From 34f6ed799b218d8c641a625f038ca1842c73c993 Mon Sep 17 00:00:00 2001 From: jwmueller Date: Mon, 8 Apr 2024 19:18:26 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@cc319efea07da004d1544c0577402d71f309fa06 --- 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 25689 -> 25689 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 131825 -> 131825 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 78419 -> 78419 bytes .../internal/issue_manager/noniid.doctree | Bin 90559 -> 90559 bytes .../internal/issue_manager/null.doctree | Bin 68181 -> 68181 bytes .../internal/issue_manager/outlier.doctree | Bin 75294 -> 75294 bytes .../issue_manager/regression/index.doctree | Bin 3685 -> 3685 bytes .../issue_manager/regression/label.doctree | Bin 108542 -> 108542 bytes .../underperforming_group.doctree | Bin 120431 -> 120431 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 4492 -> 4492 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/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 164172 -> 166882 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 91933 -> 91933 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 68105 -> 69025 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 79104 -> 79104 bytes master/.doctrees/environment.pickle | Bin 15401797 -> 15407368 bytes master/.doctrees/index.doctree | Bin 42320 -> 42320 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28116 -> 28116 bytes .../tutorials/clean_learning/tabular.ipynb | 130 +- .../tutorials/clean_learning/text.ipynb | 1474 ++++----- .../nbsphinx/tutorials/datalab/audio.ipynb | 1206 +++---- .../tutorials/datalab/data_monitor.ipynb | 566 ++-- .../tutorials/datalab/datalab_advanced.ipynb | 370 +-- .../datalab/datalab_quickstart.ipynb | 130 +- .../nbsphinx/tutorials/datalab/image.ipynb | 2820 +++++++++-------- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 172 +- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 661 ++-- .../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 | 548 ++-- .../nbsphinx/tutorials/regression.ipynb | 202 +- .../nbsphinx/tutorials/segmentation.ipynb | 978 +++--- .../tutorials/token_classification.ipynb | 162 +- .../tutorials/clean_learning/index.doctree | Bin 3019 -> 3019 bytes .../tutorials/clean_learning/tabular.doctree | Bin 60582 -> 60582 bytes .../tutorials/clean_learning/text.doctree | Bin 232490 -> 232490 bytes .../.doctrees/tutorials/datalab/audio.doctree | Bin 336296 -> 336296 bytes .../tutorials/datalab/data_monitor.doctree | Bin 145209 -> 145209 bytes .../datalab/datalab_advanced.doctree | Bin 199380 -> 199380 bytes .../datalab/datalab_quickstart.doctree | Bin 149271 -> 149271 bytes .../.doctrees/tutorials/datalab/image.doctree | Bin 398347 -> 398347 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3292 -> 3292 bytes .../tutorials/datalab/tabular.doctree | Bin 119428 -> 119428 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 154241 -> 154241 bytes .../tutorials/dataset_health.doctree | Bin 324921 -> 324921 bytes master/.doctrees/tutorials/faq.doctree | Bin 210677 -> 210677 bytes .../tutorials/indepth_overview.doctree | Bin 222087 -> 222087 bytes master/.doctrees/tutorials/index.doctree | Bin 3139 -> 3139 bytes .../tutorials/multiannotator.doctree | Bin 137376 -> 137376 bytes .../multilabel_classification.doctree | Bin 64488 -> 64488 bytes .../tutorials/object_detection.doctree | Bin 140179 -> 140179 bytes master/.doctrees/tutorials/outliers.doctree | Bin 104221 -> 104221 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 17310 -> 17310 bytes master/.doctrees/tutorials/regression.doctree | Bin 106980 -> 106980 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 1994473 -> 1994473 bytes .../tutorials/token_classification.doctree | Bin 176715 -> 176721 bytes .../cleanlab/object_detection/summary.html | 20 +- .../cleanlab/segmentation/summary.html | 9 +- .../tutorials/clean_learning/tabular.ipynb | 2 +- .../tutorials/clean_learning/text.ipynb | 2 +- master/_sources/tutorials/datalab/audio.ipynb | 2 +- .../tutorials/datalab/data_monitor.ipynb | 2 +- .../tutorials/datalab/datalab_advanced.ipynb | 2 +- .../datalab/datalab_quickstart.ipynb | 2 +- .../_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/cleanlab/object_detection/summary.html | 9 +- master/cleanlab/segmentation/summary.html | 3 +- master/searchindex.js | 2 +- master/tutorials/clean_learning/tabular.ipynb | 130 +- master/tutorials/clean_learning/text.html | 18 +- master/tutorials/clean_learning/text.ipynb | 1474 ++++----- master/tutorials/datalab/audio.html | 2 +- master/tutorials/datalab/audio.ipynb | 1206 +++---- master/tutorials/datalab/data_monitor.html | 8 +- master/tutorials/datalab/data_monitor.ipynb | 566 ++-- .../tutorials/datalab/datalab_advanced.html | 6 +- .../tutorials/datalab/datalab_advanced.ipynb | 370 +-- .../datalab/datalab_quickstart.ipynb | 130 +- master/tutorials/datalab/image.html | 50 +- master/tutorials/datalab/image.ipynb | 2820 +++++++++-------- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 2 +- master/tutorials/datalab/text.ipynb | 172 +- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 8 +- master/tutorials/faq.ipynb | 661 ++-- 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 | 548 ++-- master/tutorials/regression.ipynb | 202 +- master/tutorials/segmentation.html | 10 +- master/tutorials/segmentation.ipynb | 978 +++--- master/tutorials/token_classification.html | 20 +- master/tutorials/token_classification.ipynb | 162 +- versioning.js | 2 +- 177 files changed, 10520 insertions(+), 10153 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index 27de41706..6231a23cf 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: 1c1dcb741825cb2c57a77fc5211166cb +config: 3ad80fbb8ee164815b3a9f7204198f6b tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 43982360867aed21f6b839b8f6c43c1ca7f4d2ed..1a5320d167bae411fe61d69cf33526d7d803015b 100644 GIT binary patch delta 117 zcmdlWxj}M6IHO^jWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E YiHT*hSyHOu<_^XHPBOG{axZ5D07*q79{>OV diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index d0be6ecdb0767e0c8f7bf3552ad585a4d6d3dcf7..747c2fd7a48610fd53fee3eb35726d5b45920536 100644 GIT binary patch delta 1464 zcmX^3o8{ndmJRWYhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-@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= O;FtU=NZL*_z+-4PKKNiw$1ZkbFYs1JtS%9OEEbZ-^EjjOjglSY+1j)Dn@l|inYJ3Kb&?lBJ)0}EOPI;Db)`NVxqi(t zT0(|{H#?eNXD7?CC7bu!FXSXsE3?-_DYCT2PhPLbwYe@ekGyyVYoA<~DzJH4UVtnG zE@!&gMt2gt;itzVU0C44K*=n5b_*C&buCuJs`O!g7q= Odtvp z$eV6u&ak(qv!gc}tf=yY+?8%`*i%&*b~!vAugC3n1%r;@Ay22r>F97(xxxn>E{8F~ zJ^(U(=cl7$yjj(BueQZlR>u6PqyKrZz;#GoZ>$883MSQ80{QyHqQ&TMRWcxprWYLv zJERO4q5gKP>+MG1_Pix^s+hH^PJLiFVyJ^P0Q#?9*M>C^Zww6goJ(CR=VT^zmGBp{ zkP(^d%fz%@#;t{i9Mw)PmH~5V*4$-;zWHFTZ#fQY-~6z`HAhzrnEQJ5BCMTR8$k|( z+2@%!iy;TW)Kam+?xRIhO$M{Tw}X9w$-Z4I3X@yP*fE&w-OVOpa{oTo2CjidYH_fy zMcAHiq}h{fuo@6=-p1XOdyich0u1DUou!{SysZKgH|RadOnQHq`+>)gIcXaP&uaOr zt#}1CG2P59wCdyK;5BWQVI%2s0(q5~Iq2pU{yhuKKnuORZ#yRTi|InHiS8v{Kv6P4X-{{g{s)$7~wJfQ98zWQkuxCt}0lYLXA-pf${`ahtgFi^o9&k_PSBfOzvj z7Kwyi$)~rfGODKQBBYC**0Yt%)-#j! zR2f4aiKY!nn={C=Fq4S+y=pCuyOr@)u$iVq9VK){xq&$}k!-=QKJaK z;0mXOkT6IK3#^;GE!wz>mMsd(X%H-h!l6Kr#6{<(RNHyx4|ty6`@Hu(?>ToWkvo;h zoo;2$o?drVsG}lU?hb_9p>R0l47fbuK*hm8hN z0GYn?(=jpLtZKSP+hQy$W4_eU|2&xgIwY?*R{Ti?ljR=6k{;SuuVGYC^1H--NQ`gElnMqwGd_^o` zMCbZ4F>R-DYvCbBwUdjbz+9R&cN&pzUYP4$j>FnFKdf-g-75yneZ6`K*3PVrAcw*1 z^Guw@kb_`qsYqe>(W0p)gIQqm=S&uc$?+U^7bdS;*&~=-Zf751a%>x$fXRt`Rt=M1 z7O*HlCOITeV5nZqVgU5d_Mmq=*cX`W-Nm|La!V;Y4wF5**(6Nv-^bd(HPA>c73^yv zw&xpZ_7oee2E?1UaTnR|v5P~1fgG@N^fQOIm1E)ty*HUj?~iaF@c1z&ZNuPMEq}EY zufQgzo4JKny}S&(rp+>JBwbD-uM#r{-MqrTXMq`L;VSRjj){F@x`1nW-=&h=Zs_D82>EfXEY~`{w4J(ob9Yr?d|B1?Iwos`@zKV3I6HoUjnuuhpc8ZlK zW5^@Xv>|D87FiZ%5;4D5t)=n9%6Kc-Ow*x`61t+?z#N)LwqQSyo@9XMX$2QDxJD(# Z>IEBCyOU)4(+5%)n?LC!fAmKkzX31K3=jYS diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index cb723fab1cc5569f8250d665fa14cb598ea5cd0d..5e094d5bb61f58ed0ab1f50de67ab5c579c8bc17 100644 GIT binary patch delta 3571 zcmX@QLh$Gc!3~~_hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_K74c{!F*1<%ktqPjlEM#iktQ$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 KwgbCfElL0_$NnS$ delta 3571 zcmX@QLh$Gc!3~~_hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL@Y74c{!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 KwgbCfElL0`7fxFM diff --git a/master/.doctrees/cleanlab/data_valuation.doctree b/master/.doctrees/cleanlab/data_valuation.doctree index e026dffbb7d44119645a73b88cbf328690d47f9a..59fc8bbe189b17b02162ec783f68b2ab433894c2 100644 GIT binary patch delta 475 zcmcb4g7M}F#tqSohG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-sn7;liFtC7uN^HruUCi1jSzQEc*y3WmwY|~gs*EYG4EoJi>?mlL+ iv<7b8!=FY@xHhr{ZeAjIBY-Sh9e^Rkzxhv6EF%DYs)>^T delta 475 zcmcb4g7M}F#tqSohWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLq#7;liFtC7uN^HruUCi1jSzQEc*y3WmwY|~gs*EYG4EoJi>?mlL+ iv<7b8!=FY@xHhr{ZeAjIBY-Sh9e^Rkzxhv6EF%D{kd)H^ diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index ae3d6a5456a0dc04b30e6cfe595447c70aefbf01..eead05ac31e6ebba67f533ab0690f0873bc2a533 100644 GIT binary patch delta 4228 zcmbuC-%FEG7{@thZRwWwW<802oi;yOT)HZarkwN0#+1^ZXtkh&B{XmkC z4B5+gAs8;hL{Hs}8#?yJeTlp(u8xLGG06>=B34p&qq6?Cci#0brUE zZesJMla}I*2CB`QBrRo)19TzL&NQ2K6NE;XU6RcJs)SqE%FZKY5PFuSYCa$hE8%vw zdZ^h2S*HL>3|DI%E@zWU7t(Jb5n*Yy4cR9Hz26n3P}321r{^JRecaoFS|{Q%YVE$} zN3CZDe5m#C;2>aSg~VUf7#Z>aM$K4XdbNj^@c40ByMY;)xv0{& z9cshJr4Y?yH@i<4>>l3NMk}zR>r2j5@~S9JVd4MdP2F^@wD{;;>8Ins<5L%0kYB$^ hvr_R=-IEmX(+9L0*f%cV0c6+K|HyCU@-*$B{{RVX%p3p! delta 4228 zcmbuC-%C?r7{__vHFI0qvLEI}WC{JcQEQ&I2x(GCZKA7<3=((FnaecSNKIDK4U}%PktRnUjZxD7v{Xj zCQ^GfF1gic&6R3NRwXqOQEO$FCnDDzmg|mq+#boLNQzn~D^Zurqe@CgExSd20GK9+ z8`!+%WKrQp6V>J|PAz4R1GJH7W17Rh0YVj4<8)gAR1P<@~JCS~kgu>F%R%D+{^nPatg_>5_o$iOI^>I%(Y8{WeQES(= zCe(VS-;Y`k4h#TRW+eWiMrF_o7&UVpq}%r_z&em@gtZJUA~zGsUS{t`ej-^-^u*{5 zB&&%A$D*h;m`tG7OQ{TMeWpJ_tt*c#C_0?(LDA8PMl@?|@;z#un?in+HFG7{^jp;G z$b3c7z84pPL&r#V5H*&+3ZZDx>r+6pV!8qLxu`N*2F~F0Dme$r`OaS?w*~X+ zyZ2KYk5W30JYvOZSt~HIk>0JqJJ`X~duS2(1-XduX&WxL>Xlwv%;U#t^*Ux?<)TXO zR;Ud>cZO&VyV?D^V0ZD}R$7J~U4L?>oL6+v6c+wJ-q1x?ZG}hYavvQ79-q44g8ceT hnsF8`)xAjpKYc*EfPLcv?nicQ?T`Fs?w+RY^dCi(E}H-V diff --git a/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree b/master/.doctrees/cleanlab/datalab/guide/_templates/issue_types_tip.doctree index 1893b1e1852c543adcedeeb54d9b1213b7babb64..fcc5f9f701a0bc2acd18b2d9205c3e053f96d89e 100644 GIT binary patch delta 62 zcmZotYEs(Z#$uReS&*Dmn3t$;YLRMgVs4pcVQP|QY+zwwX<=?|oS0~kXl9XYVQ7$K RY-*flWMFKtxtyh&2LL$=5nuoS delta 62 zcmZotYEs(Z#$uRXU}lk=W{{(AmY8gooRX4km}p{|l4xL*XliU}VQFTPWR{d{nwXSk RVq%$WmXvC^xtyh&2LM%Q6374m diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index b1a4f853732805511aae6fa9a2331f1126bd1de5..ea23bfd6de8a37afb9e3e7d7559456ea32c9a1a1 100644 GIT binary patch delta 64 zcmccfmGRD3#ti|ChG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd T21&-I#%V?d#s-@k8KWx!&Vm#N delta 64 zcmccfmGRD3#ti|ChWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( TNogh~mdR#GsfL>y8KWx!>fjY` diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index e98669b840192ad8a71d5204c3d899f42c395dd7..cc50acee204e462ed360690813d5e8607ca9f7da 100644 GIT binary patch delta 62 zcmZ2yxXy4xHltyhWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<~fW);sAJ|6BGad diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index 77a23973137d0c8a7752f02fc63ba689741a07a0..f9552155a205c62984266100f74aff90162aac8f 100644 GIT binary patch delta 62 zcmdlUw>@q{G^1gfWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>e5~lzF delta 62 zcmdlUw>@q{G^1gDftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<{rk?x&V)R6b}FZ diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index d4fdd7a78843cc59f87e01553f0c582c2d726cca..ad3aa14d0f3087ee1f24780ebf64008e2cea78e6 100644 GIT binary patch delta 72 zcmey^%JH$4V?!#VVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV cL6Whlahj2Vu|e}R#`bB9jN7L%GDR~208j4~Pyhe` delta 72 zcmey^%JH$4V?!#VVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c cQksd0WwKdPs$ugq#`bB9jN7L%GDR~20C$ZRxBvhE diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index 23368ebeb46638a4cb6a12a3d7b0f0b2e4ee36d7..5f32eb1f322124d139ae296b640d6603931c1e5a 100644 GIT binary patch delta 117 zcmX@AbyRDEFQZ|aWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E YiHT*hSyHOu<~qjhf@Ek*6TZ&~0Dq7qV*mgE diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index 0805f43d51bc1f6a196004cde278382efaf0e922..d39317abfab9964b96e46fed85b4e9535ce1735b 100644 GIT binary patch delta 5702 zcmbtYe`u6-80VS3x9Q#9-ENHS$h;{xXRf-N=XvkxWV3BJak*o|L!91rjvcG!blS$$ zoij?KFgN`cU(urd;Xi@Z;vv6(q$Jc|L2yA56;x1Ah-*;{q94-ty;ov?e_nY1{rq^I z=kvVp!#n?>XI?+Ms415`Hh6e!G#iOqLy0YkWX_6j$;G0Um9!FxST-BYZnXxjrs&~V zJeJ!WjYY*IEdZ#>#=ZPg$s8i6q4MqWxxNyH*KoIYKWJX7*YJ+= z{Uxw^EkEw>$JJ|jec)M8oxRm!1!R1TTZ4>2C-~~4N3eP)_`OgkR_g@6wf-QgIu|~K zs$Oqs2HM1xjc;Ok&Tw;djR%@(MqISYcyX&AYm*KC6%D^q| zegN#7Xl*IwJFS!0pfe)X5fGne>cxY$Pp}#|)$xS^L3@RNpHJM(4Rf&T4v;oc*;u6D zXHo%d_72?J^8(gA8@>paUdz|gd2H&ouqh%341T|F3in`@mgTv6@v4`gd z&Z8;#A>iYixj{GZcScIk1la1^M>n}p7c #Df#xfL+uEiVRoMd-&FP98H9#xqRBg zFQ3Yzfv}B)rtR0k6XMV5pjg}9E~01p@v=BHy#V%=XU~0I3M$W@Gx+z{R-l2`lF_wv z(1jDFojii=hBKGNFy-77q45=Rs)u%{4GnbHMM%}z2o1Y&qVax}Z=yVEqENqD=}l>I zftOG3q$Mz+THc12$uFOxUKbqmyD6k}%6rgQ*hVx-SQU2R0k@l}NoLS&|8HWUi^ydq zKKXZ;HmVybN}vIIXve2c_RwQxE~Zo0MW(zMp$3)hBg>7QBGc(6+Nq8oqE0-thH4q2 zb{BZ5@vCLv2>k~4#c((-jS_Zqbc;g0AwND&l`4OfhVZ;NB=+@EycpE%2{$XSj7`mq z(=mJ*mND5Ek$QL1%~llI0`i}*j^W6Zo2_t|)V4FU5#Nd;iAOD*rxAQ191^KXDy04j Uw5ZLmP}m10a<9_=W4~Sb9}?zO!TbLmF9POGMwZEUuVcD7|5t7~Xk80(fcO&h9q?LKVj zwq~8^Cdyp9y~-<7u|M`lm|8iw_lFY)`b!WZI79^%6qK=AQ3~S6@V&W1`ulm|{(U~q zInTNGa2G%DEb3=BMJJBNnlssku}CbPiDia|GtqQod^p{(E#35Jyg43g9EuHPn$knr z#>RLiHZ&3y)3gMj%Io*>PeluepoYp@uTq%^SjRAB;jOITO3&4`FuaO;yazz@TD^*Q zmL4dA)vNg-{{XID&6@*Hf$Hq76l);k$G9!X7<7uS+H=jx)z{KTwebMmgf|=G;HudGfjz$%_Y1X^J8st;J<>xiMaRjGh0N<_V;lDaV5Ej z^E(cLeG_f1#eB1E8XI&)BBbcHacjCd%sz z6#Qf&fX&{8dwZY7y63=`;nJ)5^<)m4x+83h$U%$W>z~2Bmx{A#TYQ$f1J~d(40P`0 zg~9V^3VsOq_Pnm~285p=mCk z_V5d*a%dnNBcWO6b?~tGb2cb8c65k_GXr>89GYDM`^xj@zAgrp=g(RE`^#(4Kxnwg ze`oWBxohAo_{J;U5zIVa`1Hdd1{JRHB#8Fk_Q{XhX%m0-{ak zQg^+$JdgZ52+l$HZ`)jR=Q-@I=o9ve%}syo2ZO}+$}AS)9x+&B$>-xV%BTLVUIz`- z<)|tq?6xVu)k`HbX&cCEe1()%)MdcYvx!17-%h`%r-RgwCDUz&W(uo`4V1(Nx{73c zBONkuqO_Cyu-$OyvK*$YNf8=fCTDtSr`l3Sw+uq6&PHg|#EHiHRW3?7)I`2^z0#Y~ zYJitd?4lJgp<3R7m&h+4r#=IY#XS^KI_14+EF2@6B&_ng@qjzc)Fg9gw*NP=)JaD;w?`(n8qm&OUZIeJ9C){-9|r*f5>pb6n`}jaEg=61>llvBm~4g1q_&@-t@u_HNIYukJdNQC;gU#AQYQ6R VpjB;qk-|PGk^7YXAN%dv{{WTG&>8>$ diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index 320b40d28c92e5ecbd326ee9ce2558991447869f..c2ff608bc836d284f5e8218577210908ee96cace 100644 GIT binary patch delta 2705 zcmex*o8{|mmJOkdhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_)7@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&iBEqHrY`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&iBEqeLqN%Z^g{7HEl37x+X<|~E fiHT*hSyHOu<_5;O%tj<@)1Ul+U21bCYY7hkL0cud diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 0f272e72732eacf88dbdefd3c74eb4057208a5a3..f7c4e423fa451983541bfc64984fe715dd49e997 100644 GIT binary patch delta 1125 zcmeBu&(!;#X+t=pVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBmJMoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8}}oq((0Q delta 1125 zcmeBu&(!;#X+t=pVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R7iMoD(kwN2hAUa;ATvz>`7t!bMRcoewF()y4rU2&5Olz1ld$q8?E zkThl?--yk8aw=?OYv?J&+-8RWd1kU5Zm?M{WHXsjzd0dVmb}y+HyNmP ivt~M*E!hs|-F$k9Hkm;-*&%>;^64cqo7XM7F8~0e;a`6M diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree index 6feb4347cbfa02d86e270a32fe89a181c6d115fa..5260c116a51d4d5776c2c04e1b3c13487ab218cb 100644 GIT binary patch delta 62 zcmew$^+9TbE0bZGWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<}#*VTmXhW6Z-%F 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 ba37c2984386ce8424f3626aa45b900fd3471e69..16c45c4a0ad310bd8f9b75b6bf1d8f1e34580e7c 100644 GIT binary patch delta 2628 zcmccdp5?}SmJQL2hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-sn81IsyOVOxqvjlSk=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<09Ka?cmMzZ delta 2628 zcmccdp5?}SmJQL2hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLq#81IsyOVOxqvjlSk=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<07Vf+@Bjb+ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index dc864283883be64fd16b6f678a580c8f7f2f6e79..d232699a9acda6d08469b965bef5a8a565beee44 100644 GIT binary patch delta 2632 zcmaERn&s_jmJNZ7wrQ3H$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s>N+`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;S1ORKP3^o7& delta 2632 zcmaERn&s_jmJNZ7w)q8S7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfPL~`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;S1ORH8Mcx1a diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 0ca529a5b86cf2b8c328c03d80a071a7215a842b..39c2439a2e2cbf4570e435bf98656a64e3c126ce 100644 GIT binary patch delta 2563 zcmex0mF3q|mJNZ7wrQ3H$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s>N+`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`0qHY?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`0qeLqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<}OAFVE}6963PGo 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 e0107abdf887087b731f34fe558e889cee666226..d7cf83e8945d93c3068e6815adaf459c848d0a6b 100644 GIT binary patch delta 2506 zcmbRCjb++5mJN}NhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_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(NRGzUjdL delta 2506 zcmbRCjb++5mJN}NhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL@o7+;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(NT31tJgt diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 4e8e56702622281d635460cfe440147c5905f535..499e62fed6fbec0b7d5a2a438071de7b9c07ff90 100644 GIT binary patch delta 3034 zcmZ3sg>~5$)(xJFhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_K7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>j|-iJ5< delta 3034 zcmZ3sg>~5$)(xJFhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL@Y7%z~aZL(oY&UA|iM*hj)mh$65c%L(kb#v4xr+eBFp_b+kvg?cZ>iKqRb!w 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 b4cbaef8cfb764124b7871911ed2aed46a8f251b..98e2b6bd99411b7862f92226a05aec129b8c777e 100644 GIT binary patch delta 62 zcmaDV^HgR-Fr#6bWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eJOF5X5`+K% delta 62 zcmaDV^HgR-Fr#69ftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<`%|>JOG5+6YKy0 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 61d4a8a9cb0e7453ea10728ff57731680a0dfbb7..725ea62aa58f97452a66d95ae1472f39ad8bfefa 100644 GIT binary patch delta 2704 zcmccogyr%RmJObahG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_K7(bGsZSukRrpau_z>E9d7XQ~TNm zq0LgwIs9a5&z{b`IA$_=1^%qbK;4^-X3iHR)AIK^Y?EWHxi+s^txul!i#FG7-%4Io z7fr6)uCtkG&q7|Z91qeiJ$e0g?#(%;T6xGfV)OdzOqyhApS8V)k?{w44TV|T?YS8f z*+{o{`$a)UGj`JT0kh?-$>&mex8Ij!oJWSeaQ&f*jD>t;*$-+D@^9}jU_8wT0Ny<* AD*ylh delta 2704 zcmccogyr%RmJObahWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL@Y7(bGsZSukRrpau_z>E9d7XQ~TNm zq0LgwIs9a5&z{b`IA$_=1^%qbK;4^-X3iHR)AIK^Y?EWHxi+s^txul!i#FG7-%4Io z7fr6)uCtkG&q7|Z91qeiJ$e0g?#(%;T6xGfV)OdzOqyhApS8V)k?{w44TV|T?YS8f z*+{o{`$a)UGj`JT0kh?-$>&mex8Ij!oJWSeaQ&f*jD>t;*$-+D@^9}jU_8wT0Jdjm A3jhEB diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree index 794ee7ee27283129b0ebbefa9d7e9eeaf4ec68fe..3d3ab00be3f77c072ef96d72510a67ccb7a9a0d2 100644 GIT binary patch delta 3004 zcmdmgn05bQ)(zf_hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-^f7_XC|ZSuwidDCylGxASnWGN)wR*?3|3%GAy50}1zfY3 z$GF|aNOl5PSq%%7O+A9<-RmjrbIQf4e z>*fr%SOK!Nb8M~;XCg1@fNU3 zUqu6%Uf=woYCjX%4wu+mS=U3R&0vS0Z#Yef?I|rQD6#!o+fhnvPwncU#P;ny&6L>A zGQpp`5(AQCWT%8uqTOc39r7H%V6uEM-{$YLJIP4zn>Q|yAWqlU&>Dm+)*3c%n{$_Q?THST|e0W#=R> z=peyg-^sn1^?MO{&dAs*z_^0E@+4!b0HenCN*2aeUb37I(l5PzlMrJ*4+Z+K$}=9+ zAWMJKc27^nhva1%aLVV{{?w200l5a831Z}&{Aj!I^dEkVPTQ}AFs5;l<&C`U!1nVK FMgWClq5}W` delta 3004 zcmdmgn05bQ)(zf_hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL?t7_XC|ZSuwidDCylGxASnWGN)wR*?3|3%GAy50}1zfY3 z$GF|aNOl5PSq%%7O+A9<-RmjrbIQf4e z>*fr%SOK!Nb8M~;XCg1@fNU3 zUqu6%Uf=woYCjX%4wu+mS=U3R&0vS0Z#Yef?I|rQD6#!o+fhnvPwncU#P;ny&6L>A zGQpp`5(AQCWT%8uqTOc39r7H%V6uEM-{$YLJIP4zn>Q|yAWqlU&>Dm+)*3c%n{$_Q?THST|e0W#=R> z=peyg-^sn1^?MO{&dAs*z_^0E@+4!b0HenCN*2aeUb37I(l5PzlMrJ*4+Z+K$}=9+ zAWMJKc27^nhva1%aLVV{{?w200l5a831Z}&{Aj!I^dEkVPTQ}AFs5;l<&C`U!1nVK FMgSVW<%0kK diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index 63a3b6e13fa00884db6e765e89a1ecc12953533c..bb7eb1bc9968c71fe8ff5f7a0517d9246c2d7b83 100644 GIT binary patch delta 2688 zcmbuA%_~Gv7{(jsjysbvB3Tf((Aa1$cdmO!8ALIq*dWQr9l3=P8BsI&NXkN^ama#v zY)}?vyb>!Z8yhPWk*wJWEBU(l3q11&{Q5o5dCz;!)uQ=o(HyNcmFS+qZvW7L=91MO z#jSXBRd(x=sHz@SQ6xf7V*<)EBTehar zQBsWw#wQ^%OxLz#GlVkQa%5w5gRD~EXgI^iLuLc#X)q@rJFOs93g39}bS5-ZuyUsr z${r@J@Ekqxg`!EUGNWtPN&*y^ku1%j)*1N(wep@Fz)Fv0^@(857w;@!{M}wgPcj0ex7f;KH4dviOq zMMm$nuK9S(pZ-`py~lMYI54)>Yp2fsUF5klh9(5;bT)Em1D4XjA4*e6nQ}Qm+_98Q zxshk&wKwbrezyFMd{YL#v7SPK&1uwb;QQMLNl^D0%8mwr8%L+H^DM|1V$op>3t!r( w_TmYdcjSJ7-f#MWH;p3q7CRVPc;U$R6hmj+^~%%UTc!oHN56Oqe%z+~06YIKF#rGn delta 2688 zcmbuA%PT}-7{(jsj5CulB3Tfp(Aa2^_d6s- zlQh3yb1I_jSBgs%w@dcOlBi0m=2p~zD9W0ob~_!cDp6z`c8>N9t(^1UJbNtbWy|I? zI!dZB!1yFYhUwapY=lroTefVhZirP191Ugoc*v~dJPqdLW2Y6OO5qz1p3a1Z3RZ46 zL)pW`6`rF9zECuYRc3VUSV@2aGjhA;Q0t8Q1hw+=4q&Cnvid}@=Zkk1F#c{Yqw=Z| zwC89|H5z!L&WECN^>;|%)7TjEHsz43u>-lozc_iD!pJHAne%A1gCTIp=~&;l%_jz37LR66Cbfw*lc znQ|k~$ZN0Lb^L7k9r>nod}BR@f}7K*UB~yg50aqnGn5?-0ymCMW9M0rag0TWOe}n9 xq1uZlWZsec1$w{f1Ku=>+?%XmXyJt;-%|{oan~zPdvBR0&>sEbDfDrh@&j6XY!Lte diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index aa62aebfc393dcba0f5b2027d71b375aa3640997..703d1876393ec5104da4baf402dabf8eaf3e8589 100644 GIT binary patch delta 3004 zcmbPthGpIvmJPm)hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_~7_X6`ZL*sT&mLr{tx`4YUMpiOm~T z8_4xFNc%tawM^tWeshBM5=v~osC$u2n}JDHVspOX3QBB$Vtkhp+xyIID6xII;$yJ(-T5?690~v+7Dca$Nw@F1}HmOxq!G|KEyh^XFaF-K5{Lahe9LXJif0%mJzMn8+jRD gl9zp3w<`!Uejr2dc1Hh($ delta 3004 zcmbPthGpIvmJPm)hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL^D7_X6`ZL*sT&mLr{tx`4YUMpiOm~T z8_4xFNc%tawM^tWeshBM5=v~osC$u2n}JDHVspOX3QBB$Vtkhp+xyIID6xII;$yJ(-T5?690~v+7Dca$Nw@F1}HmOxq!G|KEyh^XFaF-K5{Lahe9LXJif0%mJzMn8+jRD gl9zp3w<`!Uejr2dc1HeJOF5X5`+K% delta 62 zcmaDV^HgR-Fr#69ftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<`%|>JOG5+6YKy0 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 cf8d998f0b274e72c30f89c03fee36e1085ae1b4..14d4ad14a862f3bd10fc195c8bf1de30511e3d4a 100644 GIT binary patch delta 3483 zcmbuC!Ap~I7{~e5-f7KEY^4)RkRT!|e>%6lI~1M<3gs{nQ=;Z-Ek@QB?oeSv7f7jv zKgJH#Sgk@RnDyX(5Rr$XljJVZWzh&abcv0i4#ntwz2tS@{Q;ln`+UF8^ZZ`k%DS<# zZd~qTT>T8*tx2@RhP@|^KqY3^{ZWc-0rXky1AXxRaOQt z!T7XjH^izJ)X7DYmCh>P4H*5>G`|%f%fS~S-EXGrjbn&#Ihdn2JExGWbaZdapDj?+ zIa1p0DgerOhAwoRw^WZ3&PR+_6<)fPj?PXMGgT1xj@kd*YH&sU=i(`E&*Kn()t}s)oR8K$XhDqv(}4Pb{G6-i{Q2 zX0^>viQeN4VjynSo+u0XqiLl1|7C3S>zW5id~=D;so%Aku~F;vSPfm!K-_{%VzUYF z?BN0 zTQ)=S>3CbBz)UpvbFl$0u~J~A{Scm#_YSZ_=;ZRDI+@})zj~=&K60{SMyU50k!{*7 zSC6o-*x_#QNsU)z)We=N;Y}pVBWwuA<*rfoVF$)1zf7`rxpI+3!4RZY;4!woGrD+d b(i=z3Z}2jFlTEfj?)6`zM!EEW4R8Mk4(S48 delta 3483 zcmbuC(MwZd7{>W(XIgU;Tj|6SB#4O0Gn)r@QFt{_D2Ity61APR7OTvKyQr|C3#8P- zkFkqttX3fu%zAMjMC3)$mE>-sn?)n&qMO(V>Y^B(&zrp6vp?YXKJW9q@B1B2WzAe! zGcWZq=h$VfM-QqOIyIlJ>4AXm^ts#tpW5Z?KIHCkYc9X$*SmfGpv&dfHUC*BU1ene z<4m36?WS1uf|6XY*eRfVH(~Tk%iLywEQen3bib9ZH;y90H&sYO(E>2wA4n$TxZRa5Xapi1S?5%kKNFU_Or-i{Q2 zW{u5H@!n$%d?0Q&o+umX(KOQh|1u8xb2GIsj>q=MqA$4Vj_*#fFd zTzNHT0r6coth}_U(3STOk^R9jY{U5EmkHJ`mM^j>7=kbgJjT{{T8g(O ay>Zn1Ixog=vWXVRz5F$56pIho@Ya8k;!z_2 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree index 6073bc6d610488c4a4530f46240c01b72a7fa4f3..9f27c7e93d3f0d1c05e049fe9a7d488bd3b3a48e 100644 GIT binary patch delta 3969 zcmbuB?MqW(7{}<(J03g z9W^Bn!7x%WZ|WeCGiYDLKp}_(33~ltOznHtj@BQrt3Tkk-+f)@ex7IZ1IGCQ<8UQe zy!kaAcWt$6mrcH4&$H)yWm}%tD#>!bY`0roF3Gh=_Q)2=ZM9jwyCth6x@iue{0^U} zGnlz7)`Lh_@D@ls&R^rV86flxQ?i1ggQ8S2aAW$TG{~&u2_xAitSp^Q7X{fSHf*ml z!UL2;_?p~;MGkRz?@|mjaELz@_)zQl16yN3>-D03)S6jR3|QIku%i`? z9jyV3!tV%i)`@dy&W4j4QLFD%J8CtZoRRfcv$CAP^ zRlV_mSq)6HTGxg&tfM>X?LbXJM?H;qFzZ|X(In8UH(FaU>og~{7ou5@_^x2zcA8(` zgRO@96=>E=4Lfyb!0)IR;jB!px`M`lYbx@`ZG{hCq`73EfPehMMtXu2_?K5Ae3E9qO&K}B5Ruha rwmc_;mc(=n*kOGQl+u@ET)sOyf>fv}qqGE`Lgnr&O4OQy^fTiRsARCh delta 3969 zcmbuB?MqW(7{M8QF)hth2L3LDNbyG{pkbXq4lL zj+&x}U>K>GH+7K68MH5Apb$iY1igMRruIE+N9zyR)gSQN@4l{cKhLv;LDRyZX{3^D zHMNdhx!hW{$05lM+2fIIlHKW%tb3*Gea>8`!|ryt^P*PIq3uzi^+v$}YE3ID1gz|H<)9Uf zAFlfRW@tomnIt!qOX*3n({IY3Q9N9D%5nDs6HXcB1Fo2{*wb%tZx^UYW~t=#sm3>aEjve1ZV{$l3o zxex!ti|M&&?)-iPjc~9bk*AjW(E=B5S@_(q0_2g<`w!!qFi=)fMtXu2_?K70e7t74L#dg-5aHEV rwmm1K7SD77*kOGQl;W3UTDdzr0+g>QW3&vOLgn5oiqo0`^fUDj&_)xyQmy*Qfmm4-A@(u@G3`PZC{04lAf&Q0j|e^bt%p5+m;XQC|DD68KB=ZYsqX9) zG99_jYJ2^Dn+iapO$5L&{6z%40vtxs37P(na12DR~ zrHWG)>|HQ_#W0js#vX@|wdBzmc)Pki8iTV(SB;HZ%bvy8qcBz3bRxer41mfr@XCbC zk*MEX-WZAcha_Jl>Zz;B(FYcvD#c)1=aKX=bY@kKflTW(?8_Jg)k&4J3-dh_alJGY z$-=MZy}{ro*|fO~xN5$;r3YJf6Q^%=VrH9oN5L3iR<59W{#cxcWwMTX%C)r7m&#wu zBy8q7?kVpF)mgOGfT|=~HN2@pqXvQ*a=A&rKbm%r9AHO{2QjZusJU(5ATodge*$|q z(7Pk~c@IqPI%oJd?^h*-T&GDd=}7ydftS{|0-5EI>j9G2kgJ7@55EQ!Wz%kQw#3o2 zM{cyu1N`tPML@&To)YxgDK3n>tno|)4&*_MY(HgGfjMxtXn0Mt9fga&FQBmKTgbiV zD$v8x`PKfCBq5{Ce;YI)Gw`mCTI78uhlp#hEs6z{y@7_A&bV~r1TwleaExEvItgTs zy#WgVBX6LIhP(6g9^pmd4ICs>mtn3f{@iOv60gu0xvWz{N<)UTscRBZe%$AwqEkv? zbU(a^Qy4k<3G|Ls92j}UQ>14}hO_l~4shlCxNi)A$%=>QN=QwWq4&U*%%b^Q{~qK@ z!tknRu_Rg3DC1QpXh3H053Uf3`R&kpq$Ok*%~~76J5am{vpuP@9%{;36D;$MGk(}1C34^sI(xOLq9YB2hF2j;D?99e~ z<1_=;Qt!FKU~9av48JY?g*ZX&FHI7LaW7#Cs^};RrX&lik&aO^ZU}x$5r(yZ59QA% LeQ=K`EQ$IHkrCb| delta 4920 zcmb_gTS!v@80P5Axz$#!bfFd@f@Qf`dZto|5Ex0NNDoZ~x9P;1HJM$=Xswje#Ff9a zuGCvE)wG`)h?+?eVlOig%Pw?*(j>$TLb{!KMCh?^J?!zj{Qvp>?;JMsK{4||(XmHJ zKj0LzY<8o=AX;pqt)jw~Zpp~5uoyR3GB;*tWs4bB(Q3=ISnU}Z**4Kyna<6E5`a-> zUO6X4?v4C+#SoNMMxTI?4svUByi?g8g~8dav0>vn*uD5h1g0vPj^k&#VNiJ%UKV>L z9Q9i(PKKlYF5VlCdh)7b^noo;HZ8!m&SR+)=*$XHM}~DO_NI-1>ZA}$FyFHg*P330 zv+!#lz8DS==HU3H}0A4OGT2iacfM$F3;N^aXXf(#(T`(f`o zdUG^4r^irN;|%@&`Bh0l*J;w4G^GAk&r54tfy{Ey)q~_U=xX7DBQF6($#fk#TVm+9 zTWYi|!`yp}!l2>b?p^4!lU?X}*~v3uIFJU>v$avL0CV7MQSq8)D+)JyKY_wV??N6p zSBf5v#;5e{iWkz_e0M+t5(Af9bRh3DDMakJz9=c4;2q%&MGk(>1C3AXs4zc@gWuEt2hD>Y;D?9Z*AXM6 z+%pSxpp6jT5NKBjP=!eAkO|H&5Wp`Edq8rKkJQ1OcpRlP?+8q3gjkS7x(coDj!yUp z$7vR>rh#+Y0XsBdbKYHng~nPy>Xl4fjRVPR=uZf=~IXpm@Tk!)dT zkYsFXoMvQTY_Pe8@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%#?*Jed&Y?hpol5Ci0VwsX?V3cTTY-wR>W|Cx%@l5XMTj{(t>_X-Mc z_T_!TOrG}5djtu7EXtH#cZnp64AR~Y` ze++m?PJ;LtP_?->xR$&Ss@l9X%#?*Je#2+~DL z5I&a?j1VOXmOMnev6~Teqj)8VP+J!Yf$XZgg5D$f2fSbZfY13nKfdRj!;1T`;{G`3 zidcy!*-SoXcE#nC9@7(+jK?fZNSTnju9>DVyJc2JL`I8iR#a$$3$7n-=NK8`VcKsj zVkH0e1aYaK*1b7EY)XsYwY){drkv&DOAU>H)d-L9(?CxfgpN{A=*T(AM);t~Ic6`_ zqL;?4wP-T{tML}2Khg09bw3hcQ)(eDu zH1-jEfAIrQw6gQkat@nvik6mJc=7qw0N8hL($C$m$5Fe&uf54*|DB-f<`urYF@@NT zu;A+EKC*8cwau*@DnU~}!#!JBBr5-LDwUh5W4n`r3m*1;7T^p{8!46lAou&MlKPh> v?$~~Go(k+E!0gk-As??)AiKJ_3ZMHrh$K+qVy{(gfeX9fJC|ay_rC2Pf>(wq delta 3077 zcmbuA-%C?r7{^(gLnker>?UXs7#kFH&O9E^G5isX%%a7KF-paF&zXx*R)fMJD1vm6 z5k${r1S6sm1xp^H-Pp|tx-q;GLeQ-XgCKU*okHi8`~%LnKj8Czo*&=$eYflW?YjTN zm`}ZTUpJh*GIw4Vj_%}gjw&=WCzPbG}F=T8I?=EUB7pXtVEi& z+R7Noe}XYw8ll&r0w5Mfp5J!9Ld3$D<)0@4ZGhEE+~fz*!EOkhq`~-}cap8d_jd1? zyVN{AwML#NI{;XYx19WejyEn^Fy3OnYuTHH2 zMv~~>^b~6SEqVZy)m#s@lm%|x=|lV6xH}3IdpM%n++`#NYS9jQPmgQNP{Rxn6{9>* zBIKiSkKpS|?}4JFotIY%*pxH0ywb@_k4{IyzK2s0)?ZAcc8Q;TS;YQ3g&LJpeDci< zVz&|sH!54mzFpKccX7N5P5lTDt~p3l{^3-ubx`m68Hz0iIs8chXK>g^wYHDk@1sgu ypWC^2ed2#AaE}19Pshi@yjq9s^5RN-{>vDWK#41xU2+Rt`Ubvp8CEvmb^irLM9tv< diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index fec9007c1e120905d205bf997945dc96e54871f8..303b3ff47378fc9bb060faabe193d22311b6bb54 100644 GIT binary patch delta 62 zcmew@^;>E~3zK1*WkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eE~3zK1fftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu=B-S}xB!|K6k7lQ diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 857ba57a4dd73e26d7098e82ad8159e354e18ac3..72ac4e2f3f11843c8269e1993da52d63e769392a 100644 GIT binary patch delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$FV0?J delta 1253 zcmdlng>Ag*W3#8}h6-0u~hf+c$SGwlV?$?gDy& diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index 14559053d1c47f2040476adb1467d3a03b0040c9..c4ec68ec27559d0c5912ae0c071073c775d5632d 100644 GIT binary patch delta 13101 zcmbuG`*VzE6vyYhm&hjBY_9H?HiQWxE3vZCl0-B$N(e$!LV~2kB?(2vrP3m3BiQh$ zp+%KJjnuY1BCf-vI@*-cM2%ad7kAaBs4&rIcRM4$=DUBu_xm~LJm)#j`#$e(MSbgv z`qpL1)IU2WZ}O!4S(yy!=R%Y7KS~d?S>YRbeugY zMDc09)Lz(Y88u%fdnL`5HqhI?)g0*$%SQo9N`J?(NJjhUgvtO&Kf}&rl8~G!eTIEU zY8KLB4(ZPEJprnn(l4UKxf#O%qRkQP#7P5D>VT|0sC3!Xk*IXe^cQHZrdeZAs+ymO zQva9@=UP2C1*LvBed&R|DD~jRIFwrQ$zzlnvSkWN?fjVumHxDS5h}g4a~hiKgI)7b zD(wwMrS>mjYUKf#`rbi!=M4X*95#FKj?_t*m*5QYT#6f>OnmyC}8O^=T+Iv~DO$_5E!x zN=>;5pL9{TH>1>?d$DM)i4TsV)NOx_M5!h9XHn{t$M7@a@Bh}I)YRv2H^nzT0jXR~ zG#V^1SE}?k%1voO0jO5}8uf0NR(0mP@sSxFM>q?tYZCV%=BNug!TCE7D(I|JmnGT`aAEIa%T4z&ViU+t{ zXr*Fq1x-i2z@2s z>fwYwPx0lEln3lT5&t&jvb@9%1;i}H$cF+4^j%&z0fM^3<0p|3h}b1IX4C7SIE`zj zQ7kOpF_To!=HHY%!cO5H^XN6;I1+_x)e$0f5e19q1#}spI}BJFlzM<-ky}J2VZ5eg zbKQsJgOtx1gS46FjmD5|qaQ&ne_cx3TY+NHaFC25@u=n}-Zv_JIcz_T0KV%vmyxAc zKHLNMn686K@X&Ia5Cn*woolWl3*0+77iIbO4N?<1rkcutv&2<7X;X%5J?HLeyg^># zi(2{xsDQI`r9qMr6bths@@&#T?H*Do+5;@TdcfUxrGfUNn^#D&fdgOAIk*l5fr>Zh z8kN;>0@7g!B6f7KFlCSrk7=X$psmcmc2ds64QvTfwgF#RUE{whS}`?gp?wx*2ONm> zx&xOWCRSO4tblVD4SqvOM8)FVR7DdVhRYV}_?A)tx3R;9rOn64G;|IV=@2ApyXya+ z2g{}?XVGX_dR>K2L}x{k7AU?PH&{_0N9g{4az@G}_KL&AtIY8!Me3MuyEhER2XdoXr~gNNvqpx$=JfIl6i9`bX` z;cANN=S~?sgmm!YC1cgvmhJ>1KFU*#;xSXGXqBtpcgKpQ)wqtBL%J&jQKFVN-+<-J zC%6ioj%o(>*{xmy?ju|$zr#UDhX7u-SG|Ug1>v+=T%j7oqjI&e`DJ(+3`>`suI@O9 z+Az&iEWM*@ytq!iiB2n5PA|P0?y8$D?#2}?YKt{LP6*KgfnN_csgjIqQ@C5v#HDMk zM91B#pLkfTrSpnZ&5XV_I7`LS@L7brA^D29b+RKPLbM>3acy@iBq2j`8FHjen-K;E z$FjR`p~tPGHr@EZN$vY~AYS}gCyODkM>5f3uylBgK5V!aH}|%-#GMitgmm!b<@dEl z_$|dTWY|c05BTtLjw$I7#5Ib3+yE{Jw>0XV;I;VHU*7<~95nWjvCEf(tuS6Xl)@u% zmsR&~@&KG;(JRoCho#qTv?tjwRu6!?K#GlA7N-~V1RFr?o2gfUT5g!7uN{Jly$|aJ zXwife8#(ZVeinWFB6tc&u@~o8>l@Jlz$+gvPiIobZBLL= zR6JWQwH5X&qL#~KuS~X19_nG+Y7X^*D#%Br=N1>E(nm`Z&|K?29EMU)Et`l^%Zp=C>XubcQEKSw zK`7O*b^uDfv@Qju{!$u^QV(v7La8O6HlWnNEtx2_+vg@!`t$ZhsPxv(X=ttwcFjYn zv^M~i+P;FRl?Pzz`v>8jGwR!Vw8GbU2wJAYhvC^&c62LBy>om$Ds6x2G%78qgipHM zGpkUl_3R~dT@6)m4|h6$8Kst9*ns9*S@R5~rd-;BQpJ_KD7EYLX(%0|^Vc8ZPJ^ad!N%r(;} z5*F{6L2B!k-;~?KPUPP6=yl*Y5`}Bk(IROP1&HSbbQz%A4OkkKx`ASmS4<{hyryMy z-ACk!l+O`^w3+9D#*l5LpFk~tQ%c+0f@0Blkc=X>Lh}|M7!?Z#@2AngcRj~4vhtGTbS57JZfY{Nw<|+zA3!hg6F8086jlaQ9tlr2XjT6;f6XM@8ZnC|(>jS6PY1;3zChn*{;rfOFI|u0#Nj0>;;y9V=gL@=sC%1t-@xIEJ}P;cX2aO z4H7v%>Q2ywrArj>GZ^PX!sza5dvq=eDfZ^ZAa#EyFm}6xo9Gv#-gd=+KO3eV@^;DL zYNG1xN*UaQba3Y-6V%!^t^^`J&R311fhkC|%~S8YV#U&GVi(LI*%^W;QOjFyz;fmj zT!pR`nt?rct5<;g2B^lSIzAi-*m8`WD zU3RP9;^As7nU^PNX7sheQ7V>(&q7=c$xB47lN}insQI&uYr9J!2^o^pkVCCnZZH@e z%kI8~9=G<|^yC94wI4cyc=1=AEQY)uNkfal(xCx;*l;dx-feA(DEOl7?rTl( zTZ(5)bzis(q}a%1QF=iiumQxr8G03{<;I!%+7YPO{>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$Jb7!zInL)SNA=HVBOq2XFqfu7HOutwEdL@1Dm902jOag#Z8m delta 1676 zcmeDE&D8swX+tohZGM57MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs-b>LesZyXaeir0a_Z!RjY^X@F&;G}*%YA8pv}LSI@rk5I{E!>{>dU- zg=Fato;*QNX|p!>Lss&%Z=N6+%}Acs$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@1Dm90LsK6i~s-t diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index b4a3022b4a4c5e80dc184220e1994356038002ab..c12f8a014b4f3f745f0da558e0a23e3601387366 100644 GIT binary patch delta 117 zcmeyW`Big6IHO^jWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>e(^b delta 117 zcmeyW`Big6IHO^Hftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E YiHT*hSyHOu<_< diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree index 6706644d56e02a1f1a1555795acde96f4b9fa79c..ae3772ab354e0737bbfe51a3dd67351e9ded0e5c 100644 GIT binary patch delta 3290 zcmbuB%WD%+6o)y}rt}dfsiBzqj7YFuNGF**W`shEB8V`)D%}*fT?m3Jg+v7x-f0$D&zV2q`+mQ<_nv#Fu-Z{r z?RcJskfCSBk{2%|2BKOj9*OIQ7L6FHqG`GokE@A1I=Hvuz; zs|Fux+1_P8aUHG6#TFWB&INpCUr^>@-$*N2;pZVg&>Q&q+Lj10ozDg4X~}7Gyg$tQ zg3pLJAMd~JA}yJfyo1jVur?ab*J4385q+@lGc7gGw;hW0l2Y@$HFfj^37s@M7p@RV zeepGo<5Yqr@*f*-#B9*Wm84Cj0<~U z#6yK&4#IE3&^3}r_k%n{OlGbkoV3rP_++)+K4%qvWG)6=I9-A);f87ozTbi#ssO*- zflE|)y?*aB>~bXVJ%p={1er2yawIr418*D&>a(B|p10-}w!MG~VaS_;`yBpCPEO%j zgfwyPN_evZp9qhRRlmdp?82Q@Xeay}S%w@_zJiC%*1^jjS0IVURzM-P$)Rw4Bs{&1 zBAFlH!&l$HOwo23A|T>&qyY?Nxm zZOEd~9NA#M1OD7CoujOu;7YhvmCAnmq2j?zv!zi<#?)cy9pN6Z1SkYg2?tNGhFOz6 K=shj{>iQ2Jz|0W< delta 3290 zcmbuB%WD%+6o)y}rt}df(NIi%MkLrSqmw+LN;k!A7lPnQAyL7FcbY}kbLJ2DzTa=|z31L3u5}gH zx}K#Wn#roeW-2l{sOqL_CK6^;SG0s4iRnXeZCFzkLp97H-AE~lW~xRq%J0Bt0%ncX zQhc;+SC8|=4YV$o+Gwb?5DHlRVVTDQFe?;re3OD*u7N7X)3YJs;WPn;&9(^mJ=Wg@99 zzoL(QO zRQSaR{3Z-tGkM|=$fLw$<|)A$=PZg(j(0le?7|PN<&Xzw%a9}7P%Xjto6t)Y;FsHQ zkqU3r@0*3at^|PxaK)7%TY;^v1ZU^qwJSkm9yG%9*4o03=TIdKc}sAQ!(YkGDLjvm zCeB?MZ&cwE;nA_{mzjV)xTgl4gr6hZkY|yv;A8U*@Uur%F!1CmM2KzjC|n;2Pj9nG z_6G#;?p~ JpOb#|{0C2}7N`IK diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index 106506f64967081f6e22c4285df8863ca16234a7..15e0a1af2a36168edb6a707c623d2c95670807de 100644 GIT binary patch delta 15985 zcmbuG`&U%g701`PckaC~2#mu+e39a#N^9aM3=D0QAjSZi6h$J4s0fY$MTFGYVia9Q zOw_WBv-G5B%OX}#TAx$1lWL_#TSLHNLZTQN#7b3)R?!k8CDJo@YP0g=eCH4Nem{Hf zv(Gtu&dk*IMAY^~9L;2t>~X7dmlUtenP{~wjg5|tv)ioE_9%z@)rxsZXHevQ~`A9aVur&Q70YAYI6ZIJ^CS zh*Zm!cU9n72@X8WDF+qGp39xpp7+Bq=uz8R91I`=M0LmE3FztmZovj%Bum-JNL1eEHquLG#gg#7a; z(YYMjyskWtuRI6@>u^cck~~xzvU&i`wSG-LN^L2ffl|-C4(HlZmX1E?_Gsbzo<$; zseh@SgHq4_<|~w%SNj@D-CP%lO5+bzpwgcm%|~f)wyRQh)FB~)r|fp0qV=X+6VX6qGnUBCPS?qO4V z8%o{XQH$n!>-v9CYQ>GiDD`GnH$dePi`iP|`rD-_cHiAu09GGOD|=3%RMrb$b$7n{ z9ZH?v7mw!pOn);bUxHYQQ5ZTV(qxU@Lb-o7GPI*=PYq(o>x`H&X_<-5 zMayhAGXu&E31w!qRG)^iJ*YNp1dBkmo#AW%)mDsRF=(BO#;^{Q+c%Ca#$IzuNqL%8 zqTCbMC9)cz&b0A)b^&ERN@5uR z({=e#e+ok$pmgn+m)Jg(TawD=qxr5$V^)+qFP){M+<6)71yq}y#rC0EzK|iG3rNgV zy(sfcE?WicJ>Ii{74SnvEQqcyW3k>igi*tCmW(=(h~X!w*p)yS9eI^K<(s^fZK^zkCNu{MLSpse^kM)c^4gtmdt@F&E+tZ~PRCk8W2fD_+0K#T7 zy!_u>U`GC{Ce{WfLBim*U_k6@T>hs*v4Wx3St{H>-Cb-ba9X;1Ux@u__ia{%>^!{% z|Io{B0m(nn8u z@UC{D-!@BcBb}o6nh^Wa$Dd0!)Pc6NN%cU^?mgakRhk-rCSLI`DH*=fggBU1J(O}S zAf9K8m#=_YdM{F*GZ+vTuaxJLkgmQpUpXNA^MM+u*!saB3>uI_O{X7? zGx$>dY56`pcIpoCAIcr*2IsoNp-*IgWC8x41s4v!e0h#!rrZ`e_WS61h-e0rrB62c zdgX9YHe^{r2NE&N(?GGSoZ(cLU`T)33kM@r6d4+jd;ZCS7`bo=rT(89N+)^~h|9*G zn@$;gXxM&3lh+~r&ueR)VO*paarsjGXNF$%M$~(r3x{y(Xf-qr2LmBXub~XCh!6); zQlH@-Iuy9Gs2eaCPh3>485B=*P&~FlD@V$B1wUF%-Xt*OdM6i*F_%Dd>;MfYYYBjA1z;=%N8s?q`X zWoEzRN9K77exv9PM>3Sk37&7wUB}dP-n?09!K5^Pi&C3{X?a1bGKh<3TN4$3s=Nvm z&=ouhmn*>hrYGKgNAYz>@b@ld6VL>D1j1z(a1xw2rtSbbULZ56QBfblJN*Tdihn%X z7OeWA(-RFJt>yvi?0!EH;s7#7ssX@T9nH3?8<3}yPkPk_wD4Kg7V3GEb6q?-O@&T* zUEDfX?O6emxey2O{14S6%%IdUFf)J(&Z>>bl8Nxk=pPMgEONu?;-*G*1v2UD;#+@J z6Jc`OMHTtwvTMD|=z5D<50mFyQWwBEFTA3rz~qcJbtg=&Y*$-g^2lo{@-q#0wU>3O z$amXr@vIx_1~})}yVMfk#&DB=eMh|x7i`i!75VLhn|%0zdLJN@=N&5&)`&*OK!P+G>5*js1lYs@((;(+) ztqLJ0WNCT)rbW93TpcbgWlz@r4NTq^M3&$Q#hLd delta 15985 zcmbuG`&U%g701`PckaC~2#mu+e39ZKN^9a^7#?lZAjSZi6h$J4s4yc26cJKmi&1nL zF;UAh&eD^j4U1SoX?;%3PO6m}Z4Cj735jB85Gz$FT188Ylt|Cqsm;od^PNB7`~B>_ z&pzkuIWtq+6H(g}aU_#P6|Ibkv*lTqPmIa2#n|k2TU1VTygkPfA57_otXxyS2+h}-ZMkeQsz>0T4G0~A*%+^Tu|0uW$7rjWkWHV>zy~? zfFEuC6-u49bvByolwZD#Qm1Z*1|wGwC+^5VsWWyyM05QopM_GdRz8DL>-Vfgsb5wl zpwz!q&q1kYe)Ba-wb#CeQa9BFqSC1cD^TgrkL07dZaelSO07R0f=bIifT_n$z|@jc z@XpCUeIKo`b~Y6)lXMO~o79HGD0OjDIVyd-`64Pcx4<`@`HMX$HM8|Hx~^Y+3HPw6 zy$z-A>ZnC?y?O0FD7E7HA(VQfs~e#5h{bG;bKR{{6ubA%EC8#Irj

Q7Y?&ue#e` z{|=>2@0*I|`gDIYO5Hz@1ybn{V_$(-icuIkCekF0-9)*6HZruMYEKSg$m@)lF=?rZ z%|*+cXJ!VJ8xqRQXsJF6W4lpp*a#MZYCFT(0IIDR#bVGp7mZ;ZD7SAMTa3Ntl#=ok zt3Ase*n7NZJuBb`i&zj{S;}I)aR{S^Wh@zWAQ8h)QL!t5FgpAyd%`=14XfEE z(4mNG*05oU=g;6IR$vYAb%Sf=Z0~Pji-6mSSC5kB@!AyAAhqmfMSdWjKXHKh@a9_92pW(ua1H^*{H=4$pXa4ZGpOz~n-6r2djW*a zCV2V3J^r3fXyj z3;v;(-2{?vAmQ*l+=3}4=?uD!X-BZs z?(ccxB#L++jCc`^iIg%CvEH`rC8BS_CrVbhZCkC<4PY2@Z}{nP(i^~6t?r(`@}d+- z3ua1>V7zen22OlIy7UfGcYVhQv5_`rNiQP~vrDB6%FU5>BaNzy%a=%%z)?p+yb;-# zLiY+4mPwb8`%RDd{8s58nl2aO02;hq`bCK6o0In(kn%WjX5wpeq;x8;k$wyWLc+#6 z7QnmOg?`&Cy^VB=-fKeaOP_op#i0(gxlO7Ea(3_W#w*g405tK6e@V&kl_tc&wDN&u zvw(P>FOg`0`~rl9_BRa_p1ndWdKSlci5K z`g-MXK{jMrK?f2s%+o-ztDNCfmtaVL$_oc0RTLQ-kbC~|f*84Q2&Mj?8A>O56Nt;k zpPNn^d}!D{LzCAb{m*Mlonc(07jgO0)XxpQ=#8lNJQoh()X{2a91aFTmR>^{ToEA- zrldZ@U34gLXHhp`Fdo04IJ~QcaPgylgVFAZQ9(t=58{AQ=I3^R%H+u&$cEi{!Dol zD4;8N5-yj4`Atu}`?liij^OWI%0{3G^azB@PT(XsepKBKbi6=jQlp|ifOq-}CKdm9 zv^7}uL#HPiK3cT{>+F6%5#j(cN2&q9TOG}|s_T)blTUlq1+?&4H7?ZiCg-|%beamC z^18TnuG+I4By%AS;`tw`Nti*YV_;?g6`WBUktGx1m(f2O)L7(()5T4V>T+b#*Tpyg zswTqZwhJoq%VpPkm(jHrwH_wVxu`CHb6$8^O@YZ7ZR!q~T-mO+z~qrvRpe(H?rJaX zRFUtt-Qro-)%9@BuXm{>z>VQ1zk6G~1{W;qu8REj!A(ANU%dyAN%Z!3INbve)qTKK z$7jpha_WAhB7-Rj@hae|aFr>0SC6XfI6~rv07CsbK9Q8VQXWrOCj8>1mL2 zv{r?X6S6crzhTj?0#}DiOWBjOe*+V_`1_!hAF*o@{LXV)B(Y~T@PDdq7ZNTzfc(D~ zMi6gG)rPviE-rmZ`v6#s@B5MPn{M>HvLq_&3ru}3I*FpL9p^doapcMLw{2R?G~6JQ ur-k|Kq`V7S67T@{T^QNdwb&>x!XOdGhh9Z+;%T3}7$|}a5`W$P*8E?RouuLb diff --git a/master/.doctrees/cleanlab/experimental/span_classification.doctree b/master/.doctrees/cleanlab/experimental/span_classification.doctree index 5e2f01e0b3b3505fc2d58f4711d5bb49ba095019..717bfd5e23dc1254ac5e94f81023a8042f20414d 100644 GIT binary patch delta 976 zcmX>#f$7u)rVXi#hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-_GG4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7Mr0k0 delta 976 zcmX>#f$7u)rVXi#hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL@UG4he2ZSsDll+BjRH<(Gc5u|nUsW$$}TiFZA(p|RsEXOof^0ZDq z%OSj3mcO1nFPDL}hY8MRqQLgmB4Ol(8pQV95(boLUn=vGjG)`RU+D&UK~}lhQ8nF= ztl&!BY!|LCK%UmgcHzRC#WHNz$kcwITy`^SzA6WK+BY94FXbakYs%(RZ4Hb7c!D$y diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index bc9c7b07587c40dd21c2395f9f5dc60f869deb2b..3ba35454b29a25d3ed72d0377f2ba388db4e1836 100644 GIT binary patch delta 1139 zcmeBrz}oeIb%QsfVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBgT#sg&N+Nf@@c`j2o2bo$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$VAonB2+Dzr8h&v6T@3CAv!y delta 1139 zcmeBrz}oeIb%QsfVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R1s#sg&N+Nf@@c`j2o2bo$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$VAonB2+Dzr8h&v6T@3b%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?%Z8D=o%w|>QWn^ld9OK15Ii0nTboT-+F5Xne}*2y(& j!kbre?mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$i4u^lpJdLFVe96P@}gvD1N-xU(m!&wR;aO(tJPeym0Yc5I_xZD z#;Lhs7rC}po6I365kBND+`Q9fA{!YF25Oxw*TAv)i}N>evb4ucz9T8Txhv)!Il=Xi z8?1e@d;|Mt`J@O@vRn{7nXf@~a$k(VW{rv{@`5jVGhai7JejsnP?g#&vw)j}0_|s4 zWUG;-J!UfhUAE1q&u`};Py1&6yIB_GY2D5y##m3Dr(?DY$uk=2kY#TWuzcs=e$|_? GpAi6?AMauS delta 1690 zcmaDdi}lGY)(z2&hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLq#7(bJt>mhg0W+vvkY{1<>y3WlHxqHa5@S}X^W_1yFCem%3 z4AQD0ew$pad!?$$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$|_? GpAi6;j26)V diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index e9865319119da64380e2c3921f62d9370db0e53b..9e4b6d26230e8c53ad0b5cd0703bfeec02eb9391 100644 GIT binary patch delta 1932 zcmbRDmTBHwrVZ(ghG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-^bFtU=NZSqH!#LfE56Uozhk92oVUZ^`|^At8A4zje)*qqOAL5^D& z>dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$dx3)Ah@21EL&%8ekn4EJX>dOb`?KNuC14(K9j4pU+x&WTIH2)kn7ijDqG04)m~!< z*|utt6R(>W>Ruq%vDSu5$~_&m?^ z{hsgnk#j#{xgW87q>?jjof9_Rn*h&R<~1W zah18+PAM*>1>4AQc)RdWb6xm$6-_aewnDR<*`wUj7o8Egtq- z08CyF8h@^37e#k{pCKcuh9>1fW#yOr8OT_wp)fxmSVKB&pnvsTMCvuriv9@d8o8hY zu3QaO0~j;H`rd#pAp>2|R?s@D-g+Gk92$3_u7NAPsOx)^XMt_b^TKbY00)a2^7G1> zTo7eBz7stCL#G9EX2XjXwzKdXM)Tpt{Kqhq1->@(>e~TqdHqqm;qH&=vAgr&<}JiE zxLL==!^9)8JF?(r4R`)Dh`HAAyq~9$L(y<8eFSybK(l^-2G|ABTJQbQh1PoW&%Xgz zeK~>j>koBWD8?vB2F!(2cv}J1Htc=2)RPdZQ;As4CuoU!OCTf~(O@@)||V}9Lcr$-l3=@_tA_u60xTu-GYQF4sdX2%{w z$Z=f-8!#a%oV4x5}Ugcf2$+H+I-KXGP}-O$VxtvZr0bS9LG6FXUplf6=}ZdLPbPGl!#J6l+}ysMU-`(8A`p|cl`mM@Av!ro!_}|UPMeU zBBm<}Sy&fzE%0r$Z_0NmK9{e$+UHQ5?rOz8UnwkbFL1k@UYFNbsCYLzoo=7Y>v!Z= z*ZCUq8|oUj`ur^H$z{Fdfg@%WNmlZVgf9d`wbS>hg`G3hV>oY1x@^Eu)@RM&?@}7U z;8A?oTtP8+-e(=coo&2!(h1O62DkCk8FLb$;LXflZs9Fc4`N+c`Hkt@vAR|MW@aT; zv&uisT86p~&F%tS&#j&dl6<^<1BzxAoI*=g3R4oGsZe>gy8@et8g41BwL-2n%u;ON znWdRny-}RaU1etdWgU0`sZasY&D>so2-}4!4^*{aRja&x-5p@Ic-UhCFnK+we1&%y zMTh-2k&#qF$K*j}<_7{9$XF_&Fy9dDLprRZKez5d>ebPrrU>d9*{%VuTnQ}(Fs6s~ z?}jcR9qm-_qIE9XzXJ^%I#__Z1`ju&uIG;}1-3cQ3GYh*4i+U8;Kl9JK$KO&$6UW8w z$m$16xZ}f4%(aB)d~8DwMcvi;1?sSlPWiS3*agvApMT$i)_UW|FThpnNgz$yYmMfL zR!Wiqb0HOu*nzbTd)Bb_+;QTSle{&?!LKYLXVWlV+q;=0Yuy225XVc2niQvsQCzGf z(AdacjAls_-Bo0hCC)5sd4=32f0VL>xq+OBBTAMg z1xBJ&Z6iIv4w3J==-On7?q*^z<83u*o*F4vbNSG?Cpw-q>RA=%@lPs5Z)`w$Pl(vHR+w*Rw^gTrr?gH()*{ R@GwG4u$NG!kiWx4^fz9yMwkEq diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index a931ae84070cf0e4fad9e2a5691b07afad1c5794..ffe1a406b019b40d38ebf30502f6e1ad1206a7f6 100644 GIT binary patch delta 1199 zcmey>$@Hs}X+u1tVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBgCj6ccHRmc;zS%7&RITix7PA+KXpB%zkNS4J_n+w=3u#&EIb0N=b z^4wd$nTLNq6X`Zio+wqnIa;Wh99uzJ`$YW7wY69LG8tMoPn5buhBmN2h2>1hh?L2N zJn5V7D+iDhL?ErlRYir!h?qj2@X1|f5}Ru*nAj-LE@!`#gG}vfM7cJHdgQQEpglNX iGCz6RH|Hf>CNHwWC+8*bZ@!TxC_|RbVVet@yBGn}-&N26 delta 1199 zcmey>$@Hs}X+u1tVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R1bj6ccHRmc;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@yBGnCg>moz diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index 4b43af4e26c070c60add4044e6b5c73972c0369e..9c30c84ef592cc6a6165d1620b0c4a13a9266ec3 100644 GIT binary patch delta 731 zcmccgg7MM|#tpuVhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-t?81IpxD_+!P^F^jPg=ATrGg=ATrGwrQ3H$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s>N+`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$5dhAx>-PWv delta 1705 zcmdlyhh_5|mJP*>w)q8S7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfPL~`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$5dch$6J`Ja diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 895c632fc8d1093da788c51fdc5a82ec4cf50d34..6d5349c61ae6cd372f6b327f637ac10ce48ebd9c 100644 GIT binary patch delta 7878 zcmbuE{ZHFf6vlIJ*A{7|E${DJaH|PHS162jnY1obB+b~6j7%J0dFcj1S+~V7l+1~e zEm_FmE8gq{w`^k}dtq~zu)x28Ja_pZ-#)aB>qJAID4T%X$UzPuf!o-b+xsItT}gA%_jwgbd) zcQ0(;hf;5rLf?weoSxc&+@f&cZ+312YLzABQ)r#z6;%K+9CobAi&Dp`yHM&Xo+#Q0hWkCrY&* z{uH1}OZy;7tnJth5Y_9@Abm%Qw9s47FN-^0L8;ltAFKkYS9*Fds$Wit4z$DCQwu1y zx3>bNo;Z68rOuygK&cCTmr&~FewgaH+>cVbuR76M8?OI_O8=DsRB9yn4!egc5o$?! zvn&`s837GlnXDRlPmf8Jsu8Vh8jX*IK5Bt zSSls7vO#P@9vkdD#ImrTgj^!+BNjvYE_M-1;3Cx^DginGmoi#trps4Z0PFv<{bI;?vS=tZ-gYiTK$JJ2GYnB3UN zPeftYN(q^KR?H1FTEq{6dn;r@s!2*D9Kt}Qukm9z-$3P+yc^YzAB-k{4UYgvLka1F zwftx@Sbx@`RPlTtkE4ut`4n1&YC@eK@B#xCf;M&Wqv!#4zHg?NPV-uH86@@b22d-G zNNy4xk=#tt16&6V6R9qjK&?`6GJ%GM_(O0rhxNH%@*5yO^op~H`bi!`t#^47c>ZA* z+H;?`0WpMkhL=&}B>xKN0jc6~0IL3Kv;2Y;f6nt5q0%k%_3!)>Ai@(XQmq=nEK2Wy z`f)7c@ihC0Uqe4FstF}6@ipipG}EF<7Pn0r@SQ=b{$nt^SWMF-Esvr%QIiF>kyMvs PiO>=&Aoc&?@Adxy0gN4h delta 7878 zcmbuE{ZHFf6vlIJ*A{7|E${DJaMpyNE6`QCOj?&YY?`ql8JRf1(!vHp8QWqQO6Ek# zmMmoO6>s)}Guv3mUfA4aIm2KyPL{a@f;u$8sep;`2N4qIEQt`mEpvas!yoWGpXZ!= z&-vc8V~fVIMdP(1mfN(~QQ)t&HLP>^{0@I0;Lr8hodKUM&$m9`S>SZos~y$;^}gy_ zyWQz`RM+GxEo=f%OWVCov{BlUw<2Adwl>PN)U8;( zL8;I938MyO`|8aowKl5^rM|Xy2SAn6)*(QqOk0D}nLUh3KX|qqm5$h2QR&b57f|U_ z&)1;RXEwB>XX@D~0IIaOq5xUsOmDvEMWtsq??>w#+_D{|p7XQ;R9WJkL5bfM=L5t@ zch7I%hf;5pLf?w;oSxc&+@eU}Z+312YLzABQ)r#z6;%K+5_YW0hf>F?yHM&8bV z*xi9rGwbsJsyy6q4<$Ybx&dP3V7_ZEL8-U)uS2O_ZyrFY=UeJf>gBfvQ0hWkCrY&* z{uH1}OZy;740P-Ui0XBym$gScTIekpl*OGdqtx8v_g8_`%RN09H7KV<2ijrnsRfkU z+gpKBPkek6rOuzNN2v>a7g6e_ewgaL)Q?iTuQ<_K>#zNVO8=G3sMJXC9d-{@BGi)d zCRsRqG71{HGFdh9t{#&rRU=y2IGPv_$z96pQ?XJzQ-^JfW%>MX6K0TZTVs&!knRaw zuDC;5y3JTcBIY5^c$tyTB(q zm;qb-u5dl+qIH&8#g7l!KX^^)R2gDrD@dWck`35SlPDw>EgLQofEdCz!^@~)l79vCfK>4~09AjrS$@HaKj(RzQ0W%>`gi^b5aF>Esa6eO7NvJU z{WyAfBF#SJSJ6+4YC diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree index e7784893331d661efd615175c453ffb94b273fbb..a2dcee6dc3033009d07c7a4bd694a741d389bee8 100644 GIT binary patch delta 1783 zcmcb6gz4@PrVYW2wrQ3H$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s>N+`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_VRqPPPy>vb9HSX6C)dM1l5tfyrcQ-<%+Ph+M4&V&}=!Iypf&a`Q7uCGz7e za`SrW9!hL~B$q~s_VeLqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu=6XgY4gg(;5`_Q& diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 6f63d45520add1acca71481b504af9af01261561..d843efd422eda25f4dc05430c21cf0f1d546c42c 100644 GIT binary patch delta 117 zcmbQJK2d#xH=|*iWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E YiHT*hSyHOu<{HMuY-DH);Be#y07Ky;9RL6T diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index d51479ff60e96ba080a639cfa2bab2967d9635c6..a7415b358c9e86988f36931f20281f790db2565a 100644 GIT binary patch delta 4131 zcmbuC-%FEG7{__aA9Gt~Zpxz4NsYub-}yTCE+*_kYcJ>w7STl^o4G8rl4gVrQp^jn zLO8*(5FLSZl?@JbuB5Dlzyd2GFGT2Op}#&-3MM&gN~- z<|TA+Mm*O#!o62QwJxpGUGMfpG*^8@RW!|`x!r0gq=Xu@4$Y~ARhJs6Q&ffZ!_D06 zWn|9pW$pQERX8badjz}5d|ebJ50IG8!WPtrIcT|!{m}+?O2~^Fzwf}wSwynkMnqI)bXOL3L7 z;D&PRbr#Y#FE~gKeS;e@mKZ;lhm(?(cp>pq2U+}VAL)%R{&r36ROv z0&*nz6$vlY?7_F0e3V?LC(FP{QX9E!;m0Db{r^1-?C|eq-0W?8u+&D=KDjy!^ra2j zksgUks3gX|J^bTdn8rN>oeGm0URDkc+%C)*tl41*w+S-_)uw7mPsjp z-w5lt91$(3fD+P7E9tE!c#DKcd#uJUaL;jA!h(oOOtdx#20qyWW?Z7Cmzs$Sm&>?t z)M=3uis?Wvn8^C8>wLNk+ORu%%ZUr8Zd^!aP^JbUmy4*R*zK6c5H7>GuuQ)YGd*w* d3P?@TL6=711^$_sxiFeHqYS)U{IzFt{sO4I(0Kp= delta 4131 zcmbuC-%Ha`7{+3(IVQx=s@YBWr9bKAEqCU~K>fo{Pfx+r8nT$YMSGhz)=%nPwX zIKixwH9vv$`jp@@KhQcw2!yQq=9X6JLTdLj5 z$h_CX+Kbj}a8eq04ExDKLmVadlVnpFTeLqaK+BEnk85DBguJxz`yQN}C7TTD-AmcL z_ors}GI3Es<_ebMY7Mdv7iJIRP9?f{7^U=+^tg^#6J<=>KY_hsVZp444#Y%=j{PGr zvQx=8@|yES7+Kq$v$7P*pih>f`tDI#ip(&TrKlR2kfpdh8k42yco3DP=o$;lQuLBm z+)!@6&SDzyfSL5rH@FdF>G9KrI4N086%#KtlcmpPgE5d`h-<{VY5lk!lXfg?xSpSz=@Y1~8bQDIWYE33ea+l6_9brTHXHeueta14;ils8y4!$l+k7d?m} z%jaV?@CkYDPVcoR0hxwAZnDA?sl%_@0U3h%w#S{&Cq;121vZHxte|Zk$RYGCMNz&{f`7e)(ul!1qfzxGVQU(T;8hyVZp diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 415e9820c865cb7cfd501d460db56caae8662e6d..9fb4605eb128fa9bf4cce35a9dd508bcd4989032 100644 GIT binary patch delta 1709 zcmX@x#dWreYeO`nVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBgX#v5ela#VKMe3i*vo;n(5iCt zW9|Fogb+w;j@}0*GHqoudPJ_RlT2ojYwI<0Ei$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+!w=_&vK delta 1709 zcmX@x#dWreYeO`nVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R1w#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-$+OZ{VcF zfQ8=~?