbiar$kNU153 z8gO9{p@0Z+Mv^PHQgpwO)t6Gcf9+1t4o~r@u@$oy^RUu}-E15oUN?0Xq)9I&qNNw5 zKWywq=w44zyoD96G^fGDgbO-ttwEIFXpGxqt*r1OGz!MFMoB;2(w#D%AocrmCY;1a zTj7a@L=3x-^{gn-4{KMhDQV`Z(zh>~3A4oEPaj&cqk!=A$2@p^FIL!pH4|;!~wYd1ZUzSB+L8U)aLd3`Z&dLc~Ho@e~Ex=3) zr|b7tg@w!Dtem)6oRj-dE7lRriA3c^q4=zkovVrgB4k8X72)?4k@XG`#YR~%4Q3^$ zjPTaJT3^;pu)_6Z3Qqy)M_ajy$gx&<@Ei$Ql&6%Et?_Rh%i)Oh8K2cfk|NqqbaoNn z;=)oxH-U_LPj!x5fhkswD1Bsy(Ck93p@CIe`%&aSZtj}r&}U{=UHlq23X6ubt?;|J z8d;K{;&oB=*g+HAwL7%kzRgkszDyydMZS$&2T|XqD1ql%&)iZS>m;6BkR9 z#pK9QW^cNG33FTQQtM^|9Vuu@kl%6z(%fa%mQ=Wib4M{_nJf!9yk5DKTkFv_Sfs45 zV!Gr=UhgehWyQ87dOixHwt3sSiBoH=R%b@i z^y4yelBe1%ZpOevx4X8|&Qnm={xzUa4W@rN2=sh<018;|^h(v>q^?~~0OTXgO;XL~5`@YcOYcR!ncdRKEUX+GKCJ{I&!myA*zDPrLZ(1u-zakw&C_T8N zyKuWEhdPt5d@*~Am)EVhcbA1@IRf_CYdU&}mcPrUGDV)H31pOC=geOZTR6ZgQ{DO< z{k5H${eN2FR_4m6G9@+cn2#1W?dY!Y{BcKWJM(0mLfsP@cMPM1hu|q(9e(dW_pI3G zW^&jwYZ*EnAT)P&dt#bS^jstDnN0Xc} zb35UqyoP)H#a6pCMv^Q>D^{acTDF+Mt&6$iV=H#*I3Fn|DHljEPv>A!?}>EOI1AJg zM}*9TYb7j_5K8!~6ZVcibSkE;6wbb|HrIK$&350MC7dr#eZkEDO9_eD+Pf7FazM?`N+i(LdO2o2{ze6pQ*Vc}TEOxH=67$+d8zjyb zQoRTr<3SmbT|>n;ZZ?==rttL33Jyvjh1TopC!#%Uw{gjK12y1UcjZ_S=UDP|jJpue8Oxbs)L=fhBq z8LymY1GP)YS^^IszJ`m222j82-SY`cxa~Uwl{$<1^Y^`vB5W<`?O98N=v2yv6*q$Z z1Wm`a)3_d$G?lAa#)eVI6y7K1s#`=4ENw^hfOBZaM=fp3$qEhIfX>C2!U`~7Uit$kW#F8pDxDiZwDZk8l`eO%(XhP3& zVrx}d14)rXPe1`ObVkh0-ePf0+dG{sCvi^4*(Uk+@}-9bdb-i{N*FszZCOcMe~b%! zzl>e+I%xdS0WhI8zp>3S$e_N9%gQ&qaW5QHcJ2j_r8k=BUe9(&=aHY6h==uUb{!uW zYYV2Rj=l4WjD|KWZ#g4uucIzaY{BAnBinsvO4OH7Uk~#0R?8%h?uB_h0adHm)CM~v z(}{nZ**@sp(trsVFtm=d%o6`9zdnU1^}AjWd~2y!Oyv(UP+)!$)y8&5kK`|5P*q=A z=!4H;7Iz!zrcJ7G?VKeNN(`nH>J=#Z#$;8WPa|Y*l9G>n>Zf}1TrNg;bn-|GM6f^} z6tkh1ml)C6X3D8`iumkoexVr#jE!@et?izBWu=OoHIyN@r)xt)Df(27ASkDVfkPzbSpg#r{E3 z?KsjYlKeE=+za~|ip5i$A8fdG@9id_Bv@i`2`oWQ=)3TdFOxMLdOg;LsX_7x$mw65olhm4`ka@p*Xmb< zZf(KwPu@vR36pc8%uip!dlyKXVw-@Ev2>AQNfrXM+9XWZ5L?IE{t-#KG2&^9MES!+PBb_YDgFT7TMtV zo=cPr?)cKD=MtpMzsxyDURYwAY>;?>fJY9OMh1y%+;63=IEDQi>nX4-%1IHRpl!^_ zE@`hMTwb;m?VndrvTO!Zu+)ODxZ3tb$JsWYSdToDKE~oZv$Zxj3d|`f0+eYk8qT6Y zoua{e%~IhxH%S&CW8;%?tV+Ga;va0d zo547?0C4MkhYfr38vjw;b?TjDD?*V)@cCn8km{E9V^%9L2j`@IZZp80;*+6J)%Mu_ zLWy;7=!n-H2GPq#*l~>c-Bv&Z?zeq!kT_pi$Nk6Ek8nk9$$$ugPmRyqQf=5NVKT4g zn4I$Kz|x}l&r&2zVHNT{Xv5wHC!P?zl3d-8nggnamqaoKgGrkAkx-aPcmWF*LJitnx200wMK)jIl`g%mezmdM;kPB;-9U+{^>IxeJ+s0fg8}??XxdF1 zjz}2g9cmPqW*}oRj1sM?caX4qaZHxC?#Kaxpnh)b^%z#cuW&*U?W5cu?#Ws;%!7n@ zilZ2h)rON4=D{A3m$+h=gNku^#X+ViMkkdKIFpi2b0yV=wT2|8J+{GLW=1wt%($V- z2tO*S!p;wQ;$%#IY%4*d)e*te_8q3oy&1@6{xjJ!YZJ7LALbbOLW-oh({S8jx z53;m8llG4)Euu1QcmJof+1E#f(Y}@=-NdL5a(d@1*&uOcVy^xYAclRinRSXhxzMsi zVO4TC2WekyXLTOlk#sf%TPyuWmL&cr{DHv^X_$xL12c9d&*rhWq&-9NQ9%_~`y_)5 z;+x#Ob_{J9B)%UIUvmsz2dO&7U8K9&vCYgBp7}uurC;#rudsXAPwJGVd*EoD^s-}n zG;^C%UwY<8L05jj;roUBcBl%1{=|6%e;QXThn6h&v%>^3nJ*Ds+A;_qTKzlLPizXX z!)s#7{yuO!dd?qLkT&)m>n2hP*fG&cikMKy4yDp^m*0c;967cO^%;dl@u?zqSbwIl zOV)El$8t;|P&~zp;4Cs+TyOGR^LO9MNGz{Dc2?z5Gj@I z&^@NG!M;+>UXsGPtU>(Ca&!BzkqKFgS6)!UXkp~B@Epe1qxu(Z76c*S8)YE9Q zH+ zc60l3n1(p8v$b5+620Xy!kNa<$_|qWTwmNCnK=y?qvRKAS6fSEXjum~9M1?1pi}jA z7mG@@g&u4af3}r+FzYRh&WZN+o|NoJ7Fxs zMO3VPi-F$7!;J7-NFxW>+fkp9@}84?48uGCQdljL21+SxF99j=G`1iPnMT!hn%=i@ zQXar@SyLW)?LP6SdJaQydx#VYli3H`H_X1-K<*D9^b^DFedyr>+`MTxLW%-GZ5s_) zV9h^uI#vKZ+US=9eMd=gNEW%E^_Imjk2?-UXM|`nDVHE+!aVYw2trvpPy4G=J!$ln z8F^{?wHff4)?npOcdYFG_QSDzlCciECwSp$GFiqr+qG-Fy#clx#$e9KoM6Ws#=$F- zq%b(xZHj%n9`v1N|49%2KHZKDAZGY>rhTR!447^2qXlWs9Q!rH?;_?~yPE;9`*-$G z2`A3A;{*8L#j<%CzJI>GpbORg9esnhrlM$^5mNWTS2@CLw3jo;puY%OXrHPFw=J>{ z)`KBS>;v@RlBKe9B(J>U<|;}qv(GnB>ua{r|9Q8n{t9GY_B#BRT`TOV^zl0Um!T`| zLopc`F|hEM)pAPN?Hp78B-_hlZ*(`tK*qoJ8X|7By}gEu3v29H2{Eamk0USj{Alkl zUazxv)9Jl8$U!0L!flg%iGj{+gE4m9Y@bFAcUb*J(q?;oxG+1g7sXizZnbySa4~J0 zR3%9gEwH3V4J@&|+;ozFRGC2q9 z?6;rQgZY1g>T9v-2kdhVRHcqZ|JLkEp@;S8%P;nph8^0sX2TEKs~HSC#KtuHY6)u} zvhN0@)6u3t@%FHNt&CiD)Lz0xMozZ}iVMf>5fT~mgDp^eK4~u}k-s}w0!7_3_R%AgX6ACMd9GnbUExen-Et=IkEnCJ688q&S|3jo;F-?$}aJiED(4Hk^ia-$GexZ z%^V#4xBaDoR;9q2;T^RhG;1@wcQcsX&rMQsD&@9)^lp%Y3%O&1Vv{=<9>7jzT&G?e4D!(av~1h<3&4 z{HSKA*q_&d{+KWo3V>JL=F$6^P6b%w?m&Hj>!ZZO(}7XKI7UfrvD3?e9Yu)?R0{qU z2ZMYa-E}S&{8m3lBOPaH7YT4cc$^E1cwL~Qou2eV0SDGHOya7oDkQ7cxer8@{lc7k zkOLn8_JxfaALM9=sjolebw1cJRtw4jomaFg>VS}$^k=98@Z@2cwz0b)x{2aadB9BXaI6pew8Srh6wU_T@!t*#?hPdT=p z1CuYa<1qD=9xM>$=&J=eWnv@82n{8VE)H)I(8K}j!ijb^b>Mu8gBP1SU{7U`*7bDc z7hkt@z!Ed*Kr094a1Pdsc8t-3C)+qHB-*#js>bv74lHDuw@^n%C!Kd%C&zR>h$=Wm zkk-|Kvk&IY-@`FYXF%z1^w`aP94ob;rsr*99k7<1pu_;jCO!E3K-mbxGT3yyGB}4u zuW}wT{4>~5*&y)%3SQ*+T8td(=*f5iaetVjvI~=NnC@6_p!!9yk%4Ck{i*gSsFZgQ zZclF=<)~(mL4To+mde7xwPPG7^U+i-#dCWwQ6lN>OD9Q=K<R%(HSyUIIilk*OMK; z=&`F-JD%vV%hov#>9M^wI9}otR3gOWSirl9@}Myqmdrmxy$jd&TwG2 zBO;H+`*@$@Pdzq0)p1Ub-TsTCuO2LP$njZcD1F3%WepdR_n4!X&M@|*V;jEF9*?DC z_G6OkPoq2cC+hnRjrKW)M^yWtPVEQxIH2PE>A9FOhWqX+T6Wpf&K8x_9Z$=Qv!Zacm)NF1+>-IeV_ zumC-I;%F#}+?U2xlIX7@%Gdbj*-=bqeezKD0jFARc3jefyoE3^M31s{G~Hp$8v5krq$J(&HjZ9dY@Yql8ZS^xT1~mzoA6$Z-2I$(nt4Nb6gSLFi zEPON827<@qpfkzP7Hh4s9!hzG;Q`%v4zDTIQ<)=?&8-}Ge|U|(k{IWWG=>M_wU6?< zq(~Qv!gkuDM3A2{LdG<@0jFw>zXCUUHL)O=t}$=Dp=%GsECx$phgGr8>rFw`2p1Ey!`qp(#fY!!SYf*7m5_})l=}u zCr<(7d#BMp{Mc!99)9T*n0)3GRg-U=BE5Xy6p`|AQzVctn*x*1nnwHao2Foq51Jx@ ze9bgEfS)prF2L`YqFniiDH8lEpUXt?^8HfO?Xv{6`QcKuP`+AB+}Lk?=PS$X7&>UOpj;Oy&Eb zXuW(q6fE-PP|)PFp`gh(LqU@dhDLkvYoQR3d@2+y@|{p%@{v%KD<232CSM1Qp5?)B zgQ6DlVNmpwd=(Ta$JHI%P?0+UaDqCEM|XS6%bt)?{iW(~r9R>rq>`|7LytkQk(=4bxG zhOgq=;3tk1T1KUeMO3={`5ixTP-c2iKeUR=aaS^r39m-|sw>_hQbUoMslE#`OV*H1 z>m~A#=2urrxU6@j)5qJ@FdQROb)|4c*|jqBSC{hQMOFO7aaYQyz~=ezSgQOfjGwsp zAE`tQrEpW(VVvS4C~xo+$FIH0sC^yX@J6isIe?!y?jspLViuxC$)5!L#Kr$k(`zU@ z3(EeH4u*;bO1fAvZjjR6%DJY^kw^y#IjflJ~`aEi;`F}&Zs1ojKpO3v$&F2 zPBKbbgCrJMHum|5;VX&lW7=0ob7C$zv03r1InnrhxiI%!m}f4`I~SJ6(}fE>&IRVp zg}LRzJaS>PymFyFxiH^cn13!TFc;P&7uGBnCKW}>Lh4OYywntlNySNux6Jw_52Ml; zCB@rvVUApwnhSfJ8-w+dnM!+=6#pa__AD3nA{Uk+V4t%SzRE>>lM8#B3;Q=0W+|PQ zbCE_6jmU*La$#}|$nxZvNRoqLRt{8-21(3dq8t|4m>eJ3m`@)Klh!XO-ZvNKp9_11 z(IeBzA%y~SVR9_Zl0<0)lXz5RV{&+9V{&*&Oq#?b9%tE@9B|p#>H``k2TD@>x?I@C z92ncRr1)8z|2J;i|3>Zj->4t|H)`+yMy395)XzDiW=Zp!6rYwePK!GHzfs5jH|kc7 zsHFJYxv;yru=}~NhdMS(lao0Y(V7dh=fad+*rQz7U%9ZSxv=NCu$N8@`j6)S?L--S zoeTRX7xpd}Ce5nsdTC~p*uG|Chnu-GO-^`8@yBvuC$q5Hl6WQymDu@Q*l)S8Yfemz ztE;p(7_QRUdP*IDPZ9{KU7x`i4eXZS5-A!e-au(&I4$}&P_7&D>oa0)o)K?RgD9mm za*2ylIvI}8;V7kP0r`HuoI1MQfyFO(_bW^t+sVI?(&uaWiasX5of=rF0n$LG2Cis8 zP7UA;!erV%{tRtzq|7stW#UrrF<$iRHjKmI2D*jOp*{!)wEO4lUg zc?xX;@Qy|_QChi6`wss1bR3xB9=SDDI=SmJ@+un86p>ci(-a9*`XI3v)U27(-Cduj zSI`g5(8Q6rF}-Z zzr*bIkN<>|U1Tl9zb)bQ8&1)p)=2*g?P{%*E+K6&IPbwi_Giyg3UTluSG?eU*cC#& zP8qG03Wm#6G#Y;VZE72>Ofwv$i_wsVG@!B+*v$U%Pv~FCv5U&JQA!ke%cQdL9-ZJt zT)|IV{0-{cMk#FALKE8{!~3+Qjk42lh@#sn6$(nX5;;jb3*dm+?H|967Pdv>kCGW2 zhPC9?4h=j>mD(wVn@gJu#=J1I{n^EowVnqoKD7mYLfT0uMEaaE-m?>;9^eNai@Ms|0MypA*S zGS0}WI3q9OjO_jxc?oCa6`YY5a7JFg8F~3;E?%GxEC4h-DjA zct&2f8F|%aWGBeTBhSc7HY2atjJ#k=Z-X5_z(F!D+*kr!%4UZ)v( znP%iwnvqA7k^dUP$V)V%T%pZE@h4dZats-H@ECch7 z^4~ldc~xd)?KiSJV&o;6kym6!)@UOuv5~dc$g43Ud*ic=tg1%VR3j^DBI{`)FT@gA zOA}d16L}Gq$SP`N4NYVPO=SH{Wc5ts+C>=!@K+ZlKJ1pvv{V>=u9kFW+9x6_)2lSDD`vQ>bg-+^ z4B$go*yyX&tQ#iEtF)>c%=lG$*bNr&Dpl>SbOe~&U1VdtNt2Dg_M_%lq zbO098ld+*andMkdr5Gm2`#qJq^m4Y6mqL0eQHbc>3p4EzO75jJ2DsY`=I;m<=&key z7}Hzn0`O~Zr5ix;KA4-1(5ODl`BNXIN!StDuCM;U&+0#&?fOH`x|*=|Qh~loQ^Q{B z(HDhpr^$V>SGAoE_EnkyWcEe7wo{{iN=twR06yDge%6=xDgFK{I^7p9L#5BgbML;f ziW_(e$8y46u}W)T+hZZK?esX7vx)4F+HI#<{UO2abiO|XvYm;*Ruh2PhTGNLL`fh+JA{YlIjFRLeo` zCB=U;4m*jblwjJwuZ#~R48){*h87Qmn=+Vbmj*Mf)DWd1SmK5-%TGfff;04S2>S30)gOwUK0^zJDs2Jo z421~JP_bdqf-{sbOzG&ubzHOG9K9a$tdAG9i6NU0Vp{IwDUw$ zl(7JwQ<2$u`fe)Rp!4))D$~YIL%Q?ycp55ko?@pnc56BWah{saKt0dXPcs+`o2iTj zmNXM3yrcay(V}-$WEK*ZIm!erLAb#Mf6C-S)_F=bvMeZ3o|1k>N57-<-zp-c$9tN?-5yva$nZ z^pq0hw;DNQzmd9R3Z8p-xfHg?Fc1Cwo`%eWAl}oT^B{=#)OmEi#G5|!av{E0-RKO(}6kl#IOXjFOutTUpc?oplvNOOT)hdZ$uuv~d@#&pkT7 zScwHFy#)UEJu)sqZST?ZB`AFrc`rpSt7!64^!h5wSgK3}7?K1HTt$u~j_kb*YO;#% zEJH`Fq7KWULaXT9awP$v<_gAku0XG^qM|F6$ra@`a{T^Is7quQr?_3aW{F#FFUKc? zB8?K=_*RLejFn1NEFr>IVVGHI_9_fID?M4I4E15=?*@W70`Hbtr-NC(EJ=7-694D0 z0IjpqnPld#NJ=~~B4s2%-qnmHtX2kjb0&G?kxAXrQaI2J`DkRnmL>C2@g>kjD}B8N z{IZg&7)h#0M*n*rSP?|hbm29tHgP$vNcyGf%@&_D0UulR;k50R79l%>!2Gd6S44;=(l^3$`Pm(M}Ip(MmPGOrM$`{me6MFRpo!ErJUr@Oe^wkSm zn}WW2K|Y(6!QPzo#S$cKxs*E0mlfZvlmYe5W=`333-s>={j>!N{(?$uWo*e-RP+V? zvlYU6K|QxIc6u9h6J`7Wby`6!e?UL-^_9#;U689+c0etsj;ro%1NeELvKpYxerEY& zzp@xu|5OGqQ*jjbl&1frd=KDy0H;q+Y3TvR3jd52Jf$^1+;5N2-a*f#rsAdG50RCq_+cpeb1HL~*Ut+M!+JcYXNRFK&nfzd(oIV@{u$Ew z{Y~kMkuK|9w*pQtkMnN+5r;Aki z6r6{PG+hIiPN6pr&|jyJ%K@r!8Z|vYQ%{5Q0G&CFbH4-h`7~HQQ|KA^WS^=38Mr5( zY4aJde8#JY$m}!KK8q4Q)9kazbE`b~JGf|SbWy29 zcmm8XP!|zX2SkJVCYoZ>L6r}JxjU&;_dFN&-FdFTujk=vekSh=N^^it7tqwtlyX67 zs+BUhBl0QJndbb8eDE@tS9U&;zjDPU{>s%n{VOyO)%=Z{&`$$&3mvAf)?LH;{{pnp>#nt@1xa%=H2JWJNKEh z`~z4B>~P`WDj#;XcHxDT`$}yZ{c{r!y8Zw?Dd_eC<}CLRohE3~Lx@n&g@>q%pu#3} znxIi8jy!HsI%}EX54E7lz7!=h!?Qd2oa!ZLfSGF}%$V;4`CGWA!!&T(!uj}Fxv0KY zW;tZ#@;zPnX_KIt4ychJlLHQopvDTOSV21#^njoM6;qa=St?iT zt_r0RRO=D?O3;Q!T(P&0m=^Px1+(KZH}>OWXquo_f3fJ-|HU2g>@RL?-6z~w;}a|@ z1U-DhERj#4TY|c*(WeseyMdp`(J@W^miKXK*g> zGvHDQ>i#!(Nb28E$5V7&VyCFgD>V2NO@D=XM=#y7@hvF4|YIRR9gF|Jei zKS+9=X8wc9U8mdspsLrY(p!{#off@STKLFw{&=^mNHXycNX}0UCcl|6MctekguX** zm#N-6CC*1)28jOyE^qwI2d?z;3m&7&dX2&<>dScD3(9es`uvL#e3?%C3qfH2Hxm`O zOcOM4EfbmErstXHl-tzcJveXEruQ&Px5?!L+>qNe`2%$1HvO)FiXV~7ZCdaV+H{*9 zePmjrPssB&t^EZ3zD@spLQmbM`k&Enw<+;6+I^cGpV1q)>FY0Cknszrvwx8ZRy9yZ z<9DAb^Kdh$Z2`I%)L{T84Qes$Qkx8Fdw|L=Y8QaHE@~@)YZ@q$N3Df3@Ai4X`2??( ztE~YJ=TTz-JYCg(0P(JBFMtcKYDa*eyy{$ljd|4p0ET=>w~NB^sht4E=TqAOoXv-V zc2STUM~-v@%UfFHrbYwYk=PA-Cuuh*+8qgQ&{}syZlS~ONVkQ2J1c15!u#Ptq-u!SB(O= z;>+cE`7vh)KM46gZT3@}c+1No@fY@E&wp_$oi3o_2nAYtfkPRP`TBB4+YCf{P_}+pQO^oKs!l`i=jI*=t?orp3&1{YHxs+#X);U`->xTH(e`^ zVs}$$m|C-pJOhb;h7*t+TU#G+3awk54U#;wPd@tWhOgL{hGCR!pbKHpjSW#llXTbC#Z!#8^8^Eh9Mj#t3x8SyCg^K>*RE)E;4+%caA}rSr*rx}jWlDy!Ou z>0Bv@;V|VXjea^zZAwGC4%5=oYSS=S*RSFS-vV#HyUwJg?qwz2M6V_DCaP5i-LZ+j zEu+=}I95ij2as6?UA&2!mxTm3(Wr|^mrz@#di9cZr z?E$ys5)H4YcGp<8R|d=eDs-cQS{45;8vtTpB`Dz~8d3>WyF_V~xFqk&sFRHl;p)Yugaki>pKC}W)L&gsDcr0 zrnyznA~Rj9f&%W+^D2<@U8)s{a_-XHNYL)mwMfuT(DO)W#tDk53PG->sZ|kqht^bu z(%zxJtEwHs?#LpW;20}@P?DS8nRS+B75SJ3RKt*XOg~pcn;w%#bqvVI)T_E$yLi@K z{m`$6ew|~hzDq^ks;8f-tD)Ekzb?TMdM?3U@~Q!_oytjYj#>cJcW&W+m7UM1<=Ai2 z@=22wYzjP^dT3V`^)+plIqjp965OPR5^SXp5}c=?ngH*pHb8ye8cV?io9uipgkx=| zmBM>Z-GOIQH#NwjuEHH)Yu9H{f1xqR0#46T2~N{q39i#+2`E@i>xtWwrdhdRjVD2Kwxw8d6`aUrdg| zppRD_bV594~QEzaOlYXV}}nLHDo3Jp8QOGbt2Be>NJ4uyhZaHsFMM{G*G7j zOlzpl2XKi}mjSGdQWpb+H&ROiAVD!2)Ch+BD)noj7NwJo)Zz%68^NGlrD~1U9sr9P z!-`&|CymvC0PUJ!=D12fHc^KF1T@9Gca^3z#e8{{?ltAe#?91tV0)W!Wd7!A9I)}t z8N1b-S?ae?OCS=3wuIT(*+MM`@Oujw_9IlNCG6i38r)KC3b4B+EZz~yYzc?u2sLbl znD`a+r+*`vrJc55PM?H_w`PR%C7_GJoJ|g>yPtD{HWR`c}=n137uZ%xJ)1qPB z_R`g847|PM(FP`OFZFAK`fQ~+Z7`2+CsP}>ru%kH=ELb~ClxQiwNdd;C~ehn?W`-q zg8VZqWND{1^BB>8K-`FND{%wnSX)jX)D9BcPLnins~sk>?NqrvgYVnJ-`Gx%+QWz0 zP7Pu>!MYg8b~}BEQF{XP?4S+@xX=M&+>Xx_m@`oW=8kGGZ0h@t>M-}6vXKu980hak z{t>ja6MToAbfFWbr=3)=GyIvIG^?{Z65x4fE0hNJg6NpF zi6N8Pk5SeF55!LE9|-pv{>yV+@?;hOuz{Gbwcp#@n`7j(;4B zVUbDg`m19APW8v)Dw84ypr0~n{Qz|wfZsrLUM9^PsP+f2XyD{H5A^p{g$;GAE$e%|jvjOtKAC2WY7s5K(G{LPcoMFf~St zs*I?r9)Tfrdl>qb1^EC`KeY!}(|FjGOrm&hp4V`e=gi?SUYYb@67;9aaFjS|pz1?? zN2orC4Vw(@*(PHzj8KarV$fu;d5u(iYgs?)fohJc?k-NGAbxeRVbAK17`3Y)%gwg3i zv@by|h%kD-C|-1eer3|@1T1z~+*JV3{gnXF^VI;*=@Qt-s5Jo4-w{-AEL=41^ini? zEQSg9d^nvL3k~J&FG26ea!sHE<*DU3)erGdgb*4(4nv8xp&0!*j%x^&sIIjmOB({m ztKr~+Hk78`<5_Z0iITK_yjleD(1nt8XFS&e3ZY9bOBY5@P)mUex=@ZzOyG`yLR3T) z)IfsL6V)nOiCG#jVWL_>i^|e~!xP~R=hlFC6EVSLQsGIgNYH=?nm&mI05vF0CnjMA zWL=O$2c0hgy1avyO;&F|nyiLsQCYoRathZOyE;aenGP#@ zhOSJ9c|Jq6XJA;Jp*=HTQ_oP~OpN0*^xaI1<1_SfraA#2ZWhz7%wk%-*-YCxn`y=7 zppIu~${c2~%z;rjL(RX%9D9bge2dBM40(R14$aU0bKV88#qx(X565aTWu98gohP4_ zxIcOGJ9PxO8_k8$I79pA!b+VXzj+)vW1c!Ph_eV>hAiX{=0w*){m1v0N%PDHR~jYE zhm}erF&`C7qmtjFf@!qodv!WMp+tUsawxS$|vz8E!pO*83Kw7>RZYUS#y%RI1WjH~iAgYfH|f}N?8CiDQY-u3)TZsJRoGSP zOC^@8ee_jk+D6`dW1xz@4>>&044K09Ix_ByygjeozZj$3I{i zqI|MehuIUa*1B#am~u>bftSuSd0#0u=v z4_HLLqmb?D3V{9Fk?A|?xB~^gqc=NH{dcr}Cr38irEUdg--W8ZqXj=QR(ZF&0oaw@ z>N+%9%@FIJ#XDEj_pvO9n1`{hjX zWRgsD%5hnvN zD1|>T2YicPw5sh048ms&f9$}$?hT5!6D@dyrtQT2IKGhBg~4)zw(Sbcf~dYbun6Mm zZV4N@2dUqnWP8!7H)!=<=WOmeSrz^wb~yT2k~UT_y!yZOiU>cx%W%pv6g@M z&exqGrc=6uxI4K-Lk|WTrRJ}q@kCK7uMCrzUHf+F)5%p7jpP*b5JvS(8h;3HJ!aC~ zLuikgG~_V4=uFCS1cPHHT{#li1o7)pfrb7EY=kfHM_?VqjAN+UnN;FOdMBsN5nw>wx$X*YBMIdxS1jfTmx_1&&&pm2!3U4Rx(W+CZ zfO}N>bl_Krk}^)kheo`_r%sfxNW1 zOZMm#?|Ps-^*9i~xk5+qVl_ZLE~8ihihd<9$;7!l2uWQY#KyXDHe3#rGZht}j8`!L z1GMfc`bL1tTti6%bpBdk3`C>r5_bPOdP;!0-oV{MfJWUw+dik@o4C9^r+PP0rO)Zs z%>bFtEe1_MZi6P!0j`7iLvn;t+FOCj^!ZjGHuPfMMt6Tszuyilgs5}}nLei*caZ6G zvhI3m9GZ+Y4o#+m=e|ipn)W%(i@@M|PDSpaiJsH>dx2>t=aMrKtK>{m`-&<~m>9ZZ!9$WOsu7n9=SJJ&BFdUI;QvBz*)9bMZ z_d(OCLu6nxM9Rm3Z4l=li|>~w7y#2L?^9eWrqj)*;`{X(#sR)8d5%eLI^BMbNp3m~ z{|ndFC)DE;?!V%{!1e42HGBa(o>1t^zyXL8F9Z7_#=Z(1g{bv+;1oov*MW-=&tHqm zhBtv@@b&*kMCrGIKi~^~7dQxU{9WJ(#KeEaSL=OXH+-=^1P(!*{UE+^9|L>ftMn<5 z4FZ-7=WjGZ>>?DAGH)WdOMVXYfLQ+-uMzMq#}^F3XY|__yiIsEGri5f^X-v9;Xxu+ z1lhm9mm{h@3}Q`Gdk94KX!dA`jYbrSZjXX*Z*+SEM41?z2(aQ#l_#;?SsTv}w zoC>kiLj_h<#djM;LhTX|eM0S){1r#2-3g*pnB5g(ZJ3<`;*T)9G(N2ej%gQ#C>_)O z5rSeu^8^LOvfDv)Fyc%syA6E#V%uFH7RI(aKm^CJ+e37XBViZf*gwHnFs|JZVgW=7 z`7GpqEc}(vKN$Ye`?T)ed4`3rK;qfu@H($`JUbV}{@guu^khk zk%=XN8cFQV@a;?@G1Dd$I3g)(_%q#4YPW(YpA6OTnN}o2HGHOc$p!XEE_qx{Znp+3 zmO^}sQ%GX5Q;IeHjEG1n5z3~LG?%0jl{l#-qn@cn^JZ$>LMj#0h;L&WGzq?TNo%(^ z=3nj(^DlR&dxdQ55SiJ|PkGYYVYD+@#dy$ro7V1ZzUC;@8+!PmB?WBU#<_|Ks7KF= z!BD)M&h86QExp|z;xKpN&r~|x9tyE9T+GUm!R`j%oDB8=h=du%tRWdCEHb0r(b$uK zKjla;g?>(s>Rg-2jt$+Nnd}x2$umnm4#_OlayPRSy-XG<`r<6;f1fEhtEhC&YNy6G z9lvI^$3?@7-)`U8IL+)kJ0sk6zC)0pp?DYk>^r-DyicYxq|7gu=|L2f*KW!OY=uMN zOiq;K6IID&`yfu_vd2Kw$!!mYIGfv^3DGu>Js#pk9(yvxu)Ov}h`9Oe*$^x9*;62j z=0~-BqK*0OtPsdF1HI30^9LAp3fTPL|Lg*&8$X2h|y1?`?tYf%Wv`RPg_yB9>2!ge2seTD7r5V?y8Tv9|d6BV@w0!}Q-a8@z9r%C;M z2wrBN520Gs?B>C3di3SABZ`R$wTjz45aU2`yB|c6?**>-Ub0A4Lb4cNf_<|}O45T$ z+Wk$U9}^+bkBKNxS-UwkDP_k)e5;hizg`OSh@UEzmM8~Hdv@$gk0|@nbCfnQf9l)5 zeY;+G#pkCrWza|bl&P$gbADNS5PZqXi4haZVfOMWEHAO$u?KBdm}7Mb*cP0)iD75^r^b2bg6-!j<0cQ zNX1sJiP7w*4K*Ex<9`1sD8Gi%tOJwP0gBVu4yR&2+WFw_{3F^14`@cT zZiMR$e2wfZMz0osA=23X4q|L$yE4SZ#x}zYP3$TVU7FanA@(-0JEYpmlXqWC;qt?G zf9*SU>C>*)3~L{iZi=hZd>Y);E))}g#PHnxIe$!=+SyJ%ZGB?0o7(yDM@-6Qc1?&* z&0zUKy4MU=2n?s}&5_SJ8r0mb0I|P0N`HMbCxagjuYd@hEo}y-L&_hqr z?KVj86g6%u;&NMzeU6=h(zV0Tze-(t*y(6yJ52o<|w>bgo-x=AXvx{ED)yJHYtrAj^QOb{@(H$F6p z=z&JuM%jC!hi;>pJuzOkQOsWGz1yg_5x06_p4~R9H=215)$VPVf|%UfPHucjX3(R6#RD<@t|EJ&-7}Sh-l~OA{^9=DwOyZHo!ZQ>R#ERk7`6Y}=F;h+Po>@vRwxmRm|$huXs-Rt?2Wx0JH` z?16EXa&lj~AvyfefsS_auhQ%6v-6Pebd%W74j+cPTuMua;l@c4Z;L+-3k|pP#^ldh zP>M4JV$!hjc2TOmp=mN)V{X#H;pm+=DftMy4umxVMY>6QN1#jHq|CqAjUjx$*i9gA z{DKSdO)4=GH()nu>PU&_jItsB z5$i|W4d9DD1_KOmj9m|6)fh43!x&Mi|0^b-o8E@o`R}DR#c( z{3k4GJ5NEm#R%@&y{-J2g->^;V9J_DXQtSBAwEw*ZR0Q5si>ZLwAqM6(@^R2sOvOb zROZozX(+=y$}?TW#OW}89y!x7Tj2|y86vjM!0pmJN;DIrY90-qi4x7DyAUboalh&N z812G8=;ARtx9im!m)v>ObQUTa-|fu8J@0OcoQ0HjQ!T=X+)W1wN$jRfvr+KfG-WpK zxOUTv*_bGIQ-e8}AthdFnmz|eZnTq7oVl2i=Te!um=fpGk-4}JnoAAl;pS*AJ(!2v zsJYa6J|^D%bZ5T(14O0;n2Yw)q6L`r_EXMbV3JvxaUg z!CbbQ#x1qeQrV@LomW$*rMTD$vhWqqo27WrSwOj$q1X#(?lQz$K+%4~e6)c2|AxDO z1!Vt*xpDzDST4S!%aP4t3R!_k?=V$dfvNT|`B$J^hbjL`%$0{}2497T8Hto zh%&6l7+FL=uSe@HqC4wRIU6a}2D=zUgAGXfB&FMq$~n9NO^i(pHXznXDzOng?Tuq~qUbPML=lhkypzyn(` zSD&OD+r&3{o2Y!)hAW8V-6H5dmGoKwr(H1KT{9kw6q4~+kCyBkn3@Lf zMA7fk_MJ%RE+yNA9(bL4?Lw_yr**rK^L2`|8#BRm>bo0b@j6}KjV`f|%I!gx`{q=f!`=qsqeFZ4;IfiKdh!)VxxRR0L> z=q}ReBd|zP&KIX=-%f2h_wLiW7w$79`TPh$%Hh=DsN{&`djlcmp%6&43k1?DLCKEc z0pKEKJ%*dG2UPQz-6oXR^INg@giyY*4+_&?$Lw@L%PHP*^wZ_^^GQ1wjXsWnzMS@- zL~gSIbnDCPg)I5!Wv1WJOtwBfk0IJKY7Yd2sh$$ zE{Ix$=AXjw+(flb+nHybhIN}L>uC%bArpdL)3Eb)RmyV4&H;CwGZ-STY4sW0DZZxk zXYG}tuelVZ4u{Z%xeppr(HKrVsy=gAYN~w>iM*x(=kPR$7#C3oWzOTiS`zGvz3-l% zw`)KQo#^DC_7^ZLAJU!+xW|$>`DwsKRKG+V8+wKFcyuR(()x#Ir?!`1%N3e@$!-uL zA>pTK;~6^}RsLefrgVQIlPlEnPkWV#*ZEcm9lSQ8KDE7!3&$1OciC?HpA_p|!BqNy zhFq~n{wGfJtGM;JMSCVWCFtx`Ts7BH$D4L(TKJcpn9km?W5t;vi`vnllNF8`g$_K% zZ5()P+IS6VuB98-FhbT+s#};iT3v?$YiZYYdu=Fh*kDz996~KOKBz+e8)%QU6m%1} zNRmS*2t+MJA-C*Aa3g*r2t@sXcHXip|0jOk+o+O+4Ju5^46TU|?7|f1F8b3-y8FP+LIdyG9YZ;qHTMIag?BzEP1z#w zGHfNSd5GGb7lC21lA=FCpUHj?lio@ibI+aw5q{sE1+n8ky1V3Ff?7YoeZ1sR2Lj2r z{ZGo{9%2TQlp8@H(QXh(uM};1gquf6v;<`gp!!!*qkz37XbZ)*?NRZzm~!<+xo}DQ zk;0#k!$EmJZ;dr8(hjE6ww)`4Rjg}a6#l|4Km{DkOMv9bO_4?0FI|H6!Vj1K%|H=*uNZLG6= zfg6BhwEiuM|L6r~xnnqs!p=<%U*cBs7_E6}uY&mU&Tc@fUtyvKHr%bUo8wT2qe{tCsQ;Siv_jUrH(!ow@8u#8#{U4)Gy~n&K zCgzP7t1Ui1OW&z$>kjR3k0u7Epz0rRQ4^~xLBQaQbn^pl+CI{Nk9eRgj32lge#A9f z&}590b3slM`souQKBUo~>^h;mH;R=MrxI^EgBz^i&vqGu>f^-N^PT6Ur*5BdgCY}D zOZphgNkX-PoVa9tv9lY+%s2<&+3q>t(H~z>RWcIu&#d6&4Uv>j)~1Mm?Zh)DJNbhi z&OGGsT$en^sSVL7$Y}=wi$?w@rO{EHR3W@ui#6P$v_Vc9IvN#G9?+B!rzF*j=A?(a zPc)}ys3?8G0f1tCp(mJ{YQ=DH_-S+}GjuSm<)aSKo!)TYjP7iJ7$3vw1c7!)N%4Z6 zJaD4{>VBKutVB*?>K^RGrpss?sKgI*vQw%MCkNcPf?@qy2nr+yO@lz%t>~9fCm-C% zpai`Pb<)C(XuZSka6#J5#SR;b^UTRgTf&@dL3ij@n9~7bXnLm{jgIM5g!@cPrz1p; za3=#bisiI```2)%B7KVGRD-)pY}D5s+7R2R3Gq3$(;A{x9H%S9iwsUSvQs#jC}mtH zTc~82tA8lvt-UA*^@-~=gwCnB&RB@JnViB{>)`wV_l0=Qc!=ilopC14UjsvF_|8Qc zc;0M;5G#Q*8scRFXF5ceEKXHAm(a;>l&krmoMur*%9zN>iQw9aoDmS`v%rkEiJa_k zhbMMMLZry*{zIRCt*rP68}iaMD1rNHVF31<9n2J|=TI19r)dJZ2|%s<_5Y!a?@69%iNZDV$me zZj}PI3af>8ypA|DDr_4yAI2!`(Ee(}~ulb_T$mHW%7rarTM+G>n-YdQ z$>2s~a{_4RZV+hV0+b_z6Ax~*b4se8!ASr&8ap+O$lzpv8}*lqE@qI5K(p5|#nr~{ zozW=(Ej0G`bU&l>9o%SfZc8+FE}D_a`5x}`nVb$LqPBSJ%uXSMpv6kkpP8L3aHHAt zQO+z*X}Hn$Z8%~Ur!oW@l#@gw7NiwfMHwww7YL15?mz7}JewGfR?9(WvpLD3g{I3$ zF|&&s?U#`%X2*b)Mr`<>_IsIKIv3inAl1#`B!m_kF%6B#Aw3K&$#sfGEI~EDb85hi zmfQz{Mmz+8_L~NQrsMWUtM!CHgZ)4kb2}yBMyt&Vnno-0I87m<<#pikaDP~Jbv+IY;3(L2AB%fw)rQBENSkAtYZ36e*Hcmq1sI#OKgXiT_yHprlg_A+YjS z2pBjK0=8A8r=^_oaKpes5U_1B1k7qf3(Git;f8I)XhvD5DBLjc_y5@Tpqx_*A+W6? zbu90+_>Y0PDqyY^1B=t#3YdMQ_R1a&r335N=A^e3oUW$+Q#=Tz8ZFmVp>01nS%c2f z?H`v6`k6#q_N7L2#qo!8wGX4r1Q8U+WssJXyjC&ah1@~&(iZs&Tl5}+cTjw zsoVM@w0t7&@FOat1DvHYRh*L$dz&~d$*SsDaCd6z)T3ECMTc4w(`b>{*^$r{dPh!-`S84#mtI_HAU`Ww`8;s(JR-pUzCPHo2rcgH%;Q)9%m zJE7Ek^u{){zb^~It}T312Ml?JEuAgYluF7mM*kI zUn=pVQ~5u&booao_y4bp;*BsWq%Ounpc<-E&BjjQ|J23d#^{<-4fUyG6U?Jh7i%C; z4NDZ^9Bk%ngXrHJ7Z53NQ7Yd8tu4j=fsVCsy153 z_Z-%zZ=deHI<@8x$wg}%_L)!7!5IcIw*z{E-0>EpJRLDKAJNc`&VGneot&!>)w?@& zsDp)z_9N^obVXd-{ z^l&FL74xA3ZlJg$oUF9S=M;n+`Ie?c!*B)sKvjl0O+$HGKdW6!H^Jk>oV?INf<-Cc za7>VrYD)+tSQY|l4G&sOQ%0b3E~a-QP_V@`WHhQ`;4ip*E~cHo;C5#*6&vG}qP`=Y zR&ehdgI<(%6fUHTY1k;-n@Rj?cnOY*l-NH)AYxvMF$PsHu}4B6;&ceasY3I9b-IMS zVPlgPq^aYa8q{>GlO-M#boSE4rL$L|1mkc|wskBLenYp$Vw!$KAICenLadYp!=hMk zX#6;5-nYpN8js7XC>Nw_QD?H#>p#i7os0$(RBjT1f8DcOoD1ta=JsDBHW`(A1On9 zW~0=S$WRD${NbUz(Yf_c*D$(}6}z(iJqOd5pexzKuU{J}$!F#(z5>_fir+2US>r#}`SY7_D1?9`X;}Ti~RXO(w33C^q_N^t>M6_l)rO(2m+ zb|uYfi^_Kj7S$DyOTj-41mschs+oX%3i`AVP(Z;m0}3f<+)6Zx0QgK%dbJi(OjSbL z2>4#X_X7o#RB(QffYJ)w>L|xR)>thXCK^9@KbPeJ1-V)<|d zPLP0K6jX>VV3dL%g9VIHFfCNTSQ&p(dfp)trj1t>-W3vHqJm^`1Waadge#0Ug@nd5 z2Htu%1~WY%HAtS%nvg*B=PGEHI7r6d0wupD6qUsa_F;(|l3b>seqsSD6kImo9)z(1 zE9N-L1SDm^I+O1qSuMwgk21MzKpMVpEZ?lDy0&gp09!fL9SU4icPnsB-KW4c^`HXR z)FTYsm32Nz^_WMb#!h%XUU|ovo@PL7dFA8+&UrxeFL*vHB@V@a{+|jO8E{oW8I!;b z5BR(!Z+pZ`Btn6k$O8tlWGGrA$tIv6kpYn&5VM|mK3=WIW<6K1w4i{Oxnla*3+6%+7Q!M36T-Ya-yz$XPsOq3|OJv$E@5KY035|T!+f~Y=2!jx<@Ahv>DWh72K z1@}t{NT?uvX#q(T)HWcw0?VY4O2OhEBud)ceEpL;{k4LSa8;RYR5B^R0y3_}tO^=b z5|Bf|CqrQUE2K=a?eSOhrqTr+f%@yQsAR4Va z;Ir_L8t2=_BNmpeF=($~g8`isr2A1cx+ri5M0W-5fas;b9Rht7xI@4k>lL^|V3PuO2y9j0_ISSnx5Mu;0Gp6o zZjaxqDsGQIpup|%hZVRz{tpFik3XTn?eV7-xIO-y0_pI$|GKEe?eUitxIO-w0=LKC zRN(gbI||$$e@}ti;~y$;JG?DG?!Vj~|5#Pr9{)^%+v8s_`1;!aw+Cd#f8+VA!RFTB zor1Lnd{A)50N-aNZk!;j4m5eVaiS}5y}Ae({+19B*sK1wulD#$QaKyC#-Ub2cR%&TOF0r?fInjjhl z74S~lh*DTV=}7{LD#$xUKyd}UsufX6C~#Y@w2#SlZcE-`1}fz|AZ=N}^I4^5NN`03 zxn>Edtl*>pRTXp~(WtIq=zIY+6>ME#?!Rg)Ikr$#>MF>%L_mE7NtX#|sG!+z0vajU zv0OkC1q)UPXr^GuN&zkM@cqx%rEkqDe{*#V!yY@RjS*uzte|xg+)+V_tpY3s z9sL5jC|I{cKsN=Pg%5f3WU`(!O^&0v0Qz`9GVRAc{(9PqjxA#lJdgpe<+YX`6aB#+ z5dEQ^&+4*Qf`=&>bx^zu_uOHOLc|h`*;Q6f5CnSMM3bvjVFh#+j za{{I*;AO%n#ta5N$%EGhgUnKu%@+mCR*>V8fVm2e{3&3*g0L$B7An|!Rls5e?{5iM zs^Ii(^ZfIhlDg)pbOi%h+1viE#98G5Dg7GHXJwC&;B^XeJQA=$!3zU6DR>kRjV%W7 z^{;>oeK6p5wKgq)~5pYDros!zGO*QhgL~LBRq8E_uKwTgxNU%O0_wVy}6C zs|s>{5pZ2W&nQt@$7Jtx51Zy|wyR3n|wkbjl9aX$Vx&KD0zra@Sn zY?9>7?vZ3P1>p$=#86-x5Tf9>M4}PKz$bRKPAnvrs%%IiAdZ6JNd?4H5Ryzl0tFKd zNTeWM3eiZSphrpp$rNNu9aY{xq+lY8vv~(HlqR(Yq_Al{pOq@31gBS!FQG)Z7BH8NlPOiy=8wr9ggB$*CY-0Rg!cbSWqxuL9n-ABN{waJ;a9f(qhd z$vHF%D;U541&;tZy9e_1hKKuHFd7(B+2h|nnG0jZyIp3e*Zfq^V6&xbwS zpkLVoqF>eXS+7g^B)qzk;}rzdR50lW0kswUQc*x%1s5s_sITBj6#)$uL{t^fNWu8( z0-EII>#rEfE8t;hGgVnuS3nB|yb2x~trYO-1OPuVkR|AR9sxi*4@hAiw5*oFu1*F^i{B`gMj`DnsgK}kbzGu z=W`8^&CjaB3-AGkDBz{{06qo0)E;2C0=M)d83eem%*B}k&=}(ZsgALp4~y?P*k_Gb z!Y3dg!bAnr1_+p}V9Y=PQx)*qdqkP8pz2@&GZpk7B7hVmA0}Xq0X+WV3>Pv_Rd$RJ zut34MUj!^tz{e>d-z5s%!v4nKIhUSKRDi}x4@l`(c|Pj^4q!m=8mUl;zzh39)~U)z z12!mFH&HY;DX4~{7Z7EO0zQiYV4H$QSm_VoSHMgE0d{)8XLXq>WVc7Gd(#B$RWNP3 zfc*-1CjcaJP{A*A1sqoJ%z&c`ew-&7#~AG7`gw@0OOb}}B$HW8TAIn`j0dE~&arPJ z2S@oN5iT+f57m<3g$i*P*g#}CjxxMl?*hbgn|eI zN-5yOL6B-01$+_+Ksg2b45*+WGfn`3MnwgDBnUud7vTEKyS0K;W3q&MvW3Gx0BU+b zYQ47SvrfE{;JON0y%SJh0UsfP1R5%EQ*6xO1ZQz1b~M&#mXF`RFp)f3X!wJVl0qv5 zm7`09pA?)kpsj+2F+`)ig4+gkRPZ@OG%N+TLZiw3XBQ;{!bGK;g5)s;^iVL#fL;os zW77*1rH=yM1p}a;0=KXO8OT-_6h|}$1NekU>4$3go_G>IOhKk30!AqKl1{)#1-x+v zG9AreF4yqllrmk9Wx(5JSSzxN#sm*Yo|Al@*J|~hgilfMw*k`>+{`T+GZc&|FJP8} zS``G$R&dOKxeB`eAR6;M;KLpqLKb?&T2WcRVg-Cc3(B@s0Uyc&@SB3@)dj3jkgA4& z-xV~iDPXmN3bh3I)+#wsTgZ9^N9qXJsDKY~LB5+66sa#@tAdXPY*+BMfoSYdkhqb6 zT`s`*tJGM?9#!epRKPw3eCP}EJ)j_>rGP^U+O-yNL_t`40e>hM(m}v+1y4IM@L4C7 zbm%N9rxldwD&VXF-gN|qoLBH$PXQMd@O~rE_)`HNCj)RrLDQcFTr+^LKec>9Zm7!J z5dv;0C@@;U9R)Xk6%e5yFjm0*|AFxW9x6C8K|nykvq=EB{y0i_6BF3_SOFhU1MpPA zv>5`PD`-4dzzYT4=L>kHVA4VXuNB;1B;X$b8F*-RSR&+|s(e~1;JpC4e$ZLryUmKP zw+c^;El=byR~f@eJ)akxf&uT2Vl~(%`l&r2`e{9%RcVI=r&n;wfD8)4c8Nx&{5(%f zk`oQdqAJnOh)OmEBd!R@p&;Wm0XY?1ye=TOf|NG}joz|W=UeZtXtB|IRdFXj2H`BA8ZR$j_;@3vF--cuQ~;l61TsQZ9_JD;QbA}w0izYfFD&3!1<{KO7^h%uNdXfS zIAsJ(QsA3XUdR+B^C}3K_CHWjzzhWgD+!pTU}+TrvlaYNRlr;YTdE0|uYkAGL2(!Q zl*DZyWU+!wKMGi?pnPKizbT+50#+zU-Ausm3N|+vuv$Tm76R6Kz-RG^Ot56VN34A9 z1Z-4rz<|vPYPA=QtqSfKuw6md4x+I`!AApjDHz{TG<&k1C2iv%;_%RxPlTr1)OvN#^0S@LQbp7$KC?YDmc?ezx*1JtBF$@O;+M1rq#9!K#GEoP3%70`7LY_ijXwk=Q_%8+fD{V&fG(twO2Oz$ z0@5gOtw_({6q|MIs=5AT^oZD%+4IRcUPv;lf=Ra|$?OUi-4^hjf<1Qyn` z7w-wkCjj~5)==y=#K*M36Th6H2jww5CC)q#h@A^OpLM#b z1TRv+n`xn7OBC#>E?}7g-b)J_%RS(;MwmQ)XM(;N#R{z_Dr-C-nXdDE)+rObfx!jV zzu!dkH+w+zw|YJ+PBRJKu7LO5f(bhqES2%!zKxLGLXf1Su-EfhZ`w)legzR71RPZG zsI!2>3V1Uv}1|4G4ujRL+X=(9;c5VrF&h3UOnKr{vGw+M)#pw2b{AwDHt zw+jhVFxD?1mV)Iw1;kO1XP1C@3fvx(kO6KPupSJ@K*9DT9*{1S%=6)tL7#-DP?GYH zfK&>G92Ss9L7O82(kaOQhk$Se(T)qqsKB)%3j}7WfJzEh-4akmL514_swrsTdmyBSl9vY5QZV$PXw*@V*cMPv!D~lA z0|mUB8(RHG1#WdTVX&L)XVMeVXkL)V9}}sdmKr|qF9~n0;O1umZ4`VCmG{8y6wHh( zpo4-Zi3D^~uq&B>&I(|KyeIF*`P1@u?2JF|d+3eIH} z@H2z`T=c+q0)`rZ_k@ziFbz+TOTtGeh*wO&NCvoRi(+N0C}4~SB!RJ>&kLTwK=z;; zY4j(1K=h{?AHS#EP)X98?uE;Kb!fkt46bpGw|(-!Fq^>|22F9eB{b%FK$2YG`K+7u zBzO^n)vTWer(Z&UsRu;=H~4(tgsglX2PDE;&u67;CF!kKkfVcujSALu z60ljp)7}EMD#$ey0QVo;mE;&JDmxh9`6G(e#Qd4O+XG_7Ue9OEpCbDE6{I8q2Nm#{ zoG8X&1wEDsIO+kPb!(ZBV;-@tt`KlSLGe`rPAO=+MZg&ad}t@KImZBx0=O;2nU4S$ z8GQY>_+<}BNv_KLBjJGqlEQT_+*){2z)b~Z&kMM%fRFn`9(Nfm;!Ho_=ud$A9*{g9 zdOoZC9SII7`1CMX{wH^obbKZ%j}?@EA>gTkSFZ&;S75yr@IpcI_X1uq*vMwNfAGKY zfSC1`eJ7)1ou<{=d;sv?C1yN+^n6y+C{mT56~vA$APP3n;}kG+Boz=Nu!G#&zW&LzGTNV1nJRthtdp>Jg zeF-iJ;4=}rRjzXiWq%S-&I6J{1-YePP=~>6Hes-tbnAOSA~f`TUT|XuUr*A_JRtflJfAgexTN1o0Ur*_^WRTQ zzMeJOc|cO=;Q6dF(&{@H|-vo4JaFNR}Wwn4F9*`7!c|NQ07E{K)O6IMR z2m=(9S}WjZ1v}RZ7^-0F1_8qr+}kK%q=KTG1dLHoXN!Pwh4}vOYeOfhN@JrkMS*MC zbOo+uvlO_N%~9Z5HeZ2j*&+o+ea5tH=9EW+-#02aI-nC zz|G|pgH2r4HhV(k{_Ctqq-^IsABVFQml)vH6J81&7jVS`qJPcvS-;F}849ujaz z0XE9y6z?hUwLdE4p^_yg5A5vzZKjVI;Pqb=>$@UhoX9f;+lmW#!2mDnqFAG=aU=H4 zX8opA~*uf`hR0qS1eOML=`~*lLp#2vKm` zq!?3yn?)Q3(%pO=noEC+uPSaLi5N(6$Ci`QC1oJnX1;nO*`)A**qO@nS+(v-a9Rb) z9|;Io(AE)<3BYHP#7>-Sc~(_DZt*HT#~8^4u2F-LqV=D0%{A8_divG zB-47T@*$dlh6>skvl=UK?QEvNwXCHA*Rr1!xR$k3z?SiMiXD}>rgc`}TGma0YgtbP zu4R1~Y~dPQlTvK&?*Xa7fu0Zhf=cjU7hwL)7DtFrRX!OoLP6H}qA^OrGLy%z3MwQQ zjqwWR8I4H_T(hP!c*J%Vu{ny*nxQJLA*8@HWUd0&kOc}{Ll!G=4OzzE7-y5Tj99ip zL5m**tTMoR{#&Er<YPC;B#`ilwau;7;?=c)};!f za#KM~WBeTjZWZ2B;8xK?1#T7D3fwAstiY`z-!mm{9lcQC*3sV#WU?&KRxJBRHQXxt zS2fnQ7mbgq;a1TX1#XQ*En+$d`oCKxFI~JKQyRk5?40vj?Q~T|J-GwYLQK@PN@3QO&Q-AJX8{XbfbmBr)y1k3dxWSgQ-D2YIgu3#4j8aXL4#qUu~tD0 zlg0)Gk4B5eW(BuI!)I+%(q*Km>`-vq#M!N&-Xzi3rvTg2vSkMqJQ*wCh=ShZ1RPV4 zae{!825|pBWyl#-sb;b{ub}@FiE~N8OQUf`!QsiGaa}=o6XlkI=B8ubRj`0h}1#a)H1K=~WrrUk%tBTuwe^lUh-zEyA z`(o=b1#a(crNHgIZ4|h@x4nY;v&}@(Nr~HiyD*r>^Lec`(x1C~K<4wFo{#qj<_VyW zf+Ys@S5V0$Fi3%$;t&RBrT=5gU`}M1sFJiWA zl?yQcr#mcUt*T&CVpiFppz#p_n-yT&U)I>Bz^#rQ3fv0Vt-!5-eF{tk_^g9U+zL3N zz^#B|3fu}fslcs(GYZ@aIIqC1fJ+M83b3mJo13_2ix=Urqmq#SOIT*4DigSWb-K@FBE(^Bj9fZ*aMm4{G*`Z1p)smsA<4Q z1*ft9F`|4?z#Ae1M8yWYVmRKvTow|;Bi8k+0zwsBFd&wKR`*3Cu7bL^1SC*U)qunb z=H3yFWD2mCG8^JcsU$&!kTeP!nQYQ4*nLklGAgKTG_oj2V45Mj0=HdqDsU@4j|{ud2YU_!id*qNDR3*kodUPwJ1TH1zB2=SHiz#fO-pq1 zfDDKpp3e*J?PDVEBc8vJ#_i_;i7>$Pxvwj%pA`&!BKkuWbbKmcxPnrz1&ma%^Ob-x z3JSkA*Pn4p_L?{o70fqgO;HdqV7da=@L396%jPI>Et{{vwQNx_e*Z3|cTHQWDus<9 z%N4km{jR{ZY>fifvh@sXZiCA~^5$}r2c$~3cs^^Dv zifh>o1+Hbc6}Xl~D1c>h|MftLYg&MT7I6oY9DaourLfFO6Bz6u5QV}LJk zWgl(MH?~HRZ1MaNPl=mvLIrNVNffyGCTFmiQ!SoPvPtCush>2S&svy4g3~M5pG81M z4`BU`Az3_PUCkvb*%jnBdE`{!W|~KVn@xTNZZ?G!xVaQn;O64{UWuDcDF*nAmpAhk zL&|zU%2wX72J3cJYjS?=Ils=&>3xdM0k z`dtbuc45PEwtS7MV7qb#>lNHEQ{E;8S&bE26}V>k6}XA+Qs5@K*8_1aH`N0|X#RVr zxa?it99uV`4?|#C0gGKmtTSST5m=PKVZR$O!w9SbVfe2Rr;Rvb#33WL8}YXh>y7x+ zh`;!m4Dpv4any*nMqnKUhrKuAh7nljz;LD!SYE)sfDu>-z`j{VOgG|*5zmZRVg%Mx zu;#nyaYSx5-p_RJgOgO2X(A9`=Ch99hyzA!HDbOISjoXEyzB#lmsvpYA_fTlL>^*~ z5s!`FFTDZz+iM8^q#9zW5jTz4;Z_NMs)<1UBoktu5xb4x@Am-r8^IsS!N=dfLGU+g z5UY*g56s{@Yy^M)1m7Yf_=_R<_>&w6{@exPcytUCNd6WD;;s?=od|sVaR&r{vH`(g zVnFcc6cGGr0mO16_&;y>_@8VD{>vAF|51flX$1d+1|R<+1M$%ayrOT#UVhyT!EbCJ z`0W4$KaE51Ll^|#dP49m6$IZ~K=6eeg0EK)Jhemcya&Nk7sLnp^OI9Hgsqq|;X54R zJU(Z9j6Hms7G?5`Z1Tt66xtU*GH-0qP}J-%zTx*(k658s`XG_q8`XVZPiwx^Fx4eMI@j>1HYzU_pac2ax z#lL@z)6EWMUlc zB+({t_~x&}bBAsH+M9viv&!~w*~B@^!l-!E$XFrVz*8nxzwgK(IN}Q!FP^Q8tySu=t6?XJPDEhX_s;b z;=Id7k4#0oqoJP8cp=m?Ix3}h`t4#6z8REcHW-{WdSoJ;OCl=Gqa)g}ufubN{qeOo zoQ_9F1swX8eP(v>TUIaz>Q#~rCo2ZRkA54znA!1f*|}TWrQD|!hX+Sy!r^<7VPPk} zX1T)7e(lXfKeGxxb(DfFrX?YfDX7P3zod5Y8=NFxp_X~QK6l~kppdu9eNzR2tW(^Ct+7Xm5G%`J<3x$!7zGm6O?tkq~L&HK* zD-qwa&7r7CNg_>>{P|kyK)UsfT9W)l{%7XBZ-S_D7*fS|oKia`B^t!+_P1;;vm43| zhDBx%v%mJHrwwPX$*_Fz4Nj;=-Tap8=)U_6r}GITgK0y|$Pizk4DNaQmBmjw{<+HD z+x%0Vy>0pDIC~%PPfYe^=AR4fJ#q16LL}zam z{&~mV)a+`?KSBKSlvyoyCB;u6D-Jsc^G_s)RAg5O{}kb$^8E9P13vIi5BA0{>yL~T z*(fNOFH#e-OmCeh1x|O2jE8f3d&G&%5zJSs327+7bvsQoT(sywoJc->^+lY>e8GIt zLfGQJ>~z8WukwU+Gp9P~p!R^e#f{7u%-5|6;dJlGf{ej@@tTm9g7*EE77CGcFK%Q? zoR{@6Ze-SAzIY)lUq>fxsJVts$WEL3;9weaDVvarlzbLobdOE3f%3W6_wLuO=~5Zb;| zmv-G+cWpPry%NTq5Ynf6_bz=p^_Wq!ueF7?#E&db`x2m=eT*NOFCLc_(fR5OafeDI zh%6qD3kV-yogr}0Sc1r6prbC3a(cz=eDi7r$H$Mj zk|*+9<#8a6VWn{rj&<38t8wJ@iv^sejXYQS=ALUA zPNT8TjQN=JvHxSvCu0+~;~Qpc6pnr|r^a;0nJ>n56^FeTSNLz9s|${IG3+JIbuq5= zIMBtoVjuBbi*SOAVJUHRi?z%Dxb&0use;z|>ok4xd1zCl~_eehh>qTs4u-PL_@@o(r~8w`799c+S2^sk zEUvy*-hDw)`q1!BkJ8YTn(5N`DdK7T)Jgd1!`gz~iOp`w87{NSzvtuAN|}=!<*-TE z%-8HCd=0z!T1WlKGd$}VHwizdSYru1Bs(zvd^R+QLLqx6|U_cmtE>wn(n*_@o|_;JU|kp~*$8kpa6jViz{|B5Nk z4kk%5leJ1#1`$`B`t0%#UhyoXVh(;bvKBT05!aM$yL??DO-Pv-5M8 z^$45(nJor$t@d20vHzZ78L{!6HQk@G!}AM;3-AM)b?F3%xMH00T<5UMongt(d9HjH z*yT^V@cElWK7OXN>R~rHv(wxab=AdwaE8sqHgCrDQC*+0v72FKu{)b_{jRRn*r3g@ zXV{3%xawoWHF5Q|-ebSIJ~JkyZ_}k6{gE?!+?g-phhY|uAMD?-`FZ*9B>aGBt-x+- zW*@dU*hbA-;t%=r`LFR~@l&T&CO8_lOY_ft_`FKX9GjVx!p>l3Q?V53J=X*51tzSo zH7-FkZlj*Fi)8TUsqojecu78Utn4|VA+C@-?DD6s|JQFD>+n;nRSf(2n5}*uVIv>o z8jT%%j4R3w&s7GcyXv$Q+^D#-eY$icV8ZCt7BZl zv7L@_wQ3RFb&aw-m!qy~ojvxkyXTtHlU@GYrC&^spX31N@DqWEYsDOP`LEP^QG8AM ztsIsa_vU6NiOHxparL!w;OQB)6aM{=iRt|7M~VDDet5AcSk6KU4SR;Gka0c5 z5Hqd@xHej+{GBGf3~lAR%4uA}lf2p4;LR(~6&(|XVQF!>Hmm09iq-hW^&ZRct!@5OpI>fjRp2peb;tuEuF_cjZd{RA_HJB0tamrAv=!Lp z-_Y{a$9S<;am;^efvoax@BV5?+}Ql$qqo(zehhB2VcQy~_BS2+Dth8r7g)197K>XK z{MNKrpE4HUmm<~&EdG|)BUY3yo-1W{&*fm9w+XfSvCBVq`>U$`eK$E~IM!#I^}%t+ zd9GGik!{!xti(31B3OcLT)VOM+PL;&(Y0}1!)j~e+KFY>#`QhcR~wg&h1JHj0t>2* ztKCwp@-@zk%REahirC<}{8$QY*d(lhHm-?S z{A^suvFce|d92P@?`+_h?Vf8H7C0Mr04tk~s|uDhTQ~g)^8CF&Q>h(XYXz|)*(_$J z!=7u%5zkc}i;qoc8mu}NS6{2qL21jLDOxA@XQ}^ptpdJzoYFI_@iohX2VpI*an-x( zxq@zZt{GUzYeGL`0k0baD|d}+Cl=@$SH*{(E8Qc{bq0%bO=zn~&vo)~basw;H|B5u zyD_iBIu_W?=Dfj@TC*BAd04RP+JHs0hQ&zix&FkmS!=hy;EUI{OQ)O5F;8RFt6495 z8OvRb3peg?`Sa#|lfFVMehFt~$GTN(zrSgpH+i$h;umpNp_(91{j=u1=~6T98j#-B z`TD_p=}1dG3tMUZxgWooeWU=tpR+1-fUdZXcJf@oeLPnstlc!B6~?m5|L^*LijS>> zISmblMVMy!=2EP_G_G-2c4=Jy;GLFny~JxFPa}C979m5`CNu_nzKeFiCXam#n zJ3?y*-W{05luhw)Z(K?7Ja6svuh{gqZ~Kz`3eif2r*N|zas(DL8dr0yVl=MUZ#>sX ztXniJKNc)ni~Nm}zk5Ef&Ph&Ve4G$>Rbb9!o~v#O&(%Av=SrBxbH&Q$xgO;3Trsgc z&^qa-t?#y`4Z82Ie(BwwtU<^9VISUY%@K>=VOo*RpmEoKJoCSKizVULnbr?j#%314 zWy1P3>$*Qq%YRcP4%+9>-uvJFd1LY0O{)dgjak?HYnJ_+xM(I>Hx_3eh`16hcU^t0 P%hN;9vVHshl>7exI)2cV diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index 6723a45ba20bebe67a1b5ec69349e6536bd28c03..a3a0e88627eca6d68239352f63d32cad8507bbb4 100644 GIT binary patch delta 64 zcmeA<%hYq0X+sO6VRBV+QdvQTrG9E!vY~~UiIIV6YGPuFX_8s8X{w=#fq9yRd2&)x Ua;kY!im^$Wk;Ue%jMtX{0M*SF8UO$Q delta 64 zcmeA<%hYq0X+sO6VTMI~d{$m&ynd2#lCfo)v6+#vfvHiNg<+ama=a!P7SV#?;NjMtX{0I*sWPyhe` diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index d64a0dfbfc06b86f63e65d8febca9434b2d476bc..177d4b2e1378c65e6ce654fc3de6b20d8be4d061 100644 GIT binary patch delta 63 zcmca|oAJtR#tn-Z4U?;qlgbJzEcH{EKLoQjFU|g TO-+m~lT%Vt5>pmmVax#l%0m?y diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb index 35f0d970f..835b9297f 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:00.381660Z", - "iopub.status.busy": "2024-06-28T15:32:00.381248Z", - "iopub.status.idle": "2024-06-28T15:32:01.681791Z", - "shell.execute_reply": "2024-06-28T15:32:01.681244Z" + "iopub.execute_input": "2024-07-01T15:01:38.704463Z", + "iopub.status.busy": "2024-07-01T15:01:38.704282Z", + "iopub.status.idle": "2024-07-01T15:01:39.968773Z", + "shell.execute_reply": "2024-07-01T15:01:39.968140Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.684513Z", - "iopub.status.busy": "2024-06-28T15:32:01.684044Z", - "iopub.status.idle": "2024-06-28T15:32:01.703220Z", - "shell.execute_reply": "2024-06-28T15:32:01.702743Z" + "iopub.execute_input": "2024-07-01T15:01:39.971457Z", + "iopub.status.busy": "2024-07-01T15:01:39.971069Z", + "iopub.status.idle": "2024-07-01T15:01:39.990015Z", + "shell.execute_reply": "2024-07-01T15:01:39.989387Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.705803Z", - "iopub.status.busy": "2024-06-28T15:32:01.705424Z", - "iopub.status.idle": "2024-06-28T15:32:01.873655Z", - "shell.execute_reply": "2024-06-28T15:32:01.873076Z" + "iopub.execute_input": "2024-07-01T15:01:39.992806Z", + "iopub.status.busy": "2024-07-01T15:01:39.992402Z", + "iopub.status.idle": "2024-07-01T15:01:40.303536Z", + "shell.execute_reply": "2024-07-01T15:01:40.302965Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.905438Z", - "iopub.status.busy": "2024-06-28T15:32:01.905010Z", - "iopub.status.idle": "2024-06-28T15:32:01.908827Z", - "shell.execute_reply": "2024-06-28T15:32:01.908342Z" + "iopub.execute_input": "2024-07-01T15:01:40.336204Z", + "iopub.status.busy": "2024-07-01T15:01:40.335666Z", + "iopub.status.idle": "2024-07-01T15:01:40.340138Z", + "shell.execute_reply": "2024-07-01T15:01:40.339623Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.910976Z", - "iopub.status.busy": "2024-06-28T15:32:01.910625Z", - "iopub.status.idle": "2024-06-28T15:32:01.919240Z", - "shell.execute_reply": "2024-06-28T15:32:01.918799Z" + "iopub.execute_input": "2024-07-01T15:01:40.342354Z", + "iopub.status.busy": "2024-07-01T15:01:40.342145Z", + "iopub.status.idle": "2024-07-01T15:01:40.351148Z", + "shell.execute_reply": "2024-07-01T15:01:40.350569Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.921349Z", - "iopub.status.busy": "2024-06-28T15:32:01.921161Z", - "iopub.status.idle": "2024-06-28T15:32:01.923695Z", - "shell.execute_reply": "2024-06-28T15:32:01.923253Z" + "iopub.execute_input": "2024-07-01T15:01:40.353562Z", + "iopub.status.busy": "2024-07-01T15:01:40.353231Z", + "iopub.status.idle": "2024-07-01T15:01:40.356046Z", + "shell.execute_reply": "2024-07-01T15:01:40.355491Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.925557Z", - "iopub.status.busy": "2024-06-28T15:32:01.925387Z", - "iopub.status.idle": "2024-06-28T15:32:02.457912Z", - "shell.execute_reply": "2024-06-28T15:32:02.457433Z" + "iopub.execute_input": "2024-07-01T15:01:40.358053Z", + "iopub.status.busy": "2024-07-01T15:01:40.357874Z", + "iopub.status.idle": "2024-07-01T15:01:40.885000Z", + "shell.execute_reply": "2024-07-01T15:01:40.884377Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:02.460329Z", - "iopub.status.busy": "2024-06-28T15:32:02.460138Z", - "iopub.status.idle": "2024-06-28T15:32:04.490214Z", - "shell.execute_reply": "2024-06-28T15:32:04.489567Z" + "iopub.execute_input": "2024-07-01T15:01:40.887806Z", + "iopub.status.busy": "2024-07-01T15:01:40.887346Z", + "iopub.status.idle": "2024-07-01T15:01:42.858439Z", + "shell.execute_reply": "2024-07-01T15:01:42.857751Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.492856Z", - "iopub.status.busy": "2024-06-28T15:32:04.492241Z", - "iopub.status.idle": "2024-06-28T15:32:04.502594Z", - "shell.execute_reply": "2024-06-28T15:32:04.502078Z" + "iopub.execute_input": "2024-07-01T15:01:42.861505Z", + "iopub.status.busy": "2024-07-01T15:01:42.860685Z", + "iopub.status.idle": "2024-07-01T15:01:42.872129Z", + "shell.execute_reply": "2024-07-01T15:01:42.871534Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.504766Z", - "iopub.status.busy": "2024-06-28T15:32:04.504436Z", - "iopub.status.idle": "2024-06-28T15:32:04.508609Z", - "shell.execute_reply": "2024-06-28T15:32:04.508063Z" + "iopub.execute_input": "2024-07-01T15:01:42.874722Z", + "iopub.status.busy": "2024-07-01T15:01:42.874312Z", + "iopub.status.idle": "2024-07-01T15:01:42.879185Z", + "shell.execute_reply": "2024-07-01T15:01:42.878651Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.510703Z", - "iopub.status.busy": "2024-06-28T15:32:04.510397Z", - "iopub.status.idle": "2024-06-28T15:32:04.517582Z", - "shell.execute_reply": "2024-06-28T15:32:04.517118Z" + "iopub.execute_input": "2024-07-01T15:01:42.881719Z", + "iopub.status.busy": "2024-07-01T15:01:42.881293Z", + "iopub.status.idle": "2024-07-01T15:01:42.890936Z", + "shell.execute_reply": "2024-07-01T15:01:42.890441Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.519554Z", - "iopub.status.busy": "2024-06-28T15:32:04.519252Z", - "iopub.status.idle": "2024-06-28T15:32:04.632352Z", - "shell.execute_reply": "2024-06-28T15:32:04.631747Z" + "iopub.execute_input": "2024-07-01T15:01:42.893152Z", + "iopub.status.busy": "2024-07-01T15:01:42.892940Z", + "iopub.status.idle": "2024-07-01T15:01:43.010191Z", + "shell.execute_reply": "2024-07-01T15:01:43.009566Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.634587Z", - "iopub.status.busy": "2024-06-28T15:32:04.634256Z", - "iopub.status.idle": "2024-06-28T15:32:04.637211Z", - "shell.execute_reply": "2024-06-28T15:32:04.636666Z" + "iopub.execute_input": "2024-07-01T15:01:43.012877Z", + "iopub.status.busy": "2024-07-01T15:01:43.012678Z", + "iopub.status.idle": "2024-07-01T15:01:43.015881Z", + "shell.execute_reply": "2024-07-01T15:01:43.015414Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.639474Z", - "iopub.status.busy": "2024-06-28T15:32:04.638904Z", - "iopub.status.idle": "2024-06-28T15:32:06.709224Z", - "shell.execute_reply": "2024-06-28T15:32:06.708438Z" + "iopub.execute_input": "2024-07-01T15:01:43.017749Z", + "iopub.status.busy": "2024-07-01T15:01:43.017574Z", + "iopub.status.idle": "2024-07-01T15:01:45.116344Z", + "shell.execute_reply": "2024-07-01T15:01:45.115698Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:06.712672Z", - "iopub.status.busy": "2024-06-28T15:32:06.711755Z", - "iopub.status.idle": "2024-06-28T15:32:06.724142Z", - "shell.execute_reply": "2024-06-28T15:32:06.723572Z" + "iopub.execute_input": "2024-07-01T15:01:45.119290Z", + "iopub.status.busy": "2024-07-01T15:01:45.118731Z", + "iopub.status.idle": "2024-07-01T15:01:45.130593Z", + "shell.execute_reply": "2024-07-01T15:01:45.130118Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:06.726394Z", - "iopub.status.busy": "2024-06-28T15:32:06.726040Z", - "iopub.status.idle": "2024-06-28T15:32:06.750576Z", - "shell.execute_reply": "2024-06-28T15:32:06.750015Z" + "iopub.execute_input": "2024-07-01T15:01:45.132594Z", + "iopub.status.busy": "2024-07-01T15:01:45.132413Z", + "iopub.status.idle": "2024-07-01T15:01:45.200709Z", + "shell.execute_reply": "2024-07-01T15:01:45.200202Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb index abdb62899..e5a2ac8fa 100644 --- a/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/clean_learning/text.ipynb @@ -115,10 +115,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:10.483142Z", - "iopub.status.busy": "2024-06-28T15:32:10.482709Z", - "iopub.status.idle": "2024-06-28T15:32:13.547102Z", - "shell.execute_reply": "2024-06-28T15:32:13.546477Z" + "iopub.execute_input": "2024-07-01T15:01:48.389395Z", + "iopub.status.busy": "2024-07-01T15:01:48.389202Z", + "iopub.status.idle": "2024-07-01T15:01:51.596566Z", + "shell.execute_reply": "2024-07-01T15:01:51.595964Z" }, "nbsphinx": "hidden" }, @@ -135,7 +135,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -160,10 +160,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.549911Z", - "iopub.status.busy": "2024-06-28T15:32:13.549439Z", - "iopub.status.idle": "2024-06-28T15:32:13.552758Z", - "shell.execute_reply": "2024-06-28T15:32:13.552287Z" + "iopub.execute_input": "2024-07-01T15:01:51.599757Z", + "iopub.status.busy": "2024-07-01T15:01:51.599136Z", + "iopub.status.idle": "2024-07-01T15:01:51.603065Z", + "shell.execute_reply": "2024-07-01T15:01:51.602415Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.554848Z", - "iopub.status.busy": "2024-06-28T15:32:13.554437Z", - "iopub.status.idle": "2024-06-28T15:32:13.557452Z", - "shell.execute_reply": "2024-06-28T15:32:13.557018Z" + "iopub.execute_input": "2024-07-01T15:01:51.605582Z", + "iopub.status.busy": "2024-07-01T15:01:51.605171Z", + "iopub.status.idle": "2024-07-01T15:01:51.608781Z", + "shell.execute_reply": "2024-07-01T15:01:51.608196Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.559516Z", - "iopub.status.busy": "2024-06-28T15:32:13.559185Z", - "iopub.status.idle": "2024-06-28T15:32:13.584970Z", - "shell.execute_reply": "2024-06-28T15:32:13.584357Z" + "iopub.execute_input": "2024-07-01T15:01:51.611405Z", + "iopub.status.busy": "2024-07-01T15:01:51.610984Z", + "iopub.status.idle": "2024-07-01T15:01:51.666636Z", + "shell.execute_reply": "2024-07-01T15:01:51.666058Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.587334Z", - "iopub.status.busy": "2024-06-28T15:32:13.586930Z", - "iopub.status.idle": "2024-06-28T15:32:13.590797Z", - "shell.execute_reply": "2024-06-28T15:32:13.590247Z" + "iopub.execute_input": "2024-07-01T15:01:51.668846Z", + "iopub.status.busy": "2024-07-01T15:01:51.668483Z", + "iopub.status.idle": "2024-07-01T15:01:51.672233Z", + "shell.execute_reply": "2024-07-01T15:01:51.671774Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.593121Z", - "iopub.status.busy": "2024-06-28T15:32:13.592769Z", - "iopub.status.idle": "2024-06-28T15:32:13.596104Z", - "shell.execute_reply": "2024-06-28T15:32:13.595568Z" + "iopub.execute_input": "2024-07-01T15:01:51.674498Z", + "iopub.status.busy": "2024-07-01T15:01:51.674053Z", + "iopub.status.idle": "2024-07-01T15:01:51.677796Z", + "shell.execute_reply": "2024-07-01T15:01:51.677326Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'cancel_transfer', 'getting_spare_card', 'beneficiary_not_allowed', 'visa_or_mastercard', 'change_pin', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'lost_or_stolen_phone'}\n" + "Classes: {'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'change_pin'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.598094Z", - "iopub.status.busy": "2024-06-28T15:32:13.597802Z", - "iopub.status.idle": "2024-06-28T15:32:13.600928Z", - "shell.execute_reply": "2024-06-28T15:32:13.600341Z" + "iopub.execute_input": "2024-07-01T15:01:51.679875Z", + "iopub.status.busy": "2024-07-01T15:01:51.679530Z", + "iopub.status.idle": "2024-07-01T15:01:51.682840Z", + "shell.execute_reply": "2024-07-01T15:01:51.682369Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.602926Z", - "iopub.status.busy": "2024-06-28T15:32:13.602603Z", - "iopub.status.idle": "2024-06-28T15:32:13.605952Z", - "shell.execute_reply": "2024-06-28T15:32:13.605418Z" + "iopub.execute_input": "2024-07-01T15:01:51.684949Z", + "iopub.status.busy": "2024-07-01T15:01:51.684614Z", + "iopub.status.idle": "2024-07-01T15:01:51.687925Z", + "shell.execute_reply": "2024-07-01T15:01:51.687477Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:13.607995Z", - "iopub.status.busy": "2024-06-28T15:32:13.607672Z", - "iopub.status.idle": "2024-06-28T15:32:18.588947Z", - "shell.execute_reply": "2024-06-28T15:32:18.588363Z" + "iopub.execute_input": "2024-07-01T15:01:51.690015Z", + "iopub.status.busy": "2024-07-01T15:01:51.689695Z", + "iopub.status.idle": "2024-07-01T15:01:58.269951Z", + "shell.execute_reply": "2024-07-01T15:01:58.269375Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81cceb7356c54fe6917cce0213b9538e", + "model_id": "62351de8abb94a038c8769c2df5c458f", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "251f05f2d534421ea834728e6e80c34a", + "model_id": "340785de497a4c63ab7144c513dbc840", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5cdd87483a4b48dbaf9539299fb3282b", + "model_id": "690a2c7ac41a426d9ea764ad3d62a191", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3278dd636e23462a89f4f6e23e81cf14", + "model_id": "91c85984c43b4bb6ac43cf0e512599f1", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c6db16778bc46b4aed4aaea436134e0", + "model_id": "a6a71af506bb4925baad0f1c7f46552e", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f0fe51d36484535bc14d531e03fabdf", + "model_id": "fce6374730574858a51fc6bb15b16ff0", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa428e0260a64e6da94c0ea83d11772e", + "model_id": "e3101229ea6044f0bf8820ecad5523c3", "version_major": 2, "version_minor": 0 }, @@ -601,10 +601,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.591766Z", - "iopub.status.busy": "2024-06-28T15:32:18.591358Z", - "iopub.status.idle": "2024-06-28T15:32:18.594443Z", - "shell.execute_reply": "2024-06-28T15:32:18.593945Z" + "iopub.execute_input": "2024-07-01T15:01:58.272720Z", + "iopub.status.busy": "2024-07-01T15:01:58.272512Z", + "iopub.status.idle": "2024-07-01T15:01:58.275361Z", + "shell.execute_reply": "2024-07-01T15:01:58.274859Z" } }, "outputs": [], @@ -626,10 +626,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.596515Z", - "iopub.status.busy": "2024-06-28T15:32:18.596181Z", - "iopub.status.idle": "2024-06-28T15:32:18.598873Z", - "shell.execute_reply": "2024-06-28T15:32:18.598422Z" + "iopub.execute_input": "2024-07-01T15:01:58.277576Z", + "iopub.status.busy": "2024-07-01T15:01:58.277231Z", + "iopub.status.idle": "2024-07-01T15:01:58.279889Z", + "shell.execute_reply": "2024-07-01T15:01:58.279457Z" } }, "outputs": [], @@ -644,10 +644,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:18.600840Z", - "iopub.status.busy": "2024-06-28T15:32:18.600538Z", - "iopub.status.idle": "2024-06-28T15:32:21.461322Z", - "shell.execute_reply": "2024-06-28T15:32:21.460567Z" + "iopub.execute_input": "2024-07-01T15:01:58.281927Z", + "iopub.status.busy": "2024-07-01T15:01:58.281583Z", + "iopub.status.idle": "2024-07-01T15:02:01.122494Z", + "shell.execute_reply": "2024-07-01T15:02:01.121708Z" }, "scrolled": true }, @@ -670,10 +670,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.464638Z", - "iopub.status.busy": "2024-06-28T15:32:21.463834Z", - "iopub.status.idle": "2024-06-28T15:32:21.471562Z", - "shell.execute_reply": "2024-06-28T15:32:21.471019Z" + "iopub.execute_input": "2024-07-01T15:02:01.125497Z", + "iopub.status.busy": "2024-07-01T15:02:01.124853Z", + "iopub.status.idle": "2024-07-01T15:02:01.132982Z", + "shell.execute_reply": "2024-07-01T15:02:01.132483Z" } }, "outputs": [ @@ -774,10 +774,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.473714Z", - "iopub.status.busy": "2024-06-28T15:32:21.473378Z", - "iopub.status.idle": "2024-06-28T15:32:21.477300Z", - "shell.execute_reply": "2024-06-28T15:32:21.476829Z" + "iopub.execute_input": "2024-07-01T15:02:01.135273Z", + "iopub.status.busy": "2024-07-01T15:02:01.134876Z", + "iopub.status.idle": "2024-07-01T15:02:01.138920Z", + "shell.execute_reply": "2024-07-01T15:02:01.138424Z" } }, "outputs": [], @@ -791,10 +791,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.479264Z", - "iopub.status.busy": "2024-06-28T15:32:21.478937Z", - "iopub.status.idle": "2024-06-28T15:32:21.482305Z", - "shell.execute_reply": "2024-06-28T15:32:21.481845Z" + "iopub.execute_input": "2024-07-01T15:02:01.140950Z", + "iopub.status.busy": "2024-07-01T15:02:01.140631Z", + "iopub.status.idle": "2024-07-01T15:02:01.143747Z", + "shell.execute_reply": "2024-07-01T15:02:01.143216Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.484315Z", - "iopub.status.busy": "2024-06-28T15:32:21.483999Z", - "iopub.status.idle": "2024-06-28T15:32:21.487149Z", - "shell.execute_reply": "2024-06-28T15:32:21.486605Z" + "iopub.execute_input": "2024-07-01T15:02:01.145826Z", + "iopub.status.busy": "2024-07-01T15:02:01.145406Z", + "iopub.status.idle": "2024-07-01T15:02:01.148540Z", + "shell.execute_reply": "2024-07-01T15:02:01.148071Z" } }, "outputs": [], @@ -852,10 +852,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.489496Z", - "iopub.status.busy": "2024-06-28T15:32:21.489087Z", - "iopub.status.idle": "2024-06-28T15:32:21.497056Z", - "shell.execute_reply": "2024-06-28T15:32:21.496469Z" + "iopub.execute_input": "2024-07-01T15:02:01.150516Z", + "iopub.status.busy": "2024-07-01T15:02:01.150212Z", + "iopub.status.idle": "2024-07-01T15:02:01.157258Z", + "shell.execute_reply": "2024-07-01T15:02:01.156695Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.499326Z", - "iopub.status.busy": "2024-06-28T15:32:21.499002Z", - "iopub.status.idle": "2024-06-28T15:32:21.732976Z", - "shell.execute_reply": "2024-06-28T15:32:21.732384Z" + "iopub.execute_input": "2024-07-01T15:02:01.159339Z", + "iopub.status.busy": "2024-07-01T15:02:01.159040Z", + "iopub.status.idle": "2024-07-01T15:02:01.408184Z", + "shell.execute_reply": "2024-07-01T15:02:01.407652Z" }, "scrolled": true }, @@ -1022,10 +1022,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.735585Z", - "iopub.status.busy": "2024-06-28T15:32:21.735168Z", - "iopub.status.idle": "2024-06-28T15:32:21.922637Z", - "shell.execute_reply": "2024-06-28T15:32:21.922053Z" + "iopub.execute_input": "2024-07-01T15:02:01.410863Z", + "iopub.status.busy": "2024-07-01T15:02:01.410467Z", + "iopub.status.idle": "2024-07-01T15:02:01.588794Z", + "shell.execute_reply": "2024-07-01T15:02:01.588210Z" }, "scrolled": true }, @@ -1058,10 +1058,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:21.925255Z", - "iopub.status.busy": "2024-06-28T15:32:21.924857Z", - "iopub.status.idle": "2024-06-28T15:32:21.928930Z", - "shell.execute_reply": "2024-06-28T15:32:21.928386Z" + "iopub.execute_input": "2024-07-01T15:02:01.592672Z", + "iopub.status.busy": "2024-07-01T15:02:01.591557Z", + "iopub.status.idle": "2024-07-01T15:02:01.597151Z", + "shell.execute_reply": "2024-07-01T15:02:01.596544Z" }, "nbsphinx": "hidden" }, @@ -1105,7 +1105,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0654f51f54c744d98029a5ea15c37e7c": { + "02e3e418c19240f486b6744bed9dc1a5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1158,7 +1158,23 @@ "width": null } }, - "087e012a31e74812b07d928968bc3c5e": { + "04d9fd81f1a64f5d864276ca3169c4ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "06823c4e7990422188c080cab19edf7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1211,7 +1227,30 @@ "width": null } }, - "08e9ec5f60494f048e5d58bb8dfa2782": { + "079f3f1e033f4bbca8478c880a927afc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_98055291c2ee452694ecc19e51ecaead", + "placeholder": "​", + "style": "IPY_MODEL_58ef1824a5c949b8a654f8a0277b7b97", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "09f87f9e986947dda9b2e8c0bc235a7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1264,30 +1303,23 @@ "width": null } }, - "0a9075cf3c864a86b308b079f5ee7873": { + "0b359b2ad56c46998822be17f96e77fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_08e9ec5f60494f048e5d58bb8dfa2782", - "placeholder": "​", - "style": "IPY_MODEL_c1c1bc732ed24cb9ac28092022314462", - "tabbable": null, - "tooltip": null, - "value": "pytorch_model.bin: 100%" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0b35ffe60d7c4b9799b5fce1f288989c": { + "0cbf3b1097fc425e989d58c0735c519d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1340,110 +1372,168 @@ "width": null } }, - "0b437892039948e8abb66dc411c2e04f": { - "model_module": "@jupyter-widgets/controls", + "13325f0cbdac42e292d5ec55dd02d83e": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_48337b46095146c2b808c53bea2653e1", - "placeholder": "​", - "style": "IPY_MODEL_570178b2084e4c65a1aeb887ed8b304b", - "tabbable": null, - "tooltip": null, - "value": ".gitattributes: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0c7f0a99520d4d0f86d594a5fb2f6cab": { + "14034de7bb7744e19694d7e8a08ca368": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9ac89da8e95c4b04ae98998044928144", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a088ce5c5e4a4a50a697175e3012eeb2", - "tabbable": null, - "tooltip": null, - "value": 466062.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0d6f6129381940e38ea8824966649246": { - "model_module": "@jupyter-widgets/controls", + "1c6fb4c64d13487194c96f1bdc086e42": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0f56503d7ab04c1eaaf800c590ddc4de": { + "1f3ed30be4ca4e5b97f396f778f155f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9d38a50f62054962a5ac76122754b6df", + "placeholder": "​", + "style": "IPY_MODEL_3aafc982b22a428aa27c5cd1250efa05", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:01<00:00, 28.1MB/s]" } }, - "155e84efd692454399d4426bb5713e35": { + "20de08960d534ff89e4d9748aae1b593": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "22f06b02a2e546d5b45f1f56ce9f0644": { + "2d114fd4a23f44a9b28eb2eefa18ce30": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1458,39 +1548,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bc553302eb144ee1affc907e042d6c5c", + "layout": "IPY_MODEL_f893ddf9db054e05b5719e8272e508e6", "placeholder": "​", - "style": "IPY_MODEL_45ccc4a60a034745b926bb540d0e915a", + "style": "IPY_MODEL_8c1d39b74d17432a831b7e63d65fc7c0", "tabbable": null, "tooltip": null, - "value": " 391/391 [00:00<00:00, 64.3kB/s]" - } - }, - "251f05f2d534421ea834728e6e80c34a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4873b50be2dc4a2b99b71c0258d7bd8a", - "IPY_MODEL_f04b2d469897481ea059b74c219803ca", - "IPY_MODEL_29ee7f74a6bf4c2a822d2a7326b68385" - ], - "layout": "IPY_MODEL_087e012a31e74812b07d928968bc3c5e", - "tabbable": null, - "tooltip": null + "value": ".gitattributes: 100%" } }, - "29ee7f74a6bf4c2a822d2a7326b68385": { + "3123835078454cf9b22abb8a471265a0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1505,31 +1571,94 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_58f565f2791d42f3bec1f5f720b8cd85", + "layout": "IPY_MODEL_c7cd56b89e03457e8bfa240f778aeac4", "placeholder": "​", - "style": "IPY_MODEL_a8ddb64ad7f44965bb03d2cdb1a58784", + "style": "IPY_MODEL_5e5e6673b88a48b6a1e0b40d0eb77931", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 374kB/s]" + "value": " 48.0/48.0 [00:00<00:00, 7.41kB/s]" + } + }, + "31d200dead6d4d7a9d475999bb9cd002": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "2b8cf285d37641e78f819e258a1471d5": { + "33222d3a75f94b0badb9b80898204763": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fe702e6a2dbf409c9c44ca8eb27dffcc", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_20de08960d534ff89e4d9748aae1b593", + "tabbable": null, + "tooltip": null, + "value": 391.0 } }, - "3278dd636e23462a89f4f6e23e81cf14": { + "340785de497a4c63ab7144c513dbc840": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1544,16 +1673,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0a9075cf3c864a86b308b079f5ee7873", - "IPY_MODEL_41cfe9c4d1cd4fea9b7df6ff9928999b", - "IPY_MODEL_91e70d4791c84851a1b91fa5e7c57cd6" + "IPY_MODEL_f599421afd5149bfa8a6ee654777c7c1", + "IPY_MODEL_48ac4d127e6141eb8fa1f6bf4d15e4c2", + "IPY_MODEL_ae97d0f75f4740cbb7ce3ea7ed2d44aa" ], - "layout": "IPY_MODEL_4710be403bdb45689713c7fb0b2c8b36", + "layout": "IPY_MODEL_0cbf3b1097fc425e989d58c0735c519d", "tabbable": null, "tooltip": null } }, - "3580f78a814e469999540909f5d7c17d": { + "35dadc5a2f124430a20eb48108c03ab1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1571,7 +1700,7 @@ "text_color": null } }, - "39a6f9b8d9474c6896d33cd8e0822b9f": { + "3aafc982b22a428aa27c5cd1250efa05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1589,64 +1718,60 @@ "text_color": null } }, - "3a2ff520b6424d049b8b15606b60599d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c75bd3098c8a48acbb95bca0290d883d", - "placeholder": "​", - "style": "IPY_MODEL_155e84efd692454399d4426bb5713e35", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "3c521633f0bb427da41e208cdb5b96c6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3fe3546160e64f05886cf998dbf3d44d": { - "model_module": "@jupyter-widgets/controls", + "3e413d091d0d4b6482bc9ed11d5d236c": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "4148422cf74744c5bf382eb313e0a314": { + "438118108888448cb339b8dc2230aa72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1699,67 +1824,56 @@ "width": null } }, - "41cfe9c4d1cd4fea9b7df6ff9928999b": { + "48a7c543fbdc47eb91f1afe3d775a0f8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f35cae2ff4de4ac09ee0eef4156fb86a", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_43d7d96a652f4111bce4b6480fcb9957", + "layout": "IPY_MODEL_1c6fb4c64d13487194c96f1bdc086e42", + "placeholder": "​", + "style": "IPY_MODEL_f03c166056b94c999588193b57b1ab4f", "tabbable": null, "tooltip": null, - "value": 54245363.0 + "value": " 391/391 [00:00<00:00, 62.0kB/s]" } }, - "43d7d96a652f4111bce4b6480fcb9957": { + "48ac4d127e6141eb8fa1f6bf4d15e4c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "45ccc4a60a034745b926bb540d0e915a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_91b179d4d8f345959726ca7ba76ac7a9", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_04d9fd81f1a64f5d864276ca3169c4ae", + "tabbable": null, + "tooltip": null, + "value": 2211.0 } }, - "4710be403bdb45689713c7fb0b2c8b36": { + "57162eef07c144edbbf29b08ca208753": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1812,7 +1926,25 @@ "width": null } }, - "48337b46095146c2b808c53bea2653e1": { + "58ef1824a5c949b8a654f8a0277b7b97": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "5b140acae5da41218fdb0bba821dd741": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1865,71 +1997,49 @@ "width": null } }, - "4873b50be2dc4a2b99b71c0258d7bd8a": { + "5e5e6673b88a48b6a1e0b40d0eb77931": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0654f51f54c744d98029a5ea15c37e7c", - "placeholder": "​", - "style": "IPY_MODEL_3fe3546160e64f05886cf998dbf3d44d", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "561c63a2b65b4116a28fb3b4ac4f32e6": { + "62351de8abb94a038c8769c2df5c458f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f2a5b3c6a48140f7bfb68bf54f205281", - "placeholder": "​", - "style": "IPY_MODEL_6b43fcc00af140d0ab061913b16a022a", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 4.45MB/s]" - } - }, - "570178b2084e4c65a1aeb887ed8b304b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2d114fd4a23f44a9b28eb2eefa18ce30", + "IPY_MODEL_33222d3a75f94b0badb9b80898204763", + "IPY_MODEL_48a7c543fbdc47eb91f1afe3d775a0f8" + ], + "layout": "IPY_MODEL_02e3e418c19240f486b6744bed9dc1a5", + "tabbable": null, + "tooltip": null } }, - "58f565f2791d42f3bec1f5f720b8cd85": { + "624e82a0b0ec439696797f4a87dcb861": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1982,88 +2092,7 @@ "width": null } }, - "5cdd87483a4b48dbaf9539299fb3282b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8fabaa55f275451ab3cef6d7deb821c7", - "IPY_MODEL_b30eb88d1f0a44c2a212fbce52fe66f6", - "IPY_MODEL_d6b4a4277cca46f3965f64c58a0ce1c5" - ], - "layout": "IPY_MODEL_751cbc2ed7634303a0e36ef0a4409514", - "tabbable": null, - "tooltip": null - } - }, - "66f4157cf10d4672b4b214e5bf794928": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0b35ffe60d7c4b9799b5fce1f288989c", - "placeholder": "​", - "style": "IPY_MODEL_ebdd4dfe6aec4e448aa148abafca1b7d", - "tabbable": null, - "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 8.09kB/s]" - } - }, - "6b43fcc00af140d0ab061913b16a022a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "6f4ca3b8054843dca836efad3447e526": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "751cbc2ed7634303a0e36ef0a4409514": { + "673865438c8e4b0296bbe6ca3c060cb9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2116,25 +2145,7 @@ "width": null } }, - "7b40009d218e4df1bf1bf7ab2887b25f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7c73348968774ea2bfc42fc202242f70": { + "68e32e4abb734a84a60e14567ee5ab16": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2187,7 +2198,7 @@ "width": null } }, - "7f0fe51d36484535bc14d531e03fabdf": { + "690a2c7ac41a426d9ea764ad3d62a191": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2202,16 +2213,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_3a2ff520b6424d049b8b15606b60599d", - "IPY_MODEL_fcf75ab6cf814290a0f372321afcf3ea", - "IPY_MODEL_66f4157cf10d4672b4b214e5bf794928" + "IPY_MODEL_a8d4e5776925443ebb839809dfaca832", + "IPY_MODEL_e20b0fbd46bf4b59bc4da8f83218e600", + "IPY_MODEL_aa7b347bc9c145b09318d5248c15af77" ], - "layout": "IPY_MODEL_dd2a4c676cf043739927dfa7c9d87d4a", + "layout": "IPY_MODEL_5b140acae5da41218fdb0bba821dd741", "tabbable": null, "tooltip": null } }, - "7ff82688f10e4abb980a8344fbc698fe": { + "69cbdc81324248878408dbfed717680f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2264,7 +2275,30 @@ "width": null } }, - "805d33b8fa22471abfeee9fa61e9e2de": { + "704c7b4ef65041a89cd5d3fc1f81e95f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ca4b9735d66b414697862773b6ae1163", + "placeholder": "​", + "style": "IPY_MODEL_b49778737bd8479f946ac4060638f1d9", + "tabbable": null, + "tooltip": null, + "value": " 466k/466k [00:00<00:00, 15.0MB/s]" + } + }, + "72404aea60be4c0aaa808f8f70065420": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2317,31 +2351,87 @@ "width": null } }, - "81cceb7356c54fe6917cce0213b9538e": { + "73bb6421a1694f5abed56acfff8df5c8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0b437892039948e8abb66dc411c2e04f", - "IPY_MODEL_c8d93fe968f24d46b815055f71fc9486", - "IPY_MODEL_22f06b02a2e546d5b45f1f56ce9f0644" - ], - "layout": "IPY_MODEL_8a4fde9243d3412685287673ee2eb1b4", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c9974b364cf9469292e4c0a172a720dd", + "placeholder": "​", + "style": "IPY_MODEL_9facb5b56860415d974458a4f8a98e99", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "tokenizer_config.json: 100%" } }, - "866c3e4cfe8c447384b363599afff30d": { + "7430ccc73950450389eb7ded2db99afc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7443c554c2da4d4790de7a725d038932": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7f9ca0d6a1fb49489d44ed52e9e5b6aa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_72404aea60be4c0aaa808f8f70065420", + "placeholder": "​", + "style": "IPY_MODEL_b8348d0d4075416fa30cf5f27f5fd6fd", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 32.8MB/s]" + } + }, + "7ffce27927fb488788c78c75defb22e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2394,7 +2484,7 @@ "width": null } }, - "8a4fde9243d3412685287673ee2eb1b4": { + "8658abefb5a143a0ae1d78b86bb5e6a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2447,31 +2537,51 @@ "width": null } }, - "8c6db16778bc46b4aed4aaea436134e0": { + "8c1d39b74d17432a831b7e63d65fc7c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "8e023743450746ba98fb9cff5b8ab3ca": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_af29b9c29f12466fbbc40d495ae3db5c", - "IPY_MODEL_0c7f0a99520d4d0f86d594a5fb2f6cab", - "IPY_MODEL_561c63a2b65b4116a28fb3b4ac4f32e6" - ], - "layout": "IPY_MODEL_805d33b8fa22471abfeee9fa61e9e2de", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_13325f0cbdac42e292d5ec55dd02d83e", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f650bcae88e3498c9713ed7fad85c313", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 48.0 } }, - "8d71fa197d2545bba0a3256f24725cae": { + "91b179d4d8f345959726ca7ba76ac7a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2519,84 +2629,36 @@ "overflow": null, "padding": null, "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8fabaa55f275451ab3cef6d7deb821c7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a1733c6fe59a40a9973bb2adf659b2f9", - "placeholder": "​", - "style": "IPY_MODEL_39a6f9b8d9474c6896d33cd8e0822b9f", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" - } - }, - "91e70d4791c84851a1b91fa5e7c57cd6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_866c3e4cfe8c447384b363599afff30d", - "placeholder": "​", - "style": "IPY_MODEL_7b40009d218e4df1bf1bf7ab2887b25f", - "tabbable": null, - "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 167MB/s]" + "top": null, + "visibility": null, + "width": null } }, - "924e964b896b445da36a28a097c512a8": { + "91c85984c43b4bb6ac43cf0e512599f1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d50bce29eb5246ab873d30e7b2f1931a", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1a796714b5e4b1386e7c5fc265f5a18", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b7d8f867996c41a0b6bc2bffd9f7965d", + "IPY_MODEL_9a7533d991e54982b696f43606dbe2b5", + "IPY_MODEL_1f3ed30be4ca4e5b97f396f778f155f5" + ], + "layout": "IPY_MODEL_e5831f2ec42f4838ab828634c0bd28fd", "tabbable": null, - "tooltip": null, - "value": 231508.0 + "tooltip": null } }, - "9ac89da8e95c4b04ae98998044928144": { + "98055291c2ee452694ecc19e51ecaead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2649,46 +2711,59 @@ "width": null } }, - "9e8b313266ba4a21bb5d1e6a8e8a20bd": { + "9a7533d991e54982b696f43606dbe2b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fa4457b0ed8c4544b0ca7b52eecbb8b3", - "placeholder": "​", - "style": "IPY_MODEL_0d6f6129381940e38ea8824966649246", + "layout": "IPY_MODEL_8658abefb5a143a0ae1d78b86bb5e6a7", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fa52b986ca5d4e199fbbe62d54662389", "tabbable": null, "tooltip": null, - "value": "vocab.txt: 100%" + "value": 54245363.0 } }, - "a088ce5c5e4a4a50a697175e3012eeb2": { + "9ac47759986e4465a5e745617e40364c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_fbfcfc4949a741f88d46ad1b0042769e", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_14034de7bb7744e19694d7e8a08ca368", + "tabbable": null, + "tooltip": null, + "value": 231508.0 } }, - "a1733c6fe59a40a9973bb2adf659b2f9": { + "9d38a50f62054962a5ac76122754b6df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2741,7 +2816,7 @@ "width": null } }, - "a8ddb64ad7f44965bb03d2cdb1a58784": { + "9facb5b56860415d974458a4f8a98e99": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2759,7 +2834,7 @@ "text_color": null } }, - "aa428e0260a64e6da94c0ea83d11772e": { + "a6a71af506bb4925baad0f1c7f46552e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2774,16 +2849,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_9e8b313266ba4a21bb5d1e6a8e8a20bd", - "IPY_MODEL_924e964b896b445da36a28a097c512a8", - "IPY_MODEL_e94459cf036f48d2a8288e79274ed9a6" + "IPY_MODEL_b729c768ffe84c23ab3835b3f3f8051b", + "IPY_MODEL_bdbdcb9609f647b6ac21adad814ab8dd", + "IPY_MODEL_704c7b4ef65041a89cd5d3fc1f81e95f" ], - "layout": "IPY_MODEL_7ff82688f10e4abb980a8344fbc698fe", + "layout": "IPY_MODEL_57162eef07c144edbbf29b08ca208753", "tabbable": null, "tooltip": null } }, - "af29b9c29f12466fbbc40d495ae3db5c": { + "a8d4e5776925443ebb839809dfaca832": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2798,128 +2873,79 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4148422cf74744c5bf382eb313e0a314", + "layout": "IPY_MODEL_68e32e4abb734a84a60e14567ee5ab16", "placeholder": "​", - "style": "IPY_MODEL_0f56503d7ab04c1eaaf800c590ddc4de", + "style": "IPY_MODEL_7443c554c2da4d4790de7a725d038932", "tabbable": null, "tooltip": null, - "value": "tokenizer.json: 100%" + "value": "config.json: 100%" } }, - "b30eb88d1f0a44c2a212fbce52fe66f6": { + "aa7b347bc9c145b09318d5248c15af77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c884cea631794039964063852dcfcb74", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c04125b9dc5c46b298392e2e44751ffe", + "layout": "IPY_MODEL_06823c4e7990422188c080cab19edf7b", + "placeholder": "​", + "style": "IPY_MODEL_fc9418d15a01426f91adaea86b81b6eb", "tabbable": null, "tooltip": null, - "value": 665.0 - } - }, - "bc553302eb144ee1affc907e042d6c5c": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": " 665/665 [00:00<00:00, 98.3kB/s]" } }, - "c04125b9dc5c46b298392e2e44751ffe": { + "ab8d06a6112542ae917846ddae4c46ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c1c1bc732ed24cb9ac28092022314462": { + "ae97d0f75f4740cbb7ce3ea7ed2d44aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_31d200dead6d4d7a9d475999bb9cd002", + "placeholder": "​", + "style": "IPY_MODEL_35dadc5a2f124430a20eb48108c03ab1", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 407kB/s]" } }, - "c59e2d5352f74a18a4f0864015d5680e": { + "b49778737bd8479f946ac4060638f1d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2937,113 +2963,71 @@ "text_color": null } }, - "c75bd3098c8a48acbb95bca0290d883d": { - "model_module": "@jupyter-widgets/base", + "b729c768ffe84c23ab3835b3f3f8051b": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_673865438c8e4b0296bbe6ca3c060cb9", + "placeholder": "​", + "style": "IPY_MODEL_ab8d06a6112542ae917846ddae4c46ac", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" } }, - "c884cea631794039964063852dcfcb74": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "b7d8f867996c41a0b6bc2bffd9f7965d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_438118108888448cb339b8dc2230aa72", + "placeholder": "​", + "style": "IPY_MODEL_edc251780b214e988fe3e98369e79de3", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" + } + }, + "b8348d0d4075416fa30cf5f27f5fd6fd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c8d93fe968f24d46b815055f71fc9486": { + "bdbdcb9609f647b6ac21adad814ab8dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -3059,17 +3043,35 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7c73348968774ea2bfc42fc202242f70", - "max": 391.0, + "layout": "IPY_MODEL_09f87f9e986947dda9b2e8c0bc235a7b", + "max": 466062.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_6f4ca3b8054843dca836efad3447e526", + "style": "IPY_MODEL_7430ccc73950450389eb7ded2db99afc", "tabbable": null, "tooltip": null, - "value": 391.0 + "value": 466062.0 + } + }, + "c2a16ddd367b4238aa5afd5a22f28d8c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ce23779e7d4543509b65346ec8ed319e": { + "c7cd56b89e03457e8bfa240f778aeac4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3122,7 +3124,7 @@ "width": null } }, - "d0aa47a8a00543e6ad72c388f9080719": { + "c9974b364cf9469292e4c0a172a720dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3175,7 +3177,7 @@ "width": null } }, - "d50bce29eb5246ab873d30e7b2f1931a": { + "ca4b9735d66b414697862773b6ae1163": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3228,99 +3230,57 @@ "width": null } }, - "d6b4a4277cca46f3965f64c58a0ce1c5": { + "e20b0fbd46bf4b59bc4da8f83218e600": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d0aa47a8a00543e6ad72c388f9080719", - "placeholder": "​", - "style": "IPY_MODEL_3580f78a814e469999540909f5d7c17d", + "layout": "IPY_MODEL_3e413d091d0d4b6482bc9ed11d5d236c", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0b359b2ad56c46998822be17f96e77fb", "tabbable": null, "tooltip": null, - "value": " 665/665 [00:00<00:00, 98.0kB/s]" - } - }, - "dd2a4c676cf043739927dfa7c9d87d4a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": 665.0 } }, - "e1a796714b5e4b1386e7c5fc265f5a18": { + "e3101229ea6044f0bf8820ecad5523c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_079f3f1e033f4bbca8478c880a927afc", + "IPY_MODEL_9ac47759986e4465a5e745617e40364c", + "IPY_MODEL_7f9ca0d6a1fb49489d44ed52e9e5b6aa" + ], + "layout": "IPY_MODEL_624e82a0b0ec439696797f4a87dcb861", + "tabbable": null, + "tooltip": null } }, - "e5c784eab8a94900a4d3a70982295f74": { + "e5831f2ec42f4838ab828634c0bd28fd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3373,30 +3333,25 @@ "width": null } }, - "e94459cf036f48d2a8288e79274ed9a6": { + "edc251780b214e988fe3e98369e79de3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce23779e7d4543509b65346ec8ed319e", - "placeholder": "​", - "style": "IPY_MODEL_c59e2d5352f74a18a4f0864015d5680e", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 30.1MB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ebdd4dfe6aec4e448aa148abafca1b7d": { + "f03c166056b94c999588193b57b1ab4f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3414,33 +3369,46 @@ "text_color": null } }, - "f04b2d469897481ea059b74c219803ca": { + "f599421afd5149bfa8a6ee654777c7c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d71fa197d2545bba0a3256f24725cae", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2b8cf285d37641e78f819e258a1471d5", + "layout": "IPY_MODEL_7ffce27927fb488788c78c75defb22e0", + "placeholder": "​", + "style": "IPY_MODEL_c2a16ddd367b4238aa5afd5a22f28d8c", "tabbable": null, "tooltip": null, - "value": 2211.0 + "value": "README.md: 100%" + } + }, + "f650bcae88e3498c9713ed7fad85c313": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f2a5b3c6a48140f7bfb68bf54f205281": { + "f893ddf9db054e05b5719e8272e508e6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3493,7 +3461,23 @@ "width": null } }, - "f35cae2ff4de4ac09ee0eef4156fb86a": { + "fa52b986ca5d4e199fbbe62d54662389": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "fbfcfc4949a741f88d46ad1b0042769e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3546,7 +3530,49 @@ "width": null } }, - "fa4457b0ed8c4544b0ca7b52eecbb8b3": { + "fc9418d15a01426f91adaea86b81b6eb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "fce6374730574858a51fc6bb15b16ff0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_73bb6421a1694f5abed56acfff8df5c8", + "IPY_MODEL_8e023743450746ba98fb9cff5b8ab3ca", + "IPY_MODEL_3123835078454cf9b22abb8a471265a0" + ], + "layout": "IPY_MODEL_69cbdc81324248878408dbfed717680f", + "tabbable": null, + "tooltip": null + } + }, + "fe702e6a2dbf409c9c44ca8eb27dffcc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3598,32 +3624,6 @@ "visibility": null, "width": null } - }, - "fcf75ab6cf814290a0f372321afcf3ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_e5c784eab8a94900a4d3a70982295f74", - "max": 48.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3c521633f0bb427da41e208cdb5b96c6", - "tabbable": null, - "tooltip": null, - "value": 48.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb index ef95879eb..2f2d2a40d 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:25.530965Z", - "iopub.status.busy": "2024-06-28T15:32:25.530540Z", - "iopub.status.idle": "2024-06-28T15:32:31.009406Z", - "shell.execute_reply": "2024-06-28T15:32:31.008863Z" + "iopub.execute_input": "2024-07-01T15:02:05.352362Z", + "iopub.status.busy": "2024-07-01T15:02:05.351840Z", + "iopub.status.idle": "2024-07-01T15:02:11.364535Z", + "shell.execute_reply": "2024-07-01T15:02:11.364016Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.012191Z", - "iopub.status.busy": "2024-06-28T15:32:31.011789Z", - "iopub.status.idle": "2024-06-28T15:32:31.015685Z", - "shell.execute_reply": "2024-06-28T15:32:31.015243Z" + "iopub.execute_input": "2024-07-01T15:02:11.367286Z", + "iopub.status.busy": "2024-07-01T15:02:11.366756Z", + "iopub.status.idle": "2024-07-01T15:02:11.369937Z", + "shell.execute_reply": "2024-07-01T15:02:11.369499Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.017841Z", - "iopub.status.busy": "2024-06-28T15:32:31.017501Z", - "iopub.status.idle": "2024-06-28T15:32:31.022379Z", - "shell.execute_reply": "2024-06-28T15:32:31.021949Z" + "iopub.execute_input": "2024-07-01T15:02:11.372033Z", + "iopub.status.busy": "2024-07-01T15:02:11.371712Z", + "iopub.status.idle": "2024-07-01T15:02:11.376772Z", + "shell.execute_reply": "2024-07-01T15:02:11.376263Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:31.024337Z", - "iopub.status.busy": "2024-06-28T15:32:31.024159Z", - "iopub.status.idle": "2024-06-28T15:32:32.833612Z", - "shell.execute_reply": "2024-06-28T15:32:32.832843Z" + "iopub.execute_input": "2024-07-01T15:02:11.378959Z", + "iopub.status.busy": "2024-07-01T15:02:11.378766Z", + "iopub.status.idle": "2024-07-01T15:02:12.901153Z", + "shell.execute_reply": "2024-07-01T15:02:12.900530Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.837171Z", - "iopub.status.busy": "2024-06-28T15:32:32.836718Z", - "iopub.status.idle": "2024-06-28T15:32:32.848336Z", - "shell.execute_reply": "2024-06-28T15:32:32.847757Z" + "iopub.execute_input": "2024-07-01T15:02:12.904092Z", + "iopub.status.busy": "2024-07-01T15:02:12.903651Z", + "iopub.status.idle": "2024-07-01T15:02:12.914311Z", + "shell.execute_reply": "2024-07-01T15:02:12.913807Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.850867Z", - "iopub.status.busy": "2024-06-28T15:32:32.850504Z", - "iopub.status.idle": "2024-06-28T15:32:32.856225Z", - "shell.execute_reply": "2024-06-28T15:32:32.855744Z" + "iopub.execute_input": "2024-07-01T15:02:12.916523Z", + "iopub.status.busy": "2024-07-01T15:02:12.916188Z", + "iopub.status.idle": "2024-07-01T15:02:12.921874Z", + "shell.execute_reply": "2024-07-01T15:02:12.921422Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:32.858524Z", - "iopub.status.busy": "2024-06-28T15:32:32.858072Z", - "iopub.status.idle": "2024-06-28T15:32:33.339585Z", - "shell.execute_reply": "2024-06-28T15:32:33.339035Z" + "iopub.execute_input": "2024-07-01T15:02:12.923867Z", + "iopub.status.busy": "2024-07-01T15:02:12.923684Z", + "iopub.status.idle": "2024-07-01T15:02:13.374643Z", + "shell.execute_reply": "2024-07-01T15:02:13.374029Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:33.342120Z", - "iopub.status.busy": "2024-06-28T15:32:33.341633Z", - "iopub.status.idle": "2024-06-28T15:32:34.307317Z", - "shell.execute_reply": "2024-06-28T15:32:34.306830Z" + "iopub.execute_input": "2024-07-01T15:02:13.376868Z", + "iopub.status.busy": "2024-07-01T15:02:13.376659Z", + "iopub.status.idle": "2024-07-01T15:02:14.191014Z", + "shell.execute_reply": "2024-07-01T15:02:14.190519Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.309737Z", - "iopub.status.busy": "2024-06-28T15:32:34.309513Z", - "iopub.status.idle": "2024-06-28T15:32:34.327930Z", - "shell.execute_reply": "2024-06-28T15:32:34.327428Z" + "iopub.execute_input": "2024-07-01T15:02:14.193499Z", + "iopub.status.busy": "2024-07-01T15:02:14.193141Z", + "iopub.status.idle": "2024-07-01T15:02:14.211506Z", + "shell.execute_reply": "2024-07-01T15:02:14.210918Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.329961Z", - "iopub.status.busy": "2024-06-28T15:32:34.329778Z", - "iopub.status.idle": "2024-06-28T15:32:34.332922Z", - "shell.execute_reply": "2024-06-28T15:32:34.332483Z" + "iopub.execute_input": "2024-07-01T15:02:14.213644Z", + "iopub.status.busy": "2024-07-01T15:02:14.213459Z", + "iopub.status.idle": "2024-07-01T15:02:14.216713Z", + "shell.execute_reply": "2024-07-01T15:02:14.216187Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:34.334863Z", - "iopub.status.busy": "2024-06-28T15:32:34.334688Z", - "iopub.status.idle": "2024-06-28T15:32:50.142203Z", - "shell.execute_reply": "2024-06-28T15:32:50.141641Z" + "iopub.execute_input": "2024-07-01T15:02:14.218727Z", + "iopub.status.busy": "2024-07-01T15:02:14.218427Z", + "iopub.status.idle": "2024-07-01T15:02:28.819416Z", + "shell.execute_reply": "2024-07-01T15:02:28.818802Z" }, "id": "2FSQ2GR9R_YA" }, @@ -617,10 +617,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.144906Z", - "iopub.status.busy": "2024-06-28T15:32:50.144703Z", - "iopub.status.idle": "2024-06-28T15:32:50.148671Z", - "shell.execute_reply": "2024-06-28T15:32:50.148141Z" + "iopub.execute_input": "2024-07-01T15:02:28.822324Z", + "iopub.status.busy": "2024-07-01T15:02:28.821907Z", + "iopub.status.idle": "2024-07-01T15:02:28.825931Z", + "shell.execute_reply": "2024-07-01T15:02:28.825366Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -680,10 +680,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.150805Z", - "iopub.status.busy": "2024-06-28T15:32:50.150473Z", - "iopub.status.idle": "2024-06-28T15:32:50.858162Z", - "shell.execute_reply": "2024-06-28T15:32:50.857571Z" + "iopub.execute_input": "2024-07-01T15:02:28.827995Z", + "iopub.status.busy": "2024-07-01T15:02:28.827681Z", + "iopub.status.idle": "2024-07-01T15:02:29.521211Z", + "shell.execute_reply": "2024-07-01T15:02:29.520635Z" }, "id": "i_drkY9YOcw4" }, @@ -717,10 +717,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.861090Z", - "iopub.status.busy": "2024-06-28T15:32:50.860665Z", - "iopub.status.idle": "2024-06-28T15:32:50.865818Z", - "shell.execute_reply": "2024-06-28T15:32:50.865293Z" + "iopub.execute_input": "2024-07-01T15:02:29.524947Z", + "iopub.status.busy": "2024-07-01T15:02:29.524000Z", + "iopub.status.idle": "2024-07-01T15:02:29.530833Z", + "shell.execute_reply": "2024-07-01T15:02:29.530342Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -767,10 +767,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.868281Z", - "iopub.status.busy": "2024-06-28T15:32:50.867893Z", - "iopub.status.idle": "2024-06-28T15:32:50.969315Z", - "shell.execute_reply": "2024-06-28T15:32:50.968655Z" + "iopub.execute_input": "2024-07-01T15:02:29.534459Z", + "iopub.status.busy": "2024-07-01T15:02:29.533509Z", + "iopub.status.idle": "2024-07-01T15:02:29.635169Z", + "shell.execute_reply": "2024-07-01T15:02:29.634583Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.971877Z", - "iopub.status.busy": "2024-06-28T15:32:50.971633Z", - "iopub.status.idle": "2024-06-28T15:32:50.985705Z", - "shell.execute_reply": "2024-06-28T15:32:50.985220Z" + "iopub.execute_input": "2024-07-01T15:02:29.637589Z", + "iopub.status.busy": "2024-07-01T15:02:29.637281Z", + "iopub.status.idle": "2024-07-01T15:02:29.650412Z", + "shell.execute_reply": "2024-07-01T15:02:29.649911Z" }, "scrolled": true }, @@ -870,10 +870,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.987837Z", - "iopub.status.busy": "2024-06-28T15:32:50.987508Z", - "iopub.status.idle": "2024-06-28T15:32:50.995477Z", - "shell.execute_reply": "2024-06-28T15:32:50.995054Z" + "iopub.execute_input": "2024-07-01T15:02:29.652492Z", + "iopub.status.busy": "2024-07-01T15:02:29.652304Z", + "iopub.status.idle": "2024-07-01T15:02:29.660528Z", + "shell.execute_reply": "2024-07-01T15:02:29.660066Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:50.997717Z", - "iopub.status.busy": "2024-06-28T15:32:50.997278Z", - "iopub.status.idle": "2024-06-28T15:32:51.001400Z", - "shell.execute_reply": "2024-06-28T15:32:51.000850Z" + "iopub.execute_input": "2024-07-01T15:02:29.662724Z", + "iopub.status.busy": "2024-07-01T15:02:29.662297Z", + "iopub.status.idle": "2024-07-01T15:02:29.666626Z", + "shell.execute_reply": "2024-07-01T15:02:29.666160Z" } }, "outputs": [ @@ -1018,10 +1018,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.003434Z", - "iopub.status.busy": "2024-06-28T15:32:51.003117Z", - "iopub.status.idle": "2024-06-28T15:32:51.008898Z", - "shell.execute_reply": "2024-06-28T15:32:51.008334Z" + "iopub.execute_input": "2024-07-01T15:02:29.668442Z", + "iopub.status.busy": "2024-07-01T15:02:29.668268Z", + "iopub.status.idle": "2024-07-01T15:02:29.673924Z", + "shell.execute_reply": "2024-07-01T15:02:29.673445Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1148,10 +1148,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.011016Z", - "iopub.status.busy": "2024-06-28T15:32:51.010696Z", - "iopub.status.idle": "2024-06-28T15:32:51.125754Z", - "shell.execute_reply": "2024-06-28T15:32:51.125163Z" + "iopub.execute_input": "2024-07-01T15:02:29.675826Z", + "iopub.status.busy": "2024-07-01T15:02:29.675650Z", + "iopub.status.idle": "2024-07-01T15:02:29.788802Z", + "shell.execute_reply": "2024-07-01T15:02:29.788252Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1205,10 +1205,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.127912Z", - "iopub.status.busy": "2024-06-28T15:32:51.127668Z", - "iopub.status.idle": "2024-06-28T15:32:51.234154Z", - "shell.execute_reply": "2024-06-28T15:32:51.233644Z" + "iopub.execute_input": "2024-07-01T15:02:29.791024Z", + "iopub.status.busy": "2024-07-01T15:02:29.790694Z", + "iopub.status.idle": "2024-07-01T15:02:29.899192Z", + "shell.execute_reply": "2024-07-01T15:02:29.898612Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1253,10 +1253,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.236252Z", - "iopub.status.busy": "2024-06-28T15:32:51.236020Z", - "iopub.status.idle": "2024-06-28T15:32:51.341460Z", - "shell.execute_reply": "2024-06-28T15:32:51.340903Z" + "iopub.execute_input": "2024-07-01T15:02:29.901288Z", + "iopub.status.busy": "2024-07-01T15:02:29.901099Z", + "iopub.status.idle": "2024-07-01T15:02:30.006069Z", + "shell.execute_reply": "2024-07-01T15:02:30.005505Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1297,10 +1297,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.343704Z", - "iopub.status.busy": "2024-06-28T15:32:51.343370Z", - "iopub.status.idle": "2024-06-28T15:32:51.451072Z", - "shell.execute_reply": "2024-06-28T15:32:51.450482Z" + "iopub.execute_input": "2024-07-01T15:02:30.008275Z", + "iopub.status.busy": "2024-07-01T15:02:30.007928Z", + "iopub.status.idle": "2024-07-01T15:02:30.113221Z", + "shell.execute_reply": "2024-07-01T15:02:30.112655Z" } }, "outputs": [ @@ -1348,10 +1348,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:51.453664Z", - "iopub.status.busy": "2024-06-28T15:32:51.453217Z", - "iopub.status.idle": "2024-06-28T15:32:51.456589Z", - "shell.execute_reply": "2024-06-28T15:32:51.456096Z" + "iopub.execute_input": "2024-07-01T15:02:30.115374Z", + "iopub.status.busy": "2024-07-01T15:02:30.115188Z", + "iopub.status.idle": "2024-07-01T15:02:30.118487Z", + "shell.execute_reply": "2024-07-01T15:02:30.117941Z" }, "nbsphinx": "hidden" }, @@ -1392,107 +1392,73 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "13ae702bd5d84279b8e63a3da792d5a9": { - "model_module": "@jupyter-widgets/base", + "00fc7e046c8f4c308d5d222a635bb52c": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "1535038e095c43858d008484de0216eb": { + "0a99e571562d43a7aa5ecd2abda2aa2d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_5ca0848a6ec84d39bb230c4a4572cac1", - "IPY_MODEL_6ea6bf34c19f4af8aaa9da65d1baf4df", - "IPY_MODEL_798ff95456a14a879b0ee75ddb28b8a2" - ], - "layout": "IPY_MODEL_fb0c05ec8fbf40339f9afc2d5752cce3", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4748994f76ed4bc388496a39e510d54d", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fcffd70673164f8aadd97b82906cc76e", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 2041.0 } }, - "19f26f6b8d5449568a114a9b47d7f057": { + "11d84ef51dce4f7aaa879bf5e2b72f38": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1a615b70ccc4afe8f75b0b212ec32dd", - "placeholder": "​", - "style": "IPY_MODEL_309b9b81d6a14292a59bbdd1e3761b22", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b2e03ad67f4e4dd08bf9d4b1f4fb712d", + "IPY_MODEL_b6a2cd8ff5374f089fad080858e3fd96", + "IPY_MODEL_17cad868ac7f43f3a8289ad5c1dbdd25" + ], + "layout": "IPY_MODEL_4284b301638341a3b41f91a5b5daab48", "tabbable": null, - "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 103MB/s]" + "tooltip": null } }, - "1c9cc6a7f35641d69ddc253c76d83c9c": { + "169802f2389c4c4e8717f6593ecaccaf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1545,99 +1511,64 @@ "width": null } }, - "1db3e87999ce494283770c1a2f49302e": { + "17cad868ac7f43f3a8289ad5c1dbdd25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f715cd04eb304470889617b12390a482", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8bdfb71cc05c4bac9e99957e74de9c77", + "layout": "IPY_MODEL_169802f2389c4c4e8717f6593ecaccaf", + "placeholder": "​", + "style": "IPY_MODEL_692bb6d688714e569e4dfa9584bc0ad0", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": " 3.20k/3.20k [00:00<00:00, 778kB/s]" } }, - "1f40455e27c443f1a07b5cb09a22204f": { + "1a72151665fc4b27b30d0d9a8c74e578": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b69c454713fc43798c96786b37d49252", - "IPY_MODEL_1db3e87999ce494283770c1a2f49302e", - "IPY_MODEL_c8bd4367ddb84b3b8e0e3a5262065f23" - ], - "layout": "IPY_MODEL_1c9cc6a7f35641d69ddc253c76d83c9c", - "tabbable": null, - "tooltip": null - } - }, - "2156682227184679bb5ab7dea5aa2597": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_287434e88ed84e0d9ccb4a05565006c8", - "IPY_MODEL_401cca213830476aaf131b5754a45e4d", - "IPY_MODEL_fa9ead685eee4696b3fa292b908dfc7c" - ], - "layout": "IPY_MODEL_b269424bfa5045f7b4b658254b88dc06", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "21bc7d8902984f4e9224cd00d4fdf617": { + "208e173931a84902991001290bbcd8e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "22472e38039e465a92f6cb2af7431848": { + "299a08f2fa054f7bbe1daaa618adeb92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1652,91 +1583,40 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_cffcc92eae50405295d02fa1d657d816", - "IPY_MODEL_be9e767c57dc49cdbe293ab91d54433c", - "IPY_MODEL_19f26f6b8d5449568a114a9b47d7f057" + "IPY_MODEL_9583f3f76f304e7abc2f1a387e1d3df1", + "IPY_MODEL_e0eec76f9c4b41dab7f14303a673e770", + "IPY_MODEL_f2bf833e4ea64394b10f33d310c0ee23" ], - "layout": "IPY_MODEL_95bd0f790b244919af5670fbb6054c52", + "layout": "IPY_MODEL_2d9a8d5f92fe4b3aaec557c0461f8829", "tabbable": null, "tooltip": null } }, - "2686f2dc96554990b324ec629db035e9": { + "2d6758bb73d44586a7740d66e26f26cf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "287434e88ed84e0d9ccb4a05565006c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7e85c622ae8344ee942c12255338f02a", - "placeholder": "​", - "style": "IPY_MODEL_21bc7d8902984f4e9224cd00d4fdf617", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7f01730dbf1446ca92cdfd900a358c1a", + "IPY_MODEL_f07baddc31e847c595d90dd42ca379fb", + "IPY_MODEL_3b887681781b4f25abe1998059b0ae53" + ], + "layout": "IPY_MODEL_fc74c0118a094bc3a970ec592d969b3d", "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "2c74f436fa81418cb31a0552913a960b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "309b9b81d6a14292a59bbdd1e3761b22": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "3a10a4645a7e48a590198cd1cf366613": { + "2d9a8d5f92fe4b3aaec557c0461f8829": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1789,7 +1669,7 @@ "width": null } }, - "3ca8fc03589e42e4a31d4782f3e9fcf9": { + "32de4a18c7f147a8be35c654b53457aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1842,49 +1722,53 @@ "width": null } }, - "3ea0082e65c1445c8dfc684c31820e38": { + "391af44b9e68466688f4ac70187a5ea4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8034cbf813a3405d8b1b6885128dbb55", + "placeholder": "​", + "style": "IPY_MODEL_70432221e66e4c1b925252a9aabda6f1", + "tabbable": null, + "tooltip": null, + "value": " 129k/129k [00:00<00:00, 10.6MB/s]" } }, - "401cca213830476aaf131b5754a45e4d": { + "3b887681781b4f25abe1998059b0ae53": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_5119fd3b9a554a0285d69241b4238840", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3ea0082e65c1445c8dfc684c31820e38", + "layout": "IPY_MODEL_f34dc10446834ea5b31b833757faa688", + "placeholder": "​", + "style": "IPY_MODEL_ca1c4425f72b4b4a83a222e1429dbfed", "tabbable": null, "tooltip": null, - "value": 3201.0 + "value": " 16.9M/16.9M [00:00<00:00, 94.6MB/s]" } }, - "5119fd3b9a554a0285d69241b4238840": { + "4284b301638341a3b41f91a5b5daab48": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1937,98 +1821,7 @@ "width": null } }, - "52657ce9f99944db9bf445c23ceced00": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cc2b727fb77747d48eb6de89948fc805", - "IPY_MODEL_64e55746d02f4001819ff517c805ec2a", - "IPY_MODEL_8a9362716caa416894d6eb38ae7c4593" - ], - "layout": "IPY_MODEL_fa4eedb31c004046bba082cc5ad03e73", - "tabbable": null, - "tooltip": null - } - }, - "5ca0848a6ec84d39bb230c4a4572cac1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3ca8fc03589e42e4a31d4782f3e9fcf9", - "placeholder": "​", - "style": "IPY_MODEL_eff038df27584d5cb2d841dbeac70bfe", - "tabbable": null, - "tooltip": null, - "value": "label_encoder.txt: 100%" - } - }, - "622242b975d64c2cb5a8827d70f41c55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "64e55746d02f4001819ff517c805ec2a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d032e929b7b1431e9c3ecd0fd4616630", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f1ec22b3aa7b41f3b4e8a8039ca0e7b7", - "tabbable": null, - "tooltip": null, - "value": 16887676.0 - } - }, - "6bd2d975a792427da49ecc4c18a16a8c": { + "4748994f76ed4bc388496a39e510d54d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2081,56 +1874,7 @@ "width": null } }, - "6ea6bf34c19f4af8aaa9da65d1baf4df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_13ae702bd5d84279b8e63a3da792d5a9", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2c74f436fa81418cb31a0552913a960b", - "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "798ff95456a14a879b0ee75ddb28b8a2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_86e85c6c49f343e2ac9a4a4f8a700987", - "placeholder": "​", - "style": "IPY_MODEL_622242b975d64c2cb5a8827d70f41c55", - "tabbable": null, - "tooltip": null, - "value": " 129k/129k [00:00<00:00, 2.24MB/s]" - } - }, - "7e85c622ae8344ee942c12255338f02a": { + "4c63aa921de543f9a38a79a37dbf15a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2183,7 +1927,30 @@ "width": null } }, - "86e85c6c49f343e2ac9a4a4f8a700987": { + "4fe53b9dff9148ffbaee866c5033bba3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8487dd8ed3e42a38371219c3c729283", + "placeholder": "​", + "style": "IPY_MODEL_c3d1ad19115840c0bd02e22d3f39fbd9", + "tabbable": null, + "tooltip": null, + "value": "hyperparams.yaml: 100%" + } + }, + "59b0ce30e35142928e041a6b8d2dc230": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2236,7 +2003,46 @@ "width": null } }, - "8a9362716caa416894d6eb38ae7c4593": { + "5a64304d36024180b26f4f79387324dc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "67737bdcaa0b4c8aad9b8db3c9fbeb51": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_868c56c897d2493bb075baa99c580d21", + "placeholder": "​", + "style": "IPY_MODEL_6cd92f1c94e145be925eb36473830cbb", + "tabbable": null, + "tooltip": null, + "value": "label_encoder.txt: 100%" + } + }, + "6884e940a8554f6589d336edaee67f33": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2251,31 +2057,69 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d3e7c58589c6499a8302f0f3976e6bfb", + "layout": "IPY_MODEL_f64888652c004082a1b036270e2702e0", "placeholder": "​", - "style": "IPY_MODEL_d5b79d09ca894d9db33c61c119111efd", + "style": "IPY_MODEL_78b6d5bc6077499094dfc9bd718f4db7", "tabbable": null, "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 223MB/s]" + "value": " 2.04k/2.04k [00:00<00:00, 481kB/s]" } }, - "8bdfb71cc05c4bac9e99957e74de9c77": { + "692bb6d688714e569e4dfa9584bc0ad0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6cd92f1c94e145be925eb36473830cbb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "70432221e66e4c1b925252a9aabda6f1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8ca4f7884da04fb096679c29f1741f78": { + "7687a1cfa1264a529fcedffaa6a4e467": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2328,7 +2172,48 @@ "width": null } }, - "95bd0f790b244919af5670fbb6054c52": { + "78b6d5bc6077499094dfc9bd718f4db7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7f01730dbf1446ca92cdfd900a358c1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8458463007ef42c0a82ddb9da20103ba", + "placeholder": "​", + "style": "IPY_MODEL_1a72151665fc4b27b30d0d9a8c74e578", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "8034cbf813a3405d8b1b6885128dbb55": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2381,7 +2266,7 @@ "width": null } }, - "9d23f4f000134f42aa3aa92e38319d3e": { + "8458463007ef42c0a82ddb9da20103ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2434,7 +2319,7 @@ "width": null } }, - "9ee6c4ddb70d429894715ea2b81f09ec": { + "868c56c897d2493bb075baa99c580d21": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2487,7 +2372,72 @@ "width": null } }, - "b1a615b70ccc4afe8f75b0b212ec32dd": { + "9583f3f76f304e7abc2f1a387e1d3df1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_4c63aa921de543f9a38a79a37dbf15a6", + "placeholder": "​", + "style": "IPY_MODEL_ab62c5cec180440ebb1975bcb91646f5", + "tabbable": null, + "tooltip": null, + "value": "classifier.ckpt: 100%" + } + }, + "999d991d513f43d38a94a4e22e5eae99": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_67737bdcaa0b4c8aad9b8db3c9fbeb51", + "IPY_MODEL_cf3af27e08194934863770ad9095424f", + "IPY_MODEL_391af44b9e68466688f4ac70187a5ea4" + ], + "layout": "IPY_MODEL_aef29a94b0394011a06ba738f227041b", + "tabbable": null, + "tooltip": null + } + }, + "ab62c5cec180440ebb1975bcb91646f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "aef29a94b0394011a06ba738f227041b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2540,7 +2490,74 @@ "width": null } }, - "b269424bfa5045f7b4b658254b88dc06": { + "b2e03ad67f4e4dd08bf9d4b1f4fb712d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_32de4a18c7f147a8be35c654b53457aa", + "placeholder": "​", + "style": "IPY_MODEL_b5cb5977357e4fab995dcae8e2643177", + "tabbable": null, + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "b5cb5977357e4fab995dcae8e2643177": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "b6a2cd8ff5374f089fad080858e3fd96": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0a6b2ff14e14058a38164d872d62aca", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_208e173931a84902991001290bbcd8e8", + "tabbable": null, + "tooltip": null, + "value": 3201.0 + } + }, + "c1fc1902ebdc421e8d0afcb0d1027f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2593,7 +2610,7 @@ "width": null } }, - "b3f5fd4d072b4db99ee74accb42cf952": { + "c3d1ad19115840c0bd02e22d3f39fbd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2611,56 +2628,7 @@ "text_color": null } }, - "b69c454713fc43798c96786b37d49252": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9d23f4f000134f42aa3aa92e38319d3e", - "placeholder": "​", - "style": "IPY_MODEL_c2ba3bd1eaf1447681bbf99a54df9974", - "tabbable": null, - "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "be9e767c57dc49cdbe293ab91d54433c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_6bd2d975a792427da49ecc4c18a16a8c", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cb0244a6576f4fcb8a82691d4c44cb1e", - "tabbable": null, - "tooltip": null, - "value": 15856877.0 - } - }, - "c2ba3bd1eaf1447681bbf99a54df9974": { + "ca1c4425f72b4b4a83a222e1429dbfed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2678,43 +2646,57 @@ "text_color": null } }, - "c4148b51f19748b4af9b5af3134378e7": { + "cf3a1744d9c64353a34451f3e0dc96ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4fe53b9dff9148ffbaee866c5033bba3", + "IPY_MODEL_0a99e571562d43a7aa5ecd2abda2aa2d", + "IPY_MODEL_6884e940a8554f6589d336edaee67f33" + ], + "layout": "IPY_MODEL_dfeb5c97c0174fe3b02f601346925b4a", + "tabbable": null, + "tooltip": null } }, - "c6bb2423370b4480ba7f39186635762d": { + "cf3af27e08194934863770ad9095424f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_59b0ce30e35142928e041a6b8d2dc230", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dff35f7ef39b489d8db92f12e36f0799", + "tabbable": null, + "tooltip": null, + "value": 128619.0 } }, - "c6d20ede22124fc6ba4a66d669a06d2a": { + "d0a6b2ff14e14058a38164d872d62aca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2767,92 +2749,60 @@ "width": null } }, - "c8bd4367ddb84b3b8e0e3a5262065f23": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c6d20ede22124fc6ba4a66d669a06d2a", - "placeholder": "​", - "style": "IPY_MODEL_c4148b51f19748b4af9b5af3134378e7", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 453kB/s]" - } - }, - "cb0244a6576f4fcb8a82691d4c44cb1e": { - "model_module": "@jupyter-widgets/controls", + "d8487dd8ed3e42a38371219c3c729283": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "cc2b727fb77747d48eb6de89948fc805": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_9ee6c4ddb70d429894715ea2b81f09ec", - "placeholder": "​", - "style": "IPY_MODEL_2686f2dc96554990b324ec629db035e9", - "tabbable": null, - "tooltip": null, - "value": "embedding_model.ckpt: 100%" - } - }, - "cffcc92eae50405295d02fa1d657d816": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8ca4f7884da04fb096679c29f1741f78", - "placeholder": "​", - "style": "IPY_MODEL_c6bb2423370b4480ba7f39186635762d", - "tabbable": null, - "tooltip": null, - "value": "classifier.ckpt: 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d032e929b7b1431e9c3ecd0fd4616630": { + "dfeb5c97c0174fe3b02f601346925b4a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2905,7 +2855,49 @@ "width": null } }, - "d3e7c58589c6499a8302f0f3976e6bfb": { + "dff35f7ef39b489d8db92f12e36f0799": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "e0eec76f9c4b41dab7f14303a673e770": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e6590d45e9154268897f1c416b514bc9", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_00fc7e046c8f4c308d5d222a635bb52c", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 + } + }, + "e6590d45e9154268897f1c416b514bc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2958,7 +2950,7 @@ "width": null } }, - "d5b79d09ca894d9db33c61c119111efd": { + "ea4a743f2c1443eab20e9c8135c3321d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2976,41 +2968,56 @@ "text_color": null } }, - "eff038df27584d5cb2d841dbeac70bfe": { + "f07baddc31e847c595d90dd42ca379fb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7687a1cfa1264a529fcedffaa6a4e467", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a64304d36024180b26f4f79387324dc", + "tabbable": null, + "tooltip": null, + "value": 16887676.0 } }, - "f1ec22b3aa7b41f3b4e8a8039ca0e7b7": { + "f2bf833e4ea64394b10f33d310c0ee23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c1fc1902ebdc421e8d0afcb0d1027f63", + "placeholder": "​", + "style": "IPY_MODEL_ea4a743f2c1443eab20e9c8135c3321d", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 93.5MB/s]" } }, - "f715cd04eb304470889617b12390a482": { + "f34dc10446834ea5b31b833757faa688": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3063,7 +3070,7 @@ "width": null } }, - "fa4eedb31c004046bba082cc5ad03e73": { + "f64888652c004082a1b036270e2702e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3116,30 +3123,7 @@ "width": null } }, - "fa9ead685eee4696b3fa292b908dfc7c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3a10a4645a7e48a590198cd1cf366613", - "placeholder": "​", - "style": "IPY_MODEL_b3f5fd4d072b4db99ee74accb42cf952", - "tabbable": null, - "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 770kB/s]" - } - }, - "fb0c05ec8fbf40339f9afc2d5752cce3": { + "fc74c0118a094bc3a970ec592d969b3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3191,6 +3175,22 @@ "visibility": null, "width": null } + }, + "fcffd70673164f8aadd97b82906cc76e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index fe9f08e9b..1e7141136 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:55.137126Z", - "iopub.status.busy": "2024-06-28T15:32:55.136940Z", - "iopub.status.idle": "2024-06-28T15:32:56.356852Z", - "shell.execute_reply": "2024-06-28T15:32:56.356217Z" + "iopub.execute_input": "2024-07-01T15:02:34.100510Z", + "iopub.status.busy": "2024-07-01T15:02:34.100309Z", + "iopub.status.idle": "2024-07-01T15:02:35.344393Z", + "shell.execute_reply": "2024-07-01T15:02:35.343853Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.359787Z", - "iopub.status.busy": "2024-06-28T15:32:56.359107Z", - "iopub.status.idle": "2024-06-28T15:32:56.362371Z", - "shell.execute_reply": "2024-06-28T15:32:56.361926Z" + "iopub.execute_input": "2024-07-01T15:02:35.347246Z", + "iopub.status.busy": "2024-07-01T15:02:35.346746Z", + "iopub.status.idle": "2024-07-01T15:02:35.349837Z", + "shell.execute_reply": "2024-07-01T15:02:35.349391Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.364580Z", - "iopub.status.busy": "2024-06-28T15:32:56.364380Z", - "iopub.status.idle": "2024-06-28T15:32:56.373022Z", - "shell.execute_reply": "2024-06-28T15:32:56.372568Z" + "iopub.execute_input": "2024-07-01T15:02:35.352173Z", + "iopub.status.busy": "2024-07-01T15:02:35.351845Z", + "iopub.status.idle": "2024-07-01T15:02:35.361048Z", + "shell.execute_reply": "2024-07-01T15:02:35.360394Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.374888Z", - "iopub.status.busy": "2024-06-28T15:32:56.374711Z", - "iopub.status.idle": "2024-06-28T15:32:56.379461Z", - "shell.execute_reply": "2024-06-28T15:32:56.379052Z" + "iopub.execute_input": "2024-07-01T15:02:35.363704Z", + "iopub.status.busy": "2024-07-01T15:02:35.363238Z", + "iopub.status.idle": "2024-07-01T15:02:35.368807Z", + "shell.execute_reply": "2024-07-01T15:02:35.368166Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.381515Z", - "iopub.status.busy": "2024-06-28T15:32:56.381337Z", - "iopub.status.idle": "2024-06-28T15:32:56.567465Z", - "shell.execute_reply": "2024-06-28T15:32:56.566945Z" + "iopub.execute_input": "2024-07-01T15:02:35.371407Z", + "iopub.status.busy": "2024-07-01T15:02:35.370950Z", + "iopub.status.idle": "2024-07-01T15:02:35.579618Z", + "shell.execute_reply": "2024-07-01T15:02:35.578902Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.570098Z", - "iopub.status.busy": "2024-06-28T15:32:56.569740Z", - "iopub.status.idle": "2024-06-28T15:32:56.946795Z", - "shell.execute_reply": "2024-06-28T15:32:56.946221Z" + "iopub.execute_input": "2024-07-01T15:02:35.582660Z", + "iopub.status.busy": "2024-07-01T15:02:35.582270Z", + "iopub.status.idle": "2024-07-01T15:02:35.982874Z", + "shell.execute_reply": "2024-07-01T15:02:35.982240Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.949083Z", - "iopub.status.busy": "2024-06-28T15:32:56.948728Z", - "iopub.status.idle": "2024-06-28T15:32:56.972968Z", - "shell.execute_reply": "2024-06-28T15:32:56.972339Z" + "iopub.execute_input": "2024-07-01T15:02:35.985209Z", + "iopub.status.busy": "2024-07-01T15:02:35.984882Z", + "iopub.status.idle": "2024-07-01T15:02:36.009092Z", + "shell.execute_reply": "2024-07-01T15:02:36.008586Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.975923Z", - "iopub.status.busy": "2024-06-28T15:32:56.975533Z", - "iopub.status.idle": "2024-06-28T15:32:56.987983Z", - "shell.execute_reply": "2024-06-28T15:32:56.987424Z" + "iopub.execute_input": "2024-07-01T15:02:36.011743Z", + "iopub.status.busy": "2024-07-01T15:02:36.011310Z", + "iopub.status.idle": "2024-07-01T15:02:36.023407Z", + "shell.execute_reply": "2024-07-01T15:02:36.022817Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:56.990765Z", - "iopub.status.busy": "2024-06-28T15:32:56.990359Z", - "iopub.status.idle": "2024-06-28T15:32:59.165996Z", - "shell.execute_reply": "2024-06-28T15:32:59.165438Z" + "iopub.execute_input": "2024-07-01T15:02:36.025951Z", + "iopub.status.busy": "2024-07-01T15:02:36.025605Z", + "iopub.status.idle": "2024-07-01T15:02:38.178872Z", + "shell.execute_reply": "2024-07-01T15:02:38.178173Z" } }, "outputs": [ @@ -714,10 +714,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.168493Z", - "iopub.status.busy": "2024-06-28T15:32:59.167970Z", - "iopub.status.idle": "2024-06-28T15:32:59.190456Z", - "shell.execute_reply": "2024-06-28T15:32:59.189877Z" + "iopub.execute_input": "2024-07-01T15:02:38.181613Z", + "iopub.status.busy": "2024-07-01T15:02:38.181161Z", + "iopub.status.idle": "2024-07-01T15:02:38.204407Z", + "shell.execute_reply": "2024-07-01T15:02:38.203790Z" } }, "outputs": [ @@ -830,10 +830,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.192911Z", - "iopub.status.busy": "2024-06-28T15:32:59.192503Z", - "iopub.status.idle": "2024-06-28T15:32:59.210971Z", - "shell.execute_reply": "2024-06-28T15:32:59.210474Z" + "iopub.execute_input": "2024-07-01T15:02:38.207031Z", + "iopub.status.busy": "2024-07-01T15:02:38.206574Z", + "iopub.status.idle": "2024-07-01T15:02:38.225655Z", + "shell.execute_reply": "2024-07-01T15:02:38.225020Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.213153Z", - "iopub.status.busy": "2024-06-28T15:32:59.212814Z", - "iopub.status.idle": "2024-06-28T15:32:59.227073Z", - "shell.execute_reply": "2024-06-28T15:32:59.226626Z" + "iopub.execute_input": "2024-07-01T15:02:38.228105Z", + "iopub.status.busy": "2024-07-01T15:02:38.227775Z", + "iopub.status.idle": "2024-07-01T15:02:38.243503Z", + "shell.execute_reply": "2024-07-01T15:02:38.242861Z" } }, "outputs": [ @@ -1075,17 +1075,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.229388Z", - "iopub.status.busy": "2024-06-28T15:32:59.229043Z", - "iopub.status.idle": "2024-06-28T15:32:59.248480Z", - "shell.execute_reply": "2024-06-28T15:32:59.247931Z" + "iopub.execute_input": "2024-07-01T15:02:38.245863Z", + "iopub.status.busy": "2024-07-01T15:02:38.245474Z", + "iopub.status.idle": "2024-07-01T15:02:38.267061Z", + "shell.execute_reply": "2024-07-01T15:02:38.266454Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c5cc8030a1864cbc888bc657ba9d1871", + "model_id": "d16dede2bb2e40b282d000f989523e41", "version_major": 2, "version_minor": 0 }, @@ -1121,10 +1121,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.250632Z", - "iopub.status.busy": "2024-06-28T15:32:59.250452Z", - "iopub.status.idle": "2024-06-28T15:32:59.266154Z", - "shell.execute_reply": "2024-06-28T15:32:59.265602Z" + "iopub.execute_input": "2024-07-01T15:02:38.269410Z", + "iopub.status.busy": "2024-07-01T15:02:38.269038Z", + "iopub.status.idle": "2024-07-01T15:02:38.286735Z", + "shell.execute_reply": "2024-07-01T15:02:38.286110Z" } }, "outputs": [ @@ -1247,10 +1247,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.268379Z", - "iopub.status.busy": "2024-06-28T15:32:59.267966Z", - "iopub.status.idle": "2024-06-28T15:32:59.274040Z", - "shell.execute_reply": "2024-06-28T15:32:59.273500Z" + "iopub.execute_input": "2024-07-01T15:02:38.289163Z", + "iopub.status.busy": "2024-07-01T15:02:38.288765Z", + "iopub.status.idle": "2024-07-01T15:02:38.295067Z", + "shell.execute_reply": "2024-07-01T15:02:38.294503Z" } }, "outputs": [], @@ -1307,10 +1307,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:59.276184Z", - "iopub.status.busy": "2024-06-28T15:32:59.275830Z", - "iopub.status.idle": "2024-06-28T15:32:59.294259Z", - "shell.execute_reply": "2024-06-28T15:32:59.293677Z" + "iopub.execute_input": "2024-07-01T15:02:38.297383Z", + "iopub.status.busy": "2024-07-01T15:02:38.296989Z", + "iopub.status.idle": "2024-07-01T15:02:38.317743Z", + "shell.execute_reply": "2024-07-01T15:02:38.317117Z" } }, "outputs": [ @@ -1447,7 +1447,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a6708021b7f4b4288ce7905c15eab15": { + "0dc84865474c4bf7a312e538bc8f4a74": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1500,48 +1500,33 @@ "width": null } }, - "40c3bb8a23164577a0c0611033e6d54c": { + "39e0d0ff92854bb5b45f8340a9c5c5eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0a6708021b7f4b4288ce7905c15eab15", - "placeholder": "​", - "style": "IPY_MODEL_4d70992f11b84bf9a36040fe5ef16e1c", + "layout": "IPY_MODEL_0dc84865474c4bf7a312e538bc8f4a74", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bd9368c5e9b842ca818c69f779cd5276", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" - } - }, - "4d70992f11b84bf9a36040fe5ef16e1c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "value": 132.0 } }, - "84e1f61107d940b7977e4fff0fb8f2bd": { + "746118eed9d445c9a37e681cbacd9674": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1594,7 +1579,7 @@ "width": null } }, - "84e74dbfaf7b43918c3fef375f4c2636": { + "890c2c0c04564f5da3221229c05800df": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1647,49 +1632,30 @@ "width": null } }, - "8fc926f029414a8aaaf8ecab225fc0fe": { + "979503323663435eae635a194817476f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_84e1f61107d940b7977e4fff0fb8f2bd", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_905e6b0f80c7424ca91d5ba60216672e", + "layout": "IPY_MODEL_746118eed9d445c9a37e681cbacd9674", + "placeholder": "​", + "style": "IPY_MODEL_d57ca1f4799d45229ae2f7c720c262f5", "tabbable": null, "tooltip": null, - "value": 132.0 - } - }, - "905e6b0f80c7424ca91d5ba60216672e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "value": "Saving the dataset (1/1 shards): 100%" } }, - "954e9713147d4bdb88dfcbb4b36fab1d": { + "af633ab6f6924af0b3f4ac3691d76422": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1742,7 +1708,7 @@ "width": null } }, - "b93d0824edb848df8dfe762bda5a4c34": { + "bcb7b5d047f845978925e2ef6da3385e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1760,31 +1726,23 @@ "text_color": null } }, - "c5cc8030a1864cbc888bc657ba9d1871": { + "bd9368c5e9b842ca818c69f779cd5276": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_40c3bb8a23164577a0c0611033e6d54c", - "IPY_MODEL_8fc926f029414a8aaaf8ecab225fc0fe", - "IPY_MODEL_fd7cae3437574fd3875f05820537adde" - ], - "layout": "IPY_MODEL_84e74dbfaf7b43918c3fef375f4c2636", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "fd7cae3437574fd3875f05820537adde": { + "cac3b162735445c0915d9ecfed155f4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1799,12 +1757,54 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_954e9713147d4bdb88dfcbb4b36fab1d", + "layout": "IPY_MODEL_af633ab6f6924af0b3f4ac3691d76422", "placeholder": "​", - "style": "IPY_MODEL_b93d0824edb848df8dfe762bda5a4c34", + "style": "IPY_MODEL_bcb7b5d047f845978925e2ef6da3385e", "tabbable": null, "tooltip": null, - "value": " 132/132 [00:00<00:00, 12955.99 examples/s]" + "value": " 132/132 [00:00<00:00, 11804.36 examples/s]" + } + }, + "d16dede2bb2e40b282d000f989523e41": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_979503323663435eae635a194817476f", + "IPY_MODEL_39e0d0ff92854bb5b45f8340a9c5c5eb", + "IPY_MODEL_cac3b162735445c0915d9ecfed155f4c" + ], + "layout": "IPY_MODEL_890c2c0c04564f5da3221229c05800df", + "tabbable": null, + "tooltip": null + } + }, + "d57ca1f4799d45229ae2f7c720c262f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 0ab929cc5..e8c4bda9d 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:02.265971Z", - "iopub.status.busy": "2024-06-28T15:33:02.265601Z", - "iopub.status.idle": "2024-06-28T15:33:03.540244Z", - "shell.execute_reply": "2024-06-28T15:33:03.539709Z" + "iopub.execute_input": "2024-07-01T15:02:41.409044Z", + "iopub.status.busy": "2024-07-01T15:02:41.408875Z", + "iopub.status.idle": "2024-07-01T15:02:42.611044Z", + "shell.execute_reply": "2024-07-01T15:02:42.610498Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.543066Z", - "iopub.status.busy": "2024-06-28T15:33:03.542566Z", - "iopub.status.idle": "2024-06-28T15:33:03.545721Z", - "shell.execute_reply": "2024-06-28T15:33:03.545256Z" + "iopub.execute_input": "2024-07-01T15:02:42.613616Z", + "iopub.status.busy": "2024-07-01T15:02:42.613300Z", + "iopub.status.idle": "2024-07-01T15:02:42.616526Z", + "shell.execute_reply": "2024-07-01T15:02:42.616068Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.548125Z", - "iopub.status.busy": "2024-06-28T15:33:03.547790Z", - "iopub.status.idle": "2024-06-28T15:33:03.557073Z", - "shell.execute_reply": "2024-06-28T15:33:03.556489Z" + "iopub.execute_input": "2024-07-01T15:02:42.618748Z", + "iopub.status.busy": "2024-07-01T15:02:42.618428Z", + "iopub.status.idle": "2024-07-01T15:02:42.627446Z", + "shell.execute_reply": "2024-07-01T15:02:42.626999Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.559397Z", - "iopub.status.busy": "2024-06-28T15:33:03.559034Z", - "iopub.status.idle": "2024-06-28T15:33:03.564198Z", - "shell.execute_reply": "2024-06-28T15:33:03.563684Z" + "iopub.execute_input": "2024-07-01T15:02:42.629537Z", + "iopub.status.busy": "2024-07-01T15:02:42.629203Z", + "iopub.status.idle": "2024-07-01T15:02:42.633941Z", + "shell.execute_reply": "2024-07-01T15:02:42.633516Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.566643Z", - "iopub.status.busy": "2024-06-28T15:33:03.566263Z", - "iopub.status.idle": "2024-06-28T15:33:03.754952Z", - "shell.execute_reply": "2024-06-28T15:33:03.754321Z" + "iopub.execute_input": "2024-07-01T15:02:42.636177Z", + "iopub.status.busy": "2024-07-01T15:02:42.635851Z", + "iopub.status.idle": "2024-07-01T15:02:42.823356Z", + "shell.execute_reply": "2024-07-01T15:02:42.822807Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:03.757588Z", - "iopub.status.busy": "2024-06-28T15:33:03.757353Z", - "iopub.status.idle": "2024-06-28T15:33:04.137542Z", - "shell.execute_reply": "2024-06-28T15:33:04.136949Z" + "iopub.execute_input": "2024-07-01T15:02:42.826055Z", + "iopub.status.busy": "2024-07-01T15:02:42.825690Z", + "iopub.status.idle": "2024-07-01T15:02:43.206067Z", + "shell.execute_reply": "2024-07-01T15:02:43.205474Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.139670Z", - "iopub.status.busy": "2024-06-28T15:33:04.139477Z", - "iopub.status.idle": "2024-06-28T15:33:04.142221Z", - "shell.execute_reply": "2024-06-28T15:33:04.141788Z" + "iopub.execute_input": "2024-07-01T15:02:43.208532Z", + "iopub.status.busy": "2024-07-01T15:02:43.208145Z", + "iopub.status.idle": "2024-07-01T15:02:43.211102Z", + "shell.execute_reply": "2024-07-01T15:02:43.210626Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.144228Z", - "iopub.status.busy": "2024-06-28T15:33:04.144051Z", - "iopub.status.idle": "2024-06-28T15:33:04.182999Z", - "shell.execute_reply": "2024-06-28T15:33:04.182501Z" + "iopub.execute_input": "2024-07-01T15:02:43.213282Z", + "iopub.status.busy": "2024-07-01T15:02:43.212936Z", + "iopub.status.idle": "2024-07-01T15:02:43.248404Z", + "shell.execute_reply": "2024-07-01T15:02:43.247768Z" } }, "outputs": [], @@ -638,10 +638,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:04.185443Z", - "iopub.status.busy": "2024-06-28T15:33:04.185256Z", - "iopub.status.idle": "2024-06-28T15:33:06.400028Z", - "shell.execute_reply": "2024-06-28T15:33:06.399335Z" + "iopub.execute_input": "2024-07-01T15:02:43.251350Z", + "iopub.status.busy": "2024-07-01T15:02:43.250964Z", + "iopub.status.idle": "2024-07-01T15:02:45.296650Z", + "shell.execute_reply": "2024-07-01T15:02:45.296009Z" } }, "outputs": [ @@ -685,10 +685,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.402739Z", - "iopub.status.busy": "2024-06-28T15:33:06.402145Z", - "iopub.status.idle": "2024-06-28T15:33:06.421740Z", - "shell.execute_reply": "2024-06-28T15:33:06.421232Z" + "iopub.execute_input": "2024-07-01T15:02:45.298975Z", + "iopub.status.busy": "2024-07-01T15:02:45.298607Z", + "iopub.status.idle": "2024-07-01T15:02:45.317301Z", + "shell.execute_reply": "2024-07-01T15:02:45.316762Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.423971Z", - "iopub.status.busy": "2024-06-28T15:33:06.423624Z", - "iopub.status.idle": "2024-06-28T15:33:06.430447Z", - "shell.execute_reply": "2024-06-28T15:33:06.429995Z" + "iopub.execute_input": "2024-07-01T15:02:45.319610Z", + "iopub.status.busy": "2024-07-01T15:02:45.319291Z", + "iopub.status.idle": "2024-07-01T15:02:45.325606Z", + "shell.execute_reply": "2024-07-01T15:02:45.325097Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.432560Z", - "iopub.status.busy": "2024-06-28T15:33:06.432209Z", - "iopub.status.idle": "2024-06-28T15:33:06.438239Z", - "shell.execute_reply": "2024-06-28T15:33:06.437699Z" + "iopub.execute_input": "2024-07-01T15:02:45.327815Z", + "iopub.status.busy": "2024-07-01T15:02:45.327438Z", + "iopub.status.idle": "2024-07-01T15:02:45.333038Z", + "shell.execute_reply": "2024-07-01T15:02:45.332566Z" } }, "outputs": [ @@ -1005,10 +1005,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.440265Z", - "iopub.status.busy": "2024-06-28T15:33:06.439954Z", - "iopub.status.idle": "2024-06-28T15:33:06.450509Z", - "shell.execute_reply": "2024-06-28T15:33:06.450061Z" + "iopub.execute_input": "2024-07-01T15:02:45.335093Z", + "iopub.status.busy": "2024-07-01T15:02:45.334787Z", + "iopub.status.idle": "2024-07-01T15:02:45.345460Z", + "shell.execute_reply": "2024-07-01T15:02:45.344912Z" } }, "outputs": [ @@ -1200,10 +1200,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.452688Z", - "iopub.status.busy": "2024-06-28T15:33:06.452339Z", - "iopub.status.idle": "2024-06-28T15:33:06.461491Z", - "shell.execute_reply": "2024-06-28T15:33:06.461029Z" + "iopub.execute_input": "2024-07-01T15:02:45.347438Z", + "iopub.status.busy": "2024-07-01T15:02:45.347138Z", + "iopub.status.idle": "2024-07-01T15:02:45.356126Z", + "shell.execute_reply": "2024-07-01T15:02:45.355581Z" } }, "outputs": [ @@ -1319,10 +1319,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.463689Z", - "iopub.status.busy": "2024-06-28T15:33:06.463348Z", - "iopub.status.idle": "2024-06-28T15:33:06.470367Z", - "shell.execute_reply": "2024-06-28T15:33:06.469791Z" + "iopub.execute_input": "2024-07-01T15:02:45.358103Z", + "iopub.status.busy": "2024-07-01T15:02:45.357792Z", + "iopub.status.idle": "2024-07-01T15:02:45.364571Z", + "shell.execute_reply": "2024-07-01T15:02:45.364114Z" }, "scrolled": true }, @@ -1447,10 +1447,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.472356Z", - "iopub.status.busy": "2024-06-28T15:33:06.472176Z", - "iopub.status.idle": "2024-06-28T15:33:06.482081Z", - "shell.execute_reply": "2024-06-28T15:33:06.481598Z" + "iopub.execute_input": "2024-07-01T15:02:45.366559Z", + "iopub.status.busy": "2024-07-01T15:02:45.366255Z", + "iopub.status.idle": "2024-07-01T15:02:45.375353Z", + "shell.execute_reply": "2024-07-01T15:02:45.374817Z" } }, "outputs": [ @@ -1553,10 +1553,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:06.484231Z", - "iopub.status.busy": "2024-06-28T15:33:06.483891Z", - "iopub.status.idle": "2024-06-28T15:33:06.495536Z", - "shell.execute_reply": "2024-06-28T15:33:06.495097Z" + "iopub.execute_input": "2024-07-01T15:02:45.377315Z", + "iopub.status.busy": "2024-07-01T15:02:45.377010Z", + "iopub.status.idle": "2024-07-01T15:02:45.392963Z", + "shell.execute_reply": "2024-07-01T15:02:45.392390Z" }, "nbsphinx": "hidden" }, @@ -1565,9 +1565,11 @@ "# Note: This cell is only for docs.cleanlab.ai, if running on local Jupyter or Colab, please ignore it.\n", "from sklearn.metrics import roc_auc_score\n", "\n", - "issue_results = lab.get_issues(\"label\")\n", - "outlier_results = lab.get_issues(\"outlier\")\n", - "duplicate_results = lab.get_issues(\"near_duplicate\")\n", + "def precision_at_k(predicted_indices, true_indices, k):\n", + " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / k\n", + "\n", + "def recall_at_k(predicted_indices, true_indices, k):\n", + " return len(set(predicted_indices[:k]).intersection(set(true_indices))) / len(true_indices)\n", "\n", "def jaccard_similarity(l1, l2):\n", " s1 = set(l1)\n", @@ -1578,26 +1580,40 @@ " return 0\n", " return len(intersect_set) / len(union_set)\n", "\n", - "identified_label_issues_indices = issue_results[issue_results[\"is_label_issue\"] == True].index.tolist()\n", + "label_issues = lab.get_issues(\"label\")\n", + "predicted_label_issues_indices = (\n", + " label_issues.query(\"is_label_issue\").sort_values(\"label_score\").index.to_list()\n", + ")\n", + "predicted_label_issues_indices_by_score = (\n", + " label_issues.sort_values(\"label_score\").index.to_list()\n", + ")\n", "label_issue_indices = np.where(y_train_idx != noisy_labels_idx)[0]\n", "\n", - "label_quality_scores = issue_results[\"label_score\"].tolist()\n", + "label_quality_scores = label_issues[\"label_score\"].tolist()\n", "Z = (y_train_idx == noisy_labels_idx).astype(float).tolist()\n", "\n", - "identified_outlier_issues_indices = outlier_results[outlier_results[\"is_outlier_issue\"] == True].index.to_list()\n", + "predicted_outlier_issues_indices = (\n", + " lab.get_issues(\"outlier\").query(\"is_outlier_issue\").index.to_list()\n", + ")\n", "outlier_issue_indices = list(range(125, 130+1))\n", "exact_duplicate_idx = [index for index, elem in enumerate(X_train) if (elem == X_duplicate).all()][0]\n", "if exact_duplicate_idx >= 125: # if the random index selected to create a duplicate >= 125, then the last point is also an outlier\n", " outlier_issue_indices.append(131)\n", - " \n", - "identified_duplicate_issues_indices = duplicate_results[duplicate_results[\"is_near_duplicate_issue\"] == True].index.tolist()\n", - "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", + "predicted_duplicate_issues_indices = (\n", + " lab.get_issues(\"near_duplicate\").query(\"is_near_duplicate_issue\").index.tolist()\n", + ")\n", + "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", - "assert jaccard_similarity(identified_label_issues_indices, label_issue_indices) > 0.4\n", + "k = len(label_issue_indices)\n", + "assert precision_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert recall_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert precision_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", + "assert recall_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", "assert roc_auc_score(Z, label_quality_scores) > 0.9\n", - "assert jaccard_similarity(identified_outlier_issues_indices, outlier_issue_indices) > 0.9\n", - "assert jaccard_similarity(identified_duplicate_issues_indices, duplicate_issue_indices) > 0.9" + "\n", + "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", + "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] } ], diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb index 9bd5eb804..03d847503 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:09.403235Z", - "iopub.status.busy": "2024-06-28T15:33:09.402738Z", - "iopub.status.idle": "2024-06-28T15:33:12.466652Z", - "shell.execute_reply": "2024-06-28T15:33:12.466079Z" + "iopub.execute_input": "2024-07-01T15:02:48.074971Z", + "iopub.status.busy": "2024-07-01T15:02:48.074723Z", + "iopub.status.idle": "2024-07-01T15:02:51.342353Z", + "shell.execute_reply": "2024-07-01T15:02:51.341605Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:12.469241Z", - "iopub.status.busy": "2024-06-28T15:33:12.468934Z", - "iopub.status.idle": "2024-06-28T15:33:12.472447Z", - "shell.execute_reply": "2024-06-28T15:33:12.472019Z" + "iopub.execute_input": "2024-07-01T15:02:51.345614Z", + "iopub.status.busy": "2024-07-01T15:02:51.345060Z", + "iopub.status.idle": "2024-07-01T15:02:51.349168Z", + "shell.execute_reply": "2024-07-01T15:02:51.348682Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:12.474380Z", - "iopub.status.busy": "2024-06-28T15:33:12.474201Z", - "iopub.status.idle": "2024-06-28T15:33:24.977088Z", - "shell.execute_reply": "2024-06-28T15:33:24.976474Z" + "iopub.execute_input": "2024-07-01T15:02:51.351438Z", + "iopub.status.busy": "2024-07-01T15:02:51.351054Z", + "iopub.status.idle": "2024-07-01T15:03:02.526470Z", + "shell.execute_reply": "2024-07-01T15:03:02.525870Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd367ed6b3e145b9ba56c440d63f6948", + "model_id": "bd4e5e775e0d4b5d90568b686f8fd56f", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "999579ced4a04955b6fe76b06613510d", + "model_id": "a9efee99388e4bd987cba82e4c249be5", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9611c5ddf09444219b0832f81930fdd7", + "model_id": "b13b21c3b7544706aacfbba4f3504a8b", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2bbd780010f04629a57e9a48d6241a4e", + "model_id": "dcfb76cdced842fd810c0329fa0f1c7f", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21ffc7623d52499f96e098345ad1b94d", + "model_id": "0febc72cf36d4d939a7991cbb880240e", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c14226990a04e87aa10a393e2a0203a", + "model_id": "6296fc9f1a3947edb989ab3a35afbefe", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "519660b9e9fd424bbb188e3f3d9d3b89", + "model_id": "bc98754b340343f594559442ba450aa4", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "510e84f543554f8d8f0f21ce00483d7d", + "model_id": "d60f32b2907d4a288385a30c717ef39d", "version_major": 2, "version_minor": 0 }, @@ -302,10 +302,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:24.979342Z", - "iopub.status.busy": "2024-06-28T15:33:24.979046Z", - "iopub.status.idle": "2024-06-28T15:33:24.983576Z", - "shell.execute_reply": "2024-06-28T15:33:24.983103Z" + "iopub.execute_input": "2024-07-01T15:03:02.528967Z", + "iopub.status.busy": "2024-07-01T15:03:02.528621Z", + "iopub.status.idle": "2024-07-01T15:03:02.532647Z", + "shell.execute_reply": "2024-07-01T15:03:02.532080Z" } }, "outputs": [ @@ -330,17 +330,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:24.985815Z", - "iopub.status.busy": "2024-06-28T15:33:24.985393Z", - "iopub.status.idle": "2024-06-28T15:33:36.598392Z", - "shell.execute_reply": "2024-06-28T15:33:36.597792Z" + "iopub.execute_input": "2024-07-01T15:03:02.534910Z", + "iopub.status.busy": "2024-07-01T15:03:02.534585Z", + "iopub.status.idle": "2024-07-01T15:03:13.866603Z", + "shell.execute_reply": "2024-07-01T15:03:13.865937Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "748b7bbe2c8345c6a22623d9f52f46cf", + "model_id": "70b6c17f51c948158afefdd56830a23f", "version_major": 2, "version_minor": 0 }, @@ -378,10 +378,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:36.601110Z", - "iopub.status.busy": "2024-06-28T15:33:36.600777Z", - "iopub.status.idle": "2024-06-28T15:33:55.067117Z", - "shell.execute_reply": "2024-06-28T15:33:55.066477Z" + "iopub.execute_input": "2024-07-01T15:03:13.869049Z", + "iopub.status.busy": "2024-07-01T15:03:13.868821Z", + "iopub.status.idle": "2024-07-01T15:03:31.582919Z", + "shell.execute_reply": "2024-07-01T15:03:31.582298Z" } }, "outputs": [], @@ -414,10 +414,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.070279Z", - "iopub.status.busy": "2024-06-28T15:33:55.069803Z", - "iopub.status.idle": "2024-06-28T15:33:55.075612Z", - "shell.execute_reply": "2024-06-28T15:33:55.075060Z" + "iopub.execute_input": "2024-07-01T15:03:31.585953Z", + "iopub.status.busy": "2024-07-01T15:03:31.585389Z", + "iopub.status.idle": "2024-07-01T15:03:31.591279Z", + "shell.execute_reply": "2024-07-01T15:03:31.590830Z" } }, "outputs": [], @@ -455,10 +455,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.078062Z", - "iopub.status.busy": "2024-06-28T15:33:55.077664Z", - "iopub.status.idle": "2024-06-28T15:33:55.082536Z", - "shell.execute_reply": "2024-06-28T15:33:55.081922Z" + "iopub.execute_input": "2024-07-01T15:03:31.593306Z", + "iopub.status.busy": "2024-07-01T15:03:31.592981Z", + "iopub.status.idle": "2024-07-01T15:03:31.596855Z", + "shell.execute_reply": "2024-07-01T15:03:31.596450Z" }, "nbsphinx": "hidden" }, @@ -595,10 +595,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.085223Z", - "iopub.status.busy": "2024-06-28T15:33:55.084873Z", - "iopub.status.idle": "2024-06-28T15:33:55.094273Z", - "shell.execute_reply": "2024-06-28T15:33:55.093706Z" + "iopub.execute_input": "2024-07-01T15:03:31.598838Z", + "iopub.status.busy": "2024-07-01T15:03:31.598577Z", + "iopub.status.idle": "2024-07-01T15:03:31.607398Z", + "shell.execute_reply": "2024-07-01T15:03:31.606925Z" }, "nbsphinx": "hidden" }, @@ -723,10 +723,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.096457Z", - "iopub.status.busy": "2024-06-28T15:33:55.096269Z", - "iopub.status.idle": "2024-06-28T15:33:55.123566Z", - "shell.execute_reply": "2024-06-28T15:33:55.123064Z" + "iopub.execute_input": "2024-07-01T15:03:31.609325Z", + "iopub.status.busy": "2024-07-01T15:03:31.609007Z", + "iopub.status.idle": "2024-07-01T15:03:31.635278Z", + "shell.execute_reply": "2024-07-01T15:03:31.634840Z" } }, "outputs": [], @@ -763,10 +763,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:33:55.126192Z", - "iopub.status.busy": "2024-06-28T15:33:55.125844Z", - "iopub.status.idle": "2024-06-28T15:34:29.556539Z", - "shell.execute_reply": "2024-06-28T15:34:29.555882Z" + "iopub.execute_input": "2024-07-01T15:03:31.637322Z", + "iopub.status.busy": "2024-07-01T15:03:31.636996Z", + "iopub.status.idle": "2024-07-01T15:04:03.652341Z", + "shell.execute_reply": "2024-07-01T15:04:03.651742Z" } }, "outputs": [ @@ -782,21 +782,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 5.070\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.749\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.893\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.439\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01d874da00234ef1aa34287815d37d45", + "model_id": "b69aa5fb137444eb962d31f239578d65", "version_major": 2, "version_minor": 0 }, @@ -817,7 +817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02ee74aebbc04f9a82b5829341e501a6", + "model_id": "6ca247bf72f54f03aabdd5d72546025f", "version_major": 2, "version_minor": 0 }, @@ -840,21 +840,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.975\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.851\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.917\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.491\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddb26404cd4644d3a9efa8efd7af9104", + "model_id": "d6465626e3264fa58f44ddccd18cfef2", "version_major": 2, "version_minor": 0 }, @@ -875,7 +875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c60874cc394b428786de11432b5ba1ce", + "model_id": "3fa46dee97a14f9594eb60312b03e045", "version_major": 2, "version_minor": 0 }, @@ -898,21 +898,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.951\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.739\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 5.061\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.490\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca1506e7d3f846adb2f7487be4ad5f1d", + "model_id": "76cd9d157bf74d6e93db6f5727c6f900", "version_major": 2, "version_minor": 0 }, @@ -933,7 +933,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff1c46ea9c474d1bb4645c7cbbf298b0", + "model_id": "9717f3b4aaae491d9cb2e07d49a003a5", "version_major": 2, "version_minor": 0 }, @@ -1012,10 +1012,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:29.558914Z", - "iopub.status.busy": "2024-06-28T15:34:29.558674Z", - "iopub.status.idle": "2024-06-28T15:34:29.573241Z", - "shell.execute_reply": "2024-06-28T15:34:29.572642Z" + "iopub.execute_input": "2024-07-01T15:04:03.654962Z", + "iopub.status.busy": "2024-07-01T15:04:03.654720Z", + "iopub.status.idle": "2024-07-01T15:04:03.668632Z", + "shell.execute_reply": "2024-07-01T15:04:03.668209Z" } }, "outputs": [], @@ -1040,10 +1040,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:29.575486Z", - "iopub.status.busy": "2024-06-28T15:34:29.575301Z", - "iopub.status.idle": "2024-06-28T15:34:30.062525Z", - "shell.execute_reply": "2024-06-28T15:34:30.061951Z" + "iopub.execute_input": "2024-07-01T15:04:03.670732Z", + "iopub.status.busy": "2024-07-01T15:04:03.670344Z", + "iopub.status.idle": "2024-07-01T15:04:04.150524Z", + "shell.execute_reply": "2024-07-01T15:04:04.149791Z" } }, "outputs": [], @@ -1063,10 +1063,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:34:30.065567Z", - "iopub.status.busy": "2024-06-28T15:34:30.065103Z", - "iopub.status.idle": "2024-06-28T15:36:09.855028Z", - "shell.execute_reply": "2024-06-28T15:36:09.854365Z" + "iopub.execute_input": "2024-07-01T15:04:04.153028Z", + "iopub.status.busy": "2024-07-01T15:04:04.152825Z", + "iopub.status.idle": "2024-07-01T15:05:40.110641Z", + "shell.execute_reply": "2024-07-01T15:05:40.110011Z" } }, "outputs": [ @@ -1105,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f55b4eb540fd4f368229bcf7012adf9f", + "model_id": "8b242b3757014ca08c0be26603c856e5", "version_major": 2, "version_minor": 0 }, @@ -1144,10 +1144,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:09.857655Z", - "iopub.status.busy": "2024-06-28T15:36:09.857109Z", - "iopub.status.idle": "2024-06-28T15:36:10.330566Z", - "shell.execute_reply": "2024-06-28T15:36:10.330003Z" + "iopub.execute_input": "2024-07-01T15:05:40.113143Z", + "iopub.status.busy": "2024-07-01T15:05:40.112512Z", + "iopub.status.idle": "2024-07-01T15:05:40.560298Z", + "shell.execute_reply": "2024-07-01T15:05:40.559714Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.333473Z", - "iopub.status.busy": "2024-06-28T15:36:10.333080Z", - "iopub.status.idle": "2024-06-28T15:36:10.397225Z", - "shell.execute_reply": "2024-06-28T15:36:10.396605Z" + "iopub.execute_input": "2024-07-01T15:05:40.563315Z", + "iopub.status.busy": "2024-07-01T15:05:40.562801Z", + "iopub.status.idle": "2024-07-01T15:05:40.624738Z", + "shell.execute_reply": "2024-07-01T15:05:40.624116Z" } }, "outputs": [ @@ -1400,10 +1400,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.399332Z", - "iopub.status.busy": "2024-06-28T15:36:10.399151Z", - "iopub.status.idle": "2024-06-28T15:36:10.408208Z", - "shell.execute_reply": "2024-06-28T15:36:10.407701Z" + "iopub.execute_input": "2024-07-01T15:05:40.627071Z", + "iopub.status.busy": "2024-07-01T15:05:40.626639Z", + "iopub.status.idle": "2024-07-01T15:05:40.635299Z", + "shell.execute_reply": "2024-07-01T15:05:40.634756Z" } }, "outputs": [ @@ -1533,10 +1533,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.410606Z", - "iopub.status.busy": "2024-06-28T15:36:10.410094Z", - "iopub.status.idle": "2024-06-28T15:36:10.415260Z", - "shell.execute_reply": "2024-06-28T15:36:10.414712Z" + "iopub.execute_input": "2024-07-01T15:05:40.637389Z", + "iopub.status.busy": "2024-07-01T15:05:40.636989Z", + "iopub.status.idle": "2024-07-01T15:05:40.641711Z", + "shell.execute_reply": "2024-07-01T15:05:40.641175Z" }, "nbsphinx": "hidden" }, @@ -1582,10 +1582,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.417475Z", - "iopub.status.busy": "2024-06-28T15:36:10.417052Z", - "iopub.status.idle": "2024-06-28T15:36:10.958110Z", - "shell.execute_reply": "2024-06-28T15:36:10.957499Z" + "iopub.execute_input": "2024-07-01T15:05:40.643683Z", + "iopub.status.busy": "2024-07-01T15:05:40.643498Z", + "iopub.status.idle": "2024-07-01T15:05:41.152016Z", + "shell.execute_reply": "2024-07-01T15:05:41.151428Z" } }, "outputs": [ @@ -1620,10 +1620,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.960727Z", - "iopub.status.busy": "2024-06-28T15:36:10.960231Z", - "iopub.status.idle": "2024-06-28T15:36:10.969176Z", - "shell.execute_reply": "2024-06-28T15:36:10.968687Z" + "iopub.execute_input": "2024-07-01T15:05:41.154341Z", + "iopub.status.busy": "2024-07-01T15:05:41.154029Z", + "iopub.status.idle": "2024-07-01T15:05:41.162706Z", + "shell.execute_reply": "2024-07-01T15:05:41.162252Z" } }, "outputs": [ @@ -1790,10 +1790,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.971544Z", - "iopub.status.busy": "2024-06-28T15:36:10.971137Z", - "iopub.status.idle": "2024-06-28T15:36:10.978595Z", - "shell.execute_reply": "2024-06-28T15:36:10.978143Z" + "iopub.execute_input": "2024-07-01T15:05:41.164766Z", + "iopub.status.busy": "2024-07-01T15:05:41.164446Z", + "iopub.status.idle": "2024-07-01T15:05:41.171486Z", + "shell.execute_reply": "2024-07-01T15:05:41.171059Z" }, "nbsphinx": "hidden" }, @@ -1869,10 +1869,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:10.980809Z", - "iopub.status.busy": "2024-06-28T15:36:10.980353Z", - "iopub.status.idle": "2024-06-28T15:36:11.774826Z", - "shell.execute_reply": "2024-06-28T15:36:11.774218Z" + "iopub.execute_input": "2024-07-01T15:05:41.173399Z", + "iopub.status.busy": "2024-07-01T15:05:41.173075Z", + "iopub.status.idle": "2024-07-01T15:05:41.934946Z", + "shell.execute_reply": "2024-07-01T15:05:41.934291Z" } }, "outputs": [ @@ -1909,10 +1909,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.777464Z", - "iopub.status.busy": "2024-06-28T15:36:11.777117Z", - "iopub.status.idle": "2024-06-28T15:36:11.793779Z", - "shell.execute_reply": "2024-06-28T15:36:11.793195Z" + "iopub.execute_input": "2024-07-01T15:05:41.937509Z", + "iopub.status.busy": "2024-07-01T15:05:41.937076Z", + "iopub.status.idle": "2024-07-01T15:05:41.952809Z", + "shell.execute_reply": "2024-07-01T15:05:41.952240Z" } }, "outputs": [ @@ -2069,10 +2069,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.796111Z", - "iopub.status.busy": "2024-06-28T15:36:11.795755Z", - "iopub.status.idle": "2024-06-28T15:36:11.801625Z", - "shell.execute_reply": "2024-06-28T15:36:11.801150Z" + "iopub.execute_input": "2024-07-01T15:05:41.954986Z", + "iopub.status.busy": "2024-07-01T15:05:41.954646Z", + "iopub.status.idle": "2024-07-01T15:05:41.960097Z", + "shell.execute_reply": "2024-07-01T15:05:41.959674Z" }, "nbsphinx": "hidden" }, @@ -2117,10 +2117,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:11.803713Z", - "iopub.status.busy": "2024-06-28T15:36:11.803391Z", - "iopub.status.idle": "2024-06-28T15:36:12.281855Z", - "shell.execute_reply": "2024-06-28T15:36:12.281284Z" + "iopub.execute_input": "2024-07-01T15:05:41.961941Z", + "iopub.status.busy": "2024-07-01T15:05:41.961770Z", + "iopub.status.idle": "2024-07-01T15:05:42.348365Z", + "shell.execute_reply": "2024-07-01T15:05:42.347794Z" } }, "outputs": [ @@ -2202,10 +2202,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.285071Z", - "iopub.status.busy": "2024-06-28T15:36:12.284635Z", - "iopub.status.idle": "2024-06-28T15:36:12.295272Z", - "shell.execute_reply": "2024-06-28T15:36:12.294767Z" + "iopub.execute_input": "2024-07-01T15:05:42.350754Z", + "iopub.status.busy": "2024-07-01T15:05:42.350573Z", + "iopub.status.idle": "2024-07-01T15:05:42.359462Z", + "shell.execute_reply": "2024-07-01T15:05:42.358866Z" } }, "outputs": [ @@ -2333,10 +2333,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.297976Z", - "iopub.status.busy": "2024-06-28T15:36:12.297776Z", - "iopub.status.idle": "2024-06-28T15:36:12.304804Z", - "shell.execute_reply": "2024-06-28T15:36:12.304249Z" + "iopub.execute_input": "2024-07-01T15:05:42.361756Z", + "iopub.status.busy": "2024-07-01T15:05:42.361579Z", + "iopub.status.idle": "2024-07-01T15:05:42.366530Z", + "shell.execute_reply": "2024-07-01T15:05:42.365855Z" }, "nbsphinx": "hidden" }, @@ -2373,10 +2373,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.307029Z", - "iopub.status.busy": "2024-06-28T15:36:12.306836Z", - "iopub.status.idle": "2024-06-28T15:36:12.512134Z", - "shell.execute_reply": "2024-06-28T15:36:12.511544Z" + "iopub.execute_input": "2024-07-01T15:05:42.368583Z", + "iopub.status.busy": "2024-07-01T15:05:42.368393Z", + "iopub.status.idle": "2024-07-01T15:05:42.547146Z", + "shell.execute_reply": "2024-07-01T15:05:42.546558Z" } }, "outputs": [ @@ -2418,10 +2418,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.514678Z", - "iopub.status.busy": "2024-06-28T15:36:12.514470Z", - "iopub.status.idle": "2024-06-28T15:36:12.523352Z", - "shell.execute_reply": "2024-06-28T15:36:12.522769Z" + "iopub.execute_input": "2024-07-01T15:05:42.549711Z", + "iopub.status.busy": "2024-07-01T15:05:42.549286Z", + "iopub.status.idle": "2024-07-01T15:05:42.557834Z", + "shell.execute_reply": "2024-07-01T15:05:42.557185Z" } }, "outputs": [ @@ -2446,47 +2446,47 @@ " \n", " \n", " \n", - " low_information_score\n", " is_low_information_issue\n", + " low_information_score\n", " \n", " \n", " \n", " \n", " 53050\n", - " 0.067975\n", " True\n", + " 0.067975\n", " \n", " \n", " 40875\n", - " 0.089929\n", " True\n", + " 0.089929\n", " \n", " \n", " 9594\n", - " 0.092601\n", " True\n", + " 0.092601\n", " \n", " \n", " 34825\n", - " 0.107744\n", " True\n", + " 0.107744\n", " \n", " \n", " 37530\n", - " 0.108516\n", " True\n", + " 0.108516\n", " \n", " \n", "\n", "" ], "text/plain": [ - " low_information_score is_low_information_issue\n", - "53050 0.067975 True\n", - "40875 0.089929 True\n", - "9594 0.092601 True\n", - "34825 0.107744 True\n", - "37530 0.108516 True" + " is_low_information_issue low_information_score\n", + "53050 True 0.067975\n", + "40875 True 0.089929\n", + "9594 True 0.092601\n", + "34825 True 0.107744\n", + "37530 True 0.108516" ] }, "execution_count": 29, @@ -2507,10 +2507,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.525904Z", - "iopub.status.busy": "2024-06-28T15:36:12.525428Z", - "iopub.status.idle": "2024-06-28T15:36:12.722284Z", - "shell.execute_reply": "2024-06-28T15:36:12.721692Z" + "iopub.execute_input": "2024-07-01T15:05:42.559948Z", + "iopub.status.busy": "2024-07-01T15:05:42.559775Z", + "iopub.status.idle": "2024-07-01T15:05:42.735572Z", + "shell.execute_reply": "2024-07-01T15:05:42.734991Z" } }, "outputs": [ @@ -2550,10 +2550,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:12.724319Z", - "iopub.status.busy": "2024-06-28T15:36:12.724140Z", - "iopub.status.idle": "2024-06-28T15:36:12.728868Z", - "shell.execute_reply": "2024-06-28T15:36:12.728378Z" + "iopub.execute_input": "2024-07-01T15:05:42.737707Z", + "iopub.status.busy": "2024-07-01T15:05:42.737529Z", + "iopub.status.idle": "2024-07-01T15:05:42.742129Z", + "shell.execute_reply": "2024-07-01T15:05:42.741571Z" }, "nbsphinx": "hidden" }, @@ -2590,165 +2590,66 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "00e493ba9c68410a9bfb07d35f58c61a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "013d72ca9aa146a593784e72f356edd6": { + "01c1fefd1d1549a1ac3d1168746bc81e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c91e1c0bc2da4f678e94080291cc61d8", - "placeholder": "​", - "style": "IPY_MODEL_629afeba7adb4995975ea1d03301cd3f", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "01815763b36f44db9e29147b04f45a49": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "01d874da00234ef1aa34287815d37d45": { + "0423a125c3324adc9f2433bebe77773b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_06700ad7253445b29d4575f934ab5afe", - "IPY_MODEL_9fdacce60b084ad6b7edc2e06ad6b7d5", - "IPY_MODEL_343f48933d79402992e30514270b02e5" - ], - "layout": "IPY_MODEL_e4991d19e073408f9cef1ae11eb71019", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "02ee74aebbc04f9a82b5829341e501a6": { + "070af112be824f3a9dfc9d11f6c99bdb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ceddd53d284e47e080fd5b52a11b902d", - "IPY_MODEL_115578eb2f074173835af6ef40b58192", - "IPY_MODEL_33f1930405e34e288f0f52c5e451dac0" - ], - "layout": "IPY_MODEL_169122b3abf3499a96cd266c100d3ebc", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_268de63ed1994c07842b4b562dfac3f6", + "placeholder": "​", + "style": "IPY_MODEL_2a1a89abfab4432f93e32ce870b637ab", "tabbable": null, - "tooltip": null - } - }, - "061c5f364286484db172fb4804eaca0d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null, + "value": "100%" } }, - "06700ad7253445b29d4575f934ab5afe": { + "08550632bef14f9a894de4b50f4b0628": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2763,15 +2664,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_40fdb275580d43d9b74fe903a7a8104d", + "layout": "IPY_MODEL_d78ce21ee1fc44e8b680bda13d37d943", "placeholder": "​", - "style": "IPY_MODEL_5c2bfa97829b4fdea2882b9050f5562d", + "style": "IPY_MODEL_abecf5a0521e45f78a788d98cc335e5c", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4.42M/4.42M [00:00<00:00, 77.0MB/s]" } }, - "06f943bb034d473babe64569cf34ef3f": { + "0abf4ae675284a88873e14026b73ab9e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2789,7 +2690,7 @@ "text_color": null } }, - "0a5ef6cfa8714c0ab6b0ef41dfb1a925": { + "0cfffd1a2e16481ba0fb26b13818b9d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2842,53 +2743,49 @@ "width": null } }, - "0c7b3a4a480e4a4f8a7a93dcc0cc643a": { + "0febc72cf36d4d939a7991cbb880240e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aca6b827bc144b7e84332be12838c12a", - "placeholder": "​", - "style": "IPY_MODEL_64f5f6e8c1bb4d1ea5c7e2269014d6b6", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b374b826cf5a4a668332dc70f8c2fe4a", + "IPY_MODEL_213628c3350547b8801807bf6755bac5", + "IPY_MODEL_08550632bef14f9a894de4b50f4b0628" + ], + "layout": "IPY_MODEL_c87df7230ab74cf3b4ce36554b4f64b5", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:07<00:00, 8621.86 examples/s]" + "tooltip": null } }, - "100b4c65b0f44c9b8b9c7cb4afbc38ac": { + "109113d68eb84c2eace6f4deda6b040b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_97c2e55e3c57419c81374350ea34b432", - "placeholder": "​", - "style": "IPY_MODEL_061c5f364286484db172fb4804eaca0d", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "1011f6c6efe84551a4d233008b8b790f": { + "11703dfe59744973b7ee5db1b50a7304": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2941,33 +2838,48 @@ "width": null } }, - "115578eb2f074173835af6ef40b58192": { + "126a880302694496ab5834d71ecffa75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_d8139f444e324e12818613912a49a6f9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_95453138f12b4a908bb147d1409d6b93", + "layout": "IPY_MODEL_6bb8ca43d39f43d0933376cc22b61a88", + "placeholder": "​", + "style": "IPY_MODEL_ef22307e229c427e9f2c45041e108373", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": "Generating test split: 100%" + } + }, + "160c29ae9eb04803b9e5fc42229024de": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "14620fb3ef91409c93ca2866a6b245cd": { + "182fe610a03c4f0c8820d62efd2942a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3020,7 +2932,33 @@ "width": null } }, - "15d24ab1c32544e9b1dad6314ed3918f": { + "197981c8f9c84c00abea85184c72fcd5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ac933a814c124345b0730704f11cfbc8", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2a940901f95f4ae58045f253ec9f48b7", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "19d83e638f7a404ba0eb4fdd71302435": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3073,7 +3011,7 @@ "width": null } }, - "15fc493178c94958a5bd63dc73acf625": { + "19dfb010e30849f38c848ea70dda5645": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3091,7 +3029,23 @@ "text_color": null } }, - "1640d13bd96d405ba01bbe9f22029432": { + "1a2b07452f87444f8ac98839eeafdd7b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1ab8a64cb49340769b8aa2cc1774732d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3144,16 +3098,100 @@ "width": null } }, - "169122b3abf3499a96cd266c100d3ebc": { - "model_module": "@jupyter-widgets/base", + "1ba2c9f296904da78195fe3e45321059": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_833e5dbaba7546f582547cdf7571c108", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_21904a4bfca34e3295a0b17083f1cdb9", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "1c8e1b8efcbf4cd6ad8ccd71c8886907": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "213628c3350547b8801807bf6755bac5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c71cb776520442f0baba7a63fdbf2324", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a1990d1ffd9240a7a4bf2438eaf81754", + "tabbable": null, + "tooltip": null, + "value": 4422102.0 + } + }, + "21904a4bfca34e3295a0b17083f1cdb9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2529da9ccb564a7d98cf91d4e2f454fc": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, @@ -3197,7 +3235,7 @@ "width": null } }, - "1903503bad43448ea1ac70783f0fb30d": { + "255e06fe46cb4c0eaa40336d22beaf70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3250,7 +3288,41 @@ "width": null } }, - "1a95c58232ad42d683b9174f9f853649": { + "25aea1e677d84e4da5e2f1a580b847bb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "266601a55125440a88a8a16e00cebd66": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "268de63ed1994c07842b4b562dfac3f6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3303,7 +3375,7 @@ "width": null } }, - "1ad9b1cdbd544af3a314df0c82ec6565": { + "295b6321cc6f4b1bbfa02a28f8581b1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3356,53 +3428,7 @@ "width": null } }, - "1bc6bb27d1ce4ccd8dd7cfe252d16d39": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_aea72aba96c34f99adbd16416576dbed", - "placeholder": "​", - "style": "IPY_MODEL_c27e7863116b4bf48eab88e0869c69a4", - "tabbable": null, - "tooltip": null, - "value": "Downloading builder script: 100%" - } - }, - "1c04a881488944cfb2a78e4629f5d219": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1903503bad43448ea1ac70783f0fb30d", - "placeholder": "​", - "style": "IPY_MODEL_4385afbe20eb4363b22e1ff9d6288fff", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 7615.20 examples/s]" - } - }, - "1d383e62e8714383b5a6879ad3316e61": { + "299c0f0403e1447fb83af15e00e8f19f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3455,57 +3481,41 @@ "width": null } }, - "216f04617573408697842aba4967a052": { + "2a1a89abfab4432f93e32ce870b637ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f054cf08430c477d9eab2ccc80e48b99", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_355b9a91686843b0a583fca9630ed656", - "tabbable": null, - "tooltip": null, - "value": 4.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "21ffc7623d52499f96e098345ad1b94d": { + "2a940901f95f4ae58045f253ec9f48b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_013d72ca9aa146a593784e72f356edd6", - "IPY_MODEL_c7ec958a864d4b8b9aeb4c7b4f1055df", - "IPY_MODEL_4c86867b8903466fa9e6d4e388aa0434" - ], - "layout": "IPY_MODEL_41de0713ce794701a08845a6d7031022", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "2279b7989e5940fc9318addab8b1ab9e": { + "2cb677caec644d3ab2a8b6a70208559e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3558,7 +3568,23 @@ "width": null } }, - "238898e5c2874588924e33ad1a5a14be": { + "2d09848b13f94c7a9483a0063af7658b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2f77ed4c555c4365a30f1c760cb59e74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3576,7 +3602,7 @@ "text_color": null } }, - "24f4a95a06be4e01a6b70cd2cd3bccda": { + "348018e48d954c87b48ff2a6e7470304": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -3592,23 +3618,30 @@ "description_width": "" } }, - "25c58f4aec6b4f41be25042769d63c52": { + "367462867871411f81ad39c2100a77d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_93b948080aae40babe81f47f38dc60fa", + "placeholder": "​", + "style": "IPY_MODEL_3f77fa09c365457f893db8dde97d6a1a", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:07<00:00, 8559.65 examples/s]" } }, - "25d73f1cd6f74f7f8eb5013af3fc4aea": { + "3773b260a0514063a4cb27c7bcfcdb72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3661,7 +3694,53 @@ "width": null } }, - "2ad862ff6f0f4f3a8db6362836d85b81": { + "3abd2a96be1649ed9f2a7594cfdf3bbd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f15ed2b240814bb897b777075c726c92", + "placeholder": "​", + "style": "IPY_MODEL_956d09bfdd86417caf76bb337c098a81", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" + } + }, + "3ddd27d196364f1db80581facbadf18f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_79d53959ea3648699bfb173209305675", + "placeholder": "​", + "style": "IPY_MODEL_160c29ae9eb04803b9e5fc42229024de", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "3ec84e0ec4d94afd8f15a91eb757624e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3714,7 +3793,51 @@ "width": null } }, - "2bbd780010f04629a57e9a48d6241a4e": { + "3edb1a47c3314c0abfc78f23d62ebe68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0cfffd1a2e16481ba0fb26b13818b9d3", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_70ca2521a7cf454c8f41cc8665bd8572", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "3f77fa09c365457f893db8dde97d6a1a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "3fa46dee97a14f9594eb60312b03e045": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3729,16 +3852,34 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_46c6a150015345b7a2500e89263d7aa2", - "IPY_MODEL_c979207c10fd4e0f91eb87b8a8cb5bdb", - "IPY_MODEL_75d1007bec0746c98aeda8aef0434706" + "IPY_MODEL_a50bac222b154e1e8f1af99c2d2f7cb5", + "IPY_MODEL_f306b412103a4a999f13f32ecb0841fe", + "IPY_MODEL_838e5992cb0f492aa79d9e4411b13437" ], - "layout": "IPY_MODEL_15d24ab1c32544e9b1dad6314ed3918f", + "layout": "IPY_MODEL_1ab8a64cb49340769b8aa2cc1774732d", "tabbable": null, "tooltip": null } }, - "30926f7236cb4acb82089cc6bc32bca9": { + "424a8de7240d4c088b2b2dbadf1113ab": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4367f895c508411fb72fbf123bfd2662": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3791,23 +3932,43 @@ "width": null } }, - "32020bd3922749389ef4e7b04e878ded": { + "445bd193a0b443b68d88db83f68b1135": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4751ad8ff2014ae4aefb61464aa0b996": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "3235e856e34143f1b59787649ca7badc": { + "4a97191b1a1643199e46244e6611352a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3860,102 +4021,25 @@ "width": null } }, - "33f1930405e34e288f0f52c5e451dac0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_3235e856e34143f1b59787649ca7badc", - "placeholder": "​", - "style": "IPY_MODEL_4b0f4e804c4747d3b5b564a82e7c932f", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.82it/s]" - } - }, - "340d530d31fa467c9418a8767d1a0eb2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a0156d3f29e94c14a8be31866b4025fa", - "placeholder": "​", - "style": "IPY_MODEL_3ff0016903f24827855530023693263e", - "tabbable": null, - "tooltip": null, - "value": " 8.85k/8.85k [00:00<00:00, 1.48MB/s]" - } - }, - "340d8d367bbf4aae8a133ab9a2dfda9d": { + "4c9f6ca0676a4e6bbace6bdaf151e92c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4cb59733ebbd4647b7d2611b71086dfc", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_01815763b36f44db9e29147b04f45a49", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "343f48933d79402992e30514270b02e5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1011f6c6efe84551a4d233008b8b790f", - "placeholder": "​", - "style": "IPY_MODEL_84a10c91f7e44b49a6ec73367bf116ef", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 57.41it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "353d9eda4aec4d30805d245cde63c51c": { + "50042e9a2b5145a780c7c3d799e26aad": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4008,80 +4092,7 @@ "width": null } }, - "355b9a91686843b0a583fca9630ed656": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "35d76f48e70949a588e1ac466e50a6f8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3727b7633ef444e593e7a339abd041df": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3e483d0ac53644caa94492a8d78843c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_1a95c58232ad42d683b9174f9f853649", - "placeholder": "​", - "style": "IPY_MODEL_f8d8dfbdc92f4599abc3232a34342853", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 60.35it/s]" - } - }, - "3e8bab513b2544f2be136ee1c48ca51c": { + "5594cd23fb504b0dbdef0ecfe202453c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4134,23 +4145,33 @@ "width": null } }, - "3f77865e92e04447b8130e58472c9196": { + "5695fa5956b04b45a058d58f6cb27b12": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_87900fda6f274f88ab3a99c82a5a9940", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1a2b07452f87444f8ac98839eeafdd7b", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "3ff0016903f24827855530023693263e": { + "57fa64edffdb4d37a90dbea656009ab1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4168,60 +4189,39 @@ "text_color": null } }, - "40fdb275580d43d9b74fe903a7a8104d": { - "model_module": "@jupyter-widgets/base", + "58ba69b1748d4a2bb5d07f10349efa22": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "40ff8e1e3db2418ca7a02ad19a7f7df5": { + "5b07f8803c514642801639b40c1aa34b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "5c0a9c7370bb48c09729cb766ab1781a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4274,7 +4274,97 @@ "width": null } }, - "4140430b2ab443be9abe907b644904c1": { + "5d069f3fc0fd47028eaaf3380b16e427": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_97c91dea2ffc40f1830438b9a24225ce", + "placeholder": "​", + "style": "IPY_MODEL_635f8889b38545dca62037898e9e98c2", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 71.33it/s]" + } + }, + "5d1a794999184d7c86308a072ca269b6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_e5fa84089fac429dba7a0f14fcadf7b8", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2d09848b13f94c7a9483a0063af7658b", + "tabbable": null, + "tooltip": null, + "value": 40.0 + } + }, + "5f29c5efd23943e1a08518f859506345": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a24112b64ebe4079b08768fea3888272", + "placeholder": "​", + "style": "IPY_MODEL_19dfb010e30849f38c848ea70dda5645", + "tabbable": null, + "tooltip": null, + "value": " 29.5k/29.5k [00:00<00:00, 4.28MB/s]" + } + }, + "61b5f9d9099a4161a69f1c58aaa1f54e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "61c7ca03d1b84ecdbe6bfb8765d33dd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4289,15 +4379,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_85c842922c6248a8a088b6edfa110613", + "layout": "IPY_MODEL_8c7ec74595b64ca194e8d3ba1c2a16fe", "placeholder": "​", - "style": "IPY_MODEL_589db42ac06b44af8e8131d6bc810558", + "style": "IPY_MODEL_d627f7cbc1534c10a92cc7fd0f874ca6", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 60000/60000 [00:36<00:00, 1693.23it/s]" } }, - "41642d27417146338cd059871f247264": { + "624903d3a14d4864906b939e0507936f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4313,17 +4403,98 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1d383e62e8714383b5a6879ad3316e61", - "max": 5148.0, + "layout": "IPY_MODEL_3773b260a0514063a4cb27c7bcfcdb72", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f2567d63206c480fbbdf4639222fd3db", + "style": "IPY_MODEL_1c8e1b8efcbf4cd6ad8ccd71c8886907", "tabbable": null, "tooltip": null, - "value": 5148.0 + "value": 60000.0 + } + }, + "6296fc9f1a3947edb989ab3a35afbefe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f247cc3e079b4ad2aa902a3150c5d9ec", + "IPY_MODEL_783884a15ffc4bceb98545ac1564a7f5", + "IPY_MODEL_fc671361cddb4f3e9903ef6aae4d3083" + ], + "layout": "IPY_MODEL_66aef97a11f842d89fcec4deed40e16d", + "tabbable": null, + "tooltip": null + } + }, + "62c6658ea89d458aa1198b576f9eee56": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "635f8889b38545dca62037898e9e98c2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "64e2e67bdc244016895a62ad062fa077": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_953f69d720d74dc0824fc0ac9a4ffa3b", + "placeholder": "​", + "style": "IPY_MODEL_a38689b69ffd44938ebabe7dc9710b39", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "41de0713ce794701a08845a6d7031022": { + "66aef97a11f842d89fcec4deed40e16d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4376,7 +4547,7 @@ "width": null } }, - "4275cd122298472195f82b5dbef8d87f": { + "6930211e1e8a42d88c3294247388155e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4429,71 +4600,7 @@ "width": null } }, - "4385afbe20eb4363b22e1ff9d6288fff": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4642b85d59724c77be6fd2f457c1494f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_741e5deed4024afbbef7823b6cb2006f", - "placeholder": "​", - "style": "IPY_MODEL_3727b7633ef444e593e7a339abd041df", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:39<00:00, 1495.96it/s]" - } - }, - "46c6a150015345b7a2500e89263d7aa2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bffce29067514a888fdf51ef1678bd16", - "placeholder": "​", - "style": "IPY_MODEL_cd680fba706c428f9bec61692bff4a29", - "tabbable": null, - "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "4a641d26d813428dba2094e759ca0888": { + "69835f01f4d24840900be4eafa6cd376": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4546,25 +4653,7 @@ "width": null } }, - "4b0f4e804c4747d3b5b564a82e7c932f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4b84205d415c45babd7ebcdf93517d66": { + "6bb8ca43d39f43d0933376cc22b61a88": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4617,7 +4706,7 @@ "width": null } }, - "4c86867b8903466fa9e6d4e388aa0434": { + "6c1c35f1bae449bb91822d760764fd52": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4632,15 +4721,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70ff80ef934e4944b298fa9da7d63c45", + "layout": "IPY_MODEL_8205e7ba3ebf40938d66cce708243de9", "placeholder": "​", - "style": "IPY_MODEL_845381ac8fa94650bcf7163b99d2b1d0", + "style": "IPY_MODEL_bc15b56e48ef468eb9e5db2eba40dd06", "tabbable": null, "tooltip": null, - "value": " 4.42M/4.42M [00:00<00:00, 87.8MB/s]" + "value": "Map (num_proc=4): 100%" } }, - "4cb59733ebbd4647b7d2611b71086dfc": { + "6c4353aa625f407c8370f78a1dc830c8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4693,49 +4782,7 @@ "width": null } }, - "4cc0b478adc941c3aaea012fcc71511b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "510e84f543554f8d8f0f21ce00483d7d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7b479247b91c4946888088deb8e56a6a", - "IPY_MODEL_c89e8fe224e14e5bbd1d416d2fbe1b95", - "IPY_MODEL_84f3a655756d4b028c767227ead18ba1" - ], - "layout": "IPY_MODEL_a0db81988b184e339aae03632c9b28a8", - "tabbable": null, - "tooltip": null - } - }, - "519660b9e9fd424bbb188e3f3d9d3b89": { + "6ca247bf72f54f03aabdd5d72546025f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -4750,68 +4797,69 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_e94068c8564545f282e3a4980638365a", - "IPY_MODEL_c1c5ae1b9dcc420581edd6264c6a2820", - "IPY_MODEL_0c7b3a4a480e4a4f8a7a93dcc0cc643a" + "IPY_MODEL_f035123f0a8341818f2529edc8258078", + "IPY_MODEL_5d1a794999184d7c86308a072ca269b6", + "IPY_MODEL_a774929f38e2462193ebeff227330d56" ], - "layout": "IPY_MODEL_2279b7989e5940fc9318addab8b1ab9e", + "layout": "IPY_MODEL_d460961168ad48bf8a3a86fe06603769", "tabbable": null, "tooltip": null } }, - "532e8f8731f84440bad7ccc71cd28a56": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "539affff4ad04832be7355dbe98a7d01": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "53b4cbc606f94f22a4b554b335600c1d": { - "model_module": "@jupyter-widgets/controls", + "6cb79bd433b44c4c8cd62d84839582bc": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "55b634610a934f5c9801e4876af40a56": { + "6d4b2efa08f448b0a99ccda6ee415ef7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4827,17 +4875,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bb21fd6b378748a49c86c19e35f55f0b", - "max": 60000.0, + "layout": "IPY_MODEL_6cb79bd433b44c4c8cd62d84839582bc", + "max": 4833.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_35d76f48e70949a588e1ac466e50a6f8", + "style": "IPY_MODEL_58ba69b1748d4a2bb5d07f10349efa22", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 4833.0 } }, - "562d8ef38a2d4bd3b8848f2756739a5c": { + "6d5821fefb5648caac6867e9f7a4c67b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4890,7 +4938,63 @@ "width": null } }, - "58084819006a400d9cdfa62c0fd73d0b": { + "6de62af38a2f43a9846f333eda1224f2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "70b6c17f51c948158afefdd56830a23f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6c1c35f1bae449bb91822d760764fd52", + "IPY_MODEL_766722799c914e01bd6ba03a4860a722", + "IPY_MODEL_81491c5e50a34db7ab3810ebfafe7b12" + ], + "layout": "IPY_MODEL_2529da9ccb564a7d98cf91d4e2f454fc", + "tabbable": null, + "tooltip": null + } + }, + "70ca2521a7cf454c8f41cc8665bd8572": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "72cf872836144d31ac5f693e9157dde9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4943,7 +5047,7 @@ "width": null } }, - "582d80487b6f4f07bfdddee7d7276b6d": { + "732da230ecd142e79d60e75c14f8749a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -4959,7 +5063,7 @@ "description_width": "" } }, - "589db42ac06b44af8e8131d6bc810558": { + "7662e69c3e7b45a1bc4d55996a7060cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4977,7 +5081,7 @@ "text_color": null } }, - "58ea1a14f16441f7ac9aec12aa6bcf0a": { + "766722799c914e01bd6ba03a4860a722": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4993,17 +5097,85 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c623cd95c65f4227bc6a142428b2e2d0", - "max": 40.0, + "layout": "IPY_MODEL_88f5a8e733a449bf9d33c407463abc0b", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b79a550c01b84942a32015f5d9879e3a", + "style": "IPY_MODEL_ad6f123f6f11456097bebc2f41cfafd7", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": 60000.0 + } + }, + "76cd9d157bf74d6e93db6f5727c6f900": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7f5b0df354d74d42b00745784e61cbb5", + "IPY_MODEL_d844173c6e344ebcbb4c2988636ce3a8", + "IPY_MODEL_f68300c0882c4adca9ac2341e102684c" + ], + "layout": "IPY_MODEL_ddea9d00c6b648c2ab8a73e6395f71ba", + "tabbable": null, + "tooltip": null + } + }, + "783884a15ffc4bceb98545ac1564a7f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c5cfb97d21914845a48cbeea88f4d542", + "max": 5148.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cb7c5ad137844450a4dd8bb2cd5b55d5", + "tabbable": null, + "tooltip": null, + "value": 5148.0 + } + }, + "786e4946ac9d4815956096e2294ce1a9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "5a99a0960d9346219c1f9d5f53b6a66c": { + "78c28ce7e495439eb9b5618da236dfff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5018,15 +5190,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f6039d93a76046e5a47332266efbcad2", + "layout": "IPY_MODEL_6930211e1e8a42d88c3294247388155e", "placeholder": "​", - "style": "IPY_MODEL_78ff9f12373143beb1480d97598c1fd8", + "style": "IPY_MODEL_d5b89a2622784b2684e5d0e4a1d8e288", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.46it/s]" + "value": "Downloading builder script: 100%" } }, - "5ad29a8b409a4c558b64b79f12934c9d": { + "79d53959ea3648699bfb173209305675": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5079,30 +5251,7 @@ "width": null } }, - "5af3e59cee874976bc838323e7706739": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5ad29a8b409a4c558b64b79f12934c9d", - "placeholder": "​", - "style": "IPY_MODEL_c19e17cb683a43bd955fbf81102c4165", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 59.81it/s]" - } - }, - "5c2bfa97829b4fdea2882b9050f5562d": { + "7a5e963277ae42569b466604816aee6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5120,31 +5269,30 @@ "text_color": null } }, - "5fea0aa6ab6c432499d18388d7215fb9": { + "7ec457de82a94296bda5ce8043891a1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_100b4c65b0f44c9b8b9c7cb4afbc38ac", - "IPY_MODEL_216f04617573408697842aba4967a052", - "IPY_MODEL_60acea659f564db7a12ee0db15590f10" - ], - "layout": "IPY_MODEL_25d73f1cd6f74f7f8eb5013af3fc4aea", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_a1f03e62934145f48df36ed07ef54135", + "placeholder": "​", + "style": "IPY_MODEL_57fa64edffdb4d37a90dbea656009ab1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 8.85k/8.85k [00:00<00:00, 1.45MB/s]" } }, - "60acea659f564db7a12ee0db15590f10": { + "7f5b0df354d74d42b00745784e61cbb5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5159,69 +5307,117 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1ad9b1cdbd544af3a314df0c82ec6565", + "layout": "IPY_MODEL_b56fcd9aca4c49c0b688460c48f52150", "placeholder": "​", - "style": "IPY_MODEL_238898e5c2874588924e33ad1a5a14be", + "style": "IPY_MODEL_0423a125c3324adc9f2433bebe77773b", "tabbable": null, "tooltip": null, - "value": " 4/4 [00:00<00:00, 1235.44it/s]" + "value": "100%" } }, - "629afeba7adb4995975ea1d03301cd3f": { + "81491c5e50a34db7ab3810ebfafe7b12": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c0717785491f4d29856b706b3233759a", + "placeholder": "​", + "style": "IPY_MODEL_c9c1491278bb49a0a831ab5dea82a8c5", + "tabbable": null, + "tooltip": null, + "value": " 60000/60000 [00:11<00:00, 7815.27 examples/s]" } }, - "63e9e74faee2419a8226b92ac225e31b": { - "model_module": "@jupyter-widgets/controls", + "8205e7ba3ebf40938d66cce708243de9": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "64f5f6e8c1bb4d1ea5c7e2269014d6b6": { + "8224804dbbd04b29bd71274520c5718d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_299c0f0403e1447fb83af15e00e8f19f", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8824a37abc7b47a7b37e0eb6c3e6cc11", + "tabbable": null, + "tooltip": null, + "value": 29515.0 } }, - "65e63b482d884a54ba1811f393e142f6": { + "8239b72ad4ca43fb92f0bc981abfd95b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5274,23 +5470,60 @@ "width": null } }, - "6ad9be8c9330405c950fd1e4bf6f2bcc": { - "model_module": "@jupyter-widgets/controls", + "833e5dbaba7546f582547cdf7571c108": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "6fc748b471b64f71a9cf9f308a0c6f58": { + "838e5992cb0f492aa79d9e4411b13437": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5305,15 +5538,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dbf46341abf942659d8c32c85a6689a7", + "layout": "IPY_MODEL_c07a9548b6234526a02e4a5ca84bc144", "placeholder": "​", - "style": "IPY_MODEL_06f943bb034d473babe64569cf34ef3f", + "style": "IPY_MODEL_266601a55125440a88a8a16e00cebd66", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 63.32it/s]" } }, - "70a57a24141f4298ad66d387b76ae643": { + "8767f058e5b649bfb0c2d444b49f3f9d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5366,7 +5599,7 @@ "width": null } }, - "70ff80ef934e4944b298fa9da7d63c45": { + "87900fda6f274f88ab3a99c82a5a9940": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5419,25 +5652,46 @@ "width": null } }, - "7156e47ae30c49029e1051be60fedd8a": { + "8824a37abc7b47a7b37e0eb6c3e6cc11": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" + } + }, + "88c76968e9da4cb6ae77b6883ae64280": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_69835f01f4d24840900be4eafa6cd376", + "placeholder": "​", + "style": "IPY_MODEL_4c9f6ca0676a4e6bbace6bdaf151e92c", + "tabbable": null, + "tooltip": null, + "value": "Generating train split: 100%" } }, - "741e5deed4024afbbef7823b6cb2006f": { + "88f5a8e733a449bf9d33c407463abc0b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5490,70 +5744,7 @@ "width": null } }, - "748b7bbe2c8345c6a22623d9f52f46cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c1e14573e8a14d2da05856b01902bfe6", - "IPY_MODEL_55b634610a934f5c9801e4876af40a56", - "IPY_MODEL_1c04a881488944cfb2a78e4629f5d219" - ], - "layout": "IPY_MODEL_30926f7236cb4acb82089cc6bc32bca9", - "tabbable": null, - "tooltip": null - } - }, - "759ff835cd044874ad69b6424e9c4e1e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "75d1007bec0746c98aeda8aef0434706": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_af66ef66990540bba700a8b18d8ab6ec", - "placeholder": "​", - "style": "IPY_MODEL_f8c664df72d6466aaadd0af74042c5d5", - "tabbable": null, - "tooltip": null, - "value": " 29.5k/29.5k [00:00<00:00, 4.16MB/s]" - } - }, - "7611501b359946e6a8b64ae0a49434e6": { + "8a368087070540ac84cfd57d04c80102": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5606,25 +5797,31 @@ "width": null } }, - "76dcd467640d4c13a2c0ac067cfdd034": { + "8b242b3757014ca08c0be26603c856e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_070af112be824f3a9dfc9d11f6c99bdb", + "IPY_MODEL_624903d3a14d4864906b939e0507936f", + "IPY_MODEL_61c7ca03d1b84ecdbe6bfb8765d33dd4" + ], + "layout": "IPY_MODEL_b6006aab30524546a2357c0b60b3422c", + "tabbable": null, + "tooltip": null } }, - "78e0941cdb4f489cb776e7f12e9dbd86": { + "8b62a322205f4dbd9b437035f7710286": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5639,15 +5836,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b88f2d209bbb49ab913ba214e4fa2da2", + "layout": "IPY_MODEL_bd4ca863b31b4a6087a17d6cfa52ba96", "placeholder": "​", - "style": "IPY_MODEL_15fc493178c94958a5bd63dc73acf625", + "style": "IPY_MODEL_8be92883d1f447b7b27692d27d470559", "tabbable": null, "tooltip": null, - "value": "Downloading readme: 100%" + "value": "Computing checksums: 100%" } }, - "78ff9f12373143beb1480d97598c1fd8": { + "8be92883d1f447b7b27692d27d470559": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5665,7 +5862,7 @@ "text_color": null } }, - "791e454bb7974023b43014ebc8f1c796": { + "8c7ec74595b64ca194e8d3ba1c2a16fe": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5718,25 +5915,7 @@ "width": null } }, - "79907f8846974be690f37ae0b061520b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "7b479247b91c4946888088deb8e56a6a": { + "8e4fdf510840497e9b8908d892a617e3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5751,82 +5930,147 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0a5ef6cfa8714c0ab6b0ef41dfb1a925", + "layout": "IPY_MODEL_ef43f60fb2ce4517935a3a3d655e6e4c", "placeholder": "​", - "style": "IPY_MODEL_63e9e74faee2419a8226b92ac225e31b", + "style": "IPY_MODEL_445bd193a0b443b68d88db83f68b1135", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": "Downloading readme: 100%" } }, - "7bf3c6eec63c4a8bbd6deaacc2e60042": { + "8fd06c99c9a747d8b7932274f22d7a1f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8d4f0191a9824520b481222f74ff8de2", - "placeholder": "​", - "style": "IPY_MODEL_4cc0b478adc941c3aaea012fcc71511b", + "layout": "IPY_MODEL_8a368087070540ac84cfd57d04c80102", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_25aea1e677d84e4da5e2f1a580b847bb", "tabbable": null, "tooltip": null, - "value": "100%" + "value": 4.0 } }, - "7f4c2fe39ada4e6582a507e9acd7d649": { - "model_module": "@jupyter-widgets/controls", + "93b948080aae40babe81f47f38dc60fa": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_00e493ba9c68410a9bfb07d35f58c61a", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_759ff835cd044874ad69b6424e9c4e1e", - "tabbable": null, - "tooltip": null, - "value": 4833.