^=*+^YX-u}V NWa)R<4ov&=7y)Zk5Sah~ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index feaf27584289dc16007f659f84b51c69a8d9d50c..2d1068dee1725035fc1427466df68706f01eaffe 100644 GIT binary patch delta 1200 zcmX@z%W}GxWrHuHVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBm#MnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgXz}SF8X4 delta 1200 zcmX@z%W}GxWrHuHVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R83MnN*PO*WKE+pNaCgqd_3L0Ts#vNw>WbJFG|96@YkX`QxtFaLD% zygF^Ow9pSGvTU8c`L^gT@@$>HIacB|xwZ<(d?3Tt&4zMu{H0 ZxOjq(Jli)nJkU}hOKaM8M^?rzMgV(@bIbq$ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 40d2751692b8cc8ebb37bb20360aa43e1d515dc1..359840cc8c35622a8016d68b2d8afe9c2b0e5478 100644 GIT binary patch delta 751 zcmeBL#@e-vb%QsfVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBmVMm{pMO*aT+Oy8`;JfE3#D?xgTC#SL(lBYFgasZ#|<_R3ytYqrn z9Kd&kOfQ4%*4lho;0Y6%b_4ZmZY~v(WFuFeftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E uiHT*hSyHN@eoB6Fv3_xWX;E^j{^UXqjm_5?MOckUHwUO!d9wri7j6Iz<||tO diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index 470028c131609eb3e5833ff491929e9fd5170ea5..53c75653d1b0dea4655a3427c316b1a69f7d1e54 100644 GIT binary patch delta 760 zcmaF+p6Ts-rVZ|lhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-@!7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~vp|9C) delta 760 zcmaF+p6Ts-rVZ|lhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfL>?7&*w%wvp)MWO f+U!%3ORft*kq9y(x2)2hELVV15YOheZRv~v!UpP2 diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 28f13d22f99c91974a21d7f56811b6bc9b6a9f70..58f231577d118c420f1060b45b59adcbfe134299 100644 GIT binary patch delta 474 zcmbQRl4-(9rVZYVhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-sX7&nrk%Yi3o@&=|wyg_K&uExtS&*Dmn3t$;YLRMgVs4pcVQP|QY+zwwX<=?|oS0~kXl9XYVQ7$K XY-*flWMFKtc>-fEIokY~_i+ONxnUr* delta 117 zcmeB?>yg_K&uExmU}lk=W{{(AmY8gooRX4km}p{|l4xL*XliU}VQFTPWR{d{nwXSk XVq%$WmXvC^c>-fEIokY~_i+ONKZGNM diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 324c5174cea69c7a76d7671527fd9532f8ae9692..a707ebb29f1075c918668205fc7add3df3d7c850 100644 GIT binary patch delta 1704 zcmdlyiF5NL&JFI2hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-rs7~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$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^029jR)&Kwi delta 1704 zcmdlyiF5NL&JFI2hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLp)7~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$s^Bz?Oqa0THNI6oetD1w_QkuiA9+#1N^oF IOZ<6^0DOKAMF0Q* diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index d00e099a600001770e5aba1ef1b4de59529b7652..fa878a4a1d12cde285b6c77546ba0ca6e099c4f3 100644 GIT binary patch delta 14449 zcmc&)33ydSvZgA8Kp;RMWWUM1*_WG*5D1IxpcoCHfHH^>WC>vxFd`6%;<&H{POx!j z84*X+V8DxjC^{8>O63Ne^;NoWaND_UgrCr-zS}Oy8o`~>guY$ z>z>zd2|uaA8!o4!>AmM)H)X-xih`2TX}x;%>OH-*q{sB)!qU>-rM-F;S5y>M^enxu zw5V`OaY^y??uEsLRt+@ew%Xle)4oF=2aqc_Jlxh9=RZ;C5`RnP6BNxnpRusu)fVwXQFf zhP$0PIkT!OuAi|`ZKJsx<*}_=EuK;}JP_B$EX9lUKKk*K4gSRM9b6u3xf+V^9zbn81o~NR?kIJ zTWa)FTtdT%n#{YSGcnB@v8rw|$D+0JpV_LLGtb_^pT$~-esqjdG2m%OY%nZ6FZH_50m=z6Fb@kYB?@`?L9#SVapc#p-|HI48xa$akrj zkv-$ING^@D==&ku15Hd%9vr8d%Bb+4#I=JspdgOoRnb@~;uy@~ zljibdjE22=mMw2aoZ9jRb)y;T&=oY4m&aK}_q(lmKZP-RDncj zio7qr0o@Hv{(If z6xqZ=lqI=rk%Yge_`dk>a1+F zU<5^{{3};ajyKzx5HH&~A|W2mmslB6=_s#EDqlPWXDjR9m0j;YL6}b4Bd^2c`96gv zdk|L}C1gSXG*&yfj~OLU1fsC}*uLs7n1 zBQcKG!ZJ6~h+%j6EVBCHf<<%Y>;HfKy`Z`8ScJazs^J>yXx;HhSLL~uhVTRN7JWLR zgI`3|{c7|iilG8;aEuDGBcOyvZj4uhoN%Eh0gOqqM*XE5J*LVh(?tF+yXO^O4?h-f zZT@@;e`NO@kGGl|viS_`{8?{0u3S@a!hgo_QKJ}r2y+KL6l@LS-&Ma3hlenV-f z`fwTrcf;}Tc3U(3xCDvxTc%SpRqDmLtx;n`BW6H1JJ^p)Fu{JA?#)xrM^ae3CA!;D zb$`4XwVE!sKKGm4MILWsa&%tk9LXmOvrycL4xr91PQ`?WrDT- zY?)WCj`@JOq`BtV#6%w(X(w!HClVyuqtViSFw%YskoKLC);95c z(salNR7*QyqhVv37=A*SvygY=4czZfa+()mD#JYEM zU5XwoaUYL%+^2~Ku;MyBNFs`EC9~xunq`XMapNu|&^oi`R4kf5d)}hiGpFRwU!wn# z5Nsl_>t99STfD1^;hIDfgliK2BS9F`=^*U>6+w8DD5IQPDW>mvw21#d*P0&?$st#4 zyi{z=YZ7#HwnO$^2_|GOk(b$ggLVKrWaEe+WOu8|!Wq>EQlCvpMcV&YOrSo|@4_%9 zkiVz@l0g1mqB=SON9y_;Xgi!ZuA1)nXTC~j%(C)sq`YBY_>8++T~It%n^sXrb;IWf z7hRTsTeWgXF!#@2^|Pv5f?$4!+OdRcTC&`3s?SYS%FzxJ&$|F?;yHJ8SdN|jIp3kf zH0K0%sQRTeiiaBv4OUGnDTl|HfrA}Z^Wz0P1>;AJLf?Yh5hOY~)PUtQiI>`tY6H2` zK&lNy8A!Dc@@)+*=LQ?H(?AXy$W8-!-9UEwAWbW19Dia%nhoS@18LTfuIhtYN@PkB z?V7zf$xrb~#s<+zlAp4YWa0V=>**;7#*@wgSPvw1RadM;@*HX7$_#Fz!RaU9W*How zY}}W((KWo%hO9_Zu4R-1b+nCF7|3P=!4n{tb#(lvQsFZqrPrRMAkyF~w!(WvDYzT9 zHCr9IogyOw{S0wKCqi~eBIGX;agbs|s{WFwGhrlSDJBzs10AZ^ard2+67jP>2PQZD zHBrrK=emEleY?4lC3D%siFXK+h;CIDsJ0wG_qv~hR$kmiW zoRe($XGXFK^=O}4L#KIgvT9h1WIi-mjc%eLG*A`YN4>Zl!`p+le3PX9p2Zp555xXI z(P)v7;?$&*)IptChomq)S)C5Wji?g%Vj4=??-a=xIH_Q_2tdw*|w1$V+vDSw&Er!x5f~su{m1UJW=i4L@#i% zQ*C~e9OTji9jYN5kQVyr)(9?^y8)(+(I8t?-s_(ZpGp4vvF3$JDI%7sYX5k z=C>w5**gtZ`IP;+iPAOL)mDW1jfi!N5ks6}L^R10;yfQcsIQ&-otj0c%Qn+0dQs0_ zgjcHl7PzEzC8{A?Aox$du60|eAMIBSJ7Bc3aE_x>y34KVcHn*v$e<}MJ;Ry3lxVG( z5~gn6N-p|V;2rimZFlP8oT+LSQi73z%j~LSvV1!$Ryi)WL0(#SUMeb03@@ zVru&pMzqyOEUBz5Tr$Mzne#h(n5aYz+6_0$+m7wLNKoM=E?cTg)$Syh_1uj1-eK?R zVIyT2UhR68s=35vZn!T$LJx3-%YRoxhiprU%LBHNOT4f}r{$W}4PdQU2V1fBS}`^G z5Kdbw>?N-JULQUFdwL{+ zUf*nR8Q*MhNnDVVeavM}_Md@7NZ|Jj@>2|vkRT_!!D|!>JbIYMcl4l*c+I^o@ftMR zYvw#fepho`h^WsR(l_V#_h8^x0}>5I^P5=J7NGZ$JXYPxv` zJ+KF{uBQx2@(l_LudbYS{WNdD$$}L{UZS=6=2*T&M(YVazxfg~Pogz^c`X0N+oAe< zFsrR%orebSY+si+w&@b5tMT8%#SizT z*nW5}#qXqmuDx@eo-{zKNYP4F96~Tt@B*JBrl@0w=xapL&hJtW9Y^0lib+m2UfAnJ zs-eE>p*K;VxJZp^f?0Z}szGk<1wfz=mZTaVEJ+m~oY;v{cv7mW?SvRok!pN!VybxW zEDRAJY&Oyl!G=psItoGLzu$C{UEv{td{Wg5m-s(DkjypG(4VtjsBrqQD;{Rb<~{K=|^stX3l?TnmPYy z<@~?>2EE3vG;{t_)6Dr#P7@2}0TXck6ViXDC(lfi?838bQ8Nr^u}_p_7K|3ZOTb)qT3Ta(>irLJH<;8!CA`W&@A(B(Cnc1* zYCobrjCIPchNg+B@Tf1e6skslOz~34Jd`H606n91c`Q`p9P0V`i2V%l0sH#ipQsn{ zr;w+MwI^*$q7_Rnd6$;)Pqrmnr5j8BU~~*mZ(+%Ewk6NW@&XuEO1fcKX@F~nK)}<<%J?YkKN8EYqm}&aqxfVry)M3U9{r5HO~r~JQGO|CYY>MGHg z3{y23lYwA#;iYR#_iL*rb43|bH93zK|6bJua>dZ2RrB8ixVx`(f`e{IeJHi(4#}qC z-7Z-r7Ep%zDHP0Wi&3K?l49CDsEvVZ7K$U0l&(;G1h4ZKa{lV8xM+Q(kVBc?LJsS{q>#gzQpoWf{ET)GCuExPdkp%`uCCsF zLFF3D-3J!_9M|NWOkJpn@XuV8ovGF@p=dRt58uKAGfh3BABNdAnbJ%(ax&@%SKD1< zT6DGV$K!dv-8DN?su(qyQd*duslqOTMOFiVGCMkTIifUE3Lg*KkSzw%XdwCt1&l)m zfhQZGi{C9(mOH;Y)7<%EGGTMv`MYGwo!_m^D%J1P`Y()n|7#i$k*%$1F8xuN=F(sO z4IQV*jnS)G$$$E%)r6{lWJ(R-!Yv+NZ;SgX)01|V+JvZQ+Ht;BSJ*ltBujMBzjJ<- zDaG++-_a53q6ifW1y_sm4$(Im;P3h#`5$@a7yaSBDLmVMqCrIYzZ}y+#`ww+sF2At za##jvO&r<_bd}vRYZxaIipP3@)S<^`nJj`d;YK^Z=0LfmW``&t{ox@^c2b z-w%A%53ECN007Ax^YGdxB+Xgop8S!Iu?zbpSPaw2po;nlB zeHg2jyfHpY@&+DvjpIzbM&9T_-PEBdc5iTm>T&(s;U<7q-*WX0HOzYTS{G#o4*8-T z57a|+j5QbK-nzSoem7`5j}dq{TXeZl^$(9YJ8nq=BgFAJ?vA;K4 z_Va!%FGJ0bqGaA=gl^9c+|c@Lv!V5Pi;|OEEe}F16piTl0PulqwLT1&0xwu?-bIgY)B&;4`cC;dKXun(8ywAcslLG6Nl zfIj*Y0^oHzp9K#Jig>=*%`J|ElB`~_~2)~Gsm|0xqTDM66ll>E2<5ugVa9{IJq!U2Pg%t2D zwgxNxz!gS=wSFKpi1lgEqB@P~$~&QqxgDlJ34(U`r}>?k<|*Ga<((1z01)?qDSYnp zhg@~2GtR-GT=O$t1B~t8{t@(%p45D()b514#rD5Y$kz~lGRA!OdHDs;~`ZlN0wEac-Vb}$!ptg<^3rPEY|&cIIk~<~7QLF~(mVd%xAHX6#Rmd1 z9^cyWTX{luk)!l0b$l>>fy5}yUmwg9#@jyMQ0uUKA<1$1LX!0p7;k32aAZ8$2+ep~ zE zyS;F8J44|xSLbVQ@W+&X3Z;CBXs`X>J7e5PWpqgXzg=#*QZ-ORTi8O!8H%aMT8}vs5 zYW9LmQo?A7muQ7&ew%OVI^WxM z3rsc}n=jc6K#3{<`l1R}DoJT0ELoRA8&$(N&O%dh700X7Z8?EQ7f4dVKut02F6+5# zbNM=7k1$J&@bj9*1wLK_$iVSDmeDV?=FS2`YgR!?e5rW5(RE{ip*3S+Inc`pdl&Ur zGSE%a8sO!YzCl~X{)KH_zG0Yr5KGlnr+o!x2hqw73cq}(Kz8swSVvbN-!>9Ydd(_3 zc(lOm;8DEwa?9_alm0>S2OD@!fYe60W`bHkQ{k0I3(m(YF^|v4P)3ZruRxd*9)(xp z5xkP~3&p^AE2L81#wj6{1ASdWD(x;Il~?$>gjDJ|y|kXE7W#~@4I-oxtyurYLSy}# z3dQ<2paH2|S$JOS>#v3&R9hc-AtfHz2efV-?s@nw)sV`^3ytv~Ei}eQ>nD{j6&h0c zN};j-iy{%<(0Yw1r1H5!Ln;9kQ$1QJroy9;O0*(S@}e_U{(+;%xYkF^W)B7 zdyf8Qj>~*=2(7|s34mC<_iWO&T2-lebZMPXH32pR^Po#rZXX0|ch4{W=!*(|ntFAZm<)M#Z;)QBgGG%vq z6n|n}n{?x-bX!I964f&|b1~mttp0v8uHvfLYVyRZUMqM?=&OQTKZ9J@-It5uS;^|; N3fx{XxN9x<{$Cj+)Xe|@ delta 13165 zcmb_idwf*Y@n?qcCXhf9lFerG&SsNrl1(5G0s#t<9|9VbH}VM25K#z%)bdII1$o1z z92G@g0V(BWYPX6gU>}r%AqZBaA0S$+{)$2nYiq0iX6D@8yYa_A-PX@XZtgi}9%p9G z%=gUQk5t>U%i10p3ZB_>3VKYLo;xe2plnLPl&Mpvc*?pKPA$vLE9;hD*rTwZ>*RvT zQ@WK+p5C=<;go{O(>%r!s11Uup=h{qtz{?UG;Ruxy7p@GMHn0@39br?a%pYH)?c>? zigbDH8=W=;W8Q|M<+a~N{3f`quBcrQeLFa*D{AerdxMm^dx0ZJ%G#{NUj|F6Qa%cH ztJ%&o!Ad=tu{c<%*Ru8mDRp;`+#o4yKhK*MENQEPR>6|K)8q3XNNXF41~d=qxUH!D zu+N)8NHiLXR@4seKe$CuQfJlX4_eSRNYa7Bw>=aT>4@6$eY-=0BQ?tRCD;DqwTE;5 zeR8$4KaQ+jf8*nJLDCxQZ`f-i?))PZ;I$60CGOv6L*5#p&fkR)>s8`9&{h9c;p>Wko9kZQd=5Rw#W6sAV@hU3s(RsIMg)%r=$Nfi}Cm~rHl z2xXrFMczHd@K%U-?ErWHV21Zf2{Z?IOl=whaX-C{5h`pT1XpoJC{iI6E252I!#k_X z1L3c*NZ6{}r5F*qFuGZ@a>wB2<*^kN(W>Kv7^|vg!C1kxYzKb7?$5-Jy(Zo5BAAyNivCQ)3Ge(RrV4s*h5T?xnQTN9|y@s@tfD+@0P&3rog+=#^cAUksd5C zEQSSQrx*kN*`L(iITGTH+~=dw9&<0DZAd6lvQXkDLK_2wCV8hmL>|DB7}Z#T`Or^Q zj)o}g7h_!iZGRjUV|ZT7GG>3*ibhmfbBN%XVH=b9wbYF(`89k(jFEGCCRWB6JE!In zhdU!+k*ud3LNP0tp?8?%{bD2x1>9;1+pL9c*1}$~gl#s3(F_TL&8pWJ7=<*N7>{;! zbPRa#RE&a!kWX{bW8p>r+=a1Fh!_2H{l-Ba&+)!tgQ$W#qg7)z1!??kXocIORd`=? z;W`;~;cB)`lA67U<|u@!du6KQeX+H=JOKtqb+$EYR^e?+|3vU#ztVrH^;MXq9_WX? z)b@#RigsK!33{vbU%}((wyA!-U{HyR)|cDMo0rF}t;w19*c|@<--n!Z?CFfQGY+}i z8_(>ERL;rJAA8vh_+(IfOF~7FIysp_IMsjj!j$9h@E|@!$|OJ`JfO;^LRY-srgq+k zoz?ic6!|x&kliZG1=Fns{UAminF@n&nV!QXHlyK)3pd&f&u4}5b}$FGlMbb&{FZnm zpBNkPYGG)gMmF{C0C*CMN#sU4LM|IwHX18Z)SPk{8CJ~JOld*?oaMiY)%)e(2`yrq zIR*m-aGK57UhDK0&4ft63pS(j;lkk3lh1r@x0sykY?3BQ9)$*Muo;b+IUOplYr&i% z>TFhuz^`b}FXp$o!_#pUMK*Ol9AZ`7gP3RB^fr$uh^7GDil=R6hs!9DWwC)xELm zz!q+b1<~ z5hUxg+B{aD)jKvmz%IDOVH$7j*xe4vjlQEAn#TT-v&KFuuEL&Ck#-6zIk-{$mmJ(E zd%(e+vl#Zmv#QGySfzIV2p@+WmG~3#QBHq!mt+!+d3e=-ba++WVrZvYRY6Zm62gCC z6}VF@{uRAh6vh1f$rU|Anl*D(Z&|!0y}ZTRB`R?VEK!|Gs4@`ZZ1cJByk}4(7n60CE~K$=f{xi{T54{C_q1zbtYdA@a8B=n8Nc zIfwF*Mts88Kxg&0mC&E!fTnw5e4Oa+(Tl4N+hycnXLV~8*rAWbgBF@RcKY%djMhAc zYaY`r9y~%S%(KX8#8P1-tkaawYRYXEC65s0ev6VuOiEk_kH9HS`Ie?UZ&C6HQC_tu zX+%>>3T+Y@I)(PN>lB)4=M>t!BF(NU%V9)#Uk+@^q3s>?7cJ|x36es60Y%3;-7ZOV z^CqYULSvrw8fo?XZ^ZO916pE72PexJ;1u41quQj9GL$TP+pT1&E^mR0@FQ6yz^d4x zGYCBsL#e)Z*$VIBSckf~6>RW`$*e6U%h3)&SJNFTydBl^_Uf04Mu$7p(OraDhC3+X z#i+37AOV-qSa*zuDlMh7J{=cn;?=b3st4OURB1TIg$yVCjA7S0`>3z~3Nmq*LxsLh zp|r=M-l3@v2{lQ+@;aomy$o}DEh5p!T-d{*Jda?a_u5Nv03lvAyg=E2!ES84_6a@3 zCwF(iDeNAv8b5(JDCBi%D&dddfNpBjbI=|8$NPM8;w)sKCtmx+6K|Bg=T^7dQ-K&4 zuRI?>42);$5*VWiXT+QSFiu=bCAzJ9Xep6bb;lf7syxpTMYX0_tSQ#`D6|JX@umk! z7W@C@aCGACc(pEr{9hBG=}t|4SZm4=u&EQKuq?usB2x@UI~K#m#*5+Tb>kZlu-)K$nc6xQx-dn* z$%&dU##lVLtvdEINQ7`zw#VWfc7QH4rs&cG1bUO3Ona?qR!2_2^_MH!L2Dj#KzYyoK^!kG(qZf(F`Yy@N;Q9G{NMOs5~!2gt~r+ zaGbgH1}&Z8U&^C>%b%o&z;)wd`AhJW%<_(5sPfd%X*FkSugvTSu*N^l#EFka@*_aK zcA}mdwiGiUn%x+$PK<#Hbz}d3gZtPeZ4U>F~jGIgUNAt0( z?6)ak=lW*UPu`{zZHhSiHuNFjz#>?>gt<8-p}5sQb>#$k#H5MZjF3gECCjziF;u%DU6Afi(zFE?kt8u*Aa@3;u+fq@vEi zmr$fR$c>l^RFFdmj0x2ule_JII<(8RNKFpAqdrywUJwxTOZw6^apI}b?!=L7bC*4#fm>+31J z{+wjA`$G(TV(I>o*8R5Vp2{eu{Kf<2pR5g;JbU)6E%VDW%ikg-VJ56-GCi>prO#@m z={5g7_zKSYbozZ0oz(t|@GT^&@Uv!#i%ZrXp{IBx?0q(lq5HC zMm_;iMjZLcR&^VntgG8ZCQMUvA}CjZ)p9LxsU}&c z1^S9qNPe;zp(N5@#pax-_7hY z^VG$SX8C4Dw8X%JHqs!#(-BuG1;KZEIU|Q}OtaEc%=+3j>IOxnh*>c@g}PR*t-B~{ zBh;o3h%`wJyW&s17?z?f8bW(Gv5ff7B%rnGCeDH$RBPgK(AHUB1zp{G=|eK%n+ zwN;6!jv|_t`#n=}Gj1&_X_axSBiD`4J$K|%9CFLYM2A{(8-~&CmwD-1#lqBpTScnz z`u=vRcO&Ji=Tp_jM%v6ay_xfk5Qj={riLCl3C5(09jRWAxysukuBBT21@G{?@Cs0G zm$u+gx&1M#X*>%QW@-0r(uv@HF=f z$lLX|sC82MX(UFb{h$p<*wMev^TTyx7{8fZa8R0D#K-9oZXygyQ!fof7tE9)-A15Q ze5GYM{#*Dmi^&qrWTVA|N9b0(J%9%dX&!PdZn`(=EVd!;+*!PvN{*sy@h$3p^0l~4 zn(tbCmj9w}1%fw~NG)1x6zBo61P`Y~u=M!K>`VKE_0T9RR$c*ln6>0;D^ zbp0WRG(9>E2l%3;BYBWCq1G%Eu+9`Hvs++3ZJx?;uG32w`QZQ)MWWo{$EAx4VVp_` zr5)ZHkIQgDI=hh|BMCK8%6u(#d3vBLE7P?rD`}K2)p7~imaZ>BF9i@kuZj2hh}EB7 z2WNC;`n`%V@sz?rdWn_CrHhs6Rjf>}WM!z+8{lg-&8&|@2R#LsELh^EL&OG^OE=A? zJ22fh%NL6;o6I_<;9%wQkHGR!@lfvzSx{5lo?MNLUd zhZ?Ozjj4m+-IFejci+*QmmA32dh_R8{sVLJEY-kcE*%obT-NEn;nJx0FV+@5)>~-w zZ9)1v$6Rv2iG_gPHW?c9wo^MDWO9+(DuYc9k!n*qaz>F;8gk5K9&(cEKjdzcJSTuW zQW&isEytX-{IqQSd*bG zU6bL1h8@(!tpwicGIUe4K49bL^v19FH||xCMl3W$%rGHhh7Pr_EXj>p^7lT;5^5ne zq_-qhl5OI|1niQj^Kne3PX3XZaypVTO{FCt+n7}MFUMyEHXmdcW)H9^Q&)rDCZ|Hp zX`qjj1dFA+RD+48^fHrXyry~7M# zJvlz;Gc|eNtx9`h2yV~NZeEuW=;kyvJDkw&iA*V>$1|I_d2|sz0(hBZWM;LTL|Nie zrcS|^l&1h~Ds&NLv(Gb?dlAOqEmO0tzxSGD+@_g2i}3-x zlqt=KfSLMC0TaI~#%mavWq!@nSi??)^(2v6X!vfX-r4a?|IP&E)}>h}x6zMJ89xfj z9gyWixh4FUpxlxyLAhS~{7oolLYDsAJ(lK8aJxTfaLYfmIggKJ@#pPOi(|#CEP=DQ zoP^QmY+Onel)ry1%=!V4#ck#a)nvsOtTo3EXUVthlUedD`*4O~OBG!oO)kej~gZqRIxM{&+Qn6cg&5sziaZA*}5#|xiyX?fs#V# zDJ6zBEG1?x8J50!)2pBxdS!Hzt?C~@{l%MR1V_q@;7I7(WLA(QtrGn55Kn3R!aM~^ z(o>-1^lS|!%Xy87V5Vk^edlIt1d~VRU@U2Rm48}5av8x86RC8wY5Rm=X1PruxlId{ z=B3Kk&ZMXGbOcJ)W($;jO@vA@JE~>AogL`YgW1}r2WixYB>$MLk>q8I_L8Rk*hi~@ z0#WqT$^dxW0i5doCDa&(p7!|O_5hb8w=j_DGq^6(VRd^PM!<4jofLV7OI-*)}ijLgCUBga$&s}z%nxQdS|0N{+$WHqGIcBBaZT8Brc-5jYyj{+lc&^ZX9(t*Y4En*N<%o=86#;`ya3@xjy-`Y}`m z{}PXWC_?}64@#&vM*naRtzrbjjj>tYFIVC+z#r$SlVNn{nV+ZjKZ`?gRi5&!Aw*O9 zX@@q?_(T(%+jC!w4om-8Qajz^-!`*pg?s!$YntnD}wT9X! Kw0Qp-Ec`!o6Dei@ diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree index b5294352960944b540acf428fd4c9ac9f9925fd4..e98ccf2f74abb8b6b18ac6a7dae8d167cb15ab17 100644 GIT binary patch delta 1369 zcmbPxj&<%i)(yUlhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-t?7!Q-7YvX^T%}bb8v5=>AdS@yl|KtPgg=A@W*}RN{jXaxOCNJX< z-fY6l%SxWjlO3d3H}4h_U?)%eW(TPVa^h&?f0xM)3xzi;$gFN5&t{+QCCYDk97n>YU7p+}aDuG{bHG3IfQr*}F~FW2_7 S=8TcLWEo(z9auz7VFUmR9HmJB delta 1369 zcmbPxj&<%i)(yUlhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLs57!Q-7YvX^T%}bb8v5=>AdS@yl|KtPgg=A@W*}RN{jXaxOCNJX< z-fY6l%SxWjlO3d3H}4h_U?)%eW(TPVa^h&?f0xM)3xzi;$gFN5&t{+QCCYDk97n>YU7p+}aDuG{bHG3IfQr*}F~FW2_7 S=8TcLWEo(z9auz7VFUmrHpDpq diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 03a2fb40369f8e42534bd0a4833038683059aa99..89f5b1054f5885f9ed083d5c858b4735c2e1cf84 100644 GIT binary patch delta 2066 zcmZ4ggKhl}whiu#hG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-rs7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY DjP^ln delta 2066 zcmZ4ggKhl}whiu#hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLp)7P|LL)!w{G;yoMr z`VBVgD`b%oSks~Q8*ElrTFpeJ{hJL`Pf%ocn|dI*b_2uCU~{V08jAGK*DYZo)9+yW z7aFBdT1ZF?xP-_!mBMfM+eVq_&h{v+#sg(lxwEVy~wVp$wgbz6JKUt}1C@M}jLCl;wWn{WWMqK^%dP2*3KZln7MOAD+xzA+PLd(rVcUUK^F2lY D!_IDO diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index 613056c7e0a54375a201c5cd54c9758b213de08d..a892a47f15d23a8eb19c3165f179d318fc109308 100644 GIT binary patch delta 121 zcmbOwJ4<#$Fr#6bWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E ciHT*hSyHOu<`%|wGPF&ez$m&oklB+P0MZg9M*si- diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index 5eb1fb2183f9f4a3476ae533b8a604edbcb43e2d..902f4c7dae013a19a16aa7a2dd9fc9191b649a0f 100644 GIT binary patch delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8tUS}GbEJGq11+$|TNlz9t+5hE9W{y`6tM?0 z&^ppCCZ>WekVTiBE(k0NMG}&jmu?2rTMI?@9ii{(^LSIS9C@Ktjb;Bqzd_He5s0WLUX2YX5d5gTEt-4niBg!`b zGhMMVUe%yF&1LHyBEz;n&${(LjW7A#5bhU=O}|tS4S3>dAUf`IyFj$E;wV;bzj?9O zZ4OsB#NJ(6x^;Kn8DP8l(*iYDIQ&aDTNjYWm5iYUB995)=UW1yVbL}?#z2(LwWH|t za1cAGu$Vp{LS1J^2}Rdjnn2N>Xe)pUSL`Q%$wmr`yVo5kT6^OIfQsO4*wJz4HQ<=a z^VGcqXctB9A4gqJKRN@r3eV#VY?_9}JiP-PQzfc-atqD#^5izuwd+{~bscz7in<1- zMp0LDCXTw=X1h_>Rj!gxcXFUdad24x9u9D4TcD90|QN{0EjLIP= zv(wozrS~ADQf=xtZG4ZDx%g0229>C1#q`HPp} z$OP>Fd@nZA#|x}qUxY$F*{9fGrF8|8S@eV1*V0S3dKj6+e-r02aVLFRs|L{ek^G+J QY_XoW99Lx|f8(*V-;Sc7%m4rY delta 4106 zcmbuC+e=hY6vjFGj80AJI8zaae5fo8temNFbSR(FKntw#)`fI#jg>jts8RHwh&`Br z){%BGF%@)yEV}G;L10lRLqZbs(#>FcE5hi#g7yq_c|D(h!0-FkT6^ua_MT7V&L?so zv?$JwPIq}Q>^N59_6Oa;P$=m1yF4MkW3#_>i>KVg*<36^FKP^!Ug~hvgqj3RwTH@bD52=8OA{#C6KexdVT=C+Fxf~Eare3fMH_B>08rt-4Le%yyapUI zd7iqrAMK*({iCSs=|^V(SK)k|gH6+jn4@=qW2Qv)Pi~=kUY^{Fx^_K_qOJokDo|J7 z)F|qzCz7bEX{H->UGb_4b^S35kL{{tKDL`1#oU_-JG3ksh4!_z-8f1Q_bN$bAxo}> zmim-vo^eyIG_rc$*REt!eur`b*sT94*iI^`HO6C*m$!k}7ApBXZf46U7E`>|g{T~~ zGBceWQ+f|TDs{T!rit&iG8-R?$)HkA%uS!oECrm6Of4fvQf^%i9FE3^D%f%=DqwH% zAT+vC$h;aDMCX{s9otzJjZ`odyHj<#6d@^c4Y*h&8+98nLDKE%quC}F1$vw0Tlc%k zc*W9h6Ja5D-d71}I7?9cPm9dxyijRPy3r(rH(E4?3J z&AEX6pYO$b`gno$I~Jf&Oy(&zSYup)WETBk_6_vXu7;3F{5Nqf6SvZ*)v6DzAIa}o R!6xI0%W+jn@;4sO`VH3c{{#R4 diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index d000bde45f43c91b5d791c67554a71af48262714..391cb467644a17f18e5888ed2963a735cc1e97be 100644 GIT binary patch delta 479 zcmaDpi}Cp^#tpHIhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-u77;lrI>w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7w&n_<~vN!xX9Bw`L{R!WEZ|dvUEpJz9%faIfVZoGkMxK i-xIz_hStpw#J7jdq#FcIHO^jWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>ejdq#FcIHO^Hftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E fiHT*hSyHOu<_^YMMkA87=}(@(BDUF=xt<#UCJ7}E diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index 9e5e3f8c150fb01369ce3edbd074e2e24961c08d..495534c3baa3120a2fea8de586bfbbac77293024 100644 GIT binary patch delta 707 zcmX>!f%(t`<_+$QhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd z21&-I#%V?d#s-rs7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-AjAhIQ delta 707 zcmX>!f%(t`<_+$QhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLp)7$1_MOP9@lPt%^Z~iMalaEZTYFh2VWNCGuT;D0a*{1F^7kSz@*LPmCAw#P? LYv|_N$I=-AepcM( diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree index c820b13e7d0fc35c80d80f14a0f0a0ded5464dfe..53885bbc5b3db5f223511c928c7be8124808db68 100644 GIT binary patch delta 9979 zcmbta3s}@u+V`9pV7T9AxDO1V!f+oZ8pc#4@fL#deP5dEQ;Le>qymDU@_eOWtHx#t zy^y&oV49k#h?99ObKCN>b<O_xC?EwQ9F+(rO#L+IY8i60^*+SI?PUv$#AvFTcWehs{1OKktrt)|~u&d%n$P zEicb0zcYVMz9nb2HP1RPH^-U-E7+A=;M!U@+4)yq#BYstLz9jL!$Gf_TVgMPd;Nmo zT4=cQb?-#4+Y%A!YzjDVD_+}Lr)m-Dyco3R*8CtuIIo3v+@1zePOE?Hn>Vu^&SU0Dj33YddoO21Yj)kDYWn}n*Ais~ z8txNc%qn9YU2s1AU4BvnjZxWwj#p9R%J^c9=(wD*mj4;GS)8G}K<%-P_ng}^-(dVF z+4zHO{0=&^Blve3$Z22BBeZb#Y8;Q!LfM`ouGd1(ggbc>lE*D<$5WP69-@Ub%RMx_ zu|Q9x`EU~Z4!SIVWMkox+`D+O7FEgc`cIs0EPR!li_P)YyZ8fINW7GTFYB!NqA3PG zu_p5>(agtcA#43OY;U$(0u>{?QNni`XG4#Pv9-`QDhB@6yo9%Cp}b`T2QAd}j^*ox zf@}*U+AMsB2DW}PCO~2LL-w`MX^T>r|L{~bKd6OKVCRP-$)-`LxP#|Eu1_M>l{C#p0OXH@aar`4X0R~;j4JM zR|{LS4ID{MdmKy~`54!ELC;7d^Mk&L!=d>vTKH*N4D*Id6Kq`X1&@r%LA~~kO5@xM zy6%r4Qg%HW{70wr3@-{4%R3*{KWbcY_>c8{5czr3)F)lutH?IF4)ULt>bp{Z~?_E9iCDS41T z<^=_%!`W{X_2bzZp-8J2ByP%4;-UI95PgT1cW%l=0nPSoZLH%7vGg$d|y#Rdht&}l^%C5o|RriqI&Tk(u?0C=Q#Fv!QtiI??oI* zUOe0HMUK-_;v+#f64`Hwa#r2*2;*jNNU%o3!+n{|0)3M+pm~oMGsFDJW5Ued=)Sm0 zO{KB4GQJcOr527%wqd&WP0mz(Ylh(^?Ifh^`Z6{RMwh0r>DX_CSAUzr??a_lBc21?)cXIL!7=m_%7E zXS_w6k(hR$;9sBzaHU*PToOntg?}#G!oG#F`)LK^N@1VEzWXEjr{uLZjRccoH2W|d5#a% z?2Y2NQbv|b#z-LpXAYSWk27b_h@hEU>~Y*7*W4LsoMGT-0woI)N|y68ay5LG&rKV4 zEY5YtSu`_{@x?dyBvKEopVbCOW~V_{ydT_OenIV>-&y`Dx*Q54%zv zM9*H1UM@JA!ndQ={hLy!k8m>*T2AMVKFp6}|41W$1&!`Ciyi5x%i{m_aod$%@}XUc zy#(EJNAOENF#1pw|5WE%q!; ztYZtxN%m;Z^!ZOx);P0S3S3&8sqPNRI$;hH)9!k>SUH{-=!7|iI@KIL3R@=J$%^6R z0wry@T_-kJBx>Hw(TTjNz!p3S%LNOsLeif!cAdwhK_O%I#zN;qdcIaCoYEwPtd>GR z2*Io&kKo`OOc5mg9^t4xZ8AtGZ;)gxT15nXg4?D^H^^FwTRBx$C#K9yQ{mjX3+fhB zEiPYZdZ?m)S=ExcCakuWE~;2uS8J-PGF6w?O1iKvtG2RgS*qETx~ROadSO-F!UeOl zs_W@V^q8}h;^{{`cn3`nYuVt0+m8cnXU&^=ls4Mxre0duQo(u0HQH`kdmL=Bf^QCn2RkYUoLSapkPE}i& zcUIcarNIPE=Z?Lwv8K$l3-Q0@WIm)}!Z;21*8%RSVPmu3X?3w1mPyOtj3$I%b6Jm-gfp(WnRU-X5UZ=e_+MUx*I&BFtP~z_f5_#RxvckAz@TaQz$>hfVQR`tvA1nDswqD$H;UzC4(c z&GJR>o)jxJ^dP~RUmZG?mASY|B-c#Ag)hXl$i;;X3RlhWd#Kb>=2|7WS_K!r5ZALV zE^I(9R4{49GY#hQ9yE_$W-%n}sqNbIXw*Z& zT{?P{vtC6N2d?ICVZyJ+@JxSb`gK0bR`>%HelL`s4sWkbHY(dksv-=}dO3uO$8Vuv zbs;bEhgGZXZ2SNbgqrUD86xa!^7ssYs9uxF9vC3P2SrT(86slWE=PnQJ)l~kL*8oQ zjqq#%v_vL=`LWRPVoOx5Dkj6-9Cyxx<2-MT4Ugrqr%{KDyB*5b2;?%OL&*cNHU5h< zhv&!;cq?cMA>eWEZ!%do$@Oz62iRVJQCJ`$oP63}z^^~x5F=D?NM)y>r^UwJg-eg; zaxZ^imK63H`XOAY1Ud52k&c9pbR_#NXZQ_&Sdlvdr|W*gLNC3!f+Sc+@ZbQ#%hdRU z)kcJ{1tI1{(ZOy&=KAr}P6fY`KK|&%z0U6SiHzF=L?J_nd2E0{%;N$GF|$J0+7S+> zj-hM{m^N8RR|L(0iiBX4fUH|>k*WlQ&1^o5gb{p!6j0$3P^${awigT#fXJBy5IS&^ zj8-FbX1ggr)S7N5hD+o$9$(IaHl`xyX9L`e)OOS<+rZi(yS<(t4G`Zbj-!cK(I0l{ z^9H=V!R7oHu~{#XFGp_jJrb%g+u@mKme31%6tOSAB0bs;jT;-N9RV2sDS(hI9}*~> z_JcYH_X`vVOnOcAI{~eB=-8A++_G-nL>!2mJ(wBmEZIC$0k_;5=x%0qpvW;Klw-w# z!p;($^2>41_pFUi!_ioND)~iiuKYqCvJna87n1#!=xxJ08DAEt!cx8xCE+)iWw3SY z0{rcJL85pF6t=cWTU*3T9iHrrKs%Sgu5D?^T(i#z*S6^+mGDwmcA#tLYjDPH-^0RA zC@pTd7hxwqC5%>q?+K{ek?d(G!cKl+z|i=erR;<{x$1vHPsdR0gr^p3RqXkIP>8T6 z%TT&J#^SzR<0uw{KJg^)cHs!`AQ1{Agg%pk1o|9`BM6WS^vO&RdeFwRf(Q{&Ou9yy zd0ddd5lB=VF+PZJ1e+*WX{RU>Z2y50Whe=L#szsq5-0}YCv!mc-e|VaC81JC_-~hl z&>2RWm~aFVHynZ5V^ZJ;rS*rCIlf%pH#N)~r*f~q@ms`{t^!;YWV})ZpZ-c zW0Um8^})Rn2gi;)iXI`H--#yoUH= zEKfbpH4VYNc44dv_co#418N%Rd96F?2(?T6?ORk{S3Z78`JxIpXnQP#vX&o`u6SK_ zMaAoR5zfI~(S=-y`}RoF6x_EdgiQNc`d7V?&9C32|5XqAQ?bQP-OZAGL;Ht;hlcPcWHnv?u(D+&OozBB&N*P~Hrb;wiM_!R= zxb1e(a0yf!g5mmG6Zu*xoKSJUaK{>GIlF~#LANTGRvXC^_@U9j+MwfHDL>fX)$P{x z;M=AAkNsVz-MWnL{EGjzzpF>>;%(UI2~WDKLuPxxlTFy}8Bf+x2oXLE6ZI5@a^D1_AO z5bEK}eoG-7y7)N197bS`U&U_ae)BSPf09e>1lU-(*jVt50Gnu(fGynBfmfW=L5OWQ zMJm3S(0Ye+Wlthw>2U9LdBvF*PJj)uOLcD%w}uPn7NQWmQbXqwT2r`vkvlz{(3-+c zcbwD1>5dbx#EE;F8?SZ$h8u`9(ktDmNCWn#8B%vtm2p=>_PzFVz849l?lfaG7)DTfI+egBw z*j@Tn1ODE7eL24tE-oXl3&X#b)BWHw9NnfmJ%BcC_}e#`BoMO>4)i9u)s}8KhaXhG zK7{#3*O0@Y?>gOusuvB3dQpHNp_Ch?7vBR4aL{nrzc+#3r5ACVgrwhIn|@GxQ4ZA~ zNT~imlG~R}gbb!D#YHqdUC>hF$9+qP5KwK@3#%W+?jpWYPWpu2L$ZXR)yVVpHyOk# z7hAmEy?PlPMn}B*vHC6%di6X^6MgFOpCXs#pydnuz2YpjfH{eV57?N*_v)@2%lIeK zbv-WEeJri`>L%A+7}Rz6(S=-dEhq$D{il|{BW+U3mXmt-vgP0Ejb#hm{bOdV`XR|+ z@T7CBbM<$6&aDR6^nXb_&mfZ9Vi3VDKq7-({e6PEn)K-ROapBG-o%m64sxG?b`Wp+ z3kht=_`yOQ3DyYTw#N}K(?7_3awwl~5E}~i(S{<1$PJ}lbogbWe&Whk^ ze?&tsY-sUe;eqtrR9l&dHx<~_?Zb)-Cr40>)~&vs3TZO^p;$}^eUBDjwnZi3s5|7; Iv8n9;0kX`u0RR91 delta 9939 zcmb_i3tW`dws-FtV20t%04g&eAc_hOAihPwS1Gt zWnJ-E;_s+9oUAmHR5J6Osn^snE&FxPG3}mAEA_6mzi-ccAf8{@_4jku_wBv*+UvFV zUjO~EVV%CDT(2+osAyKLAIOqs6m;p9J2i2dtxI-pm)t2+a+9)Cx=+bYOwR7yse8BX zT~czo>AIR8BD2uhi$+ym>=BtdVu09=%h=yV*DXJPqr*vk;f`I-uR7K-c0qP{JI*N` zXLL}#A%r7Is-Mem>7XFV!hR5JyVBzLO&w&c>cW53!G`K2Rt)un;`nzuuqO|qA-4~i ziOkH5CX!xyj9c~KSJs2YLPqBV&k;B=HY_MhqV}xAE#OF56zht8)KHPnx+?9x*g$IY zw(6TTo0DS?U6~Bk_mkk^t3K2pwKmaLwa^m5%joB+OzXX_y`tpzM8pT++}5BJJ@ zu<0nt3cJ#bu=7p?e@;*8v*%$_H`2bWTZRH3qAQwJ(P}3L=V)CZO|TB8h4Jm?s1dvT}n5ENX5+zr}IA zhwxbt1g0k|5pRA;28K=0n1ceM)>^nD5Qf)=lO~i7NH85(9o$VX5-&IV@E#;Q9tsBO zvHjbO(fFI*CyNjDfDJ#y8wPub46TEY`)sBW6j1B1i9E*xu72N!Pm!7_5uGfwaTA(i>>iw>;=WB-mMC2DO)eYoSG{;`hT)Bhsl=XLPG=i#v9sTO>V(8j1P3-A!j z0*UHgwi6FX^Stoqx-GfpMLuSXlqPIf0uzrJKEVo~p?BR|mwy#UZ93 zYO!1FI=gkKJz^-P*AVz=2(CX*ib0?n1J|(S5HNT-OzvwU`7$6$o+2QfXc|ftB_K9W zkuIqaHC$a{G9Ez~4;@qvZ%;xn@wM&XG-T~#L6aWUYUZ{){%4_;%#2Aeb3{y2tQl2l zyr>at_{gDrl9*1e^ya9>c;wn19Ws`ti^yz?EkIc(`~mEpSYC?Jf6NN}(=+2auD~6m zyYebAnaq)`o+3w(P>u{6vx$G?NlTC)a(jr9J5d5D(aK2Na(k&tr>-fPFd z@q`mQTkvn?&^Ufw4C6oGoYi8Fd3{$b4XoW2;;|16DTMoZ%^`oh+2ZYmwkW%nu5k7& z%@XzTaxH6vmxxm+TpdTtdg!weB<#T65F(mPLvv^yBcW{_38h-lgk&A-0j1eV(yQU< z!>yjmX|#IsJ@zzp$_PhMCmheN;A2RExL0GmVRcSQ@F-H$>QFbz)`IJDP6xh8gr|x9 z&jO=!=c4mZ<+g>Koc6G*L@&KR&r5h833=bXe*s_ZCA_~@+H3^!ez~xlZ}ieE(NZt5 zL`&iOydIYKHQswJf40VZ`=q2e{;uM^I`SUv_Z#+Fo0sCiN7pUN)_4=uBX1(1U2z_) z|MTp~oS%*YsU8+H3U78#l$9sx#cpzs@6NXaK;Q&gyV1tr$@s@0Sk_p$)Ww~xi zlN3qQr6hD2NurEHqVW;hLOw!58HdD?znF;Pq@RV8jIOg00`Eo}k0>YQVR3F64=QiF zbkrBKLwVb-oT59Cv{TM-54$HR9p|Wl^jTf`JHoFm)dI;$CRrUijwB+J&fi0L{#xLR zS>vsz-RA0t&dATxq{wH|UL#xyvGS9O*Do93WT6dL*~7xf&`)vnoSbZXB-Ig7SX;zQ zC|A$Sj}4GDJCPqkO}0l%Gn@utqRR~NwoNgrM2BAKz#YpVX--zFP-C-ZE4^mqIA#|W zY$dMADH<>V7Tlx*P+tRVq&Te^%V(K|7Z|am8x#jj#G|H}|Lb4H4p?PaZ0&-+iPQ^LqVUJ@z{^Z$XG0)oK z|8|&b3!&_M>zG9}%+s-$cu1<_FZ8t8TYX(x?pOi`UuqN9PgMaGv(Y1-k*Lkf5O~1~ z)+H6J3XQVDsU=U2W|YiXxYUck<*ghb>z3YN?+LjGXF9x#I;n8xEqYWm_ypB$ypT`8 zm&?-mF>i=^xf?qn1^B802rwP>rln<7caM)%ak$VG9x8-R!1K{AX`4EKgv+HWuQALhk++AQFO6?1_&^b2J?nf-d81{!Hh< zQ|wexJRdG3%TsKyNgUot2zA;`ggUvyB#RX`6)ld0QvuESY#fK>&t`A9I;btn!S-N@ zIB+zb8%XGE9Smuu$5}b}mi6RskfeAI!k^_Zzbu)Tn#Ac{m4N71CIX`Ty0k`=$*kUG(M=OU7qaMse(6%6 zKMF({1V>d-1V=r6FaR1j>f^?e1|QdPk3g3p3|!Zh1wnO%@D;&PRaHf@tW>mc6erS{ zM11HB+)KTf4y>49Wl z@&HRgCilHZqOU0SdqeG3kq;UVgrWy<_=e%_ND3xEOEpob;{+v~vfqwFhynR;zXQ|W z{@fwG+d^OsuCnjn+QHtF->tIOk;D2)2PDCiN0VNIfNr2RH#l*fMOg} zZjX^DZM;SwyOmHH`}E(2>Yv;DQ*t6uL-0+CsaSps?Aj2@xA=;RnP6J2ued2W=yDkq z%pVGu@#DT!Fw^S%XYY1=udk?>k;sbqYhS9E*$s7U9KWWFyNxbYQkyDf96%NGUUZuq zrWyTQtGN+OGx^D#k9+xv$b~>{%;GqpBH+rfNoeD07fd<{(+PdsNVtN8@y3 zcN$Oi6Khpj-OEiQ7;35VHKl-=|rx=P*PyyEq66RZ3u;acFVe>sG#?k zIYL+nC#R?)aYq%;ufTmF9BraqupBb?Eu$9&36-Jw?mG(Yz6>Ki_=|#)c^b~)MvFcD zg^!R(kUZL7_-L%Z>-fOrXU{;{fvzkUuD&a_3ru~y((e6U7|-&DTY25td`LRbg1;aK z#jzq9z?W$a3d{1Br;PTNFbk4uBbahWh2#G~ zFIjX+%xY84+NSF>!mOzQ6kUl~djz1J4eK+3St~xSU}K=ekw`v{)D+jc2uaG0bmdb} zfU-^9Khl}~3EN!e4hK;)0Nz8B3aK$e+Ue-XvSDMr?-1*wkK8(K| z0GB_F;8kuNw+bC=TsrPJ7VfJa4|D~;F#ygT>%=y~@WU;k_3|=FF zY<*)JULhNdDx|gvpBq(3{T8B!wH?fkWmwwc91`=8U*XM@T{?slR=SUNI4tz^fS#D3mYlG?0R4sCi!+D!VuEBxmaYU|u$nkrTD@j$5Yc)O5t3a+7 z&PRvE;tQ#OQ4F9*6}dVCFDzwz4>`@&^ZzFfVJS8434R~KIu=MPSR$-XNQ@O@Cp7<^ z2+IQFzZgJ!;>^$RCh&`v{DzP%G1j#}vG*gPeX;MQ{^1X48ugyI;aSDt^hK&35pcI^}>k~T&pv@s(2^6~&8%28e~BxvgC%Q&sT*$4_@oUIoO z`^pO@{5lbSvE@sA$oilL2~zdm2xK;du-=eZU&O}+iEon6N)`E-AW_In zb2NF$hQdf&B&02p^wJDeVFh0rB)(O?f`a3{>;x9EWkFe_(68W{i5TQZ%SvLR8XOx1 zXcT?D*lyYGy;Nw>;NvCo5WqUDM3-(7dIxDROg9zO0-PcJlfl zffq3lRK=^pj3^Ap6JAus5MI2CI%x5t=JVyx4Mg8jMC*g4XabB4M9ZUG3m9=GjXk6S zdXv6i_6*WiCYpF#Hf{M%e!lDk+1KM;-<@s27~)X;luf`XGgyRp2y%!lcJ6viYc=e?y@wX2`fZUNYGC>SGChPN zA=@u0wqJ4Ceo?XgTbJ#W-{&50Jjx;Ckx+gkIr=Xf5lUV9Ea+o)l`7ae=f_#pDIL)Y zvhTFz(Zu5VqzBi3QallFmg%AKL;!jM1&}9@D4zI0ctVytmoZ%Ka_6Gl4rIymiRhLE zMV+!aJhtS)qO|gd1X=M+Hb1goii+o{d(ZP1&EnFbSlSZD7nnsBEjP<7B2Ugm5s%p^ zW}C%z{7mF`U&l{2lQq9dU95lD9<91HO{yF2wEk-5{EQhoJ&5LK-TIzV^u6NJ*YGe> zE_gG#{g@e29=760@NX>mzeHU9Xx};}Y{I=K0@8ks0NT5dN$y>qQ1&oETzDNbi@i(q z^@8_*NuV9g+co-W{IiRJnmZ=s)2w&W;E0*_uQHw(f-1CFGl8>6D%&3>vBWhyIU7P9 znjv9gh}a?yH*ji=8E@NLwygLO3n%(V_tbQ^tyXAtB1L3{RzoLChY(qz$+KOYoA6&J zJ1ZsH%n+;xM2R+won?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dgoZjiCSl delta 483 zcmX?gh4IuC#tq(#hWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( zNogh~mdR#GsfLql82^)@>n?ljW=`hCEM#fDMV79p$q9U_o6|Y-$g?zRa{}KEW-@JV h;1=4vRcJ9e;d_@oYBEUsSCP12vTV=X{5B(&5dbFKnBf2b diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index d9dec669c9f42e8ad9b918457a36121ab1971c68..8c14692f9932360779ae85dbc303664513126a4e 100644 GIT binary patch delta 122 zcmca7cTa9ZI-_BlWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>eLqN%Z^g{7HEl37x+X<|~E fiHT*hSyHOu<{6CZ8I4HRrayTfi`?ev%*VL_Qo$xq diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index c001169fb282eaa0a9bc00c01b713741541e20b3..10161baab5f17d1aac30c51aef7d64a090ed6958 100644 GIT binary patch delta 699 zcmZp_#@v35d4oHnVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBgD#-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|nbJ7rabo1OOM$$khM< delta 699 zcmZp_#@v35d4oHnVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R1c#-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|nbJ7rabo1OTLQ*|h)w diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 71dbd62574d69cffdff5d78d4825fb943feab0e4..a225e6932ac28b4193834a50d70d928c016ceb79 100644 GIT binary patch delta 1013 zcmZp8#nSMKWrHuHVVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV zL6Whlahj2VvBBm#MkaE!ZDd-*M7nL0H!{Ufe#=@*md=>T`?v5<<`os*EXf|jN|6!b zn>Bb3kl}5h`(dsS-#nXtE_p!!GeTnX8=+PTT(Ox~bT*|fh>@saCO;@Qm&nYb!~?73 z`jg4D{Z5Pc=DSTrWZJ&@PK#g>nKtkJCbYTdyCRu3PX=lC`MKPZEZaf3hi7}gF=HVk E04e1yE&u=k delta 1013 zcmZp8#nSMKWrHuHVSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c zQksd0WwKdPs^R83MkaE!ZDd-*M7nL0H!{Ufe#=@*md=>T`?v5<<`os*EXf|jN|6!b zn>Bb3kl}5h`(dsS-#nXtE_p!!GeTnX8=+PTT(Ox~bT*|fh>@saCO;@Qm&nYb!~?73 z`jg4D{Z5Pc=DSTrWZJ&@PK#g>nKtkJCbYTdyCRu3PX=lC`MKPZEZaf3hi7}gF=HVk E0CIstt^fc4 diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle index 9ea4f1f5545f450d3f09aa8cf9baf7c7fee7d714..bc2f0eac1c2b99f470a5c5527fb561b9c9e3cc8b 100644 GIT binary patch delta 963639 zcmb@vcUV+M`v&};?F;O(%l2MTu^@`QL@co@SYuc0UF==3H&pZ>V;6f@)X3Pyf-M&8 z*fAz*OpMr4Ow?~?&g_!B-}lG&4!>MiJoB8LbI(0bnQ~^%kUMLqe9pd|^0X}#`c_Zs z)pPjJ#PXG^^{HB^YW2R=Dp%?o8&j=X^=ehC#wI4lBvz@`t6GJap0Sl<`&NvJjai)E zTM>9@e*NR3wYgDf0E)#?POHY;eOdQ##_ka=U zsnyGm8j^(n4*>lCE|pLAnErRJ{5em5hNpUX?fm*O5uk99!_2!o%lPYMJm`NLDEPL# zM4X-)4Y+##Xp|@ohEM2Tn(qa6a&6MnWWC;aVwuYWS$pqlS$b zkT`f``2jIT=UMSJ-trLFv_9+kKf;LRSc6e%y$;IZ?3XFX@*J*$lTE%4YGq|50-Koi!^UL|PKGnD=lqZU>6Y1k3!J9wFM)H|ute{|5z7g^Af~mJh zIYH9IL`>Jtx7uLm(!rPQZ!1+Eag6|(+w*07onCf6rlb|s*uZy36w83U+w#kxa!T>qzCPaF zOU{q(q|NxblbrMNdpCLc*xqIM%z2@}_1r1rk!P^EW_Gl^0im>5VIKT?h=k+ywY37+d0!!Pms`8It$oDuAO+TP5S zL^df`60xYlYo}L1S2sw!Ro#sewL#rmrEBv?kWqX1D~79srS%#%dgv%VubYF9?d}7o znv5>N$MnONkKxn3QCwNzSri$~SwY1}L2GQ%6@QpT6sz4c4K)Yg#6%|2! zNzez9iFMFb53aSlr$-5bSPdltIy8gCapTNL>yN`XI{0;EDrmxk+Uq-D@Tfi`__f_l zSX;sH;5+-m_^^JFT#?m?hxINU0n+y5+riDvrWc=Pim zC;z653Hz2>RTMpa6NmHV`#C^F_C3>*z8YeQo{{x9;@ya|;4+*Sq(#m0@@f7OMIqHP z%Ey=P=|f@D1|+OC$o?JNJ^UuY_Z2iii~P7mXA2H&&Y6DC$>;Q_f`gVuH!2qg&y$XR zzGSZmP_WJ6MZr3??RYmIU6M2foB?>`olLs_cMI^35EOz3J0W8J&!8p7izP85N)=oY zcsGxWFVHgGYKj`nFqq!~mCvvnX+Dv`Ejb|unTBK>f+Cy}lb>FlrliS&R-rxXK-bd5-* z>kHCaBB|O4((_~@ZW=3yTZr^@@j&um9+6Uy2+}bkmFPU2{Pr`Enx7s?q=Q7dI(Q6` zE)eNrsvxZ=QZ?~7^4qsW>JlzUt0c*|_Kmm+fb4naCf zq#@xm$Zv;;G-2^a$q%7@H^4l^Zeb;{(k#-R2fPW>Cju5GP<#j~bK%@@g8;SIUNY|Z$G#^RH z^W;A*-9&!OCO>{tcOQ{n5b5#o{X}|6r2grGw3A4!)3eEM+lVw^?nNR!B2tIG-w`R3 zNQ#-4iL{VNhqnpR9V{iwy}xULcD9Fam_+$7R+BK`f~7LgVa z$)0qZNE?aN_39lWeNUug7akDl0Fh289ua95k&-Xi1#vqOn^qB|D?}PIrhsIG5bcRTO@GU1}2@+{b z`JF`CN2D*0x6K4>okS|W z<#08(nBR-4+i{}?-*QXR(+18!ciM_l+kiyJyypDMJxcz-`cnMV5jXgTz{0CXS^1O0 ztMK}b8a{chj(@hh1@FpI^9M(Tg{BR}?fs(kQmK7}hlL-?^Y_-2;UmXH^WUzI;$?GN z@YP1z3D}Rj`zzB1;%)&w#Jk7&cqu=SAD!NXmu;`h=Z@{dU*r?TX#=lg&V0gbEgp)? zQKuBk*PZR=AC5Edi^i4X>t%RA53@neOSaeN+bz-Z2@@0`tEW1I*N?A^9>2zCj<3u; zC;#U0-PR$f33`6vxN_*XWB(1^6;p4(J0^A_^}fF6Ym`>nYwpr9P;8ah{paO7@Wi=yAV<2l1?fct(RSudP}n)IdUckh0tn%BRn-#Wf_qWu=rh z^P?x%;BO>XLm6?87syy~&LBQv zbSdNv0_4}g%Ih|2iAUW?uE8xu^)>NHTl63%uayGpmrvXrOG;n5x(eTKN__-~J4On~ zeS@v`H9uv6{$Es=&acUz9oreZ5qbdioq-4VK3fxbZqaIF(|V%R%7V_vMappi%;Z;R z`S_-3b&E`iH6u#x5-22$f0(8sz>#Tw1dMxH%;ZE?Bwu1mUH;YLNWxXSqyhRB_q>6M zr-QHgn2`#;>Qddme0^!A^`8!LW^7mL`a6(18}etDHsqHqjYXbtgb&Z?#%z6Fa>Gynb3<7KW;T`(;!lnw#}l<^YSP z2EN;}XznHP=3&cJ;oZ9r7&>57_wLb2;}ObE(l+2wMD$2GIm4@<3gO-xSLpNd815g< z#MBW3`E<~X!15qbl-gY==EZU&dI)#&_@PDnWB3;;e-^lO zH4fl~eDs=V?hfB|O&e|rdagaU8QpR7=QCBr>65moK~j?z5xjm&GnD-t(oQ43UuJo} z#F%oT)PdM+U3qh6INxxehVL<2Ngm9|G^4!BDUI^nBGfXJQ{uq@R#7L+{cGN~z9k9! z?}@6=iPt1a<8qe zNe0DoEg!$B6KXT@XaJX3k&oLH#pkRJMZecq;!y#qdAHdi8T6?%>X$Uji?o_E*G$83`jHgR2dSw2 zMs3AEm0fkGm5)qAQL!@yl{=4rvoi^$EAaC-nvwlWAvNGp3C54)Pi9#`%cvotI2KQ1 zt^U)=uJ7qh`=f!#x)%P}9s>eK70L<)IemsG_)1xQdDO3qqZX0n6ACq{O-C*Y-+0oWX2L@UDG!{1PIBR5p8GdvzKa-QXY1 zNEP5sn@k;_x<8gjv4N_A$2$18^8Dice*XAoHFqQEsu`IU{_|=LpSjf8@e6 zZY%%Q#qoTZOUWR4-#r;$>CzZ}{pCr#I&T7S?Y*bqGq1emw_h61&(50!9CN=@@>?&D zxkkLsSe%~arbHRtb~I! zxlk5NwpVaJ2qlANVI0Fc13(TFFq#t{P;nQeq-d~6$1(a}5ggQW8!5ZNW^P|`74yWx zjSwscdfU0g;=;{waIL9Ee|B9 z{qkHJ1psS&2Dqatx0o_+QIlhh35Z~aQl`hh;%HnzYh7-;;0EwkL+*q`aK=r|xZ6UJ zFg2dLCqy?K+n)P@W*FR=+s0hMy!3n%$Q;7Sz+d}1=-{nHuBC)j6%6RZ=|G{1v%)@o zxpWz;25|up6;O*3e>IXb(xiR@)+>p)anrclv^w2qbDhIjOG{YHdElUQ?u<>S#OOVN zN(|59ZZm6%!2XfVZJ>tOd63>Cc`eHWX2X>1RQdWdr5!q zf18^_9e}&by^;$6UkbPdN+G%WCHIWpO@GT(q>tVCjmx5Umwo2)1P?zjUi3x^lBS>% zCdoucsoOVEh(3g}s?lxY%D_MMB72D7_N85-OmVTmX8J^JDf5@5MQU0fWqDCkn(Q4b z`a)GYs=81)wBMguN{tDA|s(7LGGi>@=&1)>)F4mJOuT}6j!@=T)0 zL<#=VM^v8HpzI*gLz=V>7v<3me~lJZP_SvS8y+Cs;%0!w$)evGAn=d}UQH1hL<|ov zE)>Us$&E*=!L}D7ZAPp-98RAu`dI|r^LlDw@+{F)wU8XSRCI_FlC^)4QQ-f*G8dqQ@nL$JFOV85HcmZIMzg+`alxbe1A)ek{5~lhdDw)M6pS`X5Ad z7;FmuRkKt!)wIkQG{gurmug$!)1O2;1x6tAm1rhAo)ez>Rg}dtNJcek3DQhr1$a4P zhz`7t?-~MX|1Q!1_wnvw;PDTtP>95)Rxo#XPXk<~6|WHN0UGJWDU5`1d1~0!AU?)2 zGr)`?LimQ-@(-(cA)_s$V1ZM-myrZo<93QigB~8SnoSP`U=*z76@N#oVlE-RPQCfB zO5!WX2smKweJ$=xGc;@>o+1(M-fAJPLX-B^VjGQ;#%;x2=-mSy#5-v`jOZbrBo!X} z(ns8Zg7qID{z5}z+#vC3nruEq{D3B_4--G9$qK{8%_#1I(c*p-K{{UiQpk`!QGA7F zeLP8=Lx0ao5l^MW>P;8Vr4$~_3Emw#Uwn<`O;{ve!DvR}z%Fq3g{T`?K0~IfnYxmB zYYh(Z{ve6>>S0#8m=_cX+O85WU^8+OoVHs0ipESzrnny~%$#-N-h!&Yr;XyptT5#_ zi+52rueP$Y`LIoFplocr#DJAeo898Q6vnWRosDC^_&u$ZH(R`jl}&3XK0;xfhnd-c zUyh2yV8tWinlvD$92M`N$r{JSn`wRDoD{EQC0g#ZxHMzOoCN>K70;)&Og}GP!OG$4 z1#v24%b5iK{!ToEa+q*MoX*N&&sA|4vmUx!5B6p9TkN)|e_OnjnM3Mb@mi{fS`Wl) zSoJXRq4*TDEgn4vPpTzr_ZPDPBcr4{` z={1WUAnH15F&97dT~Gxjeq(pXp1+IhGmZmWgKo8k+F<=J;_*&0+yiqyi*+D#p2!M6 zJ0wSF!*$UkVeG_60Sb83E7?Zd!!drz8JcWXLW16FkO9w@QW7m4xm+kOxj-{)s36%v zM-bhsN;;~9ycZfso+yOmkrn}taCR%nIeNEyCrL#LwmwmEor2BmC22}aE9fIBN0ZL} zl4CT(F9ReKSl(eEABciENs@VVwA6XD+0nY+opuN%_TPNZ!bV zy3|`8tharwqy()?n=KLtr8;$+-?@95pf2PAz2 zJHmE{BwBiR>=B8R7QFeSqy}a7CRd`Q$Z|D;&a zBIzEQ^;fABEs_wgIVqPWQM&cQq&}KqOt>^K#f;NRZ_&IZbke0X?JMhK7Uk>P9-S$W>_|eAiCO zNrV`^*j7qov`Q!GS;m@>0t|3*H)+5-OHoTjCd?trrR9o^sDc;QN|(_}jNA~cM2k(* zI@B<$w@G_Z!)W(N)2UBpXG;rchAl^=ZyC+i3S_n!W&#EMZ94e%Dd`}y;CL_Y1}ydV zp0p%mzelhg@ZdvfFG{rR_tLpEV$S{`-9zur`bm13Hlw}&kTwbxqU)$wmO^8$g+}%( zz1!R_8)#!2Ld1i@Z4xcGY8xdd^#uv`+O5Fysxlpz)k3BQwZ=*(f%xjO(t>ptR+sH& zwr-##6}%TG3$)MK^<`)!iL__yzm|2PCRH_)*_cBi>9RNk{2rUM60}H=F}|~k2Lxqj z<)p#jYAe}Eq57Lz%fumUijD#?o<=QTd{>!>QGo=Z4gB0orWA2AprWsAMhLntNRmxv zYy=5nCGe_3qZDW!EsJGz=^_|A{4iGb6E)}S@v;TfnHNlvNvMmjPm$GN3`_F*NC66u z%XQ%R(4+{E^08wId^$_kkX+?-tRdrIGYTYq zZqyuHt~Wdawp}ZGI+@jv5KL=urm`HrXV$=!Gf7GgsNHf0NV(EY2Wz|Jfk94pk33+D zjz0PCCZS@9HRVaPHs8m|cT;1nt0!MZlT91Q_tIooL-|2EOuE%Pm^Zy;FmLUIVBRmS zAJSfK z_93~vSTO`q47DqE&_sf~p-D2JyFgv><@1KUII!)!+ztw7R@Q;!whiT=P^PE^a&qN2 z1@GH*R=$FIU&1B1hbE_8lh0?aP#~;U!T5anB5iTP3eI~e4~(5;zXqZwyHNfI4IZUJ z5lcC@P$>ez6Be!r1kVADA`m=dBNX3IgFUt=Vp-O;(8%r|>q>CFR}o?6Qt=X3Bk*?( z1AgQFzBat&?I!2#Berj{wnq6jK?Dz zHTo*dEPZAXLP38;rif?^EFPqoQ!F-H1}g@#1W^&2wIdW`D4P;O zytY>{v3N0X?^^|049879m68|D;~SErMT ziYyZ}Wco%RW^)rAG@VyG2t&Q}-g{C#RNPXOVrT{-f~!S4&D{?aXoDr`V8=XCToaTA zJ3LV&&b!f%ji1;)Iq3mi<+6jy@(SR3y+0ecmaKpl!Xlx30h`d!lU_IJwcJ z>`Wiips&~?guDLo%C<@&Ik%efcZ#sMma-E|H#59nNDSE3D9-}a>JT}&^1D(8lj|uD z$^)uu-c&iL*uX0IRf2L;h`{=NC*=!`kbE^r8BpSZA`Ple}=)e50~*NT7-tyOj|P-T;s{P&m^*rJr(ZemGbq{n22R zemkx-P`|LBR-UKc@*!7=Hr5ea7hY6urKa3=Mfrt-9lE3JMQvT~iL#ko$UFCi@+`|< z4`l0xASOYnfXK`f!vodtCJ(8`5?cW{HMFTiPE+!)wISuG&z3WV1dRK^91<|@9a~61 z6Q5im0ZrWYhU}t`rI!kMk6y~+09al%WFl2nWQ~wS>OY`C$VciE>k~rmQB^JI7=o5? z$YVV_hvd*?Y}XJpFC=&0bPHKWlN)-3tY)Y&L^WPXi*qD}Y^%uXf<4}@SAwMJ3OTH@ zA!Hc#L#<_$0Up^E(oI=>%(*aRx|FoL(0MGRkhUwX??P(Ps2hAghL=cBlgE{3}!k8w?5^XGD%SwW-+vY*Rxc1Ot4Y z8XC#c+7kl=ih-yY3(HpMAPS605A91Me@jMapvO#J7Mdh9huP~xPg3Py-w^tdD(T_2 zPz_b{=e?nq89a&F_UWOZZC4x#Z70|^`(o%mTF~jMLDR-x4@EEONvzzw8(NJf9Zy2h z!XCMM`p3}qG&$yF(Du!K3EJNCYiMPP+wWcIUiJ`!ccQ_b*`YFsEZoi#Xx@#6u#U_o zO`(`!P-zS6LR;N2CBo{{dR{CS_K2nHL?JXL<8atqm|rn0n;LR$rLfD?dXsC1oo2Ki zdbv&19^NP{5R+W{FeQyiTc0p1rQUFGSio9~Mur8fRe4-k32KbNlf#5ajezx%X;KSK zQ^Nw0dV5+hxpYP_*>+Y~Q;M)>epr8IbE0VSftE@Xu%yL2z=uuJBAc$Og97%iC1EsR zM{Wu;GH6N|y9A0*L+kdiziG2sa(B?W6A!RkH~Vl{9U8$2M}vlq&J9yj{kOOjG{v|Z zK~tQ(7c@n`$6?K>G0!{=TS;}A^i$YX=1wxDL!KGJcVP!`(}!mQ;G$AR`<~q)s+J6c z0zs1mc$8L^OlC)gvflq3WW%MO$G;+Q=MWkJZ<0_R8|E# zufM9O0)g`CYt<3T`cQLKVD1swS``SCp&eC$K+$(o1)8;UJyn4&ZB=hopi3LkS7oHy zSUyOlW{CsTN;HD5k*cGzKx0-oUKPSJTgFHLAt~U5NvcY;jwhz60t3|9GgN^A>VVm* zzyNj8d{tnq(0`#S@Vc}vubL?WNol$Wn7>rDo;F$Y*Qg?Bb$4%2&0(o7>i&dRxbWK@ zs*xqQv-p*AV~})RwTEF;h+ZjM@lg_?VB2b|8l+Y6wg-1_sKzir4R5S&2ugSFpojNv zsuWDl+*sWm9B5Fg8_3I7IoK$~fxMgO1>Mj`Dlh(QK@XSTRqYicEj4?n+EpA#h#YuP|HmXzP=(^mYP8Z?(Zgndqx~4>_Kj7!alvj7g*XxzkLv?J*dc|E|%Jppw za=Mj5Uj%d-F9mrZ(g?0MQM33Ia!)6S+ngT?DyI)02~zaY=ujA5tALTs)U#N$c$)K^*JFMzM!oz+iSx+C;( zQxH8$t$>9+)#p^;ew8Exyp*IKC=xg|8mVS^t%qSDf~~+3Cjm}s5OPwj1*$1dN=j-oOcW?@m*%qRCk^g2~ufYMW8W(0!HqeyEUa zwpHCrCL~YnR-a;7a{L!jmOUy6pog%cRr8d3nNT(G!&&uwRyRFLY}JQD2L8Js^_wrVe&I|dzKcU)$i(l zLR=*OqW(>YQZVZslRqG=VKDnWYSI4pT0r#hGpdKiA6Z-hkm5)FrGCZYj`-p1&uVlo z0d|L_)5Dkj?{yFu9O1$%GAKZJc!4B*Db)h{Dwd^>B3c-r2%pEyfe#JuPdS9C!&yFS zBOI!ShhJf}1l*zx-%UC6)rYfe+#nn_8p3Zfb1<93w^0t)t>LR#{UO{Qj=m};jZCyF zJc$+ND|dJvg>m`AS=tGrk;akX512jUOsVj;R3lMk*g3Q=8@`=khKwVm6}TE3E(3qo z3y+5TDul0OG;S-1VAilK)gTVdKxW^(Q24%H_;tqODME{{!*{a`hY5$v*U$v=&o#zM zFuO%~CNmKX10TkRe`a}yO^6h}Q-=Ykzl0S|?h^hw6y#kRW`I-shchjn{|iwL@(R5& zFg7V%3+nu+3D2%n}K#=$N$;A1hfwgSF1>P!I~4pzoTPP)8ufGNw`}& zJ-l`wAvx~P@O(O|P5c~=mR508!g^*+sYoF?v9{(k%f>T8+ucE<2J_RRb?|;e&1p3m z;DcvvH98P8Pi=(-?KDi^zhP{Mu&Yw${kv$&)6w+So*HyI2dTv9-Ws&&mLz`|pvj;a z<|k<$)8xdlnyoZhX^Lhq1^aE9ra#MT15)9fp?Pw!<-w>}sGqH24aH`Fz--O$ZUu$2 ze5K)-xtav#s6bd!a5*VY2@@7;ZZSti5C$L^DX5d>F@O`1W;x8`HLnb8O?Wd{eY@rw zD>=4Dy`ski!om@z!eXr@s8gZyFCHQp=GIq+(n(bm?MqTH% z=1?)%3xEIt$}{GckOA;sLmD3q{PwK|or*&uEc=gO+)e+ei4?->z#%Q9zfYBEkFdNV zz)^)NB?a+E??}OgQc)IAWUC%(E!u5{-x`3e8ZB*9HfpuUSzRhv5`3Z4_Mz0fS+s#B zrHoB$qIcW4wdf-`Qg??}i(YyQ4UJD*jkY7@N@+*2Oh5M4)h2+J<+TQIe6A$~xaw&W z!HjaKcJsf|s=?pSEv-OdM{QXUS6*9As5|+UG^l&ZYu~ce{ab~bId zy3f)6$nrfCdP_jxfZpb{X&m+@;!5NH2xaT5xKamgNN?dSB~De`UrB zS1;G%i3Wm#M^|ZYO1W!b@p|p|41ETM1dF!jn_b!`QF-F#Eq3+E5Ht9_tC*XPf)OU39q`K4Bj2*IhZwF;bX-WzQ# zd_DJ03$*CkT^ex!=QAiH-f-wD4~_T+=kKnG_|?X(6}Ip%EgO-;SQ|d4w-M%)i$KSB zVQNgo0;!NJT_pmwZRGCzsuBKB?zynKlUP53VRZ*0-8~5jdyY>y^~(SApRVaG z(O-hGkUup9yT(VC+?^Q_ zVa&E*a5xQzqBf8@2u!Rw`@crMGL`bpxcX~uMe60+G-}&Vc%VbC{n;8L3==uYU z$l=g+)1io~_&WMX#65gXI2LgYU%e+Hma5S8;H8LAjC1Bn#P9h2(Y%Q7@O94hh+FvD z?PkPDe0ATBK+drRw$6{3fO%ei7BNbUuJv9<98jR^A8#X0;Cu()N95w`l;0xyU_L+m zO?iBdn1b{BMY?Ap=o+fhjluFXjV@M$uGwxK;XBr=lSz?0rKIjU#%UF$BjxML=+0sJ zepy{#CA!wDqMMAL&#A8K6pF64uZ8CyG}N`hd}88tuP|R{YvK8VHoEy(9^77soDXf1-!(p#fc2j}iFkC+Ijil8sY!b#T7+({+b&KJ851Pq_TQ z=IBP_--oB^Nc{?VT}RBX>1y3CIA7JZx}o9dx_P(2qkonz1V1-&zwR{o{ebQi=JD~6 zP`^`0bWN~b!Y=5}E*5~^tx*53K@jvRCV*Q+XrMrRiEqkq7jpZtT=tkn-xBsQ9gZaOd z=!qX>%k+&f?q`Lb$97+0(i6RFZ2A&7Usb=J=&x}J{abABn&tE#aJ~w)^vSqhd*k%e zaQ!PK=<8s+kLslNVLr>c>wm=coH9h8fN`#l(U;Vr>+f^*xtPbX1$qh2e+uYdV17F? z^}}m(dH-K)WNDA|ENf&>Mw#6p`cS?cc6+QJpycx4`4{@_A{G8KIyCG{wdAjLv%wWV z>r1hW7~U1?%W>Q>;CZWm%FyN^f+a!L;d}*{S;d%3L!7K0eOs_Ws2rO%O_}Qgv4CGKeJv{Y?K3oFw_LSCw@Bh|sXRHmvbHb?4`rlX@ zU`*)18pBGFK%=SH!1B77R?7*KC5B&EX^?s-83RnHXqZGQ z837(uG7Kw*wnG7eRe)cs8f5TgWy26wy<^7|upq*66%XcWH3Qo4-q{cf?p8OvDpu_R zP-w?S7eilov8Exhcvd)E*WhHCqXoPw6lCS)X~65gsCL`GHhd{o?E+9z*rJhv>4bhn zt34>+$)JP-;|&8uf_uGaMcqpaMz=Mv%%Fp?V8S;BHJskg(2vn2C_4o6H_J<>01+KT z{^ThR2OaiEDMW5`nbCvVgZM;4IE?LOurqp4dob*7o&{p(VXg{B!0smn$Y=V(asv!i zSY8Gr#de49k_>hU@fomvgh2=LzUY-8ZhY+s*m|^~C2fZHj%9VB?%-KfUIq(n zKF=UxJDMGf$xA}yj*ae!)vJX z@M)j4aQ9k6ecHQ;*Bb`0acu*>QySIT8w_0;U55a;vNsuG7{VPUZa1J6AmVnuJ%+oS zkW}q8pwoKEUB!OG5t_XDtpT03P43RlHms$|ss|0|I}dVKdDyU%CeI$hc{TIOM8FBh z3~T7!s1pYCZj$7En`1cX6Ow0sFrdxJj)!M$#+EFLI7Hb~PTf(#O@1TOW|1HQX!&(`GZj^1Ek3M{Q30$|jl*C>MdNU#&;T@zH4424X#jF-8XM5g!}^tR2%AlX;vg!n6=Mrf zL|6?Ap?jW7OB!#(s)6 zG8_Tcf}vdl!BV!paRPO2*wM&x=%4`ur;Zu6Shh; z_9>1bBEZKr##rE->z0ArLyc;Qnc^ONO2@%X&mT2JC83(~HYm6-0DIj_{j%BwKBHJlrr%?rwliy)<0s;V#XD7gA zdyIEk`l_i{kbD1iDCpiDbAs+|I?d$X1hLE+<0O_!;fDcwSPkl&HL~o?M*#nHZ@A{X zaX9rV_jgR5jS#29A(xmu8={BNfxN`27hPjl3*$T%HmHXoOAju% z?VT}A!u>2P27j&>T?ric!^pUNgH}h*=ZAyQHJg!7oU|Zmrzs40KN@>6^bd8{nYzG; zzl?h1I9=LmVbxE@F=*h0lgBxehh>0`A;DmUiKW9J&vxjfh=NCzrUht3g@}MoDig~$ z9t3Myy21&Us!f|1=9&N@^aGAwCORYfTW=aoDNQq*SURo%Ae!m5@hjkUi>WQ6hWmlE zD3b#G`I|@w@48KB_x2=e1JqHCqfMKO#fzwM#26FP7bHczG`CAD;QsNZgOt~#Nv18u=XH0o=?SZOaVtc< zBq_y&H@@PAaprVWcJX<=nPH0mKd|F(vrRu!eZ8G$+FN{H9aBy3ileVGFnp0|0rmb} zX(selDAC%irKU~AXZIw-#B_!k^0HaNl*qK&)Px#xcqU6gLWI?vvHwVd(OXUX#2_>a z^%}+s+pElVC5VN29SaMHfO<1(6hOx& za~?7e_;ri<5Ss=79JbAz&(c^U;sA#4G=GN)+}~|J$V#AdmN^3Z*3kWCrawA_h>V9{ zA2z>oA&}U6=BmgVaL^<36nuU8y}3EQR()=sgReP1oA2Z6szUP?d~NW-ybfQV{b^nj zimv0O7PO1{AiS-#OvBfXY72RObA)9HmaCgAU2wh(ujLyo|5`%GH@u9H?|6A3U#m)% z9F~Cqs?2Z@b15GkeAxhfderQf7U=ZLT9&C&q-1i!&)|BN=ZxkOfFS}lW9s@Q7B!A- zUo%Txtp7eOEsOE}-&3r1S>IDpB~YRgAhbRcbk*$SVIu{>m`B1FJpnE2<3mddzdm8MwG&i#Y%W3pvB zzCUHUg{kjFxMRTD%lQU)Vy>lZfN=H#OMRSfkY<_0;`#wlfIwx!ms4{V3zk{8R3P3D zSQf|;@egY({a892WU{|+SfPHYS`TPzzHnBraAFl?(Om0@zg@10`fTdoO(uXkEH zaoh#ibhpJK=hh2vOLrWw>|`XAp|+|(&Xs&AJeh4dDCX9~35PB9MCf|#sHHSZgW4+J z(hUqeZBc`m>lQ6sdcv|1!GRj5EDT*Uz-SPmEs`KgAJ19(VQM`uS~9Tw=M~F}VpC*! z&4pN{2s6~FH!Oc6t$`_bEbISw6F>x{X4zYTo*M|F3oQyzqp?>996h)Y=z3xqgI$6& zZ`)s3&_HB8@cqmbRRMf8Z2XI*8Oy6gi~tcsXBaY6i@z-~$Y2MiSleQ|-H=#!vNY-e zRxVIoWu5rHs~daMK;ZUS!$6YBO5ah=R9kx@Qs6~|bzgBIov^#!I*`#N1!fR2`-m+! z$ZBQz9`0XNbizWLwF8doCNArEmho8tP6d*F@>&5p1Y$XKl(6;>4LI703f9HNsWAFO zu1{SmSWwC8RUn??+SdI_bcGGA_c?Us8(U4NEV!eMbpmp3SfaD_6~3?UYE6`)>zJO_ zyE1f57+}4P@0T28-G{ID23ybK>$;)VGZ?S$IO`|udc7xGqZ!&?!E*{@uGwJYY1WPm zy=oCc@MCBV+OAqs3m)4VwTJmLtdp?SN$?JsZOy_)v(C5HQgGkmM&1EO@z!ry$|c=S z%2I1N5%(?J4XkL>PURbAqoSP~c zm~^VK0@~JEnYQ!%8wMO%XJu*85z20%MX#~J>XvY;K<6#i_iVMwhE=v%+p{!qh{ZB6 zS?RaIq`lTy6=Kn9OeX`lcg%{;9j>C0jC3ZO&iOMmY6Sz_v)cH&nlH74VAiO&0{s+ zdL4SyLu)Q=q z|IwO@8}}AgWs- z=5td0f5B&_#&(3IRSWU~MAxx88+skI3P_B$?~1RaQzlzyrdcO-Cl$zBWM2TMx@>wx z3TAk07ddoI^x2rUWs|zbRkz8Orj+1eL^%PqWO)JgZH%od%Yr}vRRfOKjtlhJBZD_8u3NE;Bi^ca(es4o3EUbccpWBY%>zAKx(P&5nqu&tw#z`yMBjS&@-G{D@3{*8`AG=BlV1cHb2$4!W~W3AlN5$4>^m{?(GdGG zW~8;Pt|>r#-$*Nr)7sN8+%=v3Vknm@yfb_1vTtUv4>@|FpuXJ<|8(26EFH4;ihUH= zuuwjY!rKH}l54nxqQARJ-;gV9QhAmXFKZaaCL{Suq6P!<&@*uSu{ zz*<44hD^4f#|C&#Ix;Zi=~i#bL_1cTj50@{2uzo63r3T zAu4dh5)_8$T07ldnPp{xI9}RHJNgrTx!wgL&sI=@b99BJ*VqqW*n+hJ{El_@D3-A{ zfiKu@j{qrEJQjFjr~L+I&F`_Jp?xmwzt@h2_PH=-zfjCy-wN{SY`dDdxFOxI>EWi6 z_H0~Imos*B%vmmsJ}V&qcFz8iAwVIjh(U%-PkOzYH|?Dm#{%ogG01S}9s4r`2irZe zFJYhq1igNb%obPZvcW4q*f-)x#q7Yvr*<7kD6m`Mw=e8H&~0e=#m+RiFDfPBqe~6N zzuGTho#qwV(J((3X1%qSVHxHV*n~dU{yBN9{+At1cXMIOPl6{{zSw6ocmTYnbW~wc zD0*%M+5DFu?m!cTT=g6NOy(r_C{mC1ersEMbl?*5}yBGDip?sL`s4 z1Dy$&3%{xEKr@3}c%iNX%?xtk$tDgoGsuPWn>o-dI6S;T+~bDS=4ccO1DTsg#nW(K)%`!ENI<`KgM z8r4TSGMV8~n+AT8e+0<+Q_BUm~rPoWcyKbJe2g5~+hHea1^ zbOYO0J9J>r2?s+gfdC%;qOV!H<5w}*u}Z52O_w?DGt?LZLPW3+i}K0CaqArU3Z!?^ z442#P5K}^RcR5)0vXL4h%pssa;gUhP+tHa>@kKBNS&mNvFZk=Xj^89iXYj)z$3UhR z8Q7{paDc$I*t9I`;Y9|;YbXDoQ4yIG83ZMQjT495*SO+#8OQ#bVR@mV;@|Qp9F#zT+xo^UZw+%ie(^ ztPc+yOw$&E5cke8TR11s`P8vP@OE(TIlJ>9z(p|SCkNAMjsZ%GV2@uMcd3G!zIGgA zH5Xy?rqJ=8wPk<^p(Bl{?Y|Y1NUJ{_OiLa`YWeM>1O2&8(u`mIcQ#)58*b1}=N$^enRV9#}j2282qnUd{r&SLH@k*WKF zq}z|sB2E*JoyReRP5$)Wf>pLZ^K>>)Sa4FeeJ8780 zmyMiPIiUs@n>bypqXdAwVKB^U<($Io5~%;G2hygO7z^ulbGB#hwgWKHWNI;>m$MI5 z-Q&K_V{}}wYmoB+y}M$Va~N|tk+KSUM)iTAW1U-ACK|;01g1xYve9tz9A^^c_&L>? zs1OX3wAA@rE+qS}cB1d$a7zL!u63fXYDx0XOs9`#D7V!)EKtjx&RsM)Xs^>L74ojg zcIMN%Pe1Hz%Ivbp;QipD9A`SS_7K7#5O47gf$h&bOEbd|ul{@_Y9##jsxywYIU|F3 zhz9XCjyo>QF}g>atAR@QoUA*yKwQ2v92ESVuYi{O&Y?2yIGppuX_BIA<}>GIDR=ZA z<-GcZy_}S{`7U_xm6K)n0z@8YWNc9;fgWGG(7D+7PZrzWIyZ|m!YXLN^$$*_jVXmr zf>#D6|L(lX=na!V;byIh?C%1SFnbhm-_ojKr!UTJSleU_tV@P4N z7_!alnZh-dS$~8r*_x%P>C(cuP?w@S+vwmQlmNnF+Mq`*;1focXjSE=gea;{T^Gx~ zU7{%DA!Xp}dM?&k$s~vnE(-?xsl-UPR_} zg6ll9;}Es>YU9cg$W&_YVjNY{dY%5p#q{0?R|jgkxtIAtU5^+Cs;I{2 zT{@7}sH9uiL3MOt0RKr^0DtEaSDoVFXMVIVhYeN+pa-lDKv!DpYQ_%T&gI1OB^yAC zIwzoiYXHB=cGSKS)i`&$ppbgDD^TX%J%M}U_qwJq?fu0)15#W#Km$If@CLfmQI}dU z%=qI0{JJ@=Y@u|;DOYF4p(Sch&bv+s&;=J=4On47R=7hAALqID30UiIxa`ahnRdrz z#XHYJfLlC51KgMGmEetgu5wb)KemDa8VXzu80VCe0LF%B^VvHYF`@sZt6j0`3_|%q?{JBm&h>?_FcbKn>3P!^Jw6oJ5oxC`bjneRN$Cv|RI(>#E=awlA)h z%&v}X=M^@iO_jUR-?b)Mo)qF%GiwbRfF zN$U4T?n8p!Pd0JWAe__8?Gt`I+S)x#c(&Fz?p;FaV`sNhN}i2Mbmt2ii0I=U&1kcg zu+0$nU@5Y#k8v|ko)SLho5(5+96i>(iKX8izT7<+EQ<2FK*DeCZeY$B zx4ytzN@l$A3;MNrPMTWi-VXcCLQ{0zX+zr)h7A&0Yo~{BV zZrTlS&QkXm^k>;Hon_113~Msz7*TgFqlQyK)iZ7_SbM-@1}EN2yTb9S+zkcgz`V8Y z`w~J79^L3hf7}yugfq6g&!WEHdtxW{gmnE-+LqrlBFVT0&lD{U_9&sgFUkm46Vcb}#(eSUUtXVv6`U)<*@%z;Ap z4px|QZ{18^@a&a%R)V-OehbX{-Tg+0ZLsferU)TeNGU2<x{%j zE{Z)26MKlDma_~_f)t=n;WPZnikiC||_@Cbbh zxk2YSTs$^ajGo<;%?GoG#rcbBHqPp)z|!v`gn9p1JiF%c3}$)Tj9`*Mo+uxG9pn<$ z;d++IQ1lU=%c&|gs@1BmGE9(3vzN=Cr9Jv`e5 zXMwwVdC&=RB*W7IL4*^7f(R3a1QD7KqX_742@Ut`pa|cM^fad&N{#mnVVRi`C1;5| z5uk8F_fYWaaI7X{Z&N*}aYC*EDYBmJ;V;Ra?>VA1Fm8$my`R|cwnEJ`kBM1-f#MWo z8SM8N9&|JvQN!@rp5e@uNSN;l&Me`f1)h@^qWCKK9C$8K z!e>@^&&HB3osuL^ob)iAvMH1k{Afyf!830akPVTV&-s2}yYIAs$z@9EVEeM(k`nGF zoD$>RA_4msIvN8sn_t7Adx9SKCEp9tU(vO*v}IG1I~Mf@Zn^D4}hl`*W{VjLwF|g)g?_o|z%8q(tSw=Sm_B84dKb7_vgk{>*xnA_g zVMqa|&IJotaKWo(9SVTB>S&^cKamqwQ`_7MmN4!P>I{U!THN)bzhXhkHQW!DTks%w zck3hX8K~7fpOga>pm{P5M}x$oHCMv`Y+GfR+b}PAqw$ z^aTv53iAa#C12(1$zsS>6?_Xo!D^dcSSxI2@wH-^j0Iq#;=4nN7L(?siOiS>g=@v=O>EJ`t zEs|{A$%m#}Bx&j5L(?siY|+byrduR=xt|YBw@7mSU?18WN|J|%`_Oa?B_rU*(Y|J^ zCI`cQy(WWby0w?O($p!wKom7e2{O}6^JTCW0h8wV_EGfM`MzXU9FXpdK zQ@}Czd_xf?{PB_RGIArhzd*Q7{oZ#B%XNP6EtjI}-JgW}TYeU`a>gVEw>!B`4K8ONT47q^$8 z6PY3+fL0oAo#dCW7^VnePO|?rwodbD{!{Hf%K8QVeV9tSG$vOo0(!OBUxl$-CB72&k0s)k%l+dRoARk(ZCA8% zi8d;afQ9S*{aI#5q*5{8_#;4a70+b2e7AozD-6szfA{e>=SmnemG#+BV8|73F2XfrX=1q;VTGA#%omk*+_96d;%?a^6~ek=`QB~*AJ)D*tf}OC{{f^(M?|oJjSvD!AfX0G=q)4xENd5R zYwu!L?5k_RI_j#}dv9YGU3A4Akv+YUcjeUIR;HiOMZAHElrU$OV^JR~7r%z`DqTYbyQZtkQ z5+5$%A7@@SJ1|w$C$*Uucpo__wdlht1M>sDk3_d6AC?7TUX2nY-LWE2ZH348q+eDA z&f#WTu{LlW-fTT-t@VMZS&_w0H*E}rBr(k82W$z%yc}wQdc8FeH9@if{JkR())^U> zZMy=I3o9~OwmUG|D*GJV7dV*n$SDh4VU2zE9t=FdBg&*B*0j)`^y|^U!yM}3$-vdt zuu4B2h@C~rNZ6l&eZ^VS(er^rd2?!b1>Kwkz#;U?)j(9PO~H9Ygk`@4ZsS(#d@B%h zM!o{;`R%~JFk6fj2%GN(!ijz6%dP)F8(x&`=7YeGST+>28^qnF(^D(e^6XjQE#w%6 z8L5@{CaC9|K-f7FbVDQG1;WmeP~7n$@CT9{WwYp>rmI5uS zFFDD({L)}a3O2YA!08}P*j9x~Al(h)TdJ1iSYy$Jq8JwAg$K zFg-+rk$ENw9H`y~sntswNvb%CM7Oq(yyBYK-bS*M52c5ApTyk!JO)^N^|yf%f8+#K7@);hOkyc~dSEDW{`x(XD|MJ4 zfn8jthoO@su#3xz+0!hFik>B@YMsdg2G{3F`WO8oapz8TV1cFOjxUwK4lH}Faj~S4 zbq|9W#S?ShS}B43Ne1h+Mv`D1tnTM}j{n;GoVdw?y?={jc4eEfvurwMk_CGuixI;H zHtk#FcfC5@xKDyQ>ymdz(u*`NlVAq$h0a0q#M#-!gAy13mBjRi1C~c5_zFPJ(OTx++()${S$gztl>92ku2jDX!l%#nd&eOfBq#wO}SX*v9BbDIESWhB$%B` z26O+d1U1!Sm7ji)z)=I%j*fqpV77!9Oye(-6KGq!`X+%Bzl_5H+aOHF8^$54QqXxc zhvyDK<2Z*gm4gt|a0(o{R}!VT;~s>Waxf_hJcG)4U`whRgm}Z4*7XTW#PlF_y9JFU zccOy$mlJYo25sf?c-9HRv{Ex*FkMnN2(>d-fiSi~&{1xmmO(+77mh2iUPyypVjQIc zVSh-_PR?e$!b&zZ!h%qnWEE_-se)kHhP9s|ktoj-uqc?mJ*F=diNpoDl5(+sODEyzE$*ml@OtGY(}ii}Hfl#}bCpYYl^LqG8&!3i2b5<}~o3 zq)kvW*ZaS3Sfw4f1UjHA0| z!b?g2U8jVJAUuH<&klmDr-Jv>E^~um_Q#6ZzXW|jJ98bIAT~$pP6sUwI*0Kb7$kMB zm;{N(SetJQ(qU#o-(k7ZPTPWD7Q}?SO@m+-#ER?o2ElZZ73Y=(HNl83KF6}9_u^Pk z31&<0I~cyDmw7!14!JURCvFA7Ay-z6dl&?VTv<{5R}dU>WyLQqf?$fpiickX!68>x z{O}vLQ_RXkv(TKqUZz z_Xi_MohedJ`b8;iTZtU%Rm9#Tl#+81QgmLRMfKoF{((p->gY;gnsgMI5+g-TDTTW0 zby9ws)LtX~WJePJDe@woTImx+WD>|w033~QK?;?#?8V3L$(CY14h{(0#C(gba-=Sp z-k}1au}S*YR%Fv6Uy7O}S=fAPC{4z6g1}}Wc`+A`bDQ6*ETKDEN)z13n$Q70q(*D0 zH(8V;twAHZNZ}<)X1V&^QEqO2lsjG3LyDSMTM)=UJ)~Y_Q>EbQr0k7-OWLEi6w)UN zsmXA&#^I!p=ykQFD6koxj!TSiImR1&W)882;si7o%b zZhefjHF16@^^TkL2%kR-K#z%UZj#&6rH*vibZHqkgK?JhFs=b{5B@e=iksns*Udie zUYTy5D?P%=jGr&Xyru}^aWc^hrMRiS1>vtn(myzx`%9%KtWwR~Zixj$Cz;=z?A+^79-gk=^@M%h(QTijM%q8wr2;Q#Gq{U3*JKR zcB<)3on3<;I*OhCduPZ?#5#Rd)!;bZK@Rr~wnf;H!V@$eI9Dzu|IVC4UXH8pDQ5pE zn^lxebAp3k*)YO1KO}e#r}pbzneSS#D15uTH&>fY!(f=|GAYmf5DZgYR@~bnxEMJS z0=kx|Zd$wG&6ugK$dtqIsqXQ9!7$Zj?6L+0!&H|Q_lyjNsV*ySoe&IDT~-|SbFg`& zmpVBZrn>Cvz?s1?)n&yGbAn;2%ZfV|1jGBBtoUF_Fidq>@$8CVR6=HEaZCCf@oa0? z^}(ndI->VL4jl?PbT(kj!4N9a=k(qiyanUNV2;rw>`-uR;!+@Yre6;S2Z{lM#+(lR zfSd|(#KE29Zq&Uz7?Lb9#Xq_j+>cwP+qK{ayx8_u@D6Up;0M93m@|%MA!-r($H9#d zamfNegP#QN;X&fP%>mA`188kBFgthI6|Tr9Ub10adjqS< zqPX@B)Ry732al*p=GB$uBL$`a@Q5pJPM~abH5|?;dNy9x*2QKH4bPO>Bf2IrjZ>Wu zvIb;JmJIW%RbfBbBy!gv3!vG0*>wyBGXiR9rJrm)*)&temsUoZWDP12&%%CQgf^6Y z#3+>m`qOodW%;NO(NDHre1G(PD_P4bWZAfWKD1LWnTMT7?5Dmm!~z)wE!9J{h<>s? zwC4cXBg|M09=eA>WiRs0AZtxx2ZVZ)UPc*alm$@SQf!0%$|%`=dr=`Lf0CWG%2G2Y z%5Zadi>(_?mcgup`HikbcE&0;PSa($Nj$Kjf-7@Bxk_Xgts=5wt_(MSx2Wand<&be zrLyZ*vAMWdhWf0$;5T&BGT9GYEq<$Hcda7wX0_}&GE6ZC_NTYj$so-*^Ok!XWf!bs zvvIQwx2$0?Qr~SdNFT=7BvRQitJv7>lG*V1>9bFUT0XMy`fb0=fn&WoBs*ypFM3$E z7&Fedc`Z99o*nOYQnuVd)Kb0kvhxTwfOVCAz92(ore&3VugKiE7e`)4HEssuc|(R; zbcf32D{I}BiOxdhcV+oJ0+&3HUBham*CSaKOuxiLnfOHJ%{8*&IiACm7qVo;K$rrD zr~L6>-pQ_FRdDlz>@6muFcnUVTLc69H!XsSs3jWfU&hg}mnxtAbIb^JmCF{kGNW&eWNLnD;LhWOFRaUna=2xY5e{xmr=JG7KgtDtwQ=C zgTw{`INsu01g8r^;4K27tE0dE7_t?y`$?F7?{p1ur|o)#oV8>0NVD!C-m{m;YS6xY zLhgu-k+v%exrlzi!oeZlR5~nVnyo1R*%2Z0(DE~|=A%QZaAw2Cg+MAcrUlbamSWwB zA?x|oJ3oiKLmVO!b#s=4R3VDRAx`w{+>q%af3kB9SJ0CAA-^CZZ3Q$1Xdd!u{xS>u z1}iMZcPlM|Y+D_Yfmt+TMn1PQ#ET3&9O6WB9Amtx!=8|@_M$baAGE?71y~BOur|s} z(EL~kthKRXgA*a!c=7SckU^LgU&efs>os>V1z4vWA=!>1>0`f#XvET7cotG8$exY+QJqJ8uIw>pi z*VDfPLXTj))r`Pbcc+Ah!s;&LW^E+pU!s;%& zL7Nu}tGleYzi}w6?y}<0=Ap2<%Zh8;gu?1BE6!{e+7F{yX4olZ{(We(E}0 zz4#?(k3ON@&@cf*Lo1Wqj)7h1nL(jHRwK)M1!j_=C83xj((Lt2=)qH=yD)7>5Y<9#I!>}t;tOB{&xcmF6SXw` zQs{gf)4Q+HYbO<-v-P@sv?3xr18)B>k^`_Fpzgv99 z^dPhpp$!JJ;bG_!Oos!Nd(*$4g-#Y#KucbPzQOnh;B+UY??T;a*qcz)%RB-C1q4r9 zu`wO~5Ly=@(X46m!L;OSXgOlgwtbp>5Py1rojeM&w157nyd%ktl)KYUF7gl_7c`!7 zSO;fuA<$bs7hz)yEYbS{cJHck)Ecw}h60Qfp&?>H5Qqeby=hP*Ih;h?hd+r}0a`sk zF77#z^@rr!$by=3M;c#KK7woSbZt3elc$0-1^jb7_I#`-UuKmh0Lz~iG?1gBkXdB; zzd{kwg@CxXW%4mx+`e+F#Eqnn6mrxSj+tE~9S|;`$k|khkgr54j6#dB!}PFbBECfa zhrBIq7ca+6Sa_@SAjN6&%4Ebd_)av$DFx|3FVrDlp2#zZUy6JZQaYi%S0HyK`%~qY z#Td3KO`e2^Va#D^v0ko0H&Liui*`2vg2-gAQBJHefCuqxE_a~c8q0qc-GTnnRDK+T z2qTF?(raBPwDYcVNAmHVtv9)`C$tBhQ6OJoC$crRw*<`CA5j7GyQjF(@tx%NFuJtB zP#_y*6@nnHzZfuEbeAKiA53#TG@ysP6Axo^ddt@!{MIa(4*{%d;xSXlzVhuH^-Pg` zh1IAuY@mDvW`Bv1yE_he)tMo8A_-%8r$AxER>B$Pjl|3S9kf3^F$xzYzo$uorg=j2 zX}o+jR(=0NuJ;7_B}@qUj+`@nKUIFkI+&7K^2&7hY}~M+f(?WDVU9eR2c`{kHmH5_vy#e7k$>LQPs{jeHUhmiyMpk7B@} z|LaF!Bsqa@-X>qdqhqI?mgv}U4=Oq`SikBpk}TaTM`gjYAdrRSaxVfiOiyxPo^LQY zcR*f_aQ*M_Mr*7aOimOBRWO9q@M!Vj2xcTjfmI4$<_SE79WG9%lTM=tiT&ug^K!^N z$0iS@moZWZqltb5U;@N2?|l^)?*0d6>2=Gn zV!JNxu32gDUEyfKOlSAs_oG5X(Q`BhHQvs!SUeDQ;jL6T~!; zpGy?8XzO8$G9g&86#VKD3NK`4Xxor7%f9PFL&~+;!(1#YTZ`rv-{<0{g~`6@RcNO%j)gB3v#nARp!(CfuN0+LDM@fLfBM^NMSE^M(>n!b z+wVJR{pslU3i5xzYwt%3uke5VFL-tMVkIvM`dZFa6=kc$?D71Ms(RZg5B?vh>XN;3 zDCbq^q?~2FYt5;w-1UFJ>$QurFXuJD-8x>h#6y{k=*}$-x|w*WW2%o5jtB`|4)Ls^ zEJ3P@LXXo9F%qz_DWsMBm9P)Vh)k)YoQWhNbbW>NVqK-yIyQ$IDEn|WX+g>vRyU z3Cit=`M1RfBgtvMYp%pDUS5-SPg53Kht)=}bfLoxN~1MHi%6Q=NcqqjBSjKGaHl7l zDB;ZS-rAMmz)mwIe1wG!GD#cdQL*vU9_^LS#ZeXQ)=Aj|b5h$J%Oh!&rz;zxd~1!1c+ujK%1_p46F#mpQdx(79<7{=IVfS4&x<5ZRR)l( zaFc>YO;)b9iV_8^SZjQh3FS$j%vRpCiin^aPXaKmo+JLS;c9#o6?O+ zwkuINbIcl#WGFvVC7hgNwD#^*2D#YWBh!|6`ICaPzMf=?!Tk#P?S!%xdAY_O{P>WU zie#KnzD30Tq|_?@GhPg;K^~dh-ALqFaGuAfm0qOue$_TqbxP^tVDmFe{$AEvT8T_N zuf&|Ugoo51V}`cwL=N1kT8+HCpv*^5y?0-!A_X^<4s`8BfVn7J|3?I@1nmfuo#vu8v|z5B{3|6M)`Sb>-cKGf?$mp@X@74<+&Pm~k> z`{N0N8Sqr;jnrWe8vK`XDd%wOrSfMi2gkpa8E6jA-w5M8!8J(V_sS7i4%0p;n_U)S#vS8yPOrD_S20pSNKR zR=Y8587KXEZ`fj2@#^vuVQrnoqTi)3;vg2wehXX2s|~vu_7A^d;-fGpUTx{?u=bUC zwJ%{Sd9`x8@GvLws+Vi{lqy)g6x6y_iv|RQXK_0YQ->eIYJ)Ja12N$Xd_-p5jo}4i zyr8!mh6f;>O+CA5xCiakJUp~Ivl#i%C)}Hq9S*BOZ}$yHjcEki38(~69cNFFI8eu; z@Mkqe)|IA*pWs@)Jtw@6qj_x=qPTj|8Y7Tb>Qh> zq$-arI2rCpj$aAqYr6>-!h3LDdoPENMwr5aN&z(mqgMfePQDfX#g4r0EcKzK_rgzL znjYXZqR$_O?{O7z_Pq{=E{f^A_q*^hIN2HzKy;#`-iQB;@HHZE5)tNo3~wQ7nmT+5 zA1fBO&sWXl7n3TfM)KlCN7YJuQHH5*s$-b$#PVDM&-Eo9=fhi*)jQQyNw+F0#O|LU zM;Gept@8C26&fQ^VOAdirVs5Bq`HCBWgq%yunN|Rm{GfisxZrEjKe~?3bnq-Dt}U{ zV7GyB7^zZW7OWY}4z&t1&!SNIFn|2)7}aE)U-zL$V^v3yuGNPswW>CpLun%33>T7A zmoURWroXH-RR_-DKnC6&UuCLLb2!G~N4-T4R}FYQIGa?zqnpIkJXLqD2e(FeJ?I*% zAQuhuYS*T!K3KP^+e`(oS2LKeEmW9^0aI*fE7fhZM%K4cHRc?=+o~`p?iq*Vb}H0T zAgg?;gM~wQClz9jD8$@8v~6eASwst=h22!}(IjRDWly{cTK7_+maG}~gMCz;IfuCZ z_&C|CNOczN3ik%8ayf_OA^2d~XQ=8F!VIDf?~YLQE$|IuJaE1#_(6Ae*n!b3Ke( zh;N(QOI63wdZ@G1(k@0W$A{^SE362SF{@SP0s6DGc%PK4SKZ_4^xLGu9R6T#CEKjB z7YE=ZaT~rH?X%sAuKYb!LE07;wdd`|d*_8cs*Y&SxUpX~jC1IGP=z@r!qmCskg5}! z!}=qt1)M{R;}|{Ax+heh(2w7LTD6!zzSlX`9Bi;$Tdo?2=5X|)svqZ|zk=^Him$3r ziCviu|GBPO%{lD2salBD!~0uSbl{!tsupn$)$e111gy2IP=^zk3JyKAjH5~(t1ufP z45s{v>JGZ!%zv&r##JDDsalLRLyuQh45?qdR+V!Ou)-aue}Vy!7#+ud zQLV&sIR90JdQFoVvayXC&V#TBt*@lUycEe`=GdzrAwxq4>Qh+_r@$D8Q!Z-sSkB%} z4TrWE%ybVmy5C&psjkoa&1>Fj%*Gnih>wpN^_>k?d5Evt+(H`GP-B)?8O*$z>c+?w z%szB_J@r(sPF0{9-EVf4sB0oUqYn)ZR*&Z#4u;@8<8!F`cMkKHQr!>R(Cow2@Ub-J zV4jg`#Im_KeaeVZ&){sH#;7sde9X9R8nrok*e0mat9H>^HR@ePkwcQY5f`OTih3ee zv8Ac%2k71;C__DvbJ&!nMy#OH-r4FUXcygKP&eg|56D%cS1(O@>O*K1Y-?ySL-i(V z^!iZd59(!HlrPQHm`qHfQ?yWD;V^GnsnIKD_1dWMt5s_Y)v%S$nre7^^)#$wcIcqa zMxCk|uI|HLHXu(&>+X^b-PDfM&`sTmQ##s1jae<|JzPDQI`mTSug+dT^W?wEzwR zwIq_|I(Mp^pe~0um|=I5dQMT}&Y%rfHzua;YBx$|sK;OgoVHq>OXts3$6&&OP?i9uJk>NcTrcK1A_E?L*EsOY@>oYUwEI>6cgP zlQv?p=(RfC3!yS-X^ly4AGHU$S}(CZ-EJRI1LLCibP;)^jaP&REpU$*hEcbGKmmb| z4rzYXh?`tCU9|{X%%NCDYEfLGhJ2|V@dBY7Gk_}{91sED31H3{CW-jST~;HD_*Lvj zsJlF(E}~@;_J|El5}oOu@QAA1W$#8tpj_4r=1C{SLImC;*;k^L((O_O$A zBQcAM%()9DC)K9Cxa{1W-6J~EU!O;obFD3VZDBd|t%arXeI$GXff4rj7-^4r#SV1Rg#Ka| z)f7Wjs7#=;8wp(>=|kt+N9AKOGBTLKX~|l;wSH7Eng}3x5@YXVA5!yZB%uM4sK2<4 z{nb%s8^4Q)GTV4{R8%?F-o%)wn5zHX6A5d=#XXa?WTYYLtrY-__!K(J6eV#I4SM>A zs9T)f?q*S|Fte)jJ)?RMe^WHX&TdieWWki=Mzp9~lnikwk^a(a9+(M}@qD)o98?1M{6vi|(Tn zdM&qWz4Yj&NKt+yVSS_QN$#K&M+yz14#rVIiV7Gv`e);4B{z+f_Qy}r zy^vZMAlPy&9XUOEBVu+>$3iKvCq{B$&3u_*c}muSfni$up)MMF+C79$*K^bIc2 z>)p}tjX`$RYk%}(4(4(gClNIMX7K5)l-cx`Gtr$fqZ>0qUApl?^jz`eKW+9f+Fu;l z(Vt#KyI^!J;L_S3q8o`4l2KoxF#|+K4`ASXOGkAMbn4gW$J`$#*u+Q?CL|1TL`dos zdcrX#0If1W@S>w#V}vZi!b)ia&zSW*w*Fo#W-7lrs!q%!^dkzgQ!}VW5>p@9Ei@y5 zP<@9q#s#y43wp|@QzBy;BbA&_pmJ5BD@b*q#c?se^9Is4A?A19+jLBhdCH4zGh(_S zhQVy`K}|ozq;oG@(J}@-#|9W*vT+nlBTp2>xRNIsx|;M(tC&{Y-wXvY4=@dZjgQVw zNgYT-y2P0G0IPJ5!R&FHv8qx@j~Mt4G4nh_@0j-(A7fyP2F7@j9%ym}=aP8}7&a&982L z5c7r?cRY$|z$4z%moaa-TfTZ5bB>Qi&wq{aK`$k=NKS(s4!hOfbYj)m5cEO-z*M6- zwPN`z9PkZ_SPAccb0x7qVLmCyoM3*lw8`|LI#z{tBS3JYD`H~ba3!yei#4x>4o!&N z&plP26g$HcIYXdr@?(#PW+9P3#9}t$nXCZgN|jAx4{*bOYZm(mxeJ#|YD`X>K|ghj zMX$#Q2rl$e*VrvwMWko!I&AA98<(f8r@cqS{=%bS?Xj_#ymCxWfaOJ$>(d-)*tpnF z+^lgEV#Bz@4K0a%hUppD8vdftYtHn;?AVr=fNFk4w>o`O8XN3m^QZWhafj`(s}Q~= zyk$(_En{~Q*8aL1>A55JF#-iR`H}M5u^KYGEY^Vpm4$oLH#F7}DPIK^=BS;SOo5ol z;_|^*6MUqkVZIlMJQlmb3h6r0na5*M=~>K_I?*bpVt3h!Y;K;3#T>h?zzRGU>xkI{ zsX(}OJ{G>Tz;rY3QtT|NbaVG|>^V%|Rl%m@MIXN zFtyNovE!^#%U{38qTcNKmnaWn2XHoTAH`0vijDGdYzZc8nb~C1{1>r@oJiND`93uG zL+mMQES^oSm1^AStWUAs?3lJ`m9McMc(JCf#$0URplMr4yn#4tj`LzK56uZ1@oKi0 z296&H_M(B_niafwv#MqTFaALP7c)!w2Bu)S*XyAa}a68Mrxw0qD9WOzwS#xX@jleCBg*6Yo?0C z$fqO?9P0|w)SxeuH4CkA=QyJ6aNUs{XsU6b0a+TI85MLq9&S|){z$;K;Z#! zx`;VwQA^EaUaVT6`NoS4+G`THU-#*xf#VnKUee8S^+7L<9l!dqpJqBQ&L3oH14TnL zE4jBThG{198%~V0+^}@CrI8FDYiT5r<1LNk%}<)ayxNG#nj*{sSw(x9s(>NR=$V>S zd(oM0&eaUE#!_r#5kTITYRYV$h$EhFi!|#H!O%S7aiiZBYjz_?D8A&6b=B*Uuw&sc znqI3>+SojyZPsb#I@qiu;n9Xna$s*e2hwG$#)CB3s##$b&D?DoAB+zcu8-?TUV6ld z<1tdEiL<9Qc4+q4lEiBRz39W8nr@iwA~UQLxwc#5OP(x^Z%uOVXuRp5!lSeWeC*?fD{1R+leq^FK9*~VF_f+*EEi=58cZ!{#VkX=OK4m!H+;9S zIHVI53ysi;t_+W>9X&ZNm0!J95;vdUJ9c)Qo0C{=>B6`cNIx7& zHZ6-AhHFtH$(`kKdy#diVP%{K845?z*wt~d$jYNg;MzFM8w21{qe$mUuD)Hus9ThvRP8h&PNq8V4sGSf|kNc-#wf@noEY-#hV4 z9Gop-H_SU1_XjUdJs$@HaCWux#W+v>jBfsI?`#AW(D!ba{ zQ=AXKq2!B&`l4?Z>a%R(Pw_|ewu^tlt95gTKf#OXmE&LWVy0{SWnNTx#P8t6w_fpZ zu8}=0z$YH&FRVDFQuVI4oy;Jf1>6eK3gI+wMktq#!H4+kf{@B7hSB zcOsto@vzB}9j_Oo6S~AA=RDbYFZ5 zXY;x&eiIg^_)t82@=s`*B==}M=Cv~>)XrlT-PoRrM=T*yfLSZLBUw<6Z5-=%T!@F6 z9}{QkmH1s)t;Al7PsOZ_FYxzZuKn_MLcl%wQ2XOd$b%-CRgn;{;Fg z>`}ZAz40i1s-1brHsWc#2iH;Q3v6>>k8q-Yzl`@1-G^3v6JL!7oVbtiG9>lE)YB&6 zfSK#8%mgtOQ!o33v)oT#Iwc@m&0qrHRp2;8JqH@?oKTrF&vQ$d%Zr~q6JW=g^;GYD z62@YNf1v-tWZ|xRp5$CWo&!y&m5^j(R{f0H3AMQD%jzZ|TU!OXE3HyL!G%*-ND}h6 z{!2m<)}pDuJQ43o&erVbMDBK~38&&}CFGITs)QS68I0x8*+H%sWuxnF9PfanP*V4mg< z(xY9%9zOcebVx98vU9p5lp+TrMf7&>gm6dmK%_=*v-9T-N*KdwKOBa2n<4_}HK}?; zLMRuY*XV>6ob}D057=y@hq*PXtxwp8FF&v<3GPCgY4=f$C51|BhDLzhT-wNPurZ zF=IY^lrY{JUKFH7t&4-&86|jc?PWquJMwf=bsw7hIsugs)dEHU%#rx-YU4m}yiI^F zbPGaK$;X61UTpbKLMLm?TWMAutuO6ttCe%gYwfj1QGt!i&j5+?Q&J4Maol5>&0tPNv^@D#dMNZ3rj{UGz2AS=i}^AO#3BkF-fio~31j(IAGB-d&5zam^}U?x}_3%1jbdA1&rU5QCZBSBuIT#45{+EF4x2)M5^7F_@Qw zw3r;%VvOiHObhQ?Fg@HEf!Bj%q!yLvnh8KgYlm?Tf#dM8vd?%e<~S7XJwXeZ&6o;0 zPQr)D4U@H~9M?<*vT54UoWuSZcon$J0#UGi&7wKl*_=bhFWSXen7Q+{s2tZ!4~eB( z$l%LtaeuKE^Ra9Oqh6{-<+x^*&#tg==)D@lfhMidc0s%bDYnR#8!RpI^d@|Z^xmRH zrMG4(n6zEno}0mDCq6=F?6M*{-rl37oWqL!cz678Knv-unMXZ2q{X}_#Wdo4SPPqS z3?||jz5^Y89My@_X(zR?W6BIMv(IPkdCsBH zSM5ryN!NYTqHE>~3_DjY)#nBWUtV5-^ zW*mO*YOzJ79y;{$kiMr5Qd|oro!v)=d9jv}@$ajHbA-$>su$_d>t$^Q=KczqEPIJJS zldu6&1f)O{@{%xed#7Q;+rPF#&B1dikM zj}Dby5(I8c0LO!F`>NAmhJJuhNQ>mcTq6e{~ViJJK6o(M0YGNoMdOf0};YhwX) z#LqfU3OTS#(Vm#=9?_5X3{6yG8awkKK=33zydoTEtTOS8*riZkRU)KqWCB)-NUVlA z0x5vg(zwJhgsur=29@0iB*7j*?h|8W`q`Wh9ZWdC(K{68S1MeZ3$NQev|kZY)W>hh{FM#3qmu zyFI1r66<1_6M*rf{Wm7Yb54i0CZbX+v6d<%$0m>*yFJ~wD{&`B9Jt>K9|1&f8gwwR zImSmAr|;8b&pMG9hHg{<L4|K&eT`+b4POc)ijkX>2uQ#1Yb6 zPp2vMlXPf06$!H)n~c~tCQ~2_ zGd=4^Igo-!Np3{{r&>WbK1h>fCP4yJroe0Ok|4h_EB^c;3EnJa z#b%$9@(?++*{F{y-I5c~OrdgBF`IT*kK~73rEPtZ&3rY!$!5OK{gNS@HVd0)Yb5(& z(j!zT8nR-?(RG2zxcrsE)Rx(+BDwa^pya-o2ECz$}XV8^uXXupRbiG9=h$ z+!{Pdh6LNJc>7f{B-m!fdH*Csf^Am((Iw?KE>gZn$_s9({3h-4)F)4S<5@=E&nKq+RJu3y0X|v+$CMnlB?HSEezH!=1TctoUZFWPO&MAEm zv&~Y+cGktnegk zW-|2h-nz=H&ZdiQr$Cx*c6I-~6!RQq=OeVxtth0~_Cc-n3PA`e*Cmi) z+kwt|moh_q_ke1hQ-dmtB#XRL1Ci!vMIb-+B)Yv;>TNOFkY#mKG2=_H1jFLFwztkt)asB; zPHGWxmBc9M%Is9kS2rjiY{o4Af^bcQ1lc?3tLCX&k?LUxRVk#{hCIhi5ixC3A-gs! zhWwZc?>e($P`A`^=rJ>-#V(u$=9hr)ql@lSwHiZ=1{G}6`JvQ|l#_NTV z4TYTAkl>1ON|~OD*;Ho(B!H<(Tg*s>blOZ=qh_bNV0@i{Rix9-ou66{J<}KuWPBjFeIQ}O9Gj+N6^W?rQYHshS;Z>-K?Ku8l=-^>iVT}8l=-^ z#f2_ukhg{v*SV!ZI&D_GS1k?FX|v*^nrV_OM^_>?CPo?)7E3sgRw^xXf@8Yb5biwQy| z?Uj&8n^Q{Mlmm=MY`On#)l1Ig{p{Fwbo~CbEtvJPd&AP()3Zm@_UHkkTwhxbfEGO;^~m;L2LY(wx36j$FI_ca>W(BO`F1t zJ|EMHcyazeX%ofb_UQC6{Nic5^Z~rs$T596FFL!VkLRswiCg+_UZN2u2c$3J_Rt2U zALd1GW%?vu+!U3*g%|s3)7S8#B0YT;FJ3aFf8fPijnjwoM|5kNzPqZZ*tqWLx9r8D zyl=YNffdQTf$97e-6ez4-7!M|(AYbKm0rzFA11yBJ9k97JNNLNW79DQhyZIWtu#LU zFvjAn^6&}iBe>VOPr`F(J~_P{&Ef9U^gf(JyXomuaXKAKSItPz!R$RTJ*=5+;UJ%z zKI6Z0pa9bn{dl_t>3lKS)}$m!tqdxX=#pNQd@4ZZ2<~?x+b9qDhXE6qmdt1^m$I=*#YFqkFv^p2j zbo2TDQM=QZV@-N%Px>)5hvo;;A)kxT^%4I=>6o){Ob>m3O<#)XHlgy5bi>K?8DcUA z^6`W@0FOSKeh?Wvek6bh2mX5Wa-dJj(}(fCX5b}rRHIH;)8WIPtivC0Gu;<6w*cm? zNZ8$U7g9^sy9W93_jDf;d%)3)LJ2g=vS}zEq`yJRAY#+KkJ8VH=ArYSq|f7=GhU|G z<;6j7)AwQ)c^Um)S=a38@sH{0Xp#Z~No&)kI^F#_eSKw7?4kAo zR~{wCPq64>@ICjIe zx|wGElKPou{U!!xn)PcNl(~-U;egz7?{KB%-k@;Hy^mFyceoy|YcjEEd?Ja(*$C)i z4gD)Avl>ld01v5*T{@f7Af<`Sfu+v; zU|TP;?|9}Rgd~74i4@niaiXawGpk`@dj&$knaoWPzy&sU&t+oLgj8T9oX?y9Em*+1 zdMR_3Rg#5YVZJY5t-qeR#44<+H!_!syy)@Unbk4&2O*0{?tr!qbjtnAZkS<*8NrE! zt;lLf|9O!4+>VgNg+BD_* zinE-uD8HeVN0tjLya-nZ`DNY2eCQbHbs(nlOb^mxW>F(*te^GXQ>52CB1>4m0dVp$ zItyu4j(y@~JvW*dn>IZc8SIeZ#Dsym+=z))vGpheCu2 zw6R$v=C$;1m4%r(vq1UtjO(sM@w|6sQW90K3F*~3tK)xfJu^lwfX&WhS+!|&o2+$+ z&3+32`LR)3cha>-)+l1=p5;M)I;`13zI4rUr1QIGwLlDQ&B*?wcxYCzxF9yWXBG?% z%9XxeWftYAe!)FNwj6;3AM9&uU^PUd_Ffl>>_p;IDN3%`9po z7W?1M%5V{jXC7zOb7RFF@3YGJt@plVmB0tYgj%D#v*D#CR*dz{9wHHoM-s9xh|Pem zO3D7@AYR>?ojswdSTwZBZs{o&clXSW6mfS9%x+&i z*UGHm`sC%1tg3YMhU|TgqL!O)%;s7iwIkcl3cu#iKD)CIa&0`?mu*((#sk?Sxi*rH zWUm&BD?gvi=B9DIklls<|M;ufXAq-YvxG{rX;fAK8P=~;jU9KhhdYZ>M&B_@S@tCR zsufaJz&p~g7uiLXL<`Z^*>;@&sSnwIaBG)+%l^e#yrG-3ehBxy53c(C{Dx!R`k%RB z+tt#WHE_GGeg$Gn->iY!mN4%{Duw8GkZsrfGl}QQ>IxDTsxSKQp-;@wyl6#Ue9ZR7&I_YC7g7xH3Vbzic{DaVOE4A+O)kkW_#?l#rP=KD>2X|!5@*@3j% zUOkgE(&*7M6oD*BoDJVw91y2}g%El3tm{FvEMDIi5qF4CnLy<@5?i9LPVVURn4M2E zN+fxft@ov06ZJRk$f6!KyvWm3{Q;y*0xb?`OxL$YjE*eQF&IB`uCd;cIA;fY)2;^n zMtd^rPsmHGY^eW?8JT}4vmXWQiEd10+NGTyj)4o})0hr=SjA+;x?T0dY{lZn?s_ zQ(sLS4aS!0TR^b;mgPm3FW2w07S*5DTdA*xh~H%MzsdR24{P)%As!37F0R*OW+eXu zsNATxMa7EmdA-`KFXOxpZPR16%l?N~%^iAgt9Vf`rakoNf~p?w(J!&ygqi#Fs2sol zBiYR|{aCK5KELXJww~9%!}<-F8Sww`3OS{R3>r*dN6zRcS+$qm<3Y#=a>a@s*{7-YGU3Kwq5TquD6E-v2Gh#crsnGIS+<@^m}S;2G#aXG@UTqy2I z&PjpssZjLL=k#{_@4ieyyfi1#>s$(R-XTNLP`aUg4xHR$REBlR8OI4$?Ui$1>|UtT zfSg#)HGf3T8W@cWwB5$${EnC}v9?2p%+6WrBHE(MVzVs@mgEdWdn+J1)4r>6UU1TV zH|G4#iye35)P)7fBiU8x-2FNGxm`My7g7L;4rCf{F(z_C}+j#M{;6eb}Gnv z^Fq#ces#&EoOfJXtFPr$}8LtW5?N5<9`un;w4y zY7y<+{8P?JtcJF4GB|RI#~lp~VfG|Q7Fxw{liRj>O+znt@#?%_Lj+>6fxz&lH|Z5+ zD6?5X;X})In3lzs_h97aPCOS}_a^gW3~&E?;~@9C8Cugi8bc_iSqPO0RCXX$s~W45 zPw@uKECW!8I8@8i8uA@&R*+?BhFdsUN@-q(;R;5bz@n7C&oUgb1p@nW3>&Zn;*5p> zq%upXEYEOGAn>W7VJnuvu*L>Y%MRnd)XlK&ziWU3fp(aI4I^H)w_&Fs{F%O(XA6zFlxFoe?7&Q_ zq4G$gUDu)}Jw4RW*9}+*ACD$uCm6iRrly9Pqc2I#E~$z5isO*gJEMA(`27QFi3 zdJ9}*7=)Bo1SbFvsW7BJIMAjW4YvdhMr|?tVFSN4w?V{%-@msTwmHD>%DslcmEgDD zK?7zPlX%@Q6wu-m1_uBWKOp|gIl~N0dlHZcAT=PlL&F^C;|qp%0&&A-!wiI`DZshf zoAulA=D+pZ<}R+q1LR2Bc(mcIteLJN|pvwJZ=(nwj*@J?D>l%+>MFSvzI;)-$wG?Bf;!jP1#-X-A z<#dn{l}K06GNa-ZY;23wG6jU=0*fOG<1(uh;u&VN!}u;Jq%-N2YUFE-SEGza1p_UN zH)6((W;B0VNo%x6YK8Ifrw0>_g9O#Yr5G`1)CDRO1bdI^Su5lS$Pf+mXOSCXr-)~(Fw6bDe868 zV51ZH8k{?b-Y+ydc>^C|ptqyHafskILkAc!Z{ad-fb33}4m9?~Iu8W|DYi0!$~^YI z7-~cvw*0rr1m795UmOg5C^K%tYQNVZqYZKdGKeHxfKU#(KE27*fQT-1 z(Fvo;+2$C1dC_-A%ixbj4YGgeLIFV~gbTK=K|UG1$=!O!8ry$0IXeKkvks=Em_=wt zZoQAGGFeo`WKUW*QYvVsr)e9OCK0eGVATQNrU!jYkAO2>UDMR4QD5^^-|0To0ULdLT3jm+7W#q{}sLS|-qG zzt*%xXpz2~Osj<7vTY*WH@i&NkPg|5lpQd+kT<=2YLMHWc^&D!L#7N+fvC5HNdr;)@K^@0^WFuJW_(jtR!GhB-o63dX zc2`YCWU%6@fCphtqCm7`0L~=zv59}nW5FM$3}Mvp;E@Tl#bSY^0J#_0Epk$~ADX=A ziP(YtsO9qX+*J0qI|#OfSLP=$$X7V}dPLeiLmv z&?fhS@VDMBcdzj4WS_ej+bRWTg2y$x=1eo(a~*{_7we`m!tMP5+F_Wi7OI zY%ZpSno-^9C{6AwObZndDDavl2v?ev+gOOBZ?kg$5iIq$KKHZm`!8_#U;6~ipTs$yt|0dbup_b#xtKW~0F}`B9doB) zyoXi(t8?xqA>5XB%iV%#pA4f)kKDm%0*2nXp9KP0{c?}}cLEd$bjA!tpk5zp7?Rr_ z>41I651)-SY0qJ~m%z)!uB++T+@XTYU7nQtTyVKxr{x|J{vJ0s_pIQOjep7QRT+Md zF3a_W|6h4~RW9au1yJuo+HB2rCSm(>9qGjFxkkZ-gqT8~@5)_>G=+#s0n;RqFFu&- zApDL$k{c=rvF&uOxA6Dz^4#r+Ja2@t*0s1+hs^KWUq!pz%WZ{JeHX%D&IRN-kc3(Z z-t?FIxvntB5jC*)ajrs`@H~AX8sYkz+#sRXw)rF)q5O01VBv4QO+%%#Hgm~@&3V5f^kPOU zq_$i0TrlCS0^!%~d3$Vu%7~qLtF5APdsiOnLv0mQmhH{!El{a;Aa8+HR63XCUB@gu zRZ#hGI1h$&b4ib5XetzZZzN`vX=af^2bJd)SmXLBL{-0bO*-j9UOhVyh7Gb~FXnj@ z&(rW~m2kl4<>kD!;D~hojl5Hp;J4G=yl_lpGILfF_qEskXzb&>1%iLGDERcRyn5F7 zD92p?D(?!IgpPTWcgY5Ro4w0BD*V>@kk$R__^ zn(A$vA7TAtXpB?-K6nCs=aT=cz<#$|KFp`)(g7a%wFLvr^3K00{Pyw7M}5w+LMmmg zd>3l+&zG77qLb?8H^GefzsDqi=}3o4@|6P5b+UYBg}$Mp9AS$5R)RxKQRkl%sE72^FG3KY6Hn;%@fG(Qh%nGW>F75SCHKWOhY`Hb=5P5Eg8LuZ;_UHEPA z|5&^7xGJjl-^^tb1_TjQKo%9;5fFD#a9S>_tXDRaTnQnS)b zr!-CLtu%MbTywY0a!GT^%-?gC%bknzetM5T{$OU#oacL<^E}Ua&hwlzcV>Hb8a@_% zk-b}hW9u)o&tcA`z1j14<}~1(3=SEM?fonZ4+Fo;-pyeUt_R`a6&EM7Bjxm?*2=E5Bm5-_F|4s0KkaK3)xL*I2e5@dz`nh3N1R9{VgZhmJ8V# zoISyew32-nS5_#{BY9ls_Npggq~dn=^(SIv;Jxg*n33=AXRmn@M*2L+=K4{WOShX< z>gAY^H$^qi(zOdl4)s!Z6XoX3)dM+rmZ8&)eOzpQo2b5q z+594vviVX+btinitDE{bW_wAR8g^n<$;Ca@pW$P(4E0rfKeWFZqOn!-#Q|!F##YI% z3{u1D{wmotM7;qY12WYSxL$%QH&RMg25KcL{6rtw0xpZs8<+QHi!L=gNd)CriEWTb`-{#A0LWmJP_m#bCy`r=#kdhKep z8S@^no?ah*Pu&#L{kc)i^_w}Ok!zo-L*%Pl)g`=rfLG_K6M+q?k&m~#G*YljeIIk1 zvs(>O{VKWTH}rUCFSS2;zj_xgpK?(BK6nnf#S!(-xPHh9+Ph;X)u;U7dedb}SN;<{ z4*P}D*S$jN5C2B#%dV+k#nK4BrH05 zEX_r35qy=cDQ7$ww3?Z`Je27)$9aNSqJ{@X&9{t)^SK(Hmst{+=zNWhXYG&>w<^-C zU_6{G*399RLmjK;3vM39IyLY%2r?uBMrmH=<)PbX4ObjbNtC`FMQ(4CE zD?TsR_;d3x_Ib_gjEBhC{6;$HMVN*-Whj3zNAnuv!7xwr5--dP^EKab^YCz?W;5gA z%L>gRULNi*)^O#SWS~q#)pCem!Y>E)8=Bv^<#1`420pPv9LBUNeje7X)I@XJ6+@>&TyJi97p}|fbbCeJ7)O^6vyu9%XO|*&{ z;kmCg6}%c~xLeboqj{@)mCKqn zOksn6*39D-&h%e22e^&q`fr*S7!R|r@q5n?uWPv8qe0}b^ri+ryF_%6cAIC|Qu`lq zN7IR04*mbC=HchR`FRNcN7I3qht1cu#Y_w(DlNAcpYNsRdZ`OhMejP=R~QdJ259H= znq;FuZEIdJ$iLLpKF4^NR!_^Fg{%+NX7VibfE;va_8Wzf^3rH+kr#4a=^lpkF&`)vKHQ(MNSW; zX#Zklr#fjHdec&`G;Ji$S~zsDCn`MoU5H%OOWRCEI~dW|)j^+rTHc{dVG+Rk%HIyq z_GOH=%&hL}yea+owQq0?I~fp7Oz7*EY0opOTfRbjjFrByS_>b0 zC7sQBN1I)To^INp-3T91z;-+FQ>~S=FN0Cro!W3zcueb!(ta5pCeQv{yWW>}?WH}{ zUHe9x%`@zvYl;Ts`~%wAO#HWxXn*8EBqoq#(S*tc$Fx&9Bc+}QBOpM4=WcPQC!f%c z4IrJC|GA{~;dv7-FqV!Uj)ip!$Di5&dEr%U7#-a5)*ISH&aP5K84wGZEu8#I3$KwR zTHo-uwm*}mpP>7V4F_*;-CkDVypN8{#9dv9K;1)ie(3&(hERE*r0c+(RUJvkym9Yf z-DnPr#{mQBS%eG?EN6^g3D<39jBjbE1DCw8b@zJmqzGLh>*UYTx_V3s0nK!~S=~$V zx_BPjCx(=6IUX#()LPe=PBikib~+!<4)sI?EF(qt5$ljoN8KLA!I5fM0MG4s4j%N7r`^Y>VluM`_Z?X{wG^^m#M0r&ZVkxBd**5&NbY3PfkhDW zTVAC5koC!I(`ES3(oLgujhT|pkJD9g_o;C6i7>QR7}7|NnWA&nrDJpXi@MLahde=S zb#0I&U!9{H#dI`szD~tDQTB?i9y@)jQs>W3cP!B*)6*?0bSbQ`_glIHJkR41n*%;T zR_k=|^3FB7;ZM>X9~ofY^`^3k-JrX`>17~rz&!nW{kt%E-bXsF9eQ`y2n@WS_5fuJ z8D*CZ&i_=m{mEE7)AlV%-oI0)e@b+9oJ(UM3w}$>rvez8jQ=QkLlWS^H}hX2K?L)I-L*cFzRs8#bewh-8pVy z64>$|bsN~Y=ygT6jd{GF-*xb9CE_TKUDd5-r}M7ss@Q3dKdMV_-PF0_n;n1ZzF`$! zzoR?IPN&?fCTn%Sn(W-))nx1baglAFr60;IrqYt*5;_~G_n}`Yu=(i!WJ>k()w4No zd4T>)Zh_$jWN2V5b3d<#=v@LU3Dtkfx|m ze|=GOtln3y+eBZVb#+R-tE(jm`Ylh=Rf>%O+gFA+BGzG@-r8CZAA2FD@m$;LP7iBW z-MvQ1dfvFDCjt@sq7i9&iN0+3R!98>7KdHws^|GO5RO(TtPHShv0E0>_2;;KS)%oL zeYvEMK9gIz1ff$ueMjbGEBm^X_Q7EN95x&WW$E`d@@=1Y@Jib)FkJ(1n1pNbM8~IP`zh|d?C+qL93PY#qhj7as z`V=L{m+9G-w=6%W|CTlJ#`F3N&al5HYWK;BdUF1J{aq?1dBh^UmhpRdu|Ax8e75#J z8ZLtlR4knP=5?3&KU}77%UZZzrN7G^jI{GT8X#NV)?Z~UytCHTLd81$6mA6**xB#t z%h=@Aa06H>uO%+99dC$s?1VR}p5Cm74=s`uTtJ*nPu$31$PCY9)!-7aq19o5(8wqMc!{G3LT4;|N+FqZqB(l_O1 zIm&(<-o$wmM!D~K{dg7*E%*^8172;p5Pj&oWZPe|S#EP5m;a|A<>I ziN@U1U*?u5hD87yPPtwDz{Txme}l*^)$4*b5G@Tdu-ti-x8XPDK!5Z#usGs~pMgJ6 z!41gJz$In_i$Vk1|#>R1;mZf z>}YMce80Kj8uOJ8TNx(tvV*P%YwOE@B^gAQgG_dbw@0dBJhxR4*bbcyD_Bx{xrf2E z=&~){0G~@BlK#App(_(_;2?u5R{edj;V91!Enw0mkJZJ=40NVjc3F>0A_DB~E@LUUu5+^WFPkY_FcVh1{HHgNqUs=09xihaq@iaswG zY%%QeM#1{R5Ol$ASjX2B0+=FqaT>Hd{tddr5W0;qY!oRs55^iQpNO0E@rE8emg?rF z^&~?DKak#htvlS!Csc!x1^X8seJOx!}l%RE17AcC!O91oY%kfB2mntr<= z9QnUysE5j&h6wqi)rLgwaY10muQQCHos@5`H;mx%>n`O7q3DeU3HA8Q;3vZhbh;nr zHcZaiW-va9MNn87V8=2>ckVK@;bt`53Gw5MuMK`G%HGm%4Ewo<4+cX3<|B*y4G#EN zp|h}_JpG`d4>x;B&p-^a@JBJw)@Ml)Qg^;{>N~V zoqnw{_Tc4Th9=uk*3r*?MhNWiD7q>cxxOny#0!W`YiK`o9rOF$Lemh`whvd=Zk#Z7M`cruL2|FY#%1vJdBBpd_cQi_&(q`6-h+(m zW60?ir!h2yBeHO{z$18|%|zX28`P0a8v9WnC6*TBpqxyg4%uBXfi@$&DD@EvY4 z)?1z6;Xwu%u4j2j^Jh*Q^{n}RXN?Q^jA)WP{hV1_E0CZ4%Q#O({Zi~d#>O1&V<$HZ#U6)W-)y~WOhsjbO`-I1{dQj+8m%&Mx?u(y z*=p=_vX8f^NTh5|_cbN(gk*%xePN~mIWf@G!H0rP4mI(*fMGI#!R8T}mp^M{`hv~Y z6_KV7*{QCvDT2Ax@}?$u+Jn^1i8sN6@Z|JmeL)|0 z2EkXIG)BlfyO?(KWI@%i2w;8D^6B0_a?>8Bk*t$d8P%Pf(7U>mo%))3@yrL%SUzeO z)IU^?9184Geca75@hm0D0CtFR*+gH>Nz zfB?Z8&FGdhO@75NZ)5#RY{IcoW{QKm&SszC2dbZP&Ur%hd%JNjX=i6_3m z*a)!w(28eGKJuw4rll;%cyYRkC&;JSqvdh$n%46e zRW*zZ%)Q05ZQk(z!P44|Ca#d6nk6i+=q(?a%#5vZTTDE!((%NTH_0ZhpC#6SiQQ^i z&G`EMQxi|7R|7C^hlwkCsR46irwM*+j?ErT`@+QYE9V-3yZ&S1ifC%U7{4|>&Ge3Gqo!nT#)8w2 z2B2+sjqT-CCrw=Mts>kJn5b4`%jHi_n;=QV`64o$ae4Nlv!;4Hp%2EE?_Dx=XHn0f zE2dlAUI%aj?I^E804$k$rujAIHI*RGnue#zAg)@h&9k|*E+bPYN*<~Y1Fxl2x4 zZY#kE2oStv2zbshOJ0(e!y?p`J#zl$5FgGp$og1}+_HbpvtGh}yl6K7T_2qD0Y?f< zHbR596?mh%nK}OG&mlP{|96xvnP7G(F>h#y9Gsn#S)ZPcvF7|K{qJCvnK}JX(7d63 zX!5X}5VW~Zi+rS8n*(1UUB~?vez|Hw&Jkb27MeLNhi7gnt<0Gr@134w<;l|t@%geG z*wV*RLg#1Zto+|4g9uE@tQ-e74})LKfuD2|9=u-8;n@f!JoKBJvyq#JcNXLvWjxG# zC5I=1Bs?6i$gy$oAOmr8YOH9@>p48jMx^ZVv>QH|W|7}hV9fR_slR5CX2yqy;(>Z#7dV283oO@pM^u(1MSXUqw_Fc=F%*eEN za^MFrq%`(kPC^7d)raQR4Wy@&Vsqi~Q&J(YX>NZXdRo#lcP1;n*E+YqH!U5WTwQv( zeeMUWbaj_%gl^q)r})vjU-qf4YwVvpfZ={PG}p+Oj2fQ%F=PF*Hg_{)@?Cu{OqPV5 z(Z<}RLG(1zo;!0W$LJ@jO4^d!BaEtQ=HA@J+^Pbc5P9o?+|Eq2s_%1M8aZ(+cQET= zhcmf>Ofc$;xwlwn4L?=O=;|-I8(D=7*Q#aI=a1a?NNGJ;bvGA&EK5XC`B!cwt-Cob z&!whs#k_I`P~w+YttS7xL#*^F^7Ah-qTVyu5R))cHzY8Y}(ywLEyLjYxOil4>x^(!339NHu#WZwnL7 zl?{25d7@31M8Z+wwmg5-bxUpp>bos3UhcdtZv_*}Z~w`g^CV)S*s`=cuN@n4Lk?AU zr^%7LO4f%xr}AvfE;7#NWiY#__&Kkf2_*hnUJ^UC{#h-XZg;A|Lhj`~!zwI&nAeJ# z<}o2ZfZ^Ws$#<0|*2y<9nZF*GznUjABEtIzLChBvo$n_fte4-(k6Kb>WIoS3(NHy_ z42atqd#9V^yDaI$X8CV3xj5S7zs$tx-64N9E8W&Te;_*z>yuv;z~_lF&<1^eGgK<} z2$f^A@^e(QR}0no@AD)e(5nm?5G`yFG%L<;z?d8|B7Z8=&GFLwKn@eG5m8QjCV!`J zg1k_DXMPJWH0N-;7HHkn{D~ZraD~qia@n+eKb}-!(JT2@Ip*2??mXs&i4hR{pr}s@ z8qrvJ%*=eAFGd5@t8{D8`h5OEZ{Y;G^HTmP4v_%RGP&yIe6IIQ5aPGy=RXvIfwvar zb9`J}28>HQ&WN<#yD}d>5_bZnzMj98xAUJa$)C>6fba7BBbb4cRrx%hAts&3eKWt3 zCm00c8R&uEgh+Y&+WZJV;DWqC__vMu2G0HvI6rjgdJ}kLYjb|Ik8nbc+m@fNf@9f^ z{ApfreDHaGJ$wxQI{yMb-rSo%655svzstXeuT3ZN_u*snGx@FRz%l!${KGMPy0M{P z?}DcC8Q+4J>$-aPY=eRcoV}xP2;h9>k0J_g`2%-r;|uOdaC|GJK--LyOL?KbU@L%8 z@fQ>oG~ikC0&Ut5AT^ge*b4+K`A(w?;Db{q}Gab>~Ma6aL={o$9wS^7Lvbl$*LNWHay_p=!? ztdGR-`1a)E1;wcF&;)pgLczfL@|jNyVtKs03nmDCY%#MHk>Jk@c)UD7J&j@x4`7=) z|9oCB-V4Mu?(2dh9O8DNMay;e6m;PkL~aD%eFfJH`!LhJm z;bC7mrop|BK}zt*Mj6%dfzx_e<1zCWO6 z;nTSMv)+Y&hl?gQy%$7VO^I{<%ORVF5z)I zAWA0!q^9!EI}0~q-j;q@_$ro7%E7{D+^69u3a{bvj5CF8@Nx8oLK*iZ;m5+mxZd4s zRECaQREB@wrZQ~wSK*7e{+s_42I2Y}1+yJrNBEeD4BzoH|AA$*B*08$+bY=nCC0nn z$o!BTo0{QMKWF7Hy@oJ8-} zNiko;{NL+tj!NK@F>x@&GprSMQGW-1Swbc@s=rrPJT&1zf7u_p1 z^ZG#qelS8lRATny(IPndW)(Z$4#1C{q75r^yiwNt{Q9VLd^;a`^hk3@LD(g)9%UZm zBUI2AfDg->n1E(ZFh5aTj!LJR;Zyb#&3|!-claE0q5R9!<`f>Ujfv6vspcho4KE1O!CVg=e)ZsvKTIk1Cd$4oQV-uvs~`mV{(f^^TtDEjc`bm+o6nek z^n>HlpUm*NfC}093q5weVusIERLFOKGsEWsD&(zK&G5N^3fXbP4Bs)Wke|L|F5pOM z5$Rar7eD&TF9}7PxkZB!$jju=hIdAKBkgBDM$1WFMP`4OeBKT!ni~LowvQ-+S*JqI zYFw0q<+47m=sSE}9$z#B_y0gj5s^!Ahaw`E37v}&t|z4x`Qm=o?_Km}Am2zJqbLSh zhZnsjAVox1nq9>0$WY>I=q+_o1_zrVhfXv>XAMQe z&C!AKyFy3rQ$8uc#i45`*`p)tBw2B6u*pYEl^dc00dl z!~Y9(w7j&Sh$|X!bukH@TWSwQ8@`6stfaRGs^tV*k;Na4ToD!~FCSafm?vd=6gyDP zpH%dDJ<@wQc}@}hzyu3I7UmYg4@}7EhIvJ>7EMmaFDRPagr5Gru?UuCN$KHDMbmuf zY5vxtc8%%j@?%A?nL#QzP83yn)6n<6letu5<$87ORP`J2-UX36!E zET3Tpj-*(4ekM*-kd$h9pIZWpJ6Sek1_pJty#2pRKn4aT@?@Wc@gY4e|G{mC^y2UQ znBJCHj%*qM1Gbuh*TTV;rT%Ms=~mh)b4jCs}q8QWI*)(nd;j7|CP7c3W4a6CK5QUS7( z$Ir8j^@HQ(S1m_yy^j&av%PNFiLYC{VNpRJ>!OdAWljpu@8LtU4aQ~t0_<>J}}hXBr( zs@m|UB@KY(O0Qy~8i!9YB->l%CVs_4<%jDO6O}IxC~k9!}*>p4lqOX|RNd{;_;w_EWJTu)9bo}L27 z4yDD!l>EmPFTtW){dDnWj-O(pINaB&NykIc=nIxca!^_E94x=X`^JRH?=L8B!8J;h zm_Af~(<_SASbm4!r2Sd7ipnqVZ7M%$4VB-)wZ(O@9L|0~<#+!hTK~WnD!;iOQ~AC6 z8I|AC|4?~3zAB!M>3-TruRl6a{3@2;pyQOj@kuJb)2FEXOhF|?e(i!wDzN;%s9*9G zr~F3KjrwlUC7r+-PzN(Rsl>q{IvLb46^m(E?-F8WrF~0?nYHX+Ld@*=fD&S6Zwx9S zVtO~LgqYdaT3UaJzJ!>W!B|4f%v@MP%*j#t%R7_wo&x@#W5w`SZwnrmJkor zWO7Ls7Tfr8iq~&u2{AkGStZ2mX3A7-hEGbSV6h$9UXsftHtZPECm)YQ|9n^CBOm;# z<{?!Rq-f$F= zobgass~;L9SgSb1lI&;IpfRHL#1qi^plwse*Oz}%S^whkt1cE|&@5jo$4h5q0JvU9 zxP+S2v+m~T#>zPB9C>x9wJi_xgm_-K^@Iov*cw@RGEstYB*MzIK}(4JqpjO817F2j zIbO0O14aW5w}eRBN2ijOqVg}Z>v+_J`Oc)4FwsL*p zix3}gZ{3O+sOV^2%WHpkI$60=8p1%&Zq}DD1AEe}JddstjF_HQt|#{h@#tRGTbKb$ zU+c%b68OHKbttzHo*864fEjpisFi1OAe{&sX5G#cW`g?}DUZ=v=lBByyTXQr$#uRgh)`rMksGx0*pT6Ct7H3t3D*5)hk-)kLL7kWeX9p{|1hE^`KM#$x-tff4o)B`*a zC7iQ{px|O_u>7Ah){&U|-f$fCt2G~A@4Ie24h~&j{g<^JTyH*VBb{C?+F-}( zl5Fy_LDX_dZtg?N@A%U4^>u8Uad}aIEz8^Qr>BIc@ETYO9`phwYcE8-D_Fu`s_XTLwuQ1-FgG+r(Nj2UU0#UlVWT;&*B z31;~3iBtfcr`nd_>u<}cF4xYqoxvS=aTdMKpKUwkBNU?8Bx_@&pKt4o7VS$2Lp#$4 z2BU+Y*ou&}z&4k|OA#~9T55|&_y5x}6zLY)?xCP>^uDP7+BJ1iPp_YR(Tq=Rb5L=5MB%|^lM~9)<0-siRlN8^G z-EM0mKzZ|$!6>@Y<|8k7%_c(E=s0})bsG|{$~~6YS_#4}Orb|*$9wR6ugBGu*5Zw1 zBoxE2U%J$GOHiR3Z`mSISy(_?sF>pOl=G&~Q%?<6Ij_UP2{p(3WN(f>yXBoHG@v!p z`!r1x>!--4mfOY&LJBQB*Ca4aOiV#l<89GGA}z}@t9;P8@wUaTg3=1#5EPS;>5Vpg zV;e6HNV5^M38QD(>J#ZDa> zzQ&d+UQI?brrJyjc6}(jz(Cv;YiHV?L2R9ZR+>vPgcfMg2ew4CeZTFRFhjxbRL1XA z1;1%iY~ux1P=jB@_|Wm-L&lb^IjRk8Fs&YQ5uw&ueVj-KGY>X!d3N<+CIZ|etn&H2c-={<)tMNLL+*g@WjqZQ`b6y#yyR81hWH%HFiZ-3v_#WN|3yaI>0{?#qD zTs&)W`Qqt|TTnx&RP?~rz^{YDU7&>dDU^A!MG8v(s3b#-2V;0(&48hZcD*$sLkvkl zhkq~bgtCG~f8hpp6_Kda8J~vAWLru2(e^O3H`j$R{XIV0LeJ3Qu4Cfm*pF>GK^Rc^ ziLC)z5%qHd8aJ-F!gmVSFn1~F`d5kFg)3;~R$COOCxsRol3;^OnrPxS+YsR&3Z7LY zVDRKmZK2|W_9%J9xF~e)OCKN9Z|$03g`@Qw6xR%$>r-ctSU(kooVVqP!Kr9?p>wb( z!S$Y!FqCDohM{~%Q6I4ltI?uHjo7pH0$sh^Zen37 zO8&_<0bgU^Tn&$%x;TYZo(MNx4qcqh5OE=+h}&fwuI$xx`=Yu}rTRQI*N1pD%VH<> z^H2v5pNfSKLc|~2qh0$;lF`ADwzeU-vNI?U)bi3s-7#vA5zh-+cv#)64&|zPbHw#l@mtV-x;>sCWv=IxW8NV;;mFNsPHl4zBQK2 z7WiW2E^BMWOcC6H&J^`f`46^KlojdURR}`nv$o{Q_f-C&kIWNM9ng&rirb4HrP7JQ z%`2NIpb6}FaZ}}+ZF=zrGzl|>coq04uqUD&`)yZ+zPPm%lr?ukf|${Pj{Xc>7Kw;6 zB2xt`s4-RKF@_5$!{=Rw!bnF^V(AooMjWxi4_{RD<}6dfS=yUNTrex= zbHVo%V%g~s;AuE_E3G1Grz2-XLK5dKSblxca_=y12=?B|D(aca6YP`FKXJV#q;30mwk!!FQb|fBL zcoD^%vOSA@wl-^qww|%|7bkV3PJ4Vu>a@qe^`4R}aY0AweP8w{4?~CSK|W~rIiP%p zQNGoY`s>Y%0_r&kry0O;2JkBbfNKJ9mjb}0 z2f)nBwr7M8B$V1CQSht@y~V&zgaILjmK7dL>@5VM^6R$lLR(sNZhbEkuI9p7xo-r# zy!U5Yw$M+3(~IF~6ga&wP6Kp(woklJqyU4uE-*63;Ta5~R5w$T7}!e+ut}6Dq`-k{ zE*!$tcfSEs8x%O}8O{y`&U%WoPk{r~TsVZElxsGla9M$Kp5fe7;G8EoqR^R6tWd=R zgG{Uuov9s!cUDZSEjm-9!G(BY1>NF_wN}f+v#bTIvgRy{2f*P_Yin^}N5vG2YtkuJ z+ZoO*9Z|QNwicm>&*bi<(7%b*MhWUe4U-?x}-uab|r#823l4$6Rr_Nbo zX=gfXj>3$PSrY~lo;A_jJGP7B%OE*CYtHUWygEK+jIqLO)HggBWhHlnmyg}Eoe|#x z)@-AjimRb|5Bn%_WoMF3JvvQ(&>5Xy3)7?)0&lzRsTgx*jy(m#oPX0AiH~GbFDw^Q#VgRy z%#rcp?+opi+R*N}(aPVlcgHB^(W4{qF{EEfSY>pV4yw9cd{BA6&dp$uE()=ypoMAS zX<}fPTKXK>h47~WU03!f#)z-3w3FV3AZy~QzuV!(Vi?DO+ZZd@3FkL*?0Ld$bR!5Z zvC!{ZQ`s>V6^QmSd_m%f=b@K4jwpvp1@^Y`I+cBjSm#xL*Rs_4!Ky}RREH)l1Uqgd z6P5K*1qi?r&gzQM$}oEr)#ko6vEPYhUC_$1k_6#7+Ogow$nN5^wRLQ6muh7$I$qpE ze5MQ0qwowxJ=9;_4DDQ+5G}6ifdF4M{8nLxP347)_epI$it?%C^|!gsY04|5_X1U3bq{ zdfB>Q-jeNh3sRA^%je}&=`lwcwuX?QMJ6c2L#Jw>e@2}biI$Q4n#$VTiM&7 zzk}_Ig=Y9X65#|{6$Nie4!Y+RYyQ0{s)~+6vHdH~S4em;+N|29vZ_!j*3-XVZ)-A&4v;yfgpb$lqrlRD9o$W0I z;X8K)f^d3OQ3OS($#y_B+f{3=gGDn84&_k07Hx;0t30G|WI5`7S30il<1#j`-v7;S ziP)eU9aoLejU;dwWL$-HtHoh7?dCDAz{{9>+8e_GXd}@%bwFRSTQ}m)gfxmr98j0q z@cMVFc96uSg#i8)SRe^O85#C`5tt^yIAfX^s;dLoP^A|3(}FO9wvJbFD%WY@B}T36 zJ%sVN(4ZOtZ}K~Tu5M#jrPA5Y)URx9?;^}{-`_ZLO+33_`Ni=1=-1ZvGg z{}>lNSuQ&FdEI8hT2etA(G4wp&6??Pf9H;(X2Q1W`vVdIPr)SmFws5$-VjvX02xTE z0%nk!_}OZJE=hKs2SD^!VB$Bb?+;Ff4!P)OT84?L?r7!HFgF5o#PUaAagt@Kc!lW) zQX9gSfla^a>!NT`jO>oetE@@rvlS2;G-WtYh)tf|%n>`#a#bX}xC>i3zCk8mZ04xA zAGAd>Dx5mtdt}mMb*QO}@VX?(oQEP;m8Gk!ui~l@lIRsYhE(%N?EG$5rJX0rN4nWX zL43YD4F$@&(@+48gqz^$!>Yukz$?rf;&P}20f4XsnX~NO9-aT!0Gn+7CsRfx@Pem) zIMTU#XmV@SPU!HWs>mkd=iSNFFYKrVZ6C10X8w&|6{lhpY}ojS=T8w>#FmYZWCEqf zSQOgJ-kTKSP-a8o_HFRqv2|K49-s>#u#OEMKCW4P zr`we_q`HDO$e<-{#GzQTs=mO^RRiol(Q{ZX#cPoD(meIKWuRRW1gC;yE92P8IL6W4 z(;wPTi_ZZmjt8Enm0`hLEtXNPA6+Y-k5(FK97c3IEscf)(r%nuJOFRPr>2t+Bp2annN;eV=Np8Fb+u^aFybr2X;^$ z>F;l~q5bs;8VLdj8VdqIutZHnuuM(tysVFHir^CXG$EL-k2gUXBOypDOomU6gZ0Fr zs&DHA(`rncmKxgZtF!kLg#MI{ERXl@LG7r|(usyo2NZ48JNd-SB~QrFtx2K21f zLb?^wB@BlL0pk$#+88Ch6Wv%<=z=U_komPij`V~Cs3aIYE<~c&<4mtix zVIM580y6z0hg~Bs?n$Ot*A24d$0`wZ9`#UXBF}Q4_zB1#r?{JY(uH9-;)P-5P7A(A z=em?`q3!?Yt(Jp56|*pdZ~d>=_ke&TdLM!;nCX&ry=Ng&#p`;bAhX5mdQQlq14=BS zm`*eLhdmV;y%1#UfXY%Eju#uJqY!aI6EP+o+esQaG*Xo$q}O74Ez^-%??}O^D=P0k zFhy*TjxtV+?JtJH4R32UO46#l#o%2#MU^Q0Y#D?yNWGiVtI4{rv&4%eQWL7%W1n`_iq&=Wol*m$yixOkY= z*q=^U7|*8DwJ5j_Ib-i2{tgY|9U-_T%TTU1$aa!X24z6Zpv3hvC^1}-J&j&^MxgXfbAvw9od8XvG-x)hzpQaeM~dS#sB6nW_t}#cca7Q7nI?nm>)g7??a^ zj(w0IE_kGxR9%fMw!M`>hh`OKiY&H)Md8IZdD=Wkt;HRnG3<)ALT6xk1|Qw4Z3}+2 z9}vF>UH2(zA|B76UjDm`T1M^#fWUKY7CegyQdmB?1l!zkr@hK5%re#zgmGYjv@`C|gmz2wRV$wW>xV3o#fCFV(?Oc-5XG z_Uc7~Ik8VKIwz)6oJaGBOsI(Sh%Ar?UVs4r9DCs-iTpuwB=W}+#7k#zJp9n!1s}`D zSrgHQa!Z`}bT68~(MqwX>@|CHCi4RE#Tqw%4=PTnRtR2Q5tsB*xOErC^~{ofxEjf3 zdU!cz3h$%oYwZKX4WvsM^l_8*we@RjFS?Bg{o1(Hu2z_DU1~}IO|}u?NM;2Yn!>Zf zN6=B&MwAQh*)s(3A~c2#>1;1*NN{u;(xK7Ci^coUJ#0u1n8xna#*uGtPeb~%7d0d} zx>r|Ydeh~IIAq=i5hsc08~3ioG+Vn7$#MiBk*p1lM0da}(H&+Q>kf`Ycevc8J3&ob z!pmg1!r0Z2zhQzgu!~!|&a0zP;#mPt>1ahRB!jM62R<$8AxwH)t-KO~NhC(r(O#mp z$dVE!$5=$sa8a#jaanKl>{@$&VFj)2ez@q3+9a{IcWv6PN3{+3MB8vA+CB~KDYT7j zzK+Gh5!xO}Jr96v@xtx_TozTS8EI4f+M7m(!f&)5QO?h`v2@$591;|*_yA1e zate&dA`v@3G(qr2fBQT7(pFiTvbVPftMcv*_5cC4t1|XMpia`1mVGF%EpZu3Q%KU; zw+~HIAg9^qN>h6Gsl}Rx_o?;-B1bsxTOiw;6-5`IaGWWPqC#fFINb zxcyN;l6Nvd5^jTRiMp6;%vSvp5oUZUj$_ zqs}UrM{v}+3>+(tI_vkPQ(H)1_tYk5gg7b$v0Yy})g<>NlLOd*`Lw;3$0)k>t@bu# zx@p;$dK);xw3UQr82sCc>b`WhN!yn$HVywTfCYW4&BMLn1ZYH`aCB!-llrm@Y6|0X z;(vji>E@FR6O4!_GB~=ou-*VfvW2ybA-?`!5Z`el;;B*95{S?DR1-)tV}HiPxQl^p z|1V&B-C!;;dUTlVp93B#$|X5Xr`B zlK{@fZh{tSW@GU5EjC;7+};T=2UkKCf-BR6Y)gMQyV(!5yJ+tkdL0)l6R=ZYQ}Em# z+I-RetMDo1EUB$60i`c!$diM4=+>9zPoMNB&+o~HFT?7bu$T2eNJt3%hA;=u?zkTK zhN?I@f`^}ov={blkRYALl}LgF#^6qnguiGV@_gKFXaW!8+qjH{45jRN{XDmH1yc zy0az|yqk>dVdp9!4Yfg9-H^lqV@MoEFh)rj@hp_#xTK5%l9kG7wE@n7$~c9Bo|Cu) z07)3Z07)1DGA3aJlQ9V+*g09XOMkbxkxKz2mbg{{n{prG=HWquNdEw;8&Vf%R~~g? z3)vq92d-%){O7R*IOB2=l#Lh>ijvM3#|q!$x&~J{LB^rDtANCNIPVgF9YDkJ->5LI zHyGGm{JAz^+#Wz9aS#T{yWCdF9^V8>kmn&;?{RD-+@xNHJP&ytg4OE$3w+VWAdmnF zb0Y^*JB`F;%uY!P)@dNM(=PZ1v(pX(Yq8VbfP!aBV36eV$pfjKCR0?hDVzh(u){Mh zc(SVoG}Z=aa|4nr3;@YuJOd=^2iA%D8S6y-?A)z>R^V3u0^Gboa8>+uurC_=oTaX- zpe)t^ufF@#tG%$&vm*ZO2F}o26-!~06@JU&0Y>WNm87C~>K4{>RdgH2m$)|D2XM8I zNr;880=I>n7)UMT6sCfQ&9Q|Xt4&^)2i9gG?1@k05=y*LCBj9>_qJu?44}lD-f&Ga(jqq!&JTp& z+02UrwbT7+v~r?9%@N)oM01392f4jF%MtbfJYJ64%ewt_ZQVZV?l$qPUksuN0vz25 z!taA~)Cg(F$SDLo=JR|O2AiGUb5gzCA@kXrQIUd_pH#k_Mc5G;YW7 zG~H$y0z#qNEclFWvka+D1EL_L43-DBa_kd*N8$>OAt7qOY z#U(pl7B-Pj87%uWp|x}zlaY+&2xzWi?9j}kiV_An*TkR0kk`fikm8UZ3p|1!_}nl@ zid;X{kts;G6)dCVUXG!S|HLKA@Lky}v~iX_4E^50@phosPEDhw^(KOqvG?c^vDqoCx?5fS(fews;A0wQx0tCAJAmGUSn zRfifSs5-(&kjg)A8ZULO(ROb{#$D=A6jwYd16}Rn;K?_zxR9xEqL+r*he zQF;$YJ870j?_)+3iRB<-_>n2P{!U1UxL_y}W{yum`8^%E96yf$DG<)0#py%QuyjXL zX}YpKtgz5Gaf#A)o*9aEH31JYX{a2R;qVfqElL_}Z=u?nG@Ghv(8dMIBreIx|pVP?&$Xhoacv)(%BkeH{BFFZb!_>Y1!9~5j0PnUc$-6oDrxdqy=-KVwq&rqCDXP})C;r^L_DamL{bEiL% z{)eD@x5ZEt{ZBi8^k#oYM=4Ut#>#K3mIjY$)ESWpKSaJGMxrSL9O+Wa8Z<5G>c`^= z>FP(%WyUZxW}u@`>Zv5Yani09yJeC^3u&nG&c^dLatE(CNIA+oivq?uF%4OF=#+^S z(-kfB@ox}b3j^o(mE@x7lafNw(jktJx~&1sy$k^nIlLxbQj%i-5GVc#mV#F!l7>2> zGBFKZjd%&4;Y+qXOwK1dpYnyD?{tvhAIbAUUY@pkN2y7a{I-;c+Ey8RqV=O4{vx>e z!>ycgsGxIJ8`Wzd>RnwEn@USChHDBi7ut%&O6Y9$h+CCOCTxi?G}#YYZk>q^Hyaz< z?FmgJTb!SX%!?fvjknb7#mh>Th_@b=;rL$Kt}MPWs&ptiI3%0X8D>k5G$PN;c7%)4 zK4mLpHrZRvA9LZ4kfLT<$FI^!B_)w`H=U!a*Ci-M^SX>|E&b?G0Xw$~N>N`2dBAfc zo}yl%cXSSQbHEbolbPsOy`zU`lVrYmq^bwAyT*@fOkNTo)f=WzJNSj(h^zwF#l_6F zXbkwF(Gd|SHCI+&w9h$IY&whtf>KlE9nxzO5_TI#LqaJ{d7s!JKoGkp(L4 z3!?nq(T(dn-F@jmgF0vvX9a=EHX*Qo-YXU@%v2>xdCIni_je9eK}{60!yM_!=I|G7 zamd^0NRY-TDL@RQUpN)?BuHL1rd5qr&7a4{%Bm5L$$~UX2~yr?d>mRc($Pzrr!2nl z>!_jPtYN6{C`a;R2zFksnIqOzS0FHDiHwb&#%g2B7h6S(9|i8L{9>u zjaY498|iN)0nr8!V2yhckQPpV>S!YcWht!K)k2%8j!vEg(4F4s(Dz~fijfpYP3jET z-<9HMRMtXlDThyUjQ5o~DM885s6kqvlD5ynq;2IU&qFva4Ny{z9p|)2S;`X9n*muU z?nOtLXI;{p122Mdij)MTH$YJElEdLiK;*S1F32iPP!hOW*fPhl%#$EFW^6N5xY`jf zz3@1K_rG-X^dxX=JV#q;T}=WnEEp71QYXrj^%~1S& zM|_2u7RbtHL7nKhW9!>7L6C9v(dLBqk# z&&#ckmM?*bE%yBH#jJF+@}zPP!0nGCxVO?V*^|I6jDt!7;uD~S$hW{-cyz_x z!g(bDF+3m;Rza}fLEvuTrjo$b!Y`{F{XGfXEqG_wG;L^s%po2v;5nq8+h7_{<8-2o z4;{V4K|tZMO+_VURj@Gip`$aZx?k5{>^&T%zoW3vBKf8f#Nvdr@Jj^UzIpsY?rm34h!6UnA5#-dU|MYIuRM0rvW!}{t2Fpw!q z3apJl4DGUI*eYM==!X)5f`dE(NRy-eCpM8@Rss<16lA0PjbJbyJk}6)V=C-i6!IO& zvFuJue-R|{IEk}q5Pl~59jK*4Hj3Irg$;R6G74@wAwuC`*w3;On1g}2vl>$28W`j$ zh+Q%|<<*xpPx*fC_($rb#2_7P{8#rjQcq0l={O8$@IJx>DC!C*u80n%_K0qGzRkV*Z~ z7KjKfN&;64WK#DesGig(K8}Elcn<<>gbHhkry@vIlj#VaMhUqBs~&~c;t)k8CZ`xR zosA;ZC}M5N0I{K(&PM5b9BC?PLyh(ynT=Z0rq-+3Z1m4Q$fI^VLPf?rRPr42XlHVO zxBT^f$3sCnsH7nxUTCS)MmnY}CL%tlM$a5_^bAxIu-QCm5v&26t9Pn9N;)F8mwr*w zg0#tEq90023$%!ULRJ*B+WD^*@4*FLOoT!rTO~AEOjL-iCGGG&CfoIol`WIXS|;1o zYIN^Ig={x{=XmHXrD`5$$%^6320@aB$+rC|$9$>RBUD7TP{~8KjnQ>uYCTzd+R;If z)Jht+2gp$t6WOXYsNWd~3_S?Q0CAL!=p&6%5)ktRg1%=Vgz_M$VU3AYVp$q=es5_z z^3s=)^g2)eCCEs^&tM52yP@NO0m_BO#1zIPVxKtyC5hM&@d2#$+k9 z*%vi<@35Dk!DbzXnqG7)61TI~4_|cjlRmGBrTWa(I-JyK{oMWD@aykO5N3R*yhm90 zOoMt~c8r!hsA|Z9sdSqsQxku5+!t>#rsn{t zAO&a@%wlVIN3nr$>yaoH07<&71gvT7sZ@r4RI^Lwrle7!%DIUN8uUx%Br@kEv5OiJ zFzbF>4e>dumLTXDZAKa@-43r4YKVtYr^?SV!W6@;8Ra014mS}dgzAY;QH$Z8V$F9zU+;XfFzt*C)e>$2=dp!D#*UrAwDwdJH)xzTLs2Xc$ zdv80k_@qJDY$5K_qOErv8PYCg<76yd#wA3r?8R<7v~-tXn-<+jhTlpYzw5Xp-BZ%x z6-&<7(2#u#5L^rKw!0a!vGB1LB?pflfZCmhmG-R*`vsvke>-B3N^~|sF@HJ&(6#%H ziC(cfNM;sR^pBC6z*)5+9>!vfhL?3#ulBal5iy2&tj0a%oC52ml z`m&Md)u!%#MCj0ne_)v^Ldg%2Y!6(blxzbXm27<-mF&ES;Ci!_bpJ1s9p-MvL$al> z_wYxNmq!JoToD$}L9!KIQ=oMe{EBy+G*!t5$g{ins(@C6jBhJWQp$A}m22o@LMS53BMogttjqj3;Lm zRHUi&qZA~d|FR~9_^uA!^L1uz2+!d<7UmJ1nOw7VSLy%FGCB{k41#s=(B(fy{(P(?9R_ye5z+}Tg) z1k(eZvXrO*Mr0$Zm7e}8GC@zD(=GiaP$%_MRwFxEz4UY^OQXC)0N@VU$tqIbAv;-c zhwNmzpy7RMxzbc+H3B_}LBFKDL!jXffkw(Z1RCymK(`aTvZM{lY6N;cgWjRML!jXf zf!?RQL!jY~3mV>oW|uB2YY^1)4E3h+4xnlT0vb-<(IyE7g=z^h(7BMw6UC3wvNK+f z+?#qsHaSC3L?dT&{B#}O%TrR&y}Tej-OH;Q;S39Jjpy({{6^3gxN-7s&?Eyo-^gi- zj>pU@hh(sVd$3fzZ$~DO&Pu+?mRI7D5u96IeGph(J@y-^4oYCW#U@XUa;_7k{>pN< z8yKc6CT;*KQaA7@1#tuBniOJx1KJYf?Bm%4b_0$3;krZ|Y=i9)?$E4Q=RIkP64T9G zxw4of5K|1OV-sg9PXe+gU!kmfUK*1r7*MqD}sdJSoT;IDV>A+M}dkeEk{kO!2HwSgJZyM?!@O&LZiQlE}puNgX{T zRYS(UG?w*{G3*OYmF_9wAarM2g12!o_Ih|cz+>e$`Bn>Ojv&=HLir6`4jap4OEB0- zw*-TgcVJ5}Q*2>Gajl&#q*ls1WHT_qs0cq&0RV3XCgCR8W;2-Cu(W2OZX02XP|z6J z3sg2n_5!n{LCRL}HlPTVNtU6kjPRzOLdOVS)Gi^vxm2u# zKMZSnp0x?cu0&^9jbgknNaob1mGoF`H7a5z+T0z{zD6;ockBMee*qGEaYQ3ekc{k+ zL&ZQNeU8;1ULk^?zV4sm>?}y{DFKN(*D`gkRo)@$+{M)Sh4Kzj=VwZF?*BLHWRLwp zV?>?G#+W)UDqA7ygvvyn%F0xo9wkH*hc-Z%=27fYXQK2#iA*%qcc}|8>kIu~e=Xj9oB)8;f~BSf1} znP^j4nQGIcglOYbPv;QHqZqU~SgKTl5=DB{A*A=xozb48M3H2DZ;g_kDDqKyqDaqT zPepG17a&BD9w1ne@UvEsGkZlA89% zP}y8dtk^OK4WCrfOlqwpCr+d}+^J4UJLC|v3-Xu~Ap2TpiFqum#FRYRC4SzcgxJG_ z)y{CqqnJ2`;Pg&yB(;(!Vox3w3H{RR4dmH= z>4h52uo-tIE_Thh6ay^*qQAJk*{ zpg7NVi60x_8}eG8qlpVaAJ(Ep-yKCXQdH4pq3^tH5q<}!H3 zoPR-o&*1F*828#+90 z-x8(I^l27eg}ETl+^@uE|S&C0qGEXUaCn^5>!|HIS_%0(1l#VNL$wRcKMmbH= zugYS#*A?fgu0O8xACk?z)432C<$EP4JJ6dKZby0 z_okWzDqx$&J0ASIv-^=s3KviJCqjDfK|pviJ?*qg&ngLAJVj1|7~O-QhH$;9WAqTN z`-$JBN=!137vAn35P(ZbtO3ITXZNV|W?gUDYl`!0L6VguIQoRaG5mRFl(b2CgG}3d z^Xas`UwMa2+k5gA)Aq4^I&FW8Ey6RycN+=I@HgetxKr5}OCo+(wt_=WyeRjIGt!eW z68I^p=>$#^e2-#RM#vI;-vWhcV|97Lk|-c;hI5W*@%c@Vpx>I|Op+SapojfA%Qh-$ zm)9m3E6SYvJTb^jx*?zD=kMj?{JgE)@j2%%LF%CdC7D3i0-DuzRo)@W$S$DD&?xT^ zWn>j7l#verIHUWo%3v8CG)9sDWn)Yk6O^rxj1DT3FkD%gW^^7ULrO>OlCp{U4tfzLeW8d3U12}q3KGBbk9$~#~LEVX}7KvVmN%G<;e z?kg>!PT}J$f#mk?VaZ;v0xc3tP`1b{A*N6t)^ z!BQ6`C@}($I)pTBF^qlBVyuK|Szh7jjm41n4_1N@O?uYGnoNBa0(*~Qw+GKt($~Vo z(2YYvJ?Vk}W@yC^otjHy{ssM<-;zQCmF>Hd>=xttN##laELzW&h!nzJbI$fGCPN{8 zyCG4kP}0}ZfpM>cYgaOW2>rI#ou$(1e?kBD66d1=h(kXvagLPae?hUoVAO-h-YDWwOF^N=g5y0c>36T;fSey1wiou>QM}v=-6f7fyT9lTm!ZV-zRS zHJy59#V8?zILJ&rd64oB4B|LM)^rlgY)z+y@;({G&CCjao&qqB@#lC^$32Q;XpjI+ zl2NQ|(q(V>r!)hUO<{XeK}{OuC`oEA>f{1Hcu^-`NlrL5DLMT=*1kJF%Hnz0doBr( zY(hdvLQ4p}CJ>594}=a0p$G!fA%yFpN$(&{nvhj`?NdMf?7!x|%U>YM5I&b!oxsvQY#_1h(=8Y>}W$tfGw~Q6< zX(1Y}c;6GhaK-yx|2$T_LwL1qmQj9IytlVmCa|ltmBxzqG64ezT=7nO#ucygo7)As zV~1rSdq}9u#qO_QZn4`7U0qj3UF!X zp^R(Y6j}`_H$}?NEBHtl3UEEzm!cqLDDhCnMWY{`S$>9olY7w<_Jr$rwu5P9xgGpv zf4>XHnSw7a80Qa=3&wi;Ee+#-7Am9*#;=G@^#voI)X1iC;{b=Lt5p;%1~@ZRlq)!2 z3|#o+cw=bU1LR2!1pyWr@Rq@^S@}Nj<8hN9eed~o$`|*Z`yaA&GqDdT9J?)?R!&e1 zD0;2-;Q($uY=J#fS|67}9|#CtXR55s)+l$CBbNFmc88)GI=oN8LWkO&0ldUf%TPZE zs6z8V>d-nu(_@wuDuSmm)maopz}5hV1;;IAeG#%Jy%xxC9EXNh@`sdl!W+p;1^Q}G zQ33Arw)7&WCv=+&Fs=l_$MN~FPVZP6npi(t9mCbj17)~+nU>(fW5htY@EAo)aQGiK zkOZp9KtKel`9k07D$p>Y{tPrQ!$3uu>C??@$_1yJP#Hr7tt_XT3IvCGTm~v&G?TH< zn?uQCyijVxlp%&cecv*TDPY5!-l6z7JrIgWl^n3#SvA1J3EO$W;Tq&1WUv{UcdD+SoovyJ_v zBZK6A(ou?q)_sdw_kC$yMmwwAPdYG2?kDY`1aLnIzi_Y%5QUU0fsfcw0x5Ar={H*4 zkn%p25}zNse#sJUW}$&-5T(#yIS3U}ip61*GJ~bpmZK%uuF`{v*Cqgg|8o$E zlwJ$W%3ihXWePK|@m8-INV#I(weK~{71ox5jWMDV1!Kf%JDAV?4%R`w2)M&E&>zC> zZ(+khLGY3-S~kO1wqiHoAmTVm6JV%=#COca>^?j zI=K>Q;ytJ;*##5IMSq=3?Mg1SD?Ze|fVZ2%rA07h{U#1t; zW@D9ESUhW_AuWG(fq%lp=|(&*xkqe*;9ks^Vs;C2W`1FBxk+YFOm;$#4*c50VHH`43@Ab^ z70r;3QpFGml;Pr~d;S+M_$XEOJj_l4 zRCpnS_k0LrhA-2d4=s(gAsPIqhme;Z>cevIzpxyRENf|fGkEA9mS%pr_W#53haaw@ zsr;@%vzxI%U@P=>R zcKl#Y zi4__``k!c3_S%9K&5=Xkb-KNgl~|NgEF3q2CzX|sTg1n4OYic}${ z)~EP|hVb@T)_HyyFUV-4)f>XEF3(D2^(o)1o>_xTb#wU^_2HWW;+Dw_c*!rfiG|=A zq0&q*<>-P@<;Z8_CU5Oj+1wv!Cta>li4CM8`WhJcnxo0FaYBKSf^{thdiHd>xtRNs%pHF0)A4`3vma-n=yKDY` z(kFaVzBbtEF<117D*GbNdNQCN2qT}{8bAd_eZd}ep8A51x994s_sD`V{9L@XMn2|7 zLRDJNO#XTqIKkbMih#PB=&dWbrULEJx@OATZR^WgzvF53^nfY00P3aK3iG+qSrKN@ zvL-3E(rgtai!EFBFD=`WiSLSuKz_TtH6g!kTAazZSFn~>h5NgLwQ9ggA=rHL%h5c! zqO~Y{*IPRNWkU$|JI%q7KEw1`DO)IXd6@}HgUg28)Xo;kPuzGpBJ!}HMO_P|c(sys zT&@(TRG(T4q?2^zS#?RLTlhy(M4Q^B8kw5rLkx3MPi zcvdpP^t+e#K5tx@|Fp{*%j0WW>+lb1ST|}thjM34D?I!|g+Padn@&K;ztJ-c?X5|& z_ROzCb{)!->RT(au9P_{ZnU@JIvLH<(uT^OR(Pjv1Di*2pILjeRW^8q(KF)55044s zAkbd5~6>_kG?P#48L;EzPg) zC>G4yH@3bSz$`Epx&E=WV`1CcfvL=)kzBW7A zd-~GQpykn-acdSPZn?K+;nVUk(TT;!W=&JX?<9WW{Z=Jo{LCaRz((Gg?b^aRiqRVK z#o36KRyUhTr4#SErcEUfXfv+WCqBcZw2L*B*JwYmKU+!&^W2TiXg~ZcODys^*zmvs zJNeb#`piQ-0=pSpKx|8{x5#bD$w33Uv3DtD+&nz4R7+=$>mE@rh z7`}$&p+u19$uC7zW-Vwj(uZOXeca{DLxH%<>HN%ArZ9)5886w+k+)wdmfY@~0A46w zvd!saEnY<6$W6{+LIC&^-nQxgeB2TNr~P0O(VM=@iSzGcLyh@jgJ<)fE^H)K0`558 z>|)I@u~#V^-+G-XASj=H71alozx|c9IRE8EYdyB~U(of3T7A)Tx>;|s_5XsYFU%E8 z+_!U6B$+o4E*laepqShTug3$NeSatz=d|}#2|&P4FlBc)@qT4_Vf>L0yo^iQMlpJ4F!T5FdZ_xy%@_5Y#eo*i*=1y-EGobLA{Q^3u7`Op}$0v_d4A_83Gue2U&6<{1{R`gpHDGY= zUfVq?f(Xd5hO6oqP^ktmC&(vH91`FT`?c+O6Z@G`&Q0`acWgY)1*7vFT5N2fJf&dV zKmi2IbSVg!ViTFJNW08*McIk7i%gdmVaFcmo8~HQmuaq|Mtk&Id%%J_AGJ+Uh4X5= zURPF46u*)WPBeAWy#<*_r0A`d2>xC?2#=@i4m1HD{95Q9!Ag`k(m^W_(Yy~KZBD1(hx z2+&_qq*yWwxO}4ZINL%QPpOevp3j+Ntgf8(KczYN7TS+ftzEVIcJUV4K=zQ* zM)@Cr{P+wS{2+s{7zkZ+O`?i`dWDh zzQUG06RcT<(po#vk)}Cx<`roPD6c&KVJ2jw8d9)8Fgd(=my^89IuzdSVzaEpOsqG> z$L99XIr51srB|S(KAUar$(-JmwH`j)^rymJG`PWR3*PsK*UHGCqw7Pl1x`#>%(b2{ zu?3Womsjd9Cie~W`BP3f8aH>!(YQG+5z$2+Bkt*h z3HFaA%dnSeInMB2a^^9^%K(!4{G(H+VdaZjj4oYY$_z|#M5j#k3CMWL2Zt%Bj6*Z6 zED!W35S)&+UuAupDPZjN_hN!7v203NxU*Uj@h`2mHV>uv{-5XE;zf!)&*_ETWc28G zjxTi{0(HSloqm|&9Fj8SIEQTN%$-Bx0iM%VezK>fxe5LgCu8xN<16+BSvxqC#ocR61=Unmx&)<|Wj5lpKp5XZttS?Sr+;-;5 zi6?lPQjVa0UCu@VtjO6o(e^9r>QftUgr`$5S`2T5D`1>CXS(Ff*+LPpof&zxbA-`O zu`nS6ZzJio3Kv(L{Z*zw&}gYZM6|vU=vlWCo3}PKmG04AYsY{Gz(8i!e)nN@KQC5K4OTPuTRZw^C0?;N z#udHA>s<0k{lf3kf@AwhEl|ySTy3URzQyU<79AX|WD^Wugp)fRqbp_s3Z4O~B!1x; zph{WtjD1nD{cumbWdwOHd8@qeo$x?@%59C~;cn|-pZr$?_Yt?XzlkLaTAqa|CcqjI z2$W$0>>1vRgZ-qYxJg*)uyq{kNQlcMSk9=C4WkHDXK6{x!W@`8<%qSNi47(-IOdP~ zXUR-h?<|=KOFs&&oZwS!j8ZL~Eu}TCH*tYr& zj93%NM-G$1SD+=BW`hz;v&Fn;t;K57Vx$kn3jK2L!~1m|Xf>qYMyKDCmLPp7!Fvjs zv;;{*i9(uxXg$ED&}v9}f=+q?EkV*yf}+3lk##y-NsEy_6np67z5Ii;29n;ZlRiaD zkTjG4Y5iXQr?ePJL$QZ6CLjHD7U8)@^05;b&L{auv1IbGUY3!3j1V|7`S{#$_|++k zFFJ2cj!P3@ntQBIoI=k%ioI$UB(Uy_(EtuD;jTtLU){^?yRX(69LCI)EKc?pd})K1 zx1uN8CG;(QWcc=D_U_lUzlt!$rCPHzhDzc1^ikhp{0L7WsIa1?cZD|2xb{fJYrBTao9_WPtu7NQIf8UYR8GtYshk*Ef>WZJ#k&^b@wcp1 zwHhOMhg*=TOrmvAO$j6NP*X!7tE#SknskO^&| zAfOEt2-@JnJ!=wE!Kw|eQSxX5wYIQ9v0ovpr+`t1vsE4P*az0{S>Q--S2c~4u4cl%Jme8MRI=>!f zE6FGV$EO3XJcX3(FS!U$Sgv$fW~ zAIUQ{TSs=sn?lK4uUYz>evY`ko|ODfIG+9(;kP4YqIR6wHb8qal3z93EbO^a#LI;^ zB%tWY*1!GG2o1FTp+${y7YMRxCKlsOgbcTO4mEO=9Hu-%oGoCRuT>w#dl$52v&4VF z*S|bd)xd{Ir=Ek&ZMUbniso?hV+s-8GejN>wpBE-o|GxNShrEKe|MuL7^fYhWSn-< z5)44LQF#QQu|SN(X`&H56ARoh*+#ih-&{y};Q$LYF5ZXn<-%I<6U&)dFBFd z6tThk)Yqw)Xn-ra0j|&z&j4G9KaJ=Sr~MAJS`04B9??2ziU)Z$()0?N0vlN`vPXGh z3;jznz!+^o8B<%tyh3^j+5&1yTTm4J1qIf*5T6)hE3H*~g|CjWm17AM9b_HMPgb|( zaiFfA6jsdd+{J7a{mLp5I%Xlkg{i?sd70w2sw|bVz>x{sq}IDA>h=A<>%CS2>Sg`k z^-@aODzj<-cfCiV-qQcO-u6;ZZ*yMtG-wYduprhJ;+e6wIPJ(Qd{(TjEIUeZFy6lP zFA9F&Q1I$FTLr%gMu%4{iw>_q(Ba#bwmo7B7#;q9io+*zR>roJ{XxY%pV*7{_R=KJ5KSZLWpd1>rD!?Kv~-#=RB`*SD>h~Rp@eHadjb8TCF^GjpkNZW~q#qr0< z`I`J;+KN3IxC+eqoAyF>q+$tPtuaMc$;IZIDl5ydl3+ z&o+r|r_IN__+|pp1)DeW84iXPwI=WuRKue5l>DwTpPqvg| z$!zJmF(g|maAdZ$oe<#dF?>w2Eivvb0e&EIkopC*4W#)nw(kl~bU#S8#b_*@%8iRe zxUy1_y+pxck*KAQ#Tz7k5a1d~34Z>=-hn*6wXGnp*}@jZEdKDj5}+M;qc*mDUQg3K_rrT-Ls=^2hz|yQ+XfoBnSxQP z{*<(v)hV^Mu0IOfwzgLVfY6fY`55L zS{o-PH^<7!$xT|)f7^%!+H>P%@Y2QsDNpafQT@qS9Myeq12Vp+kFBoOZY;;x_19R8 zT^jJf7_aca7_UGu#N2zOLmX4U5aZjknp6mAW~y2TQBQTUWvO6{64%GdHR(4X4SYO< zo4VL+>_tis713#&R759Q(ySXiC=%Y)*4=C$2gmmIgv2u^Ek||0-6rwMxvx6L#jpKdE(v&`Auyj`L}~?aGmG7Hlh6S zLwFc}Dd9+Oc@VJ2h)c%H7`|b!?Q0%C%7$+Y;P&KZup8d;7%%s>ueB)Dj=f7r$N+!b zs~TPgx$INzqEaoLJ)ku(jNesiMDZaOTR#>u0R;7obSPGV3347#oR%N~C=v4j+zGEl zOK}U-;~9Bjti;SxG7tD zm@Pl=q{F6c0{1v!7<-M<#qj(7Fxzytnu0Oqx=KJ$QvGU(f{EdCA4M|8og4wiac9YR zH-yhgCU%b2#E5Zbf{YkvXbCQez6I-viy1d*i5QS$(nJF(qM(7&BJJ%|3B5MD3r-@=j|TE3f*V*07!9=A%RnV(+itT2N)HWGWs>yX zDzpR*1bV8-`z^CY@JVOGi?9@0K^TWWT4oc^?9^FqYbAz(wY@SSPj8F^`eS9l0P}j3 z0p|76*+5ECr1UhXjNVNvi^IFsLTFXgUBB0CJ(*Gub@$I0a}i!+al=4qwe{Pl_!p>A2k4`Xc@{>x6BWe4_XE)qh)Ai zd8}H2plK4mVD*>^R>z%y$=+_Q))rPt$+p$>M?`E6NB;+WG>AeL4YJcPh**f2PRcPJ z=lD4=#)~=r&Ixic(w4F@Vr?q{)?+Qnoer8Tm!BCF3vD!DavnBvP0k(daW&Fsvdo?K zq$F}zBU*Q;4zqbvE8gs+t+@~rmm(`^Z9~juK4M;6X?w!<`qajw(z_@at%$i(1&l%M zy~#4DeLxYgnWyq<=BGw8F?VW=5&FY?3YW5LZJa3(?AW)~+7j7q$`re^T2VL-8eRdT z;kWC`Tb8RlTnQ;NEL;gGvqQtsxDqNk1x$}Cp;F=(u7pZW$#W%?u+cWh&q`=ga@#;& zx5>3y{CM!ng1qr2Nd43m^gPoXTo@%0i0*LnWvn2)Y@~<9Nil2Y6@#qkWcJind-at7D^Q3EE6)357ou0s`*dC{=MS zkOw9N(mgf7FaEQi9cs9v4q3NlINA@NlmVm<2gHQk+CVF zkgg391;As6YddV8v5gdhtAaH>y5)0KU|67lek^VsrA|(4?x%2E9PASix;XGb<>%)v z3E*>cAjG`$FX(z|&IjEczSs6Y6FdJesQTPqLB(asO^RfULDvNshsu;G`Z>g>v?d0> z$5Z4KW1L+geqo$lGA++I`}N1L8{wOA+;i6U z8rwW8!C$h6$R`U?A$;$gr% zheqS)kM%BPnnptF58gF-;|$+)@!OqrWw6F4%i#!QD_ z$ENYdpW9O6jtDS~qnn6)dX5dAbLz(V^|YQWR-k%}6ZJihw6Q9$mI&vabFR z4u1_(Lxlq(WL>chV;O!B+)ci*!E-VS25z|-&C%n!+MF4gQSKVoY&H{{NU3^hLQk3~ zs2JB5QzRU^&QGZm5+a~DavhnbhxMJbBF3(*(`D@1N=tAFadJ8?A^6irB(5Kyo=03i z3v@(EEC#SdrqO7yJ~RO%j9UsLj9UuCi0dj?jaxrbX)$i8wR7W^0>-$tX}XMC>!*{L z9zKJ(I_ibZka}UX1ocvWhFn@zq$L=h%Ff6`IW>Wpf92?leBg%4QMjRU6o?VjRj{g@ zG%6S>N3ET!90iQZDKsNjIk*A12COP}rWQ<>abn(d8PKOt7DlX`D8RbMk$}Eph79Pd zC>CmLSza33VrUEldiD$%&=*q@7|g3P!(pqWi^jGl?%@EC0@`mBBL=zlb$DjlSeoxL`b2AZW82dgDJe z8jrDD87456E2F((D2(OJgwzJ|X*(Xu_C2prkKd2A*VRtXl#$VBvA$UZjBxN*7~$ZrK#a(!g4MzQMJg>0{%Y;q z!CwL6!XR9`YMTzV<}D4%_v%7TS!a9CbEZyi}x5d4M0P zNa2SnQXocrRKco>4pGriMQZI_6)9j;5!|b!D#C?tuz)q!{)GN2V$VYuG{u_tN6N); zz1sq;yB-OVL9=9tEI5m}AqJ0tS$RwcWlnpDp$IuJZPL4(sx}14BH=KIzIGzwk0ayx57)e+mgL zZ;5~EWx+pqD}S9hC5k6bCy0X_cqpX0$Q@!zc!OAY;v%-Q7>gnN$?v~_rg%<+f*p8r z)ylO(?g$;A-fr*<1MMw%c%6A6sRmH|5>7gAVE!i)_zjpQmFypd{9zF0oi01u@|@jT zVC*9S#Q+r`b1b)vYT&b*>_vIMR-KFS?Un7l^YLDAvmi$lPpe{Yz!bXuUm4bm->qVg z3kaDFudQS)GR1__3PF5zt@guAyZM%?_Ra;WLmf-vY;LM%Z&$<+SdTBI`GscoSbi;H zL$zpyhVF@vX7MA{?1N%%kcR1swE!{crSL=dQm76eCHlUOrLo}J0R9=Se}M=gvxnz? zbV!t0kfUgXC1^GrBiL7b6~9xpNF;wczj`=7TG<}VH`cJ1F%N*kh{ipV;hPyCZ4y7e zzJ4J-xoVLZa|D!}*xMk<4D#{B;KUC6o0|3}Y_cGUzA<*T{5a4A0)evsUyc#N`+a+< z5dS&Jek@dB!Ns*q{#7me$805GEoW1obm*L)ZU^qrf?peC4`Y;-{4~wBI`$xq?Vzk+ zP%XnJAL$v$zkS|5R697Er`EGK;%7R*#01oWad$r|R6M}W!($U8w0CCni0;nXeB2B6 z0g>AUH9Gy;F`M6e!G1NAvH=%xuZ-s%D}}CMmlXD6GxS%onpO^-3SabClxmOWD^D;7 z|75n-UAr?IpU2XGXl5gOb5(ZCi}>(YnvK_2J2tjAGY8E9A>eL>7Bq+N0ssq{L(C6x zE7eqx#}BeMD{3)F2Fnf`Fb5a4(I$eT`v+8(FG7SjwI`Y@dYDw4!(Rpft41(5GO6st zM1Q!V{Qt>hoz@PHVKlR+unsx`)&(KHpai~FvTCAlpgETVnvDKMZAM4_MRqY zRrrdx_i`Eqlwrdaut=3z=kTDG_JMv77G8U)44dH(A*z*qm>&e>z`YU7APPVFrWWr9 z16geK#sHbJ+t~Z5^)VS&Gki-x_6n^9JK_h2*KBLAtRi{&oxbhlqcZo6w)QGo=x2ne z-Vg4s*A}tM1TI`d?+52MXrr{x=7_iu#d~km4r)KbAMiw+0FFzu?_-ppyd2)AqrEo! zhtfOw{m?WGG?cShE5pOm?F&^bZ1(&uS{+t+F6jjxtp5M2Q9?x!O^!3XM4vd(Cc_=P zjIo zi}vfL&=kT&*J-P_v^3V9z==*Ly<~5yrOcH&ZE#o{uJwXHgiZmN(ZgQPAHf<&v~JAq zpP;L!y=5-JJ`K%HxbtnTu{Ob=iXY9QVu~qUD(PtY4SCvRd=b_i{IY$eDRe1ethey< z-uA6*C4rL`p6z3wsV&u8Xz6P|r|p!on1K+#KQyH{qZCmXV$X_loTI`3(PG&KIv}z6e-T-YHjsUGfq-h4+hZA7Yv-uA;Te z-;_tjIInQJr$yxfX-XsXAin$=W_Ll3&e>K7O5qp3IeBvV#BNOIESG zZ*)uxK51^(U_SIGtr(v)rE)yCX4==A%g+N1lnt!PD$XO_46pE&h1D!v&X5tc<~%sg z4PRLL3}z~e+H{TKt5#=~=4qfCex+U_Vuq8$7l8c64jEI^iuMu8bP&Qa7D));K z+GFs@LxW1;CnK~+@JByOGu|&ODmccE$t=J}elud5(>_w2%XBI=ydlqU*>8ldBH{({ z@FCI3Y!d;A4pN!l>l{}d8U^RloADDRqJz_xkt)y_oaW7aHV@$oW6TBl55w$(*%3lr z z*CgKtKDbcQY`ncLYeP$rFO(3zQT(5=@VHZdS`A6})=4{Q35o?JNO}S-LDEp7kWQaq zU&^v+H6*=QC%u7|AZaK8Z9#`mYhE#gmLhp5Rmi*V_3g?&qScW6`#Skcv;@gR3ErT< zZ)$u`i;+GQEA;znq3zgn^NCrI{@*(NFj|82p#+Uxik2X0DDjZSL~|ZKhKc5y>pMsC zz8# ukbt3G)lF`m)-GR3?U`o7pQPOs;R?Uq^35(?+8M14Kg+)juf{?w0T;qQ@_ z=iqCzmbwi7!Tb<(1$9K>#7*@a^ztI+uB;<%5iVRxMVgywJ?8WC++L0KqQ$sy>CT(2 zl{r;8%nqK92``3n;m6`Xn#U85IO^~-v+VgDWkuHy(>l(_x58sz5kk=6u>+L~FNNU1 zI+22LYkcxnme2KGXB2^{oW%b2=X33wH1-;0C1!rvW5!fxODR;WOtjbLyAv1K=bBUm zoVLspmFXf+;RT8ESbqGsz}{@17g7Coe~tj_ww50j8M)YAGk<}<8ow52p6j0Xy1g*m zeWCm?uY3kfEKX9Lp(S`*vdL2WX6@E|zIO89itIKmhEJ!~<2lEos+(?lIn1x~`Sq=X zYAX(ty}|K>8zGxMc7G>5^;#G~AP7z$|6K1v2dSYnfQYw7|bRrU>%a ze;iywB@dRU%=;A!tY$jH@9(gCwNd1bqAzHvFdP@+M^^VN#MiH~mp40s zj#!Ncz>nwn`PDs(^2e*}F>HjFK&e_5e1H!hEHLQn3oiQtxz%i0ARm^(75D<3u(n`< zT!Am~p}5GGqP|J+nis`oYwdlteGB;Gwe}k9fDfTPzJ$W%gm|Yx2u57}xhS6oC>J>zt%by)#thh~^NcdA}ZtFjQcCA7$e`YsoxBzL(e1a}Vx?X>^HA}JUT_Fyid zuL6dVPpydf3A^osR50!WgfEmXr)$Rja*n;9iA|>jO-)CnnRJ~@rUiyhR(N!>ik72J zmQkH-$y+Dcx=tqRI#D>GP80~VDaw6fzrB=+DR6Am1f@}l?kxxHsU~)n%7&J{theei zEx}fu=o#O`{0Lfw-vs!RmSd}ar>!zC@-yn4nCDE;?(2;LMz~j}Fv3PD5H!@2!}d(3 zfFZ)^R|+dFBF-v8oLUj_Uw9h~t>B|hzEX9<(0PjGFqF&lSgG9(_LrV<3 z3|ge?C4-iuUIr{8J;0TxUivJO9RL`iUKB>C7X^ats(cdGdHE4$-=`(0 z(p7(?zrfG;j1S^NzZ}p>y8-k>JpYbX%#G*Tb*kXIi}DEOR~E@&euWZ|iE(FmRda#G zx&aF?Wi94q&gD1jBWJo^oH6U82&1p*S3lF5-X=d_UjMxP z%Rp_}Vwv%Q|MA=96W*&8%nvVY6~_OrV~^wgR+(#8;BA;u$5n4}!g`z0eoA9aklHb`s z444l5c=+PLGHfR0jEb5{6$Nn?6jhWe3feQKAnb?pMKxewI{tfm-TZ28;_LkH@9j0& zL@EPnZJmHTAX*!zH)*u4wL*972iZZ2hgSnTb}@(Y1vNvX*fClrB9M0Qb)FRBOz?yC z^E=M6?7Tk&%+V`sp4p4{87)zab1$b=u)E>Uc9TgnE#dcDjHtoPOT3- zAqAs7YA@maIy+xd5uR1OrkebIw4=EO(kR+3yr6tAkNw?#Ilu;j@Q!Dk)tQ5eBwXKQ z;aMS$hB6a0+Mjl>Ep$SFX`b&f%$%_#i0`>oy##|F#sDQWTV#%aP|XgMP3Z^TAhPXo zf7(Z}RTK@i>-sCO3EM=$sNGddc+bCJf~O!nvv!x~ilk__NlHeTd+gu#A|~zP5$$Wf71rTkHKX#zr3S4p<4{8H(WhYL8y zvH=wBnRVAUmjlsV8{YJMbTI$&V{??8OgYcOF#lq(V_Lv$;LcZn8d#CdrE;M9UiDJn zQh)0EC&bY$pH63glBPR3hvyB<2)*~DHuK$@-);UK4-~F|@?6U-Ib88Ral;(X;D?v?PI+r@ zrQWJ#y7nf2(rN(visC)9;=V9gD2ltcu6i*4E2mWnIT9``@7QjB3Zj4Vy}3FwaUwdZ z@E@we5RN_=gJP7EsaEVCT#&ul)VlA|2^ld1$AF;+-O9r2;w z&Ih_G+aG9UV@b3fNu{&evicN)ogM1%GCzU6l*-TC*%P_!>>!V#cgMvZrFqjhM`@n^ zi}`t;{u!9z{G3)%xiH4h{bDX|wt!fZcK58rY>LFfoijNZ4@OB___%W|zPoZ(A^xPv z`GUN6ydyHdv1t&it?IY zz{)o6Hk^^tobNpd;a9c|Uo{q(EmHc2%b@mUXgdY66?!md0 zk^W-uRLrjieUIwkUO_#M$7dkhWE`L2m_5>u&%6$Qprqo(D-lvh{2bMty z%V#tPyde*p^m#)ZM~UYB-mlVtP4uZYR;iZGvS|%@hC`_l#gj8^p}c2Uz>91bt&2w~ zw!HxZC!Cvko0cFID8Zu?XJ`o?rGOGVN+AV};`5dU4rVuKH6(piCw-rmAZaK8(v3oB z0aAtn59JFj9Lrg;H;GY@Xwf%OUOv9B^g^TuKoqqOt&Rkt#6u9jPFb^qa{x=F)sSji zNtN}YB}f`dkn~Vmf~29uLmIxTIh#$THIV8=o$5kbf>fadsji|WNEJ#vRPmUKAOExT zenUQ0?m4DXf;UTb#PUM*6QdZdFOR9TdL#FkN?bG^Pr-vKrv>XUab)G}o4i1pqg7mQ zVGi2;+YtNcFO|QU<~U+vUr@1gv!z!kR15>!7jN={ogA~&`WW=?`m3AV+0j6)n@iiY zj5vaac48OD$vo;ude=9D%)EG&fVwII2G#QwU(9Fb7W;BReJlA>+9td|S<6cb{X|F& z0Tv^Pu%vhf++DuA13tE%N~_~mP}^m4cdacg!L6Vx;m)bN#zkiY58oA4%QT2D9O5X? zGHAWrogmWye$uKZe@rf(mFcL%&I(~Yr-1fkI_heDmdTBvK9mM-1ofL@s-w+XCa>7d zp~Ye!$TZWSt}Ow9VJF**d;!<C*Ee274=ypA5GZ9-sA&6(%*_rfj8#h;r8soj%%zTC5et(Z@F~bdbC71uDee<(mghRGmcE?KC;KdyTbI5r+hMtM*$$M7blHKgR5g!U4%gz2wJXC$({kLq z96=dO1z~-hHfJep$^!#*T0aa#pIEkB_K6if^xvTLH~FK#M|29Hj~@t!|MB}u$;G=T zYsZ%J-6Ngl*$E%2Z&Rvg{85c{Kzh!bDt54MftBcBH+`sHqf~$KM^*GN)P*>frRW02 zPyJXHtgJxqb1cb(6H-5CW8J)}L0Ug7QCN}ZEqozqn;+_Ri3+{Zo9BtReC)0aLD*~?c*_gv{i|4mAN zvp@RioaCE&bUiAtaH3EO!R_|a?yyOY1sc0ctKvW%T0fuxdqlxFU%0!1_nqo!sUn~) z?w*SX zYgATT+xbmcC(6yEEZiSdv@6zdu|Bk^W%z|zj(cpVLay$x@^Ek_pf<}=z+!qhbS0lO z$I;3QVMAt1HqBq%thtVkYF%i8e%fiNH$u3O!n5p6tRCN=XPu0QK6}J`VL2UZ1?6$IU3!hM2rbu*eW?%gs&prkE0P=MI67x zDnFwUjut>4JGVmL=-kjmoP#M%#0a54FhW#W3R6@Cj1i(a6%D6fN*zS4k?okOqGFi% z2sGG#a5GJxw(tqu@dX<~>7gA4LhHnFwt=(+?Eq)57HE@JNjprT#fBXwP({sMm4_Y1 ztde#ZLy4drlGm22YF@tzvVY4Y8Ke-z^OhN~cAzKbI0$T>}ni35A1U31}o* zLTMygLV=(<@tJD{td5!5rB&Sa8CdHwmDU(EFH(`e1NG%SXvUF2Q-8ooVT`>{fuLpk z0a0q%zt%W*v#`~~iP5sbtEFXwX^COkimRn%E7M}bvgKEkE>LTA9+r(?EiD^Qi5Qk` zw_3Ms2U?DnZA0mI15w3sU_X`x4rp10gJD@{BwAK!BwAL1pk;sBkeFh&GiXiTI6 zVnWVej&qj-qPc4t zJg{Jo3@FapQc8Gs*0&{7_B$H-p>+02Kq*P7@r8iWtk-{_wBdlGol42jsh#5Iq0=ST z0uoewbnq31gX2ljWVtBD$MtJKl0-a^Qyp?C;uWJcUTpLP&`JUJB!KWsUtQjE?ADH~ zkr!u`Hx7HZL4}7s@?K?0_rK5wm$CaWTSR+C%YkpD@yPt&y+ztXz{TB)(zv z8?6qy(1pBDgA|P5b1pmL*ekRE zT@;rvuTijY(NIy*Go`J;UGF(+@J450Dyt9xc~bcKFQbBqsuHA7!$GY-4oYy}IO`a! zu`LRHVJKrAvUjaqhwP_yjG&%Fjdg6TpP(+*A*GGAg8n&k!P3)!YSsC|>VZ{w^{<;2 z_9IK~qD=d8t(;(dW89Ij+_;=pf>qv-`#fiSL{#nSFfcOxYSS#&US) ztO`$_7F0g^UC|_b?Cg~Ig^!(`f;Nax0FM=q0NoS$f7ra_S&ljxJk?q7@gtd<3 z2W~nt*>8k}+`+uJPVQj-v`)VJxu$V)GGpt3zvqk-?tT?q&!^miC|`n>;EtInsWiB~ z<{^+grcz5*i&jGdiAW%d$M+4Y%93d@QW3>E72HGfqYKNA>%8~SKI;)!M(&}}`f?BL z^L2jr&@u#T+(UD$=O6#zNQ?VX=!EW}eMyuAo6?(DBG35Iv4@SKQsbbH_d`>Eg5#1C zX_*-LwNdM34no0#-qnFOnV6i*f1}@6!?t|L?9R;r{{<5aHh;s^wmX%<9;$HLjCHG zmI4!=^;hFk%$pnJbN*;th$n9dtYE$al?qP{tjvD&Vf;O1{JUSqYw875i1fo)&h8by zIJ;LMI6JTRo1>d5AkHMW13NL?EL$%pWPH7xHCEmLjr8;w!vGZoSTR7&l?@kYDH~+o zrX|Hfr8e6@l-hBF%-cAKQaR>r8gG!(#`=^1P8;u?u>_d_PY787*odhu2#K@C@wB!f zP8;N%q1O5}D8Qo9jEqcZQ|JZw9z!RKd&H}vM>w!~PampD++FIJuG`=UWavWcC zB{jsfQ4anQ8_DsNBrlXMT7ptdKRnXbr&`O6@?1)HT7rW=l;GKk!L$U=PCyBc`yc_1 z`+ihn3E${F?iVQNEG@@P_eyk_H3YKc-LXYMtQTx7YnbR9P~yoLq$GkWng1|w~(_u|8Zqtn98wJ z`tb6+%BsKw_5sDkC1zu+-(=b*0DHzo3^h+S%24x!(!nf}dOkuczDe%qm82w$^j^#+GE!F9B-49K1X1OP zMC2x!-;1PFFr!rS%)q*)O>!V^N=tE!YDmeq-6RKMmAu#u2J*O_=Sv>93;S=9+l2#t z==Y`c?f&TF8Hq?y2NyqqDu^D3Is>#Q!&o=OabF?`4~D^qY=$v zB*4$5UM}X`$+l2w(P$e$K#^nLKud(t^s@oSbz7aJL-o{+}NZa4fu|h-~TXcutNW?KV9O<0HBYy@09K&gbv++ z85x_P4cj6o(GE(%$h}$t6XZ1YBaU?6e9@k@{x5;UFSyj zo=^Zz^z{+gm;gTJB;zLi&leKBI~Ae=Lo# zkZF{5Tjfl;)mAx^&Uy2th9N$+1}L@C*+g0cXUb!h8c{s1xw9|JrqwWAvUsbUYHy$= zNB~N3s=b$%;8Yt*aH=gujN-FfI2W*wXf-7LzE1iQEkV*y0;HpO`Bu(u?0Z^_^r2Xx z-?g>V%AVUsOoa6R*6D}Q5~L3$5#MA+^N-s&OS4k680kZ?LO=6Uc)+bLt%mfIwn^2b z&=RB%B`7~WWY~!oBYh}V=;OI_3$2FqGj#f+X$jJY5>)+6T7smZ#6ucaIkk!MbQiU> z+^Vi}TFM0uttuBdiCg_HaCQo=xWGBEji;tN2gJ1$T<8L)CS@xVTa)hFkFcXuVB?bf zX$r=r&CzZ0lDvW-RvX!UYvnVgg?%fQ9j3g*``zwux;T%T*f$i8gJFDlVjw^JqVpy5 z?QQzj)DN^0j)=Fmkr43PHu3y>9dxFjrEaJRhRgd-l3jO&BW?Z zkx*UXi+Tt0(DlyZob__n;5A-h#kD5eVc*D^#FA-kY(T%g@d+j>e>;Ej!>cbXqb!C{ z1*GzTIluEDFh!NZs{wjlr2)o!j5aEf5m$!_FwOy>m*DC9i+!CnP3%=#9cP5ow#yme zG+N?sb-8N0TwSiA6^*&!3aW%{+vVI)F(Mwoe`C9x9KJ!R7?ZmI<5k4ZI`Qn+dkB9QtH3@p^isUL3o^bD&%u#p72%A ztTC|kR5XbbPX&VW7Ce8ifN>Fk=kH7GAiV@ z^WZ}h;_Ur=T5fpjoE>Dyd1FUj-a2!K^wyb_iu6{oz|PsBFR=I0a&*^Sl)>8|tT^1P zbO*7*hHEALr-Hxwp)Zz7pYM>p;Y%O#pHlMI{E^4*faIfiziBK;gSHrp>_2wMMfP7l zlpj*c0XzK&E>_u6a4*W($4c##eJpM#@nTd-iJe5}mHko1P6l#fCsTBi+sQOAJTA2r z!