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "845381ac8fa94650bcf7163b99d2b1d0": { - "model_module": "@jupyter-widgets/controls", + "953f69d720d74dc0824fc0ac9a4ffa3b": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "84a10c91f7e44b49a6ec73367bf116ef": { + "956d09bfdd86417caf76bb337c098a81": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -5844,30 +6088,31 @@ "text_color": null } }, - "84f3a655756d4b028c767227ead18ba1": { + "9717f3b4aaae491d9cb2e07d49a003a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_7611501b359946e6a8b64ae0a49434e6", - "placeholder": "​", - "style": "IPY_MODEL_532e8f8731f84440bad7ccc71cd28a56", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ab1b471e80f9406fabb0d7e0b174084c", + "IPY_MODEL_197981c8f9c84c00abea85184c72fcd5", + "IPY_MODEL_5d069f3fc0fd47028eaaf3380b16e427" + ], + "layout": "IPY_MODEL_19d83e638f7a404ba0eb4fdd71302435", "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:01<00:00, 8790.83 examples/s]" + "tooltip": null } }, - "85c842922c6248a8a088b6edfa110613": { + "97c91dea2ffc40f1830438b9a24225ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5920,31 +6165,25 @@ "width": null } }, - "8c14226990a04e87aa10a393e2a0203a": { + "98e13251741f42169572ab3c91ade8e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_eaaa22f94db549fb8e47e6917c661949", - "IPY_MODEL_41642d27417146338cd059871f247264", - "IPY_MODEL_b2d82e9c2f994037b0e0975750778a83" - ], - "layout": "IPY_MODEL_fde3ef7840f440e18047964aceeecefe", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "8d4f0191a9824520b481222f74ff8de2": { + "9a36f96ef3224e05847983090297222a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5997,7 +6236,30 @@ "width": null } }, - "8e73f65890fc4fefb308aaf8321b0e77": { + "9f1afcead928423ab651a3cfb6bc7cc4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cfc5b02015154a29bae32de6ea0acd51", + "placeholder": "​", + "style": "IPY_MODEL_7a5e963277ae42569b466604816aee6f", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 59.96it/s]" + } + }, + "9fda0321906e4bb7a73fc697ca19c8e3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6050,25 +6312,7 @@ "width": null } }, - "8f3b67fe29a34428a9dcc77323065aa5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "922abaecac1c46f4bb9ba4ff7faeace7": { + "a145248aa276456f95640b1b4a24d4bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -6086,7 +6330,7 @@ "text_color": null } }, - "95453138f12b4a908bb147d1409d6b93": { + "a1990d1ffd9240a7a4bf2438eaf81754": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -6102,57 +6346,7 @@ "description_width": "" } }, - "9599396e66b4465ea0e42e641e6a2557": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c99f3cc6eec14c488e7ae6623bdcc6fc", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25c58f4aec6b4f41be25042769d63c52", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "9611c5ddf09444219b0832f81930fdd7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d6e3dfd49a9341cbbcee99cd75b5cc7d", - "IPY_MODEL_f91439725ffb4a859b9e13acd25e5e3c", - "IPY_MODEL_d03921811ef44790b73875b7c7becb5c" - ], - "layout": "IPY_MODEL_353d9eda4aec4d30805d245cde63c51c", - "tabbable": null, - "tooltip": null - } - }, - "97c2e55e3c57419c81374350ea34b432": { + "a1f03e62934145f48df36ed07ef54135": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6205,57 +6399,7 @@ "width": null } }, - "999579ced4a04955b6fe76b06613510d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_78e0941cdb4f489cb776e7f12e9dbd86", - "IPY_MODEL_b26b9f4f9e9c4d70851630b1b5296441", - "IPY_MODEL_340d530d31fa467c9418a8767d1a0eb2" - ], - "layout": "IPY_MODEL_8e73f65890fc4fefb308aaf8321b0e77", - "tabbable": null, - "tooltip": null - } - }, - "9fdacce60b084ad6b7edc2e06ad6b7d5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_562d8ef38a2d4bd3b8848f2756739a5c", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_24f4a95a06be4e01a6b70cd2cd3bccda", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "a0156d3f29e94c14a8be31866b4025fa": { + "a24112b64ebe4079b08768fea3888272": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6308,7 +6452,66 @@ "width": null } }, - "a0db81988b184e339aae03632c9b28a8": { + "a31c56a1f6f544c1846467d596dd7b15": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a38689b69ffd44938ebabe7dc9710b39": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a50bac222b154e1e8f1af99c2d2f7cb5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_5c0a9c7370bb48c09729cb766ab1781a", + "placeholder": "​", + "style": "IPY_MODEL_2f77ed4c555c4365a30f1c760cb59e74", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a6307f65c4f14a07ad202a2748b4132f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6361,7 +6564,78 @@ "width": null } }, - "a43e1212e20e49bfb45d1ca51918abcd": { + "a774929f38e2462193ebeff227330d56": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_11703dfe59744973b7ee5db1b50a7304", + "placeholder": "​", + "style": "IPY_MODEL_786e4946ac9d4815956096e2294ce1a9", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.06it/s]" + } + }, + "a7cf6a20863544c78418cc502f39b678": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8b62a322205f4dbd9b437035f7710286", + "IPY_MODEL_8fd06c99c9a747d8b7932274f22d7a1f", + "IPY_MODEL_db1fd46972524542bbb7ec0eeef4b2ba" + ], + "layout": "IPY_MODEL_182fe610a03c4f0c8820d62efd2942a9", + "tabbable": null, + "tooltip": null + } + }, + "a9efee99388e4bd987cba82e4c249be5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8e4fdf510840497e9b8908d892a617e3", + "IPY_MODEL_de7773b079344e19b9f86e5643e773ed", + "IPY_MODEL_7ec457de82a94296bda5ce8043891a1e" + ], + "layout": "IPY_MODEL_a6307f65c4f14a07ad202a2748b4132f", + "tabbable": null, + "tooltip": null + } + }, + "aa609ade2290479691002af80a845d3d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6414,7 +6688,48 @@ "width": null } }, - "a598e468e4a04cbb8a19460650aeb41f": { + "ab1b471e80f9406fabb0d7e0b174084c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_255e06fe46cb4c0eaa40336d22beaf70", + "placeholder": "​", + "style": "IPY_MODEL_98e13251741f42169572ab3c91ade8e4", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "abecf5a0521e45f78a788d98cc335e5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ac339d14f5334e5fbce36888ade893b6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6467,7 +6782,7 @@ "width": null } }, - "a63043994e1446838f64bbd78dc7bb13": { + "ac933a814c124345b0730704f11cfbc8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6520,101 +6835,73 @@ "width": null } }, - "ac993184217a4a069874329815a7ded0": { + "ad6f123f6f11456097bebc2f41cfafd7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4275cd122298472195f82b5dbef8d87f", - "placeholder": "​", - "style": "IPY_MODEL_922abaecac1c46f4bb9ba4ff7faeace7", - "tabbable": null, - "tooltip": null, - "value": " 4.83k/4.83k [00:00<00:00, 607kB/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "aca6b827bc144b7e84332be12838c12a": { - "model_module": "@jupyter-widgets/base", + "aeda1e802b974623a7d922ff793e1002": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f4f96f825f2043db8c6d5179d06cd01c", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_348018e48d954c87b48ff2a6e7470304", + "tabbable": null, + "tooltip": null, + "value": 10000.0 } }, - "ad646ab90327403e8f0b8e3c738d3955": { + "b13b21c3b7544706aacfbba4f3504a8b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3abd2a96be1649ed9f2a7594cfdf3bbd", + "IPY_MODEL_d7462fe7497245929fb556be574c9b66", + "IPY_MODEL_e7d291738cf84330bb7fbd4947e558a1" + ], + "layout": "IPY_MODEL_295b6321cc6f4b1bbfa02a28f8581b1d", + "tabbable": null, + "tooltip": null } }, - "ad73b70f57d9434e853c8286c209c9d0": { + "b374b826cf5a4a668332dc70f8c2fe4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6629,15 +6916,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70a57a24141f4298ad66d387b76ae643", + "layout": "IPY_MODEL_f679da4310d84ae0a1679faa5659f6c7", "placeholder": "​", - "style": "IPY_MODEL_7156e47ae30c49029e1051be60fedd8a", + "style": "IPY_MODEL_0abf4ae675284a88873e14026b73ab9e", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Downloading data: 100%" } }, - "aea72aba96c34f99adbd16416576dbed": { + "b56fcd9aca4c49c0b688460c48f52150": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6690,7 +6977,7 @@ "width": null } }, - "af66ef66990540bba700a8b18d8ab6ec": { + "b6006aab30524546a2357c0b60b3422c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6743,7 +7030,31 @@ "width": null } }, - "b1ad97237b2c4011b851a00293f85c32": { + "b69aa5fb137444eb962d31f239578d65": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_64e2e67bdc244016895a62ad062fa077", + "IPY_MODEL_1ba2c9f296904da78195fe3e45321059", + "IPY_MODEL_9f1afcead928423ab651a3cfb6bc7cc4" + ], + "layout": "IPY_MODEL_ba0143519d7f4309b1af7b6461fd8a90", + "tabbable": null, + "tooltip": null + } + }, + "ba0143519d7f4309b1af7b6461fd8a90": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6796,90 +7107,72 @@ "width": null } }, - "b1b972e9e7f64377ad85c5aa38aa9c79": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b26b9f4f9e9c4d70851630b1b5296441": { + "ba30ea8e68274773937367758a4abf1b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_58084819006a400d9cdfa62c0fd73d0b", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3f77865e92e04447b8130e58472c9196", + "layout": "IPY_MODEL_72cf872836144d31ac5f693e9157dde9", + "placeholder": "​", + "style": "IPY_MODEL_e7eedd97596a479c9a69c82d61e53877", "tabbable": null, "tooltip": null, - "value": 8845.0 + "value": " 40/40 [00:00<00:00, 64.53it/s]" } }, - "b2d82e9c2f994037b0e0975750778a83": { + "bc15b56e48ef468eb9e5db2eba40dd06": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_b1ad97237b2c4011b851a00293f85c32", - "placeholder": "​", - "style": "IPY_MODEL_53b4cbc606f94f22a4b554b335600c1d", - "tabbable": null, - "tooltip": null, - "value": " 5.15k/5.15k [00:00<00:00, 804kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "b41cff4d7b16460e8fbb1e24113ab070": { + "bc98754b340343f594559442ba450aa4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_88c76968e9da4cb6ae77b6883ae64280", + "IPY_MODEL_3edb1a47c3314c0abfc78f23d62ebe68", + "IPY_MODEL_367462867871411f81ad39c2100a77d8" + ], + "layout": "IPY_MODEL_9a36f96ef3224e05847983090297222a", + "tabbable": null, + "tooltip": null } }, - "b52f0a3b663249bdbad4dfd86a3f26b8": { + "bd4ca863b31b4a6087a17d6cfa52ba96": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6932,49 +7225,31 @@ "width": null } }, - "b79a550c01b84942a32015f5d9879e3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "b85d9be3c4ee46e38ce7887f5d591f31": { + "bd4e5e775e0d4b5d90568b686f8fd56f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4b84205d415c45babd7ebcdf93517d66", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_32020bd3922749389ef4e7b04e878ded", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_78c28ce7e495439eb9b5618da236dfff", + "IPY_MODEL_6d4b2efa08f448b0a99ccda6ee415ef7", + "IPY_MODEL_ee2470d66aed4014820acd898b666713" + ], + "layout": "IPY_MODEL_ccb6f4531ee843d4ad0080744e6078d2", "tabbable": null, - "tooltip": null, - "value": 40.0 + "tooltip": null } }, - "b88f2d209bbb49ab913ba214e4fa2da2": { + "c0717785491f4d29856b706b3233759a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7027,7 +7302,7 @@ "width": null } }, - "bb21fd6b378748a49c86c19e35f55f0b": { + "c07a9548b6234526a02e4a5ca84bc144": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7080,30 +7355,7 @@ "width": null } }, - "be037b5bc2ca4ee996763a4581d3e262": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_40ff8e1e3db2418ca7a02ad19a7f7df5", - "placeholder": "​", - "style": "IPY_MODEL_b1b972e9e7f64377ad85c5aa38aa9c79", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.07it/s]" - } - }, - "bffce29067514a888fdf51ef1678bd16": { + "c5cfb97d21914845a48cbeea88f4d542": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7156,108 +7408,7 @@ "width": null } }, - "c19e17cb683a43bd955fbf81102c4165": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c1c5ae1b9dcc420581edd6264c6a2820": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_791e454bb7974023b43014ebc8f1c796", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_dea3b0f1e22b49aebd2a6671c1773443", - "tabbable": null, - "tooltip": null, - "value": 60000.0 - } - }, - "c1e14573e8a14d2da05856b01902bfe6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_14620fb3ef91409c93ca2866a6b245cd", - "placeholder": "​", - "style": "IPY_MODEL_8f3b67fe29a34428a9dcc77323065aa5", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "c2009fa1dcb54044b14d94c615567a79": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "c27e7863116b4bf48eab88e0869c69a4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "c3c185ce8c54435f908584bfc3d5436e": { + "c71cb776520442f0baba7a63fdbf2324": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7310,7 +7461,7 @@ "width": null } }, - "c4cca31f1d014a0a962d3f164bca3b23": { + "c8467f56948a44689852152948675255": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7325,39 +7476,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4a641d26d813428dba2094e759ca0888", + "layout": "IPY_MODEL_5594cd23fb504b0dbdef0ecfe202453c", "placeholder": "​", - "style": "IPY_MODEL_e466b9881b0840f9935a01d3cc542f83", + "style": "IPY_MODEL_109113d68eb84c2eace6f4deda6b040b", "tabbable": null, "tooltip": null, - "value": "100%" - } - }, - "c60874cc394b428786de11432b5ba1ce": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c4cca31f1d014a0a962d3f164bca3b23", - "IPY_MODEL_e5c369a269084adb8bbcc7348e10c651", - "IPY_MODEL_be037b5bc2ca4ee996763a4581d3e262" - ], - "layout": "IPY_MODEL_1640d13bd96d405ba01bbe9f22029432", - "tabbable": null, - "tooltip": null + "value": " 10000/10000 [00:01<00:00, 8360.91 examples/s]" } }, - "c623cd95c65f4227bc6a142428b2e2d0": { + "c87df7230ab74cf3b4ce36554b4f64b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7410,59 +7537,41 @@ "width": null } }, - "c7ec958a864d4b8b9aeb4c7b4f1055df": { + "c9c1491278bb49a0a831ab5dea82a8c5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c3c185ce8c54435f908584bfc3d5436e", - "max": 4422102.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_582d80487b6f4f07bfdddee7d7276b6d", - "tabbable": null, - "tooltip": null, - "value": 4422102.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "c89e8fe224e14e5bbd1d416d2fbe1b95": { + "cb7c5ad137844450a4dd8bb2cd5b55d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_fb73e42bc42c43d581643273c2c12573", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c2009fa1dcb54044b14d94c615567a79", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c8ba7020086d47b98cce4fb5096a67b6": { + "ccb6f4531ee843d4ad0080744e6078d2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7515,7 +7624,7 @@ "width": null } }, - "c91e1c0bc2da4f678e94080291cc61d8": { + "cf6f623149dc4d96afc8c831b7926d0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7559,42 +7668,16 @@ "min_width": null, "object_fit": null, "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "c979207c10fd4e0f91eb87b8a8cb5bdb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_c9c85c81e35e4c79900acebf8ed10e55", - "max": 29515.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6ad9be8c9330405c950fd1e4bf6f2bcc", - "tabbable": null, - "tooltip": null, - "value": 29515.0 + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "c99f3cc6eec14c488e7ae6623bdcc6fc": { + "cfc5b02015154a29bae32de6ea0acd51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7647,7 +7730,7 @@ "width": null } }, - "c9c85c81e35e4c79900acebf8ed10e55": { + "d460961168ad48bf8a3a86fe06603769": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7700,7 +7783,25 @@ "width": null } }, - "ca1506e7d3f846adb2f7487be4ad5f1d": { + "d5b89a2622784b2684e5d0e4a1d8e288": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "d60f32b2907d4a288385a30c717ef39d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -7715,16 +7816,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4140430b2ab443be9abe907b644904c1", - "IPY_MODEL_340d8d367bbf4aae8a133ab9a2dfda9d", - "IPY_MODEL_3e483d0ac53644caa94492a8d78843c1" + "IPY_MODEL_126a880302694496ab5834d71ecffa75", + "IPY_MODEL_aeda1e802b974623a7d922ff793e1002", + "IPY_MODEL_c8467f56948a44689852152948675255" ], - "layout": "IPY_MODEL_65e63b482d884a54ba1811f393e142f6", + "layout": "IPY_MODEL_50042e9a2b5145a780c7c3d799e26aad", "tabbable": null, "tooltip": null } }, - "cc0ffca4aee04bd8946bbf80a09e90bf": { + "d627f7cbc1534c10a92cc7fd0f874ca6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -7742,25 +7843,57 @@ "text_color": null } }, - "cd680fba706c428f9bec61692bff4a29": { + "d6465626e3264fa58f44ddccd18cfef2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3ddd27d196364f1db80581facbadf18f", + "IPY_MODEL_5695fa5956b04b45a058d58f6cb27b12", + "IPY_MODEL_ba30ea8e68274773937367758a4abf1b" + ], + "layout": "IPY_MODEL_6c4353aa625f407c8370f78a1dc830c8", + "tabbable": null, + "tooltip": null + } + }, + "d7462fe7497245929fb556be574c9b66": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_3ec84e0ec4d94afd8f15a91eb757624e", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6de62af38a2f43a9846f333eda1224f2", + "tabbable": null, + "tooltip": null, + "value": 26421880.0 } }, - "ce9ec70d05804fdba79263567a590fc9": { + "d78ce21ee1fc44e8b680bda13d37d943": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -7813,53 +7946,33 @@ "width": null } }, - "ceddd53d284e47e080fd5b52a11b902d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2ad862ff6f0f4f3a8db6362836d85b81", - "placeholder": "​", - "style": "IPY_MODEL_76dcd467640d4c13a2c0ac067cfdd034", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "d03921811ef44790b73875b7c7becb5c": { + "d844173c6e344ebcbb4c2988636ce3a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a43e1212e20e49bfb45d1ca51918abcd", - "placeholder": "​", - "style": "IPY_MODEL_cc0ffca4aee04bd8946bbf80a09e90bf", + "layout": "IPY_MODEL_aa609ade2290479691002af80a845d3d", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_732da230ecd142e79d60e75c14f8749a", "tabbable": null, "tooltip": null, - "value": " 26.4M/26.4M [00:00<00:00, 114MB/s]" + "value": 40.0 } }, - "d6e3dfd49a9341cbbcee99cd75b5cc7d": { + "db1fd46972524542bbb7ec0eeef4b2ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -7874,121 +7987,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_dec4f3552fbb46b28ac7bc2f8095a209", + "layout": "IPY_MODEL_2cb677caec644d3ab2a8b6a70208559e", "placeholder": "​", - "style": "IPY_MODEL_79907f8846974be690f37ae0b061520b", + "style": "IPY_MODEL_7662e69c3e7b45a1bc4d55996a7060cc", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" - } - }, - "d8139f444e324e12818613912a49a6f9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d8402ea2970a4161873c85143f7bb387": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "value": " 4/4 [00:00<00:00, 1226.49it/s]" } }, - "dbf46341abf942659d8c32c85a6689a7": { + "dcea0f344da44aa4bd657710ee8edf49": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8041,31 +8048,7 @@ "width": null } }, - "dd367ed6b3e145b9ba56c440d63f6948": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1bc6bb27d1ce4ccd8dd7cfe252d16d39", - "IPY_MODEL_7f4c2fe39ada4e6582a507e9acd7d649", - "IPY_MODEL_ac993184217a4a069874329815a7ded0" - ], - "layout": "IPY_MODEL_e9687d9dff8b454ca67f60b37790aeed", - "tabbable": null, - "tooltip": null - } - }, - "ddb26404cd4644d3a9efa8efd7af9104": { + "dcfb76cdced842fd810c0329fa0f1c7f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -8080,32 +8063,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_ad73b70f57d9434e853c8286c209c9d0", - "IPY_MODEL_b85d9be3c4ee46e38ce7887f5d591f31", - "IPY_MODEL_5a99a0960d9346219c1f9d5f53b6a66c" + "IPY_MODEL_ff86e317738e43de99250a83cfb7ad7f", + "IPY_MODEL_8224804dbbd04b29bd71274520c5718d", + "IPY_MODEL_5f29c5efd23943e1a08518f859506345" ], - "layout": "IPY_MODEL_d8402ea2970a4161873c85143f7bb387", + "layout": "IPY_MODEL_4a97191b1a1643199e46244e6611352a", "tabbable": null, "tooltip": null } }, - "dea3b0f1e22b49aebd2a6671c1773443": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "dec4f3552fbb46b28ac7bc2f8095a209": { + "ddea9d00c6b648c2ab8a73e6395f71ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8158,25 +8125,33 @@ "width": null } }, - "e466b9881b0840f9935a01d3cc542f83": { + "de7773b079344e19b9f86e5643e773ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_9fda0321906e4bb7a73fc697ca19c8e3", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5b07f8803c514642801639b40c1aa34b", + "tabbable": null, + "tooltip": null, + "value": 8845.0 } }, - "e4991d19e073408f9cef1ae11eb71019": { + "df8581440a0d42198080a4cd32ee9a4a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8229,56 +8204,7 @@ "width": null } }, - "e5c369a269084adb8bbcc7348e10c651": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_ce9ec70d05804fdba79263567a590fc9", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b41cff4d7b16460e8fbb1e24113ab070", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "e94068c8564545f282e3a4980638365a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_a63043994e1446838f64bbd78dc7bb13", - "placeholder": "​", - "style": "IPY_MODEL_ad646ab90327403e8f0b8e3c738d3955", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" - } - }, - "e9687d9dff8b454ca67f60b37790aeed": { + "e5fa84089fac429dba7a0f14fcadf7b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8331,7 +8257,7 @@ "width": null } }, - "eaaa22f94db549fb8e47e6917c661949": { + "e7d291738cf84330bb7fbd4947e558a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -8346,15 +8272,74 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b52f0a3b663249bdbad4dfd86a3f26b8", + "layout": "IPY_MODEL_df8581440a0d42198080a4cd32ee9a4a", "placeholder": "​", - "style": "IPY_MODEL_f1e89ef58c7c4a19a40750e69fa7d630", + "style": "IPY_MODEL_4751ad8ff2014ae4aefb61464aa0b996", "tabbable": null, "tooltip": null, - "value": "Downloading data: 100%" + "value": " 26.4M/26.4M [00:00<00:00, 105MB/s]" + } + }, + "e7eedd97596a479c9a69c82d61e53877": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee2470d66aed4014820acd898b666713": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_dcea0f344da44aa4bd657710ee8edf49", + "placeholder": "​", + "style": "IPY_MODEL_61b5f9d9099a4161a69f1c58aaa1f54e", + "tabbable": null, + "tooltip": null, + "value": " 4.83k/4.83k [00:00<00:00, 527kB/s]" + } + }, + "ef22307e229c427e9f2c45041e108373": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f054cf08430c477d9eab2ccc80e48b99": { + "ef43f60fb2ce4517935a3a3d655e6e4c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8407,65 +8392,30 @@ "width": null } }, - "f1e89ef58c7c4a19a40750e69fa7d630": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f2567d63206c480fbbdf4639222fd3db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "f55b4eb540fd4f368229bcf7012adf9f": { + "f035123f0a8341818f2529edc8258078": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_6fc748b471b64f71a9cf9f308a0c6f58", - "IPY_MODEL_9599396e66b4465ea0e42e641e6a2557", - "IPY_MODEL_4642b85d59724c77be6fd2f457c1494f" - ], - "layout": "IPY_MODEL_a598e468e4a04cbb8a19460650aeb41f", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6d5821fefb5648caac6867e9f7a4c67b", + "placeholder": "​", + "style": "IPY_MODEL_a145248aa276456f95640b1b4a24d4bf", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "f6039d93a76046e5a47332266efbcad2": { + "f15ed2b240814bb897b777075c726c92": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8518,43 +8468,30 @@ "width": null } }, - "f8c664df72d6466aaadd0af74042c5d5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f8d8dfbdc92f4599abc3232a34342853": { + "f247cc3e079b4ad2aa902a3150c5d9ec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8239b72ad4ca43fb92f0bc981abfd95b", + "placeholder": "​", + "style": "IPY_MODEL_fed701653a71451196013b62e55b30ef", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" } }, - "f91439725ffb4a859b9e13acd25e5e3c": { + "f306b412103a4a999f13f32ecb0841fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -8570,17 +8507,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c8ba7020086d47b98cce4fb5096a67b6", - "max": 26421880.0, + "layout": "IPY_MODEL_4367f895c508411fb72fbf123bfd2662", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_539affff4ad04832be7355dbe98a7d01", + "style": "IPY_MODEL_62c6658ea89d458aa1198b576f9eee56", "tabbable": null, "tooltip": null, - "value": 26421880.0 + "value": 40.0 } }, - "fb73e42bc42c43d581643273c2c12573": { + "f4f96f825f2043db8c6d5179d06cd01c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8633,7 +8570,7 @@ "width": null } }, - "fde3ef7840f440e18047964aceeecefe": { + "f679da4310d84ae0a1679faa5659f6c7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -8686,28 +8623,91 @@ "width": null } }, - "ff1c46ea9c474d1bb4645c7cbbf298b0": { + "f68300c0882c4adca9ac2341e102684c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7bf3c6eec63c4a8bbd6deaacc2e60042", - "IPY_MODEL_58ea1a14f16441f7ac9aec12aa6bcf0a", - "IPY_MODEL_5af3e59cee874976bc838323e7706739" - ], - "layout": "IPY_MODEL_3e8bab513b2544f2be136ee1c48ca51c", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ac339d14f5334e5fbce36888ade893b6", + "placeholder": "​", + "style": "IPY_MODEL_424a8de7240d4c088b2b2dbadf1113ab", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 40/40 [00:00<00:00, 67.31it/s]" + } + }, + "fc671361cddb4f3e9903ef6aae4d3083": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_cf6f623149dc4d96afc8c831b7926d0a", + "placeholder": "​", + "style": "IPY_MODEL_a31c56a1f6f544c1846467d596dd7b15", + "tabbable": null, + "tooltip": null, + "value": " 5.15k/5.15k [00:00<00:00, 866kB/s]" + } + }, + "fed701653a71451196013b62e55b30ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ff86e317738e43de99250a83cfb7ad7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8767f058e5b649bfb0c2d444b49f3f9d", + "placeholder": "​", + "style": "IPY_MODEL_01c1fefd1d1549a1ac3d1168746bc81e", + "tabbable": null, + "tooltip": null, + "value": "Downloading data: 100%" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index f67c6a971..452755a26 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -73,10 +73,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:16.757940Z", - "iopub.status.busy": "2024-06-28T15:36:16.757462Z", - "iopub.status.idle": "2024-06-28T15:36:17.958795Z", - "shell.execute_reply": "2024-06-28T15:36:17.958249Z" + "iopub.execute_input": "2024-07-01T15:05:46.317874Z", + "iopub.status.busy": "2024-07-01T15:05:46.317719Z", + "iopub.status.idle": "2024-07-01T15:05:47.417876Z", + "shell.execute_reply": "2024-07-01T15:05:47.417402Z" }, "nbsphinx": "hidden" }, @@ -86,7 +86,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:17.961629Z", - "iopub.status.busy": "2024-06-28T15:36:17.961160Z", - "iopub.status.idle": "2024-06-28T15:36:17.980033Z", - "shell.execute_reply": "2024-06-28T15:36:17.979543Z" + "iopub.execute_input": "2024-07-01T15:05:47.420276Z", + "iopub.status.busy": "2024-07-01T15:05:47.420000Z", + "iopub.status.idle": "2024-07-01T15:05:47.437670Z", + "shell.execute_reply": "2024-07-01T15:05:47.437224Z" } }, "outputs": [], @@ -154,10 +154,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:17.982722Z", - "iopub.status.busy": "2024-06-28T15:36:17.982219Z", - "iopub.status.idle": "2024-06-28T15:36:18.009820Z", - "shell.execute_reply": "2024-06-28T15:36:18.009219Z" + "iopub.execute_input": "2024-07-01T15:05:47.439750Z", + "iopub.status.busy": "2024-07-01T15:05:47.439498Z", + "iopub.status.idle": "2024-07-01T15:05:47.478024Z", + "shell.execute_reply": "2024-07-01T15:05:47.477526Z" } }, "outputs": [ @@ -264,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.012110Z", - "iopub.status.busy": "2024-06-28T15:36:18.011825Z", - "iopub.status.idle": "2024-06-28T15:36:18.015545Z", - "shell.execute_reply": "2024-06-28T15:36:18.015080Z" + "iopub.execute_input": "2024-07-01T15:05:47.480262Z", + "iopub.status.busy": "2024-07-01T15:05:47.479916Z", + "iopub.status.idle": "2024-07-01T15:05:47.483182Z", + "shell.execute_reply": "2024-07-01T15:05:47.482737Z" } }, "outputs": [], @@ -288,10 +288,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.017747Z", - "iopub.status.busy": "2024-06-28T15:36:18.017308Z", - "iopub.status.idle": "2024-06-28T15:36:18.025346Z", - "shell.execute_reply": "2024-06-28T15:36:18.024913Z" + "iopub.execute_input": "2024-07-01T15:05:47.485314Z", + "iopub.status.busy": "2024-07-01T15:05:47.484937Z", + "iopub.status.idle": "2024-07-01T15:05:47.492797Z", + "shell.execute_reply": "2024-07-01T15:05:47.492370Z" } }, "outputs": [], @@ -336,10 +336,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.027654Z", - "iopub.status.busy": "2024-06-28T15:36:18.027210Z", - "iopub.status.idle": "2024-06-28T15:36:18.029982Z", - "shell.execute_reply": "2024-06-28T15:36:18.029524Z" + "iopub.execute_input": "2024-07-01T15:05:47.494826Z", + "iopub.status.busy": "2024-07-01T15:05:47.494542Z", + "iopub.status.idle": "2024-07-01T15:05:47.497119Z", + "shell.execute_reply": "2024-07-01T15:05:47.496587Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:18.031982Z", - "iopub.status.busy": "2024-06-28T15:36:18.031712Z", - "iopub.status.idle": "2024-06-28T15:36:21.013378Z", - "shell.execute_reply": "2024-06-28T15:36:21.012714Z" + "iopub.execute_input": "2024-07-01T15:05:47.499036Z", + "iopub.status.busy": "2024-07-01T15:05:47.498842Z", + "iopub.status.idle": "2024-07-01T15:05:50.430868Z", + "shell.execute_reply": "2024-07-01T15:05:50.430331Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:21.016269Z", - "iopub.status.busy": "2024-06-28T15:36:21.015842Z", - "iopub.status.idle": "2024-06-28T15:36:21.025749Z", - "shell.execute_reply": "2024-06-28T15:36:21.025286Z" + "iopub.execute_input": "2024-07-01T15:05:50.433520Z", + "iopub.status.busy": "2024-07-01T15:05:50.433131Z", + "iopub.status.idle": "2024-07-01T15:05:50.442780Z", + "shell.execute_reply": "2024-07-01T15:05:50.442322Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:21.028066Z", - "iopub.status.busy": "2024-06-28T15:36:21.027704Z", - "iopub.status.idle": "2024-06-28T15:36:23.179292Z", - "shell.execute_reply": "2024-06-28T15:36:23.178620Z" + "iopub.execute_input": "2024-07-01T15:05:50.444757Z", + "iopub.status.busy": "2024-07-01T15:05:50.444440Z", + "iopub.status.idle": "2024-07-01T15:05:52.320323Z", + "shell.execute_reply": "2024-07-01T15:05:52.319680Z" } }, "outputs": [ @@ -476,10 +476,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.181990Z", - "iopub.status.busy": "2024-06-28T15:36:23.181404Z", - "iopub.status.idle": "2024-06-28T15:36:23.201549Z", - "shell.execute_reply": "2024-06-28T15:36:23.201017Z" + "iopub.execute_input": "2024-07-01T15:05:52.322868Z", + "iopub.status.busy": "2024-07-01T15:05:52.322271Z", + "iopub.status.idle": "2024-07-01T15:05:52.341011Z", + "shell.execute_reply": "2024-07-01T15:05:52.340483Z" }, "scrolled": true }, @@ -609,10 +609,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.203946Z", - "iopub.status.busy": "2024-06-28T15:36:23.203567Z", - "iopub.status.idle": "2024-06-28T15:36:23.212158Z", - "shell.execute_reply": "2024-06-28T15:36:23.211658Z" + "iopub.execute_input": "2024-07-01T15:05:52.343025Z", + "iopub.status.busy": "2024-07-01T15:05:52.342731Z", + "iopub.status.idle": "2024-07-01T15:05:52.350595Z", + "shell.execute_reply": "2024-07-01T15:05:52.350103Z" } }, "outputs": [ @@ -716,10 +716,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.214394Z", - "iopub.status.busy": "2024-06-28T15:36:23.214040Z", - "iopub.status.idle": "2024-06-28T15:36:23.223997Z", - "shell.execute_reply": "2024-06-28T15:36:23.223443Z" + "iopub.execute_input": "2024-07-01T15:05:52.352704Z", + "iopub.status.busy": "2024-07-01T15:05:52.352276Z", + "iopub.status.idle": "2024-07-01T15:05:52.361059Z", + "shell.execute_reply": "2024-07-01T15:05:52.360522Z" } }, "outputs": [ @@ -848,10 +848,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.226314Z", - "iopub.status.busy": "2024-06-28T15:36:23.225937Z", - "iopub.status.idle": "2024-06-28T15:36:23.234783Z", - "shell.execute_reply": "2024-06-28T15:36:23.234277Z" + "iopub.execute_input": "2024-07-01T15:05:52.363255Z", + "iopub.status.busy": "2024-07-01T15:05:52.362931Z", + "iopub.status.idle": "2024-07-01T15:05:52.370565Z", + "shell.execute_reply": "2024-07-01T15:05:52.370092Z" } }, "outputs": [ @@ -965,10 +965,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.236939Z", - "iopub.status.busy": "2024-06-28T15:36:23.236749Z", - "iopub.status.idle": "2024-06-28T15:36:23.246131Z", - "shell.execute_reply": "2024-06-28T15:36:23.245660Z" + "iopub.execute_input": "2024-07-01T15:05:52.372696Z", + "iopub.status.busy": "2024-07-01T15:05:52.372359Z", + "iopub.status.idle": "2024-07-01T15:05:52.380928Z", + "shell.execute_reply": "2024-07-01T15:05:52.380440Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.248212Z", - "iopub.status.busy": "2024-06-28T15:36:23.248028Z", - "iopub.status.idle": "2024-06-28T15:36:23.255901Z", - "shell.execute_reply": "2024-06-28T15:36:23.255343Z" + "iopub.execute_input": "2024-07-01T15:05:52.382940Z", + "iopub.status.busy": "2024-07-01T15:05:52.382568Z", + "iopub.status.idle": "2024-07-01T15:05:52.389986Z", + "shell.execute_reply": "2024-07-01T15:05:52.389445Z" } }, "outputs": [ @@ -1197,10 +1197,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.258126Z", - "iopub.status.busy": "2024-06-28T15:36:23.257798Z", - "iopub.status.idle": "2024-06-28T15:36:23.265482Z", - "shell.execute_reply": "2024-06-28T15:36:23.264904Z" + "iopub.execute_input": "2024-07-01T15:05:52.392057Z", + "iopub.status.busy": "2024-07-01T15:05:52.391736Z", + "iopub.status.idle": "2024-07-01T15:05:52.398743Z", + "shell.execute_reply": "2024-07-01T15:05:52.398311Z" } }, "outputs": [ @@ -1300,10 +1300,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:23.267847Z", - "iopub.status.busy": "2024-06-28T15:36:23.267498Z", - "iopub.status.idle": "2024-06-28T15:36:23.276176Z", - "shell.execute_reply": "2024-06-28T15:36:23.275581Z" + "iopub.execute_input": "2024-07-01T15:05:52.400864Z", + "iopub.status.busy": "2024-07-01T15:05:52.400548Z", + "iopub.status.idle": "2024-07-01T15:05:52.408413Z", + "shell.execute_reply": "2024-07-01T15:05:52.407979Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 475827b74..94ec2b5de 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:26.272789Z", - "iopub.status.busy": "2024-06-28T15:36:26.272608Z", - "iopub.status.idle": "2024-06-28T15:36:29.104013Z", - "shell.execute_reply": "2024-06-28T15:36:29.103502Z" + "iopub.execute_input": "2024-07-01T15:05:55.109624Z", + "iopub.status.busy": "2024-07-01T15:05:55.109456Z", + "iopub.status.idle": "2024-07-01T15:05:57.756143Z", + "shell.execute_reply": "2024-07-01T15:05:57.755510Z" }, "nbsphinx": "hidden" }, @@ -96,7 +96,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -121,10 +121,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.106890Z", - "iopub.status.busy": "2024-06-28T15:36:29.106333Z", - "iopub.status.idle": "2024-06-28T15:36:29.109810Z", - "shell.execute_reply": "2024-06-28T15:36:29.109260Z" + "iopub.execute_input": "2024-07-01T15:05:57.758704Z", + "iopub.status.busy": "2024-07-01T15:05:57.758362Z", + "iopub.status.idle": "2024-07-01T15:05:57.761689Z", + "shell.execute_reply": "2024-07-01T15:05:57.761157Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.112003Z", - "iopub.status.busy": "2024-06-28T15:36:29.111592Z", - "iopub.status.idle": "2024-06-28T15:36:29.114674Z", - "shell.execute_reply": "2024-06-28T15:36:29.114249Z" + "iopub.execute_input": "2024-07-01T15:05:57.763881Z", + "iopub.status.busy": "2024-07-01T15:05:57.763378Z", + "iopub.status.idle": "2024-07-01T15:05:57.766675Z", + "shell.execute_reply": "2024-07-01T15:05:57.766123Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.116836Z", - "iopub.status.busy": "2024-06-28T15:36:29.116485Z", - "iopub.status.idle": "2024-06-28T15:36:29.142651Z", - "shell.execute_reply": "2024-06-28T15:36:29.142099Z" + "iopub.execute_input": "2024-07-01T15:05:57.768614Z", + "iopub.status.busy": "2024-07-01T15:05:57.768315Z", + "iopub.status.idle": "2024-07-01T15:05:57.808437Z", + "shell.execute_reply": "2024-07-01T15:05:57.807887Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.144877Z", - "iopub.status.busy": "2024-06-28T15:36:29.144489Z", - "iopub.status.idle": "2024-06-28T15:36:29.148574Z", - "shell.execute_reply": "2024-06-28T15:36:29.148059Z" + "iopub.execute_input": "2024-07-01T15:05:57.810722Z", + "iopub.status.busy": "2024-07-01T15:05:57.810309Z", + "iopub.status.idle": "2024-07-01T15:05:57.814281Z", + "shell.execute_reply": "2024-07-01T15:05:57.813706Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer'}\n" + "Classes: {'cancel_transfer', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.150742Z", - "iopub.status.busy": "2024-06-28T15:36:29.150368Z", - "iopub.status.idle": "2024-06-28T15:36:29.153564Z", - "shell.execute_reply": "2024-06-28T15:36:29.152998Z" + "iopub.execute_input": "2024-07-01T15:05:57.816292Z", + "iopub.status.busy": "2024-07-01T15:05:57.816001Z", + "iopub.status.idle": "2024-07-01T15:05:57.819153Z", + "shell.execute_reply": "2024-07-01T15:05:57.818607Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:29.155686Z", - "iopub.status.busy": "2024-06-28T15:36:29.155353Z", - "iopub.status.idle": "2024-06-28T15:36:33.062605Z", - "shell.execute_reply": "2024-06-28T15:36:33.062024Z" + "iopub.execute_input": "2024-07-01T15:05:57.821168Z", + "iopub.status.busy": "2024-07-01T15:05:57.820783Z", + "iopub.status.idle": "2024-07-01T15:06:01.454864Z", + "shell.execute_reply": "2024-07-01T15:06:01.454218Z" } }, "outputs": [ @@ -416,10 +416,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.065550Z", - "iopub.status.busy": "2024-06-28T15:36:33.065329Z", - "iopub.status.idle": "2024-06-28T15:36:33.965085Z", - "shell.execute_reply": "2024-06-28T15:36:33.964485Z" + "iopub.execute_input": "2024-07-01T15:06:01.457576Z", + "iopub.status.busy": "2024-07-01T15:06:01.457191Z", + "iopub.status.idle": "2024-07-01T15:06:02.359759Z", + "shell.execute_reply": "2024-07-01T15:06:02.359194Z" }, "scrolled": true }, @@ -451,10 +451,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.968066Z", - "iopub.status.busy": "2024-06-28T15:36:33.967676Z", - "iopub.status.idle": "2024-06-28T15:36:33.970602Z", - "shell.execute_reply": "2024-06-28T15:36:33.970111Z" + "iopub.execute_input": "2024-07-01T15:06:02.362504Z", + "iopub.status.busy": "2024-07-01T15:06:02.362099Z", + "iopub.status.idle": "2024-07-01T15:06:02.365173Z", + "shell.execute_reply": "2024-07-01T15:06:02.364692Z" } }, "outputs": [], @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:33.972948Z", - "iopub.status.busy": "2024-06-28T15:36:33.972596Z", - "iopub.status.idle": "2024-06-28T15:36:36.086496Z", - "shell.execute_reply": "2024-06-28T15:36:36.085881Z" + "iopub.execute_input": "2024-07-01T15:06:02.368303Z", + "iopub.status.busy": "2024-07-01T15:06:02.367393Z", + "iopub.status.idle": "2024-07-01T15:06:04.354878Z", + "shell.execute_reply": "2024-07-01T15:06:04.354255Z" }, "scrolled": true }, @@ -521,10 +521,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.089588Z", - "iopub.status.busy": "2024-06-28T15:36:36.088943Z", - "iopub.status.idle": "2024-06-28T15:36:36.113521Z", - "shell.execute_reply": "2024-06-28T15:36:36.112998Z" + "iopub.execute_input": "2024-07-01T15:06:04.359326Z", + "iopub.status.busy": "2024-07-01T15:06:04.358175Z", + "iopub.status.idle": "2024-07-01T15:06:04.383863Z", + "shell.execute_reply": "2024-07-01T15:06:04.383356Z" }, "scrolled": true }, @@ -654,10 +654,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.116126Z", - "iopub.status.busy": "2024-06-28T15:36:36.115727Z", - "iopub.status.idle": "2024-06-28T15:36:36.125325Z", - "shell.execute_reply": "2024-06-28T15:36:36.124905Z" + "iopub.execute_input": "2024-07-01T15:06:04.387331Z", + "iopub.status.busy": "2024-07-01T15:06:04.386438Z", + "iopub.status.idle": "2024-07-01T15:06:04.396138Z", + "shell.execute_reply": "2024-07-01T15:06:04.395755Z" }, "scrolled": true }, @@ -767,10 +767,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.127442Z", - "iopub.status.busy": "2024-06-28T15:36:36.127118Z", - "iopub.status.idle": "2024-06-28T15:36:36.131240Z", - "shell.execute_reply": "2024-06-28T15:36:36.130840Z" + "iopub.execute_input": "2024-07-01T15:06:04.398058Z", + "iopub.status.busy": "2024-07-01T15:06:04.397776Z", + "iopub.status.idle": "2024-07-01T15:06:04.401475Z", + "shell.execute_reply": "2024-07-01T15:06:04.401092Z" } }, "outputs": [ @@ -808,10 +808,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.133167Z", - "iopub.status.busy": "2024-06-28T15:36:36.132866Z", - "iopub.status.idle": "2024-06-28T15:36:36.138900Z", - "shell.execute_reply": "2024-06-28T15:36:36.138496Z" + "iopub.execute_input": "2024-07-01T15:06:04.403322Z", + "iopub.status.busy": "2024-07-01T15:06:04.403036Z", + "iopub.status.idle": "2024-07-01T15:06:04.408720Z", + "shell.execute_reply": "2024-07-01T15:06:04.408332Z" } }, "outputs": [ @@ -928,10 +928,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.140862Z", - "iopub.status.busy": "2024-06-28T15:36:36.140553Z", - "iopub.status.idle": "2024-06-28T15:36:36.146597Z", - "shell.execute_reply": "2024-06-28T15:36:36.146207Z" + "iopub.execute_input": "2024-07-01T15:06:04.410591Z", + "iopub.status.busy": "2024-07-01T15:06:04.410423Z", + "iopub.status.idle": "2024-07-01T15:06:04.416683Z", + "shell.execute_reply": "2024-07-01T15:06:04.416154Z" } }, "outputs": [ @@ -1014,10 +1014,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.148465Z", - "iopub.status.busy": "2024-06-28T15:36:36.148170Z", - "iopub.status.idle": "2024-06-28T15:36:36.153742Z", - "shell.execute_reply": "2024-06-28T15:36:36.153299Z" + "iopub.execute_input": "2024-07-01T15:06:04.418724Z", + "iopub.status.busy": "2024-07-01T15:06:04.418385Z", + "iopub.status.idle": "2024-07-01T15:06:04.424043Z", + "shell.execute_reply": "2024-07-01T15:06:04.423521Z" } }, "outputs": [ @@ -1125,10 +1125,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.155782Z", - "iopub.status.busy": "2024-06-28T15:36:36.155479Z", - "iopub.status.idle": "2024-06-28T15:36:36.164116Z", - "shell.execute_reply": "2024-06-28T15:36:36.163656Z" + "iopub.execute_input": "2024-07-01T15:06:04.426089Z", + "iopub.status.busy": "2024-07-01T15:06:04.425788Z", + "iopub.status.idle": "2024-07-01T15:06:04.434068Z", + "shell.execute_reply": "2024-07-01T15:06:04.433526Z" } }, "outputs": [ @@ -1239,10 +1239,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.166281Z", - "iopub.status.busy": "2024-06-28T15:36:36.165967Z", - "iopub.status.idle": "2024-06-28T15:36:36.171549Z", - "shell.execute_reply": "2024-06-28T15:36:36.171073Z" + "iopub.execute_input": "2024-07-01T15:06:04.435974Z", + "iopub.status.busy": "2024-07-01T15:06:04.435800Z", + "iopub.status.idle": "2024-07-01T15:06:04.441070Z", + "shell.execute_reply": "2024-07-01T15:06:04.440586Z" } }, "outputs": [ @@ -1310,10 +1310,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.173603Z", - "iopub.status.busy": "2024-06-28T15:36:36.173270Z", - "iopub.status.idle": "2024-06-28T15:36:36.179096Z", - "shell.execute_reply": "2024-06-28T15:36:36.178548Z" + "iopub.execute_input": "2024-07-01T15:06:04.443100Z", + "iopub.status.busy": "2024-07-01T15:06:04.442719Z", + "iopub.status.idle": "2024-07-01T15:06:04.447928Z", + "shell.execute_reply": "2024-07-01T15:06:04.447468Z" } }, "outputs": [ @@ -1392,10 +1392,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.181331Z", - "iopub.status.busy": "2024-06-28T15:36:36.181023Z", - "iopub.status.idle": "2024-06-28T15:36:36.184720Z", - "shell.execute_reply": "2024-06-28T15:36:36.184156Z" + "iopub.execute_input": "2024-07-01T15:06:04.450005Z", + "iopub.status.busy": "2024-07-01T15:06:04.449608Z", + "iopub.status.idle": "2024-07-01T15:06:04.453217Z", + "shell.execute_reply": "2024-07-01T15:06:04.452674Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:36.186917Z", - "iopub.status.busy": "2024-06-28T15:36:36.186527Z", - "iopub.status.idle": "2024-06-28T15:36:36.192238Z", - "shell.execute_reply": "2024-06-28T15:36:36.191670Z" + "iopub.execute_input": "2024-07-01T15:06:04.455383Z", + "iopub.status.busy": "2024-07-01T15:06:04.455056Z", + "iopub.status.idle": "2024-07-01T15:06:04.460142Z", + "shell.execute_reply": "2024-07-01T15:06:04.459596Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb index a40cddd8f..71ffac131 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/workflows.ipynb @@ -38,10 +38,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.361660Z", - "iopub.status.busy": "2024-06-28T15:36:40.361112Z", - "iopub.status.idle": "2024-06-28T15:36:40.809139Z", - "shell.execute_reply": "2024-06-28T15:36:40.808612Z" + "iopub.execute_input": "2024-07-01T15:06:07.601006Z", + "iopub.status.busy": "2024-07-01T15:06:07.600505Z", + "iopub.status.idle": "2024-07-01T15:06:08.023065Z", + "shell.execute_reply": "2024-07-01T15:06:08.022566Z" } }, "outputs": [], @@ -87,10 +87,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.811963Z", - "iopub.status.busy": "2024-06-28T15:36:40.811488Z", - "iopub.status.idle": "2024-06-28T15:36:40.944953Z", - "shell.execute_reply": "2024-06-28T15:36:40.944353Z" + "iopub.execute_input": "2024-07-01T15:06:08.025689Z", + "iopub.status.busy": "2024-07-01T15:06:08.025283Z", + "iopub.status.idle": "2024-07-01T15:06:08.152849Z", + "shell.execute_reply": "2024-07-01T15:06:08.152350Z" } }, "outputs": [ @@ -181,10 +181,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.947272Z", - "iopub.status.busy": "2024-06-28T15:36:40.946935Z", - "iopub.status.idle": "2024-06-28T15:36:40.971280Z", - "shell.execute_reply": "2024-06-28T15:36:40.970561Z" + "iopub.execute_input": "2024-07-01T15:06:08.155131Z", + "iopub.status.busy": "2024-07-01T15:06:08.154741Z", + "iopub.status.idle": "2024-07-01T15:06:08.177601Z", + "shell.execute_reply": "2024-07-01T15:06:08.177069Z" } }, "outputs": [], @@ -210,10 +210,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:40.974379Z", - "iopub.status.busy": "2024-06-28T15:36:40.974119Z", - "iopub.status.idle": "2024-06-28T15:36:43.938511Z", - "shell.execute_reply": "2024-06-28T15:36:43.937823Z" + "iopub.execute_input": "2024-07-01T15:06:08.180286Z", + "iopub.status.busy": "2024-07-01T15:06:08.179872Z", + "iopub.status.idle": "2024-07-01T15:06:10.839277Z", + "shell.execute_reply": "2024-07-01T15:06:10.838727Z" } }, "outputs": [ @@ -280,7 +280,7 @@ " \n", " 2\n", " outlier\n", - " 0.356959\n", + " 0.356958\n", " 362\n", " \n", " \n", @@ -315,7 +315,7 @@ " issue_type score num_issues\n", "0 null 1.000000 0\n", "1 label 0.991400 52\n", - "2 outlier 0.356959 362\n", + "2 outlier 0.356958 362\n", "3 near_duplicate 0.619565 108\n", "4 non_iid 0.000000 1\n", "5 class_imbalance 0.500000 0\n", @@ -700,10 +700,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:43.941207Z", - "iopub.status.busy": "2024-06-28T15:36:43.940593Z", - "iopub.status.idle": "2024-06-28T15:36:52.261673Z", - "shell.execute_reply": "2024-06-28T15:36:52.261071Z" + "iopub.execute_input": "2024-07-01T15:06:10.841884Z", + "iopub.status.busy": "2024-07-01T15:06:10.841353Z", + "iopub.status.idle": "2024-07-01T15:06:18.620342Z", + "shell.execute_reply": "2024-07-01T15:06:18.619784Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:52.264189Z", - "iopub.status.busy": "2024-06-28T15:36:52.263727Z", - "iopub.status.idle": "2024-06-28T15:36:52.413280Z", - "shell.execute_reply": "2024-06-28T15:36:52.412618Z" + "iopub.execute_input": "2024-07-01T15:06:18.622535Z", + "iopub.status.busy": "2024-07-01T15:06:18.622344Z", + "iopub.status.idle": "2024-07-01T15:06:18.765943Z", + "shell.execute_reply": "2024-07-01T15:06:18.765367Z" } }, "outputs": [], @@ -838,10 +838,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:52.416001Z", - "iopub.status.busy": "2024-06-28T15:36:52.415579Z", - "iopub.status.idle": "2024-06-28T15:36:53.780189Z", - "shell.execute_reply": "2024-06-28T15:36:53.779592Z" + "iopub.execute_input": "2024-07-01T15:06:18.768372Z", + "iopub.status.busy": "2024-07-01T15:06:18.768184Z", + "iopub.status.idle": "2024-07-01T15:06:20.089155Z", + "shell.execute_reply": "2024-07-01T15:06:20.088659Z" } }, "outputs": [ @@ -1000,10 +1000,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:53.782813Z", - "iopub.status.busy": "2024-06-28T15:36:53.782410Z", - "iopub.status.idle": "2024-06-28T15:36:54.243902Z", - "shell.execute_reply": "2024-06-28T15:36:54.243296Z" + "iopub.execute_input": "2024-07-01T15:06:20.091428Z", + "iopub.status.busy": "2024-07-01T15:06:20.091067Z", + "iopub.status.idle": "2024-07-01T15:06:20.540474Z", + "shell.execute_reply": "2024-07-01T15:06:20.539777Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.246679Z", - "iopub.status.busy": "2024-06-28T15:36:54.246127Z", - "iopub.status.idle": "2024-06-28T15:36:54.255761Z", - "shell.execute_reply": "2024-06-28T15:36:54.255221Z" + "iopub.execute_input": "2024-07-01T15:06:20.543076Z", + "iopub.status.busy": "2024-07-01T15:06:20.542728Z", + "iopub.status.idle": "2024-07-01T15:06:20.551688Z", + "shell.execute_reply": "2024-07-01T15:06:20.551243Z" } }, "outputs": [], @@ -1115,10 +1115,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.258144Z", - "iopub.status.busy": "2024-06-28T15:36:54.257783Z", - "iopub.status.idle": "2024-06-28T15:36:54.282979Z", - "shell.execute_reply": "2024-06-28T15:36:54.282497Z" + "iopub.execute_input": "2024-07-01T15:06:20.553781Z", + "iopub.status.busy": "2024-07-01T15:06:20.553342Z", + "iopub.status.idle": "2024-07-01T15:06:20.571023Z", + "shell.execute_reply": "2024-07-01T15:06:20.570476Z" } }, "outputs": [], @@ -1146,10 +1146,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.285367Z", - "iopub.status.busy": "2024-06-28T15:36:54.285016Z", - "iopub.status.idle": "2024-06-28T15:36:54.507945Z", - "shell.execute_reply": "2024-06-28T15:36:54.507290Z" + "iopub.execute_input": "2024-07-01T15:06:20.573201Z", + "iopub.status.busy": "2024-07-01T15:06:20.572911Z", + "iopub.status.idle": "2024-07-01T15:06:20.803461Z", + "shell.execute_reply": "2024-07-01T15:06:20.802856Z" } }, "outputs": [], @@ -1189,10 +1189,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.510812Z", - "iopub.status.busy": "2024-06-28T15:36:54.510405Z", - "iopub.status.idle": "2024-06-28T15:36:54.530061Z", - "shell.execute_reply": "2024-06-28T15:36:54.529521Z" + "iopub.execute_input": "2024-07-01T15:06:20.806333Z", + "iopub.status.busy": "2024-07-01T15:06:20.805945Z", + "iopub.status.idle": "2024-07-01T15:06:20.825160Z", + "shell.execute_reply": "2024-07-01T15:06:20.824612Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.532229Z", - "iopub.status.busy": "2024-06-28T15:36:54.532035Z", - "iopub.status.idle": "2024-06-28T15:36:54.700650Z", - "shell.execute_reply": "2024-06-28T15:36:54.699991Z" + "iopub.execute_input": "2024-07-01T15:06:20.827381Z", + "iopub.status.busy": "2024-07-01T15:06:20.826963Z", + "iopub.status.idle": "2024-07-01T15:06:20.993329Z", + "shell.execute_reply": "2024-07-01T15:06:20.992779Z" } }, "outputs": [ @@ -1460,10 +1460,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.704472Z", - "iopub.status.busy": "2024-06-28T15:36:54.703989Z", - "iopub.status.idle": "2024-06-28T15:36:54.716459Z", - "shell.execute_reply": "2024-06-28T15:36:54.715941Z" + "iopub.execute_input": "2024-07-01T15:06:20.995655Z", + "iopub.status.busy": "2024-07-01T15:06:20.995315Z", + "iopub.status.idle": "2024-07-01T15:06:21.005096Z", + "shell.execute_reply": "2024-07-01T15:06:21.004627Z" } }, "outputs": [ @@ -1729,10 +1729,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.718610Z", - "iopub.status.busy": "2024-06-28T15:36:54.718408Z", - "iopub.status.idle": "2024-06-28T15:36:54.728637Z", - "shell.execute_reply": "2024-06-28T15:36:54.728151Z" + "iopub.execute_input": "2024-07-01T15:06:21.007134Z", + "iopub.status.busy": "2024-07-01T15:06:21.006797Z", + "iopub.status.idle": "2024-07-01T15:06:21.015972Z", + "shell.execute_reply": "2024-07-01T15:06:21.015502Z" } }, "outputs": [ @@ -1919,10 +1919,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.730717Z", - "iopub.status.busy": "2024-06-28T15:36:54.730400Z", - "iopub.status.idle": "2024-06-28T15:36:54.768982Z", - "shell.execute_reply": "2024-06-28T15:36:54.768461Z" + "iopub.execute_input": "2024-07-01T15:06:21.017811Z", + "iopub.status.busy": "2024-07-01T15:06:21.017637Z", + "iopub.status.idle": "2024-07-01T15:06:21.046279Z", + "shell.execute_reply": "2024-07-01T15:06:21.045826Z" } }, "outputs": [], @@ -1956,10 +1956,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.771243Z", - "iopub.status.busy": "2024-06-28T15:36:54.771054Z", - "iopub.status.idle": "2024-06-28T15:36:54.774061Z", - "shell.execute_reply": "2024-06-28T15:36:54.773608Z" + "iopub.execute_input": "2024-07-01T15:06:21.048363Z", + "iopub.status.busy": "2024-07-01T15:06:21.048043Z", + "iopub.status.idle": "2024-07-01T15:06:21.050539Z", + "shell.execute_reply": "2024-07-01T15:06:21.050117Z" } }, "outputs": [], @@ -1981,10 +1981,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.776076Z", - "iopub.status.busy": "2024-06-28T15:36:54.775901Z", - "iopub.status.idle": "2024-06-28T15:36:54.795979Z", - "shell.execute_reply": "2024-06-28T15:36:54.795395Z" + "iopub.execute_input": "2024-07-01T15:06:21.052583Z", + "iopub.status.busy": "2024-07-01T15:06:21.052269Z", + "iopub.status.idle": "2024-07-01T15:06:21.070692Z", + "shell.execute_reply": "2024-07-01T15:06:21.070162Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.798272Z", - "iopub.status.busy": "2024-06-28T15:36:54.797955Z", - "iopub.status.idle": "2024-06-28T15:36:54.802433Z", - "shell.execute_reply": "2024-06-28T15:36:54.801877Z" + "iopub.execute_input": "2024-07-01T15:06:21.072780Z", + "iopub.status.busy": "2024-07-01T15:06:21.072455Z", + "iopub.status.idle": "2024-07-01T15:06:21.076730Z", + "shell.execute_reply": "2024-07-01T15:06:21.076273Z" } }, "outputs": [], @@ -2178,10 +2178,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.804497Z", - "iopub.status.busy": "2024-06-28T15:36:54.804166Z", - "iopub.status.idle": "2024-06-28T15:36:54.833324Z", - "shell.execute_reply": "2024-06-28T15:36:54.832732Z" + "iopub.execute_input": "2024-07-01T15:06:21.078798Z", + "iopub.status.busy": "2024-07-01T15:06:21.078478Z", + "iopub.status.idle": "2024-07-01T15:06:21.105961Z", + "shell.execute_reply": "2024-07-01T15:06:21.105424Z" } }, "outputs": [ @@ -2327,10 +2327,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:54.835614Z", - "iopub.status.busy": "2024-06-28T15:36:54.835280Z", - "iopub.status.idle": "2024-06-28T15:36:55.211393Z", - "shell.execute_reply": "2024-06-28T15:36:55.210819Z" + "iopub.execute_input": "2024-07-01T15:06:21.108003Z", + "iopub.status.busy": "2024-07-01T15:06:21.107678Z", + "iopub.status.idle": "2024-07-01T15:06:21.447286Z", + "shell.execute_reply": "2024-07-01T15:06:21.446728Z" } }, "outputs": [ @@ -2397,10 +2397,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.213606Z", - "iopub.status.busy": "2024-06-28T15:36:55.213255Z", - "iopub.status.idle": "2024-06-28T15:36:55.216591Z", - "shell.execute_reply": "2024-06-28T15:36:55.216098Z" + "iopub.execute_input": "2024-07-01T15:06:21.449527Z", + "iopub.status.busy": "2024-07-01T15:06:21.449200Z", + "iopub.status.idle": "2024-07-01T15:06:21.452276Z", + "shell.execute_reply": "2024-07-01T15:06:21.451757Z" } }, "outputs": [ @@ -2451,10 +2451,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.218693Z", - "iopub.status.busy": "2024-06-28T15:36:55.218365Z", - "iopub.status.idle": "2024-06-28T15:36:55.232267Z", - "shell.execute_reply": "2024-06-28T15:36:55.231672Z" + "iopub.execute_input": "2024-07-01T15:06:21.454363Z", + "iopub.status.busy": "2024-07-01T15:06:21.454023Z", + "iopub.status.idle": "2024-07-01T15:06:21.466646Z", + "shell.execute_reply": "2024-07-01T15:06:21.466203Z" } }, "outputs": [ @@ -2733,10 +2733,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.234822Z", - "iopub.status.busy": "2024-06-28T15:36:55.234454Z", - "iopub.status.idle": "2024-06-28T15:36:55.248860Z", - "shell.execute_reply": "2024-06-28T15:36:55.248321Z" + "iopub.execute_input": "2024-07-01T15:06:21.468583Z", + "iopub.status.busy": "2024-07-01T15:06:21.468405Z", + "iopub.status.idle": "2024-07-01T15:06:21.481924Z", + "shell.execute_reply": "2024-07-01T15:06:21.481439Z" } }, "outputs": [ @@ -3003,10 +3003,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.251109Z", - "iopub.status.busy": "2024-06-28T15:36:55.250759Z", - "iopub.status.idle": "2024-06-28T15:36:55.261210Z", - "shell.execute_reply": "2024-06-28T15:36:55.260768Z" + "iopub.execute_input": "2024-07-01T15:06:21.484047Z", + "iopub.status.busy": "2024-07-01T15:06:21.483628Z", + "iopub.status.idle": "2024-07-01T15:06:21.493274Z", + "shell.execute_reply": "2024-07-01T15:06:21.492857Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.263582Z", - "iopub.status.busy": "2024-06-28T15:36:55.263219Z", - "iopub.status.idle": "2024-06-28T15:36:55.273514Z", - "shell.execute_reply": "2024-06-28T15:36:55.272922Z" + "iopub.execute_input": "2024-07-01T15:06:21.495372Z", + "iopub.status.busy": "2024-07-01T15:06:21.495048Z", + "iopub.status.idle": "2024-07-01T15:06:21.504141Z", + "shell.execute_reply": "2024-07-01T15:06:21.503595Z" } }, "outputs": [ @@ -3206,10 +3206,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.275723Z", - "iopub.status.busy": "2024-06-28T15:36:55.275369Z", - "iopub.status.idle": "2024-06-28T15:36:55.279072Z", - "shell.execute_reply": "2024-06-28T15:36:55.278640Z" + "iopub.execute_input": "2024-07-01T15:06:21.506165Z", + "iopub.status.busy": "2024-07-01T15:06:21.505846Z", + "iopub.status.idle": "2024-07-01T15:06:21.509379Z", + "shell.execute_reply": "2024-07-01T15:06:21.508840Z" } }, "outputs": [], @@ -3241,10 +3241,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.281212Z", - "iopub.status.busy": "2024-06-28T15:36:55.280883Z", - "iopub.status.idle": "2024-06-28T15:36:55.334857Z", - "shell.execute_reply": "2024-06-28T15:36:55.334288Z" + "iopub.execute_input": "2024-07-01T15:06:21.511428Z", + "iopub.status.busy": "2024-07-01T15:06:21.511040Z", + "iopub.status.idle": "2024-07-01T15:06:21.560989Z", + "shell.execute_reply": "2024-07-01T15:06:21.560473Z" } }, "outputs": [ @@ -3252,230 +3252,230 @@ "data": { "text/html": [ "\n", - "\n", + "
    \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
     AgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_scoreAgeGenderLocationAnnual_SpendingNumber_of_TransactionsLast_Purchase_Date|is_null_issuenull_score
    8nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.0000008nannannannannanNaTTrue0.000000
    1nanFemaleRural6421.1600005.000000NaTFalse0.666667
    9nanMaleRural4655.8200001.000000NaTFalse0.666667
    14nanMaleRural6790.4600003.000000NaTFalse0.666667
    13nanMaleUrban9167.4700004.0000002024-01-02 00:00:00False0.833333
    15nanOtherRural5327.9600008.0000002024-01-03 00:00:00False0.833333
    056.000000OtherRural4099.6200003.0000002024-01-03 00:00:00False1.000000
    246.000000MaleSuburban5436.5500003.0000002024-02-26 00:00:00False1.000000
    332.000000FemaleRural4046.6600003.0000002024-03-23 00:00:00False1.000000
    460.000000FemaleSuburban3467.6700006.0000002024-03-01 00:00:00False1.000000
    525.000000FemaleSuburban4757.3700004.0000002024-01-03 00:00:00False1.000000
    638.000000FemaleRural4199.5300006.0000002024-01-03 00:00:00False1.000000
    756.000000MaleSuburban4991.7100006.0000002024-04-03 00:00:00False1.000000
    1040.000000FemaleRural5584.0200007.0000002024-03-29 00:00:00False1.000000
    1128.000000FemaleUrban3102.3200002.0000002024-04-07 00:00:00False1.000000
    1228.000000MaleRural6637.99000011.0000002024-04-08 00:00:00False1.000000
    \n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3551,10 +3551,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.337385Z", - "iopub.status.busy": "2024-06-28T15:36:55.336901Z", - "iopub.status.idle": "2024-06-28T15:36:55.342582Z", - "shell.execute_reply": "2024-06-28T15:36:55.342159Z" + "iopub.execute_input": "2024-07-01T15:06:21.563215Z", + "iopub.status.busy": "2024-07-01T15:06:21.562809Z", + "iopub.status.idle": "2024-07-01T15:06:21.568340Z", + "shell.execute_reply": "2024-07-01T15:06:21.567916Z" } }, "outputs": [], @@ -3593,10 +3593,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.344705Z", - "iopub.status.busy": "2024-06-28T15:36:55.344399Z", - "iopub.status.idle": "2024-06-28T15:36:55.356329Z", - "shell.execute_reply": "2024-06-28T15:36:55.355779Z" + "iopub.execute_input": "2024-07-01T15:06:21.570390Z", + "iopub.status.busy": "2024-07-01T15:06:21.570048Z", + "iopub.status.idle": "2024-07-01T15:06:21.580186Z", + "shell.execute_reply": "2024-07-01T15:06:21.579714Z" } }, "outputs": [ @@ -3632,10 +3632,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.358690Z", - "iopub.status.busy": "2024-06-28T15:36:55.358150Z", - "iopub.status.idle": "2024-06-28T15:36:55.575548Z", - "shell.execute_reply": "2024-06-28T15:36:55.574952Z" + "iopub.execute_input": "2024-07-01T15:06:21.582185Z", + "iopub.status.busy": "2024-07-01T15:06:21.581851Z", + "iopub.status.idle": "2024-07-01T15:06:21.792455Z", + "shell.execute_reply": "2024-07-01T15:06:21.791912Z" } }, "outputs": [ @@ -3687,10 +3687,10 @@ "execution_count": 32, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.577840Z", - "iopub.status.busy": "2024-06-28T15:36:55.577511Z", - "iopub.status.idle": "2024-06-28T15:36:55.585510Z", - "shell.execute_reply": "2024-06-28T15:36:55.584968Z" + "iopub.execute_input": "2024-07-01T15:06:21.794636Z", + "iopub.status.busy": "2024-07-01T15:06:21.794453Z", + "iopub.status.idle": "2024-07-01T15:06:21.802233Z", + "shell.execute_reply": "2024-07-01T15:06:21.801679Z" }, "nbsphinx": "hidden" }, @@ -3743,10 +3743,10 @@ "execution_count": 33, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:36:55.587717Z", - "iopub.status.busy": "2024-06-28T15:36:55.587380Z", - "iopub.status.idle": "2024-06-28T15:37:01.427411Z", - "shell.execute_reply": "2024-06-28T15:37:01.426755Z" + "iopub.execute_input": "2024-07-01T15:06:21.804166Z", + "iopub.status.busy": "2024-07-01T15:06:21.803993Z", + "iopub.status.idle": "2024-07-01T15:06:27.357122Z", + "shell.execute_reply": "2024-07-01T15:06:27.356570Z" } }, "outputs": [ @@ -3770,7 +3770,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1966080/170498071 [00:00<00:08, 19658053.51it/s]" + " 1%| | 917504/170498071 [00:00<00:20, 8269545.10it/s]" ] }, { @@ -3778,7 +3778,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 9469952/170498071 [00:00<00:03, 52230874.01it/s]" + " 6%|▌ | 10387456/170498071 [00:00<00:02, 56842742.05it/s]" ] }, { @@ -3786,7 +3786,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 17760256/170498071 [00:00<00:02, 66158713.83it/s]" + " 13%|█▎ | 21495808/170498071 [00:00<00:01, 80950295.23it/s]" ] }, { @@ -3794,7 +3794,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 25395200/170498071 [00:00<00:02, 70095628.99it/s]" + " 19%|█▉ | 32440320/170498071 [00:00<00:01, 91992107.79it/s]" ] }, { @@ -3802,7 +3802,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 33718272/170498071 [00:00<00:01, 74822583.06it/s]" + " 26%|██▌ | 43483136/170498071 [00:00<00:01, 98507606.82it/s]" ] }, { @@ -3810,7 +3810,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 42041344/170498071 [00:00<00:01, 77646889.37it/s]" + " 32%|███▏ | 54427648/170498071 [00:00<00:01, 102169111.94it/s]" ] }, { @@ -3818,7 +3818,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50823168/170498071 [00:00<00:01, 80964433.57it/s]" + " 38%|███▊ | 64716800/170498071 [00:00<00:01, 102261059.68it/s]" ] }, { @@ -3826,7 +3826,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 59080704/170498071 [00:00<00:01, 81434669.67it/s]" + " 44%|████▍ | 75759616/170498071 [00:00<00:00, 104794429.63it/s]" ] }, { @@ -3834,7 +3834,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 67862528/170498071 [00:00<00:01, 83414756.69it/s]" + " 51%|█████ | 86573056/170498071 [00:00<00:00, 105786259.21it/s]" ] }, { @@ -3842,7 +3842,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 76218368/170498071 [00:01<00:01, 79826592.63it/s]" + " 57%|█████▋ | 97779712/170498071 [00:01<00:00, 107658431.70it/s]" ] }, { @@ -3850,7 +3850,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 84246528/170498071 [00:01<00:01, 76490866.06it/s]" + " 64%|██████▎ | 108560384/170498071 [00:01<00:00, 107631887.95it/s]" ] }, { @@ -3858,7 +3858,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 91947008/170498071 [00:01<00:01, 76082662.08it/s]" + " 70%|███████ | 119635968/170498071 [00:01<00:00, 108510039.53it/s]" ] }, { @@ -3866,7 +3866,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100368384/170498071 [00:01<00:00, 78428938.84it/s]" + " 77%|███████▋ | 130514944/170498071 [00:01<00:00, 108504773.07it/s]" ] }, { @@ -3874,7 +3874,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 108888064/170498071 [00:01<00:00, 80391787.54it/s]" + " 83%|████████▎ | 141557760/170498071 [00:01<00:00, 109047203.13it/s]" ] }, { @@ -3882,7 +3882,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 117735424/170498071 [00:01<00:00, 82707343.94it/s]" + " 89%|████████▉ | 152567808/170498071 [00:01<00:00, 109231770.68it/s]" ] }, { @@ -3890,7 +3890,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 126189568/170498071 [00:01<00:00, 83202299.90it/s]" + " 96%|█████████▌| 163512320/170498071 [00:01<00:00, 109164928.11it/s]" ] }, { @@ -3898,39 +3898,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 137068544/170498071 [00:01<00:00, 90724902.11it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 87%|████████▋ | 148504576/170498071 [00:01<00:00, 97755628.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 94%|█████████▎| 159744000/170498071 [00:01<00:00, 101954468.01it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 169967616/170498071 [00:02<00:00, 101064380.25it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 83450279.11it/s] " + "100%|██████████| 170498071/170498071 [00:01<00:00, 101603839.38it/s]" ] }, { @@ -4004,10 +3972,10 @@ "execution_count": 34, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.430525Z", - "iopub.status.busy": "2024-06-28T15:37:01.429953Z", - "iopub.status.idle": "2024-06-28T15:37:01.498795Z", - "shell.execute_reply": "2024-06-28T15:37:01.498158Z" + "iopub.execute_input": "2024-07-01T15:06:27.360097Z", + "iopub.status.busy": "2024-07-01T15:06:27.359363Z", + "iopub.status.idle": "2024-07-01T15:06:27.426972Z", + "shell.execute_reply": "2024-07-01T15:06:27.426512Z" } }, "outputs": [], @@ -4029,10 +3997,10 @@ "execution_count": 35, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.501417Z", - "iopub.status.busy": "2024-06-28T15:37:01.501047Z", - "iopub.status.idle": "2024-06-28T15:37:01.542795Z", - "shell.execute_reply": "2024-06-28T15:37:01.542166Z" + "iopub.execute_input": "2024-07-01T15:06:27.429234Z", + "iopub.status.busy": "2024-07-01T15:06:27.429051Z", + "iopub.status.idle": "2024-07-01T15:06:27.469917Z", + "shell.execute_reply": "2024-07-01T15:06:27.469464Z" } }, "outputs": [], @@ -4066,10 +4034,10 @@ "execution_count": 36, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:01.545456Z", - "iopub.status.busy": "2024-06-28T15:37:01.545096Z", - "iopub.status.idle": "2024-06-28T15:37:03.018066Z", - "shell.execute_reply": "2024-06-28T15:37:03.017446Z" + "iopub.execute_input": "2024-07-01T15:06:27.472017Z", + "iopub.status.busy": "2024-07-01T15:06:27.471718Z", + "iopub.status.idle": "2024-07-01T15:06:28.881795Z", + "shell.execute_reply": "2024-07-01T15:06:28.881232Z" } }, "outputs": [ @@ -4143,10 +4111,10 @@ "execution_count": 37, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:03.020409Z", - "iopub.status.busy": "2024-06-28T15:37:03.020049Z", - "iopub.status.idle": "2024-06-28T15:37:03.815261Z", - "shell.execute_reply": "2024-06-28T15:37:03.814627Z" + "iopub.execute_input": "2024-07-01T15:06:28.884131Z", + "iopub.status.busy": "2024-07-01T15:06:28.883805Z", + "iopub.status.idle": "2024-07-01T15:06:29.648363Z", + "shell.execute_reply": "2024-07-01T15:06:29.647874Z" } }, "outputs": [ @@ -4161,7 +4129,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "451c772a56324a01bb8a79944d07ccb9", + "model_id": "1fa7dd59335f4faa818bcf4e966c7c70", "version_major": 2, "version_minor": 0 }, @@ -4185,7 +4153,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "329aa1acbe77409c816dff4173888262", + "model_id": "bc30a28b7d6044ff9e4159420fbd7a37", "version_major": 2, "version_minor": 0 }, @@ -4435,7 +4403,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02bec9b1c6084729a3637a206e447d9d": { + "0e8630e122434af98c6e33cb01b60aa5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4488,33 +4456,7 @@ "width": null } }, - "03f6b9d07c1443d598ec9f33572e18d2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_02bec9b1c6084729a3637a206e447d9d", - "max": 200.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_be287562794f4a518cf017c87c9bb835", - "tabbable": null, - "tooltip": null, - "value": 200.0 - } - }, - "0d838fb608934bffb704a7530b16efe0": { + "11db28fa3e49460cb17bc5b1cbe02474": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4567,7 +4509,31 @@ "width": null } }, - "0e34b193c4d34137bbcad321b8cd1de8": { + "1fa7dd59335f4faa818bcf4e966c7c70": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b80a9a6f3cc8460ba1eacd1eebdd1695", + "IPY_MODEL_bd44e36d29ce400580eb9c39706e9764", + "IPY_MODEL_6681d426ed2e406a80e7d1e331ba04a8" + ], + "layout": "IPY_MODEL_11db28fa3e49460cb17bc5b1cbe02474", + "tabbable": null, + "tooltip": null + } + }, + "277e9c9f4f2942e8ba4a67175d4da165": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4620,72 +4586,7 @@ "width": null } }, - "14edf79b75964399b033d0bf68999408": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_d8d1b1a783f6402da95cc6eb9774aab6", - "placeholder": "​", - "style": "IPY_MODEL_5419a131323c4d419c12f8229ecd6339", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "329aa1acbe77409c816dff4173888262": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7fbb77d6ea2e46569c100c09757f2a52", - "IPY_MODEL_e3a97118610a4a8bafdaf9ffac12612d", - "IPY_MODEL_e7d739ec00864423a21b98a67fe17219" - ], - "layout": "IPY_MODEL_7c9fe377569340acb6e777bce2670127", - "tabbable": null, - "tooltip": null - } - }, - "32e466d1f0be4c2b871e95c442638fe2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4360dc3380b14f92b9552eda6a8e20ca": { + "2b75c32350ca4a21bcc6c23534605260": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4738,31 +4639,7 @@ "width": null } }, - "451c772a56324a01bb8a79944d07ccb9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_14edf79b75964399b033d0bf68999408", - "IPY_MODEL_03f6b9d07c1443d598ec9f33572e18d2", - "IPY_MODEL_dbcbd0cc37774c279102b607f1d2f129" - ], - "layout": "IPY_MODEL_acb8a5d64588422b98aa99e642774766", - "tabbable": null, - "tooltip": null - } - }, - "5419a131323c4d419c12f8229ecd6339": { + "5c30ade94053466c8c64d7b0abffbc55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4780,7 +4657,7 @@ "text_color": null } }, - "55dd8a5d604642df9c9d62f2c24684df": { + "5daa3a2aa6bb4c9f84ea2674afd64ead": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4833,60 +4710,33 @@ "width": null } }, - "7c9fe377569340acb6e777bce2670127": { - "model_module": "@jupyter-widgets/base", + "5fe8b6850a5246f4896ff6995970d3c4": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "FloatProgressModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_277e9c9f4f2942e8ba4a67175d4da165", + "max": 200.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a1e2e6018ec643d38a6a7f43c30c3d0b", + "tabbable": null, + "tooltip": null, + "value": 200.0 } }, - "7fbb77d6ea2e46569c100c09757f2a52": { + "6681d426ed2e406a80e7d1e331ba04a8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4901,15 +4751,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4360dc3380b14f92b9552eda6a8e20ca", + "layout": "IPY_MODEL_981dd7e875cd448284efead3ec98c9ab", "placeholder": "​", - "style": "IPY_MODEL_87bd60144c07417a8293887d0866a96a", + "style": "IPY_MODEL_5c30ade94053466c8c64d7b0abffbc55", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 784.17it/s]" + } + }, + "6cc234a88c8e4669af66844757fac7f7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2b75c32350ca4a21bcc6c23534605260", + "placeholder": "​", + "style": "IPY_MODEL_7055ef739a1f4669a5783ae634c9e715", "tabbable": null, "tooltip": null, "value": "100%" } }, - "87bd60144c07417a8293887d0866a96a": { + "7055ef739a1f4669a5783ae634c9e715": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4927,7 +4800,7 @@ "text_color": null } }, - "98ce6e6b751848419553bed6e9d74379": { + "7d1e93d6b2384e198a1aab9f26b5c1fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4945,7 +4818,30 @@ "text_color": null } }, - "acb8a5d64588422b98aa99e642774766": { + "7df90a685b4747759b5a5df590246cc1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_0e8630e122434af98c6e33cb01b60aa5", + "placeholder": "​", + "style": "IPY_MODEL_f812040db2d642d08a03d0f598afe058", + "tabbable": null, + "tooltip": null, + "value": " 200/200 [00:00<00:00, 847.08it/s]" + } + }, + "981dd7e875cd448284efead3ec98c9ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4998,7 +4894,7 @@ "width": null } }, - "be287562794f4a518cf017c87c9bb835": { + "a1e2e6018ec643d38a6a7f43c30c3d0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -5014,7 +4910,30 @@ "description_width": "" } }, - "d8d1b1a783f6402da95cc6eb9774aab6": { + "b80a9a6f3cc8460ba1eacd1eebdd1695": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ef85a24fdc2e4cf783b12b95dc654a54", + "placeholder": "​", + "style": "IPY_MODEL_7d1e93d6b2384e198a1aab9f26b5c1fa", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "ba235d6a06cb4c11a73df384a365fbe8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5067,30 +4986,31 @@ "width": null } }, - "dbcbd0cc37774c279102b607f1d2f129": { + "bc30a28b7d6044ff9e4159420fbd7a37": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_55dd8a5d604642df9c9d62f2c24684df", - "placeholder": "​", - "style": "IPY_MODEL_98ce6e6b751848419553bed6e9d74379", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6cc234a88c8e4669af66844757fac7f7", + "IPY_MODEL_5fe8b6850a5246f4896ff6995970d3c4", + "IPY_MODEL_7df90a685b4747759b5a5df590246cc1" + ], + "layout": "IPY_MODEL_ba235d6a06cb4c11a73df384a365fbe8", "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 746.44it/s]" + "tooltip": null } }, - "e3a97118610a4a8bafdaf9ffac12612d": { + "bd44e36d29ce400580eb9c39706e9764": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -5106,40 +5026,88 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0e34b193c4d34137bbcad321b8cd1de8", + "layout": "IPY_MODEL_5daa3a2aa6bb4c9f84ea2674afd64ead", "max": 200.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e84e667dd53a4c65b4378b383e66c2f2", + "style": "IPY_MODEL_fc145740c00f4075a0ddec1338af6a17", "tabbable": null, "tooltip": null, "value": 200.0 } }, - "e7d739ec00864423a21b98a67fe17219": { + "ef85a24fdc2e4cf783b12b95dc654a54": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "f812040db2d642d08a03d0f598afe058": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0d838fb608934bffb704a7530b16efe0", - "placeholder": "​", - "style": "IPY_MODEL_32e466d1f0be4c2b871e95c442638fe2", - "tabbable": null, - "tooltip": null, - "value": " 200/200 [00:00<00:00, 831.61it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "e84e667dd53a4c65b4378b383e66c2f2": { + "fc145740c00f4075a0ddec1338af6a17": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 3194f04c5..2c0bb8a06 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -70,10 +70,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:08.113610Z", - "iopub.status.busy": "2024-06-28T15:37:08.113258Z", - "iopub.status.idle": "2024-06-28T15:37:09.278340Z", - "shell.execute_reply": "2024-06-28T15:37:09.277671Z" + "iopub.execute_input": "2024-07-01T15:06:34.327040Z", + "iopub.status.busy": "2024-07-01T15:06:34.326556Z", + "iopub.status.idle": "2024-07-01T15:06:35.430619Z", + "shell.execute_reply": "2024-07-01T15:06:35.430106Z" }, "nbsphinx": "hidden" }, @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -110,10 +110,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.280904Z", - "iopub.status.busy": "2024-06-28T15:37:09.280564Z", - "iopub.status.idle": "2024-06-28T15:37:09.283499Z", - "shell.execute_reply": "2024-06-28T15:37:09.282980Z" + "iopub.execute_input": "2024-07-01T15:06:35.433295Z", + "iopub.status.busy": "2024-07-01T15:06:35.432846Z", + "iopub.status.idle": "2024-07-01T15:06:35.435562Z", + "shell.execute_reply": "2024-07-01T15:06:35.435139Z" }, "id": "_UvI80l42iyi" }, @@ -203,10 +203,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.285782Z", - "iopub.status.busy": "2024-06-28T15:37:09.285368Z", - "iopub.status.idle": "2024-06-28T15:37:09.297090Z", - "shell.execute_reply": "2024-06-28T15:37:09.296514Z" + "iopub.execute_input": "2024-07-01T15:06:35.437647Z", + "iopub.status.busy": "2024-07-01T15:06:35.437334Z", + "iopub.status.idle": "2024-07-01T15:06:35.448830Z", + "shell.execute_reply": "2024-07-01T15:06:35.448366Z" }, "nbsphinx": "hidden" }, @@ -285,10 +285,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:09.299174Z", - "iopub.status.busy": "2024-06-28T15:37:09.298899Z", - "iopub.status.idle": "2024-06-28T15:37:13.501981Z", - "shell.execute_reply": "2024-06-28T15:37:13.501494Z" + "iopub.execute_input": "2024-07-01T15:06:35.450965Z", + "iopub.status.busy": "2024-07-01T15:06:35.450626Z", + "iopub.status.idle": "2024-07-01T15:06:39.722894Z", + "shell.execute_reply": "2024-07-01T15:06:39.722312Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index cc0a526c8..86b92fc2a 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:15.853544Z", - "iopub.status.busy": "2024-06-28T15:37:15.853352Z", - "iopub.status.idle": "2024-06-28T15:37:17.049498Z", - "shell.execute_reply": "2024-06-28T15:37:17.048960Z" + "iopub.execute_input": "2024-07-01T15:06:41.824444Z", + "iopub.status.busy": "2024-07-01T15:06:41.824251Z", + "iopub.status.idle": "2024-07-01T15:06:42.924912Z", + "shell.execute_reply": "2024-07-01T15:06:42.924299Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:17.052466Z", - "iopub.status.busy": "2024-06-28T15:37:17.051871Z", - "iopub.status.idle": "2024-06-28T15:37:17.055531Z", - "shell.execute_reply": "2024-06-28T15:37:17.054963Z" + "iopub.execute_input": "2024-07-01T15:06:42.927716Z", + "iopub.status.busy": "2024-07-01T15:06:42.927441Z", + "iopub.status.idle": "2024-07-01T15:06:42.930770Z", + "shell.execute_reply": "2024-07-01T15:06:42.930234Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:17.057853Z", - "iopub.status.busy": "2024-06-28T15:37:17.057415Z", - "iopub.status.idle": "2024-06-28T15:37:20.475550Z", - "shell.execute_reply": "2024-06-28T15:37:20.474905Z" + "iopub.execute_input": "2024-07-01T15:06:42.932829Z", + "iopub.status.busy": "2024-07-01T15:06:42.932448Z", + "iopub.status.idle": "2024-07-01T15:06:46.102573Z", + "shell.execute_reply": "2024-07-01T15:06:46.101934Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.478721Z", - "iopub.status.busy": "2024-06-28T15:37:20.478099Z", - "iopub.status.idle": "2024-06-28T15:37:20.519804Z", - "shell.execute_reply": "2024-06-28T15:37:20.519187Z" + "iopub.execute_input": "2024-07-01T15:06:46.105551Z", + "iopub.status.busy": "2024-07-01T15:06:46.104970Z", + "iopub.status.idle": "2024-07-01T15:06:46.139232Z", + "shell.execute_reply": "2024-07-01T15:06:46.138546Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.522898Z", - "iopub.status.busy": "2024-06-28T15:37:20.522445Z", - "iopub.status.idle": "2024-06-28T15:37:20.573083Z", - "shell.execute_reply": "2024-06-28T15:37:20.572396Z" + "iopub.execute_input": "2024-07-01T15:06:46.141703Z", + "iopub.status.busy": "2024-07-01T15:06:46.141464Z", + "iopub.status.idle": "2024-07-01T15:06:46.166417Z", + "shell.execute_reply": "2024-07-01T15:06:46.165807Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.576361Z", - "iopub.status.busy": "2024-06-28T15:37:20.575827Z", - "iopub.status.idle": "2024-06-28T15:37:20.579483Z", - "shell.execute_reply": "2024-06-28T15:37:20.578971Z" + "iopub.execute_input": "2024-07-01T15:06:46.168919Z", + "iopub.status.busy": "2024-07-01T15:06:46.168681Z", + "iopub.status.idle": "2024-07-01T15:06:46.171591Z", + "shell.execute_reply": "2024-07-01T15:06:46.171158Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.581933Z", - "iopub.status.busy": "2024-06-28T15:37:20.581534Z", - "iopub.status.idle": "2024-06-28T15:37:20.584738Z", - "shell.execute_reply": "2024-06-28T15:37:20.584114Z" + "iopub.execute_input": "2024-07-01T15:06:46.173661Z", + "iopub.status.busy": "2024-07-01T15:06:46.173228Z", + "iopub.status.idle": "2024-07-01T15:06:46.175926Z", + "shell.execute_reply": "2024-07-01T15:06:46.175394Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.587053Z", - "iopub.status.busy": "2024-06-28T15:37:20.586691Z", - "iopub.status.idle": "2024-06-28T15:37:20.615756Z", - "shell.execute_reply": "2024-06-28T15:37:20.615148Z" + "iopub.execute_input": "2024-07-01T15:06:46.178276Z", + "iopub.status.busy": "2024-07-01T15:06:46.177828Z", + "iopub.status.idle": "2024-07-01T15:06:46.201962Z", + "shell.execute_reply": "2024-07-01T15:06:46.201387Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9cb302a654554b6dba5964ac8c805ba8", + "model_id": "4fd339b3d01f445392d6c990fdab5a89", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10f5bddcb2454f329c7a54207b2bf28e", + "model_id": "0e05780aa0694da2b04037e49f9ac6f9", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.619003Z", - "iopub.status.busy": "2024-06-28T15:37:20.618607Z", - "iopub.status.idle": "2024-06-28T15:37:20.625885Z", - "shell.execute_reply": "2024-06-28T15:37:20.625314Z" + "iopub.execute_input": "2024-07-01T15:06:46.208658Z", + "iopub.status.busy": "2024-07-01T15:06:46.208178Z", + "iopub.status.idle": "2024-07-01T15:06:46.214827Z", + "shell.execute_reply": "2024-07-01T15:06:46.214300Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.628461Z", - "iopub.status.busy": "2024-06-28T15:37:20.628012Z", - "iopub.status.idle": "2024-06-28T15:37:20.631873Z", - "shell.execute_reply": "2024-06-28T15:37:20.631318Z" + "iopub.execute_input": "2024-07-01T15:06:46.216919Z", + "iopub.status.busy": "2024-07-01T15:06:46.216531Z", + "iopub.status.idle": "2024-07-01T15:06:46.220056Z", + "shell.execute_reply": "2024-07-01T15:06:46.219618Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.634235Z", - "iopub.status.busy": "2024-06-28T15:37:20.633791Z", - "iopub.status.idle": "2024-06-28T15:37:20.640374Z", - "shell.execute_reply": "2024-06-28T15:37:20.639910Z" + "iopub.execute_input": "2024-07-01T15:06:46.221958Z", + "iopub.status.busy": "2024-07-01T15:06:46.221652Z", + "iopub.status.idle": "2024-07-01T15:06:46.228007Z", + "shell.execute_reply": "2024-07-01T15:06:46.227489Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.642498Z", - "iopub.status.busy": "2024-06-28T15:37:20.642312Z", - "iopub.status.idle": "2024-06-28T15:37:20.691414Z", - "shell.execute_reply": "2024-06-28T15:37:20.690793Z" + "iopub.execute_input": "2024-07-01T15:06:46.229956Z", + "iopub.status.busy": "2024-07-01T15:06:46.229562Z", + "iopub.status.idle": "2024-07-01T15:06:46.262926Z", + "shell.execute_reply": "2024-07-01T15:06:46.262323Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.694197Z", - "iopub.status.busy": "2024-06-28T15:37:20.693811Z", - "iopub.status.idle": "2024-06-28T15:37:20.737170Z", - "shell.execute_reply": "2024-06-28T15:37:20.736554Z" + "iopub.execute_input": "2024-07-01T15:06:46.265438Z", + "iopub.status.busy": "2024-07-01T15:06:46.265079Z", + "iopub.status.idle": "2024-07-01T15:06:46.292805Z", + "shell.execute_reply": "2024-07-01T15:06:46.292125Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.740010Z", - "iopub.status.busy": "2024-06-28T15:37:20.739710Z", - "iopub.status.idle": "2024-06-28T15:37:20.866813Z", - "shell.execute_reply": "2024-06-28T15:37:20.866236Z" + "iopub.execute_input": "2024-07-01T15:06:46.295480Z", + "iopub.status.busy": "2024-07-01T15:06:46.295249Z", + "iopub.status.idle": "2024-07-01T15:06:46.413036Z", + "shell.execute_reply": "2024-07-01T15:06:46.412421Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:20.869660Z", - "iopub.status.busy": "2024-06-28T15:37:20.868914Z", - "iopub.status.idle": "2024-06-28T15:37:23.945616Z", - "shell.execute_reply": "2024-06-28T15:37:23.944963Z" + "iopub.execute_input": "2024-07-01T15:06:46.415958Z", + "iopub.status.busy": "2024-07-01T15:06:46.415232Z", + "iopub.status.idle": "2024-07-01T15:06:49.386585Z", + "shell.execute_reply": "2024-07-01T15:06:49.386024Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:23.948221Z", - "iopub.status.busy": "2024-06-28T15:37:23.947849Z", - "iopub.status.idle": "2024-06-28T15:37:24.007400Z", - "shell.execute_reply": "2024-06-28T15:37:24.006793Z" + "iopub.execute_input": "2024-07-01T15:06:49.389098Z", + "iopub.status.busy": "2024-07-01T15:06:49.388711Z", + "iopub.status.idle": "2024-07-01T15:06:49.445289Z", + "shell.execute_reply": "2024-07-01T15:06:49.444750Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.009682Z", - "iopub.status.busy": "2024-06-28T15:37:24.009342Z", - "iopub.status.idle": "2024-06-28T15:37:24.050753Z", - "shell.execute_reply": "2024-06-28T15:37:24.050188Z" + "iopub.execute_input": "2024-07-01T15:06:49.447480Z", + "iopub.status.busy": "2024-07-01T15:06:49.447130Z", + "iopub.status.idle": "2024-07-01T15:06:49.487292Z", + "shell.execute_reply": "2024-07-01T15:06:49.486835Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "08a301fa", + "id": "6cb95977", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -1327,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "d17b4a6d", + "id": "7616eae0", "metadata": {}, "source": [ "The instructions for specifying pre-computed data slices/clusters when detecting underperforming groups in a dataset are now covered in detail in the Datalab workflows tutorial.\n", @@ -1338,7 +1338,7 @@ }, { "cell_type": "markdown", - "id": "467b92b3", + "id": "c8e20eef", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by Datalab?\n", @@ -1349,13 +1349,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "3793609e", + "id": "5c7c2dee", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.053187Z", - "iopub.status.busy": "2024-06-28T15:37:24.052862Z", - "iopub.status.idle": "2024-06-28T15:37:24.060784Z", - "shell.execute_reply": "2024-06-28T15:37:24.060277Z" + "iopub.execute_input": "2024-07-01T15:06:49.489589Z", + "iopub.status.busy": "2024-07-01T15:06:49.489256Z", + "iopub.status.idle": "2024-07-01T15:06:49.496732Z", + "shell.execute_reply": "2024-07-01T15:06:49.496311Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "b5be9b33", + "id": "02fbab1c", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "beda2e53", + "id": "80ce97c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.062996Z", - "iopub.status.busy": "2024-06-28T15:37:24.062565Z", - "iopub.status.idle": "2024-06-28T15:37:24.084314Z", - "shell.execute_reply": "2024-06-28T15:37:24.083768Z" + "iopub.execute_input": "2024-07-01T15:06:49.498789Z", + "iopub.status.busy": "2024-07-01T15:06:49.498386Z", + "iopub.status.idle": "2024-07-01T15:06:49.516684Z", + "shell.execute_reply": "2024-07-01T15:06:49.516122Z" } }, "outputs": [ @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "d0ec4783", + "id": "f382c568", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:24.086577Z", - "iopub.status.busy": "2024-06-28T15:37:24.086173Z", - "iopub.status.idle": "2024-06-28T15:37:24.089622Z", - "shell.execute_reply": "2024-06-28T15:37:24.089095Z" + "iopub.execute_input": "2024-07-01T15:06:49.518671Z", + "iopub.status.busy": "2024-07-01T15:06:49.518371Z", + "iopub.status.idle": "2024-07-01T15:06:49.521416Z", + "shell.execute_reply": "2024-07-01T15:06:49.520910Z" } }, "outputs": [ @@ -1622,7 +1622,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a7b959af14b4aa68731b0af4b7e7ea2": { + "0111e2bffb134a42896020ea74d7e0c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1675,56 +1675,49 @@ "width": null } }, - "0df3eeea26e74ea597b01f7334be29d8": { + "08c89a132fac493d9d7986ef465e2458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_15f2ae0eeaf24047857abbeb37fad136", - "placeholder": "​", - "style": "IPY_MODEL_27cc3de215a9489c88ed634cc42daeac", + "layout": "IPY_MODEL_852d38b7e7464bfb919e2fb9d8164e6e", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_091b3aa8f4304d839459836b00997219", "tabbable": null, "tooltip": null, - "value": "number of examples processed for estimating thresholds: " + "value": 50.0 } }, - "0fc3523ecaed4cdcb1fa6d452d860686": { + "091b3aa8f4304d839459836b00997219": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_f62440c0ecd8491b8ae0a9a2541dfa01", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f30a962c2cdd422483e4d9516c8e0f05", - "tabbable": null, - "tooltip": null, - "value": 50.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "10f5bddcb2454f329c7a54207b2bf28e": { + "0e05780aa0694da2b04037e49f9ac6f9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1739,16 +1732,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_555a49f5dd334422994f7c81fd0d7f54", - "IPY_MODEL_e9d99b127d7348f0aafad88df30a6f0b", - "IPY_MODEL_dc618dccc3d74be5b2b53edece0ec7e8" + "IPY_MODEL_ac61742632624d8e99ae7cb5f710a566", + "IPY_MODEL_faad99f698554ee5a74327e7ca036115", + "IPY_MODEL_ddebd597ac7544839e8b864a9d0ee839" ], - "layout": "IPY_MODEL_3f5ca8079afc4b2cb1683c22361efd61", + "layout": "IPY_MODEL_70ea901211b24a5e83c22f533f32e36f", "tabbable": null, "tooltip": null } }, - "15f2ae0eeaf24047857abbeb37fad136": { + "1447fe2269d345b3ab6d777d2314a43f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1801,25 +1794,7 @@ "width": null } }, - "27cc3de215a9489c88ed634cc42daeac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "3f5ca8079afc4b2cb1683c22361efd61": { + "1def5cc260bc404184c09a073ccd4bd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1872,23 +1847,7 @@ "width": null } }, - "4ba67748833f4377b63cdafd5f5d997c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4cf14dfdb0b34b0d8465da1f8b07606c": { + "40e84f876df34c24a0156c5fdf89b318": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1906,66 +1865,84 @@ "text_color": null } }, - "5296ebff86d244ff8e6b3474a6249a0a": { - "model_module": "@jupyter-widgets/controls", + "460a43df0d3b417d8996679fabe135e8": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "555a49f5dd334422994f7c81fd0d7f54": { + "4fd339b3d01f445392d6c990fdab5a89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_99f33331931743bfa9147fd8a8656809", - "placeholder": "​", - "style": "IPY_MODEL_4cf14dfdb0b34b0d8465da1f8b07606c", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_be7586025bf846d68f14028779700b35", + "IPY_MODEL_08c89a132fac493d9d7986ef465e2458", + "IPY_MODEL_664279e521a34f979477f41a406cb5c1" + ], + "layout": "IPY_MODEL_1447fe2269d345b3ab6d777d2314a43f", "tabbable": null, - "tooltip": null, - "value": "number of examples processed for checking labels: " - } - }, - "7a59431350034e848cce8fea99c3e2fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "tooltip": null } }, - "8de3b33011fa44cfa55abf7791a9482d": { + "60b07213fb4d4ba79fddd321add6003b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2018,7 +1995,7 @@ "width": null } }, - "99f33331931743bfa9147fd8a8656809": { + "651b9c830e68446881c37217e971cf46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2071,54 +2048,66 @@ "width": null } }, - "9cb302a654554b6dba5964ac8c805ba8": { + "664279e521a34f979477f41a406cb5c1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0df3eeea26e74ea597b01f7334be29d8", - "IPY_MODEL_0fc3523ecaed4cdcb1fa6d452d860686", - "IPY_MODEL_bbe7d6f1caa5408ea1c6baac6048752d" - ], - "layout": "IPY_MODEL_0a7b959af14b4aa68731b0af4b7e7ea2", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_60b07213fb4d4ba79fddd321add6003b", + "placeholder": "​", + "style": "IPY_MODEL_40e84f876df34c24a0156c5fdf89b318", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1061876.00it/s]" } }, - "bbe7d6f1caa5408ea1c6baac6048752d": { + "6887dcd8b8cd4f4d89261e3c68e959a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_8de3b33011fa44cfa55abf7791a9482d", - "placeholder": "​", - "style": "IPY_MODEL_5296ebff86d244ff8e6b3474a6249a0a", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1028519.86it/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "6957d92bb0cf4c47bfffb9903128dcf5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "ca982934b4914b838423f2e8743bf780": { + "70ea901211b24a5e83c22f533f32e36f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2171,7 +2160,23 @@ "width": null } }, - "cc94bc38ba4a43e8a19bb8b38b129392": { + "762d2f4c294343aaac239f8510aee6d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "852d38b7e7464bfb919e2fb9d8164e6e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2224,7 +2229,7 @@ "width": null } }, - "dc618dccc3d74be5b2b53edece0ec7e8": { + "ac61742632624d8e99ae7cb5f710a566": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2239,107 +2244,102 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cc94bc38ba4a43e8a19bb8b38b129392", + "layout": "IPY_MODEL_651b9c830e68446881c37217e971cf46", "placeholder": "​", - "style": "IPY_MODEL_7a59431350034e848cce8fea99c3e2fd", + "style": "IPY_MODEL_6887dcd8b8cd4f4d89261e3c68e959a9", "tabbable": null, "tooltip": null, - "value": " 10000/? [00:00<00:00, 979954.67it/s]" + "value": "number of examples processed for checking labels: " } }, - "e9d99b127d7348f0aafad88df30a6f0b": { + "be7586025bf846d68f14028779700b35": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ca982934b4914b838423f2e8743bf780", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4ba67748833f4377b63cdafd5f5d997c", + "layout": "IPY_MODEL_0111e2bffb134a42896020ea74d7e0c2", + "placeholder": "​", + "style": "IPY_MODEL_dc2d26ad64ec4759a2171fea5ff47da6", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": "number of examples processed for estimating thresholds: " } }, - "f30a962c2cdd422483e4d9516c8e0f05": { + "dc2d26ad64ec4759a2171fea5ff47da6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f62440c0ecd8491b8ae0a9a2541dfa01": { - "model_module": "@jupyter-widgets/base", + "ddebd597ac7544839e8b864a9d0ee839": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_1def5cc260bc404184c09a073ccd4bd2", + "placeholder": "​", + "style": "IPY_MODEL_6957d92bb0cf4c47bfffb9903128dcf5", + "tabbable": null, + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1591222.73it/s]" + } + }, + "faad99f698554ee5a74327e7ca036115": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_460a43df0d3b417d8996679fabe135e8", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_762d2f4c294343aaac239f8510aee6d2", + "tabbable": null, + "tooltip": null, + "value": 50.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index da76b3390..4c0344124 100644 --- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:27.714360Z", - "iopub.status.busy": "2024-06-28T15:37:27.713879Z", - "iopub.status.idle": "2024-06-28T15:37:28.959982Z", - "shell.execute_reply": "2024-06-28T15:37:28.959396Z" + "iopub.execute_input": "2024-07-01T15:06:52.670679Z", + "iopub.status.busy": "2024-07-01T15:06:52.670319Z", + "iopub.status.idle": "2024-07-01T15:06:53.828911Z", + "shell.execute_reply": "2024-07-01T15:06:53.828417Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:28.962815Z", - "iopub.status.busy": "2024-06-28T15:37:28.962469Z", - "iopub.status.idle": "2024-06-28T15:37:29.160428Z", - "shell.execute_reply": "2024-06-28T15:37:29.159774Z" + "iopub.execute_input": "2024-07-01T15:06:53.831480Z", + "iopub.status.busy": "2024-07-01T15:06:53.831210Z", + "iopub.status.idle": "2024-07-01T15:06:54.013309Z", + "shell.execute_reply": "2024-07-01T15:06:54.012774Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.163692Z", - "iopub.status.busy": "2024-06-28T15:37:29.163188Z", - "iopub.status.idle": "2024-06-28T15:37:29.176473Z", - "shell.execute_reply": "2024-06-28T15:37:29.175820Z" + "iopub.execute_input": "2024-07-01T15:06:54.015638Z", + "iopub.status.busy": "2024-07-01T15:06:54.015446Z", + "iopub.status.idle": "2024-07-01T15:06:54.026660Z", + "shell.execute_reply": "2024-07-01T15:06:54.026227Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.178819Z", - "iopub.status.busy": "2024-06-28T15:37:29.178582Z", - "iopub.status.idle": "2024-06-28T15:37:29.391572Z", - "shell.execute_reply": "2024-06-28T15:37:29.390996Z" + "iopub.execute_input": "2024-07-01T15:06:54.028614Z", + "iopub.status.busy": "2024-07-01T15:06:54.028276Z", + "iopub.status.idle": "2024-07-01T15:06:54.258453Z", + "shell.execute_reply": "2024-07-01T15:06:54.257871Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.393993Z", - "iopub.status.busy": "2024-06-28T15:37:29.393620Z", - "iopub.status.idle": "2024-06-28T15:37:29.420116Z", - "shell.execute_reply": "2024-06-28T15:37:29.419615Z" + "iopub.execute_input": "2024-07-01T15:06:54.260736Z", + "iopub.status.busy": "2024-07-01T15:06:54.260424Z", + "iopub.status.idle": "2024-07-01T15:06:54.286726Z", + "shell.execute_reply": "2024-07-01T15:06:54.286174Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:29.422535Z", - "iopub.status.busy": "2024-06-28T15:37:29.422200Z", - "iopub.status.idle": "2024-06-28T15:37:31.677275Z", - "shell.execute_reply": "2024-06-28T15:37:31.676561Z" + "iopub.execute_input": "2024-07-01T15:06:54.288988Z", + "iopub.status.busy": "2024-07-01T15:06:54.288686Z", + "iopub.status.idle": "2024-07-01T15:06:56.272596Z", + "shell.execute_reply": "2024-07-01T15:06:56.271981Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:31.679930Z", - "iopub.status.busy": "2024-06-28T15:37:31.679401Z", - "iopub.status.idle": "2024-06-28T15:37:31.700730Z", - "shell.execute_reply": "2024-06-28T15:37:31.700165Z" + "iopub.execute_input": "2024-07-01T15:06:56.275147Z", + "iopub.status.busy": "2024-07-01T15:06:56.274650Z", + "iopub.status.idle": "2024-07-01T15:06:56.292694Z", + "shell.execute_reply": "2024-07-01T15:06:56.292260Z" }, "scrolled": true }, @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:31.702898Z", - "iopub.status.busy": "2024-06-28T15:37:31.702688Z", - "iopub.status.idle": "2024-06-28T15:37:33.227201Z", - "shell.execute_reply": "2024-06-28T15:37:33.226576Z" + "iopub.execute_input": "2024-07-01T15:06:56.294630Z", + "iopub.status.busy": "2024-07-01T15:06:56.294450Z", + "iopub.status.idle": "2024-07-01T15:06:57.712997Z", + "shell.execute_reply": "2024-07-01T15:06:57.712387Z" }, "id": "AaHC5MRKjruT" }, @@ -729,10 +729,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.230165Z", - "iopub.status.busy": "2024-06-28T15:37:33.229351Z", - "iopub.status.idle": "2024-06-28T15:37:33.243878Z", - "shell.execute_reply": "2024-06-28T15:37:33.243283Z" + "iopub.execute_input": "2024-07-01T15:06:57.715656Z", + "iopub.status.busy": "2024-07-01T15:06:57.715048Z", + "iopub.status.idle": "2024-07-01T15:06:57.728518Z", + "shell.execute_reply": "2024-07-01T15:06:57.728061Z" }, "id": "Wy27rvyhjruU" }, @@ -781,10 +781,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.246084Z", - "iopub.status.busy": "2024-06-28T15:37:33.245762Z", - "iopub.status.idle": "2024-06-28T15:37:33.325997Z", - "shell.execute_reply": "2024-06-28T15:37:33.325374Z" + "iopub.execute_input": "2024-07-01T15:06:57.730397Z", + "iopub.status.busy": "2024-07-01T15:06:57.730225Z", + "iopub.status.idle": "2024-07-01T15:06:57.801036Z", + "shell.execute_reply": "2024-07-01T15:06:57.800476Z" }, "id": "Db8YHnyVjruU" }, @@ -891,10 +891,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.328410Z", - "iopub.status.busy": "2024-06-28T15:37:33.328174Z", - "iopub.status.idle": "2024-06-28T15:37:33.543623Z", - "shell.execute_reply": "2024-06-28T15:37:33.543031Z" + "iopub.execute_input": "2024-07-01T15:06:57.803338Z", + "iopub.status.busy": "2024-07-01T15:06:57.802990Z", + "iopub.status.idle": "2024-07-01T15:06:58.016553Z", + "shell.execute_reply": "2024-07-01T15:06:58.016010Z" }, "id": "iJqAHuS2jruV" }, @@ -931,10 +931,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.545784Z", - "iopub.status.busy": "2024-06-28T15:37:33.545592Z", - "iopub.status.idle": "2024-06-28T15:37:33.562693Z", - "shell.execute_reply": "2024-06-28T15:37:33.562099Z" + "iopub.execute_input": "2024-07-01T15:06:58.018803Z", + "iopub.status.busy": "2024-07-01T15:06:58.018381Z", + "iopub.status.idle": "2024-07-01T15:06:58.034965Z", + "shell.execute_reply": "2024-07-01T15:06:58.034432Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1400,10 +1400,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.564886Z", - "iopub.status.busy": "2024-06-28T15:37:33.564699Z", - "iopub.status.idle": "2024-06-28T15:37:33.574692Z", - "shell.execute_reply": "2024-06-28T15:37:33.574212Z" + "iopub.execute_input": "2024-07-01T15:06:58.037248Z", + "iopub.status.busy": "2024-07-01T15:06:58.036814Z", + "iopub.status.idle": "2024-07-01T15:06:58.046245Z", + "shell.execute_reply": "2024-07-01T15:06:58.045792Z" }, "id": "0lonvOYvjruV" }, @@ -1550,10 +1550,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.576695Z", - "iopub.status.busy": "2024-06-28T15:37:33.576495Z", - "iopub.status.idle": "2024-06-28T15:37:33.663696Z", - "shell.execute_reply": "2024-06-28T15:37:33.663087Z" + "iopub.execute_input": "2024-07-01T15:06:58.048367Z", + "iopub.status.busy": "2024-07-01T15:06:58.048051Z", + "iopub.status.idle": "2024-07-01T15:06:58.130967Z", + "shell.execute_reply": "2024-07-01T15:06:58.130377Z" }, "id": "MfqTCa3kjruV" }, @@ -1634,10 +1634,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.666354Z", - "iopub.status.busy": "2024-06-28T15:37:33.665907Z", - "iopub.status.idle": "2024-06-28T15:37:33.801045Z", - "shell.execute_reply": "2024-06-28T15:37:33.800289Z" + "iopub.execute_input": "2024-07-01T15:06:58.133536Z", + "iopub.status.busy": "2024-07-01T15:06:58.133071Z", + "iopub.status.idle": "2024-07-01T15:06:58.249759Z", + "shell.execute_reply": "2024-07-01T15:06:58.249159Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1697,10 +1697,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.803432Z", - "iopub.status.busy": "2024-06-28T15:37:33.803187Z", - "iopub.status.idle": "2024-06-28T15:37:33.806941Z", - "shell.execute_reply": "2024-06-28T15:37:33.806446Z" + "iopub.execute_input": "2024-07-01T15:06:58.252274Z", + "iopub.status.busy": "2024-07-01T15:06:58.251852Z", + "iopub.status.idle": "2024-07-01T15:06:58.255729Z", + "shell.execute_reply": "2024-07-01T15:06:58.255184Z" }, "id": "0rXP3ZPWjruW" }, @@ -1738,10 +1738,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.808893Z", - "iopub.status.busy": "2024-06-28T15:37:33.808719Z", - "iopub.status.idle": "2024-06-28T15:37:33.812775Z", - "shell.execute_reply": "2024-06-28T15:37:33.812256Z" + "iopub.execute_input": "2024-07-01T15:06:58.257572Z", + "iopub.status.busy": "2024-07-01T15:06:58.257399Z", + "iopub.status.idle": "2024-07-01T15:06:58.261023Z", + "shell.execute_reply": "2024-07-01T15:06:58.260496Z" }, "id": "-iRPe8KXjruW" }, @@ -1796,10 +1796,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.814864Z", - "iopub.status.busy": "2024-06-28T15:37:33.814431Z", - "iopub.status.idle": "2024-06-28T15:37:33.851921Z", - "shell.execute_reply": "2024-06-28T15:37:33.851323Z" + "iopub.execute_input": "2024-07-01T15:06:58.263055Z", + "iopub.status.busy": "2024-07-01T15:06:58.262734Z", + "iopub.status.idle": "2024-07-01T15:06:58.298627Z", + "shell.execute_reply": "2024-07-01T15:06:58.298174Z" }, "id": "ZpipUliyjruW" }, @@ -1850,10 +1850,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.854343Z", - "iopub.status.busy": "2024-06-28T15:37:33.853885Z", - "iopub.status.idle": "2024-06-28T15:37:33.900346Z", - "shell.execute_reply": "2024-06-28T15:37:33.899841Z" + "iopub.execute_input": "2024-07-01T15:06:58.300556Z", + "iopub.status.busy": "2024-07-01T15:06:58.300384Z", + "iopub.status.idle": "2024-07-01T15:06:58.341152Z", + "shell.execute_reply": "2024-07-01T15:06:58.340674Z" }, "id": "SLq-3q4xjruX" }, @@ -1922,10 +1922,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:33.902629Z", - "iopub.status.busy": "2024-06-28T15:37:33.902277Z", - "iopub.status.idle": "2024-06-28T15:37:34.010055Z", - "shell.execute_reply": "2024-06-28T15:37:34.009418Z" + "iopub.execute_input": "2024-07-01T15:06:58.343232Z", + "iopub.status.busy": "2024-07-01T15:06:58.343056Z", + "iopub.status.idle": "2024-07-01T15:06:58.437535Z", + "shell.execute_reply": "2024-07-01T15:06:58.436855Z" }, "id": "g5LHhhuqFbXK" }, @@ -1957,10 +1957,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.012994Z", - "iopub.status.busy": "2024-06-28T15:37:34.012514Z", - "iopub.status.idle": "2024-06-28T15:37:34.121092Z", - "shell.execute_reply": "2024-06-28T15:37:34.120466Z" + "iopub.execute_input": "2024-07-01T15:06:58.440127Z", + "iopub.status.busy": "2024-07-01T15:06:58.439842Z", + "iopub.status.idle": "2024-07-01T15:06:58.527589Z", + "shell.execute_reply": "2024-07-01T15:06:58.526960Z" }, "id": "p7w8F8ezBcet" }, @@ -2017,10 +2017,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.123432Z", - "iopub.status.busy": "2024-06-28T15:37:34.123168Z", - "iopub.status.idle": "2024-06-28T15:37:34.337858Z", - "shell.execute_reply": "2024-06-28T15:37:34.337368Z" + "iopub.execute_input": "2024-07-01T15:06:58.529972Z", + "iopub.status.busy": "2024-07-01T15:06:58.529737Z", + "iopub.status.idle": "2024-07-01T15:06:58.741167Z", + "shell.execute_reply": "2024-07-01T15:06:58.740717Z" }, "id": "WETRL74tE_sU" }, @@ -2055,10 +2055,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.340126Z", - "iopub.status.busy": "2024-06-28T15:37:34.339793Z", - "iopub.status.idle": "2024-06-28T15:37:34.547731Z", - "shell.execute_reply": "2024-06-28T15:37:34.547093Z" + "iopub.execute_input": "2024-07-01T15:06:58.743495Z", + "iopub.status.busy": "2024-07-01T15:06:58.743153Z", + "iopub.status.idle": "2024-07-01T15:06:58.920954Z", + "shell.execute_reply": "2024-07-01T15:06:58.920411Z" }, "id": "kCfdx2gOLmXS" }, @@ -2220,10 +2220,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.550178Z", - "iopub.status.busy": "2024-06-28T15:37:34.549981Z", - "iopub.status.idle": "2024-06-28T15:37:34.556248Z", - "shell.execute_reply": "2024-06-28T15:37:34.555797Z" + "iopub.execute_input": "2024-07-01T15:06:58.923453Z", + "iopub.status.busy": "2024-07-01T15:06:58.923009Z", + "iopub.status.idle": "2024-07-01T15:06:58.928872Z", + "shell.execute_reply": "2024-07-01T15:06:58.928426Z" }, "id": "-uogYRWFYnuu" }, @@ -2277,10 +2277,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.558418Z", - "iopub.status.busy": "2024-06-28T15:37:34.558088Z", - "iopub.status.idle": "2024-06-28T15:37:34.773972Z", - "shell.execute_reply": "2024-06-28T15:37:34.773468Z" + "iopub.execute_input": "2024-07-01T15:06:58.930892Z", + "iopub.status.busy": "2024-07-01T15:06:58.930502Z", + "iopub.status.idle": "2024-07-01T15:06:59.148406Z", + "shell.execute_reply": "2024-07-01T15:06:59.147826Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2327,10 +2327,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:34.776118Z", - "iopub.status.busy": "2024-06-28T15:37:34.775925Z", - "iopub.status.idle": "2024-06-28T15:37:35.877501Z", - "shell.execute_reply": "2024-06-28T15:37:35.877002Z" + "iopub.execute_input": "2024-07-01T15:06:59.150754Z", + "iopub.status.busy": "2024-07-01T15:06:59.150391Z", + "iopub.status.idle": "2024-07-01T15:07:00.213417Z", + "shell.execute_reply": "2024-07-01T15:07:00.212813Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 8af61e177..b426f5b7a 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -88,10 +88,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:39.503284Z", - "iopub.status.busy": "2024-06-28T15:37:39.503099Z", - "iopub.status.idle": "2024-06-28T15:37:40.687920Z", - "shell.execute_reply": "2024-06-28T15:37:40.687390Z" + "iopub.execute_input": "2024-07-01T15:07:03.695403Z", + "iopub.status.busy": "2024-07-01T15:07:03.695236Z", + "iopub.status.idle": "2024-07-01T15:07:04.786480Z", + "shell.execute_reply": "2024-07-01T15:07:04.785971Z" }, "nbsphinx": "hidden" }, @@ -101,7 +101,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.690877Z", - "iopub.status.busy": "2024-06-28T15:37:40.690286Z", - "iopub.status.idle": "2024-06-28T15:37:40.693591Z", - "shell.execute_reply": "2024-06-28T15:37:40.693053Z" + "iopub.execute_input": "2024-07-01T15:07:04.789244Z", + "iopub.status.busy": "2024-07-01T15:07:04.788665Z", + "iopub.status.idle": "2024-07-01T15:07:04.791892Z", + "shell.execute_reply": "2024-07-01T15:07:04.791444Z" } }, "outputs": [], @@ -263,10 +263,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.695825Z", - "iopub.status.busy": "2024-06-28T15:37:40.695507Z", - "iopub.status.idle": "2024-06-28T15:37:40.703252Z", - "shell.execute_reply": "2024-06-28T15:37:40.702722Z" + "iopub.execute_input": "2024-07-01T15:07:04.793920Z", + "iopub.status.busy": "2024-07-01T15:07:04.793593Z", + "iopub.status.idle": "2024-07-01T15:07:04.801265Z", + "shell.execute_reply": "2024-07-01T15:07:04.800810Z" }, "nbsphinx": "hidden" }, @@ -350,10 +350,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.705494Z", - "iopub.status.busy": "2024-06-28T15:37:40.705178Z", - "iopub.status.idle": "2024-06-28T15:37:40.752328Z", - "shell.execute_reply": "2024-06-28T15:37:40.751695Z" + "iopub.execute_input": "2024-07-01T15:07:04.803286Z", + "iopub.status.busy": "2024-07-01T15:07:04.802900Z", + "iopub.status.idle": "2024-07-01T15:07:04.850047Z", + "shell.execute_reply": "2024-07-01T15:07:04.849570Z" } }, "outputs": [], @@ -379,10 +379,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.755037Z", - "iopub.status.busy": "2024-06-28T15:37:40.754588Z", - "iopub.status.idle": "2024-06-28T15:37:40.771776Z", - "shell.execute_reply": "2024-06-28T15:37:40.771317Z" + "iopub.execute_input": "2024-07-01T15:07:04.852247Z", + "iopub.status.busy": "2024-07-01T15:07:04.852061Z", + "iopub.status.idle": "2024-07-01T15:07:04.869485Z", + "shell.execute_reply": "2024-07-01T15:07:04.869018Z" } }, "outputs": [ @@ -597,10 +597,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.774143Z", - "iopub.status.busy": "2024-06-28T15:37:40.773746Z", - "iopub.status.idle": "2024-06-28T15:37:40.777708Z", - "shell.execute_reply": "2024-06-28T15:37:40.777230Z" + "iopub.execute_input": "2024-07-01T15:07:04.871391Z", + "iopub.status.busy": "2024-07-01T15:07:04.871213Z", + "iopub.status.idle": "2024-07-01T15:07:04.875222Z", + "shell.execute_reply": "2024-07-01T15:07:04.874787Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.779858Z", - "iopub.status.busy": "2024-06-28T15:37:40.779536Z", - "iopub.status.idle": "2024-06-28T15:37:40.792878Z", - "shell.execute_reply": "2024-06-28T15:37:40.792415Z" + "iopub.execute_input": "2024-07-01T15:07:04.877103Z", + "iopub.status.busy": "2024-07-01T15:07:04.876935Z", + "iopub.status.idle": "2024-07-01T15:07:04.890567Z", + "shell.execute_reply": "2024-07-01T15:07:04.890109Z" } }, "outputs": [], @@ -698,10 +698,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.794938Z", - "iopub.status.busy": "2024-06-28T15:37:40.794619Z", - "iopub.status.idle": "2024-06-28T15:37:40.821318Z", - "shell.execute_reply": "2024-06-28T15:37:40.820726Z" + "iopub.execute_input": "2024-07-01T15:07:04.892340Z", + "iopub.status.busy": "2024-07-01T15:07:04.892165Z", + "iopub.status.idle": "2024-07-01T15:07:04.917921Z", + "shell.execute_reply": "2024-07-01T15:07:04.917510Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:40.823690Z", - "iopub.status.busy": "2024-06-28T15:37:40.823369Z", - "iopub.status.idle": "2024-06-28T15:37:42.858786Z", - "shell.execute_reply": "2024-06-28T15:37:42.858257Z" + "iopub.execute_input": "2024-07-01T15:07:04.919909Z", + "iopub.status.busy": "2024-07-01T15:07:04.919740Z", + "iopub.status.idle": "2024-07-01T15:07:06.770405Z", + "shell.execute_reply": "2024-07-01T15:07:06.769771Z" } }, "outputs": [], @@ -771,10 +771,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.861902Z", - "iopub.status.busy": "2024-06-28T15:37:42.861153Z", - "iopub.status.idle": "2024-06-28T15:37:42.868329Z", - "shell.execute_reply": "2024-06-28T15:37:42.867757Z" + "iopub.execute_input": "2024-07-01T15:07:06.773094Z", + "iopub.status.busy": "2024-07-01T15:07:06.772569Z", + "iopub.status.idle": "2024-07-01T15:07:06.779189Z", + "shell.execute_reply": "2024-07-01T15:07:06.778660Z" }, "scrolled": true }, @@ -885,10 +885,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.870554Z", - "iopub.status.busy": "2024-06-28T15:37:42.870224Z", - "iopub.status.idle": "2024-06-28T15:37:42.882956Z", - "shell.execute_reply": "2024-06-28T15:37:42.882441Z" + "iopub.execute_input": "2024-07-01T15:07:06.781060Z", + "iopub.status.busy": "2024-07-01T15:07:06.780797Z", + "iopub.status.idle": "2024-07-01T15:07:06.793132Z", + "shell.execute_reply": "2024-07-01T15:07:06.792613Z" } }, "outputs": [ @@ -1138,10 +1138,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.885139Z", - "iopub.status.busy": "2024-06-28T15:37:42.884799Z", - "iopub.status.idle": "2024-06-28T15:37:42.891477Z", - "shell.execute_reply": "2024-06-28T15:37:42.890929Z" + "iopub.execute_input": "2024-07-01T15:07:06.795311Z", + "iopub.status.busy": "2024-07-01T15:07:06.794896Z", + "iopub.status.idle": "2024-07-01T15:07:06.801219Z", + "shell.execute_reply": "2024-07-01T15:07:06.800801Z" }, "scrolled": true }, @@ -1315,10 +1315,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.893662Z", - "iopub.status.busy": "2024-06-28T15:37:42.893329Z", - "iopub.status.idle": "2024-06-28T15:37:42.895941Z", - "shell.execute_reply": "2024-06-28T15:37:42.895509Z" + "iopub.execute_input": "2024-07-01T15:07:06.803175Z", + "iopub.status.busy": "2024-07-01T15:07:06.802994Z", + "iopub.status.idle": "2024-07-01T15:07:06.805670Z", + "shell.execute_reply": "2024-07-01T15:07:06.805234Z" } }, "outputs": [], @@ -1340,10 +1340,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.898071Z", - "iopub.status.busy": "2024-06-28T15:37:42.897574Z", - "iopub.status.idle": "2024-06-28T15:37:42.901102Z", - "shell.execute_reply": "2024-06-28T15:37:42.900664Z" + "iopub.execute_input": "2024-07-01T15:07:06.807492Z", + "iopub.status.busy": "2024-07-01T15:07:06.807328Z", + "iopub.status.idle": "2024-07-01T15:07:06.810895Z", + "shell.execute_reply": "2024-07-01T15:07:06.810453Z" }, "scrolled": true }, @@ -1395,10 +1395,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.903184Z", - "iopub.status.busy": "2024-06-28T15:37:42.902860Z", - "iopub.status.idle": "2024-06-28T15:37:42.905499Z", - "shell.execute_reply": "2024-06-28T15:37:42.905046Z" + "iopub.execute_input": "2024-07-01T15:07:06.812899Z", + "iopub.status.busy": "2024-07-01T15:07:06.812510Z", + "iopub.status.idle": "2024-07-01T15:07:06.815130Z", + "shell.execute_reply": "2024-07-01T15:07:06.814702Z" } }, "outputs": [], @@ -1422,10 +1422,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.907596Z", - "iopub.status.busy": "2024-06-28T15:37:42.907270Z", - "iopub.status.idle": "2024-06-28T15:37:42.911435Z", - "shell.execute_reply": "2024-06-28T15:37:42.910901Z" + "iopub.execute_input": "2024-07-01T15:07:06.817057Z", + "iopub.status.busy": "2024-07-01T15:07:06.816734Z", + "iopub.status.idle": "2024-07-01T15:07:06.820893Z", + "shell.execute_reply": "2024-07-01T15:07:06.820448Z" } }, "outputs": [ @@ -1480,10 +1480,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.913391Z", - "iopub.status.busy": "2024-06-28T15:37:42.913217Z", - "iopub.status.idle": "2024-06-28T15:37:42.941833Z", - "shell.execute_reply": "2024-06-28T15:37:42.941368Z" + "iopub.execute_input": "2024-07-01T15:07:06.822875Z", + "iopub.status.busy": "2024-07-01T15:07:06.822704Z", + "iopub.status.idle": "2024-07-01T15:07:06.851357Z", + "shell.execute_reply": "2024-07-01T15:07:06.850916Z" } }, "outputs": [], @@ -1526,10 +1526,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:42.944033Z", - "iopub.status.busy": "2024-06-28T15:37:42.943856Z", - "iopub.status.idle": "2024-06-28T15:37:42.948433Z", - "shell.execute_reply": "2024-06-28T15:37:42.948007Z" + "iopub.execute_input": "2024-07-01T15:07:06.853186Z", + "iopub.status.busy": "2024-07-01T15:07:06.853017Z", + "iopub.status.idle": "2024-07-01T15:07:06.857526Z", + "shell.execute_reply": "2024-07-01T15:07:06.857095Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index fa739c4ac..f9fccade5 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -64,10 +64,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:46.062655Z", - "iopub.status.busy": "2024-06-28T15:37:46.062439Z", - "iopub.status.idle": "2024-06-28T15:37:47.278130Z", - "shell.execute_reply": "2024-06-28T15:37:47.277493Z" + "iopub.execute_input": "2024-07-01T15:07:09.805934Z", + "iopub.status.busy": "2024-07-01T15:07:09.805760Z", + "iopub.status.idle": "2024-07-01T15:07:10.951874Z", + "shell.execute_reply": "2024-07-01T15:07:10.951332Z" }, "nbsphinx": "hidden" }, @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -105,10 +105,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.281006Z", - "iopub.status.busy": "2024-06-28T15:37:47.280461Z", - "iopub.status.idle": "2024-06-28T15:37:47.487135Z", - "shell.execute_reply": "2024-06-28T15:37:47.486629Z" + "iopub.execute_input": "2024-07-01T15:07:10.954229Z", + "iopub.status.busy": "2024-07-01T15:07:10.953974Z", + "iopub.status.idle": "2024-07-01T15:07:11.145898Z", + "shell.execute_reply": "2024-07-01T15:07:11.145328Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.490066Z", - "iopub.status.busy": "2024-06-28T15:37:47.489490Z", - "iopub.status.idle": "2024-06-28T15:37:47.503413Z", - "shell.execute_reply": "2024-06-28T15:37:47.502810Z" + "iopub.execute_input": "2024-07-01T15:07:11.148952Z", + "iopub.status.busy": "2024-07-01T15:07:11.148446Z", + "iopub.status.idle": "2024-07-01T15:07:11.162195Z", + "shell.execute_reply": "2024-07-01T15:07:11.161701Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:47.505906Z", - "iopub.status.busy": "2024-06-28T15:37:47.505486Z", - "iopub.status.idle": "2024-06-28T15:37:50.206513Z", - "shell.execute_reply": "2024-06-28T15:37:50.205911Z" + "iopub.execute_input": "2024-07-01T15:07:11.164347Z", + "iopub.status.busy": "2024-07-01T15:07:11.163932Z", + "iopub.status.idle": "2024-07-01T15:07:13.785011Z", + "shell.execute_reply": "2024-07-01T15:07:13.784429Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:50.208774Z", - "iopub.status.busy": "2024-06-28T15:37:50.208544Z", - "iopub.status.idle": "2024-06-28T15:37:51.579425Z", - "shell.execute_reply": "2024-06-28T15:37:51.578910Z" + "iopub.execute_input": "2024-07-01T15:07:13.787263Z", + "iopub.status.busy": "2024-07-01T15:07:13.787077Z", + "iopub.status.idle": "2024-07-01T15:07:15.129078Z", + "shell.execute_reply": "2024-07-01T15:07:15.128523Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:51.581852Z", - "iopub.status.busy": "2024-06-28T15:37:51.581666Z", - "iopub.status.idle": "2024-06-28T15:37:51.586007Z", - "shell.execute_reply": "2024-06-28T15:37:51.585531Z" + "iopub.execute_input": "2024-07-01T15:07:15.131446Z", + "iopub.status.busy": "2024-07-01T15:07:15.131256Z", + "iopub.status.idle": "2024-07-01T15:07:15.135063Z", + "shell.execute_reply": "2024-07-01T15:07:15.134547Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:51.587856Z", - "iopub.status.busy": "2024-06-28T15:37:51.587684Z", - "iopub.status.idle": "2024-06-28T15:37:53.735997Z", - "shell.execute_reply": "2024-06-28T15:37:53.735310Z" + "iopub.execute_input": "2024-07-01T15:07:15.137004Z", + "iopub.status.busy": "2024-07-01T15:07:15.136825Z", + "iopub.status.idle": "2024-07-01T15:07:17.134343Z", + "shell.execute_reply": "2024-07-01T15:07:17.133735Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:53.738554Z", - "iopub.status.busy": "2024-06-28T15:37:53.738136Z", - "iopub.status.idle": "2024-06-28T15:37:53.746671Z", - "shell.execute_reply": "2024-06-28T15:37:53.746192Z" + "iopub.execute_input": "2024-07-01T15:07:17.136852Z", + "iopub.status.busy": "2024-07-01T15:07:17.136398Z", + "iopub.status.idle": "2024-07-01T15:07:17.144283Z", + "shell.execute_reply": "2024-07-01T15:07:17.143851Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:53.748672Z", - "iopub.status.busy": "2024-06-28T15:37:53.748467Z", - "iopub.status.idle": "2024-06-28T15:37:56.383626Z", - "shell.execute_reply": "2024-06-28T15:37:56.383027Z" + "iopub.execute_input": "2024-07-01T15:07:17.146377Z", + "iopub.status.busy": "2024-07-01T15:07:17.146026Z", + "iopub.status.idle": "2024-07-01T15:07:19.687541Z", + "shell.execute_reply": "2024-07-01T15:07:19.686980Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.385815Z", - "iopub.status.busy": "2024-06-28T15:37:56.385620Z", - "iopub.status.idle": "2024-06-28T15:37:56.389671Z", - "shell.execute_reply": "2024-06-28T15:37:56.389205Z" + "iopub.execute_input": "2024-07-01T15:07:19.689886Z", + "iopub.status.busy": "2024-07-01T15:07:19.689435Z", + "iopub.status.idle": "2024-07-01T15:07:19.692913Z", + "shell.execute_reply": "2024-07-01T15:07:19.692502Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.391657Z", - "iopub.status.busy": "2024-06-28T15:37:56.391481Z", - "iopub.status.idle": "2024-06-28T15:37:56.395227Z", - "shell.execute_reply": "2024-06-28T15:37:56.394761Z" + "iopub.execute_input": "2024-07-01T15:07:19.694737Z", + "iopub.status.busy": "2024-07-01T15:07:19.694568Z", + "iopub.status.idle": "2024-07-01T15:07:19.698060Z", + "shell.execute_reply": "2024-07-01T15:07:19.697520Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:56.397415Z", - "iopub.status.busy": "2024-06-28T15:37:56.397086Z", - "iopub.status.idle": "2024-06-28T15:37:56.400385Z", - "shell.execute_reply": "2024-06-28T15:37:56.399921Z" + "iopub.execute_input": "2024-07-01T15:07:19.700087Z", + "iopub.status.busy": "2024-07-01T15:07:19.699691Z", + "iopub.status.idle": "2024-07-01T15:07:19.702785Z", + "shell.execute_reply": "2024-07-01T15:07:19.702302Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 19c4a295c..013f8cdd9 100644 --- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:37:59.018700Z", - "iopub.status.busy": "2024-06-28T15:37:59.018517Z", - "iopub.status.idle": "2024-06-28T15:38:00.265105Z", - "shell.execute_reply": "2024-06-28T15:38:00.264576Z" + "iopub.execute_input": "2024-07-01T15:07:22.044165Z", + "iopub.status.busy": "2024-07-01T15:07:22.043990Z", + "iopub.status.idle": "2024-07-01T15:07:23.190619Z", + "shell.execute_reply": "2024-07-01T15:07:23.190110Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:00.267828Z", - "iopub.status.busy": "2024-06-28T15:38:00.267338Z", - "iopub.status.idle": "2024-06-28T15:38:01.587223Z", - "shell.execute_reply": "2024-06-28T15:38:01.586436Z" + "iopub.execute_input": "2024-07-01T15:07:23.192992Z", + "iopub.status.busy": "2024-07-01T15:07:23.192742Z", + "iopub.status.idle": "2024-07-01T15:07:24.642885Z", + "shell.execute_reply": "2024-07-01T15:07:24.642213Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.589871Z", - "iopub.status.busy": "2024-06-28T15:38:01.589663Z", - "iopub.status.idle": "2024-06-28T15:38:01.592876Z", - "shell.execute_reply": "2024-06-28T15:38:01.592418Z" + "iopub.execute_input": "2024-07-01T15:07:24.645453Z", + "iopub.status.busy": "2024-07-01T15:07:24.645208Z", + "iopub.status.idle": "2024-07-01T15:07:24.648320Z", + "shell.execute_reply": "2024-07-01T15:07:24.647888Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.594798Z", - "iopub.status.busy": "2024-06-28T15:38:01.594621Z", - "iopub.status.idle": "2024-06-28T15:38:01.600889Z", - "shell.execute_reply": "2024-06-28T15:38:01.600425Z" + "iopub.execute_input": "2024-07-01T15:07:24.650220Z", + "iopub.status.busy": "2024-07-01T15:07:24.650036Z", + "iopub.status.idle": "2024-07-01T15:07:24.656028Z", + "shell.execute_reply": "2024-07-01T15:07:24.655606Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:01.602887Z", - "iopub.status.busy": "2024-06-28T15:38:01.602700Z", - "iopub.status.idle": "2024-06-28T15:38:02.098288Z", - "shell.execute_reply": "2024-06-28T15:38:02.097701Z" + "iopub.execute_input": "2024-07-01T15:07:24.657894Z", + "iopub.status.busy": "2024-07-01T15:07:24.657721Z", + "iopub.status.idle": "2024-07-01T15:07:25.141231Z", + "shell.execute_reply": "2024-07-01T15:07:25.140651Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.101009Z", - "iopub.status.busy": "2024-06-28T15:38:02.100809Z", - "iopub.status.idle": "2024-06-28T15:38:02.106483Z", - "shell.execute_reply": "2024-06-28T15:38:02.106007Z" + "iopub.execute_input": "2024-07-01T15:07:25.144290Z", + "iopub.status.busy": "2024-07-01T15:07:25.143822Z", + "iopub.status.idle": "2024-07-01T15:07:25.149165Z", + "shell.execute_reply": "2024-07-01T15:07:25.148739Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.108382Z", - "iopub.status.busy": "2024-06-28T15:38:02.108204Z", - "iopub.status.idle": "2024-06-28T15:38:02.112634Z", - "shell.execute_reply": "2024-06-28T15:38:02.112164Z" + "iopub.execute_input": "2024-07-01T15:07:25.151192Z", + "iopub.status.busy": "2024-07-01T15:07:25.150870Z", + "iopub.status.idle": "2024-07-01T15:07:25.154548Z", + "shell.execute_reply": "2024-07-01T15:07:25.154108Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:02.114751Z", - "iopub.status.busy": "2024-06-28T15:38:02.114411Z", - "iopub.status.idle": "2024-06-28T15:38:03.057670Z", - "shell.execute_reply": "2024-06-28T15:38:03.056990Z" + "iopub.execute_input": "2024-07-01T15:07:25.156514Z", + "iopub.status.busy": "2024-07-01T15:07:25.156335Z", + "iopub.status.idle": "2024-07-01T15:07:26.038062Z", + "shell.execute_reply": "2024-07-01T15:07:26.037425Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.060460Z", - "iopub.status.busy": "2024-06-28T15:38:03.059841Z", - "iopub.status.idle": "2024-06-28T15:38:03.286800Z", - "shell.execute_reply": "2024-06-28T15:38:03.286263Z" + "iopub.execute_input": "2024-07-01T15:07:26.040299Z", + "iopub.status.busy": "2024-07-01T15:07:26.040059Z", + "iopub.status.idle": "2024-07-01T15:07:26.281733Z", + "shell.execute_reply": "2024-07-01T15:07:26.281238Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.289126Z", - "iopub.status.busy": "2024-06-28T15:38:03.288767Z", - "iopub.status.idle": "2024-06-28T15:38:03.293054Z", - "shell.execute_reply": "2024-06-28T15:38:03.292569Z" + "iopub.execute_input": "2024-07-01T15:07:26.284005Z", + "iopub.status.busy": "2024-07-01T15:07:26.283674Z", + "iopub.status.idle": "2024-07-01T15:07:26.287739Z", + "shell.execute_reply": "2024-07-01T15:07:26.287303Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.295148Z", - "iopub.status.busy": "2024-06-28T15:38:03.294835Z", - "iopub.status.idle": "2024-06-28T15:38:03.761009Z", - "shell.execute_reply": "2024-06-28T15:38:03.760370Z" + "iopub.execute_input": "2024-07-01T15:07:26.289717Z", + "iopub.status.busy": "2024-07-01T15:07:26.289415Z", + "iopub.status.idle": "2024-07-01T15:07:26.747330Z", + "shell.execute_reply": "2024-07-01T15:07:26.746844Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:03.764257Z", - "iopub.status.busy": "2024-06-28T15:38:03.763873Z", - "iopub.status.idle": "2024-06-28T15:38:04.100471Z", - "shell.execute_reply": "2024-06-28T15:38:04.099895Z" + "iopub.execute_input": "2024-07-01T15:07:26.749504Z", + "iopub.status.busy": "2024-07-01T15:07:26.749157Z", + "iopub.status.idle": "2024-07-01T15:07:27.049969Z", + "shell.execute_reply": "2024-07-01T15:07:27.049390Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.103987Z", - "iopub.status.busy": "2024-06-28T15:38:04.103475Z", - "iopub.status.idle": "2024-06-28T15:38:04.445171Z", - "shell.execute_reply": "2024-06-28T15:38:04.444553Z" + "iopub.execute_input": "2024-07-01T15:07:27.052016Z", + "iopub.status.busy": "2024-07-01T15:07:27.051834Z", + "iopub.status.idle": "2024-07-01T15:07:27.386953Z", + "shell.execute_reply": "2024-07-01T15:07:27.386354Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.448311Z", - "iopub.status.busy": "2024-06-28T15:38:04.447953Z", - "iopub.status.idle": "2024-06-28T15:38:04.862739Z", - "shell.execute_reply": "2024-06-28T15:38:04.862176Z" + "iopub.execute_input": "2024-07-01T15:07:27.390094Z", + "iopub.status.busy": "2024-07-01T15:07:27.389720Z", + "iopub.status.idle": "2024-07-01T15:07:27.826810Z", + "shell.execute_reply": "2024-07-01T15:07:27.826201Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:04.867087Z", - "iopub.status.busy": "2024-06-28T15:38:04.866704Z", - "iopub.status.idle": "2024-06-28T15:38:05.319876Z", - "shell.execute_reply": "2024-06-28T15:38:05.319267Z" + "iopub.execute_input": "2024-07-01T15:07:27.830888Z", + "iopub.status.busy": "2024-07-01T15:07:27.830547Z", + "iopub.status.idle": "2024-07-01T15:07:28.275927Z", + "shell.execute_reply": "2024-07-01T15:07:28.275306Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.322789Z", - "iopub.status.busy": "2024-06-28T15:38:05.322420Z", - "iopub.status.idle": "2024-06-28T15:38:05.539025Z", - "shell.execute_reply": "2024-06-28T15:38:05.538465Z" + "iopub.execute_input": "2024-07-01T15:07:28.278580Z", + "iopub.status.busy": "2024-07-01T15:07:28.278386Z", + "iopub.status.idle": "2024-07-01T15:07:28.478171Z", + "shell.execute_reply": "2024-07-01T15:07:28.477537Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.541213Z", - "iopub.status.busy": "2024-06-28T15:38:05.541026Z", - "iopub.status.idle": "2024-06-28T15:38:05.743328Z", - "shell.execute_reply": "2024-06-28T15:38:05.742791Z" + "iopub.execute_input": "2024-07-01T15:07:28.481029Z", + "iopub.status.busy": "2024-07-01T15:07:28.480514Z", + "iopub.status.idle": "2024-07-01T15:07:28.679630Z", + "shell.execute_reply": "2024-07-01T15:07:28.679032Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.745675Z", - "iopub.status.busy": "2024-06-28T15:38:05.745487Z", - "iopub.status.idle": "2024-06-28T15:38:05.748456Z", - "shell.execute_reply": "2024-06-28T15:38:05.748001Z" + "iopub.execute_input": "2024-07-01T15:07:28.681815Z", + "iopub.status.busy": "2024-07-01T15:07:28.681633Z", + "iopub.status.idle": "2024-07-01T15:07:28.684760Z", + "shell.execute_reply": "2024-07-01T15:07:28.684215Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:05.750297Z", - "iopub.status.busy": "2024-06-28T15:38:05.750127Z", - "iopub.status.idle": "2024-06-28T15:38:06.726754Z", - "shell.execute_reply": "2024-06-28T15:38:06.726181Z" + "iopub.execute_input": "2024-07-01T15:07:28.686736Z", + "iopub.status.busy": "2024-07-01T15:07:28.686404Z", + "iopub.status.idle": "2024-07-01T15:07:29.599883Z", + "shell.execute_reply": "2024-07-01T15:07:29.599378Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:06.729839Z", - "iopub.status.busy": "2024-06-28T15:38:06.729460Z", - "iopub.status.idle": "2024-06-28T15:38:06.872733Z", - "shell.execute_reply": "2024-06-28T15:38:06.872148Z" + "iopub.execute_input": "2024-07-01T15:07:29.602491Z", + "iopub.status.busy": "2024-07-01T15:07:29.602156Z", + "iopub.status.idle": "2024-07-01T15:07:29.724845Z", + "shell.execute_reply": "2024-07-01T15:07:29.724400Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:06.875073Z", - "iopub.status.busy": "2024-06-28T15:38:06.874723Z", - "iopub.status.idle": "2024-06-28T15:38:07.017945Z", - "shell.execute_reply": "2024-06-28T15:38:07.017419Z" + "iopub.execute_input": "2024-07-01T15:07:29.727049Z", + "iopub.status.busy": "2024-07-01T15:07:29.726723Z", + "iopub.status.idle": "2024-07-01T15:07:29.857120Z", + "shell.execute_reply": "2024-07-01T15:07:29.856610Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:07.020497Z", - "iopub.status.busy": "2024-06-28T15:38:07.020139Z", - "iopub.status.idle": "2024-06-28T15:38:07.772705Z", - "shell.execute_reply": "2024-06-28T15:38:07.772059Z" + "iopub.execute_input": "2024-07-01T15:07:29.859645Z", + "iopub.status.busy": "2024-07-01T15:07:29.859295Z", + "iopub.status.idle": "2024-07-01T15:07:30.599850Z", + "shell.execute_reply": "2024-07-01T15:07:30.599307Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:07.775031Z", - "iopub.status.busy": "2024-06-28T15:38:07.774718Z", - "iopub.status.idle": "2024-06-28T15:38:07.778431Z", - "shell.execute_reply": "2024-06-28T15:38:07.777967Z" + "iopub.execute_input": "2024-07-01T15:07:30.602022Z", + "iopub.status.busy": "2024-07-01T15:07:30.601697Z", + "iopub.status.idle": "2024-07-01T15:07:30.605345Z", + "shell.execute_reply": "2024-07-01T15:07:30.604899Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 6169cfa22..de1ca9206 100644 --- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:10.079179Z", - "iopub.status.busy": "2024-06-28T15:38:10.078994Z", - "iopub.status.idle": "2024-06-28T15:38:12.976778Z", - "shell.execute_reply": "2024-06-28T15:38:12.976174Z" + "iopub.execute_input": "2024-07-01T15:07:32.630513Z", + "iopub.status.busy": "2024-07-01T15:07:32.630022Z", + "iopub.status.idle": "2024-07-01T15:07:35.339624Z", + "shell.execute_reply": "2024-07-01T15:07:35.338990Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:12.979625Z", - "iopub.status.busy": "2024-06-28T15:38:12.979083Z", - "iopub.status.idle": "2024-06-28T15:38:13.326543Z", - "shell.execute_reply": "2024-06-28T15:38:13.326026Z" + "iopub.execute_input": "2024-07-01T15:07:35.342314Z", + "iopub.status.busy": "2024-07-01T15:07:35.341942Z", + "iopub.status.idle": "2024-07-01T15:07:35.677074Z", + "shell.execute_reply": "2024-07-01T15:07:35.676543Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:13.329227Z", - "iopub.status.busy": "2024-06-28T15:38:13.328804Z", - "iopub.status.idle": "2024-06-28T15:38:13.332930Z", - "shell.execute_reply": "2024-06-28T15:38:13.332475Z" + "iopub.execute_input": "2024-07-01T15:07:35.679709Z", + "iopub.status.busy": "2024-07-01T15:07:35.679377Z", + "iopub.status.idle": "2024-07-01T15:07:35.683566Z", + "shell.execute_reply": "2024-07-01T15:07:35.683132Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:13.335012Z", - "iopub.status.busy": "2024-06-28T15:38:13.334691Z", - "iopub.status.idle": "2024-06-28T15:38:17.730900Z", - "shell.execute_reply": "2024-06-28T15:38:17.730361Z" + "iopub.execute_input": "2024-07-01T15:07:35.685686Z", + "iopub.status.busy": "2024-07-01T15:07:35.685365Z", + "iopub.status.idle": "2024-07-01T15:07:42.517988Z", + "shell.execute_reply": "2024-07-01T15:07:42.517428Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 851968/170498071 [00:00<00:20, 8336065.33it/s]" + " 0%| | 786432/170498071 [00:00<00:21, 7820176.68it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 10977280/170498071 [00:00<00:02, 62438830.70it/s]" + " 3%|▎ | 4980736/170498071 [00:00<00:05, 27792797.45it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 20873216/170498071 [00:00<00:01, 79009100.57it/s]" + " 6%|▋ | 10944512/170498071 [00:00<00:03, 42298222.53it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 30736384/170498071 [00:00<00:01, 86709726.78it/s]" + " 10%|▉ | 16449536/170498071 [00:00<00:03, 47151386.97it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 40763392/170498071 [00:00<00:01, 91578171.64it/s]" + " 12%|█▏ | 21168128/170498071 [00:00<00:03, 41939697.62it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 50528256/170498071 [00:00<00:01, 93630346.09it/s]" + " 15%|█▍ | 25460736/170498071 [00:00<00:04, 36250418.01it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 60653568/170498071 [00:00<00:01, 96035464.51it/s]" + " 17%|█▋ | 29261824/170498071 [00:00<00:04, 31518178.43it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 70418432/170498071 [00:00<00:01, 96491443.11it/s]" + " 19%|█▉ | 32604160/170498071 [00:00<00:04, 29639430.93it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 80543744/170498071 [00:00<00:00, 97924676.51it/s]" + " 21%|██ | 35684352/170498071 [00:01<00:04, 28240309.80it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 90341376/170498071 [00:01<00:00, 97866529.70it/s]" + " 23%|██▎ | 38600704/170498071 [00:01<00:04, 27686377.59it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 100368384/170498071 [00:01<00:00, 98505652.49it/s]" + " 24%|██▍ | 41451520/170498071 [00:01<00:04, 27880418.29it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 110362624/170498071 [00:01<00:00, 98885459.16it/s]" + " 26%|██▌ | 44302336/170498071 [00:01<00:04, 27339381.04it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 120258560/170498071 [00:01<00:00, 98548885.72it/s]" + " 28%|██▊ | 47153152/170498071 [00:01<00:04, 27588406.47it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 130187264/170498071 [00:01<00:00, 98667276.16it/s]" + " 29%|██▉ | 49938432/170498071 [00:01<00:04, 27519027.46it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 141361152/170498071 [00:01<00:00, 102538275.84it/s]" + " 31%|███ | 52723712/170498071 [00:01<00:04, 26308629.52it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 152797184/170498071 [00:01<00:00, 105995144.12it/s]" + " 32%|███▏ | 55377920/170498071 [00:01<00:04, 25864377.41it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 163414016/170498071 [00:01<00:00, 104278726.49it/s]" + " 35%|███▍ | 58884096/170498071 [00:01<00:03, 28360977.09it/s]" ] }, { @@ -388,7 +388,183 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:01<00:00, 95344245.43it/s] " + " 37%|███▋ | 62717952/170498071 [00:02<00:03, 31182568.80it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 39%|███▉ | 66846720/170498071 [00:02<00:03, 34093308.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 42%|████▏ | 71303168/170498071 [00:02<00:02, 37046963.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 45%|████▍ | 76513280/170498071 [00:02<00:02, 41418183.98it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 47%|████▋ | 80871424/170498071 [00:02<00:02, 42002402.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 50%|█████ | 85721088/170498071 [00:02<00:01, 43883828.77it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 53%|█████▎ | 90144768/170498071 [00:02<00:01, 43748354.63it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|█████▌ | 94633984/170498071 [00:02<00:01, 43921848.70it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 58%|█████▊ | 99057664/170498071 [00:02<00:01, 43385313.39it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 61%|██████ | 104136704/170498071 [00:02<00:01, 45560088.81it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 64%|██████▍ | 108724224/170498071 [00:03<00:01, 43798020.18it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 113573888/170498071 [00:03<00:01, 45129830.46it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 70%|██████▉ | 118554624/170498071 [00:03<00:01, 46326838.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 72%|███████▏ | 123207680/170498071 [00:03<00:01, 46221454.72it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 75%|███████▍ | 127860736/170498071 [00:03<00:00, 45331471.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 78%|███████▊ | 132775936/170498071 [00:03<00:00, 46431225.49it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 81%|████████ | 137756672/170498071 [00:03<00:00, 47136533.89it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 84%|████████▍ | 142966784/170498071 [00:03<00:00, 48566305.03it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 147849216/170498071 [00:03<00:00, 47837503.41it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|█████████ | 153649152/170498071 [00:03<00:00, 50699005.24it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 94%|█████████▍| 160006144/170498071 [00:04<00:00, 54457105.34it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 166297600/170498071 [00:04<00:00, 56798743.02it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:04<00:00, 39742139.91it/s]" ] }, { @@ -506,10 +682,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:17.733413Z", - "iopub.status.busy": "2024-06-28T15:38:17.732871Z", - "iopub.status.idle": "2024-06-28T15:38:17.738007Z", - "shell.execute_reply": "2024-06-28T15:38:17.737546Z" + "iopub.execute_input": "2024-07-01T15:07:42.520241Z", + "iopub.status.busy": "2024-07-01T15:07:42.519920Z", + "iopub.status.idle": "2024-07-01T15:07:42.524659Z", + "shell.execute_reply": "2024-07-01T15:07:42.524205Z" }, "nbsphinx": "hidden" }, @@ -560,10 +736,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:17.740340Z", - "iopub.status.busy": "2024-06-28T15:38:17.739963Z", - "iopub.status.idle": "2024-06-28T15:38:18.297381Z", - "shell.execute_reply": "2024-06-28T15:38:18.296749Z" + "iopub.execute_input": "2024-07-01T15:07:42.526805Z", + "iopub.status.busy": "2024-07-01T15:07:42.526366Z", + "iopub.status.idle": "2024-07-01T15:07:43.068980Z", + "shell.execute_reply": "2024-07-01T15:07:43.068376Z" } }, "outputs": [ @@ -596,10 +772,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.299866Z", - "iopub.status.busy": "2024-06-28T15:38:18.299513Z", - "iopub.status.idle": "2024-06-28T15:38:18.793592Z", - "shell.execute_reply": "2024-06-28T15:38:18.793002Z" + "iopub.execute_input": "2024-07-01T15:07:43.071278Z", + "iopub.status.busy": "2024-07-01T15:07:43.070855Z", + "iopub.status.idle": "2024-07-01T15:07:43.585255Z", + "shell.execute_reply": "2024-07-01T15:07:43.584665Z" } }, "outputs": [ @@ -637,10 +813,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.795690Z", - "iopub.status.busy": "2024-06-28T15:38:18.795502Z", - "iopub.status.idle": "2024-06-28T15:38:18.799031Z", - "shell.execute_reply": "2024-06-28T15:38:18.798587Z" + "iopub.execute_input": "2024-07-01T15:07:43.587754Z", + "iopub.status.busy": "2024-07-01T15:07:43.587325Z", + "iopub.status.idle": "2024-07-01T15:07:43.591527Z", + "shell.execute_reply": "2024-07-01T15:07:43.591015Z" } }, "outputs": [], @@ -663,17 +839,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:18.801250Z", - "iopub.status.busy": "2024-06-28T15:38:18.800914Z", - "iopub.status.idle": "2024-06-28T15:38:31.916783Z", - "shell.execute_reply": "2024-06-28T15:38:31.916167Z" + "iopub.execute_input": "2024-07-01T15:07:43.593739Z", + "iopub.status.busy": "2024-07-01T15:07:43.593377Z", + "iopub.status.idle": "2024-07-01T15:07:56.035606Z", + "shell.execute_reply": "2024-07-01T15:07:56.035038Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "098cb2eeb14f4be9ad0d532925ef1c3a", + "model_id": "6e0af51d1d7c41f6b28e94e107a2e2dd", "version_major": 2, "version_minor": 0 }, @@ -732,10 +908,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:31.919440Z", - "iopub.status.busy": "2024-06-28T15:38:31.918961Z", - "iopub.status.idle": "2024-06-28T15:38:34.027699Z", - "shell.execute_reply": "2024-06-28T15:38:34.027090Z" + "iopub.execute_input": "2024-07-01T15:07:56.037942Z", + "iopub.status.busy": "2024-07-01T15:07:56.037579Z", + "iopub.status.idle": "2024-07-01T15:07:58.157290Z", + "shell.execute_reply": "2024-07-01T15:07:58.156719Z" } }, "outputs": [ @@ -779,10 +955,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.030317Z", - "iopub.status.busy": "2024-06-28T15:38:34.029979Z", - "iopub.status.idle": "2024-06-28T15:38:34.271466Z", - "shell.execute_reply": "2024-06-28T15:38:34.270826Z" + "iopub.execute_input": "2024-07-01T15:07:58.160021Z", + "iopub.status.busy": "2024-07-01T15:07:58.159723Z", + "iopub.status.idle": "2024-07-01T15:07:58.413397Z", + "shell.execute_reply": "2024-07-01T15:07:58.412322Z" } }, "outputs": [ @@ -818,10 +994,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.273998Z", - "iopub.status.busy": "2024-06-28T15:38:34.273648Z", - "iopub.status.idle": "2024-06-28T15:38:34.933909Z", - "shell.execute_reply": "2024-06-28T15:38:34.933303Z" + "iopub.execute_input": "2024-07-01T15:07:58.415968Z", + "iopub.status.busy": "2024-07-01T15:07:58.415746Z", + "iopub.status.idle": "2024-07-01T15:07:59.087448Z", + "shell.execute_reply": "2024-07-01T15:07:59.086838Z" } }, "outputs": [ @@ -871,10 +1047,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:34.936344Z", - "iopub.status.busy": "2024-06-28T15:38:34.936010Z", - "iopub.status.idle": "2024-06-28T15:38:35.228743Z", - "shell.execute_reply": "2024-06-28T15:38:35.228088Z" + "iopub.execute_input": "2024-07-01T15:07:59.090441Z", + "iopub.status.busy": "2024-07-01T15:07:59.090110Z", + "iopub.status.idle": "2024-07-01T15:07:59.428255Z", + "shell.execute_reply": "2024-07-01T15:07:59.427685Z" } }, "outputs": [ @@ -922,10 +1098,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.231241Z", - "iopub.status.busy": "2024-06-28T15:38:35.230869Z", - "iopub.status.idle": "2024-06-28T15:38:35.477957Z", - "shell.execute_reply": "2024-06-28T15:38:35.477300Z" + "iopub.execute_input": "2024-07-01T15:07:59.430501Z", + "iopub.status.busy": "2024-07-01T15:07:59.430156Z", + "iopub.status.idle": "2024-07-01T15:07:59.674251Z", + "shell.execute_reply": "2024-07-01T15:07:59.673618Z" } }, "outputs": [ @@ -981,10 +1157,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.480716Z", - "iopub.status.busy": "2024-06-28T15:38:35.480496Z", - "iopub.status.idle": "2024-06-28T15:38:35.566978Z", - "shell.execute_reply": "2024-06-28T15:38:35.566454Z" + "iopub.execute_input": "2024-07-01T15:07:59.676994Z", + "iopub.status.busy": "2024-07-01T15:07:59.676767Z", + "iopub.status.idle": "2024-07-01T15:07:59.764794Z", + "shell.execute_reply": "2024-07-01T15:07:59.764299Z" } }, "outputs": [], @@ -1005,10 +1181,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:35.569419Z", - "iopub.status.busy": "2024-06-28T15:38:35.569233Z", - "iopub.status.idle": "2024-06-28T15:38:46.264047Z", - "shell.execute_reply": "2024-06-28T15:38:46.263378Z" + "iopub.execute_input": "2024-07-01T15:07:59.767244Z", + "iopub.status.busy": "2024-07-01T15:07:59.766860Z", + "iopub.status.idle": "2024-07-01T15:08:10.069096Z", + "shell.execute_reply": "2024-07-01T15:08:10.068139Z" } }, "outputs": [ @@ -1045,10 +1221,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:46.266718Z", - "iopub.status.busy": "2024-06-28T15:38:46.266270Z", - "iopub.status.idle": "2024-06-28T15:38:48.609210Z", - "shell.execute_reply": "2024-06-28T15:38:48.608559Z" + "iopub.execute_input": "2024-07-01T15:08:10.071585Z", + "iopub.status.busy": "2024-07-01T15:08:10.071346Z", + "iopub.status.idle": "2024-07-01T15:08:12.388556Z", + "shell.execute_reply": "2024-07-01T15:08:12.388050Z" } }, "outputs": [ @@ -1079,10 +1255,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.612144Z", - "iopub.status.busy": "2024-06-28T15:38:48.611517Z", - "iopub.status.idle": "2024-06-28T15:38:48.819480Z", - "shell.execute_reply": "2024-06-28T15:38:48.818936Z" + "iopub.execute_input": "2024-07-01T15:08:12.391205Z", + "iopub.status.busy": "2024-07-01T15:08:12.390795Z", + "iopub.status.idle": "2024-07-01T15:08:12.593036Z", + "shell.execute_reply": "2024-07-01T15:08:12.592540Z" } }, "outputs": [], @@ -1096,10 +1272,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.822006Z", - "iopub.status.busy": "2024-06-28T15:38:48.821667Z", - "iopub.status.idle": "2024-06-28T15:38:48.824895Z", - "shell.execute_reply": "2024-06-28T15:38:48.824452Z" + "iopub.execute_input": "2024-07-01T15:08:12.595509Z", + "iopub.status.busy": "2024-07-01T15:08:12.595162Z", + "iopub.status.idle": "2024-07-01T15:08:12.598275Z", + "shell.execute_reply": "2024-07-01T15:08:12.597815Z" } }, "outputs": [], @@ -1121,10 +1297,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:48.826934Z", - "iopub.status.busy": "2024-06-28T15:38:48.826600Z", - "iopub.status.idle": "2024-06-28T15:38:48.834980Z", - "shell.execute_reply": "2024-06-28T15:38:48.834539Z" + "iopub.execute_input": "2024-07-01T15:08:12.600173Z", + "iopub.status.busy": "2024-07-01T15:08:12.599856Z", + "iopub.status.idle": "2024-07-01T15:08:12.608170Z", + "shell.execute_reply": "2024-07-01T15:08:12.607771Z" }, "nbsphinx": "hidden" }, @@ -1169,31 +1345,30 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "098cb2eeb14f4be9ad0d532925ef1c3a": { + "2727be2db0d2497f9fb52dc8697095a2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ceaa8379ba32458a89a926eadac8b9f7", - "IPY_MODEL_b7832f8930e04ecb82387de27ccda85c", - "IPY_MODEL_34e4202f5faf45f4bbdd37ca23b87fa2" - ], - "layout": "IPY_MODEL_3c8bf8f7c6de41b08aca8334534c44d5", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c995655b1f814ffb9d408cda2ffbc566", + "placeholder": "​", + "style": "IPY_MODEL_f100e8e508354e3a998f09e41481fe4a", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 311MB/s]" } }, - "27730e8449934c789cd27668388c81a3": { + "56a99359b74d41799a5e62b4bf03f499": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1209,7 +1384,31 @@ "description_width": "" } }, - "2d2e5be95459464d9398638bc99b5ac9": { + "6e0af51d1d7c41f6b28e94e107a2e2dd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bbb9fac4ce02410c9a7cf006ad612d8a", + "IPY_MODEL_beaa49c5ac66403896b3c555d2a06c91", + "IPY_MODEL_2727be2db0d2497f9fb52dc8697095a2" + ], + "layout": "IPY_MODEL_b4c1215d79fe4c049a90606906d2692c", + "tabbable": null, + "tooltip": null + } + }, + "847a911faef949b68edb66c688cd3c4b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,30 +1461,7 @@ "width": null } }, - "34e4202f5faf45f4bbdd37ca23b87fa2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5b4e0dce4be640c58c64a1d670de2e81", - "placeholder": "​", - "style": "IPY_MODEL_7a728423519d43daae91c1085fdb4228", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 143MB/s]" - } - }, - "3c8bf8f7c6de41b08aca8334534c44d5": { + "b4c1215d79fe4c049a90606906d2692c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1338,7 +1514,56 @@ "width": null } }, - "5b4e0dce4be640c58c64a1d670de2e81": { + "bbb9fac4ce02410c9a7cf006ad612d8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_847a911faef949b68edb66c688cd3c4b", + "placeholder": "​", + "style": "IPY_MODEL_e010df9fbdef472aa0c2e3f8a393bb55", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" + } + }, + "beaa49c5ac66403896b3c555d2a06c91": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ce770c3a1d5d42a98a4d28d04bc1c7d7", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_56a99359b74d41799a5e62b4bf03f499", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "c995655b1f814ffb9d408cda2ffbc566": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1391,51 +1616,7 @@ "width": null } }, - "7a728423519d43daae91c1085fdb4228": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b7832f8930e04ecb82387de27ccda85c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2d2e5be95459464d9398638bc99b5ac9", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_27730e8449934c789cd27668388c81a3", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "bf29183e5e5f4369a7a8bb198f9346b3": { + "ce770c3a1d5d42a98a4d28d04bc1c7d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1488,30 +1669,25 @@ "width": null } }, - "ceaa8379ba32458a89a926eadac8b9f7": { + "e010df9fbdef472aa0c2e3f8a393bb55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_bf29183e5e5f4369a7a8bb198f9346b3", - "placeholder": "​", - "style": "IPY_MODEL_f5a57583e54c48e280d535fd0d90277f", - "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "f5a57583e54c48e280d535fd0d90277f": { + "f100e8e508354e3a998f09e41481fe4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 8d011fee3..46926446f 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:52.976102Z", - "iopub.status.busy": "2024-06-28T15:38:52.975670Z", - "iopub.status.idle": "2024-06-28T15:38:54.204177Z", - "shell.execute_reply": "2024-06-28T15:38:54.203645Z" + "iopub.execute_input": "2024-07-01T15:08:16.815662Z", + "iopub.status.busy": "2024-07-01T15:08:16.815212Z", + "iopub.status.idle": "2024-07-01T15:08:18.061264Z", + "shell.execute_reply": "2024-07-01T15:08:18.060688Z" }, "nbsphinx": "hidden" }, @@ -116,7 +116,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -142,10 +142,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.207000Z", - "iopub.status.busy": "2024-06-28T15:38:54.206429Z", - "iopub.status.idle": "2024-06-28T15:38:54.225068Z", - "shell.execute_reply": "2024-06-28T15:38:54.224435Z" + "iopub.execute_input": "2024-07-01T15:08:18.064012Z", + "iopub.status.busy": "2024-07-01T15:08:18.063557Z", + "iopub.status.idle": "2024-07-01T15:08:18.081205Z", + "shell.execute_reply": "2024-07-01T15:08:18.080744Z" } }, "outputs": [], @@ -164,10 +164,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.227793Z", - "iopub.status.busy": "2024-06-28T15:38:54.227339Z", - "iopub.status.idle": "2024-06-28T15:38:54.230442Z", - "shell.execute_reply": "2024-06-28T15:38:54.230003Z" + "iopub.execute_input": "2024-07-01T15:08:18.083492Z", + "iopub.status.busy": "2024-07-01T15:08:18.083103Z", + "iopub.status.idle": "2024-07-01T15:08:18.086376Z", + "shell.execute_reply": "2024-07-01T15:08:18.085828Z" }, "nbsphinx": "hidden" }, @@ -198,10 +198,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.232620Z", - "iopub.status.busy": "2024-06-28T15:38:54.232254Z", - "iopub.status.idle": "2024-06-28T15:38:54.268547Z", - "shell.execute_reply": "2024-06-28T15:38:54.268041Z" + "iopub.execute_input": "2024-07-01T15:08:18.088481Z", + "iopub.status.busy": "2024-07-01T15:08:18.088156Z", + "iopub.status.idle": "2024-07-01T15:08:18.174903Z", + "shell.execute_reply": "2024-07-01T15:08:18.174413Z" } }, "outputs": [ @@ -374,10 +374,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.270959Z", - "iopub.status.busy": "2024-06-28T15:38:54.270511Z", - "iopub.status.idle": "2024-06-28T15:38:54.454589Z", - "shell.execute_reply": "2024-06-28T15:38:54.453953Z" + "iopub.execute_input": "2024-07-01T15:08:18.177216Z", + "iopub.status.busy": "2024-07-01T15:08:18.176851Z", + "iopub.status.idle": "2024-07-01T15:08:18.363421Z", + "shell.execute_reply": "2024-07-01T15:08:18.362759Z" }, "nbsphinx": "hidden" }, @@ -417,10 +417,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.457298Z", - "iopub.status.busy": "2024-06-28T15:38:54.456939Z", - "iopub.status.idle": "2024-06-28T15:38:54.703303Z", - "shell.execute_reply": "2024-06-28T15:38:54.702725Z" + "iopub.execute_input": "2024-07-01T15:08:18.366181Z", + "iopub.status.busy": "2024-07-01T15:08:18.365737Z", + "iopub.status.idle": "2024-07-01T15:08:18.613007Z", + "shell.execute_reply": "2024-07-01T15:08:18.612399Z" } }, "outputs": [ @@ -456,10 +456,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.705458Z", - "iopub.status.busy": "2024-06-28T15:38:54.705190Z", - "iopub.status.idle": "2024-06-28T15:38:54.709668Z", - "shell.execute_reply": "2024-06-28T15:38:54.709113Z" + "iopub.execute_input": "2024-07-01T15:08:18.615413Z", + "iopub.status.busy": "2024-07-01T15:08:18.615032Z", + "iopub.status.idle": "2024-07-01T15:08:18.619703Z", + "shell.execute_reply": "2024-07-01T15:08:18.619083Z" } }, "outputs": [], @@ -477,10 +477,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.711751Z", - "iopub.status.busy": "2024-06-28T15:38:54.711415Z", - "iopub.status.idle": "2024-06-28T15:38:54.717104Z", - "shell.execute_reply": "2024-06-28T15:38:54.716684Z" + "iopub.execute_input": "2024-07-01T15:08:18.621922Z", + "iopub.status.busy": "2024-07-01T15:08:18.621693Z", + "iopub.status.idle": "2024-07-01T15:08:18.629203Z", + "shell.execute_reply": "2024-07-01T15:08:18.628679Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.719115Z", - "iopub.status.busy": "2024-06-28T15:38:54.718793Z", - "iopub.status.idle": "2024-06-28T15:38:54.721262Z", - "shell.execute_reply": "2024-06-28T15:38:54.720825Z" + "iopub.execute_input": "2024-07-01T15:08:18.631920Z", + "iopub.status.busy": "2024-07-01T15:08:18.631513Z", + "iopub.status.idle": "2024-07-01T15:08:18.634527Z", + "shell.execute_reply": "2024-07-01T15:08:18.633964Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:38:54.723261Z", - "iopub.status.busy": "2024-06-28T15:38:54.722946Z", - "iopub.status.idle": "2024-06-28T15:39:03.562442Z", - "shell.execute_reply": "2024-06-28T15:39:03.561823Z" + "iopub.execute_input": "2024-07-01T15:08:18.636916Z", + "iopub.status.busy": "2024-07-01T15:08:18.636464Z", + "iopub.status.idle": "2024-07-01T15:08:27.681165Z", + "shell.execute_reply": "2024-07-01T15:08:27.680590Z" } }, "outputs": [], @@ -572,10 +572,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.565404Z", - "iopub.status.busy": "2024-06-28T15:39:03.564765Z", - "iopub.status.idle": "2024-06-28T15:39:03.572338Z", - "shell.execute_reply": "2024-06-28T15:39:03.571880Z" + "iopub.execute_input": "2024-07-01T15:08:27.683915Z", + "iopub.status.busy": "2024-07-01T15:08:27.683447Z", + "iopub.status.idle": "2024-07-01T15:08:27.691061Z", + "shell.execute_reply": "2024-07-01T15:08:27.690544Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.574497Z", - "iopub.status.busy": "2024-06-28T15:39:03.574149Z", - "iopub.status.idle": "2024-06-28T15:39:03.577840Z", - "shell.execute_reply": "2024-06-28T15:39:03.577381Z" + "iopub.execute_input": "2024-07-01T15:08:27.693260Z", + "iopub.status.busy": "2024-07-01T15:08:27.692915Z", + "iopub.status.idle": "2024-07-01T15:08:27.696508Z", + "shell.execute_reply": "2024-07-01T15:08:27.696074Z" } }, "outputs": [], @@ -696,10 +696,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.579710Z", - "iopub.status.busy": "2024-06-28T15:39:03.579448Z", - "iopub.status.idle": "2024-06-28T15:39:03.582873Z", - "shell.execute_reply": "2024-06-28T15:39:03.582423Z" + "iopub.execute_input": "2024-07-01T15:08:27.698591Z", + "iopub.status.busy": "2024-07-01T15:08:27.698265Z", + "iopub.status.idle": "2024-07-01T15:08:27.701394Z", + "shell.execute_reply": "2024-07-01T15:08:27.700844Z" } }, "outputs": [ @@ -734,10 +734,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.584887Z", - "iopub.status.busy": "2024-06-28T15:39:03.584561Z", - "iopub.status.idle": "2024-06-28T15:39:03.587419Z", - "shell.execute_reply": "2024-06-28T15:39:03.586979Z" + "iopub.execute_input": "2024-07-01T15:08:27.703468Z", + "iopub.status.busy": "2024-07-01T15:08:27.703137Z", + "iopub.status.idle": "2024-07-01T15:08:27.706217Z", + "shell.execute_reply": "2024-07-01T15:08:27.705750Z" } }, "outputs": [], @@ -756,10 +756,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.589440Z", - "iopub.status.busy": "2024-06-28T15:39:03.589127Z", - "iopub.status.idle": "2024-06-28T15:39:03.597072Z", - "shell.execute_reply": "2024-06-28T15:39:03.596559Z" + "iopub.execute_input": "2024-07-01T15:08:27.708232Z", + "iopub.status.busy": "2024-07-01T15:08:27.707899Z", + "iopub.status.idle": "2024-07-01T15:08:27.715999Z", + "shell.execute_reply": "2024-07-01T15:08:27.715525Z" } }, "outputs": [ @@ -883,10 +883,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.599177Z", - "iopub.status.busy": "2024-06-28T15:39:03.598840Z", - "iopub.status.idle": "2024-06-28T15:39:03.601388Z", - "shell.execute_reply": "2024-06-28T15:39:03.600963Z" + "iopub.execute_input": "2024-07-01T15:08:27.718018Z", + "iopub.status.busy": "2024-07-01T15:08:27.717680Z", + "iopub.status.idle": "2024-07-01T15:08:27.720242Z", + "shell.execute_reply": "2024-07-01T15:08:27.719798Z" }, "nbsphinx": "hidden" }, @@ -921,10 +921,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.603502Z", - "iopub.status.busy": "2024-06-28T15:39:03.603179Z", - "iopub.status.idle": "2024-06-28T15:39:03.723060Z", - "shell.execute_reply": "2024-06-28T15:39:03.722461Z" + "iopub.execute_input": "2024-07-01T15:08:27.722277Z", + "iopub.status.busy": "2024-07-01T15:08:27.721939Z", + "iopub.status.idle": "2024-07-01T15:08:27.850295Z", + "shell.execute_reply": "2024-07-01T15:08:27.849694Z" } }, "outputs": [ @@ -963,10 +963,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.725504Z", - "iopub.status.busy": "2024-06-28T15:39:03.725028Z", - "iopub.status.idle": "2024-06-28T15:39:03.827464Z", - "shell.execute_reply": "2024-06-28T15:39:03.826856Z" + "iopub.execute_input": "2024-07-01T15:08:27.852484Z", + "iopub.status.busy": "2024-07-01T15:08:27.852300Z", + "iopub.status.idle": "2024-07-01T15:08:27.955847Z", + "shell.execute_reply": "2024-07-01T15:08:27.955257Z" } }, "outputs": [ @@ -1022,10 +1022,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:03.829919Z", - "iopub.status.busy": "2024-06-28T15:39:03.829592Z", - "iopub.status.idle": "2024-06-28T15:39:04.313676Z", - "shell.execute_reply": "2024-06-28T15:39:04.313059Z" + "iopub.execute_input": "2024-07-01T15:08:27.958252Z", + "iopub.status.busy": "2024-07-01T15:08:27.957880Z", + "iopub.status.idle": "2024-07-01T15:08:28.451750Z", + "shell.execute_reply": "2024-07-01T15:08:28.451203Z" } }, "outputs": [], @@ -1041,10 +1041,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.316205Z", - "iopub.status.busy": "2024-06-28T15:39:04.316016Z", - "iopub.status.idle": "2024-06-28T15:39:04.389000Z", - "shell.execute_reply": "2024-06-28T15:39:04.388436Z" + "iopub.execute_input": "2024-07-01T15:08:28.454335Z", + "iopub.status.busy": "2024-07-01T15:08:28.454151Z", + "iopub.status.idle": "2024-07-01T15:08:28.527356Z", + "shell.execute_reply": "2024-07-01T15:08:28.526736Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.391365Z", - "iopub.status.busy": "2024-06-28T15:39:04.390985Z", - "iopub.status.idle": "2024-06-28T15:39:04.399547Z", - "shell.execute_reply": "2024-06-28T15:39:04.399087Z" + "iopub.execute_input": "2024-07-01T15:08:28.529697Z", + "iopub.status.busy": "2024-07-01T15:08:28.529341Z", + "iopub.status.idle": "2024-07-01T15:08:28.538428Z", + "shell.execute_reply": "2024-07-01T15:08:28.537958Z" } }, "outputs": [ @@ -1189,10 +1189,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.401759Z", - "iopub.status.busy": "2024-06-28T15:39:04.401238Z", - "iopub.status.idle": "2024-06-28T15:39:04.404123Z", - "shell.execute_reply": "2024-06-28T15:39:04.403592Z" + "iopub.execute_input": "2024-07-01T15:08:28.540454Z", + "iopub.status.busy": "2024-07-01T15:08:28.540269Z", + "iopub.status.idle": "2024-07-01T15:08:28.542883Z", + "shell.execute_reply": "2024-07-01T15:08:28.542447Z" }, "nbsphinx": "hidden" }, @@ -1217,10 +1217,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:04.406104Z", - "iopub.status.busy": "2024-06-28T15:39:04.405929Z", - "iopub.status.idle": "2024-06-28T15:39:10.119031Z", - "shell.execute_reply": "2024-06-28T15:39:10.118402Z" + "iopub.execute_input": "2024-07-01T15:08:28.544877Z", + "iopub.status.busy": "2024-07-01T15:08:28.544701Z", + "iopub.status.idle": "2024-07-01T15:08:33.972038Z", + "shell.execute_reply": "2024-07-01T15:08:33.971430Z" } }, "outputs": [ @@ -1264,10 +1264,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:10.121458Z", - "iopub.status.busy": "2024-06-28T15:39:10.121040Z", - "iopub.status.idle": "2024-06-28T15:39:10.130156Z", - "shell.execute_reply": "2024-06-28T15:39:10.129695Z" + "iopub.execute_input": "2024-07-01T15:08:33.974153Z", + "iopub.status.busy": "2024-07-01T15:08:33.973956Z", + "iopub.status.idle": "2024-07-01T15:08:33.982773Z", + "shell.execute_reply": "2024-07-01T15:08:33.982320Z" } }, "outputs": [ @@ -1376,10 +1376,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:10.132289Z", - "iopub.status.busy": "2024-06-28T15:39:10.131950Z", - "iopub.status.idle": "2024-06-28T15:39:10.201214Z", - "shell.execute_reply": "2024-06-28T15:39:10.200720Z" + "iopub.execute_input": "2024-07-01T15:08:33.984722Z", + "iopub.status.busy": "2024-07-01T15:08:33.984546Z", + "iopub.status.idle": "2024-07-01T15:08:34.049986Z", + "shell.execute_reply": "2024-07-01T15:08:34.049478Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 1b5222eab..3b1c6435d 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:13.402150Z", - "iopub.status.busy": "2024-06-28T15:39:13.401954Z", - "iopub.status.idle": "2024-06-28T15:39:14.646183Z", - "shell.execute_reply": "2024-06-28T15:39:14.645441Z" + "iopub.execute_input": "2024-07-01T15:08:37.513049Z", + "iopub.status.busy": "2024-07-01T15:08:37.512824Z", + "iopub.status.idle": "2024-07-01T15:08:39.014630Z", + "shell.execute_reply": "2024-07-01T15:08:39.013915Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:14.649149Z", - "iopub.status.busy": "2024-06-28T15:39:14.648692Z", - "iopub.status.idle": "2024-06-28T15:39:50.257887Z", - "shell.execute_reply": "2024-06-28T15:39:50.257232Z" + "iopub.execute_input": "2024-07-01T15:08:39.017371Z", + "iopub.status.busy": "2024-07-01T15:08:39.017127Z", + "iopub.status.idle": "2024-07-01T15:09:39.584116Z", + "shell.execute_reply": "2024-07-01T15:09:39.583459Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:50.260572Z", - "iopub.status.busy": "2024-06-28T15:39:50.260205Z", - "iopub.status.idle": "2024-06-28T15:39:51.486151Z", - "shell.execute_reply": "2024-06-28T15:39:51.485480Z" + "iopub.execute_input": "2024-07-01T15:09:39.586690Z", + "iopub.status.busy": "2024-07-01T15:09:39.586340Z", + "iopub.status.idle": "2024-07-01T15:09:40.720146Z", + "shell.execute_reply": "2024-07-01T15:09:40.719576Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.489186Z", - "iopub.status.busy": "2024-06-28T15:39:51.488560Z", - "iopub.status.idle": "2024-06-28T15:39:51.492413Z", - "shell.execute_reply": "2024-06-28T15:39:51.491891Z" + "iopub.execute_input": "2024-07-01T15:09:40.722658Z", + "iopub.status.busy": "2024-07-01T15:09:40.722386Z", + "iopub.status.idle": "2024-07-01T15:09:40.725657Z", + "shell.execute_reply": "2024-07-01T15:09:40.725218Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.494943Z", - "iopub.status.busy": "2024-06-28T15:39:51.494455Z", - "iopub.status.idle": "2024-06-28T15:39:51.498706Z", - "shell.execute_reply": "2024-06-28T15:39:51.498166Z" + "iopub.execute_input": "2024-07-01T15:09:40.727650Z", + "iopub.status.busy": "2024-07-01T15:09:40.727470Z", + "iopub.status.idle": "2024-07-01T15:09:40.731254Z", + "shell.execute_reply": "2024-07-01T15:09:40.730747Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.501145Z", - "iopub.status.busy": "2024-06-28T15:39:51.500786Z", - "iopub.status.idle": "2024-06-28T15:39:51.504649Z", - "shell.execute_reply": "2024-06-28T15:39:51.504101Z" + "iopub.execute_input": "2024-07-01T15:09:40.733340Z", + "iopub.status.busy": "2024-07-01T15:09:40.733016Z", + "iopub.status.idle": "2024-07-01T15:09:40.736638Z", + "shell.execute_reply": "2024-07-01T15:09:40.736162Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.506798Z", - "iopub.status.busy": "2024-06-28T15:39:51.506520Z", - "iopub.status.idle": "2024-06-28T15:39:51.509682Z", - "shell.execute_reply": "2024-06-28T15:39:51.509112Z" + "iopub.execute_input": "2024-07-01T15:09:40.738709Z", + "iopub.status.busy": "2024-07-01T15:09:40.738283Z", + "iopub.status.idle": "2024-07-01T15:09:40.741139Z", + "shell.execute_reply": "2024-07-01T15:09:40.740716Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:39:51.511926Z", - "iopub.status.busy": "2024-06-28T15:39:51.511586Z", - "iopub.status.idle": "2024-06-28T15:40:25.616678Z", - "shell.execute_reply": "2024-06-28T15:40:25.616008Z" + "iopub.execute_input": "2024-07-01T15:09:40.743089Z", + "iopub.status.busy": "2024-07-01T15:09:40.742768Z", + "iopub.status.idle": "2024-07-01T15:10:14.851046Z", + "shell.execute_reply": "2024-07-01T15:10:14.850360Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0cdc6dee74c040a69b3494b8aab06e7a", + "model_id": "e93b88c996c44feeb3673439eaaea41d", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b11ab931e8144b04919807741d6347a6", + "model_id": "cae016dc953549ce807817682c42dc87", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:25.619646Z", - "iopub.status.busy": "2024-06-28T15:40:25.619138Z", - "iopub.status.idle": "2024-06-28T15:40:26.311249Z", - "shell.execute_reply": "2024-06-28T15:40:26.310687Z" + "iopub.execute_input": "2024-07-01T15:10:14.853695Z", + "iopub.status.busy": "2024-07-01T15:10:14.853439Z", + "iopub.status.idle": "2024-07-01T15:10:15.523116Z", + "shell.execute_reply": "2024-07-01T15:10:15.522614Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:26.313705Z", - "iopub.status.busy": "2024-06-28T15:40:26.313226Z", - "iopub.status.idle": "2024-06-28T15:40:29.212189Z", - "shell.execute_reply": "2024-06-28T15:40:29.211650Z" + "iopub.execute_input": "2024-07-01T15:10:15.525482Z", + "iopub.status.busy": "2024-07-01T15:10:15.525022Z", + "iopub.status.idle": "2024-07-01T15:10:18.415066Z", + "shell.execute_reply": "2024-07-01T15:10:18.414464Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:40:29.214460Z", - "iopub.status.busy": "2024-06-28T15:40:29.214101Z", - "iopub.status.idle": "2024-06-28T15:41:02.133359Z", - "shell.execute_reply": "2024-06-28T15:41:02.132825Z" + "iopub.execute_input": "2024-07-01T15:10:18.417219Z", + "iopub.status.busy": "2024-07-01T15:10:18.417035Z", + "iopub.status.idle": "2024-07-01T15:10:50.808150Z", + "shell.execute_reply": "2024-07-01T15:10:50.807678Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3c4b5bc26264823bfb0f6b3ace17d05", + "model_id": "731093637bac464aa707d2bcbb8b8fa8", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:02.135440Z", - "iopub.status.busy": "2024-06-28T15:41:02.135225Z", - "iopub.status.idle": "2024-06-28T15:41:17.058471Z", - "shell.execute_reply": "2024-06-28T15:41:17.057815Z" + "iopub.execute_input": "2024-07-01T15:10:50.810430Z", + "iopub.status.busy": "2024-07-01T15:10:50.810008Z", + "iopub.status.idle": "2024-07-01T15:11:05.045003Z", + "shell.execute_reply": "2024-07-01T15:11:05.044449Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:17.061274Z", - "iopub.status.busy": "2024-06-28T15:41:17.060907Z", - "iopub.status.idle": "2024-06-28T15:41:20.968204Z", - "shell.execute_reply": "2024-06-28T15:41:20.967621Z" + "iopub.execute_input": "2024-07-01T15:11:05.047402Z", + "iopub.status.busy": "2024-07-01T15:11:05.047124Z", + "iopub.status.idle": "2024-07-01T15:11:08.765062Z", + "shell.execute_reply": "2024-07-01T15:11:08.764520Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:20.970611Z", - "iopub.status.busy": "2024-06-28T15:41:20.970161Z", - "iopub.status.idle": "2024-06-28T15:41:22.495041Z", - "shell.execute_reply": "2024-06-28T15:41:22.494478Z" + "iopub.execute_input": "2024-07-01T15:11:08.767118Z", + "iopub.status.busy": "2024-07-01T15:11:08.766928Z", + "iopub.status.idle": "2024-07-01T15:11:10.139794Z", + "shell.execute_reply": "2024-07-01T15:11:10.139251Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db5e31d22ec845c096d00b1290219c66", + "model_id": "94d7b260381b4b7da5eca25e123790e6", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:22.497895Z", - "iopub.status.busy": "2024-06-28T15:41:22.497483Z", - "iopub.status.idle": "2024-06-28T15:41:22.530031Z", - "shell.execute_reply": "2024-06-28T15:41:22.529445Z" + "iopub.execute_input": "2024-07-01T15:11:10.142331Z", + "iopub.status.busy": "2024-07-01T15:11:10.141916Z", + "iopub.status.idle": "2024-07-01T15:11:10.170330Z", + "shell.execute_reply": "2024-07-01T15:11:10.169827Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:22.532973Z", - "iopub.status.busy": "2024-06-28T15:41:22.532548Z", - "iopub.status.idle": "2024-06-28T15:41:28.764066Z", - "shell.execute_reply": "2024-06-28T15:41:28.763451Z" + "iopub.execute_input": "2024-07-01T15:11:10.172647Z", + "iopub.status.busy": "2024-07-01T15:11:10.172452Z", + "iopub.status.idle": "2024-07-01T15:11:16.278293Z", + "shell.execute_reply": "2024-07-01T15:11:16.277810Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:28.766438Z", - "iopub.status.busy": "2024-06-28T15:41:28.766065Z", - "iopub.status.idle": "2024-06-28T15:41:28.822918Z", - "shell.execute_reply": "2024-06-28T15:41:28.822414Z" + "iopub.execute_input": "2024-07-01T15:11:16.280555Z", + "iopub.status.busy": "2024-07-01T15:11:16.280188Z", + "iopub.status.idle": "2024-07-01T15:11:16.335969Z", + "shell.execute_reply": "2024-07-01T15:11:16.335497Z" }, "nbsphinx": "hidden" }, @@ -1038,57 +1038,46 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "061c85291828496fbf52c0bd1e246e72": { + "0ce80ae00bf44c64b8d09bc5ac656e27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4daa6952ccf640d5bb471ac97fca937a", - "max": 4997683.