(%T~An0Lu244Z=@-CfPXC2ldMYcldbcArl)(X-Ut7mWd^H7A?U6 z)jHAnuJ+na>6i;>vEi6=sgmB@nU`a}x>GvltCWf{jPBVf9cLdcMkDN|^pE;80)$u9#gV-_Wlti z8@NlJy{BY3p1l`xi?jEN+{Afy>dr&yOXr~wbe^L#o!e9+!ULl%S;8*j*V1`>P;1X} zZdXyI2koRDl*qTuc0OXADLr(cv|ZAH(r5`f5Wd1`*`+^LXrtxAxTZ`VvB9aoe>4iH z_>4K?5l*;9cXEZZjRtJ6BPnc97YYP*5&IXs(4v4bUi|Z#Gnp-;VxU5liilt3abQ;! zAAQxBA@15GXNWnJ81@QrD*?V5WHjvXt~@4)WYQNc=U0j#>5H6+stm#?ruKrge%(tC zIqr6govlsm29*UZb5*y@Ra)Y)OgQgX!WqQJ&mWm$ehfCkOIiQWnrNdxNmB~!_G2TA z3%^Sn0YkKr!VqnwKv2iavYm~X0ye&MU2V6w(<&7ae;o&>Rlum9uYL5B;4b#26CTA) zr$kXd9iTm8=<7gBP(R^aZQae8yY-iXEwmi9lEEYPcszH6)QZ=jAsz~bJDyN27N5x0 zfEnsWVTQUsSMq`L4`s8^~ImR%6U@7N8Pj);uyYz$-1#HpBBlwp)ivb zTge$P;r)3~J^7#}ox}&pZ-bRz>paTNQ5ml98(xOzJPB;hzNBCfErsW#HV)B1jcHE< zYp7V*p*(As{@M?_<%;DeO2X(+w~4+V?)KB6#EPXTS+SIsJ?h+Uc}7DK*lWR}SEX&` zuwZG+YxWDR!;0mou9(8_HBJu+5m3*1MdU2AwFQm<=T|y5X#-DUi+ZsfjP3P65KA zA52k-#p%Nn&OSBFZ{`H?PAz&?VasVbHjLATZIli3>IhlQVk(lga5-rhV8zdb2lY{T zVrt^Wh@d*`2xTQw6QM#l&pE~V{6bLB3%uSwXJJ1C1{Miw!_ImWFbkFiB7st5U9+pSu|Mbd@j|@mWh|R>+bW$-Q~;N^sR)d{o;o`#Ke zJ8!Wz2A;@oz zIuDqbjZzlUYnglaFUMgEGn1Bhk<>o|HAp4N`<`@WnCC(3uXm_fir+j58KXs%3eumy zhYx?pnabwV5+h}{o`NxD1_+jSVR@<`cnPE*`<&w?P{&iwF6MWoKqdI`Q_d#r14yCo zNdAZJ1)sv?f0+1%$^Wpuc_#nMf9T}C$$ze$agJe?1cCeFL?*n$W}K-hN5D|wSCRc+ zk^k&}#9e21ej+)jsLcMSmulQ8+7Am$-Mwn|wglhwv9qd7;nMmtg&VimNa4m9?|XC* zvSSLj>t2{!GzjV&S66^&4mX~*k1yI4oXB0J`vkbFoO6ydv6o3HN4LZWIR{ZNy5-Az zx#c{#je>x)U_8h^iXwJmY<6rX-i@;>5Q7O8+xssK2F3KI`vd_6C%uxfD4Ig}|7 zT==E{J)wYF`*Ib4I36mU)lh>m9!x}f7@Ii<9M9=sb zR_U9twm(9ZV2p=u0@f`tZ{stKha>o3-#I6-PYGeU2YL>a-p|rrJF}0RCj7yt+AXD8 z6we8((wAxbNuzL|?a4m5j~7l$aGwoIaGx!Xmf$`cl;A!aNPzonej0E2aG&=`J^4rY zHZZ=#MC;3udN0`6&uPMzf;EoRsr&iMKRJiReJI-b0Q}tZ@$mhm;qnx@yTs4VK_>Ph z6&s(jW5r$h9NosU&XgJ+(6dm8F;EQ=VC;yX0X(30^`7$tn?Y;ivUkdUeY#Ie`fnSt zKwGh2=HXT;C6KDomhC6~Z3_?(xtdf_&9mQzdARKTxPsAu1uk}{P(rxaRUkM5Jc)C? z#1t@wDcoE5nBt2trB+1zn(?mMDi}NQ#QkzYISxdEH*xr14`J4LpVC8R{H!bEXIg^F zD0o0BBlrNc(lch^;1hI!D5L0syp*9GkjelSs0@V#Dno%_V=x7*fI%7U*$Y$#)Pqt7 zQBOT~_F@Vc_3(q29)A4W`6cU3>7gEaKr_S|um>%{TnL`D8hJp5yH{wj5$=XljZ8Tp z!(FOSsqP*a?wki?xN}k>81C?Cls9#SFQ>(FnMuiS%v0e&AH$tOKR4WI&_uL@(nPd_ z0zqX~FuN>q`V-pEJvU)VWjVuEDx!M3fnvn=54aiTQ$~Y7iP$j&x{?TYKLFs!T2fcl-z(L}BQ3vz#J@Y}CN_a}y8X^8~(4mm>M^L#g zC8iRZQ+|e&O$FE^<%L47N36R~MLhA;g@RF$BEEAL>%!+LlOpULO$Ygu2zwRZmJOvX zdquV^l=={abhvI3J5ab44#fHyCxvP8P*>MnerTa|ASK$`6m+V$6{EQ_uwpbGIoj1T?n@yX zjn5~EuzGx+66306VhMtPXFz|(_-yEuv8t3VM(XAiLb{UxV|N187?HX+tt|&-w>~J- z5)4iw+;V^zrId(BJJAwI63hb37NV5J6?U zV<-dTNjgZ5qZc$(j9!i0GM==cEDYCbCcqxoG7i)BJ*01d_NQnVTYDcO{$f3Z{t{Y2 zs8jbBeFL<|AsJC#q!cisOx`*q$OMQ&%w~sj#l(Q}2CZ#~xztC@Ppi95vzP=(i}8m^aB?r6GAx(=2a zI>11qj0+fOl%dJ6ml0?J-U9v@XadDA3^ajn)o0gtxq-iLG-`c^N}!eHlZd-ZEkc zNyN1fVq*;H@Kz9ip-WJgxC8;B@uL`R8sGC;cy)K#7hKKEtSc$tA{$>S#v8YjQH5V< z2H&T1ZHo?$Rw(Lbhvpb&w+b{gyG{H;v)iQEW0VW(Ix_H;Z)(?Q>{{(-k^$G!H-*kH zWSOmhlKwRUlAol{IT}<=oAH)dUF+YZpF-+@Ea*;KpUIu7@tS2?r}B=ygSxPFgt|=b zUVG5JVHAPtJS}VAlHZtzYv&RCYRT5!*dbDjW5QrR2!6{U@1FB<-Q;8wlMJFnTszNs zOU!8+@P$FaG0}dSCfI--N@?SX!FZm%}`N(MMS}XRZ9n+^GdON9=b(UcVRzKZelyhU3ySZaZ_mcVQ7Wk znPDHUZ0i(OjDUno#Pg4yf2kaQxm@drn0Eu7drtpI`oO-B2)!q=ec^|BVp~^rMro0T zR`;TpssTdvcCOKW5LTzUIs{ zR*+dA3Pv+PMc&vNbk_%?JWpR&w-R%CV`$wD%Z#(aLP!^9NLNtmib@CDPp3;1<*%i? zYOz^fL{oVBpyI*NN_D-f7Y22Aa!q2(C<<&4wc!7Bauw3D4|A|@QM2WZ!@P87SBZc( zpdR0KDk+-xbOt4|?Gz0f+$ejeIgsD05gZsJYURoAgV=~ zD}lm#yMoF$DGT`hEu(~E5J&k_gTHYi;zL1c9zX61Y8|cgA{|fm$SQoz+!ht0g!nix zo)^DxU_5^~&v+RAqTk{0&tX9`*d0N@H;{ctz)%5)yB*Iy+|}VfC$*j&?T5PM(P25_ zTsv%pDD9(pe8o$yT3W#){QOI<%(zEFEIQtNOXZYD{Ed(4L>wV)!4KetGdM zjQj`P&Hb5Yu69jUNfJze!0s@h;Dk@+BMWx86`91^%K|xd?APCZeC>3q?##Y@L9`Y79(pV$fIpjCP*ks@q>+x>nsgCivEkH~OjHt)b`LM1!rXfODd+o6=abIBSA^s^p*UJ=_;ksA} zZl`Z>nCLh7oDiMCdElh@h4aA4FUy?=DxD0i$X~tM3qJYe`rb%zvZb#4hSSZfy()WI zG?l*K3l}=91@@k^#BE|sK)_kzm47%(lqQ({`T4K>BKTLMAki}EMz2XMmWs!POw?Lg zEvZ8xxcC7GR7;}a8-KpFSW^ndk_muu1r)Z{SOGnY1gLZcOhP^_OF~RU6fJOHEMGC>Ol)Kwh$NV<*9KR&Lkiv0G zxd6=i-$AY97ZX1MHjr)!21}8FZ0XoBA!4LxLYwIW8AOUB%fVD6tPNi9N#wJ$;k!g% zQ&Hk1h#4?>(dxihFIvvd@~dUu1@OrmIwi5)loXF6mryMaua(D!`Fd9oFmLNDYHtVS#ep@Upr1_ zbv&?fR<1#tjm&`3jBYI0$?BL}uhZ{(ZA;0}jV;#^H+EX*)r}7(#3$q%e$>}?imW5g z6m(=t3)&zN7z!IyTIaF>W(Jfv7BgU!GLoEz5CbzidV)|I=e~*J7tVbX*Ofc>rR;+n zKI?VuRGIG>536pP9T0EWwkn=EpA6l_!^OwyX80G3Yc=rcJezH9KFoke-YbaE z$|P%Eb*|I~g$k~wGNBT@Jbcu*8jJY~j75T4*Gng6wLHK&hcsSLlP}ui8^Id8p|s(t zWxiGU`NumoxtDINlN)U^_fYUL4-_<t>jj;iPT@bi z;bu7j!S7tXLHXh{&FRRc6yh?uAT%v0$YP&UFh1>si^YE589}Ar`c7ioC>lOZw|>Pf zvmsCpP>3szOj~)uyAWlkJfLWet=#f)3;0g)>DsDZ~stnln55+cIH>8Isxvh=v$I)6z)Hd{-97$d++ ziIJWQ3SMKoDH_H|$=5PQj#7vbBPBd%8N^7L2Ncx>Vq>Z?uUDOVk4U*AImftB=?1=o zYRPcn4}U`Jgx=AK8<$I*vk0`#bOZmLP|-O*=0@sGtvk!hQOi;;F$T>roa0SjXODPz`N2mLo0*qk*bU4Xuq+kpn;aob&Brh$P#NYgn>@&vB-t{te zPElG{>}Ucko>SL6z!*FLHi9Q`!~Z9EG~W%9b%Gn2ZVUmQU7$}k4Lor0Vc=xZ$^(qr z0T^Sbvm5wt#E?@vp0b7nh?A?KSiT!;{W0oPY^(>^$pWM3B{%Rrq6oN(C~`SvksG;g zIdBSw(Q*pLhn3TtYbnHtBLHI@ZKYt0BjINnM~(V+n!=7!Gz_N0=(DD*j6#fH5?~Qb zcRavo6#yH-6!y*kE0}`6k*sUEk?AH8U{^3T_Q1hla7qF)p9;{eYwbxvbXQc^ftH&AGn)H_N6#aOBx*924sXu8`1q$LU`byOCP2W!tG@~k zVtMBKH6r=;7JgqAFMOu+qbBwh;m2_TNqnwzTk{s^P407@Yq6~qj#;hmP$xY(`ST{3 z)l%n2knCV?L--SX8Yp8Xr_6BLnd9@J6SY?3bwXPgfVOnAv8>8aI7*D+Oli>e9! z(60f!E}GF8OCT*EKETU=FQ{!|G2cSFGo^yb+K6vuvNnQ3&>k5NN7pvj{}v94{uG+X z8d5meW3cI4GM_dFE+SW}nxkl2Xgo28{%$Zxl$XF#jwlfmuiZc*CSG$Cc(oA&v@9ah z)Q6{ChO_EaqRhJ*6OSPfZ<_$Y;+}<8y9;adk zNilbiTtf~EJZC5p$DnYoe}Dy z#Nb?fZz2p%7Zt|kuivTj>H&Yt74{uvg<<&(1Qmv5W%scB6+DQGfCH2W!*VaR<*~mL zmio&-AWeoPm4;z?+ZmP>wz!AoEh;T=%e}+W5Qbq1LS125eTyEJH9f>6ZXt2mNQ$|8 zTpDs@T+&YA9NKYnlZa#0uhYgP){4d=wx2a(uKWrZz4Sybt(+Tui@i<;uSCFLw;|hNi|~NPNC~io~!y3L^3O{s$F_ zv*i&g@|-t6;(-8T6=z7qums7ju=M+056gh>-J`$a_arPMzQ^dldsrHB@cHi9GmOpp z-^-b@p@+n}RAO@}QC)Gn2dGP;3{a+sbVaG!jL&yZL=mql;^dTJHF@p=zm}|*qN)g3 z4|-gmDy=w$j<`3ae7-BB7`pFoafL3vO%e!eP;n69`X+f0;mRg?7~>r`5&E8NfoOK_ z$?$EG(Z6&W&Q^P1<6(>yv>6SDFZV0WI3Cuw$_d~HD*GpzH$&)bXw|3&`<~L`p!k+L za@Y4bC{#z5)U+fe7!j&pvO>kd#<+@q8lz1d?eYom6Lwhey~_@GAcO8(IUY$<@{)!z zj5GUzTfs(nEF(z#!ebdhTgyF`@lzekbdSRrt@*-a%V<_pFmP^ljk6euq*?dJjDPFU z*+SNuaF?erns1dAgOr24>#;5rjO7Rc7FCPJTa5!{(Oz`SPv0t2!Bw{!sbKv?S+aTf zR$qQ#gI~{tbRmEqCW|K$efg@wlobBlH-1Bzm9+DJ<1kq*zGGB@FB?tyVuttB@s4mj zWioBYxW#nKbWj}AEw55IhU_a~5!YETzE#fS&{2}A#dOOg(L__VlehAWbjyRPNL+ZW zOtsB|{VS{(*$gFlnL?C44II*=t$-c*8_`Qdp6d*w&KH zloqr>CNLB>Nd3WO1Dx`d_`oSo8Gwc%j45xSP#vedMdBAud5eB1cgj2TfW^xf9{5)o zmi_F1)C$Iw_c>|SeetgyKYNSqAl&7Y_am5I96a0dgFJYKmDm#$4X3`}e~@MUD-;58 zqt~YeSX5$tegvIKSm{MS02`IqXg8d@p9#^zf0V52xsmCt1=v-IZQ+4~wN59CjvipF z!~z&Av5&fe|4t>=$$FR@tvBt)c;G17z2E_MYKP_6*WAGOD93_gSdMji<`Xw^!!uZp zT|>cGj&%}lp%C;qss&&y$L^tEEXN9Gi>D++6qaMXM0EaUm}ARWMEsp{Y#BJs zRgU#8LOz36!l-waV}0d?WPJ7pIJTIYu_HKeu6!3V>L*`5C_@c>o$%kEaIX{nIbi})@HyV@ zCt0j*mS>4Jv1#E|b-%1h)`|c)bj}aht5zDk9xH zsH1+p&8XP&nRbhRG&LFNH|`cp+et`(GxXHJZJ|1jtrEo2#XzzvEoKEVSVXXUh$*0ACP*=EJ;Q>nH#wLV^XwU> z#pZ)}@jZgqJtWSh65sXKF_{+gB2iC^DI$%un9_{9GNncCheTRTZFZ%_HYuu#w3v$M zEZZur#>~9u-sCbjrqJVts1ih!*bv>`CO1Skx5-W6Uv4b*fWIM{m4L@wSojY2;engN z5ZY{P3YBIY1V%2b_6|K4mP~2INHEppnIBs84<)4Uz=6P&sKiv5;kzXHV5ZXaR2Xf- zRG2A9@J{2UmurM+H@3S>fqO$bFUWM5vJf=vVeAcu3*~Wdm@j_e-Y|a$?hP5e!a}m+ zGYsp&FEsC(8Dhy=cfNna8qtL=Zev~C#(HoY|BATmBhe>0f59vKWfuIoDE@2|?`^|7^YE(u zOv3mT!E|(msmO6Ea+`{rrXrWA$YCl{Pj%#9fr}T!U393;c$YS(#4pt5j8F-+dCH~D z1eZ3;#jgcmhOmh2dK%cO>}-BZK6_`6`+P;b11}vkGGrnu;xabe(LPUj^-b}om*{#= zml4j2UmahEKaYq%Kfeoq_K82&--kc=sqaP~!7uzKUczVa3$IrGY&HDCt2@6IcY5JX zVR%&t-uE*2IdRJc-svH(B0w*qrSs>Df0l|r`2PKM@s=vS`8rD+HpSDcRYjsIQT$o? z1N;$NC`bM~G5}r!pSz-hAL$%YzoOun2bc0DB=K%D8u;>*rJaL$=}gO7wps*;bI*_0 zBBeeCH!Rpcv84M5Gw`zgk=3yv|)!_D(v0CEpsec+51Tg@D#&q#(;nD z7GH<(Xx^^Q$RK`axuqh1ywboyeEwWZAFpzry?;4JR$AKglVkE?i{G4QdC}Nw__>aC7LwzCv#*2;*tiz%e zEU0su!g<u?lWKnGs+eJY>-5k$J;M&CO3N5$l39KC^d75TE^~rB#HXj(#0U zUF6gKpUEq^G%1JiQ11V|yQ|d7IHUx3G zSg?vGvS|)#%1gJzhqd~rvDxypB_x>Y0dVaVZ6f%Ny`8JG-xc!+AC}(5hkus9iwYytD}fJXHX}QKoIjjb$ysh`!q*>tDwKZ} z-{^i$*uxm_!wb?!Bxnap_|}gsY3u-1Ot?P0!cMnQzEAovkyQ=T_LT6lv>}c7`RUmK ze7K`e1fN}FemGA&?(fH|ePD^3Uj}zSH2T=`IExf)%EX;TFb9YfM#O)FC^~(F?c^J( z!&(rSu1X$}Y1*X+OPLlXti37)hWm~xRbx$X8P#e_JYV!h<@%=2#qah!HzX=BMhOOe zAp=r)k=7)Shb^~E_Zk~>|50q@@kKUABR+10D_7S$Y1G9UP7U=B-G57y7d7F6t zH*gdLjOa?#M|+WyKVql6v8&tKfxf)!XO$m`^kiEp8Yb|++bQ3>*-IhF03b=? z+sDDhFwY&MV3E!>eaDM-i&ZjcYr3p%hs`{>$Kj51U)> z^5r#$49sF}-C0B!EGo)%Ps?30rPYj}g2orbP=0JDX7qTsp#!(GM+t%);RY}LsnK2X zit)}1O!iz_chY@b1Emx4q7f5|63mHA-z9S* z(%^+Jz|D1;Xfq@)+5H$7m*+n z%czP>+^{PxxK|DM7K+hIGw!&%opCf^KT)MbaxR)85;H`8XIZW?!EN_cbVPfYV&Pi$ zhQ+ULw)8Wx3zR9E=uh25e^LmTC|mRWnJ3i;>-hgnq8F{>^K&`Y3I7?bqebrGNk>N1 zC&tl}ryvcDqew&JC@pB5X+Kz!n9>Z!d4$h6Bp>TfT5<+&j2{B zgJF4zr7N3Gn?=txAP~8?U`jx*wRxLT;I%e@DX+DmBs{0PttKznWogJ(Pzt9E^p-+^ z-cnl7Tlqg*rm5Ov@2x+JsYOH~oQo3w!&H!Em&|{R+9fa9J>@1^PX!$p&Cby-&j-BS zdX`pkx4e_tZ?}73ns=8Im|?qJfjRlTVQsWKKjS64F*k`|Ll)#Op6}UFy9PcHiL2v3 zyR^l-cH2?8U?^SN)}oneTRdRDWroSzYd0hURu4>LeJC9cq$kSLcd*nKra9cyxlQK1Ck7sE!Iui&o4qF~o32^0a%;7rkZj=}wM%7B;n93a5?DjCy zlk*BCe)Fm;^h6l3H8@Qi2?3XjlvfO$@eR9y<>M-MX8)s-(8A)r#&T)SzL8V4IOVA2 z4b8k6jGZuH7PkiwuzS&aWcnkPv;a)lWyX;EUvg)P^hevVwqQQ{g2j*5J!@Hz zHl$_2zyX#~c_~d>3~t_}dCMU!nl>5IIJHHKmMxk$Z=9Exn%Atwz!qt#0~$ANJS07} zaq671RWX7-lTqz>UZJ%QuiNj*aK5e3%I2k>@~@bm3hHx9ktrsdqB=V=<-R-ORG}qr z+rLXh@zQgaY9{s&v6alRbPz2U!Dh}6-cT*%TbH=;eryj!n%HRlLo>b8a zOJ~@4^9L^6Pgpn=&J3n1=zDIZNq6%7fBy(Y?a-_b@oFWVqrbRhdCR0dw?}Tf;s46E zJCOhSUFSGHsh+J4U-Z;~Fdn+Mb0D8&>ypgz!=|Za-730JGPHO1@Ujl}6!yMiCb0ou zgcgDuSpy@sxF?&I@8PB?wsb!Jn#F#f(dO(>gyi}v6?Vez-U-;$iP&>?0{8ZF z^mr6|dvK|-w;v|}`rh6le(QUCFAskJ*hO3{$(Vab)1Q3$dI2(WGwL;=Vd08Y+}e;hH4Ro+Y7 zh)g5*$}B={3PGj-0jBz0oJ}YgSp(R~8o#UpcbY%MvM3sI=>llZ>&#gn3P$k)ERL2z z*YTSxwxJgD>!&VJ}lWd`ADdZ@Th3&sDsSET!{lki~bLukOpHQ?Z!RI2dhht9`eZ zm&DsM*%k`MJmY%Ng68p&>$6P|Iq)h`^ag?oD7a!8(cI7VY^c=~Bd$Nn;ZzS2rv*3N8v~#KD)>HjEivjzv zr!n4pifhyE@kWPq`zRKEEzUYgtGka!##@K7`cx$DCmPInIGv@_W{jWu`{a&SArMc8 z2t!(c^hTY1@*vE)GvR^pm1P^%fOrOTKaaZ!(>GW>MKih(rc_A&3@y%0wEAewLeX(I zJ#wGiO^>7y{Ep2l`{dqnx&jg3v3Yr)+&g-#4CVndNcm_}VLA|d$0_@8?`Rsy)0$eV zGDU{i90$`D{cG;?YFUff=Ts(|7QVgb%O}>hHel;04DI{*KJHt`YEh|#eTg^qlW$a@ zTrg_M)}Prv$`ikFyc-hxb*+VLH-)(I)H5{-&xCr`tJ=kVJf^-in}6L_J~*%kuhqAX zVYetdRQ~clo^rsJ?83z_=q5gFZ^J@Mnx zbKxT2*#iR_uyl&+PDQ@d`v7IT^=uGOevW*P|~3FA{Yohro;Hxs@x{*FX;D zWfVD>mr+_Ki_ah7hYpqIlBU)t*vC{6VOpl(AnEUCyR=Z7(KXvY@^8h~{SEqc&8^i` z`jYqIEj`x4n#8vM4f+pTSZ8>l&t*?0u_O1UpWn*bNF|q1nBKYxyGY4Jw74M(%LTT! z=Bdphqh)$ZI3kU&ywftw84!HpgrOCSTeq>kU}BZ`llaD{h}_SU#*B&!q5$#1$NAs% zn5m`im*0C#r%?RfW9h(Q4SCX2R#_bPn3dY>$B#dXS*d96AovWxe)-MEdXy0sm?t$4 zsB3<7Kjbi11tqe_DI5qk_G%OBtB{@6^RuJ3QcwaDdrq{Pi5U+W(EnzAxHa?r|B# zy|KjQjSczs&slrQ1f9|DfOJMX3c;_TuJ4`L%iQ|_EMQJoO=3?`INIn*ZpyR9&sljh z!E^2$0$laEGbhK|*z~Bt?W}B1)Inl zcZcV+_`YkMgCr2a`1;zgrf7Dq4UJMk`-Aei+ksMH>OJFNIm^B5gRbDga8?m zvF`nYvRJo-5@1K)Ew7_1jgE?9U0uViHKbdq6wCsj7Kz=;b$RL#TP;SR{K#tCBkVgW z8@pUiA6^^^gF~UmKs-7W9@u*+6P##NL^1cz7-o%D>0xl{$*)tC9tWo<{ms*bL44wH zYqAFuecyZQZ!pO%@(JS8-+VZdZyRWhT=zpyBgdPpAjjTX5$EK0=g!Q;6+;MRc57;M(Jj?SS`50YtEgn%D< z5u)B6EJC!)Zz&izgFmh!glHi}2m#B-6rISI=hqj1T8JzJq(%7R)v>w!PT>3Xc(u5= z9;~mxCHqzwD_@NC)eeF9cA=5{QH!;)c?A5+^m}AW-aYvJFxFd8;P?fNAXV6_0qE%b zT-LIQP;$|`XfQSi;A?vcaS!tqR_hbymw|0q+g=&k3x{}8+VJk?X3**NpS4Q!7Z!n1 z#(KS!VYfC5`oN{Id?7W5y3yAaob@7SsZ#;+Ew zGX>9^Q!``nS zqj1u_3>17vK}r?AbDT9RW}#q)@q)iF4g>}UEBw%dbv3vYDrQacZwmZt@W&ObqJ;#j zfaRlH(V_frfER@5JACYTYj<y~V9+$Y{Y_sI=x9g7=Gwl*=br-WotP3~Ggm~ha@O#W(c;QiDgg^oO^U`$9bm9JNvlqTKU|KQswAgdE!-@{f} z9QCYM{OOC~(kD%ER(d<)?>b_2`}GN{mlhX|2IW zwO$7GqKqN%<;xk?HtY=Lg`cfEJ!`c!S+e3tcXpNLYJWhQ+&un~)bUvlI__wd!Qu+nl2T&)z3$ZC5I^Gfg7 zv-l6wtoM;>b~CgVT*_}3!MY=uDuQ-S6D@Rz1eQuW`;|&Nzg5SU!_N+PwBy+g?N9J? zbF3}dLzE3}_D&?*GI-+7KB4Arr8Km16ig9XS>^S-Pbt12=d%a=$IDjbMDy9xtgWN+ zr@Kc2NarV?c69YrWB!}=XEj?X&u(pdkU6Mme7)LIp6H3t*A;^r3!~5e#o8^xi%6LU zRyvOM^FVDc?Pv1iW$%k(;mKNIB2-{K)SQ>jsval?qpM1&&BlQihev?58SszX{7iWU z*vq^iOd;xod3?kI>nuhw#7CG~^USxb$JlP#Z!8p+kQVSZPUx@yqbXM(7n6>558}Bw zeM5P_cdXg$cOnN*_0DfyxiyV_U3mUcI3&K%dWhYi1Xzu_0xl9)XIv;Hb;9r?WcVOj z$PtRxozG5`o3;eGghYbxtU!B~6)FqX}n zieeqIkfI@fo6i453PJt=!D8eb3PIKYQCMR!@)L@NtUuIQub~iR4G?6#g+h=uK%A_x z8hM0bAlHLB*K-ttTmfQKBmbgcWDQ^^Yoi(&{TuNdatZ&9R3mFrFp3vos79JDh|$wp zeqh;4_=*U!EVoXpbPOWMs6M{J7e7!rDu_lE4ALa+M;Mdh6!e4@*4p8ML{=Yjgn0O; zJOzDJw9}bz8{q@v4yup5+=tE+N>4@>Wbxo6`=Hhy#$cKWpuAKa6jUTs`|7hZZLZ>{7N zTNii`mcJ()zGrUS6SY?Sg zDziONM}ZA)C&Zdb6!*f8mba)lDziN~FuFu>FZd{Cdo&P&xk5z<<_eV-Ov%K3Z5_yz zX3P~D#k~iqKA4kGu#rix^-$|mnsN1z+sZyvd*Y}((bS9b!RPj4<@IYIkcj6t^h-Rq ziBI$$nj$Lc?O}~n;h4O*B)zSb{U&eSKC#hyi_M@)phKpD4Mj$MDutj!-q#(nM1cs0 zyjz~)E8QIOj_wc;f(}uHphJ`vv?bQOlxD1X{Y&arOf3F)66ENI*x!X8;({muBXR9E zYc5Nn&7$`j;NubUSOTF($TLYh$0Ou`DUXm-68SOuMSih9e9jJ75%i>FPC@89g-ZA? zn6~Jp@&zT#nbAIRjcU#A?d6eFWfp>nFLvaxWV+_3YdpT3gG9V#Ckvu;1L(}tq z=XrC7XLn5A;&Y<(K?+Rw+;E%E1*32(LQSqTscnl#;7@n zQB##2q!3p<>^iWTG`KdqNuki+=6vN>Q4O@K<#oE^AF@0N^ADCMFa9pelNTu;G%J=T zlSNREJ$KzdT(dWpCqW7p!xSm7&PkSabLc9@q0&{HLzEVr z2+~U7#D?1Js!!%nB{4^>BD(66N;8h-Uscx>@Bhu(%f!Y|hUgV5^i0gDRtj->W#%8! zE3+xow6hx%Q5~b3N5$bbHecHTUW-T@GJ3+QU4OUZV5Bo7$nY0rr#ZZ|XZf38O zCUNE0gM7!HsK7{L?$v8cbHQjyuRayYk6(pM>k<#Dj3#{p86X*}Xw&II4T~3Evu-u9 zVv3HPo__jrB6fP^_4=&>heMTxoSW~D&Qbe!)97~EO(7aw4vAY9+zt$*=@hLI@I3^qR3U`b1D z!;6`%rL-FiQ7jJs30nYBbUg?|Ruo--g}s_64!`8qA-y;}#bp%O9Ak0#-2A>L%!|RG z6RUQu&fcSB#x%N6^rW08&!@*-&$qBghPKug9GBDR0?JA*8z!5YXx|=}JCn^6jI-#* zy9g;c?wTcVa{@x}zAZ084(BulV{gcQx)wX_A>;U6WL)+lZ2!Iwpx67a6>W@>&oHN{)BvCu07?6lLx+Tn@^j?SO^w~ zP%p%y_p|)S8AnY%Da7^!pH!z_2vfMYO%S@kb|>US+WLfCUT%%Fy=0zt0&b}JPOHM# zkM0-93!`kcw4xLIohVy9Hiv3}ot;%)XWuqDyDr*BJF8!^KSS^A49XK;^~_gIkCsITnv0E6gpUnhfC^s@h)g z$Pu4m;U@vpnKr=ma0qDu7^a6``$y^FJf8B%zz9An!PcI)X)d3sAgV5({bXWXQkNss{}$;f+0(P34zMOJ3S+co2A-1nYH$(_{7Gx zX{-a~jxVaWYyz)5KSG;v+0x+@-_XQXMNPiq|G|TA883gwuq``82NSDj7Bax#fP@CwN(k6v8H`;N|yPSD|Di#%gXENe`QZK z8ExH2AN5OrLE^DCHmfSpt&ckDt41I3c_6YDgrRPD(j1Xk@NCuk)g!tlwi}MfEp&Rwmg3edauYv}(P{+!4f2eRAje&D68~>UTMK6U8}uVO+2Xv= zb2wJ7N0ri9j4GuCqpDKF(RG;8jH|vXXUK>UQ>Ka|NKIL;wO=%!lwphD z<2%|y-0(!0Ey;m6X+Rlbm?}b17mO3NSsnL;$-rpws!DF4rPjQ z(*KMc_x&jZ;^aB=vu7Zcy{Kw+HlBiT**)$|InRTaftx<+SA93AiRL&Xvo;_FhrJ>N z9i+6NQMNp3o6Xc_VHA9teoR$CqpFBv?5Fp(4OHndYxC3@`Cx&8gFECKr{uc&Bs^%u zy80MQw_=BUh>F08bo~paK(=2rJ7-ek`1JKNa&o*$snAAO%d=6%vo0Iqp7o-%5j#g& z;Kng@`cPjJSX3@oGT$&=m4vNRBJekeT16%j@nZ;m&DBg1SHR06k^&eF4TrSlP`#+A+okU)HzQh3gKdkdZ$3mFa= zO1MR(s}*^KTiHbzfr?wy>VHD$;uf`r_=Q{48h^?o+;oeIN4P5{=l6omYR1jM?P3t< z+?wc2y8;l;#=;N%Y;0FJh>5q!G!X3T!XMY!SkXex#tK+Io{$nU@HUxVaB>i?C~ox+ zPn+UN&KmUa6}sd{aashAssxDL1#H>m-7=zQ;IR+pnTFU{uFwj*+51m<*tRce0T^z+ zhu;6@+j$oIP_{S5FWx`nl9y~LQx>lI1?oCSri&!(x!Ax~|AN9H!Bh|Z(5=#>Tm2tgtqR7m@PawjBa8b@uvOHw z&;OL?ZQ=iL-gW>_nQS|5-Vgo0v8{7`c2McJxUyxR=rZPDd8mI#n06fY%TsJ0+{Xc1 z&}5vp`%m8E1zTgbn+g(>LHG@KAMF&9+iTnZl!ra5F0&nAA?HYtiUYK*{+VgUX+h`k zOsQ!*&zuR;e*9COoE`sNdLXYeWlREJ*$z^1xi9UF;O{&&0BQ(RZA*Nbf*QPG0NioY zoGOIJXB(fx=bf}x<~ey}_S&Dk)ihh5`?XriIi5M))|91CX~Jp|Jw*$R>;|$wTHSNf zY=;{b2J_q*wjwr^68|f<8>HK=E-!l*Qeb$V)M>yS-*sku-%Q&d+RSr&{Vd2D%%<|u zR5Pfl-ZV@Fr;BUx(y2CoUTu;+lvgdXRk>fwO+Ckt6xmv{sZ=;x?h|**y{KF6dEIi? zU$Z^OpLrKjgfJoE0ftZALv)>PfaeVZn0^$2)B?}b!o|7pJB_bD9ACxsnFwe7(w*fv zsQCKL9jiur8SnX@{>c~3vmIp=hR0}4BX~)8VGJ*R!`6jct#zW{aCw(d{^bOFZ{`nA zS=R&(Db0b|KBNU;ID!9zn{inPHyi_4EbkI7S0AHL!Z zIIQivre;mPe%_deSwn&>6RCB1(JE&mwbpr&NbSxZqF5rGY0C7#isC6IpYE(LMa7(G zuk$k9I)XwlCkhZuw~nI_Oosvl)1gvZ})02(ktUvR+Oh$QmFD z>%_yu`m@ax4Ows0S?{J0WDO8xeUw6wH9(xK;p{BDIUi>4!ERABWPMF%9SDzkSEL|o zfFSG26oRY);$)3yXIFn<>%`J18nSL!CT;xyg&=EyAnPm&LDm3KSmPx@LnshajFJUdB*?*IcuA0#pcheymjn%d$)0A8J@3oExEP$sD5gwZ-+}Qg4&%nYY_A&* zczWu3u@EZAOijBqNQf5gbjmdmL&F)Wm*G3Eam#G`*-}MUaqxD>=qK*y;4KckE$6F; zu=goTT+~N?V%y5Tpv_{civL`+ppyC_TZIyjfNrHk#!hszXvQrbSPd_EnOW1GWnzC& zbllq?DU*BqBNT!;y}!!jw)Ktz5jnk^{K!_ioCvrecdLuRiz1JARlxOIHh@3w>J|QC zS07AU^iyjK=h~C`#HqP{eDsvuDBiuUeJraB-EhS@?i9}AlL`+k7=3ZM94a|&hkr?5j=Zr^Q;Ir1);ys1%{%_gGX$vm!U*CeO zn%?%1@&=W%)FlNT;n@-1q+p_6>54EyRhJQTeTWg{^Yr`#Q=sXPh>rH!{xZA`G8-XGpKK8y93o5{!o#$rBf3 zM)CxuH8PUn7iI8Af>T}5hpCXzi-t$Ego{e#Yrqv#kP25=spv=Nc3zN!LFo^sxRe%5 zaXs*@EuSgPn9(rKLwBUA!7#{Dv60D??`)mjnnj)r@A`R?(qo=Xk%sgOH{0f^^wLD) zJoVET3KW9ltlKr)=UUtqX|)6jHmp|V3JK!mE2`C^ zuSly!Q(9x3WdLh2&e~Hj8m%4W+7%R};|#dsID`M=I71H%x}tkvhzGL)lv#nn3_So$ zaGWVB8Xf@Spa&G=pa+x|^uVUuHakIF~R8`*}9E=;bQ zm^?DiUhwp2`_sb*G%6S;ej$WU4tqF`AHGnn9{ZSf3ca@Uiab8OltP3WCOBoguzNDU zGCn;=$E&D8n)Zt2vVJUdGi=F3fp1fptV3RM(@QV#m5F9p z*H#dFW~@U_YEs#zRk|v_w;Fbpi~y0tGX?X>)$JrdT=goxDsNs~2A@-H(8wXk2(ktUvK~hv$QmF{*7%P5YZL>y&eXXsq7dW?5a6ocxV@Z$ku`vwtT7E% zUZjZcbzvH8;#EBjMlod?Z17bh4HgD?dK&D25IXTH|2o#5p75M#h8>IRqX|QaU!j!M zu&}i(JN!mf#XCp0Oy-yy<)2-F6{Kz*HiDwPJiG0k-PBH-(Bw{dBu3|ss6sXhkXgE8cLC{*^S^JDOz-xj~{|EI0Aun#wn1maz(j!a{bSDVk62X@@U%i`sC_ z8*+mVHpVQYA+(Zw=!V>oSw;^bGRxTJhRiay^N`YtNa<`y!7QUUDVSwcqzEg-d7u(2 zkR8)R8T8t75F@baEKX`-hr?}!R4a^0+YK3$HVVN6_^cZ;2Uw&)L<0O((vt;1Ph!&a zih$|KZ{Uq_sqn_dv(kc#XT1MFX?Dx^eNEMXh|FRNHZu99rQM=7<7NZzKiCB(7hiPQ zP5GevOGtMP8;-#%4-9MTSrN+qF zSXU|ulV^`AEns;cfx}JT!%e>`HVie6#vXQ48atno3S(Zw6WK7&!KS~}-u0%; zhACp)CIcOnc1TZ$&85;Y9rhM&!F1T%n=&1y;2P<$<=zM|9i|WnmB=Gj&!^13N%CQR z<@7<-l=(2IC*h7qkCfsmUF|bW>@XFKL9$;DlKm876fY9&9~$} z9qf+PfHF{2A|cmW%h`{4D(^Y1o!ERiePrVv;fAzKv#@|Q;pJsvqYW8?46m~EUE(AZjWihY*Z_( z3aE(U^z4bk!b~spSXFrOZ_p2W!d}%2{rS^_sxy9f`VFB^$MEbY?Gaw+S7?RJ*hhD# z$NoRi%l@)Tk2~+ZN*p%gul2Tf;M; zVOV-ghG8kCb%h~cYPE;)l^q60F!q;QAo2{Gy$wFV`Kva2GcloH2m(h8LHNIsDr)o> zrizk++_{(<@JyR?A<*hCS2Say2v}eMD#{rF2xh?mRLp__sI;I1o*e*%U8NZvuo+Az zQXeo86b-bWU=FGu+TH>>3ENvJ#FgKC^)G4qSrqCry=gk>+#7$X6Tr)VNz1=XNzw9{ zTw5hAmdI99xR?P3*6e%mdqw2<3mev7;9iU_=KA@H_*$O3!phr%BF zp|oH>@V*758J9VDC42@Ik1HGn5$W;11*I8R98gTAcEHp7<6yTSj>QkVF0*EXVQ(l- z@7vtO=vlJ@(d^vDVAd=&GVxPw=53z04_*hEO{s+E`5UY3>EbhZa^3u#cI*u3CS zzb@KTX&v?gC3NM>w8?-kv@5vn(hd*pZ>H$RCTkPb4iD`&8e{Ki(hl9`Qwr^MSScmL zN7Di7x>JVh7LK*oBU=gmVE0~1e{8J1DN|a|Nu9^pXR-=+NQ|J96hw4V&NKF}RC*lW zyY((_GP;bZL1m|jsX-+VVR!{ogN=m(m>O&%eqm~`$(?djgKv+wzu+Y`IBbIbQh zf~mnSq(u*h8TbFqI}V3_vc&nvA?zu_nB)Sz$jk3@N4~3Ed$N52vr;;dU4kKR`Ouk^ z%fBNNf_NHlng{AjUQjW2_^t;kY`{IZV(xH_2kI9pDrTz6>4$M(tKS_xQ$;alrmDjo zBU2R)czUMlxcDW=N{b64`LLJmu?c-d6U|q3B+BV;9RKvP9j*g8O8{IXtmcou|9n-o zY4&(_igLtsh*rVR$T1ls^^QQqc?29O-lycVr`um;@e~_p<=6^5Cv{9!7E2*GD~CS% z^b=as3VcwLqOgi8 z0ViffCXU&JS@sqxB97VU3OuvXxJ)gg0#BM%*pS0HvNk+rzTJ;k^7jqr=a<{Av9VM` z?3!Kgnw>&0(KDNuErUeQYYISY=VsDQy=8O?6Ft-PPT|*BfI0R`VJ>&sw&Y zO2hQdCfb6&!}Jay@{sxVW-5h|-r3_umSq+cWkYRkJSg=1&Izv+C%FA#l|pQw;%|!3 z!T8pbinK#C!BfFRLr+iem;>Qb;BKvL?HQwB+;#e!jF2%$1Kz6nZPG=|h)sT^Ay$ELB{9$jQ)NXonkR#Pi(jf(lTA@TA|5A_erT_nT!SoZ zGw?@PoV*VWxMMgf+;Na8EjY+9$)hy8C3#j+J@DzOU?UT}LWfGa4Q|9X zQ$`p98%c-ECB!k9=P|6UUu~%fDEh&g5=1%T+%UjI=Y~ccZ??Qa(a`)?bn{=K5Hx?d zrkg)fBk_!LT&QLQtd^j;%#YInL!fSdV2}1!*rWZG7Bn5+X{j`0f~P$dk5*T3kQDE< zRGJN|KMwsst3OE@q17KH{V)#zJUZ~mfY)!bR0QBnmP!kn{i0!ZOJHH977E{*803Jv z1jWrkci>?vO2oHdMI;uxZ%~bJqFWh|926v4ozE!Oo2usjIQ{Vjr9yvvM*5+xck{<` z;t#fr(&Bn|(y4B~P#-pwoUie+gAP;@*Su#b2`Y(&g;P{Z<9?&$VtYN!>~Hqt8UEps z{GF49(JavH9zeck5YC)7*C2eZd6%j;4GgMiU=J4xckX=rw}5BuCwQD!^ybHR~AsX%2!rz zMm&mDe#}y#BChg36u)ql55M^2uVA*OCjgwUdOizh0iL*0*dYdidg;%(5*r0TeAN?v z=&yQ?`cr?^bCuw~9R9do^%O1SRZjsEM-3|E3mN#T=dsz-{rg` z-fF@Z8wQJdEY@~Ah@}Cno$;kl@iNna*DP5)p>LOZ><^(IcKEQFuL_)&mHiuO0hk!* zGe+J20SA2Pv(Fa$MX!wK+&|--E%w?x_kg_#&)oqhC))P!5>Z@ftG%I#nSDr~Ccil@ zD_#>VbnQ<&$xSOAQT%Mv;W>Q$kMXn1^|=fJT(rk252_Cwt%IxSjxJn znxhTRZ9crQfcbOBPxf$gm=Da-rz*kB@{@hMxi0)Gv%Wc23-iI1ii_=$hKg2tUD{Kt zNgw4Tn;!%&_)_XaRC?you~}NCkDM9B3#t5p2^sfOR#yxe5=@nVzH~?);MH@V|8viE z~)3%YMr|TfoH% z0S&zW>JMVslPPurU;Vdl30*YsiKRK9h${l)uud(deX)H1<54cHwy+@=|W zAI69gevDWlT4;2=j9$q!zW_8jXHpvRF?;Quc*a}(g1KX_{V}$YvJe3pF~FV0ddWh2 z!G|Z!gXi=5v*ti;vhHpCR0o&{AgTCnp*9qIu79dypbyU*>(D>dp}13@CqbI{R7Wl! z-gUE`||05^mV6NQ?&ls>|2E##&|jlV(7`x#k}VQB`@?LMwcj=)EqnJpQ1)A@5#lAH>oqpH=UU>8UlW zD2)4vR<|Ng8V~aZ<~h1}pgm-u75R?o&hjV*OdQF4_Nm|oCIbtZ%{*$K!^Tlmlqpmm1NiD2?aOpuQQ zve;0Vq1*EhckUBm=&qk~%JAjST(A#RMdG8&165IW@<(HyWn-xd8|pi`idnSPkp<@#7l%6H*enV-vc3?W#Jv+kd2W@_zWlkFjt8}mVP!Vc zQJsBC5n;Fw(mopv8W&UQ7B=EW@Us|}Adkq@a&A>Mq`9;Q{{n+_BaeUkw!oKdJ{`>DQZgFtjBUqjN_2@p^ALYN`Zj(Tj^oj!17V^L6ZHpHZGhKi1G@6!n=OZ?MRb=0Wfv z`_UV2wE*aX!W_GB$Q$md0LK$5E-3OKJMRtmbfBYyitE;0{Q#I}ch>|t7BPQ+8oXjm zq1!@fGj`YCpT`9|MyLdCqV&T?iYScs8n7Ct(Y`Xo!A-2SB1FV^bAK7*%_(FteB>7DCIsysk04 zUyY#H`5P(*v&9t0c*495rgM&M^T%Ko=M#n>*%pvV9x=`@Z}{`3l^y+5k#7FfQ59v; zpH2L!KN}UVi*&wQrJlw8X)~u@bKe7@G=hZR|?O_uh@Xa z2NE-))2fIT#cAD7`G*#FYv6c8Gq(tY)w8(juN9>ewlOv1X%+`w*vdm;iK%r6l&Mp+ zGu-eOX$%VeQ}pS;Y)7RoPc!&o_!6JPU#%EZpY^8AI1$3NOu1=}CMp38o*KNM=a@wH zG$jz@#w-nk+obZzHLE`mP4{(`1B8B=_x43jO+adYGWN1Rr{hu?X?@nG~}&#Y?xMaZ~bDanaCIPTr7Psa4qM(QD0&HvFY&bIS)F<9hagdqxG=*^j+M+xL~yYhO0b9) z^uK-^x}pU?65gl*FY4qNa(`BXJT`=X)5Y;(@Dic>hvLsN2*T0+1G|J9-O-D% zZ@_^_N4)$1JmsYuI3Eb)2jG(*arBMuE12m{F)(C+0;V3If~KgT^6|$16X;*SA6MZ+ zw2;DwfaRlD(WCrHK#1ReKa}lwQaglWGnMBS`GxV1MmZpZ{$5LeKB-=KjCnd>ici7& z3ymO{cJZVOk)3(B?yy3+C0L=7SMVcG2ZFlyHHGy76j@;$`nfuj(SQIwC>hG9(pOcd-_R@KQ_}Kko;E5l* zhabiW6Yh=pPPEYQdk3ABXdDP2}cf_9}p1ZSuF#DCVfC%o&+kRoXpD$-9 zgG4H-{UU{H3z$(CF~1I)O#aT?iKp&s8OXZ~c3kr@1;aFo-?ulzFV~#mvhJPotQ#DR z)=kqkF`o8N;bXi_uH)ZQ{wCv7hQZSHCbbY6xtdUt8u^-TYg``eJwP8B<+=xX9O(X5PeP zE_j@~*Bb}(6Hhy`ROu`0#8u?yG74YP@`CxI|BQKr4Wx2i3s0?od4flRU|fNMU|!T} zU=07Mec=NU-o%TeUM}%yH4m)RU@?zmYL`iLXs_%4z^WMxMp$?1RkH~g1hQ&odWPqY zb+nh?xA!thWnnF7F2bM68m< zQXF_V!Pgg{K%_;5sQi9?#?hUnQCt+;FhrKg9-t6|3xJ_?)tY5_fU#;Nz#=kA_xe@U zq~`P45Q>XJ2IxZU6k-Sgu&WsNyax&j5-4thqWHmyjtOi5B|vFhm-Z2bNNKS(8yB@p`w>k43DvPCjQAHImP1oYppv~ zykP2Fh2qBl&{Rin)`&K{QR*ur56a?=uQ(c*Sev_$>a{q96bo$KDG{b^vP27RKi9$t z6a}_VXE@;E$PpA1t2jd|$qe^U3h`FOnNmqsabBc|BG;o$1{UxviSSsT%&H{QbyX_K zYTI~3)GIn57}MB_U`%7v7FupwK_z2S19SFOIuM%mfI8pO!Iv24HwI83NEf@ z4R7lgAW*&JYTIwF<9&97@^tgIeubq%38NxY8&pXqtn(_l%54WtObaFPgzmYmyXQ6) z;>|r(LZy4+C<}B?<t zZVG`>aLc6xnCJrnEV!uzqI_zlxK8F+a8q#I{Hbq?X-7!GZ5rii^y4+!jH0H6%7U9a z!Gr95Z@5@+Q*bdPzwm~O1vdp36nT(s@rH{9HwD+NyCE_q<=I^8KKgV7$L`tvh`5qsnMMzVbsy zcXRzPu^p{hi#4Q}u+AK00v2AUqvl~U*AI^Z9u_mGt+^KP$oaWjTo7e}i|O2fh4r*< zfG_ISITVaTDyuvZeZrI>g%XbRda8;d0c@tfZVkP~L8kN;2N`YA2U+B&jMex@zD zpHKYPae$>#9&Qg6{qQs;<(3;`Q~2;Tj$S?w0wvFmsus%+uW{67ohc*q!b9Oa_e)1l z_7H^#FN7OUyjbZ4xM6>fLw_&0UATBsMHY`)>+mHI)Pcs0OdI;s0p+4)`dl?O|p%)Jz~D719Zzr;Dis#6#)flL29_7AfTY2sG#`PF8F-UCxU{Y&q}cu5NwDIzjM!>-8nNm8_N5C`uh!= zd++Jz+;Z-{GnGlo1`6V(@!;Xg!A1b25&tR_@1g*xcwp+^uU57(0sw^`%0FmZFY^2T zSy}4WHd!e0KUM&HU#pBa0^m4y`87B=@*xHAa&!B?phGdJ2m=O9b)EunmTb@vPZL-` z41J|CUAbz8kU`-1*DHq^0l0C4b>lW=tKO@GckKOL7$$XLVP95eZEjN=t{Z<685ys) z(pcf)8UQE0g{p%PND>-E?MECE6<`eXe>NC(T5gu_1K`HI6FZhXe5rM zAQ*{210#__ki!!*C;*xO05B2_02qn1M2o~*-m0`KODGOgBqoY5p`crD0ATt zoK#X+QqMY#_HQK4Y&a=xTu0h@30X&`vc*59hq3=Q8q(M#>5s1cX z5yUy}lseit4LpqjKlCNLKG*vgnBsH2)3h3Cd>Dwv*Mf$ut!|OT+$*U~6axt`ly)*> z6+un3iYGIcM+mcI?x_ftBB)iglNn7S3EOCzC|+VP4W7*C|5asAo7^!HPL)@tDV-@8 z7e^{&-Rj!$C9VD;)hE(7dwuG=%FGD8J83QaD>t$(D>*&4odQ2}En=TOw=Q7#y^oe?H+J@ylrVVFAX*+@3=Dl2 zbaeTwsNcBYNRlKW!GWho`cS4pO6~LrO{VD}o_c>&{$i6yMv;Rf)-3@B(}+D-a+1_U zu8v~c!{);Qq(`F~MA4k07xz0M;uOhD&JLa;xsCtAQzY;UFD6D^=3Ci#j0CqIyg{D% zgrltb-}a4`TMiHYA8k1_Wl1aEk6;DohGw&#vi)^sIaM5lkhb@57b(_+tNK6Yq4fX= znOO<&CW6g`n7$PeZNWoDH7F4;@nqDtA5`{HK+J6Ki4r>qyQ0KK!)vq!_=UF7a7b^4 zcVu?ewU;R$)3%81{V1`~@GY%DEIZz+-3}ZT_ZhDE)nbMmfWdXOeFoqM3B~P>AESgt zgG#-5Wh4|5EeIvj8l7FJE)BL9DrtVzNC~Qqlzh4PKq?HFi@z6S@yo?iEFu@b5X@l8 z#dAoJiyzOqgg;qQ%lVn{U@>DZelb-S=Hg+?Ghb3lsc_hT!Jg;6%B{2*R5C~|jb`76 z+6Nf{aIDw2DHleIZOTOkXy5%9cXWVB_sVE((hWk8ZOTPvA%F+$5)DlAE-~J(=_JYm z?jj6`u~9zW z4o5F;&I)tjJB`OE0QU3%@O@KzOCtcPRzG@pE?OKt{ER~D>zOlwRC|G{R-8f9CDvYL z0`zCc+b0?6qc3m?*@wAeVD<{PJi*@C*w*9*(J{gen#7QZ(|L`I3E&1PG1qVdo!=J8 z_IXAgOm3hxH5e23b$U^zaMIVEm*9A?2s$XfuQMve-a}GKXiHpuPXNyO?VJg;2A5r# zch{a}Q-Ob+RJ&+BPI&D?X8-Bo(ikzR=AM55CeDu z*W9CG_)It5^K7~uUPK(*gt{$XZbvr}c8{htRtfa>O|%4hE^UcRw=BS6nL+Q`L@eF7 z2Ve$04UpggeE6Y{SiWVIG@8xr1BdX((pq$~k%36SkdHU?#z*6xp^a0(Rh5APXiZC@ zgS_Z=26@r#Xo==_opS9DD9b4`Xy3YdVBfX07~O7p6SgDI-p2@l)A5~FZ83pvKyI?F zmO!U`Y59Nx`*!6yiWAwwEc#(ujBGvEgzYSZT)jU4`rSz@033=804CnFw7Y>f^t*^A z2ESu%JK0YwR}D0{+x@P&-S4zUbT@;t#@nKr!eF^0wwV&sl=umHLPXO5&ep7{&l8fn z+H>SkY^e1%Ko8AspvTRwm`kEq<04oI8j5xG_NM%B^+wT#q_b_<%|65e3685{t`|w$ z?)I)mBwz;Za?P*0qA^o5JceXyy2pr|K-UfUqmynS`%hbHx)Bb_&VbFfo zqS?)qJroQ>eODmuKh;zZ^#i|+>R{UeNHEmlhefE%M*x6lP>#}SwD5ld(Rfd{Fu#q9 zBz8vM8XHVeh|Yv3g1!e1aLl?y>ygCyKqP(_By8R>3u$0~Vi3dEpG0jXxQRwFOS7dZ zF$o+&!wWRRBwCuLZ7k6Z+NBjQG1vvuG+zz0ueaHH$HFX`U!dtr;ZUNUu>rzjSgbw@ zpMaY*;rbj&dtY`@EIU?gU!@dLr20jg>9iOpbpU`DX$$}mBicn87X{!kVuOa_B8>q8 zyT&SWV;QjHMH)j}Q}?5B-iZR^O!&@`_9|r;Eymcz+cf)VG3P6UH}vLQ(pYj@WjH%K z3WDE&z(iWR!{bjHuh_gsX_)4@Z^epv?s3{ipF18E7W6-2`5|Zz^Y+{+&GXzOvG9K_ zR@}8Q&^K|eHTC2Cku~G(l5&}HrFVE&X)%r`m*IsM6YX0q0I+iljUz6NTIQI&TJFlE zA@(Bn{Um#c5rK(U?Ou?9R~%moWA&Kg-`rXYDLqW=KwLos?OqU_8cI#oj;>cSss2-a91lOI&h# zgp=9DAN~1h<38}vFFEnbl~_S8IbmbWj`7A7-S~r3=a-z2jT6cB3YdGg{r~6!L5lGp zFt|UQHmp^FhY%sxQL6${AmZi|Pu@ZGifU=veB~o96YAmJIB~)0eNqB-d}OGaee=0H zyxWIbI*aZBHu00Oa0l2`=BRJ`X)8Z)(1$PIM=k;v-VEABn9mU zmRMSS_=e0=lwF^Go}l`9QRpYjHXnX0cHa`sjxMzixJgCHkH)i;%k1ryM=4>{(_3ae zJ)-Gpmsd|`(i{&cpHjg8Sy^W^W$iL43vaMwoXcpy94qbL+Uh4jN_*}6CT!sar!UtyT6+#YRb@x?1uQ>Q=Q9co289 z{pD5H_Lp+tq~03)8l@wJ=A&nLE3>i-1hfR7Pw;w;)JOb2`-2=NWs2X2&crvB)6hM% z7o4?@=h^&jY)5tT;3(2B0e3j(21?@nW^b&sZ?NnMcKuI1!3?(Iv%Wd3?*{v~%2FyP z7)IMDSxkyGwyOSDqT@E%M=Bc$VzMb>L9b#1_aOdev!T`Y=asz_5O)`LCx}gvo$zn0 zW3qDCufNk}5`bgahB3|{TqHt|wA+oI_Nr3D2*O3e}lr5swLQ-W%w)ZMQdDe-DQN`JL* z6f5H?08$#QQM#4ZAf=+8C%2Q3P$V1I%F#@5_&0g6Uk~2Ij~IR?a~*Au#Huu656~J; zZ1cJ6ajR^qj+f3I-l0!0_cAMne1+z^mrQ@@9~YpJz6-#U-h3S#8^e%7YVD z&;F>PrNMOKzQyy z?2pRzl3BdR-d|~uO#Dxfk zAH|RdB#D%8-z40MV5fUK{-yLV@WxZZU4bpW1J#w*h?H>D*l}z}U&ly!e6o1&&qUe= zU3DxGO1>r8=c@SLpOGNs+*woNf+-lh_NN6q+b=9lUZi1NLaPz$LW*^@1=enJro>9g z{LMefEGaoG-u4hDbu2VV+3kmHCq?$8KxX4sPVQHpC67!N@96o3@IQ0KTEl>~5@qlO>4(tX=ea{p$cXBPJ@9eo4K|C$Zv0}>DfYoP8 z8N|ZA(jXT0m9#|km7aZX+Tog?i!y-L#H(y~(qe8++0{&3WitSvd*vHAr83SS7seGil&_}e!%7uRQy3lX5CA!cr#MZJcMeK9%xJ4iO z!Rtft!^($Z0f6sw#HWyI^r0pxx@*cUQ+!<(?rm_i=tKszx)Xt5=tKs=(1~b?HsD_T z!2X;vfHHu_!uxi^X)*fHfE01x&H#WTG``@noC4r}007{9J0k!eX&tn!7dhUyGqg21 zo%R|=Du{48e8J^fic^<^(PAXEHbvaGGXwNc_FA>Y`*w!57&k9iwZ;2(hPJ>+59OFu zTfA>)XloKzd!?ekxOm_03yM=0_W~_OalZg7@cVWK023+gDv&>^4qw>sQEE3QE{dd* zvODG|A+(s23gHdC;XIL@cyoR?cKk~?ieW&&Nt1Ba?uQW4B%I$jg>%Z+_TiFJU?9xH zIj6Z85prk^g!5PO(B@)hFpSn~iU>K|OcCu8oZ;ZaUkC^( zvGsKwaSGrwbu0Kfz!x>FFVkwo`aH$@9}`v?xWd-~8qk8NwBr$=VC+^53dVVcxg?5> z`q4gBF%+ZEeDx!|wfqugi~CFzZ3q%+s~7h0Pj+TR0?cUhgskR7ShKc*d5Ns!b6xXg%7qEkODlq%c*tC-(0x0TAa1@IyC* zK6xxo6^=KYR-++?P`ndUjgH6BqMNi#C9}s$D8<>M!w=hhifxI&rq3P$3!2ryq;6JF z4w}`V92_lZi8fkf*d5;~yD0-`SNu@#;?sMiA_APncWFKm$*t7?|Sy3z?~C`?6FXF=)ImIUZHMp*Te^%D>zD11&~zzDZ?8 zHphA+04GJdGHoN3kRn}KL*SJkyz;Jg{s?j`QsP<=-$8ecX(8M-hSuFom!w%p&P7s4@)7dL~R2V(2F~AlnyvFf4?x^Rvl)bz+W&7oZ%fg z3L);uJzCGvLYYbdaQFuRbS?t`_8I1qD7LLWXx&h(hkGOt)-;DFT6!hvTfK5lk~lt* zl_XBIY^Q8sQe-PH@pdctzS#X+CWo=mw5B2OcCFBI+w-7{6KkWBl@}>2XjSa9JWDid z`!}@3*BbGDeMzT&+44cG7U_+q_VW#X`138(6~E&nP6t% zk=pLGHHZgxr`y^ZEZija(t=v*8`O!`5I_#o@cH7!l{CTS1DrgklP-agW?)iQa$_kT*BPh@NBEQnxl$1bSN?)j{5Iy)TY z_=|OI z_W@diNHC!e)tKgf#jhH3w7lAvc?OP=5+>B&rvOOllt$@GTBB2fYNYhDUo}$V)qa#P z#~sp2w=qLX^;-#taHGIM0g&8$jodO?qmzSbB!@c%8~xfKIo^hn>(<&a zUD-$5BBebVrRQmlP6?`kQWU$iyiTg}AHO!o-)Y%{Ync+lKQMbAC@A#oeIE!B%-++c zB75Hss(jh|FleY{?=N#E`hd%)IkMv+#Ei-N4C+&uyvHrsKPl&!f=@R?{zjW%^1fb% z<9nq+YvOYzNG*A9fW+i|0tLcxDwdaEhnWtn^E3Gevm9_;rI5D7IYMr0F-ORyHRt$h zr?|B?>7+Gc(g`rW`N722V$ylJ6~ooefY#bv7Jm4;NQTRu%GP4J^M z$Dn$#+~Fv39y_45Sng~AM0~k(KShMBF^XunV9mx6;rq`K5r6-=fj7*>;ezKS4eu*{ zcweA+-!S2o2CzeeU>Rk=>YEr#x$Nv{csaU3!#FRdC3VF&TyWds0wu3+Tl_+caoYkK zl5GnE7|cJlqmp&o5IaDq4YF}C+_o@4;Jmnrxv_i!gyq{7hPF5_)_q#rwxIIRZHp|5 z)U<8Uv5nZa=uF#yPiWr+Dr}>>nypV8ZQFty)b~2Z2%QgY+XCo`Z3_co+_sq9Mr>P@ zT9cbd$<5Hn$>7?!ZDB!3tQib+a3b&5l<0;`1YNxHr${P+ZOj3c;hLF&A=Al zw%AN-AV`y0rf=KgsWu{957IUm6Z?r!^2=?E;kplm#HAWlfDS!`oYzKK|i=UFqddM z7lvY7s^QK>dRw9c9x73^A=G)hRNT3sbqu4Y=z8IC=fVIF!e|+Zjcuq;qufWz05uWYOLyO@@603iAX{LpZT1-0|(E6+?m9hhs&|ezl!v2z$X#M4+F^((B2}&QW zi5Y_9( zX>9Vh^=f-AOmb|Klsp4*?n~M2gfC^&8Y^EK3|!(X&W6&K=t~0uhtHSBwKMrrUjRg3 zf*)4CG!Fo{FD;BMQ!P2>0%kufmWkC z z^p86obtJ`2`9gtUiis0R2dx2tzLGZrAN*@q)wBj>Sx;rzE@bh=#+r03;^2oN;)cr} z&<_9F)pN84WB3`0@t=@m<6pb-t@b!d47WAuVzu{?rshv5B4q6Yis&nonr(=Pul5YQ z>8riJHM~*=VIA@QMezn_n9K)!VFVk{>Jbb&#t1g(7%gfpiDF-_cHF8MiqY+FuX3a- z?I~N_?W1TzVA(qvZL-GE+yV&>LcPpLVyuxspcmg(H^GPoy(@F1t+u?t;9jSc3Ikd` zgP4{fJZl=Q!BBy(qK>uQnE`p*?_!gcrL-QEy%->Uv2qVl_RjYt?G0+TV-xKdAW%a6 z8YrQD%_ULn_YID*ilG?wyLcmL>?z6#*KZ7MsO#6O8d}S9M%r|JN`^1;_>KZw+0Oy* zjfYnP$+4|PUOuL^*iQI@62vskr@X{#M%+%o&(DQMBt34cl?fVqJGO-)XPVvfD%F^h zsk`T#)06tizY}FeT_!XME!;%-DT8bxe40U-)vk^fQqstA9xpL)j8{kBzRyu@3mO5H z?8r`-V2`4J+{)50cKCz?-|;0*dX@;bc}L>j?nnvOC$-H)$2~Zc9oPuLD zc3O;s0s!EY#~%QvJS(gK%6B@lEhwOW+-C*Q<3UGTBLKc{>;4B}s=to{;I0+0wD}>& zOGW^29lCtb#O+UkF&}t(m*XqtHCn9u?onEd(!Z9;=05Cr+X%o(k>x@Qp;L8{40jvw8ZkI7WLLEfC7O05K7boaf123QHk7PEJooH%bhOD0vdBh~bxRp6!n><}vD`xXA3pL97mg;f z<4-~+w-e{Xdw?54K-)MfAjO&xRsRR{BH4ula0g%j!A`b|EQnV02kqc?(V3?l4yBX= z@=U!1XKs!;vcv}`#%A#?qo~czlKLr5zm}DLEhj7M{aURtw$jt~ftb_N_Je>`Oxx3@ zB5j`yReIVU8ft0#m-(+jS?r9*ksc4C8q@ZsNzbqji{p&oP4^HZVkLb^Fy%s9E8@HiF6iZjlVY%$|}0&wyDh5Zy4 zGPuu#>v^Jc++sjne9~#)P@i$SQKqYT>88396gLkV06-U9AYREoVf--a{jy{(})Du zmTj&VKI*8Ww-Mg7yU?Y**j*?xVD#=TNS#>e7mkkN)@%Rv+U5dx3*Y8Kf*$3 zvCTk>7;ZC^wHMnAQ?2Qi5W2H8y4=V4E<=2c6vYzTKoDb-&(dKChj{Pq4NS3_zziJl znfk`|JTBvcX$AH-zP$wLNw)rsBSkqto1ptYt}`yZz<&4)vLxQn+zWDGXu=OeXo`&l z>7>BxIWX1OvS83G28b z^nRKzOr$Th0hljM-pvv-ymzw1(qs-LisRR9yo8Kj^vl?*b435WhC*>$$}4loT8;?| z_&j78+`dH*fgc7B87@DWBRcN`v<7npk5h~<(lJwj63NZFQEDOVo%)%i<+|M)TbEwWFzI8;}b(^6NE@U=Mubh*AboU{p}QTw=S? zLJ)4dz2CwVO7E1k?T%g){^xHUX^NplA5m_j4RJ)F4GF(EqR={~k0^^M5)3NZkRZVk zh1Q9nT7SLpA6|fA%>a*)jqmn(=z5WS@x7ze0?GV~@G7up%t&G=HC+;K)`8ZNtV8!) z-8-Z#)~&t@uQ8rJDHwk1OEx-B)iE<%vYj#UA(m{>)*!G_NX?988K2Fq=PCNpv0j!h zuMt^yAmUIrL^Zwvo{ z?`_$(cEHWNw*?l2__~sYX~uVcz|{u0FkGtw?-9e{qgoYsCJELvwJKnlBi`G><%5Q2 z+4={=5?H%G992qp&LWE3DNnq;r7J0cIv)O2LvH#Iq-XM$Y{g)r1K&1^U=yc}jbfn#8i%nVR~@5l%PMunvd8ME&Zth7cvy$J>U8BKsY7!{9VaCOU06NUp?ps2*x?u4!eq!jaJ=Ar)-YJj zQ&O#(M#?ey?8LvEF#{{&czrxPrf;mWWu|3qsz`)Cs$~8vng6Oit#s!6@=X;f{Ey%{ zrL)Rsre(taS(_?_5_N*#1*-*00kpH=ZLFdOd#QFeQ_U7sD{?t3r&^K6A7+x-I!Ugm zSoV>krpR&mn?uwrS&h*R1@>r>QJ?HeHs1$BaO&sVNbYQvdA#CIa?W`hVWmR@+M1K+V@k|=ku{%qjfqA zw7N>zvWL*>J%Ad>vIy-Z>J7d_S#_Bft zNlPYL3V?l@DqrDLypCv6rS@F~Xo{-b7NI^PAG1W7=0ob$VzgJNP6na+1QZH9$X_Vo zB)3GVMNzdlB*r|AD?TbGu}`AZF@a)tK(ui9&{YJJXnwO|Vyt?H(ukBZ{Q6Mc0+tu2W?DgPj8g|I$yN}Z;?*k(rGcMs z-n_G33tMghyx{Vw@Rmxx0nmG)p?!gvG={d`7iQ}QZReh9JdT7yuFw~7^>LyKZ$zU= zaCY54NlgzL3&pJ4uaBlH<7oq!mWpc=qbZegpi;WBaToS*vfA8|)F;X6J4RAqqCsR( z(YT@pK>zY|NKt>0Os5fmd?AJb0Nw0Asj5BEy2}QO;RptXd?+l9t!kk@plqe$ zaVLo0LQ04eu+riuntGmUseUUfkCG<&t0$A9e`%&xXk``HeHT3&f) zb|>W@v^lJyAb+0|)=qwk=ukdXz>;UGUD<^zWewPxc4`sPV-F$gqXq12JGH-&x=D{( zQz`@3V}$2m8uUQ&$9{V3C2$)7@E!4)>Osl&Bgn9%S?xB;&r}9nom`^qUoGIvtY)mJ zy*i3;K2TskU%>XZS63lokpx_S2a=DMC));He~D$g{|qT-X@<7V$x%E!j15qRhTSxDpKDK;b>uy z!%x=0b+XQC66?}UZD?$+lUv?RZKk~FM{afr3~>(@va8+H&ho>BZ2c0oMGcg9cUMoy zRfSCL0cH;%*!do6w(@B}N-sIo{A*Gg&j$BWALEq%=%r>DDVY>M)IV}OJ9?{)CAo12 zcCe4yRf*_8{JOYx)o3M_mf++tq60h7S4}szJ$z^NL?w;3#kn}NeW)KyIt_TS&+gM- z?G)4v09d2ZW0RB~6cmCroz2-+lm~AUIZ&>4WH>Sm>Hu33(}Tv4U>z<50xEcF$Uue5 z-dG$)QUjFY930?Ti#G%I<$j7B34w# z?^1)=A8*4qSnnF9PO?1)<+!2(+<0HoA9wHJ>TKmDE8O*2oe}DI9k)6{-DEpPFt!<~ zW*NCfwOo3{mS{T%?dGHgB`Kd#)Ozom$DaZ#QB|C2C!z18sm-^>#JT0mQPI$saZh-BeY7Uiq}Cvu`WC zb=IZ|bMIpy0sUS&i}k?w-MW;jRdRMmcC-{+%FqUlRPuXxFdJ5;PLj(yvZu;mG?{AP z#5V|(bR>hotd3$3*mp*0z%EWvvx&W?37rDToT<=5_>+VWMb>7UrmC~$yF0Slv@CJ)t`QeP407LVSlo>bnUoS=FB zeow89NY)3l8~EBlag`!#Yqml$QbFv>d}MJH(>WMWZkYG4u9O#N?D zFUUci%rR6UP#fCgl=k>6b#Q=d^6MKE0>QuVZY7D(1huTW%M~e*s_sx7*z?-AXfCXn2 zOBblS8O1RASh)^O4T@aw3nJDyZtzJ2#y`s=Gb$@sm_y?rzuZ3Fx`5)x=ne3 z7JCP#`fT(D^96a_rtEBZ z-+Z#l(rjRG3_J>TbrDQUzoUGjLnIvx4P}F03J+6$qICv!Pg$b2^YaP=D)frqmZ)px z?>ezncfzdBP>qWE=1!RCH|b3L-m9p3Z1G)cp$$NMBlt{P>&`+uSX}(f)+R=jrpR?$ z!~;g+)urk^*H~Q66N}6Dmq8aZkn)FPTR&1lym;Z=>RDwRsWV6DV!;sw7v0_0%OU=! zQ%F!x3VVHpx=xuxi*@xpvQnLA0}!E}%vGw#w%Ds4Ui{(v^V5weP3pPRf*DlL%d6Ev zfjX+>Ds`Ij5akFJvV)Wmh1|c#73&FGqgKeu0n&mU;5qBt7jAE-JV%RhdVHWW%UB1~ zV}F2!cWiH^ylDk6YP~wb2q5N%lN!%ZKA`}*`bu7m7;Xa^LVfEdH}1uTj%z%?cEPJd zUiZV9{Dct7h?XO|yb(t7zbRtO5drIqHmNO)oPez3m9Q?v#WgWKw^XZ*Bx7T4qC~w% zUoK2!)Gj*^y)WD&G4(ykR?r0wv);WuJlfdAm-Avv8b&ttBy3UPtKD5F54s|ryH6cw z>i~Fg>j4!p`+jw-Z7>vJPt>6c`|^IZSqQZ8r4uzh+vuyz9>!pHOn>%3V=E_cWn(dI6kv&~- zTqF~BNDTgCEc#{3bqalBU@ka!IMDs^e{)mT1c~ygXrH(13a~9k>8|LlFP*j7M zn}wL?fmCH#X~oZV*p@wN*K3M9VK1yrN9S{zh5XTxKRWYASN`b6A3gY^7k~8OkAD0y zfIkND#}NJ~=8xh0F_J$<^T*h(?1RVD-f1cPj|uRof%`pz`!-##%SbHxadnqGuPb}$ zakWRmT#kPMJjyGOlv*>XLxR-Q>m%i{J*g(i-b|Me{ANDE&Q>SKeqG7gZ!}@ z9vT~a;b#pJzb@EVEZB&$c1yn7+?4%tK<#slVR#wa`PW0?Y}Qk1-)QUYzPjvcN-UCyz33QUlN{lX+59n+Kc41~{rvGVm+S@p_=)crT;h+- zJh!=#KN9%DB9=eWc`~Xke+=c1LHzMFf9&Uv5BTFf{`i|e{^XBm_~QPl4vXOVU;gtH ze>CB9sVM#!&9RQ)kKTL?=>dev#(Vjmt`C}V@+{YiQ z_+vSL{DX7|3V`^%738|kMmz>#LN8GQ`~aZJYumUI8Q@DjE z^2gR0{JEJw-r;tn>vn&gOu5 z_L9p{Y0UF1EWWN<1OK4`zheS^5`Ygs*SEP1LX>5$Yd4VPyr|}|mM=nzC85a6M@*48 zR}o7}pbpPfOuG4V6>%@APsvIeK`JH=f4rjJt>n^TedIoINS$s25HV;EJ*+-z>ji(| zbSJGyD`MfNl9Y==AQSFnw21!`hnS^xq-&fTM?4FNdeXfZcJ&~}WiCY1f*gea# zV%f|81XIl-EpWxqT&JX+3Ns>synR(3pHthj-_HucLDM~M z6i=wYbN7GLFO?SvMTXLNG-*7v22(jY6ZINI3(;0ptxw%^`m{)l(NeDo=~TxcjoADimEq` z6#hdS^I05q-k(mWPqHVz6>h^V&FX#!GdHi0Jx{8Klrl4?tn+!bOD1*Eex!*%AJN1` zPoJ;JMQg?rBk1;kWvjmzjC&K6lUbLGY8adSU$q5uUDT-Z{4sY)_U%Qr z6XX8K@-L~4*h`x`r3#0Py;9LWSl3|__TnWtgIMnawMu!J(&W}=kN=={Nx2S1u$||7 zbWYaKRvAs=vm5)XFLX+;f2jVz(k}~+QJSRuvX)HwSnbIs{R|?Neypak)jyj>`r~J> zNPT}Xh%~7^oPYZE?b0~5`5(5h2;Ti^oriWr=lyQ%y-(C(KBC;|NGGVZGBORe=+ccX z{ze^S>3i|l_Pq{g)J;lkcM@3G)kJp}9cMJH!H}DC7S@Pqv=~DU0IWOC2-4KwD-(6E zRJxIt_~uCe-m_+8NdlSrsrr&KoFYS!hG-%Up*6y?q3Q5K#Y6uNN1`YiW1bd&Jo$1G4K+a|* zrG?6b#$1`L{5sPB^0OCa!n<9LcdnDt`kL;oIb9;#nIcE9sb8rFLmnawXi1G;Uqd3| zF;Xs)8V_}6N559{6$-(RC{ah%Ufg;^j+%OHM~(be-J+bL9HCW@YgRo@YtT`Leg~fY zB`rqX0)Vxn9wSZt9knHK)ZaIeD z(3oqjrJqMy3(7)%RJ&YrRQJsIQH8fpO`y~;Aob1jX}}k5o=>NZP}->`Y3Er>s~z&2 z(1X4Ei#paw4E<#MWwpDqf}%%>0pQAIb+-|~G!f8nQ&;C|ieN3TsPm%?Gh^SpfbGAc zx|GKZ^!Qfs9#W#aAKUVqTBbZl>O?wSw`P{GCIx4(;pLW&vL&I;IJU33v%7MFBH-dl zZ?Udn&Lrh6T7kMR`LTU%_Uj*Ny7UFy=y0YhU(qIflYZmkUxV1>C}&&gGm{B^=)uDN zQcFnhr+vxwEwBw7y>6_ttyLW17`jKjPG}I}aYwBGRdu8s)|2hLs?JrSdXi{4@cE{} zQcqSC=1f$=X$8KN=jFfENLzKCTI_U~Jt3$K)Uzz9O0 zAvu$YP&q>7;XTblX%&q)nwo`bC_87$qfn?=_IDkp!kWs?#_So{nWv1W45BZN(S#gB zYv$UVGlFJwv83eaX!gW@_!!-3*%_kDrLBwt-ewZetqB-k+gV5i%oPFxS(DYYiV@co z(34Te*<4a~QdUruZ9Ul&b)8wtHd*&b@ey#z0uCxZ_s0wrTlt=Yqrz4$BSXz$` zF>LF_!VqN)ZD^Ee1eK_)mx$8faA$iWR!1QgkTr>=Rdi%U=@6wgiWT9UD&MJz_5Hj_ z3VS@l*;83g8AI8YYO*b*HD1}m+3{A+dYktRXv8WaoG?p_bcV|Jqqtp^Z4|;NunjmVc+!Ma#L2a z1YVTzWP&q8&g;$2Ccto7K#=gaXz0VKoYR}cY4_g3tLrB@Q;3GNQ_)$yS=S_IrIJOl z@!5%g)3n~=)JYrCROeotI@yry{M|Oq%k=7~`dPNy;ICJr>bFs56F3vYGbqDOjp47O z>n9N|dJ2xSM?Vqih2cZm!&y}8lg;j)d)Ub zvtF&jO><8gDPl(4G{ZR{P`JV2$vk%P_wIF=Cj;g`?fRGkFRC>uA+9?i(^(zV1L}lH zl`Jr6FVd88fu(M2?ja{X*&9Ztp96$fcE&n< z4FhFONcw2Db9f+0i?^=KWBuAY=P0uYHE}m@CMhBGTje-kW^ayhVlv${uP7E$P)I)a z&tR)_oipTheZ+Vip66_?R1@r|7kNV;F&=NAHF&jpzsuQHd4v|D=K#Ph`OZ;_0f2iB z(W`cv`W~X!)dUfd3+MLt7r-?5b&bz;A8!a^tqOtJqXa}S`-aBs8?*+`p`WHD$S1U2 z+QC_@7}}cn93eFKuj}LUk*7O4B_*gYug$irY8jL`9Cueasm|vspMcxbBRLPl_+to%8^|BK`Eu|< z{)poJS2%yP;C4>ok6Za;8h;GpVEy5tZ|HJ#D-qQSM6(i6tVHmY2)Yu%R#sMAhEaQA zpG~aHXq>c-Fq>l3kq>f!1QpeEe)GI(qc&FM1qO-#%hYe);y`2M|c zXHB=Tl2en2l}zUm#-Y4JWX+9-uuaZ;vF5wkH`%O=jj&c_>91F zWTtbwtQ@0wxHpCC#?2InAT7jp>;rRQb$o#qb3NFg zAxl1QOEN-mMG*4Yg8aD}$$NnO7)fc=A2{;Ht1<|fZ=$Q^?1KB( zD7HSf`YFudC5O{*pVFL`=&FNX7_}Y18=HXj_&xSZRS$Pp`cS@2A+F7*d(tN89)CKV z|5ZlPVm|NK>~ywMCel*W!^r+@xzm~A-<~a6=YkcU6~F`+1c`xwJ~v!q1#o)3tCJDH z$I(Ws_APFAW*FOZ%QV;ZtF1z)>^L%~~geTkLerF}5`+XqqS*fV?nFyPP4LGdZmTc4+j)y5g(C{K zf9Y+BEU7TGEiq4tKctuXod4dH53g4?P*1lq)68q&Xm7+}DZw0n9E^uyV$ETwvgAix ztCb-GNLPYco`IyqKYH(A-ClB*Cf3|&DLZl79qW{71e53>VTsd9nMI4yF{TY*VYBZT zV+7#FCO!K~Atog(3}w5=c~X+@CWb0ncy}6sTx|h~{S?LJFDZk5O3D}g)Sv5|qoqeU zi8wZAJ*07VaT##l2Xmt0Zy<5~ATPlw!1e*2#v7c^*`$98fUm2aBa}lF;D>*?I>>tm zu+l5AEP9y$!b08^$6nd&%uLeXhhfbo+xfBc)~En=_b}(W022=ew>R?G)n)reDOWgU z?=XV1v|mUG5inzmvmz=$-IH6K|5h3eB$!NH_#vSc3XcBSoR;Xs;1>=P|JKznJ;ah< zaK*44`Du<=TUY|o;Z6T+pq(2uYmB}bFOIlR9{b*OB}7ZX-<4$$*~9y;*b`<#k=b^!a~kTa3I*kvYf==C?8 zeji%8sBXuqpy>;4n+b0Ys>N?OpwYBOx(JK`3w^zA))#S;*SJt#H}RT* zYi-ok4SQ|!mjl_{`R+-n7FrvWE(y*)6DRpsI(;cuqHyx41| zxtCUT?)VCnRMnnYx}aRWE;9{ksd4wq01Wli8eM$a8(O{7G7=%F{tary9*}3nW33bo#K2g)s z^+kGsuF~`5VdpkkS;9$s)ywD}(qzxaduA#wc78)$syen6Fz+Z#+Q_*>c zK{R-;QG*li4fWjjZ)ba(2ck5kUo zpxV1M)t;tpa4E6%sPkv#A}!`tN!6{g%mNfWjU2#k^_J74)EZ38rY{%j(PEtN)EdmH z-*!$m0>Ff)M;;&h3leF_KRj%tGFQ z9Jc`g^R}}F)vv=QrMd?z9ju^W(6cz=Tr-O+*ojcs2@Gv0#gP+H)C{^43Fh+tddH_p7q^!FKYx3>s(P}sV6oi8i1sJvN+R}GFN1v(}6 z(tFMsQ8O&-JYD|lTq7yVNF|eh( zTw?ZWUFS*q#CfY6@+ncHmKwXQ)y5pip2AL<|AldV6pTo5#L{#7`V8#yE=@!uo#&XHyISCSuQ9 z5yLLuT035kI26bu)=BOZxyKOy333tVFMvyQhC1CP_ypPW<~Pnhk~~2}M4e(RMT9+n zuOa3ca%X^pdC5(9MPkizpAaFwJ#XXtfmS$gUx0n{dkog#Ck$7U5~7KZzK4LeuJcT| z=qwIWb`wBzK!2*4k0KBOO-pogUh{opVZP&?cjn5kT3GHCs&Lbg{UX$vEceGR&Sxa~ zBMZyDPZ6QzUh}iui$emeZL9^tNKUn`^9p;}Usyi7&KlxQm!BHI-ulhil1yA{4>eEh z{2Mst~ukQm5G+l23M;b1=2a=9e9x6g8 zz%-qzT z#}R61&Z-n?73#<^lV8r62+$oZr3Pov9i4&2FgFmK+rtkkoEDFSrns-fm?Xz9=! zcjo3)be%`|g;TB+E@%xab(Hs{zr4pOyF7@)_58#!A#W9|x%oPstk)OTaXnUFx~RJt z&tzJz2-|6fJ3o{!+;K&?bI7s`5?J316>Il*IW_az{aDT3c7${99rO62um~50*S~Zh zlu8`y+8UHoiv35hq+;D1<~|}G>U{km?IVWRHs!GW+xA7Vqyx^n9(SaxyX0vb>3Tqt zyA}st(7QEuI|;n7pdTFV+HRAF76)F?-))9r$-$e_jl?@hp6}vZ8ziZOE$-wRW@*X| zK0JorB3Mtu6tV*aF8CB)w`KL^RN%;%NQ?*F@xeDh&(V~tk=pXc7x)W&#>BoM(D->K z$+cTjHc@(}?&d)sBEl1`%xd-(7R7gKTHQ?g(}+)%C1$jNmg*7M|0DrPxH@N=y8?c*UY`z4Veh zoLxF`BvrBql&?4w0s7X@q0Sd5yl)NleACL+QiAOgsi&cb%~EO&BY3cjJ@4mipKHz* zAWvc~8O|CKt?RtHe@kffOk3BVN*m6I*JVvEbn+&(`hkU3_fJ}$51;x$MbPAOPpSyh z46GZ}nH1VK!!@5rxSv8NQaq^8e#6Ys+ijSL-WykFl(j5BsU`fnfoL{QwH$dnu{bbzX1$ z!(UYI9JcOLJVn?LMpwvplkxVeVdg>Vb1EktqzVdMd*xcg0}n@%P0T(25*T1On$`gn zB!>+TJRH?E!>~&oU2Em!;cQ7KSFz>D)6fTE7?)hcp0v;-8V z^;2F&N3atI-4UL(U0gx3GI%%?2Z;M78m|=H_4KBRHi%Z!Oci?5e0j!j|0&0G&Om^k zv>fV8f&W=|=t1SB8X|qNnx!nEh|q0s1JfDD?Q(#tVbM$lC0udmy+&8sh8@`Ej$mis zemKZ;vX`r!O?j*a#b(^3VskANn>ltZe#XWPoe3e9l6zrzsxP z>k&V_zIxMo4bY={N(Q+KC8geoz~0&OCY9LVLWw&n9?O?gNBDbZGtNK&?@WX`lXreI z)RipBT{J}0J3CTDsKu5e%-)$e;{Uu;9qBLZwK~=5aQV)-X;DrErJd^O5v~{HB_jfN zs^c{q-61eQrg<$>*laC7jh3k(H{tHwqJ{iT&y+TO6k4JO)zHq~7t~cw+$s^6D zb8r3MPv?fRq~AXXX7?>zse~tRF3^1Z7jW=ZU)C-2ZZ|ouYiuI=ge>5TAJ+{~hsSjr z&vi{#1`zz>GDcrg0<^J<0d)MhZgcB8XvJ4d1Nf_eygPH3#yZC-9OI4iWT>uCNE1`GklXLy*)&^uoEGccK7Ml3PZD}WT4e0Plt$L@+ zE-P0k9=n7EkzB4j$$z@Tzie} zxdsSb?I^D@swTR4^Ut$=rRz>f=|IW&hE?I3Sn&BXNswSMX{ zXc`Zy(Gp#8@C$>efJPiYP&<$E%}qiguA5j!%%)AmF8DpJuaw2K*mrz&1uf-zut7Uk z%)jXA2Ov(=-e-o87ms3iqSgSQ?@{lw0>Bfs1^^#RFI%;LYlAD-$dS*K$F16Cv{l2~fTN}ty@KTHQ@bsXA>Vv0kYC~kY7>sy(B z?HN$$2W5%NX-A61_x)QuO=mFM~jD1d3`D#M@vy-!$ymv#fJ8Hw0NdfTRd89 zXlv5gcwu=1Uwq*8YHaCsXl$ZC4tlhBlSyO5UZwt!UVGuu;$24S@O@hnn8)Zl)`RngqK&WqRMuVFkkc#pq^S5wWH){iToPS-Fb#KYKFkLxWs@9n4H zT@+1L{pyaWUb1JqVoZQFvb7|r*%a$Kuat*@H|y<26!yRV!k(gBa$(6%;|U?GX{S*+ z1uL7@OExWpMbgKfZiieS2C>YCogtoHhg_cqNk4HK@TS6r&M0zZSnnr}hzPkfCeY5~ zg)zAE81(_CZr*u3AzU&*`tU6-jMriK#$(r-HXd=QqpeoF3jC+wj%&F_RkEigcYKA1pxht6ZsF04(EJ5qjm0P7ayPOaanB7i9pxH_mQ zYTh@ru*INS8cVarh)ivbu0N=Y|G5#ykG0AVjP=ij-Cu*T?xn^OdtPh5b8V4ikA{f$ zq6aA=%!Ta$!x%HQ_X1oEnObYH#8DaRI?Y)J^|uh&chFD%+(PbilwFgPp7xi!;F7Dg zB>!b0_wN)D%KfFE+^5Ihc>k7Kv&5tPCAQ1~lHKo?>~MrTi^1)IP;z*=`8ex{WO41Q zd$LzAyV|fOIn`a*_{*;5?EGa{BD?<=xK$E<*_FyZ$*oRe4Ssb+v4YERmgvjYd*IGk zPIcZ5fk_M5iN?1_z=5FRrf$CjNqSsP(1M_|U>N3<&*xRc-Q3@SksiOpEP7mFbp%^> z1vvbppt?PK=Qm(;*A-W6qbbu%XWddhVe*_?XSHmX)RLVk+8oQe{^nYAv%qr82`sB4 z9MCD|JX@ZB()pg^$9L%;t`4lYV|60S>QtS@p6*cHpWWF3&KfoU3s`Q^sk$M1?GN}h zry~rN*KXHn;ihGq_$VZL>{6X4)4?j8rx`w;41FANmJY&MhECWRM+WNVa4DdM{3>-> zv1vt1^ijbZw)0r=i;qUI5W*Er!a3%r_WV>nDVBg5L%I-7LYXS}J84pu5*h4%%&fGKZ9`}FV zL#*%@_gX!~_m(}xcT`j|X0yzyMlb`8AhLHv7rsz6|7GK_aeE~_`sw1HXnvS9xk!M z^ztZczhloS?l+W?AhyAi6Lm57T6l6@c4=ATG}f}A`!0Fp>qyDF zDe>@4%t>tn8EAc^dq$$Q@jy;I%JdX=QJ#z__nVUPIprE8YR=lkxbv0sv>1i?d^~$G z#+_jV;KC&6#`jnl|9Yl(FZstcyu=yKK!?MiWWL@RIzcy>d5s(bb-uZ{nBEn}x`#`0 zI}H)-#9C8C*oj3>Fn3}h0Qdi8diN$5*}FZWUtAi+dGgMzO@qWKO}oQs8%)`bPjr8; zOr^yp(Dwp?dfrWP=Sj*u3X5LQu^D)Qix#67%$vZDHFL-L0VK1i6nBAAWd*P|#l6)J zpgBVd4_E=zO?5Z-14w52m8AVvQ2a%S#24SU64|u5L*wLn2KP_J;9lC&eN;I~c`&K_ zE2}stZNIjO=cQKeL|HjUVR3^fVO;f62f#U7~6gz7QM zRSJrh0)SmsI7m zn3R*1lT(nFm!DNuHYF!FzpyMTH)mS*)SRhP3#LxX$!pI_bKNVh-Rg&d?ihLLMBI=j zC%p8|+NK@#TLNvgW8$WZ!*_uG$GW>K`n~SyLbLKSaLIUtn_lTI=XUXST-Q7nZj}-E zi#X%WD*K>NHdzZEpw2~SdKrc8m*is;15ffAYWg`MFu){lZU;A(qN^ z!Uw~GJGr;X-%kuYtBduKG0f_S2faSgd?w^TKr=jHHp7i)X6kATX_=X81V$1+4SD^6 zL6&2!*!ofKZX_;JCt*JjC@yjf^Kx@@re;l>nwgnXnw?!ZbxLM=;ndu`tjw~~DdmMC zF0R=MldS@yxwpBs5YdVW(pe8qGki|1C6{ z`AmWux+!(fMRk+Nm?hLL_7iAi?Ca_F$g3s=j*RviJIe(Ih>Wj$xnm{y{z-u&qtFb) zZtvq}@?(<%M@Eqk#1I+85=+q%n_u1=OBVFA0vck5*$_7#9NxP%*B2bUZaO%=FbBt{ zLf3(U0|Fv%TKO~>IUq)+wa+UpoKlvTS5{t{Uob7FAg5FtG_KVOgX3xq!7)g)$sd7& zYUuXUsOK@j8*!xY0*riBINkwl7L&2X1qBBf-&jGMSUm;D{dT_Q1V>IrH6< z$<;u;_ZXvb(^DXTK^AeO4YbIdFVT@`L+o7{tf)$rvA2n=OONy&$aA;f@Px zRiVB5g9J!7E>|`W5VG^vmbZ_$=%`u&&xVfb#^a;dXAE5TU1RX5xas(Si|$}BGI(qk z`nHG<>CUP<60-`XOf4@gZC{p?ms2`5t2D20YRX?eL*acNp3zqzC(4B`1o3=+kfx~J$NLN zvGMMw>`*A@|i%Z~Dbg7z?IWVO$pnx9i% zR#;k?l{a;2Y2mb~rPIo@rsRkaxn?U25w#@nvV&;~sDw7w%Z_;^=4Ho(l0b8-u^^P8 z%evw4Sno5)4Tc9%_8y^Z5+A#Tvi;*D@fP3k)#DlFsbG2)QD!g_&-k#rZKmxE+=5$K~y%fd`ONK9E4cp_ZD1 zqb#5)CYw!hT{V(GNZ{Pv~o3(IqIb278D z3i79A=46+)pE9j5vn(qwQ^ZF5Yqi4I__2oAIH$>fF<@+*_lu490vhC`*&sJOdVJ)w z$IZt^voiBGMq(La^N)?pp+042!Y#Zp65jVPJl7N?Z*%0QwsQf|gjC&U|?uAVw+Z1Ac+`IUr zOzZ+10N@aJb2e~`yS)-=1(4*Z9%Q6o+U!Wtlwpv7Z+0Zn5@5x6YH(0%?RuL_t#)1G zeCq+WGig^k`{RE1OG-Bbll%n}T}X*PnG74gU?R%8m1oa3_Zv243_&5@DWPW`I%x7y z1R@w{i9g{Y|Ere_-zia4XL)nEa*F?E&OFXVfI}p+p$>vebY0>Q$>E3Gx5@Wtc<5fv zYKjNnmZB#5Zlaw!8v$M$3B&o`4M#sja|6E#Hdb;g**o*pe^Z^lgI5 z)SESYYR&Rav5*&ERWZh2-jS3~(~-(ye)6&zPq`nH=K6~}i=shs$C@_$>nwZ~#N*4(q&587D2c&vB1c=1>^r(!<--v(H% z;Us2D&b8N-Z)%76ZsJEZYi+TG))tzzMi#R!p*8;r`@73`&h>0P?rtS{+8uZAlH{%B zfhX_pY5Z*w*kEDHR`1-(!T%q7-yJ7Kkv(oVOAs0afdvJ2mMoIGr)Q>TM2{etNFper z2u$b!mYl=R?(DL&v%3V@B^4mJ1Qi1YOpS^No_dO)aON!P>3O2Uoq9(6y{hh+sg47B z-_Pgw$9_H@H``tBRlRz#s;l1X$e>T(C~S?k4PMa{EnKzz*383>?ESFwU>DmTH=%rH z_4a8PI-gM*y^%FR|J5`< zNu1+y)|4OBbTY2@3u6p-itgQTdgr$^T1w!~z}(c!0in{bDiF?k!Zd?hGX_W6Bx+DW zN|+kKJHSMRP|8;y6%J<@IPH${&_<^VjfaF#&7(#}G>ZNodD3PU1mg@P%osDlqv(Me zJn0BC!Pk6NSivwe%mf$EcnHBamrh$i%d{o zpC}y5FvrMrlQKEB$LxhPter);H)>q@uhRt3<~9+nw(2Q7`bkDl;1>y41L)Y$4Xu05a8V^y4o|BC>p!4MaBO5T*g5YbL#3W{x znc(U4Kn*rvs*Yfsj1aZ^rEn@!X{NWFMnmYGW1@HJm$ zNDUtw9##pq2(%tOU3hW*bJs7tQoTH)d{{5$tGu<D_Cr@ zvuwPb%u&-qlqIqer*tA^hcgoYfVo(|4?-ZbM zACIZ8UV5VusC=NGP~Bb9H}(tnr2|IJ4r5nqjDVXs+Vks!M(G3E4GodRHkfMG&3aR* z5gBazIq)v6n}|5Ga(C=zmUU@}Xm8e@MntT8qp3s%s1r=*pi_^OwMNzb>oqd3rLAdF zuiRR{OVOKerq?paqs-~m=+uL8H+txSCFfBQ{1r68DPyD$^ca8a60~G;VN+D!9~;Dm zrrO)5ZW{Y0s#sxJIOp0}KU5ruU4~QEH$~&~SM|n!Ud_&fsj^!N_BNpza7k`Z#cb}#*un3;#BqJDpgdG+lLW@ez3LJ%|a>tAAShI`$F_2^VoH6ZpWca(l(zM?D1C;+wg) z#>OmQUwTFC4t?A7XfC#D|H+lHh5Gge%7<7{PNHRNj;0iDs9E7q{#rHVMgGg z*@VkO;a$eUkW|)pUlV(SVGA@ww1t^WBO(@Nbb-;y4lS^WAFT;G6YFxLnOIBfLT#?B zD=;o0YiL4KmIYrO88tF?8+(6&y%m|D@#tO^gK9;7VfO>+b`~RI0{O?N*b?@Y0{g&b zS{lSIu-R2$Tprc{j~oITdd8j1gfePO?D`z;7tGz1oiKz53;&>;7Gc)GjNLgdcB9Md z4F|naG!zNRA-I&o2Yiw{DEK1*+3S{MK`AX?HY>*^OM*Axc1PrJAn1vD1%D{w7vTbf z9ESg7Kib2uY4!NLvK5R=gxfsUJ34QgoKDkl>EF>`fKcICO=z`=wQ<%-yz`CBii#&Ohh@;W3Iet zAmWz%z9=sSq8>>M1SK)zj)=SviuywmVm>Z!t!#_MZpmrSM?+FD;CBbz9?=J< z9pweD+b7G2IaLkqwwLc_@`6Gj;Elrhu7Jk_w@yMK$s6&BqCX;pB9Sm^F#&kf{8;Q> zCf~VlkC#y53d&AB2hqV@F&-U_R5e8BFNk&P>61+QF_tDIbTxQdB~vaktD)YaFHn>c*r z;32~%W)an(zIDKP@}y3;^g;_htj=|1qip->WnIy?U$3s87YRpYuaAclsuB;!jNsyr zB=`eSUr6+d9=B3bzHD}mOAH8Z*&X5q0q)BQJ}D3ud|o*3&4S1^2t&p;1;4PYl+(BJNFut(PxR+$ZXvrW@u5@4GUsTE_SPWj9BGkMNELS>Zu+}Zkr z3;fFjwD8v0g)WaJI!559lUP)B) z2N!|3Ww~*N^|H05ZCO}E>~}`nvPh_;*S0J(7sT53`jnd7JZGXd3@y3{BbGuzKe#th z0wwW)+Y0i2uP-P8>0VLv1Qg~(4RF>Aq6o?bo&r=d81e^s+3%4+fZe=bmPA-suU5TW z9z5a0QMbp-^Inh7>-U2l42MBfyl&CwMx}?AwN{;6?<_q>@#s`12U?42?l?RsFUu%S z-Eo-j0gW{;=N0|n()=K465RgqML>~cw-AU#Jb|bb7L<;Ii-0t6Zg9OY2qGN{c_caN z2crw#Mesvow;-$bp`?2?I0=tO6u@kRqu!uLkc9x4$%qJsg_q$%Nf;g5Q`=VISHfmV zewMC7s?28Vx-@T_fIfRD*4`BkMT34B_a26m$PsUdkGlQf6u>cn6^JUJS#xqaM1Wej z5EkIES_MNMIpT(UV*YT{1ExoT;T>c;0JoySkQ=}~enAQZ0)Wrw7d!!QjBt|e@%uCq zvUQ0LG))R%iyZqz+fOXhwC>v{VqJSml+KB~M~DUk9zpW?J#ddLAO}5R7=siI3gKWx z3$L==cT?@ewcU!AGx4x9dwhB|a+cGGN8j2Tah$gRR=V|Hug-O4p$hUM#=4?e*H<^l z!yXReKzAVM_rP@}pg#bn6U==?;H7|a{F9p5IeB3bIK%V6A8-f)T-A`=SowTzkb_W! zQac|X4*4Oz4D&%AxE&0H{1SvP9=8{ZI;D2`AP!C)d(eTGVxwLDEIngY>+SV`o{K(F z(>FOF!i_{Z2psi+alq7&h|wL8fGTvb$Klp$+~RUa!VqdgK;wl_m>01L@`VKmmt}7> zDhR61k-)4&RC0&G97FgS_V7M%KYqU(q$L`bAQV6~_pffP#$_(&-r*ZHC{xysiN@XZ zTC7uk07BgeoN;!;pujeyn?Mx5y9A#Ft_i$4q`01h6S62K^#j1A@R(@Pc4PBsCVvbG{>|47|0hmWhKM zb>NQMphV6xYE(H09GvD$PWd@k7J5+WbyZqaDM643)?Wli9{?X8@dqS9gn7Uw-5*7J z*fnI?Fg~Y)AO>X_R&fxqdcetrAhL}5c{v)6NTLs&`n_+L|ukx$}^S2AqhE$`3mB9Q2?)Ct{ZYbHK}q!C*M#^#CdUupeSB zSf+-+5%C_>@uuq33LzQ^`y?0+uo_P6`$f1w4{KU442};0B}^FHu^N#{0SNH$`Y7TF zdLTN5KOtPk?!}AY5qQh=63DrKL5s*7yY{Hj<(uEfTwpZ8FZ;b>SP}#eEH?uY@N9l5 zEQNi3KX@)RI*vx7LJ;Qy5J9oL9w8vPA-(`}DS|x=2FXf06s)$>(a;9hj&w|25|(KZ&*n35G#NI z4~2bTJ7A&eh3Dz8Kg(S^<#?}cw=?l9{0ZqDmgp>)24@I2IGpAyoYu801gW})-CH{^ z+z|2eAxY*1*ldV`V~fHzi7x`S8;xqet&MV^9Mu8 zAr#2?HQu^n-vXr@gU<)U4w%Y zy8AWH2D%-V;D){A^MX`L&Fkk1SL zF(C48c!Zh@hubLIb}zdZFG*$aNl1EpxE$ZfixYa9z`Gc?zc0>e-dA;C{0un@oUN0Y14Hns4a7Dqa0MQZ%EG8!q zgkkd7fChNK==CeYbLFra1xT2PU^Eou-Qc5fpbwj4-mt)fcNch!FclPM>Dx!lMxJxG zNH`P|)QyvyI~H~ACNeu~f- zD_j;Xh4l)TML-RUxGb~g(-f{vo-;ieeanN09(2RQ`(Xpi8wr3LfWSZC357%mHQWkw zeHnz4z+?$_4)Ds`BZ88_Jx35Y*qOj_98Y$Zh+oSWWjP3o3|Ke$f`G^$;32f*VRKLL z<8F9m)Pt+PV)FgYy}D6fp=gu~%a&8Ou+6}uSB0Xcu8=H>vJX}$kmJJRbGu=0Gz3em zpyZY!?l5eVe1aFF5D)v^;B{e-EC5>zQV2puSU;NVC3O&AS_1%9t6^xlFKZ;dazsyF6T}os*XK>NzsKaSa}6SH!Rdd;6lXf4!ON< zUV=~~6!yZVt44(fHjE))^a|i&flV^_31|(H3JM0MQ#qA)7}U!v1OSBut2x-c@<4bA z5r`W+niql=NOPccY@OWC-vTT@Th)CFaQFmK_bs6ATR`2nfVyu1 zPM;y_z6HQ}gt~75b>9N&z6I2M3(&q-)qM-7`xa35Ex;l>U){F=I7pdgtF`W1fbB-va8s1=M{DsQVTG-%ifzTfm0nhx?&fkCZiH&PRI|6svXfaOv!etZIOjS=~SA zxPCz^^x^9JYwRu`PW(&pZMh`#xpu9Ax(6c!mkw-5a)A9dM#+Ee)higa?$yGE=bSU5 zU=jRVz+B%DZNI1K+ic%yXvM(dp6s+6H$;|pX8VOv-Za#$;bU-faOH3~hxP2r_UM3b zZGCid(3`#L6?C75ZW>g4$=JTr8lF>CFb%S+R~7Vw{8Lp07@`W^!@mm(erRycIk;Nv zAo#O@T%Wug{~|?#2jgEPyV?-^i`>J%0{?frRGt_=5`KVvrjUo)sA~OcYM76f(y~dEw7R&hH3Qc*pKh#b)lbR zW+Eju7l2Nr>XV&ZdDkFLrpJvhR#AO)Qb!k}o8bGD==E^%o7_D5F}@9Ce?8^>= z3YhVzH^VJ37l3WqGJdfS&6r+1o%=#Jfsl>T^%?cV%>647)4w)EYeJ?CdT~baChjx^ zkI23M>i5@N#5I@!4O7ZPxO#dzYB8%AN|ez61`~CiN*FyRJln05|I8}h%rbds&+Wwm z;@>+j7o8lD=tNEX7z;=&-MKU_YV51#6(8UvdXNTDmTeGru|^1}iIJTKEYx3>$AwtE zkO~QguFv~23iqBe|AwpbBj6oiqH{lOT39>{r$Qol=zFXMG#+w~^@bV7d#od7sCO$r z8VY@WJQSTeu*8*rjs$-YiL|DP+-A)pAjN9ZgugE-UY2VkeFaO4=X166R1-hAg?d6w z#PGu6f?Vr&%8|n2p$zvp1*l5gkfP#?x&8EGB5{w;K!=LJp6UP$61Q5N0J=k>N?qtD znc=}N!shj6HV=6HE-&*BGn+rbiu7yG&1^kXvRU1y@U$>Q}E0PiM?2XXza02Wmg zKcELN@aXP8#-j(!JR-H?qchEy%ENeIHcXlX?=nmxX)ER|FWy>@jcbT#F2!g>giBLq z8kKR@t<=48rE^X3)hzqsOmeZjOFI4r?~+o9 zmsBo2uVR2G{^Z)?wJiG%kPjEjEkTYML4Mg#{1d}|In$VmBGb#eDGFQ=bm#6$UVbMaLSmpjX-5)-x*58(3X$3!J^XQ7|A6c_0M3=;mu zACvH}f$I>&8QUKcZC$@)R39tW{M(E^6#xmf#h0-oRanaa=m#^bx@!2Xwm8O3 zq{$_Qfy}~W`Z1Y>iL=na?Zp@B0rsq0+!49ns2a>Ip#jLU0|0O(S1OGA**uaI70j-c z{n=FxRQzg&RRfE0JBtrBVzA>ijw`=`_`l*Q3hXn1E*SN8D-iZ5cg7w8!wGkg7m#lyH)=*MJcUzmk{eXw|}9stW%US>cS z*$t?x^MLx;6?nyU<=OAathLt_mj5>%pN%2?7T#qHDap>U{o&#whHW-mwaBy~Z8Doe zL=5SFXBiFYx3kXPkY=ISZ>Bg~b`Jk+V|ETNj6F>#WkpfPZlxlcq!gcv`Y6R$vZH1j z^K@ib)?Z`U2o)Eo`DKrmj65W!AQG6*oceXKOM+R2{x-mKQ0|Ew~8f;(?6JWZJulHVw! zF11z@O7`K#FOR>(b)Z2Zq1Z^SEB%D9yu)1d-ir8CeOm+Hwp(>W(tSCTvCD#QnX>W2 z=9-shSL5;7aPJCuXW-tNTjP5eR@M;F>V9NCG69uta}Wk=OZMHD9ooeNvw$? zeCuNUF2%P;Y`q)a96BFe>sj8JU2j468hW^f#IdVYx-Hz?oVD@a8?bxk+Pk@Z8s{E> zHr8(LzPV%>m5XpM#{96W(W%Gc?a#faz$QWLN<8e8Z2lpa_ZTHwb}o;ayMI{B-6zJm zqY^WIq|TiwPrvfk);Lp-W#-w(W#4KeJFTLCxNJn5rI$mRsQCDL?D_NT1G7|J`!t}k z;Li9LEZb?G?P_;8r##d4NUo(vYS^9GVkUX@!