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b5eda668ac604134af3aae92fff71d06", - "tabbable": null, - "tooltip": null, - "value": 4997683.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0cdc6dee74c040a69b3494b8aab06e7a": { + "0d422e6ef3524fe18196d3c8b173b4b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b1c27a9f01dd463ca9687d81a5be93d6", - "IPY_MODEL_6a21cb9490224017b4f63b3077027978", - "IPY_MODEL_f2261b979563426081a86553acc28756" - ], - "layout": "IPY_MODEL_5d4cd4bc65ce473890303c09f2290548", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_58f2dea7d21a44d288433745eff48fb5", + "placeholder": "​", + "style": "IPY_MODEL_a6f5a4d370df4f5ab60b6aa6323dfe0c", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for estimating thresholds: 100%" } }, - "0f957598d78841ffbd5ae356a219a0cf": { + "0fba3cd87f5e4a30858fae2027c96f2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1141,25 +1130,30 @@ "width": null } }, - "13acb4b4c3314dbb8349d08e4f86c0a9": { + "120e14c159a940bebfa008af4846e1af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d7d60f679eea43e79193e07ed20fc5aa", + "placeholder": "​", + "style": "IPY_MODEL_2d90c5dd6d1848b38f0aa931986b4799", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 768.06it/s]" } }, - "18001b1ae0dc4bf8bfcf7cf650b5842b": { + "1a7a00c51cf841f9960806391ff7b8c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1175,70 +1169,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_591d5a7b1f9b4ea8a0347e83195b4778", + "layout": "IPY_MODEL_4bb2732aa35d440a84e89690b8a0f610", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a63d2edb73c64989b5e20ae6c7095773", + "style": "IPY_MODEL_a88b229f72f34e9a8c5e66a993f56a21", "tabbable": null, "tooltip": null, "value": 30.0 } }, - "1c1b2410e3cc48bb8d4e3d5e81387088": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "22cb6a36455147f5b1bb1c434d6a9e1b": { + "1d6b185c42f944568fbf955268ac9154": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1291,7 +1232,7 @@ "width": null } }, - "27597bf2a1be432eb70769222d89dc7d": { + "2535e62f9cf1494fb6cc24a0ac2907b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1306,15 +1247,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_73a809c622b348c8affd0c44c2e1a8fe", + "layout": "IPY_MODEL_1d6b185c42f944568fbf955268ac9154", "placeholder": "​", - "style": "IPY_MODEL_4b8c4cb8ba7a4ff4896c57ad28d3eca1", + "style": "IPY_MODEL_880d91c28adf4495881e409632e6f2c0", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.38it/s]" + "value": "number of examples processed for checking labels: 100%" + } + }, + "2d90c5dd6d1848b38f0aa931986b4799": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "2816574fe4494f77a0d739edadb1cb21": { + "3889edbb1fe4425ebf7afefb82fe5802": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1367,7 +1326,7 @@ "width": null } }, - "29d91937de334b2391a824c0115c189f": { + "470506ccc55441d4a3335d76966e3c9a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1385,7 +1344,7 @@ "text_color": null } }, - "2d212f1218b849149ab1dbecbab9a1e1": { + "4bb2732aa35d440a84e89690b8a0f610": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1438,7 +1397,7 @@ "width": null } }, - "458bea55f4b24c5ab5cc1e7f3aa00697": { + "58f2dea7d21a44d288433745eff48fb5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1491,7 +1450,43 @@ "width": null } }, - "4635042d0e4f49e3bf6aa1ac992c08e3": { + "5ab028ba9f524306aa0c67b32b1e5513": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "620edc866142421db8ff2794fa79153f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "645ee85b81c0443e9b9b94ea8c6e87fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1506,33 +1501,114 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_458bea55f4b24c5ab5cc1e7f3aa00697", + "layout": "IPY_MODEL_7e82ec8863a94f5d8e6065f6c467c1ba", "placeholder": "​", - "style": "IPY_MODEL_b0e946ecf2904b21b2a0ea7e127f9d7f", + "style": "IPY_MODEL_470506ccc55441d4a3335d76966e3c9a", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: 100%" + "value": "images processed using softmin: 100%" } }, - "4b8c4cb8ba7a4ff4896c57ad28d3eca1": { + "722e544a67304c41a49646191b43f64e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8dfb6569b11b4e579ab5f1679787b922", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f781332402924b45849cdca8f6978ed3", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "731093637bac464aa707d2bcbb8b8fa8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9b08851d974b4b75b2758e2517483e68", + "IPY_MODEL_a7e45bb52c5c49fd91aef928ec848223", + "IPY_MODEL_8df97aa7051e4cac814f7092dcecc401" + ], + "layout": "IPY_MODEL_998f6677b81d4e86955affe5f63bdf7b", + "tabbable": null, + "tooltip": null + } + }, + "742abd1e12e941f0ade6da7a2b14d0d0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f6a973b86f594108bb655affcc8de322", + "placeholder": "​", + "style": "IPY_MODEL_620edc866142421db8ff2794fa79153f", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:01<00:00, 22.07it/s]" + } + }, + "754ca065e07f4e7eb99baf4c7cf19ce4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8a70293c3224c66a3f27168557b9ba8", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ec84cd07627b443a8c8ce0e1f099c72c", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "4daa6952ccf640d5bb471ac97fca937a": { + "7e82ec8863a94f5d8e6065f6c467c1ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1585,23 +1661,41 @@ "width": null } }, - "4eca08c04d174cabacd8a567f0036242": { - "model_module": "@jupyter-widgets/base", + "880d91c28adf4495881e409632e6f2c0": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HTMLStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "88577bae8f0943e6a91fd0f7eb9523ad": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, "border_right": null, "border_top": null, "bottom": null, @@ -1638,7 +1732,7 @@ "width": null } }, - "53366ac2a1b242d29a5e4f1f927c7fb4": { + "8df97aa7051e4cac814f7092dcecc401": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1653,15 +1747,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_70fa5a11ae724ba4825380abd56877bf", + "layout": "IPY_MODEL_b53c3a56cde9432497a763625dc6e49a", "placeholder": "​", - "style": "IPY_MODEL_29d91937de334b2391a824c0115c189f", + "style": "IPY_MODEL_a513f46efc83419b8f4e22c3b2c064d2", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 4997683/4997683 [00:32<00:00, 156404.46it/s]" } }, - "591d5a7b1f9b4ea8a0347e83195b4778": { + "8dfb6569b11b4e579ab5f1679787b922": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1714,23 +1808,31 @@ "width": null } }, - "5d0717a9ba494648b412c84882ae83be": { + "94d7b260381b4b7da5eca25e123790e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_645ee85b81c0443e9b9b94ea8c6e87fd", + "IPY_MODEL_1a7a00c51cf841f9960806391ff7b8c0", + "IPY_MODEL_742abd1e12e941f0ade6da7a2b14d0d0" + ], + "layout": "IPY_MODEL_0fba3cd87f5e4a30858fae2027c96f2f", + "tabbable": null, + "tooltip": null } }, - "5d4cd4bc65ce473890303c09f2290548": { + "998f6677b81d4e86955affe5f63bdf7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1783,7 +1885,30 @@ "width": null } }, - "5f7ffcb3dfdc431ba05c649dcf5c676b": { + "9b08851d974b4b75b2758e2517483e68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b8bda17c986d4f7fa6a7b2aaf2a0be0a", + "placeholder": "​", + "style": "IPY_MODEL_e3090d513b8a48f380607cc86cd1895f", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "a4188823d8964bafbc43b207d512faeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1836,7 +1961,43 @@ "width": null } }, - "6a21cb9490224017b4f63b3077027978": { + "a513f46efc83419b8f4e22c3b2c064d2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a6f5a4d370df4f5ab60b6aa6323dfe0c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "a7e45bb52c5c49fd91aef928ec848223": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1852,17 +2013,56 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1c1b2410e3cc48bb8d4e3d5e81387088", - "max": 30.0, + "layout": "IPY_MODEL_88577bae8f0943e6a91fd0f7eb9523ad", + "max": 4997683.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_fe128ed3c0eb4ce7a6a8220516bcf5e8", + "style": "IPY_MODEL_0ce80ae00bf44c64b8d09bc5ac656e27", "tabbable": null, "tooltip": null, - "value": 30.0 + "value": 4997683.0 } }, - "703c0ea26e484c1badc31d97206f4521": { + "a88b229f72f34e9a8c5e66a993f56a21": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "aa1f2aad145f4f339c1cd410fe718472": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f5afe4dbdebe45929344b9d66f6e48a8", + "placeholder": "​", + "style": "IPY_MODEL_5ab028ba9f524306aa0c67b32b1e5513", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.33it/s]" + } + }, + "b53c3a56cde9432497a763625dc6e49a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1915,7 +2115,7 @@ "width": null } }, - "70fa5a11ae724ba4825380abd56877bf": { + "b8a70293c3224c66a3f27168557b9ba8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1968,7 +2168,7 @@ "width": null } }, - "7144445ec5d64b2aa78a1ed6ab26adf1": { + "b8bda17c986d4f7fa6a7b2aaf2a0be0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2021,7 +2221,31 @@ "width": null } }, - "73a809c622b348c8affd0c44c2e1a8fe": { + "cae016dc953549ce807817682c42dc87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2535e62f9cf1494fb6cc24a0ac2907b2", + "IPY_MODEL_722e544a67304c41a49646191b43f64e", + "IPY_MODEL_aa1f2aad145f4f339c1cd410fe718472" + ], + "layout": "IPY_MODEL_a4188823d8964bafbc43b207d512faeb", + "tabbable": null, + "tooltip": null + } + }, + "d7d60f679eea43e79193e07ed20fc5aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2074,71 +2298,65 @@ "width": null } }, - "740679addecf42bda4edfb03c3cd2918": { + "e3090d513b8a48f380607cc86cd1895f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HTMLStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", + "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2816574fe4494f77a0d739edadb1cb21", - "placeholder": "​", - "style": "IPY_MODEL_13acb4b4c3314dbb8349d08e4f86c0a9", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "85558cbc107944af8618db036067ddc1": { + "e93b88c996c44feeb3673439eaaea41d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_4eca08c04d174cabacd8a567f0036242", - "placeholder": "​", - "style": "IPY_MODEL_96271a40a7334ed09a07a521c01f08ad", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0d422e6ef3524fe18196d3c8b173b4b6", + "IPY_MODEL_754ca065e07f4e7eb99baf4c7cf19ce4", + "IPY_MODEL_120e14c159a940bebfa008af4846e1af" + ], + "layout": "IPY_MODEL_3889edbb1fe4425ebf7afefb82fe5802", "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:01<00:00, 19.97it/s]" + "tooltip": null } }, - "96271a40a7334ed09a07a521c01f08ad": { + "ec84cd07627b443a8c8ce0e1f099c72c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "bar_color": null, + "description_width": "" } }, - "9fd930ccf17f479a9a9231dd8ae6d86e": { + "f5afe4dbdebe45929344b9d66f6e48a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2191,278 +2409,60 @@ "width": null } }, - "a63d2edb73c64989b5e20ae6c7095773": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ae4a8a8bae08447f943f64b763e74911": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b0e946ecf2904b21b2a0ea7e127f9d7f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "b11ab931e8144b04919807741d6347a6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4635042d0e4f49e3bf6aa1ac992c08e3", - "IPY_MODEL_ef6b1b176d644c0ab075adf2a0a6236f", - "IPY_MODEL_27597bf2a1be432eb70769222d89dc7d" - ], - "layout": "IPY_MODEL_9fd930ccf17f479a9a9231dd8ae6d86e", - "tabbable": null, - "tooltip": null - } - }, - "b1c27a9f01dd463ca9687d81a5be93d6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_2d212f1218b849149ab1dbecbab9a1e1", - "placeholder": "​", - "style": "IPY_MODEL_ae4a8a8bae08447f943f64b763e74911", - "tabbable": null, - "tooltip": null, - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "b5eda668ac604134af3aae92fff71d06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "d108010f360b474c8406b52ef77971ad": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "d3fece7453394289bcb1e8b87a24bfcc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_5f7ffcb3dfdc431ba05c649dcf5c676b", - "placeholder": "​", - "style": "IPY_MODEL_ed3903b8ba3b47e89556f14ce1662a29", - "tabbable": null, - "tooltip": null, - "value": " 4997683/4997683 [00:32<00:00, 149866.37it/s]" - } - }, - "db5e31d22ec845c096d00b1290219c66": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_740679addecf42bda4edfb03c3cd2918", - "IPY_MODEL_18001b1ae0dc4bf8bfcf7cf650b5842b", - "IPY_MODEL_85558cbc107944af8618db036067ddc1" - ], - "layout": "IPY_MODEL_703c0ea26e484c1badc31d97206f4521", - "tabbable": null, - "tooltip": null - } - }, - "ed3903b8ba3b47e89556f14ce1662a29": { - "model_module": "@jupyter-widgets/controls", + "f6a973b86f594108bb655affcc8de322": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", - "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ef6b1b176d644c0ab075adf2a0a6236f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_0f957598d78841ffbd5ae356a219a0cf", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5d0717a9ba494648b412c84882ae83be", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "f2261b979563426081a86553acc28756": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HTMLView", - "description": "", - "description_allow_html": false, - "layout": "IPY_MODEL_22cb6a36455147f5b1bb1c434d6a9e1b", - "placeholder": "​", - "style": "IPY_MODEL_d108010f360b474c8406b52ef77971ad", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 776.76it/s]" - } - }, - "f3c4b5bc26264823bfb0f6b3ace17d05": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_53366ac2a1b242d29a5e4f1f927c7fb4", - "IPY_MODEL_061c85291828496fbf52c0bd1e246e72", - "IPY_MODEL_d3fece7453394289bcb1e8b87a24bfcc" - ], - "layout": "IPY_MODEL_7144445ec5d64b2aa78a1ed6ab26adf1", - "tabbable": null, - "tooltip": null + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "fe128ed3c0eb4ce7a6a8220516bcf5e8": { + "f781332402924b45849cdca8f6978ed3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 153b10754..8650ebc00 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:31.234194Z", - "iopub.status.busy": "2024-06-28T15:41:31.234011Z", - "iopub.status.idle": "2024-06-28T15:41:32.436580Z", - "shell.execute_reply": "2024-06-28T15:41:32.435971Z" + "iopub.execute_input": "2024-07-01T15:11:18.503218Z", + "iopub.status.busy": "2024-07-01T15:11:18.502735Z", + "iopub.status.idle": "2024-07-01T15:11:19.975527Z", + "shell.execute_reply": "2024-07-01T15:11:19.974839Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-28 15:41:31-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-07-01 15:11:18-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.246, 2400:52e0:1a00::718:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.246|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "169.150.236.98, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n", + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 4.92MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.71MB/s in 0.2s \r\n", "\r\n", - "2024-06-28 15:41:31 (4.92 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-07-01 15:11:19 (5.71 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,15 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-06-28 15:41:31-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.117.65, 52.217.130.65, 52.217.92.116, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.117.65|:443... connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-07-01 15:11:19-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.28.244, 3.5.24.72, 52.217.13.252, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.28.244|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -160,7 +168,7 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 48%[========> ] 7.93M 37.9MB/s " + "pred_probs.npz 35%[======> ] 5.78M 28.9MB/s " ] }, { @@ -168,9 +176,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 60.6MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M 52.3MB/s in 0.3s \r\n", "\r\n", - "2024-06-28 15:41:32 (60.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-07-01 15:11:19 (52.3 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -187,10 +195,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:32.439449Z", - "iopub.status.busy": "2024-06-28T15:41:32.439055Z", - "iopub.status.idle": "2024-06-28T15:41:33.746197Z", - "shell.execute_reply": "2024-06-28T15:41:33.745549Z" + "iopub.execute_input": "2024-07-01T15:11:19.978352Z", + "iopub.status.busy": "2024-07-01T15:11:19.977882Z", + "iopub.status.idle": "2024-07-01T15:11:21.215995Z", + "shell.execute_reply": "2024-07-01T15:11:21.215505Z" }, "nbsphinx": "hidden" }, @@ -201,7 +209,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -227,10 +235,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.749019Z", - "iopub.status.busy": "2024-06-28T15:41:33.748505Z", - "iopub.status.idle": "2024-06-28T15:41:33.751942Z", - "shell.execute_reply": "2024-06-28T15:41:33.751495Z" + "iopub.execute_input": "2024-07-01T15:11:21.218513Z", + "iopub.status.busy": "2024-07-01T15:11:21.218132Z", + "iopub.status.idle": "2024-07-01T15:11:21.221470Z", + "shell.execute_reply": "2024-07-01T15:11:21.221045Z" } }, "outputs": [], @@ -280,10 +288,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.754199Z", - "iopub.status.busy": "2024-06-28T15:41:33.753868Z", - "iopub.status.idle": "2024-06-28T15:41:33.756996Z", - "shell.execute_reply": "2024-06-28T15:41:33.756443Z" + "iopub.execute_input": "2024-07-01T15:11:21.223694Z", + "iopub.status.busy": "2024-07-01T15:11:21.223257Z", + "iopub.status.idle": "2024-07-01T15:11:21.226332Z", + "shell.execute_reply": "2024-07-01T15:11:21.225848Z" }, "nbsphinx": "hidden" }, @@ -301,10 +309,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:33.759117Z", - "iopub.status.busy": "2024-06-28T15:41:33.758809Z", - "iopub.status.idle": "2024-06-28T15:41:42.901434Z", - "shell.execute_reply": "2024-06-28T15:41:42.900715Z" + "iopub.execute_input": "2024-07-01T15:11:21.228084Z", + "iopub.status.busy": "2024-07-01T15:11:21.227917Z", + "iopub.status.idle": "2024-07-01T15:11:30.310755Z", + "shell.execute_reply": "2024-07-01T15:11:30.310211Z" } }, "outputs": [], @@ -378,10 +386,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:42.904438Z", - "iopub.status.busy": "2024-06-28T15:41:42.904121Z", - "iopub.status.idle": "2024-06-28T15:41:42.910537Z", - "shell.execute_reply": "2024-06-28T15:41:42.909908Z" + "iopub.execute_input": "2024-07-01T15:11:30.313310Z", + "iopub.status.busy": "2024-07-01T15:11:30.313004Z", + "iopub.status.idle": "2024-07-01T15:11:30.318459Z", + "shell.execute_reply": "2024-07-01T15:11:30.318009Z" }, "nbsphinx": "hidden" }, @@ -421,10 +429,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:42.912924Z", - "iopub.status.busy": "2024-06-28T15:41:42.912455Z", - "iopub.status.idle": "2024-06-28T15:41:43.306159Z", - "shell.execute_reply": "2024-06-28T15:41:43.305530Z" + "iopub.execute_input": "2024-07-01T15:11:30.320517Z", + "iopub.status.busy": "2024-07-01T15:11:30.320198Z", + "iopub.status.idle": "2024-07-01T15:11:30.659248Z", + "shell.execute_reply": "2024-07-01T15:11:30.658770Z" } }, "outputs": [], @@ -461,10 +469,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:43.308933Z", - "iopub.status.busy": "2024-06-28T15:41:43.308556Z", - "iopub.status.idle": "2024-06-28T15:41:43.313173Z", - "shell.execute_reply": "2024-06-28T15:41:43.312627Z" + "iopub.execute_input": "2024-07-01T15:11:30.661698Z", + "iopub.status.busy": "2024-07-01T15:11:30.661301Z", + "iopub.status.idle": "2024-07-01T15:11:30.665925Z", + "shell.execute_reply": "2024-07-01T15:11:30.665448Z" } }, "outputs": [ @@ -536,10 +544,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:43.315174Z", - "iopub.status.busy": "2024-06-28T15:41:43.314861Z", - "iopub.status.idle": "2024-06-28T15:41:45.999753Z", - "shell.execute_reply": "2024-06-28T15:41:45.998935Z" + "iopub.execute_input": "2024-07-01T15:11:30.667958Z", + "iopub.status.busy": "2024-07-01T15:11:30.667632Z", + "iopub.status.idle": "2024-07-01T15:11:33.481219Z", + "shell.execute_reply": "2024-07-01T15:11:33.480521Z" } }, "outputs": [], @@ -561,10 +569,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.003076Z", - "iopub.status.busy": "2024-06-28T15:41:46.002321Z", - "iopub.status.idle": "2024-06-28T15:41:46.006640Z", - "shell.execute_reply": "2024-06-28T15:41:46.006164Z" + "iopub.execute_input": "2024-07-01T15:11:33.484626Z", + "iopub.status.busy": "2024-07-01T15:11:33.483787Z", + "iopub.status.idle": "2024-07-01T15:11:33.488254Z", + "shell.execute_reply": "2024-07-01T15:11:33.487491Z" } }, "outputs": [ @@ -600,10 +608,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.008550Z", - "iopub.status.busy": "2024-06-28T15:41:46.008360Z", - "iopub.status.idle": "2024-06-28T15:41:46.013890Z", - "shell.execute_reply": "2024-06-28T15:41:46.013387Z" + "iopub.execute_input": "2024-07-01T15:11:33.490509Z", + "iopub.status.busy": "2024-07-01T15:11:33.490170Z", + "iopub.status.idle": "2024-07-01T15:11:33.496148Z", + "shell.execute_reply": "2024-07-01T15:11:33.495591Z" } }, "outputs": [ @@ -781,10 +789,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.015898Z", - "iopub.status.busy": "2024-06-28T15:41:46.015719Z", - "iopub.status.idle": "2024-06-28T15:41:46.043665Z", - "shell.execute_reply": "2024-06-28T15:41:46.043105Z" + "iopub.execute_input": "2024-07-01T15:11:33.498276Z", + "iopub.status.busy": "2024-07-01T15:11:33.497940Z", + "iopub.status.idle": "2024-07-01T15:11:33.525403Z", + "shell.execute_reply": "2024-07-01T15:11:33.524817Z" } }, "outputs": [ @@ -886,10 +894,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.045840Z", - "iopub.status.busy": "2024-06-28T15:41:46.045642Z", - "iopub.status.idle": "2024-06-28T15:41:46.050758Z", - "shell.execute_reply": "2024-06-28T15:41:46.050230Z" + "iopub.execute_input": "2024-07-01T15:11:33.527747Z", + "iopub.status.busy": "2024-07-01T15:11:33.527322Z", + "iopub.status.idle": "2024-07-01T15:11:33.532159Z", + "shell.execute_reply": "2024-07-01T15:11:33.531610Z" } }, "outputs": [ @@ -963,10 +971,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:46.052700Z", - "iopub.status.busy": "2024-06-28T15:41:46.052506Z", - "iopub.status.idle": "2024-06-28T15:41:47.506317Z", - "shell.execute_reply": "2024-06-28T15:41:47.505719Z" + "iopub.execute_input": "2024-07-01T15:11:33.534578Z", + "iopub.status.busy": "2024-07-01T15:11:33.534006Z", + "iopub.status.idle": "2024-07-01T15:11:34.915561Z", + "shell.execute_reply": "2024-07-01T15:11:34.914971Z" } }, "outputs": [ @@ -1138,10 +1146,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:41:47.508579Z", - "iopub.status.busy": "2024-06-28T15:41:47.508376Z", - "iopub.status.idle": "2024-06-28T15:41:47.512426Z", - "shell.execute_reply": "2024-06-28T15:41:47.511988Z" + "iopub.execute_input": "2024-07-01T15:11:34.917877Z", + "iopub.status.busy": "2024-07-01T15:11:34.917550Z", + "iopub.status.idle": "2024-07-01T15:11:34.921657Z", + "shell.execute_reply": "2024-07-01T15:11:34.921191Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index bc81ce806f0966bc5ac0687a9ae54669172fd5a1..bcc71851e3b5a1787b65bcceff90d82e2fc56ae0 100644 GIT binary patch delta 62 zcmX>tep-A(E~8;`RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x=6Q^|TmX_A6Se>V delta 62 zcmX>tep-A(E~8>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t=6Q^|TmWpS66^o~ diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index df18942c5dd0b8cd8440e7c126eb2c988e8f803f..49658f8e3e90dffd74a4717e35bade3aaecf60b3 100644 GIT binary patch delta 64 zcmcb6i}~&?<_%{!4U?;qlgbJzEcH{EKLoQjFU|g UO-+m~lT%Vt5>qz+L3B1ccpsAX zk&DdC;eVX^>TUgHyO;Oh`SSVwn|3dMaL;+CO!rn@%p$@ z-Nu<)cWj*5>UQo*JBEMQuE7!4^-a}BuR{S~%-(-TvwK?O!?ap><*C!D?(WxNRYAnc20&ZQGgCj{c$p z%O2dbWD0-kKfV9_zBsU~{|3Had*Jd#?arHx_jm7C|I7Cse)PcMkM|VglE!45_f|N< zO)y&7z+#p`NR#`2KXPAr5jnQ>@W&1vKc&YJNk+ybtac(O?!2Z#1W9@H!3fg4u3K2t zUwiDF!^eN|@+m!KiDN;#AgB{eMVp*f!bKhxbJWsybEQSi=AJl;*I62jv_=HMz3^V) zy*y-+BxTat+`g{3sCnI{7tUJPb;@b#vLwoTVVU&66Uj8_ER>Y2f7{RY_X=y$VRG#B zg}oe-;#z15>vbl1ren?_3|{(i z=hg+wNle68MWRG9$;KuTEnIUhQ7>(M#Xmbs2J7GGJWzExXN*Pew9Hm`;$8IC8$66X z3$5CvN50YdT~)MQzx{A^ZI6w&&1*tCYwPbnQZ4FYV?4%eqbE`%BE8hqCc`vKl9*H< zf2dkA`1bVFrUlDsQM}v#4YaI*|I_s&n?>Q>yc1)nk!Tk)v>oXCa*R ztT~;OvZE#>K3aPuO+8t4$JH<|ph^*`0Qz99mBvuZ;l8KNivfML`eoI{b?G1l&v;zuW=MicQn?t`pTc zy^=#sWL{EV+wGj(rJiaVMKqZUX{FUn1*bjIpF=*hgDYmH;5tB+qiL>1i7aD7>l*7g zMFtnHK6+`3vaci1kn194h*>IV@&>_}6%ZV$Od_y0ep{5;g6(?ik!pRH6l`C#7cqI- z(dx9GMp{TrTc#13#%N=L#wSQ3d&UwOT=836V;2cs#TdELUNDYKAzhBrF$OIXO_+vy$D`5ni$joT+-@v1;`+n0hYQg3sJjVs#FJ$z+X<5}Varg1zh8 zHguN${ORhZ1x1E$5C5CbR$r_3mM-c8I2s$I^H&CQ~&ixVB=8Mx@JYURpd!fCP% zkHpeQqVP%>=7UX0=TSz+RT!bpbBUx5HdR3=YeBBeC#thmN$Xs1_#sGR$ZfsEh$l#& zqJbKC&%-}wvf+%Hq{u8449Trzy#C`WAU87JxNphr?!SQClu4W=0b!@Pa7I84KS0RDIm!DS4#~St{k{fT- zHzcR(JKw5SP4hX>gUAyErTSK;MfoBvnuLmDUM+J8TDlux~aa&$OYwA|E^s1_=o9m@Jt=ai^ZTk>FkD}A zQs=s&-4=AOozywAC%B=sN%Qy+h$GJ-8%+$1fdc@k!An;)#EoA_stxgs3Zz;CRfTU{ zS%h{>jpjXfaAZ*jlFn;r0HYA`$TrjwuY6!WXE49EN)C?iY(!YeY#X0xyxE~LCI=s# zTG6!;3@k}jXcGvFVoO4HC=?Z1A~D7q0&$c(xO2c74w*5OXrOHd%@`mxB!S{3qr+}< z2hBW1PCOKcLlW`e8jh>c+GHwa9<6P2M_~tfM0X|YL&P0Ab?Tf?(p6h_T}H^IgiLR| z9Sp9SSwq*or#6#>sLw2c{U9~rzBkM$E&}n9nLIf930z#Ugq+KMg{zD6fmMpy`KwDu z`7Zkv+#0gqC9T}HXaQx5I8;{X7@Nu^A;BWvxO8)pYY!z52_a~1B@wdWhy-67x#F!w zIJ7wI`iR28qX@KFA<06yLbE{@846M|u}0`F8MK-cOUBvu^$V*z8;W*Y9Debmoy9$+ zIg$!s5lli=$t+SyYNT->3WC6nGD@=#qMV|LSHU1D07?NF>JXv=8*Z<&m1$lx3lNx` z6IuuiDvld2J*x4sLc04I=jN_jz{H?&bq zYmVbgl(yw@%i`!k07;sofoH(ssHU3+2i#R0_=>(!(Nw>E+0?pT!RV-+@~3M$i zTeADub)C<5Q9iiG3&;dn0+4%yjT=CbCgKH&sPVLh zh@1m8)NPO^9M@n36m~{ZWsu}}PE)2JPusOa(kof7UnU(|Ha>*fW7kftT%*?rI!9=t zD}an_C}#p17nQgm25mfq5Jg@ib6CC5YaDI5E%I;-<1Wd}$J*Gys#E#SF=anthL4O=^lyC}WT z)J4x4DO5;?U8aD38Cu%t_a@h3t}9K-)G*;BDir8Z2WkY~2#CaMZ`(SjcMx?>7sSCc z!kMFaiE$9>Ml@@TArAc;Q2U%{?G2VLh95Vdvgx}wpLP9Y zgfJN)OhyR9vDRdSFc~3CMhO4rp9ETwF&QCXJU$sAj2&W3MhNYwywq!-j1VRxgvkhD z{t#m_LKr`dneDA~CnJQ(2w^fpc;C2ZGD4V)5I!)vn2ZqK`^SLE2w^fpn2ZqEWP~s~ Ra`{h22zv1Nol|!$`WO4uFRuUq delta 13396 zcmeHNZOENv9q&HR#5tX^+?%G)xt%YoT+goemqMhdKxu6vNZQuJ^{Pwv;xrp2L zN`#!&jmpd!B}I@r>O`P!1w{pc?MojLwKoh*8Zr8iVZZyFXWKc`o~sY_Avh0%v9s&k z|NFYG|L^7h|33#GJpb_r&wu8p=g`~C?Cj^aZ=GH5x#u=>#W;~H8=6%J(pX7755h^o zZHOtlc=+$gUo~%Tsa9RRc3r(Txa}d`v3YjSF1LMmPP^LAZcTgkY~6nA=Goml-LABG zHn?5Af6T4v_2yQe8*X#kZ^dUjw{GvfJa>8T(tAJNod3CM)zsR}`+ASOeo61)dvEz4 zJ$>1qyS?-7-`^a)u3FwZbpNW}bNg2|hidnQzEJ8tO%sNi%6-~Y;dR33oq);99wmG z=^rn9U-j(w*H=pqRNrc@IZ$2K)j@MEj8#T!ksR|m2$lmQjNxzHKmJ^2$D&$g5ghl1 zNNk74fY2&s7m*Y?Nv8VuRh=iQI+$pAPIy>mBH{=Ql|;&{X%1Ex)nM6${jaQ=`b||A z0+Z&S2dhsP12>d*);EtmQ7!2rtGM<;5s;Qpf#GdUklbTaO7hek`dPL72hUcYT~td= zDDIdMBa#S8I_i=)MyF^zC&Kq%3R5pu(_V0GEF;|F-IHN1XxXar>usP4Nj;ZC|jh5%|t zP7q*HNN<_)UJ4RPvQ#$DZl7A+KeD9rbX7B!iSmRwmZVTodLc8-V2EV#A42rqk2}v- zbs=sT?4vHkb3+6&Axr>>NUoR9Ft|p9WE$SMlz$7<|(zC^kR@0 zgUcyVQ-9O7$h0}K@a5z+2zuS7n`bv{{P-t6HCxKd@ztf%TC-(cv*)d9WnJ7AlJkmY zW4CiwZ6Yw>sMV24T$OjkaV8aDqL~aXjT`_HH)LjHiaCK=$vNGVz8VgbdI}>TW7J*S~NdLH|^o%G!X1IY9CD>N&G@p7edY_W+`O52hzO#^XlTRG{}C)nzl58=0Fkw8_Jpy9DX!| zGahn4?zErdLShaSBYcT>c0tO?LIjBsV4a^fAEo-UW2Oi{Djx8+fEqAQ=+Fv^7 z^*=4C5Yl6ofvWe zKu)8r4g`Dz?ut*l?L<|s5eC>RRs1aZ~^GoHDa72wWt|D|uYdcdty*?LGY)F3F_d&IXQ zd;^lv+g7id*`uBJ_UFD^Jqlic4{;9S%rqE@I{5sQRn!stWHbQBttOOWH?JaN>q6)} zMBcVHs!O_&z^7SwNXjXI1}=&MJ_6K=n-1W8spp|4VBM1qBGM31z&I!ZGCe~)z-7>f z!I93J$U<<}Tz^*Q#<~=E2(>rP>RiwzCfO8_MMPN07JQ$K(s-aMI}{s@dtu&0b^{IA z0T@0-Ca?h)m?3bBjB&U&)u*plFIHWkm1r3<4+bJ)6zLA49QHOR6F_F%2+tLl*1}C@PU7jz?7}q@LTqHzBoQ<9?H}zt zgD3V(p2-{*`a+_Bb_OF1sDpY7b0d=uQirJK-DL3v2CldeyQ!b*=;p+#&dTPY6Vts*Ueqm=iu33z+!r6mQz=!)SC`K1 zl&>sq>lFHGq@A(RA!{LDo}grf7lb8)S79K}5vA)oVhjWeMgW}YBuYsfmzIgcGo<7P zsXg?SHNhdaIwKkGIg)HpGgy2lLv2)WV-PI9!7tZ#Zm0{v2YI``x&8f}rL~}_F&PVR zQ}CBT!Y~?C37ql-(@aOURFl@l!H*$Vi3sq&E*nCEv?Rh##x!rvTxQnWz-*>WK&?H> zdu+@~mw5DWUDH#mn@vCIoYUX*&FUcPxzZc7nOh9rLUCg^GbbUf%E6HdXqwM3gdXNO zGc?-F$*OeoqPE=3ArRuwxIhhuEh%czwV}#^R7T4=sc29fZW(qIM5-J!=$}9egUH~c zlK?D)9+sswBy^#;gK|zaJ3idGa=n-las++o2Ebu>2o`z;8V(|-go4s33Jzs))NdA= zCe01ss7C!};ljc=u1eWDzPfaHr%+hj-f0v9RVW3L^9G$6iz^h7J54N%mqk@EOxgaB zhk|5Oah{3ya4ylI#6u$j1qDo~3iF+mZbte`=TS2uZJ0%kpSfn7p%(x#Ndr}#*T7=% zw?8tqp+*6)VBDbwzH)tMNmolBIbJ@XzBMj*^bLJL4gysMEvFITpi(C6J4c1mzU>iP zly-wI1c*5C2360=76j5YXYdIGAFUy%t2Z7N0c{C|qSeCD4O$SP6np@^LR{G#y0LRp zmv}DW;1z*q6y9oh2btjuQCu6a;ew88budh~11RD&g%l@`9i{ zg#$+lWv7EhTQ!Q)W@~!k=Hh`@r&jc9I`vRBSGt&S^c3>)jM}brXs57N*q3XwbJ$7g zMsy28Le=Oc$Q+Q<=qZr|qmrrRbcl>2a~()Nb=oRTg@@#M%8hCr3VNHU643KAt>655 z)nx(=OW{#3c_CzUyya6zEY_l!Hlud45Zg6Bx&vB=hTVd3hfup^TW4uEqfU_86LcN} z00adOPGe&!0d~d@?BTEvK_aS5$}C4SNTA3tX#X09j)YQ3Klsf-AELGizK5dipgcsL zQ8WZ2MQ*qKOMver%nEV&u+vwH*=~e1o9^mdJdGRSTHAyq1P@4Jp-pJXr(ja#wC`eP z=b{?I3|&As9F1jx3qVtlz!nnYd9HKS44I)L>ZWrxXjB>i9)<}dXh=K!5UBdmZ_6XgT3ufi`>e7jw!dY=s1DtKUdrB`maP?d78T{OK_Li^Sw(K*LdBJ2}Fqsz&=UC(D zo6HN|F{GHx3nue|e?QeZ!zE7U1%+wFWL_|t7rgU#WhV22;b^YZgdHv5Ci8;vDaB-7 zFqs!j<^_{^!Jzj#nHLPFTjSN;WM1&DbBf8lU@|Xw*FnxWdM5LN1!fhKdBJ2}@XlZ7 P|G%3T^f51ZX34(*x$5lW diff --git a/master/.doctrees/tutorials/datalab/audio.doctree b/master/.doctrees/tutorials/datalab/audio.doctree index a709b1c363d83847551e0de86b98a8884856f1d2..fd49abec46ed53db5fc1ccfb3b48ff2cf23ec117 100644 GIT binary patch delta 8841 zcmeHMO^9As8TRwLu}oFc(o~Az+?%xiVaS|w-t+6qiWMrYHFOivn7-%FfYQbYQk0T~ zi+~lhTD^3k=&EQXCXxN_{Lzu1!L%PD^8!qZ`=)Fi#tN|?f&O|;1rsfDZFr5ICjF_W&!fAz-J zQOfb7rhF?rsVAS+mGSVoy*;`i(YWRCpc-W%9_|Qo078P zm{~Ea)u&!~sr%DnXuLokpFH0^HZ*FN@vpyJez4D8dzZ5>nTl3So}5%NBuY*NMLSDm9<_hB*860~4%jIP%BkclvDv}0YR+xuwL6{|j4b>}|pc-L3D z_nq|eb06OM;@K0=Jack5k!y)21($?$I9!TCRVyTE8OK64Po8`9v5!3T$hig_^TDSZ2Sz8(o*eJFwtUCX zXozP}J}}<=*Ye2VtamI^WvQ%Yqe4vu$YQpvi?(ih@M8C5*BeIDLT9o!v)+#YM;CSNKmNyp7!sGQnFCQNob<^C*&iL%B z%cH&5V2vdqT&XAqSgD*r6IMbBit`kKP03D4Bds+x>8M;Jkwp&dlhY6ezryC=DzJb* z#VWP7iGxbHq7T7je3Cr~a+o&pVH2a|O5P!H- z_D*H1I8EZpEH1(q;4P43*ql%yNFgK)43-Jk@FU=M&$RP$_w~iLWndAqZwO)~e5jm- z7vA_nKl}-;% z0B3JfG{HGCuIS1!KmZaKk2z|RZcqKADuNg@uh5x{7Ge!BdI?#HFvl4f_l-YzdAT)R zeWd&TqHok*F%2?)Xzdl#Ufb&4J4g#EsH(_^q?0b*Ne2@OR1&AebuE0bfCx7O^&BQ> zY{1J_4v?A%iGlE4TOOO9c(wbo*6M1XY(v2SMb@SSX|%%70-&0- zw9!ZmEUbqr1PELNgdKq-k?`4M2q;@JF&cH7oApJMf(%&!5}an{IAfoHT%^t7+-m+S zy0war*H3hh4~^P;j&^@{wAaZJ!+K;c28arf4tDtMW?M46qWUTVbF?a%R1m&l9-_o3 zgF^(10b*n82=LuE{pITN#l-;Z0-ea!6^33M$D}H-;$4xMa+PaqYZSLDpf>8lWDp(4 zORsc8pCqmZ(6$nD(hA`K@>Ek4SqcT_tuk<5ShGX3D#3WEa8#m%}-Dg#^1X4>;yVxZnHPo7y~^us9Hch zW_X)HkknRIj~mD&ugsDd0t;VNWr3vYB8(V>WD8f!B?h(5j;+ZYc;^5Pb6Wh18 zR!6tk_Lg@cN2EdmE-)t#<-sTkv-4Tyq^56PTVCi8MJpkv6kK5-Kp}fXGT==Wvhd~* z*3LFx&bAXovnmkN%b){kZ^=1`EN&2B^Z3t8-MzE3y|U;JtxRJ4>_gq@cIjPbyW^wX z+Xu*YGEjryvO?t;1;_*Hg=DZEi*H+M|xnkIVKHlBGEl4NuLe6&+VX}`hLIRCcC?3tM z0qYy{;Dja!o-Bx0I!TPg;F|+nBC~}C?A|m7f_YS}V<;BU+Tz2ihP?@iI|J_bdM^bZ zUf8S}Y1Nz?apTc5-2*#P9LSJdstOF23s4bcI}^`q%cyI<$;IQ#thyU%Ra zBb)WeW<9dIt$RcKgghO#PujP*&3a_B9@(r%=KbSlJ@UUdGn@6uW<9c5k8IW>oAtp1MRZoG zR46{Mh%G-NQ3HjbLP zS$plZzQE~cD( zdgki-vsc$A_`Gwty55{WGHHhI+|z76J9)Feu-!g5ErzR@808=tkzv+4%}wA!I_AlW z;rikTb< zD~(~1dr`Z^UKY6-6F|FLd^nbnBd~oWLkcA3kj7uscyqDfP zk)@T~C7I{xOrr>1hQzZk99e72LnzMS;S&BC8m@e){nca|w2P$3a9JWr<7{-=SRJHE zIlIWbdyvtZ#qQ9GOO)1j{K4!3CA5gD0Mp$Bd#h0xA+h3YTr+Z%@ z%n)|G|HkXhfvL+`M?y;(mPk13fDx6w2w)=S@}i=h&{h|bfEuO9IToa#9JO#MFsX9? z*5mEr{=)B?L&HBVHQz_2s_`Q*Jq$cJwtt$4~ASL2Z}fJuko znH7)*j~2*gxcpj+Q~-WuVs@Z9N0fpKg-AsqV~#no{>sJn@B(L*il}Ae)OvFHbmgsf zaQ3cl@1*1nn5}gRZ8MVmmfmZIbq zyeo%uKWcw4>4-t@yx@=y28r;H3dw^q_+e6+`g8l*jfH|$!m<}W3jwP{wPPB``&%dN zty7b&0!)O@RuU=-0gYS;rKGfl-4Fo5Lo~@c1_VOvz%YV9D}ZE4Me~IK2x~}!OKeV- zGE3lMXa!SBE5>x}k?qEC>0|AWC)K#KzMNLV&SZe~*Vo(kb<%UV2u;eHOx7#MI7Bq@ z2xbh{Zu>=W1NM%RXtVa?Ye3WVm?YL~v{@pow!e zuEp%X0^nCe&ZX?|ZS4o{VY`$p2#s1$C|*IT3Wz#rWrd<(ZHlb-|86#Z{#NsNyUCT- z7P@7)C2d@QG6$2gSKbTddT8?w#47c%%b=-rWm-O6d$Li=Hr@>>t0bmxVr;5I63H2q z6vz@Lc>>`_irjjxz1>lB2)7{k7G*RHSq#uJJRVO3agTxL;Yinh9~LXSY&7`Sq)0HW zAqyvcz(EBQAmeTst3~erzSTb3RRZ^KT!hd1=AG?<&PGqgfESX7y@1iss8kfLa03B0 zGi0EekF^~d|)E1@1M6Jh6y6_>yfgDzE_en4=VF##FOBrjj|Zy~T|@ zhi(fXGK2&lERE(tERfcLM1?Lo*s%ysX;hYzcLADg3R7eE-qHW?m1cc-M7C#9#Jw+& z=Dbl0K4Q-F&#U&m$2i-qYEA{1B?JF}j&j`jqO@Tp1kDsh@WXsY0sEQJ>BBOC9~5jq zrlKHx0sjGobE5m!+s)S}Pgg3Xmr+#f(dE;X$1^uDS4-0%#Kt zzF-*I4Zq4IT>r!a?a5A5ZgBtpwY&Q-zuxTcZ}~{OzmxD&CBh9_6isCE5)@&qC=&i8 zmYvKVYp1ysCc*T=C?FPtfxuh9*=HYt%97_Q(J9zv3$4*P;+RB#V@3unNEs1*9L_y7 zddNyxH}BY5$ob56{iE%jn|dbMpfEh9%mNcKhBm^38pfO*FdxHsK+?e?0pyu9MG+wi z)`CLO%*3PW;cxF4-ni79hZH+opnar9qf|FybVZBAC3lmFUftu`mK-T!KHvTI?p+MKL5CuiDeWr4BU kob0u;S#3^Mn-j6xoUAq{tIf&w|9f*{hO_(Ees;@$0HK4Q>>NdPz58VZ4u2~k0ocCF~vMYp)~xgUj&DJB_$Ou&P*GIGwdvks+7 zP_1qiYi*$V>~^vAwd9d3SdCzSAY|i}kO`FyWHWdJ)#5fgRiE5r_h>5N0JlvG+PP>pX%EA+yF6$XsXb@SkD(azz=7smf#`-rWS zK5M0X&{;bPKJ%a&e@JO3qG`e-r`Mktb&i4w7&$Z%0(8bYiAkocqgTpeb!D5)?N>w1 zoFKF`3dg77PGEG2a*6^m1N~Oh%3z`<9K1YLizCLXH#_XY5n(x8Qo7pQVRL=g+?qIX zKn)oirc#Q=3F2rukNJtV0+qIlNk)L_0GTwl7oeDn6zYd4nLV*59` u?QVky^VRw{HruXk#LidU@9cSd4G!ygSjXw-a#+XkpX+GqhbKBOkNpDH3PH~R delta 1967 zcmeHIyJ}TI6y?kfh}WQ^qQN3L1S<_@9(!JmF`$LuBc-BpnLRTbV_{`sr9~PaA8@eo z8)6YoC4!}ei8g|%atTj9lxx^)?X?zrb7^#QX>@Vruz27GgNKji z2h*rfc~V9yt|d7^CPwYN7N{Zw4a&!qQ%DO{zpeV!?XMG2$LP4?K{x{uqjL_U(H4VG z!rA7@TD8yxYR?XLD3&u@&^9iwA2 zyUHHdUPJa)C;>JBCUh=QLSnN`2KOHh+iH08W}TfsB)!%by>FJ+ z+1LbR3kfNZ#gcd7qM}TN5kZtjnLS?B9pM+-*i-GwXzxY!Y=561PEOU6*XHgFZrr+Z z_4=SIvwx$z_BMHcs#*NO&U9;AW#^jy27BJ!frBz0l<}a9I|t-Hm(jNK$9gZ1{08_2 BOd|jQ diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index 27e1b7eb24cd773300ff5d9aadff8a27a7786790..8f2157fbdc2d6cd8b7a91ecf305d9bc1041e9f35 100644 GIT binary patch delta 72 zcmaELjpNldjty5h4U?;qlgbJzEcH{F{LU30L!Wt-v9sr delta 72 zcmaELjpNldjty5h4Kpm_EKLoQjFU|g cO-+m~lT%Vt5>uL4x!PH|7`L->F{LU30H5a;6#xJL diff --git a/master/.doctrees/tutorials/datalab/image.doctree b/master/.doctrees/tutorials/datalab/image.doctree index 0304ea67982cac8f0de7d3a32905c038cbfbf963..f288175e090cff79dad781b6e32b4a886925e6d6 100644 GIT binary patch delta 32713 zcmeI5ZLFo$b>DO5+y5}j-Ny+Hg=rWirol2QciGG1FDj$vQo#YDkW`|GJTO+B{&9E@dsL^ zzkQx_XU@Ig=lK@*!$AUxd-vRR_RCu9|NpPG?tJSTvu}N4_S82I%ktx8`slqME6&tS zDT6ag*gD3_CRf^88sVGZ%aqF6r%Ibfg>!#;_&0xIo+?w@+Iw4z$|PGA!n%-La7}0` z>zcFAz5O4}1{R%_pFMu=p75?q=CP=ox-JSGLTyr2WRo$d+LX!^skZs24K6zO!0m6o zWS*o>N|ri#Ym65z#ol39DM!U*Xg)X8m3dzxiAe0gH@`DX6I(PN^rhw`Mdx0rrkBi8$zfZQv=Q3q#umo%uTh+s6*0E=y>axZ z`M@G`?mzwfhd3@L8m*Elgs)6gA?RSE7NwWIZgf%QPc<7@bngFr^!_(Z1&>eq#>%oV zLD~?Tx~X_oL5ryL*+(D$#{)~w$qir#tCJ+XFRYdKjX!8JbLHOD6}8EesrvT?DEmc_7g`&myS&# zSLb?Td;G}gmcA9s6>nbs@qDIrd+TMR+kVJ5yAy3+xqS4MwH5n5LU*dYpW)`k zZyy;wacJg@vrf5MD`%Q$Lt{);m1QME$y-xP2dV}ZEskC}dh2C#-9+OmqL+`-GzH#M zCnvO2N$6UbE_MwqTD&qFl{;gvm+w{cDoi0Roa!Z~N4LE8_qylWK77OIzwIr!x&5^p zM~5%`gzjK};>OW!JquQc`{GTbuk5Y5_!*YR`gdOa^_v%8Jw5tohvqp%7L~O5iJ8jP zg|3ootXIBtO(EFEDn8Ad_4aVnt$Y) z@z>fXzkFz`y?Z)7(SBz(zUr^`0M5!ge!TtF**N@YKgId)&su+KoPH!%aU#Ej&;8ua z2R`?6SNzP|M?dys^X{J?$%k1!%Y}H$_{&!k&XO;Hx0+yON>hfiB0mQcNY6o~g?xPL zBU>{YQglU<-h_;Z5;z$=EkRn~-KZ8<{MODFM|0pHQ4Ua8&>{=6`Se&)HAE`MQlKx5RwFa%i7sReR=-x36e-KezpsDPSFR zu1V+7kVYUA&U!CJU5C1A7Uw^@bMMy7$z+=X&QcW7NGG`@06jP}MhQmhc7E4wTVET^ z>Y4))m2w(dOy|>jg&9xYQ`(5w(;5**+wma_p;LuCmzk1;4^tB&2-TvI$b0q zN*SXzi@&WWUmQ&<*sIqpEqE=pEK<<~aDg+LZ|L?7w~vo4o_yovk4EzW%ep=OFSF+e{f-@ym*2YRc2XrZF9;~g|;wpw1V^bvy9R-m_!?1DGDh2*z zNK)$d`TxbXySEB=ZYo%l2u7H~*9A^1SYN2<;9%Wboeh|6&;P-enhqF!{N3BKbGm1K zd;8>6!YXR`rZ&>phF}NBtt$cTvqtI#A2A0*-$Z9{!lp{5(mocVN_7N>3jup=Kl$Is zM;8x$WBco)YwFTiU)4cH;!)vJ6>V7uVRR^DY@hq3?PCk^JL9j71`M~4zj0g62kgo< zuY2~{?VE14@3`o5(P^HNv!!XIiNeQ92^AIbJh3sgxbJg$X9Ma*cSZ9Dw;vuoIB-RJ zzX$a{(Yo5d+V>uttoQuw^PiqY8F zkygA@t1#Im2ndi;G{3$c&F&R|peE1WDuwg-^J?@D@47l|_5s zow!i9kHWf26bZEfDNGVu8t+ol+LxjTF?5f#^EM5Q=7I9@r6ZqLMWdpJ{gwEjPHAz) zzux-RXwdeDPHxHhfYsF#CCObktD0chg%J_mjbIqa0nwC|Rxr?L7BBhnN4BQMTN$~p zMv=0hQn7B5%ds#LM%eI;TgSSXF|B>23Q5W@8?uTP1lEQml4T`p!{X^`>siuUztPw} z{wq83ntq$Nmt)x;`Pr?V_HF-l`-anE-OW^i+(>9IA&e%;lmJ7xSy-A)a&g7&Y-%bv1}^A;_YAyEx8K+Lk1fjVYj6Dr?9OosdiyTv=v$_Tz?DZ9t{Y``HX2!c9?S7 zZ@Rth-P>y3Z&Cr>-($4FY)DH5aDK%IWS0Jbap{K@qYf~({XMorJ@dy#C&%SAY<>=(6=L4pBESb#R za%^(>92*6cM@s<`y6nDatXwH>e0!G z6_O;fm=s8kjg2Ncgmv$PE_hB_t1Fw2iFC4o2kX5Hy}%^THT?I-V=4Vh*;qJ73sUPqFJ zReBXG^kt}ADsmv9o+k0Ks<0W5?BU9$uF#>K)zL$dq%54EPU#dND```T97#njEm@n#|I#ZR@%hcse86;CKG)fR)mHwyttE}SZdNv~aY{>5R2l8N z*dS>IEm64?hMZ1d*;lA?piH{% zZT;6%m;3!^;9VZ@wt@4yD3%QDY6U1m)CA4|$srS=ScObW;M4-C5}dM5d6bQHl;i|Y z=HPastreAJOWX}4u}&?SQPmf4rJ|&4ZCJ^}(5Qhl1gMH5&QM?vP!8BuZTp_do$~>+ zOS!~Pp(14rPA8rsiHIFV=_~S5w3Mh95E(?Tkm@3rn@Q4u=j4;}u27YtZp8NO;=Ui? zT&#kj@|kIHAllGmkc{?1lR1zF6$08PGGFUAShU}LY9faWw&&3u+BcVz8>b4<&O;;X z(jiI0NE9sy4q&KqcwL--TmJms5eQs?73ut0-2~KD@DG+Hs)Kt5cQ`&R00o%I$>(5W_nh%&= z$t3^z`N?I}0|MS4aH0zGXauEOcn>f)QoPlE@ujb37LHN_o{YkI7vT%E)|C9W%5+uY zhU<@&iUN-4LYM zPDb~2U|-ahLscTVcKK|o@qMHQJkJ3c(2R5GI#f_hVoYqm?~#C4VvozxRd7C#zjH!C zUeQIVq<|6lDq~2>00UOLbVkEKpH&3TWl3ZH@$IYI|L~c~l@mh2uCwj;J~KHnD~Rh& z;pnG<;Mb6BrJE3I2cG>>M7jU=%+e^-N=4O7hpi;i91K>j;14tee+9~oQ z!YMr!r9yg;F)Y6HV)i?_QXme#R>%iNA?`N{m0TF=cOqz?L(QrLHqH-w2HL24pys zJ(2CB4{br&1dUK0u3IcKHok$1BV5vo@L`J;Wh;vkQit{nd(K@{=%&z9TS2;kqAN+N z%W;$y=ts3RY@29-wwi!3=*!$3M<(+w$Z-Q^SCnnY?2@uQ_nFDD_LYB6ilg6P7eSR2 zcXEnLE)PG?BUG?Dx>!V0Y%PAh7J({D**S_u2XnuVvtBH zbp&LfNf16Ag}PkHpy+}0vpD~j2_$sDVk$jJ+ou4@aaxB~$Un9y&qb)@P57eO0 z$)^F58waZlvB-#gkFN~07Iz>574nhMgS|K7{r=oE@Ft7>s|U`@EWPV*?M{x(2eu}b z-Zial6QMR}K@k*H;AcKJ^Yb<%F1C2`#W8$bfx)QXK;s&q42=r3b{s6dw%Asq~BcR}U-2SSV-ES~;el;JkxD-P~``l#bk{Nh6s9ZVA@e$k@ zONK5o9HcOT=Cyy0oa9r0fB>2&m|hrQr&@tncPr7^ibe<8fM_jfPMvP5jZK?niSk`)nrLTPW$cU z!0?`3bp3U|LXzP#`7_&z!xHK@z zkzdh1a;Ez)>VU9FvhRDN2LR*1$)!D~qv5xAYoWu7hGR##vPh{&s*;`v)o=kyQ-fmM zp>vQfk~&o)H&VC`MAF>tN*yXBdbt{$uC^@#-(DrV(J%=kL5Z+39G5(-(VXg%9ta~T zs^v%Bx!u4Apq7r?p)>c{Zb$E(sAYbeT2Nw9dae<|!q5h~7E>T#@`&$+1)5yW&`4;M zaY}1x%|pE7G3JRpJTc*}h{=y;!WAvbA2Nzc0Jq%coWU2th|)mAfZ>`BGViyFCYKv{ zhs?GQ9oe}x^RkHSl$C5vbaS*a(ST(sV^E!xX05g08t#WWV6-G)G;uO8EoKz%Y5Sp! z*`Av39Pa|hoDwIgvOv%>#Yo2yEnAchQbw;$_BdK}=w7ExDO1*T?%kU9&(h^P+?UJ- zuIPZ94ZO|+cyr+N$_s8bu(OrC4+KPHL{cdwkBugD0U7WDtb~A=R@r=>+rlY%NC-{t zW`d^B|D!B@!wBe9rdKMXEU1l03T;3b?w?{BnU7vOD4oIyx~JLYumQV!EN#H<1wT7H zrG;OB{|zt|(+t$p+R6M2sR(q70)LTb7onz;oEipcm4;rR1RW(wqm1U)T<@i_OxqCf zgMgCqlVif`=>8-p!e&>mbXE85x4rZ;F&i-3r80&qklt!~x&gFgSUiQsw}cc9yrtV| zZ9<5uE@&SahoNsGVhJFF`JmW?i;?g$3^}=Y;fbBE68{8J53?K41j;cAJVN8Rrrm(d zM`a`DvVTP_t>5-)JZQkIx*+LK+BbZC>(m)9clA1aL#lqaMVt;YGc zCE!BL)2Id_q#3}WRfZOHzn2vVI#jf2{$&V-_9cQczi)y5WOyeuuv()xvwo{hmlp=C zuJRi7^138_=5~1cUL>RBq0%WzGJ$j})ZJlu8UlOdo8DFH-p8DL{skR$z%9F3Xb;hrNejNr@1vla-Az#r3~8@>-@vt{{TOlJtsY=` z;F-Q-%u-}~$L{1fzqz4zS7rO9Z|}UX{pKgP-@6$j-HegK#x`T5`v;!)d+BD3bbWYr zsZVakNH=4oeUrYMG1AQ#>8poVH)EumG15G)J}}C;86(|{k-lnxwHvY7jFDcLkll=t zZpKJAW2CP&<$OT2+>DXF_5>>tbu&h~86$nwl<#JYbk$d~86%~uZZk%@86(|{k#5FF z*N0a(W2CQ|zuk57TZpKJAW2CFU zRqq0B+>DWK#z@yEST|#&{EbZm!4GuRCocbgV54FD delta 32532 zcmeI5ZLDS0dEay9oO>>VL1WCk*@k-#n3%Wmu-0CCt+f|86{PkDM|Mm?Rht+Kuf6wL zwNql_+T?>(3{*{{f)s;?YNtxEAulbB(}>APIfPRm*~rPrl$O&eO@p81P|zxu9u+bUy2Yibo_scTnz zZJM$atx?gqvO4v``~Fe(p)((<+DqonIc;4N+R}^CX<#3R9cKvBDSsV-zxLanfgbLT{17~y0w)MQbbXj!g+0j^JUeV zqLjW2%eTrsbmq3Zc6cipV`JIaRu`pn(U~AzrAqBvS-Y}dVD6zaZ@-7Psv21qg>g=> zSD~`rOW~DMq41&*$}c}v?x8c^Yk4azoiUsnP;ZuwTZhtB-x zpZZvOD-o(Pa!0f-W7Sqs#6mVfh_X_xT`q8zdFagl`RNDVwo_KMt?N)UX?q&uSu5M- zA~&)$&8aUu@sBf)oRMGrTOZldCxsQAzkYQ1)P1jAGp?LE_4sSooci}K``3TzlI{y{ z8XY|K)VI&=f9BY!U;nE&t=?ki)Hna?mxf>7>0Y{hbnuI>{^I_}c0a_m-M4QZz3tT7 zU#l+i`kBXH`?m+?LbjDHTQ5YR$`GaZsw^s7N!=>jmO35GEc4Kr%DQX+y8<9>08ay!-s2(WPT2?P^~==Rrrx!>0AdzX)XdEYbpK1#p2na^_b;x`YCo;omV z>%x?xY}(S4Wvjdk(N`h1)<-8rx!jvJ^U&h(m7^cLY~C2bi8YOKWxYHq(*`L^#dg;o zKUlt3?xDr^X6*CGbw74!bfo+6iP1dW5F_`t_>vQ&TVMC({`Pi{-Z1)aYtJ0-e&NQ^ z!M*qC_xW8nj&2`*X0_Qb-!%H=wFl3Cp5;FOor}J7^WxbPqknN=-gv2n3ARS6D;^lXb9<=jF zeknt(OY%WUwyTQ5)lIZQ7QT%xruUj<9y%qzc=TuQcwn@+*)9`-Lc~b4tL)>e&DU$<;TWHI&*B_&;I<%@!{^D?)~f+j*U-t z|8?(w|M;Z?Z|e>oA0NI>l~`(Un`SFhX``)02m0Jo)Bp#*^;OtNG8jo<7h&-kqEGFI>_;cjHy# z`5SKZv8%^l>F(NLRew4gU;PFw^OSt=Roy?IjjK1->bX~DoxNsUztJ0f#giZ3{^*k* zzv9F18~wl!%+s&VUo}4V2HvZMxNiK5SI&e8rBgML##AN9%GI$J#4sIo)fn0NPi!Av zJpL2ok4$D(yBI5_n^u$rHo}^5wE;*4!c>sm6?cpeFP{Fx@nfSM8_TAMK{?+791SVh zYmv}o12!_<{ZDQkSv>ug>3(ZGI-WHw(nMQ#C z_ylz;O(~7Grme%`nPi70EUVyau%M_68{xXsr=~|2FWx=*%4nx< zOkEeYC8?3`+6qkF#8BvhcWqVo=_j`iFT`(+zcQK)8*V#)d?MzMz*5~ zo&3z?@Z!$fS8QZaY;^(*JY}mBz z7xd=8H<@;S^W)>|-(`gT`#*fkXFhuEgAbmVUmKjMEbyC)RhPPKys;68uB-*`ht+A# zGNo5{#qUjyba%cmzHD*m^yI=e_U0clffF7d)NWKXL6x zPjvtLhRM-+W@&AA$C=5EGhK_Lk~4Z!G$d=QTCsTM<#b(DwA@x5ToLfS z($g!HFnw)g$dKuGocl6(E zeQ-8x+I8pNvL$Y+suJY3JW<>z7HuIM1c;f5h z-yO{}W{ckYKWryVbtQ&-`#qCG*i$8{J2#z`qEeyFDqYJLwNBb)C>9f$=616KWx_bYspv) zYmWE)^IJFFTz%lY+f~6rTEKM(&LM_aD^Yn*a4%#ng6-o)AN4adMECXoxV7@VPu;rp z`O!m}`*}VtlN%aE3Uv zIQP@r_fKY;Bo_Hax=XcvQL}YA#a~wuFvZHDmcB<71!rWr8L{TBXb06GxzTN*gAS^D z)%L#kXQSDm<=S-r>`%AunrBRdBM(h(>FkSJhh}Zy9Qj;DOtnH?RKBq_DYz0&`$8;8 zQ=|zCi((_rYMd?Wz-^7n`XFGQq~@tt?@O1oH9@u13eKmxnVO>s!4a3Nu65Jc1t-oW z%~*8HOS&^`v74Ws7Ae`ibLLBEwF0j$5+DJDl$w<5VPo)+y6MyQtRXJOhP)MsgLuDV ztu>4E0uL4S;?wV*rUeM)3Ry`RBC(R6TUGJlEn54n!UW1SyZ#~E!R}Ksbo+Z+?1|?0Elo_p@ll@PPZ(o4=|L%o z<21QX22h!aMcu5tX6ay!v#t=ffuU^6AXs3isjvy59BbWnB+F%T!@&m4Yu&x|wXHjM zhD~>8ubIg1w_e-2Y+iHgLg9q5I%NS;TpKmlOB!1xc)2)zdi=@BtZ7XpVhSoS!$lQY z7Z}>Og1=w-q6dpT41?PQAO=*nxzHNnv%n)S()x0k`ZV#NqHG z)=%~3Kz9Azintb6 zp{%RgL8{`&pfD-Of8Dtc?fKfU>CpZB;pvahGp4y#sf4@r$n^3VkT%FhkiQ6>5f=k} z5jzVf8{DZ~$R{SOC3Prb;0{ya(PPliwZTimm=d5x5Le0tP|NsJ(RfGS09H;rWvepS zf@Enz+nU84KMUlV4O^^w#FeqQ$^}bLv*f8tgh&P@ot0%xg%!PPfeeKHGWpPMSq*en zmGn(|y)>F-M=Oo0oofrOabJ)vgT}mUDpMH(9xxNnN!LWA;+DpG1lNuqA)?|@T^BDShsoW5@R%T(Mj6%nfC%2c;#mNZ0c} zJ;#tSz4E+_>D6UjdR{HT6Sh#cE}BwES;Hi!#*%}U*Ns|TOsxoJumr;JAtMwXdyIgMP_8{fp z!a@vvii?1jCPJ4eL0bgY2+po(nua(_6V>%K&YU8MT;(Kzk|%57b)_g7oPjWDjW&zX z$;p>SL?)BE+>(Q&tc3PsTN8&?I%B=i3;F!?FGur?N!N9EPv1Ywm|RjM-7AMCm&}}P zilC_l8;y^HQKf=?!-;5k*5&Tyq#A29of;sdDJy(PlCNr_MhThc#oFbZfxfuNM%U>| zG%n@o)Fvu@tQ%GNRjHFPySkehv&*}A_bgp=a2DJPK*LAn0a>UI8hnJ$Vb(KSL1J=M z&<(xZQuAl1Vx^D(cug{%7Qq_f4<4Md_1e`H9c&QIds*d87Z<8XzwWYDMo16v0yAN_^NOeeRj;#NdVZ2JGCx zp#nl$Sz#T(0nt=W?Q=LNVkvw`Os$;`@pU%6SZ5L9ekTSx*3EbgTM zN7n!p+1Q?IeDU=3o(Y6E{zYgmip2DVx-GmhRyNY&Q{3j>)1aia56C{+hN4JlYC>=q z$t;N-s-i#No{=0j?M(MWXQy|LJytZ|%v*SpuS_qOzqOTk6Qi4~mdc2}AZb z0ISq3>AL|L%bh&LRUI@;mAb@WPyftR?%?>QhF7eiA)~}6r zhU^wS7daa?TzB&Kx8L7=OFOVclpW(|8-(Ul^=;;Nb}E?aVS zOWH`D*9Ya^$8vVNU-{DX&RH{Twu4g;Hxtx^SkeCES(t?eK?%sU5rjl=QUppz0GV4d z`nv^>QNbb7p*0l{mL-i&>RA-NDd0&Jtk{x;q|$`!Nwh32Eeaz@7fOs4!Y63x**#aI zPnEH~tarpLV>ZEI+2LzfbU*U!bT+qW=7{^X(u5DdJUj_dD+t`3N-OMAK3akPV`X9V zD>^d*=xo6xM8OskRxe&SHHMKyDyB$rfJW-IS`=N4hh4$^jB0JYW?K^e z!{>CN%MD*tz2D_B+a~vFHd-cwORIVWNh4Rrq~Npwl8Te0o^GUrqeTA_SIsuyTx+36 z=poQ}LIp`*nDU%@8wr^=v7;JVktFjZ-J}vveRG=4aF>-JdD77FvE)_)HdDQ}Gi3+b zy^U{Bqo}3bgPZpwgFC}1aVNI1u0K6ev3;LDF2OuTu z1}xt9V1@;s3wX6JjULLK$bLV`UBmrPXZFkOCbFI31$E+2ChzQd5N)WDL!{s+2yaPC zOHVtZTrid(qgT9gK?B#*juHkvhtC1nmtinfvh8&&8d zSC^7&#N)`Ka zcaHxx#Luwl)y2%1Zo5YgZQn-IIw{#9`T>a=@&k{C6BQM(ifoXm@cSoML*NK_)@TEm zf+%MKa2J7l9g)N=?tl06h0%^GRcg5_7*z0C;ZPr-c0-PBIh>+<=J&UDKfWBcNvC|t zI1bqi>#ejm|Jvl*llEjO*XlBOR3}@awOCf{5&b+$VN|D~w%A{}S4JhAqTX)1ddWBr zo=^c4eDl<dZa7fbLMk+qFw~ z9GeZUCpDJ8JGp6AL;KN{FQsmX&>n68rUO|tb2!lTX7TFJOli>2q2|y59Gc-u5%7R2R(N?YNujr}`+U^~Tj=_-aRo^V-uek+s z(n#h`%2?8HB4eUnqS*%?4OEwlJAR4A*$$c}{KON>DXa+Vfrg$$4I(0^2H~DK3@&EU z3c`euqoamwO+`rK(!B;CK}T{^73>kFGdD3Y1!3^g(aFS)hDib zgP<7toY@fgIhbSzo+Y?JQS#%u*zGKp!!}nfvYgG{xA-&=+Ng54WM~h`S4`1CLO5@u zZR0w7k7krUTB2$oO%ddhKQ&CQ2j!y{x80K6?&CxajbKS{a97B^5sQ(tVHW`rWRAkD zWQ`fyy8E#|g6Pax1q$7LvS8S4&9~Sw(IPW#9qfuAibLC=&!eqJbnCk+i5wlASo%Kg zNA7U;Bg5evsW0%a%zh;yWB1qJoF1KJR@R+wMbpg$geb-2TN>{o=5tE{KtN;Ejd4|i z2~=_D;F)`c;Go&!UbQAMu;#0-L%X;LsR ze-mZ{zvH79AZ8#%srIb^l6Amr*~>ArcGpta3$IMBy+xcrygI1SE|ja$Y+Jv2@Ms)RT})(#dMHrRu#S;k<` z(tqj?CYSCoOAfWk*oZ)h(m>ka*sLE&2-Is9L@CtUiQPqYQsai`eaR5;yogYVCUjlo zvRi$8@zPV<&(i0Liar!t3by^tzsL2^Xt% zvI8oXVnmbFlAaMAEJAWNm{tfmdsjI;%S?dwkg0D#L+lJ4(FHZu@U;{dkovF&LtD2; zD9$sh>ZQZT|M*e{cvJ2Ztt438K(S)D|HR7%j$^5so)szFk)F78X)=TY1_dqDl!4+v zUa>g)Pqq@9NN`nr`#>7mKwQV=rRPR1Bx&drG+38|QRK3u7#_6REPKy6t0^IiF14N+ zwodvkzBr*qVN@V*Xe(kon4hwva=y3RYr~czBvsc?8q6U;ic^f0nn^SYaah$A&29Kb z1-RqVN-jDQK|mNuMKQPm=8Yu1>yq|;*s5J#%~{4^_b@c#3`!_y#Gybcq_u$YBt7B~ zV7F-wnw6whp&X^Y1b8f2DfE?`R%$KPj7y-^>=pSSk|LELK$HRzDS}}WX->k1a>UHW z^3WAGs@B8d+Lfk6EiF+R4lhdDc`lMSg&Zt9D!+_t& zC>eMF@X+(4O~MQs#ldQ+nHlL7bDue;YCmqw+)umz>C869-DEb;EGTx*{PFfjy01R6 z^%I)`P$I)-0CY0|`nuD~7iu6k1EA}(r<(!LmF6}xj=2&$W=0q{1E75E$J)5^MM}Sc z@!8D)=w<+PGXQ%2iw!mdpszQ7x)}i741iwbD-OS}39uOe-3)-Hk<-lp=w<+PZ?<)< z>y{bB+zfzj20(WQPgf$6%>d}y=^&7N)sKsN)R7x~`y zUWvB)I)mW>-3uDmn*q>^44!TVKsN)RL;tqHntZGO#>dS7=w<+PIe5AhK5Yg-Hv^!X z0nq;YLN)`StD)1)0O835f3fL`R^=~~TLZw5d& z1E9;H)6D>AW=t~8GHnJx)4w&48^zoVfWFS)>1F_Q`H%Z#h9oxwpql~E%>d|T0Q7>{ b+06jxW&m_E0J@sf{r_VCbin}VQ!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x=1GiGxdDJs6U6`k delta 62 zcmZ23wOndLBBNo3MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t=1GiGxdB?;68iuE diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index 07268fc3663cb33c69f187ad0819b45d4abda6d9..40134de46eb4885628d6398b614274d90e856c91 100644 GIT binary patch delta 68 zcmcb(j{V{~_6_?u4U?;qlgbJzEcH{EKLoQjFU|g YO-+m~lT%Vt5>uMrb8dgn$(VE&04}2!UH||9 diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index c7bf7574e3365c35fd206e422b4fbf2de9885a8f..760690942d84c7b65a623e8d7c87e9e95fec1113 100644 GIT binary patch delta 448 zcmZ2EnRDf2&J8;_4U?;qlgbJzEcH{^OkQ?Fc(Q+r&g8yix#$e6G%?XOHR#+i7zQi%qvbyEt8jJsN*P5b zA4r#%D9bEPjET=LiiyunEG|hcN=__FQP)vWpKiZ{QE_r_x(riH?evRkOfr+LGNPz# zrr7j?N=DAfyEB{lA&y13^DFqwfL$_M5AK delta 488 zcmZ2EnRDf2&J8;_4Kpm_EKLoQjFU|g zO-+m~lT%Vt5>qz6!W;yyCRfqREF-yh+yMoOW5FEVDQ9p=5d(^)r>ZkMJBgp X>tY0)cD5Pmx|;oRw)^EUM&AVhpJKSo diff --git a/master/.doctrees/tutorials/datalab/workflows.doctree b/master/.doctrees/tutorials/datalab/workflows.doctree index 2dc2a528ea337ac46c992dadc39df9b83d2f43f4..e67183aaa81c61480d72b9ade150927403743e1f 100644 GIT binary patch delta 10641 zcmeHMYiv}<749{2dASXs60jP;3#4fa#-6$FOKf9<4Hz)kU>*(ud+w_a5Y`XCkYJWa zQy`cSV%kxp6pU20jUd4;Kej4OAnhN11PZFEMwMEuR%$C13`qQG6(ya$dwurqW}Ex7 zse4q-YgmV^`t8M72Dd^x!XJrzCiz1_Au&^2z})wz=wY!r;Eg7ZLNgS*8ES8M2vBH!?5 z#Q%(RoDuk+&Kg-jZVpWE`ek~= z?tfe~`Pjwo;}^RH{#%s1*gfv<8-kGR5s)`OAz?ziw(fW@ZnNXNa9M1AOIx;$2HzINz3oQ&PC%P{eHiR#Yzf!Lv(SQDaVS zjIi!Nww}k9gYzZk;Cx#S&X<^lvjW2_5i0PMC+)>=vwc z1De4SN`0883vLZ9p?w zIY#q?S@+$sM6yAQmIl^74;)Y^E2mKwWH1Ijpf$B3-C3d7XJ^Ne1^V*j{)B_nrl_?;qQvp%!JJ5x3$N+zCFWc7+R~rVGOHV+Fa`1x>ZB5=6XH2@XaoLBySI zv^*23#)fzBNC&IXw}U8dRl%%j)#0RQ>7;a4qXhxZwrJUX|p4tFie)j|rpVm( zqj0%MU%YaUhk1_1`A)I<4zXEh*x(5J#S?7daxr$gFI+By#x7hgJXW||TrONL;DAxM zT%@;c`*9T==a}xLl+^AOBxnE|UCmaieIGjESmIY^ZG0Tc|$t?~mU?w~FB@ zu4eT8qS@^20o0OZo%Z*lQa1A(N`$6fIESRnzsWzILp`lR2U|Oe>PpCSyY@Vf@nX#H zFa#3~%Z6;0%LXH|I6V^7C5tmy#Y1GX+7@9EyZ#U@DIt}4+`Y7?f_1Gan#2CuB9wnQ zZlLxLbJ-`4&}Lx^!;ew*{Qu^#XWO}s3H*0je;W97USRSskI}%C;vc*+LHJ7Qr}0zP z^#Cnov0tF6$!mhRp*%)BsymKq%ChPy9;F5`EZb9DQ`2nS)-nGKRy-PsO5VZZsVOnb zmNAtKOLsJ^D4u7!3QkYY*0_5L}z#Ji(gOn9}`ar zG0RaDSJoWKv|R;b)xfT0+Onx*%{6sRNpkEzg-G_=OPDrt3oB&E7TTvK*5OOkoK#55?O)G|Fuvs9aSN&G~-&SNkzHL2#w zwrwaHwkUC_&1brn;}FeM8F?bkOeUE46Cs-Y%Fjg{i{Pbg znzpF*nT?LfoH1`(8M7yjpAm};ju-e@*8Vq9j7hrgn!Mrox7)mdUBjXZ)pVk(j^a?F zso?_il_$9xCWgx^D?7a5G^%@w%&TC_4h|MWy7N?z`_c>*Q&mzlPccl*^EA^`dG9&0 z9duqa{e^eMwXsNIzx5=GuToi(Rc!MhBx2dNX7hjfV#KPX;czHAR#n&2Ws?$*uT_(( zyi=*G$$ay1brP)Am}YYa3N;MdaDAA^YoI8)s(aMdJw4QWr4jV(;x&=9G=QI6wk#f;~iC! ze7|4(slZ#`Gco0LvG^L-b=9;~syZCX7d;P_gaV4;C<*>%5B%P($4`0YKksY>q5uE@ delta 10717 zcmeHMYj9P?9rwmPC@>XKMjaJ%2~}GR$?n-bXZK(d5=eL&2qG^LZ_d+*$PM8UoI*mh z3L=Uc+8t-ChM7JG)KEITPV1}f7pEf>zjT~VJMgW3kc52bv~?Wq-gD0V--oxJkAAt# zFxmS*zun*O|Jpr!@3SA?^3;d7jQ??ZYoEwuw(op2)9g@(S^+h6iVZywBrsjq5SBq4 z>N4A4)N(!Fv%O|7>+Wveo!vX&`lGw9Za6z{#)1tg>+F4*-=Of%<ihDea+~;$1fGlXAkZ?ufPiR;pm)Qzl%olvybX# zJauy5@X3Lc`d^njIdJvIFQl%@?M|U!{;+50*vkxL2XV&D?!b-d`442uZKR4#uVSNB zY^;jSD76h|2T2v;&{hV_4(e5Kp{)W(tKdRg1&-x$GfO;{`?sPdEp%R<+KRd>Vk1g* z$Fg}9TLmtZSb+;|6}V7h8BWC}G*$pZ-DSY+U}&qr8F~LIaG|XNXI8<5w&4mOuL2Bh z6*y4^7uqUtRux=mi*dt+(j;MRR=5%wE0!c-;Z!0+VNF;cFSx9S^T$&;#*sWiQ$q z4}aB*HpRp2J!n}x?6J}P@o;t{8jOd}Hv(X%gIZ(jcyaj9La@G899~kx8O7Z~cC1G? z$>AoX$xmD~xAe*QVDzNE)JRwXzQsd##H0s2v>_h0H-oBqKJcGwM(xE_h+`1 zG(Vp8nO_K`9K&cyY&{sj0`CWCQEdG(K#SwyX#%{jJ_5Y!^imIXncAF}OqGW#`_LWJ z!FRc9tx^#_IM#>i<2A?oP*+i^E=M||*>}g!J01m5mnn#9WFV@afvDc?Xn8^Ec9=G? z9m<3}2GcGY5Q6s~gAnwarM^%Z=A?%!U;1{STjRB-OtiSftuxOmw-lxju;Uz5?0Otj z%oU)bSD?j3mzkXMmCHSrB6)Bpx-%x35NJsW3E7w@v5+1=o|^@}hqB<*SQcXOc@|WR zFG8)wrGC6wt#t8T#rDg7$ihecu*UVfN)uv42~qe$LQd|2rrOp5B3^BQ1unIKhzkQ~ zS_ zd!bHy+tJc^v9^7XjFEk4Nl_w?c<~Yyo=^8dG7fZ&O~x}A6eLh^bigk-7{LV~+WV=Cks zk`Mn<<=cWEAS0H2z6)f$^#h2Iy$rR-fR+P5b9ot%pDS92mIL{P1bMJc%zk$-Sd zkxw52@;6q%zWw45>Y53g1;P0s+${SKsR>^Up>!-}-Z1JcL#gJ8QOAZMUg=e+GoJ9( zFj^ZA-(7`T;^CL8P)FG^Dl`SML!fSKHK@D1y6E``#AE0_)KSbEkym_liyRwKk`Fuy zVy^E7F_TY%m^ZpX%tUwjD&wTE%4bi3mKWE6mVc}PE${vi(zIhO(5!wMdOy_z)=N(V z`D;Buez6C2#6kJ}GjJM?tV1hf>tEIZ+gpdh^WOVmTFVi%yd;)1+A*$M4_*H85gz4L0OGNuK)h}POk4O|88M59zuExAjT?dZ&_*Es!$w%)C&z&J zzsG?1%qAdy>3N86`(~JS`wL~n@g6y`8GS#F+3j0^xPJ=}pV7ja|PE*GAxT`o@7 zE*J2EQM+8^zk-pq%SG*SQM+7(tx&sMMBk5pTbGNRx?FrvH=PBpM}31>a^uUWtK{QP zUq)OqpFnLr&DNj)^*)q+_2aA(cWuPSLOf74w^D@@1?YjO=)Hjmvh}V zOsX>*6UPxYw=Bjy%w5;9VF^VLP-<}9C&JSsY|qo$C#fWM zx%{xUwmyMecKk=fa(Ggkk@kc~9Aa~!BrvRdwq>}k={uGRlx;g^PRZPf(gq`zO(>zd z`ti|IA6%k-lhk#~cRiC*D^fUqOgojTC(QQ(ZaLhrO!X_J;$H;RQ|9Wc}JTg z6%^$r$F-lQ5+%shuV|z`NkGq})_hMxzE~R$9&AiE7>4Hw+we@xOs0E* zQ!p=x9kYb%o4%>U62XKbF%0VXp6Od2XOx#Zmi8Ri@v!Ssu7cyL%o0@!CguV0^nmCQ zw$!sE4SCOSI#EL2HTmaPw0o2*O^k)j7CEUZI`oklLNHx-EMeQ0%p zYFnHTVG?XJTR3*$*?|=Vwkmm(=u)-!oLtz8>2K!tg*^rS z*K_1r{a4yAQg?i_1UdC@ji}u}K|rAm7Z5>7lNp`WQ6H*JJ@D1GprT9}R@G~&gN8e< zubUhbor8}F$52Th@`Uu2cUD5tdKKt|_(2~g`4#q|eCAKuk)(5}b}H6R#rV9hor>k% oSUVM~9?7*+QN4F39!lYfQ9Bh2FE!EKLoQjFU|g hO-+m~lT%Vt5>uMra<;$aWCUU+AZFhFmXk&QJpgY#8~p$P diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree index 4198ff1392adcd03d29726b3c29355aedf6f6b7d..b0e93f410ff20a257f1c1ead450ae461b988d70a 100644 GIT binary patch delta 3801 zcmeHKO>13M6!o5@XiGsvn~9p-Je(Pmea_kEVj9DlTS?RrxCqpI*NC@Zj02E!M0x zMV&!bM59S%n~0$15KZuO<*RSL7%aL8*Z+8`g#yNT2mvf%4k`kIb_J}WRtvA1RM^B4mtv4!bA6;>1G1f?uisG;ArNv@PAAT1f&$mD_ z>JWTI4Vb+qG~~6707Onn>fX5^p6%fWvDqy8rQ+DKSsc216Xu6*+i|4xS zJ7S@K;hgxn8N5OF_ow3c@CBBy3EzU0vZz>Vlo|snPZkuE;4r%aNln@0q_VOhGsA?= zP*BpzT7sz+T@JQqHg}pf7H2#kU}E)Xv-OqduDo;P0HQX1xGQcqgP`5fdji@a%#I1H zb(b!R*jk9dP1QAG#uyycoU>Lk-E6A@n_QxfcuHr+z{}t*{qR~lGjkIN3`C{q*=DEkduk+J~bQv;d078`joM9c#yKP z=wy8%wjNE*#&IScDyx-MTvUhx-#(h^n69yW+OFA#KqvAX!rU|4tKH{YV!4&MRFjfo z@h1e79urUirxV{f=6`d;l5v?-jKKUjIXGxIDhaeyKBsD?RB|pc8*(8zgUDf7n7qY6 z8A{=wQz}ECQz`?Pdro_qBUvh?R-JytYL3&7Kq@ICM}%Scq*OIpSFMV+yH?xD_kle# z_1e&o_Pb|Dll!ZOy0+)FAxaakZT}@)x7dp(XPa)e>1LbmKhI&aO}Fz{IoouzO}GDz s>VLQCdfs&7xrOl?ho0E|Y#unXQD5<2Z@zfy%*J?i?)h8exzVA20D^u(6#xJL delta 3887 zcmeHKzl&W(6!zU+F$n<+V^WB|yNhWgX6MJ5nKKAcM2mpIADw8joH;Xtu`m%gf?x<3 z8%+>HvRo}v8P{q-3GrcJmiz~jMhihf5Sx%TjluKY+bp}y-dfn(Jh<<0zxmGjzVF=o z)AHWY^4{-PcI$U=aq->vPA)Dan{2Gs8>^hDsH#zq{od{m zziuNZs|TW4%xDj8UguPgZs z&Sdn;V@H2U>D`~3-v({Z0d#l&qvlxKgYCMxs|^mdt47XW3RMFN0=hscm1IMutnEl( zPtCR_Fxy;=b`$|4BbMwfFoVfdh)1uVYiYd z?v&2W+sz?s(CK&>{mG?;501~DJ$tB~R}@lok*bCSL>`0HNl}FqN|2s!X4q$^aqYg* z+ON%LgL8dDn-}b(uf4f=?A4cEer2&orhaR+C~4as&L2P2{jk>TANG*z+N;gMgOfHe zMKng0~ByTxW&91r}W8i7&#C~mwaw};Q zau9#Gigm~aVC&Gs)471oyGABl;fhB{p%Aq*rn*F8?(7TPs<9Oi2zR_*PrffL&vZhG9Br@McC zY>w>g!<~J&vk$j_ZQI$0EKLoQjFU|g fO-+m~lT%Vt5>uK#a<+fuWCUWS?H@Urw*&zI<-{5c diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree index 8802c2b442cd29de405a6117c4a1fb0f5dfc64ef..3f8ef0c260342596f68520ce98e2b24df6f96eb0 100644 GIT binary patch delta 62 zcmX>saadwQAfsV&RdP~UL4~D$YFe_Pg_((wfoW=DVv1>!S+Z%Wp^1TcnuU3CQc`lN Rc~Xk8Nt%(x<|f92TmXqb6a)YO delta 62 zcmX>saadwQAfsW1MSOf#US_<0l5vu;Wty>>k+FfPQJRHenptwPsfDGfVUlsONusHV Rv1M{fYD!|t<|f92TmWOt6FL9@ diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index 7f76de2e1673d94f30294e107ca2796a57e7be79..e03ae1aaad0cccc9dfacc6749278086def75139f 100644 GIT binary patch delta 72 zcmeyif#cf-jtzS_4U?;qlgbJzEcH{EKLoQjFU|g cO-+m~lT%Vt5>uMra<;$aWZeFilc|#r0Hb#pj{pDw diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index 13adc9c3dc1a4a15c5dc2a2556fd08ce565a871b..0da910c90669252c030ac8f175159f92c6612409 100644 GIT binary patch delta 80 zcmaFyo%zLg<_(uPZIi2#lgbJzEcH{EKLoQjFU|g kO-+m~lT%Vt5>xb3@{^18i}Op1l2a!aGHY$NEKLoQjFU|g cO-+m~lT%Vt5>uK#a<+fuWZeFdlWB=00DLqUF#rGn diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index 4743fe3bcb40bd58452c622b8338feec14e752fc..2c4d181cb68d30cd7e3b8d7987cdda5fc7cde038 100644 GIT binary patch delta 2160 zcmeH|yK7WI6vi2mKro2t8XIM=i`uws=5gk6Vk#enl0^|kLJVP^5{OB_AV!E$!9qwO zZgGj&sMr|@1Y9gcv=f{B1!};5BjU|Nh`4eqyEK=9^9}R;&Y5rS+G5w*V%O4AH}iMB zyFGOW`(pCUDBx5^YlC#k%gBK0WVCnAN9}@u#Le<*_p@t<;Kca!DN6e(t!Dx?a3-*U zto76m{j;{q{pf{(zc-Cdx4yp4T8I63rBk`}+`sEw7z=|P3(KFwtAR{KYLjGv1?_<) z<+zCmkx^}V4qT!l0b+i1f*feqJ((xfqS1g}TCWsPVNf~YrIdkNsywm{Y5paW z-nWr_B3X$Goqsz+u9O@2FhY*ylTETGe{_TF%M56Xj3pZr0y<@4a7uY)j1R(t+cdlZ zSB;d>kTAIr6KHNN^*oAXtU%$M%vWbfC9mEj``c5O$pXoobC|3LA1Mb$JzDJ(rOF1y zcrdnIG~+!fxA1e4(9**{-Y3V`xf+>AluAm+l3|dN!vM6PvAkG&snj;4EHO%>iIN&2 zA&O#nCQ*#Qz_1j-u4@nF*#)w9i-~KlU2%UL4IEIfcSG{vjY3UPTE4|p>Y-p_)w08X z&#EVXJVQ=41Y^z9eYZwxv$OTA=D;(~WYAa`^%T$|Ra6z9z!h@89kVP9>)J|weU9|B zpWGv}WUg#2{X_s802uWgR{7!+QLC_?md*`H;I203wHQqUl9 z5Z%GSXj0KEK@hq$h@e?C@fTD=f5T{73WnMlHFLb)eb##3wO-D~a`(n^_sU8SyMwLP z-Ra5JkQG+wC~_{SNG9~CyT`_bfV`|F#mecVm;?Nx5SaPRjnO#0D13#*^~>yeCVbe4x;8APx_ z8&ACyTyq&BbHcP2Up^`ot=3-+FJ;{GAk!x#NDVo7Ei~o=t<%~{<8*OqnjGt_HOP2Z z#+4E@Ab{2aoWf)qS9y*C(IGS9kGAH>A~~s0B&&7OQwYu?7pQ#T$~kXP7VC4QulN#3 z0+iq4i}GalcSl9Tr(6w1{p!*+9-q8GL13KpWCf@6Wc`B zpfpm+O)WUUz;V-18I!M?ChWM0Hc(_&$dS&y>tu;!E*OJCX=_Bf4>YKa=6NvQIJ7#d zPI~Zxl#lRphR`gQb>9Z7XVOBT-i5UG9B4XYmxEzGS2VdBknP-;GKwQ`A({3dyfHyD z$0#N)3!LLFC;jF2TW25|=ZEe$ ztMl`=a!P7SV#?-hMq?)cgS`_> diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index ba57af50a48eccf07b7ae08a2e49a4939bc89298..d7fd3bc72f4a160c768dcfd896031477f488bf18 100644 GIT binary patch delta 68 zcmdmUm~GEtwhdc24U?;qlgbJzEcH{EKLoQjFU|g YO-+m~lT%Vt5>uL=a&CXh$#{J_04vBBeEq^$|nIegZp3>%4}wD3xWaH;@ADUJFl~t!u?wuc#Gnms&l^Y zJLlu=-g;^G)=O{xVO^&0rFi9q@A~ahlVnRum=Z!Z(I!(Q30K*r7*laEYpRNWxV!$N z-SrOfA;gCfYlx2^?m*m$IDyzZ^HIcIh`SN@AU=k;7jYlre#FNS4=H$IiM75Ntt<$7E?pgxLJU#i^<`3GZu3y|dHUIBpo9~_& zQz$~(oV?M7d@3v)OJ`XnQaQ0(e6e+EjxWA`eso$2udQ}5B(fBO6E3TW!g1spc=563 zsr~Dnm^RP+bo7*6R90w~k)q4NloS+NqO|j(*!?fg8mFdLFK%x9eyji2nOS7qNqL>L zQk1L>x+*6v8{?vnVgBCD{`s}BB&L*wv&uyx9XN3+qR1+|Pfn$I_p|=z-RKOZAgn2X z2NhSM$0sT!m#Rc5X}a-3|G<3wPJgp&C7y;0!%j2r^zOx;N5fU+5>Qzgh_YfPtsR+C zD{+lxj6Lqiho`OQ`Y(*xWh<(6z5v6zI4`|MNlR9u zCKJNqJu{WPNG4~SB@;)@RfOQR^lT$JKYwv&_;WXt3Vos^jSrqgEU^Y57+qnqMTY6U zZa6vrw;g`fHS;cB(@5+0da^y8zubRbSUxB0)<4=6$(Z?>e zFnauSqk8r1vs=@De%x=2t*7l(t1UC|r#5A(|@DZpYx9QU`FY&>gK6 zft`vX8HO6=6u4R(ELk6N4mdSL0lFtB5g6btS?Td@T`iR*&W)|i)9k|V_}EIkAf3!M zN;Xi%>WPd6(W*B_Sq{ldLkF*57TL?BIDmBI4YMpHJV;k;RY@$9PC;^u#rGf=gqZ&7cJUWRAJSLdHW4iK5TeR$GVur4+*z1{bOS|y``5J zhZ)Gs5I6=Zpv5tfjtWD_NL53Li!RL?<7~RI29fyd>EV^`TB}LN`QsYsJbt=Wwj;K+ zp0&(2HV)&rKpj2A)CMnW7Ikkp8Kpx$sDRj;^cejj0;UPI;w*fG+9pu7sQn5|+gJLH z`MsYWzTJ(%0V**Xl|TVsf$>nPSG+eVSt&ieKk7zqTw(g%XNRZ8#tqzb=5xarM=TCG zNS7e;nX0S71~4N8zzyOq!hwWp;RSk(w4qR6}8thUPJ1kU+` z5jO@rp<+(e#G0hX&O}A21PCW#G3k#u_sed`6(7MeV<0doB$guPL3gWu^-E_iqC(cwxr3l8f`GBQ;rS+VyEU|WOJs3~Kd zEYXe{H`tqYJ3}M?jZ3|F?dg7fbcGej%~oTNswQ)>*mNs=hjmF}Q(i2Y1ON)mtz);5n|uyW)>@IVFQRHfvviDY}}p-YG4byI>(}e1+I~If!Jo+eZ>Ce zsp0%sQC%#tjBVIsByq$tvWHE`LoRRu2aU1V8{v1Xm83|?4DP`h;OPPTsL@9f^4o4u zA!Z2&0xH81gj{S!n=ltCiwx%Nuga~o3-M@Wg?Py6k<0zY^v`F8`?r-kC*0Dw6Ey~% zGKyykN-r4JAQUAO1QYAV;?mNK>qt8h@dNCno>EX!`~{szCYgH+1q&S)pYze=+-UC}IXheycd~icUVA;y zdY*L-ZoGJKBDN48LEMG78*vOVKl4$U0m3oeEt9J8&SRW%%~QNWQW8ejfNcY5~fH~ZgD0EZ-P@KsRG zY*Y%~IGY^>9gA1l%y9N^lV;-a+S8pFLMt(f*c*R(qJL=I{dV`@AZ3asD8pJ4?x`b1 z+=mrs6MUgKd-J*OnXNvPaDoI#Dk6gkj)jR@mTK|TG+y~dcWU;^Ke|6nhAO3@=&jcP zvor#AIHNW2n547%W;Knx%W95J8}hs*d*j8+-52((xnRxZ@9ll}sdHDa?hfa4=9D=m zqf=q4oEJ&}TORlGLh}lA4RViHF7>CzYya%-o!vOoU!AeV@9e)t>T+JE%xU;Wx6 z`wfV?!>3y>ckZd(@x7mS+e4$Y1yIj_cJT_LdTq6L**eEmYs@+nrRNv(L8Q8D?^I`D z2^fq4qYYyuGD-B#a0!95XReOIYSf$Wt3FWYMjLM38Dmx0RtvxTi4kK z#%aMai^7$C!TdBKP)Od(BwSG|_O&uE@H%ZI9_e=-jXyil?@z4?PGFi!2|01eg<7*n zcuZM9n7VlJ*>I_Qa%+IrBu){-#0IXFoi|C_kfqD2q;P1KNHp65Px_P%w zu9L9Uvct4$VfFCo)&tH-m|yEoPmiu|f!Fin-(TzQ>T`f;&=PN9SY#bBKNJEm2V-1FMldN3b6^)QYps^DFtf?8yWdQv!ccNrdU6sHWNNJVx`>&I1nTX0 z?YRzLTqqwOojig~w<4eP8HPsL zT$BR&ln0(cRzai_IloT6saDFEkmhie;GpzDgN9VSw8_>uzPW|B`M2r+IB8~|LzeoZ zbIZ-|xbWI%6&~P@<#XD$v7guMX&$qz(he;<4=5fxDM_fAxE*UF>N-BYWw6x7j%E?CVC0v`S7jXUa zo_{?n%6u%zWro>zaW3XAL88HUX~zDVaM2b zLQW7oZ>2Dmas5@N+I!P&YG+}!k7(F!CXyO3JxsdR1KuKCqqqg>#=X?r@dwwt$2Ke9 zX64(geE;KDiQCw^S@}ME>D;V*o0ae8?=G8_Z?p3K*TT73`8F%xVx8Tre4CYTv+{k| QFBAXIm2ZZX?~Qx@0~8>>NdN!< diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index 33f2f9f028f8a94caa247cac17b70b8dcb762400..ad65bcbbd829555db6b2b4a2f9fa83250fcc6834 100644 GIT binary patch delta 1684 zcmZ4fgsb@p7fS={RKATYM>q|WtCEw-3Mwr1Q`3?SEzC@e3`|oK6H`o+%#ux04NVNp z(=5!Blai8C&684$P11}kHvi=OZ7OfX%D`Y#P?T!NrK@XXU}U0eV6JOms9lv8>xj?5)KIj+Bg-{K&(|dB8XQLiarLnJ*ez5`&WacRt=ouGt zabbE93>$^AY` zq$i5yDu^h?oggM}_lreN3yS{NNKFR^Lj9+&Pi0~snSNgG;I3zHW<~}OXmw-U J>c;eNBLG@Gn@#`# delta 1611 zcmZpE!nOPf7fS={RECW#M>q{LEaKy{@-pM~lZ=y$Ez^w6jEoITjnXU()69~SO)V@< z4U>$MO%hE_j4hK>Qd1IBHvi=OZ7OfU%D`Y#P?T!NrK@XXU}U0eV5Vzipmx3VVy=P)SX?&ZCohWlWZ$@j+NMT&Muz5khCpm)s-pno8XF*U zEscN*%qI6cNASWFfYk#nKf_`MR66;f3#z3?3L0Q>$7u7(30|CP8SJrBG6W+=YzUMv z_D^qMX3`hM74xB!^Srr8kNtwcbEL-lUEk#-#ky@sELyA=1cpxDA8$U{Ak+(g?5_y* zpPui^$UZ_nzfMvGnB#H9I7N9~bn;vu9#UhT8u@+i= 125: # if the random index selected to create a duplicate >= 125, then the last point is also an outlier\n", " outlier_issue_indices.append(131)\n", - " \n", - "identified_duplicate_issues_indices = duplicate_results[duplicate_results[\"is_near_duplicate_issue\"] == True].index.tolist()\n", - "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", + "predicted_duplicate_issues_indices = (\n", + " lab.get_issues(\"near_duplicate\").query(\"is_near_duplicate_issue\").index.tolist()\n", + ")\n", + "duplicate_issue_indices = [exact_duplicate_idx, 129, 130, 131]\n", "\n", - "assert jaccard_similarity(identified_label_issues_indices, label_issue_indices) > 0.4\n", + "k = len(label_issue_indices)\n", + "assert precision_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert recall_at_k(predicted_label_issues_indices, label_issue_indices, k) >= 0.75\n", + "assert precision_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", + "assert recall_at_k(predicted_label_issues_indices_by_score, label_issue_indices, k) == 1.0\n", "assert roc_auc_score(Z, label_quality_scores) > 0.9\n", - "assert jaccard_similarity(identified_outlier_issues_indices, outlier_issue_indices) > 0.9\n", - "assert jaccard_similarity(identified_duplicate_issues_indices, duplicate_issue_indices) > 0.9" + "\n", + "assert jaccard_similarity(predicted_outlier_issues_indices, outlier_issue_indices) > 0.9\n", + "assert jaccard_similarity(predicted_duplicate_issues_indices, duplicate_issue_indices) > 0.9" ] } ], diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index b2fa92ade..2ba045367 100644 --- a/master/_sources/tutorials/datalab/tabular.ipynb +++ b/master/_sources/tutorials/datalab/tabular.ipynb @@ -80,7 +80,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb index 3599732a0..51da2d9a6 100644 --- a/master/_sources/tutorials/datalab/text.ipynb +++ b/master/_sources/tutorials/datalab/text.ipynb @@ -90,7 +90,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index 0d8105fd1..ce8be3372 100644 --- a/master/_sources/tutorials/dataset_health.ipynb +++ b/master/_sources/tutorials/dataset_health.ipynb @@ -79,7 +79,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb index 8c1af8a11..7d89efb39 100644 --- a/master/_sources/tutorials/indepth_overview.ipynb +++ b/master/_sources/tutorials/indepth_overview.ipynb @@ -62,7 +62,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb index dd83004f8..ddf90b86b 100644 --- a/master/_sources/tutorials/multiannotator.ipynb +++ b/master/_sources/tutorials/multiannotator.ipynb @@ -95,7 +95,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb index aab5b8442..542b9cfd4 100644 --- a/master/_sources/tutorials/multilabel_classification.ipynb +++ b/master/_sources/tutorials/multilabel_classification.ipynb @@ -73,7 +73,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb index 337283453..79a87033f 100644 --- a/master/_sources/tutorials/object_detection.ipynb +++ b/master/_sources/tutorials/object_detection.ipynb @@ -77,7 +77,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb index d5d8c9d0c..4f7e0427a 100644 --- a/master/_sources/tutorials/outliers.ipynb +++ b/master/_sources/tutorials/outliers.ipynb @@ -119,7 +119,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb index 0976d0919..c0d2c1d07 100644 --- a/master/_sources/tutorials/regression.ipynb +++ b/master/_sources/tutorials/regression.ipynb @@ -110,7 +110,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb index e058ebb4f..e65d991e6 100644 --- a/master/_sources/tutorials/segmentation.ipynb +++ b/master/_sources/tutorials/segmentation.ipynb @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb index 2ed9014c8..2777086e9 100644 --- a/master/_sources/tutorials/token_classification.ipynb +++ b/master/_sources/tutorials/token_classification.ipynb @@ -95,7 +95,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/searchindex.js b/master/searchindex.js index 6c2df3a90..2c6f7b4df 100644 --- a/master/searchindex.js +++ b/master/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 96, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 96, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 96, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 96, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106, 108], "746043": 10, "794894": 10, "98": [10, 97, 98, 106], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106], "signific": [10, 94, 95, 96, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 96, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 96, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 96, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 96, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103, 108], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": [37, 96], "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103, 104], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": 62, "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": [62, 96], "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 96, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 96, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103, 108], "93": [87, 97, 103, 106, 108], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103, 108], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "card_about_to_expir": [88, 95], "getting_spare_card": [88, 95], "beneficiary_not_allow": [88, 95], "visa_or_mastercard": [88, 95], "change_pin": [88, 95], "card_payment_fee_charg": [88, 95], "supported_cards_and_curr": [88, 95], "apple_pay_or_google_pai": [88, 95], "lost_or_stolen_phon": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91, 108], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106, 108], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "070": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "893": 92, "493": 92, "060": 92, "975": [92, 96], "330": [92, 103], "505": 92, "917": 92, "476": 92, "340": 92, "951": 92, "328": [92, 103], "310": 92, "061": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103, 108], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103, 108], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": 95, "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "523": 96, "991400": 96, "356959": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "mislead": 96, "pinpoint": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "enhanc": [96, 99, 101, 103], "alter": [96, 98], "darken": 96, "reduct": 96, "aspect": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "83450279": 96, "11it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": 97, "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106, 108], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103], "169": 99, "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": 103, "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": [103, 108], "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": [103, 108], "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "anomali": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "95344245": 104, "43it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "718": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "92mb": 108, "mb": 108, "directori": 108, "inflat": 108, "116": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "6mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 96, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "between": 96, "vision": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file +Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/data_valuation", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/_templates/issue_types_tip", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/guide/table", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/multilabel/index", "cleanlab/datalab/internal/issue_manager/multilabel/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/model_outputs", "cleanlab/datalab/internal/report", "cleanlab/datalab/internal/task", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/experimental/span_classification", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/neighbor/index", "cleanlab/internal/neighbor/knn_graph", "cleanlab/internal/neighbor/metric", "cleanlab/internal/neighbor/search", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/clean_learning/index", "tutorials/clean_learning/tabular", "tutorials/clean_learning/text", "tutorials/datalab/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/datalab/workflows", "tutorials/dataset_health", "tutorials/faq", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/data_valuation.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/_templates/issue_types_tip.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/guide/table.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/multilabel/index.rst", "cleanlab/datalab/internal/issue_manager/multilabel/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/model_outputs.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/internal/task.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/experimental/span_classification.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/neighbor/index.rst", "cleanlab/internal/neighbor/knn_graph.rst", "cleanlab/internal/neighbor/metric.rst", "cleanlab/internal/neighbor/search.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/clean_learning/index.rst", "tutorials/clean_learning/tabular.ipynb", "tutorials/clean_learning/text.ipynb", "tutorials/datalab/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/image.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/datalab/workflows.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "data_valuation", "datalab", "<no title>", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "<no title>", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "multilabel", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "model_outputs", "report", "task", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "span_classification", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "neighbor", "knn_graph", "metric", "search", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "CleanLearning Tutorials", "Classification with Structured/Tabular Data and Noisy Labels", "Text Classification with Noisy Labels", "Detecting Issues in an Audio Dataset with Datalab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Detecting Issues in an Image Dataset with Datalab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Miscellaneous workflows with Datalab", "Understanding Dataset-level Labeling Issues", "FAQ", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 85, 90, 91, 99, 101, 102], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 90, 91, 99, 101, 102], "generate_noise_matrix_from_trac": [0, 1, 90, 91, 99, 101, 102], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 17, 41, 46, 48, 49, 50, 51, 55, 56, 57, 69, 92, 96, 97, 108], "method": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 54, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "ar": [1, 2, 3, 4, 5, 7, 10, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 27, 30, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "us": [1, 2, 3, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 84, 85, 90, 97, 105], "benchmark": [1, 38, 84, 85, 90, 91, 99, 101, 102], "cleanlab": [1, 2, 3, 4, 5, 7, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105], "": [1, 2, 3, 4, 10, 19, 33, 37, 38, 42, 46, 49, 52, 54, 55, 57, 62, 63, 67, 69, 70, 71, 72, 74, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "core": [1, 41, 44, 76, 78], "algorithm": [1, 2, 8, 10, 32, 39, 43, 54, 55, 57, 62, 71, 80, 82, 84, 96, 98, 99, 101, 108], "These": [1, 2, 3, 4, 5, 8, 10, 22, 38, 40, 42, 43, 44, 45, 52, 60, 62, 63, 66, 70, 71, 75, 79, 80, 82, 83, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "introduc": [1, 89, 98, 99], "synthet": [1, 101, 102, 107], "nois": [1, 2, 3, 37, 44, 47, 57, 63, 90, 91, 96, 97, 101, 106], "label": [1, 2, 3, 4, 5, 7, 8, 9, 11, 13, 15, 16, 17, 21, 22, 23, 25, 30, 32, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 90, 100, 104, 105], "classif": [1, 3, 4, 5, 7, 10, 11, 13, 15, 17, 33, 35, 37, 41, 43, 44, 47, 49, 50, 57, 62, 63, 64, 65, 66, 71, 72, 80, 81, 82, 83, 84, 85, 86, 89, 90, 91, 96, 100, 101, 104, 105, 106, 107], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 26, 27, 28, 29, 31, 32, 40, 41, 42, 43, 44, 47, 49, 53, 57, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 87, 90, 94, 100, 101, 105], "specif": [1, 3, 5, 9, 15, 16, 17, 28, 34, 35, 40, 52, 53, 54, 60, 64, 67, 70, 79, 83, 92, 94, 95, 99, 103, 108], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "modul": [1, 3, 14, 15, 16, 17, 22, 25, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 49, 51, 52, 54, 55, 57, 60, 62, 67, 70, 71, 72, 84, 92, 98, 102], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 17, 19, 24, 31, 35, 37, 38, 39, 41, 42, 44, 47, 51, 52, 54, 55, 57, 61, 62, 63, 64, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 104, 105, 106, 107, 108], "gener": [1, 2, 3, 7, 10, 19, 24, 26, 34, 37, 49, 52, 54, 57, 58, 71, 72, 74, 79, 88, 89, 90, 91, 92, 95, 97, 98, 99, 101, 102, 104, 105, 107, 108], "valid": [1, 2, 3, 5, 10, 13, 33, 35, 37, 44, 45, 47, 48, 49, 52, 54, 55, 57, 62, 64, 67, 70, 72, 74, 75, 83, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "matric": [1, 3, 47, 98], "which": [1, 2, 3, 5, 7, 10, 13, 14, 15, 17, 19, 23, 27, 33, 34, 35, 37, 38, 42, 43, 44, 47, 49, 53, 54, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "learn": [1, 2, 3, 4, 5, 9, 10, 15, 17, 23, 31, 34, 39, 40, 41, 42, 44, 46, 48, 53, 54, 57, 60, 62, 64, 71, 73, 75, 78, 82, 84, 87, 88, 89, 90, 92, 94, 95, 96, 97, 101, 102, 106], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106, 107, 108], "possibl": [1, 2, 3, 7, 10, 37, 38, 42, 44, 46, 47, 49, 64, 65, 66, 67, 69, 70, 71, 72, 74, 80, 82, 83, 91, 96, 98, 99, 101, 102, 103, 106, 107, 108], "noisi": [1, 2, 3, 10, 37, 39, 42, 44, 47, 57, 63, 64, 66, 72, 74, 75, 76, 78, 79, 85, 90, 91, 94, 95, 96, 98, 100, 101], "given": [1, 2, 3, 5, 10, 15, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 56, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "matrix": [1, 2, 3, 5, 10, 17, 19, 32, 37, 44, 46, 47, 50, 52, 57, 58, 64, 67, 69, 70, 71, 72, 94, 96, 103, 104], "trace": [1, 90, 91, 99, 101, 102], "valu": [1, 2, 3, 4, 5, 10, 13, 14, 17, 19, 23, 27, 28, 33, 35, 37, 38, 39, 41, 42, 44, 46, 47, 49, 52, 53, 54, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 83, 88, 89, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "more": [1, 2, 3, 4, 5, 7, 9, 10, 14, 15, 17, 19, 27, 37, 38, 41, 42, 43, 46, 49, 52, 53, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 84, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 107, 108], "function": [1, 2, 3, 4, 5, 7, 10, 14, 15, 17, 24, 27, 31, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 96, 97, 98, 99, 101, 102, 103, 107, 108], "noise_matrix": [1, 2, 3, 10, 47, 57, 90, 91, 99, 101, 102], "py": [1, 3, 34, 38, 39, 44, 47, 49, 84, 90, 91, 99, 101, 102], "verbos": [1, 2, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 41, 44, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 90, 99, 101], "fals": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 48, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 103, 104, 106, 107], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83], "prior": [1, 2, 3, 37, 44, 47, 49], "repres": [1, 2, 3, 7, 10, 13, 17, 19, 27, 33, 35, 37, 41, 44, 47, 50, 52, 53, 55, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 108], "p": [1, 2, 3, 5, 10, 37, 44, 46, 47, 55, 57, 62, 70, 71, 72, 76, 94, 95, 96, 99, 101, 108], "true_label": [1, 2, 3, 37, 47, 57, 99, 101], "k": [1, 2, 3, 4, 5, 8, 10, 13, 17, 19, 20, 24, 27, 29, 32, 37, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 65, 66, 67, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 87, 89, 90, 91, 96, 98, 99, 101, 102, 103, 104, 107, 108], "check": [1, 2, 5, 6, 9, 10, 13, 17, 28, 35, 38, 41, 42, 48, 58, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 98, 99, 101, 102, 106], "learnabl": 1, "mean": [1, 2, 7, 8, 10, 13, 14, 23, 27, 39, 42, 47, 49, 55, 69, 74, 88, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106], "achiev": [1, 2, 38, 39, 42, 74, 98, 101, 108], "better": [1, 5, 10, 44, 53, 62, 64, 72, 74, 75, 84, 88, 89, 91, 94, 95, 96, 98, 99, 102, 103, 104, 108], "than": [1, 2, 3, 4, 7, 9, 10, 27, 29, 32, 37, 44, 53, 57, 61, 62, 67, 69, 71, 72, 74, 78, 82, 87, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "random": [1, 2, 3, 7, 10, 19, 32, 41, 49, 52, 62, 72, 74, 87, 89, 90, 91, 92, 94, 96, 98, 99, 101, 102, 104], "perform": [1, 2, 4, 7, 10, 27, 29, 32, 38, 42, 49, 51, 52, 53, 70, 74, 84, 87, 88, 90, 98, 99, 101, 102, 105, 106], "averag": [1, 3, 5, 10, 23, 29, 37, 38, 42, 49, 55, 62, 63, 70, 71, 72, 98, 101, 104], "amount": [1, 3, 92], "paramet": [1, 2, 3, 4, 5, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 91, 92, 95, 96], "np": [1, 2, 3, 4, 5, 7, 17, 19, 32, 37, 39, 41, 43, 44, 46, 47, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "ndarrai": [1, 2, 3, 4, 5, 17, 24, 26, 27, 31, 32, 33, 37, 39, 41, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 96, 108], "an": [1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 52, 54, 55, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 84, 87, 88, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 105, 106, 107, 108], "arrai": [1, 2, 3, 4, 5, 7, 10, 13, 17, 19, 27, 33, 37, 39, 41, 42, 43, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "shape": [1, 2, 3, 4, 5, 17, 19, 37, 39, 41, 43, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 89, 96, 97, 98, 99, 102, 103, 104, 107, 108], "condit": [1, 2, 3, 47, 53, 56, 57, 72, 92, 99, 108], "probabl": [1, 2, 3, 5, 8, 10, 17, 24, 26, 29, 33, 37, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 107, 108], "k_": [1, 2, 3, 47, 57], "k_y": [1, 2, 3, 47, 57], "contain": [1, 2, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 46, 47, 51, 52, 56, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107], "fraction": [1, 2, 3, 10, 21, 39, 47, 57, 62, 74, 94, 98], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 55, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 101, 102, 103, 105, 106, 107, 108], "everi": [1, 2, 3, 4, 5, 10, 17, 38, 42, 44, 47, 56, 57, 64, 72, 74, 75, 87, 89, 90, 91, 92, 94, 95, 98, 101, 103, 105, 107, 108], "class": [1, 2, 3, 4, 5, 7, 9, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 54, 56, 57, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 105, 106, 108], "other": [1, 2, 3, 5, 10, 17, 23, 28, 37, 38, 40, 41, 42, 44, 47, 50, 52, 57, 58, 60, 62, 63, 66, 70, 71, 72, 74, 79, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 104, 107, 108], "assum": [1, 2, 3, 13, 44, 47, 52, 56, 57, 72, 76, 79, 98, 102, 104, 106, 107, 108], "column": [1, 2, 3, 5, 10, 11, 13, 14, 31, 37, 41, 44, 47, 49, 50, 53, 56, 57, 62, 63, 64, 66, 67, 70, 71, 72, 74, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "sum": [1, 2, 3, 27, 32, 33, 37, 47, 49, 57, 63, 64, 66, 69, 74, 90, 91, 92, 98, 99, 101, 102, 107, 108], "1": [1, 2, 3, 4, 5, 7, 10, 11, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 97, 98, 105], "each": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 15, 17, 21, 23, 24, 26, 27, 32, 33, 34, 37, 38, 39, 41, 42, 43, 44, 46, 47, 49, 50, 52, 54, 55, 57, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "true": [1, 2, 3, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 49, 52, 56, 57, 58, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "return": [1, 2, 3, 4, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "type": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98, 102, 103, 106, 107, 108], "bool": [1, 2, 3, 5, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 49, 52, 56, 57, 62, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 83], "is_valid": 1, "whether": [1, 3, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 38, 41, 42, 44, 52, 57, 62, 63, 64, 66, 67, 83, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 106, 108], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 19, 23, 24, 28, 31, 32, 33, 34, 36, 37, 38, 39, 41, 42, 43, 44, 47, 49, 50, 52, 53, 55, 56, 57, 62, 64, 66, 69, 70, 71, 72, 74, 75, 80, 82, 83, 84, 89, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 107, 108], "perfect": [1, 2, 37, 74, 99, 103], "exactli": [1, 3, 10, 37, 38, 42, 44, 65, 71, 90, 91, 92, 94, 95, 99], "yield": [1, 38, 42], "between": [1, 5, 10, 16, 17, 22, 23, 25, 27, 30, 33, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 52, 53, 54, 55, 60, 62, 63, 66, 69, 71, 72, 74, 75, 78, 82, 83, 85, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "below": [1, 3, 4, 5, 10, 37, 38, 41, 42, 44, 46, 49, 55, 62, 63, 64, 69, 70, 78, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "we": [1, 2, 3, 5, 7, 10, 14, 23, 38, 41, 42, 44, 49, 57, 58, 61, 62, 69, 70, 72, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "loop": [1, 3, 47, 57, 92, 103], "implement": [1, 2, 3, 4, 9, 15, 23, 38, 39, 41, 42, 47, 51, 53, 54, 57, 71, 74, 84, 87, 89, 90, 94, 104, 105], "what": [1, 5, 9, 10, 17, 34, 37, 39, 41, 44, 62, 63, 67, 69, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "doe": [1, 2, 3, 7, 10, 41, 42, 44, 49, 52, 55, 58, 69, 70, 74, 76, 78, 82, 88, 89, 90, 91, 92, 94, 95, 97, 102, 106, 107], "do": [1, 2, 5, 9, 10, 37, 41, 42, 57, 58, 71, 72, 76, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 104, 106, 107, 108], "fast": 1, "explain": [1, 10, 96], "python": [1, 2, 42, 61, 74, 90, 91, 96, 97, 104], "pseudocod": [1, 105], "happen": [1, 10, 44, 64, 95, 101, 107], "n": [1, 2, 3, 5, 7, 37, 38, 41, 42, 44, 46, 47, 48, 49, 52, 53, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 87, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "without": [1, 2, 5, 9, 10, 13, 15, 21, 38, 42, 54, 66, 74, 84, 88, 89, 95, 96, 98, 99, 103, 104], "ani": [1, 2, 3, 5, 7, 9, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 46, 48, 55, 56, 57, 61, 62, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107], "distinct": [1, 19, 57, 108], "natur": [1, 10, 101, 104], "number": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 82, 83, 85, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 107, 108], "0": [1, 2, 3, 4, 5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 55, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "count_joint": 1, "len": [1, 2, 3, 7, 37, 41, 47, 56, 57, 58, 71, 72, 74, 87, 88, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "y": [1, 2, 3, 5, 8, 19, 31, 32, 42, 47, 49, 57, 58, 61, 70, 74, 75, 88, 89, 90, 91, 94, 96, 98, 99, 101, 102, 104, 106], "round": [1, 41, 44, 57, 74, 96, 98, 106], "astyp": [1, 101], "int": [1, 2, 3, 4, 5, 7, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 39, 41, 42, 44, 49, 50, 52, 53, 54, 55, 56, 57, 58, 63, 64, 66, 70, 71, 72, 74, 76, 78, 79, 80, 83, 89, 90, 92, 96, 103, 104], "rang": [1, 3, 5, 7, 13, 47, 49, 55, 57, 70, 74, 75, 92, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 13, 14, 17, 23, 37, 41, 44, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "pragma": 1, "cover": [1, 3, 85, 96, 97, 98], "choic": [1, 8, 44, 53, 55, 92, 98, 102, 104], "replac": [1, 56, 61, 72, 87, 88, 90, 91, 92, 95, 96, 97, 98, 101, 104], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 52, 72, 89, 90, 91], "05": [1, 10, 27, 31, 56, 70, 74, 80, 82, 94, 97, 98, 99, 103], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 90, 91, 99, 101, 102], "none": [1, 2, 3, 4, 5, 7, 10, 11, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 90, 91, 92, 96, 98, 99, 101, 102, 107], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 27, 40, 42, 49, 74, 87, 89, 90, 91, 94, 96, 97, 99, 101, 102], "max_it": [1, 88, 89, 95, 104], "10000": [1, 41, 97, 98], "x": [1, 2, 3, 5, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 38, 39, 42, 44, 46, 47, 49, 52, 54, 56, 57, 58, 61, 62, 64, 70, 71, 72, 74, 76, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 104, 106], "diagon": [1, 3, 5, 44, 47, 57], "equal": [1, 3, 10, 13, 52, 64, 69, 79, 105], "creat": [1, 2, 9, 17, 19, 38, 41, 42, 44, 57, 74, 84, 88, 89, 92, 94, 