*SSTdCG$1y)+_1@*O6Ux6HFyKv~-}V(YRH z+s{mFw%GI^^Nb6qPiR7^1(foG5--bPPdn)6AWda{QBlA?)aiZk-*VXV=G&|0H71z( z&&H?bu&(*`>!s0Ym~=>LsHyhjJY!9@Pc#X$F`M+HlSPw@ev;9Ilv}wOZO^;2mGa$- z@dU#SpHJCbLp(4^8!L_OOcXY1<|V=UR{qv1-doN8VEw@9D##iL9FU+_1D+@Iq{Z&N) za(nX|@j!j{EeE-+GQl)`FFq%S{lYQuhLz99FnGu@Yh> zvvTPtlvK<)9)Bj^`YTaNrb`)gd}oW^sOXdU-JELyCDV8*l;56ul0lpTew-z>^IXoY z_Mw5N5T(olGrP!G_f zj;qgGr*CW+v;S$tlnM9DkN!UQ(2|& zMfFN{>ANs6_tQVf+%GMdi>oBD!xx%;%QbjtHr%}u-etH;c9mTXO1@&)sD_BSk*8NKv~e!h98-fL>vu3}{JD7e4`yPsIhX2%M(47MCX{k62x`fC0qNUTey-F?2|AZ`aIAXd&p`9u`+PaHZ8f6J+#n1 zN;)?UV&`pkE;M?ZRlqj~Z$roMZef7i_H2fPv0kgwURm;2ccbH`e5++H3tT#do)v)NI;& z-y);--c6H9Yww2RxA#YRT}txM$zN}8jT(J&yO92~5sF{GmSdk=WUtJAjeiGK{GiMy zKe@d?#nAlVwp_I|#BHD6K0f`d6Z;0tnw^#pFd=;Cv)cn0*o6j+?bZ~v{>SY-@t-}} ze=M@!y0|)p2AvvGl9wtML4}$77ZJs$ICnQ`YF8$L=8>xR^b<vC*NG0+$>d8a=P-vY&pE5sK7Izn~VdthutJJ=BQC+)l~4!QH%UCAQ=< zK;Y_<-?*P>Q0C>eV>xCCg^Xy&Pm9swE*pC5+max&?}Q1xxRzD`ZwxKzr3Wym(-Et2 zoq@}F%`9Swlw7+62_>2JK=tTbdo{rWvr#4wyfY}%f4gs}VMeQ1XkEINMniPz(j`V+ z@+>)fUFzb@;SVsOmY;c5ddqb54p_aiCs#s`)bORCNF~_fOTqcqmF#T9)Dm>*l5&<` zU_aM*wq=Rk7lTbp)SABKxHsb$gQ6vlDzFrfJ|)F}=!3yWluS8K-fQN`J8GOf>QliH zb@I%g3i2kDbgjpHi?KO;C-19OBRip@0r&iVe8oWY@!%CX=)6mBZLfS2E|D3vpznjF zD@6U9IShNwQhQf@i4nQ-wp`Mr9@~7Wy#c!-4P$4(zEl~>zF`veBhFkrWaQ%6e^5x3 zB>jCM*`Lg{))VD}D>wYYa)XyrQe&`vH)v$i2-I_Hhi8`T5c@K(^!akngi0J-zuHD^z6-37FS&*GAi4Ew%=x-2rn zpdXf$9AQs5XiGc|Vy7+7FEzUR2Z7)JLwBDfWp2@Hv64r*3kvNee#IXY+Zr=BN%1Yl zhxW8C)$g*2xPKe5<>8%PY|&fsl5xyP6%FlHhtX)T+%kUDVoymcZa==BKyD=Pg=a6e zS-k3I`@*fb*ccNx(;K4)YVfK{RC+DE>ZG!g^-Y-DFuLogdU~0_bEn3tjcCu_66^Y` z>+vf$`gmaZxot{uPezZrq|i~y7UGfrDUZ5^k|$e_N+#}A^TZuCP8{V?Us5N|>`^DI zDe2gN`3hrm@TjT!p8r(Qfad3HDtW5`^DBmPIC^%DcIh?)H)dN&!v-uDvv-zl(l9#p zr-~lJg^v`Upl-H7h+lH%(F;Z%oxQ*OG|eO@e~F&HyX0GLK#URzgS&KVgzDTS_IWlQ z7;U+?WG6S4#z+oxPrt9E3pbH|Oul4{jiImZE9q?kFl09X%&`I(_(0`_dH};R;xfB2 zXK`Pbn+x!vkMFu)VmHRjj(R;FnvKO<1Me~x?;$1GLh1WpiJM{fXozT=_W+HE`0C9u zqpw~QJA0#-h1lU{Vl90&CDQMq()OjMTBdK)#M1s6&NP>?-^J`*^%#v$r&J6O0>eq> zuh{xU_JQq9CJ;EpJe6%-WUs2hG>A@B*>faR)3vnUhW+=nquk5e>P%alT-Ip-{+OA? zt3>exTdvhC=b737yQ+x9MAXUUAw@)>EL@88M9GrzY_Q1wNY_k_{t3{=`bgK!MaGlM zL*R)+q%sJG^XGiX6m3v=Qvc{Qtx>0mav-bKX)-wwgikKB_Z8WTVv#nUyHyMziulwr zyU#%sQ@I*4Ao#>Gd)Pq~m#2Hti6X`NJBy4#<7#JCtTeLX>|Mp8G%tv&aM6OEv2pN= zeCw!2doGQ&LId+k@8HfUrsN=F<{QzEiJ3pA7}dB+19||1)n1<-28HwtR#&%~4cUgm z)h$uej-_3gzG%)mL#WydkIu$$_keeL!;Qdj--7sxk_M=_Q|YS=)KCfFhB+O4szQpKVfab^pdC$e?4 zZB#7Za+~#G6DT(3%KNqZgEOeX`v;TR0g3@zc>l)8`_xqt5`I14;#Gz z=CD$D&jdy;sf01ub(V7aNg6^YrX?SS%z>_tQuEked!y_<0>xNu0BHJs=0SVKUjzx_OMX= ze{NR)ZKl@JtWqre*d(1F(4^ARiDP2sCrqn@N&QJ<&krh=R9xJ+#6B+m-2_77;tNac z(W&TwXTaXB%$YKO#`JaC zhRqJVqoo5@j{j2K8r*CMq!+&2{QD*~G^WmDtC}owV`XuVP5W#|Irm@*)!m ziI0yth-BY1h@G{(x5Q{I*EzGI#>k4Zx0dbGyf_nU>BWMrPxL~<(OcHnqtCJYvYLC<=`zHf-0P6wu1D!QoU`wJrMl;`shv=~ zO}weHuII8jo!Hk)i7B-VLMLf6bPRWA9fZC@rU*@HwCY?c2;B?f>|jqH!;<}{g3z0^ zk^LEaT7Q{QJ=tZHdeRb0_8an+*-Pv!Z9MH%3@LlscbUD!E---*d)j}Qy~M`SAUcVq zLYKD7jP{gW=E#cQjjT9(dpb4E3#YAVWyuZ8{=;zdV1m-DH|$MUq!CeV&S1x271kq| z@@eCi-3WEZgHWOoO2t84V5n^Cg$l?}%~Xbp*pMdDi?%mA-1_l1?t_Yx9w_@LK_yg4 zggU1_zgvL%aePn|gC3|Fh)TxCn7nXAfC9!)${5Pm^HAl?=MD*hs2r4`z@D5c zfalyRFQC7iS2ofm_?2g|Gcs$E?*F8j|)&pHX=ZU(?~QFftvIlcQh0VL+Q(? zvWH&-1@NL_T*ngX8se&=o(L2w^oHDiIf9G5dP89pTmTu>)T*w$CO6R6G|w5-#QkW$-*uR5~h% z(E!xl^n{=yU_b_*1brbWBn|A=iYI63QADSHe_3!jcL+tbBUL{1amTXmy`l0URa?>* zg_@Qg;0Kg;#x;b4vKRjIyMs_UQxYS%#3IlG^*%+&J_=>MpNWSBTdiPYD@%Q9h^>d9HCQVZdI`}OMjEf_;Hu2BFDORQ@o`X@_yAY*Hj|&FW2lLoST@?_+_9{dHyrCOJI@8h zOT%s<>h;NCsC(&$l9^$sff>PtjwPQUD92wc2S$cOKU6#n^B})a{xk&T9HGLdTa-dj zgAuaKs!Db;ry44I!$bf&LGrpI9v}w_r9xS0S&)F`o`8&kTVhc7e0}U5CO=Ep5i}}N zCE!|nPCa84+pr+Up^^Q{y1IZ7o)8qP7D?%8C_o#O_#l`L9*T;GMK4{^RpQ+dKU6gj z!OY_#x{Lvfmx{@lctUqAAM& z)UvLwY<F2E7qklA}myS_Z;(Q|#s(7Zl8f`oUlm zqfn1L>X8FccT|MZ!g4?gL-A7u$ggGc1c~>%Vfx%r57a952_YyXF7nuH`6PD)1+Ro6 z=&Ig1_w4ncz?ia;`B{2}V%sNTeS3mU6trT>BG`Yy3+`F)Mm;_Ws+Y^&fPf3FgCK(8 zS6YlbJS!&;2=MU&FUw$bC2-VHFsM*g9sHXd^7~a@s_N(VL?RK<4f6sP7o<@Xpf;%p zwQVJ+oEl2GxHY?=Sg~`@#KYx-qjc(7P%FK@d`nsPyfA1QbP1CTL4n&3^;x0ZE4XVu z5|qUtG^~cXEMGG|Cod#LdEVm#bLsX-Zcjh}KFKl^DE32TSC0a27<;*ZAP7EB$Pbe1 z2|xvGk2efqjU0q>?*i{rw*R(zKjU)l)J;l4yF}*>(Y8pHgg$s;S@+%$NzhKtExN(a z@sThD88GERs0kl}8ob_c6iV6$6o`*z=eVHQF4W`=L;czy)Q%1WBzO(AyFK8f#SpFz zyr3EiC_D4T;NG0Y(kJQ_GRsUwLB;Y>?)BBJT~IdMi68{AS677nTZeGaFG2z95Dyvz zWo>z=XdUoHgFYZ!5|r7GRnN_74^bHeTOf{M2!kM)0%Ie1B`}mBi1p@FROc#u&#IM7 z9uM9HLI4nN9^}Ix5TH7CC@9E2A>!8Rnrlkt*eefp!yjK*HrmBIb?gqI?XTAI=*C6h z+`wT)BH*rqdj6z}zgc@Q(GS`JoC4TJMMfd~|Ij{3Z^8)}(HA+D2y z9vNW8far%{@AKMRt>8Kotp-~O6_mj-f`{fqB-HST5ET1?;j}l)(sfuZrR>;q5Nnqo zO{yyE*4rcX5h*|B3xNQ5qYxapq3APMVhB1yvLHhpcru~H_!5HwfBRwe5dz2SiA3EY z00M{Raf{e;`c+r0y0R=>GPpAE2Tr0H)$3W7iOaY$XTp4_#ldA4yCebVl_e=6dLkYf zVrQ@~5Vpus2`aWnduzkD9RxLln2&8tyDGZAiI5n z-!FurGC72Q5Q&4J^RWJlM8IaD<`cj*YOc(=XKFJmzULn4L2V4|A)SGQKG&np_A(*d*xmI=`?FG0<6;3otiybuZlQT~90 zP8F41pnNM zQ%Et_-A}D6vP(d=>Cv*s?5c;7Vy@)2aJrZ)`3o*lnTomYT82JS%KY^<&1i@Quy^!m z6M_WwE1OQA6^!(rKSk~x3C<6$E4W|*x@m9OB@KQB6e|azuTQ@-n5|ugKHgi#Pu>Mb zZ>kCa9{$C^RnQv$i`!Ne)Z(|9>aI`Xb2=o0=TrC>sT;Qs{~`rHp2ojO?$7=B7s<=^ z4E_b@u}GcQM!1XuoXU9))$C}I$HLv+kNV3ZMGA8u-R z+KWLSJzK`Huc@q{O28KU@e;7XqN>iMU-X6Vfo3g_;<1tgX!sY5(_9Qz-$XqF|0xU! z7lXBVM~cCAeZFi9_Zx*@Ee88D^#uARN9sRara&>+a)xVCmKhEr>EK$@Pe_8ECS}Tu z7t6XC8sj@x9X`dl@iF66^(;f5yCC-Ru@*+7e1`jg?^GDH8sSRFx$E^qhsmb@rf{sp7!dI_Azu5lGkX#D`j7BGX0oL z+YI^%p+qi2t&Wxr(YGaeHse-p+Z-!P=-XndyJTiK&paX`KydsFml$ObVty{Wjw-N~ z0nImqs->x}e67q^kJ*JG>6(`r$SxL;hEk&zlWr|X92-4x;?qi<4AGObg*AB+xu54Eq4zEk!D_XX9G`bK*Q@F=*+fCm=AfkNMw*0Pmwl*cYselm5)dk+R#U3 zU6_l)VNrnXHu%ORMS^e&0H5viz;{{+4&k`r+cJFJ2}FbNegA=v%R1zoRr3=p`sfQV zn!UBL(&xx8rpI~;l5nuO3FA5PW&no|?AoNnx1K$cQn8EmyA;dkTh1&bw6mDhvGhO< z8S%&HCY79=|M_2KZ!zpV3yNpchzP~wO%#v$!xU!`@-nqhYV9{=%X4j%r~kKQ`?x#t zAT=MP`iLFWlZ<#N1t-gvG2A26OVvj_ML)sDt|@K<^ZgaLf|YBLFl=`#^v%h#IQnLH zRWtH3)t{CPxadD+-MAw(8k`Nd9-u*^s_I>61#-oA0P>L~$ef=qYi|Ye^aE8Fai@$R z>>~+8GC$}DGx(SlsyfpXqvm!B{7`m`JGY#gL1O<~&`o_ulkq}9ANGD^X>N#c%|3-5+DQ|=8 zIWlAPI4s@H7BdbMtgXyNBYr7c#6@)Ur^_@>ryt`h-wYf{yfiV0@`T&WcvEA;RT>$% z$3xR+Ikp{U9I8vad2+HotE}jh_-i9&MWe)*jhSkK4n35Z0oh55lD(;s+-1g+8VRo8 z$ZXS(gbMD{Go3C2N%K#=9z;JeaddbiyAnr#`E5;S<{8p6^DwS3bO9py`}X0P)xtlc z=Xe6%89c|@+{E8nF>kAQXsiAPjfdE{gXP4kQ+ZaOz&yj{du+_QHEC+5TX?+jeN(w&f4Hex|@kVZsk9&Vy}2x?J~$g}g^){G=f&b5B0@V>-ImRo_l zPB|!YRU@M=rCL288o`;UO9h@p%>uGJl4P;nH&e+r)4@uM4pK@@O4&ZF0$VC`wFNK= zzFt+sJxkL>GF*_Vw1?=&#N0iL=d?X)-i+ziH8ZMp7Kl@$BBg0RbQskJ3t-jSB`-^K zYQcV!M9iV8DO7ixEg(|ek=nGU9lCqT0{Bc=bj(7|N%6A$6IGp=4rqIMLVrj6!U}u! zZUgTOvi{3ift*;x#LPr5q0tbcWfRf2RM=edv?l3H>~~W$xt5na zsjFpoRSoUrJORwqS6JPtvDc<< z5>2m~8JvDXG5lmf;t9Lp`iV-XdqtvkBX&cT z@&0qh7ERO`wN^y|`~8jAB{nr;cLDMGEBhIk5)(}Ot%+Y6vPzZl26P5yM;eAGi-l9E zt}@<$UJ7h-xQtFutm;(gCmFF&8t+QLKFnvB!6}(1R`mq^gjm(js+7U^B>rM(Y>@32 z?PR-+b^i^~1LF4fX!U)Gm9|Q~JMkdbb~!ab;@VnMPcrnNk@tgZvwo)>y+7e$xV{vi zs__#2gwWb|IokJN;$nT$&dc{XKJd#+?R%v)>y-(73h~Cd#Zq)k%fOD|$)}*Nx-uj)g z^|3@pmV1bvW5Yu2d#jnmpHP%*_9n(L+EjpP1K(X@$1` zkZ8{q(Clupft{hNv1-~VBDcXcMYqeXcPT&)xBJCVqvH=B^_>qKJ~zFHdAC;edb z8B$5?a^aywIfEkK00N3mbmSWq0VWZ({5H{=-9Hpv{B5F}x$Khln2Gpyv3{3gVK3V% z7?wT^XL>z&GI0&H5=p28Q7e(-Pb_T%pPkXb0fj~RaK%-^m#f?%NH`GoLOKYa0JqG& za2pmbB8egjdJ8MFw>mYfur0d(--*#wYPgJR@@R!AwysF$oxqPeAZ+*HG_S$zVSQ*W6wNGEgoIZ?-SQhNmq~1!a+p==&A1$ zUD4Jz6CxV%T4EL2_CsP4l?F015&gBne6AvbSYXoYiK`hj^T)&(OMoFp0QAL=2^a3_ zFDU+IVhY;*lg*qDOGE0y0Zi4zpA$DwS$mGGATgK4>>o1DzHH=}!}1*F$sM<)d@0_VJe1L+`rFyIKTlV^)$t4I7l0Sl->P8W?Rl z)bNaclCi?dhhM@D3o7!LH)fAQ4jnj1K&_RlwQ$}s;ITKUyyxCM( znsth<=j}Q7tPSUs-oGUVwO~JVU|p&j+h&FSk=lJmrHKuGg)UHfEMG9{Mp^%JjMw zC4A+#S^)Hy%CF_Fumbp(RK8XZU|9Q1)@FkqC=nZ*B{rH7D8J8cA>Zec@@KfYx^dv$ zxmnZ`%C?X1SAIEHLcKG3zXjUZXn6G;MbAsif8=g;08N!r(t(m?8T}+P82Ck2;zbt4 zhH%wqbob(N-5Q0v8~1LrCc6{f85Sz@`j`KWVV*-R=41CqyUl|%BH}jhzt!kA??l2( zOSfrFjkypMdQjpV&&EL9xz8s-JHgaIxZ;}@z9nrsOsn)&Cq8{%e!iSuZWey zr?}O`IZ`P&Gl5In6gJdiO(t-mrf9@!5=K*@A(Yr!Q{LW+sa_yg$JDd;iK(JYvu-8E zt&6j6Tx^?JACVI94}(N@UEH4Dv)~@YgQV>Nwa(wI^2?wf`V6PrCYKk~M>ox1(Nwu( za{1i)>?;mRBWlz8vaQm*xSCYE%zYGhZLH0;n{dU>i``?KZpXCIRtw8s+VJo!+v$)x zlPsK%GS1}h#+jrd!(Y{zG)0CA=uu5QHm}-Vc~)zKYFAALMJo>i<+)M$d13huRf81J zAeLqOR@9dC8of9+B$mODq_=5N2Gb8Wcn4soc;`9g{u1n@Y+bVc0bqBHC51rV$ZW zS*T7FwPP}`+U6^jH9=?MD&Md-2ZZKlEogp*9xC3)q|CuSm1cuEz_(2XoL-(_&_nJ! zbI^`BWvs}LfbS3 zokgJ5S{I>?gOdWP{%2WZwiS#;Up$qANpj%Vxp=;4ex1EpV6_oyj8e0HT$ z8m}$C(FJ+qypZ|}QiMVFB2j`Qxo(^h5wb|}IFqlIa5fr=_(ZoD;dvjVq4NZT0SS^A z$&iOM5cEeOY2d$C*D@~1n&yXGpL|5{MBYQUct|+x!hbK zuN(5Q2E-5~_w+y_RY+Xt#Yv{ZICYaRiXd5VD}|(c>;Oq11CSpT2ofOCGbD?Jbi5v~ z1Uc+r{|ME*Sq@3~NV3%|Jwv${oNP5q$0W1ffM&^d=<%=0FL6Qs&2R`(j0Homapez0 zA=4c642jL)+P4Uq57peU9yuJ6A>AFY4w6Z_qd~|I3#rZ^TQp>A4hiU#zpSmA?#l(K zD}y5BnuQE^669-z&l3ThT@iYKbeNDCj3ipUHXm~AxtzO(3|ND!g}f|1lT_13BUKI2 zN8gqE@7e)0Me#?M#_JllRdj3lYeb8-|3kkcAJ@WOcE+Gw~!g{-iW8#1esOtF4Q(&&lG zq8DsKM2c#%DY%1>;S{ot!Ur(Olj`vUk#GTt54r;p$Xcuj&y_y|Sz;k$br|MBglxx< zyc@)VheXei1sT$$2ZGAM9)}MwF6aItYQpd={h>LK8WPQTvHbcxuvd`!PJle$kh2|< zu|q~^$QkaFVU8gaB4QTRq=Zp|gt#6^V+fhgA@LvNDTU;EL7+S$Ll#TOjz|)qmvqOu znWK>1HY`UVcPS*g^uq81UN2-!hxEvPe=wq^k*#)>-N!)oKZo9ZsWiq}de(9?pO4Dh zBt5PW!r=fUg!Mqu!;lAOw1wQ|KFECSmXsC~aLVv3+uU@nM`yOkhU|@5WKqy7 zB4inKnkoUOEq;AUO`AMtqOd@AMSO!Y2@QCsd~BT;tMg)YUaZcG)p@ZxFP6?O=NMf4 z|Kr6r96x+1y1@`LPkC2Q?I^~}gmzG|0Hg5(Dwd%@}a zAmlHr{2<$E(2>qb$o7#y`*T+IVNX<`Z*o?88~y`MZLjP%842Mx`_(Hre>r-(OR_IJ z{Z{l%mn6?_tU>u*lLO=1V1WxcK;V*YRly0k2~kym;i{lL{&zZTKUNiN#BDMeLLR|u zVsgRxQT&Ts1Ah$vB6l(#$G^zAbOrw+M_TvdU*!AP6ZjX|)GW9ia-9^RcOLG%s1e=) zg`Ec_oiilfE%`e<~43chsy;vKh+flg(Nl!UG`-d;AMJHzxwoH&M6W zK7b+NL?AZrNFtCmLUJkhUkbl^$@@F%3G_`ix&d9GJ6X`i`dvdbCvw4s==VW0>MQNU zWQgIKt;vj9J(rtHKOrAEo2@}!Z}N7Fwij8oUEwpf#osqOe1J!bcO-_kQjeF=LsT!D zJSrvIvfN-3(gABw_e+wYrUR%q^i7c)sON~>6dABak@_W9F&qto4CBbP7zarSGV-$I zYv;bK7ov|_UL{dd zq|KI2iU{|99h|(E`-<+9oUQ*4{e;N%S8LFOE0P!M+m0H)q$7Ilie!kZzm`&s8rOnL z(IjbVBQ*KSAGk6p>Ol?S<+2b6a8+%_3dw;5E;*s^nDRSg3d9|4liZIgl263^%+!;ss? zV(QJgZPwTi?s6NR=2{>w09!oky5t?)1G*{1xoz&Fo|q+I6dL&Sl3PW024BDlPO_$VrY3^0p_r<20Z_=q6YI9{&Jp{HQr~ zzP0DjBnwEQ67(R<6EXu-!kkmb%QjnQ`X14w$+|SWx=nu|5pPa?+-d~x0UiBuYyZv3 zB@EkLLq>hvYSP7sjO_V0UuWF&Zv?Oo*DN z0oM)Glk^;<-P1BvsGJ*Bq88UrZKd=LCi^nnYzm*6Rw<)uTGA34oB9!-eqtU3m9&Jd zSV!VtDp^Sey_T#*j|{j5=7ZeWM=C6p2qXimB`Kj{sU#)4XreN=_8+7l6DPlG9jf`Y zY#?RRv@9hw;*?Dz7xw?^FsL1>;M1__>?C)$W?x%J?(0*X-DL(ybw;l1f2wvytsc@d zkWN6N>x~?=qMw+!W0`=2Wa41goAvB>JY#CeX{l$w!h5}*A(D-worX(Ovy+jX?#Am4 zsEC&3tT$@eug+R#O`NJ_*6)<<%aV|tVi4{>rAp+qcV;fzXsHiq1nS3l`bm2HDRmp( zfzD1bZaooo>M(t#gH9G5q&Q7veX0s<$?0wuz-o4iB{WSLIWMIj6FFb9p2&IE%!n?w zK$;p65%l#ABkE-VtV;TStCAPCWba(BW~Rt=$6G+8x+9|gm_v8dEr8F2J@cJ1Q?RdF z$ony)kQ)5TEAZYR@4ddR+QojYm-pGn3xy{CGU6fv|E5XcUs-Q+Zo*pjh^Tk9eg_h& z9;P%Cjz7!`BMbX9e0V6dYIZF;bzq5$IsnsVgYj%(s||!!>aYaK*6_s6_3Ospi!^3f`o&2 zCnI)#`I>u@htSF+$;RxV8sd`k7f}Prj1qnkULZ#G$qnm)ym0H55X1 zxxR^hGH~{~5zGciHM9H&eN!CztwchE3ir zYx0OCa9O`gN#9;opr3s*d2hb;>Lt|@ za{5WCEy)+CYip3GmoD!XMh2GC67jgS& zfb`zN`eIPN9Y{^^qU(h~Zhj$I!5y=NM87c0XJF8e|CVg0@01Mia~g`w0kplW-tuqt zZ4Gm9#9|IqGtuWz@({zFrm>QK8f>Jc1E=vgjnG4flke->8v1$Fsvn}g^z3J33w{v; z`i6z}9=&Y4m+6fTewWmxmTGb_9+^YD+8y4f^;W%A_wK7ng}FjSMXT=RMpQ&~`))L< zZugCjt3qqqRMoY9m!kW)s=AZ@o}6Omi$45&@)<5hbAv>Ci#MVlUxR(C#q=BC#^s2A z{DQ{03#iBG>8e4SUk6pTdDrfZ0u32erH7`Q4gB#48Kxh z^0S3{WK!33+GKWau1#qymd>p`yw51f$&ZpRvHdhOw1W7JXo!Mz*<@6Z_M2?tTWh-1 z_}2Peie`)CYuVykd6V&GNQfpAgU_j5(iW!xfb2Dclvh7ZzSfW}-ef$unsHu@wFDG} zDjooY6gD#u08W4W>*Rrk>}r?_eJ+~}a8DC3Mf|t8txyau!-78#(!OVM{ciK}8pEU>)~X7#tWLo|6NE z_Zi(J*TRQ4toRfh7(qqr=jBFJssr0h^pm>a&>bDyUtx7KoMu9LR5fR!pXFwqz$tf!;Q8C zdB+6u^(7U*vreZbsIhKZ$hT+#9{y2_2pC zW&_l8>J1G!{4(7fltBv}KB68|gPCwc#k5@OcgjaMR9sMxYj_)lkND^V`$Jv0^XSJ! z8ynt+?zpMq89e~8C>^cZwi{ouNZ*zq-!^^MFix}rAWH(>Xbh@ep)r#tN8N0qpJa%K zUnESu)qI^B;k-i3O=!+v4O%`HkIX^K$H4oHmLHf{@dh(b#X>9iY$Fz;;Nx#II^Z$4 z**IWpf>Z^!ewQM4qnX%j3cmF=qk?aysiYNrYPjN9F7r61*g?T-HAX$6q5%beW@$xH z8|H6hrbwRjZKGoOmYROT1pC;U3MH3$D+617cictYo6TtZCE8ukM{cw8y${`H^u3!^ z7FcAHq34+>d{_EOT1?VzH)*~noo(hDZZXb$&K63(sF_ElyDMtiOr1Az)`I!dr^<6D zPMtSzf!wXL80gn4A2q$dqKz`({)#x8waK>(dVfy^$L4RbkDpd)^VmTh48%_}9zq>Y|@e}8TXU#H2N_n_ zVjrh%G!1go-inHR;}lUtBqLN{gj3ivYW4|(?tL1jYTy?8aPN-vDAFQ#YTo*bsO{Se zx1jBRiw~o&ZrYKHW=ta*OE4#D34( zf|eYtsMZ4*Ozfi?IdqR_Vq%{(%Ra(+%-Gw3R>Ni1Cb!|CIoQ~Zs@PGVh|##`DrT^X zhKshej~a0iOS=u^Mt>`8(K=c!$)T_6mmAxyI z7H#jbYI~?p<^B4$1_N-8cJh@Tm}&s%CmAx}7m2YN;9HJ{Ga|5@lGvkL%?98I9-4yz zI0WxAK^9p`{Y|KhvtMesXan%M5f?E4e+M1V8-PPwZ47`lT_n`%V*M^fb8}4dnJ4I@ zd_wN+#P3u0pIgG=S-?hp(xcNJx&l%-43{f|Q4ww*hGi)z32>*)BTIfySO~b$ByV9= zewIdlL~?8Oz1?_KIuk&p?%wu-HriEbgR1wq-I#c!CE8O{X_=#inhj@S z3cO}hkj*b1HSy?(9go@#tgKDb*rt@c#4@;u@q{H08?5ar(9f zb3Z!GSsEXde(Y-9JG3I^ew>-dYVwk3t=Zg%@z5O1{UmsAF!wF4f%%VXxM*`9GvXrV zeoC#;+)t`Kdvl+K=CGOOY;t{1t#N&K7fmZ=dzA6RD?8`1&(_)-pQ#$Jo>5VN@yUr) zK9$S92E^NKWvzhf8!E42xWN>ldhB<=HuMpyyqOz7zahKfH_%UrdP3XN z8!K7BexQ6O6M0yzNrhC`TmH**p(nyIFSV%u1-OSvEj^0)o(6@!1xGw0y`zs}` z(hA_^EtMnm00jAFtG36sR$iuWn?b(Y3V>|$=|*lK`erMlq0HTO?l~HtVI6Rb6%@Ju zard^$#q3+#P^axc?>jUK_`cW$Rc)^nxsU0`WZvG{hT80?9Hj@qBh6gy7(P+!^9>Yy zq;gR1-zg-NAi;%^3zaK&R_C7rX|(fxN-y7cXF=dCia+9a*> z)7uasbIPvDHEj3o=+|A9i<F~59VHaCR*WT=y*j*9`6#!VMoPLt z^bK&-+Q001kTSGV7t-8rBKf5gCXTZxVBzC`r{5*w6<`9hTh4&fx88B~U3pDFU zM8N0h9Rb-C+}qItWfz>vu4CqBDEUG-nG&nVrU9^fXN?9gMzrv{Guv zUbxdf7*#Y@wb@Cg3xZK}qVl75Y|owc!D#D7YBZC9`LC?ZH9NOt9c zJs7VWCyMfmkkuywxpx9#Eh~>3vROf43ZEd#yeAxh6zBno4?zAdAr$b)K6GkHtd+v| ztXj!*2+EMCCIqQ*0-^_siHBg9M-YONB+H?w7meCo-5MwIdYj45(sKlT-luAWD_hTy zpNH=OnTz`HMMb^xJMm%2Yb8Rz?f~!chJ_F$b^yx45y%k)nZ=ajpVUA~n~2{ZmLXwK z)F-(ixjv+_VOASvl_gDb1TzZ)4)X zR42yx^|^xtN1+tl`qo)yNy3?i7JOKp>vE<>Lcq=OILlB|)U{jHHFc_1r)qVo_J5>m z2QMw2!cN&~OoNhHV!o~UUD`F`NTwA!Z${PXEITtJ)IFSIGpefXl53EpD5Se|QWWyn zLP#VsqF@pHTfki35N*Gw>Dz4iPW12DRU$ij9Z8DPa6a7gSlKoV5z3ow&Rth)Q`ylm@j=<>OMq-ZBvJh!S}yd0vJs)EsQRkEr8 zBdj8LstV@9k>RR>_u)uYRY3$kFjW<_h80XzK@o1B$(ynf{wyH-Q=9NFa+P>9{zYy_ z-G+aWJ0M%|FLI=OEB-~!jol8v+7^(nmbLgH*$~@?f5B{{V-I(ZH^N)-5OqA9PNH(^ z|FQSpVNw*$(s)J|H^_w#&zfABufdoDd))s?!sx~8XkMA5M- z;!9j(sEEq&e^f-}hPvzMuxuYff5DL2(kba(cmVx65JF3*7(8R8Q>slYS}T2k@bl6s zCr}HF&Ej!D$4aN@KjWoS60c7x>L^M-B5?j@^bg#^HXJ`LOstqvRA12PTyv4aFHhY4 zxR8vUR@9;fMvT9sYK9@$cT@n;YkJWK>DcLhrs?aH6dI%_3$0*=c=qn*qqBR8ofnb6 zW)$_3uEMz4R|4Keyuiu9Jz_P4u09W+)6lWhkFBU^qO5{VSGkRC!Exx7c=7AEh97LjnYXk2<=TJIxP>X>*do$rPSHc@eU(ol ze$WFLHMeMTIsnyPOGRvQu@g*}y!m5AMa1eau%!f51yEq+f+QFgR~2bIcTdqEBxZ+> z2vns+bOf#`B_di)Nb06Dov12vbTAi!bL#g@(Q>KJ3!qZjRdQE$6KYWuPYRzcD$Fr> z_T{rh`=tjF9Ct#&7kJT-Abz59Dg8Oo7%_7xtZ2w23`l1SlBis5Bn=Q14T-4~AlDd4 z>(1;Pw z(oT$smB3%~f;xk@1mH|Ec=}wiLm);kq-R^jXhqmV77U!IS=LjE7Eo;TW0qTTwPTXL|_1Oy;~kQ+&?r zKTAAwpa|-f)L){^JO15tO8kB5$=?cM1Q#O1##Dw$3ols0EF9Gg$=D^lQi(ELfyr>pwu?=ME7SqL5XC&AW8CZ`&ir#lEyQI@v=+XYO3uK-7s4}@ zg|jLpugQ3@g};jNuw`w<67{mSaEZa21uRuW4!t|HmHsp6#ojE8(=wdt&BD1Q>f?-G za6Zv-MpmKZrSFP;w3wVQ!94NfQ@BMLFyZ~72r86x zpfn`IIQAA(F|#5)OJOg&Sm}dnFenSQJ1^y>4?5!~>@`?F+>-3e*2uKJG{(zS2uPMl z!5mzOsm{`B4i(cFy;N(eqbPj_ruqmxQ<>_>UyFu{vt&HjRAU$qGu5$6nW>_$>mDKf z8WlIG+NRK;k_P4$QAI7bzW%eyO zyn_MKi$ZK#%8Np1Az4v~v$~MHCb0wVdG>k<3%CT`l`lB)~LaH{ z_$mA9)r$Wu^+=-;g5~@hP>W&@GHjpKa_t`TUN7O=J@+GoEQvwpTjW)b7RtDP8L!)u z)=edomu1=GQE}a#iFgQlzK3`oo!Ud1ft#5ECNAT3doVy{9>U-J%l%hKPhkj_ZvqHb zw+BP;x;-m!UuNgf2i5IKgHSQFS&t$1^)zNtEB@DXTn(-KA-=Or>tzm50u8*(K6sYy zWfJ`w6k7%HD;W`XGoNEb%*`BGrd|>EEi+gV^%>%yRO&xV+S-0Cv$`uHx}vHc+0mo8 z8#;4dXF1{%ziR(ilj2_lp@0b27i+$#RF;HyWj(dyXyA4Gkz6P^BV0q_@F&v>$Vm;z zE6s{O6oi{82>)any=+(DK1so)Ubd^gXlt{a+}pDF4istES#G*GWAXCE8Mh$gc&1hH z+eUlpiB}Y_l4b(`sdbxGKbnSG5T>VF7Z1+Sf0k&{rnsFT%|n1Zr?~{TuxUDPIr;0# z;;A})U)Jl}xn1$F^uDU;_PZ=wOjx2>`{Hs@I)o9jBcsP~3nS;yax%JO@!s^l%*B7D z*Y`I|aVJ&ZR9WFabN~|HT8s09bO;p#f9f#6LlexBq6hfJg35~8vI9Icxy4c3LTtH0 z>ol6JP_U@at>nV9bf-ZEIE#;oSIKy=&*+5luzlhcWO5kFnQf!h_P&EOePQkj1DB!C z6L%T<&yxJs)bgwAGSD|253EqL8N_TNpFyg6i(e2Ytx%uNQRZ-tTrOb(#{m1vhdYgf zIUg_SOjdpytw-{IA5(+u?NnSO&RU^9u%n=9szLL1=i-RCV1@c)K?S5$3MAz{R9_H{ zU7x*9$gz9ASR9F%$J;u3o{q(gdxRj(-e)^LY>MxJ?|IZ}dyOQ~q|AB2kdhrrptdaOaBZ+T&s^q*V zylO1*2{Q9rQWF2hS3W39IG7TeiNvRBfDeYs-xGz?DPVnxM^kVqiK}e<<&|n1U$~O> zHTup9I}jd=;1HOK_RR+pA<%&E;sozf_!7Jf#WL8v?k_#;&Eh zT)jkr6P_>L(U?^ExHy;9JyF#zQ9N2<3NmHtD(6zZ3I-pC3rS2^b#bPik1bCz5%|S| zv|U+b!5<6jlK6_L(R|abd!O@i0izY$i^odeQ#(mc5*n1}e~VhQqL$Hj^*w>5aNVRcuDq}+iZpp4s9(|)YBvxI`UrD9GtES~KS@M`1 zzNw$`W-$?^_IMCBcK6-I5y^p@m9e+~7%3FnuO_iQ#VvI}KipINtkhEWwfMzz)zNVb&77pMHf1_piD@>{C?)1+& z!zkG<*A7TDe6M(1Rk7L{_Bu&7iu|vvtjf?w|8n#SYY3H0{qN`~>dVnTUv27vKBZJA zJwwI{0PGx@tkk5azD34lDm3xxHQG&N=QYM=;(=!=Gr=pwA0HQcv&8;ujJH=e$dy;_ zJ|)P1{(r-jXJ9*XPrnp-#V`1#^Fg0u#Y&iqZ+ABx*c3Ea&5&6+jj zs~?MdkedG~u3Q5{$;r}N{##ri?Z#j%SqfnL{!{ErABTPIdGC+KcIgBLK;!hSNQW3f ztp6$QDt(PRC9xj+Z}EV1tSaaCNJ=P3zU<{u_RqyPl7mmyszEN_v$qOq@^f)7sp4AH zOZ6IRq#mXVaHC_jWr=txrsiw46C{rkVBklaz_Zi@;T4$^ze0iR&N3eCNIe)2bEGZT zGK)knfSQo={UdtSEcFfNLLBK7%B#L3W!0w!>#+PFCROE0L!4zDPulg*Vp2hzvDVm= zMrD0Xg+BV8bn;r}NpptMQPiF^M5U8!v}^HGqVaU?`f6`D@Lb`+kwb^fmT^7bOX;I% z6@IZbXBsjmbD?e45!xoT&`@f70~eaMoJ!Y9kptp;YmNPALZ;~m#{g-nS+Qw1kq;_I z&WoRe{iIhlP-5k68n_-+BOAn@*Q)DvC?IbpL713zs`4J<$`Q9}>~@2xX4b1UbMcOJ zZAvxFk>=>nhs6C-1P-URU#Do9milfzIX5=wFi2Hq!PSRa^5c_LIrl%97@`&%GxI=_rfFFBb$B)s9Qo=;Ct}v3m z;T=yYGjL;L;F-#eK{$9hSzkADmGC6TgzH-_^O*3Au`6R-@6vMp z2BuWy$zJ1J>v*!f`jIBt!Y2$J+mfywF%Fm0ROZZ=vgXdLGxBI3tmCoa9y*%ZqrJAy zGy-{*QvZ)!+Q-PU3|$%(p4ec$cIm6P-q>d9z_XOi;Mj0cLFC68qGP?WPa7uJqqA}p z5F6%R6{%TMykWhuKbxz8S=>8vqK0_KdgIt|TMEV~HY`}rlp{8Rk<(+tIv@^{*svy* z4A#uWyR-*o>Mkrc{FGRay0bUl8@WrYQG^DU-4$~_vrs#)Tv4>1G#VJOOBjN{o3oZ= z!oY}ET8IIdM+AV{zDP5}AvRzL>JeE_ume_zxhh%kXrO-jXz=c>CArZb=^(v@VUwfn z_eX4cqZRCqR3i33#3CKTV9W^@txs$VM5YPp1F3xKmnrcgO|kv_fl%ZIK{}7oDvQJ~ zxS0`oemyz*KxBRm3?NH=;e(O3QuPXfr6%EPV(Sd0*?1cL6T{hl(I<)$wu5 z-Lu`I?M3bU?MO*8@cHfGS!({W&^7VY;7A8S>?I?@4!;LR#2mikMfHqn|Kk7L;fsdV z19>H_aXLJY#-yq|{wU6~j>rG+u*l&mB6*R;uBhOg;_0i)C7<9@!xtk>DzqtF+?y=8 zd0Izwq5-bSelgOy{scOT+S88$Qce8HBa~|FPcAxmtsv7!BO}nOe#Fl#Wv`CZcB!R? zg4Z{=)U?5C?tPOQC;E(!RLc_2zG&?3Z2*oi<}0a@_9uv>3DqpatF6)DT-^W$`Mxlru-$Xν*8Th;sc$V(- z$k(Njz2Zt44|aLWFdpXe=DehKc_l9yxIBG+_;y|YSyK8>8M{2j@<|<*k6}_(F7GI3 zS;yslK07k4n)vfe#@qGhWqth!ee}2M-@n9z+Yjg{YL|BuNHuYJhbh(n$mKna?8(sO zQK<>-iSIMje(1^##&&WAJWJULj%|-VANi?*(1T(#If?dzOxFz@4LH18mqsR36z-;Q z`X|w{kFW_e8cAnipUfvYh zkq*FKKrMMWvR6uA0ObYLYp+DgvtSI;>|9oUI;_fNT`9{K)6QRfRoD`FK$PCW0|OpV0yosqht)D%7Bx2KJm-9u`HTN#^8H$oMzh_wdpF9*wC zy$O7}F$4>i0b=-Dk$-DPAip1t-6ZwI0LdXv?uxY18v-^BF6kZ&ksRXH-I0FjL#RY) zl|_lAz}F_+qR0h)vDMP8Eiy$|VXY@NYS*TTlr)32X*@hj`5bb~zQ|AF(=rOYEIo-) zu$et|qk35yztLb>(r1M)OZv}}Y<8t=>c^Cf+r2v6?!u(1mZdj1w>rzxV+SLVD&pCV z#>-MqSyi7vAN^(N%tpQ}y-i0^FH3I#sV2+PYm{p2_%1pUzZRL2xzrjbv^LTpVQeGy z;91H>@VYekWMps!(VZ|3!Xq+K4vqrWq*k_S`6~wy|#zFYX z6pT?2ZY4tZXlJo044j^FsRz<9QA`~w7_69!Uy-J!G{b{%eVb22zm7B#q$nPbEh+ab zn>|{ZjhmGv{4j>7Xi#-_WC& zZrAekjSr;4)|I`yG|uN&h61sgKq=o{2Kk(s0t0MF~nlZ zRE;6_uSe5s46XYz_n5GWJX9gtSN#43R$@rJb{YAmLeyFL2{7_R295yv=Omhe!Ph|a1;5aVzlMN;n1t7@O${Ezo_t9_?4X9^cOqV zHIx2gk}RUXSX5V~nEnK9@QOn8H9pLh8L5gwT`Nalts*|Ri7iN|qEO)PR}?zm`|&(7 zvF7m_pRvAVaqdBMUb=-xn*<^U$eH9W#*=<;*`hFo|B%?pY=3kZNR=T|jJ@2p_2tA-GPoaP-siD{%$#k5pdZ>eRS896

~`6o*!Eul?+kfuh7Etszxn3MvnBy$iuYN0aDdA($TJ_ zwvo^;195IX4#vfE6}UxfJJ*nYcP&~V4NVg$WL9$uQHugbERJZQ(X+LV=u^^E1jo~? zr6?bg1V}g<4Ig=7#lO< zmbgX1pX6e?uGZ?1tN$o#A$m7!ccv~%gMses@J!JiD+&JN4bdmXTV*_0cQ<1^RCnM6 zCJ&3^&kgOH4Kf(|Eb$D6{<9>zr5bkW2HJY_xPr>->8Y^GZz1QmkI6%Ci^h{P1EV&w zcJ-K=$V$gxPSGBm4D1;lRKejW@DNMyXk+r~P0?#A+8hP$KE!v+xQ670o1*uq9*Z`| z++NYf;+)NF-`?_Y4@JO9vY+J2E#sqt)1B8VT9U$Z7oY$)9Bs_ufZnqPjPYwb zj<|RBwAqtqPngY-ZrrRseWt7vOOoTJ@&@!XCZvuyRYw;xaO9lY=)=@8+){Kt9>eCq zzG7;DTeOm11qPy<;mTwuUn+KjZmT=XHKxsQk#1ebO~|)|{GPDg9tya84u{nf_SnL{ z5L6PkSY1Jn#p5PExUMcQQzv#?UDg)$TWdwC8@N<2gD!HajD9hLr1}RX{yJ1d=B%MW zKcVwB>A>ho^=r%ySh8J->@AYUyoOUl^>9(bMxOxNx2Qj3vTQ-F2Tw@xKKwW*mk5>HmSfD~azr4|B-buc^al6TMIL?{hnm!$ndA+hl zT~uk2p)wYzm2uJY;Yd;!e}6uFrdNGYoAkf>V_XK_WL54uweL7&QU zRf-f0IX*PHMqI!(g}!@IKx(Hzj8rvtD}T8hhJn*_`S*b=Omg{nCFWlk;}#^ciNm8) zr2SNg$%TgklZnLeOp~o}i-Hk;vHWT)ZNVDC3Nn7Bqs7lnwD>93B7w+vnNWf3 zD206wj(LS`3JF|hUu$Ic>DG%6dNXj@(SS?A%1MMnu6-dk?HZ4^lL|12s;yZ&cm}4g zHPyc!jrJ7!P-L3(5Q>gmvW{-zSRjaf`?2Uc;XXzKJ6}D&-}wA1bi&_0UDTkEr9EGx z4apx9tt338AW(fXtc-nK3W1Rkci+YpV&A4V;&$6E&WJN8qo3XE=N~?V^gS%+`x6Os>6;BubNK( zw!lrA{>nZSolmgk3m1n+WO`?_hvJ>;7(F%g}93EY@CQeJCMHJFJUIqsh(Q#0z zIfx}`^lk`DnlDUi7+s z>~Y>}(OLEN=U-*A<(?HTGGS-*2`O&}z$DjucH*)LYEked9(q0cF_d3M4|x$&CvIVH zgF5UWHQ$Qft?D~qOfxd)t>|-7cMPDsBcHM>Iz|N0KX*m=5K}iJV?6LeW8H#JQ`goZNWNXzWHU; zqKG9qN}P22b7FR4)UoJUft=V_A|*y1i~cA`dk~b`P0x}o8e`^q#4Xyfh>jlD-Z#T7 zoYkuE6D)ew?(qyfQ>pOfQ_&jLA>ckEmZWc#|G;>d3ct{(@XQW_4U0ZI+%4-rONw=` zmeovY|F-0O)e>C4w)Ja>O`M3HwCTwlvi7TJU%^ghxl6L4)E`Sx72T9(V=A_KdW zG$Rx1lvE)D&qV{~eX@n?sLV`)?RR8lcjeT;D$$^BNncX++vv3jb#pfIq;H~o^%~(V zOTY1wa627JQ^`y54<|K4Ev*NvwEkvw{( zaTQgJZSJ;ZjnR9e)~^}m)Ujc(Xsab|(awtRsHt4|-WCx?n8l#2?QE_iovkfL)40Bt7<@cH7gk*HrlB^L#=?+N`V-e_zz4O zDic3tsEgxjU0nQD_4kxAE@b7Xr>#?_>rPSrq~A_;BBJk31Alr=tCF}_xKn-DT@hdt zS%4343`crwIA0w_BHt5+f#Rx-tclrY6Kw5Kh3Pmm}=E^MB=r3JeRR zWs>wZE@|Y4Hgh={`Js^C>J8ehp)g!^?QnVg-moR;^7;H>m)#N!Se)DsW$eXXR=>AX z{b7fqHkT~WBhUxEtwsVpv{RjA+iWP%O~BMYb3hiEo`2$iDwA0qN;HQ^D!guB+SCh) z`L7$Bb~l-(rW^w-@5}>PUpMwZ-8CT019f}d*aP)Vfuxx>TFV+yzPgb4SB=A`F)F-n zWZnYMo2HG6cR`&}>bMXWblU~GAjH#XU0sbb(p567t3xK`lyu5+TVW#9E^&$uJ2_Lg zB#(@*Q&OFVbghk_*B57*=(@kma4e;32<2kQf!dK{*(KHG(e~)JUd7@#@p%*D2x`WF z819}VHiel1IVJaLTOaRIQcGNKVo1YM=#301qH9Q_3=IhfXBQH#jlMWPiOk_s#T<~Y z{D>>V&{ujx7MH~q2!=dPhsS64+bq_wHRKI?93HpJYLR_qre4ff{`tD$!Ja;oWWoMM ziPVqSex{G`(VmYCh5Dg-cA9?lrkBbdE7Nn)z7l!&f;ZG5K)W~0{ABkxjQwOprssN& zA?YXYdc)XH&eDJ|Kl$Jr#(wh26iAwRBc8gwp$-AsyjC8bKqoZdGb#$@r zXXC}WA0%n%P4%15s&BH{VB~s&R=3^m4125rI6mdJhdlPM)9nxVz1FbZ=JvaY)V^d^ ze#TzR^|X4^I856n3$!`((GSz|-eh4~b>(Whs$@sslBPN*+A0B0X*Pe+o@bLx&Oh-y zcW_ywed4WL*4h&fEJAzeO=HiqTc&3)$B^_q6W=uUJO?x&%=5(GG(1Mt=V%gy3Ksc) zWIzwT$y^V5sd1~37H(E+;o_sS*HUV@5YKbs;yurs8XfI4(vcFG#a$4YIbB|wcXMVr z+}`wTTWez6AzS>=M9Uw^?4C?%nWfo|dP}+{$7@Hf@s?C)gF>RMh!{WP`8M%86B9Y7 z8IR@Kbjg=}@toti)RE^L!=DtT0X6<70ESYAjzsq=~v#CLN{MLGxag6Ov zff(uNvR!H&eQBtpi}goW|80NNf0x=H-O1%-6u;Sm0lziq^MnH)H=JhkdxAEI-uxE7 zGwcue><;daGWKHe+lXDp@!O5EK!-yg{rGL@F0~uFbC;1Dx*eGMXKtvgOwT`YLkqdE zksF%Ng{|*~#4RRf-7iT`(yV!k?QIjYzEcCj0=QEqW_@1@#K^2SYRr1Rp%$K1YvJNu z&rL~!c3g<-30%DE$=r?S9SzLCx8yGV zJzUFsO9qNp?>2Tv!_;WVANQ8@5&P~oc1Mqm9#H>}5H zr5hYH(PxUCRWMLLsX4gh!wg4D;CKizPIam}N_Hepwy6G))hnV)S-`-Ir4vFXi^nbME-{}C1%Vm0f zS3Xt}Oz*3*`)Bl|$xiEH`D)d*P{;0BCHI4Sw08eCCC$L@_rNp7?r#}evQGR$MuAQK zGmL_r%z0;z`f$}AfTN%N7}&f%L;P@+{@l_;iodR}bLJCrS#^ z0a!ts8>W;jlLle{<*?q>Q%lN3VC0>tFj>;*N-;hPLo$C0{dj51Gys*|t^b>Lw@JtD zSY7F7_G<0!DN37x-OYt(irv8p@ml1Xneb+RgNz3o-+GLP8Q*hz)kk*clG~?5*&9uS#aIZSbIf?nC;1ACm(?Eo;AfAtuG=?(RdyRKkyJQ{x$WZ{y zwega#v&5?VjANRk8knW!B|?_iaG!BD@zW#>6`@`k+3)wNbBQOJw6h8~^d{?WAD5SN znHz-0j;~{pvNv?CaEoFONg;oK@j~voPc7sd_aS4#LT0zNivE4ZLjF}|{(g=Egv_pO z6-Sr|S&+;3D=_S~R&k1nkgFzP$oT{pvg)GN0sGiRtter8BWYG6rk6Iy66J3}af?>Q zHR-hh7k+3BWswtH)aVW9OI%Jy+gOJi-gCO#4v)*{a#-9hpU2^|Sl#ehx6|tj)0gD* z)LO<~Y#V!UpYb-fzAVnSp^yGHcF#WbYY3>OCz;hu_C^ zKy7RDOPUL9DPmlyTFJDvQR5_SR+o$sI#CE-s9HDE)-eTUv~8;Uw%Wz~VWh2J)!MrF z-EQNQy7+F_eEK-Fw{B!w2Oiew@*%DTO-3Toe0|A10-R?zS7s-f;u%S0Cd8LY-VlVR zjdgXc2Bho8l5T>qB7?GerQnQ|HI*w19k6}qZPPt2mM?QydALREux=w~-kn=OR=f-` z_zB=RH96Ur6YrxI1(x)ErQ}oT3-nCAbGb)m48wE%;}!)Y{9=1dpH~0;27*cuq^S3p zS^E_P>iK*bJX6Iy^lE7_PsW2|o+cO%i+QT-R|nje?Pqy8;Y0c%cYeOI29Z8X(X4ZzaQid`V_veqQM|RZ8=KU_ zgQm9B;TT@C0>@-vm6EFHp!eGU8Ox7eJj*{RETatXe~j#Fcb(WYL9BJa*uEyndarRn zUKkUHvtwTiVzUFr_BBHT8QdToY$136`9%&xC zNsxA9ShywPPU#SCVabZ!2jHU0*p~FZsxA9!y>VEo2#>5~`p=R|+eUfS z-RW?ayUC>Dtnk&CRMkxdo#7c^M24TdIlOzjSl6oJy$6lAC~wL7>JNS7EegFd4DGJ& zJjibpZzNw%ykZMvy$3v-3n)Bbwf2s-H|1( zI%w>hYsnSUmva=b_V4K&dn-%acF@>2H`c&Z>l*8vCGJ0H?3-JsVA6auQqB^t9I+fm zPPf~Lxn^dnHFNR%ld4JT4zTz%&)n#3{PLWgH^deQl6VN&1C*$V!m_$6$uh4x5w_SDjcWi^uGYTRo2yehm5@Hkwe_8Zl{B& zz3Qezre1XeCHfzE)n3S!481CqnRxj-+U2+UJH}R01)im>1nsEizOkxlqN6X?K1*!- zjpQ{!S0$vzpN2@Rpqqpi6 zsUWc;FWw2yPHB=S3Cx}FRyspKsI;_)IPo187U+uYwG>)~LZ(;%rX@YZI0x1(+skvX&&ZUDd4dLH?smB^QiFD1w8iXAWu?83wVf|O(@%^1I!C|R2dPgQ(4^0 ztUZkCbsW@zNDfEqb%-ZShWknfn5ij8UrprrxY!EmCoBk-OZpFLK~wKsAJT->nh<+h zs(4sY6EDvG{)E^SQVrb9R;G%F$=#2~y6OPD`8WXN=>c?~7`rYVK(#XcCbNmjz#3Im+4IX+xWyX}Yn{$@lr)2-sVh87IUT+<9iJR47w?zx;I(N0#>3X8 z9*5OyQ`f@=Ym+`dJY}W-EXiv%Ew8#+6cpPP9cHOHw8Ndw`9v!c&7hQbeWfZnF)cPw znumGA8(f6-8IVbO5xD8lViLn+zIa*`we-KQq=l zUw>1a#JRi@IkT{K2Qt58ZYSv!HNq)j#{(?HLOAwv=Hki{wj*W9T;DszUobK z<0?F!#Q6BB5qC5;U66jkuqUq=rh{3$k zXNd2W^`8ON%tsVZX7%^VjOT}Sczy_zstW1`bDq%@O74}1&5gCLES4N$i=ysl2X0wm z#iZZcG5!0sXV6ET8WfJ8!>Gf$!9c6YM0=2uowVz}wOO_xi!vA64jr*=Q;Q94mN#>; zX*bJ%&5zX=#ZyO&L%NYkqBsiJEI++477@j7zJ%7Bp26<;?Y=?9m0#bXr=VWp)V!mYK7VS`r=yXg5kp15^4j`B5 z43LbQSO8hbfpr7ORXV^tfXrPR+b@m9j41)+6S$cj03G`-nf3dWZYZ2vr-M037K}SdQ9j*B!^?s<3^L=e$fHG5FH6sK}V{vs@9_m-Qv59 zzVy?k?@*x`tPcm^8KA;D{^DJ+r^Rn%Ja~Ed3gcnR!+Y)kW>KAQt~kfsoEV?hg9{T#l+KNp7{F$+=2#t ze1B|8p1!L{O5hnDZtd$fyPwgUg?}H2O_YXEMv|)w+f@ugEy|dQnFnJ-MQJR0sCFKW ztMV`jBj91u3Oum4ElLMt)GjJ&?cl5#1`3n^9iuID&LVd&5HY_Pw?o> zx1K>Q$l|}riVtq3{|tJu4bNM2W+fx6-?+)^rs(#`^roZZz%0V5{yv*8x)Ihv9bg_| zRjU*~E;W81nY$8UwZP3R!fO1!I>I`lgPF~1;E6?8ZWFSO>HzZy>ys++hpLI)-&aRi z$8{i*!?6hKPLtvO(gEfXR=QA=o;sn^*{hA5U7dqYm?wVF3AZQ;Npjin;<@}o!{x8~ zTt2r94mownjSrOS9#cjp23*!Ursne3{~4FfFP_V8$JDyK8khi^Rq3+)K%y#{`N1A9 zdG1Nj=bab8>Fu0TREFhh(yd<{sv~L-?PKj-h(6n0Jg3t%oIat;DVf{~toFy6vDVVl z7r^C{8ZMvspKCK>e3NpSYxMR@qt6`NqRf&+ zlXme&f>iA|G$lhY_EFM3%Vv+3>fmPOqoh-v$OB@vZNu5af78*pC!JL(tnm@H$d4n-bgX7ztwVm4pXa2aeu+NM$S38 zkE(f1{Od|$^%E>E)cpoxh^)0L&`19`Sj7|k;KF%2ih3LM#c|WT$7hu4_9qqze7#G4Y-g#v7|; zGOc%Utgx|qxmWx;Q5_y$27f5JHNy*&k!miR{R{O;g76T894>8HfJ zAR&`Pm5T}sE9~O8RnMn1#=WZkbRf}N<8!1J@o>yLu0<_KZ>6`vK1%-?^kRPGJvysARKD@G!L*e<|_9#pKr4CebSawOt6&Z%D zT%9kfSfzVm2SPl$V~F|Q6>e~ zp3MJaVK%vCaC~Hrfj>DuIBs~-{>~xs$PXt%IA*ne$ek1w%k@)30 zsM?H(_m-|yV-ahfN*ovw@2MJG6{ETI2xsYT%!(q5r2M;R4HsfGw|L7b?RwjXl4cN} z-Nac%sgJ~ikHyOcagdA%Ptq_(#3t#jr`RM#$L($+4?h*x`@UYEA0D9TKTGo3lk#d5 zpt;4TbXcB?DOCk$<(y?SU6a9CyYcZtft21btpeFLA)ZT4zr3n4$(sznwg;vakZO;| z>yd*W$Lf<6ljAkX?~$h)k^3KyXY;ndXxrmg{JSCP{6w;C_v6qubnH_NN$d%LEv>w& z0m+>hznl#1KCL0Sdt$Qfwd?5=;2X1-W9xnm|Zg6Z-4M3@0;p8xUuhGfFbcvUigX7ZoCXTU#)rJnX3 z@O-5zE{nCA-Qls3Zn1b{GHFJ6O_E;{uTa4am%0!ko&?P+0Z{+6RoSG?%y=ErZ)Uu~ z|M6hr!dSLg@dFlX>jrUgS?uBma-5Cf6znW zVUG;;jT|)Wp#k@g2n>5ZO|B0kt`}gGLWM?Gq`F}~o_pK*47jr%^-g*36EkSas z1eTuvd*#-jU3dyi_ukoT$-AqkaM@+~^nc{$nej&dw{}%i{)~hNt5o4wg#IH1q(#NJQ=d{}8T>KVmX3FRA7~6b7Tc=&T>D702$nE9vR^q@9(w6W^(UtA1 z!<;f&!Ta<1hE{OmpBsp7Lsl)%DbUg)AKo-Xw`-w`)g9%1QNh6W43URU5S6{P9_g7cdIX@nh)|ZiLo(9 zJjTW?NFBHQJAO#|gi?@PwNxSYu9R({J~YJ3F~C^+nfQbZr*Uo7`Mb~IYi3)dN*^M` zERN1XEeffLvCqb5R*tx z3OcZ>7sJb#8}(uDTN1yK4oeS@q01(Z%Cf`^@)?I)6qex^OGTWb76?1`lBMyxa@|(D z1Fqe0K^=UkIur8y?KZd=-0t;;yk3u!2-{}WOIZ4kzqdkDi`!ze_f1!fd^K1 z0LlkhoSuNwLV&UciPH~^Un=A}{XUn~;qip+A%`vKh1;n-cAp!rQgmCqEYypPR40q4)h6jM9CZ*zuyUVGT%4uM^g z^JB*IIhGr;dIK&W+>`_60S4j+d{(>PYVr6%ieb|1*HW-vHpTqrBL~RA!|_`(j{NYt z6&5n=NLz~b=w63}(KkPDr>P_!A!%6lD-kOiiz-EMOQ>`sTx;k754)tkPJ zISR8;;VjklrB;f{WIVFq+!Ge^=U=cz7<)OWLAN_m`@Si_nkD21hhPtZ zC4mij!X8`L7YaKp;Ms#7i-$n3?8GfsOehv6I?c5dOrJjH)){i9_s$?YE1hgd9-Nhv zi7PTWcYZ7K?bq>&dEhU+z^&6A4mhEdnge{P&Ekg}_}u<5%!kC8UDGK4exKLx@lpS2 z2W^5Chutvatl&LE)_{$fGGEdxVB=xG&k24w;P3{lUT@gp_Bq2=kJ}&gy4m6<&$kRi z2g%_7#{1=F7#S?pN~-@9ub$_0h5go$)#eUZg5WrSNT&xlv-pBxZ^-6N2zO9TxQfL}>*j&I2+;v!x{i_77F`RVuCz<=04o3@bKYLRDKz#n$lLoiM0 zQsN2vePNr^57QK!pWWpRb9PJfrmPV1%tv+d$(CVMI@vrVuWD)QiVm08<|e(WmNv=* zO9;83_&nTO7I1(&0}8!ipUV%ShBpjrDt8~g5E!1z=5o2LP|nW~wlns0VAAIX^_`HK8@7BLE?ZKNJ8T5%M~$ zL66TGwuC}Hzr_pDl*OAk(+eyz*X6c)LV%r8D zg8TQoLm{U%;DwwH#3vq*BBXZ$?jQ|R+}sE8l`O-+KDLtOn-A=BuwYSM%D`89t-fHu z84NglR;M3U^RUGV3#!d!35W4g=7w<53zrJRRJ4LavG{>w(6&F|0uSH|C(h^YdsWEy zfyc4CJPxoYzr*LTzznt7LU5HGaOZ)=I&o(7EQl4&hjlTVY-&0pdH9|pJ9(;k=@qbY zL!jie2ZI(mm0@WDpXLM$v0AMmm`Yq7c@TS8Ac_V}cps=rug9+w>DBtS+7ZVf+#y*L+mcY~2cLeuhDVWI?dV z#TT)i?MmxZh6!#huv_>N=5Yt@K}#3{46DTv08xA3smBh1hb86*4&UU2u6$ZkO)#-WdevqI2x_gR|=AnllAgE|s;-vxA=i z|6uclJP;_`EfB1GAsh}md^She8{~_3uGj9cfpZL4gRm`e2Vr3gK)eh)GdCEWm7Je2 z1@>+2ilU%ZtH)xu+MzQ9SfF@V4E^A3ETMoKgl9?gTND3E$j>mUn|yU|X`g)aQC)0y zS%{@eX_dTS(BXnu)aMR^2Y?mU6Sg>Eo`;~Ysnz3$xxm(7r_~0*m)GI9gkbw=g~in6 z4EX~P$@;*t2kFFu7||2*gxn#A6HF6kY|szOfdj&StKaVmg&YK8um*|YCsr&0e{VWy zko)@#gOc-$_E|{&4W-rdY#|4Ph`@mbyo1~0f>j2@?(@T96oxo}$8|i~g;>L9g^e?; z#6E}BY4-=+VQ}(6*uy~>7$V>ovlEkAm#z|;z(VK@`hsw2k^?vZKjQQRA;Rzm!Ivf; z&Vi|zpJ8AtXC%{@lW#5Jn=#U;Z)wZ+7BAY0Ic*Td*zFK-!;BPx zvM|3;Jz=djpRQl#3R3N^U?**Ak?C_Q;5QRj&`ammoUh|Kc^McYu)CJS!vIVGh>#&@ z_FG^N69#7(1Xl-9Ki$SSWz+P*z69bd2i)TvfSBF~m&({}Fn=Jtr&l>ACVyNyPsnoz zz!u#uxB&}74VplKSq2jj7JXkRWcLvW1LSN+UWQ@Gm-m+T&C4(<4RS4{W0%sZd11RR z=ywL4;G5x&=^*4N95z2}@_iO?k(T715~5Uy7ehhNvlo&eusp!ja{4`X$nMwz0Uj&O zzh(lBm0-R0x-76LfIqc>O@I^hy22i(mu_xgSE+1bGK|eq1(v)_W3%Xx&9i_d5DL3s zQ)>^|9X_uQ7FW1y%nu=k6IQlx;><@hX+tA~5bUe`u>W#E@a=~M3^WMZg!tVaBCw0j zPB2~|F7-IzF2_K~4lW}Mxvh}ZZil_F6(%P{aF*AnHB4Bd;};A08HRT7t!0LhAMOGk zgJwPGL~GX3=`66>&|HJ9Al#Sc^te2bIP&@+-DUx22y_L&30p(t;f*Vjo8}C6f)j|7 zlbf;(`m(0aqYQ?siZRXQ7xvIuM>8NzO*td;|9s*KGVBgWfH<6M9JG7g zVV^w|1O*0QO|x;^IxWo~09h@#!wL4J5CR6k<=Vl`hJz3_!ny^!pD^>IY*&`=^urAH zSm2q%?|^Kj&kFkntIOsLxxhhtlPjCYd}u3M+44LYhGnkw!%3yp@+pZHTE!wz)_IQ`uK^qO(8J0K;RT~daSUL+x(E7vp~cGIVea7lO4ZIW0`zN z%t5Z$sKkHgAl^*P+Itp;VnZYB^8`ylxbvk`VQ zkko>^9&sMubZCd4JpFuW-(2%i>H2IT@i$7V<-&FzqFzYGL6!}a1a3YE!-jpZpMfw? z-qOHc8q&eAsKNDa4yzA#XOQ^zd7MrNVjy+EGx(NCG=mQ-Suh-i`3h+p3#1AlpB!?9 zVdlbq)1A!VLo&i_RJNtb%QPtCfvsqeXS4a;c3;>D#6UHF*eb%R3W2Q+@@rv8+?=B; znjb=0h<#l^lf_|$*AD9l81C%5-~OFY(=@`-+5#5Ve?TNR`XaV3?|C(`7C=mj7VsMRUe1 z#JYM`+q@9u0R!MHEg?I^NZ|MbemlHQfzZ$CazY}XXFuGqW`QmGoECWB0wRJy8=^}{ zMcTu#gJS7+uS zUu+u=fiAo}bzoD`pG5VD#L&74LP zvyn-&niK4)$gz2Wx4`8+6MJ`oZ!`wRkmh{!kW-c~2UCl;)P@0pMfhnHHXF;RW9-wb@;AJzUE+AE9u{*3@ z@Tf4|+;$u1J|B$M4{3hL+S51FPPjb<)+=}`>hOjGkaV;p9=`91=LB$gG(`(~K@EGvo{UU_ayV!#fDr!Gzrq z`+^67s2E58HVdQ}AxG|p)QJP$micV%5bSGh7PsF4uAT+Ie23E%UiSs*TPS$d4oSKo z+C^CqS2p28p$;=0noabYMT(4zH*4L9}Lr%1Otu@6mLK8_hI;gA_D) zHtMipOYgBl{B40i!4b5;p$MAJhd1u#_su5Gw5Qn+Q_VoCBz@XsuuIiymO)x9)5un{ z4E(TkeltktL&7KQv<1OsdLh+efh;k+3bVs91TW;njs!UE;qZP5!m%(U=0YBtLxwBB zVS@ti%3L0J(F7+I5{I6nsSrQx4V-pJ?RaTY+2@8GgxzU@Bm;OIkITi4m*?`$M|HvZ zB>Gl5!?2WxR%|_EZ>HhBGQ8IUBZ7#?VFM$w!G^>|)8P=M1l$RD2}UC>NXx@(ngB$w z5Jv&y5FLa2xB6i&LgXwPrPT?qEUZDu`T;I@+YPa?1?C|n=|UC{yikJ;yPOHnGauDX z`UQ)udFF!#$@%L`JLLw0R-4lWyFQ3_!QH`i#-JH+`hHgs7O8}_Kb-lRPFlt>1>c~w)-++cmG-sbXD-cx7biqzimL~9anF>B7H!&jFM zUtKzU}4uLwTEYXUc$Up74djZIK`OD>3}Eywj_ z1+w6eg>}j6^UF$ep4RP3tiG|`DzB!`{M9P;BTRDM`f~L zS&jEAMWBdSw1E71`EiR_Wi#1UAzC07Y$v@ImD$CHJIJI(Wi7;wJID)*%A8`WJ>=w~ zvYRSRp)iffg2AM5yV-?H4#2;vKRPJ%ScgYK!-qXGa(3Z+aMf^B8UkNF59u&y#E4O$ z5wi;q943#vP-bl!hCAP)mk%2?a>$_2BdMqN(bk5Kl5V9(8)g+=cbt4vG1@4ru=8>9 z%L`>~8hs7bjic3rzDEXk7&O%P0LAwyZOVTXE)hSPpH+C-hvdG+Wlh9!ACmEl%Nk6i zY!ww&*-ZZyY^Q%4?x24+vcIkN(BG7eB1V1@qqqoYq%D-fqC!eh5l}+^c4mJm5k+j= zA{dYU9!LLND#pU!DyVYX_{dMNO zE8*`-`jZuyUPXVgqSUMDFIGEm4gCd`-^ii$EhdUjZz94iqxz7drDa{EH|Zm0fZIPL zv?yNRou~z#(SpcPU&oO{htvOuy~6|khad8FgwJN(4!0LR0fuwtP_fGEalGSAU`Vik%K>gs(=W<6g!SA^RYVr$EGRuS8t zW)9~1!#w~Q6LaVvirLcOculh6Ut^@Ytto*_t@IZRezphcW|=-N|1&z+ca+ieE?j_} zfTVr13v&#f`QD!GBGWgQZIo_D_?g+{^g}H&KZSs9oNanxef?*Z$-}{hjS@F*EnAaS zV=x9MXQo!jC$|O0IHVD%ot$4?)JGhAIE3co0G> zj5qB>`!b8k=2F3=^6L?9K03RnIPWz1YerEoX#vK~u0nYBG_WxWj-~Grt08ptb*$V&_ueeqDoUF%9$E;qu0lye4w^Vkx)qG+mh%$oiUiwi#F)m3NA{Gp5TwJX2fKR* zA#2Q$9mtG~OguldN@Co;vd(bzJjS@A{-l1=54eS0#`D!_^3eXWp6Pw5P|@g3`RHfO zN9WV9ZVxw-pDctlblxDhaM|tYeOclBNin#`=(!DbEK)B$s5_68 z%}a-(qHC#GL4lSyxnMMz)o1zwF>r=?kJc&g0kbVB1&bDzUZqhlvG8cw3_%=mh6sC# z9z>FG=m0DzeEhmA8}tO6%w6cny)TK7 zX5S~hkS0J_qjH`{EkGc1*WhdlpDijhdba08*;CRM1V@;fN>7%xkzU8mOs`wcklMo@ zzbhR;@_8;LHppuIp-!kCIT8X|Nl?howHd$AXP{1@p}r@Wfj z`ZQZ{@~)$Vrq;M5>(Ivh`83jP>lj~#P7$)8g;0X3R?I}vR`tXQceYiyt zTat#ky6US#uKoku_E;|Z^~BnDTtmWC_!t(wN`)ignWDns;@Dcmajxu?P_ETrDMrII zIQ}EG21kBmP`+B9Bc{6+`p=Tw&Ij^zub-ecD>J=Q%k*|kt?CYnt(<98%{qAp#ns=H zy;x0vkQL0geC=;JWbKG?c}?}4;WDnflOGw~!f@;(eoxpNbTIWT3|l`kEr0zAB|f>l z&}$g3c@?f@j#hYZN0rIO$7T93EVxy_`F`ze?_ch>I%4pCv zVTphJS+*=o=t!|OA(L(xcU?<8(f!G3YtOMj;A($yOydedS4ISr`uu+5^IPeF_NgkU zVc$xOiDwD7D6pkVC+4B6-BQa}pL!`+CVNr7ei>a8cIC%RWn#UL)plLuV^e)qqr$@a z>csR_afokZX!M0C?5Cu{m)d*>-EF=T>tY|`U7L3(j*g| z<@Ka(bV{?Z=*-_1Wd_T+k#d?0We4uBUyF3OZBBow?I*~9RYJX47HVtUhX%A{cf;}* zBoA(8!B1@U(3VnH+{}U>&nKi&<8o&@fHg9)C0RRmOgrgTJ%EZ$%A2PH&}b=F(Re>S z;%JPSqr{kL)?gh!X;=R>_mQkd0#<#8X@F2o+``^k(tHlxOw9iEg6sKm^WKI^UoEv5H^uR4wduYK#4GL z{_Ejc%K3Ajv86@%AmI>4gUx(DM#G%OE1#%6>-tZ)XEksd`i$|I&)}KGHMMEts#9Zn zi#2{m!mP}<|3ll9JEY4#1^$x$nr;7o{)GJAru<0>Ly#R$%(+*pgZr|*IrLp{X3S&h zeN}cjSx;4Gx{R)uxTjtDuq>%9#>#eqHr&D{OxsV%%#P(RrT1ksY{h3|u9vRY1Gwqz zn3klywY+9Jl#043depId&-7)QwC7?YLn;-RHm;2v9x$SITO`$oV~ci7w@L3(^S;%M7KYdEvFv$GDkU z)`?FO$FDBWQ$eVVWVPOC%t-L~3Uf&#c}d4e4qmmuDOUSTYaf+AQxK>xu&e;jRQA#R z+VYx$*g{5x?IRx}V)h|@rnZj?0B2$!`fPDuWALoX4dwHUEMw~p<lsjl~F9`t1qa;@da9 zukaah_A1|@({;8^*VVnted%3QCUiuPEM`KOP(=#xi>;f-bxepYShqNeTZo%=>}V4u z&%n=YfM+T@y7%UCuOJ@Lv7 zURe}9XW2bsdfVncRYV@(W|IXc%5!!0V71Q@;oEV&Q7rr$m$tZ}H(7W4xV#)CA%sOr zXkeE?$k`&8LHf2SlaL=gs}^#IODO#wLi+k;+fyayMRBr;kQ>X)Kb{iuH@@;gSz?)q zkeh2@4u;C#6UFCEgxod>lM?dz5p&ug(;0V`EO={TZ**~Jh;^;J0n!Y~%-cT@v{fr; zdc|5@(zVizmg3Q~+I8SCh{S0A9>fyo5$7|ZVyF16j0Z0R=P({NecuP+!X0af*MpQ- zy-QNDJt1-yoE$SKgJpHnGMve>8h);}tenr0pTg6(+OYC*O#~YeK9$ody19|Aver9t zG+>d{qUCLy3fED%+jKz|X+TyjEB7@KZlfTb$)s<_)z_WHRkQdk?nne-w%#mm|2dn* zNa#7N>jfK_!BR+sUFAciDZpXcqFR3vYEd*-nd}HGZJhXJSNZRPGz)bh!rt;brKfO9 zx8bwh3!<>>B+aPlrn?W z>=k&HzM7Gi`^)DD$2b~18IEE!Y%;v|xq33ZLiM6I8T7efYgtSEXFxMsy{A!5^;5Qt z;i_bEB|0QdH&*)p*!#}FsEX}z?xay}0wE!xg(MJqOWhE$s`|GC#_R3OYh7ZT~%8SCg=gtZfuKul1)YkAcDEaO*uiWqEvVCwxy>6ks2+Yc>_;5;)Rl6oNL@ztAMe`V!opH3f z^ld?)k2IZ%Zj>-Ke5nxh497si$-=V*^L?ZjJkpNDXBOnu76P9$0MGid;3|@eJir#A zmM(hWn79S`!DOx_H0)P>cK?`n6%Ev@$oqFjMLPP#qa)_!#&WN^ZJz*rRlQLtE5n^ZmF5~?qpMlNeI)WJV`Az|U36PXm<#2*VVT?o z5$koXEwtWX&m~?}h4recc)*(A(UsSLRZ_#+T#|#&1Jhsu>vFI)MXm#jS-=WDj~CUn zJ}Ur-39)vTTZ#bQ4Y9V=0_Y;#S`Voz6|Rg>>nKU?3K8m}+d2W5H(v4XFza};|CbFS*Ot%*pU1}b@4nFmXlz^0wYQa$S`ZCf4x+Z0A%XFN>96198s zizQpH2*Na(Y9&kS$r(vqn08RgOwvjlsq=(sKFM0tz*>mc4qOq4$*(eLKSv0I_8Sl( z3)&mc>xX6Ad6UHwF<(5v6xX?V-fteX8SCebtp5Tj)dlSzIqSxtjjv404{?ocX#Jov z3b?=yMhPSK`-<*-Nt=@S5i|yr4OrY3Ot_^BJh%LvcBPNlKc4rRTb`q&KUBzm8`zd< z$flA*?JpRo<|!AnL9|?t=M> zQ*8sxPs!FJK4|6z^A)EUHw>1Z7S2@%aowN}q+{(u0r?s=0Wq7i##6aKy;M9t`zy3% zo@cHe>D}zx;<%H^9iisYyfI_(@yB22}DG`9D~~V&pd$@V*|_?b^1w7}-VP3wFk1 zBrH+8chla>kJzX#m5m*0O_r+RvYEOVS^0MrUcW%&N9sJ*jz76;3&yYaw%(IsI2srk zqahj=83TXUo0lIxm@iCc#oX}7D6VsJ`Y7d8oX#?a2OAk41S!=;#{QgPU1XF$?-fv~ z`9dqbukej)@}t(7zBqRCtU%YaN39Edr75fr?>X);MUxYMH;b6Lzw;b-2qi%uG5b@= znL1`)$`N$T&J{d8l|C34nshrQd*R4GiyW@EM_W_;$Syp!Xzz!pUN<0^e5+^#?Y#|h-5h5vM$%aojcMY0 z$c=_6Ly-9H<0?d(4VGW(Re$UB`m1FgNG;1?$ zdtF!OL|w7HHt)>;A_$Jo-0jI|%td1^kU>c^$sk9PIyE}7qons&Sp#wUbJl7&;W=v@ znyX-hfpRv)$O2`~MSTi6@}fx!Ddy}!0_7F+K*^ZjDPaC(NUAPUZsE-9A|>4xvd*$L z_D6>l))7KEgkR%^6B3b@Qdl5kOLvZ60v99#|8_idJ|OlhmBo9_~> zipOWt7FP-Xopz;h@uUCcac)`1D%cMPc<@-5gKn`GYrC93hds_(Wg^OY-K z0h}?xt;)bp%QaxJD!9>ag+7KVU%{O>tQ=+r+Sn_o>(s`TTO|SPuFWe)%k9LlBe$&V zLAzajXHgnB#eG^49%PUYXs|?O*{z+kc+W0tDBkK=)5A=ZtM*&hnw`+=^p^D(vrEbB z0R(o|$@2gL`|CO50|;pH75v7#)&Z#VxuP+%8=-ms!)*>&Bhl_3@qh!?$I*bF@a6;7 z7HI4heCCZsO4gS2Wykaow3V)PfGLdV3Zf9~oC_i@|Io+I} zyPKTu%+DP_j(+9mj%BqJeSR)Qn4kM6F`4|_QM6orWZ+*^IqX*4BKnKnt9zdQV&~!) z(_id7>kbJ-yi&PdcYs5q%b=G@AC(^oUWo%E~jhIvCHYlzO-gbax}z4qi2W;s@%(t z_=8_guM3(2d9lmsHBMXeB{>zeV25D=GUal5(gPry@r`wzBoBhf*umJLu!K#)2mOH$ ze`npKZA;||I#%wbr+e>My_as_Un4p?I>o>eRT6Fm8t$d%L%OMqfwq-<=`X-0>|VNj z&Ke@g7?!ge2@Y7ogn<9R-~M17uWfs9e_jl(vAbqld5-{~=tq5fDzq4tb>&7nBukmm zu^Z{qA@W#_vZ6&VV}hIMDh}mlI>h0Uezv|PkyB9=8t;C7VkGW7_l4F#9?I=>2!wUF z(*@Eu+)kG+Jph4S_+%C0<4F-LQT2;Y|7Nwy)vl5fB}DN!QW=zh*595d5y_-(#0BeG zxgmf97t~w8Vy57RSNR2X4S+6c4CB1O{`fNh`4_FV5bAQ3KLem@XM_NN88<|svO|8c z=ro=LW!=<|-BQoEYE0%wQcm1qmCl2}BEv9J=Sk)9GQRk+v`0`U2aW2tz@-r1w&9U6U+YbKG~K#3{=w*(im z+h;M}8@*^0GrdZR4hh8k0hEqT%>N;Es+h_0Uoal(n*A|4Cy?^@5D$~`5rdTfbJb*q zEoO(ww~@HcEsxcNK9dPH<+$dbM#HN9rz%#q9R$uL%A}59)J?FpyCZ1_W}jAeN4g)F zea;uP`BOjZRvo2;J4#E^M7$2z>`%T97)pEA&$nelzLyzY70QV_nv{E|oaHx)L%GFQ zz{#UB_Abawfgnfe1qnQKI2WXGy1ne9+1IL|d4IC$Hn>Z!itI~HQy#}al1%>-v*%Vt zEB|D(Z6V07dXPKc&OT8EIsVk|A*;RIaf4{)+F*ZwCRYp^L;BUuvxj@v%22&lyx$xb zspS*5TG)uv*sBYq2N~~~V0zOTof7Emq!*o?fI4FfYOW(~3g=4b4+Rm7s+S-l=I>t{ z1a;!iiu-#x%+@n9Ti&Vs`q%WEzICpFxCV|m;Q9%LnYd13VPibv`s{|d>W$g8amU2M zI{3o%*$wc#gu+Jn+oZy-c+|Do0l4Ja>})(bp|C+kAR6LRHRs4E>6(7I$zz%6(aIg1 zcupnR4BTfaoR7QSnjL``|1~=@GHYDMq_NqDR$j zW||q8$+%7K#&2(57={xcDU87XZdX_r=iHfHBam{TGSTnO?7FzP9ickrF44m5M+iIT z+7Z(8@62xh5b!nTS{GaCMmKk{A=mWlaVqWZrw8J%nZ<~<*Yr1=>hQK^J2%ji+4ZI{ zSOUhSl8%K1@*-jZp7_odr&Qt|Q3MO&A0%3IF**=`b%kCWM@~Y1sfdJ1ztDzq^I;~Ty1(6R+CV~ zb#n{O^uU-|@O6U)pZSL@xK+Qxe7Vncodrj#a+xHs-~fDian08Fsr16JXvB3~c5h}q z{9SrsqMQj4v3bp?>v&@S!YAcXu!hcSLe(y&C|wM@&KDt`1Z{y)(?tlpaX{g3@=Krr zX5Bo09sfG8aG?A=tkHEdRq1Ad_iorK!RRjBU?s6&sB*bzt}4E6#mXcIm6)$ zYTL%QKi+`E>H^0Ch1h??HthVuu*kfN@&~Y32l_7&sP1JB$Q6rl-X}Ea%h*d9c-oM{ zkL4e{^u1c?TZ5myzHsb`!Wbl9fv{|?yunk2&&xMqvAUr4ZOgoDbmcl8I;?Pk7L*+i z@gGhME9eHWU1kde;LYKM&uIauEi_O~u0TX5of?;;$+gBP#p5!2hau^PF)qhb`b^?- zTT(|X1MH2u=8mG#qQ8O&#^t^c5sS;oH}rA2?TvptE^jb$TwYwBc|)JdO^1xaxI8?o z@CRSC`i6O)^|GR=6&wYbXslb3H_Q*MC(LIrcof`7P__H20cP^5yhFa|)f?tPc)th6 z6NGhlcbDE^cXz>F^mL*oFB3nF6R_uRU@5ERiqsjW zHBZxM65k6tOzgO6R==kdjtIizub@iQJ*aQWp0tCuiGXt+L@y7w@ z=QoqnKqY@~HuW#O+H!O<(LTeXdK9sMBch{Y@@9#s==Y5!{UV9cHcDf!D7t}vy|BwSfZoi1Fp zTT&Py;X%6!wa;wrhFDmvvi-$k6}Xpd#RmzP*^`rEmiStpNnMrYh1a6QChQi)=Mq0l z-FHRdZ23ox8t9u+=Rk=n1~>3+zu??7m}WmW`B zjNAtqFWU2wkG%|%dedl17D}&)DJ5{q!N`X<6%{T-k1BZJz&8NmVaC+{rhec{AaGt* zhQ+LLYZBMFzA7&4faF=UZ?_hr*O6DL7Qzx1k7wO*=O(uxpD;I>yw8~+;n;13QFzkef)LlSO@%`x6n4uz zS4mV97jlb7_ z1LMhE_E_?w!7^!$TlyuCTU;H2$6cVz`Qt9IMD=O67BVWXg|WBvS{TDcW?YHpkGm}J zBK2_!_jBD+ck#zvR(p~1WCIL;+@;KmlxKKgJW|$Wh_i3$GsH2syk>|{uzaBjmZ+k} zE8i}BUp`J4^aLaJeq)=a3tGxw!eSPYj^Dyn-YtyQ0_cLwB83~+Azzt-B`OJSZRZ=c z&0f<$_Xw-{MXB4AG?O9VFRm^)1klH2q;S0O(IV{=)}gln8WvtE-PVr)f8F}W!^;{Y ztL254wA*^a?nr3&gi#n?7QJ7%C_;LQa%;GAqGrIwikhC_Xvp+u>AAvT4WtQ7N}}r~ zD(oEt>?o;de1w#z!s_1aRbexfcMqPp?J;&;NEv)vA5uEsu}Oa)T8Xc{|m-d;F!dj>xT`1i&aG!V$d3o)_xc8(l)DLjp+szl8&{T6U)6TW{F-g-Va!dG}BBWZtHF4)nQ_rcYWKl z=r)pl?*c>6E!7V3AOdY@rf-JwqrOF0lIwXvnPG;qv6xD?XO1;_&H$y{HIC~^lmL_9 z6Hn^Y;KNS+yfUEZQ-lU7h+y>T4-v8G)9$W*p&%X~EF{6NB2lOuUY|4aTHe5(cUM2K z&xCw}fgL+XZmH!O|9DZngsgYXJ!y`jsMQ<~@ua9Hie8b>CL#c{h0W&;FujHrk$t>> z-({WzZj`7a`EoZ56{8a7bVK=yyZT4tX0parn&q0@ZG*PN6IWOQjAPvJqB8j$)v`Ml z>Rg~iWUuUWWZ{USRg!$!3#?59%O=WVYTUNI-l%^}Z)u~zdq!KYdJnLpn1QG)fQcf!X}e8;)7OC-{S|Zo}|6#hundLR4+qrI;GleW`AqQ z-3<)NbjW2=+bodU2EEk4kn7}9GYq+}KU-w?L+{-)j}x~Pn%?CYNStUmyJ)CCIzf!b zEKbPE#ZeXJ&jm$`{Ll~g%tzcv4~%BS1&au8aowP;2D5c~SBzb+n2Ha%S3R2XI^>>u zsc5q#hu;T=z><0Ppi(3(X4!xEecWkj(S9v}E}6F|a)BP`jRySNvZC=};;9WDzr1Lw z+*d;feYCt6C{d%0tM`f`^3c&xP|2q;8L)&gGW0%w=t$F6$4EDY5!e}@mB13Ud-99r z?0wAcslRmJ7%dl5(o8age~~(Ew8ZtSMPH&a1rH3Cr4SDbmP_vI6N7)@gU5?RFKCMS z;lWZ|=jL^AIk(e46|npo`c%h9l<0?wl|z6znZ`OB2x&a%MM-`rEQtungH@QH2c6Gcn6D&gp*#iD1-Zg3k4|AF?R z_w=v5o6%R4YG_7YU*!?acij)pP-&aGI+I$QWOVg5lDVt5By~bpKk{}_Z$FgI(0Ot7 zK;`~wAC8B(`r7{#ZSq4S8K4(ek2CGPm6~7$m?N$x*1t-4wxY&%^vQwyuhjoi;<9E8}|OcItDi4#4OF|gX4WF#S@Biy6NO>(2# zoM^ZkGUi*dd{kG#10!ZQ0(e-&^g()iBZn2l5mU?$kC@^*w<5-xKa;^&?jvBiCnQxD zF}smEq9;MbTzR%Ay9&x-_{0%&iejT#$Sh)xM$8q%`_W$X5wjce>WyiXYUqs>j+o;W z`lv)rBPNv^z7T=6kBVcx$iOVp%0*_FMY_lr7%^!sVL^S+8+CBwUgg%)azP zA2L8M;qr(9ki1;h$Pax<49_ex`pyGm7A|*l9U+^+e68LUv|6vAiigWrJsQG$EI2FD zyX}+rK*$3=t{MpUljLeXz&zOWl08%w0*l$KteOw*=8K=z0_Y;oH4hyCXenQIN7+|m( z2{F)MCH27vl5i+EM%l&ZACFc30cKvuI9+YzbOoeS7pazVP6d$)SHnO1NjoXJ_W1Jp z+2O)DLiGTCjpbz@vuL%&hexXd+LJz7E%osltzM#(KUB1;tkCBzS~0ni*9r9&@||98 zAY6UTTLjASPxG? zsDZC}pc>+VF$+{TxO$`?^rri@ch!8QS53v^l*OYP7N;6}k5ie{?sa#!qYB8If=@ER zY3JaN`0Mo%0HPJBL!7xX0Y#W0Bz zv-BV-$plk3$BgST0oSFFP@SW%<6H|Iy>Vll?}y%I_{1s6vx=hj`I z$=CUMb@F0L^+P%NM2{gc&5E~^nbfWbq;^p+HQ?swxzvnq9^M8Yl2D)@^;s`j$$Evd zs(y?z;_TV6xIOV%2I$4vUp4^NNx)!fR>;ZmEO%k~nPwW?rQ|G;_ z=oh_;D(>v-JUU{|9wzn-SBqr)4w5s8xI8AIKG>BDi@{wQ`e0Wk5zJwGT`j0CAvxfo z0idT@g$Xry;3GKCY{GZ;5t75Kg%q)?14W=jom9EL?tnvl}LZ8(YW@J>sAb55I)IuEB{ofjYKjr)=`k?`x~WZX}RqIz;HfuYm(8-Ei4l9EVr^vZyMG zC5VU~z~I-6ftjuUK!Nl9bXcNVo9jY49^oe!Q>nT~1Kq6Po|yrMnV?J^j9>Q&d1ye`@MhwK7k3VQ+jz3YMrW;Uh!wi)Qg- z+*5e0`~x73KgV+hl&F|o9fx7^9^VyE$sg@v@A2J)#cEun@A3H%5S+GQ`q*F38K3C$f0D4j ztSuf||8yMMe3d=Z7xWiyFab9S_@3O3SvFt{KKS0U`gqC#@==$80oXnP$F=TG7Mk_% z^u0rtQjG8P75G)gm;9F1@yY$xABR7UBYk=WRK{~RuL++|d(O|@Odd_i&kZ77<>%54 z^K%EXa@s|HE=8K3OOTL%za<8epG&~ui+vNwpSet6Pt#xQiJ^(~7rV+giT+~OktWk$ zY}0oN{Y5s5n^nr4N`JCN(`V=}HkwbPznCXHOMelK@)!Hax;u``8>zh5S2__NtSr5R zgI8nzraXOt3p~d+=AoYBtB0pVpRS32wd|5Z7E>x%D51YdI5s}Vr)|Q=t>OTDrT329 zFq68eeRf!|?|Ua>3tsv@5PvcgcaUEN9pQsJZ0TXIfD%Fk8xINIgY!4n!*~{cT|NLB zVCk$a8qc1M$H*VS;F7WJPR+ydT7W$z>(e8OXqdG@#szfKArBI zA>RYdu?}+($9(GnFuT5+kB^df@++$v(_^d7u?1IVbcTuP#AkaK4+4@=nA1pxu8GCK z62PV+&|1V;+?U!fFWcNjZm%Z{^wEG5mI9j`be7e^J)g(l%e`SSQ*>0XnHG5vET)PM zqzy^8Y;AugUhwIf1~~8qJWh*%Dn3TlU6iqFhJ<7ctJ7a7L*zAEGCvsv-nUa>8yaEp z!5iCa;f62a{b&}0jK#5ESGNFOn+SmD<_|v5e^-;{wR@9zeVJHfu0XtoNAA!{FVw{w z@$h+r0CO(!tiP6Eo4g$gmHJ`iR#0M8b7%IJS(vyHPWxy}{i1B=KKVU>gjppJNMBAT zM8TyPIDBIe{&ofa1fN+!Ue`Fj0{@0SQG^=Rqia2YV}Jk$Gg64vNc~V^ExPp(_Lkl6 ztQT!o{u^b%9fhJ3*TJ{!ekOGU72Kb>xf&ND|0;Z+2d3pdRRA8g^?#|dJ}v*bGN0~$ z-qsjWOrIgLApR3Icx4APfQevG8#UWj{bvT(nid&{jdRTFfJSjxq3`7xVKECC;l=nF zc`~3K$4%duEzf`@d*(UP(c~)l%JhwCAQsFw;&jIXY0I)sXsH)^78+rx6^FWBEWr_* z&^iXng8}Gqu@TDM|Irw{qSm?_zEU!Sm9J{}m~Y1n!nk z^Xt(4x)hhmCk)~{UIq8+THIAWuBjW)Jd2LJ@-A zl)!WXuU>jM)RpyUagZeMfZa0IOCKv9Chyf0Q`UPAF7ASMRK?2&7Z1`x=|p}~AaXVe zd8g_d67t-s(&&%FYha%t#e?OqAsy6W!EpB@@8~PogxSlvA;o9qUtuxJmo7h1oFZR= z#ng;^NJD(s9@1CwY)W@+hnM2LFx!1K2*EN1(e>tsCPBvFx>;Up%h(nlCQ+y!a9Q@kkXa(g1aJVsW-F+DXJlmfOLqnF^EWhQYyi ziao1l;86Jsgt@9E{lH2Y`HUTrx!__t6zl_-Q+iJ-YT-Udc@jUvB@UjP{gz8gursuH zdU4BI=q3|6+!<13Fi?@fb%IrVyzgo?9DMX}mugG+~^{JR2bA?r=DsSLahn&i>t#S3NBRTgdnsY3cip{g^-Lrn6slH&A0 zG?)PzHi1+?_k$)XVDZ-Cu`(JX>oIAt?gS{$#F@2puVZ~+&Tku9PmeAhv6gEJS;U15|V$*E* zC7ji4t(O?`qyfq?ctJqXqP_z#cMRKXI|5g;K9LSsmL=nG0 z<|KkBvhQH=shUzWE=yZ$I7p(}?KDLa4G3m&B&iq3B8jX<62Cw*yG4W=jonr_lsv7_ z^Dv?0Q7$F0pFDs|$+(~V%9q92)yUHaL`a5>Ayr8C6{5y*G$fL&{h_#CkAWKl zDoCVq)mjA^a=Ex~HE9{w$AXJ_@L-P$*VzMSW|`T9J8c{pV5*OPdVN$pj{N0O$b-a@ zQO5TM${QK~W6;(Ck+!sNvA0EQZz253Tk8T{31}_yk-jNMg*l2`PkB_h&u{H4fBeEK zZ?R>80cwZ5_Ip2a&WL%HsTS9H;LNmm(xAlyrdq6MeA#RIBtm(*hz-)mm)1dM@nvD7 zl3B>LAbRbW{``}{-?b{4zzTlwN4AM z%h%-@`gw!CelgP*PhNbhmy>O7Qb?AnGlwDi;>k-ZONp1{WtRbpCohewn=9)L4~&_z z?&+2Fi>b0I8e0yS-u)Je99Ynk_+Uy&8c1Cp=aLenF5QQ&O-n{onaIVHz(a}*CQ+o~ zmsTd=LBpu%KY2a?5^^3cki4f5RpCGaq_mz%3B(Zxmy#il;ME&TGF_v$ zti4^+b$|2PaUp1bc_#XTa=QBskBMH`x3+Inbd&*lnGpSA0Nj0U?cOHn8!uMcS2;BD zMs+hQ{Wnz*3@Hv%-PrWHsdz}4uki3-A!Ud0K$6hD!Lmb04gCbRMh%k*?2mFuEq~Oi zMmZtHeXy`O$3sHO{E(77e>}a+TSz&k2wsJnR=4CVx%pPZJf!^Mfzc=naMy%zr_DNR z)G+g;ipCVF^52~gE#NW-L1iwNl_02)d}Vu0w3Z27ysF|kXGE}y6O)1(`ov_esT5`t zN**qr_$a4#DjZLM-VPhA@gM^St5^BPU=?Dv>N)80eXoduNeS? z29~^775N668|Eh-7&F8C)?k<)nyTb|y-F$`Mpk)r@*vZp^VG7n>(9b40tSHgI4W^n z8W;dlf=!|bPfP{}mlH+)P$(Y6@puxGBfZvnNlb1Vpm<_3-HWpPs_<3(LBVFq>L09+ zA}PWDR1^uUQlTgUM81`Y90ZX~TvUP}GH`53<96sElesvEgegLJhp;LRBKw2&L1dGu z5Y`h)9xjMfSLms55CH=ELvNFp%xsb*CI4-Ls?{vVCQnr5mQuNjM#=kXGpP}>;U6Pd3SSm`AqYQTqc!QvHUG!g<9fDfbWPwJwt5EC)$7!y0 z;7uza1wR~Jur9vnoo)5NH0YA4>MrP2SMgwTR1wpI@2rpT?=B?E%(RER(T6Hbq!E2v|M-9581saEXfUHuCV7k3IlsV zqiOqpVlSw_{CDHcCrs)f-W=o762zO$iR&7NqFOyxuj4Q z%khxGaKw}A-j)$LUiW1+G&saOd5QJFXvRYjQd(0c;S?x5#B4YW zB@}D4Qt@!|i$^UFlDiBDtZ+D4D%9CaW;){u%*OI`HbfC)nMY@QWhMHmx0o`@0L2rS z|9Vl_cn^%3!bzLFF13mmsJ#p0{pnO%xnTO1 zk3DpvP3!xR7dP>q!FiEkm6F|O0UjX~KV&eu?@`Hv1e2Vg2MZ=ZU#}VTwcAWzyWU(E z=#P$;r>}{M2oHJm)%n1>Y5q8CxXsH<=tToms}I+m@ zNcK}%KX@PkB9E%2-~NfH#au)eM6`QxJI}5g7tFVS_MBZeC0I(uajR@z_MG2Wq+=o7 ziKj>jwe-;?qL$gfSeKBfvE>R!m$wyq9wxfva?ycQWh@t+F_rFcecj7p(jvV2X&Zd1 zK;6Olw{quV0mne%%F}_irs0y6%2mAG{G$OxZftuoOxnm*K{qOe)1I&zJYQdc2NnpU z%3Q*icHM8RsV2tgHBs>tWtYOogUxul*Qjt*`P!hTugvts(i7=Vb3OG_gm=ZGCzhBX zzp!$W$;S_ zYK5WNP<-yIfjMlBS-fzv&hkfQ1Wn*W4uDo!$giXTtXmntmG0hMvnQT zY{7wOI}?zXyyT8T)mDy&NVe8Q+lfH*R(W<=KbS`Y6)+>uc0)!-xm^l(^E-H8G&wUE zRW=a@wIO7ssY;gVRZ{T~a?PWY2T4qZ2Uj?R_=M{d$x7iSiR7uJHkX9zhnF)e%~WJq zhvOmFU>tpywRa6%VLKzC)(qN9HaSrb?b^1=RzO}g4L6^f&ho&RX)TO!snuGgaMsrB z!nq-)m|f>+YqcFRyH*rbXxkAkz5!Yp2X~s+Ie-n5o=d{)FVQ988AO!J+Tf`-PKIFr z=+hA>z6xGgY-@oIRlz%pZ7tE{aD2Mh)~6?Jn4cR@POj$X9%BDarX|yJuagUPbFIyC zmykdC#k!^R7rPv`jQ(O*W|q@mgmrf1wlbxh*b~X+)(1DBA+2VKZF^-@##A^6+_oM1 zP`7Qv@tiVS75uYhS5QbPC4imYrN2n$#@n{qCj98GQLuxEC0%ayp*HlBPGQG=OYNIAUCHdpqot%?T6(GKT7F;vDIUbbbSJA`2RZixm| z*Q+wa(ez5MW`w~KAc5c=wvBR2*eSaV*$kAZt=+8EVV5Bzy*G-)XB<xDM>J&GePK z0DN4$*{|B>$bB@$l-s!1Y{OBP+IZz_wy6TB)_ZN!v1tlsbx9H-qbm8U9TEaH?jW=xnlDC06 zOfT^HY^q%uUm4%Ej!b@d{08eddxUcad&dit?6q9&_SyO%`5**kw`^2P?I-}HH_Kej z-?lw1$)_PGyHR+uHop9hZM=L^Q$ue@sYp9>q@UI1cd(Cxrm9qM=Q_vgxsngqtUhw3 zI)J${+p+#qZJd#~zb=03CtFoi8qP1!O?=P~8OPLN8^xDt}@7SduqD`gNA}lmL`Qr*KGuY95vGifQxe z;j>@bCd=0& z;`;PoRYUQIKiEdg!$s}>^*=pgBL4yMBz@aJsw0vku-6X0!)agJzQcL#UI@|>r9PLi zK#zcGBK&7tk`_Q0KF6Bb$(KLdzL8`bM9N}daa|nrn{Am~3~Lw_AO2>0THXbVnR*Gp ziu1Of0swTu)<-@d0@!lFwn_`2BRf|>HuJ^zez#SV?~PXAvPLMxb>c5uJxR_6_;@~5@0M+(JW*3j$Bsj{Y&}tS zJzVoPNi8%`I!h|xN{2MC%qRwy7)f7CG-XtJP2BPBnns8`cWBVOm6GVrzjd1%t4WMi-HIGastG|#26W&_vP@2f7q8@r^D@YkURp)kn|#Mn&4EBY8cjWN&*k@Z?kJvT~^#BH(lL(GBg7AbGbJ*dP%sdjg|bWBZ@- zdk~Z*c<)5u8BOew@;k7G4OGsjxAc=ghsA85A^^Ef?d=5spEb32k$(~aOm1d>ObbBO z0a&gfB%3@L=IYOB>*l zWV=fX!2Et*3i0Vp5P+?BGQWRr3)ycOnrg2dNIN2Njm`e*;Z+uUeO!`i57gq*>9?tX zZtidi?d(I4d;oSz6AEC0%|)=x;c9iT7okrY;GG@p!{yH)3P$(Pj`mgP;|6rOD;a-s z7*z#GTZ)jbF6%fZ?AwAQ{zR@gLm2|d$A>V=2uwZo)l&E;|cWL$sVd6U1 zs4n(4K5|GTY3@$8n3?YDZto-4gT>5DLn8609`+0Y!0$cm7P++uU|vuAcr8HM?F}(F zyqEoPxswRs!(K$zdXfgngG6oPrf;ohXd8{$8;V(afIy1WZC5jK^=|f=_hq5HJE7F)oY^S7|q!C-WSm5OQ^#S&&{^&ym5zHgrhlp4nu{%-AS z`Me^}X9<1EaSWY2r$*_IV+@NjJC5-r;mH#%%YBE4ZCvWc*m(*)R1YK`{jCElTez&j zag2>z)`sI4SaKd7jqhjMtKj}8o^ObkIS)VT`Lh=O4l7IIE$GaHT@>+ivs6 z%gYYGNYVTceOjIi6dHi1ZaDlD1rEXAd44%=J|BqAdT|Sz2aTCq>@&EDB1{ z2!}|IFM0IHXVBjLs;pvu1rAP=Wv%D!arnUV^J@U#svXU&$IQ2KQZtg`;$t&2k}P8q zQAM+Hm>@ z4lk&tzvo^%+RV*r5%wO=%{F+{Q$aTaq93D*9=KWt7roKdhVS5Zt+?9JDfZ{kTqZm( zzO`4Ob2i6Hg5x(k4=<$dVZrmBv2R5y7zQt6%svAK$29xbs5IJqj5+85(Kr)eeTGMC z^}$853{!=T)GMsw4z<&xu?IK0PysMP@SKdsWWnHUM*Z>ru}7fu{7oiS6i)QW3 zPDsHp#uA1|SYk{VzQ=>!dO6f(ookPg2fz%$0ihyG{i;o z?cD@W0SoM%q;^XzqSQM+kDS_J9@W1JoVB0cV1XdkEr&}HQR^Ozyv zg}pDk?O*mqK8qVx#!-(>sl$GxU7DFHFVS>ESF%1|L>9hYY^cr*Lshl?q@Z8Ks?)+3 z0B9gx{-%DN#r6X}Tirdwj(8hh7%<__NaE<_GA}Rre+Pi;~ZA z(o@;q=laN>8hYxu#J(RL6ZhnP%nye|w9+$p>~VWd_d3hEDzYrPDA3rui`_DYI=p-2i^-^OBja4J7bV$buGgK^5#ssC z3WuH2+%n}YyCe^QNOcQ^y+xp8 zU}#&EGR@aj_Z>1BlE*-JLhAteX;^ab@eTc@7`*o&8H4mqqcK|^o+oJk`Fmu;MAM#f z1qPMA3TdNb@amx(x`Y87s#WlZ-d*YM+fPZd6Exum?Y>>OAyM86i`3vHK%?f&ie#A^W59cOrnd57{3P05myF z5{Jto0Ctd8!-P(T>52?(T!ys(5>#1YOhW5EZ7lq2_~s-u$cBANE^C`tIBxKpCMsdSREyU6d9yE z043``w`WUo4(#&N<{P7N;z@fmc@iv+erIbs%4v*UC+!Uc0O(758+m~UfEk{#hZ7WF zjR`;~9=%BR_oGnL@al0(L-ES52tgY~&1;?_%h?+II(a;%$RqOs(k zA^nd)B1Xl(>|F(fl}?@2P3|rNu>NRI767oY_k;-G#h>gkS^%A9hAI;CX~K^@ zR{~h{A!EtU_A^MH1zPZ^%aYTDu$T#QRukOpynTQfK!FG#>4JTP8NenHz`);03^gIi zB?9Pkk%VI-z@dxw1@a*g01LVr0G$YjDI%QiU4-RY_pQsO2@lS@V&4!cz70XN#P&Vg z>tTeQ(){g$%?uMSX1@th8>IeVLtDzCgQYv^Fv*o|FqwdSWL~phcx7#VT7n2TKbe6uZKXjn_~GddxW5iA%BrA=58W@8-LlS3jo&Mv=5Po zhyeQBvaiwtP_=^X&u0|1LYeSI9av(N$ z4NLn|u}hO2;$+LkUxD4@Cd zwm^sn#>_j<5H7XedAONZ?}Tz@-f89<>k>+nq;eec9X?D)B$d7-cWO?Q1^qGV5m2I9 zpIfDkeE2Z!y;XC($DSAJdeu@|gybOrAwP3JJ+*YFJW^9k*L$C7Mx$9=T(n3iK zrgOeFii9B*{E}$h4yC;$c^1U2OVHy)u9FkVrQZ9nUEUc_( zj@PD@=4k0R1O{tXs0X`E}0 zcl0i8q6J`!Q}=t9o{{fp0I0Ux_AT9v2-KH-OY^l*I--*mqQ83pqC2IRzJ}!J7Qm<( z(UAj7yU5L9F(n$eKUFH@*L#=N*Mg{Ix7UDp$c}D-e}1%dr4~v@cB(>lL<>HxF_AUB zS_o1;x4{@J8^1HSbeWt9>CqW>d!<7SC|ehYCBW*GtUb;69S_|3$kJpqzlCu`nnMXN z8Ifj@I^Bp=@e~&av?;-hNOip*pLL?Z=8i!e_ty2ewvscHX_A{>j=?o zhVr=S=Q23U9|>6gFQim=Hs?LgvhMtV_RXY@g{51Q@S$^MfvzrvrMr{R|5zvDV>Tld zC7x~JIc8HfU~yZJ*?rdnQ*nhIrd{V&A|Fl9RX^AB9+g=AD$U3{VCt7)!DC?WQeNEQ z{%@VTc>-7&QMT~h4VPu@mPYfb)zaL2YLYsS`M`lbcG=6d@RW~B|3ZljrI&I3zVa-8 z9LGh*`QMM1=1HhK1N1V^`}^^YCl4S#`)TP_^mt42VLr$MVm8dTX~|TD>JltTI0T#k zsM*r%a9^E@396~$#{m9PB=HCti|K(`?|xAW?|Fp0NcZ_0wj6mm?3|uO_Q`Ll+!!W5 zRJv%q1Cgt)A`aLQACvc;EX9(%3Bt0VJnyU0G4f7W%tn$;EpdlarR}r;y3Xp0I(vXv z*U*nxnC{-)z-n`n(dPnV{gjes66-!Dbsl5oGvg1xF1?M=?+PLq>wbfXSgbo~u!xUa znxx-Qo`4?qu}E34W5efk~R3NQ-2vgmB-7bvzB^>Hr_`G}&Z)*KCS z$jj$SdrByc0eW%BZhAnx`+ueT(V$k$A;AM@>Y0FaHwYe7%OQF;u?69dq}^bI31v;q z4sk&7@~j({0A-B)xpcqDmZPifFQs)PIiK+F@o2Uj_5vtTQMq+D%#Y>SVd7SCQO!2x3{PJunycQ1d5YVfQ6mQEWqA5l56FxeCA2Xgo_=zJ;6W2jj96}? zH{$F6kP*9L!yL6FlQ9Yp3wL}dParbzSU5WtGY*uf{@_|&+wqI9JPTCPvoC|>g|LJT zJhR&1xCV|WZCl+~a9t4#?2Ip@!VU1hU)Z6ixKN_gufyz#Yh?ufd+v?|YacxbqcQH>q0}$7_`CVz`R~RzE z$?Vx!de&k1j=~JctUGMd>qD0xb^MG97{^{R_##DLt2i1GB8Lxl43p4C2IwV3mg)g< z>=4H>w3i5veD7VjueHSkqM1?y7MD{N;a3!9aLEhqY(ep|@x3uDQH{eb@Ld%b`2U0g zKPwWrHtmWU?r7kXl91v`8}2wQ;oJj7fjDNkqprK`CkfpxPci>igmufU7`$VoV*tf~ z3D)0T=SMjPO6s32GaW$^3Lm+PwDTSi zkD=*qC~hS@YPa3i#8%A6Oh#s$GTreXljO=3{G5Y4DHae%Ov*i2vh2zilmK&EI>Rv` zOkC%ZXFBXYasvR!H`G>T;HTv#nqsmn^%sOUw{y-pOITu_Mq%vnZ7YCN}Y%LC?CMo${6nn-x1=`7szQ<~C4d z1bg9mNM`N}TK8%N;LB@)b>$c8xengqA%L@!?9x0XZ-p_z25RTdj9= zk!!(XmS+aW<3Xz(%d`NxJhP#wFPdkDL8epz<6~|uK}iFp8sZfV6j|-005rM)&q)-~!=^V-lB%(gU(B!D(XSAbL2~y7$CHs+aao!1 z*|Aw!@s`ZQ)YR0JcuPiBO5&KTxYVrdF-fjHv1=y#Mp}|AmYkf}oW!iuq{O7`*o>4h zi5Xdzq`1W7q}b$mSM*b>98zRvPKsqrN@~WKg!tIRoVb+C>{Lr^PEs=YUshI55}x|{ z_E6UWdG1$IdEMf9I~@I^%IP%@udd}xiHwU+iHoJTrQ@;_$6k*9u z$x2Mg%E`z`w4^4aW+WzMrH}^?5@WLn)A-Xpj@qu+Wpg%5QRVbZB1itJxCZ5PEcKn+ z7+=2am>3x!mzk1~oRE+i7oVM&k(rv2k{y?mm6{Nrk(`{FU~y$kr>Y=)Wu;_g#U-X% z5)w1waxxNAQILqCdOr0ELoP6#Ms2- z*x01l6wYmpPv^WWMU~TQ5*{=rKQ7XHr(F*zYKBQ80aR3#?FrDi6^S`y-tGU7AFB&Xs% zXiY8dV^LoF9TOXe=MyJu;I+?0d~nO0SbU(Cvr6O`^72P~auVq;IW>{ckYOPVSaLFm z$y!pg5?pED9hu_WASEjo4DCit3q81ny%U* zMUcQ@Nl7NY6_=bi#$riHO(YB?B#p_4Ps+;1aaSF+T+Bkt>zJ(jIpd;;IK6coSD1Em zabs?X#IdL!q!9gOC8oy3XC^0EQj$neNJ%1+Oy!2yATyQ(!<hcw^NN|SV*W$BFat7$VnoPq>RbQ$jOc;;UzvH0~5Orb*+kA zv|Wnu+A&@jxF$Z*d$;j;>YnX!II7iAf83zTD)Nyr>TQ-J66)gHSS-NXGBYf3sg{hy zjFgnPvAQY#zQGGg*!ipDiSkC6{~Or;Z(#esfej`F|2MGx z-@x{N0~<_2{y!Vo+F@&3XALO`r;T%x9H8Co=w>H+8J`60ESeU6`XhNdH8Iw?z--y! zaIEu*aPeoGluu-_Wi8Jqve;jOPh*D`IU0h=uh@=cNhALy(8L{ z{$fvUbfdr6)!pv&7rO%0gZ^R{FnZEo>tQ($`Sq~3TAdEVhq^d} zLPk(p*a|fLMF=o{Jxtq#FKSLn;9n0jsbgOcYv0YeRbB${^RI`!2ueubY%`XiV_y%O z*xh+qE(Q(w5*hn?m{U_sS7+GQ!-^B|oSx3kS}3|46fuPY&}Edq)%Y=9*fXY=J8(uX z=TdYa0blIpTq(Z?JD^+WAu0ytOJMIL@MHV?K~um(=RVF!=*tAWyN~k;`4niw9)tM-jo{x zbj;U)p||A}Lu8nIR;(7j{_UDD*RUMtj}nSaqWkx;II2h2dH^&*07{vSfyj*HcOavH z_DBu?^iVqMMY|{7hf?ki;kpBeJxLuwrM!qWCD(ZsA+oegD6OwKOPd98u5ZjXr7uQwBxoNZN;o(+1CsM3?Rhw*I?&f6YB)J$WidjM-C{ab> zhJBn_LX7uD!-$Z`8h_N_l^xJ4Og@9!sM(_Gyra1~wa7SZoMT=G^e%^ODuNwjgyqM$ zUwL-pbMkS(1c_-Iv*j;g2@3_slh~$4j7rZqBTlAp!i%(JStoSf3q8uGwjeLmszY5} z7dpSF=1N}Z{Jk2w%@FXf27rDinfJST?hBnzH4C+|K|(nkGs3cCh2^N|3!_W0j|8<~ zU1?pSsYSH|{ppDqQs;3A{*jXXO#RpEM`wE?XuvSYn@MO z0oa>Nc&)RA{E-NtehC>mH9hF^(%%Kv$cE!%CC-oJGY}`sVcuKkoGAaIDW;?1e4Dc` zI%B~{ZO#!oD0*~ah~3#yz6+t4Llb~Yc4td1fR4aR0s`4^DHbZO;&3{{#Niab>u~-@ zZk-I|&B$n;jK6g{yUEQpH6`o2;@H>gR1IqaidF65Rb|dxlR8(8_0D7@_XmLd8#12A zmF}-R773B_)5Wkv?T-9n+p)6Xu}J!*D`^5A^u9Aff=Bbm(f&h>-FaT#4&_K*VvE7w?Qo{b zufbw={9-#Hi+)g91Hks>Rl&PIoC160CrE1Va$+C(2*k>ckDq`gte+#vcQob<2MY1o{Jk(f03oWK0M9VBDC`E5E z{wXHohL}fwG+A8d=HFuEzr2BC0NLIqpEUsQfA@oIgX4uR@~+3b2#sUx8qOE02G&9G z>}-yQq#_H?7IbtiSh?IH;WG!EJ+QOe(m+?G+gTW;{2d!gj=<&6zhch)Yyg3xE>X=|z?g5z#&xh`fDjQ2r5 zl&6WA2Eb3U3%*3?suy`L@W6QFO^+Od)2>HT@YHdRe&D$A>nUE74JcxsY`_v#=y=5m z=lgPWDk*X28YE!F;5JPcw3M5}VjAImNJCup-NGm>gf2`iQ`mt$KFAoh#;ABYy{n1o zhw`L>IC@}NeUuBsR5~TlWF+fD>U3esmc1lN8n57iaVZDlVR7lvR2G-Ooyk6U>Ql6T z!=*{cCLo7hcox@@R&3B-XJoj%xKxm;k4vi{pD->hIPF|sU)n+09o1Ga^GYvM^t6p* z_?&D5H0N(xf8%QFU$!KON2CP*vNb`{+e~aYwpSimLt2nm8(f)G1BajTY>@|}t7EOQ zIrXJu9$*W&FR8-iy5XpdMK__zqLl8o?SmnyI1qynNaq&Sz5RtvFwOkr5$h$ znil%_wX2LL{eH3=UeI$+fK2{UU57Y=k{lvX42&YfQZ}KnxXx`9r;U$L@lQO(wlj{? zt!XzV4$y+sc^tqMn^LyU2lZBPK;!QLaWLbLZ>JxWTeLG7l*PQTK{-ZT=jQVQEf{ESPH)n^Fdqb|99m|eaL4^z-{GN$wf7cZITZ7Yb%9Q)M z^cfRy>0>4SbV6CX+PQhO8~rmT)7p6r##1QS?(kafD<*8?LeZ&PSQixnsYtzx?T|oh zAJG0ytmHo&qv5kAuD1QkuK1uI89Fa%O*iFzm2(^q5%;EnW!b*yIs^2QvJ5Z)E_%G| zOCO0GHh-Z^y}N43lsYRers6Sez`+?&>cHY-wu?hY9qhcknuEs&#bZcR?i5YNMa-Dz{Dkuq^FpK zHB-VTXs0F?GMLnP;t0QQ`rnadZT+Na91n~oQy?A|ONO`C$CAPAO=5|dC!XMo>)cXV zWaPKJfz;k!KaiF{KEXhWKgub)Pp`PYP-b{(KsCvHMd`0`EF`@rAIoZwzf0I$#kFc& zSyM^+P&I7bn*%DARR%1vxn)0+I}hdiM6m}{;}al>+)Y^4ChaBdy~ZslbUup064hc{ zl_!=Bmt@~ZNRiuh*aFn*NoBFJ42#){mG2|C{^YXm0)Q2h%bLjzL;%54%Kp#-=(5g@ z3jdHq=6_MJL?zm7J1_g-vb!5XaE)imO3YmKz%ymX!o`b6m2um4i!J!{v@*Lq1QJej zg}_Hi3Gp5F=v{lfC#ft1Uw*dCBGYn@s61Ii)BsRyq;$99`~Q!%=MIdb_}ZON0!-+H zo&*TJBfa+?dKCx+si8Ne7pbB55fw0ZmupJ1oj)6(*Z@UrD2jlJ3M$2}hN%$`uN-P%ueyIE&1C!y1>>(_dTr^%5y`kB zop^IWW>zfu67b<`!KN3V&`t4tOSg}reclqjv{mvg(ThEsIMOnzSH3K$Hr(uAWfRAA z`h-|rvk6ml@vWRTQ>>M{%@pQ!*#ia&q>+zSXSRwV4FFw~l%lx)DX_W{4^JuhZI=Tv zqyqp(NhuN?fQL6_R*E70Te+`w)Xau)3!AlC$*-c7;#jOVVGFm4s;D?2Bdn;qPbK1X z>Uy`7p)9&1G|nY2>Mhujd4#UTy~FLV)u;q%FT9h_Nz`?`6=WUlb8&szHeTT=qJY9Q ze4-ndaFWUEvQ0yVlXVYg_SeV}9T5&EFJnXyPC^b9WVUh%Cu)XdGNG4~wsiaBVYB_Hd#JF^+P?_+OOdQWR4E|fvI7u@M2JfP@ z!zHjl-mEp-?a6FlZ3}NiTtAghQ=%k`0h=l3zGw(<0oxjy8FSjv`*dXR&rjhb^!KXbVcMhTElbB zZMpDdP0t5(A0`tbQm764tCpuf4Owck7YhBT7}>!~wLN>t@iuID9nU0s619O1a?)YR$D*t0s9jBWALxxwIQiSiK(BU#$fnVr zyc%uM4w;25jKt};U@!uCII-dw&rI4a3TPn}sCZ8}(rm2fV~vi+urN@hwqxnzJ?S*n zQo~O)qiA15$9iA8NV^H1!x~+N8^cMg+UcHDnqeuvcXuoH-mSQNCD-v{-O^hn^AEAZ z+qKKu!SPA+4nRsVy_ZF-$fKV2+I;c9`a5gn&cmjkD^UWGV>G;vqlojiql0m`tqXc9 zM21fGOx5V~xEz>$rX8y|)w7;HW2xcHA~H(oWbn^>ax&sEa?>}_v%*+QsKJ$Kp5N(t z+z-r#KSw2I>qws&p8gvB1y#ydE#8~Shs?imG0bRwaR{%BPW5$3^^XX|?1uGO>FtY# zIs*t3C8s2~#DwR+AcQB0uW;BV=J0Y>&Gydms#<#!fqn72GOshoD>(PX9`NjoB^`A{ zc)V(d5y5y>yFH9oXuG^JTNd^xzR9ZQN6yRDbq233oxCdNiNtqpZ{~K=M1gX0r4&(S4?7uST_CU$9>tN?Vh}p|I3t(xsYED6`Q2+U*NS<^>1QD zZ#7$f6Kl>+&lz@b=Dxga+D^|E+7V&4A0C~jiw5K6m-IY!BL6VYc#RIlurOF`-Q^ic z(=5f}*lxQ=JYC7q4pJEwD4`5A04&gZ9N0nLKI?~2nS9%$odIQ`6S8w$V!|78Tjq=a z@+~sf!5Qtg3sPLN^DVs27VVz$dVa|r$?|&Q@{of%Ivn%%VRR7lc5(Sw*MqlkDRklM zK#n6qYPMw1Q`Z?Rdz>sQgC5%EJKMn?+|OWkQNUyGjrW#{?0HfwCHxFLM~QktUDekm zG9LAQIp7%@!@l^?laT!(b>%suLv~1X&Zz#go*^3Bot{Y}4WIR-X{2~Z_ssLfY($pK z6Y>5d=iR!+d8R|uNdcC=428G^S-vd`0_o{yjtcJ+q`7$?uQR<5zDQd3h({yjppMAmcNmfMJ8mJ|@6dR+ zg{6JzQ60@wGbH^^U1x~@yp!1+{jQ_K?=Yjt?;_Wa@o-Fj26m&k+$F=_B_8f_pTFTb zt&v=vgutUD8b3GytGw;$#+|E^d!q4+0V9N|I)nEk=a2gqlKUBG>UIVX3s~+!;1bh3 z43XdDC;C_&w(BIHS#|8hOATj&biExaF=5&KcRV*~KU8NQ6JuiKEC<6&YsV!fMuVDs zvD`#$nKit;GYy1qUF@GM2yn6XiM-CVcF3$>f6r4o7g?ht!q&bLBLZum!Bu7*WhQoV z8D-QANo!Zv8O$a+nN?alN;;onH~w;_)(0@N*y5i$<9Usc6Tos5i?6I}>n(|gTl`;V zJ@sSA=Q&w?T?e4=3r~oUAEd=ASF4+6!&rtB#N_LON&NNh1D&FdCRoaHG{GgvM%sSm z$%;CO(wTkyt>+cm8-ajhq1ky)U3xv(mN;*HcJOwyA|<<`_NWVZ zE;2IfvZsSa$DtwafwbJ@H#U( z!DXJ2S~nW${gY>xMt;(<;ShBVV}lU&S!a8Q`hbg4nFyhGTR;?wq&CI0RiL>hGwqKp!(Ch2z2Kw{-b{t^bwZ^4fI z;pxkg{+<`dMimX$7XKVUIs+~_Vpma}#h+ovm6uItDW%wfKRv6+pf2t?@!V`cOAw2> zr%7J+LUC_1eqeK3IdpHNbeE_R3@brKFkE8VQKZ`6p4=K;%UQRCBC@IrTXT!AVy?2( z7(pn0IVv`>v9y0Y|DzsMCl}q|+FTeH!z~ovkX^h`sJsKBH+{+8n0UJEc+%U^(X;|9 zuX$V3cNOjBwAVt7ErnjC3mtOd(jqx_5Hyqf1>>?)0P1t+KNq#hs zUZW$t+(pjUx90Nl7sCs5MbZ@!3_+~bA@cHjWbOB~Be}YY?5tjPj8Qb@$YGWC8%!0f z(w2zzHq%J`uB=Z!Z!>x??iSW%>OvP30~t) zu+fwwcM!~km|2fv-m5Xsc8z5fGGc4d2<{fBep*fScAbU;I@O&^crR%5GzPa(y;FyH zN2mI@JJm*0j$BD~Qp8ir8_~$`UD>cQ-Zu14+^zQG?p~Uj=!bO@ZUE7>BM+AKJ`+QW zbwd`-FSU1|WpRm^!^Vj=d`N;bv&Gq#a^B+XOv18)4%kYpev&td*0v)c#k#R4lf31v z0JhBG5uLn3-K6`_Zm#ZQH07K)CFC})8xC!W5O9J1Kt=CMbOa_^C=aCskLjdYK$pF8 zye_NM^z2-gk?h?{r($@RA5B6fD6p=r?449uUB_EN=9lFhS>aVTvaXvmmslgZamgiC z@;Wn@fSJyus@@ZMh)+j^(}+xr2-1iR-Rx<^O0FDb8lh%LrV;8ogW01_W^+m--sPrY zy$XB+Gm6rPNdEfX2Xd2df!!#H!<)K>zm|CTEGKsp@44LM4*-lZufN~`9M;O)J2#2# z?w%n0l?`J_5Kx|U5o{k{EIBYcxq{XI+f#>q(aZZL4PiyWBrL1@&OY7|G|N&UlDX-bj~6I`N=*{j$v)os^eC#6 zx!nBujIt$ty+i2RxbEVwzFve+K5tCyP}&M*%em_7mg2EMi%;eK)X)0_`KCMD+uz%P zeut}JkE754?^t@xQY`j3jt%hkAm4RoMF#Q=!U6@`R7(bW8`E4pIA4b4!Vt{?V28>A zU>jTR)hYeDJB%$QFQUFpIq65T@(uQmCdob6l)>I{v z+uRCZ(|Z$@-eI!{_m1SM4{NQQ5yE_bZ&eRxlvpmvbBPj5d7T+0U})=~>K&Vh?9~zB zDDfCZ1W{s54||kY+QVgNQ!^!pHgz4R8piP<(HK6dVEAoJr)`LP12c?=IKE|cnUL=Q zU6dhiq^`9~5)U8ZT2J!!jUj&mV3Z+lq608?n)ehT`Fgq!akH~wEJGYx$oo>5c=MR_ zcO8m&qes-C4vSn4b-2Vdn8-V`yv6t-_@2DT2pEu;HAW?7bJlwf&&$+xhCbIi^(kL~ z8`RTjy!}NtF2>u3*JT@TBwooXy9+d*jJw?1u&X!o>m1rptwkCwtm2TysB!V4 zZoDqri{v%vhgW;|6(AFIMA((aVnpCdeN*hN)QyWkIlIAJB6{EhdFncY;Y)&H<%wRC z?}rt9@4)2RPW5h;d?U{?E^f@(=}pN`o&kL7iyK#UZ68W;ySTAGMPA%^MD$`m(7QDy zs?Ti_R2$a+uP$zUtJ5c>nzQ7-Q%LTTU2^E0*_Tpsjzhhycg*&(kx#O`bG67TS>8Q- zL${Z(#VEsRtNe1Br1b(q`EYtM;C&*7lmNge!)aGLAgfSddx=PyC%q(wRPN@TD1?Yv!YcYngSu}?v5YIRJKJ-Nb%U5aAO1 zLcGov{1RXE-i#q{>4PV*YNli!p{_F+UKNeuUlk1hgz2>9 zzt=FsM*eGg6FG9=HE+7cChk}o7kTou70Z3hg`y`H>z*eS zfLa)HYNV58wforaMn4e@Gm*cXlr zL1O*DU>1){OmVUzUwhxl!4$YQ#Rps>MJ{>AxQ+H~-8bIDZii|{ed~RnMzACx5qP=} z+wh&YAAQ8)xy)N_6rLHr~?;EEt!^qsT>!xJ#MIYz1rNWda89nS|IcO<9(&1`rR4*`6Ia8n*IC{{7dot5gFH) z{iOMNkgbi_vWFk5K`J$2lZdYtx!sKIBEA-+XG``m@pT{_Te7k-z9z)mh7E|}?H_H! zGGly$Na+sj&lq2KQltaxl*^YwI(KB-#s8~xW?$v<4JHYlS5szS()`eQMtTwW+nJw+%}B4p zk0E8GC&1r*yw8mEy`1=rbiqOfCtds%glBLH#osJwBdE(r-@OF>mnw)bA7Yv(>3;um~Ya*_B2-*j6ne!%=cu6j(2Q z!JxT8{DPr%qxc1pb(8odEF2zoVu7bi-^Xs}_syr7qQY=QzF5l`1QVA2d}}t1-DqDlDOUIp4{E2_mL;SMn_AFUkDkh=^KDKVALDzSL?t>g z>xce7U!NLY$oGJDD)K=g-~C#n??necia*z&EAi)N@#k0Z=XdevPx0rb_;XAAx!srF z4eC0NKOS1dSET4UQ6yOFp?@FZ3wFAxuROa@#8)$3L4EqUzN|t~Us}nx1t91Ieu0=d zGIq2&09Wskxg${F>lp^{Wwek{%2PDM*ecwqV4ITBx6?kZ=3-B zFHx0#vM;M&!nZV5pyl%2o&BQMo+&(#M0%C?wbj^?SC+>`^2}s$P|#B&4f_f0_eGC< zjrsRtuAk^@R;<1t1{f5-I3>=fy{t{7sdelppZ0R8gQvaTP4c}$QxSeJmXgCz3GW+3 z+Oc0OTi9ULMAyn0V@j8a?5yY;N$4B|FK=K}tjw))5iW)s7<2kX_Eq-H)~pa>5Gv36 z)9sb?Z(#SKKx5+^{o zIN|I}Dc0_d8O7MOs=nMTQr%b3ijUtS8A+eT2!Q;@`muF2d_HSiNdHXg^=FkH?kAUU zcOWFDXj$9R@05+i)$%pb$VWOuXHZLd_IG=2-&FbqE{4?WOuxv8IzE0m+X7)Dqofq# z2a=4EYBKa)*7o&$Uz30Ov1;{wwdg(lP3x0nEUWM9O7r4kAmg6?EV+U2uob{Yh5l^Q z-*-So*HeRzIuOFp)n89764oKrKJ!m_m8Rzc%jUpK#Tdo z?rranw|9#f+&^~bnvCvrsDq$^(2G0CMY<{L|9^k;80^G&3)Fgn=VHVG;( zF-((Ol$?y3nii$2GOmShJ6Y48d0Y4%rfX44IcYPICP<{K?gVM+R=#J+-v0bU%iO6m zF;d7)Yq$0-r9oT_xoKv97Tboa&H`XlP@<$__Z?6X8Pe9*nb707ADf_(B(^ssws^Im zVA6&3qf6JKZg`OH>khtG2>lNC2Zr}`e|D?0?*{!E*MOy#?doey|H8#OK;)UOzJ;0< zLP!FUQBl%vAp@-L#qPdiwCDijlyJrE$sWEsvq#g?jon(^?n+ zh~ofoWW&;fjkd}P+Qb=yWK1nGMLmw!F0aT3LpfKY&uJ0 zCvh;6@I&oeQdVVZn^}d~zJBQ8DigG+6IAAoe-Ge%X^#uY`mEk(h2q)uUMnghiL_Ke zinr9*kzV_&R4>(ye~SgaP@zylFG#F8DleE zDN={M_sR0v^nEN;Sh+lfN=(CGX+wSK@h){2hx&HW%Lq;$jN1J9)@t+yE*5Hz;SE`Z z4f6(DA#8R#QgVhnl(LT-?)yk1`3B0oT_yZ91t?sj$H)+8H|IZDj{TGB^V5n0k*nH_ ze3lE=WHnZf2omIZT#?qj3i+^K8(_e?Ptj6zuP1Q!lbOwU9Y-Zsaw8Ygd<6&@qT|2| zAp3{cFpFdu0a%|{0Uzo1Nf)!SlxEht1TpLo` zC|`4o-e^0^tVD`M3Qq9V(P$>Z$8N;GlguVhfhO>)X>AZ_~><}cAOI&>H!2)2j?_JWIFo*E{ok+rD-)4e<{q03Sq*#b-ze4;7_XFz+$lN*Iu=V^TKP#i?F=geFI3`V7Z)*gMa?P zNG%Zje;Z`q!n-=iWeYEgER)rByfaweswr4i9?oHcW70||6|>wNlWIGd(}dS?jA%E{ zIGB^ceCNuMp@0t`%rVu~P*>NW!EOh0`VW=|bJ~br><4q2434_d*HBPxM3Nj2=D=!> zu^^+%sx~sG=u+FDklI?i)X<@v)lzDXLphOmBEB;PNC?n5PU4u7`&y^WFY)k`IMt8w zFJ6-u0MKy~#{~St0eJqTFD)-QKG=Q;$ApQ~pMCL5c`F`b^GY?5O&p8$gvILUI$B}Z zQS?JNDkYuERuY8G970J2mYI-MoE`qyx1RokJGVuWDmu^DneHSk*HXNjWIl3Oc8H7v z*ttp#L2vyolG6KK|V2)yN`17sW4H>S~)W@o>O@z7W~>MgWZB7o8n| z4_)-}`!{>I46P3ontstY8wTQ#!Y>{iB2Tp3@6g1sAyH!&mbpBnfJ;m*M6^r3?=^Z# zh|~ymkSm@T!n%Cxd!C-aH9WaYW_7>w)ub13F--J$Lw51aRb{LYkenZEnX3Z**$zQ? z7Yi@*)h~M&71-68seNefp$eKH)wTeL{1uJhqyDAejT2^QEP{-Bw`5^fyZ?^+Xmw14 z&BdqbT~)zt#8ObC*pI$)8f}8hZLl*`u&|o-;#J=oLc3vDP)Mhtkv%tj?KIj6SD6|a zhKoTXydj(MSF3VX2>4F@mp}We(gF87z5wl$Rb(%>m?*>BuUVb z;enoIa z@?~8iw0{)XkO~%Vcd5xS?BRey6Yy-{bcGV8V7b}Gy>wn@RYK(bf?2CIlA%z*DvSse zFw?Gp^!xs61r#WnRZ?^DCf2lA)@SrZEMRb^=TV7ifQImn5xH;_z149@*dj?KviQRO z`v{@jGkZHeYXCiqi(!S}{rlM5l38P|0Jea0N>@7WUoPuRER}ViMt{OsZN_$52|IAl zm}CUxWciH3Ub0`rBZY=JJ(mtMvDiH~AFs0-4J%SE>myQ4$Ag`=GR6bhVUc0>>@eT3 zs78b3goqLsLgHQO_OyDUJbN}F>nYk>^lx}B3>DiGvsTc-xEO|t-ox0eq^yBf0GsB% zag%-trSv1uSIFW!Ov@d8Eg2R$ot#xrqf2m==&ZOIrEln+Ef^*@H|L_ZNDTZ_%1v0( zsMMNnvM5_MYf@#koH!a7UVc}Yhxq{3a|-i#S`g#1wsVlz+05hbYFWd`DTR5Q#CX6w zo_CnXK`sF0O(8W?5YCg;bq2%t+{v41w-tQaD zbU!$20Y0p5Dm|b6Hd(3RZYvWdhRc-+Z8(>*eP!ZL-IJc>pY~6`(1YDLGA}Wne+x<9 zN%~DtZ#cuhTA3g^eL}ok$LjT*Y}3*B8Enq7WmT{zr`yePx(S|){R=z`d#Y(xyIk5N zfz5Hlz!Y9Towx}S4WER~ZIyL8m$uLpyzPd82|LCC`)d2Fs=2iFCamp-feHIyHY~(3 z#e8h6R2u0!+#dV74VSUcMPr?Xw6MnRex0I6w#wL1K2W%Xsvh04Hfr<)?heA@o5NYd z9$Dk*o4CdnsW$38yf$1$sw1c^QnnGP%CLhyv&y=KtBXBz3|HHQ_uBT$#CvSoZKt{Y zEogBGRDbfiY;%J}X!dHatSO|_2(4S|4B*4xVmHm)ud6wYE&Dx7{YXiwsA^HNyXeIpo7#_v zYUynR)!5RbzE7fBklUMW=)j{oHi6V0R!D7!U2166bgPt_bJg_d^sJku$TNU0%IeS{ z{RHG8iH8TKc5Ab|WytFQ7-e;6g9C6xR@TK*T}5{vjC$+DJ%3-cVD$Krtj7s0o{B62f>GgA_ULO_|Ios?#ukjU+;U@#a$DqI zhKtXK+9DUu4n{HQ*M_r0uVMNzCjp;&8>h6c>QJg%vgA*d$XM zsiA;$R!YqokbIwKHH#&G1G*>yX{AotpArubNTV-horooIBZa+22}qd^K>yXO#8^^( zq}<&bUDUqTSB;~gyorGbl$&Z7eCrrhjqW5HHPlS=;1urBdCOvq=&a1<^7Z5 zU8?r@-=fbTD0ww-`sCrw=u5a5g!s%zR)hHKSpjS^`9tRm_a|31afwMB{{>3@M-jZ? z0l2Tg8LX$TE(p?Gg7-OIXA9opT>c38TgQWg_n#OKq?4Dp2U)i;&yAGv$z?H9&6PaX zpsq7Gj%CZ*>@6Otm)Adm&`P6tu@UBMrV^*Wb%xR1iAakn-Vua%nc9wWj)$!T0WKnK z&g)E(!iT56E8rhb$WR>-mgyjj2xQuRlwGFHN4boMm_WwF-c{+%kk*vQkPKPe~eZ_a47uM#KjQ)Dy7Nr zX92K6w1269 zISK`|J+ZC{d0C-=7qH-LdpQwaXDR@KWw{#uDY?kU3JrXK5rGB{J2VjCGkxXs0gDG2 z1O!WUondXOoXqAFEbkd@500x`5KCFcOI%{=ByzHe z|JN8go^$CIF9U9N>lV8Xwe(+hi;xFf`Omm**6nWXf0k~+ltA>{h)PVt+0i!sNr~z@ z!e_6_e>bQUo>85?nl*}2idF{iaMcm?Nmuo7!(_^KelB{m-B zbvD=hxr6_8LcY)u;o$fgMg*?;CO0l?Zg_Zf)ZnOQO9n@my7aF8LvEU@-_5^>7QUbJ zXNosU|;v~|0%%W9Eqhf+rapRu6#_be4}w?KmT`x4#KGU)vHSEv4Q@UG!+-a)vH1G zvwH{mhgbn%OY!+Z{H23 zh_*{c`X?&do>aBnGs@q~+SbO&*D6jKFG=C<&hC2j%`~j69d;Dv( zpC47bg(~gw;j&HzMj)>S%oyw6N8-k?q;Xusg~lKc0FC`M&L1EJ#)y?hc`p>5DEnT8 z6s{QIZcIAaf(icJZXDN{=)X}?eeqlWwg#-zXMbA*{-yf1MvF1*;AH=xm=*lO%Hdq= zYm*a0;o};4pXDnb>qm`ewTotrib-ELn!P*4- zcSibh_${zy0G136&3~Jpqv$1M4Y!4Qi(hcxsE_ysr?C2pUvNaGpZEovz5T^6p5j-% zCw+kU2?OFl@e3l}An^-gz+my~hx;Q(rui#qbfWl|5!QG*sDA-s+!!$;*Jnq(&0^Wo z8bwRi6e88v5Y2BosJEF}WQPAFU9QHw^#6r<=qxUHVF-LU`8fLn2K#6E&%~0wV?@$} zzf>^o|MjJUa{Ox!XUnjR#{&h6J(kUk_{BSSezU;ZL=J@q#>j6LxYWTn3obR_Pj~&Z z*gt^2jXHr37sS7TO1Pe2cs(#Ca&(FRQVczVD&t!u;DBGwG{x* zt>E3>umi*i%$!H~kictf%a|`qu`5gcPq2jYi3Jr{rmpfQ)0nY_Aw9-5%%AcgXU0T+ zT*W8TRtOj|t`B}V7puO;-;kETU@+U`)WYR63w!Y4te6acGg{RJpg0?~oaTw)Q0(+W z`HQo+GW<1Yvpa0Oc&)zy?Qw^VTdw1nN2nSfUFR=jdl!>9)Q5f{f3c;BxM}?I2LE~f-VH8?VUFPvp@KNk#?b$G zrfu|(XOWu43OZmbFt~IavLhgy#aq0LdJN>R7Uph8H z^=tJQR9+jay4`<_Xr~1vx;9qMgsdcY#}Fi|@AD_v?;I=C;kE+@?r7DnQ}tBSvOYZ`gDnZeX@U4E#u5Bhm-Dvd%411XFtOv(JXR z^E!@7?ghy&GkYKRA0}j?jtI{!#$iM-x9B&{KDX#TF6!Ju&6QktR@WIE-!DIbRxiun zg3!$tJ&7E3Ze7vKZ(qvv`S8cko%$wtEOS-x52GKk+9;zD!C|O(PdQ=_CW<{VuObr+UI|m z-mvg2F01~DOW-K#jdAj_s)=)f6Q>by#AQ_yHy-i_B|VFrurlIwIp}|>JSh*L$Ujh= zTjm5cx)nE7N$n`z{^5j$IFe`;s^J^DrKQ}%`o0t{7CC>)Uq6O+9?y#*!{Y-pEcj)M zBqg_gi3_8XD+9R1bUvel0*Z^9;ub8qADFRv?#J2+%g_A3(w8t_ zF5g@I-+24?R=wk8oN`$r!#v3)GF*Z*^3plJ@_|PxRCjedYs_B$G%%O`Bxp3O2nMHX zsKf+`JbvCkDi^(H0%{K5XQ}-)-@uH;#llTvctiGhuYu*P5d3ktO04$ycamrs3;}87 zJrmfHOa2p90LUsK8L5Q}>`lr#>xGh8t-nQmAXr`DJX)7A zznl;?)ljn~Qw?>U!7_i=h7SPGwhQp9P_-uVQp0mWsnsU3>x93)SPj>hF4r0tgUfM% zb1{J`R)8CIU+h8q;I`mF)h0%s%@t^-SsNQB)|igQ5C%C<<_YY!0@!rl$JL%c${X0J z(Zvp|^Cz-5@&#JZ`M3rio!pE|fIr^$iTr{46m31Kwg(CXE?e84-o!tq@)qT3+A|mc z*yG5-v@L|#*z2!i5AvNK;sS^1NsQDK<%hUf^cBM!%I~n)A$rqq?2XCEiL6!Oz-%k_ z%=rC%=^v`LGqwfGSlil|80gAGp`wAav~W7ILt7p)SOt0qu02BG+UB+I+UZXJt|6#! z@$ag<&gS1uN(AN*(pg7@{ksE31pZw&-R|F2(_NAgwfD+oL|tci_9Q3A%47tccb(1- zwF$IC$LFSCKG6nZWNDeeDSr12xOHqEnuE&>U0bUp9=>__VYxsXLLLD?$L664xX=N( zJ~8mDM#5Zj*3%Xy%$jT%h?U0e0>s~P!C!-&uY}5?tB_ODqh?1~$dDZ+;u6zD46$!x zeNPoH&K}+#+?b@!m5i2EHKwg&b=W_XUK~rm6h>miUdWoxqY|?lrd1AnNUx(hF{STK z|G*{S;k=FXaC>8YEhhgY)FZ^7>*;KK)xcCMcAM)ystXxYC+7jU#H5%1f}u6jX^rC3 zT}7F=606l`TY3hp*I()g@?6}nCa*Ky4<6&JR3k7$)4EGM*!#L*Jm7uzPO^Jn%}Fla zr{+m|pSsTA_h~1;O7BC5J7@E2JJtteXJ9^&`>_di0%x=hf?3BcAJZTHr>knM#KI@y zqdr{Gf-T*&A(p-O)t+kXNc}(pD}40vc=-%{n*;lXv%~cRiK48cwg+Un?ch!RDd)Zm zlPa6f(VJZyb#$?+e&A(ozX@u)3uJ;G&jz)4E0V`*E)VNo|BOjdT@`CYx+*R)&B@Tw z`;hyvQAsm%M+P?xgb4ji7>nVB+^>>Zy(WR0^ebEpuJYL=_FR*|K`Q|4iSB3`Xi0y? z0FXFvfLhH0!>j-{-#YUzee0al*7z4nGaHd|lbyp_>B*)b?DiJV>ui4YQH#JPjWp5` zVaIBK5rJbROa{k7j|9ZC`eOp+NcwqJzt@7KB=druS}}LxSXZ3P=Hyr-C)*hvju}Oc z6=~Ww!0&X=1$O1QR##>B*L6+Jk$67rn)jaS%eF7tUM>A|)}}+?JLymzIs~qh48Rp- zs53tJlZ`9#WXHf1jqKvmA>WzFS?0cjR52Wy=5<*E!1|DD z0Jy}omYCk;$rZVHzu<^-2)q}05-EPs566myptiNxZ*Od zc~|>ah3)?&=yMrUu1QtutHUs+%uC^83Mn|nIb|y_1t9|4|J`Bx*ZzCkFYn|yC);m2 z#lF7Oh||uU+`hhaqHo|g+6(jLvcA+~ihX^l(UiLj%kfy#NRT}T1QyYSf&{~~AQ75} zO3c=grUL`jW9V8`XXLwN5BMgb56jIqh-dOT zTTnf8U!bf;jwlT4WsC?6D>TKPtz=Gd$yU@{$)KvPGdOPJvFd!SoEhD+bdA4RG-$plmEOnWY;1Z|h4PzIt9WTyKj|~h@R6B{GG5!tD zVp(2NMEtIG^UC1x*?jPZeo2~9_p3}*2&aS6oj^r`IAbdK867S|0d$!XNVWn1H9=L|N>c*^t!>+t-M^c@U}-B(YSk&Xe=y9OuJ0w3g{l{Bf9^qBzf*3Z}>Cp_X^cJ7y%n6H3GZENRrGmW98ot=hxq)9v zZ5zb|xvZn+3SwBiOI@o4fsfpVlxhnDd+A6)mNBH<*mR;- z0xB>$H$;~qKM&P^QQ#J#Q&3a6dA?|AU>u!;i@{^2Ok=H=1!`CUVEw!F%H0RqwEykS z%@%8u%Cf@We_DFy(cE!#wFQT;-OnBhyii^A$OR6|{-jp^?JKv-%o09vZXpL2%g-+l z#Ax&}2d)My1FsTJ)Qvg5cIKInly$m24L1SAxrP#K;;O(BH!1$JDzK8iVnU38Ge?Op zq7tVVH%T!TS`(;5=t=TLbFV%^}iqqMx zZGqOgf8g6`>si`gpC+)>?SZ1?^K^E(e7H_bndyAN=lSK8^QI5tfo6SicJboL5_!|V z68~t*u6I3~$nx(9l+81gi*S7#cA(qYYOMc`Kxy*)G&Xfdpe89gojtlEP<2Vg>Eg7Z z=q4lmbH1*Wkt;8>`HFo-`N=st(V|6=?Gkwpo!0WN3?{pE? z@R7c8|Ncl{X7z9>7T5D^33;Tim*^kjhxo;P(|M%N+CTEA9Y4)ijvdT5BzL5MH?WA%4Acyg-|}gqL<|7N0Wyq9ZtE5E2Yw@z zVNeKDjYEMobg!jY#H1yIhf-E19GFVjbhb7e_`nJ(d<^v(CyyamdWGGI@16)8qc2#n z&+%1YpFSCQkG_hFVQc6GE;kac9Jd{-~2h zL>CvZy6oAf1Fw-1GuZrR_=LIS4AbW1xYPZgxs}bsp%c`8Cmte zKz@y;VXTl|zBkiDS^3`slj%fU2R)`ademQ48XC=f$t>xgjvKLaBY`9J^k(~Ro@{H2wFGoz;eYNjClPgd6%43AMVT$HuGwY3t- zRB*iq6Kmu8Q76~We;DXb$O{UtBN!2I?X`3L=)dMVUd=Uo`IEpiiE4{CB+^+veGP}Q zeh@V3DU3|sFQXDu{RZqpc>?vwS-x^Z|3XcLC-kOd7D5MMjkl3u&E8lKYsKvlFR_t&9Y)O+BThb~R0LD`e5a0C0bSr?((H2W@!OY35=gRrO0~&3OkwS(5(~!=% z7^WeuXR(D}^3@3^z&l^^G-((HfLTb^SAqG2Lsj@Xu)_)k0-J6D;89()oqQ=!&Dz$+ z%~CgR62A$&rqT5nDTrvzEY|8f9_iNL8sPJX?*dQJJ(gmT30=J$*h)CmLstS_Y)~RS z6PjSB;)40b@MZL{;rqb-*0wgXA5xGFAq%U;X>)6w9t1CU41X5#Ync2vv zY#E3Cz7fVIZ`;rPH87bUpu(WGG^!r116Sp(S%n{BlB{4h9qP{_S$ez!9cDUp2#=Nw zp6%4(0703H4*T*tn-2T_75H5v({)5xhf^>j(BaV8_MOPS?BeL4Y9|tNi0Bbl^pn+f zhW4Ix@S4+_`6ILKn=wrCY2S>gc02GoD_t*Cl^(>rx$Ma7n+-cM1=fj=nZhy=U$Ph- zOmN%m!)(dTK3rl7A+lEs{-M#&1PR920nzL$Tmm-5+sN>5Z>)#^U+oa&v)Qa>?qDA) zb_i9$+`%z4?;O-AMA+eZg1uw(e&S;#pB%}_Ji$e@oTK%wyuksYk862@$E=1E+TW;^LDp{9p%pa`F{w@+MXhp!6?~~cfd&kwL0XqVoiyoM1mm0J`@HlV(KYM#2 zus4bY>svdxF)LVu)h-^KNKaS*#8CE4@!&AtOT9V4a#kQPs&yrTA$q|9v?4yZ9i)+V zwr&|Kn9WXJQrHPh`i_(gK1gq3%r;|)c>tTTFov6R*hdS49j#Ed{thepyMqAyx+5sb zV^&E&WK<$G9&kqCs)9O~D4fjetWlUP>6@CDtt}huPCDqwa3pSvkwGM`^?*GRC$lX} zgXJ~Hkq~tZlBvJCjuQ@Zop+=0JYB)_6ilWq^-sq<<6X?l3Bi)_WIdphyO_!5(F}9; z{E^gKhD5_pJg#UItQSvq1Ds>WG8-=C8U09!30S*laCZr^j|td1oJB`zHiP z)3dk+eB<4hMz^Ndaj^mL@x7JM?S+L@NjowYlZk3beZ& zgm5_xo*Epb04TU?Tm?GZ4iG2$Fk*2F)^2RD1v~pr&w^G2CRbDK2t<#|rw6Mk01}Ul z?n9R<0cHf}SOLsvzf}craAvTl0sz9jPXz$s-eN2~+;5}LS=t(rUhn{yzbyb}s6XL= z5}{r|#XcCUZKVrDmGH*shV+63N`z_-aO1&XKPv!e6O{9l3gFt@U~4M?Sj>od!Rj>E zT!j%|oENNR1!#BEo6K6x4-Ta9Dgd}9VBuVh9tmb-n5%!{+2X`7Ty0wrTuAF+q^3(Y z#>L>0b>_;E$^u~PYoV$yF;rE?y>qtpQHKA5E$W99UnkE#5==(Sb&gf{3Hn^ds=>U@ zHdc*a5*$Vz(DC45Y8J)=!_>&R_DSF1xh|({)J(xRl>@`;qB87NF#I?s)HYD<#th?u z>XV0p)ni!4K(IVp+G#-tba2 z0a_2{o1KNSVcZVZ{BN!trLr7q*gaRyz3+68iaz5`d4`>SW zX$f2+>_3LLk@FgRV|`vz)eb@8=COQRx&K=LU|DPS)}V(r!vL_N!U5pIj0M2vV*hh< zv3M72T(CX(M7(-=khRae_yW1gq^`98l8pO z%T>VzxCF$<+eq=*8|&iRV22>H=CK94f=^npgZKtK!bhyV7y!h_0nR)UeBKIR6W^P< z_%Ms|vqfbe3qC+27%7~;F>Uv46)0@iHQ61UM2^p6zTLrT^rRhyxfPdkLPzlyx4mF} zcJOwyA|+o(?UAtAmbO#Mu(5lC-C4`M!BXVnJT_==a2)*#HHVE}g#M(C{=A7kUX4Do z^zmRNzMLBc**iM2-*Q9-yJ~2}F}SqI7$(x+dxHG2rsDIFx0wnphl@d>#pg5r7Ty&r zfK8#NR9b-{2X~JIs}DIJDIzeOlp0YD z`pj}=~Qt;&2 zp6t+o1^5vlEM1rYRmLTzZ5Z#FL02}37zLTF1enpZ1E{eqSI^f>=Uoef-Ti`D6g4rN$0?)TVj_kiWEdIy&QAc(x zYdQVIC8qQvi7y5BYIFo=HhcQXFXB{S-@mc01|5rw;Udn61#J2&!MRodNP(}u8cd}R zVgN{iIl#MzgD+bFZ1JeHE(c7aT%a#`G?=W>Z5S&=xlIe$&R6+j-X>gQl97drfea21 zcr92}0r2jQ6J2RU1+eE>aJv=2Mt4~i-6D=AVNOh88>U_%m)u(%`bMx9{S0>v+v=a7 z5)+uU+`6wkJ91!ru1J$NgKul}GHND2OIG{sU>$k`7lY0&FJRBT9qgt6sCAqt1u+Ye z?hSwg$N4&@r3V}D)pg!6u~KKVPXyo6XjzQamKD@g!NT~1v6-wJa;u+<4;s#G`J${( z!?l&Prfk{Id}Rz@ENCQ%a2bOd@H%r0Ix=!~5w`AB@ECjG$6$hn$Ddvp35-9@7uv_4 z1`A!rA2maA{885#%(ieco74ESfWJUyy#hHGGwB$AwCzGj+0SBXx_N;*Gm!MeqOEF&T*|N|r7H;I+SqGoFQtFDq2a8q?KDfj* z3PacfoWhn1zhGJ4IW6lFHVT{2zjP5gJ9I91g5+A1!vHi)7oN6AAAlm-`QW`8DFJ{{ zMxZo1Alr8#c!*S9-nA?S+3=)aVza@!}v5(gb5;u5D0rn1vL zb{30F|0=jbqy0GZiUH|9Tnw?Q-y-I}6#UByAlGC>V4sWu#7Lm^6RehJNylR;?_X%nc-dR%C|pvWaAW$-#PCc)l&@N)18jRbW>I4)&jL=cy@ za82_MpP*}t8NjS_w?NI*orI<3Q5pV7!SMT-PMfQo!VKg1H0f%vgGO!ux+uxoT3u~d zB_1B2?!C_Cr4e>IIV9h}wB5cogWFaI?BJh*)itfC2`jE&qZ_0q?4t&(yWf1jNczng zhf+=fko(pFRt`8$5-u^VhyQ|^iZ>U_yyY#%KEHT!OuRbtU~#_%ce$--y!0F2-0QcP zqcb)jU`3-hDsgrilHPj#cj2w0P*Zu>@6VB;rgQ=>7S0zZ8gc`NebDBt&mY0&Y~Ejd zAJmC}f4Q_Noo`1#MlEJtZt`5l0su?tM{e?k>Gdjru7C5LQwxACmw7_hCgxZ=KQ3{~ z;E=)>;DN}e!GtWS$U#Awi`(y$Qo*MLFZ~m&ol83@@nEli8{^s1XKxE_u{{~tx7Z~a zQF9}`UR}p&1`~VD$!Si>$Zw17$;i)`P3-L7VXXpu&e z05HnH_?{h*&CVD4lhj+{J}{m!Kv-t2km`zfz9sf-0umV86l-)JsZ=B9jx%gAl7G1sPMM)hx4&=1w+l)m6GAA^a0*H+df@R z%FJ3~KPfW;#N(C$U@m0_fJ@B&48AWxOkvX!Q*%eE6%IW|Xr`dbh)T9u#1)m-!jf5f zu}}+*9>B1$WcGgXP&FFC#bPoUCmI?-={h_8TG0}0^PEe$*xq8HPZ({N7H36e3m!kH z$e1yEUwq{I_z-_X=oIeH#==z8HgZkc(ijphz6Y3>J(>`zP2=rg zOl!l6^bFzC+fA0b1ZFjVGBB&_ z4BiVkUzN)#FfUzd56p`&-zYG%M!iC{*n3q&m&q>RI7%jFJVLos;^NC@r+SC_v*9&E zefct8NdHLA&OV_|*<}?;7_df(+@mJinkL6<* z>hhRAbPhlLc+WDE4(1ZmZ!NWdkK)^!@MA*wyF(R0Bbzf%B zzuGVR&)vMMli8fyeBCm;o3Fu)A~%m*Y#u725g)J{#l?H;n#z=TxQqXIYEKJx?GSgt zO)WxwSna-{67pLUEnD%?`&mF3#k&VO5Kd|x`klPaCF-_EF+3Z@&58GLzO7EYM(Tr# zP4Kkzzxege9fTdqT`eKr{GyV@`fmIZoK_i^Agk8LoV2@F$gff%$en+Zf^>K1FOKG8 zWjb(o9$1jObAg9UI^5hj_944F|FrDx+&M8i_&P>ap+Plsyi9C zS323u$)R^XWOwLom__W+eB3UfkpsYO6o+1~>*z^|hp(RSF?$K$Bm}@Hj=R+k$oQ~) zlf1_T=XTg~mjS{q&QMOMLYzJBw`lQd8%X^>7<>2Jy|C|DZl6!xl0u5&tgq!`U1Gw` zSl|)vtntfpcGhCc?enQyARV{)6gD>)dgF14*wr^a>VP!{z4#%sfe(d^)&~iEh_XdkkQ&EY@82<$s{|hn*-A)a?LzkeI zXzgh=E`}|@CCl0RQK6@-0JbT}OFC<~d%3H1JT25#qkAz{nDjKcKh%&0a4~eYcR73G z{?JEO09$9qgXNaaU>gjVIJrOOG-vsg>buLGA@YPE&1Jsx7O%?=k@888@5hCj#E@@w zL^w`u%mrdii3+X&MJZw{)`pzd;1bi84EL}>1csEH6}QwCRD zVs;oQ@L*_`Mqd&%xCGbVRZ-*)9Q&42mP|a+t9=S)U|CQ87|Xzx`g|b8iPw|gogdmw zw4Wp%?AF&X9&qc=R@mM818FVJZ6vjX?!>M0IiEbu$*s$+w7Yfbm1?(+aJMeRKfAh8 zgu^KLPhtJ(#0C-%a@|sFo+vJLM^# z`289z$+|^+5lh78+h0hTV&CqzAyk!+TRI}_+<#$2;N0JEi?hCs@rAUR|I)eJJDF8F zcL8($U6pM)5W?TiPhO?=Z8>C5-x~Ugv|W{he|OWB)k@;R{vD~gJyb;_eE~%jKkn^7 z0e*aaN9YtuTjlP(gR&!_3oI&~97s^XW6CDu_Fby#x z2rx;j?TaU+nD$nv96#>h^36&$TQZ4I*BLCYaI(yjwms2=j8X8MhDo&r)l>`5eTF|= zI8q`UsvAQVDR`cb5dqKR96YBAo)rs5YJTp7*R4)ol`$1vUJb0aCrm!fC!PoO?Y*!B zt9>xEiW~usqa;ncbo+c+;^IMd+U?^o-)YY_zZmMrK7T4yob5ftGpA8~7gBN>5Jm~H zdmRY3yu?!_j%FCS$R+ExqU1N=z`k--OZW3L=+^7IFK`K1*OYvq7{$B?Yevp{aEa+I zhV4CZ_xdQZ#=fRha1HpHdw@NEELN^5{dhZ2l~(1=v)$HZ-jlq>o+%Yv<2I&X4)28L zXYQWofs$wRo$G#*XPLmLY_KFtd?7SVo65GF35`@9FMUzh{3MAAnP4daTY@ckF|=D- zA`p~g^|~%dbU&$j)Paf{-!g4eHf&?%Y1Ox~!7Rf&a=r#!w%bf zDJR<-`9>`J=DE-)`q3Sj-TMx+d+9%8_v5=~Hzva#`tHdJ(h9F+BXG0AcrFL472cEaUqyp!cdwQyQY|ROOqUXLkANdzk7bV?=P71*5T7e9 z=go79fJ-y%5imU?YI^AigNeAr?9d4Pf52QZ;yoF`< z7cX&%XsH+ZbsWirw=2z6Qk|q)a1?un~Jfx3d@qoR*2Bxpw;qIFZwT=2Cm}gR1OEP9Qv2fUwd^eDD#L~^Z4yg&G zd&42!%R;*U>2znKlh>T$LW6boxKNjq&LgQa)lB?5^gq%An2j>i*`e#Ht3<;``Gi69 z<<1UEx*e*>QvWfQhes6+cg`-WNQMLID1qZq2kJWc!c|z}?a(|jd7b;Rk|!I^ZN%@y zrC=TL>#qB+M*P6tE9Eh%6g1-RlG1b=@e8xoG2!;Ce+NF=KZQ-tWxjJ@oqe?5^`G&3 z@b3A&;4s2-QH-z_Tbd`lf&7`1B}R0$|K_m7Qv6Hw{K!Gl-}B-aH^NThpPfj4{=mTl zEQ_EuvZ!amc-re+$WrMhx-p>xxJNKWd;_W+B1zKwC&Q5298?b}>=M_Tc*JXXw zDR<#*U%Z-+EsWzb{pdJ1!BM<<_8^5#5Op*{bkv-UirtV8eMGVgJNokz<^O|~hWGED z^^GR$Xy(06vaSq=wHYlEuE8!84v!`Qu%0M^@l)Ltd=jB0Fmg1N*vNm3gMi#7*OKRR zGRN}{gb)r32wYDV3lAc1NyAj$#rnno76L~J71B^9XlQ3-DZU%W!|Z>fpzrTqL4TME zLbmy9PPUoK&ja!)c{BEG9A9rQxWUCX(K1AV4G#}y+0~J}H(fbR= zq>r2@UvcTVm|sueueijdcU;WYeS5Ne-t_Z)p?-Z;R;$<9>SWS#wk09likw-+-b)CV zC#^TIYYE|MOFDB=W~2+W861`P%MtOv9pSIQkdZ!#AAre7KLfv8i}vQ5EklJS;Cn&$ ziC^&1@nPZ@d|zm|_yza!Mu=Zop?nU_JQ!S6765cmV2RJ(MR7+%7tuQ5YA8H}rKC0X}oJya?#r&h=o!Ezs!#&A9 z{;Y4^@Kr06ICoWkr2r5+CC1w>xNF#)VZA@z-Yw?+4Y51dWOSz=;tt@W6Yp(6FOrz( z^haBq=%zLZr_paQ8aOfbcY|9emmcAjRw$cX%gYW+Z;}rV#Uo6nEObv8oD%L%Xm`{^e$>pgqC|2pTI8WIyLqvQ#(YlCDLxEbY~#Bv$#p@G*J}vkcA* z#(WYN3uBJq4dr!AJ4A2#g}pI3wuvnm9$sz5ZWCKKNx6kE5iSg|{T4-RJ)E#oZ2MEg zkHnBXn}x)stdOF5JE06kO+T}|35f@=zIDv6#+Y(W%>lNGgjFhev=bKAITNkC8#xKL ze8?WsnYNDAVLy%ukEQK5qc{LZM%thf(^uHhvEf~`52}+93Z6t7f{Q_^ydkTwVcuXX zge^qPl;qJ849vCK-O7^^cWJe_7> zqD5Fo=vTR*PmYd$J-0I3rU(C?Z6o3bPONLkUeUdgEZSB#ALn%(mA>l%hdHiH3Ac(N zhjm1Fb@L^R2v#?Po9){kk8hUSM=ndpYOY}OBi^O%)0yFib1<{%`tw7x!lUU8ENn2d ztEj}3L}cUa@M1!5qe__=HhVBUmFC^TTk6R@gEt|5e=t0d+}_N3&E*-N1qwpWp}FDx zw44Qsha3(Nd0}37kfwlWwIDo!)=@w(_~f#s3!6EIrO{xShbX*A;1 z8*Ly<3-J|c{4ub8f-V=E>CNkGHZyK<`1kz$6awJECNl}+0h1ZL#cndax4@dKbEizr z4H!!foSt@Yn$v>#!&~eN;yW;#XcIhAZ+$pU(+&w{Dx- z?65zUgRrmMwXn%nyRdJ&2%CMlE!@9k!zz> zSD*GAuS#k`e{w}x+20@b{;IHWiW*^NgAK*lBP`senu?r>v+K?pp-)I8&X!y4yQ!v? z+%~$+wvrpOc2q+PU?sM)858#Qq_1Ru80GezTS;YXH)izEgHK8>?XJyaio;COrG^}< zvXL^6hWl$|6;d=hMUpLnl>GY37t0MzM}B%Ne3*X$3u#OVSj+)fV{f>bMgmgY=){TH zS@~?3YxTs~kREzCn&f46d1kOPoTlqOl^o{B_tyPk0eM!Y1ZBo$!gqyY8VNdac` zxnIln9KX&nHz zt@n@~K)Ky{EtLQ&rF`Qym2#O@Mxs(K;3hQYCXi|WQN`LXVNR>oXJesGrIVBP=8dGu zXek=3k_#~!rjnBut5tF=!1W{zw4rtsG2DbouG5lRmP&rKSgn!;BAL1>*=h5cilqNR z_{iM95$NS-n9L}8c}%011&i;DUbZ`ww>35PoZE^FC{0QtsfY7kO4i>UA%VkrXM`F{ zkapAQIJFuok!=Bfu{$eU$bg-BHG?$@^S1Ir8fq$Tk6bOzGYQRcF*A*{C1g?vzJDqJ z*xN}hy4W9$;QDpFNS`S_YT}dkq}|?7>^M#^YAWFo5vGNL@kN3d7)31cyd#} zE-brJ9g^AmLo{VFpQl52E*`GRDXiDhUFUROYI%OY4&8e&B1X4gL-+b6QQU$)RpJU< zzb;Ji*OcP7NqWKfI^nf5C;z9h--OHx+vqBzYJYNV&$v`Hsa##6_6}D>dXaay_~H24 zJcOT}J589BW^%i&R)g8&akwoOv(?~mIUP2K)9rLxOtx%~k+`fAUMaiPwcZoz@D-P` z=uB^s-RFv=@8yY+i*j}%jvOoJYfmm5eYpauNwU{=2*l{FtTclU6cFIq6-( zeoL9@^3uH~L|tzxlRjuoYg@{+T}K>d7_dSd?U*>qjc`4m{nE|J<61->H{%K`@gOth zKCbTMiO9)Q3oAF8u-0D5yT^khocVnL60lO{j~D*(91s z$m!qm8tVYC^9gtB00awv&uh;qA=pUUNvHC&Gk6ctnRV&-qRqNcrUom67`nkdQ2yFg4;P?fZxN2jo}3ElZ3TPL<3In zd)}AI@y8gT=md3lO!}z-E8Nv$+Tuxzo9CP(rSZPDKmH<#?h_x5U{IJDqIL&R ztK4{6FG@kjDPmSL6Dx!HkKH=+6PKx{{Hn{ClA$SoE1A#MsFTR3&1VwG&IF&6{F4L= zf@aV(qN`CB1R2ZJ3)QO27;o;BU-6K2DI1c42P*mC+|2`YMB!PT`IX6nuaod7E@pn^ zfn}s_72jedfXe8fx=o`Wr4<9A`PTVq%e3}B8S_vTZBBqX+1}a3sDG;YTpYhbLWJ#| zU_>n1oUu$DWlvak#|?g*me{fk{(wg3_KJk+=-fx-hjyT8XE1S5H0`8D)Art?rX^#8 zlT|yoIoUUj7s@SH>lT}{s^DU#TjiFMzw7#TDgjiw_3CZv)&ecNSrTc^LPmG@DZeBd z>Byo?C7DpCQmurBzIQmTkBA3r*8La{(=6L^wPs~5zvG&0`f>%sB{4_17?^Sj|u$uhKrcqBX|z%kQW^+aUV0)C^w@PS{Qd zr0{2KTWt?6W;(ZhIl0orcSs4K(z(27I#)*IwuOy|>}}@zLAZ!}W)7AO=sR4@26S;b zd8&oat^`mGXuIBkSZe%MEqw!dfm@-bKEIXkA)z8JW}R^>$dJ~)tx5n@XFK#dV~4s{ zWctns=@==yAS=kTqi;@m{t*cg zHrIzRB4)0gR;bO@vEq)K>nSavp=m4B!N+8gP+e2qb67IffxTfyAec&QstEt$ z73$by@`_ut)hIIpS$&UhkFbvpL1CP8JNO=Eyzg-_>+H)F!FRg&hN&P_y>w8x2WYBjp*o6$A8GSsOw zdTtgmJ~^1mC>s6bo9cTq11Q}ocPf~U7axEgwySb{ zuz6qKIbj#Z#th-cL(s`VT+9q%*PG;{fxf0n0QP8T;={gH!buFk z-e?1WWe@uvR|2pN`}9YA9}3@L07gd3LB16{Kz%$2@M*`z!o%wdD0&ta8j@-GgHx3d zDg&*q#|3Jj(L7SgqrQ{TK6(^9=6gqIxe^JI@r|9;HQ{2)w>W4>JddBuBvX&%RZxP# zAiEO72X!l@HbY>tmBTOg!K49Gqu^IVd`;m5IBF_pAd_d-6p2iXE;wzNZvih1!eH`v zhv8x-=Rqq;X_n8W12Dt`?)W)9fJ)iE)j9x-BQNU#c)h;6l>kgJKJxl*2ul?J^h-{a z;l5owjH`IKZ@m&qCFV38`o-N#%n>bvo9Vnlyf(*Y=k}0`(^boWU37R+0*f6`7jeVE zEl>OE#zPiYh5|d_6Bq?^!24FJLy8?M#i=#Q7aVR4y`oZEMI<=cFu`g z6`k0-pe{nJn62ULbG|?vpDH23Bsai_7|98%)Fj`vDoPZkPteT}d#{$*vgVxoR;l;T zdSF5+_~d=n}kB{@M;@yne82e{^C8ZoHqJjOh$>V($IDQNRjPViSAvGYmKEXGG zp8ldm4;sCSg^%Y&H+=2Ht-N6X0)`)3auj!b` zk92HN{J~|YlTC!}I31bctHSY>R*SZVO{BtVgov5Q&l(fCeCJK%1ud~n z_6VWaMDk|)w(@-#wJ}U&q9myMMKoX{7hdr-;rJ&QU>qQfsQ1sIV@sYw$isw z=%Fa45z~}M1`A}#Dp)mifT0$ys;2~1`N?;0Q|We4LBKp{U*j8;n&mK9>?V`RZLoPP zc9+|2%<_2Lo-DV+ZF9N2LGFizqv9KS-A1p;Vsx3UZo9?hFu0s%v&CUI8LU>PiCkDT zGx>utGv;!sUZd5OWptR#F1y!~ZM8T(F1yiVaF{Gsm&@rUu9Xv$f_sEXr?|8-I`)!k zMZSkpV|HwJ!!Bg8fxNTHS3TA2aM}$fi#sdJ1--eA?kuarX0>{n`vYD;UtZbvj9vuCFZ#S0$M>pIyhr!{nI?a|WkJn`e zbY_>sV$L=h>||kJeyw2jV-xeZv@&|NlD$=C8B=0+O4ePUZX~|Xe2r2J278u`4K3Sk zF`Kg;RE!%GOxGdIer^RY8xU$SPo5f?Z+KpL8r;7lk zNx`)pC;7M(AkAq2DLK6$U9-XB^ct)Nm)+nsWm!B{a_s2BFd?yfwm2yvhBCYEJn@Yt zOUF*EL3V!bYtq_eGxq5M7X%tjn$#+-ks-@rcY6&^uh(w1Sj|RGwx`jMR%zt; z@P)Nf%bCES_xIq81 z!ydiq^VrwE-D&!#W?>E&w{_yg7u@%M*<{hk;I%F3O@(n=)V+d4r@IJTETBQ|IG>-b}$m(q`%m$*Ol}aJ661k{$i)G zSJPi?|8Wid)odl%Giu{_a&@q!2I>2QufMRB7Kg(c2#D_`hJkVA_r$#xRwU5m_>I{s3C(CndgP<`29MHrg=#;r6#t71Hi3h$ve616x ztwQ8`hGu5UBvt?PP3t=;G9LDevV7j~+t15%t@q^GWZpw(>JZ^C-w9zp!Y*b|nukil zBA&Z8IN)#J_;_Izs$^Tl4d>Dls^Gn(;yVvCE0@&1XzyNY7ExG3qn z3|@CA=kLY&$k9xHGU>nfrOM=)a{i@p``0Fr&UeSbSs;J{7O})bizvlh%q@6V?SktnPo37ZMazO03m& zu(HP&eKq}OI3WXr(rp36ni@KwFvHX$)sy{GQZdI6BP59nG^ao&IhX7oBHV-fU^ge* zq7oT4xT&`PQ;r|Bju>XGNvf^i2zKDA)~XxaTi1UePIwgIA&Hf98r78ZF!t-i?IKWIU9-3882_bheOAWz87Z+0fyOjW}i{dnYdtsRl zKrk!a|1_rq@mC|iMaa{EAg?y|&sGAc^tpj3G|W6vi&kd%AB?NA4wc`rO0BgX!WEh`7s*ZRmG42$w$2F(MJ;c?*U$3TY z=qY0F`__@yTlnn)?oCV&@!uByjlz$(6ZRGEQcM3l9-xk8`fHK7cl(Rtu0s#Zs40%H zQMV;~za9q~29%~F3z#aBg7r-P=H>YwZ;3Of;cPV= zQN+XOKOu^+kp5$*lDE)*9-|e5=syYcpK|n{mh_)y@Xy$s*Aco`aww`f49zPH#T)=1 zLW?NuD-7-xIYeZ=Gd{82*)P`n0XhWMJ9DzEcP3WW`wcoQ+IvnqsK>f@>#imfJNWPA z=exwspz*1B7X~gpo7uZSw6fe^+H&cH83-mw%YB2m`Qi3IN#lvI4n^e#vW( z6^ELvF9k6#oknyD;nM>f8Mp)qQPSQFuslUxy8U>_Y*LW>Xf3Mx9^8ZlWJf%H_)c>S zm#=I1P6oMfc4Q0TG%jXbJN7p5d;M*c0N0X38DvZQ_;lg29>DA;VNzD~zyKCp7?dvj zqXvkBhUANd@AT6h&M1-MaqpO%7q49zcWV{Ld)?R7~qCj$AP? zIzfb)wlD_Mx@%!6(~^R1pZ6c-`E`g_NK8^TKfNAVZoREyk0`gE%;$%-J%&ZhVE4ut z?W0;)cKRk&Ig-eHCE2W-r00&GD}0W*%ho1mP>F2nWWsoVPI8pGzsCEQ3%?*ZlydKH zTukL12MxuPdkP3DKQs-E5vjr*GQ|xxo8+&_b4kRt-ESliIZ7G#WL}Qdfn+`mHrhU0um(C{HHmtw}=OyJ-83!Vc&DD z8`PgTTW=6Qf=BtDt4|XUXZq&}lj$I(5R1h@sS{9% z%!n{vxc6COs=IN_HY>HfW|`#R*sPk@%P@~rpUsy*U4+-c1+Vz0aNK)ZHgCl!7@JpV z*t}#zlv7ap#4-jq)~^dQA{T}xUs+NBD>{NJU1ATm zf6c$V5_w>+KV{LDzmmz8F%weJ{d3tawupA|&jyk{cUePvNNHWanQhP^r9U^YLj|?J zpLR~~j@FAI}?+erqE??zu$p@#b^7rSt{uHs8k`E>EiZHNGIq7b89}y#*mw9Nd zzgaoX#RkNfzjnaHG+o%*8rWYp_$S13qh;6yo2I0qL|}5iEhDhe&d%Jwkx7oTk!huV zd^t9AW6UEocT>J$skM!HVO>vb*TZ6Cb563pNd%Aj{X;on1MZGa@Jpw>R-U-?{U#xR zt5{U9VI%n|A9k!XK;+@g{w~69J%FD#`wuDs=&X_=vof3^3lB5qz&CC6kLQF7iq6Ka zPbdA}^=Al|a4`$aFKiS8a|M8E480|BYR1q6m&ghYzt~5T2WUzR2wzyvw2HYb=0i7t zWg;KZ>44b)0JEHFbwyy5LreyKOb1vz4nJY`tcgO4T%<89tCb)`1Oo%u<_W$LRQF9boZXb8JD) ziU-7!jN5X633Q zipjaRKGl_>nmReF$~w(>D3=k6X1D_)p=gF88BUOnu8nG+H=Aw!@mT;ydo-lpRCqbUwe{lz!gL``0)IF0Bi zq9{%()TtC_U$MU$&v%dzVZ~{O5i!MSwn?oxsheVkqWa7e&x`BVg&Dg;hwh{Ith4e@ z>ySJgk5m;9W@|~lc+CG4$G@yY@-&QykvvjEa`yi(l0T*-hqp|ImDH|IT0gktQ6Zm> zDLf&ow5n9nDj#mcHu~zG@P8)k#KrP9$N^l;Cf1#s$nul$K}HL}wn0wl0ZcpP@23P% zO=`#V1{c{WNS3qkV@w;^~`HMLr6#){qgV79^FqWpiLkwT|vvt}!_1X@;0NhmImA8m`=>a@+(LYTI zpkn_I68kYF;{LJgOaBE<$ii3|5l`b1Mnu*-q}qaUCB{@0(UtR}6P*BcDxw?yA@GT(Y40|bO2$`%JjW&UeNfnKaLan;wsq^J-AqcAQyh|H&Ozqw6Usm zDH)4Fw8Ync^$+KTi5RQg*~_??bv7}Ny!Ai-JJA4^>H+-zhrgo|KsCB>wl2l!Zo*cQ zwYP{_HW+S~R%3S1kwr0+Ak;-PX6fz;4{|58y7DPTz;tD|MpuG)QG!@Z85#%8M4F*r z2d$VtNToFDZ`jbpH(Yr#x~0lE;(VwJ)rQSoPzMN6I2+!u`QwJaW<{uoQQ1LdbM^G z#V47rR~rPmdVx1M;Slas6?ra;phF|O39qqQ$7>AxX_1}#N~;kUY5!5Y#(7bmcu)QE z7Zdt$+&>~7Jkk7#@i6D{jYa_HB>_a)Q`hI#&CIUv=nxFEtDS2}xV`~M?0_$XHS%ZWbk1-`EQzrJNyR0ApX$JaCrd2;&ej+&dwaT$nncj;`77*8qRl zGBB9qawEVt-L2T-2wXI?p9&-@uQBHMV|rrDWN|@+OJo%Zv;8wM1?2;oflj>e85Mol zSE#J^^le)07h1Wp^eEglN%yc4|A=)@wHm*SWlJu&)+Yx4V+#Dqah1rrCt)VVI$R+j zQz~9<{8^*JmnBBtakJG+2*VN)*u{a$1h=Y;$El*%2z5%MB|q5%qu{%OhTJ|F4KrD1 zfSD{x24f@xMpK`mNd}{@AIv|M5#sz#MXMj;FnXt|>AhQ+gh#3hamI@T>x4M3w+lR9 ziCf9gMZQYuTGU9jX>m@}R-DQf1J7wO=`=5bq5&L7B#V)O2( zmUkgF?@*LeDDqAl<-ljDCGp%36kCjMQ3EBmeJ7#;?>Dn=ph-EH5vi`l{ucG52Db4- z0r(bGh3uUPeS+t->3u-#Gd>K*c&-!jd?QMV|3st9aC|m~m=3AMbn6jMINd|Jcf`** z`Zn^xV}Tx=&^aGz1>4}bnz+~@+=q+V60CDRDL*8TqXbZ`s1{00!5xa1mm zM4&1sxG-v!n_i@cfCg7-7^UnT;TM~JmrM7U_6`dar|Eyd4>!HYiCbf5T!Hbz3_KRP zHI_CNm1z4}6GuAl8s|!l-D(5NKM_3BYT~n;umT|zljX?EnmmbIj85d&knF%YVN(oH zzaA(HLoqTf1>1CPuJ^Az(as%Y3a^aH_R#@Ei9>foozlSv=L`=V=eY9{B0Qs=#fX@T zKa{VI%68{RiOTc|5<^h^y0EPGXo)Q=Dofa`j>^hyMueiMjC2}11&*hW3>e6`Cnr=P zoBubh0{zeSeEMekaYB=9LYDi-lJHVQ)bJ^?_cMWicoT!u4x(;_ z3|SfwNcRpC?~XK7`IPfIiXI&Oe4r;MEK*~Oo4+}MyxM8vmje8dJ5pi1po3A|m74xy z;5;YzFf7|2eixU}VaGumGP&DCx3V$#&kRz%`^3A2gK7xgx0$>??d?VyfOK-S*XwPC zlX?K@69WZG05+WO^xCeS6qu-Nt0MbLJ+g1YV_ec25zFgQeT()JT$L?I{Hlns64a?2 z#)zqbFF3x1ga|u~rWg@(7&W%29Y&=scidt8rlqwkhw}K(&1d&6x%OH+GIdtqV}25Yjo~2pc<}^E1eWZc9hkzu2E0?|YmyyA#Ry0= z2jK+@l@6jmyq~jZWy5GW3b8wgVn!X+%I`gsevzBsT-r)`z3h$-Q=Lzf2HDBH42k_Afxe+#J!0D4KX!m_|79(vfi zEYL~`#g5)v6ae7VXxk;r18!wol^1Cx2?UcahAi)`2xO2QFB_^5_+=D&WArMiX6hkf z_bTST85qD1-Aasu3+@v<7zN{jYb%+)GSEYC;Tk$Z`L07BgM@qt9K8-0$(9?=#M+Od z_OcMAXYF%W1rBh+tEdUt>&WX1uhM?JCh)E>3s|-m-|ZA?~7e7K*u7&qg3#b zd1G9H#wsbcJsy0h>S%7-BA=b#1&`>ePekmXLyr=_1))ymj%U6dfa3utBt+O9e~J+? zcf5P6dWjw+>yE56r(Xv{XQtd$hvEwx19m=co0j6cwneA-A53xZYHnZ* z&o_{8U}EcH9E{j<+tkFu((=CKrxo3E0Zb$O^ zmOv`G`uFNu9roGFFg4A8ErIl3{Bko9 zVfDRhlS$g&nvlglj3@rp9+kRHgP=@fr~bA4F>ioC$6sB zVoqFGcx>Wof=gu03ajP|T9GZCFux|Ll0Geie^*C6w_w#$#YX`wIx+9rtX~61_+lLu zJ%aHp?7yZRJUFh(eHPdjB|6e4N!&5huM3kqjgqUoVHd1W8`HYGQf6X--ulj3aYUkQXDv0hz5Gg!^;%jpxcQU7Szg$M zdsoFne@4*CBMfpU;PZe`gu&SA?^9gDqN1XAgS$@!nyVVK&@kx?tRTNuK+w=I^=#k; zzUW8uEj3I+6#+0c*D8$DQIWRKM$`#C&ZRj@KjBwHs~nes=V zT}6H&!#6!h)s7qNorLhMoy$N!fB#t2jVYCt(m2|}3i3KtAk%U{Lu{0Gr(d3ywh!j_R7 z&oVjkhcs%wJGKyas)cy#0m_Pq6mKR#*-wlo>`B2-Yv%9c1=|Nmh1i_j0hh2y!}bB` zQ#*gTvMrlt*{s=L55PGhx2+Dq+7Y?E1-BkR&yl$qN&q&R=k?kysF%M%*|u~2{8hp% zMOzy2WH!iugookJZjiq~380$84@WqN*Z~h%)D7k~%wNq58!&2i#cSOMWNljhaA6&; zVM7k$62?GiyC*%rxw0)|APbp}=mEUdD1W~afCWKk_1d<`$Um)Y%LZ~qukDT|>Orem zQKZKTw)q;{^?p)L+(rN<<*%#F{E+0efZI|)BW0XqThx(^lkC6rGEUYH zl7@HZXU4s>A%Sq7@r~kmI9T6i&*GHwli;J{y!*(gN6z$$JFz8!Jl17$n+iEKgHUQC zy9b@IlX}ZnR(i5N6wJv1aQGKs;a>_kCkH^_U)mfcoXmv(%wZpx+t6R^R=I)xVplng z^cVY5ZKA)}7PXoF0)CNW+ZsO&yRkbt*CD?>KP5=&b<7{F zgrYjz@DU0?r~O`)#RwdC%{*ZSE|CWl2CD)52;V@uI&WhaAs-KcF4s^WQcfSAqKS1? zt>DPc`Q3P7CoTuu?k60;CCm@(43a0imK`47hdQerqarkM`J z)yE1t2$wKKxQhxs^9Shwut#zJ)B|XEZ+BiQtT=Y7ko>23 z-lIdY3nO9__t8+?{lnW8ak9RMNmY0Ld$skgI)SWu{3R<%bB8>_QaZS>Z)LW<2$jep z4_?g5pA{#pN0rpL`w4zr!t5H_kc$_pHPkdtC(QMKq=w+ve@Olqo_|sU&`&t3Zd-+p z09eoR>b4^{Jo)WNaZY|!4Xo7JmudvGvt>``FH-`r-O3>&^7je9C;({eK5ArsE`_S| zO#X{XC>A*%&7N$pyaREO%}~(3%+l$E(Jtc(7eU1~|A(p9l*66Fz`PF%$JI2bAsSSSei55a1T+GPnz9YEl z`TW705<=D4L~UoYUwk1S{x-wk7Wmr=f7{^iUHE$s{@#bb58y8df7{{j!{CcA?C7?I ze{Kg$yPJ&1E$bq4ZMF6563EI;08F^Mc96@N1$Qd}R7EquJQ{Ig3!GKxH{_jq%#O1@)EfRs3C~!zq@XI>%JdofDd1oG`2m@`bse zna~;+v#~WPATCQmJtct5sg4+caS8yO)`Bh?fPTUNy|zYMK}Stn$uVW=0c6?>GL!%; z_2d+LL3?2=1_%#7*-?VRi4Lo1JQU5eMmY;!6;^1vT3SFJYFp4;SgNQ|u6@=< z=_(_NrAkYtjoLYR*wXb@elG||{o&yw+I^I$s{rccIhj2*ces5)U5-B^A;M9}NsNfj z$=rT&v2Ov+*HlmtWm8z695LR~uY*Ce)!|kRsbzh3h~KIH?7;0rWa7^b?{z8|$v50d znI`UCW<-|p5Xpy!5EO+}Ps9RCh7Fwyj`NueOx(FdNfBkRT`Dkn?C50@Z4`LpY?2IRr+1qXzu zf$B)G!zP3=xP+Pb(>qD1N5RvYw&`S~zfT8YrXIlco?xE}lH|^J_XRkyTCoAg4=BjyJMJpOfUk+-YA<2|1HLk#AjJ1)V4?x*+WqfpFw=ttz4&3f zqFbz#O|Jc}Xcp@P(v%k4SZo{ZU@o@LSQZ-`HL&0qC%gtEMy!<`S%|okJ{6@zBMOvo zgfo-eu#YJh{QeP$1NgO&5rH1$+<*}=BY$fbIX<}HobVQ|VG}>w{|e$_3J?bk#r-cW z1l#{QqK4q1i#d-Mn3Y{nD^HTCs4P*jdKXRq-8uP9ghe)%;M5@n5PAQED#cx}%ex5M zF}sXwm|NpMLabvHL)(S_Sy`8i>OQ7|pa5WrFNO(&8dX*U#nX0V^h_uftXe`(jHNemm8EI8LwO|RYFJ~19obVuq zRjE{UJy>=m77tMt3;beVdXsh1xUjt=mMQGnkF-(DNIHrro-haMWKYO;7P>!K(3t1v zNQkf(oP`lFFZkR?YA=}cQOunMecD(s6K$@k_jCbCjk2>K1v)PhZZ6p3^`{H6g#yfT zy2FsV9hJzO2=lW!P3=}Q_~VFzCcJP2H6@^_-GyVggsJwCkI3#(5SuF6vd2!6o-Jr4 z{09Tjj!87!O+RCbriKTWQ)#>zQtP z0BNrmj?@7tSyFVb@UR{L3yc(_QE}O*!zyt{BW^+AE4=Uw#tM{V5Q9DUZs7%7%*c6W zH|e~n(5VEF!?st{0Pz$v+4}2)sY(#Gr*pM-`DVfjH3$!Vlv`3bSqY#b$gD@u-hrz< z8AuC})FiLt)bTnjPv73%T8Hxy9a$8IQvh|c!(nG>j*-IC{22)k_BSUn9_DZM?^gSp zg56P~E`2Q&mreS0FlgpsyHHwn&(f3?eO1`QqA!%$GJX#uS9^wrbnY}^QmWTsa9WKX zc&^uLw3wZ4SGK|KHkrI8o5gIj+Q~5MggI$tG;)w@y(iS+)Amq7;G@IxJaxY$^oB4# zDFO6W+k|wq9ICg6WzeiFXMK(*>Dvn%#O1u-n{0h(LTd6|cKArk9`xgYrig1P^QbWU zk}4@fn8V$W65cMa`-{9rr*8U)yw;q$336)-;XT8r7=twDY-BDClt$$h@xVEo*VYw2 z&GF+Ipyr623>Z$F8v!Jv-!A-?e|1mPXt$3$+!y6?iq3`lEFce}U6d4}PgH^2uNFw` zlQen~=^K#@wL* zUbE9|_Y#+N!YgIBVixjw3=0`0(SIhYg`Ct_$lj;|`A990nCH{<3-lE3XAn>Ks|m$4i51ETRDeITt7#%JFsv7{frOssYKG zF{>UTd)60KB-J(-Rw9#6yj+#E*iz`_d+t@AMU(rV9YGUqjhF@`E7ypX_Okhb*SfI^ zK!w7M-MZC`iAafAP3$RDCpUCi^T}k51Sdq3VDcyUC6kH25ohT}Jt zCBbECKoTD;4Dj1yNNY_5B$~7qYNRz`uef@MvImFfZMLE>BesW!3utV==flF@obVl% zH(TO;y_Y;$P?#osjceGl{JnxgoA4JdWH~!T=|B6RK1X@eeYH;-KLL(e$M3rLh4cT-yOPyc@;#qxMBBO*~ zEEalSC+>@6O64Eirw#u4(@{hT{`x>2%=gksg$w%&v-uGc3LN)6g;B7$Z{R)__bFz> zYiA1^a+(ZD`sA>%uZezLnADH7q?To$OZTbcy+tC0XgXt=%x`>NICarx@OGOI6*|ez znMKV6cpp#ZQ`;ZC9cekchq ziJ6(R50s5@F|*qGa8h{erurIyeu7oqHV%*y?nti`P2+TO^^s)`p{E*x2kcKCE$pEL zQ2B)8w`s0twNfRK9KZZC`?co!G#y_Qa~%$KvbnOg+CQHZp5&LBT+Ia8io`q=LzKO6$1J%p)@VzdYLtBn?t z{{Y>0z-X`BI=R1U$o+2&a=$oJ*pb|K04S||0O>^(N)ryKDgE!AqV%_0r_^~sEm#}S z0Q##V*nOn#bRe?dO%T~zBvO!9#K@h1}XC|6c%v8cse4j zkD88)nU5NOK=e^+fPTVab=x>-Bl;+HW64KtR73E9efw91?KQ*cCu~=@W!TyID=l{B zUklX`6#J}i3ioTUOT#&*2T<`+VLv5+%4Nn&>H09UrL#wu3XgNbFBmKH{^MULvIu|U zV#e8D4v?{z3m54C^yj<96Xy?w8A8>ANK9dKNZeQ!=bWV2fvuk1*Eiac~=Wx67I#FvoNzeDv^PM3$GR4&j}Bs zO7XyL!q0`hg(q;aYs99$G{;)us(Q-yDvl&ewBZiLuoRcbLkN$*fli`QnyH(gc~CpW zO{XJ^GQ~}SI@J_c_cz#%TrJ_jQ{0;v51Zm<1Bvhv7=@)%NaqnndYL=*=@GXh_3Og4 zHi??Kv#hDE=%Cso9u~<(9#P6nPOjw^PEIu%oQ`ax*=;eKEiQ}Q?RI5Z+;*$MY|3^S ztp;(k#a>n`w%PI(W1#+aWNS(47h!z*+mYuFs%MtNjIny#kq4<*!Y=c+wj-M%Ed*t_ zt<9FCj~TDgW=rD7NVTY_=sSQsd_jlAShdI#*9+(Itv@bf$HgIytfhzxc3ei?C@jbE z9T;GY9hVMjz$I6TCh>hgj=s6lJpvJJx!w#&RBo;$ejIIcr78?qA(UI+TrozZax)&` zeyv9+%RFXkL^eH&$by&Si_#PM)n$omfHdScBjQ?^TvV|dzqu@Ng|oKHfHj*I&92Jt zjv=s@Bb`MP*fNd4rbiVR_CYLa$-pJD0}Xqnl~KJCdmV!Rm65DGlJj4x6xb1?4<$c| zEYo_XU}Z4QbCc;3bOi18t$iZk*kh}VhXN3WgHgVtvjK1(&)M$WKJkq-zb9fMi#xu z2_NA8R8id02qHA5V_*Afk@=&FUN2)GGl_JrxgnF}JX`drP>MTY=NpPqiL5E`i-mg2 z$<|d9l7jt57k$bJpQEN??ACuwQFGyIT->=Jw=Z{yd^)BmQ>T6A^F^J6-*ws_dA_K( zPJ4#G;$cLzg#X~!qE~d;a>!jLVthq{O@iYcw;jVtF z`t;lkt?y}REh{c4I;;tsMLN;s6yDXHwn#V%S$?J!Imz52*yp^6sf@C1a{e$2Zqbgy zVUcc~9U@G)xUGvzWL*r4cGsjw@&JV8dCemAT?zUkeYH9r25oNkm}Wt+W(G`TB=WKpSeZ$3fp?Y z)_B$aY-=1H=pY(W7snvAa2}8vw+o~ixCv78G^8#*5_2Nfh&CCQAT`?cdeLji`db%d z*!-fI!hh)S!c#h%{Vt#q858_sv)_4^oIZE-f+AkHhFYq>kA<^jV{GDSx_{m%`ca50 zMBTD)hc}Lp9*bfB=mtj0P61y2cy3Ff76xFKm;j*FlA^In0Jaasj;A%l0BlDI07fq@ zYNP~E&3WN0*NS1Yt4XP(Xc;eb#8_1k$yd^Rseok%P;rTtrQd6nkv$T_KVGPvF9*{B zM42xihC0=JSz%?-W`3-M2hW$!VLWX1c%o3f_wq2=yG?iRMV}sV??t~ZtUfnjo&0{xPye<#hc}aD- z-LYDctGy@G|R#Lfy?eVrYUw%buc)QHL zEHWF#VwbRPh1W%N^QGqcqLX}lQ5pL#@lt9)u8659z%6`zX1mk5eDE>u5@ef5&mJIsz+zhQ&Ev>rV9&J%rNg`L$8Et2oVCW!S zm=_|le{B3$&1#6 zDw2_VfZ|JsU;**zO;CJPm*O3FjN<;cPBBMDb0H(y^YWDHNVl&?C!1WLlCFLL=-#*o zBwafsD;tTT+(}|5{U2XcnOr#fas{N}*Y6ndgKwSq?z+Tx0g9p+Hpx3w)P+nq3G#mI zCg>g%o$fB7n9C__%*?!#aS7u1>LW$_g;{hg;Sf!=26*x|XTgz_oe&ibD=He4>ULPY z#%#CMYsj`)tY(A5Zt&W?Zm-kg%<{OLB>m9^Nx@F3YafqKvAaD^r!m`Pfa?x!Ll#83 z5cJyYPLIRlG&{*w!y8E-jF~Z)OEKH5PP@}&HhMf(i^*trIb859(q)1&gT+9qKQW_L zaE~zQ6qi;;??!U<P7zi<0oNyG~dp4pT^^$y&ZT>8!D}hSySY?2wr=xgWjl=4EThF{ z^fU3tCtsa}rYqgmz*=`dM0RLxodtF9@$?XYFn>l?{e5&1U_BgU#aJ=4bv6#IkmnYlm zwq!Y-F0;Yx%_8aDCM5-@He2B5QnKxKkHKU1WZU7Ru)*OpfLN>+yV>EfW@Wp{v>^-Y z1Q$FvJ(o+3-E%hCTVARr8ZUTtk@j1G#b(>jeNX(L9NuXC|h@ZdK2=~ zmHFk;3`UTI(Q9xRv$HJ5ETao}0@8C>vR&qEhrF00K7YG$!HjSf&= zu(xcx0Zi6Ou6?tn7BjmvhuLYdy0g7mCO~Jh8(m(T9n{tCv}d_I9-|>RVDtRTT$#Nx zn@e-Y?A2uqW_;AXC8y z0DWG!$pe%)U2ZTVo7wBNn$1SL-QWnOcbWl)=YjJI;DJD=K|`$$@JQKagUjs72HiDT zOrjx!;rY1KSRFgT<_FAKU`&nOsb@*&;`ymwZ#K1TtI_RnS=>gG(_?TNEG~!L3LFGt zk8NEO(Z(#R*=90jWm!#j3oQHH9smWOnC-{{yCVxv%uEheueQ*~H8i_DW~(XNo(=l~ z-~b&qo55?f7+iKUNSEw=xVV<+KhnzR-6q*c+UYF~j_2H<_#^ zuhr@`TZ04q3!aTn$ue3UF#kF1W?)Q~+Xei}rblVA-ENQFPFy|T5REN0+W<3%HOrfA za@s9Ur^(^A+3he(*bOeLH_IVvsW{2S?$~G|Q`Z+ikQ%#Z8(CMn#z1WKimRr0ZAOPH z%k8ve*_{@z(d{yuO)%fVkiBM)i*O$;WP*G=zM%(vzTIH=dQDkW?;PeVvn303*JiP} zy=2(RiOIpW{^>A-SzS=_)fSLAVPuBf1*1bElIG@XRqa zjMcA~?9H7B39Y&>sGJHDki!GBk=JR1D`EzZ)8n;llpcZ&U7LmP0zV{^f@Zvk4fopuidGg)x^%xiXd z$+0gZl$6o8ky%}e(;2hlbuTVxN`^hVpeAY7s<^e$VK8S}jcA&)fQPr+9JXwy6-?ja zbsDWk3;cOa76?x~hTy`&iOF{8lg@<~M3;hAgj#dRi;Q zq8^jWY`1t^*;Y%oja(c&T{O9rESn?S3s}t7Y~W-T=&H%?a(V&UYBIw_Qx$YTvbZuj z7Q;$Vlo%bG$+VQU1~$W`nzBp|JH$=~v&(47vSzt~9=P~xb(%~rTb4yM_65}@4T*0E zQ#BY|me*;vIIMOD{0ESeooxnrTcn0ePgAH30u@%T$7P}BY&X~dBHIa5g*yw#?iCI% z)5_>wRMa%1GiKLD;Lj`rnS8XkL#n|5g0MP_HW;170D-B~mYr>KSS?0Vmfe&cT-fXl z>I&W923#hOH9MQ`YP-O7I&B`C+XxYe%}QJ=C)N_ZM=J2qZSla$zy!0t(d~3+L-cL4 z*-ZuzmId~|Y195`3-Y+s*gc!Xna)#2*UW!}XS$5m2E{Drbvo=WOSUu1YOz=$CN{!g zO<-|m7@sHG5KQb>JSsk=jLmq6M8Y#1+~$rYD3{XcV(X~Mp=@0S<4kmhd;i`!$*I|~ zsS<6@P=1D;bUHn=3K{%x@%{gc&2hez7zui8T68@rv3k5t8(!QvQl5J&?t& z`$QhdV*jQ0K-Rtx>GMsov-|>hS7BZo(s^-iBhufUUlnc+Lf*!2iZkMNgdhd~_8Cp$ za=IKORlhAx@M#a`iosLIZJ=BL$V3y=uh~-D?X`3c?92H^CM4cUAR;nO5m@GqC`(=<@_I? z(t7N_#m#G8p?x!n&|lE6_7yE<6Y;duT_xfxT2bn@{#^XAP_INrEk2-C8dC7C+55FCjU96|Yl5(PLl@@1g+od~o;y zE!;Jk_IL3UeE$-1@$X_YX^NB^(Bc-LS1V2LIaRTo2izWq)tzAvt@ zBw^_*bM6<4aj*2iHV2n3nuHwWXdzz-VM`K6puh%so!gKlXPSBMQ8o>$GN})osLGjMk zpA|#Pgxa{6bzZGh>|6mrJFhA0ZEdmh%B5lk=r44;SF>Civ4TnTpm+u@L8F&!t0DLc z#Vd0?@sR0B6eNhI;=sD$CQPjBtAthx197nmvbhc<6XDlYLnry+r8Fsb3OQFTbhj`P zqhUPDDJ2UUhMEdFxP~SEoq|hf+bXmTki?z>8bCT<3DqT;(?b==(8N$j9Rv&22zco1 z{u-fn%C=0Mw$%uY5Z=}UxHfiuXC%LtBC%dkezmg@2oiwkcseG9_VBwI{r3q6P)l<0 z*Y$lliSoU;f>C|~mq<-tOb$IGXq#TrX}Yj>$e?V>Nc^#OsJHN^PJ0!FcZ(G6k~CDG z!eFPmpX_LWg#tkD^Z3uXkj5;5u6d4 z)FRZ83yy9PdW7RoF$B_!i88E32UhRJL>-T)eKGOJ7=!F0c7*Y;W9hYV2{!e!-!Hb3 z{)cj>3zd!`YhZa?DxeY>hE&Q7EhqQqh=C1LmIdeDXfC9o)-2`>@wG(9F)9Y>$tjtO59_pN6A8v34-|f}AnkEbSSf!7Ctk)uuO$v-T`A7pSAKv} zBF2w&mK9I1qT3<;pdN6d7ZZcKOd%h~KO-SSr_d`utwvUT@;jBeW9p;#{fV(y436Qbo~@41FjRBZe*EmzE>ksVSKxuS=-1EPtkmLmMe{>!x*ZTGvp?9Zh^#4cF&QyJww8+>KwQ{2buU_s7l>`S)`f{!}c)Qp zalo}aoR&y-aa+PJF4~<=A57QEgj!3E#k}}|LkV{ABLkOcBZy;~2to{;I{foSW4_!c z;*Gm)#MpO2UD9d(yt^y&pG1)822M9{I6n%U ze4>t=Zb(E&$#bGlB8#7D>Bdha%@uktSwCJT-@8I9g#L77;Vl)GMW7EViLf9NiG52h zHm+GSSS>5`Q=ISwYD(m1OTD<5-S~gv6SC>a&>|&(yf%9Y1F!`t0I-KqqxA491_<}S z9!6CFu!m7s;H(BK8|tJ4V4S^L zoL7!?9u3I;(1SWMI@DeXrCQN^C)zAl2e$mmz$ID*xeA5`OVG53=HJ8D`;;v~(Yuzi zOZZibKD>N2u9fcU^EX6KmT~$&Rt1R3!jB z`wQkDT<-J z00rSu-c1t!cs_}YnjNZ5R=+W}suBq!bAlS4|14qYwilWxo2tlaDv{MsB&!y}A(JJ( zn5g9i))z6M^4_^2NL9TCVIjxn!!~CwE}`OzgEr!G=jz58ByRys5g)1{_%)xA-(C-u z>R>NYw+&-Yt|&Sd+S#$`m6AyA0@%_2T-}_aZoUvCqZo@ylbh>lGW(j)=#9{1{!gaP z^~tU`VBh$LriZ^jBM&bM%@_X0H7Z$z-|hP!lSSv+zoNFXFeDv2usHNEJTQJ-Z8z!1 z$=Idv5hxwks5-h^BBkMR@mr66M{22uho_J_`w%m5S?E3CKHMR@Oe`x*YdzkvghCh@ zqEX!E>SLzPHavR{JI-d$Haat4ABRo{;n{~xpU;ZGx>G@W9bhTL)0)W@$*MO)vxTXc zIyNW1f=k$(IQ2N26VVLSNe6RySZq>U8Dm&obb!TO>5SE(O$of8!D?p39y(y*KH03e zGe)0Y9pIap6`#?W73HT#&$1qsQ{yShz8F*Eaj2K4M)v&Z_wR&y#`C{O$ne~FO^uAr zjh`P^&yB~A-{HBDy#DFhhD*1Ez831AK$ZyeR6n6HE@3_o+K4_+-B^0L(yE5w>z^Pm zZVNe-*j0{c-fhb44IP=WhZ`S1pw66`gNuG0jGkqETcahqtgZ7kC)8W#D>1R*t#f5+-Sm$_b>hjH0dH0b zHu)%&7SC^Ie9)xUm3`y}NsRA{$RV|E;l9wW1pWvE)FhRX0r#r`KPV2F6Zq4>PS}4) z(Q}tyMnIx%Kx_k|l^YN%PsH4Sz-(t55UIE%qCDY+bDz6N=j&6Ff?ZF9{^o@8CzZw{ z*+W%a%q$2RiWa1X=qIG98}sE)l4GYs_baihjHjHGe;E&!(C52@d(MQOEGKl;VC{I4 z4ErI}Qs{_lSO7K_$@Bqp_D=R=*Oe=p@ei9>Lfh|C>&&moY z*5Nq4w(PUAqAlxzeO8{U2f#inD*ynw^jUe89)RO(_{gmVIP^8VSiD&c5C;uOwGmI( zA&-9(vPJ{js|KUIy60Pn_7wdvO4wGxr+NT|mqL#!0aSrc1xecNPO`wKwgX{7D#{K? z1mnIB@x1T{YC^ax5XDRK{}op-aa2BqwODLwyAm2HXqz_FY1;2cbyL;oD@mijCXPN? zfAs3GEljgQ!)fdlWw}R&!->IE{XH_Ek9~jq7QL!H5R<~bEkA%tWUdC&{u}xwPRK%) zbn8cY7Wrvh%+x;X6e;{I)J6y3qs=ee4GCTlv%>(Je}|=^q6enR>@Ds2dI0nP2WNlO z0NsUkdTqUbgr+Lns(eWuNj{iFP%!c*$Wc;Bg*v6DWD;=V$Zx}n!Yp9=}6ksMXa zhRoY!s6~gNk+;scCS-K45_pIO?`}7uqlm%KRH#=nG&qkd32SJ2f)K(PpRB z98EnPCA^8kuf2=NQ%&^i!m89sOLkg zhax>BflU@M0Lb8qC2iukHyD?Q;ob?U|p$D*|lO4Q3vRz(osY zCIE@bU?%%?%wPsHSxlpaOCqXL5?q&9@+>cuQh|p*8ZeL3IjJOFIEjl{_yP^Z@I?W^ z_GBy9DrqKsr-tB5Pm|YbmF!h^!5)X7oLtgc_)8C_zMroHEAwVu71!3`n{ ztCcq7u%!>ABa30_4?w-j((i3h(wO5$i@30%KcmLQ4E@nF%+S%(PY;m93Z;5aKj~|g zxQ@}UgTb?P%=223%QEyg&!`Q38KxB*ddIX9}R94ekx{sq6H4E@?Nkr>QNaFgEbwVfH7Yp0&K_znQVETrV z+MLiARf-wXWv|~(8URK>;~q~20+KPpFQ&qswTTzS*EOC#tBrVuQ&cg`HXG_yX8X#f zl3zLgWeFL!+i7ZK%x*^lua!A-v(HAEpfRP9alrw9Izj8#g^BK~CAuuT%|ENQTOTGC z+wFir$?k^yAx3hHt%iprfgKR>z%;$2sANGy{uBd@vDL6x1GxITlDLNaC172&y@ulv zm}t}UULa68J^RkaoSrd@#k~exA`2wU^jc(V`-w?pYeMOIq1HJl4bRmu^Y#)N;u1C` z);cFzouaME>gtsfKP}*%nbOyy$|ZFm4+V|`l&(tCH^}fXo$=)2+lj5oUzJN=74E~M zVh0g=pb{Bz7`Q2;F_>7Tv<`fFLQU1y+FTkqMklUPaEZ2q%yM8~sHz*=LsRJxd?t`GpcPY>2O`kugIY|GC-_pZh$DA?gz*8lrw(nCSm#i7v|!3qDsH z;&x0dHbfTh@}Du1#d%XV>itfV*f9}L%&3=K`J&VWr_#q)ioW7~sk}IcMDU40FaIlW zG}?5UP$3Kx%{ohfZl!f@|2(F3V$O@>csoJJxWUl3%wM@^mtkIhsSp3yPg*(Yz36c+}22p!@V+d zOU5N=9G#Apz7v(AW2O6R>)&CPE~T^ZIC3eS{g=+A^zsvdpm}z}EzYe)MjS8AD1Sd3 zPE+1a?{`YPo!;g=tRCQYdWD>>FdfWmN-hjN)4W1s(8YN+FIm{O)RvIbjG}8Wh8!&_ zZ4j69{CTqJL}}Y8w39hG6!V;%ez4s)CkJrBzZB1$9ExEMgPa2p@LRs8J|F%whiwHf zpugC8%Z2n8+f06g{$lE|i2h=c&|>7gMB!rSvConcY*TSEj!|S=yYtKx&>U z{San+*Egk&$WNzA>v8Xr^lwT#lIvfUHYRJ6kEU>Y#DbrGgMurkOB-+@vEcCMP%z?5 zX-y86b`<2k&r37NYoAo9MJ||+mFK<{OWXem&4ztnT8>OPpHzuFd$zPZ_lsE7=R2r6 zeYUg?4{ypPUMlTCPJII<_pDEBPM&!5lk$93sd)88DBjVkMhe+7|7dx>zEr&FDirU| zf+yaFE!lqT~pNF_t#pv{pBrSXv2tHAeDS4 zK%04=R7qxh;#Wzi}iUkdRhJuj=$Lnyd#De+PrT#fc9#3&>{2K~>0ETlt#DdFT zL&5*!>%0S_D84xUyP+hcUjhlFCxj%FkkAPwB%!y2rbsUqkQ!7JMJZxOAPD5CC{EH7&r{du+S z+t$P?P0nI1yZ$v~&)Aq9F0)r?*-QAd9K49;zgEk5`z|tiTq;YpY|%0{oYnXkmyghq8V~vlgv5Sj$*^9U1&@EYVsM%*lm zw=~x>UI?_MP{mC|IrFrPJO0QJO`$-`_$2@t*-t!HQ#QLHTK4_bkS%15)iSnoS)zDT zwT!Jdks(OWYZ;9ykWqH0JlV29%Q)&`yrX5*t8_Cy)-sv~LCV~)!&#P1T1EkXfNgin zaxJ^HjM8gmDN9D$>R}!vl+yDb{)4863oOUAjBdw}@s`Eb%yL1?7;zREnaq}JxgmeS zAGu54!@XJ6Uqw@1(jL11ci7=}%&KM3(7&Pkk0&1<$l_&AFgJNWRkgKa4P{O=HyITc zJ4TCYwrJK`W`DuE_w8!7!K|~)`Hh>^`1Oa|vwkwi!kh3QKfp%FoFHx`j{k7j$|lGh z7casDe_Ma{w9Lu8h@7r*N21tEGUpSXGcxhWaJES1JZZtB3=6PzVWl#s0q?kN-}@+= zDuOW}eJZn4L-4~@fwrD(v&`}1c4Ypo)xEWT^_|Ra$%m2?J=SDtfA5mnyZ*tim$qKp zR?9vmvo~_{-sia^UD*|p)1JygY~eHKRsG-p#%JOg!nWro#k+Snt2v05NsTiL6ji=Ii>LZ?N!BaM#%IB$VJXLsdCXyFMBni;i=m^^#)JXIEB=QJQc}P-|=LR^3*Y&TFFxg!dXnEc@5;{Eb)?C+GfqUI5{F)ZLT4^1?o z(uTHLRJ!P}m1YgFM_Bx5N~p zuhU|?7Zq=_1aQWRbXy_)9c4>lPn_msNh|7;UNr!#fB4(31XMVk9P0|C&9yD@)S;av zlK!r3vsxzc@0;R=5nEiK_ff`c#pd^4SjN(l^WOaZlswPmqGs+j$XnD}ygAO*+S`oOU?EC09m29JxkwPm+d@ZSn} z$F~6YPOuHJ{AmjQ-4xt+mc5WRCE8lD^fQX?>(QvD(5+}Yq5_5nIb;PC4QA=|V-hML zng6Iz(R~8ho7?i)ShR%KZFvm!%v+76yCzw*n^8}cd)p14Ua~P~ly;gqwLFF1ZfR?( zYoMdf@CKSeeOfpIMYAh%6;jcTqMFhJd{r&{OKRb#mRo7*Iad%}eCuc^mDceKq0-7} zez=ZcS%77Zcmky(%sS)SG_85gJ~VRZhi#KOEv=jC2k-{3hLpDtdB_CD^p{osT2`~oujaq|Mc}5d)dyFceZzD$`#b(%&SsN)p^w3s%5k)ni z0IukgKdXe&j=klP@R@ZY@Zth95ikM;w9Zi;N}n}-7w?&R(;3XhsKOr73#;f*9z=IC z;ggQi%NTwS8PDP|dNr|Su*H&GJjQ9z)2u(Go+zuSEz2@r51(reKQ$0da#Ax}8r!87 zdprNNyC?B#$hzbRqV~;g*_dkTRP&WN*{zyPc10y$)RWx`iKATWBr7dw>k7A=)5}1E zGgJSlL9hd>TG^VgnBNpTP@C-CeM%o&)!mhfTicpjYVq&Y1{99-%f5}%t(Oq0$VPOf zn~LwO$GepiN4EG$0d)MAD*j|^11p@b7la6fPSEUE?S0ASm>fi*zoGS=%Xfvcsj9Tc z^wQR~v-Pk{){`d~$nHl#+f1JlKo_%ZA?!Vsyj)LqKSwI&RjkxL*A~jkR6z&yg1mJe)_hVOs%!^c{h$}L*CR;SWf(bOm!IQ?q%{@y$1n^7 zEAobGd12)aKkECot+g<=e2nLjKNfvXvDLZjY?@C&f8TG*R*Y!PbG}B@{*AMcj&;IF z+z!prGrnh&bqKdLZ`vXWtZ-UpL1!%OY)iMa)V|7^YmZlkmu5USpJko%*w9@+!*e*? z#g@(M7lfP-;WM z8OyudnplcWp;%wl72Dn%o$W*qTPoYFLU-z+lOKSFOUDF6QAKZmf34tn?;xoCU3vWq7AYk@7#5=W;V#`qLXv!C~= z#^43;4-XXDx>Ncn*bZGuYx1ABrXue9DK#muAuIg@s8L@S-5wgdtDap-U=RNNy|9wE z{1a*W+t5l*i}C?Xtin9*GK#)Dh3|>X+7N>zw{AXti{T|GR)3Gc&_v>6&Y}|VGwIX`%~Z($Ajp^7zpa53PPaOBWOe@ z9%T7ATWvaM4+*5T&JcgvHWs?cF%{X?T%>SmuN-(SmilBOq&G#CHpxU<3(I&@JR-Sf zPlR!!Kiy1&Cz~~4QFXRV#V=Lx-dag~APl#cPDu5miiwT@%bTXsmfSv$Et%T0a&oOibglW}@N`J!Xq!376>F6Se}S=f++V zXN;KAvMg)SvVMQumSpLtU<*~OXt-ORur;TvFUs+XRXa@$UP4?~A;H(^Pp4lFl-BChC%@Gy~+XTdVuM@l)?yb7;4+n12H&K5z9U$Zq}Ee)7v=9m_95vv&L ziz%IJ%VvWNg!?^&zH8u_lqQS{w4m1chz}*XM?s;aWx?UKsZVt%d#*3~{du+)>=gt0 zMGeh4qOG6?IoGn9iWl43(F4uw*;U55gQ(v94!b2hv5h@dctF&5h0g?Qo8)f3!abC> zG<%cF8&vadkhr20lH#_xwAmWFsFhQhSe>JVOKolVRBu$n!!2pa8yLTX$+`+dJN_)3 z0vFlpvVg1lD)hUmM2&x=lZ$NYSPINLcbf2>P+`@po_01W^KdJgyu?=5(pzC)T7s}u z4+FcaFZ=rvupg3aeCKAnt7pnKl>62D!jt8K>pVrm(KiwBdCefPKK86LZ zb4}mBBSlUg(>JM3cOzoy?k8wc9So@9t8MjJI|C})ixQR)O1pSz{ zppO3(1O0ddb?h~h5wZ7LG^_ar#J;t#Gjk1yS7gCn#HZSAT6L_MNISp8Gp;wF`mTdP zS!+P8z6WZ^dJL^D1Lf!SwoGO>P!8RLa_(~sj5iFFF&j{pKMa&>_n-t9)lAkNEBbeR zEBO5jTc#z7uI<;B2NjD@Q)FtZ0(wQ=*=TFd+8bbVH`&@++L*yw{ReD$BS$qFvZ!Wl zQAQg7y{(&O^Af(YHJ~Hk+p?8X4K@c|`^q+&%`p^c&DRK5&oV2}bLJe;9=B~qWBSkl zYqQ1H5SO2usH)T)<{6))P58lpJo2Wkv1P9ra@3KW$3iXhJZ!nqcWq@?QuJ{%P^1zq_Tj>d=AH75q&xy1-ALpS38RmJyhDQUVx_+7g! z@IFH4SOYy{y+_w2A2_7jb;=<6gjBW1Ww?svses!xBV{A0{)cxyWvPNaw6?Cmg;Q=9 z#11jT_NUS+wZf?)zE&VD*oP?M|M+jTn2X{W{Hv=`5$bzsKMZY_Dj>rvps=MakmlNK zsbp(is|JOgK&YsJm#y?cY>}&g)0Fau$9jqh_xlH->tT|fJEwb~mp!u%3P0fxg0BNj zi31G86@1lhQyF*S)bhUUWzMqM`dVHv#m)BML=8e0q6Tkz+op)rjB{XVYPX8#+n}Ys z6P>n>mTg|VqT9`X`1TeA3GSyt7oPsIgoRa`>V5i>Bzr7X?l=-fx#fs-@GaBE7};s9 z$fvwI6`_~RlQut^bQr!)