95, 98, 107, 108], "impli": [1, 10, 37, 63, 70], "float": [1, 2, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 40, 41, 42, 44, 46, 48, 49, 55, 56, 57, 62, 63, 64, 66, 69, 70, 74, 78, 82, 89, 90, 91, 99, 101, 102], "entri": [1, 3, 5, 10, 37, 38, 42, 44, 46, 50, 52, 55, 57, 62, 63, 64, 67, 87, 88, 94, 95, 99, 102, 103, 106], "maximum": [1, 10, 71, 79, 83, 107], "minimum": [1, 8, 10, 21, 44, 46, 64, 69, 82], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 17, 27, 38, 42, 44, 52, 69, 74, 90, 98, 99, 101, 103, 104], "default": [1, 2, 3, 4, 5, 7, 10, 11, 15, 17, 29, 31, 34, 37, 38, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 90, 92, 96, 98, 106, 107], "If": [1, 2, 3, 4, 5, 10, 13, 14, 17, 27, 29, 35, 37, 38, 41, 42, 44, 46, 47, 49, 52, 53, 56, 57, 61, 62, 63, 64, 67, 69, 70, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "have": [1, 2, 3, 4, 5, 7, 9, 10, 17, 22, 25, 27, 30, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 75, 79, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "all": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 23, 34, 37, 38, 41, 42, 43, 44, 47, 49, 50, 52, 56, 57, 61, 62, 63, 64, 65, 66, 69, 70, 71, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "necessari": [1, 2, 3, 4, 7, 10, 13, 56, 90, 96], "In": [1, 2, 3, 5, 10, 37, 38, 41, 42, 52, 61, 62, 63, 65, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 104, 105, 106, 107, 108], "particular": [1, 5, 6, 10, 14, 15, 17, 20, 21, 23, 27, 28, 29, 32, 38, 42, 57, 62, 66, 70, 74, 79, 83, 84, 87, 88, 89, 91, 95, 98, 101, 102, 104, 106], "satisfi": [1, 3, 37], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 31, 36, 38, 39, 40, 41, 42, 44, 47, 52, 54, 57, 60, 61, 64, 71, 72, 74, 76, 84, 85, 89, 96, 97, 98, 99, 105], "argument": [1, 2, 3, 5, 10, 11, 17, 24, 28, 31, 32, 33, 38, 41, 42, 43, 44, 49, 52, 54, 58, 61, 62, 63, 64, 66, 69, 70, 71, 72, 74, 78, 79, 80, 82, 88, 91, 92, 95, 96, 97, 98, 102, 103, 106, 108], "when": [1, 2, 3, 4, 5, 10, 13, 15, 24, 27, 38, 42, 44, 47, 49, 52, 54, 55, 57, 61, 64, 66, 67, 69, 71, 72, 74, 75, 87, 88, 90, 91, 92, 94, 95, 96, 97, 101, 105, 106, 107, 108], "The": [1, 2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 38, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 57, 61, 62, 63, 64, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 106, 107, 108], "rate": [1, 2, 3, 10, 39, 57, 89, 108], "set": [1, 2, 3, 5, 9, 10, 13, 14, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 38, 41, 42, 44, 48, 49, 51, 52, 53, 55, 57, 61, 62, 64, 67, 69, 70, 71, 72, 74, 76, 78, 79, 87, 88, 90, 91, 94, 95, 96, 98, 101, 102, 104, 105, 106, 107, 108], "note": [1, 2, 3, 7, 8, 10, 11, 13, 28, 32, 35, 38, 41, 42, 43, 44, 49, 52, 57, 61, 62, 67, 69, 70, 71, 72, 74, 75, 79, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "you": [1, 2, 3, 5, 7, 9, 10, 15, 17, 37, 38, 40, 41, 42, 44, 49, 54, 60, 61, 62, 64, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 102, 103, 104, 105, 106, 107, 108], "high": [1, 2, 17, 41, 44, 52, 53, 57, 69, 72, 74, 87, 88, 90, 91, 92, 96, 97, 99, 103, 106, 107, 108], "mai": [1, 2, 3, 4, 5, 10, 14, 22, 23, 25, 30, 33, 37, 38, 40, 41, 42, 44, 47, 49, 52, 57, 62, 63, 67, 69, 70, 71, 72, 74, 76, 79, 83, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "imposs": [1, 10, 99], "also": [1, 2, 3, 5, 7, 9, 10, 23, 35, 37, 38, 41, 42, 44, 49, 56, 61, 62, 71, 74, 79, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "low": [1, 10, 57, 62, 84, 90, 91, 95, 96, 99, 103, 107], "zero": [1, 3, 5, 38, 42, 46, 52, 57, 58, 90, 92, 102, 103, 104], "forc": [1, 2, 3, 5, 42, 90, 108], "instead": [1, 2, 3, 10, 14, 17, 34, 37, 38, 41, 42, 44, 47, 57, 61, 62, 64, 66, 70, 71, 72, 74, 75, 78, 80, 82, 85, 87, 88, 89, 92, 94, 95, 96, 98, 99, 102, 103, 104, 106, 107, 108], "onli": [1, 2, 3, 4, 5, 7, 10, 11, 17, 24, 27, 31, 37, 38, 41, 42, 43, 44, 46, 47, 52, 53, 55, 56, 57, 58, 61, 62, 71, 72, 74, 76, 78, 82, 83, 84, 88, 89, 90, 91, 92, 95, 96, 101, 102, 103, 104, 105, 106, 107, 108], "guarante": [1, 3, 5, 16, 22, 25, 30, 38, 40, 42, 45, 47, 60, 85], "produc": [1, 2, 5, 9, 10, 17, 49, 62, 72, 74, 76, 78, 84, 87, 88, 89, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108], "higher": [1, 5, 10, 37, 44, 46, 47, 49, 55, 61, 62, 63, 74, 91, 95, 96, 98, 103], "opposit": [1, 108], "occur": [1, 3, 10, 37, 56, 69, 90, 91, 92, 98, 104], "small": [1, 3, 10, 37, 41, 49, 52, 55, 57, 63, 70, 88, 92, 95, 97, 102, 104], "numpi": [1, 3, 4, 5, 7, 10, 13, 19, 32, 33, 41, 42, 43, 49, 52, 55, 56, 58, 61, 66, 69, 74, 75, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "max": [1, 44, 71, 72, 91, 92, 96, 104], "tri": [1, 38, 42, 105], "befor": [1, 2, 3, 38, 42, 55, 57, 71, 74, 79, 87, 88, 95, 96, 98, 99, 101, 104, 106], "option": [1, 2, 3, 4, 5, 7, 8, 9, 13, 14, 17, 24, 29, 31, 37, 38, 41, 42, 44, 47, 49, 52, 54, 55, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 79, 82, 83, 84, 87, 89, 90, 91, 92, 94, 98, 99, 102, 106, 107], "left": [1, 2, 44, 46, 55, 57, 64, 67, 70, 90, 91, 102, 103, 104, 107], "stochast": 1, "exceed": 1, "m": [1, 5, 38, 42, 48, 49, 52, 53, 62, 67, 69, 70, 71, 90, 91, 97, 101, 102, 103, 108], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 38, 42, 61, 98, 99, 107], "length": [1, 5, 13, 27, 28, 37, 39, 44, 57, 64, 67, 71, 72, 74, 76, 79, 83, 87, 89, 102, 104, 107, 108], "must": [1, 2, 3, 4, 5, 7, 17, 37, 38, 39, 40, 42, 44, 47, 49, 50, 55, 57, 60, 61, 62, 63, 64, 71, 72, 74, 76, 78, 79, 80, 82, 83, 89, 96, 101, 105, 107, 108], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 13, 37, 41, 44, 50, 57, 58, 62, 64, 70, 76, 78, 79, 80, 82, 83, 87, 88, 89, 98, 101, 102, 103, 107, 108], "ball": [1, 97], "bin": [1, 3, 64, 90, 91, 104], "ensur": [1, 2, 10, 38, 42, 52, 54, 55, 57, 58, 61, 69, 72, 74, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99, 104, 105, 106], "most": [1, 3, 5, 7, 10, 17, 37, 41, 44, 49, 61, 62, 63, 64, 67, 69, 70, 71, 72, 75, 78, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107], "least": [1, 4, 10, 19, 32, 37, 41, 62, 63, 69, 72, 82, 92, 98, 101, 104, 107], "int_arrai": [1, 57], "can": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 75, 76, 79, 80, 83, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 102, 103, 104, 105, 106, 107, 108], "model": [2, 3, 4, 5, 9, 10, 11, 17, 19, 31, 33, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 54, 56, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 90, 91, 96, 97, 100, 105, 107, 108], "For": [2, 3, 5, 7, 9, 10, 12, 17, 23, 36, 37, 38, 41, 42, 44, 47, 49, 52, 55, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 78, 80, 82, 83, 84, 87, 88, 89, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 108], "regular": [2, 3, 41, 61], "multi": [2, 3, 4, 10, 33, 37, 38, 41, 42, 44, 48, 49, 50, 57, 58, 63, 64, 65, 66, 71, 72, 84, 96, 98, 99, 100], "task": [2, 5, 7, 10, 11, 12, 13, 15, 16, 17, 26, 31, 34, 37, 41, 47, 49, 50, 55, 57, 62, 64, 72, 74, 84, 88, 89, 95, 96, 97, 98, 99, 102, 104, 106, 107, 108], "cleanlearn": [2, 3, 10, 24, 31, 38, 57, 61, 73, 74, 75, 84, 85, 87, 88, 106], "wrap": [2, 38, 42, 51, 61, 71, 74, 84, 87, 88, 90, 91, 94, 95, 99, 106], "instanc": [2, 3, 5, 6, 7, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 61, 70, 71, 74, 79, 87, 89, 90, 91, 92, 94, 95, 98, 99, 103], "sklearn": [2, 3, 4, 5, 8, 10, 19, 32, 37, 42, 49, 53, 54, 57, 61, 71, 74, 75, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106], "classifi": [2, 3, 42, 49, 57, 62, 65, 71, 72, 84, 85, 87, 88, 89, 94, 95, 98, 101, 102, 104, 105, 107, 108], "adher": [2, 42, 74], "estim": [2, 3, 4, 5, 9, 14, 23, 37, 41, 42, 44, 47, 57, 62, 63, 64, 69, 71, 74, 76, 78, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 98, 100, 103, 104, 105, 106, 107, 108], "api": [2, 3, 15, 61, 67, 70, 71, 74, 85, 96, 98, 106], "defin": [2, 3, 5, 7, 10, 15, 23, 37, 38, 39, 41, 42, 44, 72, 74, 76, 90, 91, 94, 97, 98, 101, 104, 108], "four": [2, 10, 97, 99, 108], "clf": [2, 3, 5, 49, 74, 84, 87, 94, 96, 98, 99, 102], "fit": [2, 3, 5, 8, 10, 19, 40, 42, 52, 54, 60, 61, 71, 73, 74, 84, 87, 88, 92, 94, 95, 96, 98, 99, 101, 102, 104, 105, 106, 108], "sample_weight": [2, 42, 74, 99], "predict_proba": [2, 5, 37, 40, 42, 49, 60, 61, 87, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 104], "predict": [2, 3, 4, 5, 8, 9, 10, 11, 17, 23, 24, 26, 29, 31, 33, 35, 37, 40, 41, 42, 43, 44, 46, 47, 49, 50, 56, 57, 60, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 88, 97, 98, 99, 100, 104, 106, 107, 108], "score": [2, 3, 4, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 43, 44, 46, 49, 55, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 104, 106], "data": [2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 49, 50, 52, 53, 54, 57, 60, 61, 62, 63, 64, 65, 69, 71, 72, 73, 74, 79, 80, 81, 82, 83, 85, 88, 92, 93, 100, 105], "e": [2, 3, 5, 10, 13, 23, 33, 37, 38, 41, 42, 44, 47, 49, 50, 52, 57, 58, 62, 63, 64, 65, 67, 70, 71, 72, 74, 76, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "featur": [2, 3, 4, 5, 8, 10, 11, 17, 19, 20, 24, 27, 28, 29, 31, 32, 49, 52, 53, 54, 57, 71, 74, 84, 87, 90, 91, 94, 95, 98, 99, 101, 102, 106], "element": [2, 3, 5, 37, 43, 44, 46, 57, 62, 64, 72, 79, 80, 82, 88, 89, 95, 96, 98, 108], "first": [2, 5, 10, 18, 27, 28, 37, 41, 49, 52, 57, 62, 63, 67, 70, 72, 74, 87, 88, 89, 90, 92, 94, 96, 98, 101, 102, 103, 104, 106, 107, 108], "index": [2, 10, 27, 37, 44, 51, 52, 54, 56, 57, 58, 63, 72, 74, 79, 82, 83, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "should": [2, 3, 5, 7, 10, 15, 23, 27, 32, 33, 37, 38, 41, 42, 44, 46, 47, 49, 52, 54, 55, 56, 57, 61, 62, 63, 66, 67, 69, 70, 71, 72, 74, 75, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 107, 108], "correspond": [2, 3, 5, 10, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 37, 38, 41, 42, 43, 44, 46, 47, 49, 52, 56, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 76, 79, 80, 82, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "differ": [2, 5, 7, 10, 14, 16, 22, 25, 27, 28, 30, 37, 38, 40, 41, 42, 44, 45, 49, 52, 55, 57, 58, 60, 62, 67, 69, 71, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 104, 105, 106], "sampl": [2, 3, 5, 8, 10, 17, 21, 44, 46, 49, 52, 53, 54, 64, 67, 70, 72, 74, 75, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "size": [2, 10, 32, 38, 41, 42, 44, 49, 52, 53, 64, 69, 70, 74, 76, 78, 88, 92, 94, 98, 99, 101, 102, 103, 105, 107], "here": [2, 5, 7, 10, 15, 41, 44, 47, 61, 62, 63, 64, 66, 67, 70, 71, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "re": [2, 5, 38, 42, 54, 56, 62, 74, 84, 87, 88, 89, 90, 94, 95, 98, 106, 107, 108], "weight": [2, 10, 38, 39, 42, 49, 52, 62, 69, 72, 74, 88, 89, 90, 91, 95], "loss": [2, 39, 61, 72, 74, 92], "while": [2, 3, 10, 38, 41, 42, 48, 49, 57, 74, 84, 92, 96, 98, 99, 101, 102, 106], "train": [2, 3, 4, 5, 9, 10, 17, 19, 33, 38, 39, 40, 42, 49, 57, 61, 62, 67, 70, 71, 74, 75, 85, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 105, 107, 108], "support": [2, 3, 4, 5, 13, 15, 34, 35, 41, 43, 49, 57, 58, 61, 71, 72, 82, 84, 85, 89, 90, 91, 92, 96, 98], "your": [2, 3, 5, 9, 10, 17, 37, 38, 40, 41, 42, 44, 49, 54, 57, 60, 61, 62, 63, 64, 66, 71, 72, 74, 75, 76, 78, 79, 85, 87, 88, 89, 92, 94, 97, 101, 102, 103, 104, 105, 106, 107, 108], "recommend": [2, 5, 7, 10, 14, 17, 41, 44, 62, 90, 91, 92, 96, 98, 105, 106], "furthermor": 2, "correctli": [2, 3, 10, 37, 38, 42, 44, 47, 52, 58, 63, 64, 69, 70, 74, 76, 88, 95, 96, 98, 102, 103, 106, 107], "clonabl": [2, 74], "via": [2, 5, 7, 10, 11, 14, 17, 19, 23, 37, 39, 41, 42, 49, 53, 57, 62, 67, 70, 71, 72, 74, 75, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 102, 103, 104, 105, 106, 107, 108], "base": [2, 3, 4, 5, 7, 10, 13, 14, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 43, 44, 47, 48, 49, 52, 53, 55, 56, 57, 58, 61, 62, 63, 64, 66, 69, 71, 72, 74, 75, 78, 80, 82, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "clone": [2, 74, 102], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 66, 70, 74, 80, 85, 90, 96, 98, 99, 101, 102, 103, 104, 106, 108], "multipl": [2, 3, 5, 10, 13, 14, 35, 37, 44, 55, 56, 62, 63, 64, 66, 69, 70, 74, 84, 90, 91, 92, 94, 98, 100, 102, 103, 106], "g": [2, 3, 5, 10, 13, 23, 33, 37, 38, 42, 44, 50, 52, 57, 64, 65, 67, 70, 71, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 105, 106], "manual": [2, 74, 87, 88, 89, 96, 98, 104, 105, 106, 108], "pytorch": [2, 38, 39, 42, 74, 84, 89, 92, 98, 100, 102, 107], "call": [2, 3, 5, 6, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 49, 57, 61, 71, 74, 88, 89, 90, 91, 95, 98, 99, 102, 104, 105, 106, 107, 108], "__init__": [2, 39, 74, 92], "independ": [2, 3, 10, 63, 74, 95, 96, 105, 106, 108], "compat": [2, 38, 41, 42, 54, 61, 74, 75, 78, 82, 84, 87, 88, 96, 98, 105, 106], "neural": [2, 39, 61, 71, 74, 89, 92, 98, 102, 104, 106], "network": [2, 38, 39, 42, 61, 71, 74, 88, 89, 92, 95, 98, 102, 104, 106], "typic": [2, 10, 38, 42, 54, 71, 74, 87, 88, 89, 91, 92, 94, 95, 104, 105], "initi": [2, 3, 14, 19, 38, 42, 52, 62, 74, 87, 95, 98], "insid": [2, 42, 74, 98, 99], "There": [2, 3, 7, 52, 84, 99, 101], "two": [2, 3, 10, 19, 27, 37, 38, 41, 42, 50, 52, 53, 54, 57, 67, 69, 70, 85, 88, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106, 107, 108], "new": [2, 7, 9, 10, 15, 23, 38, 41, 42, 48, 52, 56, 57, 62, 74, 88, 89, 90, 95, 97, 98, 104, 105, 108], "notion": 2, "confid": [2, 3, 10, 23, 37, 41, 44, 47, 49, 57, 62, 63, 64, 67, 69, 70, 71, 72, 74, 78, 82, 84, 87, 92, 94, 95, 99, 101, 102, 103, 105, 107, 108], "packag": [2, 5, 7, 9, 10, 12, 16, 36, 40, 44, 45, 57, 60, 61, 67, 70, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "prune": [2, 3, 44, 64, 74, 85, 103], "everyth": [2, 70, 99], "els": [2, 70, 90, 92, 96, 97, 98, 101, 102, 103], "mathemat": [2, 3, 10, 47, 102], "keep": [2, 14, 15, 57, 84, 90, 96, 97, 98, 107], "belong": [2, 3, 10, 37, 44, 46, 47, 52, 63, 64, 65, 66, 71, 72, 76, 80, 82, 83, 91, 92, 99, 102, 104, 107, 108], "2": [2, 3, 4, 5, 7, 10, 11, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 50, 52, 54, 55, 56, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 75, 79, 80, 82, 83, 97, 98, 105], "error": [2, 3, 5, 10, 38, 42, 43, 44, 46, 47, 57, 63, 64, 66, 67, 69, 70, 72, 74, 76, 78, 79, 82, 85, 87, 89, 90, 91, 94, 95, 96, 97, 100], "erron": [2, 3, 37, 44, 47, 57, 63, 64, 72, 74, 75, 76, 104, 106], "import": [2, 3, 4, 5, 7, 8, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 41, 43, 49, 52, 55, 56, 62, 66, 69, 74, 75, 80, 82, 83, 84, 87, 88, 94, 95, 96, 98, 102, 103, 104, 106, 107, 108], "linear_model": [2, 5, 37, 57, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logisticregress": [2, 3, 5, 37, 57, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 104], "logreg": 2, "cl": [2, 15, 31, 74, 84, 87, 88, 98, 99, 106], "pass": [2, 3, 5, 8, 10, 11, 13, 14, 15, 17, 24, 31, 34, 38, 41, 42, 44, 48, 49, 52, 54, 57, 61, 62, 64, 70, 71, 72, 74, 79, 80, 84, 88, 89, 90, 91, 95, 97, 98, 99, 101, 103, 104, 106], "x_train": [2, 87, 90, 91, 99, 101, 102, 106], "labels_maybe_with_error": 2, "had": [2, 3, 74, 103], "issu": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 37, 38, 40, 41, 42, 43, 44, 52, 60, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 88, 93, 100, 101, 105, 106], "pred": [2, 44, 57, 87, 88, 105, 106], "x_test": [2, 87, 90, 91, 99, 102, 106], "might": [2, 5, 10, 52, 62, 74, 79, 87, 88, 90, 91, 92, 96, 98, 103], "case": [2, 3, 10, 14, 37, 49, 52, 62, 74, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 99, 104, 106, 108], "standard": [2, 3, 5, 31, 37, 44, 61, 63, 64, 66, 72, 74, 84, 87, 90, 91, 94, 97, 99, 103], "adapt": [2, 38, 40, 57, 60, 74, 104], "skorch": [2, 74, 84, 98], "kera": [2, 60, 67, 70, 74, 84, 98, 103], "scikera": [2, 61, 74, 98], "open": [2, 41, 96, 97, 103, 108], "doesn": [2, 10, 74, 84], "t": [2, 3, 4, 7, 10, 18, 28, 29, 38, 39, 41, 42, 43, 44, 49, 55, 56, 66, 71, 72, 74, 80, 82, 83, 84, 90, 91, 92, 94, 95, 96, 97, 99, 102, 103, 106, 108], "alreadi": [2, 5, 10, 17, 38, 41, 42, 47, 52, 61, 62, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 103, 104, 106], "exist": [2, 5, 10, 13, 19, 38, 41, 42, 54, 56, 61, 67, 69, 71, 74, 84, 85, 87, 88, 90, 91, 95, 101, 108], "made": [2, 5, 17, 38, 42, 53, 74, 87, 88, 92, 95, 96, 98, 101, 103, 105, 106], "easi": [2, 12, 47, 74, 90, 91, 97, 98, 99, 102], "inherit": [2, 7, 39, 74], "baseestim": [2, 42, 74], "yourmodel": [2, 74], "def": [2, 7, 15, 38, 42, 61, 74, 88, 89, 90, 91, 92, 96, 97, 98, 99, 101, 102, 104, 106, 108], "self": [2, 3, 5, 7, 10, 13, 14, 15, 17, 32, 38, 39, 41, 42, 44, 49, 71, 72, 74, 87, 90, 92, 96, 97, 102, 107, 108], "refer": [2, 10, 17, 38, 42, 43, 63, 64, 66, 67, 69, 70, 71, 74, 78, 79, 90, 91, 92, 94, 95, 96, 98, 99, 102, 105, 106], "origin": [2, 5, 10, 42, 43, 44, 56, 57, 61, 63, 64, 67, 70, 71, 74, 75, 78, 80, 82, 87, 88, 90, 92, 94, 95, 98, 99, 103, 104, 106, 108], "total": [2, 3, 4, 37, 41, 57, 63, 83, 92, 98, 107], "state": [2, 3, 5, 38, 39, 42, 48, 74, 99, 102, 103, 108], "art": [2, 39, 99, 102], "northcutt": [2, 3, 37, 71, 72], "et": [2, 3, 37, 39, 71, 72], "al": [2, 3, 37, 39, 71, 72], "2021": [2, 3, 37, 71, 72], "weak": [2, 70], "supervis": [2, 10, 90, 91, 98, 101], "find": [2, 5, 9, 10, 14, 15, 17, 20, 21, 23, 24, 26, 27, 28, 29, 32, 33, 37, 38, 40, 41, 42, 43, 44, 48, 54, 56, 57, 60, 67, 70, 71, 72, 74, 76, 80, 82, 85, 90, 100, 105], "uncertainti": [2, 10, 46, 71, 74, 98, 104, 106], "It": [2, 3, 5, 7, 10, 13, 14, 17, 23, 28, 31, 33, 34, 35, 38, 42, 44, 47, 49, 52, 53, 55, 62, 69, 70, 74, 84, 90, 91, 92, 98, 99, 102, 105], "work": [2, 3, 7, 10, 13, 31, 37, 38, 41, 42, 44, 47, 56, 57, 58, 61, 62, 72, 74, 84, 85, 88, 90, 91, 96, 97, 104, 106], "includ": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 38, 40, 41, 42, 52, 56, 57, 60, 62, 63, 66, 67, 71, 72, 74, 78, 79, 80, 82, 84, 85, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 108], "deep": [2, 40, 42, 60, 61, 74, 95], "see": [2, 3, 5, 7, 10, 14, 15, 34, 37, 38, 41, 42, 43, 44, 49, 54, 57, 61, 63, 64, 66, 67, 70, 71, 72, 74, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "subfield": 2, "theori": [2, 99], "machin": [2, 4, 5, 9, 10, 15, 17, 34, 40, 55, 60, 74, 87, 88, 90, 91, 96, 97, 101], "across": [2, 3, 5, 7, 10, 14, 23, 37, 41, 49, 63, 70, 71, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 105, 106], "varieti": [2, 87, 88, 98], "like": [2, 3, 5, 6, 7, 10, 15, 33, 37, 38, 41, 42, 44, 47, 57, 61, 62, 63, 66, 67, 69, 72, 74, 75, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "pu": [2, 57], "input": [2, 3, 5, 9, 17, 27, 37, 38, 41, 42, 47, 49, 52, 53, 56, 57, 58, 61, 70, 74, 84, 85, 88, 91, 92, 95, 97, 98, 99, 101, 102, 103, 106, 107, 108], "discret": [2, 35, 44, 47, 57, 71, 72, 76, 78, 79], "vector": [2, 3, 4, 5, 10, 17, 44, 47, 49, 50, 52, 57, 71, 72, 84, 88, 89, 90, 91, 92, 94, 95, 99, 102, 103, 104, 107, 108], "would": [2, 3, 5, 10, 38, 41, 42, 44, 53, 57, 64, 74, 84, 88, 90, 92, 98, 99, 104, 106, 108], "obtain": [2, 5, 8, 10, 17, 44, 62, 64, 67, 70, 72, 75, 89, 91, 95, 98, 101, 103, 105, 107, 108], "been": [2, 4, 37, 44, 47, 52, 56, 57, 62, 63, 67, 69, 71, 72, 74, 89, 90, 94, 98, 99, 101, 102, 103, 104, 107, 108], "dure": [2, 10, 17, 52, 54, 71, 74, 87, 88, 89, 94, 95, 96, 98, 99, 102, 105, 106, 108], "denot": [2, 3, 47, 49, 57, 64, 71, 72, 82], "tild": 2, "paper": [2, 4, 10, 62, 71, 80, 82, 97, 99, 101, 104, 106, 108], "cv_n_fold": [2, 3, 74, 88], "5": [2, 3, 4, 5, 8, 10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 42, 44, 46, 48, 49, 57, 62, 63, 66, 67, 70, 74, 75, 82, 88, 90, 95, 97, 98, 102, 103, 104, 105, 107, 108], "converge_latent_estim": [2, 3], "pulearn": [2, 57], "find_label_issues_kwarg": [2, 10, 74, 85, 98, 99], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 64, 80, 98], "clean": [2, 69, 72, 74, 75, 84, 87, 88, 90, 91, 97, 106], "even": [2, 3, 7, 9, 10, 37, 41, 46, 47, 57, 74, 89, 96, 98, 99, 101, 102, 103], "messi": [2, 74, 99], "ridden": [2, 74], "autom": [2, 9, 10, 74, 84, 91, 97, 98], "robust": [2, 47, 52, 74, 91, 96, 98], "prone": [2, 74], "out": [2, 3, 5, 10, 17, 29, 38, 42, 44, 49, 52, 61, 64, 65, 67, 70, 71, 72, 74, 75, 83, 84, 85, 88, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108], "current": [2, 3, 5, 7, 10, 11, 14, 15, 23, 38, 42, 43, 44, 49, 62, 69, 74, 90, 91, 98, 101, 103], "intend": [2, 14, 15, 16, 17, 33, 34, 35, 45, 52, 62, 78, 82, 89, 90, 91, 95, 99], "A": [2, 3, 4, 5, 7, 10, 13, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 61, 62, 63, 66, 69, 70, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 103, 105, 108], "follow": [2, 3, 10, 15, 31, 35, 37, 38, 41, 42, 49, 51, 55, 62, 63, 67, 69, 70, 71, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "experiment": [2, 38, 39, 41, 42, 43, 64, 85, 98], "wrapper": [2, 61, 87, 88, 89, 106], "around": [2, 69, 90, 91, 103, 104, 108], "fasttext": [2, 60], "store": [2, 4, 5, 10, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 71, 74, 87, 88, 94, 95, 96, 97, 98, 107, 108], "along": [2, 49, 64, 82, 90, 91, 92, 96, 98, 104], "dimens": [2, 57, 76, 79, 92, 98, 104, 107], "select": [2, 9, 10, 27, 51, 62, 72, 92, 96, 101, 104], "split": [2, 3, 5, 10, 13, 41, 49, 56, 57, 74, 87, 89, 90, 91, 92, 94, 95, 96, 97, 99, 102, 105, 108], "cross": [2, 3, 10, 37, 44, 47, 48, 49, 64, 67, 70, 72, 74, 75, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 102, 103, 106, 107, 108], "fold": [2, 3, 37, 44, 47, 74, 87, 89, 94, 97, 98, 103, 107], "By": [2, 37, 63, 64, 74, 90, 96, 107], "need": [2, 3, 10, 11, 37, 38, 41, 42, 44, 52, 54, 63, 64, 66, 71, 74, 84, 88, 89, 90, 91, 95, 96, 98, 99, 101, 102, 103, 107], "holdout": [2, 3, 74], "comput": [2, 3, 4, 5, 7, 8, 10, 20, 21, 23, 24, 27, 28, 29, 32, 37, 38, 39, 41, 42, 44, 46, 47, 48, 49, 52, 53, 54, 57, 62, 63, 64, 66, 69, 70, 71, 72, 74, 75, 76, 78, 84, 85, 88, 90, 91, 97, 99, 100, 103, 104, 106, 107], "them": [2, 3, 5, 7, 9, 10, 12, 13, 28, 33, 36, 38, 40, 41, 42, 44, 54, 60, 62, 71, 74, 85, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 107, 108], "numer": [2, 3, 4, 5, 10, 14, 23, 31, 35, 49, 52, 53, 69, 71, 74, 79, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 99, 101, 102, 104, 106], "consist": [2, 3, 38, 42, 51, 57, 62, 96, 107, 108], "latent": [2, 3, 47], "thei": [2, 3, 5, 16, 22, 25, 27, 30, 38, 39, 40, 42, 44, 45, 52, 55, 57, 61, 64, 69, 72, 74, 75, 78, 82, 84, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106, 108], "relat": [2, 3, 10, 14, 20, 21, 27, 28, 29, 32, 47, 57, 63, 74, 91, 95], "close": [2, 3, 10, 41, 47, 71, 89, 90, 91, 92, 94, 95, 96, 98, 99, 103], "form": [2, 3, 10, 38, 39, 42, 47, 56, 57, 72, 74, 98], "equival": [2, 3, 38, 42, 47, 71, 104, 106], "iter": [2, 3, 37, 38, 42, 44, 57, 63, 64, 74, 98, 101, 107], "enforc": [2, 38, 42, 57], "perfectli": [2, 37, 63, 99], "certain": [2, 3, 5, 38, 42, 61, 70, 74, 90, 91, 96, 97, 103, 104], "dict": [2, 3, 5, 10, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 41, 42, 44, 48, 49, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 82, 90, 91, 92, 98, 108], "keyword": [2, 3, 5, 10, 11, 17, 24, 28, 31, 38, 41, 42, 44, 46, 49, 52, 54, 56, 61, 62, 64, 70, 71, 72, 74, 79, 80, 82, 90], "filter": [2, 3, 10, 41, 43, 56, 63, 65, 66, 68, 70, 77, 78, 79, 81, 82, 83, 84, 85, 87, 88, 89, 92, 95, 96, 97, 98, 102, 103, 106, 107, 108], "find_label_issu": [2, 3, 10, 31, 40, 41, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 98, 102, 103, 106, 107, 108], "particularli": [2, 84, 101, 104], "filter_bi": [2, 3, 41, 44, 64, 85, 98], "frac_nois": [2, 44, 64, 80, 98], "min_examples_per_class": [2, 44, 64, 98, 99], "impact": [2, 4, 10, 90, 91, 92, 96], "ml": [2, 4, 5, 9, 10, 16, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 101, 102, 106], "accuraci": [2, 39, 72, 87, 88, 89, 92, 98, 99, 101, 104, 106, 107], "n_job": [2, 41, 44, 64, 76, 78, 80, 98, 104, 107], "disabl": [2, 38, 42, 44, 104], "process": [2, 3, 7, 14, 17, 33, 38, 41, 42, 44, 52, 56, 62, 64, 70, 76, 78, 80, 88, 89, 90, 96, 98, 101, 105], "caus": [2, 44, 49, 90, 91, 96, 98], "rank": [2, 3, 10, 37, 41, 43, 44, 49, 63, 64, 65, 67, 68, 70, 71, 73, 77, 79, 80, 81, 83, 84, 85, 87, 88, 90, 91, 97, 98, 102, 103, 104, 107, 108], "get_label_quality_scor": [2, 40, 41, 43, 44, 45, 49, 62, 64, 65, 66, 67, 68, 69, 72, 73, 75, 77, 78, 80, 81, 82, 85, 98, 99, 102, 103, 107, 108], "adjust_pred_prob": [2, 10, 66, 71, 72, 99], "control": [2, 5, 9, 10, 17, 41, 44, 62, 70, 71, 74, 80, 82, 90, 91, 96, 97, 98], "how": [2, 3, 5, 10, 13, 14, 15, 17, 23, 37, 38, 39, 41, 42, 47, 57, 62, 63, 66, 67, 69, 71, 72, 74, 78, 82, 84, 87, 88, 90, 91, 92, 94, 95, 96, 97, 103, 104, 105, 106, 107], "much": [2, 10, 37, 41, 44, 74, 96, 97, 98, 99, 101, 104], "output": [2, 3, 5, 10, 17, 33, 38, 39, 42, 47, 57, 61, 62, 63, 67, 69, 70, 71, 74, 78, 79, 82, 83, 84, 85, 88, 89, 90, 92, 95, 97, 98, 103, 104, 105, 106], "print": [2, 5, 7, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 57, 62, 63, 64, 69, 71, 72, 74, 76, 78, 79, 83, 85, 87, 88, 89, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "suppress": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79, 107, 108], "statement": [2, 41, 62, 69, 71, 72, 74, 76, 78, 79], "big": [2, 41, 64, 70, 74, 99], "limit": [2, 5, 17, 41, 52, 64, 96, 103, 107, 108], "memori": [2, 38, 41, 42, 64, 70, 76, 78, 90, 107], "label_issues_batch": [2, 40, 64, 98], "find_label_issues_batch": [2, 40, 41, 64, 98], "pred_prob": [2, 3, 5, 8, 10, 11, 17, 24, 26, 27, 29, 32, 33, 37, 41, 43, 44, 46, 47, 48, 49, 50, 57, 58, 62, 63, 64, 66, 67, 70, 71, 72, 76, 78, 79, 80, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106], "threshold": [2, 3, 4, 7, 10, 19, 20, 21, 23, 29, 31, 32, 41, 55, 69, 70, 71, 72, 78, 82, 90, 96, 103, 104, 107, 108], "inverse_noise_matrix": [2, 3, 10, 47, 57, 85, 99], "label_issu": [2, 41, 44, 64, 67, 74, 76, 85, 87, 88, 89, 92, 95, 98, 99, 102, 106], "clf_kwarg": [2, 3, 10, 74], "clf_final_kwarg": [2, 74], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 37, 41, 44, 46, 52, 62, 63, 64, 66, 67, 69, 70, 72, 74, 75, 78, 82, 84, 89, 92, 94, 95, 99, 101, 103, 105, 106], "result": [2, 3, 9, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 41, 42, 44, 46, 55, 57, 64, 66, 67, 70, 72, 74, 75, 76, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 106, 107, 108], "identifi": [2, 3, 5, 7, 9, 10, 13, 17, 28, 34, 37, 41, 43, 44, 52, 64, 67, 70, 72, 74, 75, 76, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 102, 104, 106, 107, 108], "final": [2, 10, 74, 87, 94, 96, 103, 105, 106], "remain": [2, 74, 85, 87, 88, 92, 96, 102, 106, 108], "datasetlik": [2, 57, 74], "beyond": [2, 5, 7, 9, 10, 12, 36, 84, 87, 88, 106, 107], "pd": [2, 3, 5, 7, 14, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 48, 61, 62, 63, 74, 82, 87, 88, 89, 90, 91, 94, 95, 96, 98, 99, 101, 106, 108], "datafram": [2, 3, 5, 7, 13, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 41, 48, 57, 58, 61, 62, 63, 74, 79, 83, 85, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 106, 107, 108], "scipi": [2, 4, 5, 14, 53, 57, 71, 96], "spars": [2, 4, 5, 10, 14, 17, 19, 32, 52, 57, 58, 94, 96], "csr_matrix": [2, 4, 5, 14, 17, 19, 32, 52, 96], "torch": [2, 38, 39, 42, 88, 89, 92, 95, 97, 104], "util": [2, 5, 10, 17, 34, 38, 39, 42, 45, 52, 61, 62, 67, 70, 74, 84, 85, 89, 90, 91, 92, 96, 98, 99, 104], "tensorflow": [2, 57, 61, 84, 89, 98], "object": [2, 5, 10, 13, 14, 17, 33, 34, 38, 39, 41, 42, 49, 52, 54, 57, 58, 61, 64, 67, 68, 69, 70, 71, 74, 82, 84, 88, 89, 91, 92, 94, 98, 99, 100, 102, 106], "list": [2, 3, 5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 39, 41, 42, 43, 44, 50, 52, 56, 57, 58, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 78, 79, 80, 82, 83, 85, 88, 89, 90, 91, 92, 96, 97, 98, 99, 102, 103, 106, 108], "index_list": 2, "subset": [2, 3, 5, 17, 37, 41, 44, 57, 72, 79, 83, 87, 88, 89, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 108], "wa": [2, 3, 13, 15, 41, 55, 57, 62, 63, 69, 71, 83, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 105, 107, 108], "abl": [2, 3, 10, 74, 89, 98, 99, 101, 102], "format": [2, 3, 5, 10, 13, 33, 38, 41, 42, 44, 47, 48, 49, 50, 52, 57, 58, 61, 62, 63, 64, 67, 70, 71, 72, 74, 76, 78, 79, 82, 83, 87, 90, 91, 92, 94, 96, 97, 101, 106, 107, 108], "make": [2, 3, 5, 19, 38, 41, 42, 49, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "sure": [2, 5, 41, 44, 49, 87, 88, 89, 90, 91, 92, 94, 95, 97, 101, 102, 103, 104, 106], "shuffl": [2, 10, 57, 89, 92, 95, 96, 102, 104], "ha": [2, 3, 5, 6, 10, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 43, 47, 49, 52, 56, 57, 62, 67, 69, 74, 80, 82, 83, 84, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 102, 103, 104, 105, 106, 108], "batch": [2, 41, 57, 61, 62, 76, 78, 92, 98, 104], "order": [2, 5, 10, 35, 37, 38, 42, 43, 44, 47, 48, 49, 55, 57, 62, 63, 64, 67, 70, 71, 72, 76, 79, 80, 82, 83, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 106, 107, 108], "destroi": [2, 57], "oper": [2, 38, 41, 42, 52, 57, 61, 72, 84, 87, 88, 95, 98, 104], "eg": [2, 5, 10, 57, 67, 70, 90, 91, 98], "repeat": [2, 57, 62, 101, 104], "appli": [2, 35, 38, 40, 42, 44, 49, 50, 52, 56, 57, 66, 71, 80, 87, 88, 89, 90, 91, 92, 94, 96, 98, 101, 102, 104, 105, 106, 107], "array_lik": [2, 3, 37, 44, 57, 64, 71, 75], "some": [2, 3, 5, 10, 15, 23, 37, 38, 40, 42, 44, 47, 52, 56, 57, 60, 62, 63, 64, 66, 67, 70, 71, 72, 74, 76, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 105, 106, 107, 108], "seri": [2, 3, 41, 57, 58, 74, 82, 98], "row": [2, 3, 5, 10, 14, 28, 33, 37, 41, 44, 46, 47, 52, 53, 57, 62, 63, 64, 66, 71, 72, 74, 79, 80, 82, 83, 87, 89, 92, 94, 95, 96, 97, 98, 101, 102, 104, 108], "rather": [2, 3, 5, 10, 27, 37, 57, 61, 62, 69, 78, 82, 88, 97, 101, 105, 106, 107, 108], "leav": [2, 44], "per": [2, 3, 5, 7, 10, 14, 37, 41, 44, 49, 56, 62, 63, 64, 66, 69, 70, 72, 75, 76, 78, 82, 91, 98, 103, 108], "determin": [2, 3, 10, 13, 17, 23, 27, 31, 37, 41, 44, 49, 52, 57, 62, 64, 67, 69, 72, 78, 82, 90, 96, 98, 101, 103, 104, 106], "cutoff": [2, 3, 53, 104], "consid": [2, 3, 4, 5, 10, 14, 17, 24, 27, 29, 32, 37, 38, 42, 44, 52, 54, 57, 62, 69, 71, 72, 75, 78, 82, 87, 88, 89, 92, 94, 95, 96, 98, 99, 103, 104, 105, 106, 107], "section": [2, 3, 7, 10, 85, 92, 94, 96, 98, 103], "3": [2, 3, 4, 5, 7, 10, 11, 35, 37, 38, 42, 44, 47, 48, 49, 50, 53, 55, 56, 57, 61, 64, 71, 72, 74, 75, 80, 82, 97, 98, 105], "equat": [2, 3, 47], "advanc": [2, 3, 5, 9, 10, 17, 69, 71, 82, 85, 91, 93, 96, 98, 99], "user": [2, 3, 5, 9, 10, 15, 17, 28, 33, 34, 35, 38, 42, 44, 52, 61, 69, 71, 72, 74, 78, 82, 99], "specifi": [2, 3, 4, 5, 8, 10, 14, 15, 17, 19, 32, 34, 38, 41, 42, 44, 49, 52, 54, 56, 61, 62, 63, 64, 67, 69, 71, 72, 74, 75, 83, 85, 88, 89, 91, 92, 95, 101, 103, 106], "automat": [2, 3, 5, 27, 37, 84, 87, 88, 92, 94, 95, 96, 97, 98, 101, 102, 103, 106, 107, 108], "greater": [2, 3, 4, 5, 7, 9, 10, 29, 41, 53, 57, 69, 91, 97, 98, 108], "count": [2, 23, 27, 37, 41, 44, 47, 57, 63, 64, 70, 85, 92, 96, 98, 103], "observ": [2, 3, 47, 54, 89, 90, 91, 96, 101, 104, 106], "mislabel": [2, 10, 37, 41, 43, 44, 47, 62, 63, 64, 67, 69, 72, 78, 80, 82, 83, 84, 87, 88, 89, 92, 94, 95, 98, 99, 103, 106], "one": [2, 3, 5, 7, 10, 27, 37, 38, 41, 42, 43, 44, 49, 55, 57, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 101, 104, 105, 106, 108], "get_label_issu": [2, 40, 41, 73, 74, 87, 88, 99, 106], "either": [2, 3, 4, 7, 10, 38, 41, 42, 44, 53, 62, 64, 69, 71, 72, 76, 78, 91, 96, 98, 102, 103], "boolean": [2, 7, 10, 23, 41, 44, 54, 56, 62, 64, 67, 72, 74, 76, 78, 79, 84, 88, 89, 91, 92, 95, 98, 103, 106, 107], "label_issues_mask": [2, 44, 72, 74, 85], "indic": [2, 3, 4, 5, 7, 10, 14, 23, 37, 41, 42, 43, 44, 46, 49, 52, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74, 75, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "its": [2, 5, 7, 9, 10, 17, 38, 41, 42, 44, 52, 54, 55, 56, 64, 67, 70, 71, 72, 74, 76, 80, 82, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 105, 106, 107, 108], "return_indices_ranked_bi": [2, 41, 44, 64, 80, 85, 87, 88, 98, 99], "significantli": [2, 10, 92, 96, 99, 101, 105], "reduc": [2, 41, 44, 57, 89, 96, 98], "time": [2, 10, 38, 41, 42, 57, 62, 83, 85, 87, 88, 90, 92, 94, 97, 98, 99, 103, 104, 106, 107, 108], "take": [2, 5, 10, 37, 38, 42, 48, 49, 52, 54, 57, 61, 72, 87, 92, 94, 96, 101, 102, 103, 108], "run": [2, 5, 6, 7, 9, 10, 11, 12, 15, 17, 27, 28, 33, 36, 38, 41, 42, 54, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 108], "skip": [2, 10, 38, 42, 74, 89, 96, 98, 102, 108], "slow": [2, 3], "step": [2, 7, 27, 49, 70, 92, 96, 99, 101, 105], "caution": [2, 5, 98], "previous": [2, 5, 14, 57, 71, 74, 85, 87, 89, 90, 94, 95, 101, 105], "assign": [2, 7, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 38, 42, 48, 49, 57, 74, 87, 90, 92, 94, 96, 98, 106, 107, 108], "individu": [2, 4, 7, 10, 14, 27, 38, 42, 43, 62, 66, 69, 72, 74, 80, 82, 85, 87, 91, 94, 96, 97, 98, 101, 102, 103, 108], "still": [2, 41, 42, 57, 71, 87, 92, 98, 104], "extra": [2, 38, 42, 57, 61, 62, 63, 74, 92, 95, 98, 101, 104], "receiv": [2, 10, 38, 42, 43, 63, 66, 67, 74, 76, 80, 91, 103], "overwritten": [2, 74], "callabl": [2, 3, 4, 10, 27, 38, 42, 49, 52, 53, 54, 56, 61, 66, 98], "x_val": 2, "y_val": 2, "map": [2, 3, 13, 41, 42, 45, 48, 56, 57, 70, 72, 74, 79, 89, 90, 91, 92, 96, 98, 99, 102, 108], "appropri": [2, 10, 17, 35, 53, 64, 72, 90, 94, 102, 103], "earli": [2, 92], "stop": [2, 92], "x_valid": 2, "y_valid": 2, "could": [2, 7, 10, 23, 37, 57, 71, 87, 90, 92, 94, 102, 106, 108], "f": [2, 7, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106], "ignor": [2, 38, 42, 56, 61, 74, 79, 83, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "allow": [2, 37, 38, 41, 42, 46, 54, 57, 62, 70, 71, 74, 76, 78, 88, 89, 92, 96, 98, 105, 107], "access": [2, 10, 14, 38, 42, 74, 91, 92, 97, 102], "hyperparamet": [2, 66, 71, 92], "purpos": [2, 52, 90, 91, 96, 98, 102, 106], "want": [2, 5, 10, 37, 41, 52, 58, 62, 64, 74, 88, 90, 92, 95, 97, 101, 103, 104, 105, 107, 108], "explicitli": [2, 8, 10, 42, 52, 74], "yourself": [2, 5, 41, 91, 96], "altern": [2, 7, 10, 49, 54, 57, 61, 62, 72, 85, 88, 89, 92, 94, 95, 97, 98, 99, 101, 102, 104, 106], "same": [2, 3, 5, 7, 9, 10, 13, 15, 17, 27, 31, 38, 41, 42, 44, 52, 57, 61, 62, 64, 71, 72, 74, 78, 79, 82, 83, 84, 87, 88, 90, 91, 92, 94, 95, 96, 98, 102, 103, 104, 105, 106, 107], "effect": [2, 10, 28, 38, 42, 62, 71, 74, 92, 94, 95, 96, 98, 104], "offer": [2, 5, 9, 10, 88, 89, 90, 91, 95, 98, 99, 102], "after": [2, 3, 5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 62, 74, 88, 90, 92, 95, 96, 98, 99, 101, 103, 104, 105, 106, 107], "attribut": [2, 5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 38, 41, 42, 49, 54, 71, 74, 87, 90, 96], "label_issues_df": [2, 74, 92], "similar": [2, 10, 37, 38, 42, 54, 57, 62, 66, 67, 69, 71, 74, 78, 82, 90, 91, 92, 94, 95, 96, 98, 99, 103, 104, 107], "document": [2, 3, 5, 15, 17, 37, 38, 41, 42, 43, 44, 49, 56, 61, 63, 64, 66, 69, 70, 71, 74, 78, 79, 80, 82, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 108], "descript": [2, 5, 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 37, 43, 57, 67, 74, 90, 91], "were": [2, 3, 5, 10, 37, 42, 52, 63, 69, 82, 87, 89, 94, 98, 99, 101, 103, 105, 107], "present": [2, 3, 5, 10, 13, 14, 21, 37, 57, 71, 79, 84, 92, 96, 98, 104], "actual": [2, 3, 5, 10, 37, 52, 62, 63, 72, 91, 98, 99, 108], "num_class": [2, 37, 41, 57, 61, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 104], "uniqu": [2, 32, 57, 79, 90, 96, 98, 102, 104], "given_label": [2, 5, 11, 26, 31, 37, 47, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107, 108], "normal": [2, 3, 19, 27, 32, 44, 46, 49, 55, 56, 57, 72, 96, 98, 99, 104], "trick": [2, 98], "distribut": [2, 3, 5, 10, 27, 29, 37, 42, 44, 48, 55, 62, 70, 71, 72, 84, 90, 91, 92, 94, 95, 96, 103, 104], "account": [2, 37, 62, 66, 71, 72, 88, 95, 98, 99, 101, 102, 104, 106], "word": [2, 3, 56, 82, 83, 98], "remov": [2, 10, 32, 37, 38, 42, 44, 74, 84, 87, 88, 92, 95, 96, 97, 98, 102, 104, 106], "so": [2, 3, 5, 6, 7, 10, 15, 27, 35, 37, 38, 41, 42, 44, 52, 57, 62, 63, 69, 72, 74, 78, 82, 89, 90, 91, 92, 95, 96, 99, 102, 104, 107], "proportion": [2, 10, 44], "just": [2, 3, 5, 10, 14, 33, 37, 39, 41, 57, 61, 72, 74, 76, 84, 85, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106, 107], "procedur": 2, "get": [2, 3, 5, 8, 10, 11, 14, 32, 38, 39, 42, 44, 49, 55, 56, 57, 62, 64, 66, 71, 72, 74, 75, 76, 84, 87, 88, 89, 92, 95, 96, 97, 98, 99, 104, 105, 106], "detect": [2, 5, 7, 9, 14, 15, 17, 19, 23, 29, 43, 52, 55, 65, 67, 68, 69, 70, 71, 72, 73, 74, 77, 81, 84, 87, 88, 90, 93, 97, 100, 102, 106, 107, 108], "arg": [2, 13, 23, 28, 32, 38, 39, 42, 49, 57, 72, 74], "kwarg": [2, 7, 10, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 43, 49, 52, 61, 70, 74, 76, 78, 79, 80, 98], "test": [2, 5, 10, 27, 42, 49, 52, 61, 74, 84, 87, 88, 90, 91, 92, 94, 95, 96, 105, 106, 108], "expect": [2, 3, 10, 38, 42, 44, 49, 52, 62, 71, 72, 74, 87, 88, 98, 99, 101, 102, 103, 106, 108], "class_predict": 2, "evalu": [2, 10, 38, 39, 40, 41, 42, 70, 74, 87, 88, 89, 90, 91, 92, 98, 99, 101, 105, 106, 107], "simpli": [2, 10, 37, 72, 88, 90, 91, 94, 95, 98, 99, 102, 106, 107, 108], "quantifi": [2, 4, 5, 7, 10, 14, 44, 66, 71, 74, 84, 91, 92, 94, 95, 96, 99, 103], "save_spac": [2, 10, 73, 74], "potenti": [2, 10, 37, 44, 56, 64, 67, 70, 72, 74, 76, 78, 83, 85, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "cach": [2, 88, 95], "panda": [2, 5, 7, 13, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 57, 58, 61, 62, 63, 85, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 106, 107], "unlik": [2, 10, 44, 46, 49, 61, 63, 64, 66, 82, 90, 101, 102, 104, 106], "both": [2, 5, 10, 17, 27, 37, 38, 42, 44, 52, 57, 62, 64, 72, 76, 78, 83, 84, 90, 92, 98, 99, 101, 108], "mask": [2, 41, 44, 56, 57, 64, 67, 72, 74, 76, 78, 79, 84, 97, 98, 101, 103, 107, 108], "prefer": [2, 72, 80, 102], "plan": 2, "subsequ": [2, 3, 38, 42, 54, 88, 95, 98, 99, 103], "invok": [2, 38, 42, 99, 105], "scratch": [2, 52, 74], "To": [2, 5, 7, 9, 10, 12, 14, 17, 27, 36, 38, 41, 42, 43, 44, 61, 62, 64, 66, 70, 71, 72, 74, 75, 76, 78, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 105, 106, 107, 108], "share": [2, 10, 72, 74], "mostli": [2, 57, 69, 74, 102, 106], "longer": [2, 35, 48, 49, 56, 74, 85, 88, 95, 98, 103], "info": [2, 5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 74, 82, 91, 96, 97, 108], "about": [2, 3, 5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 39, 41, 46, 62, 63, 66, 70, 74, 79, 82, 89, 90, 92, 94, 95, 96, 97, 98, 99, 101, 104], "docstr": [2, 37, 38, 42, 57, 74, 97, 99], "unless": [2, 38, 42, 52, 74, 98], "our": [2, 3, 10, 61, 62, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "is_label_issu": [2, 11, 31, 74, 88, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "entir": [2, 10, 27, 41, 44, 47, 63, 64, 69, 72, 74, 76, 78, 79, 84, 90, 91, 96, 98, 103, 104, 105, 107, 108], "accur": [2, 3, 5, 9, 10, 17, 37, 41, 44, 53, 62, 63, 64, 67, 70, 72, 74, 75, 76, 78, 79, 85, 91, 92, 94, 95, 96, 98, 101, 106], "label_qu": [2, 62, 74, 88, 99, 101, 106], "measur": [2, 5, 37, 62, 63, 74, 84, 87, 97, 98, 99, 101, 102, 106, 107, 108], "qualiti": [2, 3, 5, 7, 9, 10, 14, 31, 32, 37, 41, 43, 44, 46, 49, 62, 63, 64, 66, 67, 69, 72, 74, 75, 78, 80, 82, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 106], "lower": [2, 4, 5, 7, 10, 14, 29, 41, 49, 55, 62, 63, 66, 69, 70, 72, 74, 75, 78, 82, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "eas": 2, "comparison": [2, 38, 42, 70, 99, 101], "against": [2, 38, 42, 90, 94, 96, 98, 101, 102], "predicted_label": [2, 5, 11, 26, 31, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 96, 99, 106, 107], "ad": [2, 38, 42, 91, 101, 106], "precis": [2, 53, 55, 64, 67, 70, 96, 97, 98, 99, 107, 108], "definit": [2, 7, 35, 49, 74, 87, 94], "accessor": [2, 74], "describ": [2, 10, 19, 62, 71, 72, 74, 80, 82, 99, 101, 102, 103, 105, 108], "precomput": [2, 4, 5, 47, 52, 74, 97], "clear": [2, 38, 42, 54, 74, 88, 95, 106], "save": [2, 5, 17, 38, 41, 42, 70, 74, 96, 98, 103, 107, 108], "space": [2, 5, 10, 71, 74, 92, 94, 96, 97], "place": [2, 38, 42, 52, 57, 74, 87, 101], "larg": [2, 9, 10, 41, 52, 74, 92, 94, 95, 98, 103, 104, 107, 108], "deploi": [2, 9, 10, 74, 92, 94, 95, 98], "care": [2, 10, 38, 42, 52, 74, 95, 96, 98, 99], "avail": [2, 4, 5, 7, 10, 13, 15, 34, 42, 54, 74, 98, 99, 101, 103, 106], "cannot": [2, 5, 13, 15, 57, 105, 108], "anymor": 2, "classmethod": [2, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 35, 42, 49, 74], "__init_subclass__": [2, 40, 42, 73, 74], "set_": [2, 42, 74], "_request": [2, 42, 74], "pep": [2, 42, 74], "487": [2, 42, 74], "look": [2, 5, 7, 10, 17, 38, 42, 57, 74, 79, 87, 90, 91, 94, 95, 98, 99, 101, 102, 103, 104, 107, 108], "inform": [2, 5, 7, 10, 14, 17, 34, 38, 42, 54, 57, 62, 63, 67, 70, 74, 79, 82, 83, 84, 89, 90, 94, 95, 96, 97, 99, 101, 104, 107, 108], "__metadata_request__": [2, 42, 74], "infer": [2, 42, 57, 74, 79, 83, 87, 88, 92, 101, 102], "signatur": [2, 38, 42, 74], "accept": [2, 38, 42, 54, 55, 72, 74, 90, 91, 98], "metadata": [2, 10, 42, 74, 92, 94, 95, 108], "through": [2, 5, 7, 42, 74, 88, 89, 91, 95, 96, 97, 98, 101, 103, 104], "develop": [2, 9, 42, 54, 74, 98, 99, 108], "request": [2, 42, 74, 87, 88, 91, 95, 96, 97, 102, 108], "those": [2, 3, 4, 10, 41, 42, 44, 51, 61, 62, 64, 70, 74, 78, 82, 83, 84, 89, 92, 96, 98, 103, 107], "http": [2, 4, 5, 7, 9, 10, 12, 19, 36, 38, 39, 41, 42, 46, 54, 57, 67, 70, 71, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "www": [2, 42, 74, 96, 104], "org": [2, 4, 19, 38, 39, 42, 54, 57, 71, 74, 98, 99, 108], "dev": [2, 42, 74], "0487": [2, 42, 74], "get_metadata_rout": [2, 40, 42, 73, 74], "rout": [2, 42, 74], "pleas": [2, 38, 42, 61, 74, 84, 88, 89, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 104, 106, 108], "guid": [2, 7, 10, 42, 74, 85, 89, 90, 91, 92, 93, 94, 95, 96, 99], "mechan": [2, 38, 42, 74], "metadatarequest": [2, 42, 74], "encapsul": [2, 17, 42, 69, 74], "get_param": [2, 40, 42, 60, 61, 73, 74], "subobject": [2, 42, 74], "param": [2, 10, 38, 42, 61, 71, 74, 98], "name": [2, 5, 6, 7, 10, 11, 13, 14, 33, 35, 37, 38, 42, 48, 49, 53, 57, 61, 62, 63, 70, 74, 79, 83, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 102, 106, 107, 108], "set_fit_request": [2, 40, 42, 73, 74], "str": [2, 3, 4, 5, 13, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 41, 42, 44, 47, 49, 52, 53, 54, 55, 56, 57, 61, 62, 63, 67, 69, 70, 72, 74, 79, 83, 89, 90, 98, 101, 102, 103, 108], "unchang": [2, 38, 42, 74, 108], "relev": [2, 17, 27, 42, 74, 92, 94, 96], "enable_metadata_rout": [2, 42, 74], "set_config": [2, 42, 74], "meta": [2, 42, 74], "rais": [2, 4, 5, 13, 14, 35, 38, 42, 46, 49, 52, 55, 74, 98], "alia": [2, 38, 42, 74], "metadata_rout": [2, 42, 74], "retain": [2, 42, 57, 74], "chang": [2, 33, 35, 38, 41, 42, 46, 74, 82, 87, 88, 89, 90, 95, 98, 103, 104, 108], "version": [2, 4, 5, 7, 9, 10, 12, 16, 22, 25, 30, 36, 38, 40, 42, 45, 46, 57, 60, 61, 72, 74, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106, 108], "sub": [2, 42, 69, 74], "pipelin": [2, 42, 74, 106], "otherwis": [2, 4, 7, 10, 35, 37, 38, 41, 42, 44, 50, 53, 55, 56, 57, 64, 74, 76, 78, 79, 83, 88, 95, 98], "updat": [2, 14, 38, 41, 42, 52, 61, 74, 85, 90, 92], "set_param": [2, 40, 42, 60, 61, 73, 74], "simpl": [2, 38, 42, 44, 62, 72, 74, 87, 88, 90, 91, 92, 94, 95, 101, 104, 106], "well": [2, 3, 9, 10, 38, 42, 46, 47, 62, 64, 70, 72, 74, 79, 82, 83, 85, 90, 91, 92, 94, 95, 98, 99, 101, 103, 104], "nest": [2, 38, 42, 43, 58, 74, 80, 82, 83, 108], "latter": [2, 38, 42, 74, 104], "compon": [2, 42, 74], "__": [2, 42, 74], "set_score_request": [2, 73, 74], "structur": [3, 71, 94, 96, 98], "unobserv": 3, "less": [3, 4, 5, 10, 32, 41, 49, 62, 71, 72, 76, 78, 82, 92, 94, 96, 97, 98, 99, 103, 108], "channel": [3, 89, 99], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 37, 47, 57, 63, 88, 91, 97], "inv": 3, "confident_joint": [3, 23, 37, 44, 57, 63, 64, 85, 98, 99], "un": 3, "under": [3, 10, 38, 42, 63, 70, 71, 91, 96, 104], "joint": [3, 37, 44, 47, 57, 63, 64, 97], "num_label_issu": [3, 41, 44, 64, 79, 83, 85], "estimation_method": [3, 41], "off_diagon": 3, "multi_label": [3, 37, 44, 57, 58, 64, 102], "don": [3, 84, 91, 92, 94, 95, 99, 103, 106], "statis": 3, "compute_confident_joint": [3, 37, 44, 57, 64, 99], "off": [3, 44, 57, 69, 92, 96, 99, 103, 104], "j": [3, 5, 37, 38, 42, 43, 44, 64, 67, 70, 71, 80, 82, 83, 90, 91, 99, 107, 108], "confident_learn": [3, 44, 64, 99], "off_diagonal_calibr": 3, "calibr": [3, 4, 44, 57, 62, 101], "cj": [3, 47, 57], "axi": [3, 32, 47, 49, 55, 76, 79, 89, 90, 91, 92, 96, 98, 99, 101, 102, 104, 106, 107], "bincount": [3, 90, 91, 99, 101, 102], "alwai": [3, 10, 38, 42, 57, 87, 88, 89, 99, 106], "estimate_issu": 3, "over": [3, 5, 10, 38, 41, 42, 69, 70, 76, 78, 87, 91, 92, 94, 96, 97, 98, 99, 104, 106], "As": [3, 7, 84, 90, 91, 95, 99, 106, 108], "add": [3, 5, 7, 13, 14, 38, 42, 61, 70, 88, 89, 90, 91, 92, 95, 96, 98, 99, 102], "approach": [3, 37, 41, 44, 61, 87, 94, 96, 99, 102, 104, 106], "custom": [3, 7, 10, 12, 31, 38, 41, 42, 49, 56, 72, 88, 91, 95, 96, 99, 106], "know": [3, 10, 90, 91, 92, 94, 95, 98, 99, 101, 106], "cut": [3, 69, 84, 99], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 33, 103, 104, 108], "underestim": 3, "few": [3, 9, 10, 70, 84, 96, 98, 101, 102, 103, 104, 108], "4": [3, 4, 5, 10, 11, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 48, 49, 56, 66, 67, 69, 70, 72, 75, 82, 97, 98, 102, 107, 108], "detail": [3, 4, 5, 10, 15, 17, 34, 37, 38, 42, 43, 49, 54, 57, 61, 62, 63, 64, 66, 67, 69, 70, 71, 78, 79, 80, 84, 85, 89, 98, 102, 104, 108], "num_issu": [3, 7, 41, 89, 90, 91, 92, 94, 95, 96, 99], "calibrate_confident_joint": 3, "up": [3, 7, 10, 18, 27, 28, 31, 44, 49, 51, 61, 62, 88, 97, 98, 103, 106, 108], "p_": [3, 37, 44], "pair": [3, 5, 10, 37, 44, 99], "v": [3, 10, 41, 63, 64, 66, 72, 90, 91, 102, 103, 104, 105], "rest": [3, 5, 7, 9, 10, 12, 36, 63, 64, 66, 74, 87, 88, 90, 91, 92, 94, 95, 98, 99, 101, 106], "fashion": [3, 5, 76, 87], "2x2": 3, "incorrectli": [3, 37, 63, 64, 67, 94, 108], "calibrated_cj": 3, "c": [3, 10, 55, 56, 64, 72, 84, 87, 89, 90, 91, 94, 95, 96, 98, 99, 102, 103, 104, 105, 106], "whose": [3, 4, 5, 10, 29, 38, 42, 47, 52, 56, 62, 66, 69, 75, 78, 82, 83, 89, 90, 91, 92, 94, 95, 98, 99, 102, 103, 104, 107, 108], "truli": [3, 104, 107], "estimate_joint": [3, 37, 99], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 64, 70, 99, 103, 105, 107, 108], "return_indices_of_off_diagon": 3, "frequenc": [3, 27, 62, 63, 70, 79, 103, 104], "done": [3, 10, 61, 74, 90, 98, 99, 102, 104, 105], "overfit": [3, 10, 67, 70, 87, 89, 90, 91, 92, 94, 95, 105], "classifict": 3, "singl": [3, 5, 9, 10, 13, 27, 37, 38, 42, 43, 49, 50, 57, 62, 63, 69, 70, 71, 72, 82, 87, 89, 90, 96, 98, 99, 102, 103], "baselin": [3, 38, 44, 88, 104, 106], "proxi": 3, "union": [3, 5, 13, 27, 49, 52, 53, 54, 57, 58, 64, 70, 74, 82, 98], "tupl": [3, 32, 38, 42, 43, 47, 48, 50, 52, 56, 57, 62, 64, 70, 78, 80, 82, 83, 89, 108], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 5, 10, 41, 47, 52, 53, 62, 71, 76, 78, 84, 88, 92, 96, 98, 107], "practic": [3, 87, 88, 91, 92, 99, 104, 106], "complet": [3, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "gist": 3, "cj_ish": 3, "guess": [3, 47, 99, 101], "8": [3, 5, 7, 8, 48, 49, 50, 56, 66, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "parallel": [3, 44, 70, 80, 97], "again": [3, 61, 87, 98, 104], "simplifi": [3, 15, 98], "understand": [3, 9, 10, 37, 63, 70, 91, 96, 99, 100, 106, 107, 108], "100": [3, 4, 38, 42, 52, 53, 55, 71, 72, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 102, 103, 104, 108], "optim": [3, 38, 39, 42, 61, 92, 96, 101], "speed": [3, 44, 88, 97, 98, 106], "dtype": [3, 24, 26, 27, 32, 38, 42, 56, 57, 66, 82, 89, 96, 103], "enumer": [3, 38, 42, 89, 90, 91, 92, 96, 108], "s_label": 3, "confident_bin": 3, "6": [3, 5, 10, 42, 49, 57, 82, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "num_confident_bin": 3, "argmax": [3, 44, 72, 76, 79, 89, 96, 98, 99, 103, 104, 107], "elif": 3, "estimate_lat": 3, "py_method": [3, 47], "cnt": [3, 47], "1d": [3, 5, 13, 17, 33, 41, 44, 49, 50, 52, 57, 58, 66, 75, 87, 89, 96], "eqn": [3, 47], "margin": [3, 44, 47, 49, 72], "marginal_p": [3, 47], "shorthand": [3, 14], "proport": [3, 10, 37, 63, 99, 105], "poorli": [3, 47, 87, 96], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 99], "variabl": [3, 7, 15, 28, 57, 74, 75, 89, 90, 94, 99, 102, 106], "exact": [3, 10, 47, 52, 87, 90, 91, 92, 94, 96], "within": [3, 4, 5, 10, 16, 33, 38, 39, 42, 43, 45, 64, 69, 78, 80, 82, 90, 91, 92, 96, 98, 103, 107], "percent": 3, "often": [3, 37, 47, 63, 98, 99, 105, 107], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 57, 58, 70, 87, 88, 89, 90, 92, 94, 95, 98, 102, 103, 104, 106], "wai": [3, 5, 10, 52, 61, 84, 85, 87, 88, 89, 90, 91, 94, 95, 98, 99, 101, 102, 103, 105], "pro": 3, "con": 3, "pred_proba": [3, 105], "combin": [3, 37, 90, 92, 96, 97, 98, 99, 105, 106], "becaus": [3, 47, 53, 57, 69, 95, 96, 98, 99, 101, 103], "littl": [3, 41, 97, 103, 108], "uniform": [3, 72, 97, 98, 99], "20": [3, 7, 43, 83, 89, 92, 95, 96, 97, 98, 99, 103, 106, 107, 108], "Such": [3, 92, 104], "bound": [3, 24, 26, 38, 42, 56, 66, 67, 69, 70, 103], "reason": [3, 23, 38, 42, 53, 71], "comment": [3, 56, 96, 108], "end": [3, 5, 38, 42, 54, 70], "file": [3, 5, 13, 40, 41, 60, 70, 87, 89, 90, 94, 95, 97, 98, 103, 104, 107, 108], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 99], "handl": [3, 5, 7, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 41, 42, 52, 53, 54, 85, 87, 88, 90, 91, 92, 94, 95, 96, 99, 107, 108], "five": [3, 67, 70, 99, 103], "estimate_cv_predicted_prob": [3, 99], "estimate_noise_matric": 3, "get_confident_threshold": [3, 40, 41], "amongst": [3, 10, 103], "confident_threshold": [3, 10, 23, 24, 41, 71], "point": [4, 5, 7, 9, 10, 19, 27, 38, 42, 52, 54, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101], "valuat": [4, 9, 19], "help": [4, 37, 38, 42, 70, 84, 85, 87, 88, 89, 90, 92, 94, 95, 96, 97, 98, 101, 102, 106, 107, 108], "u": [4, 87, 88, 89, 90, 92, 94, 96, 98, 99, 101, 102, 105, 106, 107, 108], "assess": [4, 10, 96, 103], "contribut": [4, 10, 19, 96, 103], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 11, 17, 19, 20, 27, 29, 32, 45, 51, 94, 96], "metric": [4, 5, 10, 19, 20, 22, 27, 29, 32, 45, 51, 52, 54, 55, 57, 61, 70, 71, 87, 88, 89, 92, 94, 95, 96, 99, 106], "10": [4, 10, 19, 20, 24, 27, 29, 32, 38, 39, 52, 70, 71, 72, 83, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108], "shaplei": [4, 10, 19], "nearest": [4, 5, 10, 17, 24, 27, 29, 51, 52, 53, 54, 55, 71, 91, 95, 96, 104], "neighbor": [4, 5, 10, 17, 19, 24, 27, 29, 45, 52, 53, 54, 55, 71, 90, 91, 92, 94, 95, 96, 98, 104], "knn": [4, 10, 14, 19, 27, 29, 32, 51, 52, 53, 54, 55, 71, 94, 104], "graph": [4, 5, 10, 14, 17, 19, 27, 32, 51, 52], "calcul": [4, 10, 19, 27, 41, 49, 51, 52, 55, 62, 66, 67, 69, 70, 71, 74, 78, 92, 96, 97], "directli": [4, 5, 10, 15, 17, 34, 35, 41, 54, 61, 62, 88, 91, 95, 96, 98, 102, 103, 106], "lowest": [4, 10, 62, 70, 91, 92, 94, 96, 98, 101, 102, 103, 107], "fall": [4, 10, 69, 78, 82, 99, 104], "flag": [4, 10, 23, 27, 44, 49, 63, 64, 67, 74, 84, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 104, 106, 107], "approxim": [4, 10, 19, 41, 54, 71, 96, 101], "top": [4, 5, 10, 37, 41, 43, 44, 57, 64, 67, 70, 72, 79, 83, 84, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 106, 108], "found": [4, 5, 7, 10, 14, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 102, 104, 106, 108], "arxiv": [4, 19, 99], "ab": [4, 19, 99, 103], "1908": 4, "08619": 4, "1911": [4, 19], "07128": [4, 19], "embed": [4, 5, 10, 17, 71, 84, 88, 89, 90, 91, 94, 95, 96, 99, 102, 106], "represent": [4, 5, 10, 17, 35, 38, 42, 50, 52, 64, 84, 88, 89, 90, 91, 92, 95, 98, 99, 104], "suppli": [4, 102, 103, 106], "2d": [4, 5, 17, 33, 41, 49, 50, 52, 56, 57, 62, 87, 89, 96, 102], "num_exampl": [4, 5, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34, 37, 63, 89, 90, 91, 92, 94, 95, 99], "num_featur": [4, 5, 17, 38, 42, 61], "distanc": [4, 5, 10, 17, 19, 27, 29, 32, 51, 52, 53, 54, 55, 69, 71, 94, 96, 104], "construct": [4, 5, 7, 10, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 38, 42, 49, 51, 52, 54, 61, 96], "nearestneighbor": [4, 5, 10, 19, 52, 54, 71, 94, 104], "cosin": [4, 10, 52, 53, 55, 71, 96, 104], "dim": [4, 71, 92, 107], "euclidean": [4, 5, 10, 52, 53, 55, 69, 71, 94], "dimension": [4, 27, 53, 57, 89, 99, 104], "scikit": [4, 42, 53, 54, 57, 71, 84, 87, 88, 89, 90, 91, 94, 95, 96, 98, 106], "fewer": [4, 10, 44, 57, 71, 96, 103], "stabl": [4, 16, 22, 25, 30, 40, 45, 54, 57, 60, 71, 85, 89, 90, 91, 92, 94, 95, 99], "exce": [4, 52, 92, 96], "transform": [4, 10, 33, 49, 52, 55, 57, 71, 72, 87, 88, 91, 92, 95, 104, 108], "rel": [4, 10, 37, 52, 62, 63, 71, 90, 91, 92, 94, 95, 99, 104], "adjust": [4, 39, 44, 52, 66, 71, 72, 84, 96, 99], "closer": [4, 10, 69, 103], "highli": [4, 91, 92], "influenti": 4, "posit": [4, 5, 10, 38, 42, 55, 57, 70, 96, 97, 104], "convers": 4, "neg": [4, 10, 69, 70, 90, 91, 96, 97], "valueerror": [4, 5, 13, 14, 35, 46, 49, 52, 55, 98], "neither": [4, 5, 10, 15, 53, 103], "nor": [4, 5, 10, 15], "larger": [4, 19, 53, 74, 76, 78, 92, 95, 97, 98], "55": [4, 56, 96, 97, 103, 106], "525": 4, "unifi": 5, "audit": [5, 9, 13, 14, 17, 89, 92, 93, 94, 95, 96, 98, 99, 102, 103, 106], "kind": [5, 6, 7, 10, 96, 97], "addit": [5, 7, 9, 12, 14, 34, 36, 38, 42, 49, 52, 54, 58, 62, 70, 79, 80, 87, 88, 89, 90, 94, 95, 96, 99, 101, 104, 105], "depend": [5, 7, 9, 12, 13, 14, 36, 40, 44, 46, 57, 60, 64, 71, 74, 75, 84, 96], "instal": [5, 7, 9, 12, 36, 38, 40, 41, 42, 44, 60, 61, 76, 78], "pip": [5, 7, 9, 12, 36, 61, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "development": [5, 7, 9, 12, 36], "git": [5, 7, 9, 12, 36, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "github": [5, 7, 9, 12, 36, 38, 39, 57, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 99, 101, 102, 103, 104, 106], "com": [5, 7, 9, 12, 36, 38, 39, 41, 46, 57, 71, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "egg": [5, 7, 9, 12, 36, 84, 97], "label_nam": [5, 7, 8, 10, 11, 13, 19, 32, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 103, 106], "image_kei": [5, 10, 92, 96], "interfac": [5, 9, 10, 54, 84, 98, 99], "librari": [5, 10, 42, 54, 67, 70, 71, 84, 88, 90, 95, 96, 97, 98], "goal": [5, 106], "track": [5, 7, 14, 15, 84, 90, 97, 98, 99], "intermedi": [5, 9, 91], "statist": [5, 10, 14, 23, 27, 37, 62, 63, 70, 91, 94, 95, 96, 99], "convert": [5, 10, 13, 35, 38, 42, 50, 55, 58, 62, 69, 78, 82, 85, 88, 89, 92, 95, 96, 97, 98, 101, 102, 103], "hug": [5, 10, 13, 92], "face": [5, 10, 13, 17, 92, 97, 102], "kei": [5, 7, 10, 13, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 49, 62, 63, 69, 71, 90, 91, 92, 95, 98, 99, 101, 103], "string": [5, 10, 13, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 35, 37, 38, 42, 53, 57, 62, 63, 75, 79, 82, 83, 88, 94, 95, 96, 98, 101, 102, 108], "dictionari": [5, 7, 10, 13, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 38, 42, 48, 57, 62, 63, 66, 67, 69, 70, 90, 91, 94, 95, 96, 99, 101, 102, 103], "path": [5, 13, 38, 41, 42, 70, 89, 90, 98, 103], "local": [5, 7, 10, 13, 38, 39, 42, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "text": [5, 7, 10, 13, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 43, 49, 71, 80, 82, 83, 84, 86, 90, 91, 93, 97, 98, 99, 100, 101, 104], "txt": [5, 13, 108], "csv": [5, 13, 87, 88, 94, 95, 106], "json": [5, 13], "hub": [5, 13], "multiclass": [5, 13, 16, 49, 57, 62, 102], "regress": [5, 7, 10, 11, 13, 15, 17, 22, 31, 33, 35, 88, 90, 91, 95, 100, 101, 104], "multilabel": [5, 10, 11, 13, 15, 16, 22, 26, 33, 35, 50, 102], "imag": [5, 9, 37, 42, 67, 69, 70, 71, 76, 78, 79, 84, 90, 91, 93, 97, 98, 100, 101, 102, 103, 105, 107], "field": [5, 10, 38, 42], "themselv": [5, 87, 88, 96, 106], "pil": [5, 92, 96], "cleanvis": [5, 10], "level": [5, 10, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34, 37, 52, 56, 80, 82, 91, 92, 98, 100, 102, 107], "load_dataset": [5, 13, 92], "glue": 5, "sst2": 5, "properti": [5, 13, 14, 35, 38, 42], "has_label": [5, 13], "class_nam": [5, 13, 21, 37, 43, 63, 70, 79, 83, 84, 97, 99, 103, 107, 108], "empti": [5, 13, 47, 62, 91, 96, 98, 102], "find_issu": [5, 6, 7, 8, 10, 11, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 84, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_typ": [5, 6, 7, 8, 10, 11, 14, 15, 17, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "sort": [5, 17, 41, 44, 49, 62, 64, 67, 69, 70, 72, 78, 80, 82, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99, 101, 102, 103, 106, 107, 108], "common": [5, 10, 14, 17, 91, 93, 96, 97, 98, 99, 102, 103, 107], "real": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "world": [5, 17, 84, 90, 91, 98, 99, 101, 106, 107], "interact": [5, 17, 95, 98], "thereof": [5, 17], "insight": [5, 17, 70, 101], "best": [5, 9, 10, 17, 48, 62, 72, 87, 88, 90, 91, 92, 94, 95, 96, 98, 101, 102, 104, 106, 108], "properli": [5, 10, 41, 48, 52, 57, 58, 76, 89, 90, 91, 92, 94, 95, 98, 99, 102, 104, 106, 107], "respect": [5, 38, 42, 67, 70, 89, 90, 91, 92, 94, 95, 99, 102, 103], "lexicograph": [5, 48, 57, 89, 90, 91, 92, 94, 95, 99, 102], "squar": [5, 57, 74, 97, 106], "csr": [5, 52, 96], "evenli": 5, "omit": [5, 69, 70, 92, 96, 103], "itself": [5, 33, 38, 42, 52, 96, 103], "three": [5, 10, 37, 62, 63, 74, 79, 87, 89, 90, 91, 94, 97, 99, 101, 105, 106, 107, 108], "indptr": [5, 96], "wise": 5, "start": [5, 7, 10, 35, 38, 39, 42, 49, 84, 102, 108], "th": [5, 10, 43, 48, 56, 57, 62, 64, 67, 69, 70, 71, 80, 82, 83, 95, 102, 103, 108], "ascend": [5, 37, 63, 92, 99], "segment": [5, 76, 78, 79, 100], "reflect": [5, 10, 52, 87, 88, 94, 95, 96, 101, 103, 104, 106], "maintain": [5, 61], "kneighbors_graph": [5, 19, 54, 94], "illustr": [5, 96], "todens": 5, "second": [5, 49, 57, 70, 72, 90, 94, 98, 99, 108], "duplic": [5, 9, 22, 23, 38, 42, 52, 84, 90, 96, 99, 106], "explicit": 5, "precend": 5, "collect": [5, 10, 14, 17, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 62, 96, 98, 101, 108], "unspecifi": [5, 17, 44, 64], "interest": [5, 17, 23, 79, 83, 87, 88, 95, 96, 99, 106, 107, 108], "constructor": [5, 10, 11, 17, 24, 31, 52, 54], "issuemanag": [5, 9, 14, 15, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 34], "respons": [5, 17, 23, 54, 74, 75, 97, 106, 108], "random_st": [5, 87, 89, 90, 91, 92, 96, 99, 102, 104], "lab": [5, 6, 8, 10, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 41, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 106], "comprehens": [5, 84, 92, 102, 106], "nbr": 5, "n_neighbor": [5, 10, 19, 52, 54, 71, 96], "mode": [5, 12, 19, 38, 41, 42, 104], "4x4": 5, "float64": [5, 27, 38, 42, 82], "compress": [5, 10, 52, 57, 76, 78, 96], "toarrai": [5, 52, 96], "NOT": [5, 41, 95], "23606798": 5, "41421356": [5, 52], "configur": [5, 17, 49, 91], "suppos": [5, 10, 67, 87, 88, 104, 106], "who": [5, 69, 87, 94, 96, 99, 108], "manag": [5, 8, 9, 10, 14, 15, 16, 17, 18, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 61, 90, 98], "clean_learning_kwarg": [5, 10, 11, 24, 31, 98, 106], "labelissuemanag": [5, 10, 15, 22, 24], "prune_method": [5, 85], "prune_by_noise_r": [5, 44, 64, 99], "report": [5, 7, 12, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 63, 83, 84, 89, 90, 91, 94, 95, 98, 99, 102, 106, 108], "include_descript": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 34], "show_summary_scor": [5, 34], "show_all_issu": [5, 34], "summari": [5, 7, 14, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 43, 60, 61, 63, 68, 77, 78, 80, 81, 82, 85, 89, 90, 91, 92, 94, 95, 96, 97, 99, 103, 106, 107, 108], "show": [5, 7, 27, 38, 42, 48, 57, 70, 79, 83, 87, 91, 92, 94, 95, 96, 97, 98, 99, 101, 104, 106, 107, 108], "suffer": [5, 10, 14, 23, 64, 72, 83, 96, 108], "onc": [5, 23, 37, 38, 42, 87, 90, 98, 99, 102, 103], "familiar": [5, 96], "overal": [5, 7, 10, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 43, 49, 62, 63, 66, 69, 70, 74, 78, 79, 80, 82, 84, 85, 89, 90, 91, 92, 94, 95, 96, 97, 98, 101, 103, 108], "sever": [5, 7, 10, 13, 14, 23, 38, 41, 42, 44, 66, 69, 71, 72, 78, 82, 84, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 103, 104, 108], "compar": [5, 62, 71, 82, 90, 91, 94, 96, 99, 103], "issue_summari": [5, 7, 10, 14, 96], "With": [5, 9, 10, 41, 88, 95, 98, 99, 101, 106, 107, 108], "usag": [5, 41, 61], "usual": [5, 13, 33, 34, 92, 101, 106], "ti": [5, 62], "exhibit": [5, 7, 10, 14, 79, 89, 90, 91, 92, 94, 95, 99, 103], "ie": [5, 74], "likelihood": [5, 10, 41, 43, 44, 64, 69, 71, 72, 76, 80, 96], "wherea": [5, 10, 57, 64, 87, 88, 105], "outlier": [5, 9, 11, 15, 22, 23, 32, 45, 52, 72, 84, 90, 91, 96, 99, 100, 106], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 99, 106], "global": [5, 7, 10, 23, 38, 42, 97], "non_iid": [5, 10, 11, 15, 27, 91, 92, 94, 95, 96, 99], "hypothesi": [5, 96], "iid": [5, 7, 9, 27, 94, 99], "never": [5, 89, 99, 102, 104, 105], "someth": [5, 7, 10, 38, 42, 72, 103], "123": [5, 90, 91], "456": [5, 87, 88, 89], "nearest_neighbor": 5, "7": [5, 10, 49, 50, 61, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 98, 101, 102, 103, 104, 106, 107, 108], "9": [5, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 43, 49, 50, 66, 80, 82, 87, 88, 89, 90, 91, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106, 107, 108], "distance_to_nearest_neighbor": [5, 11, 90, 91, 92, 94, 95, 99], "789": 5, "get_issu": [5, 10, 14, 89, 90, 91, 92, 94, 95, 96, 98, 99, 102, 106], "issue_nam": [5, 6, 7, 10, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91, 92, 94, 95, 99], "focu": [5, 10, 14, 95, 96, 107, 108], "full": [5, 10, 14, 41, 61, 70, 92, 108], "summar": [5, 14, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 37, 63, 79, 83, 84, 107], "specific_issu": [5, 14], "lie": [5, 10, 71, 72, 88, 89, 90, 91, 92, 94, 95, 96, 99], "get_issue_summari": [5, 10, 14, 91, 96], "get_info": [5, 14, 91, 95, 96, 97], "yet": [5, 18, 28, 61, 97, 101], "list_possible_issue_typ": [5, 15, 16], "regist": [5, 7, 15, 16, 18, 28, 38, 42, 90], "rtype": [5, 15, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42], "registri": [5, 15, 16], "list_default_issue_typ": [5, 15, 16], "folder": [5, 89, 90, 92], "load": [5, 13, 41, 70, 92, 97, 98, 99, 103, 104, 107, 108], "futur": [5, 10, 23, 38, 42, 62, 84, 90, 95], "overwrit": [5, 90], "separ": [5, 37, 49, 66, 90, 91, 92, 96, 98, 103, 105], "static": 5, "rememb": [5, 95, 98, 99], "part": [5, 10, 38, 42, 44, 67, 69, 70, 89, 90, 96, 97, 107, 108], "ident": [5, 10, 23, 57, 95, 96], "datalab": [6, 8, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 84, 87, 88, 97, 101, 106], "walk": 7, "alongsid": [7, 38, 42, 90, 98], "pre": [7, 8, 10, 38, 42, 90, 91, 106], "runtim": [7, 38, 41, 42, 74, 76, 78, 89, 92, 98], "issue_manager_factori": [7, 15, 90], "myissuemanag": [7, 15], "myissuemanagerforregress": 7, "decor": [7, 15], "ll": [7, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 105, 106, 108], "thing": [7, 42, 88, 96, 99, 106], "next": [7, 62, 84, 87, 88, 89, 94, 95, 96, 98, 101, 103, 106, 108], "dummi": 7, "randint": [7, 32, 49, 90, 91, 96], "mark": [7, 10, 85, 103, 104, 106], "regard": [7, 91, 99], "rand": [7, 49, 52, 90, 91, 96], "is_": [7, 10, 90], "_issu": [7, 10, 90], "issue_score_kei": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "whole": [7, 10, 27, 38, 42, 91, 96], "make_summari": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90], "popul": [7, 95], "verbosity_level": [7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "std": [7, 103], "raw_scor": 7, "bit": 7, "involv": [7, 41, 79, 83, 96, 98, 102], "intermediate_arg": 7, "min": [7, 49, 69, 82, 90, 98, 104], "sin_filt": 7, "sin": 7, "arang": [7, 96], "kernel": [7, 96], "affect": [7, 10, 38, 42, 53, 76, 82, 95, 96, 98], "easili": [7, 47, 85, 87, 88, 89, 91, 94, 95, 99, 101, 102, 104, 105, 106, 107], "hard": [7, 42, 97, 104], "sai": [7, 10, 38, 42, 96, 102, 107], "anoth": [7, 10, 23, 37, 41, 53, 56, 69, 72, 88, 94, 95, 96, 98, 99, 101, 104], "try": [7, 9, 10, 41, 44, 61, 62, 76, 78, 84, 91, 92, 94, 95, 98, 99, 107], "won": [7, 38, 42, 90, 91, 98, 102], "issue_manag": [7, 10, 12, 14, 16, 19, 20, 21, 24, 26, 27, 28, 29, 31, 32, 90], "instanti": [7, 17, 41, 61, 71, 88, 89, 91, 94], "477762": 7, "286455": 7, "term": [7, 10, 47, 57, 70, 89, 90, 91, 92, 94, 95, 99], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 20, 29, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 103, 104, 106, 107, 108], "003042": 7, "058117": 7, "11": [7, 10, 61, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "121908": 7, "15": [7, 55, 61, 74, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "169312": 7, "17": [7, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 90, 91, 96, 97, 99], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 32], "group": [8, 9, 27, 32, 97, 103, 108], "dbscan": [8, 10, 32], "hdbscan": 8, "etc": [8, 10, 23, 33, 38, 42, 47, 61, 62, 80, 84, 90, 91, 94, 95, 98, 99, 102, 106], "sensit": [8, 10, 55, 96], "ep": [8, 32, 70], "radiu": 8, "min_sampl": [8, 32], "kmean": [8, 96], "your_data": 8, "get_pred_prob": 8, "n_cluster": [8, 32, 96], "cluster_id": [8, 10, 11, 32, 96], "labels_": 8, "underperforming_group": [8, 10, 11, 15, 22, 91, 92, 94, 95, 96, 99], "search": [9, 10, 21, 27, 28, 45, 51, 52, 53, 56, 74, 96, 98, 105], "nondefault": 9, "Near": [9, 98], "imbal": [9, 22, 66, 71, 72, 91], "null": [9, 11, 15, 22, 91, 92, 95, 99], "togeth": [9, 10, 47, 88, 90, 91, 92, 94, 95, 99, 106, 108], "built": [9, 49], "own": [9, 38, 40, 42, 54, 60, 66, 67, 70, 76, 80, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 106, 107, 108], "prerequisit": 9, "basic": [9, 42, 61, 94, 95, 96, 104], "fulli": [9, 10, 38, 42, 61, 98], "platform": [9, 10, 84, 92, 94, 95, 98], "write": [9, 10], "code": [9, 10, 38, 42, 47, 57, 61, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 101, 102, 103, 104, 106, 107, 108], "being": [9, 10, 14, 37, 38, 42, 44, 49, 56, 57, 72, 87, 94, 98, 99, 106, 107], "100x": [9, 10], "faster": [9, 10, 41, 71, 74, 76, 78, 98, 99], "intellig": [9, 10], "quickli": [9, 10, 39, 87, 89, 92, 94, 95, 98, 102, 104, 107, 108], "fix": [9, 10, 62, 88, 95, 96, 99, 106], "scientist": [9, 10], "million": [9, 10, 108], "thank": [9, 10], "ai": [9, 10, 84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98, 100, 101, 102, 104, 106, 108], "suggest": [9, 10, 37, 62, 63, 69, 88, 92, 95, 98, 106], "power": [9, 10, 92, 94, 95, 97, 99, 108], "automl": [9, 10, 84, 98], "system": [9, 10, 89, 92, 94, 95, 107], "foundat": [9, 10, 84, 96], "improv": [9, 10, 62, 87, 88, 91, 92, 97, 98, 99, 106, 107], "click": [9, 10, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "tune": [9, 10, 88, 89, 95, 97, 104], "serv": [9, 10, 14, 17, 101], "auto": [9, 10, 87, 88, 91, 97, 98, 106], "free": [9, 10, 84, 89, 91, 92, 94, 95, 98, 99], "page": [10, 91, 98, 99], "variou": [10, 14, 31, 40, 58, 60, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103], "why": [10, 95], "matter": [10, 37, 63], "didn": [10, 96], "plu": [10, 106], "ye": [10, 11], "near_dupl": [10, 11, 15, 20, 90, 91, 92, 94, 95, 96, 98, 99], "class_imbal": [10, 11, 15, 21, 91, 92, 94, 95, 96, 99], "data_valu": [10, 11, 15, 22, 96], "No": [10, 11, 87, 88, 95, 96, 98], "reinterpret": [10, 11], "your_regression_model": [10, 11], "_score": 10, "badli": [10, 69, 87, 88, 108], "issue_scor": 10, "atyp": [10, 71, 90, 91, 92, 94, 95, 99, 104], "datapoint": [10, 32, 44, 49, 57, 72, 75, 84, 87, 88, 89, 90, 91, 94, 95, 98, 105, 106], "is_issu": [10, 23], "primarili": 10, "former": [10, 38, 42], "investig": [10, 89], "expertis": 10, "interpret": [10, 97, 98, 99, 102, 106], "annot": [10, 37, 48, 62, 63, 64, 66, 67, 69, 70, 79, 82, 83, 84, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 103, 107], "dissimilar": [10, 94, 95], "preced": 10, "incorrect": [10, 69, 72, 75, 87, 89, 90, 91, 92, 94, 95, 96, 99, 103, 106], "due": [10, 41, 44, 72, 76, 78, 89, 90, 91, 92, 94, 95, 99, 106], "appear": [10, 37, 48, 63, 64, 67, 75, 91, 92, 94, 95, 96, 106, 107], "now": [10, 41, 85, 87, 88, 89, 91, 96, 98, 101, 103, 104, 106, 108], "token": [10, 43, 56, 78, 79, 80, 81, 82, 83, 98, 99, 100], "hamper": [10, 92, 97], "analyt": [10, 84, 96, 98, 101], "lead": [10, 69, 72, 92, 96, 103], "draw": [10, 90, 91], "conclus": [10, 95], "let": [10, 38, 42, 71, 72, 87, 88, 89, 91, 92, 94, 95, 96, 98, 101, 102, 103, 104, 106, 107, 108], "sort_valu": [10, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 106], "head": [10, 87, 88, 89, 91, 92, 94, 95, 96, 97, 99, 101, 106], "97": [10, 87, 97, 98, 99, 103, 106, 108], "064045": 10, "58": [10, 87, 91, 96, 97, 99, 103], "680894": 10, "41": [10, 96, 97, 103, 106], "746043": 10, "794894": 10, "98": [10, 97, 98, 106, 108], "802911": 10, "give": [10, 49, 72, 99, 101, 107], "li": [10, 71], "especi": [10, 87, 88, 92, 96, 98, 106], "veri": [10, 37, 63, 67, 69, 88, 90, 91, 92, 94, 95, 98, 99, 101, 104, 106], "rare": [10, 44, 70, 90, 91, 92, 94, 95, 98, 99], "anomal": [10, 72, 90, 91, 92, 94, 95, 99], "articl": [10, 41, 98], "blog": 10, "unexpect": [10, 38, 42, 95], "consequ": 10, "inspect": [10, 88, 89, 91, 92, 99, 103, 106], "011562": 10, "62": [10, 96, 99, 103, 106], "019657": 10, "22": [10, 89, 90, 92, 96, 97, 99, 102, 103, 108], "035243": 10, "040907": 10, "42": [10, 49, 95, 96, 97, 103, 108], "056865": 10, "smaller": [10, 71, 96, 102, 103], "extrem": [10, 90, 91, 92, 94, 95, 96, 98, 99], "record": [10, 38, 42, 89, 94, 106], "abbrevi": 10, "misspel": 10, "typo": [10, 83], "resolut": 10, "video": [10, 97], "audio": [10, 90, 91, 93, 98], "minor": [10, 56], "variat": 10, "translat": 10, "d": [10, 55, 87, 94, 95, 96, 98, 99, 102, 106, 108], "constant": [10, 32, 74], "median": [10, 31, 55], "question": [10, 23, 84, 99], "nearli": [10, 23, 91, 92, 94, 95], "awar": [10, 85, 99], "presenc": [10, 52, 54, 99], "36": [10, 96, 97, 108], "066009": 10, "80": [10, 39, 87, 94, 102, 106], "003906": 10, "093245": 10, "005599": 10, "27": [10, 94, 96, 97, 99, 103, 108], "156720": 10, "009751": 10, "72": [10, 96, 97, 99, 102, 106, 108], "signific": [10, 94, 95, 96, 99], "violat": [10, 94, 95, 96, 99], "assumpt": [10, 94, 95, 96, 99], "changepoint": [10, 94, 95, 99], "shift": [10, 52, 54, 94, 95, 99], "drift": [10, 91, 94, 96, 99], "autocorrel": [10, 94, 95, 99], "almost": [10, 94, 95, 99], "adjac": [10, 52, 94, 95, 99], "tend": [10, 37, 47, 94, 95, 99, 107, 108], "sequenti": [10, 38, 42, 61, 92], "pai": [10, 95], "attent": [10, 96], "realli": [10, 88, 95, 101, 107], "mere": 10, "highlight": [10, 79, 83, 90, 91, 94, 96, 107], "necessarili": [10, 62, 70, 95, 99], "wrong": [10, 62, 67, 69, 85, 88, 90, 91, 95, 98, 99, 103], "gap": 10, "b": [10, 19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 37, 56, 57, 82, 87, 94, 95, 96, 97, 98, 99, 105, 108], "x1": [10, 67, 70, 103], "x2": [10, 67, 70, 103], "10th": 10, "100th": 10, "90": [10, 82, 87, 94, 99, 105, 106], "similarli": [10, 38, 42, 90, 92, 94, 96, 98, 103], "associ": [10, 13, 17, 33, 35, 38, 42, 70, 96, 101], "blogpost": 10, "proper": [10, 57, 62, 67, 70, 87, 92, 95, 98, 101, 103], "scenario": [10, 52, 54, 72, 90, 91], "underli": [10, 43, 54, 71, 80, 82, 96, 108], "stem": [10, 71, 104], "evolv": 10, "influenc": 10, "act": [10, 69, 90], "accordingli": [10, 33, 52], "emploi": [10, 102, 104], "partit": [10, 105], "ahead": 10, "good": [10, 38, 42, 55, 61, 63, 69, 72, 76, 78, 79, 84, 92, 94, 95], "problem": [10, 33, 41, 49, 79, 84, 90, 91, 92, 95, 98], "deploy": [10, 87, 88, 99, 106], "overlook": [10, 69, 103], "fact": 10, "thu": [10, 37, 42, 63, 87, 89, 94, 95, 99, 105, 108], "diagnos": [10, 91, 98], "24": [10, 89, 96, 97, 99, 101, 103, 106, 108], "681458": 10, "37": [10, 90, 96, 97], "804582": 10, "64": [10, 42, 87, 92, 94, 96, 99, 103], "810646": 10, "815691": 10, "78": [10, 87, 94, 97, 99, 103, 106], "834293": 10, "Be": [10, 42], "cautiou": 10, "behavior": [10, 17, 37, 38, 42, 70, 98], "rarest": [10, 91], "q": [10, 103], "subpar": 10, "special": [10, 52, 56], "techniqu": [10, 103], "smote": 10, "asymmetr": [10, 37], "28": [10, 92, 95, 96, 97, 99, 101, 108], "75": [10, 49, 90, 91, 96, 97, 101, 102, 103, 106, 108], "33": [10, 38, 42, 96, 97, 103], "68": [10, 87, 97, 99, 103], "excess": [10, 92], "dark": [10, 107], "bright": [10, 96, 108], "blurri": [10, 92, 96], "lack": [10, 61, 96], "unusu": [10, 96, 103, 104], "cluster": [10, 19, 32], "slice": 10, "poor": [10, 96], "subpopul": 10, "faq": [10, 84, 91, 92, 94, 95, 100], "get_self_confidence_for_each_label": [10, 49, 72], "r": [10, 41, 74, 90, 91, 96, 106, 107], "tabular": [10, 84, 86, 90, 91, 93, 96, 98, 101], "categor": [10, 71, 86, 87, 90, 91, 93, 98, 106], "encod": [10, 50, 70, 76, 79, 87, 88, 94, 95, 98, 106, 107], "71": [10, 96, 97, 99, 103, 106, 108], "70": [10, 82, 94, 96], "69": [10, 99, 106], "subgroup": [10, 96], "wors": [10, 96, 101], "ratio": [10, 96], "miss": [10, 28, 38, 42, 57, 67, 69, 98, 103, 106], "pattern": [10, 96], "isn": [10, 18, 28], "scalabl": 10, "sacrific": 10, "One": [10, 57, 71, 98], "quantif": 10, "39": [10, 88, 89, 90, 92, 95, 96, 97, 98, 103, 106, 107, 108], "32": [10, 89, 90, 96, 97, 101, 103], "valuabl": [10, 19, 96], "exert": [10, 91], "possible_issue_typ": 10, "label_kwarg": 10, "outlier_kwarg": 10, "near_duplicate_kwarg": 10, "non_iid_kwarg": 10, "class_imbalance_kwarg": 10, "underperforming_group_kwarg": 10, "null_kwarg": 10, "data_valuation_kwarg": 10, "health_summary_paramet": [10, 22, 24, 31], "health_summari": [10, 24, 37, 84, 97], "health_summary_kwarg": 10, "tandem": [10, 97], "view": [10, 38, 42, 43, 44, 78, 80, 82, 84, 87, 88, 89, 90, 91, 94, 95, 97, 99, 101, 102, 103, 104, 105, 106, 108], "ood_kwarg": 10, "outofdistribut": [10, 29, 71, 104], "outsid": [10, 98, 102], "outlierissuemanag": [10, 15, 22, 29], "nearduplicateissuemanag": [10, 15, 20, 22], "noniidissuemanag": [10, 15, 22, 27], "num_permut": [10, 27], "permut": [10, 27], "significance_threshold": [10, 27], "signic": 10, "noniid": [10, 22], "classimbalanceissuemanag": [10, 15, 21, 22], "underperforminggroupissuemanag": [10, 15, 22, 32], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 32], "filter_cluster_id": [10, 22, 32], "clustering_kwarg": [10, 32], "nullissuemanag": [10, 15, 22, 28], "datavaluationissuemanag": [10, 15, 19, 22], "codeblock": 10, "demonstr": [10, 41, 52, 90, 91, 92, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107], "howev": [10, 38, 42, 52, 57, 87, 88, 89, 92, 94, 95, 96, 101, 105, 107], "mandatori": 10, "image_issue_types_kwarg": 10, "vice": [10, 63], "versa": [10, 63], "light": [10, 92, 96, 97, 103, 107], "29": [10, 92, 96, 97, 101, 102, 103, 107, 108], "low_inform": [10, 92, 96], "odd_aspect_ratio": [10, 92, 96], "35": [10, 90, 96, 97, 101, 102, 103], "odd_siz": [10, 92, 96], "doc": [10, 38, 42, 71, 84, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 104, 106, 108], "label_scor": [11, 24, 26, 31, 89, 90, 91, 92, 94, 95, 96, 99, 102, 106], "is_outlier_issu": [11, 90, 91, 92, 94, 95, 96, 99], "outlier_scor": [11, 29, 90, 91, 92, 94, 95, 96, 99, 104], "is_near_duplicate_issu": [11, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_scor": [11, 20, 90, 91, 92, 94, 95, 96, 98, 99], "near_duplicate_set": [11, 20, 22, 90, 91, 92, 94, 95, 98, 99], "is_non_iid_issu": [11, 91, 94, 95, 96, 99], "non_iid_scor": [11, 27, 91, 94, 95, 96, 99], "is_class_imbalance_issu": [11, 91, 96], "class_imbalance_scor": [11, 21, 91, 96], "is_underperforming_group_issu": [11, 91, 96], "underperforming_group_scor": [11, 32, 91, 96], "is_null_issu": [11, 91, 96], "null_scor": [11, 28, 91, 96], "is_data_valuation_issu": [11, 96], "data_valuation_scor": [11, 19, 96], "studio": [12, 84, 91, 92, 94, 95, 98], "data_issu": [12, 16, 17, 34], "issue_find": [12, 16], "factori": [12, 16, 17], "model_output": [12, 16], "except": [13, 38, 42, 61, 72, 90, 91, 92, 101], "dataformaterror": [13, 16], "add_not": 13, "with_traceback": 13, "tb": 13, "__traceback__": 13, "datasetdicterror": [13, 16], "datasetdict": 13, "datasetloaderror": [13, 16], "dataset_typ": 13, "fail": 13, "hold": 13, "sublist": 13, "map_to_int": 13, "abc": [13, 23, 33], "is_avail": [13, 92], "dataissu": [14, 16, 17, 34], "central": [14, 108], "repositori": 14, "strategi": [14, 49, 96, 98], "_infostrategi": 14, "basi": 14, "collect_statist": 14, "reus": [14, 23], "avoid": [14, 38, 41, 42, 44, 52, 57, 64, 67, 70, 74, 76, 78, 90, 91, 98], "recomput": [14, 88], "weighted_knn_graph": 14, "issue_manager_that_computes_knn_graph": 14, "collect_issues_from_issue_manag": 14, "collect_issues_from_imagelab": 14, "imagelab": 14, "set_health_scor": 14, "health": [14, 24, 37, 63, 84], "get_data_statist": [14, 16], "concret": 15, "subclass": [15, 38, 42, 71, 90], "regressionlabelissuemanag": [15, 22, 30, 31], "multilabelissuemanag": [15, 22, 25, 26], "from_str": [15, 35, 45, 49], "my_issu": 15, "logic": [15, 35, 41, 44, 76, 78], "issuefind": [16, 17, 34], "modeloutput": [16, 33], "multiclasspredprob": [16, 33], "regressionpredict": [16, 33], "multilabelpredprob": [16, 33], "instati": 17, "public": [17, 96, 99, 103, 107, 108], "creation": [17, 42, 96], "execut": [17, 38, 42, 90, 98, 103], "coordin": [17, 67, 69, 70, 103, 108], "At": [17, 70, 98], "get_available_issue_typ": 17, "direct": [18, 28, 38, 42, 54, 61], "vstack": [19, 57, 92, 97, 98, 99, 101, 102], "25": [19, 27, 38, 49, 55, 91, 92, 96, 97, 99, 101, 102, 103, 108], "classvar": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "short": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 56, 57], "item": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 38, 42, 57, 90, 91, 92, 98, 99, 101, 102], "some_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "additional_info_kei": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32], "default_threshold": [19, 22, 29], "collect_info": [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "info_to_omit": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "compos": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32, 38, 42, 88, 95, 104], "is_x_issu": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "x_score": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_a": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b1": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "val_b2": [19, 20, 21, 23, 24, 26, 27, 29, 31, 32], "report_str": [19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 34], "_": [20, 21, 23, 24, 26, 27, 28, 31, 32, 49, 56, 57, 84, 87, 89, 90, 92, 96, 97, 99, 102], "occurr": [20, 21, 23, 27, 28, 29, 32, 56], "median_nn_dist": 20, "bleed": [22, 25, 30, 40], "edg": [22, 25, 30, 40, 69, 84, 99, 108], "sharp": [22, 25, 30, 40], "get_health_summari": [22, 24], "ood": [22, 29, 71, 72, 104], "simplified_kolmogorov_smirnov_test": [22, 27], "outlier_cluster_label": [22, 32], "no_underperforming_cluster_id": [22, 32], "perform_clust": [22, 32], "get_worst_clust": [22, 32], "find_issues_with_predict": [22, 30, 31], "find_issues_with_featur": [22, 30, 31], "believ": [23, 107], "priori": [23, 99], "abstract": [23, 33], "applic": [24, 62, 98, 99, 101, 108], "typevar": [24, 26, 38, 42, 56, 66, 69, 70], "scalartyp": [24, 26], "covari": [24, 26, 74, 106], "summary_dict": 24, "neighbor_histogram": 27, "non_neighbor_histogram": 27, "kolmogorov": 27, "smirnov": 27, "largest": [27, 41, 49, 52, 72, 76, 78, 103, 107], "empir": [27, 48, 62], "cumul": 27, "ecdf": 27, "histogram": [27, 94, 96, 106], "absolut": [27, 31], "trial": 27, "null_track": 28, "extend": [28, 50, 61, 92, 96, 103, 104, 108], "superclass": 28, "arbitrari": [28, 37, 78, 82, 90, 104, 106], "prompt": 28, "address": [28, 88, 90, 91, 95, 98], "enabl": [28, 42, 54], "scaling_factor": [29, 55], "37037": 29, "q3_avg_dist": 29, "iqr_avg_dist": 29, "median_outlier_scor": 29, "issue_threshold": 29, "multipli": [31, 55], "deleg": 31, "confus": [32, 33, 37, 38, 42, 44, 57, 70, 88, 108], "50": [32, 42, 96, 98, 99, 101, 103, 104, 106], "keepdim": [32, 98], "signifi": 32, "absenc": 32, "int64": [32, 89, 101], "npt": 32, "int_": 32, "id": [32, 62, 90, 92, 96, 98, 101], "unique_cluster_id": 32, "_description_": 32, "performed_clust": 32, "worst_cluster_id": 32, "convent": [33, 35], "subject": [33, 35], "meant": [33, 35], "Not": [33, 54], "mainli": [33, 104, 108], "content": [33, 71, 89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "fetch": [33, 41, 89, 91, 98], "datset": 34, "exclud": [34, 43, 79, 83, 90, 108], "get_report": 34, "enum": [35, 49], "qualnam": [35, 49], "boundari": [35, 49, 90, 91], "continu": [35, 61, 87, 88, 92, 95, 96, 98, 101, 103, 106, 108], "binari": [35, 49, 57, 64, 66, 99, 108], "simultan": [35, 106], "task_str": 35, "is_classif": 35, "__contains__": [35, 45, 49], "member": [35, 38, 42, 49, 90], "typeerror": [35, 49], "12": [35, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 106, 107, 108], "__getitem__": [35, 45, 49], "match": [35, 37, 38, 42, 44, 49, 62, 63, 72, 90, 91, 92, 97, 103, 105, 107], "__iter__": [35, 45, 49], "__len__": [35, 45, 49], "alias": [35, 49], "is_regress": 35, "is_multilabel": 35, "overview": [37, 52, 87, 88, 89, 91, 92, 94, 95, 101, 103, 104, 106, 108], "modifi": [37, 38, 41, 42, 52, 54, 57, 96, 98, 99], "rank_classes_by_label_qu": [37, 91], "merg": [37, 52, 56, 84, 97, 98, 108], "find_overlapping_class": [37, 98, 99], "problemat": [37, 63, 79, 83, 89, 103, 108], "unnorm": [37, 63, 99], "abov": [37, 38, 41, 42, 54, 57, 62, 69, 70, 72, 78, 82, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 105, 106, 107, 108], "model_select": [37, 49, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 104, 106], "cross_val_predict": [37, 42, 87, 88, 89, 90, 91, 94, 95, 96, 99, 101, 105, 106], "get_data_labels_from_dataset": 37, "yourfavoritemodel": [37, 99], "cv": [37, 49, 87, 89, 90, 91, 94, 96, 99, 101], "df": [37, 57, 83, 89, 96, 98], "overall_label_qu": [37, 63], "col": 37, "prob": [37, 56, 99, 105], "divid": [37, 63, 72], "label_nois": [37, 63], "human": [37, 97, 107, 108], "clearli": [37, 72, 92, 103, 107], "num": [37, 63, 97, 99], "overlap": [37, 84, 97, 98, 99], "ontolog": 37, "publish": [37, 108], "therefor": [37, 72, 96], "vehicl": [37, 97], "truck": [37, 97, 104, 107], "intuit": [37, 63], "car": [37, 97, 103, 107], "frequent": [37, 62, 96, 98, 106], "characterist": [37, 96], "l": [37, 38, 42, 67, 69, 70], "class1": 37, "class2": 37, "relationship": [37, 96], "dog": [37, 57, 63, 65, 79, 97, 98, 104, 105, 108], "cat": [37, 57, 63, 65, 97, 98, 104, 105], "captur": [37, 89, 103, 104, 107], "co": [37, 38, 39], "noisy_label": [37, 90, 91, 102], "overlapping_class": 37, "descend": [37, 38, 42, 49, 63, 70], "overall_label_health_scor": [37, 63, 99], "half": [37, 38, 40, 42, 63, 97, 108], "health_scor": [37, 63], "classes_by_label_qu": [37, 91], "cnn": [38, 40, 42, 92], "cifar": [38, 39, 96, 97, 104], "teach": [38, 39], "bhanml": 38, "blob": [38, 96], "master": [38, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 102, 103, 104, 106], "call_bn": [38, 40], "bn": 38, "input_channel": 38, "n_output": 38, "dropout_r": 38, "top_bn": 38, "architectur": [38, 42], "shown": [38, 70, 89, 90, 91, 92, 94, 95, 98, 99, 101, 104, 105, 107, 108], "forward": [38, 39, 40, 42, 92, 101], "overridden": [38, 42], "although": [38, 42, 71, 87, 94], "recip": [38, 42], "afterward": [38, 42], "sinc": [38, 42, 46, 58, 63, 70, 78, 82, 98, 101, 102, 103, 105, 108], "hook": [38, 42, 97], "silent": [38, 41, 42], "t_destin": [38, 40, 42], "__call__": [38, 40, 42, 45, 49], "add_modul": [38, 40, 42], "child": [38, 42], "fn": [38, 42, 70], "recurs": [38, 42, 49], "submodul": [38, 42, 51], "children": [38, 40, 42, 108], "nn": [38, 39, 42, 52, 92], "init": [38, 42, 99], "no_grad": [38, 42, 92, 104], "init_weight": [38, 42], "linear": [38, 42, 88, 92, 95], "fill_": [38, 42], "net": [38, 42, 89, 92, 97], "in_featur": [38, 42], "out_featur": [38, 42], "bia": [38, 42, 92], "tensor": [38, 39, 42, 89, 92, 104], "requires_grad": [38, 42], "bfloat16": [38, 40, 42], "cast": [38, 42, 89], "buffer": [38, 40, 42, 96], "datatyp": [38, 42], "xdoctest": [38, 42], "undefin": [38, 42], "var": [38, 42], "buf": [38, 42], "20l": [38, 42], "1l": [38, 42], "5l": [38, 42], "call_super_init": [38, 40, 42], "immedi": [38, 42, 104], "compil": [38, 40, 42, 61], "cpu": [38, 40, 42, 44, 89, 92], "move": [38, 42, 49, 85, 97], "cuda": [38, 40, 42, 89, 92], "devic": [38, 42, 89, 92], "gpu": [38, 42, 88, 89, 95], "live": [38, 42], "copi": [38, 42, 74, 87, 89, 90, 91, 94, 96, 98, 102, 105, 106], "doubl": [38, 40, 42], "dump_patch": [38, 40, 42], "eval": [38, 40, 42, 92, 102, 104], "dropout": [38, 42], "batchnorm": [38, 42], "grad": [38, 42], "extra_repr": [38, 40, 42], "line": [38, 42, 84, 90, 96, 97, 101, 104, 108], "get_buff": [38, 40, 42], "target": [38, 39, 42, 74, 75, 96, 104, 106], "throw": [38, 42], "get_submodul": [38, 40, 42], "explan": [38, 42], "qualifi": [38, 42], "referenc": [38, 