^q<~#!J_^Y^K8*DvN}PxGk-#hgNzs&b_6rnnt$qN+*ZVA z^Oxl@MZgNa_&Tc(w&Be>ifL9?RX`_2KFbeen&Kgly-bG%rgYN*Ck)rb6RMaTv| zOUlWeoE?Z=3p`>go&yR`r?8*#sLvS0JoTqR<4>_qBq(ffKNX~@dv>h^(S+Ft2G|N& z=4JSlqrY3%$?S-#1(#AhMa{EoMbV-|wpz??U>`EFy=v}u3OcO%mr{>#;XKa|HTPq8 z48&VrqR=o@q1UkWB>GCiTeSkEhTE$m+9)DIDjc!2^E6c4>Mwl*X%25v9RkopCa^Io z=^=%L%Boiq9EkwWWrSBo8EC`*(wf^4e7Zd>Zs_*^W{YKuC6%wks~XUmxEbFZ38K}H zpgGSs@aO(T$>(i782CafD7{`QLhr*~jSQxu3-IU;s*kcyd6bBYSdjS6AYqSAA6QzO z$Zn{RKfI8Vd@PepI)*-2`amR3-(QDPt{IS5q*5+=l_G+G?XSQSPr0GAi=-RgcCljz z`Zw%yBo#dzS)CHEVkb%b4Sn^++|U#bwonL)uUClmhHU^HNUY^P1TjoXKH={uG^|ner^?PAvXvdaKpE~By~j0_2b{+9K5VP$qNeDR^1NwdJS65m1X!R z{xYg|wgEgtdlJiZve&SSeOy!iuszPcP^s$`YRGL2Stx1sk~OI7P)DR672LK3(392e z&BQ)2#d5EfTAGf(=bM%A5GGoBgk6@xxE(iBO;h%pq1>m<6w>k^L_VU(8ez|8HUIYN zn5&}XNMvnVz8~e~3mMG%x4uq8u+qkDb|5}wCxR*l(5*9(5p;sk7*70QtBz$*fzMO$ z&`A%LV*s}G0>$H_$nM7%|A%iP{{NG7bvBnP;ShdI2K&(=3rjhC`QCP0I+XOC)vn*W z_3GbR^KiR4XnciqCn?H(RAaqX=(Sp*IV;@TiNfNeqR18;mc ze>LK{s08eGWJ;06AeQEDPiCVfwBnzZYLUe{`tfLc995J?!I>Np6(*JwEJIDj4Ehf- zc>xf!KrQ?0{MVXpu##SAdDWEpk~tH$x{!ti*;Ck7mHD*~6TNwAR1LZ~KfFdsCwl-6 zI(W3LC})GY1nW*{e0qqzAv-T6WGzosl`|p(V`I^Ts6?6>imLq86kcHt$0npg`Y6oa z2)k4*jnLI2Zi=q1wUi>OeIN_FsqfvvH?`ia)Swu$T;cY1mW~=HSLI-@XDo$9+8fcj z=c9rt_cyB_&3w`xz}g$wZH#QO=QF96y``l{V^37swm)F=r(5l~<+e@jn$$7U9?r%Y zuw#r^QRLB4_J)>sHSF68w&Wm&#lS#xyy3O&NsJ7@#a^H=K?3=$4X2_OmyuuDS1OMh!o^zlC&7PC1qXFhfkr<7{l(qbew z+hHvbkF#*rwstk_8hXoj?7J3ce~7iZ!GV zAEKv=y;Osf?dfcxO2_s`54~)Iy#eLETqlI?7S*Xm1<&C@`Zb4{)3IOCv&EG2wG?|A z+pe;=DD2`8hhQfQtOZ*kO$xnHfLTM-3m>zgd|rc=Af7EZm3%+0+`GUg&T9TEYf)@w z{&0MGish#Gz2XeqxaEyD&@&UT33Xe#{XI(r<#mMSM92X@5wb9?^T?V%?68ov{$tGl z_f?eDv2>=sIrs}PC>D)!@43$x9Nm`Y4!5;EEbd`Zbf0#=)QONmgfSeIZ+VFSZC3&; zT-!8+Wo3UFdEiJAjc!mS9D85!y#bVe~F$+M8RpYxFG&J=5YyrrbKwjVLSz8$9^J zGTurj9WsTz(U&bM7>~h~PNO^E5k)mXYoZ$3X}=bG%I8cgSC?3$A3EY~0iJC|Ec{=s zGv_{XM6Rl>>1Oa%g8ucb6zFX#YyPj+op+Au5=c#YqbOjc(T@}C4Jm7FbUGETsHxds zOJn}Mx|yiqzrGbAYPn%6dowm%QpIpTMB(0QWiP@tY8<4f=RTexRp0MlkLty~-KVqd zP1szO_lm*`J&9i1r%?=E26!;ekM6d??vfYv_~*^>VyM{D*4~nRCGoS4)>f5}Fbv%m z@Jwtywa4&l-+YVxnK@Uep>~eFH9Mmg`xpLesou*u_Nh20uH*e|&P&R*zs$n#C}ua5 zrVK^DnKAxIICajmKM5m&48w+`8a&siR?(t=$hS9V9aLyL1&V6wPqQA02_oBWyA?;C z&G0SF@#68vcD2XQ406P<395`o6&Y>2LB+bd}wpWGjR-j9-qq(i?Vb8;!HZzrMGbanL>~2qZW!F`5r9w`4N^5HkD8z2B zg++QZ`~`Eo2qNYb*fUt-U8R-8(d-HCRw4?HrrelD7TP2DzSarG_7cT&jqOELC<17i zthb8qq2P0-K`%v7G4*MAUwa*_l$znOk5$DBBM1{BOkTRy^o1FOof3I9Jb8gM;)vb5 z3qV{f@aA1*ji<>`dkXCtYcP3l+%=fILb}^5Hj#2$*sHV6@_7#c;aliR0dmanH=WF&ZD5vcb%9%SEPUpx{cuj%-qZQ*F>V`4AA#3(t{E31R zHTmZdSf(7wYwO{yp95aen%EYUHQHfitqr^u_uzFMj;a|id1F1i*+EAlsN*?%I)~!W z`3TH0A2vWo8=){ybJ&}bx5UGP3ZG?f7yt{50P&RPN5igvD=FW2C>hhVr^FfeUt)u( zI55dy+iJGMK;CL3ixH#c!yx}I$yYt(yo$pS^xQ;8x>hNP566aM;R4f^O9t|JBiUW% z7;K5R$FKy8sBTLvotlk)%Dp>Oxwr86Q4q~)=m@ve;lEJF(nwnI9HwUaTDLz6yVgr$ zyZd09B)~;l`t#BH)aOO?jCHTTMe3r*b~IpxQ45Ekz@C=aCw;I#Ou!iTxSmsF;0Wv0 zYZ9#2hm!NY564@)#G?rOSB#FWsu{6TJ^NiFo5%iNErRKCN;WnkC>mY=sVkO7PO%Tg zMKC%&?(@Kep+b)rMrp0qNrj<;brE?BJb8iCagGiCTC6?VbGSrYdJ@lQO8((4mVLBv z2ad~s^NPV#)n;_<#^IJ#)A^<;F{cQAKQ2kLQ@KoQ4()070(W!R2S1rxs;M>W(d4Jx zK3W_83)M$!#c2H(+O|B=#wI?6aetiTjq%~3c|8Z0oC6fAG>T`)L18$fh12cs6MhBs z<qvVCb%GDePceE6pccsK?GX$F83aUuTab?(!iv=*GM5{_?__nKs5pO~UbJo-o~Iu{oqA}$y8R2<4N(*exEi!8p1RyQ%!cO%ym zLWZd2^^+YaY<7H8Dw_=lspukaoNv#<-h$j!u*mhRYD4!vsvPWSTXHZygC;MqTUlyV{m5Onsy1??O;{rx zIHbYo)M5CX&9!DIY!A$Z$gAba3#7_O<@~YtW~Ha93fn`|VsP$)pOdG{9~jKfl&aiL zw9?Fv*l#fAWPB1`eF1>xizL_ zc0v&CZ0)e(#sCjivCfltuC&hMtEtwxknUTKK=TKZzCxjkDCp|-_BK@fp1nGiwy{Uz zn2#BLsX0EcD;B|CTw$-zu&OTfaY(@H3L13Be2j zeqImWu@!~oErd?m68wcF!HErBv7a@k3-4me8haW-8iM|rp6+gcjm3DlM$i4&oGYTN zKdrL|iL)(!S#L^TAarHVm)4P(s=m@E&5TN^AzRvbnP%F|jJx5sEQA}iUG z6-dkaD|1$Ubse_xK%(X&(BuJjTtl_bV0~uzDb@##c0kFDM9ui{3)T2@zb^|sVw63c z=6z-F&q}odD-+ar{XFJIbg4aGW9h=C{ry4p?_b|+AHnt+9{4-$f%o`%Op569|7@|> zw;6R3>ZCv6=mj+7Y02P_D%RZ6yHGm3?AhEvn^*0!qUXY;KDhR5|#~TNd zw4fN9VL(ndBE{og*#n<@y+p26kua5yu+;|8DiaXjDE85Q)cDSa5mm$Gmm(g3Hvo8* zhiG&I%CaTm9LL#(*rkK|(p7_9 zd^$j}i=xij{){?X5uiCB%P8*x95NunNwm_U>B(tyG6!di`QkBdI>Pq`uk6hLUo-i| z#^e<0a|g|TzvO@)+!F!K;y64$6`aXy^2E()%%$0&BJS_Y1A_) z;a*$*=t?19@FlM}eK_ADO~(?&oWKSMbAmu4H!;GTw3)1}Q;Ti*YyP%Ju%lQFL#&OK zPLpfYv^~h7-?)W)irn)t(F6YZ2R-1XEIPKu@wx&aoZ3q?4IqW4Sb0V8J!PCMc8!b=K9*WCsb;ihq>{9s@Su+3%f$zK ziYjXD`qGoQL#iYtNV8OZCBHz&Co#?A0;uTSlmu-S+0!dQQGYj{o6kDqys8r3TQbJc zkj;?v>3TXw?~#^irnsr5IC_UUny}T9w@S|wv&KGMQ=)0s>J+Os!CPsH#|zx_rRC1i ziK>nac2wfa^?0u`N()l346_UB6Kk6eQyFDO8Qe!I4s_JiCO6f{lH$n6R=A|PYNZlp zPMwrh&AX=b+vfC?t0}l37tWWZsYcJ#)6p0oMm$N#)P?@)^ISt6@)BqAi$i!}hw5=C z>}cE(VurilfD;z(K(Hg-^16n5RgWuMirqf%ggE+IUNU9QF)(2|3h7p;BgOKy#@wW5 zx}CWMT6g_OLdi!6rs7t2u5;b9Bf!iWRLe6@eEa z=u|8`(Y`euX)GdC8;`^A$=zjPJ20EX1_Iuuua2#!P0|@|YSE1*0ioJxg6=2WH1v1j zm%*Bd|HD)Xt5>F38ca8F8K6lO*Pa)ngp>*(Vb4kq>~YVC40BIGKki z7W=?}S|Oi)S*XYOh?dqW&XLZJNTf?ex@!%KxGPhmwdGH{fqTfv6$917364xwHB7U~ z7TVR)Auq5K&x;<>kSc;wQ?*slI|l46BUTvYmM_-}qxl0ck2#rP4`ofHfW~?OqDep> zk+gvSSeT%O{733=n9eign62nVxGm!w@FOtC>jhERgAg=a3L2spBmzW25PmE}Ef44g z-DfUH^qd;0j#M^R;$P9>#k{CbLkHGT3;3-r@RJa#(mlrW=5#SDY?=;h{gtF|)X}w} z>!&)7=488Z1g8z&1O9V8{xfsDTT6}D8HxWzj~BYxIV%;fRyY9iNj({_f-=)TLPt|r zxK-=uq4?w-T{IhnfbVBPR(;Q|VbyxJQisCgy|_BHwl+EJAZhIsnpd}_Um+IFH*BzM z1G}Y>Ehe59vK;l;1c`lA!P1&0jzR1Z1L#37NSGV+ZDD?%z$Xvk!)A#8--iy$r-^ND zYMT(nR?^|k9W~jPR?YNOyM^nargt+~o%L45>WG->nih^kc1#u$UYgJ({@hBFt=P9c zYgcN0DhjBh&E1&OP-dIeV}C?-uWaRLgj0mqdSaCOAMNS~=S|R-uSdW*Hzh5NiXXsM z-+v9DoATjr;7TXy`iSm)GTYJ6lBEGN6kt{&>_b+Sv}7%6-hh8)*msr2b7l8cz{1ar zYUgNT8K!{;d%-0`G;S;=Txrt#8E6V0U=A0yZE%jGx#cwt{<0TN6_@yKzA9<4R5mm% zf?nu=;Q0%t^x5~M7t;H=7^lC~=wB%GxN^{o$E79G+B|^Qn}XNg6I?(C^5LW4K3!gW zrxdWLJyGlv9UXDlHDji1yeiO4*+L)wogG=sik+@}$tHxZJ;ZB;``@B*920vSxmLnn zgOe^6Y6G<&>3j=+o}15UE$E?NPJs8elTCGK+?@< zSf6ht{u@0W`=EtN;h*z_^}8fr)sscR`-G-7&}PB7+lkk;t{Tr9 z$imY&8Jj6XazA({XvDeh(_EoM13njH140p^W z^n>eaxpn}+?D#6&yLJ838!4f-L;QO~sBOPMipZacXB7-}G-r3D!fz=^M6#a1(w>K* z%v1m5xl+x)<9dq#snUDs;EqF?cF2H>ebSZ)yY^%=bZcQ6g|j8TrGnS?hV#ew z#PTf7Oz~Of_`Gi%gJ}HH^av%K_lOkqpdtt>OhV6%#UcUT_GI=5!^~v}4YivBbH7NE zixskXgyO14n=5nEd8YW+{saHwx>b0>>Xb}_)k%$_CNYkDvMh2p91$ym z)xpdlxj`NJ>}hO-Oyu7i8crO~l??}QQFs_PVJAPU(gDQKeW=<&p95q%=VT{iR#UW^0=^~4TQ~vomnp# zYZb=XnU0ybRn6?_R`Fc#t4e%kzGF7~+Q8X#56&}h;Be8hXBvi6yJAPE<&s7`uM!6>LK*Rs2pYN&(Ur3X z_OJJ5cVu&(J8wJYu(u6dx(Bz> zJ1F2m3EHQEs=bQ>erEvfF@l66uxJ?^ftwO^Lj`>|3jT5Naz`Qi!$7-cq>27?snpSo z9^K%Wt5x5Y6^?0i-xrQPirW;A4K=7Gr6&pxth{lfB(A7xw64+~!UjZZ_Ny=dHH-?M zCy5pzO0}-IK|2wOpbs}fAb&AU6}*^+`fhS$Ye%#dyeQb4EAg1KC3J=!I_^WqbaH*E zqGo=jpoFD{jTDx8x@MPzmkYxy@`ify0_lM$?XDu>fBvIEhY`A$fb8~yO&06bP} zMJs!09X8}?8y-c03&Jzpm$zJz#lEN%8>nz{-|%CloyQy1q32d(|^ysIUL5K6N4MSNd&y~hLD8_5- z`Gpj(taa37oh2Rqxtp%NTPSdoBate?j@G1?KLfslDZZUKUTl~8aGfKWJtpzkhv~-S z1UxQULE)F&$?&ItY=BQNg+J;G7nOS^TJs@(iS{g-iP%v04Tue`mcmx)gcWCJB+(bo zwnWny zza33uiR8bb@NsVy4&FYMQI{UwfljhO2vKV4^;l0$LAB4Y9g?wCVYnB65DMxraw?jw#q$^(btJRYB5D#a6ljo8;U!|ku}-z0N+zAw3kKm zW*!Nk8pl*aS$fQED8Ul717+%KNA#sz>l;POJv=j|gl9DgqU#oIMZ0=uA3TSr3vdNK2VXll&vf0r%mmRm&qigP#;5egR64AN2Ey(mRf!)LcMs?lfrGyFug;a)lMbKS^*d*x1JIEFgx zJF(9JuNvVy6(Y2<2bXA<2DL zS$hlaHRiRgQ}jg-Q)m{&6`sRFo-H@1K}#aO5 z1kI&LY>vpDI7abmrD0l_ouFtP7nY4=qY|{9Jsh9h$;O|D6K*JL#fBv4``iO~`?NeH zAi8gZQy2>8O28{BK)hl_+?G(xG9#$)YU2?4xw5f8H9Czh^P++Le1c|tW+r&_i_3Wi zQ0}jeDE5_6!t;*_02Xn16(GmyVX!=FU)`wYN7(iZ11bOebX>RL(- zWacwPIj|=ksMC2zJ1V~k^DdCMst7N#uAYgdf~lFI)N&biD}Hgy$J1`9=ZI>2;v)R7 z^S3oloOXd)ZZx(jRm{o^q#qaIeyQF6`fwi9bHsL{IhP#?bn}k8Jh+@LmcWUm?P-~Q zH2#t!fDYev^uY@rmZUp-s*i)Z9#0mFQ$C4KIg=|L(PXzc)j7j9eF^ZEfkL8d@U5>) zl~>{?s>;_rfvfC=wogW5+wr;kND1Y91==Q(&`i=Pz|S8olhfi zJ>129jiafLUuFo+{R2KlH2+>*H%CaN1}_r7F-oaSH`$*)2+nlxY3(hA^thLhs#N@! zBNk`bbwWD%3K6CS9ak9f`|$ZQV*M}!;{Pqt-FU43tsdog0V#h`!mQKf9r&cceR*wF zDz#?@P-utOtFsl@|HY&KSI){U95|K3A4s4<_#|o$5o%Y$Jwkr&X-9Z>j2ke0DX5G&RwM>U-l)-!6G0dE1iZ1<26+m z^yJ^n$- zWbWJ6#C^pZZU75MqwDjCLwltYt%E>amCZ@^RN1u&n6Ld&)ftJHo+#lerG#M*VU8(Y zMs;VRGm7RNZ{kmNH-uO*+tC&HJ*~jYlRcBCyhzsw#PtNTT+dwQ=WJzh2#7L(mwAho z$6q?SJ-`{wSY566-PJQtBY`w4${9mc!&wKD99@zA(Tem}vZoPwp_vkjJKf^k)hr%% zYM?U;lNscy9XXX}$d0V=gqQJlkTaeQl{{SD=F5Yfs7)13n}$%~#AdiXY;rS<0Ksr! z?$^ucZ!SaFxm6*~81|}^fy>-{Wx#yq;`OS!GGi&utjuEezc$Pn$2LhGu5R@| z`BrBvZsFHKKQ)I6cXw8@s~T+$ch+RTN^*ss41caY-g~cu{%8&rPySU+Jb74()&#Jb zGR@hQL`4}fnhSXkC+9(uv8)|N8rAIY4pfGs$r@ghE zv22_~jZsht#dO7SbzOl*87ShhT+w(el9a^?1$CK9lY2Gwr(Om4XVxh+m4$lfd;?TG z<&Uv=$~_XgOM$wdGKQWR&@_~;Yy)(=9=gQuXe^-Jj2)- z#mU&C$*=@HHE=gQc-gZxZ zvAmu$8|T$^$jRnNp^3Ap(8Nj&`JN6*LK8dFV4IigneUi0MKM>-ZEg)#{alB}FVpiy zM?aBtG?2>bJFS)jdLciU3-R{$-y1kW zN+W0*ai=^qkouID{R}}+CX4^a!eYCn85wr*qMwi#sYl59*i!W zO2;#uNtXV4W*>8=FbQEzU@BkIm~-?@F#r@5H?2!MZb1vP^x)_I1Dx0l;0+ph-Mzr! zOP*~JKo!)~k1n|&;ZwbYPwpXsUk%%+1)5)lCgHeV0-9etyb@cd-6Q5WVJsTAg0Tor z)2wv>SqgpX&1*5Ptc1k~X^qnQ@gJ#`SCwZNd$*{()NCi-iY@W2^mtKwnJGuZN`|$@ z<-U9112)&wo0!vu`E1h`=5vgsKcuI7ZRpGt%q9}rq2-OzhYqPJE%3xw)`rJm zT90fWcJ!mkSI}y4`3V1BSH1}bsA#pT?so>$j_%G{>^+Icg(DukXthP>aL}~0hqE?b zTx~{v%Rm;}5%%_Urn52$#dRYdsJNPSL|a!dtqX64HyZzj!L;H|4z~RB(&GvelhW@s z$iQr_5B=8L*^u3qGH~OFM+O4wfmCF}vk4icl~^l3)C+iAKw^T&OCWTWCx(&WKF&;5 zA10pPCWc+M>!%>vj6>vs)bl=P3aw2$8sXJDt6N_mqDri<1(FnxPjIvQIh(QjCAYui zBIX)M8CTpz20Vm{BRYhMaUaJPzNwoWoW zl?-oV*cs5QI?ekB=JXQ-@*^WsY? z%4n|05T**PDU2=*bH-bmm_i#HphBOKBQQ2RB%z}esL*NSM(765euX*va8u|I15^ZQ z8jNx#u!Ryj-wSm+dolD}i)J+`aWtC!TvPBX2C%SMtsjKV+AhIcykP#mnN(WW0KQG! z<1OSU_LV7lqk)X5dLiXM3`28KlFupRt23N+D6B`bP>Ov7hWd;t^cMqEOjp{C!E~i| zL#Mw&Rw$;aD!l_v(<2BPw&8t!dWy_W~{6$+*{x?D8n)jGZMN z`>K5D!i`K^l3k6WW}@63^z?S7bm3g*k9S6~$0T^N9xS};*a`5iC+L}vnlj;PJ4&km(u%Mm{uNlEjJBk?evo6iO}@`QfG5u%6F+eB0QXPrT8k(A;$SCvA= zyOJdBVV5;HqMPEmJqjY&`8t|%4n1@XYmCnUk9QuKDCe{pzJS`# zaMnWDGwBUDO;NOBF$_P~Ji%_XvDD-l2K$avFH3yYEV^B*S(sFG6(p*=$k|NX7pcL} znPn*#G(K8`qGqF%d8X_f!N#HQ*(*?>9R8tQO=rQoz~;Hz-pD)J;U94;_@Rrq6&y!B z-{A8HzR;R|7u(k7aFDtZIV(eKSzi)-IFvfP;9Loxi~mBqAp&2D2L7CuCl=uH>uE>2 z#de+^m&%eYhZG^NXSg@6fB%*t(E5Fbr-_T$c$dp26I0hC- zL&hCTrmW1Pi8S{WDCdHyauCXf;&Dc0*CGs1{PGGOE}i7x&X2zTvrVuj0g>$Ypb7qN zZ`7-GCF)OH%PwT`mQGoR+SH<*6OQ3f*Y!3v=$+Rf5N}Oc>0`aIrS>BqS)#9QtKPP@ zCI*3Rj~LuF<}2ppScF_=w1*=qWX-JJHk>xT4l#Ipim#YFy%^!)p4idG{hHaQG&#kN z0eCThj}#nGlX@#F8W~L$^TEgUWWM}K3O`=kWwm~RbDZS^rT8m!VDkFWwqj=sekmEC ziUnKR*i*e?E8T^Ku+=zk;86%J8}lhDVXLq#Yh|7G5frwvT}=wBZ0ApF2HOHGxP{EO z)ZdCyvv1(^$ioGq88un#Y-GV91&4?LHxQ@(M1l}Bv z`c*}Uk@>+AXA_p5sX6BL@W~g8Mbp7a`#@?(&ihz$rrsS-$kg0%rNDxQ%X~7~eUjEs zrFk2@R?r{X1(keK)t_JV#R?7lUiaXazwK_9&JeTK1Cnlx@ONB z;7=RjVn=`7ccF^UBz%nu$0`2W6n4B_E6QC4SN&rH@$$o3cw~ zF_tv!>u}r4ig@>hH*anl&Ang2ICP=BEf_EG_u=Fo=VEA< z4V!B-pE?|X_p0D~!|;*Ib7iQfeU_(%iv_s_Zs|XR+K0)lC%D{~lS>t{^tys6>|=15G!idb3`&6`JLf3JIF5cL=GnX`ulYpi>HMgaut_nMSWcs(c1 zIs>R^9h&d}3GM3(jiVjY;P)i9JX(jA=jGs-gcTE}0=d!&sQN>3S%_^=1Xn`vN*xE1|3P(1MN7!74opVR|!^=nbfUxTTvA;`>QLkLscB zrjTPP*OL*THzNzDb@NxwROa7QtF3BewJW_f9DPiS!qlT#b3266)~_)|!Zoe@N4n?~ z!t`b`&=>|$K}N3c&X^WbUQ@k1w2mwqF%+Ss%`p&_X)3A_&&_8_EB5N>_EzXIZ?&=qEU_O3uhO#0_p6DUFEywqmHuT=-jKRz5b;TWHj)&10 zM6>oIu<)*gzU_fZqrvrDGB>9hJ#qjone^br=3sBRiw{E0`y~B44_y`+^jLc45JI|p z^yFRUA`0#)VnSO)TfNIvJ@sV?mZRHAQ1AGPHaDZ7gcn#~719<_| ztu2BZ)mjKajNy}e?LKZr%q2iUeKh@ncOx>?MP!IYv>!Vg2f~vp)M;vNtJg{>n<> zsN!83o$_O7#~G)UZ7^`x8M$s#Q#ox%R~)5PDQ&Hd3v!i z2G~RQfW36p+062`1|t;~Uk(o&+Q_8hN-SC|HV_vYiQ+KZj%&{5aP-BK?@@`xzdMJq zT?Wo}BS$;+e%;v`-o4;lQ#qJ=j$&5~oQp<|P*=`hSj)#Q!eHFD8c$8aTynOC3y#GD zHl{u|oDKPAoG_PRqJ}LvVJ_nk;I3@EEbo5QbAd+hsn9)^O;mG2Vh3$a%Yv;rCStG` z$4rbkobl;Rly15vVVWYry)4z;f(jVHfjMh6w zSc>0yR>Ro+1}XiGQpBp&B0pCQTzc#Oq_U=ptObbnV(Hpf7nWu%I^Qm@?3 zSezaMU2oFR8x%B&`O&gbSX1t~Wo`glXTW}H#0p<^TcE2Ze0@>a3YCcU%i3B&ZI5)Q zM!m)&jF2!B2LHH${iBiXb)OCfyCPUvw&ofHX`4W2;p zeV8>I2zSM>hYZ|NMlR-EgXnIAs}>{4U956NwN-S+l0j@qjla?}o}l zrKM7COqWpVQ3t3$45(|euvfA@ku@vd^7Ux6D~d<`n!-$^kR`)C5@6*SJC*ag;70c@ z7>)~KTviMi_}(xa#PeJ^94K_r)F#Hd;w%F+dS8VuX0{r=L6=|}9tV1XDZQsTU0CdA zi=>ITDE^%t#enyr>Apcy%*b;Hgu^;57&d8V{JvhR)U0g&9RtR^ryM0=-VwMwyBDB zcf?$lsS%afXrs28rBK?2`{{8_ZHL!uve6PbLPffj(3%Z3AO{(dqA|bUz!k+7NZRWv z&D)%3H+0ozuNsgq8IfWS~+U}@Y zOi7QUct21lydNZte{Ja5!94dKJsVqc{b_$ww6l&se-{wE? z_U5iQRwT(2b>xz{T|#I_3mmf_X9^x;4))gPyAu4i0v1O-$dtF(oae2=eUgXMr5-I$ z%7$UxW6IlQ&O@*0LyOzE>arV>hohw)o_p~zA7LM}%DQ3>6@KSu?ObWF?tSlvkx!x~ zgHY9C?N@{X+|FlbJ`)(^@(uPN!`J|P+nA-V1}@Np=XZbx;Q#wF;q03+#Yn#)*OhFU zq2Z?Mac&!vfPD?H>ZN%YTHyQp(&6@-(Z$}y1Np9oIMyVzuu4Z4*PSo#=<0!(jt&Vo z-wY`t3O72rQgNzDAj|bgxg3L+LUwl5$MGf|7k<6U6$^qG@cAMg_aVgplQf@#ACBOw zLE@y>wTa4YP`xjgC9%3W+SZzFld!=dk#-GpsT&;Puy;Zp+dXxkYclJcqnU(!{zFtQ z-x`6xw9?%GytWhTM_B2K>|T?K&%iOsefeE}C5QN>ZUv#85JXvP9t^~W4EKVx820DZ zI2^|wlX52O<&1odZ#~QIUYCa3y7}>?UD`PeO;X2y)ReEa=gH-1Y`NsWqvsFLz@5}f zd%2RRqE>focHWEi^wQm!Nxh~0QYWXxM~+ZCR3_F+qqVQZwvW6!+PUi$eO%qy390d& z(Q=6t!?;fEN4;tnQMtHz-J>a|pQ}3iUk9xM{?&>jDgYpH?mCbz^>a1C;+~ntZ)n9S z+GYu0e|S|6yHQA~16(OALnE^W_~b*rpbA&>pEtWF(629kWMyd`^sanL2W@~>kfNas zmb8H??ehJu7Hoil(bt4A(DfvHSu$Qw8Q5wUNOvD_HDR+2pczJx@D|z+c6DH1NZNXp zCbkFPT70Y(4H<$_a;*Wm+KU{u7Zy-fZn>sSQMr#UR2IVfRFp-s>m9T{)%Fd0J#+xv zjiJ6z@G`!|J(o0z?4dMsxN9X|9dv3cdXu;W2;Yice4&GOzP4Tig+6GcD~Ywu)#!5@ zx!X@`@%OrenJsepdHq)6njZdIz73;X_1G{hl=B;72lKy>h8&!59v6))>D4ow-W`ol z-fxc^yA(WeUPyidna0dGiJ@qU8Lvg|skt9ph} z$Q4&8tDUE5yA~zQaa&6P7UOZyc(kW*?JJ#G3FW!@`oa#GfL7mGGV}FJ+^Zthk@(iJ zCNyaxrbQi0>FxA%Oj-KS{7LX_ACvUSdb)5}3nsgoV~4AF7+r}U)idGXJj#|!+&g+4 zrVW%vhu9j72C-St;@@oX%@AMOAsl(gRJWqoY+jBNP0S*5z-)&;OwT3^pQrQNKar39T z`oYAS>EMP|)IYHQOzyA}hCD2<3jzJA)FQK?@jQitlTV7aPKIrn?%kmnHrM1%G!VhQ_t8 zxbO;~XI&jIcQz~d40Ezr*O>U6t3LZelGm%`1LZ-KGXoCPT2tt1bEp`*N@im0Iwhee z6zHsVu4pQnCI-z$MEq~sX#{BW}aNl>+ zSL-MT#5@uMMxNGQ%SF@E?!0-jsdnd$XsX>S%Mw^`h-i6apmz64_M0yKA^WZ!c{`Pt z77H)w#nA;;pB2o>va}OqY2VJqr8h+%xl*b0nF1?i=J)ZVwI8_x*rSFbKO*R2_8k%DMgnR3KaqHi65iPMGG(?z*T&u#?FiTRZHESS5b#sMmp;)$vsTT&_)uq zJBI@%T}|zhAW6YBV|10l_BOS=w>0*XBkA;79pnngsF%r<|2go z`DZcA&qtE}ftOBV{p7$BS9i+_Q|K~ts9P;oN_f-dkKLe9y-VQp6}U(7C0`d{0ofDiY=-N9xN0W15d(0$-yu@tdT6@fTdAoYsC zpbZhRRG1DeJ-ag~fX&xLD3QRqwChWkxFS+i@3j@KbhceGx2R0vfD0qAb4OtSoj6-) zrRUC}?04UTt@_GP_KmXapLh0{88Q2h`T%kMi&DZlRlQBi$} z(nX)#*$sWtGZUxpcge|#5+^X5(^FAcQ3@p9@Jc>97C`g+ z76j6k(P(o1UG;TWt*fCgiuLr18*o^utz@@W**m{*jbbefpr&4sPzOeJp^i>48T=}x zf~Lyhko}cq@$4Cl0Q}t3zpdoK5cZT{sME{ zcBcWWEtG6^)nR{0>~$53tTl(V%`0)asKZdXqPFU`s}-CH(O}OLul8UlTQ6~I z72N!@uzAJb;z2(-EIW&0oLBh}r$m-(2bQpQj&we*v=GV=@xq2(l$UI@boEP?wy#n? z|IFTa{@jPlak9tw^cTEOFIFJ``W`Eg;oY@5!W8#3>Ii^v?Q9ID!!=4y_DiGlKOi_C z#J@I7i)!#(Sx?owdyJqsMpR?JE1q?bbWCwQbZ$ccj-iCJ{X#W*wkf@(Ib9f%%oqFn zQT>BhvX~%6V8-hafjtXy?mg>rzjTT^geQ2!RK|nmGQ{BCstkksTN00XuLqAo1=aay zzXo)%99K~+G9?$^gKWb*e6NJ=_Cj$xb0CeZaMj@8SgPFGKakQMKVr4)G^KAdr;9Q1 z&uRm#6tf@0&vhvWC(_)77yIEvl%{gsu?BSE2Xw;ArXp|-QLS9w32|mkG|AtTCi&5e zP7z%acUboM9q$Kh3O|~6#zGuA6H_ScD;agg&>0)yK0F!A7W49HM=&oR&_ipEefeK> zqqM7-4>3T?iM3xH3dd(-Y)KkdOZsqIN!_yvx9iDp(J$1Ik&rt9p zrrPlHV+pjU=Q4iGKnI;>4$b=s6}eXORx7-W_wd%l%eUdykNg?S8msiYmF7IrCFdST zm;6aWk19}H;~7W=ClF39*Yh0aJmDw2d=dc^f1JMHj!`vy^6?f*jpI!%kFlO&a8+#4 zU2~Z5={`FJpRR?3HTA-9gWe?<&VD{PAc7WDU=YjHu!=v>sHZk&E0rR43vK=tD`cZ3 za)b|(ugD_6Rf8%=4X93crwjiCC8R6pL_nyJbXn};kuEJrDyRcUcpqGpw z(V>3%4GWlCCFpAv^v*eW)td~U4Ol5V@9M#n#}#ex(s{JO^AdDc1&Ky@>kBkO5%~Vq zfUGcQ>yURn)D;8WJHsFWNQ)=Td`1@|&k1vWnwQvC0(>NKY1fM5!};tF9M zB{Ek<;*4(~RsO-7_Qqp)>%m9&2h-h3c&_aYV%iwR2;aC`CFWm65|Kd`TSe;1dm`igAo}|ev`{j@7vBS(_6I`z`y~83Gk81KOv|{PoW!cpi3Hl3Cu69hG$c3TPiX zHug!ahgr%SW4TxdE;9!U8_|oEC$MTbLC4pH7~V%zE{8#3B1Bc5JUF1XcKY(Jp3L9( zY*J*{!$PXNY+&sr1?54ksibErboUBC3*61Nu$GS+>hla^%RuWJ%SK4lPz5F090t@- z%JM6Z;r^>H?|yThnAQyPFR#a5m(W)gDCP|{Y2Ai_v9$dh+VH`yI6w80o;=5#Ed0@N z0p;=RYf0Xuklo!c6Zf&{O1jRRC)(GfMnf`GjXSg?z|7gQ{(g1sv?45vh~;L~Un zv10EPtg$PCdbfKq)|kW=OY)6IW8&}4%--DGE&lG0otyGL@6F7cH)UsLVXUvI0!*!p zrQz^a4DVwXOM}iUaTrSGm3S8+8Kbgo8L`8(Z5ia=lb`zc_J+L+%`OHDVIK{lakw?Z zOreA!4tAl|;VkW#voH5{g;OQ50sJFK{Wp^uA#jEL6~4fkQ_9p3PwwYe9C&=6OZ%oX zAI;JQtBa*g!BQ|@T}Z4%C74&6G*GhpgCfuUE0?}`!j-{Qz4651f!-caqD-z2-qAx- zIuuJ;5*j55JgB|+y?sYlc0!_NrSgI!rO{JbrF8g@$9#lh40?q_Cck|ulVn99#kPwk*GrElIm{) zm4@AI=p>ViK)%nd3(fz;{S0NgunE>1`K~s>DhaX8OyVbpd`SR#olU~oYpw=^*`^o# zv_n1EQ^izFYOUeypQ3$d08jIi^nzALU=s2X(4BBU*mMY&Bq&~LH$x$`9Lw7x3)~OL zSv2`u*c=i1zgxwmr5cM|*V~$?OB`7wcOeZ&dznTn?-cYYsV!{m$Mfby#rF^@O~nz5 zUR~W7rJ|ukodubBET$|Ips zQcf4%3a!XK5Ty1g1TPxqdP%NbnXm;RuLIf%DyJ2`vB3c2TiW%j|LH(!v#VUoR8gv< zL4^xY)XEi(B7-e+9~bbhZSpnLbOfyqc^&N~rE{U$bxfgBtOhzz0Hqs9ZJ=kfp5ic+ zY$NUGh}>I;Oq;@(1|}bP^m8A)Ke3+4OIoV&SR(MC8(^C>#3D}Z*4GU>)<^hcNBE?# z!Us#IG|=PufHp83afCWZq3~T4DyX)Www)@gw51E*&rf@m)5bmlnx_3v%rDX<$;g)%U&x1HY7osX}YB)Dvpgw+L#9cL)+2nnWytW~N_E(C%C#=H8=2zY-AG-sB>+>#nVEPqo_o zXb6wBv(+uT+bd$TfJU91SdelS+s+q z3?-pa(%|k~59#Zcg`MzzuC*HQYDeIkxCS;Or+-8EzB883%6x%Sb-+{uvtm#KFKNIR z9DvCI@WSwI7aVe&%@;IV2TJwO)YVj5`cnga>i|mEME7?ywS;bQ`a$Ch9U>j+Nf(`6`!CkhBvi(F?n$jv7!q4Tvi7kDUkB zfZ(ZSZz)=b*3yPXwF@hsYF8wYwgKeEn7{h*kCYxvm0oqXzQr<9F!3$cq8?nA*jxI3 zxJ!4hCgd)3$ubiH;GPz8^Xjgo?WG~Czp0kALnrB0ger(%pXy=Lp8=-1(mf4K6~XZ6 zs|FU4JkSk_IuEJg(XiaQd)rJMczXGaN4$48MlXE#p|(LBb>12FCvEq|PeNKsu?^y>`AYh&An9V9z>D8QZ#2>rh9425MSj+i&*?1O z1jmYe-QZww?S^(HJ6{$j?O9N*#@tY?9!=t!K#L6au7M7P>yJj2lzK5;est0LuHg%X z5lZB9Pd0=-2Q$Z*4oM|@u@C29FopieprzV*(VsBT3at zt#ghx@Rv-w+~J4ft^1Yf4)RZ$I3JiefV6lzoLJ*sKqj-tw&Sp*7p|@TPbb?uO}5us z(IYgK?Up86{90k!Fvn?VN$mo!hA@7qRof%J%c}^JHk!m+Mr-$GHG2=jW?;>B?#*fz zgDHGiZ#E6oN|v z%QvA8d-Y}Z2|pT4>=VU&P-=K(Xu1KfXY}anSXu6PT`L*A2$*zWkdSJsg8SMjW)qJ2 z_~IRZXtvzcLTac(s;5Vy<379>uqy6mr%0nX(z(m#VlZGncG-`{kMw|tW{V%(3LfqX z>sDgj4A-F#)uYp9acC7bi?s}WHAkOy94#v5y&?=yEXCk-8V)s9>M&FFn6#4aug1mB zOAPY@$F$Z_lyp{yl&wdiqn1x=Or`MFW}5f69BHsSmg1?kID`6KhxFnrNT0tm)x&dR z;k&Vp1NvF(cy1AE9sk&BcujIFLn__Rv5Nir>8h9t*Wi0obwfu6*^WbY$-L_ig*TX5 zOVK){mNq2XKv27B138HbNyaqm7igW)VpBaJ8+Z&U1l~F`Sz_&#j1)&~CyV-#Ua20t z<&`cL4SK)R+F$HtGVWqB!p0@oL*d(_deRP^gj@UZ_D1sO=jE z?n1t>Oq&J_aRy_=7*BXP3XfcWWDMZQd6Rb6H5GnCTNM7|er)~NX7efNxA`)leSOG5 zd~H;D0KPA}9R*i@K;h`=T?+f+ps6mDyQm2)6m7r&+A@VQpu@zF!&nC022e!<(B|MToMNHyUk@~t9S1nJ_I5f<+7LP%MRPNkVM?>nB^^7F4%hlUq1w$cRq?*(Vy+Yw zdR*1ZIyHaXamP)~q&*t$P5~G22!{vkoP&3$abRh?PTVaFR$7S{N0RM;sBJ`1aYlp{ z&;&}i$Yp2T$QzsZQzk#@)c~ff%i3G5$#V)JT9aQf4X$oi-dC_dffU!c_(>B+#Z6o# zS2wy8NbwjIKXlCGVR6GpjUi!SMi)pQgKs>;@vUUGsaUZO$u0%tKV1qGXz#&3IV%lh zO$b406Z&}oYeIge+w2m3vY<2Q)2%@KcG0<9t4)}sXba=8InU&H&ZOo*!P?Oy>f_Fg zE3S=>dlHl=soOwSM+l^S(6R7MQy}dB3qNJ~d!r47q!|1zhW(kCjstm1#pA3oFt~q0 zDGhWk1D!2`vUTU1xO|&_-sCII(BVxT$U2Tm18w7t;M|G0Xip{FDT2`zl4U=jRoSk? z*`mjxuhRTOa?9h_swkKuf|2WdVdEvV@OO1!d3rFa;QMczykK9g?~B3NHFrEE*FkIy zTM%8M>a-Yx9+;}Z;PXzU=v~#gm(-yE|H}uP46r(c=Y~jiG{kTLkq#~&Tr)*VHFaRs z2kA-=ZyfIep*OH4#%f>#MX;Nu9#TIYPVcYd6op65@xHJ)hb+)*h)e7dYmy`Z>}sQA z9d?o)o7(vJ+PPJvQySuN0g=rNJ$^C;L8wQ(H;k!+HxC`r;U3iES}Q43dZyw2BI44j ziSS0qWsPyN`9uf#P!CCs<&OHf;gauQc)ZFK1)JVl4KJ;4IyKsFGPN>zktlInj0Xw8 zfeHE0ub|)7+~d8M4RcFLQG;1;-`o+IPLQz}%0g~nd?C31#L11V`8RQ7)G&WWIC$*> zxwjKM!0R_BFL)M($8i!gZetv|(Yfdwiz&h|$`O8e{_xg(V3PYu^=A`*{HX7Lc1!3*Vu`_}2Ul%l zn0m3Sa%+>++8Fgwf1LV1e1aMi#zGy2S|Jpf?z=ElBmbMQ^V!INc3Xu>SaVSQnV|F{ zg2|e6+X9q|_?E>%v;;x49(eTwXi}{r2{oyZ61RUB!?91{-%R!K>!LKuLA0e58G=5I zrb1|fAv_ffj}sHY7Dq9`=+`$IO`1?lD?u}ru`!H(j(g14wAR*740kA|Fk;EYbfjlJ zYu~f}_CVjWzJ8$ftZyFBp4I0NCbH{UUlDXoP6VcM-ehv7t!HO6=!#w75LUgTzc*Ku z$_yc9wZ5?0S`s#W!JcvM$*EwZ9u}kL>VS#PaJK72 zac!s35XUZ|K4ljv)fvM3K>M{FX{^RY7P;)Ns&Rp!QxpAQ>06u6nFFAzi zyDu^$`d+F(pHH=uc59{Zs()@pDN|Pp+w4n$Zg8b{eB@~IUDxD8dT+Ku#Y%g(FuA{6ZE{T6lf2=uCTeh^pD2iZ;`?2Gq@FlGDv@quigIALH?nK zq$BlvA8>&vcqnU{mB8R(js_~Stw^cJW#>*R%9fqWlSH~5Qe{b~X5dk5TE5$~^4+YJ zZ_h_;7DSurh@ni&Za%?j%-rsI#qiU>1cID35ws1zAn9%ngg0#zx};3kP>q_ni@Fp2n8@KWX|sWNUr%XeK? z8l>^*@4yGf6)^|F*%nh-)5X1x`N87{!4V$6X8W!ft0`~%yfyt#@4#>+(c0VJsteiM zU%G0zK`WJ*%)6SVt)oc=JG;hKDz=tM_!G`6f!r(&r{_m=rIx+ zB^|bvgRS-cu7SRA1f}QAu&e`MnaiZ&(79GF`8-vA@K1H{)|Old&Ul)OOQqwOA@)O; z7-A|k7I9mtc$}lb^^Rk6;=@9zt`SN#jWE+n_1l3;0Jr<}<pq`8;%c5^)UxZ(-@9m4kp8<%4T{3_rNPu zf>CbMO1iUol$=6(4x}u}Mm_NOx^0YP((bDW9y%s^f z^)q*oUg|)eIv@T~Fvm6KWLFTTN95DaR2Qvsihl`+6x#lpgg1J+p^39QNp$V~5hsKVZG%6u} zm0S)(bk^L)ahG&_OFGH@HKqe}O#A=;On2BZbZU25cUniOMX+3M0D@Z$Zv|IJGG!LF3ILFHo0XBq~n@ZPp09R}Pw2rVy zX&sfuLLme1N|#x89UE%yYjA;aQE1cF`>E_8zNE$qfH1xB)2(J%JKU~LolR2+I3+)AYU&`YFQ&4`^8B_5rU3=HZ6@jtF}k z5gt!OAsW4&UY+$jW&z&?? z!$z`pj73SR1GixkPHZoBcnLOP>(bPWE6Sr-Gk)uDU8$~#U#Yck%hgBaQy(k4V0|E! zRv%xwn8n=@?I=hjbQ)lGfwY-yXG`P+r6D{xJfjMDHBWJoCfZp0z{bPaD#qzpkIff{ zbb)==u_J%N_Y7-Tr&DE+FVB}Dzj=qLLW$JgZ;55 zoEvJVuZ2iJl))92#|cBNEp2fGSX-p_Z)dKE3q=mpA^B1Vv^Tech6Ax?DdVj6=Jv34 zpj}Ig$(QhH2Xi-Ry2y5UM{^(izMccyiH_8?i6rbWhfC{4>Q6hcdEhmh_F2XoHZjYR zD}JqsyoSM&qviihEC0A(*m{h27xR4SE`wYB+B&JrqiDkDC`Yze>!9&CM*fqN`9SM# zIJA4~D8%nn2t18$SMIkaU|o~|!#Hy^vh;!fx|`cb#YQ_erJ^*1oMpG8PMu`#U}$Ki zE)Lgwnq#DTI_lc_Q~%w|+z3xcaNa4s%@I-`9d*zAsWbbab|u=VoAotEO0#s-)AOh9 z-_P8_kYS_#*bgU68+Fv{8MX96t2!)uYL~9l(uJTVA02~J@+&ro!5 zcSqY--A`Q?g=vQVBPaBRXM?a~eXpbbb2Nls#x0to?FSd12Q3QHRvP`su;z#Cye$q+1n<|2K_P^`=*I{JqB)At=_4wi<9^!J9DL#07F`u;k4oZLl9 zbJpOiLR|YvGv>4?a>?kICB>7b7FzWiB_`<}6`ZO8hVj`c4B>#{JnYH|=HR!er#%H|hQfC;0` zEu<$p0UqWrK)G}xKn*-W5j&5qz}ZSz@*N8SW6kx5Yb)E`W6^r~;Maxh`&X~AR!yR2 z4V9akwLH{MGR_;ZSQtWOCYXESjjhNr8kUm1k7N;0>ZQZ%KGwd45aVW;&geF*#r5+x z-LP#;(a}w$A(7I!vG$FF+PC$buV&!Z>gZR0g`QftGXnk9p60GnmX7|!SLmsYdm_+N zBll28|C5d0dRG@UD$mX?uE&NQ&he~mW9j+8o@rPkQoLi0d~qnftR+jVx7}0{$t%q; z*ObcZ$jj1@NXZ|+4y~=}Q6a)+;!L592#}s-t}3fGS(SR$R8;~)gP{k7jplEwfn*F6$BHGPe=JJN; zj#B*UAVu~va}@)==H7wkj8;~&RC5U4fxUdYU1hn@0Ja$B#Qu|Gpc2C6D-LOBhQ3JoHI3d*FFfn@FX=T*t88Q6xOv#YNi0{ztryD~unq zwn}7YjrOm>iGf8xt-01*jhrpF&*&W+D$zST2)YjWS%5gZ7uO*_Izqg+hhQd5t2%6e z)Q=X4O1S_VBr$2<7=#P(9vSkSA<;Rvv=lL(wd@!Pg>MSw_SrS7Mi@)p{NAu%<9Pf~ zzsB+5p|-~HH%98#IPPMg-5N(v%8IRVAQvL4p*{65Kg zY3Wd1ENdfVi#(G5M8^s(MMENS<{NuMTZOP+^N%GP(ZRD3MV&N(ee)aA+7EqE7YyLz#C6jiDgrs|ww(4jy zH2xcHB|$PlJI3oGJ?R*wD?0j%`O|Y%lU|D;blmt-2l7k@GCtcp5C=cucd>&-4e)`g zr*Sq}WRhb=7N#MQcov#hB!AIVM<6E#UJBQd*JR|<#)+D0PGqWDLi3}7p{hpV&LV2u zjeP_y?c>~E|Ck2qyi?}u5m=5Hy9)wJ{3!2>B3c#7DHC5o@B}95ekmzm< z+g#A;{v>y3+hR>)csoXMk?v1oGeXR&58S?tYXd*(xG6Snw54O#Y0FQ*ERfEzXEmYx z?q9`2y5%RcRvuIxx5wkQk{5RUqW@%GbhLdJxsJv)QX@GVhJtLkj*BkMD4mn@Q#UyI z$Q%JNjg-Rp0aRGl=+0=4X+d6eP2BS_ar<^<`C4kYU3H0ysSdQ!RNmgXsgwrUXuRR&1)To%*Ae#7r0Iz>PjThq*)z<} zd=X&kb8~AcQ3o(f4?veCL+eA)(n=}s@J4pabvvdyEKN#Vr$V`xxD1DGQL-qBIrN6? zm*)0ThK|`r8#C*0k}@mYh%y7u-_2f=(GI-9h5j!((LQUUeVob;X52#8j4#-+VC_Wh-Gr4(ZToVmGtItS zd_1Pv7El|*fx4TD!P`ljLbY}i4&Kg6G!rQG4|6}M<}^n=t3C~^yNjtE-|(k-kTg)t zNzeb8hfDo*guU_Grnqe#duVz{3vu$z$iC*R%$L>3aEF-pxU8J&kOyz4Z>PD#KQ%Ji zlh>iHg2-JznEOhLb)3I(5P8tw_-^l{D6;=YG@r+GgokNJq;x=&8G)=aTeX_%Nu?G{ z=Cdm^+_moIm7~zo^J$Rq5B62h1%ZP;n+Hn2>iGVw@pY`^t}R(5*R!u=|LOLX?Cn%) zI^LRgGGm}7OsVODf^PcHEK4P(JJxHl>Gt({`U|RS>*+QPP3x>Qms;rPA~58|ohV!r zrqzlt)*`ag&swclOGIm+Xf#qQQH)Dua4jCekQO?Q?X`_zEdRsZfx+o{4TS5`QL|0{rbVEmT zm4-x0m$X7a3|a_lr`tEuUF~3;yHZ;sTdwS1r2L118e%s0&;K+lMKOdpu8eZ+$+%lw%}Br{ZA!pV2EcGT2StA7*<4S;?#GB zb${Q74meP4zV~`C#DwO$NU=04Y=0k3wQSzz(%Pm#8hb(lrWkGT*W4RO2h;S)GSH>GL521%Q57(q4Ubba%RwtA$biA)0 z+<3e>01_VJH@*%D5=t9qK+j@IFmY`GkA|lYvo?f<`^ww_r<#T z%~u(KFDD%`rU)6ki-%LiuJaqq=GGcg0lZ(9cN~}elx}!ef&)aWPyTa>lATA9&ybs~mXnqU|nqR{hv!YrimOm|rNY;!wJmc}!MGO1lg=>yR< z6>$qk-I>JH&>oAj#4!bBSs#Z6WDZhc(jD8;!sr0SPnxS`@7pPNv`O4-iXm=koq=g^ zg|5;q`xUwuUEFkg`3zlQW1uoTSnBGgU!c2>1a=E_t7kGph772fQ}J5~M9d}g1u+K) zDFdY|nwb3;+^HyC)Jb@LroAT6Z2`a8Xz&V)@=ApCR7d!j6`0`x#MV~=ZPzA#TR~}v zd#(+Qv4=*U_-&(#NbibQO4=`u6{I1NI{GG+l<|fTf!?LEGEJ(cqpu>;2a!`IRFnHx zQMwpnMDk#zx71Nb-fosH-xF}(((Er=@f>1^;)3hPHrdx##j`nMb;L5`EsdCEul#q) zZ0k;U>EQ8U8_2NgO01Ns1NqhlLNyL0B2}p?kl+xd8IJO8cxOVCR?--J5U(irc4D%Zizc1tt>#zl-R zOz!w6n^#cHsGgqD^wh7ZFw7iKD7Q73!hmI!a62BH}>bM-z*D2e#*I`~4rgEvSPc?)f_-FwJt1{@U>E zZ8hni3H0u9aW>N`tW9RLnV-C>K-FQh$v>inOa2;XY;OpUJab(PX^hvL4%Qu{D=06ak6bp# zW+BMpVBe6NjkH2O%!z%V+jyn5;eex{dzqlS@WVD@?EV98+H9@JY*|Mvh9We?Xu@(WVc2Pp4<2t}23=rz|!7ZM2Sc=L* zjdu#I3#NIGXR(8ww8(Z8&TTLKsbN2ytyGlW>R^Ajl@?vcnB5&mh(2>!y){q7gD7s; z9Pr?DtZdgRJV-#4+$dax!0%*<)}xT$?qIEAYKbW8hTv>1 zREJW7qtL9OG*m@@qeZ|}H0_+w4L@kNJB%7fN!HNMUe-EeX@m}a*gX5p(U)jO8=>|R zZIO;}0S$?i=Gt=S_*Qqf2t)R-OPM;DZF(45KklQHzS1obAbXTDSh}GDxcU_Ux<&dA z5rA%yexn0;^%a19W0YuvFPvL|ohX)|55$aBI!ZqC9c$T(hR}kb6@(>9buShRhUngZ zCxvy;bq)45W<6I&hJ5oBu7$SKfkw}_uNI-#8p8>_Atq6YmPYBQhif7Yoo~~;3CeK8 zG8^gYiD=bQbd*LLrM0V|23#+mrhoK@k1W8{4kf^;59`Fm2T}2Fh!~NO;w`cu@PB2KL!8A*wHu=PE-6j7>DZ!t z7dY0K4-JXb0niS%u?TRcEp{9YbO3c3fZ-~HC*xdqH*xR=*`W#^!6N~9(J%?1-;ijD z*&=FC$4Qvd#mvTfOVl`}_Os4BXX!XiU!d0m&30_i$oCJpQyhAXS6WKzbmVK8pi&xs z=fi4(P!XhaHr77yWv)_RI<2EUWup~UL3%3El97w_SV#N7LG0OEa%yT~7hK3H2y^WN zr57rVBz&RfFsb}+0pz{99fjw+xzt1>!jmwQRYdw*NlH7ZqK>}YLPvTuO@pL%BI%58 zuv?ARk+$Tdv@v5wXk!)&kci@GM8T(vc$->5yw^DNF0Ow8*vKC+lk{R z=Q3Y5aZ1TAiBm*AP8?_XDsc>Q25}0?hlx`_UY|(ZBJxG%TqBOBe402;au)NQAdZ)O zmpBGFjRoE(P7&F}d`ZOdl2eIekk>Ka8sZd_cM+$!e2O>)<;}$LlphnPfV`w&BD&7< zQR1yHKO>H-{DL?Jc{g!9o4PIg3srMjDCNN#07Fg7U8{ zauIP1@*NhJ%;Ly_()RB18REFeTM94qB7!Z;u9D4Bkz36#S5F+<93NkE(X3LZ? zUs9tCiNa~G5`MXS!9@@PyRP06D%M@RDLOdegx zqZ@hjAdg<;(T6+|C*U)Vst*x$9XTGk0UvQxGf2?S4>9Oh@_0@jugK#KdHg{hf8isJ zss>5>k}PcgP9AT_;~ib+B#*zy;{$noB#(c{<6rWSzI1_-%asb$_N7vNScX7EEvb^E zQtco?#JZ(Y?I1o6@ONw5(w4Kl2 z&KGFs3$pY1p<)t-#B8R@rR;oV?0f-szQnS2-g0)n3U`5!J`A9P% zK4QF6X*(f4(ri-YPl;2UXe6<>sq$w#-+y+#0#n75lD%(6L5f05a;jX=&gWw1bF=dm znt^=}k%kd^*hLnx^A)x8kp@lZNCQu$E!*ZRe}}~pbDb(zwDVQA^I_Xhq%g!7q*5zD zV3?1X1DlVS1L7mLJeAr7n~&H9;v;q%n9)k%d90J#>F9sm>P#5G&~MLiPQ`^BqK)2A&8hLhah6G9D<1XatI>E z%pr)FHfvC#CO@%sB#dR3m^_=0*gl(&7(ttlSVNnSm_?h9*hia>7)qOuSWMzeWDJSz zv_%plYV#3mYV#4ZYV#5MYV#38Yx5C{Yx5D)Yx5BsOnqeRl}gPo@si#=mD*vOkJw@2 zBc>6}oSlzYW?LLF(Ka8k)ixh7+T1re#dE}%gdu|_B@8{bf3*@}z#;fMP-=R#NOCU_ zEFYVVYPArmui+tWA_^9eGa=3v@?#qsve6cj(V2!=Fr*a2IsOB7m2}ioLN0m%|9fA6 zptVXlEjl~L7VUcz|ChK)qJw>y(zoFCjb9%Z01R*2z);JHFe{e7!OtoD9biiVVlm z*~lEyVWDq3b4c%mz7QPK#LC%lbG?#ecnRIUS2~4}K{>%q8;v8I5()UIIRziHr{m-N z3_1p4#09adiIp=T?|a4Hjf~n6a6SBm7o0KtzQ}A|^ z;*U4^duAX9GHAz?OJHDzQqFMn*xU@IjKS~>)^Ap-7a^SpM!v#+BUauGrp?G9^H|v} z%1T2Z8M~9rdUeOz{HF&#hW5e7_I?GiIMX3_t5Uff=>?Gff71&hJsH;%f;njT<(Qcy z*$VL6rc^gr5RmXvk1*&ZKH_Lkf^fvKj1U`OjO-7Vt-asJ!*8zjxcf^NRFMJ&Aj}Il5q*$z_s5cyg=E(SHFdiS9rxWii ze1y!y$JPb-=(dOukPZt)2*my?mNrI$zuuUL309}zBYF)!Od0t2v;`lbnfNGs5FdvQ z;p6O4e3UqWkJ;q$_#{3)ox(?IHa`5X;p4z{!iLOh2=b>~4C;QH1d$mI32I@+poR(w zI?IIl@qtSsN<-#6BthSYnBe9kd=z?4Qjtjz37_>Eg9^OG$M6sMX!V6e{;EY93gRf# z$(f8o**IKYgZ*bLoh%XJk{%d;$`c=RN{~ck_C&%z_+!w_z(jn8RKZ8>a1u*;0W2jl zsX~XCVRXo(3LP@3LWfMM&>@p5bjYL%9Wtpx=Nsk_6OBGHsX~X$snA))oc+upb1DqH z)sDGjQiWbJsX~WLs?Z^mDs+Bi&UefqlPV0{OP$G+QzUsJwS0||z_d##m)Hoc+U!*+ zo+D!?4vHH;IhNd2#8MCgXQYb@M!L3;OqUjnbY;Ov7Z!|kUBO80n30+>BOP`dsSz{M z5w=mzrZ{9l!ARE=jC6Euq^k+WM7o+_l-JYfi}ZPoK8Z~-(scx*e1iTb2FXYl5sdPE z`kyQz8094TB(}&%*AI*`Sw1k*)dM44JTTI=10!8JFyhJqi9Jf6iO;C>fk*aGl)z)OHtjTo#pG>F!$#jfqq?7+-s;0?QOOvURCQ}_Xl6imC0?9H_(PXNj z$&=-p23!QdEif7A&c=4f^fNfc7Y>xfW%Ye~mFhu6Gv%V>M#RFNDE}lpjW17iL+>k8 z=3sd_eA=sYb+vlB?Grq^_bCH-Ows+yNbX78uMDfK(GdJEW%04Sf)&4hMUKA^+z%*? zHNu6^{(#b)dzK$iT5!+314>)&seVw2F2T;@T)<24=D1Q)gS-tzPAK)br~L_~DfcWp!K>glC?}LC9uss@ z@QgYs(CiUC?@lT$IZcyOTnTQ&oKphyrBghIx4}1ytH*8Vm&NPwHXO|2MRXh7vIVqu zq9-L=sT{(xO$LQ>4YJl{Q~c|7(aWNVrIkC?HNb%~_+g!xe~XuQv&KM$(_EFZVd`nF znAvdow9-w>R5p}5qqO3lv1gP{#RTYo8zHOc#s=8rrDODKdY-3V)Zum(22J9=3(=>X;4#E*5yBr6)|=Vz^O2lpMbC|E${-E$IMlkX4CEfTu7qly zt8nu=Z@?Mw>N;;e8PNC!FNh3SeM1?d(PY4v8=Pi61m0AJYccC#$xUSf_Y}-g#&gfC z9Aygke9Td%a?jLT$~5kIe@g&J$W*GiUR3{OXlpYXv-_Sq(0vKOJt9VJ#Hya*@maFmM>a#tC{W76+( zfiFTSlQNmdq?rU7ce4`DW9FE7!CZv*V$5iTYsf|TQQ;+f5u#Ng=BTQS((*wDZnF0= z6#N@}uzd78&hv2%T3L9_U4y+AC0;8;GS-trPh-kWr%54(L(!W7Vb(@*4UoOH@UMaQ zJudz=SaeTG(DHH(KHpO&h=@t&5wY?GSbk6G2@xLxJZ*?Ce^N$qx_}qcZ+;4H0`>w`Q*VO`f?dxcY#%wJZpJs0`CY$s(Dndlyr_ybEuyC>0>a>|GU7 zGxA&@rfwM*jdK?K`;ZrF7L0ks#mIu69`W%?7DPPejVTMZJyr&D0M94NFz%W5M2X{` zr=ln3XJwF^br1ektSdT1yoAp9v;!xOOfDd&7*^)FmIPQch- zc$J)hpMMcZ+W*QkcmhuTstnOehz#uI3V$Knaqr>H6RiHh*Ya?e3o|SyVA*e6ZBD@7 zzbXB>ME#yAeYq#=nGjRBG+6dR z8K=Rffy+x}iY90p5ft1cob$TFcqcU+QWuxU;{~Ovdh+D|yyWdJ4W_>0Eiw(>zT#~) z4Tk=%$eiZx??O!UYu;AV;M8knf>u|Q`2}BO{&xw#yz zDEpS%_xoUc%S-b<{PC7o$$f}@CnUT6jw{oBsP~65LMsC@=aOeuMZW&k;N4$n@Xn5{ zSPIfTZu91KAH4ol28!u%OI`ul6N{^uS^*vzD+aIjzU^$S;QOFpa;-4#LzDk;^`yMs zg(GeITGlL2uFbP-f^+}lqZSjC`HMFU6U_UILz>{%zj$6u(B-|5?AUv*#U=>+z_rN) zi63}jnBe&bAz94dyrG+5-`@gF(T_?$t+vRdRDRM5W$oSCDtgmsE_xCy|HymeBzX6c zt6~xi_{616f-9f6o+LrTf4H6`!R~*!QAmQKp9O$fpLugkg6E&PAxVNh{|Yge{^glU zf{_1sjggtOT(BR?I*1;HqYVC>F!E=LZ?TOdH2uYp6lRd*DjodTbNws$6>)?TtnxHA# zIwE%ci757Ryf?r}9SFILDpY>i#o{JvpLhgz26k=3QUJGlW zZxMAmPxfOGA*R2VI*Z3V^ir+!t6lJ)muj8W?t)H5RqH^07i=!7cGYNh!RMl?bpA@DBn@Kw8L#kz|WYsy|M*5E^s>#Mec+`JGsUaY&|qOTxRkM@>JQO|EN~uxWBxy5|?XSxS z=yFYhvlsC6!6TPYPy9B0sCB-!87`LM!fghh(yDb@xEThPR;@GV&9J?+YF*aY3~x%S z*16SYXj(>%)-psHe!7VaujE)WWVyvNv;=mR;bJd=zsjiA1(hYx*q`Gsfd&4mb%|mL zobl&Mc@!S_tJZn`Q79jvHq^38R{3Pl*T^a{&Iuygs)6vRR;?n?eqn8QtsaiT&Hyg< zQTRK6=k6#(26DNMLQ0_8TvH(m`^wF!fV{sDcvn|Lz$3C29;;=laTLmwRU^furwU_I z{MMnNo?4a(5Hq2+S8?mCl)}yO#56^UFt~&cwQL%B6h0#!7q?}Qkk{lO)w)n~6mABo z5jiwG>jy{SdL1&mbT~ zwXOh>g>t#ySS+Ug@lLS64z{H9>WzYmX+=b%$HZfF%>?LzYQFkm!@`g=%;Kk2z%%fx z$#d`w`qtz^KZEp|YIV(2kd=7Zf^PzaC9bu&X+&eQ8{XAat@EDU&^wfOKf7U1DDQrD zgG(5<;k#i(m}*^&+zr`b>PStd-B2T3wT?e`!}4&|y1cL(K7|W>Vr%hH!)~}!OSP_< z?1rYbx$^CXrL}oMX2PD@s&!R16TZ||t;<@O(6m1M@aU*4(}&3p;KM1Kbf$p zF3)KuysyhuCKFoM6Oyg1C!oEor*_knoV?VK!>?dDUbsT4r#=ko-^^xVGGR@9f$&9r z-n=rQK?AO=nea^mp507P8}R0p384*zWOEv-okY$qdC2)x90HcUsEZQ!&1hD{+9ETdT4S|~X5?w+B2`Sf<(?BnwpMGv;C;=C2u$)C z3mGcggpVaMVR{oG-`AT6#b2Q*SEo#v)l`VN+f=B`&}Q~o-SP_M82TE#!_bmMylGZb z5aX9-0_Wz5QCVYtG zx|<0DqlA1MixOzOTL|?xvV~9&*)6yc$b>>I1qB<>Qc%4;ErmM&+)^mxcCA$F3yw_K z&`JP!*NWSuOsLyh09e?Xo4ZW7)ml)rplIH&Wx|MPLAJxud|;Bfx(&B!nb5Y4Ai(!+ zY^mUR8-Y!ow!FW=N^UD8yV6!rjMD7{IY)_}9g`ytOUXb8ad$pHlbTXk- z2SJ&}ci=YT0i@bu9>B8>yha{C(~f*F`vBH=QN| z0|Pm;E#NwcXJ!iw9>mSw7C1JD7tI!M8_ZkW78o#C?V`PR+d`Q2cf$QSPn?~#Y#wt# zFH?SUGC*$R;!NJVm4JIA)gmysLw7%J_@6-8ShcBE$ux0sUnG840&c~TNCopFS*McW-X$ZGYPr!GmYJE5T1bPkS=HLme8>&W#LWEUA zA+CqOykTl(7`(G*5zPoZf#6}hx}U(5VSJ?a1hPbr6sI;30sX=e@I`G1AAz|V7pHn~ zzy)z?BkuVjPHiBP^{>l+FF*_UPT;+U1tw46<0}h1oWKW87HBRzk#Nb+ATAjtyadJ1p$C&6PXH<>3PZYr;ylW=k>SKO1}IZf@PF(bQ`ePTpO>jad>{z`U7HmnDmW~lWvJ*MGp$%e_H5%7ElpI^}MiDVDu)z;8yrrM^y zCMMxx!EKfDP~1()y0mn%rIJOHt(NkiWS^zA4&ha^qr<@V`#g?YMQt1oG}= zcsEB4tihd*GU&|Q{N+Y{dr{Q)|#;-aY_-$-; zzS2gMv?hC3f5?CviE1NFR2qJlY-fFr+gUYHmxqom4ne<-En^jxrus$i3Ru; zaPWC2A8Xyy*qUS`Y!TcD%P<e0?8)Xj6>i#PwgOvA0+!`_Z=nSbs>G8 z>L*5?A^T~aPk_8Ytp%YT@rCRp@+Et0$DCjiMR>=1+h=zciOxL<@$=Q_QbOT9BD->* z+$?pq!h6%n+tP781PqBO%V+HjxlO4qj+DM}N z5WlsZuS%&6v2RUf=qJkktuQp*4YCRO<1;HKSU!6~2}rLssg$+#(p2uaOIfafo0F+L z^50~eGGn%tFq`=rUMBGY6%G3G29xyo01X$ZO*BQN;c;YNa~<5*%yP!~<<6eu27z-Y z`Dmq0Q?|rJ@(w3gjprV+`xl)wY64iG)Tg|(Q;+n6V0##VBVcJ0X41J zC3~jjB#gBci~IxGGR;8mkZ3;-fN#|Hnv&3?o$))bly0*8m~MX#mY<_5zraUI;f@KzCXb106+dah^i?Zy2P)rfIu6Tbh9ZD5D&=|u*K4?qAYeHiB0^1yGU)(bkik^rn?-c<0ed7EZyYDVlNTmFx>QL zi|z6VgIFv%*)7j_D8?>YgqdKme5WANLc7H{6ldYtFkrFjm&iPPOPY-V-PXloaUO~W z9k+SeVher48C)f??`Day<>uf%4n~_nVt>WsCpOyu7W3Hkfh2kk2H2wMt|vx(5xkeE zewx77Ak^l`f{y6Xq^EmF#^9C}8_1yo_@ak_&^;5a&~J;qLAFe=Sh~GnAo^{wbaw%Z zrEl>6!hHv}So#8=#U@edZ{fRrTXfn04!sTxDO~zBP}b(z1x?YTNzdO8+Cb(3eSPUy8BsM&OzxqkTrDuZICP;u4E_wZ4FN zHi378*S9=n2jb+L@kl4J3kHa>&6aQ$rAuJZ$qZbObmGimHr^EK-r@Ef5(Zp!DE}ixP)W#tc8H3Tza~8@CFb3*g)vg0pqcS z$Tty}5^S+_uFqnvbA2H;qa6;YS?m!)TM7L(+O8=)=R2wNWNK%NUQ1?nj5r7KP)1Gf zZ^9d!#{!B(fm7ER|8jQAv+v&l#Y*I!Dx2ubM}nk79%_>eKQ4Tuh9S*$jc6=RE|snrUJ zj|y$HbWq4>FOc&TI4Ts0=?H^GYa=ik7tyVkX@yiFR)y7_=(?#S+h8zXu)~iuYtMFv%SWx?5qRW6%EQCsH z)S>7Y*Qou_`DKkd0-cU))#2zIUaJm9$M-uLIQu(QM&GmVXkgoQY8?9Zt)sr8>uHi{ z>(wTB?(@icwW=5C)sg=Y#vhl%(sAoss_N#02`YTAc1CC7_i9UYj(@LqM8{==+6tWx z8xSf54sSr<6!6`s4nb$yMwB@PZf~S1gVHgZDUd*%OK>_Jb4x}E$Za;`hTlGJQ=}9$ z%TvAK^;6HnFz$|85aJ(rdSD4$f|yNe3|_9ZX_H#loeUt5aN$xIze6nv5!=)nnC3QA z&QN<}n#2sXKRQ2UU}4>cMw_X#W;2a@$DDRs)Ik`xbBo#&9m7^iFmS6n)PoLAJ~?5J z|H%nr&M^3p!Xx4)4H^|cJb|zY*rtv~D&saR+uQKJZ4{&ZcIsqoSBDw$z;%Zj>z>EN z%%ozr#?a$C)KP9ERQ4&3Tl}wi2SRkF+6H4%GZ8WmK4+@&=#1WpRhb93cd8X!Y4Y7( z;JE`|b;waZC|Dj;+oe{-1f6%O6Wlfuy!XBaNL`j&8h+lTHt-@rSU#6n!G^)$J$RU) z!)~>x;WrqtTa74!9SBKqoh5*B{fZQWqI=Y$kh5F$#zc>HW7YizjrO3P{RSKMs1xw> z9o~D@p6=TjrDZ9M+l%G89dh=n3*1Pk9O`d?=JR~Y!Hj*VBHQ82J}lMk;J+W0Zad81 zk1DbqUhK!LZHF!gFr(Yy>;ZKWI&}}KqtMxSP#wac+JzY4@y^_mQ0|c0l?9EjZGgnG z#RFi?A+<9L%Bg99+?vHpgZp8%3)1&HtWIGuH$n~YUDx7)B&I&pJffCn;bCD02p>}1 zALfwo%|}#!BE%7OAv&#&s);Ps0sLOgrenFK;FqJQGuxrrF?BkNNvdUlE}M~~<(S$P zV`>~nHOhsQ<7#a$GH%5(4O&L6LiLpg<`jpTCscQMc^nHq7d{_XqtI!7LXALY?g=c8 zYWtbKlcb-aNnFojtz^U4vuHxH;p$l` zP53!=B>KKPhs`(}{yC?PL1*N7bu2pO^E9yj1sb^Jf;!fN$Su=jBUQTLrEh8_7<^In zfKETCHSxgr>>t$Dg~&J^3uP!~E%l^YktAq>S$l~e^g*Fs!nTX*EOaVdQYWGVm(;1~ zcwJVfqhn-_(-n0t`W9YM=b__rRZT?)uBuDWsc=nAL}&9gN>}DO8r$QLa$RlaMWz7A zcs{F*L0B7QVBk%)354BH3t^(hH&CaK!`>U}0(5HKROh2}ZR$>D2?(%k z88pek&?k#mTI)`qdJQ>hD7u&>o_&!(d>rM_xVA74t zR_Cd!Ffc7o-Gxr%ZFMI)uWqaR&{=;+-H%SkyXpaSzTBlrcA3=O=<98!jP6p-|>A`x4+bCQ1o74VRsUWmGNmA?0>0tr?&>kwQm@p z>BGPPsQL<<`DvK{3Z=<{YQL+!-N_jPOz)fuP5)CvSEes40_*=!TVr`A^)REF8)uQ zf!7v9{DsOzriI9~d@3yei-vl<$AQ8L*zg{8{se@6P*=m-pHyeK_5oR+fZ>1B=)eC) zE+=qrwz>iyKEUYUPc;1GCv^pecl<|9LFdPRu=EB$#^_O>)np9#<1-FFPQcuM(E{9s zpZ>*MlVi##Knt4dBv)-B3VA`6`m4XFCEQ5>#jC=y4#Z``^g1oarl5u zd1_5eTs&>RWdNoqVz3;5x4$6U9}Jcq7%*0{>_;ci$+8EX8%~z(5cC|Qrxmnp!hq7w zmhI@AcczI)yUn<3W7^o|a|k+v92Z7M;dLsPB3a zODg*Mds*7z)e+mgEUgQf80&?|y4g*9;TN`{QZX3dV~IqyFu~ZOmZg~Tb5TSw!D4UZ zj=S@HXy6_n#56(EViqGhmy20epwqWF4SZ4D@(ubX_|mw~zLv%4`=*3t4qjE^;%Dhl zD2cJ%glx~;!uOryzEmy>i2;^qWSay(`B?x{PAX|xjZWcGmi6fDC}r7zPIPJHkOWF; znk1nNW;zMn{4MLy+2T)qO#@JtB*+cG+~UsoK+6gb!cUGTTj}HbLAtvo6mp%axDk$R zU`|rpc;{JDfuNBxx% zcH&fWr8BWJr}DBhrxMXc?*I!b$GYfk-RMaX%G!1fw_{(PgfqJ6MTKWqy#bW0UG*?H ziCy){aB6ka+ra2bU^l&GQF@Amm_1O-{ufcpjBgtihVgH`4r;hd{N4@mc8O8l^-wt9 zOQ&uRJs6>w9{MCW?|bOu;RN>7$HBSUlVuHh=|Kps>ZMPB^G-T}z4eX`%f-&#x{nJz zoJlDd9U%9}8#AK+gT$K+b){APyxB;%t2eb2)`{9EO-l^M}x%JU`2w zzlJ^~z8}Kz8V)tfS)(orQKN2LxL#Fw4ni5857pg}>pw$v4bJ#soWb2;I8>)Z`{A7V zp5gj5ger{CC(4@iZH7ep;*C@N1+t9Bd2>pf9j(tS&h1ygA4%d*^jof15gkY9O~tA)dI{)k9m6q-25=)q z25^y#06x&I#`0m0;^pHj6+IkIZU|D$7u`eANApE|D0<~H@hntt=kiP@ z^=6Im9fyWHBsPxI2e}=R^qoApW{beXZ5jxF4a2o>qlaG5nmry(a!C9+Ubn%SJ^|Cr zA@OknMukISP8epsL!y+XFNd>5!#r_F_)Nsqbx53>h?(J#7&J*=?Mj(wpSGjbK5Z8X zvvD~4ecQT%Xdk9G5FNL*E9wZNN24dBvEs##lQBic%R-f1>mqgpqt?DtFnz|0*;Dk{ zaNbTq@#956II15n?uO&Y;d93b9QSzfU4$M5$8Rbc1K$i!)u*{qVp{ZNWFCE)dI9>< zS9(V=cN*rPc(HhzKFc9KF49!IN1q7ayZV{pqexbK5~)uv&S`mHhYJ3L!wP+X@Ql`r zIt53E1g_KK#rIKq1QKl$txtz@AR0#oUpr3cGOe7>W%`JaEYp>rkQPWxtToXU{sG6Vh)Gg=5m8Bo680KG#5i}yqG_aT10%9hXal_{{q8uy!iM9 z2Jm?Cl?|5{_{?*@9s_67eAX$!+)oVbWR>R_%7B5 ziup@%!Et377xdvW*6g~R6Fa&b6G6NvAEQr{rB7*qc=+QE8n;|;EuO?+a6<2l(Wl6y zPxwLQgkRhWeQ5DW9KeXcX@Qe*8G)~uS73IF7iCtmGjJt_pm_1qN`0n`XlaRv_#P~6 zh3@Cp6cMNghqM9=X(bDqu}UwCD1WZf=gHz+=?W?S#MGJSlMp+LQLFJ+BB{F*#<_U$ zd^K7sO$4mb2e_rl)9ch)o=`QhU=6ONUaZmEL&0Y)xdLa~6`#iUrhz6-t7 zz#NsdR&0;OsIpyT#_E0H_^rdqxn0azr}u+%V;vf5yJ)&zZwX_vlh^CbTW|lo`2sxv-_Z)R+FB8^0rgrd9&ErUwN?!L1|#TNarzsKr)!1BMjZ3KV);hBpW9v; z|2J9ZXz_Xzj(5k&*6O&)B$@Ur?zHhIO76rwQF4pkhNQ*C);Oyx(#Y6^D;IowyBTN0 zUXiyMXUbkNVGA?(QYpeNWgZzB&w7w=5Iqx>^Zv~ z$t8=gw&OfX7NvIJJW3YJrBiq(E})Xd!ksv)lH+!F=aQ}I9a6Z zM%PRho%Wy<$>RJToDs>wZ?7KVO4(~?@L&{w;>c0GqUg9!w-gHq2o9VyDIkCfQe__} zCH7&=P8N0cbNbu&qiK^xiFlml$znr18uJ_RZ@k{qNJUfKsR*Alck=1T-^{_t5h2Atl zr$?;tIfByf7CVmU3*gi^im_+6*mV?@-z};hLxb!Vn~tIBc8e+rxTMMwVF|jg8@-D{ z{9&=&k$Z~;-|9{H%Tu&4}oT%<9dHMTaTk`_y+Pg+Ad2BKcNqYbK-YB^9TJ`hf7 zBInoWG;4l+8WV9Ap5AeqozLKc4rMvR5ni6rMl-+xXYEp@-`4&86Niwh^9`>Nz%cdm2bchEj5&VHx2bvY?pXH%^B z^c|{sQj9o{A?>8Navn`^QZ&54PTU35{iJwzfn^~V^;Qmt#ny{Bf`?`NL$M9XEVgnzFW#_7c$QeGK_;$9gg zrY7mV#PB4H^S8vfB(&)*k(`8vz9qcAN5{G)7JZKza+DSm5Cw1VSAVbfDjFF*d2%2= zNPxM)PCuYFx5VZjaFX8=jz6+9;YV~br1c~E<1IWFM62HtvC{eTvhEN2gx#*_ZQSVF zNEGyeEa>1k|B)4O^Mhgk&=tKkl=80Nssf)@CL@8H;=5$D%1u%CD#opw;_IvE!8gTk zSJCx#5%iPZ!%dgT<;&zE#hSHa8i;?c;qqzQvN07MHC;5khI?XIMZAWqB3)Ror9mBtxQ4_eh&lcixvnM^`Km9iaVi!F5>=8y^2GUczshJ>OyZqk?_Y@(f<~jHddUxrFU|nH=v*e z*NIQJFoMR4=-YT!5F2*~2UCcScW}c+uR)>Wx{gn)VWQL>y|y?PtGkF*_w-W2H*sGD zbT%P!@8Cw}k!W;R@8|Cj&nUuK5%n{U&smZB zGfwxjFz$k>9q&!=Vc0n<8r|p6w)=V~Cw_k#)<@*s$88)1kV?IEF5-&^y1zI!XM6=m zEk~^R1!Gu_a81GSz{iy-xai6e?@};mWj0WLWKps|koLY&#k998*emAgUaW<2u@wbojG;#Ei-n%r%Q4OS96ojoP19SsQ2@H3Qn$O(t&hQm75Nj$^)bRBT_51egui&~zCi?} z>q8{rZyoRN6iMm&AW8VU#`n90R|alXITnA**f3tK$j}3dG2xFFJ;cOJOzNpm!ploN z{&ey92{9=XmzAu?-z#1`B{DMg;gaw-ie=AG>Q>R~%ZFto=5G_{o)HOIxa;Mp{8eJc zS>c(j4=>GxzeA)yG~y6h+mboDFMo+B2c z$zLEIyDl!}=-r$Ws7K+WL)RyI3l|EYWxrl0`aIExy0VPFJ{dG1nx~1} zCwgNS3P5|+I^mV84=c?w{^pSWP*$JV*9ql?NAxroaWGeRbK)-u-ld78Tx3N7M2=f0 z9P;#DPD`m6!_!6oJiVbC1t8lb5AZ;-ph096vFSO6M2~zO+qVsftbjw!QTWTl8yVtd z9wt#H{FR~0Q_(M9@9Sj$?m+C#*PFUg0Lku_$@Ud@7e`hT4IUubjHh~8k>MLz0w0cY z6#np#{!ooU|F`1U#ShE6vKoJf82Xnu@>CC$gugfJ`bkuNrjK^YrApxwL)$aGbx{f+ z_oEoaUcJz3ic2?icTw#(WK5%bY1ZX$59trtwiErYMpcu;ddRPOdFREH+NokraowUT zh)utuoroaGQ?f$+WrT=1frZ8VTS&a*bG@?rJ&Hx&N5mAG7C2?%I;33Ze~KlhMMmMP zVwujlbzI_xelV>EA(YneAPm< zex>(x=0I#qCvmL&%-Z7EE8X9XQROyhxsFX5diE4kJ+|QHuqIXUE0O{uJ@DvjqSwH-}U-3aH4})`nx{b`688VKu;&}`rORsqUIaDnhPP6@HtMo zH+m`o!>?4DUk%zUfYb0dcQ3asbJeS%2%pjQ@0Pa3Y`!CRz%oua%NB)tCA zr@2r7x`o$^&423soLTnL;v_B&G3toQf9X9W3iWpq|13J>DVF`E&zC7I>MKgU)8l2} zFm|?lr!SJq`GZjV)+vp}{P#%mA!XNKqLU~&b#^`B@Im)>BZN#V$^v$y0@e}%AN1-{ zdBQMz{>MJ(7AKD1GFT*3HR=nWzxA0?>G%XRVCR%U!r>o%xKye+Q3U>@FLQoMIqv?_ zN%%P4X)fyhtJf?>2swHqN8wPyC@)G{zbGbV|EqgT?YPNKV#lN}>Wc^eqJMD$0aHbp zkNQ+8FBk13taEH_MDj;nlj!|)CviK=)=PZ(N$(}mX5=}))cm^Q?@xGN@FNvt%Ni#! z_42|l>l2Ln#fYF3?Wq)=rBI3tN2830>}dEnvGV4xM5kg#6EV}l7%iheUE(B?;+M7* zWgQJoqO7l-gx8Ri&f+IWW0*v4%TW6HU-^pR1&!e{sj!veOhF^omD63n20gr4bK%ue zlo6LMEh`~xBU9YPibBR1=R_*ym%E(AYQM#eMXka{BNsv_=HT^WbYWwJ6KnLtRZd|e zTB=WtbBYU0sUn6JF(yjVT{wv^;um)iE>1>oiNZHHiHviLJ;W?06p9nkHwxX!SSbU6 zTf`J+BUT1VZx_xk#%dXuy-RF!F%CN)r=m?h?Igz9mRiM*qK2OfArx)edhw{J5#Yoc zFZWQZ#Yy!J@%9EQ?`lLyWIJGQu)D6PFGqQL*xq2{iyLEQDxs&vt>T7F%8Q(G5^I(( z_YjdKj2==x`m{(YVN7;jLWTM|-AVYCj%iGttGuwe8O4hcN6BL7*iJ2D)DUOga9CNp z@Hu%{y-FGbWc>PIK?8ON*i6Ie15vPP}&$M1G{gF3T2E=GI0N%7+b~&lreAI!^tvtWh3#fjL}5O zgHuFfccZ@yj7$;B+|4{Y8dTG&qDWbzs}#31ME|nJP#JjkSR5^D43-IJJw|g6SXEQ_ zmNVK&^lzGzNSL&$l2}pBXegu1Oc&pmGdjE6ptf_!z+km~_e^);RoRV7`O(mU{sgN?Xv8n#ia^HM~UX-*hj0n z6^&U=oaEsrG+K=ij+G3HRO*xKB;K`%tuN*)6oP!^`CAFi#PLS`O3mmldQ~hS!9iz^lL_{@o2M(Nb z5NE3yeWdd0!p^d6bv%s@5*bCDMaOocnpok9&dE`_;Q)FXLC$20U5hI3EY{2vjm2m$ zqm~OH6xtjEiwkv(6%sYC=PbfXZk;ZEs%!L< zQRX)gp7o4G83<}9(&`!KWZ_aa{&#bQkZN8i!;umAgBOJ2!SV6pxzW_;Jn$ z`iV}>jh|#-bMM4bEevM|8Cus{c(yeDEyj8|eVhx#j1ZaryUL07t&AH|<#8WTsqrB_7kt#8At6Y_7{8Gqr$hT&5I2Xl{y%8T_}JyUlAuP9gLaITS(S2#955-d@x8< z>xiCB2pVhSgtenFP--j~>?{%nq%;x_I$~JlNLPo8MxBf^GH`r^c-hIgE0w3Zh*%oLHQf zZ-`jk%~)3~g-Tm30*%m8>{!09spx3iTUbo!Zuq*82&FzACw6o0y;niI#@HSxNS(M%XUFmf;{AK@$_*6*(?M)fqhNi=YZvsf@_Uj>oW)0iev2GG&g z*Z_S_FJq=FM;sE43_AD36PbN@=GWU8A~jEjW2Bh7ub#Nt+vp{c8?qR&WM380%wi08 zj-z^XS>Y_+@7UK&T(=l?TnM53x8ua$7GsnXYh+Io3&t3&#fCn{07*v8qOyM>6X^AY zz1Z8V7~DBVC!oOD52}cQ{-_+I$BS`t{JFoOSniKr%V@$v@!H>*kejuUI=uNEcy<1&Z?F6*7e^T-3WMSOn@G>jb9#|sj?qpCo#=`#dKqJzH0w_TNywV$F^e@J;raKU= zw%E}xt%4X^4qyF#9E8KbqG3DH-GUy}EU{#r7P1rj$uV6fJ`Obe#qGhkp4mvLS4b39 zhZz2D6hP|51=p{wxHH(OBI1S^)umeW9&vGq5$w!yKJ9mwcLBYJ8WB=H`-n&yYV45m zd&itb|N3(qi_OD~ky2hVQG6U`tS`2o^1OHxrEWsqx~vFqd8C^7dAL!-g+wTUmk=L^ z;~IoDSDcaELX0rFIx{(b!CCYRx>-jQ9clEGDDkGfb98ez+=OK~uBCU6G^$8phs)05 z$c1YjB6lQCXHMcuvS>fbh>~*SCuebECd>jp9%ZzZ=qS?Tp4E1=5$b$|N<1sgS@_R; z&`#uzHtM?&LP^^QQE3dW8(E{r&s5UIQoYYZdm)<#82uz_fE4(|I~9N)&QUr(7H(rP zH*vr-)mb!J^q{p!8*B8G@|t)n7ii3q@~Ma)e)G1kNDDOFZDsKxHkK#Q3WUYjxrRd3 z%n&V1(B$d_$m&su{#dgCh14oPh#Q%J#eNi8LlvfFeh@b`0gIcPA)D6FmE|o>qFr%g z&{~0qr5UtUpkNgS?G>PHCy+-+0$)+4w0IDpiwQW>Ze~b3RFB0yR=QwoNRD8%V$w^g zl=o*~QJ{H0237@T4Pek$fjfg4^jF}oaSY@UH0oNGJR)Ds*K$N)zu29`* zttWuZ1YEZTW=M0y7Ge-DB=9ro)9O!v#S&mw503D)lJCW8QOK7maN{|H7zNy3Fj%R; zw-T&YK)hs)wF*3c&tRPblRq+u<6?8UN`GRqL8-XG5H||4QGu-v3^pt90c%B|u~mWM zMHp;XprL5%TTCT!7|^ONK0z8k=b0P zG`5ijmZng?S53eLzeb@v5`Qf3P{0QHO_Q+vwi(jyVBcJbe^#IbA^`lNz@HL4B(RT? zoYjOi9+`ksOf^H=03Q}-DA34Og-Ny|Jv|uYDq!(s@Kk|L)fqhhAE?FPr2?yKGx%MB zE_E2ZRp5JX2Jcqd%#!Q%nf$F(un>kS`%wWbgCXF6bq%tzSOi0$kOGYxF>q1sMQGEq2of^zd)n9 z3HZ3wM93!PtEIe-Dc3GZ;H|*#68I1}Pf23S7)sLD1e|1JGo&qQ$>OF8^lrtVxdOWh z*swREA~{l}wE}&xcMS4qs{m~#1JGUp+CK)MqXI&L&I){zpsNDS+HsWb3~W@DRg&~n zDz_!*tw4+R9H);04ZAb&SKto``YAyB$)IEd6liW?Fi3%ledPH!M3EaHh%-!q^ZpD* zD6qXRgHZ|$?8jh?0$m3%7^?s+6GEav1b(IJ4jaTElz@#0AG+~MK5z)j!xZQ=jKM?& z(j=Iyz?|W%5w5`6F$|_Ea5aEIqymq|60m8}inIu3l^F_nhccL@K*Tr(a}>BSp20i? z5+*RPDL^~Wp!N$0oTl27aRPv^BrxZ{#Y$c_oaJ9D;DdcK5n-7Eb7wJ#Q6LHXXF_8o zfuAUk1~6g(u*L-3z_AqCN#avWSsVvoW5V(cNy^^p#W`tgO)jI0-JXA8zzY+(MoJ%a7KX)3C<}%d*2|D^8_wa?Pp{68-PnD;M#w0 zhO~Cr7Y^bd6==VY!If2X{mUs7+Rx;wQh6`IH3fPcV2v9JR6NMwmI5meF}S0^y~7NC zR^aLp2KN<+v>juTqR5$(3?3@5{1gL20opMKwRue78rA+c3DQl#wa+v|yb=uh*$U9k zIRH-x*f@m*N%E9R&_z~xsz8BD41QIhV-kbk6rkO6km^eXXz>`pYXvfYWbj6TI@cWd z`uD9OYp$`%UkcC;I!NR_feTdG$u}7MV*;-1M>9k#%Shh=>qn%1s04)w(9$yPTM3*b zFwcJ%C7*#!dXPdf1!ylGfZ_`LErFW?r+;CMQVK+;FeszI4-XiWRbc%?2IXyvRMweP zRA7e$l@+M*h&8Gz@YQ1m)fAwuc~Dd@0<@$}BTFX$HBG>+Roe_{jh@JnrmiC0UT_3& z1(GFbpuo(RtkF<`a<3TpDllJy#tQWKgEg8eK%4iVSj|_{`NsvF|0k1{O2zLl2CWr% z{Ek6e1*(5!&|ZNU2|6n9rv#k|+^6RF_=%%*Gr@!wbp6|dg1nTDOuqp1HUT$RA2Xym zJFwVa0a|(o{eBAUbYd_-0owfs8iN#g;=*8v9iaZ7iZL0cRQ_;fFhYTCB^ZoS;7J(< zV-!eqXE0WQcG&m_`35OKI{^WN5J;uAAVW4Z|Bg2aS2oNHY4bv+3SgO*%w^|l2965sS}V`LLW;~?$0|h> z@LA8mS%HUf42mk?v6+FZ0$)o|LV@2UC`sTTHR*+|9Hq<}n*WG!o0e7bP1{*sUV)9f z7*tfib2o#^3ha`gssc^-utqfnitK0LrN9>eH2>95HfaQqN! z)K|d!FasY2)=1Dufp-%45wLN4(7sA2NE4+pCV@dS1yUqvp+GxqqJ$`|6rf#|0NN0s z)vwx@=NYs&0k?5SGo)pHOJbYWS&^A%IYL(jXg4J!*`2^HO7ih72E9zcNm|U1_BU*= zLEoyt*eeYBDv&2Ze+6t)l38V-BDBR45*e&OxoZrDD)5H{!xdP4oi#=(;Cq9?Xa)94 z5THPno2(INQ-pR}LcYNSmQ!WPRvf@M6L4iGm?7={T^4H!w0*!}k^<$k7)((h^9h3p z0;ed8OTWtVFVZBON3EQs7ci21^v6y_uk~Q~}zE31GPb9wiv8P=HntLt~W!(IpG= z`L{+9T0snzSOv06Ggz-cS{VjHf%)zXzEPlmIR={)C|91r76oX3Cgie>fQ_4^VI?Lz zluBS_2D=o9smfrF0<6dAZ+kOHR-iy120#4|U=1|#y{<3sOYsL2u=pP{qy^e$bM%jjJdvOP);!7-YRqK~M+NptP)LEQt5~Cm0-HB5 za8{t*Mg~O{c)p2&>sq@0Nd;&vIBHs2fttG+xD$w{%1+$Npu7pV zvK7scc5y$8D=Y9JUY>td6=`;mRjMiAd5M9S0v<^WYA6sVK`jN^e$N_p6xi_tgL(?k z!gAE4K7dV@JmLxyAEiROPC=!S0<^{)z)yi^KQU;c04*_xMl%J<++ff`fekkqv{E4U zmOTI3DAMdUtF%*qHlaeQ9Td1OK_>-hNjfyTC_wv90d!Mov4t_ zNChHaFc_^sp;rt76sZ1&L7)QO?-&Ft;PDTGPzA1hVlbZjKR1A5!9tX8m{K`Vn88E^ zP8MM>`G3HfLAV06cphm?Rp7f~3?dbv_4Ck(R={?%IFlKQ1eRhjOM$xO8O%|jeMJWI z6c}BZflYygDhw7V(AH;J6<8I;;5LDklxdaO41P8NEdQ?WLpaL{nX3S|adtU_i;Yj%Le zMQn<+!nf9l;H*H;1O`PF7>bn^&~Q~?+zAFH6gcoLgOUnd$Hg6@lvZFomW=?oo4}?y z;jJi0Ig{{SVgMBsa5%#nl@zFg4*;N1MS;?n70!s1``xG z;>iG3aqPu9R-M5l1+Ld*FhzkQbr?h_K)aTqWYZLQ%r-Nmd0JV#LxFm>zD#y064Q^t9tEcLXRuF!P6HXlD^PkcgM$PP zQz?4ESUyT|!~|T5V-&)ui>=9EJs;u|1Z+fDev*Q>NFE{Or%k}(vt|eeIXL=v3Ty~s za6y5l!3-`bkR-wP3Je{`8b8L;{U2vahC5KGD@x^b7=x<{(6(yOxTZiz1cMt26qw53 zmI5mzxT8RgDAxE{0b9Z8Oztc4)eHtH1a4AuY?;YGHvu=tBQvBOn9Jf+1)eQukgh=S zI0l&t?B2v68^9*>_^_SH6Qx2sv7xeg1avCJQS8bF@XQ2UisxoXi-~9P3k8ynFnFcF zwc`waSAceAqwmlDAc9d}?vH>x{xSjQ@!kw+o@Y6QzZD=`BGCVrKoTX;7@H&j6u@$A z2AqH+g^AySD)pBOoXR4I3!{lrRBjP|^%(&oWqC zn!qE{Z}5ykSrf2+c?z8+@rq|QmRB+f%d40nt>JGh_E4bJ9}GMdIQu7q>I(c_pa_+) zrUGq?F{rHoZT1F@x&(4?35jh$v8w|}eI@{03Li719jwaYMheuc$-qwmQIA0r1#Ar& zG*f_ffI}876d(&H0Jc_&kaZJ~HVTkM6M%LKko^*X4g_%NCAXsh=xhQmcvmx|k?j(Q zyDLDZO8|N*kYK~!Z6LiBNx^Py0DTB7qdc08W6;+GoJW5%#P&*{KTrX(R01$q0kTj6 zFjN6DPy#TVfQ|DY+aw?(l?oXp0T`_SnIi!RAaIn5)f(Gn00fzUixom4T>8n~G$0;t z0v3mvA&rcUKs<3Bjei`0jE#UyRw`s`1Rz`iGBpA)RROXz0uZSH85#kIR)B1b0L)N; zOpE}`Qou$gMnL8$LdHb^<|#n7MF4CHkW~?Y1qzTQ5rBmXkPQ)lMGBDh5P&5Fa9K~r zLl`XMREcl{#V9!$5J7~M1g=tq=Gb%$V2ue_Kh_LsWIY7p^#pJ+UO;;%^*5M+^*2&z zDT!&5GU#vN{E4u9o05}>5Xg5ZKo&v(b`ik64(&X~V6O={f&FGkBRe3_KcE1a{Qx+m z09pN@@$U$cTuQP4HX4IUf(bau6J|&w8y^s#B(RwDVb_DeX%n#iSqj}FF&XfH_`C^N ze9;VPWT%7jPf~=eaUjAE1iq#e{>1KL09Q=FDO@!}8kyXH_?iM_Wdq=b0%T+Z;1+=l zO7R+Y5d*ku0vjiJ&kSnkIbGzA@lu}R4!6MVQ_;Id0_%B=PNU$!P*9i-zZR~JA*$JsL_MLdj-Zx@Q(tp zyTQ-@KM{FCnUdKJr0R%;=?pm2LKMQiA(_}|%yASlOW1t_r}$1_3t(7WHLN zT7kp1{!Gd$0#h3lrvd@oOtPU30+mg`nN~GJ8cb`D*i(U)!3=6B0J|EbQCoqU6B*Pa zV8gv2o7Nze21E#O9u3Wq23r~=_ER8WDubp9z?ueWv`_%HGzhd-0G2cev?G9fakirY zVADF9gfs1IhP2IJafEIPz;Xtq&{F}}%^+Y=09G>y_$v^$l0kn3$ZiHS1`)uM2-F{j zGKdT{372BH8PfW#WAP{jU@3$20~COv3<5z4z)S{#PzCgj3??WLxP`$)0GljE>FrFW zD3#f=SW^k$u?t(xpj4wwz~!87hP0P^IDuITz-$KT&s6|MGYHrefXNIVf4)>C^faq1 zQUF#nD9+akjFuY92`r&v!EOd=tTX`^Yqc5D3ZG+ftOBr;LHcnf!1G_2$pHDrB-#lH zHY)%t8KkjIf%!7ioeJ!}%^G_YxPOnqeg$AJgVH#tKyV5J+Yv=ZN|K;JM}t+qRiLE= zi3&V>%o=AEfb|T@=DY&1ok8G|0yAaVe;{z95Z2$Z`3$IBu@ianU8PVqwJ=O)Pzu*g zz%6{!3~4ZyLE<|Kz*Gi-dkP$W!XQNf*vTLboq&z=fSC*;kCn=(Um2t;aQQidECpaE zgQ7f9V3bTFpTPH2@WD>br17f>xZuB;Aq~bdAhv0*6e;$OlYFB9>}602e<}cL83f)d z(D@T<{G-4*2iEwc0BmMZ6i2M~mc@d_3<8BEpy#jMWU9_Y)=|k}G=o%%nSe`P+ze@r zTv%LE0T|36{W1znk_nU}fXAw2GJ`cLnt)TRjF3&r7gb=nhbh;(Rc7F&0L*7l9yJws zo%|;j2LNdNw9a6q(Fke@N-8f=0Q_)YswL##r0%T|d;Hm;Ju|eRv0&WrV{JTXY zjtUC<8l-a91YFR2W=PAP#^MwOM$KZND**c%l)z&JU|oYix&pARK_H6(KmVApkjWFJ zLgqD~lCJ<6)&TgG0A5b9Neu!oOu*%QWrnbS3hTd70CqG;|4#)RB(S|#1hzCtI9iA>x~!bz4g zLt6A67P~6|gBeINx~^7Uq1i`R2Yb=k6NS+X%C@QkC1oa_1ol(5WHtjDH3W@+L^#`8 zO1|O@%j+rtTNxCgz5=k6L7<@mu#Q2%Pk}4R44Ntc`xvCrLIGIE;PJ0D5jI@`;~1pU z&IDX6n1Hj3d;HAe&I-Uf2I+TG0Jbp*^i-hZV+IxlU>$=r{OzDD3|z=K{gukpG_hc} z(bTpA4Ko^#9HjG=bYi4~w|69?mHTiWO9!v#D6~yFcnLXAY} zNoS{Y@D7vUW;!+CUY5=^>0FQwt>l6XFDA*sTR?K~W|$nj;3MZ3=`51YYUyl}&Iaim zl+HQn?2-;%_fhoacr{HfUZ9ebFP&%7IV7EU>7+?#yL9MPJ0j2(psUGWfp>pi`^UhS3 zIJ??bQT3qV72Ea#=ez!M>f?e3ww#t-soi-OG4G&J%ppZAJ!rIJvstTS8i-W~jhe!8 z)^MQ)-fNFT#|e3gHJ|p?pbWR0TueP=RFBQL$i=_|U(O+psE^B*&x6%t)00?f>wl>D zbjT>?f(OI)h+apKT)+>kdEmb^{W4QLX8v4v>?Nk#KBqNB#;N!sRL*_>#coFo7nePs zGar{7Du_aR#g6%&+iw4Vs5o*22V>WNSnQZ2);wybJ4C`!qheXVCIPz}s>U9@&PB#UbuNRQ*1y3NPs#sBJ;lpoXe&HQ{~xR8lJt4dvvgy#qId-V zKQ)h7*E?J`JZ1lky0-}MGxab0MR|RS1;gmC4*hxKZ*@?NQ^=%oVUuWM!0$ID_Wai9;oxvxtUhV@I6M>=P8!}tXp)Gj zDn9o4qME~xqQohqei0gtW9o|4^%mE4mJ>#dH-##ROGS;+7~eLZGHNB0RuTC3P%hAj? zPtJUBs8Vo#6p5#eo+YS0*g{>~gC_ZGk}0R{1MrXLDN)INXcB5v$}Xa5Qi{Z?Ge+%3 zc1-gn(xmbfrjMP=lyr>DFMAbKyJ{z-(ma%6*J)SO(hxtdu-BjTLuiVN#F0Vz$SfFVR}va zDteMHv6bVa26tU370VNAc?Yo|<7xS#oM=S{>quOLEWLf7FcJSN9+J^!SSEWNV8*_N{?!Ht9PCqioXWI?(jJ z8EX1I!2X()&V!jX?W-jrzdT9|`Q`DCZj`e3lgD*ExZpopa$q^WM8LGrkm%sh0CVe3 z=tMkuT#IzYlgEw8z5e8J6>^^GY$a6Mb+O3f*`kE@dAirmK?$i0>O znCs=7_qcTlZl6|#ta}O}-x{LRQRf7TPZ2;F}KRoyvL1g94o1? zHMOw)P}PIkHfi830QTCXuNMrpNnZjiv`OFJFwG`?-C>hW`mVzmoAiA+WBPJoeND1i zFuNvwf56@v`=S#!=06@)p6c@M)8ht>x#b#JI-mw6Ljt1$10p8`MobEw6)xy`j0)rX`py?x~(I!(!eS2gox8(WA)Jl|_ z9+~Pv?)%8pYUEaml5U$QsW&Vtm5<5vsMH!H-xMX|B}S!sa{Q>&I`z5jN;_I>Iewlm zZIKr?Wn8!{ueFY&wYlRG(Y{fsT5eneYfDFKSBl{&O7<*OoqLP5yQ8%~F;}znk%7}g zx#ZiTWzOfKQ|pYRTSG1375>=Q=5N#I;0S|d%tjY7eGx@W-*i}PlhO?^)h2z#ikZHU z;^a$gH$Alj_ogq7q;l`NcqFwW)z#r>YSUKS!s{&CQARf^W5a++;ejCmbdc-^W~b$d zSpl^Dq|hn$rk`>&wR{_ne!_AF8pUPwfC+(-6Ql}`>N(4I5b?2V7(6L3GBRLF;AEQ7 zkpHEl@|dI^mB;Ag(bT$BuDZuk>z3q%f3X;d?Li5PQY%v{$$tOEu~Z%%;*X_P9?i$9 zjDxit&Z`*LK7m1@lTgagsb&eR6&$P{Br9dga2&&;LMJN)PY0_PDU>i3g2Kaj4>J_S zD}^TkbF`YirTxg4s2@*#+lBJH^Jl6zx&Qo`+LPSw zf29r}cjI5F{^UOUE4353KJR4H`oBx%Az~`r;@q_9R!^!<74bUx0iQ7!-=)^4D9_)e z)*-j-`_%ff>cI}yairTo1THV{Dehc-P=XY`c%RCH?#cJ^aAdqsZ84q?#|8)MK}6sg zV(Ac+oStB4Z^xLv_JO8vBdl0S=~S4nlD<>0TP1zouumm@>B@HyCaSbw6TkS7TAOF8 zy|vT4d2qF?vj`N@T^y^O<}O~ znUTh$Nc@a69$jwDNaLRRenuM4Uv+1uH6{HqGt-)o8#^pEXJZCl z%!mDd!1nffN?Pf%^yEq#1p7(STGLh-PSUQOdXTn-+j*OFdRs~-)j6HIbDT>$p9KwE z(re0ty~`56L?r!DO&;u7F6lfWZ+A)O$soxky?#@A8m6@{kS_bSz)q0#<-$Oa^zBTy z`=T}XTYLy!xAgK&>{m4X7e1VInx^wWI-zMgj{qy1rZYd&G~Jhq_iNMiX0m#BE$>l1 z-xFT$*vi+dICnw+X6a4j1Dj>8J*VtHL_>l3fKaYzHeXzh<(x8 z#9S^?Lj8K)qT`F|r4lPPPv6NS()r%$UFG5HY#ENj*Fo%9R=vFN?UG(s_Sb%v^oCTx z`IhuX{fTZmU}YJ2ga3AmVELms$wmb*A`Uh`^~5!)`Nm##(+ zNHyPfNS1v;FiswcZt`% zNaq3J+KY6al3ib>^QhMPrR?Z4U#7R<1NAb!Ik`_?;(*apTFt#b@@3x~SccISCHlQe z*SR;kmd)V7rBm6Arm`~2ESpi89fc}-h%IF^>PooH@(bY9g5Fiz^O^Y2>R_JA4FZrN zJ^j_jg_*u1n(5mDBPde38`e*>O^M#+GA6Z?&Dqy7W{D^@y1uWg#)*!#tU+b58IgB& zkv_4032x;QgV1QGOkcaZdQb+BLU#sb^dassIHL`@BL`<#$lX6UqdU2;24}P*x6zP{ zj_v5Fv{nRWO{BGwez0dEeN|VOzC4&Tk<53E>FX6szQjXAGWwUMv_4J9;Gw}YOqOA6 zSO)j@rC}L8-wIBV-h}rzUl$Y8k%LG{;2P6_G|uUMQai(;|`` zhQSc&bBAFN=__}@^bJ33`YtKo_b~M#^$NpahxAP{ebHL!9gdhEVmyNXZJ;G+9^s~ zB%E&Kl^C%+<1C+r4K`&|q?-5Glu@1BiJLO`+F`lfJ-8{us~Ei+(1u+_O1lytY|2O* zO`3`CGq`i6zR##5?@j9B9H*1N#DvhmNl_E9`?P)kP3kz-NC)fhBrO#{m)%pMCkKSi z44jNx2EKHJ!dnNcD=w`08si15Ye+L3Lts`z`ut!