42], "attributeerror": [38, 42], "invalid": [38, 42, 95], "resolv": [38, 42, 108], "get_extra_st": [38, 40, 42], "state_dict": [38, 40, 42], "set_extra_st": [38, 40, 42], "build": [38, 42, 52, 92, 96, 107], "picklabl": [38, 42], "serial": [38, 42], "backward": [38, 42, 92], "break": [38, 42, 92, 96, 103], "pickl": [38, 42, 103], "get_paramet": [38, 40, 42], "net_b": [38, 42], "net_c": [38, 42], "conv": [38, 42], "conv2d": [38, 42, 92], "16": [38, 42, 49, 52, 61, 78, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 103, 104, 107, 108], "kernel_s": [38, 42], "stride": [38, 42], "200": [38, 42, 72, 97, 103, 108], "diagram": [38, 42, 105], "degre": [38, 42], "queri": [38, 42, 52, 54, 91, 92, 96, 98, 102], "named_modul": [38, 40, 42], "o": [38, 42, 55, 56, 89, 90, 91, 97, 98, 99, 102, 103, 108], "transit": [38, 42], "ipu": [38, 40, 42], "load_state_dict": [38, 40, 42], "strict": [38, 42, 49], "persist": [38, 42], "strictli": [38, 42], "inplac": [38, 42, 96, 101], "preserv": [38, 42, 57], "namedtupl": [38, 42], "missing_kei": [38, 42], "unexpected_kei": [38, 42], "runtimeerror": [38, 42], "idx": [38, 42, 57, 58, 70, 90, 92, 96, 98, 99, 101, 103, 104], "named_buff": [38, 40, 42], "prefix": [38, 42, 89, 108], "remove_dupl": [38, 42], "prepend": [38, 42], "running_var": [38, 42], "named_children": [38, 40, 42], "conv4": [38, 42], "conv5": [38, 42], "memo": [38, 42], "named_paramet": [38, 40, 42], "register_backward_hook": [38, 40, 42], "deprec": [38, 42, 46], "favor": [38, 42], "register_full_backward_hook": [38, 40, 42], "removablehandl": [38, 42], "register_buff": [38, 40, 42], "running_mean": [38, 42], "register_forward_hook": [38, 40, 42], "with_kwarg": [38, 42], "always_cal": [38, 42], "possibli": [38, 42, 87, 94], "fire": [38, 42, 97], "register_module_forward_hook": [38, 42], "regardless": [38, 42, 90, 91], "register_forward_pre_hook": [38, 40, 42], "And": [38, 42], "forward_pr": [38, 42], "register_module_forward_pre_hook": [38, 42], "gradient": [38, 42, 92, 94, 106], "grad_input": [38, 42], "grad_output": [38, 42], "technic": [38, 42], "caller": [38, 42], "register_module_full_backward_hook": [38, 42], "register_full_backward_pre_hook": [38, 40, 42], "backward_pr": [38, 42], "register_module_full_backward_pre_hook": [38, 42], "register_load_state_dict_post_hook": [38, 40, 42], "post": [38, 42, 52], "incompatible_kei": [38, 42], "modif": [38, 42, 52], "thrown": [38, 42], "register_modul": [38, 40, 42], "register_paramet": [38, 40, 42], "register_state_dict_pre_hook": [38, 40, 42], "keep_var": [38, 42], "requires_grad_": [38, 40, 42], "autograd": [38, 42], "freez": [38, 42, 88, 89, 95], "finetun": [38, 42], "gan": [38, 42], "share_memori": [38, 40, 42], "share_memory_": [38, 42], "destin": [38, 42], "shallow": [38, 42], "releas": [38, 42, 61, 85, 98], "design": [38, 42, 52], "ordereddict": [38, 42], "detach": [38, 42, 92], "non_block": [38, 42], "memory_format": [38, 42], "channels_last": [38, 42], "Its": [38, 42, 49, 63, 69], "complex": [38, 42], "integr": [38, 42, 54, 84, 98], "asynchron": [38, 42], "host": [38, 42], "pin": [38, 42, 88, 95, 97], "desir": [38, 42, 52, 56, 70], "4d": [38, 42], "ignore_w": [38, 42], "determinist": [38, 42, 89], "1913": [38, 42], "3420": [38, 42], "5113": [38, 42], "2325": [38, 42], "env": [38, 42], "torch_doctest_cuda1": [38, 42], "gpu1": [38, 42], "1914": [38, 42], "5112": [38, 42], "2324": [38, 42], "float16": [38, 42], "cdoubl": [38, 42], "3741": [38, 42], "2382": [38, 42], "5593": [38, 42], "4443": [38, 42], "complex128": [38, 42], "6122": [38, 42], "1150": [38, 42], "to_empti": [38, 40, 42], "storag": [38, 42], "dst_type": [38, 42], "xpu": [38, 40, 42], "zero_grad": [38, 40, 42, 92], "set_to_non": [38, 42], "reset": [38, 42], "context": [38, 42, 103], "noisili": [39, 99], "han": 39, "2018": 39, "cifar_cnn": [39, 40], "loss_coteach": [39, 40], "y_1": 39, "y_2": 39, "forget_r": 39, "class_weight": 39, "logit": [39, 61, 92], "decim": [39, 57], "forget": [39, 49, 108], "rate_schedul": 39, "epoch": [39, 40, 42, 92, 98], "initialize_lr_schedul": [39, 40], "lr": [39, 40, 42], "001": [39, 72, 96, 98], "250": [39, 90, 91, 99, 103], "epoch_decay_start": 39, "schedul": 39, "beta": 39, "adam": 39, "adjust_learning_r": [39, 40], "alpha_plan": 39, "beta1_plan": 39, "forget_rate_schedul": [39, 40], "num_gradu": 39, "expon": 39, "tell": [39, 88, 92, 95, 99], "train_load": [39, 42], "model1": [39, 99], "optimizer1": 39, "model2": [39, 99], "optimizer2": 39, "dataload": [39, 92, 104], "parser": 39, "parse_arg": 39, "num_iter_per_epoch": 39, "print_freq": 39, "topk": 39, "top1": 39, "top5": 39, "test_load": 39, "offici": [40, 60, 96, 108], "wish": [40, 60, 104, 107, 108], "adj_confident_thresholds_shar": [40, 41], "labels_shar": [40, 41], "pred_probs_shar": [40, 41], "labelinspector": [40, 41, 98], "get_num_issu": [40, 41], "get_quality_scor": [40, 41], "update_confident_threshold": [40, 41], "score_label_qu": [40, 41], "split_arr": [40, 41], "span_classif": 40, "display_issu": [40, 43, 77, 78, 79, 80, 81, 82, 83, 107, 108], "mnist_pytorch": 40, "get_mnist_dataset": [40, 42], "get_sklearn_digits_dataset": [40, 42], "simplenet": [40, 42], "batch_siz": [40, 41, 42, 76, 78, 92, 98, 104, 107], "log_interv": [40, 42], "momentum": [40, 42], "no_cuda": [40, 42], "test_batch_s": [40, 42, 92], "loader": [40, 42, 92], "set_predict_proba_request": [40, 42], "set_predict_request": [40, 42], "coteach": [40, 85], "mini": [41, 76, 78, 98], "low_self_confid": [41, 44, 64], "self_confid": [41, 44, 45, 49, 64, 66, 72, 80, 82, 87, 88, 98, 99], "conveni": [41, 54, 87, 88, 89, 95], "script": 41, "labels_fil": [41, 98], "pred_probs_fil": [41, 98], "quality_score_kwarg": 41, "num_issue_kwarg": 41, "return_mask": 41, "variant": [41, 62, 107], "read": [41, 46, 91, 98, 99, 104, 108], "zarr": [41, 98], "memmap": [41, 107], "pythonspe": 41, "mmap": [41, 98], "hdf5": 41, "further": [41, 43, 63, 64, 66, 69, 70, 78, 79, 89, 98], "yourfil": 41, "npy": [41, 97, 98, 107], "mmap_mod": [41, 107], "tip": [41, 44, 61, 98], "save_arrai": 41, "your_arrai": 41, "disk": [41, 97, 98], "npz": [41, 108], "maxim": [41, 62, 76, 78, 107], "multiprocess": [41, 44, 64, 76, 78, 92, 98], "linux": [41, 76, 78], "physic": [41, 44, 76, 78, 103], "psutil": [41, 44, 76, 78], "labels_arrai": [41, 58], "predprob": 41, "pred_probs_arrai": 41, "back": [41, 52, 70, 90, 98, 103, 104], "store_result": 41, "becom": [41, 96, 104], "verifi": [41, 54, 98, 101, 104], "long": [41, 62, 71, 101], "enough": [41, 57, 96, 98], "chunk": [41, 105], "ram": [41, 97], "end_index": 41, "labels_batch": 41, "pred_probs_batch": 41, "batch_result": 41, "indices_of_examples_with_issu": [41, 98], "shortcut": 41, "encount": [41, 44, 76], "1000": [41, 89, 95, 98, 104], "aggreg": [41, 45, 49, 62, 66, 69, 72, 82, 98, 99, 101], "seen": [41, 98, 104, 108], "far": [41, 62], "label_quality_scor": [41, 66, 69, 72, 75, 99, 103], "method1": 41, "method2": 41, "normalized_margin": [41, 44, 45, 49, 64, 66, 72, 80, 82], "low_normalized_margin": [41, 44, 64], "issue_indic": [41, 69, 92], "update_num_issu": 41, "arr": [41, 98], "chunksiz": 41, "convnet": 42, "bespok": [42, 61], "download": [42, 89, 96, 98, 104], "mnist": [42, 84, 89, 97], "handwritten": 42, "digit": [42, 89, 97], "last": [42, 49, 67, 70, 90, 91, 98, 101, 103, 108], "sklearn_digits_test_s": 42, "01": [42, 72, 74, 89, 96, 99, 102, 103, 108], "templat": 42, "flexibli": 42, "among": [42, 62, 99], "test_set": 42, "overrid": 42, "train_idx": [42, 57, 104], "train_label": [42, 88, 104], "span": 43, "sentenc": [43, 56, 80, 82, 83, 88, 95], "token_classif": [43, 56, 80, 82, 83, 98], "encourag": [44, 64, 72, 75], "multilabel_classif": [44, 63, 64, 66, 72, 98, 102], "pred_probs_by_class": 44, "prune_count_matrix_col": 44, "rank_by_kwarg": [44, 64, 72, 99], "num_to_remove_per_class": [44, 64], "bad": [44, 52, 64, 69, 72, 95, 98], "seem": [44, 99, 102], "aren": 44, "confidence_weighted_entropi": [44, 45, 49, 64, 66, 72, 80, 82], "label_issues_idx": [44, 72], "entropi": [44, 46, 48, 49, 71, 72], "prune_by_class": [44, 64, 99], "predicted_neq_given": [44, 64, 99], "prune_counts_matrix": 44, "smallest": [44, 72], "unus": 44, "number_of_mislabeled_examples_in_class_k": 44, "delet": [44, 84, 88, 98], "too": [44, 49, 52, 71, 92, 98, 103], "thread": [44, 64], "window": [44, 97], "shorter": [44, 67], "find_predicted_neq_given": 44, "find_label_issues_using_argmax_confusion_matrix": 44, "remove_noise_from_class": [45, 57], "clip_noise_r": [45, 57], "clip_valu": [45, 57], "value_count": [45, 57, 98], "value_counts_fill_missing_class": [45, 57], "get_missing_class": [45, 57], "round_preserving_sum": [45, 57], "round_preserving_row_tot": [45, 57], "estimate_pu_f1": [45, 57], "confusion_matrix": [45, 57], "print_square_matrix": [45, 57], "print_noise_matrix": [45, 57, 99], "print_inverse_noise_matrix": [45, 57], "print_joint_matrix": [45, 57, 99], "compress_int_arrai": [45, 57], "train_val_split": [45, 57], "subset_x_i": [45, 57], "subset_label": [45, 57], "subset_data": [45, 57], "extract_indices_tf": [45, 57], "unshuffle_tensorflow_dataset": [45, 57], "is_torch_dataset": [45, 57], "is_tensorflow_dataset": [45, 57], "csr_vstack": [45, 57], "append_extra_datapoint": [45, 57], "get_num_class": [45, 57], "num_unique_class": [45, 57], "get_unique_class": [45, 57], "format_label": [45, 57], "smart_display_datafram": [45, 57], "force_two_dimens": [45, 57], "latent_algebra": [45, 85], "compute_ps_py_inv_noise_matrix": [45, 47], "compute_py_inv_noise_matrix": [45, 47], "compute_inv_noise_matrix": [45, 47], "compute_noise_matrix_from_invers": [45, 47], "compute_pi": [45, 47], "compute_pyx": [45, 47], "label_quality_util": 45, "get_normalized_entropi": [45, 46], "multilabel_util": [45, 102], "stack_compl": [45, 50], "get_onehot_num_class": [45, 50], "int2onehot": [45, 50, 102], "onehot2int": [45, 50, 102], "multilabel_scor": [45, 66], "classlabelscor": [45, 49], "exponential_moving_averag": [45, 49, 66], "softmin": [45, 49, 66, 69, 78, 82], "possible_method": [45, 49], "multilabelscor": [45, 49], "get_class_label_quality_scor": [45, 49], "multilabel_pi": [45, 49], "get_cross_validated_multilabel_pred_prob": [45, 49], "default_k": [45, 51, 52], "features_to_knn": [45, 51, 52], "construct_knn_graph_from_index": [45, 51, 52, 54], "create_knn_graph_and_index": [45, 51, 52], "correct_knn_graph": [45, 51, 52, 96], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplac": [45, 51, 52], "correct_knn_distances_and_indic": [45, 51, 52], "high_dimension_cutoff": [45, 51, 53], "row_count_cutoff": [45, 51, 53], "decide_euclidean_metr": [45, 51, 53], "decide_default_metr": [45, 51, 53], "construct_knn": [45, 51, 54], "transform_distances_to_scor": [45, 55], "correct_precision_error": [45, 55], "token_classification_util": [45, 108], "get_sent": [45, 56, 108], "filter_sent": [45, 56, 108], "process_token": [45, 56], "merge_prob": [45, 56], "color_sent": [45, 56], "assert_valid_input": [45, 58], "assert_valid_class_label": [45, 58], "assert_nonempty_input": [45, 58], "assert_indexing_work": [45, 58], "labels_to_arrai": [45, 58], "labels_to_list_multilabel": [45, 58], "min_allowed_prob": 46, "wikipedia": 46, "activ": [46, 48, 61, 62, 84, 101], "towardsdatasci": 46, "cheatsheet": 46, "ec57bc067c0b": 46, "clip": [46, 57, 89, 96], "behav": 46, "unnecessari": [46, 98], "slightli": [46, 87, 88], "interv": [46, 49, 104], "herein": 47, "inexact": 47, "cours": 47, "propag": 47, "throughout": [47, 57, 74, 83, 89, 101, 107, 108], "increas": [47, 55, 69, 71, 72, 89, 90, 96, 98, 101, 102, 108], "dot": [47, 82, 98], "true_labels_class_count": 47, "pyx": 47, "multiannot": 48, "assert_valid_inputs_multiannot": 48, "labels_multiannot": [48, 62], "ensembl": [48, 49, 62, 72, 87, 94, 98, 102, 104, 106], "allow_single_label": 48, "annotator_id": 48, "assert_valid_pred_prob": 48, "pred_probs_unlabel": [48, 62], "format_multiannotator_label": [48, 62, 101], "formatted_label": [48, 57], "old": [48, 57, 85, 97], "check_consensus_label_class": 48, "consensus_label": [48, 62, 101], "consensus_method": [48, 62], "consensu": [48, 62, 84, 100, 108], "establish": [48, 61, 88, 106], "compute_soft_cross_entropi": 48, "soft": [48, 97], "find_best_temp_scal": 48, "coarse_search_rang": [48, 74, 98], "fine_search_s": [48, 74, 98], "temperatur": [48, 49, 69, 78, 82], "scale": [48, 55, 87, 96, 97, 98, 104, 107], "factor": [48, 49, 55, 76, 78], "minim": [48, 69, 104], "temp_scale_pred_prob": 48, "temp": 48, "sharpen": [48, 97], "smoothen": 48, "get_normalized_margin_for_each_label": [49, 72], "get_confidence_weighted_entropy_for_each_label": [49, 72], "scorer": 49, "alpha": [49, 66, 69, 90, 91, 96, 99, 102, 106], "exponenti": 49, "ema": 49, "s_1": 49, "s_k": 49, "ema_k": 49, "accord": [49, 64, 94, 95, 99, 108], "formula": [49, 55], "_t": 49, "cdot": 49, "s_t": 49, "qquad": 49, "leq": 49, "_1": 49, "recent": [49, 108], "success": 49, "previou": [49, 52, 92, 94, 98, 103], "discount": 49, "s_ema": 49, "175": [49, 92, 99, 103], "underflow": 49, "nan": [49, 62, 87, 94, 96, 101, 106], "aggregated_scor": 49, "base_scor": 49, "base_scorer_kwarg": 49, "aggregator_kwarg": [49, 66], "n_sampl": [49, 96], "n_label": 49, "worst": [49, 101], "class_label_quality_scor": 49, "452": 49, "new_scor": 49, "575": 49, "get_label_quality_scores_per_class": [49, 65, 66], "ml_scorer": 49, "binar": [49, 50], "reformat": [49, 89], "wider": 49, "splitter": 49, "kfold": [49, 92], "onevsrestclassifi": [49, 102], "randomforestclassifi": [49, 99, 102], "n_split": [49, 92, 102], "pred_prob_slic": 50, "onehot": 50, "hot": [50, 64, 70, 76, 79, 87, 94, 97, 98, 106, 107], "onehot_matrix": 50, "pairwis": [51, 53, 71], "reli": [52, 71, 88, 89, 90, 91, 95, 103, 104, 106], "sklearn_knn_kwarg": 52, "correction_featur": 52, "discourag": 52, "flexibl": [52, 98], "manner": [52, 66, 87, 88, 96, 101, 106], "701": 52, "900": [52, 87, 94, 106], "436": 52, "000": [52, 88, 92, 95, 96, 97, 108], "idea": [52, 72, 103], "dens": [52, 61, 96], "33140006": 52, "76210367": 52, "correct_exact_dupl": 52, "mutual": [52, 63, 102], "vari": [52, 69, 91], "exact_duplicate_set": 52, "main": [52, 62], "front": [52, 97], "consider": 52, "capabl": [52, 84], "come": [52, 57, 90, 91, 98, 107], "misidentif": 52, "corrected_dist": 52, "corrected_indic": 52, "sqrt": 52, "distant": 52, "suitabl": [53, 62, 87, 94, 96], "slower": 53, "decid": [53, 62, 88, 95, 97, 101, 106, 108], "predefin": 53, "met": [53, 108], "euclidean_dist": [53, 71], "spatial": [53, 71], "decis": [53, 87, 90, 91], "That": [53, 99, 102], "cosine_dist": 53, "knn_kwarg": 54, "html": [54, 57, 67, 70, 71, 89, 90, 91, 92, 94, 95, 98, 99], "kneighbor": 54, "metric_param": 54, "n_features_in_": 54, "effective_metric_params_": 54, "effective_metric_": 54, "n_samples_fit_": 54, "__sklearn_is_fitted__": 54, "conduct": 54, "is_fit": 54, "trail": 54, "underscor": 54, "avg_dist": 55, "exp": [55, 71, 72, 90], "dt": 55, "right": [55, 67, 70, 88, 95, 102, 103, 104], "strength": [55, 70, 96], "pronounc": 55, "differenti": 55, "ly": 55, "rule": [55, 56, 97], "thumb": 55, "ood_features_scor": [55, 71, 104], "88988177": 55, "80519832": 55, "toler": 55, "minkowski": 55, "noth": 55, "epsilon": 55, "sensibl": 55, "fixed_scor": 55, "readabl": 56, "lambda": [56, 89, 90, 98, 101], "long_sent": 56, "headlin": 56, "charact": [56, 57], "s1": 56, "s2": 56, "processed_token": 56, "alecnlcb": 56, "entiti": [56, 84, 98, 108], "mapped_ent": 56, "unique_ident": 56, "loc": [56, 90, 91, 92, 94, 96, 108], "nbitbas": [56, 66], "probs_merg": 56, "0125": [56, 82], "0375": 56, "075": 56, "025": 56, "color": [56, 79, 90, 91, 94, 96, 99, 102, 104, 106, 107], "red": [56, 70, 90, 91, 96, 97, 99, 102, 103, 104, 107], "colored_sent": 56, "termcolor": 56, "31msentenc": 56, "0m": 56, "ancillari": 57, "class_without_nois": 57, "any_other_class": 57, "choos": [57, 72, 87, 94, 98, 99, 106], "tradition": 57, "new_sum": 57, "fill": 57, "major": [57, 62, 85, 92, 104], "versu": [57, 99], "obviou": 57, "cgdeboer": 57, "iteround": 57, "reach": 57, "prob_s_eq_1": 57, "claesen": 57, "f1": [57, 70, 95, 99], "BE": 57, "left_nam": 57, "top_nam": 57, "titl": [57, 90, 91, 96, 99, 102, 104], "short_titl": 57, "round_plac": 57, "pretti": [57, 99], "joint_matrix": 57, "num_possible_valu": 57, "holdout_idx": 57, "extract": [57, 71, 88, 89, 94, 95, 96, 101, 104, 107], "allow_shuffl": 57, "turn": [57, 84, 103], "shuffledataset": 57, "histori": 57, "pre_x": 57, "buffer_s": 57, "csr_matric": 57, "append": [57, 89, 92, 96, 97, 98, 99, 101, 102, 103, 104, 108], "bottom": [57, 67, 70, 96, 103], "to_data": 57, "from_data": 57, "taken": 57, "label_matrix": 57, "canon": 57, "displai": [57, 70, 79, 83, 88, 89, 94, 95, 96, 99, 108], "jupyt": [57, 89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "notebook": [57, 62, 89, 91, 97, 98, 99, 101, 102, 103, 107, 108], "consol": 57, "allow_missing_class": 58, "allow_one_class": 58, "length_x": 58, "labellik": 58, "labels_list": [58, 64], "keraswrappermodel": [60, 61, 84], "keraswrappersequenti": [60, 61], "tf": [61, 89], "legaci": 61, "newer": 61, "interim": 61, "advis": [61, 102], "stabil": [61, 71], "until": 61, "accommod": 61, "keraswrapp": 61, "huggingface_keras_imdb": 61, "unit": [61, 108], "model_kwarg": [61, 74], "compile_kwarg": 61, "sparsecategoricalcrossentropi": 61, "layer": [61, 88, 89, 95, 104], "my_keras_model": 61, "from_logit": 61, "declar": 61, "apply_softmax": 61, "analysi": 62, "analyz": [62, 84, 96, 99, 101, 102], "get_label_quality_multiannot": [62, 101], "vote": 62, "crowdsourc": [62, 84, 101], "dawid": [62, 101], "skene": [62, 101], "analog": [62, 97, 101], "chosen": [62, 72, 96, 98, 101], "crowdlab": [62, 101], "unlabel": [62, 92, 94, 95, 101, 104, 107], "get_active_learning_scor": [62, 101], "activelab": [62, 101], "priorit": [62, 69, 103, 107, 108], "showcas": [62, 96], "best_qual": 62, "quality_method": 62, "calibrate_prob": 62, "return_detailed_qu": 62, "return_annotator_stat": 62, "return_weight": 62, "label_quality_score_kwarg": 62, "did": [62, 63, 87, 88, 89, 94, 99, 101, 106], "majority_vot": 62, "broken": [62, 70, 97, 106], "highest": [62, 70, 90, 92, 105], "0th": 62, "consensus_quality_scor": [62, 101], "annotator_agr": [62, 101], "reman": 62, "1st": 62, "2nd": [62, 76], "3rd": 62, "consensus_label_suffix": 62, "consensus_quality_score_suffix": 62, "suffix": 62, "emsembl": 62, "weigh": [62, 97], "agreement": [62, 101], "agre": 62, "prevent": [62, 98], "overconfid": [62, 105], "detailed_label_qu": [62, 101], "annotator_stat": [62, 101], "model_weight": 62, "annotator_weight": 62, "warn": 62, "labels_info": 62, "num_annot": [62, 101], "deriv": [62, 101], "quality_annotator_1": 62, "quality_annotator_2": 62, "quality_annotator_m": 62, "annotator_qu": [62, 101], "num_examples_label": [62, 101], "agreement_with_consensu": [62, 101], "worst_class": [62, 101], "trustworthi": [62, 101, 106], "get_label_quality_multiannotator_ensembl": 62, "weigtht": 62, "budget": 62, "retrain": [62, 88, 106], "active_learning_scor": 62, "active_learning_scores_unlabel": 62, "get_active_learning_scores_ensembl": 62, "henc": [62, 89, 90, 101], "get_majority_vote_label": [62, 101], "event": 62, "lastli": [62, 94], "convert_long_to_wide_dataset": 62, "labels_multiannotator_long": 62, "wide": [62, 87, 88, 89], "labels_multiannotator_wid": 62, "common_multilabel_issu": [63, 65], "exclus": [63, 102], "rank_classes_by_multilabel_qu": [63, 65], "overall_multilabel_health_scor": [63, 65], "multilabel_health_summari": [63, 65], "classes_by_multilabel_qu": 63, "inner": [64, 78, 96], "find_multilabel_issues_per_class": [64, 65], "per_class_label_issu": 64, "label_issues_list": 64, "pred_probs_list": [64, 72, 92, 99], "anim": [65, 104], "rat": 65, "predat": 65, "pet": 65, "reptil": 65, "box": [67, 69, 70, 97, 103], "object_detect": [67, 69, 70, 103], "return_indices_ranked_by_scor": [67, 103], "overlapping_label_check": [67, 69], "suboptim": [67, 69], "locat": [67, 69, 96, 103, 107, 108], "bbox": [67, 70, 103], "image_nam": [67, 70], "y1": [67, 70, 103], "y2": [67, 70, 103], "later": [67, 70, 71, 88, 108], "corner": [67, 70, 103], "xyxi": [67, 70, 103], "io": [67, 70, 89, 96, 97], "keras_cv": [67, 70], "bounding_box": [67, 70, 103], "detectron": [67, 70, 103], "detectron2": [67, 70, 103], "readthedoc": [67, 70], "en": [67, 70], "latest": [67, 70], "visual": [67, 68, 70, 87, 90, 91, 92, 106, 108], "draw_box": [67, 70], "mmdetect": [67, 70, 103], "swap": [67, 69, 79, 83], "penal": [67, 69], "concern": [67, 69, 84, 91], "issues_from_scor": [68, 69, 77, 78, 79, 81, 82, 83, 103, 107, 108], "compute_overlooked_box_scor": [68, 69], "compute_badloc_box_scor": [68, 69], "compute_swap_box_scor": [68, 69], "pool_box_scores_per_imag": [68, 69], "object_counts_per_imag": [68, 70, 103], "bounding_box_size_distribut": [68, 70, 103], "class_label_distribut": [68, 70, 103], "get_sorted_bbox_count_idx": [68, 70], "plot_class_size_distribut": [68, 70], "plot_class_distribut": [68, 70], "get_average_per_class_confusion_matrix": [68, 70], "calculate_per_class_metr": [68, 70], "aggregation_weight": 69, "imperfect": [69, 98], "chose": [69, 101, 103], "imperfectli": [69, 103], "dirti": [69, 72, 75, 106], "subtyp": 69, "badloc": 69, "nonneg": 69, "high_probability_threshold": 69, "auxiliary_input": [69, 70], "iou": [69, 70], "heavili": 69, "auxiliarytypesdict": 69, "pred_label": [69, 88], "pred_label_prob": 69, "pred_bbox": 69, "lab_label": 69, "lab_bbox": 69, "similarity_matrix": 69, "min_possible_similar": 69, "scores_overlook": 69, "low_probability_threshold": 69, "scores_badloc": 69, "accident": [69, 88, 94, 95, 98], "scores_swap": 69, "box_scor": 69, "image_scor": [69, 78, 107], "discov": [70, 91, 96, 108], "abnorm": [70, 92, 103], "auxiliari": [70, 104, 107], "_get_valid_inputs_for_compute_scor": 70, "object_count": 70, "down": 70, "bbox_siz": 70, "class_distribut": 70, "plot": [70, 90, 91, 96, 99, 102, 104, 106, 107], "sorted_idx": [70, 104], "class_to_show": 70, "hidden": [70, 104], "max_class_to_show": 70, "plt": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "matplotlib": [70, 79, 90, 91, 92, 96, 99, 102, 103, 104, 106], "pyplot": [70, 79, 90, 91, 92, 96, 99, 102, 104, 106], "prediction_threshold": 70, "overlai": [70, 103], "figsiz": [70, 90, 91, 92, 96, 99, 102, 104], "save_path": [70, 103], "blue": [70, 97, 99, 103], "overlaid": 70, "side": [70, 97, 103], "figur": [70, 96, 99, 102, 104, 106], "extens": [70, 99, 101], "png": [70, 96, 103], "pdf": [70, 71], "svg": 70, "num_proc": [70, 92], "intersect": [70, 98], "tp": 70, "fp": 70, "ground": [70, 97, 99, 101, 106], "truth": [70, 99, 101, 106], "bias": [70, 96], "avg_metr": 70, "distionari": 70, "95": [70, 80, 82, 94, 97, 99, 106], "per_class_metr": 70, "Of": 71, "find_top_issu": [71, 72, 104], "behind": [71, 99], "dist_metr": 71, "subtract": [71, 72], "renorm": [71, 72, 98], "least_confid": 71, "sum_": 71, "log": [71, 72, 85], "softmax": [71, 78, 82, 92], "literatur": 71, "gen": 71, "liu": 71, "lochman": 71, "zach": 71, "openaccess": 71, "thecvf": 71, "cvpr2023": 71, "liu_gen_pushing_the_limits_of_softmax": 71, "based_out": 71, "distribution_detection_cvpr_2023_pap": 71, "fit_scor": [71, 104], "ood_predictions_scor": 71, "pretrain": [71, 88, 89, 95, 104], "adjust_confident_threshold": 71, "probabilist": [71, 87, 89, 90, 91, 94, 95, 104, 105], "order_label_issu": [72, 85], "whichev": [72, 105], "argsort": [72, 88, 92, 95, 99, 103, 104, 106], "max_": 72, "get_label_quality_ensemble_scor": [72, 98, 99], "weight_ensemble_members_bi": 72, "custom_weight": 72, "log_loss_search_t_valu": 72, "0001": [72, 97], "scheme": 72, "log_loss_search": 72, "log_loss": [72, 95], "1e0": 72, "1e1": 72, "1e2": 72, "2e2": 72, "quality_scor": [72, 104], "forth": 72, "top_issue_indic": 72, "rank_bi": [72, 85], "weird": [72, 83], "minu": 72, "prob_label": 72, "max_prob_not_label": 72, "AND": [72, 95], "get_epistemic_uncertainti": [73, 74], "get_aleatoric_uncertainti": [73, 74], "corrupt": [74, 106], "linearregress": [74, 98, 106], "y_with_nois": 74, "n_boot": [74, 98], "include_aleatoric_uncertainti": [74, 98], "sole": [74, 87, 90, 101, 104], "bootstrap": [74, 98, 106], "resampl": [74, 89, 98], "epistem": [74, 98, 104, 106], "aleator": [74, 98, 106], "model_final_kwarg": 74, "coars": 74, "thorough": [74, 98], "fine": [74, 88, 89, 95, 104], "grain": 74, "grid": [74, 96], "varianc": [74, 99], "epistemic_uncertainti": 74, "residu": [74, 75, 98], "deviat": [74, 103, 106], "aleatoric_uncertainti": 74, "outr": 75, "contin": 75, "raw": [75, 84, 85, 91, 92, 97, 98, 101, 103, 104, 106], "aka": [75, 89, 99, 103, 106, 108], "00323821": 75, "33692597": 75, "00191686": 75, "semant": [76, 78, 79, 100], "pixel": [76, 78, 79, 92, 104, 107], "h": [76, 78, 79, 107], "height": [76, 78, 79, 96, 107], "w": [76, 78, 79, 107], "width": [76, 78, 79, 96, 107], "labels_one_hot": [76, 79, 107], "stream": [76, 104, 108], "downsampl": [76, 78, 107], "shrink": [76, 78], "divis": [76, 78, 90], "common_label_issu": [77, 79, 81, 83, 107, 108], "filter_by_class": [77, 79, 107], "segmant": [78, 79], "num_pixel_issu": [78, 107], "product": [78, 92, 96, 98], "pixel_scor": [78, 107], "enter": 79, "legend": [79, 90, 91, 96, 102, 103, 106, 107], "colormap": 79, "background": [79, 96], "person": [79, 98, 103, 107, 108], "ambigu": [79, 83, 88, 89, 95, 97, 99, 108], "systemat": [79, 83, 101], "misunderstood": [79, 83], "issues_df": [79, 92], "class_index": 79, "issues_subset": [79, 83], "filter_by_token": [81, 83, 108], "token_score_method": 82, "sentence_score_method": 82, "sentence_score_kwarg": 82, "compris": [82, 83], "token_scor": [82, 108], "converg": 82, "toward": [82, 96], "_softmin_sentence_scor": 82, "sentence_scor": [82, 108], "token_info": 82, "02": [82, 90, 91, 96, 99, 103], "03": [82, 94, 96, 97, 99, 103, 108], "04": [82, 94, 96, 103, 104], "08": [82, 96, 99, 103, 106, 108], "commonli": [83, 85, 90, 91, 102, 108], "But": [83, 95, 99, 106, 108], "restrict": [83, 98], "reliabl": [84, 87, 89, 96, 98, 101, 107], "thousand": 84, "imagenet": [84, 97], "popular": [84, 101, 103], "centric": [84, 92, 94, 95, 100], "minut": [84, 87, 88, 89, 94, 95, 97, 101, 102, 103, 106, 107, 108], "conda": 84, "feature_embed": [84, 104], "Then": [84, 87, 88, 92, 98], "your_dataset": [84, 89, 90, 91, 92, 94, 95, 98], "column_name_of_label": [84, 89, 90, 91, 92, 94, 95], "plagu": [84, 91], "untrain": 84, "\u30c4": 84, "label_issues_info": [84, 91], "sklearn_compatible_model": 84, "framework": [84, 102, 103], "complianc": 84, "tag": [84, 102, 108], "sequenc": 84, "recognit": [84, 89, 98, 108], "train_data": [84, 87, 88, 104, 106], "gotten": 84, "test_data": [84, 87, 88, 99, 102, 104, 106], "deal": [84, 91, 96], "tutori": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "feel": [84, 89, 91, 98], "ask": [84, 98], "slack": [84, 98], "project": [84, 106], "welcom": 84, "commun": [84, 98], "guidelin": [84, 103], "piec": 84, "smart": [84, 92, 94, 95, 98], "edit": [84, 98], "easier": [84, 96, 99], "unreli": [84, 87, 89, 94, 95], "link": [84, 89, 97, 103], "older": 85, "outlin": 85, "substitut": 85, "v2": [85, 87, 94], "get_noise_indic": 85, "psx": 85, "sorted_index_method": 85, "order_label_error": 85, "label_errors_bool": 85, "latent_estim": 85, "num_label_error": 85, "learningwithnoisylabel": 85, "neatli": 85, "organ": [85, 87, 94, 97, 108], "reorgan": 85, "baseline_method": 85, "incorpor": [85, 99], "research": [85, 99], "polyplex": 85, "terminologi": 85, "label_error": 85, "quickstart": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 101, 102, 103, 104, 106, 107, 108], "sql": [87, 94], "databas": [87, 94], "excel": [87, 94], "parquet": [87, 94], "student": [87, 94, 106, 108], "grade": [87, 94, 106], "exam": [87, 94, 106], "letter": [87, 94, 108], "hundr": [87, 94], "mistak": [87, 88, 92, 94, 95], "extratreesclassifi": 87, "extratre": 87, "ranked_label_issu": [87, 88], "branch": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106], "preprocess": [87, 88, 91, 94, 96, 104, 106], "standardscal": [87, 94, 104], "labelencod": [87, 88], "train_test_split": [87, 88, 90, 91, 104], "accuracy_scor": [87, 88, 89, 95, 99], "grades_data": [87, 94], "read_csv": [87, 88, 94, 95, 96, 106], "demo": [87, 91, 94, 102], "stud_id": [87, 94], "exam_1": [87, 94, 106], "exam_2": [87, 94, 106], "exam_3": [87, 94, 106], "letter_grad": [87, 94], "f48f73": [87, 94], "53": [87, 90, 91, 94, 96, 97, 102, 103], "00": [87, 90, 91, 94, 96, 97, 104], "77": [87, 90, 91, 94, 103], "0bd4e7": [87, 94], "81": [87, 94, 95, 103, 106, 108], "great": [87, 94, 97], "particip": [87, 94], "cb9d7a": [87, 94], "61": [87, 94, 96, 99, 103, 106], "94": [87, 94, 97, 99, 103, 106], "9acca4": [87, 94], "48": [87, 94, 96, 97, 99, 103], "x_raw": [87, 94], "labels_raw": 87, "interg": [87, 88], "categorical_featur": [87, 106], "x_encod": [87, 94], "get_dummi": [87, 94, 106], "drop_first": [87, 94], "numeric_featur": [87, 94], "scaler": [87, 94, 104], "x_process": [87, 94], "fit_transform": [87, 94, 96], "bring": [87, 88, 92, 94, 95, 101, 106], "byod": [87, 88, 92, 94, 95, 101, 106], "tress": 87, "held": [87, 89, 94, 95, 97, 103, 104, 105], "straightforward": [87, 89, 94], "benefit": [87, 89, 105, 107], "num_crossval_fold": [87, 89, 94, 101], "tabl": [87, 94, 97, 101], "212": [87, 99], "review": [87, 88, 91, 94, 95, 97, 98, 99, 103, 106, 107, 108], "iloc": [87, 88, 89, 94, 95, 96, 106], "92": [87, 90, 99, 103], "93": [87, 97, 103, 106], "827": 87, "99": [87, 96, 97, 99], "86": [87, 91, 92, 94, 99, 103, 106], "74": [87, 96, 103, 106], "637": [87, 94], "79": [87, 97, 103], "65": [87, 90, 96, 103], "cheat": 87, "0pt": 87, "120": [87, 90, 91], "233": 87, "83": [87, 99, 103, 106, 108], "76": [87, 99, 102, 103, 106], "suspici": [87, 94], "carefulli": [87, 92, 94, 95], "examin": [87, 90, 91, 94, 96, 103], "labels_train": 87, "labels_test": 87, "test_siz": [87, 88, 90, 91], "acc_og": [87, 88], "783068783068783": 87, "robustli": [87, 88, 106], "14": [87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "acc_cl": [87, 88], "8095238095238095": 87, "blindli": [87, 88, 89, 98, 106], "trust": [87, 88, 89, 98, 99, 101, 105, 106], "effort": [87, 88, 106], "intent": [88, 95], "servic": [88, 95, 98], "onlin": [88, 95], "bank": [88, 95, 97], "banking77": [88, 95], "oo": [88, 95], "categori": [88, 92, 95, 96], "shortlist": [88, 95, 106], "scope": [88, 95], "logist": [88, 90, 91, 95, 101, 104], "probabilit": [88, 89], "drop": [88, 94, 96, 98, 101, 106], "earlier": [88, 108], "sentence_transform": [88, 95], "sentencetransform": [88, 95], "payment": [88, 95], "cancel_transf": [88, 95], "transfer": [88, 95], "fund": [88, 95], "cancel": [88, 95], "transact": [88, 95], "my": [88, 95], "revert": [88, 95], "morn": [88, 95], "realis": [88, 95], "yesterdai": [88, 95], "rent": [88, 95], "tomorrow": [88, 95], "raw_text": [88, 95], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "getting_spare_card": [88, 95], "visa_or_mastercard": [88, 95], "lost_or_stolen_phon": [88, 95], "card_about_to_expir": [88, 95], "card_payment_fee_charg": [88, 95], "beneficiary_not_allow": [88, 95], "supported_cards_and_curr": [88, 95], "apple_pay_or_google_pai": [88, 95], "change_pin": [88, 95], "card": [88, 95, 97], "utter": [88, 95], "encond": 88, "test_label": [88, 99, 102, 104], "suit": [88, 95, 96, 97, 98], "electra": [88, 95], "discrimin": [88, 95], "googl": [88, 95], "train_text": 88, "test_text": 88, "home": [88, 95, 97], "runner": [88, 95], "google_electra": [88, 95], "pool": [88, 95, 98, 104], "leverag": [88, 89, 95, 98, 99, 101], "computation": [88, 89, 95], "intens": [88, 89, 95], "400": [88, 95], "858371": 88, "547274": 88, "826228": 88, "966008": 88, "792449": 88, "identified_issu": [88, 106], "lowest_quality_label": [88, 89, 95, 99, 106], "to_numpi": [88, 95, 96, 106], "44": [88, 96, 97, 102, 103], "646": 88, "390": 88, "628": 88, "121": [88, 99], "702": 88, "863": 88, "135": 88, "337": [88, 103], "735": 88, "print_as_df": 88, "inverse_transform": 88, "charg": [88, 95], "cash": [88, 95], "holidai": [88, 95], "sent": [88, 95, 108], "mine": [88, 95], "expir": [88, 95], "fight": 88, "hors": [88, 97, 104], "duck": [88, 97], "me": [88, 95, 96], "whoever": [88, 95], "consum": [88, 106], "18": [88, 89, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "baseline_model": [88, 106], "87": [88, 91, 92, 103, 106], "acceler": [88, 106], "19": [88, 89, 92, 95, 96, 97, 98, 99, 103, 104, 106, 107, 108], "89": [88, 90, 94, 103, 106], "spoken": 89, "500": [89, 96, 104, 108], "english": [89, 97], "pronunci": 89, "wav": 89, "huggingfac": [89, 90, 91, 92, 98], "voxceleb": 89, "speech": [89, 108], "your_pred_prob": [89, 90, 91, 94, 95], "tensorflow_io": 89, "huggingface_hub": 89, "reproduc": [89, 94, 96, 99, 101], "command": 89, "wget": [89, 103, 107, 108], "navig": 89, "browser": 89, "jakobovski": 89, "archiv": [89, 108], "v1": 89, "tar": [89, 96, 104], "gz": [89, 96, 104], "mkdir": [89, 108], "spoken_digit": 89, "xf": 89, "6_nicolas_32": 89, "data_path": 89, "listdir": 89, "nondeterminist": 89, "file_nam": 89, "endswith": 89, "file_path": 89, "join": [89, 92, 96, 98], "7_george_26": 89, "0_nicolas_24": 89, "0_nicolas_6": 89, "listen": 89, "display_exampl": 89, "expand": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "pulldown": [89, 90, 91, 92, 97, 99, 101, 102, 104, 106, 108], "colab": [89, 90, 91, 92, 97, 98, 99, 101, 102, 104, 106, 108], "tfio": 89, "pathlib": 89, "ipython": [89, 96], "load_wav_16k_mono": 89, "filenam": 89, "khz": 89, "file_cont": 89, "read_fil": 89, "sample_r": 89, "decode_wav": 89, "desired_channel": 89, "squeez": 89, "rate_in": 89, "rate_out": 89, "16000": 89, "wav_file_nam": 89, "audio_r": 89, "wav_file_exampl": 89, "plai": [89, 97, 98], "button": 89, "wav_file_name_exampl": 89, "7_jackson_43": 89, "hear": 89, "extractor": 89, "encoderclassifi": 89, "spkrec": 89, "xvect": 89, "feature_extractor": 89, "from_hparam": 89, "run_opt": 89, "uncom": [89, 96], "ffmpeg": 89, "backend": 89, "wav_audio_file_path": 89, "torchaudio": 89, "extract_audio_embed": 89, "emb": [89, 92], "signal": 89, "encode_batch": 89, "embeddings_list": [89, 92], "embeddings_arrai": 89, "512": [89, 92], "196311": 89, "319459": 89, "478975": 89, "2890875": 89, "8170238": 89, "89265": 89, "898056": 89, "256195": 89, "559641": 89, "559721": 89, "62067": 89, "285245": 89, "21": [89, 90, 96, 97, 99, 103, 106, 108], "709627": 89, "5033693": 89, "913803": 89, "819831": 89, "1831515": 89, "208763": 89, "084257": 89, "3210397": 89, "005453": 89, "216152": 89, "478235": 89, "6821785": 89, "053807": 89, "242471": 89, "091424": 89, "78334856": 89, "03954": 89, "23": [89, 92, 96, 97, 99, 103, 106], "569176": 89, "761097": 89, "1258295": 89, "753237": 89, "3508866": 89, "598274": 89, "23712": 89, "2500": 89, "tol": 89, "decreas": [89, 96, 98], "cv_accuraci": 89, "9708": 89, "issue_type_descript": [89, 90, 91, 92, 94, 95, 99], "lt": [89, 90, 91, 92, 94, 95, 96, 97, 99, 101, 104], "gt": [89, 90, 91, 92, 94, 95, 96, 99, 101, 108], "9976": 89, "986": 89, "002161": 89, "176": [89, 97, 99, 102], "002483": 89, "2318": 89, "004411": 89, "1005": 89, "004857": 89, "1871": 89, "007494": 89, "040587": 89, "999207": 89, "999377": 89, "975220": 89, "999367": 89, "identified_label_issu": [89, 95], "516": 89, "1946": 89, "469": 89, "2132": 89, "worth": [89, 99], "6_yweweler_25": 89, "7_nicolas_43": 89, "6_theo_27": 89, "6_yweweler_36": 89, "6_yweweler_14": 89, "6_yweweler_35": 89, "6_nicolas_8": 89, "sound": 89, "quit": [89, 104], "underneath": 90, "hood": [90, 96, 98], "alert": 90, "introduct": 90, "mayb": [90, 91, 95], "your_feature_matrix": [90, 91], "toi": [90, 91, 92, 96, 97, 99, 101], "inf": [90, 91], "mid": [90, 91], "bins_map": [90, 91], "create_data": [90, 91], "y_bin": [90, 91], "y_i": [90, 91], "y_bin_idx": [90, 91], "y_train": [90, 91, 99, 106], "y_test": [90, 91, 99, 106], "y_train_idx": [90, 91], "y_test_idx": [90, 91], "slide": [90, 91, 97], "frame": [90, 91], "x_out": [90, 91], "tini": [90, 91], "concaten": [90, 91, 105], "y_out": [90, 91], "y_out_bin": [90, 91], "y_out_bin_idx": [90, 91], "exact_duplicate_idx": [90, 91], "x_duplic": [90, 91], "y_duplic": [90, 91], "y_duplicate_idx": [90, 91], "noisy_labels_idx": [90, 91, 102], "scatter": [90, 91, 96, 99, 102, 106], "black": [90, 91, 97, 106], "cyan": [90, 91], "plot_data": [90, 91, 96, 99, 102, 106], "fig": [90, 91, 92, 96, 97, 104, 106], "ax": [90, 91, 92, 96, 104, 106], "subplot": [90, 91, 92, 96, 104], "set_titl": [90, 91, 92, 96, 104], "set_xlabel": [90, 91], "x_1": [90, 91], "fontsiz": [90, 91, 92, 96, 99, 102], "set_ylabel": [90, 91], "x_2": [90, 91], "set_xlim": [90, 91], "set_ylim": [90, 91], "linestyl": [90, 91, 96], "circl": [90, 91, 99, 102], "misclassifi": [90, 91], "zip": [90, 91, 92, 96, 103, 108], "label_err": [90, 91], "180": [90, 91, 103], "marker": [90, 91], "facecolor": [90, 91, 96], "edgecolor": [90, 91, 96], "linewidth": [90, 91, 96, 104], "dup": [90, 91], "first_legend": [90, 91], "align": [90, 91], "title_fontproperti": [90, 91], "semibold": [90, 91], "second_legend": [90, 91], "45": [90, 91, 96, 97, 99, 103], "gca": [90, 91], "add_artist": [90, 91], "tight_layout": [90, 91, 96], "ideal": [90, 91], "remaind": 90, "modal": [90, 91, 98, 101], "132": [90, 91, 99, 103], "9318": 90, "006940": 90, "007830": 90, "40": [90, 91, 95, 96, 97], "014828": 90, "107": [90, 91, 99, 102], "021241": 90, "026407": 90, "notic": [90, 99, 101, 103], "3558": [90, 91], "126": [90, 91, 99, 103], "006636": [90, 91], "130": [90, 91], "012571": [90, 91], "129": [90, 91], "127": [90, 91], "014909": [90, 91], "128": [90, 91, 92], "017443": [90, 91], "6160": [90, 91], "131": [90, 91, 107], "000000e": [90, 91], "000002": [90, 91], "463180e": [90, 91], "07": [90, 91, 92, 94, 96, 99, 103, 106, 108], "51": [90, 91, 94, 96, 97, 99, 103], "161148": [90, 91], "859087e": [90, 91], "30": [90, 91, 92, 96, 97, 98, 102, 107, 108], "3453": 90, "029542": 90, "031182": 90, "057961": 90, "058244": 90, "54": [90, 96, 97, 99, 103], "039122": 90, "044598": 90, "105": [90, 103], "105196": 90, "133654": 90, "43": [90, 96, 97, 99, 103], "168033": 90, "125": 90, "101107": 90, "183382": 90, "109": [90, 97, 103], "209259": 90, "211042": 90, "221316": 90, "average_ood_scor": 90, "34530442089193386": 90, "52": [90, 96, 97, 103, 108], "169820": 90, "087324e": 90, "259024": 90, "583757e": 90, "91": [90, 103], "346458": 90, "341292e": 90, "specfi": 90, "new_lab": 90, "scoring_funct": 90, "div": 90, "rem": 90, "inv_scal": 90, "49": [90, 96, 97, 99, 103], "superstitionissuemanag": 90, "unlucki": 90, "superstit": 90, "to_seri": 90, "issues_mask": 90, "summary_scor": 90, "9242": 90, "is_superstition_issu": 90, "superstition_scor": 90, "26": [90, 92, 96, 97, 99, 101, 103], "047581": 90, "090635": 90, "129591": 90, "164840": 90, "lurk": [91, 92, 99], "thoroughli": 91, "8561": 91, "001908": 91, "003564": 91, "007331": 91, "008963": 91, "009664": 91, "0227": 91, "022727": 91, "conceptu": 91, "856061": 91, "355772": 91, "616034": 91, "821750": 91, "901562": 91, "betweeen": 91, "859131": 91, "417707": 91, "664083": 91, "970324": 91, "816953": 91, "375317": 91, "641516": 91, "890575": 91, "531021": 91, "460593": 91, "601188": 91, "826147": 91, "752808": 91, "321635": 91, "562539": 91, "948362": 91, "090243": 91, "472909": 91, "746763": 91, "878267": 91, "examples_w_issu": [91, 98], "013445": 91, "025184": 91, "026376": 91, "inde": [91, 95], "miscellan": [91, 93, 108], "428571": 91, "111111": 91, "571429": 91, "407407": 91, "592593": 91, "337838": 91, "092593": 91, "662162": 91, "333333": [91, 97], "952381": 91, "666667": [91, 96], "portion": 91, "huge": [91, 99], "worri": [91, 95], "critic": 91, "60": [92, 96, 99, 106], "torchvis": [92, 96, 104], "tensordataset": 92, "stratifiedkfold": [92, 102], "tqdm": 92, "autonotebook": 92, "math": 92, "fashion_mnist": 92, "num_row": [92, 96], "60000": 92, "transformed_dataset": [92, 96], "with_format": 92, "255": [92, 97], "cpu_count": 92, "torch_dataset": 92, "quick": [92, 102, 104], "super": [92, 94, 95], "relu": 92, "batchnorm2d": 92, "maxpool2d": 92, "lazylinear": 92, "flatten": [92, 96], "get_test_accuraci": 92, "testload": [92, 104], "energi": 92, "trainload": [92, 104], "n_epoch": 92, "patienc": 92, "criterion": 92, "crossentropyloss": 92, "adamw": 92, "best_test_accuraci": 92, "start_epoch": 92, "running_loss": 92, "best_epoch": 92, "end_epoch": 92, "3f": [92, 106], "acc": [92, 99], "time_taken": 92, "compute_embed": 92, "compute_pred_prob": 92, "train_batch_s": 92, "num_work": 92, "worker": [92, 108], "train_id_list": 92, "test_id_list": 92, "train_id": 92, "test_id": 92, "embeddings_model": 92, "ntrain": 92, "trainset": 92, "testset": 92, "pin_memori": 92, "fold_embed": 92, "fold_pred_prob": 92, "finish": 92, "482": 92, "720": 92, "749": 92, "329": [92, 94, 103], "88": [92, 97, 99, 102, 103, 106], "195": 92, "439": 92, "493": 92, "060": 92, "851": 92, "330": [92, 103], "505": 92, "491": 92, "476": 92, "340": 92, "739": 92, "328": [92, 103], "310": 92, "490": 92, "reorder": 92, "hstack": [92, 98, 99, 101], "vision": 92, "grayscal": [92, 96], "max_preval": [92, 96], "7714": 92, "3772": 92, "3585": 92, "166": 92, "3651": 92, "27080": 92, "873833e": 92, "40378": 92, "915575e": 92, "25316": 92, "390277e": 92, "06": [92, 99, 103, 108], "2090": 92, "751164e": 92, "14999": 92, "881301e": 92, "9569": 92, "11262": 92, "000003": 92, "coat": [92, 97], "shirt": [92, 97], "19228": 92, "000010": 92, "dress": 92, "32657": 92, "000013": 92, "bag": [92, 97, 104, 105], "21282": 92, "000016": 92, "53564": 92, "000018": 92, "pullov": 92, "6321": 92, "30968": 92, "001267": 92, "30659": 92, "000022": [92, 108], "47824": 92, "001454": 92, "3370": 92, "000026": 92, "54565": 92, "001854": 92, "9762": 92, "258": 92, "47139": 92, "000033": 92, "166980": 92, "986195": 92, "997205": 92, "sandal": [92, 97], "948781": 92, "999358": 92, "54078": 92, "17371": 92, "000025": 92, "plot_label_issue_exampl": 92, "ncol": [92, 104], "nrow": [92, 104], "ceil": 92, "axes_list": 92, "label_issue_indic": 92, "gl": 92, "sl": 92, "fontdict": 92, "imshow": [92, 96, 104], "cmap": [92, 96, 106], "grai": 92, "subplots_adjust": 92, "hspace": 92, "outsiz": 92, "outlier_issu": [92, 95], "outlier_issues_df": 92, "depict": [92, 102, 103, 104, 105, 107], "plot_outlier_issues_exampl": 92, "n_comparison_imag": 92, "sample_from_class": 92, "number_of_sampl": 92, "non_outlier_indic": 92, "isnul": [92, 96], "non_outlier_indices_excluding_curr": 92, "sampled_indic": 92, "label_scores_of_sampl": 92, "top_score_indic": 92, "top_label_indic": 92, "sampled_imag": 92, "get_image_given_label_and_sampl": 92, "image_from_dataset": 92, "corresponding_label": 92, "comparison_imag": 92, "images_to_plot": 92, "idlist": 92, "iterrow": 92, "near_duplicate_issu": [92, 98], "closest": 92, "counterpart": 92, "near_duplicate_issues_df": 92, "plot_near_duplicate_issue_exampl": 92, "seen_id_pair": 92, "get_image_and_given_label_and_predicted_label": 92, "duplicate_imag": 92, "nd_set": 92, "challeng": 92, "dark_issu": 92, "reveal": [92, 103, 107], "dark_scor": [92, 96], "dark_issues_df": 92, "is_dark_issu": 92, "34848": 92, "203922": 92, "50270": 92, "204588": 92, "3936": 92, "213098": 92, "733": 92, "217686": 92, "8094": 92, "230118": 92, "plot_image_issue_exampl": 92, "difficult": 92, "disproportion": [92, 96], "lowinfo_issu": 92, "low_information_scor": [92, 96], "lowinfo_issues_df": 92, "is_low_information_issu": 92, "53050": 92, "067975": 92, "40875": 92, "089929": 92, "9594": 92, "092601": 92, "34825": 92, "107744": 92, "37530": 92, "108516": 92, "lot": 92, "workflow": [93, 98, 100, 106], "histgradientboostingclassifi": 94, "cat_featur": 94, "boost": [94, 98, 101, 106], "xgboost": [94, 98, 106], "think": [94, 95, 98, 102, 107, 108], "nonzero": 94, "358": 94, "941": 94, "294": [94, 103], "46": [94, 96, 97, 99, 103], "7109": 94, "000005": [94, 95], "886": 94, "000059": 94, "709": 94, "000104": 94, "723": 94, "000169": 94, "689": 94, "000181": 94, "3590": 94, "051882e": 94, "683133e": 94, "536582e": 94, "406589e": 94, "324246e": 94, "6165": 94, "582": 94, "185": [94, 96, 97, 103], "187": [94, 97], "898": 94, "0000": [94, 95, 97, 99], "865": 94, "515002": 94, "837": 94, "556480": 94, "622": 94, "593068": 94, "593207": 94, "920": 94, "618041": 94, "4386345844794593e": 94, "issue_result": 94, "000842": 94, "555944": 94, "004374": 94, "sorted_issu": 94, "73": [94, 96, 97, 102, 103, 106], "deserv": 94, "outlier_result": 94, "sorted_outli": 94, "56": [94, 96, 97, 106], "96": [94, 96, 97, 99, 102, 103, 106], "style": [94, 96, 107], "font": 94, "18px": 94, "ff00ff": 94, "bac": 94, "unintend": [94, 95, 96], "duplicate_result": 94, "lowest_scoring_dupl": 94, "idxmin": [94, 98], "indices_to_displai": 94, "tolist": [94, 98, 102], "perhap": [94, 99, 101], "second_lowest_scoring_dupl": 94, "next_indices_to_displai": 94, "wari": [94, 95, 98], "dive": [95, 96], "your_featur": 95, "text_embed": 95, "data_dict": [95, 99, 101], "85": [95, 103], "38": [95, 96, 97, 103], "9710": 95, "981": 95, "974": 95, "000146": 95, "982": [95, 97], "000224": 95, "971": 95, "000507": 95, "980": [95, 97], "000960": 95, "3584": 95, "994": 95, "009642": 95, "999": 95, "013067": 95, "013841": 95, "433": 95, "014722": 95, "989": 95, "018224": 95, "6070": 95, "160": [95, 106], "095724": 95, "148": 95, "006237": 95, "546": 95, "099341": 95, "514": 95, "006485": 95, "481": 95, "123418": 95, "008165": 95, "313": [95, 103], "564102": 95, "572258": 95, "574915": 95, "31": [95, 96, 97, 99, 101, 103], "575507": 95, "575874": 95, "792090": 95, "257611": 95, "698710": 95, "182121": 95, "771619": 95, "data_with_suggested_label": 95, "suggested_label": 95, "withdraw": 95, "monei": 95, "lowest_quality_outli": 95, "OR": 95, "636c65616e6c616220697320617765736f6d6521": 95, "phone": [95, 97], "gone": 95, "samp": 95, "br": 95, "press": [95, 108], "nonsens": 95, "sens": 95, "detriment": 95, "duplicate_issu": 95, "fee": 95, "go": [95, 96, 97, 99], "strongli": 95, "p_valu": 95, "benign": 95, "curat": 95, "bigger": 96, "make_classif": 96, "5000": [96, 104], "n_featur": 96, "n_inform": 96, "n_redund": 96, "n_repeat": 96, "n_class": 96, "n_clusters_per_class": 96, "flip_i": 96, "class_sep": 96, "faiss": 96, "x_faiss": 96, "float32": [96, 103], "normalize_l2": 96, "index_factori": 96, "hnsw32": 96, "flat": [96, 97], "metric_inner_product": 96, "a_min": 96, "a_max": 96, "create_knn_graph": 96, "assert": 96, "indices_1d": 96, "ravel": 96, "distances_1d": 96, "sort_graph_by_row_valu": 96, "warn_when_not_sort": 96, "50000": 96, "523": 96, "991400": 96, "356958": 96, "362": 96, "619565": 96, "108": [96, 103], "500000": 96, "651929": 96, "999827": 96, "031217": 96, "933716": 96, "627345": 96, "998540": 96, "530909": 96, "296974": 96, "646765": 96, "942721": 96, "332824": 96, "803246": 96, "625202": 96, "999816": 96, "474031": 96, "706253": 96, "655108": 96, "997703": 96, "131466": 96, "912389": 96, "639200": 96, "4995": 96, "998646": 96, "504755": 96, "746777": 96, "680033": 96, "4996": 96, "894230": 96, "340986": 96, "816472": 96, "640711": 96, "4997": 96, "999100": 96, "428545": 96, "592421": 96, "658949": 96, "4998": 96, "986792": 96, "273710": 96, "618033": 96, "4999": 96, "986776": 96, "273524": 96, "618084": 96, "instabl": 96, "proxim": 96, "analys": 96, "comfort": 96, "explor": [96, 103, 104], "third": 96, "parti": [96, 108], "newsgroup": 96, "alt": [96, 97], "atheism": [96, 97], "sci": [96, 97], "fetch_20newsgroup": 96, "newsgroups_train": 96, "header": 96, "footer": 96, "quot": 96, "df_text": 96, "target_nam": 96, "enlighten": 96, "omnipot": 96, "19apr199320262420": 96, "kelvin": 96, "jpl": 96, "nasa": 96, "gov": 96, "baa": 96, "nhenri": 96, "he": 96, "nno": 96, "ge": 96, "nlucki": 96, "babi": [96, 97], "tfidfvector": 96, "feature_extract": 96, "x_vector": 96, "data_valuation_issu": 96, "147": [96, 99, 103], "500047": 96, "500093": 96, "499953": 96, "1068": 96, "1069": 96, "1070": 96, "1071": 96, "1072": 96, "1073": 96, "concentr": 96, "seaborn": 96, "sn": 96, "distinguish": 96, "strip": 96, "stripplot": 96, "hue": [96, 106], "dodg": 96, "jitter": 96, "axvlin": [96, 104], "xlabel": 96, "ourselv": 96, "make_blob": 96, "center": [96, 97], "cluster_std": 96, "n_noisy_label": 96, "meaning": [96, 98, 104], "silhouette_scor": 96, "gridsearchcv": 96, "silhouett": 96, "cluster_label": 96, "fit_predict": 96, "param_grid": 96, "grid_search": 96, "best_kmean": 96, "best_estimator_": 96, "underperforming_group_issu": 96, "328308": 96, "tab10": 96, "domain": 96, "knowledg": [96, 99], "dataset_tsv": 96, "ag": [96, 106], "gender": 96, "educ": 96, "experi": 96, "highsalari": 96, "indiana": 96, "phd": 96, "male": 96, "bachelor": 96, "femal": 96, "kansa": 96, "school": [96, 97], "ohio": 96, "57": [96, 97, 99], "california": 96, "59": [96, 97, 103], "34": [96, 97, 99, 101, 103, 108], "63": [96, 99, 103, 106], "47": [96, 97, 103], "stringio": 96, "sep": [96, 108], "simplic": [96, 102], "ordinalencod": 96, "columns_to_encod": 96, "encoded_df": 96, "salari": 96, "573681": 96, "underpin": 96, "caught": 96, "whenev": 96, "generate_data_depend": 96, "num_sampl": 96, "a1": 96, "a2": 96, "a3": 96, "375": 96, "975": 96, "non_iid_issu": 96, "796474": 96, "842432": 96, "922562": 96, "820759": 96, "873136": 96, "887373": 96, "825101": 96, "855875": 96, "751795": 96, "835796": 96, "ylabel": [96, 104], "coolwarm": 96, "colorbar": [96, 106], "strong": 96, "evid": 96, "inter": 96, "mitig": 96, "risk": 96, "deeper": 96, "tsv": 96, "tab": 96, "pars": 96, "annual_spend": 96, "number_of_transact": 96, "last_purchase_d": 96, "rural": 96, "4099": 96, "2024": [96, 108], "6421": 96, "nat": 96, "suburban": 96, "5436": 96, "4046": 96, "66": [96, 97], "3467": 96, "67": [96, 97, 103, 106], "4757": 96, "4199": 96, "4991": 96, "4655": 96, "82": [96, 97, 99, 103, 106], "5584": 96, "urban": 96, "3102": 96, "6637": 96, "9167": 96, "6790": 96, "5327": 96, "parse_d": 96, "lose": 96, "intact": 96, "encode_categorical_column": 96, "placehold": 96, "dropna": [96, 101], "category_to_numb": 96, "_encod": 96, "gender_encod": 96, "location_encod": 96, "focus": [96, 99, 101, 102, 106], "null_issu": 96, "833333": 96, "sorted_indic": [96, 103], "sorted_df": 96, "nice": 96, "styler": 96, "combined_df": 96, "concat": [96, 106], "highlight_null_valu": 96, "val": [96, 99], "yellow": [96, 97], "highlight_datalab_column": 96, "lightblu": 96, "highlight_is_null_issu": 96, "orang": [96, 97], "styled_df": 96, "nbsp": [96, 98, 99], "160000": 96, "820000": 96, "460000": 96, "470000": 96, "960000": 96, "620000": 96, "550000": 96, "660000": 96, "670000": [96, 97], "370000": 96, "530000": 96, "710000": 96, "020000": 96, "320000": 96, "990000": 96, "rarer": 96, "fairer": 96, "randomli": [96, 99], "class_imbalance_issu": 96, "countplot": 96, "xtick": 96, "rotat": 96, "ytick": 96, "filtered_df": 96, "xy": 96, "va": 96, "textual": 96, "get_ytick": 96, "nbar": 96, "nimbal": 96, "get_legend_handles_label": 96, "title_fonts": 96, "mislead": 96, "pinpoint": 96, "blurry_scor": 96, "odd_aspect_ratio_scor": 96, "enhanc": [96, 99, 101, 103], "alter": [96, 98], "darken": 96, "reduct": 96, "aspect": 96, "cifar10": 96, "markdown": 96, "root": [96, 104], "selected_class": 96, "convert_to_png_imag": 96, "bytesio": [96, 97], "seek": 96, "max_num_imag": 96, "list_imag": 96, "list_label": 96, "num_imag": 96, "img": [96, 104, 106], "toronto": [96, 104], "edu": [96, 104], "kriz": [96, 104], "170498071": [96, 104], "101603839": 96, "38it": 96, "dataset_dict": 96, "from_dict": [96, 98], "apply_dark": 96, "transformed_list_imag": 96, "transformed_dataset_dict": 96, "plot_imag": [96, 104], "num_images_to_plot": 96, "num_col": 96, "hide": 96, "get_property_scor": 96, "_spurious_correl": 96, "get_specific_property_scor": 96, "property_scores_df": 96, "property_nam": 96, "standard_property_scor": 96, "transformed_property_scor": 96, "295": [96, 103], "light_scor": 96, "415": 96, "325": 96, "odd_size_scor": 96, "grayscale_scor": 96, "015": 96, "refin": 97, "instruct": [97, 98], "studi": [97, 103], "mnist_test_set": 97, "imagenet_val_set": 97, "tench": 97, "goldfish": 97, "white": [97, 108], "shark": 97, "tiger": 97, "hammerhead": 97, "electr": 97, "rai": 97, "stingrai": 97, "cock": 97, "hen": 97, "ostrich": 97, "brambl": 97, "goldfinch": 97, "hous": 97, "finch": 97, "junco": 97, "indigo": 97, "bunt": 97, "american": [97, 108], "robin": 97, "bulbul": 97, "jai": 97, "magpi": 97, "chickade": 97, "dipper": 97, "kite": 97, "bald": 97, "eagl": 97, "vultur": 97, "grei": 97, "owl": 97, "salamand": 97, "smooth": 97, "newt": 97, "spot": [97, 98, 103], "axolotl": 97, "bullfrog": 97, "tree": 97, "frog": [97, 104], "tail": 97, "loggerhead": 97, "sea": 97, "turtl": 97, "leatherback": 97, "mud": 97, "terrapin": 97, "band": 97, "gecko": 97, "green": [97, 108], "iguana": 97, "carolina": 97, "anol": 97, "desert": 97, "grassland": 97, "whiptail": 97, "lizard": 97, "agama": 97, "frill": 97, "neck": 97, "allig": 97, "gila": 97, "monster": 97, "european": 97, "chameleon": 97, "komodo": 97, "dragon": 97, "nile": 97, "crocodil": 97, "triceratop": 97, "worm": 97, "snake": 97, "ring": 97, "eastern": 97, "hog": 97, "nose": 97, "kingsnak": 97, "garter": 97, "water": 97, "vine": 97, "night": 97, "boa": 97, "constrictor": 97, "african": 97, "rock": 97, "indian": 97, "cobra": 97, "mamba": 97, "saharan": 97, "horn": 97, "viper": 97, "diamondback": 97, "rattlesnak": 97, "sidewind": 97, "trilobit": 97, "harvestman": 97, "scorpion": 97, "garden": 97, "spider": 97, "barn": 97, "southern": 97, "widow": 97, "tarantula": 97, "wolf": 97, "tick": 97, "centiped": 97, "grous": 97, "ptarmigan": 97, "ruf": 97, "prairi": 97, "peacock": 97, "quail": 97, "partridg": 97, "parrot": 97, "macaw": 97, "sulphur": 97, "crest": 97, "cockatoo": 97, "lorikeet": 97, "coucal": 97, "bee": 97, "eater": 97, "hornbil": 97, "hummingbird": 97, "jacamar": 97, "toucan": 97, "breast": 97, "mergans": 97, "goos": 97, "swan": 97, "tusker": 97, "echidna": 97, "platypu": 97, "wallabi": 97, "koala": 97, "wombat": 97, "jellyfish": 97, "anemon": 97, "brain": 97, "coral": 97, "flatworm": 97, "nematod": 97, "conch": 97, "snail": 97, "slug": 97, "chiton": 97, "chamber": 97, "nautilu": 97, "dung": 97, "crab": 97, "fiddler": 97, "king": 97, "lobster": 97, "spini": 97, "crayfish": 97, "hermit": 97, "isopod": 97, "stork": 97, "spoonbil": 97, "flamingo": 97, "heron": 97, "egret": 97, "bittern": 97, "crane": 97, "bird": [97, 104], "limpkin": 97, "gallinul": 97, "coot": 97, "bustard": 97, "ruddi": 97, "turnston": 97, "dunlin": 97, "redshank": 97, "dowitch": 97, "oystercatch": 97, "pelican": 97, "penguin": 97, "albatross": 97, "whale": 97, "killer": 97, "dugong": 97, "lion": 97, "chihuahua": 97, "japanes": 97, "chin": 97, "maltes": 97, "pekinges": 97, "shih": 97, "tzu": 97, "charl": 97, "spaniel": 97, "papillon": 97, "terrier": 97, "rhodesian": 97, "ridgeback": 97, "afghan": [97, 108], "hound": 97, "basset": 97, "beagl": 97, "bloodhound": 97, "bluetick": 97, "coonhound": 97, "tan": 97, "walker": 97, "foxhound": 97, "redbon": 97, "borzoi": 97, "irish": 97, "wolfhound": 97, "italian": 97, "greyhound": 97, "whippet": 97, "ibizan": 97, "norwegian": 97, "elkhound": 97, "otterhound": 97, "saluki": 97, "scottish": 97, "deerhound": 97, "weimaran": 97, "staffordshir": 97, "bull": 97, "bedlington": 97, "border": 97, "kerri": 97, "norfolk": 97, "norwich": 97, "yorkshir": 97, "wire": 97, "fox": 97, "lakeland": 97, "sealyham": 97, "airedal": 97, "cairn": 97, "australian": 97, "dandi": 97, "dinmont": 97, "boston": 97, "miniatur": 97, "schnauzer": 97, "giant": 97, "tibetan": 97, "silki": 97, "wheaten": 97, "west": 97, "highland": 97, "lhasa": 97, "apso": 97, "retriev": 97, "curli": 97, "golden": 97, "labrador": 97, "chesapeak": 97, "bai": 97, "german": [97, 108], "shorthair": 97, "pointer": 97, "vizsla": 97, "setter": 97, "gordon": 97, "brittani": 97, "clumber": 97, "springer": 97, "welsh": 97, "cocker": 97, "sussex": 97, "kuvasz": 97, "schipperk": 97, "groenendael": 97, "malinoi": 97, "briard": 97, "kelpi": 97, "komondor": 97, "sheepdog": 97, "shetland": 97, "colli": 97, "bouvier": 97, "de": 97, "flandr": 97, "rottweil": 97, "shepherd": 97, "dobermann": 97, "pinscher": 97, "swiss": [97, 108], "mountain": 97, "bernes": 97, "appenzel": 97, "sennenhund": 97, "entlebuch": 97, "boxer": 97, "bullmastiff": 97, "mastiff": 97, "french": 97, "bulldog": 97, "dane": 97, "st": 97, "bernard": 97, "huski": 97, "alaskan": 97, "malamut": 97, "siberian": 97, "dalmatian": 97, "affenpinsch": 97, "basenji": 97, "pug": 97, "leonberg": 97, "newfoundland": 97, "pyrenean": 97, "samoi": 97, "pomeranian": 97, "chow": 97, "keeshond": 97, "griffon": 97, "bruxelloi": 97, "pembrok": 97, "corgi": 97, "cardigan": 97, "poodl": 97, "mexican": 97, "hairless": 97, "tundra": 97, "coyot": 97, "dingo": 97, "dhole": 97, "wild": 97, "hyena": 97, "kit": 97, "arctic": 97, "tabbi": 97, "persian": 97, "siames": 97, "egyptian": 97, "mau": 97, "cougar": 97, "lynx": 97, "leopard": 97, "snow": 97, "jaguar": 97, "cheetah": 97, "brown": [97, 107], "bear": 97, "polar": 97, "sloth": 97, "mongoos": 97, "meerkat": 97, "beetl": 97, "ladybug": 97, "longhorn": 97, "leaf": 97, "rhinocero": 97, "weevil": 97, "fly": 97, "ant": 97, "grasshopp": 97, "cricket": 97, "stick": 97, "insect": 97, "cockroach": 97, "manti": 97, "cicada": 97, "leafhopp": 97, "lacew": 97, "dragonfli": 97, "damselfli": 97, "admir": 97, "ringlet": 97, "monarch": 97, "butterfli": 97, "gossam": 97, "wing": 97, "starfish": 97, "urchin": 97, "cucumb": 97, "cottontail": 97, "rabbit": 97, "hare": 97, "angora": 97, "hamster": 97, "porcupin": 97, "squirrel": 97, "marmot": 97, "beaver": 97, "guinea": 97, "pig": 97, "sorrel": 97, "zebra": 97, "boar": 97, "warthog": 97, "hippopotamu": 97, "ox": 97, "buffalo": 97, "bison": 97, "bighorn": 97, "sheep": 97, "alpin": 97, "ibex": 97, "hartebeest": 97, "impala": 97, "gazel": 97, "dromedari": 97, "llama": 97, "weasel": 97, "mink": 97, "polecat": 97, "foot": 97, "ferret": 97, "otter": 97, "skunk": 97, "badger": 97, "armadillo": 97, "toed": 97, "orangutan": 97, "gorilla": 97, "chimpanze": 97, "gibbon": 97, "siamang": 97, "guenon": 97, "pata": 97, "monkei": 97, "baboon": 97, "macaqu": 97, "langur": 97, "colobu": 97, "probosci": 97, "marmoset": 97, "capuchin": 97, "howler": 97, "titi": 97, "geoffroi": 97, "lemur": 97, "indri": 97, "asian": 97, "eleph": 97, "bush": 97, "snoek": 97, "eel": 97, "coho": 97, "salmon": 97, "beauti": 97, "clownfish": 97, "sturgeon": 97, "garfish": 97, "lionfish": 97, "pufferfish": 97, "abacu": 97, "abaya": 97, "academ": 97, "gown": 97, "accordion": 97, "acoust": 97, "guitar": 97, "aircraft": 97, "carrier": 97, "airlin": 97, "airship": 97, "altar": 97, "ambul": 97, "amphibi": 97, "clock": [97, 108], "apiari": 97, "apron": 97, "wast": 97, "assault": 97, "rifl": 97, "backpack": 97, "bakeri": 97, "balanc": 97, "beam": 97, "balloon": 97, "ballpoint": 97, "pen": 97, "aid": 97, "banjo": 97, "balust": 97, "barbel": 97, "barber": 97, "chair": [97, 103], "barbershop": 97, "baromet": 97, "barrel": 97, "wheelbarrow": 97, "basebal": 97, "basketbal": 97, "bassinet": 97, "bassoon": 97, "swim": 97, "cap": 97, "bath": 97, "towel": 97, "bathtub": 97, "station": 97, "wagon": 97, "lighthous": 97, "beaker": 97, "militari": 97, "beer": 97, "bottl": 97, "glass": 97, "bell": 97, "cot": 97, "bib": 97, "bicycl": [97, 107], "bikini": 97, "binder": 97, "binocular": 97, "birdhous": 97, "boathous": 97, "bobsleigh": 97, "bolo": 97, "tie": 97, "poke": 97, "bonnet": 97, "bookcas": 97, "bookstor": 97, "bow": 97, "brass": 97, "bra": 97, "breakwat": 97, "breastplat": 97, "broom": 97, "bucket": 97, "buckl": 97, "bulletproof": 97, "vest": 97, "butcher": 97, "shop": 97, "taxicab": 97, "cauldron": 97, "candl": 97, "cannon": 97, "cano": 97, "mirror": [97, 103], "carousel": 97, "tool": [97, 99, 101], "carton": 97, "wheel": 97, "teller": 97, "cassett": 97, "player": 97, "castl": 97, "catamaran": 97, "cd": 97, "cello": 97, "mobil": [97, 108], "chain": 97, "fenc": [97, 107], "mail": 97, "chainsaw": 97, "chest": 97, "chiffoni": 97, "chime": 97, "china": 97, "cabinet": 97, "christma": 97, "stock": 97, "church": 97, "movi": 97, "theater": 97, "cleaver": 97, "cliff": 97, "dwell": 97, "cloak": 97, "clog": 97, "cocktail": 97, "shaker": 97, "coffe": 97, "mug": 97, "coffeemak": 97, "coil": 97, "lock": 97, "keyboard": 97, "confectioneri": 97, "ship": [97, 104], "corkscrew": 97, "cornet": 97, "cowboi": 97, "boot": 97, "hat": 97, "cradl": 97, "crash": 97, "helmet": 97, "crate": 97, "infant": 97, "bed": 97, "crock": 97, "pot": 97, "croquet": 97, "crutch": 97, "cuirass": 97, "dam": 97, "desk": 97, "desktop": 97, "rotari": 97, "dial": 97, "telephon": 97, "diaper": 97, "watch": 97, "dine": 97, "dishcloth": 97, "dishwash": 97, "disc": 97, "brake": 97, "dock": 97, "sled": 97, "dome": 97, "doormat": 97, "drill": 97, "rig": 97, "drum": 97, "drumstick": 97, "dumbbel": 97, "dutch": 97, "oven": 97, "fan": 97, "locomot": 97, "entertain": 97, "envelop": 97, "espresso": 97, "powder": 97, "feather": 97, "fireboat": 97, "engin": [97, 107], "screen": 97, "sheet": 97, "flagpol": 97, "flute": 97, "footbal": 97, "forklift": 97, "fountain": 97, "poster": 97, "freight": 97, "fry": 97, "pan": 97, "fur": 97, "garbag": 97, "ga": 97, "pump": 97, "goblet": 97, "kart": 97, "golf": 97, "cart": 97, "gondola": 97, "gong": 97, "grand": 97, "piano": 97, "greenhous": 97, "grill": 97, "groceri": 97, "guillotin": 97, "barrett": 97, "hair": 97, "sprai": 97, "hammer": 97, "dryer": 97, "hand": [97, 99], "handkerchief": 97, "drive": 97, "harmonica": 97, "harp": 97, "harvest": 97, "hatchet": 97, "holster": 97, "honeycomb": 97, "hoop": 97, "skirt": 97, "horizont": 97, "bar": 97, "drawn": 97, "hourglass": 97, "ipod": 97, "cloth": 97, "iron": 97, "jack": 97, "lantern": 97, "jean": 97, "jeep": 97, "jigsaw": 97, "puzzl": 97, "pull": 97, "rickshaw": 97, "joystick": 97, "kimono": 97, "knee": 97, "pad": 97, "knot": 97, "ladl": 97, "lampshad": 97, "laptop": 97, "lawn": 97, "mower": 97, "knife": 97, "lifeboat": 97, "lighter": 97, "limousin": 97, "ocean": 97, "liner": 97, "lipstick": 97, "slip": 97, "shoe": 97, "lotion": 97, "speaker": 97, "loup": 97, "sawmil": 97, "magnet": 97, "compass": 97, "mailbox": 97, "tight": 97, "tank": 97, "manhol": 97, "maraca": 97, "marimba": 97, "maypol": 97, "maze": 97, "cup": [97, 103], "medicin": 97, "megalith": 97, "microphon": 97, "microwav": 97, "milk": 97, "minibu": 97, "miniskirt": 97, "minivan": 97, "missil": 97, "mitten": [97, 98], "mix": 97, "bowl": 97, "modem": 97, "monasteri": 97, "monitor": 97, "mope": 97, "mortar": 97, "mosqu": 97, "mosquito": 97, "scooter": 97, "bike": 97, "tent": 97, "mous": [97, 98], "mousetrap": 97, "van": 97, "muzzl": 97, "nail": 97, "brace": 97, "necklac": 97, "nippl": 97, "obelisk": 97, "obo": 97, "ocarina": 97, "odomet": 97, "oil": 97, "oscilloscop": 97, "overskirt": 97, "bullock": 97, "oxygen": 97, "packet": 97, "paddl": 97, "padlock": 97, "paintbrush": 97, "pajama": 97, "palac": [97, 108], "parachut": 97, "park": 97, "bench": 97, "meter": 97, "passeng": 97, "patio": 97, "payphon": 97, "pedest": 97, "pencil": 97, "perfum": 97, "petri": 97, "dish": 97, "photocopi": 97, "plectrum": 97, "pickelhaub": 97, "picket": 97, "pickup": 97, "pier": 97, "piggi": 97, "pill": 97, "pillow": 97, "ping": 97, "pong": 97, "pinwheel": 97, "pirat": 97, "pitcher": 97, "plane": 97, "planetarium": 97, "plastic": 97, "plate": 97, "rack": 97, "plow": 97, "plunger": 97, "polaroid": 97, "camera": 97, "pole": [97, 107], "polic": 97, "poncho": 97, "billiard": 97, "soda": 97, "potter": 97, "prayer": 97, "rug": 97, "printer": 97, "prison": 97, "projectil": 97, "projector": 97, "hockei": 97, "puck": 97, "punch": 97, "purs": 97, "quill": 97, "quilt": 97, "race": 97, "racket": 97, "radiat": 97, "radio": 97, "telescop": 97, "rain": 97, "recreat": 97, "reel": 97, "reflex": 97, "refriger": 97, "remot": 97, "restaur": 97, "revolv": 97, "rotisseri": 97, "eras": 97, "rugbi": 97, "ruler": 97, "safe": 97, "safeti": 97, "salt": 97, "sarong": 97, "saxophon": 97, "scabbard": 97, "bu": [97, 107], "schooner": 97, "scoreboard": 97, "crt": 97, "screw": 97, "screwdriv": 97, "seat": 97, "belt": 97, "sew": 97, "shield": 97, "shoji": 97, "basket": 97, "shovel": 97, "shower": 97, "curtain": 97, "ski": 97, "sleep": 97, "door": 97, "slot": 97, "snorkel": 97, "snowmobil": 97, "snowplow": 97, "soap": 97, "dispens": 97, "soccer": [97, 108], "sock": [97, 98], "solar": 97, "thermal": 97, "collector": 97, "sombrero": 97, "soup": 97, "heater": 97, "shuttl": 97, "spatula": 97, "motorboat": 97, "web": 97, "spindl": 97, "sport": [97, 108], "spotlight": 97, "stage": 97, "steam": 97, "arch": 97, "bridg": 97, "steel": 97, "stethoscop": 97, "scarf": 97, "stone": 97, "wall": [97, 107], "stopwatch": 97, "stove": 97, "strainer": 97, "tram": 97, "stretcher": 97, "couch": 97, "stupa": 97, "submarin": 97, "sundial": 97, "sunglass": 97, "sunscreen": 97, "suspens": 97, "mop": 97, "sweatshirt": 97, "swimsuit": 97, "swing": 97, "switch": 97, "syring": 97, "lamp": 97, "tape": 97, "teapot": 97, "teddi": 97, "televis": [97, 108], "tenni": 97, "thatch": 97, "roof": 97, "thimbl": 97, "thresh": 97, "throne": 97, "tile": 97, "toaster": 97, "tobacco": 97, "toilet": 97, "totem": 97, "tow": 97, "tractor": 97, "semi": 97, "trailer": 97, "trai": 97, "trench": 97, "tricycl": 97, "trimaran": 97, "tripod": 97, "triumphal": 97, "trolleybu": 97, "trombon": 97, "tub": 97, "turnstil": 97, "typewrit": 97, "umbrella": 97, "unicycl": 97, "upright": 97, "vacuum": 97, "cleaner": 97, "vase": 97, "vault": 97, "velvet": 97, "vend": 97, "vestment": 97, "viaduct": 97, "violin": 97, "volleybal": 97, "waffl": 97, "wallet": 97, "wardrob": 97, "sink": 97, "wash": 97, "jug": 97, "tower": 97, "whiskei": 97, "whistl": 97, "wig": 97, "shade": [97, 107], "windsor": 97, "wine": 97, "wok": 97, "wooden": 97, "spoon": 97, "wool": 97, "rail": 97, "shipwreck": 97, "yawl": 97, "yurt": 97, "websit": 97, "comic": 97, "book": 97, "crossword": 97, "traffic": [97, 103, 107], "sign": [97, 107, 108], "dust": 97, "jacket": [97, 103], "menu": 97, "guacamol": 97, "consomm": 97, "trifl": 97, "ic": 97, "cream": 97, "pop": 97, "baguett": 97, "bagel": 97, "pretzel": 97, "cheeseburg": 97, "mash": 97, "potato": 97, "cabbag": 97, "broccoli": 97, "cauliflow": 97, "zucchini": 97, "spaghetti": 97, "squash": 97, "acorn": 97, "butternut": 97, "artichok": 97, "pepper": [97, 98], "cardoon": 97, "mushroom": 97, "granni": 97, "smith": 97, "strawberri": 97, "lemon": 97, "pineappl": 97, "banana": 97, "jackfruit": 97, "custard": 97, "appl": 97, "pomegran": 97, "hai": 97, "carbonara": 97, "chocol": 97, "syrup": 97, "dough": 97, "meatloaf": 97, "pizza": 97, "pie": 97, "burrito": 97, "eggnog": 97, "alp": 97, "bubbl": 97, "reef": 97, "geyser": 97, "lakeshor": 97, "promontori": 97, "shoal": 97, "seashor": 97, "vallei": 97, "volcano": 97, "bridegroom": 97, "scuba": 97, "diver": 97, "rapese": 97, "daisi": 97, "ladi": 97, "slipper": 97, "corn": 97, "rose": 97, "hip": 97, "chestnut": 97, "fungu": 97, "agar": 97, "gyromitra": 97, "stinkhorn": 97, "earth": 97, "star": 97, "wood": 97, "bolet": 97, "ear": 97, "cifar10_test_set": 97, "airplan": [97, 104], "automobil": [97, 104], "deer": [97, 104], "cifar100_test_set": 97, "aquarium_fish": 97, "boi": 97, "camel": 97, "caterpillar": 97, "cattl": [97, 108], "cloud": 97, "dinosaur": 97, "dolphin": 97, "flatfish": 97, "forest": 97, "girl": 97, "kangaroo": 97, "lawn_mow": 97, "man": 97, "maple_tre": 97, "motorcycl": [97, 107], "oak_tre": 97, "orchid": 97, "palm_tre": 97, "pear": 97, "pickup_truck": 97, "pine_tre": 97, "plain": 97, "poppi": 97, "possum": 97, "raccoon": 97, "road": [97, 107], "rocket": 97, "seal": 97, "shrew": 97, "skyscrap": 97, "streetcar": 97, "sunflow": 97, "sweet_pepp": 97, "trout": 97, "tulip": 97, "willow_tre": 97, "woman": [97, 103], "caltech256": 97, "ak47": 97, "bat": 97, "glove": 97, "birdbath": 97, "blimp": 97, "bonsai": 97, "boom": 97, "breadmak": 97, "buddha": 97, "bulldoz": 97, "cactu": 97, "cake": 97, "tire": 97, "cartman": 97, "cereal": 97, "chandeli": 97, "chess": 97, "board": 97, "chimp": 97, "chopstick": 97, "coffin": 97, "coin": 97, "comet": 97, "cormor": 97, "globe": 97, "diamond": 97, "dice": 97, "doorknob": 97, "drink": 97, "straw": 97, "dumb": 97, "eiffel": 97, "elk": 97, "ewer": 97, "eyeglass": 97, "fern": 97, "fighter": 97, "jet": [97, 106], "extinguish": 97, "hydrant": 97, "firework": 97, "flashlight": 97, "floppi": 97, "fri": 97, "frisbe": 97, "galaxi": 97, "giraff": 97, "goat": 97, "gate": 97, "grape": 97, "pick": [97, 98], "hamburg": 97, "hammock": 97, "harpsichord": 97, "hawksbil": 97, "helicopt": 97, "hibiscu": 97, "homer": 97, "simpson": 97, "horsesho": 97, "air": 97, "skeleton": 97, "ibi": 97, "cone": 97, "iri": 97, "jesu": 97, "christ": 97, "joi": 97, "kayak": 97, "ketch": 97, "ladder": 97, "lath": 97, "licens": 97, "lightbulb": 97, "lightn": 97, "mandolin": 97, "mar": 97, "mattress": 97, "megaphon": 97, "menorah": 97, "microscop": 97, "minaret": 97, "minotaur": 97, "motorbik": 97, "mussel": 97, "neckti": 97, "octopu": 97, "palm": 97, "pilot": 97, "paperclip": 97, "shredder": 97, "pci": 97, "peopl": [97, 103], "pez": 97, "picnic": 97, "pram": 97, "prai": 97, "pyramid": 97, "rainbow": 97, "roulett": 97, "saddl": 97, "saturn": 97, "segwai": 97, "propel": 97, "sextant": 97, "music": 97, "skateboard": 97, "smokestack": 97, "sneaker": 97, "boat": 97, "stain": 97, "steer": 97, "stirrup": 97, "superman": 97, "sushi": 97, "armi": [97, 108], "sword": 97, "tambourin": 97, "teepe": 97, "court": 97, "theodolit": 97, "tomato": 97, "tombston": 97, "tour": 97, "pisa": 97, "treadmil": 97, "fork": 97, "tweezer": 97, "unicorn": 97, "vcr": 97, "waterfal": 97, "watermelon": 97, "weld": 97, "windmil": 97, "xylophon": 97, "yarmulk": 97, "yo": 97, "toad": 97, "twenty_news_test_set": 97, "comp": 97, "graphic": [97, 107], "misc": [97, 108], "sy": 97, "ibm": 97, "pc": 97, "hardwar": 97, "mac": 97, "forsal": 97, "rec": 97, "crypt": 97, "electron": 97, "med": 97, "soc": 97, "religion": 97, "christian": [97, 108], "talk": [97, 108], "polit": 97, "gun": 97, "mideast": 97, "amazon": 97, "neutral": 97, "imdb_test_set": 97, "all_class": 97, "20news_test_set": 97, "_load_classes_predprobs_label": 97, "dataset_nam": 97, "labelerror": 97, "url_bas": 97, "5392f6c71473055060be3044becdde1cbc18284d": 97, "url_label": 97, "original_test_label": 97, "_original_label": 97, "url_prob": 97, "cross_validated_predicted_prob": 97, "_pyx": 97, "num_part": 97, "datatset": 97, "allow_pickl": 97, "pred_probs_part": 97, "url": 97, "_of_": 97, "nload": 97, "imdb": 97, "ve": [97, 98, 99, 101, 103], "capit": 97, "29780": 97, "256": [97, 98, 103], "780": 97, "medic": [97, 108], "doctor": 97, "254": [97, 103], "359223": 97, "640777": 97, "184": [97, 99], "258427": 97, "341176": 97, "263158": 97, "658824": 97, "337349": 97, "246575": 97, "662651": 97, "248": 97, "330000": 97, "355769": 97, "251": [97, 103], "167": [97, 99, 103], "252": [97, 108], "112": 97, "253": [97, 103], "022989": 97, "049505": 97, "190": [97, 99, 103], "002216": 97, "000974": 97, "000873": 97, "000739": 97, "32635": 97, "32636": 97, "32637": 97, "32638": 97, "32639": 97, "32640": 97, "051": 97, "002242": 97, "997758": 97, "002088": 97, "001045": 97, "997912": 97, "002053": 97, "997947": 97, "001980": 97, "000991": 97, "998020": 97, "001946": 97, "002915": 97, "998054": 97, "001938": 97, "002904": 97, "998062": 97, "001020": 97, "998980": 97, "001018": 97, "002035": 97, "998982": 97, "999009": 97, "0003": 97, "0002": 97, "071": 97, "067269": 97, "929": 97, "046": 97, "058243": 97, "954": 97, "035": 97, "032096": 97, "965": 97, "031": 97, "012232": 97, "969": 97, "022": 97, "025896": 97, "978": 97, "020": [97, 99], "013092": 97, "018": 97, "013065": 97, "016": 97, "030542": 97, "984": 97, "013": 97, "020833": 97, "987": 97, "012": 97, "010020": 97, "988": 97, "0073": 97, "0020": 97, "0016": 97, "0015": 97, "0014": 97, "0013": 97, "0012": 97, "0010": 97, "0008": 97, "0007": 97, "0006": 97, "0005": 97, "0004": 97, "244": [97, 103, 108], "452381": 97, "459770": 97, "523364": 97, "460784": 97, "446602": 97, "103774": 97, "030612": 97, "110092": 97, "049020": 97, "0034": 97, "0032": 97, "0026": 97, "0025": 97, "4945": 97, "4946": 97, "4947": 97, "4948": 97, "4949": 97, "4950": 97, "846": 97, "7532": 97, "532": 97, "034483": 97, "009646": 97, "965517": 97, "030457": 97, "020513": 97, "969543": 97, "028061": 97, "035443": 97, "971939": 97, "025316": 97, "005168": 97, "974684": 97, "049751": 97, "979487": 97, "019920": 97, "042802": 97, "980080": 97, "017677": 97, "005115": 97, "982323": 97, "012987": 97, "005236": 97, "987013": 97, "012723": 97, "025126": 97, "987277": 97, "010989": 97, "008264": 97, "989011": 97, "010283": 97, "027778": 97, "989717": 97, "009677": 97, "990323": 97, "007614": 97, "010127": 97, "992386": 97, "005051": 97, "994949": 97, "005025": 97, "994975": 97, "005013": 97, "994987": 97, "001859": 97, "001328": 97, "000929": 97, "000664": 97, "186": [97, 99], "188": [97, 99, 102], "189": [97, 99], "snippet": 98, "nlp": [98, 108], "mind": [98, 99], "alphanumer": 98, "facilit": 98, "seamless": 98, "classlabel": 98, "guidanc": 98, "labels_str": 98, "datalab_str": 98, "labels_int": 98, "remap": 98, "datalab_int": 98, "my_dict": 98, "pet_nam": 98, "rover": 98, "rocki": 98, "speci": 98, "datalab_dataset": 98, "number_of_class": 98, "total_number_of_data_point": 98, "feed": 98, "alphabet": 98, "labels_proper_format": 98, "your_classifi": 98, "issues_datafram": 98, "class_predicted_for_flagged_exampl": 98, "class_predicted_for_all_exampl": 98, "grant": 98, "On": [98, 99, 103], "merged_dataset": 98, "label_column_nam": 98, "datataset": 98, "fair": [98, 99], "game": 98, "speedup": [98, 104], "tempfil": 98, "mkdtemp": 98, "sped": 98, "anywai": 98, "pred_probs_merg": 98, "merge_rare_class": 98, "count_threshold": 98, "class_mapping_orig2new": 98, "heath_summari": 98, "num_examples_per_class": 98, "rare_class": 98, "num_classes_merg": 98, "other_class": 98, "labels_merg": 98, "new_c": 98, "merged_prob": 98, "new_class": 98, "original_class": 98, "num_check": 98, "ones_array_ref": 98, "isclos": 98, "though": [98, 99, 108], "successfulli": 98, "virtuou": [98, 101], "cycl": [98, 101], "jointli": 98, "junk": 98, "clutter": 98, "unknown": 98, "caltech": 98, "combined_boolean_mask": 98, "mask1": 98, "mask2": 98, "gradientboostingclassifi": [98, 99], "true_error": [98, 99, 102], "101": [98, 103], "102": [98, 102, 103], "104": [98, 99, 103], "model_to_find_error": 98, "model_to_return": 98, "cl0": 98, "randomizedsearchcv": 98, "expens": 98, "param_distribut": 98, "learning_r": [98, 99], "max_depth": [98, 99], "magnitud": 98, "coeffici": [98, 106], "optin": 98, "environ": [98, 99], "rerun": [98, 99], "cell": [98, 99], "unabl": [98, 99], "render": [98, 99], "nbviewer": [98, 99], "cleanlearninginot": [98, 99], "fittedcleanlearn": [98, 99], "linearregressionlinearregress": 98, "unexpectedli": 98, "emphas": 98, "crucial": 98, "merge_duplicate_set": 98, "merge_kei": 98, "construct_group_kei": 98, "merged_set": 98, "consolidate_set": 98, "issubset": 98, "frozenset": 98, "sets_list": 98, "mutabl": 98, "new_set": 98, "current_set": 98, "intersecting_set": 98, "lowest_score_strategi": 98, "sub_df": 98, "filter_near_dupl": 98, "strategy_fn": 98, "strategy_kwarg": 98, "duplicate_row": 98, "group_kei": 98, "to_keep_indic": 98, "groupbi": 98, "explod": 98, "to_remov": 98, "isin": [98, 104], "kept": 98, "ids_to_remove_seri": 98, "assist": 98, "streamlin": 98, "ux": 98, "agpl": 98, "compani": 98, "commerci": 98, "email": 98, "team": 98, "discuss": 98, "anywher": 98, "profession": 98, "expert": 98, "depth": 99, "survei": [99, 108], "scienc": 99, "multivariate_norm": [99, 101, 102], "make_data": [99, 101], "cov": [99, 101, 102], "avg_trac": [99, 102], "py_tru": 99, "noise_matrix_tru": 99, "noise_marix": 99, "s_test": 99, "noisy_test_label": 99, "purpl": 99, "namespac": 99, "exec": 99, "markerfacecolor": [99, 102], "markeredgecolor": [99, 102, 106], "markers": [99, 102, 106], "markeredgewidth": [99, 102, 106], "realist": 99, "7560": 99, "637318e": 99, "896262e": 99, "548391e": 99, "923417e": 99, "375075e": 99, "3454": 99, "014051": 99, "020451": 99, "249": [99, 103], "042594": 99, "043859": 99, "045954": 99, "6120": 99, "023714": 99, "007136": 99, "119": [99, 103], "107266": 99, "103": [99, 103], "033738": 99, "238": [99, 103], "119505": 99, "236": [99, 103, 108], "037843": 99, "222": 99, "614915": 99, "122": [99, 103], "624422": 99, "625965": 99, "626079": 99, "118": 99, "627675": 99, "695223": 99, "323529": 99, "523015": 99, "013720": 99, "675727": 99, "646521": 99, "anyth": 99, "magic": 99, "liter": 99, "identif": 99, "x27": 99, "logisticregressionlogisticregress": 99, "ever": 99, "092": 99, "040": 99, "024": 99, "004": 99, "surpris": 99, "1705": 99, "01936": 99, "ton": 99, "yourfavoritemodel1": 99, "merged_label": 99, "merged_test_label": 99, "newli": [99, 101], "yourfavoritemodel2": 99, "yourfavoritemodel3": 99, "cl3": 99, "takeawai": 99, "my_test_pred_prob": 99, "my_test_pr": 99, "issues_test": 99, "corrected_test_label": 99, "pretend": 99, "cl_test_pr": 99, "fairli": 99, "label_acc": 99, "percentag": 99, "offset": 99, "nquestion": 99, "overestim": 99, "answer": 99, "experienc": 99, "prioiri": 99, "known": 99, "versatil": 99, "label_issues_indic": 99, "213": [99, 103], "218": [99, 103], "152": 99, "197": [99, 103], "196": [99, 103], "170": 99, "214": 99, "164": [99, 102], "198": [99, 103], "191": [99, 103], "117": [99, 106], "206": [99, 103], "115": [99, 103], "193": 99, "194": 99, "201": [99, 103], "174": 99, "163": 99, "150": [99, 101, 103, 108], "169": [99, 108], "151": [99, 103], "168": 99, "precision_scor": 99, "recall_scor": 99, "f1_score": 99, "true_label_issu": 99, "filter_by_list": 99, "718750": [99, 101], "807018": 99, "912": 99, "733333": 99, "800000": 99, "721311": 99, "792793": 99, "908": 99, "676923": 99, "765217": 99, "892": 99, "567901": 99, "702290": 99, "844": 99, "gaug": 99, "label_issues_count": 99, "155": [99, 103], "156": 99, "172": [99, 102], "157": 99, "easiest": 99, "modular": 99, "penalti": 99, "l2": 99, "model3": 99, "n_estim": 99, "cv_pred_probs_1": 99, "cv_pred_probs_2": 99, "cv_pred_probs_3": 99, "label_quality_scores_best": 99, "cv_pred_probs_ensembl": 99, "label_quality_scores_bett": 99, "superior": [99, 105], "timm": 100, "glad": 101, "multiannotator_label": 101, "300": [101, 108], "noisier": 101, "111": [101, 106], "local_data": [101, 102], "true_labels_train": [101, 102], "noise_matrix_bett": 101, "noise_matrix_wors": 101, "transpos": [101, 104], "zfill": 101, "row_na_check": 101, "notna": 101, "reset_index": 101, "a0001": 101, "a0002": 101, "a0003": 101, "a0004": 101, "a0005": 101, "a0006": 101, "a0007": 101, "a0008": 101, "a0009": 101, "a0010": 101, "a0041": 101, "a0042": 101, "a0043": 101, "a0044": 101, "a0045": 101, "a0046": 101, "a0047": 101, "a0048": 101, "a0049": 101, "a0050": 101, "na": 101, "60856743": 101, "41693214": 101, "40908785": 101, "87147629": 101, "64941785": 101, "10774851": 101, "0524466": 101, "71853246": 101, "37169848": 101, "66031048": 101, "multiannotator_util": 101, "crude": 101, "straight": 101, "majority_vote_label": 101, "736118": 101, "757751": 101, "782232": 101, "715565": 101, "824256": 101, "quality_annotator_a0001": 101, "quality_annotator_a0002": 101, "quality_annotator_a0003": 101, "quality_annotator_a0004": 101, "quality_annotator_a0005": 101, "quality_annotator_a0006": 101, "quality_annotator_a0007": 101, "quality_annotator_a0008": 101, "quality_annotator_a0009": 101, "quality_annotator_a0010": 101, "quality_annotator_a0041": 101, "quality_annotator_a0042": 101, "quality_annotator_a0043": 101, "quality_annotator_a0044": 101, "quality_annotator_a0045": 101, "quality_annotator_a0046": 101, "quality_annotator_a0047": 101, "quality_annotator_a0048": 101, "quality_annotator_a0049": 101, "quality_annotator_a0050": 101, "070564": 101, "216078": 101, "119188": 101, "alongisd": 101, "244981": 101, "208333": 101, "295979": 101, "294118": 101, "324197": 101, "310345": 101, "355316": 101, "346154": 101, "439732": 101, "480000": 101, "a0031": 101, "523205": 101, "580645": 101, "a0034": 101, "535313": 101, "607143": 101, "a0021": 101, "606999": 101, "a0015": 101, "609526": 101, "678571": 101, "a0011": 101, "621103": 101, "692308": 101, "improved_consensus_label": 101, "majority_vote_accuraci": 101, "cleanlab_label_accuraci": 101, "8581081081081081": 101, "9797297297297297": 101, "besid": 101, "sorted_consensus_quality_scor": 101, "worst_qual": 101, "better_qu": 101, "worst_quality_accuraci": 101, "better_quality_accuraci": 101, "9893238434163701": 101, "improved_pred_prob": 101, "treat": [101, 102, 106, 108], "analzi": 101, "copyright": 102, "advertis": 102, "violenc": 102, "nsfw": 102, "celeba": 102, "make_multilabel_data": 102, "boxes_coordin": 102, "box_multilabel": 102, "make_multi": 102, "bx1": 102, "by1": 102, "bx2": 102, "by2": 102, "label_list": 102, "ur": 102, "upper": 102, "inidx": 102, "logical_and": 102, "inv_d": 102, "labels_idx": 102, "true_labels_test": 102, "dict_unique_label": 102, "get_color_arrai": 102, "dcolor": 102, "aa4400": 102, "55227f": 102, "55a100": 102, "00ff00": 102, "007f7f": 102, "386b55": 102, "0000ff": 102, "y_onehot": 102, "single_class_label": 102, "stratifi": [102, 105], "kf": 102, "train_index": 102, "test_index": 102, "clf_cv": 102, "x_train_cv": 102, "x_test_cv": 102, "y_train_cv": 102, "y_test_cv": 102, "y_pred_cv": 102, "saw": 102, "num_to_displai": 102, "09": [102, 103, 106], "275": 102, "267": 102, "225": 102, "171": 102, "234": 102, "165": 102, "227": [102, 103], "262": [102, 103], "263": [102, 103], "266": [102, 103], "139": 102, "143": [102, 103], "216": [102, 103], "265": 102, "159": [102, 103], "despit": [102, 108], "suspect": 102, "888": 102, "8224": 102, "9632": 102, "968": 102, "6512": 102, "0444": 102, "774": 102, "labels_binary_format": 102, "labels_list_format": 102, "surround": 103, "scene": 103, "coco": 103, "everydai": 103, "has_label_issu": 103, "nc": [103, 107, 108], "s3": [103, 107, 108], "amazonaw": [103, 107, 108], "objectdetectionbenchmark": 103, "tutorial_obj": 103, "pkl": 103, "example_imag": 103, "unzip": [103, 108], "_separate_label": 103, "_separate_predict": 103, "begin": 103, "image_path": 103, "rb": 103, "image_to_visu": 103, "seg_map": 103, "334": 103, "bboxes_ignor": 103, "290": 103, "286": 103, "285": 103, "224": 103, "231": 103, "293": 103, "235": 103, "289": 103, "282": 103, "281": 103, "271": 103, "280": 103, "277": 103, "279": 103, "287": 103, "299": 103, "276": 103, "307": 103, "321": 103, "326": 103, "333": 103, "261": 103, "319": 103, "257": 103, "283": 103, "243": 103, "303": 103, "316": 103, "247": 103, "323": 103, "327": 103, "226": 103, "228": 103, "232": 103, "219": 103, "239": 103, "240": 103, "209": 103, "242": 103, "202": 103, "230": 103, "215": 103, "220": 103, "229": 103, "217": [103, 108], "237": 103, "207": 103, "204": 103, "84": [103, 106], "205": 103, "223": 103, "153": 103, "149": 103, "140": 103, "124": 103, "246": 103, "268": 103, "273": 103, "284": 103, "110": 103, "136": 103, "145": 103, "173": 103, "297": 103, "317": 103, "192": 103, "332": 103, "324": 103, "203": 103, "320": 103, "314": 103, "199": 103, "291": 103, "000000481413": 103, "jpg": 103, "42398": 103, "44503": 103, "29968": 103, "336": 103, "21005": 103, "9978472": 103, "forgot": 103, "drew": 103, "label_issue_idx": 103, "num_examples_to_show": 103, "138": 103, "candid": 103, "97489622": 103, "70610878": 103, "98764951": 103, "88899237": 103, "99085805": 103, "issue_idx": 103, "95569726e": 103, "03354841e": 103, "57510169e": 103, "58447666e": 103, "39755858e": 103, "issue_to_visu": 103, "000000009483": 103, "95569726168054e": 103, "addition": [103, 107], "visibl": 103, "missmatch": 103, "likelei": 103, "agnost": 103, "vaidat": 103, "inconsist": 103, "000000395701": 103, "033548411774308e": 103, "armchair": 103, "tv": 103, "000000154004": 103, "38300759625496356": 103, "foreground": 103, "000000448410": 103, "0008575101690203273": 103, "crowd": 103, "alon": 103, "resembl": [103, 104], "000000499768": 103, "9748962231208227": 103, "000000521141": 103, "8889923658893665": 103, "000000143931": 103, "9876495074395956": 103, "bonu": 103, "uncov": 103, "irregular": 103, "anomali": 103, "object_detection_util": 103, "calculate_bounding_box_area": 103, "num_imgs_to_show": 103, "lab_object_count": 103, "pred_object_count": 103, "000000430073": 103, "000000183709": 103, "000000189475": 103, "label_norm": 103, "pred_norm": 103, "area": [103, 107], "lab_area": 103, "pred_area": 103, "lab_area_mean": 103, "lab_area_std": 103, "max_deviation_valu": 103, "max_deviation_class": 103, "deviation_valu": 103, "deviation_class": 103, "mean_area": 103, "std_area": 103, "class_area": 103, "deviations_awai": 103, "max_deviation_index": 103, "num_imgs_to_show_per_class": 103, "class_num": 103, "000000422886": 103, "000000341828": 103, "000000461009": 103, "train_feature_embed": 104, "ood_train_feature_scor": 104, "test_feature_embed": 104, "ood_test_feature_scor": 104, "ood_train_predictions_scor": 104, "train_pred_prob": 104, "ood_test_predictions_scor": 104, "test_pred_prob": 104, "pylab": 104, "rcparam": 104, "baggingclassifi": 104, "therebi": 104, "rescal": 104, "transform_norm": 104, "totensor": 104, "animal_class": 104, "non_animal_class": 104, "animal_idx": 104, "test_idx": 104, "39742139": 104, "91it": 104, "visualize_outli": 104, "txt_class": 104, "npimg": 104, "show_label": 104, "data_subset": 104, "resnet50": 104, "corpu": 104, "2048": 104, "embed_imag": 104, "create_model": 104, "strang": 104, "odd": 104, "train_ood_features_scor": 104, "top_train_ood_features_idx": 104, "fun": 104, "negat": 104, "homogen": 104, "bottom_train_ood_features_idx": 104, "test_ood_features_scor": 104, "top_ood_features_idx": 104, "inevit": 104, "trade": 104, "5th": 104, "percentil": 104, "fifth_percentil": 104, "plt_rang": 104, "hist": 104, "train_outlier_scor": 104, "test_outlier_scor": 104, "ood_features_indic": 104, "revisit": 104, "return_invers": 104, "train_feature_embeddings_sc": 104, "test_feature_embeddings_sc": 104, "train_pred_label": 104, "9702": 104, "train_ood_predictions_scor": 104, "test_ood_predictions_scor": 104, "lost": 104, "unsuit": 105, "ok": [105, 108], "convention": 105, "aforement": 105, "hypothet": 105, "contrast": 105, "tradit": 105, "disjoint": 105, "out_of_sample_pred_probs_for_a": 105, "out_of_sample_pred_probs_for_b": 105, "out_of_sample_pred_probs_for_c": 105, "out_of_sample_pred_prob": 105, "price": 106, "incom": 106, "sensor": 106, "histgradientboostingregressor": 106, "r2_score": 106, "student_grades_r": 106, "final_scor": 106, "true_final_scor": 106, "homework": 106, "3d": 106, "mpl_toolkit": 106, "mplot3d": 106, "axes3d": 106, "errors_idx": 106, "add_subplot": 106, "z": 106, "errors_mask": 106, "feature_column": 106, "predicted_column": 106, "x_train_raw": 106, "x_test_raw": 106, "randomforestregressor": 106, "385101": 106, "499503": 106, "698255": 106, "776647": 106, "109373": 106, "170547": 106, "481096": 106, "984759": 106, "645270": 106, "795928": 106, "141": 106, "659": 106, "367": 106, "318": 106, "305": 106, "560": 106, "657": 106, "688": 106, "view_datapoint": 106, "preds_og": 106, "r2_og": 106, "838": 106, "found_label_issu": 106, "preds_cl": 106, "r2_cl": 106, "926": 106, "favorit": 106, "968627e": 106, "228799": 106, "646674e": 106, "402962": 106, "323818e": 106, "952758": 106, "422144e": 106, "456908": 106, "465815e": 106, "753968": 106, "791186e": 106, "110719": 106, "485156e": 106, "670640": 106, "225300e": 106, "749976": 106, "499679e": 106, "947007": 106, "067882e": 106, "648396": 106, "synthia": 107, "imagesegment": 107, "given_mask": 107, "predicted_mask": 107, "set_printopt": [107, 108], "sky": 107, "sidewalk": 107, "veget": 107, "terrain": 107, "rider": 107, "pred_probs_filepath": 107, "1088": 107, "1920": 107, "label_filepath": 107, "synthia_class": 107, "maunal": 107, "100000": 107, "244800": 107, "leftmost": 107, "middl": [107, 108], "infact": 107, "rightmost": 107, "discrep": 107, "3263230": 107, "783381": 107, "275110": 107, "255917": 107, "78225": 107, "55990": 107, "54315": 107, "33591": 107, "24645": 107, "21054": 107, "15045": 107, "14171": 107, "13832": 107, "13498": 107, "11490": 107, "9164": 107, "8769": 107, "6999": 107, "6031": 107, "5011": 107, "mistakenli": 107, "class_issu": 107, "aim": [107, 108], "domin": 107, "bunch": 108, "conll": 108, "2003": 108, "love": 108, "n_i": 108, "optional_list_of_ordered_class_nam": 108, "deepai": 108, "conll2003": 108, "rm": 108, "tokenclassif": 108, "2400": 108, "52e0": 108, "1a00": 108, "871": 108, "connect": 108, "443": 108, "await": 108, "982975": 108, "960k": 108, "959": 108, "94k": 108, "71mb": 108, "mb": 108, "directori": 108, "inflat": 108, "17045998": 108, "16m": 108, "octet": 108, "26m": 108, "3mb": 108, "bert": 108, "read_npz": 108, "filepath": 108, "corrsespond": 108, "iob2": 108, "given_ent": 108, "entity_map": 108, "readfil": 108, "startswith": 108, "docstart": 108, "isalpha": 108, "isupp": 108, "indices_to_preview": 108, "nsentenc": 108, "eu": 108, "reject": 108, "boycott": 108, "british": 108, "lamb": 108, "00030412": 108, "00023826": 108, "99936208": 108, "00007009": 108, "00002545": 108, "99998795": 108, "00000401": 108, "00000218": 108, "00000455": 108, "00000131": 108, "00000749": 108, "99996115": 108, "00001371": 108, "0000087": 108, "00000895": 108, "99998936": 108, "00000382": 108, "00000178": 108, "00000366": 108, "00000137": 108, "99999101": 108, "00000266": 108, "00000174": 108, "0000035": 108, "00000109": 108, "99998768": 108, "00000482": 108, "00000202": 108, "00000438": 108, "0000011": 108, "00000465": 108, "99996392": 108, "00001105": 108, "0000116": 108, "00000878": 108, "99998671": 108, "00000364": 108, "00000213": 108, "00000472": 108, "00000281": 108, "99999073": 108, "00000211": 108, "00000159": 108, "00000442": 108, "00000115": 108, "peter": 108, "blackburn": 108, "00000358": 108, "00000529": 108, "99995623": 108, "0000129": 108, "0000024": 108, "00001812": 108, "99994141": 108, "00001645": 108, "00002162": 108, "brussel": 108, "1996": 108, "00001172": 108, "00000821": 108, "00004661": 108, "0000618": 108, "99987167": 108, "99999061": 108, "00000201": 108, "00000195": 108, "00000408": 108, "00000135": 108, "2254": 108, "2907": 108, "19392": 108, "9962": 108, "8904": 108, "19303": 108, "12918": 108, "9256": 108, "11855": 108, "18392": 108, "20426": 108, "19402": 108, "14744": 108, "19371": 108, "4645": 108, "10331": 108, "9430": 108, "6143": 108, "18367": 108, "12914": 108, "todai": 108, "weather": 108, "march": 108, "scalfaro": 108, "northern": 108, "himself": 108, "said": 108, "germani": 108, "nastja": 108, "rysich": 108, "north": 108, "spla": 108, "fought": 108, "khartoum": 108, "govern": 108, "south": 108, "1983": 108, "autonomi": 108, "animist": 108, "region": 108, "moslem": 108, "arabis": 108, "mayor": 108, "antonio": 108, "gonzalez": 108, "garcia": 108, "revolutionari": 108, "wednesdai": 108, "troop": 108, "raid": 108, "farm": 108, "stole": 108, "rape": 108, "women": 108, "spring": 108, "chg": 108, "hrw": 108, "12pct": 108, "princ": 108, "photo": 108, "moment": 108, "spokeswoman": 108, "rainier": 108, "told": 108, "reuter": 108, "danila": 108, "carib": 108, "w224": 108, "equip": 108, "radiomet": 108, "earn": 108, "19996": 108, "london": 108, "denom": 108, "sale": 108, "uk": 108, "jp": 108, "fr": 108, "maccabi": 108, "hapoel": 108, "haifa": 108, "tel": 108, "aviv": 108, "hospit": 108, "rever": 108, "roman": 108, "cathol": 108, "nun": 108, "admit": 108, "calcutta": 108, "week": 108, "ago": 108, "fever": 108, "vomit": 108, "allianc": 108, "embattl": 108, "kabul": 108, "salang": 108, "highwai": 108, "mondai": 108, "tuesdai": 108, "suprem": 108, "council": 108, "led": 108, "jumbish": 108, "milli": 108, "movement": 108, "warlord": 108, "abdul": 108, "rashid": 108, "dostum": 108, "dollar": 108, "exchang": 108, "3570": 108, "12049": 108, "born": 108, "1937": 108, "provinc": 108, "anhui": 108, "dai": 108, "came": 108, "shanghai": 108, "citi": 108, "prolif": 108, "author": 108, "teacher": 108, "chines": 108, "16764": 108, "1990": 108, "historian": 108, "alan": 108, "john": 108, "percival": 108, "taylor": 108, "di": 108, "20446": 108, "pace": 108, "bowler": 108, "ian": 108, "harvei": 108, "claim": 108, "victoria": 108, "15514": 108, "cotti": 108, "osc": 108, "foreign": 108, "minist": 108, "7525": 108, "sultan": 108, "specter": 108, "crown": 108, "abdullah": 108, "defenc": 108, "aviat": 108, "jeddah": 108, "saudi": 108, "agenc": 108, "2288": 108, "hi": 108, "customari": 108, "outfit": 108, "champion": 108, "damp": 108, "scalp": 108, "canada": 108, "reign": 108, "olymp": 108, "donovan": 108, "bailei": 108, "1992": 108, "linford": 108, "christi": 108, "britain": 108, "1984": 108, "1988": 108, "carl": 108, "lewi": 108, "ambigi": 108, "punctuat": 108, "chicago": 108, "digest": 108, "philadelphia": 108, "usda": 108, "york": 108, "token_issu": 108, "471": 108, "kean": 108, "year": 108, "contract": 108, "manchest": 108, "19072": 108, "societi": 108, "bite": 108, "deliv": 108, "19910": 108, "father": 108, "clarenc": 108, "woolmer": 108, "renam": 108, "uttar": 108, "pradesh": 108, "india": 108, "ranji": 108, "trophi": 108, "nation": 108, "championship": 108, "captain": 108, "1949": 108, "15658": 108, "19879": 108, "iii": 108, "brian": 108, "shimer": 108, "randi": 108, "jone": 108, "19104": 108}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [12, 0, 0, "-", "datalab"], [37, 0, 0, "-", "dataset"], [40, 0, 0, "-", "experimental"], [44, 0, 0, "-", "filter"], [45, 0, 0, "-", "internal"], [60, 0, 0, "-", "models"], [62, 0, 0, "-", "multiannotator"], [65, 0, 0, "-", "multilabel_classification"], [68, 0, 0, "-", "object_detection"], [71, 0, 0, "-", "outlier"], [72, 0, 0, "-", "rank"], [73, 0, 0, "-", "regression"], [77, 0, 0, "-", "segmentation"], [81, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.data_valuation": [[4, 1, 1, "", "data_shapley_knn"]], "cleanlab.datalab": [[5, 0, 0, "-", "datalab"], [16, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[5, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[5, 4, 1, "", "class_names"], [5, 3, 1, "", "find_issues"], [5, 3, 1, "", "get_info"], [5, 3, 1, "", "get_issue_summary"], [5, 3, 1, "", "get_issues"], [5, 4, 1, "", "has_labels"], [5, 4, 1, "", "info"], [5, 4, 1, "", "issue_summary"], [5, 4, 1, "", "issues"], [5, 4, 1, "", "labels"], [5, 3, 1, "", "list_default_issue_types"], [5, 3, 1, "", "list_possible_issue_types"], [5, 3, 1, "", "load"], [5, 3, 1, "", "report"], [5, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[13, 0, 0, "-", "data"], [14, 0, 0, "-", "data_issues"], [17, 0, 0, "-", "issue_finder"], [15, 0, 0, "-", "issue_manager_factory"], [33, 0, 0, "-", "model_outputs"], [34, 0, 0, "-", "report"], [35, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[13, 2, 1, "", "Data"], [13, 5, 1, "", "DataFormatError"], [13, 5, 1, "", "DatasetDictError"], [13, 5, 1, "", "DatasetLoadError"], [13, 2, 1, "", "Label"], [13, 2, 1, "", "MultiClass"], [13, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[13, 3, 1, "", "add_note"], [13, 6, 1, "", "args"], [13, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[13, 4, 1, "", "class_names"], [13, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[14, 2, 1, "", "DataIssues"], [14, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[14, 3, 1, "", "collect_issues_from_imagelab"], [14, 3, 1, "", "collect_issues_from_issue_manager"], [14, 3, 1, "", "collect_statistics"], [14, 3, 1, "", "get_info"], [14, 3, 1, "", "get_issue_summary"], [14, 3, 1, "", "get_issues"], [14, 6, 1, "", "info"], [14, 6, 1, "", "issue_summary"], [14, 6, 1, "", "issues"], [14, 3, 1, "", "set_health_score"], [14, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[17, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[17, 3, 1, "", "find_issues"], [17, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[19, 0, 0, "-", "data_valuation"], [20, 0, 0, "-", "duplicate"], [21, 0, 0, "-", "imbalance"], [23, 0, 0, "-", "issue_manager"], [24, 0, 0, "-", "label"], [27, 0, 0, "-", "noniid"], [28, 0, 0, "-", "null"], [29, 0, 0, "-", "outlier"], [32, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[19, 6, 1, "", "DEFAULT_THRESHOLD"], [19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 6, 1, "", "near_duplicate_sets"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[24, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 3, 1, "", "get_health_summary"], [24, 6, 1, "", "health_summary_parameters"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.multilabel": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, 2, 1, "", "NonIIDIssueManager"], [27, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "find_issues"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "report"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[28, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[28, 3, 1, "", "collect_info"], [28, 6, 1, "", "description"], [28, 3, 1, "", "find_issues"], [28, 6, 1, "", "info"], [28, 6, 1, "", "issue_name"], [28, 6, 1, "", "issue_score_key"], [28, 6, 1, "", "issues"], [28, 3, 1, "", "make_summary"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[29, 6, 1, "", "DEFAULT_THRESHOLDS"], [29, 3, 1, "", "collect_info"], [29, 6, 1, "", "description"], [29, 3, 1, "", "find_issues"], [29, 6, 1, "", "info"], [29, 6, 1, "", "issue_name"], [29, 6, 1, "", "issue_score_key"], [29, 6, 1, "", "issues"], [29, 3, 1, "", "make_summary"], [29, 6, 1, "", "metric"], [29, 6, 1, "", "ood"], [29, 3, 1, "", "report"], [29, 6, 1, "", "summary"], [29, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[31, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, 2, 1, "", "RegressionLabelIssueManager"], [31, 1, 1, "", "find_issues_with_features"], [31, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "find_issues"], [31, 6, 1, "", "info"], [31, 6, 1, "", "issue_name"], [31, 6, 1, "", "issue_score_key"], [31, 6, 1, "", "issues"], [31, 3, 1, "", "make_summary"], [31, 3, 1, "", "report"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[32, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [32, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [32, 3, 1, "", "collect_info"], [32, 6, 1, "", "description"], [32, 3, 1, "", "filter_cluster_ids"], [32, 3, 1, "", "find_issues"], [32, 3, 1, "", "get_worst_cluster"], [32, 6, 1, "", "info"], [32, 6, 1, "", "issue_name"], [32, 6, 1, "", "issue_score_key"], [32, 6, 1, "", "issues"], [32, 3, 1, "", "make_summary"], [32, 3, 1, "", "perform_clustering"], [32, 3, 1, "", "report"], [32, 6, 1, "", "summary"], [32, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, 7, 1, "", "REGISTRY"], [15, 1, 1, "", "list_default_issue_types"], [15, 1, 1, "", "list_possible_issue_types"], [15, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[33, 2, 1, "", "ModelOutput"], [33, 2, 1, "", "MultiClassPredProbs"], [33, 2, 1, "", "MultiLabelPredProbs"], [33, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[33, 6, 1, "", "argument"], [33, 3, 1, "", "collect"], [33, 6, 1, "", "data"], [33, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[34, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[34, 3, 1, "", "get_report"], [34, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[35, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[35, 6, 1, "", "CLASSIFICATION"], [35, 6, 1, "", "MULTILABEL"], [35, 6, 1, "", "REGRESSION"], [35, 3, 1, "", "__contains__"], [35, 3, 1, "", "__getitem__"], [35, 3, 1, "", "__iter__"], [35, 3, 1, "", "__len__"], [35, 3, 1, "", "from_str"], [35, 4, 1, "", "is_classification"], [35, 4, 1, "", "is_multilabel"], [35, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[37, 1, 1, "", "find_overlapping_classes"], [37, 1, 1, "", "health_summary"], [37, 1, 1, "", "overall_label_health_score"], [37, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[38, 0, 0, "-", "cifar_cnn"], [39, 0, 0, "-", "coteaching"], [41, 0, 0, "-", "label_issues_batched"], [42, 0, 0, "-", "mnist_pytorch"], [43, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[38, 2, 1, "", "CNN"], [38, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[38, 6, 1, "", "T_destination"], [38, 3, 1, "", "__call__"], [38, 3, 1, "", "add_module"], [38, 3, 1, "", "apply"], [38, 3, 1, "", "bfloat16"], [38, 3, 1, "", "buffers"], [38, 6, 1, "", "call_super_init"], [38, 3, 1, "", "children"], [38, 3, 1, "", "compile"], [38, 3, 1, "", "cpu"], [38, 3, 1, "", "cuda"], [38, 3, 1, "", "double"], [38, 6, 1, "", "dump_patches"], [38, 3, 1, "", "eval"], [38, 3, 1, "", "extra_repr"], [38, 3, 1, "", "float"], [38, 3, 1, "id0", "forward"], [38, 3, 1, "", "get_buffer"], [38, 3, 1, "", "get_extra_state"], [38, 3, 1, "", "get_parameter"], [38, 3, 1, "", "get_submodule"], [38, 3, 1, "", "half"], [38, 3, 1, "", "ipu"], [38, 3, 1, "", "load_state_dict"], [38, 3, 1, "", "modules"], [38, 3, 1, "", "named_buffers"], [38, 3, 1, "", "named_children"], [38, 3, 1, "", "named_modules"], [38, 3, 1, "", "named_parameters"], [38, 3, 1, "", "parameters"], [38, 3, 1, "", "register_backward_hook"], [38, 3, 1, "", "register_buffer"], [38, 3, 1, "", "register_forward_hook"], [38, 3, 1, "", "register_forward_pre_hook"], [38, 3, 1, "", "register_full_backward_hook"], [38, 3, 1, "", "register_full_backward_pre_hook"], [38, 3, 1, "", "register_load_state_dict_post_hook"], [38, 3, 1, "", "register_module"], [38, 3, 1, "", "register_parameter"], [38, 3, 1, "", "register_state_dict_pre_hook"], [38, 3, 1, "", "requires_grad_"], [38, 3, 1, "", "set_extra_state"], [38, 3, 1, "", "share_memory"], [38, 3, 1, "", "state_dict"], [38, 3, 1, "", "to"], [38, 3, 1, "", "to_empty"], [38, 3, 1, "", "train"], [38, 6, 1, "", "training"], [38, 3, 1, "", "type"], [38, 3, 1, "", "xpu"], [38, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[39, 1, 1, "", "adjust_learning_rate"], [39, 1, 1, "", "evaluate"], [39, 1, 1, "", "forget_rate_scheduler"], [39, 1, 1, "", "initialize_lr_scheduler"], [39, 1, 1, "", "loss_coteaching"], [39, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[41, 2, 1, "", "LabelInspector"], [41, 7, 1, "", "adj_confident_thresholds_shared"], [41, 1, 1, "", "find_label_issues_batched"], [41, 7, 1, "", "labels_shared"], [41, 7, 1, "", "pred_probs_shared"], [41, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[41, 3, 1, "", "get_confident_thresholds"], [41, 3, 1, "", "get_label_issues"], [41, 3, 1, "", "get_num_issues"], [41, 3, 1, "", "get_quality_scores"], [41, 3, 1, "", "score_label_quality"], [41, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[42, 2, 1, "", "CNN"], [42, 2, 1, "", "SimpleNet"], [42, 1, 1, "", "get_mnist_dataset"], [42, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[42, 3, 1, "", "__init_subclass__"], [42, 6, 1, "", "batch_size"], [42, 6, 1, "", "dataset"], [42, 6, 1, "", "epochs"], [42, 3, 1, "id0", "fit"], [42, 3, 1, "", "get_metadata_routing"], [42, 3, 1, "", "get_params"], [42, 6, 1, "", "loader"], [42, 6, 1, "", "log_interval"], [42, 6, 1, "", "lr"], [42, 6, 1, "", "momentum"], [42, 6, 1, "", "no_cuda"], [42, 3, 1, "id1", "predict"], [42, 3, 1, "id4", "predict_proba"], [42, 6, 1, "", "seed"], [42, 3, 1, "", "set_fit_request"], [42, 3, 1, "", "set_params"], [42, 3, 1, "", "set_predict_proba_request"], [42, 3, 1, "", "set_predict_request"], [42, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[42, 6, 1, "", "T_destination"], [42, 3, 1, "", "__call__"], [42, 3, 1, "", "add_module"], [42, 3, 1, "", "apply"], [42, 3, 1, "", "bfloat16"], [42, 3, 1, "", "buffers"], [42, 6, 1, "", "call_super_init"], [42, 3, 1, "", "children"], [42, 3, 1, "", "compile"], [42, 3, 1, "", "cpu"], [42, 3, 1, "", "cuda"], [42, 3, 1, "", "double"], [42, 6, 1, "", "dump_patches"], [42, 3, 1, "", "eval"], [42, 3, 1, "", "extra_repr"], [42, 3, 1, "", "float"], [42, 3, 1, "", "forward"], [42, 3, 1, "", "get_buffer"], [42, 3, 1, "", "get_extra_state"], [42, 3, 1, "", "get_parameter"], [42, 3, 1, "", "get_submodule"], [42, 3, 1, "", "half"], [42, 3, 1, "", "ipu"], [42, 3, 1, "", "load_state_dict"], [42, 3, 1, "", "modules"], [42, 3, 1, "", "named_buffers"], [42, 3, 1, "", "named_children"], [42, 3, 1, "", "named_modules"], [42, 3, 1, "", "named_parameters"], [42, 3, 1, "", "parameters"], [42, 3, 1, "", "register_backward_hook"], [42, 3, 1, "", "register_buffer"], [42, 3, 1, "", "register_forward_hook"], [42, 3, 1, "", "register_forward_pre_hook"], [42, 3, 1, "", "register_full_backward_hook"], [42, 3, 1, "", "register_full_backward_pre_hook"], [42, 3, 1, "", "register_load_state_dict_post_hook"], [42, 3, 1, "", "register_module"], [42, 3, 1, "", "register_parameter"], [42, 3, 1, "", "register_state_dict_pre_hook"], [42, 3, 1, "", "requires_grad_"], [42, 3, 1, "", "set_extra_state"], [42, 3, 1, "", "share_memory"], [42, 3, 1, "", "state_dict"], [42, 3, 1, "", "to"], [42, 3, 1, "", "to_empty"], [42, 3, 1, "", "train"], [42, 6, 1, "", "training"], [42, 3, 1, "", "type"], [42, 3, 1, "", "xpu"], [42, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[43, 1, 1, "", "display_issues"], [43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[44, 1, 1, "", "find_label_issues"], [44, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [44, 1, 1, "", "find_predicted_neq_given"], [44, 7, 1, "", "pred_probs_by_class"], [44, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[46, 0, 0, "-", "label_quality_utils"], [47, 0, 0, "-", "latent_algebra"], [48, 0, 0, "-", "multiannotator_utils"], [49, 0, 0, "-", "multilabel_scorer"], [50, 0, 0, "-", "multilabel_utils"], [51, 0, 0, "-", "neighbor"], [55, 0, 0, "-", "outlier"], [56, 0, 0, "-", "token_classification_utils"], [57, 0, 0, "-", "util"], [58, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[46, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, 1, 1, "", "compute_inv_noise_matrix"], [47, 1, 1, "", "compute_noise_matrix_from_inverse"], [47, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [47, 1, 1, "", "compute_py"], [47, 1, 1, "", "compute_py_inv_noise_matrix"], [47, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[48, 1, 1, "", "assert_valid_inputs_multiannotator"], [48, 1, 1, "", "assert_valid_pred_probs"], [48, 1, 1, "", "check_consensus_label_classes"], [48, 1, 1, "", "compute_soft_cross_entropy"], [48, 1, 1, "", "find_best_temp_scaler"], [48, 1, 1, "", "format_multiannotator_labels"], [48, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[49, 2, 1, "", "Aggregator"], [49, 2, 1, "", "ClassLabelScorer"], [49, 2, 1, "", "MultilabelScorer"], [49, 1, 1, "", "exponential_moving_average"], [49, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "multilabel_py"], [49, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[49, 3, 1, "", "__call__"], [49, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[49, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [49, 6, 1, "", "NORMALIZED_MARGIN"], [49, 6, 1, "", "SELF_CONFIDENCE"], [49, 3, 1, "", "__call__"], [49, 3, 1, "", "__contains__"], [49, 3, 1, "", "__getitem__"], [49, 3, 1, "", "__iter__"], [49, 3, 1, "", "__len__"], [49, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[49, 3, 1, "", "__call__"], [49, 3, 1, "", "aggregate"], [49, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[50, 1, 1, "", "get_onehot_num_classes"], [50, 1, 1, "", "int2onehot"], [50, 1, 1, "", "onehot2int"], [50, 1, 1, "", "stack_complement"]], "cleanlab.internal.neighbor": [[52, 0, 0, "-", "knn_graph"], [53, 0, 0, "-", "metric"], [54, 0, 0, "-", "search"]], "cleanlab.internal.neighbor.knn_graph": [[52, 7, 1, "", "DEFAULT_K"], [52, 1, 1, "", "construct_knn_graph_from_index"], [52, 1, 1, "", "correct_knn_distances_and_indices"], [52, 1, 1, "", "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"], [52, 1, 1, "", "correct_knn_graph"], [52, 1, 1, "", "create_knn_graph_and_index"], [52, 1, 1, "", "features_to_knn"]], "cleanlab.internal.neighbor.metric": [[53, 7, 1, "", "HIGH_DIMENSION_CUTOFF"], [53, 7, 1, "", "ROW_COUNT_CUTOFF"], [53, 1, 1, "", "decide_default_metric"], [53, 1, 1, "", "decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, 1, 1, "", "construct_knn"]], "cleanlab.internal.outlier": [[55, 1, 1, "", "correct_precision_errors"], [55, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, 1, 1, "", "color_sentence"], [56, 1, 1, "", "filter_sentence"], [56, 1, 1, "", "get_sentence"], [56, 1, 1, "", "mapping"], [56, 1, 1, "", "merge_probs"], [56, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[57, 1, 1, "", "append_extra_datapoint"], [57, 1, 1, "", "clip_noise_rates"], [57, 1, 1, "", "clip_values"], [57, 1, 1, "", "compress_int_array"], [57, 1, 1, "", "confusion_matrix"], [57, 1, 1, "", "csr_vstack"], [57, 1, 1, "", "estimate_pu_f1"], [57, 1, 1, "", "extract_indices_tf"], [57, 1, 1, "", "force_two_dimensions"], [57, 1, 1, "", "format_labels"], [57, 1, 1, "", "get_missing_classes"], [57, 1, 1, "", "get_num_classes"], [57, 1, 1, "", "get_unique_classes"], [57, 1, 1, "", "is_tensorflow_dataset"], [57, 1, 1, "", "is_torch_dataset"], [57, 1, 1, "", "num_unique_classes"], [57, 1, 1, "", "print_inverse_noise_matrix"], [57, 1, 1, "", "print_joint_matrix"], [57, 1, 1, "", "print_noise_matrix"], [57, 1, 1, "", "print_square_matrix"], [57, 1, 1, "", "remove_noise_from_class"], [57, 1, 1, "", "round_preserving_row_totals"], [57, 1, 1, "", "round_preserving_sum"], [57, 1, 1, "", "smart_display_dataframe"], [57, 1, 1, "", "subset_X_y"], [57, 1, 1, "", "subset_data"], [57, 1, 1, "", "subset_labels"], [57, 1, 1, "", "train_val_split"], [57, 1, 1, "", "unshuffle_tensorflow_dataset"], [57, 1, 1, "", "value_counts"], [57, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[58, 1, 1, "", "assert_indexing_works"], [58, 1, 1, "", "assert_nonempty_input"], [58, 1, 1, "", "assert_valid_class_labels"], [58, 1, 1, "", "assert_valid_inputs"], [58, 1, 1, "", "labels_to_array"], [58, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[61, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[61, 2, 1, "", "KerasWrapperModel"], [61, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[61, 3, 1, "", "fit"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "predict_proba"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[62, 1, 1, "", "convert_long_to_wide_dataset"], [62, 1, 1, "", "get_active_learning_scores"], [62, 1, 1, "", "get_active_learning_scores_ensemble"], [62, 1, 1, "", "get_label_quality_multiannotator"], [62, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [62, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[63, 0, 0, "-", "dataset"], [64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[63, 1, 1, "", "common_multilabel_issues"], [63, 1, 1, "", "multilabel_health_summary"], [63, 1, 1, "", "overall_multilabel_health_score"], [63, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, 1, 1, "", "find_label_issues"], [64, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[69, 1, 1, "", "compute_badloc_box_scores"], [69, 1, 1, "", "compute_overlooked_box_scores"], [69, 1, 1, "", "compute_swap_box_scores"], [69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"], [69, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[70, 1, 1, "", "bounding_box_size_distribution"], [70, 1, 1, "", "calculate_per_class_metrics"], [70, 1, 1, "", "class_label_distribution"], [70, 1, 1, "", "get_average_per_class_confusion_matrix"], [70, 1, 1, "", "get_sorted_bbox_count_idxs"], [70, 1, 1, "", "object_counts_per_image"], [70, 1, 1, "", "plot_class_distribution"], [70, 1, 1, "", "plot_class_size_distributions"], [70, 1, 1, "", "visualize"]], "cleanlab.outlier": [[71, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[71, 3, 1, "", "fit"], [71, 3, 1, "", "fit_score"], [71, 3, 1, "", "score"]], "cleanlab.rank": [[72, 1, 1, "", "find_top_issues"], [72, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [72, 1, 1, "", "get_label_quality_ensemble_scores"], [72, 1, 1, "", "get_label_quality_scores"], [72, 1, 1, "", "get_normalized_margin_for_each_label"], [72, 1, 1, "", "get_self_confidence_for_each_label"], [72, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[74, 0, 0, "-", "learn"], [75, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[74, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[74, 3, 1, "", "__init_subclass__"], [74, 3, 1, "", "find_label_issues"], [74, 3, 1, "", "fit"], [74, 3, 1, "", "get_aleatoric_uncertainty"], [74, 3, 1, "", "get_epistemic_uncertainty"], [74, 3, 1, "", "get_label_issues"], [74, 3, 1, "", "get_metadata_routing"], [74, 3, 1, "", "get_params"], [74, 3, 1, "", "predict"], [74, 3, 1, "", "save_space"], [74, 3, 1, "", "score"], [74, 3, 1, "", "set_fit_request"], [74, 3, 1, "", "set_params"], [74, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[75, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[76, 0, 0, "-", "filter"], [78, 0, 0, "-", "rank"], [79, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[76, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[78, 1, 1, "", "get_label_quality_scores"], [78, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[79, 1, 1, "", "common_label_issues"], [79, 1, 1, "", "display_issues"], [79, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[80, 0, 0, "-", "filter"], [82, 0, 0, "-", "rank"], [83, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[80, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[82, 1, 1, "", "get_label_quality_scores"], [82, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[83, 1, 1, "", "common_label_issues"], [83, 1, 1, "", "display_issues"], [83, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 87, 88, 92, 94, 95, 98, 99, 102, 108], "count": [3, 99], "data_valu": [4, 19], "datalab": [5, 7, 9, 10, 12, 89, 90, 91, 92, 93, 94, 95, 96, 98, 99, 102], "creat": [7, 90, 91, 96, 99, 101], "your": [7, 84, 90, 91, 95, 96, 98, 99], "own": 7, "issu": [7, 9, 10, 22, 31, 84, 87, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 102, 103, 107, 108], "manag": [7, 22], "prerequisit": 7, "implement": 7, "issuemanag": [7, 90], "basic": 7, "check": [7, 96], "intermedi": 7, "advanc": [7, 90], "us": [7, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "gener": [8, 96], "cluster": [8, 96, 98], "id": 8, "guid": [9, 12], "type": [9, 10, 99], "custom": [9, 90], "cleanlab": [9, 10, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106, 107, 108], "studio": [9, 10], "easi": [9, 10, 84, 92, 94, 95], "mode": [9, 10, 84, 92, 94, 95], "can": [10, 91, 97, 98, 99, 101], "detect": [10, 89, 91, 92, 94, 95, 96, 98, 99, 103, 104], "estim": [10, 99, 101, 102], "each": 10, "input": 10, "label": [10, 24, 26, 31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 106, 107, 108], "is_label_issu": 10, "label_scor": 10, "given_label": 10, "predicted_label": 10, "outlier": [10, 29, 55, 71, 92, 94, 95, 102, 104], "is_outlier_issu": 10, "outlier_scor": 10, "Near": [10, 91, 92, 94, 95], "duplic": [10, 20, 91, 92, 94, 95, 98, 102], "is_near_duplicate_issu": 10, "near_duplicate_scor": 10, "near_duplicate_set": 10, "distance_to_nearest_neighbor": 10, "non": [10, 95, 96], "iid": [10, 95, 96], "is_non_iid_issu": 10, "non_iid_scor": 10, "class": [10, 85, 96, 99, 107], "imbal": [10, 21, 96], "is_class_imbalance_issu": 10, "class_imbalance_scor": 10, "imag": [10, 92, 96, 104], "specif": [10, 22, 96, 107], "underperform": [10, 96, 98], "group": [10, 96, 98], "is_underperforming_group_issu": 10, "underperforming_group_scor": 10, "null": [10, 28, 96], "is_null_issu": 10, "null_scor": 10, "data": [10, 13, 84, 87, 89, 90, 91, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108], "valuat": [10, 96], "is_data_valuation_issu": 10, "data_valuation_scor": 10, "option": [10, 96], "paramet": [10, 99], "get": [12, 90, 91, 101, 102, 103, 107, 108], "start": [12, 97], "api": 12, "refer": 12, "data_issu": 14, "factori": 15, "intern": [16, 45], "issue_find": 17, "issue_manag": [22, 23], "regist": 22, "ml": [22, 98, 99], "task": [22, 35], "multilabel": 25, "noniid": 27, "regress": [30, 73, 74, 75, 98, 106], "prioriti": 31, "order": 31, "find": [31, 84, 87, 88, 89, 91, 92, 94, 95, 96, 98, 99, 101, 102, 103, 104, 106, 107, 108], "underperforming_group": 32, "model_output": 33, "report": [34, 92], "dataset": [37, 63, 84, 88, 89, 91, 92, 95, 96, 97, 98, 99, 102, 103, 104, 106, 107, 108], "cifar_cnn": 38, "coteach": 39, "experiment": 40, "label_issues_batch": 41, "mnist_pytorch": 42, "span_classif": 43, "filter": [44, 64, 67, 76, 80, 99], "label_quality_util": 46, "latent_algebra": 47, "multiannotator_util": 48, "multilabel_scor": 49, "multilabel_util": 50, "neighbor": 51, "knn_graph": 52, "metric": 53, "search": [54, 90], "token_classification_util": 56, "util": 57, "valid": [58, 92, 105], "fasttext": 59, "model": [60, 84, 87, 88, 89, 92, 94, 95, 98, 99, 101, 102, 103, 104, 106], "kera": 61, "multiannot": [62, 101], "multilabel_classif": 65, "rank": [66, 69, 72, 75, 78, 82, 99], "object_detect": 68, "summari": [70, 79, 83], "learn": [74, 91, 98, 99], "segment": [77, 107], "token_classif": [81, 108], "open": [84, 98], "sourc": [84, 98], "document": 84, "quickstart": 84, "1": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "instal": [84, 87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "2": [84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "common": [84, 85, 108], "3": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 102, 103, 104, 106, 107, 108], "handl": [84, 98], "error": [84, 88, 92, 98, 99, 101, 102, 103, 106, 107, 108], "train": [84, 87, 88, 89, 96, 98, 104, 106], "robust": [84, 87, 88, 99, 106], "noisi": [84, 87, 88, 99, 106], "4": [84, 87, 88, 89, 90, 91, 92, 94, 95, 96, 99, 101, 103, 104, 106], "curat": 84, "fix": [84, 98], "level": [84, 97, 99, 108], "5": [84, 87, 89, 91, 92, 94, 96, 99, 101, 106], "improv": [84, 101], "via": [84, 99, 101], "mani": [84, 99], "other": [84, 101, 103, 106], "techniqu": 84, "contribut": 84, "how": [85, 98, 99, 101, 102, 108], "migrat": 85, "version": 85, "0": 85, "from": [85, 87, 88, 90, 91, 99, 106], "pre": [85, 89, 96, 98, 104], "function": [85, 90], "name": 85, "chang": 85, "modul": [85, 99], "new": 85, "remov": 85, "argument": [85, 90], "variabl": 85, "cleanlearn": [86, 98, 99], "tutori": [86, 93, 97, 100], "structur": 87, "tabular": [87, 94], "requir": [87, 88, 90, 91, 92, 94, 95, 101, 102, 103, 104, 106, 107, 108], "depend": [87, 88, 89, 90, 91, 92, 94, 95, 97, 99, 101, 102, 103, 104, 106, 107, 108], "load": [87, 88, 89, 90, 91, 94, 95, 96, 106], "process": [87, 94, 104, 106], "select": [87, 94], "comput": [87, 89, 92, 94, 95, 96, 98, 101, 105], "out": [87, 89, 90, 91, 92, 94, 95, 101, 105], "sampl": [87, 89, 90, 91, 92, 94, 95, 101, 105], "predict": [87, 89, 90, 91, 92, 94, 95, 96, 101, 102, 103, 105], "probabl": [87, 89, 90, 91, 92, 94, 95, 96, 101, 105], "more": [87, 88, 91, 99, 106], "text": [88, 95, 96, 108], "format": [88, 95, 98, 102, 103], "defin": [88, 92, 95, 96, 106], "potenti": [88, 101, 106], "an": [89, 92, 98], "audio": 89, "import": [89, 90, 91, 92, 97, 99, 101], "them": [89, 97, 99], "speechbrain": 89, "featur": [89, 92, 96, 104], "fit": 89, "linear": 89, "workflow": [90, 96, 99], "audit": [90, 91], "classifi": [90, 91, 96], "instanti": 90, "object": [90, 96, 103], "increment": 90, "specifi": [90, 98], "nondefault": 90, "save": 90, "ad": 90, "A": 91, "unifi": 91, "all": [91, 99], "kind": [91, 103], "skip": [91, 97, 99, 101], "detail": [91, 97, 99, 101], "about": 91, "addit": 91, "inform": [91, 92], "fetch": [92, 97], "normal": 92, "fashion": 92, "mnist": 92, "prepar": [92, 96], "k": [92, 94, 105], "fold": [92, 105], "cross": [92, 105], "embed": [92, 104], "7": [92, 99], "view": 92, "most": [92, 108], "like": 92, "exampl": [92, 98, 99, 104], "sever": 92, "set": [92, 99], "dark": [92, 96], "top": [92, 107], "low": 92, "numer": 94, "categor": [94, 96], "column": 94, "construct": 94, "nearest": 94, "neighbour": 94, "graph": [94, 96], "drift": [95, 102], "miscellan": 96, "acceler": 96, "knn": 96, "obtain": 96, "identifi": [96, 98, 103], "explan": 96, "vector": 96, "perform": 96, "visual": [96, 99, 103, 104, 107], "score": [96, 99, 101, 102, 103, 107, 108], "synthet": 96, "result": 96, "predefin": 96, "slice": [96, 98], "i": [96, 98, 99, 105], "catch": 96, "valu": 96, "encod": 96, "initi": [96, 101], "sort": 96, "6": [96, 99], "spuriou": 96, "correl": 96, "between": 96, "vision": 96, "pass": 96, "relat": 96, "transform": 96, "imageenh": 96, "induc": 96, "properti": 96, "origin": 96, "understand": 97, "evalu": 97, "health": [97, 99], "8": [97, 99], "popular": 97, "faq": 98, "what": [98, 99, 105], "do": [98, 99], "infer": 98, "correct": 98, "ha": 98, "flag": 98, "should": 98, "v": 98, "test": [98, 99, 104], "big": 98, "limit": 98, "memori": 98, "why": 98, "isn": 98, "t": 98, "work": [98, 99, 101, 108], "me": 98, "differ": [98, 103], "clean": [98, 99], "final": 98, "hyperparamet": 98, "tune": 98, "onli": 98, "one": [98, 99, 102, 107], "doe": [98, 101, 108], "take": 98, "so": 98, "long": 98, "when": [98, 99], "run": 98, "licens": 98, "under": 98, "answer": 98, "question": 98, "The": 99, "centric": 99, "ai": 99, "machin": 99, "find_label_issu": 99, "line": 99, "code": 99, "twenti": 99, "lowest": 99, "qualiti": [99, 101, 102, 103, 107, 108], "see": 99, "now": 99, "let": 99, "": 99, "happen": 99, "we": 99, "merg": 99, "seafoam": 99, "green": 99, "yellow": 99, "too": 99, "you": 99, "re": 99, "One": 99, "rule": 99, "overal": [99, 107], "accur": 99, "thi": 99, "directli": 99, "fulli": 99, "character": 99, "nois": 99, "matrix": [99, 102], "joint": 99, "prior": 99, "true": 99, "distribut": 99, "flip": 99, "rate": 99, "ani": 99, "again": 99, "support": 99, "lot": 99, "method": 99, "filter_bi": 99, "automat": 99, "everi": 99, "uniqu": 99, "num_label_issu": 99, "threshold": 99, "found": 99, "Not": 99, "sure": 99, "ensembl": 99, "multipl": [99, 101], "predictor": 99, "consensu": 101, "annot": 101, "major": 101, "vote": 101, "better": 101, "statist": 101, "compar": 101, "inspect": 101, "retrain": 101, "further": 101, "multi": 102, "beyond": 102, "mislabel": [102, 107, 108], "given": 102, "hot": 102, "binari": 102, "without": 102, "applic": 102, "real": 102, "download": [103, 107, 108], "objectlab": 103, "exploratori": 103, "analysi": 103, "pytorch": 104, "timm": 104, "cifar10": 104, "some": 104, "pred_prob": [104, 107, 108], "wai": 106, "semant": 107, "which": 107, "ar": 107, "commonli": 107, "focus": 107, "token": 108, "word": 108, "sentenc": 108, "contain": 108, "particular": 108}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 58}, "alltitles": {"benchmarking": [[0, "module-cleanlab.benchmarking"]], "noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "classification": [[2, "module-cleanlab.classification"]], "count": [[3, "module-cleanlab.count"]], "data_valuation": [[4, "module-cleanlab.data_valuation"], [19, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"]], "Creating Your Own Issues Manager": [[7, "creating-your-own-issues-manager"]], "Prerequisites": [[7, "prerequisites"]], "Implementing IssueManagers": [[7, "implementing-issuemanagers"]], "Basic Issue Check": [[7, "basic-issue-check"]], "Intermediate Issue Check": [[7, "intermediate-issue-check"]], "Advanced Issue Check": [[7, "advanced-issue-check"]], "Use with Datalab": [[7, "use-with-datalab"]], "Generating Cluster IDs": [[8, "generating-cluster-ids"]], "Datalab guides": [[9, "datalab-guides"]], "Types of issues": [[9, "types-of-issues"]], "Customizing issue types": [[9, "customizing-issue-types"]], "Cleanlab Studio (Easy Mode)": [[9, "cleanlab-studio-easy-mode"], [10, "cleanlab-studio-easy-mode"]], "Datalab Issue Types": [[10, "datalab-issue-types"]], "Types of issues Datalab can detect": [[10, "types-of-issues-datalab-can-detect"]], "Estimates for Each Issue Type": [[10, "estimates-for-each-issue-type"]], "Inputs to Datalab": [[10, "inputs-to-datalab"]], "Label Issue": [[10, "label-issue"]], "is_label_issue": [[10, "is-label-issue"]], "label_score": [[10, "label-score"]], "given_label": [[10, "given-label"], [10, "id6"]], "predicted_label": [[10, "predicted-label"]], "Outlier Issue": [[10, "outlier-issue"]], "is_outlier_issue": [[10, "is-outlier-issue"]], "outlier_score": [[10, "outlier-score"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "is_near_duplicate_issue": [[10, "is-near-duplicate-issue"]], "near_duplicate_score": [[10, "near-duplicate-score"]], "near_duplicate_sets": [[10, "near-duplicate-sets"]], "distance_to_nearest_neighbor": [[10, "distance-to-nearest-neighbor"]], "Non-IID Issue": [[10, "non-iid-issue"]], "is_non_iid_issue": [[10, "is-non-iid-issue"]], "non_iid_score": [[10, "non-iid-score"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "is_class_imbalance_issue": [[10, "is-class-imbalance-issue"]], "class_imbalance_score": [[10, "class-imbalance-score"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "is_underperforming_group_issue": [[10, "is-underperforming-group-issue"]], "underperforming_group_score": [[10, "underperforming-group-score"]], "Null Issue": [[10, "null-issue"]], "is_null_issue": [[10, "is-null-issue"]], "null_score": [[10, "null-score"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "is_data_valuation_issue": [[10, "is-data-valuation-issue"]], "data_valuation_score": [[10, "data-valuation-score"]], "Optional Issue Parameters": [[10, "optional-issue-parameters"]], "Label Issue Parameters": [[10, "label-issue-parameters"]], "Outlier Issue Parameters": [[10, "outlier-issue-parameters"]], "Duplicate Issue Parameters": [[10, "duplicate-issue-parameters"]], "Non-IID Issue Parameters": [[10, "non-iid-issue-parameters"]], "Imbalance Issue Parameters": [[10, "imbalance-issue-parameters"]], "Underperforming Group Issue Parameters": [[10, "underperforming-group-issue-parameters"]], "Null Issue Parameters": [[10, "null-issue-parameters"]], "Data Valuation Issue Parameters": [[10, "data-valuation-issue-parameters"]], "Image Issue Parameters": [[10, "image-issue-parameters"]], "Getting Started": [[12, "getting-started"]], "Guides": [[12, "guides"]], "API Reference": [[12, "api-reference"]], "data": [[13, "module-cleanlab.datalab.internal.data"]], "data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[16, "internal"], [45, "internal"]], "issue_finder": [[17, "issue-finder"]], "duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[22, "issue-manager"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[22, "registered-issue-managers"]], "ML task-specific issue managers": [[22, "ml-task-specific-issue-managers"]], "label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[25, "multilabel"]], "noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[28, "null"]], "outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [55, "module-cleanlab.internal.outlier"], [71, "module-cleanlab.outlier"]], "regression": [[30, "regression"], [73, "regression"]], "Priority Order for finding issues:": [[31, null]], "underperforming_group": [[32, "underperforming-group"]], "model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[34, "report"]], "task": [[35, "task"]], "dataset": [[37, "module-cleanlab.dataset"], [63, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "experimental": [[40, "experimental"]], "label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "filter": [[44, "module-cleanlab.filter"], [64, "module-cleanlab.multilabel_classification.filter"], [67, "filter"], [76, "filter"], [80, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "neighbor": [[51, "neighbor"]], "knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "search": [[54, "module-cleanlab.internal.neighbor.search"]], "token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "util": [[57, "module-cleanlab.internal.util"]], "validation": [[58, "module-cleanlab.internal.validation"]], "fasttext": [[59, "fasttext"]], "models": [[60, "models"]], "keras": [[61, "module-cleanlab.models.keras"]], "multiannotator": [[62, "module-cleanlab.multiannotator"]], "multilabel_classification": [[65, "multilabel-classification"]], "rank": [[66, "module-cleanlab.multilabel_classification.rank"], [69, "module-cleanlab.object_detection.rank"], [72, "module-cleanlab.rank"], [78, "module-cleanlab.segmentation.rank"], [82, "module-cleanlab.token_classification.rank"]], "object_detection": [[68, "object-detection"]], "summary": [[70, "summary"], [79, "module-cleanlab.segmentation.summary"], [83, "module-cleanlab.token_classification.summary"]], "regression.learn": [[74, "module-cleanlab.regression.learn"]], "regression.rank": [[75, "module-cleanlab.regression.rank"]], "segmentation": [[77, "segmentation"]], "token_classification": [[81, "token-classification"]], "cleanlab open-source documentation": [[84, "cleanlab-open-source-documentation"]], "Quickstart": [[84, "quickstart"]], "1. Install cleanlab": [[84, "install-cleanlab"]], "2. Find common issues in your data": [[84, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[84, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[84, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[84, "improve-your-data-via-many-other-techniques"]], "Contributing": [[84, "contributing"]], "Easy Mode": [[84, "easy-mode"], [92, "Easy-Mode"], [94, "Easy-Mode"], [95, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[85, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[85, "function-and-class-name-changes"]], "Module name changes": [[85, "module-name-changes"]], "New modules": [[85, "new-modules"]], "Removed modules": [[85, "removed-modules"]], "Common argument and variable name changes": [[85, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[86, "cleanlearning-tutorials"]], "Classification with Structured/Tabular Data and Noisy Labels": [[87, "Classification-with-Structured/Tabular-Data-and-Noisy-Labels"]], "1. Install required dependencies": [[87, "1.-Install-required-dependencies"], [88, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"], [95, "1.-Install-required-dependencies"], [106, "1.-Install-required-dependencies"]], "2. Load and process the data": [[87, "2.-Load-and-process-the-data"], [94, "2.-Load-and-process-the-data"], [106, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[87, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [94, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[87, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[87, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[88, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[88, "2.-Load-and-format-the-text-dataset"], [95, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[88, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[88, "4.-Train-a-more-robust-model-from-noisy-labels"], [106, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Detecting Issues in an Audio Dataset with Datalab": [[89, "Detecting-Issues-in-an-Audio-Dataset-with-Datalab"]], "1. Install dependencies and import them": [[89, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[89, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[89, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[89, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[89, "5.-Use-cleanlab-to-find-label-issues"], [94, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[90, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[90, "Install-and-import-required-dependencies"]], "Create and load the data": [[90, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[90, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[90, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[90, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[90, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[90, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[90, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[91, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[91, "1.-Install-and-import-required-dependencies"], [92, "1.-Install-and-import-required-dependencies"], [101, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[91, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[91, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[91, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[91, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[91, "Get-additional-information"]], "Near duplicate issues": [[91, "Near-duplicate-issues"], [92, "Near-duplicate-issues"]], "Detecting Issues in an Image Dataset with Datalab": [[92, "Detecting-Issues-in-an-Image-Dataset-with-Datalab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[92, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[92, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[92, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[92, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[92, "7.-Use-cleanlab-to-find-issues"]], "View report": [[92, "View-report"]], "Label issues": [[92, "Label-issues"], [94, "Label-issues"], [95, "Label-issues"]], "View most likely examples with label errors": [[92, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[92, "Outlier-issues"], [94, "Outlier-issues"], [95, "Outlier-issues"]], "View most severe outliers": [[92, "View-most-severe-outliers"]], "View sets of near duplicate images": [[92, "View-sets-of-near-duplicate-images"]], "Dark images": [[92, "Dark-images"]], "View top examples of dark images": [[92, "View-top-examples-of-dark-images"]], "Low information images": [[92, "Low-information-images"]], "Datalab Tutorials": [[93, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[94, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[94, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[94, "Near-duplicate-issues"], [95, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[95, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[95, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[95, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[95, "Non-IID-issues-(data-drift)"]], "Miscellaneous workflows with Datalab": [[96, "Miscellaneous-workflows-with-Datalab"]], "Accelerate Issue Checks with Pre-computed kNN Graphs": [[96, "Accelerate-Issue-Checks-with-Pre-computed-kNN-Graphs"]], "1. Load and Prepare Your Dataset": [[96, "1.-Load-and-Prepare-Your-Dataset"]], "2. Compute kNN Graph": [[96, "2.-Compute-kNN-Graph"]], "3. Train a Classifier and Obtain Predicted Probabilities": [[96, "3.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"]], "4. Identify Data Issues Using Datalab": [[96, "4.-Identify-Data-Issues-Using-Datalab"]], "Explanation:": [[96, "Explanation:"]], "Data Valuation": [[96, "Data-Valuation"]], "1. Load and Prepare the Dataset": [[96, "1.-Load-and-Prepare-the-Dataset"], [96, "id2"], [96, "id5"]], "2. Vectorize the Text Data": [[96, "2.-Vectorize-the-Text-Data"]], "3. Perform Data Valuation with Datalab": [[96, "3.-Perform-Data-Valuation-with-Datalab"]], "4. (Optional) Visualize Data Valuation Scores": [[96, "4.-(Optional)-Visualize-Data-Valuation-Scores"]], "Find Underperforming Groups in a Dataset": [[96, "Find-Underperforming-Groups-in-a-Dataset"]], "1. Generate a Synthetic Dataset": [[96, "1.-Generate-a-Synthetic-Dataset"]], "2. Train a Classifier and Obtain Predicted Probabilities": [[96, "2.-Train-a-Classifier-and-Obtain-Predicted-Probabilities"], [96, "id3"]], "3. (Optional) Cluster the Data": [[96, "3.-(Optional)-Cluster-the-Data"]], "4. Identify Underperforming Groups with Datalab": [[96, "4.-Identify-Underperforming-Groups-with-Datalab"], [96, "id4"]], "5. (Optional) Visualize the Results": [[96, "5.-(Optional)-Visualize-the-Results"]], "Predefining Data Slices for Detecting Underperforming Groups": [[96, "Predefining-Data-Slices-for-Detecting-Underperforming-Groups"]], "3. Define a Data Slice": [[96, "3.-Define-a-Data-Slice"]], "Detect if your dataset is non-IID": [[96, "Detect-if-your-dataset-is-non-IID"]], "2. Detect Non-IID Issues Using Datalab": [[96, "2.-Detect-Non-IID-Issues-Using-Datalab"]], "3. (Optional) Visualize the Results": [[96, "3.-(Optional)-Visualize-the-Results"]], "Catch Null Values in a Dataset": [[96, "Catch-Null-Values-in-a-Dataset"]], "1. Load the Dataset": [[96, "1.-Load-the-Dataset"]], "2: Encode Categorical Values": [[96, "2:-Encode-Categorical-Values"]], "3. Initialize Datalab": [[96, "3.-Initialize-Datalab"]], "4. Detect Null Values": [[96, "4.-Detect-Null-Values"]], "5. Sort the Dataset by Null Issues": [[96, "5.-Sort-the-Dataset-by-Null-Issues"]], "6. (Optional) Visualize the Results": [[96, "6.-(Optional)-Visualize-the-Results"]], "Detect class imbalance in your dataset": [[96, "Detect-class-imbalance-in-your-dataset"]], "1. Prepare data": [[96, "1.-Prepare-data"]], "2. Detect class imbalance with Datalab": [[96, "2.-Detect-class-imbalance-with-Datalab"]], "3. (Optional) Visualize class imbalance issues": [[96, "3.-(Optional)-Visualize-class-imbalance-issues"]], "Find Spurious Correlation between Vision Dataset features and class labels": [[96, "Find-Spurious-Correlation-between-Vision-Dataset-features-and-class-labels"]], "1. Load the dataset": [[96, "1.-Load-the-dataset"]], "2. Creating Dataset object to be passed to the Datalab object to find vision-related issues": [[96, "2.-Creating-Dataset-object-to-be-passed-to-the-Datalab-object-to-find-vision-related-issues"]], "3. (Optional) Creating a transformed dataset using ImageEnhance to induce darkness": [[96, "3.-(Optional)-Creating-a-transformed-dataset-using-ImageEnhance-to-induce-darkness"]], "4. (Optional) Visualizing Images in the dataset": [[96, "4.-(Optional)-Visualizing-Images-in-the-dataset"]], "5. Finding image-specific property scores": [[96, "5.-Finding-image-specific-property-scores"]], "Vision-specific property scores in the original dataset": [[96, "Vision-specific-property-scores-in-the-original-dataset"]], "Vision-specific property scores in the transformed dataset": [[96, "Vision-specific-property-scores-in-the-transformed-dataset"]], "Understanding Dataset-level Labeling Issues": [[97, "Understanding-Dataset-level-Labeling-Issues"]], "Install dependencies and import them": [[97, "Install-dependencies-and-import-them"], [99, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[97, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[97, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[98, "FAQ"]], "What data can cleanlab detect issues in?": [[98, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[98, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[98, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[98, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[98, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[98, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[98, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[98, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[98, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[98, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by Datalab?": [[98, "How-to-handle-near-duplicate-data-identified-by-Datalab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[98, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[98, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[98, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[99, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[99, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[99, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[99, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[99, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[99, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[99, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[99, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[99, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[99, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[99, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[99, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[99, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[99, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[99, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[99, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[99, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[99, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[99, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[99, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[100, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[101, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[101, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[101, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[101, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[101, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[101, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[101, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[101, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[101, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[102, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[102, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[102, "2.-Format-data,-labels,-and-model-predictions"], [103, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"], [107, "3.-Use-cleanlab-to-find-label-issues"], [108, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[102, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[102, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[102, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[102, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[102, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[103, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[103, "1.-Install-required-dependencies-and-download-data"], [107, "1.-Install-required-dependencies-and-download-data"], [108, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[103, "Get-label-quality-scores"], [107, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[103, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[103, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[103, "Other-uses-of-visualize"]], "Exploratory data analysis": [[103, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[104, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[104, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[104, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[104, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[104, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[104, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[105, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[105, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[105, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[106, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[106, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[106, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[107, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[107, "2.-Get-data,-labels,-and-pred_probs"], [108, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[107, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[107, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[107, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[108, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[108, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[108, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[108, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[108, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.data_valuation"], [5, "module-cleanlab.datalab.datalab"], [12, "module-cleanlab.datalab"], [13, "module-cleanlab.datalab.internal.data"], [14, "module-cleanlab.datalab.internal.data_issues"], [15, "module-cleanlab.datalab.internal.issue_manager_factory"], [16, "module-cleanlab.datalab.internal"], [17, "module-cleanlab.datalab.internal.issue_finder"], [19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [20, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [21, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [24, "module-cleanlab.datalab.internal.issue_manager.label"], [26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [27, "module-cleanlab.datalab.internal.issue_manager.noniid"], [28, "module-cleanlab.datalab.internal.issue_manager.null"], [29, "module-cleanlab.datalab.internal.issue_manager.outlier"], [31, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [33, "module-cleanlab.datalab.internal.model_outputs"], [34, "module-cleanlab.datalab.internal.report"], [35, "module-cleanlab.datalab.internal.task"], [37, "module-cleanlab.dataset"], [38, "module-cleanlab.experimental.cifar_cnn"], [39, "module-cleanlab.experimental.coteaching"], [40, "module-cleanlab.experimental"], [41, "module-cleanlab.experimental.label_issues_batched"], [42, "module-cleanlab.experimental.mnist_pytorch"], [43, "module-cleanlab.experimental.span_classification"], [44, "module-cleanlab.filter"], [45, "module-cleanlab.internal"], [46, "module-cleanlab.internal.label_quality_utils"], [47, "module-cleanlab.internal.latent_algebra"], [48, "module-cleanlab.internal.multiannotator_utils"], [49, "module-cleanlab.internal.multilabel_scorer"], [50, "module-cleanlab.internal.multilabel_utils"], [51, "module-cleanlab.internal.neighbor"], [52, "module-cleanlab.internal.neighbor.knn_graph"], [53, "module-cleanlab.internal.neighbor.metric"], [54, "module-cleanlab.internal.neighbor.search"], [55, "module-cleanlab.internal.outlier"], [56, "module-cleanlab.internal.token_classification_utils"], [57, "module-cleanlab.internal.util"], [58, "module-cleanlab.internal.validation"], [60, "module-cleanlab.models"], [61, "module-cleanlab.models.keras"], [62, "module-cleanlab.multiannotator"], [63, "module-cleanlab.multilabel_classification.dataset"], [64, "module-cleanlab.multilabel_classification.filter"], [65, "module-cleanlab.multilabel_classification"], [66, "module-cleanlab.multilabel_classification.rank"], [67, "module-cleanlab.object_detection.filter"], [68, "module-cleanlab.object_detection"], [69, "module-cleanlab.object_detection.rank"], [70, "module-cleanlab.object_detection.summary"], [71, "module-cleanlab.outlier"], [72, "module-cleanlab.rank"], [73, "module-cleanlab.regression"], [74, "module-cleanlab.regression.learn"], [75, "module-cleanlab.regression.rank"], [76, "module-cleanlab.segmentation.filter"], [77, "module-cleanlab.segmentation"], [78, "module-cleanlab.segmentation.rank"], [79, "module-cleanlab.segmentation.summary"], [80, "module-cleanlab.token_classification.filter"], [81, "module-cleanlab.token_classification"], [82, "module-cleanlab.token_classification.rank"], [83, "module-cleanlab.token_classification.summary"]], "cleanlab.benchmarking.noise_generation": [[1, "module-cleanlab.benchmarking.noise_generation"]], "generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_n_rand_probabilities_that_sum_to_m"]], "generate_noise_matrix_from_trace() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noise_matrix_from_trace"]], "generate_noisy_labels() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.generate_noisy_labels"]], "noise_matrix_is_valid() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.noise_matrix_is_valid"]], "randomly_distribute_n_balls_into_k_bins() (in module cleanlab.benchmarking.noise_generation)": [[1, "cleanlab.benchmarking.noise_generation.randomly_distribute_N_balls_into_K_bins"]], "cleanlearning (class in cleanlab.classification)": [[2, "cleanlab.classification.CleanLearning"]], "__init_subclass__() (cleanlab.classification.cleanlearning class method)": [[2, "cleanlab.classification.CleanLearning.__init_subclass__"]], "cleanlab.classification": [[2, "module-cleanlab.classification"]], "find_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.find_label_issues"]], "fit() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.fit"]], "get_label_issues() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.get_params"]], "predict() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict"]], "predict_proba() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.predict_proba"]], "save_space() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.save_space"]], "score() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.score"]], "set_fit_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_fit_request"]], "set_params() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_params"]], "set_score_request() (cleanlab.classification.cleanlearning method)": [[2, "cleanlab.classification.CleanLearning.set_score_request"]], "calibrate_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.calibrate_confident_joint"]], "cleanlab.count": [[3, "module-cleanlab.count"]], "compute_confident_joint() (in module cleanlab.count)": [[3, "cleanlab.count.compute_confident_joint"]], "estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_confident_joint_and_cv_pred_proba"]], "estimate_cv_predicted_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_cv_predicted_probabilities"]], "estimate_joint() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_joint"]], "estimate_latent() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_latent"]], "estimate_noise_matrices() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_noise_matrices"]], "estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_and_noise_matrices_from_probabilities"]], "estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.count)": [[3, "cleanlab.count.estimate_py_noise_matrices_and_cv_pred_proba"]], "get_confident_thresholds() (in module cleanlab.count)": [[3, "cleanlab.count.get_confident_thresholds"]], "num_label_issues() (in module cleanlab.count)": [[3, "cleanlab.count.num_label_issues"]], "cleanlab.data_valuation": [[4, "module-cleanlab.data_valuation"]], "data_shapley_knn() (in module cleanlab.data_valuation)": [[4, "cleanlab.data_valuation.data_shapley_knn"]], "datalab (class in cleanlab.datalab.datalab)": [[5, "cleanlab.datalab.datalab.Datalab"]], "class_names (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.class_names"]], "cleanlab.datalab.datalab": [[5, "module-cleanlab.datalab.datalab"]], "find_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.find_issues"]], "get_info() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_info"]], "get_issue_summary() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issue_summary"]], "get_issues() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.get_issues"]], "has_labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.has_labels"]], "info (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.info"]], "issue_summary (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issue_summary"]], "issues (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.issues"]], "labels (cleanlab.datalab.datalab.datalab property)": [[5, "cleanlab.datalab.datalab.Datalab.labels"]], "list_default_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_default_issue_types"]], "list_possible_issue_types() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.list_possible_issue_types"]], "load() (cleanlab.datalab.datalab.datalab static method)": [[5, "cleanlab.datalab.datalab.Datalab.load"]], "report() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.report"]], "save() (cleanlab.datalab.datalab.datalab method)": [[5, "cleanlab.datalab.datalab.Datalab.save"]], "cleanlab.datalab": [[12, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[13, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[13, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[13, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[13, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[13, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[13, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[13, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[13, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[13, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[13, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[13, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[13, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[13, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[14, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[14, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[14, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[15, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[15, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[16, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[17, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[17, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[19, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[20, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[21, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[21, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[23, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[24, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[24, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[26, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[27, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[28, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[29, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "metric (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.metric"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[29, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[31, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[32, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[32, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[33, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[33, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[33, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[33, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[34, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[34, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[34, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[35, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[35, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[35, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[35, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[35, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[37, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[37, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[38, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[38, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.forward"], [38, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[38, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[38, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[39, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[39, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[40, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[41, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[41, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[41, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[42, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [42, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[42, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [42, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [42, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[42, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[42, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[43, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[43, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[44, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[44, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[44, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[44, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[45, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[46, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[46, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[47, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[47, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[48, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[48, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[49, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[49, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[49, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[49, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[49, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[50, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[50, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.neighbor": [[51, "module-cleanlab.internal.neighbor"]], "default_k (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.DEFAULT_K"]], "cleanlab.internal.neighbor.knn_graph": [[52, "module-cleanlab.internal.neighbor.knn_graph"]], "construct_knn_graph_from_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.construct_knn_graph_from_index"]], "correct_knn_distances_and_indices() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices"]], "correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_distances_and_indices_with_exact_duplicate_sets_inplace"]], "correct_knn_graph() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.correct_knn_graph"]], "create_knn_graph_and_index() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.create_knn_graph_and_index"]], "features_to_knn() (in module cleanlab.internal.neighbor.knn_graph)": [[52, "cleanlab.internal.neighbor.knn_graph.features_to_knn"]], "high_dimension_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.HIGH_DIMENSION_CUTOFF"]], "row_count_cutoff (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.ROW_COUNT_CUTOFF"]], "cleanlab.internal.neighbor.metric": [[53, "module-cleanlab.internal.neighbor.metric"]], "decide_default_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_default_metric"]], "decide_euclidean_metric() (in module cleanlab.internal.neighbor.metric)": [[53, "cleanlab.internal.neighbor.metric.decide_euclidean_metric"]], "cleanlab.internal.neighbor.search": [[54, "module-cleanlab.internal.neighbor.search"]], "construct_knn() (in module cleanlab.internal.neighbor.search)": [[54, "cleanlab.internal.neighbor.search.construct_knn"]], "cleanlab.internal.outlier": [[55, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[55, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[56, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[56, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[57, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[57, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[58, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[58, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[60, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[61, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[61, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[61, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[61, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[62, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[62, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[63, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[63, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[64, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[64, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[65, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[66, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[66, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[67, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[67, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[68, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[69, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[69, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[70, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[70, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[71, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[71, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[71, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[72, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[72, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[72, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[73, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[74, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[74, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[74, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[74, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[75, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[75, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[76, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[76, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[77, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[78, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[78, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[79, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[79, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[80, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[80, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[81, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[82, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[82, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[83, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[83, "cleanlab.token_classification.summary.filter_by_token"]]}}) \ No newline at end of file diff --git a/master/tutorials/clean_learning/tabular.ipynb b/master/tutorials/clean_learning/tabular.ipynb index 35f0d970f..835b9297f 100644 --- a/master/tutorials/clean_learning/tabular.ipynb +++ b/master/tutorials/clean_learning/tabular.ipynb @@ -113,10 +113,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:00.381660Z", - "iopub.status.busy": "2024-06-28T15:32:00.381248Z", - "iopub.status.idle": "2024-06-28T15:32:01.681791Z", - "shell.execute_reply": "2024-06-28T15:32:01.681244Z" + "iopub.execute_input": "2024-07-01T15:01:38.704463Z", + "iopub.status.busy": "2024-07-01T15:01:38.704282Z", + "iopub.status.idle": "2024-07-01T15:01:39.968773Z", + "shell.execute_reply": "2024-07-01T15:01:39.968140Z" }, "nbsphinx": "hidden" }, @@ -126,7 +126,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@0a675a1c4bd93cec9a874c1dbd565866d1f77dbe\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@7a801c5ee1e11be3732a7ea01725de8aca8d147d\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -151,10 +151,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.684513Z", - "iopub.status.busy": "2024-06-28T15:32:01.684044Z", - "iopub.status.idle": "2024-06-28T15:32:01.703220Z", - "shell.execute_reply": "2024-06-28T15:32:01.702743Z" + "iopub.execute_input": "2024-07-01T15:01:39.971457Z", + "iopub.status.busy": "2024-07-01T15:01:39.971069Z", + "iopub.status.idle": "2024-07-01T15:01:39.990015Z", + "shell.execute_reply": "2024-07-01T15:01:39.989387Z" } }, "outputs": [], @@ -195,10 +195,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.705803Z", - "iopub.status.busy": "2024-06-28T15:32:01.705424Z", - "iopub.status.idle": "2024-06-28T15:32:01.873655Z", - "shell.execute_reply": "2024-06-28T15:32:01.873076Z" + "iopub.execute_input": "2024-07-01T15:01:39.992806Z", + "iopub.status.busy": "2024-07-01T15:01:39.992402Z", + "iopub.status.idle": "2024-07-01T15:01:40.303536Z", + "shell.execute_reply": "2024-07-01T15:01:40.302965Z" } }, "outputs": [ @@ -305,10 +305,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.905438Z", - "iopub.status.busy": "2024-06-28T15:32:01.905010Z", - "iopub.status.idle": "2024-06-28T15:32:01.908827Z", - "shell.execute_reply": "2024-06-28T15:32:01.908342Z" + "iopub.execute_input": "2024-07-01T15:01:40.336204Z", + "iopub.status.busy": "2024-07-01T15:01:40.335666Z", + "iopub.status.idle": "2024-07-01T15:01:40.340138Z", + "shell.execute_reply": "2024-07-01T15:01:40.339623Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.910976Z", - "iopub.status.busy": "2024-06-28T15:32:01.910625Z", - "iopub.status.idle": "2024-06-28T15:32:01.919240Z", - "shell.execute_reply": "2024-06-28T15:32:01.918799Z" + "iopub.execute_input": "2024-07-01T15:01:40.342354Z", + "iopub.status.busy": "2024-07-01T15:01:40.342145Z", + "iopub.status.idle": "2024-07-01T15:01:40.351148Z", + "shell.execute_reply": "2024-07-01T15:01:40.350569Z" } }, "outputs": [], @@ -384,10 +384,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.921349Z", - "iopub.status.busy": "2024-06-28T15:32:01.921161Z", - "iopub.status.idle": "2024-06-28T15:32:01.923695Z", - "shell.execute_reply": "2024-06-28T15:32:01.923253Z" + "iopub.execute_input": "2024-07-01T15:01:40.353562Z", + "iopub.status.busy": "2024-07-01T15:01:40.353231Z", + "iopub.status.idle": "2024-07-01T15:01:40.356046Z", + "shell.execute_reply": "2024-07-01T15:01:40.355491Z" } }, "outputs": [], @@ -409,10 +409,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:01.925557Z", - "iopub.status.busy": "2024-06-28T15:32:01.925387Z", - "iopub.status.idle": "2024-06-28T15:32:02.457912Z", - "shell.execute_reply": "2024-06-28T15:32:02.457433Z" + "iopub.execute_input": "2024-07-01T15:01:40.358053Z", + "iopub.status.busy": "2024-07-01T15:01:40.357874Z", + "iopub.status.idle": "2024-07-01T15:01:40.885000Z", + "shell.execute_reply": "2024-07-01T15:01:40.884377Z" } }, "outputs": [], @@ -446,10 +446,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:02.460329Z", - "iopub.status.busy": "2024-06-28T15:32:02.460138Z", - "iopub.status.idle": "2024-06-28T15:32:04.490214Z", - "shell.execute_reply": "2024-06-28T15:32:04.489567Z" + "iopub.execute_input": "2024-07-01T15:01:40.887806Z", + "iopub.status.busy": "2024-07-01T15:01:40.887346Z", + "iopub.status.idle": "2024-07-01T15:01:42.858439Z", + "shell.execute_reply": "2024-07-01T15:01:42.857751Z" } }, "outputs": [ @@ -481,10 +481,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.492856Z", - "iopub.status.busy": "2024-06-28T15:32:04.492241Z", - "iopub.status.idle": "2024-06-28T15:32:04.502594Z", - "shell.execute_reply": "2024-06-28T15:32:04.502078Z" + "iopub.execute_input": "2024-07-01T15:01:42.861505Z", + "iopub.status.busy": "2024-07-01T15:01:42.860685Z", + "iopub.status.idle": "2024-07-01T15:01:42.872129Z", + "shell.execute_reply": "2024-07-01T15:01:42.871534Z" } }, "outputs": [ @@ -605,10 +605,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.504766Z", - "iopub.status.busy": "2024-06-28T15:32:04.504436Z", - "iopub.status.idle": "2024-06-28T15:32:04.508609Z", - "shell.execute_reply": "2024-06-28T15:32:04.508063Z" + "iopub.execute_input": "2024-07-01T15:01:42.874722Z", + "iopub.status.busy": "2024-07-01T15:01:42.874312Z", + "iopub.status.idle": "2024-07-01T15:01:42.879185Z", + "shell.execute_reply": "2024-07-01T15:01:42.878651Z" } }, "outputs": [], @@ -633,10 +633,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.510703Z", - "iopub.status.busy": "2024-06-28T15:32:04.510397Z", - "iopub.status.idle": "2024-06-28T15:32:04.517582Z", - "shell.execute_reply": "2024-06-28T15:32:04.517118Z" + "iopub.execute_input": "2024-07-01T15:01:42.881719Z", + "iopub.status.busy": "2024-07-01T15:01:42.881293Z", + "iopub.status.idle": "2024-07-01T15:01:42.890936Z", + "shell.execute_reply": "2024-07-01T15:01:42.890441Z" } }, "outputs": [], @@ -658,10 +658,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.519554Z", - "iopub.status.busy": "2024-06-28T15:32:04.519252Z", - "iopub.status.idle": "2024-06-28T15:32:04.632352Z", - "shell.execute_reply": "2024-06-28T15:32:04.631747Z" + "iopub.execute_input": "2024-07-01T15:01:42.893152Z", + "iopub.status.busy": "2024-07-01T15:01:42.892940Z", + "iopub.status.idle": "2024-07-01T15:01:43.010191Z", + "shell.execute_reply": "2024-07-01T15:01:43.009566Z" } }, "outputs": [ @@ -691,10 +691,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.634587Z", - "iopub.status.busy": "2024-06-28T15:32:04.634256Z", - "iopub.status.idle": "2024-06-28T15:32:04.637211Z", - "shell.execute_reply": "2024-06-28T15:32:04.636666Z" + "iopub.execute_input": "2024-07-01T15:01:43.012877Z", + "iopub.status.busy": "2024-07-01T15:01:43.012678Z", + "iopub.status.idle": "2024-07-01T15:01:43.015881Z", + "shell.execute_reply": "2024-07-01T15:01:43.015414Z" } }, "outputs": [], @@ -715,10 +715,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:04.639474Z", - "iopub.status.busy": "2024-06-28T15:32:04.638904Z", - "iopub.status.idle": "2024-06-28T15:32:06.709224Z", - "shell.execute_reply": "2024-06-28T15:32:06.708438Z" + "iopub.execute_input": "2024-07-01T15:01:43.017749Z", + "iopub.status.busy": "2024-07-01T15:01:43.017574Z", + "iopub.status.idle": "2024-07-01T15:01:45.116344Z", + "shell.execute_reply": "2024-07-01T15:01:45.115698Z" } }, "outputs": [], @@ -738,10 +738,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:06.712672Z", - "iopub.status.busy": "2024-06-28T15:32:06.711755Z", - "iopub.status.idle": "2024-06-28T15:32:06.724142Z", - "shell.execute_reply": "2024-06-28T15:32:06.723572Z" + "iopub.execute_input": "2024-07-01T15:01:45.119290Z", + "iopub.status.busy": "2024-07-01T15:01:45.118731Z", + "iopub.status.idle": "2024-07-01T15:01:45.130593Z", + "shell.execute_reply": "2024-07-01T15:01:45.130118Z" } }, "outputs": [ @@ -771,10 +771,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-06-28T15:32:06.726394Z", - "iopub.status.busy": "2024-06-28T15:32:06.726040Z", - "iopub.status.idle": "2024-06-28T15:32:06.750576Z", - "shell.execute_reply": "2024-06-28T15:32:06.750015Z" + "iopub.execute_input": "2024-07-01T15:01:45.132594Z", + "iopub.status.busy": "2024-07-01T15:01:45.132413Z", + "iopub.status.idle": "2024-07-01T15:01:45.200709Z", + "shell.execute_reply": "2024-07-01T15:01:45.200202Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index fd2594923..87f58e815 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -817,7 +817,7 @@