|L;C)PAr1CLYdh~zr4ko@$k^l7 zj60}@r5bW^?c(o`tH!V?VNvE~p~cVA1CnAS85cfnM&Ps%zGxj?G}F`b6jiYc%xg%4 z2wt!4zG&??7~w#(Wt5s#Bk@wv%tF;=C#q{1hQ!JXKabYjfWJ1#T- z5*mf0rUizXcSJKR4}qLRrw2}o4vgZv23%1;w)|cUvzdJD`S5IJ19|9g zJ6eC`o1%cAz^LE}0l^bOgD1+n4eJX>>qqmrCc$`sT<|{-Rs*E(6U+rj-*(sukiJTr zO}ACN4RbIhBXi55H&f^+d}znLHxRdXvc`<=!>B<@ z{*lT3+xL%5z5wd`MyU0y{WL6r^{pb(NM>JCj>Mft|#La!VfP=LNHKG&u zr7&!V$Jp6E`*9bGz~h9lU{i$oZx9~Ub(+<^ERUGA-)7bbji)2+egHLK-$Ym+kiJbY zJ0N|tJ+UJ*OSd&JeU3h+FI4%;H8$CSCU#%6Rw*7mLX55du%am0tCxG?qqmtkeAXuw z&fg3p3l~J0Tv3XEDG>pI)20PtFJG1wFK8`Gvci0V^I4pKEQ=4Ub3zs$*gAH% zdqS4Cy#3!&(0T=txC%*uC@#IR)H+NxeeqG`OWc%@wUHllwD>8jx;r<{K=VOH*c_-l zNSypr)~Z%in~#p!Jh#*-n9XCMwP3c7Joy({E}(oxgTp6BL`Q`NMA%DiNwVU_7zUyL z31-^sjVnF&MLTKVBQ{2?H5t}= ziS-I)SK||*cb9A)3BtQ%^8xs#OLjeZpgvfOQIRT%HNjT<{rGQo<DY?YH#3z(Y~#D~$O=`U>qfeUtG{Rok0*q-(Zy47KFH zJG1$)RoNxoHoLNU2#Vg7-AE3&<1N!r%8DYvr*B0$Yv0_J-Kjhupmi1~;l#o)c!+{0(>&2AwF$!U(( zUFJYI5pTBTO6gzmT3Y&wpEP|{aIYX)b39&qZ)@zu@7pN5tcA}hjqaFZ@IuYI@9!}W|_XyPwc*EtuqwSH~WVTsFK(#F?)3x zYR^a4vTJgCS}YcuJ;i2tq%D^d&wWn5M33v)-D*tW@>I99LwVfrCM7C#T0mszl&H`t z!RBn*kqQ-49OGATC>~CIK7>uO#6sjWIWQt33@?o31#O%q0kGKQ&}rjwo?_IKkFu`6 z$mSt3??tvpFL|sTtToK8U-b>jgw9+*bAD_3ioZ8~b^bAZZ$Fy88CYGc?N0Q6nSFIC z)wx1oPL;}Bo6eRHDnRh0u!sPl>I;ffU06b9hqT5SYUq`b=OBUuZlE%&8+A z?Js$^Rw6VqDr_ z$AWS^`Bbwmcd)L7j_7x5NZGb>#9i-T-9gdZ_;QBI84wvUNzHt_X<>a^|ILEso$7Zb(4JCP5(ZA3yW$W7M1V?KG(W1eT(*mYPMg^*Ckw{1D97x2AlSBFF zwhYPPVP|NFY}8qHcT-3XUuvHY$>B@u^pKqD@+8i4w7y3QJWSum3UIkpydjo;OJ8NI z`If%PSnMr*m$ArO`nqCuxAa}bvTo_ShV|Ui_XG>M?NY4ZmcHItx-EUSKe1?AdQW1p zw)90|mA3RX#`0|G^TWDq>2q&t`kb1ZzUf$kEv3`3^jiA%Va>Jly~JW`>AQke*3!2H z%d6p|cP*Q-uv)?gSWzu~v$2#~`r^8nzT;RsE!k}>nwGv{SS`)IXw9cgVHyQIix01s zIAvVU9iEvwF3YJUhx&<@C45XfX%BfOUbZZU$Lzz)%^rO2WLnm*>3Yt6ZP&a_Y)^-U*iQB_i|R;bn#A z!{PP^gA2aP@siT%mgP%C|5hVv@P*vn?_>{oXLqZe&*>rcF5`AwyiOZYNo+qbqF7Nm z+B~qNF|1WwhBNRiljh#Lr$$uf^j@6L;nH|s$l(Wj)(bg&fjZ|xPIDW52c`Xk_11FH z^wMC{7ZhUpN{%;uyRf8M>J`V*X?qOiyNcD*k`2ZxY3XY?&GbEvGJWA#AT6c(j6&pH z()40ZV$+LHo&?h9RM{)Hxg0bi9ITz`)av;2NcpU8o>wliHA<Oi8E{FR_GwRYIVz0WLjM~w9Bnt!ak!!S*~$Gyn5!^ z3-UZTmk(yekX*jn?G%#h?yXbKkFZW#uJF#m0&VGg_pRL*t#v=ZC(7$T0~#bA49VTw zr6_m%s(2`h$3L6KaGu&gmQv}8W!GA4qH#O-LmzpG=xdpa8kG(ZNnc?TdM<*^@*_|s z;rAxje$nVrHIFmxUNw&=s_|9xxOgk7=J6bKxN08XqUBc2^KQYT`z$%n*dGZ*U@n(0 zlma8{136}$J(w9V*lL9`Q7Dnv!y|9yMdv;a4h85Zwq1Y5d3{Wkd3dDd@zq|hG#L&{ zlVw?!md6#}pO(jGQ%YJM_oKq;dED#j*3Lqwxvae%to>1x7-zmk z3JZ_67jck-)kfTv-jW4`PY<0oNfuvZr012RQ{-{&1J0Cj@EFTXmkgEPp{_2rNgD=1!6S=MBu!TilW5o zJo~tHB{Pq2QC?=|@wwoYmB(je&n(HOX5}@K*Ei0Etf4q7d1mX0mG5$m{J=j<-+iog zm#nNq5xZ{)R=3;r%8^ez&C2t09JM&H)TQ*F9YqJ1tdfZzpX6QkDcZo(+j}COxr~oY z{Gm*~cVW?Ze0Dq6u@RG^8b(eCpHa8oSaG{sPG#{^z_T)OBfcswTKMMgO}tSt|650A zGp3kdCBNI}DP69T|D+(NRO|DUJnP~zC}L80)TFSWh7q&K$C=doe`T@MD}S|v$|A91 zjr?#2QMzDO8PRfLPVvNzHS~)e z=`*gf8ELSi6_0tt@+-;fo>h+4wWQ?6{XZxud?xP5Lj%o+ck3Okn@KF6Ajs1=Yzi*D zaA9uhY;&~kCLPxRIwE-QJU(%Yx{C?n(-7S@#&r?JD^nAYIotd8By)B>9Pw+s%tXh446a_bmSjsib^j#lq z`W6S-ebL%d|HKdJ`ISPcROPxo<-xF1x2HVVMRa@0gW$?;Px%hwShuIXa_-J_usR?s z5s>Q7w*uw5KW#gL3(?roI@BzL(2xy%J+1YJkp=1d4%QWy_q<0+5F7c!pm6<=nCEPBbzr#x=A z&wR>HBBSl@q0FZ|=iJYH%2RsbtfyRoMp;sC9Kxfo2Yq>ZJ(axCw!dUC zhn2)S3JS^!j>*ZX1tq|qv_fJ^N@huBeqLgZ0;G&r01mJ#Aj-!%Y>Je3P>v1Unfs&HabpDc8vDMS#&IJZf8CC!51hG)E5BnO%Nm-AjWow5CuB)EI(z0w!BNF6bzkW>k2 zia<*zF-Bjz>5f3f;AKy~j*Px}K-J(vNAd$OD1n1nzy)f+6CNO}IN;F^U{-0?!jIc~ z+Fvn>K%!aRyA%@5vcR((!21b`v*g&=LD39sLV9~DZND)4)jM!XK5+UKxYhLF^ebiX z_@(!EUnQ8OSAl0YKn{7BAqHguM|=4}22GEzEz{T@cIH(XczW?1(`#@8={?hH87pv( mwqW#4hM6-Jc!C4us0Vl8(G3umuOXCGn#HdGGO@HYNe=)rn)iGF delta 955734 zcmb@vcX$*xyFPqo`+B|WE#BUXDF%$`y@cK|5D1uN115A#@7Si9V!$wf=$PJn5xw_n zy6FZ2At8VXB!Lj%lQi0Oc+c;T@0AbNg-6e<=GIdsJtNJ`Fx zuH9q%lq{dvwS3oZ-MW@aEL*8tVr=Qea%C!2tW>^im-1b@mP_o?y=>V^UCVb#Dm5#o zn<9A8&N^Iwr=oOnudcDZyLH}qz|~LGro1P%Y~|ABE0-zdDHe5=NgR~at>?fw z8I?*7?wgGN2Lb+nx5}q@&Hp{G{26Z#hN}{M4$cU?@^~{pySpA}pX3 zsNe&-!*_h#9MCy^pj(vT0xj<9UHqetCHV8*qQE$GlFu&W;fjInD|N;Ay!dK->F&jO zU-8o5+7_!{l;Hu1H!FD%kO^#m+Q!1C7Wac4eG`lEr3?AE8vL37+GQ6Qn zG0p*&pUSbw`*lkiJ|`m@3>eut+Bhd8qHCX|#D0AeyOiwSv(Mlpq+AbZF`$c&iw535 zDn}#kG7k^;@!u6I#`~fTe6M&LAD*BDj}Om|1SxxmOZf&}%W+X)`T1dmxkBLWR;~zF zj!&CZ4qcdBsjpp>5e2SZG#3UdIt~elGK?VW`W++xx_cq6JV;w-E03zByo|K-nT6e) z8w_|?${V2xan!Z{kbZ;th(tesqEjJ$XS>q;oGyAkyNHbMQ^cW-7(8Tf|A9Rd`wS}C z6AQ)Xb}7P(I+f%fC&Z%vMS=F7ON#Joqz`n8kk->~sLlM-KqQDeo8#bP6QlUNLN5MR zyn{o({URp65%AB&E&St-b`ZDBDC1WYK|I&&D@FP*24eg8RKWeSOU5rR;^fQySeXAk zz9>GcMeV#jJlw_eQRVpL*;3V<3@Kr1PDUy4IMq=K{hr%5F(xp-|+IY=zixG+DfdoHG8&9mlmn!7fsq@{Yd*Xn9*(W)$ z$|}mRfV7O^7TyzW3~K*MAunI6STtt?35j=Y{IO0&cwJ&0@O1BR2Lg`HcM4I)HzDQ+ zT>HEJa1>&p7I{^dGW?~avJvW#j}J`j*Lx0F-f>72AJ-|G=en0dHpX#{1V|{;xCq~> zXf*K5D5ByQ6>$(xL@KocWIrq4xnpTiGku^#l;H&5sqZ@Z%Mw9_z|Sn~L0wr<9A;oZ zrM~C~p%de`6m_8hZOf6a$h`COkLATFYb5{kKU3vxstx};$tdfUL#W8}BM7yUP#x1# z3AL3_eG5+_)M`SNP^S}W1EGqmXA$Z=pdYA2zReiW!1gfa{hsM9Hg?6O!OGYB>7 zz-$8Zh)_MA3lt!fqV59nTNa_Vty@H>^@KXzW+|a25lS;$pl%atjUj^c3P_hpKb&pVKuQrn3(h0St-&R7MBGmCzftp3A zl5@9{-!2nse&t<++9>&-6x94vD->~002<`Amg9VMZ56ACm{5$YVF zOoIgKA)!h%4=2A(Ae7pyCe$uMeMyNG$m4`ei!hNNcMz(2c`Koo63X-1MyLyfTCH^w zY8|1HVg%|Mp%zc@lHZ;aDk0KGsALQ^UUMxgvE+W*$K}864mr(N`2-GG*&2<(fzd=H+E?P8&kP8TzXkSmL zTZBruGvFU=wN-@LyixT9Q+Eh8p%C{KQ!5BH=wnAhJs{NTmyl4?301j%TU~cla|)pf zCkWJaLV3zQ(52uXXA-jh(N%g(EhNpd~R|>ZVf+uz8To#MQXm;01cPJZ(mxJpERJ168GFQ=M>2(!zavl z@Y@Di`7+y@@*4(M<9!Qz@qZ2M$uFKCt)28@B~6-U@V@^hBj_`up2ab~a8{Ns5IQN5o0 z$?=Iizp*3#II}(H;oqhkKuQCRS~4fI*9KgNi{^EKLLj-RMk+#_zD@V=^M@PxdLw?| zcg>4N|9v3AVwB_hJNc{;<+#)2?<4fL3>wqW1!lh3$Z*so;JKB_qKuA+%R2mnk@5WQ zc}4jNTk4^*lHWY42C9<9mm0lai3bWSPepWb49FH24&!UjY0TdmTao`WqcU$ATa90n zUXzzitHakzsmnhbSBpRy$Hs!pmX2^Hin0F;{Co1DG5 zxG3LqNK;-n#qq86xo;(#6>1hMWyR`pe9N_E`KYxeP-~aTZ&-2v76Ds)?zDKy(MD1y zYF!*rGO8!as7EH2bnF)Lypyd-bhEt$DAx19G zD#VwcU7N2vLdQRvr2^Vj1BjCx3e~{cL{7&~o1VzmnbU}9^yCsNe{yyLYV!zI&A*K_ zYi_uM>Bl?>BECcaT8U0e0lq4kaELvDXwq(c}c$!lhOGY|u@Khe&+76L`FQ zR3pCO{My_S6p*F4C*SZN+Uy-9{Vk5XxmN!ENc!{H!KRMWk%7;;TAUBmCys|jA zjbFa9DEF91n6}D?YWTll@c0rNx?&Fb9Xndz)xBs}w40n;xw^a@56oEQ9j2D$6W5eS z^&gVocCToNHUD6CyARgnr-_TN?Q55x%F)={*(;XilyS@^) z3q`yc6syuz7ty(M&wf1zckUdMJOXtE$4Q@zez5SR*O%i9uc*WAAWgN{P=YVJz6}3v zLu+m+Qb92i^tTol;S)EPBHmFO=!nDP4j`mI+`nDm-)>4K-7Qva;^tU%ZXLE4@&B-j zeO0&E9r&vZ*R`ZG-8tx|Iq$FavFgKqFn!BU5D5DVX z-r)et_`zY}h&4Qi_!g|j&PJ#aJe$DhOJZl{U+>h&@r(i+YSw03z7EugbuHg{v71{& zTG%%lQ$`S1d{`KNV=VH|!Uzn{4KP?5NQ0A-`DME!m3V@HPff(9CZRfbj(|_3;}e5H zo@8ny3P?Oxz>OX~FpO`$cO=LxH&e-<-amxThACVb{@DHzpl0q2<<92&`f>cZy<@Vdmzy|kb%_RQT z;c47aRId-;?AR>6^YJjg{-Gqk*~v_hbsSyy;yAiZawLU+bS!mcr&Ao?`DiNN`NVj> z%ZYUUm*YeEPN$W8kE0`%WoVPz`Cg}o@)gewI+ zUSP1tR&F3N5cuA~4Ja_!WGB}W>+qh7Q%Vr%Gaq-jz@X@MKwEUe9HRk(t9yBW5r%Kd=(+)|xukB?#Bar=?Y;i-Du6{HL} zw=wq|H3UaD=YBm6QzX1uWX z9PXq&$p4V7LH-A9<8Bp1PhR+854RFqVDWygCbrCjBU~rUa@CVuXM9|IRyh81Q8;$E z%6-D;+uq{lV~f7c;XcdJ@l7tbM2U{mf93wf^n~BJTDabsKe?lrp8SQ&MV`95r-;KE z>Mavp!j4->A(Ap{#$)D+!HBzlE$JAA>K7@MAsO2hiJ6DjV<^~N6~qF+?6OQ zj5)iXB&vZe>gX+cg^ytaM7j9<%ORrg6l^-`gh%F^+)6MjMf4W~1fFZaE2$!*h~eT{ zdE%Pj!H|*)klZIx2G+k2>E@M@N5JvpMK47lE-yw0QznU?YJ}pTxuSiXP^<<-B3dDC zfoKIiD_$h}i=I_24OMVui5?XeF4LS9&7)wuZ;6z0;p~+MqSF*%%_Gr8S{(maq!9}h zR{SKI%wR_Fzs1X7Q&KZjj+<~6m-B!GbdjG%#@o1@V+1Dg-TME{rr}!%kgyFr#Cup%r zU-5lftkhrpGcA@HAZ|=?rws^_KXr=rd1z}79XX*Z%Y-YQO0VG7f+!q z+@Bme+i#lqDy)5&ZW4+i&xv_5+ z16FQYZ4qy$FvcD1+&FiN-&3J{d&DzYxoHW-2PlkdKQlMrw}awvSo(mt3Jr*{2gMs{ zvGQT@8Y=JGW8y`ujFvbdj%FN-li`O`;%QXMIcLQSS$Vi}PMpEmb0))oE{OY59!6dk z&tc_Z>lJZ0vmQEJ3yo#+o9wozeM`KKnTL!!;$>71Rquf1MzWYTReOu9zuEO z_JrLQ3!aKkGV}1|nRqSbVb2Tk0#-fb{UYAUY>PUt#KR~L7vHex0ivO!Ds%Jw-i1_9 z=udWc?DChmHsf@!B^cYSp902y6{F3Ec)ABBe-Rr%)>M%VesM|;(2nc8SHd`mkp?ti ze<4XYyzG+z+9?hVNKVpX<3f_gf(_u>A`%^)zMLy5IY%q3EG5}UrxBeiNZO&>pE!Xy zS4Z+#ArucZ4KfPTTS(5(vz_B5r775oM9DP@HnFRu5p69uNm7CqT|Fd+XocT;N=CBW zXF!fn6dp~MOr=w%_CqAs>AbGS1W6_pAa{ynAFZ%qhGZh;ujV|-TbUqBttBD39m^zz zs4T74N}QC{wDpqTXmRW&iH8=qZjn@_#ecR*{-m;qcS9r&{KK}7WGK^j}=AC3!7_Iw7@>oFF9gu#cSTQ2$R$BG7RC=7A zJtmhXQ+8{GOZ~LM&b&>|XL*7Li47|r(rDLeF`uC8^sOcv4mDXezXMj`#w58*1Sf<|a&LF9b z%Cvr9P$uti=?j+i2ti-80XbYcM%tVDf-Nm1)BABDnZ~6{)l{ae45^bb=$nJo_kE?H zgs%S-c!8HjONFdCY=N{yfe{t(`^S-W3T!i*zeeghh~_kpx4 zWx4nh=@c3}r+<=erDrGoBE3XA)NUW7^;JTg9Tdw_Y4kPK%3jm6O&qdbcD5NsbC9=Q zq644LD8j(!Qq>Z{f(kMNnAB9J0ab@dM}y{-WYL0&XH=4HVKy;t1YW8s%b|kqsV%!l z`=}LlW%1Oc>c%oVb0}!f+zto3Yb7rLP2*&Ycc_{Jg3_x_axZYDh3uFh`RbN3aTuF= z(O!9P{id*aN12FGcLbpo{L)pX6mct|qPuKD7&=Z*mW^TT2?=6Z@Tye(RA?C@E5qo` z`7jRn$1vG1)SPcd$fi>lo<3G4q4r*pDyz&GmgF9i0^}W*8^Fjh$$F6bce_;hbdszc zqpSP}paq_rhIA>!*AFvgDU{`(XURHIzib0!^%!TIMM3hH`b|KI%wz*>y-fCG467d@ zn3mvVIXS+ytc4FcH&AjwZ-+-ObdN>~ z)g~Q6u$oTtv$XEd-9mNObPv^S-Xm03*Gry7>mE-I)lC~1svA8xRQIQX~>ucyhk(YkXchw8SP8mjxxv{2o{(?e=VpA)KEZ(gYG8xX3yeSv%j#T}d# zs#|77sP6rhp}I>~%a78!{Wpc`#%u}Iy}dP5cfoe~13J>B?~}_56h{!nQR@N+P87U& zLVlJ8%Ieedh19F!F3P>MIPR)^8Z)QCn9+kpEe1@5&2!{4bp;6=IQ3U~aCRhn9Sn^< zdGZf5GL#BM8OnH5wIUc9;Sq{pWbD=|f{`&yulRwQDrf3k& z1uJ(~SXf5Fe1zN{iYyUgC9St&KATdAU@o|}kD?b#=;X7qY@lK&X2mo_!E#eSAE?w& zMSx|Rk&p0ngkk|^Wz1;ByaH)u`4~mVf*B|yRnUrfykbUySaDBK9(Y9c(0_~#N3Q8YPj4qH&RSyc5DtA=DvW6D2 zKa4-7D9w^XA%yxMc1=SAG@n)64@WYQon(reiXsd>8$@tb@z}TYz5?w)+zPutR6ItC zf(;%k67c!;&lFzd4DiAWMI7oQpyRdTHm>*njUoY`Z~abjL5+@HPC3woj&sb)-l*Ol zr_!KDWMoNYHzhibsHl`L+9a8ZBgnOtO@er%sZ3;%vja^f&h|(f&kACE2FR> z!cQo3u$`ZsQvQwU-1Ex4*q#e6DI?Z9kQqL@5#8PMMVMNeZ?l2W?r3I2x_A!Ayy&1WU&EZ95RPa~1oKN3Hq^ z+g)l?d2nPG@~DWveD$eLGvpj_K&|kfMespk)f%jv2}MNr;M&MSgxYFtxI!2W*n zj;aAZsvoOXWB=ayOtk|ayZ_ z)jAM`(>y#pbuSd&3d^v+MEGl#@dP>Z(0TjAr@)-j;d`(tW|j?qfKAZ1TKIKF6HLcS z{8}%Z#Kqgz;Z__Ms-*B}%xL*O;Y8^vgTjf@qlSl<$I9(6CY;1csg!UMC-Ss#5+|p} z3CD>Ogkzma;qkcsvT5PNnT>&bI}CVaE3N z2m|#u?P#EFL-;?qM>1~_jMjTMyV3US53hkks^&q#UanK&F09{b7X>}{xGv~<`&~iL zKRgQWfbFvDY4}F0zl2}HAF&Q6aM`=?OSr!w&j6fPs%gi)B~0CnVZ;=4g)rT4luo@B zb63o)eug^)ol8BwKyH1~rzUPaDxfBA-L8b1)vZydU#Fa!bowvLtBG?zsH?t!*<9a5 zjpxMB*iucLyHh(gac)T`HR(}c7d3J2jBaY;++Dh>3*q`xdaGS5PK=_qK6DRKA4H7{ z^U1ss>M)i}3?l)Aq<|wwtIJ|pwvJPiY1Xg_YBJ4gm#!w$tdwbLGHkY;p(gjzO7ZHs z*a6SXRqwYJ1wn-fwFJv}^bKF((D{>kD8sZ0 z{0EHjxq6ct^xikf2yeYpPuCD3;b4(wu~aB}6`C`gQ2eOWlnoP#)gv``=-DDBO|o1# zyWXjpAfgpKn(vfCF|(-V83mhEQq!0gpOn>fGO#J~6_Au@*8m*pR73|NcG>LUT0;$s zvmxhofozzg0u#&o5}~-UW*v)0uinwr0x7LEZdk3U<_b$p1`$~9d!uU;1SdV*Mw21t zHh{+Qnl%iHML<2^)b^UEEJF*rxDkjMtWm(cE}AoH#05#eFD7exi4aj?kcQ=M5k`dw zwG>Od1XSbSb<0Y~0TJHmb=HD@K<23R3QbDu-U$TZC^d^|T!vk4!EP7sd% zNt$9NL^ofod8tCjD(f_ZWazkci{={3s?#?Nv1~*@013kCMwR25`A7tC4XjY=%QvBtFM_aYnUFZBHW+K)^x=T6uqutdFYy8bh)89!mNS) zw=|6~1JXMzCV+^6Dl8KT!uaa@n!dPg!y^sLjb(z-ELY=aW+3dTrY2^f@iPs}j%C8Y znCF@{EO9;Sv*t%33Q~U4{D}e*41LGs0|=-;-18pwPz)YSE~$hR76Z=W`I{k^uP@K zqmE#ikP!wdMMPX?P`+TfwYrEcn1MEt5nEVIu*w*5gP8$~C1O2h;J7VfGb;lTjtC8& zG`QRmOwTmp7^?1x$i^5Efe4n~fLNkIRK$H|S2$TD;(M$JYcX~PS{9Gkz;Xiuao+-5 zDH9v!FYqL^#g~X&D>|08Xe&mcWAAF(>r6X`unTzFYBgY5W{d&etEWAo zp;CkA-)jvZcB;k(b6acKo>W5U5TRGVEVu2Tt%2vk^Sfv-V$qViY5Pgh@kUSW8hn0K zvi1!=_8z9)i;sb^+GF^-=i{`)Su%Q}{FFNyIj9potPG4y*Rn=fV?bcm<#e`zyh;9O zICP3Oj@j9lW$86AZkF~Yv!7!GfDojh#x}1J9Eq~XVK%RQWn}Bk8^cN)w9MfTCvDO` zGBLJ1@3>Y4#-7*8$qso?@+3;Ovd)?N!f7Y95DB%++J8yp{!Ob2 zN(5`X(e5jN9Qn=2=1nOi1K_=u^eI}n>v!#298_yQ2thXJZ*4ggOuN_X&fwDnWV#D1 zc{+|IB%Tx=QtMV@DN2D>MzCJ1(+eHMDxK~yt2<*{fN)jtwL#YzbJ@(QBRz!Ot}Be` z+8*6GY%H};hxQMm9>cF|fO`gK5#0pFRGKxTopgp?b3suV;x32-*h*+KOfT{n! zU_3TX$Mygc9*Wv^(uKpU*19>!zQWksFkUx@wflz%?ig04y^HQ5_Lvvlb^qY825Lm1;SLpOJj6txHD=#S@#Fa10pCtrgs!? z@w!Y7?403r@%?|3!YOlgMZ_R>vr`9-&)4-~=&aE_&j9*pgbglPphGW!!BFtvV%-fX zw-d}-p?ksz3EYQrEHHPaPAdW0tX?|Etr5p=(7EYOax5H!kbZD_aPY7qj&TXmS2E z{V7`PcSDc1>XQnwxAe&4NU=+fzL!QQK6tK2?-?OyTmGtFtq_WeclxdLva|2?yJ>O8 zpZeBRiueEIv;IZjk0O*68E%CM2zIrhyBOql3e!VgYw&4>v!^|Vpv37u!=F@rGYT8f z1HFWY#G-~Ezy4wdNYB13ZfK(vDzq$b=u2D6u4HJW63!N@8^Zmqo}o6CqGof$11fP$ z%Mfl}E5j&yc61v9iV)IpzqW=Z0y}FjbTH80?{_jhp}${AFkCVURW}dCqLHftBMs>3 zZBi_hW++K7>ptGFfnMgFXt+b`s-_r{Ob2J|K#f>nQ+p;LrV z+_NRb`0#CpziA8OcN%sE>+Uw}pn443AJW*h1BOb}T^#2OduiQg=M85l!l6qBIW6A2 zVpu{ejK69?50Dccrd~Ior{ziU*-gWBT0ECy$fXtb-!%kXcItgYPpME>^29Kh3OwbR zp)#^WRiz%@d=;|rx;KXD)IRk-7`oEB`#%{ zIr1je-i*GHH7R3{hDMqVf|T;9k-I79m!?O)rxmV&$Q&x#fvm{HYT)s{&i`Jr%X}Eg zvSjyou*CzGUd)lhPLCpcD!E7S?2E_^B6J+|GO{_tG>f;6bVBXhNG-F_Tgamk|cF}<+|3q$Jlm_9sV9_s;f3nQ0FhjeS8W)KKHX4bIOpnnBV!2?7 z#P}O48$=F;+&Go;vQlYedb%jc%at%=D+Z?pNF}Y%JJ^g~VIkz6*7!v%Xy?~R<9IeB z5x}C*W-|WGs2+-N%wkNY`l)3%PACvJgB-?RSh+!OxHGP&j4bpTrxb{hY@hKiRq=sB z1;lz=*m#uL>NOu`hl2;@jB@ZP+1MY#7~=#nm@=S?5yq7^j;7*1C~Hh+6&E)EQ3E4c z^$vn8szLSgMjhzW&e#UDuV~!C$`P`o3S6mZL`g(@V;OL#lJV9536vCMcQAH`=c^bK znax3X>-RWY29JJcw6Ns2!B|#-K0jq^!JF<##0_KN@GVx*C7DzJ*$nejFH|7?%H7NBuuEGf2$E~q9ksp-fl#%lO;vP zP9w_sNb$xlBYOEWDW>l+E~CW?dyVK$2sx|VZ`?$Srw@ecjyM#mTl9z#CA_5W??;Wt z{6g{MPe%0GIdXQvbK?@m?z0u#|BJB}l|}v9=rs$FHKZm5%Sw9CK#WOCt9fcuIQTr* zL^Bt+%2boi*pf9Sp3S>Mpf0#Q!nBOyB^pe#3yils(o~PJ0}!}E3*^n=X}hTz%Q_cP z_9cTU9IgtOt}urKMgUFgMl^xV3!Ba`J?Jprh~72zC0hp6HB53~OEdL{`qHKWN}(5M zRK_HX9;6pIRmD_?_5!x*roL=85SoLixj06-^BGpdyqcyPQo-?W)-xs0aH!YVG=mnO zH8=HT42OTU15i6U5O>;y-t(Mfimbez{WZ_X^vywX5`yd3F&Hex+n7dD=Z5V}EN`X# z2Gs@sjyFAJ)-GlaBIagLTdNaHb6IU26x0P3#xA8y&Vy2p=GT0C!f(Fx(w&SLm`mu->L|_?K(6yt4&Gjfi3>$6HK&;KNO(YYfdpba5t2qiL6sM}OKE^5}L)Lmq8D!Q{~dvDit| zXa+F=z8Ij3HK4|66U#FM2;koy4VRuZ4WQoSxxnJgn5FTs??tBW9-@oU`(a53G4m>m zGYic>xMu3l;?b!2B+&e>Nd~^+O^c2k(-KCjBtZmokI}>8!ONW68gO@$sUrOJ%(Rq| z2MW_`sHr(jdtsW&!VMZ!$l8M&u779BlyF(X;`oja|~eYnxSD}aT`kltnD>#l7P1-8ybPqgH{cbznE;WR3S53QpGj|8AZ%2`8;Cw zM~K>E`iKp_7&D8Wcl^Pu1D*!v+93D5wlwTp+>F*?iElkFX)c0p5o3?Dl{3HRgrd8G z`HNC0#(ZbC(_(xhT#P-{NDtFnnNP6Xk@HhJI$2N684M zJ^GuO)=A05aRbe}sVka>nAfmH@kBUqsF~?u7Q(}h5$1iAhtZ?WYgu`?GsgUw(P9%} za;jNE`;Up^&3jmRcss$|oMCbcksgw1^S@6uZ)a`3U55D`qY9|gM$9x%r|!8q(|ne? z=cKvj)vP={o@Zuyr`C7N&69+_-MqxykSb?D7E7E!gw=$lTSwO=!2%DL*ZIf}CTo!H`} zV`hJar5wwI2DR7&i@th*4Cng&9oM@oQbC+@S@)G z7Cp^_NBGM&i-Tpp9Bjb|wv9v1vNX>nhnu1-XjdI+p=uG!Hb%7*CFH}Z!R_J}^l*{b ztc5p=SOFd@Q5X2)DONGPPO)XiJ$J#9{by>PNH1c!=uPo+B7~R%_zUV@x zs#D{GTA0}0!aB#KusIzq?TEefuy$8VCbid3KU&akQsR9ZdRlhSV&C4DwTwcLjK&R* z4z$QPZv9-Ydjk+L*s}b;AGILb(RPQ$HgA_(lELTIRyQ0u+%jDR64qDOLCYvh4_cfx z#*!)n$Md;$0AhTIZ2fVe|@|}bz2ozavVb}-<5mq-wy#nZ5 zZOIl)1-`7ctpD#_3!vr!?7!ZU!_uFViz7B!(C3`6kARn3EG$pI6PWhfEPCo{V|QAZ zzI%gkM!>rJEw9{y%htYYDJ&QVj(TY6M~isfICHMMH{k7R6JK&2hw6sjWvk7M{gZR&o(q$o6yVF_^kCMAj?}r55E?& z4xkm%iiIw_TrzZ7Vp%KGw{npR27u1FIbq;y9jgp9{;er`-(*#5niN!dSIr3f)Uy7} zXlMZzBG^dEd7z>73n!?dOk-;)YP=!MtZB4@yp`2R&z5Lottu7jj%<%FONMpzaB4?u zX+f3_F@HN?L#E+lZV8$TaIu;u&7-%(s%;^>zd^*(nfJp)W z#+C#0keLil8DSkv`$^4cYZNWk8EZwu2yu?ERO=ur|BUh0Axu3@rgfI^*3g_OR)ZkU zp6S-w0xbAG)5>tK1|m(N?-vmsx6ci+I}BLI$c3}gW!4{9Mm^+yWWc$=O%iTYYS&s< zG4x7g3-^-8IZE)gcw;+^U1#mVkh$=)QtW)Qr7HMllQo_Ly*dujz#q0)!{yvu|>V9i!^vDw~UOH&CvGnMrO4$m-8b_??NeYYu8XdQ? zd|Vg900dJP4nKiIk?<6D*7^fwZ{T_BBzjhP#maP#B-lh7P)Tj;1eRU1vb@ zPOyc8136YYx~E;Y{v)Ur%(!i3+1l|fG(^y=ShBWnVL(itRRJnD@EL$JfeVA~$JU`} zheWH!M!4aHbvU(JnU^df3I2Ce8ra}BYh#vMm)~MR#I`G3aD$HjSUrO6ch9iZp^$o{r7^ktJ5tszQ2fb}YfdK&m^v;_dt%M1Yww|o9iGzyxD*DzZ z!UL&JP620_ZTG3=N7-yFUx)Zs_J2X=*==ZBBnjVkZd*^5;p2Z}t3dx>e0G3d!?6H5 z3)y<81P5MN$~No&MlUMhe(Ki1+_E;GLSWud&9+i06wlPNT|m!$;$CuP1DjkW6c4nr z^^yz4^6hO8XoWyWTMP7HD6TuHi|w3DC?@o@ou(Bk^tPexc%;IweQf9!HYslJXWK(fiu(BAF*X;&2=Z-M7Kcs8*}i9)K7{N5QCAlb3p($aZbQixVLE!6t+)b24o-Bz z!Mtr3OA~m=gsF3F=;N`eQD`UaVPG38;g$){xuVyJRsx3?+nCo2YyO(!0h^ZCSRNV} zptp|&M{lTQVD#|@3TR(uW7>-JEetrY+{Uyjqqon8+Lg9O5-=)OuLHf;+J0i_Ul23% z0fI5jJF8K`3hQmHS$a@{GDaD&!{qI@GHPV47DMBW;O-$CdJS`JyA3`&Y`Z5C7`=MT z#*$l+CLZunXv5A4+h!KW3L$`MA8k5t^nqOqvKw@Xf&SCB^~_RZmN4LW;HhF~xg!v? zKvC$;wxO@U5^GkxX=8b4Jcwly`t(P)Y-*Oy@IPR#-?1I0##?dUcBCL)25UaFc?-l6 ztohjXh;kGAlkH?dxDm8;@0pG1eUbm6rQ0v?07|TP@}=!m!TCylWh+-8RSDu{?TG99f$D}`!7^2-^=aC z*wk_yDwKA%WkJ3DIDD=!(klA9vVaOl7_UOW1xC@Bu`+pDu|K}Wa?K+Q^a+1%7ZJk*y6VOJ;_!WNaX z7iL+O31Vx(;c7)~AbX*EFq~C2)WV+Xp%&6=+G{Yh0N*yU1HmY8K{GpgoPy}0Uz{E7 zav;U>EkkW^--phgYi(!xW-t+G%io1`aBYIU1LG*T0Hk)ehlAu_d^S)qP-rq7)zkg} z&)LY!n)~*%=P~vd6xj3Bt%fxR+kc>nSvSmHSLi?Bmr?dqmV|=Ho%s{m9hWuTYlZ#C z*++1G#V%n8EszhN`XCSvCr`6qqz;}m!+wQYvTmlmAEU;TL3V$4VGt|# zw15}q+uJhgm%^m(_G;kYOYBKhzsbw(hp2w5ud&~!XNzsH4`qz=WZ=1p-Zgb}sH-tt zx81&8%huQA!)QQmS*HwoPuP`NO8$M zyPsCL`oxZ2SwYUW_}RXh79)PMyM&n-tp3)XLlsc?4|@w{UyBnvWZ>v1{|fl)C%c9^ zgYru#fUkZHu{WP{tYdBmb$n77p>S-XO{oI7g!_$8-HtU3 zCM0)5b89;+@T13}V;Q=2mmPz_R=&N=AOfs#cXcNruh zScjh3T=03c;~h&>5QKyV4h76A?eMF?$k|>a%&FlJQ^tO)bA zn>kpfF8LN580TPmd1*d^p_OBoNZ=-?wPQc4riefF|G{yd*=z*C6z@Q@N}`*O9ULra zWWHqcIysnfkbH!7iH=K@n@QaqEXiX&man^`3`<{z5N3k@Lmc$ACXM7NfPeR80joFI1k9CDAMUme33J+k+|QkGp|&Q>fb3*wQ& zkd!4S!iiEA!ecXo6QwMq=&(3Z%0h}thqDi3^!NT($}-IFT*Z>I5O#x^4_-=gqUVbU zV?S1MqHKi}?|kP(*-CDLR}Zf>bfUK^kh6;#J5jbmid~vIQMN*g^S*bYY=sm}?VKoE zA;lXVohVx&MR8Xr%2r76x9-mR%)UJ1sLKuRjPg%`i~2fIwn7m1_jd++jBx`(>@^(Z z%w%rIGybX^W{q%epiQ(N?Hs`jmN4O}0yOU)Wdk*Jj=s=8-sz{XKTLGaW{e4vZn)v* z=}s93qRROifKM}=%*l`EQ;uDDn_ow-qZG)@afQKcna+VM1LpS2uEAhI4l-2rBhF4> z!xE(&2hd#dvaR%(6){F&U*}FFTo-cQSn0O6PD;B2<{_J1wg4$ zRggL)G7P?3?u=3Zu+(paB{n!wl0;biZnKjmNeVWgfpCknJ)`bt0>q*l%$V$=-~Z~q z-8oF)3%=gvL@!>&EW$tbIeW2mbwM6kawl>v%5?wVxl_&K&c;-Ki%&UOW}O6|n7I5I zCsXc}kC1cDiRK8zn#(RaSu(kNtcsVNOu17<GP!)LKkhr3awmk42{KPQRlw875e;2Wof`$u26umEbsh?st+Kmd>Mu^F zw|D0AMPL$sbKaqq^F3qF@Nbwa@0{l;UmO1Xzu>FI2Pey75BLv# z{rR^OC3__7u6%aFg6k{!tFt3xXHWHomV)dcyME!8P$qXBVM(rtj5S}Qr+Z?3#d^cf zTGuYd(J&L_W?t2Sl=9v&dz`LG%qYn(oOE5J=7_>N@W@`j4ZKmrHCaT4GSIWI%K&O_ zaN6L~Vy==bX$%pWv_ck1*2cI{E<*;MrR7{BS*B71G2vI5%j~J@8X*?O-5E7pGwJvi zQ`^Oqz~p0-1V&xgg%TLT?XUG+D1jlx^9@~YmdRSs`ZBn!g=;L!#%qMo2V{;fGz`}2 zY7>MQ@>pBXt}c|H5TPD*ccJ`*6gT&Fq5Omt7xs7cXAGJ?Ah%1=B&ZtZTFa7} z5K$9K6_0^qCcDrY3Ss9dOgG`ITt!Zv4J z(F`yU>8v)(4H0zRV2CE_c8;4Nte6+MOVHAe#M{#9sr zUT4+9viDrAWZVom`LW9^MaQh?u1iwx-oILX^&5MuaT6TCeK1D~@4s?IGW1r^o98mI z465k5(IBC&+b=w|Z2#T0MvSC7{)g)s!^jmxD+W{ka$RBY1V9k-*67p#Z+Ziga7R&m zyFdfuzq;0AeUpW6i_|??gox#G_ZU_-Nh4(2+jE6Gjahdg7@)1<*1?)8x1wZ$S;AN> zTNi`+_Y1p$(E82|2~tz*@7%{(c|yKX48E!5W=cjdKM)~Y!p4ee;AWZj2W2V-&op#1 zB_a6;J)5}E7hmpysOIj|1yaa~IQLm*mmw)byI*SC@ zc1r}RMe;G=B!4vaMWy1O{D!)4xf+wjJ?FgWwBdjOgmKrYwamSOHB%sDU|M48?@8A6Ku z)tyuz5kYP*w|ZV(mweu}fC0>Om+b{l<+=U1t0fbd&+pyC&^!wc`QVOZ?wqhLgAJ)+ zr@!47u~LhDc0alJu-gfY*$D@j%jDpCL9`tR%;>e`2u^y(K`axiaupchG zj3*iMJEnqXfC3%g)bRWzN5`M*d(b!I?!oO1JtV@0H};gl=Qp+Vq~Q9+fAAc_$6wof zVz7@|6Fo1myrLw}L`Lf@g01>``Y6CnTZR*)?zR>KearcZfRaN!tVs{Z4ZmY1izjf% zFwbfhhb(o?(+A8f>T`p*KRunmRKf$OFq1I>AGhs;sDx5@{ z?3V5J(lwG1i#_#_p5W1Co|h7I+_cJrzB`)^2XFA);?S}2CeIFh{IVH4YC1f%)g#9B z&+YJ>#`QCyQ18cmLOt67q2A+zL|FLvh-U%QlcrRMgkhGPhPXm9?~B}%71c}xe?8+_ zLX>~r!?NXx_(SAH4^uirE?#}vvluf_>#FDQe{X$=Fqqy$McVFm)3YD9?auL>{O{Wa z1Y_GB57Xiaxp>EY&u+{>k4GMssX4*;kn3T|o#5Li9vkin!k@E--C{WFXU_?Y(fXx_ zWiuh+|Nd{DvlwG_o`+>ojbN1c-NW>x&vuD-5vVydV1?WM^1MZH3s(HY6bA$d=|T-F zfAO%qpBaP@c0cppz&^2G>}5!*A;MX}GN%Z&5B&=78O*@TFfY?e9@Z&Q#e{J0Czeh* zpOv-|-ffr_rJkLY+=u8<%#{Z3{(`Ym-sIhaS$SshvbcJ_Ov7#7QY@n@wOhKoqIB-H4Ka}=HqQU7+b%V5=?iv zv|#g5S1Wj^mbVR~f>J=7wWtAj1{aQmzc%z{mFv!~h<6A~}$FSr|gwt&zv=udPWM>t4wZDvZ-u6b3pz;wp`rfXrOB?uG ziuVcPkSGKUAL~Uo0(N?AP&>|RX6!IhK(IjtSw{M6g10A@Ju%&z#wypvfL^}d0jk4Uw>>qRLcnn6GGqLeTlR{7bBQo?jt=8YGngz4~eo|nwD zPyX&jDPcPN@X?D>!gM(Nix;JY>9D-mR{;l5KZOsagz4~ya6~iA>WG(QkGm!XM@IQ= zu%q5rjYU%e%p^bh2Jo(;z2HHs?=3SoB|qO*g1GKcy@xzCA5JO>7@)7AWvUq zcksF4xbi+XOW*W%gKr$Dy2I}VeLMO(fy#Bz8-yNq^s$5_f?6^RQ@Ja*UKq>gx zT4V$j8X>Xs8u*mt(^2D_`OpW@3I79HFxfuXx(2>&>FdlgoeCj<4jmEKGhKd~Zf|Mr zL+@I{%)x7IeLGk(<8QE8GGh#YEL{|ST^7|`1fvsuhp4_jboH?$$Alwd?`26orVKeB zp>Yr2LCTG#w~rxH1{ez>ZeB1Oj);=n-*=ZX(te_cyGCH>&(Wj>Y+ z7m0ELj$H0zT5=>87hUDMOnFeOWi{>zxM!U&jA3|$h=;AzAGU8{S3%M?AJZH4QS+;X zwN2ArA9{9&bo*8I`_Ln6q$oS+E5kC$LAVP;sV!aMTyiSZ!tpb-1wEW`&ZlLZ3@jvB zFL_=~SdeUWGt|QH+o+otWN3QFhaN#8;u!CRB+I=YI=k+n?=8#vBoUHixw$>;XbP~f zsS0eY*)R+)`^6`gkbxh_`gEM;vaqJ zh27-ro4eEhp5B-K@Z7M`@1mOZ{WZ3tJYS3 z3zqx}@hHH+WS1YkaF(dyz1NR6PLksBLVmPyk`%W_`v)-2^alRx!LFjE{VP}!Ey8Iq zU!#fElG=W>kCJfbYT!rvC`mCh&X4v{lH%mHezcF06kEpo(LPF2w07{LeUzlww5uQO zqa?*kKl;%=N>ZHG$B*_=lH&dWezcF06ju)MH(~b!l!if+hHa<*l{VHNjH-sIAgZHkh(e*znmQ*qU?v`By^DEB!m;d+80}KNYOhu5*w^zczdH)8M4l4-b zgPB+TSI}19W$lb`@GXBumRv3b1`wD64!!F~?~=zlhJQTtAL4}KiQG_e))W6`dbasb z{wY$S!Y{vsD!`Ya;>1^>y0zb+zDcN?_1=F^EEF?7_-nBAwTS5{Brk{al>wEWXkhJL zJ9_yj@s4w@0Lu**ggO(J@&s7&ZHO+;po;5MD6o^t>MI^VU#=nQd{ZLO1%2`sYqm?- zKy6xKPo;pBCCpKCSvaglvAJOyaJLAU$X;8rI1M(}3&>%UdVz%^L4|)c2(Ww>3$aC@ zU2u4#08<{Gj}YE8kVUyU)I6|_QBa5kWy-_z8M)mmfZlaUJZnvxz#c|Mu*O}mc-sI| z9-hxlsrCW%`8LAMr;Y)ZH(}&srFIT5<>C1VmM(#Dl$(v+0vigXo2c#q^a&{<+2@{t zO$EXl(K~RFVzucX*jym2>&XG8Je+9h==gw7*wfx_Xka@XKkTE}qYD8>!*!zr5|$x3 z$Wk<%oDw)e4cB2@faTT47z-k990g-!@1(#k%E*{0%!~k{7S*%>)188RwXB#PKyNoA zKA4or?t_GzSF-};8OQfD5G%>ifb5wLtFX+~Y(Zc+<2aE9JkD!!^x{jm0F}2Q(1RtT zBT9<>A)qIZK*9xE0z+6~;L&}7Kjp&2<-)N*Lv|0^7l;(z*phoPfSz*1hJ&{+1iCQJ z^3p)bZI2>^_dgo01!OXT$C#YJXgXy%a4*0#=O^03;1E6Lm?O9t?3o)t^L@f}!zY11 znVkh0Scl&}+6VUgHE@Q-Drjch<6r4|y*~q0Skm=;81gcf=AQ!SVMik0sV@PPvy&n( zj(SGVPLW43y^4vn6HK-n!#kR&43>H1H!w73FX4$oIXhu=OCSp6?4;-^5ruMgQnZwd zLODArJ}DoCa&}T&Uoi^h?4DeUOJ{}5(GQ}8F^U6a zGmAF>uact{GmP9MbItUl%=P1dD1`)UyHH#Q^+TfqEZt10+fltht5H#Im^VC%X|fbV zSjZp>x^@)EJsm|q9&=)J6uJ$8g^wP;KF} z+neS^)%pKe`|g0I&hPy%B14cR;1(PugpmXiW`KmfNdQM3b>QB6R;{?}#`$Vn#l2DM zI&O(|?>*|SRg0}v>(<)-&imdQXg~k;@cTpWCCU5T=ZyEf`&>zhVDpXN1InP;3R^jo z%3FgUOCHMJt@NYxQw0}v5g+Kc+K)8kszCU()^DL~veg^>C_V5BtokK>q%)5d2wOJ$ zVHz0W?ZdYEQM#xVSTDEvk;YvW2!HJK!?{txX8UeG%HX&HOS6|8N<=vD0n2{BFJ(5_ zrGBd`6$$oP{0_=dW%?0vMFsT1pJbRzC;iq{ic;!nKhls3RlHalZ5_SfH%hKpO|Mca z7H}+Li?Iq;{^*CJ8sRNHZu@PbDPt8pz2o;gWri8KuY&LI`+X;KX!kSKuB+hIL%;WA z4jX>)!>rRn58Z#I>*2TG{BBeFB&dhU()y?SD?eP_;zS_)jUTRV@!Os6{LWC$G9m0J zwse=D{flH$Mg?hX_D_5l_IZZF6^arRIOiv{+&)=>gEVn# zOjYC)-Eb7UXHry$%xpy&u`-PUMCfbB_Agg?vv14_+ty^`RSo#MSb@2G#iyoa3eq8D;iN^07abVp#+(ed*C=rHR3Lh+ zSHzK-|Ig~_)Gg((2ev8}RI@1)@Ah82PeEDml15^x8+Bmweg$R!49mN#=+9c0Dkwee z0w;gmLUKCJ3ocm{hiz@j*uo=<@$ON9+`%mHo23PYZq(ucJ(5EUEKj-RC*-0$;cX0PV z&!bnUmp^51w7~em+n+R`N>KW*mIhSW_58P?Bnuk)Qz|3kY=Y&D{7I`;qVUAe{Eu=& zWc&LQwn779ETQya#j_6t`S0S-9;T)|n+c4%A^xPbDN%U4)*o~Dl(AmnBsUTmj*wOK@tU;m z)BQK{2q(@B2F&&!gP{u6&GRRn8?3+<$v|f;@aN4Bv|i+o2M5bQ|E<3ZS=|k7{KcT9 z4~$she~#ib*rn<5kC931&HhHp$fN>Bj7++3_s7ICWpEb!@!((?e7DaZ4-S^Wlv4k$ zl=>>*E7r!I9rG`utd0E(MqV3Bzu}Ju2g_jhZGSvCSO!6l{PEym88rFb9}fr4 z&_kI+(G>SK=ulfZh+3G9X`mFZ7w83l`YMAdVG-3V1~|U(i&E)sV{<{;v>6|$q^y|m z0T;vvDTh@>AUq%#&Kk~Cy2B@pvSU@7o#jUbmF!%YlALpA>3yXy>(W-IGLcuyrBixl>1(ud^U)>z*9A&9&3e$Wgp+C*5&OZ2ifs!=3 z7KOJgQsOJUggZ?BPDvS52$;wvO40yZ6#jjw@@JXDgB8jXv=Fin7Ohf}#@K?x-nB}6 zdAZQTh7C%}IA6eYC{dEOCy2t{Ta;^M4rjM2DH%Bh%<%0>e2JzIW#}#?Ww0t>8t+m5 zA;b6`P*Tnf3Yd!rm6S0rEIX{keeFVun3J^1aadUj&rd0DQl=g7@ujTjj!QK-a#o24 zfCZ5)7nDaR-pR2dL&wo_@Kvs(&vhlVOH_!>S%)dwT zvU0WL{G{wciqwad>;Ew*{!peYqxAkzyT#?_Cwv2sd~U82dNEz>x8L(L>sR43W$;0yRT{mR3RF&81OOQy3!oF*m`tIN>TcJQWJM6 z^^Ul6Yur!|*#QABYy@@44+>Z)6aJ_UAT19H?UohORl zQs=4M0V-131R4TdDQTWC=>7A^Wz)=n4wT_(1&kP;PBRDK!kG}#rfC2!ob}ggYry%> z1Mm=!_;g>JfI?Cg;&dy3U(PI0&owfxZOK80QYc; z+ig<hb|aruXH;2utK`*3*x?%@=- zXIBTbAq^me?XBr=q&=LyN&-kZaIAsE5ikeNCXzWg8|!LdPZ+Q-U@OI;3(f_sVL3(J zJi+xyfVUJsAnJ6$JHqe=5NwF7`p1_72Ftg-uLnGoZ#&)&*eP2q;9)>@%3eNvMlrK1 z3us1&M!YNpJPFt<2ZYzZ1}?!qjid|x0>{@RfMMky2WMjgJ38C! zhtTvuJ4%Bjtx_9q__G}ufx8JT3I+#GXZLafeIYX|@CJo~6@fAr~ePCnrzV{6VsPMI=r=6zF{O)!58cbKum}4)C$Jrh9v1A$`kMkN9jppViS_NP;{qSp zNeVgnb>P`bS!&+YK+;IuO34SB%?QN7hVUC>QQ(pI8kkq8<=S`UZ2l?^yiqAOmzD*R-p4MfWo6*!vRZ1d4J2)2kpqPfyjmCdlo+Pu z$Z&gO;7i$C?r#pfSSht^{yva2b5UWWLE8f{WtZ^XcnCaJDK@sd18wB^>AgRYG9^^O z>&F9u_A=IsLxCqN#S0DxE~9kk1)B~}0_9Vvy-x$+g$elyUS*gBLjL};?6{+&R{i$HZRFkcSMMGph7S4vC$9|zW; z@&e=)Co5>v(@GMV`7AJz)I;WRL#X)r%Rn!g&8Ihk*D9r!Tkis2lVgxzbK_&6gUsf| zU-THnv38saB1dLPH5YAz-pV29NBbahKoo*)cMLi}RSo1i2U(lXVXi@^D`lfB_n@yR zV@YcTul|oIvWB&T{3zSf1=jO7LH{_x{B7ePFUkaofGS-O=mAUALGQ>2v!a9Qz>Jun zU1WrVYjLcQm>%?u+KU9YWeC&9@BlS$L*a*RLE9)3 z8bZH!dIh;br@ldFZ3R8ns!x#Tg5`mAVbH*!yHa(9PD6t(kslB;GRPDBM+eQRBFTSt zOwc!E`2|?3@j;jpRfs-%QV^zK6SwBC%WoS^4Z@^1;?uj+g5D5z3NfLZT;J>7EM!@b zJ)B)+<+HlcrvhaPa}eb6C72!acC!M!Q956cy^ugn21P`7$8&It=Kirj*6 zPFUQw^9sg;CU{!|vTFw~q%<7(h&ilus81IB;TwE};+|Fnc_w&fXfVzM3wmx5!8j8v zZpRveaVA*Yjz|r*x>!!}6wL~o&M5U61g&}olh&gILWjY@)ubHgZUcjRQ}i!jd`1O3NjcEZj0o;l z6DJghr?XK-!IV>sVs6bn=Z1%PNf&!HJ9q)1<_>UFcV)Fd*7St1xxrNjD*yi)9 zb}VRNFeMG$g^j`Eq>a#Si-Lct%AP$R?!g?F1XB)WAl6hESseU?5?n;#hRcJOSCKee zS{eK;L8SsFV0CauO4BI{r>+a$D08sg7`*tua{z={NPhfhTgp{%Z7W>`s_ns~yer7P zH%z>{jqd*=YCf1QYUP0wOl0SP9F`g2O+R`;7Tva4|u%0%p^r;N_Hd2MZ5?KYk0I zA*le0o&~?6_y^+jVa0EPT_NOEFzKufM*u`{uVSrEhu;M^BFHp3!aU5fkZyA&%x>-egqr7Tu*NVT0UuBUQ<*m|llvi43l zP!ZOSDo6w3pOYid`^Ku3m9hk4`9O!yRHRsB6f3m0dUt3sp^(+0+PAtsyE5uuj-g`lMDOwV3K>RY&L)t0IkFtT1lOee^viR`{C= z6LY-^R@%rY zWmJzcwpQ80-_2FiBu9X6TdIyz5V7$tVEs1+V-4@6a$xV@RPkgt_XhWcxgAuiZLNGw zoy&t|bT?A4{MU0_U~+fWeTqKIVF2m+R|>~xsHhomYA&AhOH~98db(cff!U3xF z1n;#9=FJeRrnE)WVUTKvjCy9MYIUViA!N8}HD$?4kh?btc{QA?a%6E6_6K5FbkvFe{c5yq%@GN{8{k z-Vnwts6++0#}QgCR3(&$q)m%d8FWIER1IKtu}VR%k*JvlFmjnHkrNUr7k*i$s=@l- z3$6=)FINpF$2Otz4ntH`!FRpN+OOWfQFWAph;6}OhTX_1@cnkx^78Q5eOGySY`T{e z9tCjiI@paY-lx)0hA-s^Y{_|*2g5<8J3IJItpIlJpz1uq_y2`AUSGMMB^qu~dx`%9{QkD;EG0q+Ui|~EJAvkB)oCKac&TrA`w69Q2uR_8 zHPW#szo~G?7!NEqzpF?ohJ-{VKVNiYEK3#5{(Px=LaSjzm_X^Cqw2-}eyzGJ_qW)I zS5}JNZ&f{sk!lFr{HMyEVJC4otoTdiDfJY098-@VY#{`ku*HSAd}E(z4ufTwuh9Lhi2Yt zQ)LPGvf>`)Ruk67tbF1jtB(4Jq$bv=ftoPbD~DnTRg;~68CVSl`KYVd zvWC6-)_}>4)Q<>t(u$hS^y!T~=Nqz}z(=Y6)t&uXv!6HkM5_l%24%}))V=?E<*>q= zU`?#LjSS^!RQIn8RD*amzUD)?*?<%^zOGQ*HcVI7l5f*9)emf>Ph+#y&#bp5b!JuR zQq0fz1lKUL8tjs)Gr=>wlv#M5zi> zig=Y0(!QUgJ|Mg9@rCMQnep(&YFs!LwWG^2^-s<=w^*Y;ywX`wiJG#sC9OHOx#0-Y zHmTcIVH>{l@PPMQ)Sn3QunToHCg!qR?aRVT>a<`_wy6vLyC(@0IJsSYm|&Q43Y5bE zNWUp7{2Qy6NQBpXkGiL=PzXD}UrkwWumWNN@q_B`DP0ADQ2nmj5llzbtv%7$B6ZCE z8|tBwtyuCcbw8Stn=%A;g_PUsB%-mK0wNR;wpDO5iN|9m^k3DK_cB>kE4`NqqJLA@ zsw&y4;JKQzY;FbgfkiLW#Z-lG_wj)rU#dIH<}trflhy!bSM-7LZ`G`FZ1%k`XA}Bo zCE0ZOL}vr2n^m$}BC2R8%Q_WC8eprjR8B3I?KGogHU*BF`P4cj48E|MW_RV-ymZzK zlGzM%C9{DdH%%g;$>sHNxU^$qmbV5s0r6&lx!2Vc{dcthVvQrT(wsvfRQ1t}ko7RL zp@z^=l3*@1(iq4b4t=J<95qKg*7d99|k%poRRGm9$9$OsMTm6GTg9ie)hW(Q$#%%yL}_PtR%vnsKCttUlO zNQP;jrE!MQIT{nOEpG<7%`}gQW@yH+uq&Ksp{ZeO?Fc+?rNLK-h)x@8uQ@7JT2LL(dteqEyZv2tt>!I}Bwn-gH%GL0?KJ({t8>olIwV2$Pskxer| z1eNTlC7U#lsZ18QXk0+CLvx8rL;y?!4Q@pfm3ZGijlZ+a&usQ`43=eK8WXhJBhz7>};XvDHzrJmEO+nV0E%^8WX#t$?z|GRvESbmg2 zFP7^Is~&3>NqS)BCz>h${qan|4Et5%Nz`Fq2>4yILgsM$xn>%bgTo)1G%|;$uXwjh zm|@Uc%@`_&+3z&1D6KpyF_+zTXkQN|{jD*3v0q;KdczaX5DU?;0?ZM@YKIK-lDvFh zaL7K{_osw}G;xtW-5(dSyfIB!9Sa#1(vTU;@;u>9hmhShCDMn7g*Z~02GoEK5HUPd zVm7oS1YhkSDyU&|$V!>-%Y7lss!N})IuX**NxH3lIfUWCDqfjOe+=0u7aM&mE)kA02Agkn=rY_t3IodJGH#9`H<7i#z zA*xDPh>Yk6N1{TRx5TlJDYSzWG~iCtP+y|Q=@zsMb%Xw`LxbxG+p%{8Lp@pP;gGs; zXHY1qufna!Q5m8-ESRFO2Zy1dztxjiSDh1jLe}@4g`orS^as~>!P3wR^=SGQVcnN) z-xs=*4f*qQ16H`BP7vF=KXf$#4;!1BP<$ZNhS2`Pg@d85C|1C7Em-!kP{NQEJdTHA zCg@FU_NmbA1bMM|S7~|r=hLC2O+=P(Z5}%ta@~O)zZxn}f5u%5?aN7Ryb?+o0+&Mq zLMBl(YDK693vP$L;#9=kXYu{e6O=lLaLwT9qtLz85h;{4^@am4Lzh>0TU}sgpEEnxDN@G&a zm*LZDF}Dk$?by0qIxp6{hL*4{#@Y7-cTa6CPh_T4Xel$~h%pd)`fEwaA4TC`0<;6U z5!(c72`3V`5CdU}N=ur)6NNu$v_m<80b1>HLgf`0J9S#h7z(iPVgBq%QCdo`Ts-?| zwDu^`od$x&pl#0y%!;RL|6+pnGNrdC*k`0@yKn-V)9C*9B3(-wstE$!vLp>0%b{z) z$*lc}7(WKWtUPTWu7OWY=^8LL*Y4vOe}1kVNA=-Gt+e?5flci9Hd@L;q)=vXTkRdP zB1+n8n{xuT9krCBQi4EYCoO4uP82@XMIz9syLJgxiyeDt&k`yF6!g~OyMHz@-+pxA z+x6Fyrig{`mVw$HoIubJdKm0KRC|_e{`0x*tpFLbwNJV$9mU$?WDPW2A=QEbtLRa9^Xf_j!KiiG6SF$VIJfI!T z3AD0kDWhni#pQ>z-N^(>j%XKi0$Il?8h}P8v>(XNK5$yQj6b`>IW6S`q!50?dF^mA zfuonSgE@hitMt~O@S2vCAyu&d^M-aEC$RdKmXeG@V7$Fui3Yj*J?&CXpxOgUB*4jV zEoli~5I^)t>JAl_X-lXr(fKFZyX2-Y|EcyE7ryRu?e|myeP2|fH~j3S_BeIOflA=QUs}>pMBxL?ZFCPgftaegJyZezeSU@VKXra{N&!)7x7|dJ}~xJhGNfREDIwI?DW} z!1$(~t~qfQaUjfTteeHP*w{};ZVG!TboGd?Fc1O)bdx!O?}O;B@G)5T6UVrt(M_QG zvt6j}1G>V3aNSBOL|TMyE~jxbN=MlUB#h~z*NNEhN33o+l}DsOM>>or5lGNA<3e;y z(oq(D1x9hQ?jgB-@K4hX=LD8!=m^u6Fd$R6oa~G{b962Fv)yuaNf3Cy#O>bgPTj@qqUC5=4?kdOlxvh>oW!1R7jy_|wp+Gl;6KK>~w~Q)8 zmoBQ*WIEB)0_t)*>tXO4p32)-ND`f=pJKHM71Muz(2b62t3*C=4cUiOP!U#*J5pd^*dO)HW+Ooq-htV;>o$SZ7%jIf!m z1U(D?em*pQAY5x1ww7z7uq{m+RwP%5=n$4c&;}D#VgE2)b#`YGR)rzm!`@P01d%&) z;;X~^{$XZHRj?w|fSZHDe&^11a%k9jULU?49_Gl^y>et&0KcC7D(o>=+RpJ|*C^Fi zJjuN%tOhik5!R2&6%e5rr_p6z*i%ZK5Hw~j3v+>ci^A-2dj~vTLiYkdq^4xc&Da!H zgEgEKfv3e2dcq&;!s=2b0)(>Rp<~weu;G;VdkM8Xdlp{{icf|$M+LLjXTv5?YZ(GH zfLiCnK5(GFO>nq z!rp9i<79jG)+@XbTyY35tcQeJGzrfp+w6LLc=a`!=y2}IA%<{LbQh8#P#D>hpXG!* zGi+o$S#EsT5NMbYZlY=l5NkgVI3;=E&&YA!eOy8*+%5M=4KL-28gwcA8dpr#mGG_n`q$O)GyHn`dU!Hn6EP(G8a|oA49j(9?OemU z!Vgcw&vPBkdnqyN{aRw?`!@V8XZH8IaBs@dE|k3m`_eALg?;6hsD`Vy5e+CrISe4o z;0FKQZcO5kl4yW!O(FuQk7r+|B)T)xfJARr?`Sv!Uq!_4+?KAo2w}@dVG+WXiy|WK zagBW$6_M^mU@NL$VZTHJ8=DhBIb&l55M*bu^9~Q9T7;_&IgEfn+J-`10ygI?B&Yh}RnQ z6Cz9$an6Y(AY{LuP{nf&v- z%@I>5O?<(^$kr_PW}*cYyCX;`4Xg;B%-BD|9)gZUJm9uNhdqmEUjwi6Y$8XK1-R!GS&Pluk>ttBR7qch$vM)2va^?q z0*Lj73vjJwb7rcIO) zNhIH91`C!(e&o#OESH$?S{rHL%-`;e{FO6)uqUzszuFv#e9y~${DHtZW*{&~^@ z`1VX>4@#fGiqHr)UyNLYyTu{nQKTBXLoobVWIc*DIU+Q87ugI2V*@`$QuNt?o(U%$qI}6}LIe*OUp=abo6gHUYCjKh7wbnA^S=i+ zjCxB(?vR$6cZa|a+#T)`@ z>F_9bcKoGE4YtFg>Qj75s2yQS1CdL?im1P+LoB$rHL50+0mH(MFlKwyU!2BQccb2M z*BW*|O2MtRe?|F`2hMF0Q}9Us z9-Svl^@wi%E!H;ZCe;&^|x<2v&OL_jxpB{a3I6iJ z9Gg9+N*e33BYG`COny?7;Ym?f=05C(3+uNtx{QE=oNBZ4ccS&|tI}wD=3g4>39lg9 zfhb=E7G-OSl}raIDZv#>wAl`|chQA=%0* zSa$uRmRkr?0T`@73P14?rm#x4yybW?N}AB&QSeDV$p4<~q`%Iu z9o+OcZ1Af0(Bo3~Iq>$>@8s9hUV2QIeGaD8)K{yFbTP~|;d&kBq}OYqQ+<7AWki#( zAk#-GVEyn)F%u|D)cQ&GR$|fN`iM#q!|bflHKCeb zKeAHHBp9DqeUX(K`;egbWas_$b>VrUeo5u5dv?wxdv>6u-T{0w^hOt1dbYl~^$|;3 z=;u{V0R9NTN37xg-1Q6nVt%dKLGQ->*Q>KWhhIB(*MBDby0`TAmHv89{&(46{c?Vt zK0@;It|Rrp{n}@={u}=J?y=J6bH_{m-*=+q|9+Dt|Nr@G{Z#(DelyUvm5AXn?AfDe zM;JR#kHbH?s^40qA5j_Yi;jRVdt0n8wOK56Q2t)3-$-C-?Vz~8-^=uSh#iVr?B|Vj z8ncjNq1fBqpx4;gEQaYw$LR-FR^0@_kW$5uAJ zQ0J-sDjA{8OT8(ZU;?gJW`KJNFKW zA^J=quXB#DCN!ohwmiJ1?MsgN!%ez9*es@PHR)E>E~bjJbQ|9zrnZxG+hb77HhcL8 zU&eeP-+ogV(*X09bFkwRV>(HOhuA4G5fWV(KO-ht{`7iL%y;r{CoYI_ag>U!SQ68Q z=yqe-mX$G-2?12^Sax?+%syJ>8w)vWVo0x{;}3@DburPzusM$TZHSpoV?B=b*cfx{ zzn29hU60BaG@D~=9VAO9?To1<`&BPXOas{~79Ebci7$BI9yIZ2%zpW{>G2r;+6Vq= z(~~g@`L|Qg#QZIP`pvnRpXJ+G7h+Dxw>>V!;H$<&F~!xGZSw7t>oFVT+toK?4#>Ad z?!@4m4#jV`{S;FmKf>=(%wO^k!pdSk$+(HX#%SbE$32Z1B>&d_Ma(<-*6nr7X88x_ z-p1ftAq8&F4>8{I4~jmOQ(yXbIraHAv8Uum46u!TA{XmzAA3T+O|2IDLcUF}9(zT; zRlCLRly6^q#MY4?;p-jS+m;o2w5$P(YsbEkf3UJ%td+0QH`Z7FZOg{7FXRWe{w&sN zfK)|nl>CEP0kJkR!m^;)GxBf8sA8{EI$-xt#SVc_VX+nu5zpAwh*(eNJ|VX5&a_yw z^cdD5BbM-j62_q!LOU||{8(Hs$c)XhXU~nF*I>7EV<-N1cdq~h1XO+|Su$g2>=8M3 z`HqUcB*#SC$+08kPiIVzT`0%Ih`F)dWG!x55UZDc?EN>f>GG%fOJXlk`Xm1ql}&9L z>%n_Dl$81iwNjciGOdc8BUMoLVq@%1n!+~1giW!VB=K0%*4XJ(n2pKS#Gx$HCW!!*r`;QUB}9GQ{`0b zrvDBDsGALvJF>;+V<-K01YS8d?z|W~M@Ct3HFh@<1){IVCR66Hgn?V%jKxIhJjk-# zJF(wWIo!D$>q_Y(B8QpGcaV!SD{UU<&VG9w>kT&_$Ih~4&Rej%Hs;q@H(5i) _ zJirnDcpmE^#|^Jnu{Gsz6Z1YckVyPXaJPv=f8+N^~s; zV+iH|qu0h+d(jCS;ttE!saX=YpU6~9{75r2FlBq(CKp!Fxged5ITS}|(0HnGv8IQ4 zh9}&>`ILyp1#E9kQw>63H{fkl!8G^g`TG(g2Q&Q2|qWDehnuBvp8oWGkVR3bZTN;!=pZ zZN&=D$9XWrdbBCG44FdZMpjo|9K^*E03jy;Cz zlF$I<97?KfG6EibdLCDg>qo3@<-Uv~C7`qt!m~rq;~bdJ?)LU@^L3mafuZL6xNv^W z{xfcPWsF*NenUeo=uyR>DyO@_&Ty0z$YkjSB&)T%y$dv{W=P^JyHz&`wu{{iJBe&N z!Xs}3CUuZ>%GT5}c(SD)hPtq{w&6?ualLv5(sXQVLq~SQ*We1d^$iKcN-Ge2S%;yC z?h9@NE7%Ah!x>t&5JPgF?rFvB_;i9*;MZn#I(MIppT876Uy9Mc=t zQwcPVHIR}9W1T64=?248PM}tTfij>K7=sfHl%&BtiWI`%$%d|+K-VB4VmXQ0OH zLP#nwjO7H5bfOFI+r@xe4Y4unW+1ITa5r@8VYtUJp7%1WqPk&f9|I|Au<(K_{S274 z*20Vf4XddH77Q|wQoslTs-Y4AHrznj!6q=Ck1$Y@21}8k-)IA-@V2mXW9S-Cj5Uxl z2MhLWyaDr6TA24FdMq3`*+AKL1OuiRFcFo7wU|y1gPUd;NJ)c*@PV@p<2iv%bLql6 z&qpDs&bxG>VF4!)`K^I6WhR7J^qql}G*}dlFE(JFR13Sb%&?g%gl>g_lr&fnIJ;UR zP_T|d020<4dJ+!YNtN%5O;Y9Cw}oE$JhvK1`GSS;({~s;bM3zXdH_z_U5WU2XRiS` zfdvQX{`c;n0rLf0*pG({l#@V05l)8c;(40{vW!bSHf8W_-s9{87Vbp%UoqWh5mH7AAO8)A*hfcwWbN zm`b2aJ>z3?9Qfp8#Qgadwycqn+#j=TVl3boUHpvX_He$!7|GkDGXX|Q&R(HRZIF?a zG*}c~t&%FkxDX@ZvwQ(giF1%`EBqaeknst} zs5aC{p0jE{%t)UydNaZ}g%cz*;A`V)jy7RBHS|enfCO)HppCPsvFo3-Jm(rQC9#F=UqG#Y|3quJ(Ab2! z%p}wgcNQDJ=8;*f-;G>ubSL&WWL6002y18ScBLgoH@0xDt`jJ?7)eXz z7wqGk@if7yEOMvu9l^t`0JRzB86E>!yNshKJMIO{%%jE{fEkB3R3#DtM8KDMT0nQw z*o<<(T+q0^N9QbM98S4v{E0|~A&Elzk*o-4N!>$(bX=FW3XczyHEC@@%WUOg}aHCS&p^3P) z!W8}uobMd}2iMRK-Qw3$GD8UKVankUUP&8`s)TY7#1(FS72k=X98o{+PC$galj0|k z>jzIX%uaOo?hGx9;%%svWDUY_U%5L!9`lk|U<-@KJi`{){Y^aP8MeTK#qpSD*aD}P z$G;#`<#~n~<{9n`ura<7l`2D++Aw5ud@Lulbz3|sagV5lJmWCKjKiH_^X~Xv9IpF; zO1J~Ud4j(szBR=i1fhTD9-e<9K7?Fz5UMtGJR4uey*&GJ{2WS79zlvHAZD0=xHH^& z67PUoVvl~0pFn64M8!k2x(-Om>23UEvL}@%Al_j}SVvY7qSS-Qb_s4g{xau;`IH32 zLPeNEcn%~rNidSRRAeD`Xp!IwA%!!;^M=)f=mac8L6I$=W%0 zD6Ie>(sHst|4#`XblQo1`9&n{^0;Zd{z_@`4)!e8qh-rwkoY0NGn4UnH6BKTY z8Hm5;fwF970wy1}z{1%Hn0(j*JLV=}@?i@Eu>{N_Vu32(Bw+Gk3wW+hz~sXgXtFi| zlMiE!Jd%LPhw*pE65f$j!jlg(Og^k;4^AgMBC3O@FdhV!vk5jlQv7f!L3E9_TuF$g zDwy?a5@F9eJWg<7S-4{ajX?4vdwK;z-sqH|^ zL`+m{f#TMQUr-Vii&~0_ilfOyv9LQ}M&*-yG*suyL`;^7Lx)j`7fGWbRZ?4aa1N$t zE>BVnn51|fX}bhs0b)(y0VR2UA|@%eK>tOO`+Z)VXhVtD1)N~?ga|z}T$4yU(9CI_ zSerPL$Get05-~}!1;*}5#3aQQaNeJYNs2A-$B9HtQfz@)XA&_S-YNzDLYIyDHnH)}3cw2_qhNr`h z`pI`N2(WpLk|`ZARI~tc1$=$SGp^QRzvO(Xq=5N`_fWcGf`&*|d_5!?lNDQ_M_4i@ zE5^B;gk(%sY=L9R$<7p7HrdHTS#?E}8rEbcQ(g@Ki10mSA`@k5!P5-yf)}lmw-L1> z5WD~r75~D0UD+`ia}-;kUbke-QEY))y^|M^yO@|?xCj&SC!gm2Giz+}1ZszdCn^R^ zRLsvHfPP}K8zoD;=xktS;?8Jorkj&Ii^>Kuyr9k8WK2qIfq@H>>rvcB3`r}J66b!G z+?d=6LYQr^eo1nZKqPebcybMb#lYiwa(@Z~EcXS&e8gj5#*fLjIfGtyDVUVl0-YUF zFe$MGrd3P9q{J4O>73%iuiv?(U{YcWT&S6XNr^3RtzHTyCAI*3mJ&f3MDO;-(yamCU!{Sycmnn6V7iL(v zI@q>P!F1}b4{^-=s zaQ0}53g@k)rC9f$QdXmLG59&9h~VM~*b{zwm@=9YIWcqP01)E~^0RFhr!L{bFE36l`R~tWg1_Ce)U{*+omQrb{hkhMQYl*v1%Wu?yCsXHg6Y%{&?EQ&U`Rw`lv#AzhWa!2a z(-D3?pE{nm3EeNJ&O!;{-PP2-yqDSSR;oXxqmFFbvXFbJ&TPboR&`mopHjV9^g#y? zz&oKrv7<@zF!dFYw}cM+9;cq;e5XH2UC;SOK2Hth*B-A^Pg5o!1@HKT%T?LZkEwQW z`h995nIMM&ggJ(7wA2ocvP&b)GYM7=UDF()pn6&;l@K7pCu^I`hPb53-qYSI?Ks88 z*t-g6A&t^NB z>O*pPS{d)2t0U7E^P&Cem^3d=^nP5LiXyErWog^%Zp>J-jXi5K$j%cEC#BuD+6DGx zq$w%Df_MMF(z2PVsDBMuZB8o*C&VZ#Og=bIO6$c#!muf6@3{sBOiO!K3$Fz$(pvH= zwr6b`=BT&8i1le}`L*%JGz-7}T#{DGJA13POW$|iDShv1XXVboP{s}LdqR3ms-PSzANI9R7apPPBuU%2OS&7ERy#U9 zmrFZ-oFq-+1WB4-CrZ+kOio|VrENJ=`o30?^nK}U>HBeW(;skYyMCKKpE5o}Q;4if zzOZ6NdPYq)fBl*UtZ+x2AQrYKeGj1$RAiSrzw$;c)@pD1M*_(@Ubs#DjH-kOMlP7$3tckUEg5Ek9JAVgabu8WR} zjtJ`*(Xmr_LC3CPVbPr;I(7+#gdfs-+1V^$CCAe*62!pb(^+8y8%Ib!nO>6;QY#RA z&!lguf>h3*OJ7?lDsdOmr`RIUvCHYJDg_jJmHYGpw)jRmAfj!ycNNbg{;nK3V%LKf665jJ*~gW(pTCeyA{vVF?cV4KVPRi zJK)vvQ+gP`##PNotBTi&jv2@J?{eHS>Q%#QkJ=f(QKpTNOBZH7pYF!StQph{OieQ0 zx?8!l4$GLyiIqiW5N%fhjmh+Q&dK=b3_Q@c0Q9jL^C)eCpu5zgR~@!xZb5f=n3z$_ zYm89cqV$ZtoNn{%49cmba%k9+lhKcRNW;90%Qkp@ou6@>Uk^0PI7Ao`0EP%Z8=E=O zt0#QgW-O&t1fECFu3g3jLfL|rQ@vQ}kc|2e**;?C@cH8QG4~ZON?+u#s+u+{sA8%){u#aHcGy zv5UCf`8MOc{L}k?XB2r#-;eXmTrFco*UB8JkUl*Umw8dD2Cz0M^Mg!bTW01ItjQcU zr+wxZ?$Yg^ewpDC?#|(vovTS74jG$?st`XtH!t(C{M=)UGZQFlDivz)|7AvP7`G|& zfP1{DWhjS<_@& zcdDOdRmq)3S*uCgBxIG;FAw`3tZGo!PMbASTjsu|j+%u8XALDpN6~d(x;n4g+;gq% z;A3#sXhQFM?)N30b4^Q}*|UvxYOyVj*^cliG%Lu)ZB5nx`@f!z9sckC+Rjs&rq^WO zKWI@4B6V3;>}}4o*@J7Ovzmi1d$At+EK<*gH^!VhD_n=ejv+BwF9{kLyBH(Z()g@2 zR%*<$hmWyYQIv@FFBnQDNDctVLthnjb2B;X98nf{o|3hX&_7~Ag8t33x-zHC08i+Z zleO87ntc5luyvn3A-RvyNjC6TznKQ(V zjf-g9nVsJmU4w^AJ+jjWpN{4V^j z|J=r25M7+r#vXMve^u6*%0l`;<26||DS`Q4%zWV8`mB>xkkk()S(E|A|Db6$XH}up z@Be{$@qJb)C$)8Z7G;6xf6(gf%<`-hDL@44V;zh}MU*P3s#a1>nvzrN)`wFE5hvv8`l z1PVW9ttV^sykmAf(J_OcZL&kSC}IjAz&@LlY5G%83_xWuNBPoch^9 zfW|)ALV$O^*(GG+g&S|wU}JW+r~_XpvL|r(`>Jd`xe8k0>?Io<3CZ5gMHmy7jSU`B zkI1fKhu7Sg?AH8Q8xpgdI^y;3%M9 z8xS#_V9?s^XPj-P&DnqOYm1%Pp`7*C2eQv`y|*mQ#<$p)K*uf})Q$J+m>w-&Q z+>z`I7rdUnn0@O%CcP{<>Eji9C zdWyRz9DjxKTCLXlL-t9k@H@8TIBT`lAj?D`*ENsubyi9f2 zhu9p_i@$p3FsW0NXUNINdDHQ2JlOV>97?|k85KibTFzC9Mn&Pb897JrHBOwvne3b; zgmz27#F%n?i7G9Iz`UGuG6$z-IV-3fMmNuKCyX(f5TI@AoC`7sW80jSR1VwP<#ZwI z;g^D(Lo$cGopY8^IRtmjNg;Dc=uytWt9K5e9|Ne)PL%G3FoV~CoL#aCZV#e7Ub1G! zker>Akt%W+%M2Ua)PvKbat67uy+_-4vx!r3JlU3(Io0MCee!^@Q**8mYny;}1os&^ zJtP@n>Z}}FqRaMXvo_zT4iWQm782>m2*VcStgI#^g^;B=7b$HAs;&KkEjN5%%jz7R zt)#Vpb^p|w-};;pL_q~ILu7llRQb6*wA`F?TUKA{)||`uLL>}c5VSq#sC-*xXUFX`ypb2)PtuF6;ohUiQW@?({3_7jtH4f zrXrc%uhmWSh~cNAgYm%TYiSnN=xw6B6x7;b@?yC)P45YfNdcHzvO6pC?BRMX(p9FB$5=q8phgn^3J-J}|$ri8M!2 zfnfGCjjAHCx#e#nEoyRIqhvnd5n$>_)iof(ahb>!wTbllqjDndAtqaj6Qf#su>Q#= zdFJn0gz2bksZFsaN(a*l=mS*^COe{T1SKD^#G6LQ3d%|{QO?G2Hh|*2rL?*gqWGqj zTk2hgiO|pDl|dFNJIgeqa%!1vDpyOrJkxAiu#nkw&Nnrw92;YE6Tb9QRPHt{O(m6L zQ`*YpNNM(kcDH&rOakek%^SvyS%C^=9m^xIzFh~S<<9K(L$41j>pdzo0idZ z!=P2MsV7k_45lqNO_nv%WEGvm^wlN>nZuXsOy9~J6dO&esCt-NVlt39EZSn4Cvz}t zGf^fT(5?*DZzpxn01MZazQrj+fxnA&Qdc5k3TctqU;So8NylNSd9sWg_?H}=?jF0?BjS%*3K~V45BW> zS^O-GFUtrut06wlyn{>zVYQ%s0hl3gBam0pD zX7lS3O*A|=n@cLk24S4RC*NF13_q=yxuv-tB(*Y+AQF)fn0AVLT^QNM+{#J#I+S%W zhdJ4N4L<$NOC6<$G%3_U+;Q^|xw-z~q`5b(%OE`f4xcj9UMCACnPTb#vkx=v&#lc4 zzOrizN6(f=vFn%2yJg!hzhW+xZwFm78;Aj|;)#nYA+!?Z_mSBhX8dlxDpMN#hncdiL{KURWC$p~{{7-h^DuIF z5P-%$*K5G@59Z$`|AS|r%(!)ykGBB+E;sSlHo1r7Putk$Zj^6p+vU!tR$s0kFGzFE zb*L(}pp!guV~b&XKFMrcUlgxgP)4AlFg8wQHRFIi(&a49V@m zT;g&a0SE6_s!Q?kmxx?z?d=<#OR2q9SXUUQ&wW9OWE=sI*lbzA%?Y_Fa_F_o%>7L^ zSe5MDU*+4sa&nhANOmr1o|_^Uvu&LlL-Ba40P@iPsy^41^}UyCuis0blSRz0rIDqv zL++ER%zgN<8Z4$uE~P7vSViz%*WB3@zY>Li?~%Jj4$+%?=PoBiD*;oZZ|;|54mksI zKgt|h4bCOJ+YXRJ4@&0-%MFB_k-43TzB!P6{?Swq`i;)L>>yQ|mJ@SF$^Q3ndhTu6 z|1QnW-70^&U{US?*;l)Ko74-EpTG-_2b;(+XuPT?IqLfE6Y{O-eje#?KSGRPv%N`{P1h{?5{3%*(|D4L?|*uJC2s?|D|~k|?^IR1cE6VwN{VK3 zZH#7rpS@fQqRR4?@LC|YdH?!*UgOF*jf}bRMcy^E3ru*Gha0>fL#sD=NBOnkyS%={ z21A%Vnz>)N?96mc8q|fDzw%y{Yifi|{y#J|ph|vNaroGH_C588E9DRe>CHm3^5&G zlpdYNjq9_f|iKcH#M(_|+vcKaqc)8=H@N-5-OGAzxVW zr7`~`aWGvG{(V`ewET@Wzs$Gkn$8yQQv|W`>G|Yij`11!&j``TW>J1JbG?*r59>4Y zNm0^@;LCdF(wY)Jk~JL$IN$o!@b((S%+`LpC7d@&`zru>8PGxKu@2ZR9m z^mLL~6t2k6BigA8bX%QYO>!6*yFNcrCU|*Eez<&F7xJsgw_*G9Rr2k?AM$tE{6E&d zJ0Odr`TxCp=o|>5sGt-TdjTxi5U@88c=|(qC{MFt?*?P6v0yZAVnt(&8coosXJSK> z7+VxICYqQCmL$fg7)^}E@|)eghvy!Y{N8u{@&|Xfw=?^h+1c5d-Mf3JT;0D+Et7<4 zcrtZ5%cKXwkRc?Z_w|zpAfInik1^Pa{ZTo1*YfbCFf_Mw<1Ex~as++bR@AavM#bz>su&o;+1bxnO^+G#0ZGp(B4a_EYETQgJCPKrZ%R_Ga)~uj&bE~prz%h=h;LV zw&!YU0*k#!Kw63JH&VCrfMNG9soNMafIzLp<~LKBeu1?L$kSV?YcP;icT!m%52@ng zU732B#VJ%V*#1ZAR1Bo$pQ#&PMI+1pO09e)AOjzz!Y^LmK;Qn8y7^Ur^m&^4=v9F1 zRw&^Uu{Y2LUismx;0SP0c4u+@QqKtDU!F?SD+nUI{ZJJwh6+k}d-etmYrED`ehni~ zbg!#~x5I9TUk57Tchhc&g~7_ZP)RHZRbFI?Yyfg0dg7^UCpK@c9LRuK1P+bd>@d%_ zQ{KfeKkrCk&hM;@!PkeoD=%W$D`S<%@i8w>2`|Cj5Su0_@8J6Z{gn{F-4L%0P(lEA zLwswH@+*8a3{jTgqj!=r$Ti>we7Rwv=a_s`-l!2q>&WG{rhlWuZ3lEj#Y2>S;xwgl z3RcBKjj|rY(iFnIhtA|H1H=Ql$_WgrARI*CPy!;+{3j6Dp~4@OLE_X_LTG+NTr*WU5Y#FDGE?b~JNVITWj4c9ElKnFB~S5Lkt8Ar)&o>XpihYVDFKv!^(d!+O%UzND^*{El<$n!;{oD#jlm{oyQyE zI;DG}?Ecy36(b>tm zXjD?H@J5WHS>XGs*2f2eP*a8Ns3uf$nGnS%v5Rky#@LYBy z9W)Q7Ax0gFA1_e7BVo`jQq5;&GiR~t8)gj8i&VQM3|}r&vAlId?ChWADyAHi5Klu@ zdWcxbt_S5R)$h!DxV}aOuLF?zYg#cohOO&WAYzt5)u`Kj}02(xRRd%qrIz*l0g2dYO>mqHa8def}tAfP%ChC=p36@4QHdp6S zuOoijT75|JMVs2HKbB5S?bREk(*{xMP10#uwED3`_Ct)izAN>uF0pE+Uw{Px{?^8?gp|UuB;NUiCP^hktq%8 z#w@G3h@EH>A^dXR5!GPG`;mH8J@6gm=_RanM5bO%E#7mN)HEjelFc ziebcwz|kOSe%l&#g=BDR*QvjgO6R<}Qa2Fi9#L;|r&7&7UM1BDbt=nXbK>nLW}jBimNfs@IrYyh97*p-=Bfh3 ztnbuQ8RMfkggGGK;D%yvrqmTDT~d$sCPItPuB+WV8B79z#iQiE;gi`T{#3h*MR(M7 z>9`g@yQgl)C@SSB0>^a848DJ?{!%i7k|*l^k~Td!&F|7kaCOz3lqyuXX_&0rE{Y$D z{No2N)FVLSf&SU43lL8U8oRUd6f}|}?(L@;$zbs~Rv_ZhRzzrJtt7&&Kn*;)OiX=G zeU05EPYBW&C2@ZZ(bSgo;oVeoOsac5LKDF>#R90fh>>FOqHY<-lM9tUr)_pUn+6^M2$bQ6n?M4>e#6wiwFJk>ldk- zzop5gw#MFH&or73nI*F~;48)$G+-8_lh)>Fc1kiC&6))Gau`lIJ{h5DBz0277)>#= zOvNr2+|cy7DqnPh8xkajPS)6J(V@C_p5_p<2XL@$d3TVHcxQoTgw$KZ7i-}C1tLS? zTbkO^>3b_QUef8om6|9QlwDk}w#co+r=)K0_cW(jp7bN;N6=(%)zlW>-K=4HlD>+K z2y8xdr5Xw=(OhM8J#h#y1&fPzX_)r#?Qnv{8GAIRDIBC1?MAqGzvcilu*ega>WF7b zHEQP0#~e_%S(XOq>Jbe*pX)Nj2Mv5z6)q}1*YuKfXE>(m%51bxPig|hz|$I&6t_54X&suBYbzCCp z>usxq9@@T2yarKP)?g-1Jl$CfZ^YtG6K{3XGX3lk88W~pK%5w>g)g8Hs9e0ZoY{@F zKLu_qzmGPFS)GJKOh0XB$-l1XYwx9x2WuBd<8M&1_EdegfEME9DHlV~$d%d#;sLAn zAy4hzYq-6czlLkCF~<^i-fk%5(=0b}Y=QQaWDLpUv`-~t$ef`4RyyrFNel0;5xpl) z(+*+QJG9$Sj40Gf+ufr0y7s)(#Hu&635>yi94edwZYF(sw?!>$HKG?ZN+g56(vRL~!a|p8bYxo#$-X`sBsf7==*jre( z6%3x$f(YAr+qJJrL#BQSm@6x@%7z!}p#!B_ck%LW?EuN@r+j7yYTBnQW0oKD7J+x6 z1T^V$?LcOrkYBo>nP|!}JLL8kv~}1mxvG`z_fiD0 z?4mYb0^jckZ4*}TE5a`XprQA*!J>PGcB~W)E%{knhgG$9hVJyky&Y~|qLSK!gUaOBS%yU-yON)l308d)U>!uI{>&lr;_3Wk^%RTcNtAtdfb( zBXoM^84QRUp}8UIK=GgEy1SC&eBMeokrfWAoOUtrgC~(XyPu4*Yqw`d-B@O$z+AP!INp}NFQA{NxMFSboM~?_F&xwmNzpX;vkRLg^I~4T`Lc? z>r%)7bR=EJ@^E?8(}K&Ar+OpJz5#it;OS`T=dp_B=)6!;rY?=aEpyHF$zrQ4U451b z0*I~XvQfwM-c@t`AQU!V*NQ$x=$ETI?8+TP=PWvwpUDO!gj=-O)uvOkI64Q1?xS=& zcz|+yv~CldMkOfmV|6`QEY<;~^+eq=4CT;d-G*0!;yYFMG0Px#K(S5NZNX6L6uu&q z+?l#rucnn{vvu&c@j>J{Psj4vU@VgemZxVld8v1sE!2I0QJh}Rfa+xar9Q*Xpm97woNyc#iQFH`l65>Isx@O zr1KDAB|6?i0xKr()9GKu7${>A7!#Z&hjl%eaYjzj)e~oYrSnwS(XBqA`g4`EH5RHo&ks+BJg|-qw~9|YsHMN z&{OX%mfq1Rs&sepo-U4gfMIqB*!hTCALv#APqE+MI(YTwpcwjCx6K8Pr=ID4!$(n} zPhv$aLNjgbfYGlWdTHF;5%f$y#1k?f<6(}J5KM988zyo-^wtX-n*plfeNSUeZ^jV^=mk;L@e*8PgTIN!yx_s zP&igv^-cU=fisKZ0aX&}F;_4B*udx0^dnqA#;BQk#D;`$3lZ~Q*O!89sM#E5Mk2sU z#yl1+(6da`cF{t_qi^avf>h!^OY}hu;{_2;KW@@{h^trVU-JM+-1(GF5X$M zpT?>z;OZ-`-=Gi2f`0zKp5FxT5V5Q>GPxQ9|skm#uUWJdIU+M?3 zq#<_MeNp)-J20{F>uHh`` zQvA4q;XIy%X9XLM;A6i=hE~{rj&5SOgs%rj7}kL(;;rTe#Ih%lpfqSut)2#7ls48i zKx`FlP`OK@puJr{_r>4qgT&IVhGQ%V4CXEZ_d+a6Q34euuQwH?d0#^+i>n8wnW(*Q z{{S&K5n$OB|97&1WqC&gc4Zh|Z7skHD5?J1dYBa%Zz?;Yy9PrYFz9U$Gu53T4pr-#pYrpE>pXw6^d`=8nUXa+0A6=#G=%SrfEv?&q!Qdhyn9Lz{>-zn_+-2KTbBR#=SIYx`8EZsNyC9w;y}~-qb~g!FUXOwal;r z+kWN>!&w|lM6Nb4#ST?15WGm($mC;yiwcEpF?gZv+YM5jn)1Hk7!D zL>%*>VH=Bcu3{qsviGp5q?G($;Muanz%CPHz)7kLH3kDZvkUxE7@DdBYe+7`30|E|i2m-FU2!a{2_~~T>&hfGfJzQYsirh9bWpK_nh`>}u05XSNk4Pkmcr6J7K=Y~xz zAq-$oLxmn`Zm3qYVVStyEA6)`DYp5iJ!c7AoOpVmwbNbQ#KD1S!?8Sj8>EqtY+-O3 z3CV^wN;5J{xgxaD8&3`-3hml5jp@}eV3I4|ZJjm-Bg<(|k#&xu$R0(fb!7=tfT;>4 z_Du5-4|Ppz$7}_d0|E}N;%nX0%CVEcqN0_tX;N6ZzGvDK2F>9dfvi!5iY@!6&2T~2 zzG~kH74B}(8r>b7R>Clekaa!3eOa!kR#KW5`g2Iy_y0SWJxO3|T*aQ$w9GnOg_vbZ zbM^V(xvWS^>xX<7C3>JqL*d8TclT+Li8Nc%;0IN=GJm{ZEFPD3&Yi13W2dFD%p3(P z(q0o!PERwjEOis&io&$JJODU5D{aUBt`kIfM9)sMG6NVqFKsgh@L)mO237$57N+fB z2Jpd>vqV?=J#nAI5=uArwQG3R$@JyS8U{}O;%BGRyI>vNzmQIJdHlQd zG%QQRPw9=Y%)PIryI@%c{6b~_>1H}SVNoF#-=(s5{3HDUzW(DOy`J+peG^>oZks_= z`#GPn6yH{RWKh+5Wt8LV6Mh+~xYZ~c(cagQ<<;PQpfGrHlC z_Z62JjOnkrWs>U<&rBOu-h7|T-OOgN&U&E^suhyyE}p5K83VAXDuRPESw7Q+h@vr1 z5qR#$=uS1xBqp$-Y33d*wZ2W}Dy*G9J7(_2*B|!C)Z?RTpUg4dY~wxwm1r}Yq5{5W zfEbpXnTBT@(xFAl%#T)0qLdugyG%0eoMa$?|j31VH+R z%oQx*5)i+Jo_ysIEFRdB8RP*dsDs{-sbiGqHFPT8%~#}iXNI_O%f;}0nVAYW79Px; z<^sp3M>1>Uqu*DVSMl-w$;{!)`D7g!DiIU8pkLYWru*Bo46||>wy=b+Y*uWNPy#e(OK%Ij3~t-ZPsVN zjA}kBC#xRIJ{6G{I5ihLS+Y2+`IwPelVDJbC1bOeFq(!LZC#QTjthvM0h ztg$ZWM;MfCXBZ-S9?$B^GK3r)+)rh}=ab`$&jlQq$mh_?Gwc3p3BCjQ}_ zJwl+T1@*HJyVKKNVcDSydOD^_b};Q8ac%SLO;V}4b@o|LS|O%WcD7VubJuM65)M|L z*u6)#Al22yWy9;er1VVhYIIE)yd{k6nl@Z{`7sfjJqvwKT*pU%$yrVg8x*-)c- z7aNK5S7ravi1d$G^lA1=w^w3zwKV%v3FNjfvu8_6?R6&GSCZIxDf^04A?2s+hSKSb ztJxn&Vs`#H`>;gjQCZa&lOI&|h48SdFIqm%o+NSD`7HaHq_e}EQ74tQax>cdLi8|R zl`3rYHrhL@o1bwvZEa7G@vd~%)Clj;kgmEAVT7LnCa0U48#ha*SuKs*rPD^OjfbVv zyKRgcrPGb=jGLs>w8*Nu4Wf-#BrSNx8ii(Tiq1oJzqN%sP8~5!7y^1e<3cSIw$2w` zxB6^=8@fN^LO8lx-p(C8$}_ThXapWQNG!`YdazgpOhHYT-TA~DmCqhi50$J>b4AIE zGwYy&vF+W&k;9FhIW+L22EO9X5yl)h4t}T%e;2#pnJognKJL{Zqk^eM*SABzcSY&X zZCU8S(l)ix-QmUtsQmdQP|bK_K7;O478tWp`S9^>;@t_xHZHHg!54it)wqz2!4i^3 zppa|eEXIE_I$}l1NjEWZx)E0G&zu?Hk1o$Ne#yoeiPl+0riZUVGe|6$WBik+7$(d& zGE6lhu&K*pXFxYu^jm8DlVcNnGAg{5?2WQhGW^h@+9^q5{pH5je9+CC1L}&~)*DxQ z5~)P}hsGoYJ>C0}aX8ht*y$6an{+xvG?x3)3b99x&0Xo~_>)HXKq~GV(fhRVxpW$Q z*7&XuE&cK*xNY1aou0U3Tq`k~e9!nqYHiL# z<4}f79$_7#doVR?~dY_DGG9vwv zDJAC}RP-7@0Fl_XAT@{CbH>-o^+%Da9Cy_A@;F~qI>!G7l$M~U= zkr6G>h=6u1{pElCQe$*QmopU2QRRHfAi14EXO#SGbRF@sI;Vss)U>lX@d^+kQCLP! zZ5l)SorIk1aMO4oju1E1C+r`Ah3#jf-;=t|Bko}PYGk@L1x zs`@2oj0Y|K?ru)0bh`QPoKarfE*fuSDRNnsPsn;685gIoU-Cr0oks+VOI&hSv3Q&C z?zzF@1h?FLj@u<(cF*nV#vPFb5VL%9Sx2r22;CVWC83)Wj19yqez^x&1``OCgo5kj zvb@j%wA1MlGqm*w#Q(gl$nb3bC%!19>f-59{2 zZn>NOcMXUDU_489PJj=I%l!hk?ca-C_))!cnSRR`0GO*sb47!5*9w5gm^rHM2Yqz8 zjDiuq9`v`@e@n~dYI8@#-$&%a3wlSy{iAZ>1-&EUu+h1&t9e9hH#XN79FiD2Ik&z+=AQyH?kBDOz<&O4%%;=nk=G6|B*YD3ALRa0(R~cy6(^1SYRx!aLFTmVRg&fi~FFlM;__pvz~dRkC%Jr z)yJZ3_s@eZ%p>CQKw5uw{k#LXz9A^j1NU`qc%B=+Z)=u!3YW*U$eWGn4z#1!3nKGY zdct)?H%k9^_q>C+o*0`qJsOUk3i62Uc#X+hiB7@Yo(I_> z_btqW^?FH%+n40EWEvV+hnTMje2rLhXE#tuHg2Sv%h*IUCv2vgJF_LP7S_O(kE!PV z*+uK0-a|FFa4*%|+lQ#;R)0Y?Wj&fV9n<}CieB$Jo%c4@+@On;zR~wobC-Xhnlt$3 z6V0{v%U^~y_j#TCql}sxN%Yk{BtHg*J+-;Hk@;2zeTmQyQ?ZuT^v);tRnRw|*jLN` z`NY014#+3=wQ5j4(b9*>`NY1yQq%e?wfV%pbozW^U&icwVqcd0d}3dn%=yH=_Kl#| z^G4;nVy!J6pHF;E<4O6+SZiZvQhxnr7r+x*wDBQ;*izk`KM zE~i37T+J`QgK))7s*K!Q`KR$C!9Ex}l+SC)5#tQJ5aHI8r-=P&C`!#PD4g5g1f@ zaywDu+NNU+Vyq80EfC)iFtud?Ply){nKqs-(vt<+M7OR?f!r$6Vp#? z5%d>3m_EY*mUT95VFmCo#>AAf5CC!AP4h8;4{Rc~Ux0P|+k>&&_4AKW+%G8UN|A?;#w7qoPAgJ9&_ z(42z;mzY`h&X%4uXQSao<~m~kB6Dqyi$N(fEp^eTrRK(nUmF*M%KLZ@5}z+MZ-h6K zR>I3)_ObKMN;4TdXvr$GjXNejUS)2@@u#EE(jQHb`4shJ2_UP`pw?^60)H|Jad|Lu zb!*KJ_#dN?(bc34xE$s7AKQJm|NLjL!d3|f8`Nt3m!&y6^uRR(#jG{^q7km91XTJ4 z=f&TN5>c^v495w`f4f=7d7#n{%%N~d*!wazGhA~d-#8kb{UEPf?VZ)%+m1wMH<~;0$D+{Lb7q4KoErt_5skucnYus&J4(#( z$;@c9-k6`j^U=uocvw4h^Jmi?PEWC^DK@nXTkK@>SWYT%U>hxA8%wc`stMclny`6N zY-$Ocnqo_J!q!&&2a7b&K>uh09$1h?C(ypsGK`ju4r%DGa-U!wxi6}QMt?wX?#f29Lj ze-Q@qJz5slVq9-rc6j&L?%Z=ywyf+*eN>*p_rm2fo97S4EyA%IDsuCxSCc>$r}EQL zb}MtITI)c!3*E-#oAO5GF1OLp0=@zUINR{EJD{bj%$feUj?LEx^xw84oUQACGLM@( z2du`$_pvYI=#ZS-0Ts74cNNQzn?pFhLr3aOPkz~k(VI@;BRf(b+UAt`H@;U#;zMgE zU=XBT$%jUwnP0#)*m*trv7t&N8&&EH=(Ahbp$sT8LvWz#{cN!74U)+eY+#N_i^aK=s)w{SMVl!_wN3RljCId(w>2f+K70k_?q@&_f5pHYzL*$FT`GIX&!rI!w8 z`mF@?q6GAte*=28CZLi>g(fjj+D4IHh~>nWpqz2Z{DkvHQ5RrBaPLI@syn*(y*Zt0 zgiDi9dHYG;;8KX!az{JL&EY7_sHnq9=fs&3iiW7Yb0B&5;R#$aF6xC1CZT~@TX!zDO=p*3TaEm$nUe!2 zbb?#NAx*?*#34=WBy&i$uA9H&=A-Yfo9CekSF|)lflurK9{APjd0{=07GgqY?h6sS zLn48Ad{2`X;(sv@;?~iYl5@BofiG#vwzd;-NZbrmaos!vO<3j{go=JO_lIYA0b&Pu z!9>xT-_Z#f!@wJC2g#1-Cv#uZdttf(ZJ=-Lx1z$v1)|DMfv#xnZ@}P&j6ntH9J`+T zw5%|QZ_Ps)c0D}T8I6A1JPuzImqQz=i^`h?xuU;*H*5Kz&WL-(4a6Yr zT(=0mSmFSMb`DhznEzDbuv_BrmBazAN%I#ZVz{;w6BobbFKGUbMEsjX{6r##YeMW5 zLsbXYPQ)IM%zC~_4AB}FiVi6+QLm=XRpr9@nGZenbqMt?z_BeP(VEuof4~z5V=7)Z4dxZeGCgV`GT7UvV^! z#o0SXz!uKp!xzNhpm;VvF9v&j$)nH4XoyFL0f0R^@={o?@$Z5Az+QbF=ni5$e4LqU zX^o0pEH@PUV%(5XKcO*yAO@YSZyCXV3P%WqU-aT%#h{8Uc^&u>G018SKACc z)7gu84f)+MbZ#ivU$-TH1uFhEHk_}3&e&@j$^Tdr$6G*^YUzkM?s#U2##|~k06;sp z?3r9gg`$faT0h6NDeu|^vm3>AsR3WDE>+x8J{Z>na~a)mbPzuJ_sg%lBBX04MRQ;_ zvtO5{d`rS?Bp*=|w+;?&<=&Q9#ct43#nm_h4j!3mhlUB3mi$1dH33u$ZQKt)F%13aV=2TJWUede-O3HLNV0}^p)(&3Yb&;>Wq}`Exwa=f z2#xI2xH(_gg?8YyF0=#TNIGzFZOb?OI{=|DzZt(0B*Fc+q9%zpcB$&Wr5E#h@^3*! z+;?y!^Ll)!e>A_p3-V7fH|7sY`uVITZpR$@8Qnj>8|G56DKD6C`)h1Zz8tQx5nPoR z{Ztd9-yMvI5nx8x2;i7f-;$FS1-+UkdK@HP7SmXSQ)wTAxp>slq>dKYt7#aqzV z{zk7L%*oLySa-Nn;7B?JA4i!WlQ`h*C;k;=N#g3f?IJC}OkduDhHV|Y;!3U2l!lh& ze2=cA&lMd3nC~N(P#*5i9Ag^FC&_NCURbLkitGMsJ+yT~t}m`~c$c{~s=Srgn9l}o zR*M!FhL1Bf$4B7a4owQRMDb%}OcpfV+zQ`~3bUB`H|Py~eLu|7TB7QNF0IOG#jkMO zH2#togl~e#+JKqZBM_J5K_vlkA>|GcBiH7v=J<~Qd9f)H9#!7ln%^VwfWok;rf$;B zH2#=`=p&HVcrs@yzCna3UvJvPe=i9Gg{0f0n_@^GOMQ<2o!(Vo+RGbrf-r40VBS^0 zino{s^OM0Ga0YE33nN-t_9(b|D504p7?n2|(Ua>&BdP#g)DZdk6l~?&cOzqfi=x#^ zEkR1YZMPciq;ua*!gkvx&sr2a02XsUCf=2|?Hi}yF4LC^vVt5NWST1wiEPA(iePjSy(a93W zy-8ay9;%2{%mPRi<+U2wYn4CgAB1LiwnRgEMle>@G8b-+u-B@5JTd@TVl1(ca#hvh zXLVcA#S&((Q~G(Wj@)6V+oAb3w)|^%t-hdpf@+S&^u`SHeSpR>JdktZWqcP-BD6Afur8tDHcd>wu}- zpn^rNF6hea&26~uGIkxhlhNQ*sifG{hD&r*QgrA}hX$?H5S6Vr?cuU$1-!t(0~b6x z?Dw;W^L})MaHE}TE$wT0o7N)GYdZ$8zXTmE?PnRn`+>gK;u_XfZs=Xl!q3&h4Sq$S~R2y@p{@3XrAgo(z+Zpdub79}#}b3hw)% z?8KC8;(%)!%VmBg zw2b3`)xZYg0Fe7%U~cN++_}qv2*(0&bi@J&d(c>*vocxh9r9fojLjxR_z zmb}xz&AAglH(L5};1+;T$EU^8;hBangHimjCjO|TRnx`%#8^6)O_oVE{@)~<<&caF z)X}kYppK@TD`zz5#;<}p5RKuf0;`MI@w*PDFRpukfQnNsovI>p;7MjQi6@@XxHMFe z2}4&BfULSo=VUP^-eM`X->giu1fW`0OC)y&14u%pR?f?QlhppqYBAdj$ij*aqpKPK z=(f%B1@{0G>-b8lI@Bct{2Ec5YPjVTYypIi0O#h5DlDK-;@rG?*5KUg_pGu8qBA$B zh@Bfuu@>ws)4h5rB6~vreK)Z&7cJx1t|pE>92_A#>dpg4V*hYl51L1jAA~egsDNeo z4(~A7;~^l4uS~6pWj2sXzGP|gm}mfZZ?vTfV~HxbA>}&B&UK26>x7!PzTwPO;Re)r zAW+$<59TUK-%<_Zo~37`r`C+K45pW4oxkpQi;CMu8^)6~kP?F?b@70pbxEJzuIf{= zKE49-QCJ`6XG8K13sn5;kP><4=Wu^P%_RA~uCe^>o)pOCo>f4M4Il=E zBpWZ414UCHefMl&K9-oo5viLa1TAA3Q&~lOE^>NsTb|9KCx34qYP z=FYE>ZoXCRX5lT1hW|*qxiyY1Q|*tVD=u&yJ`X&^3BZ9Dy5O3uxBzFzI?*+W_=-e) zPa=kEveffjB8F=_v17TW2B93wH8zmC`f?4{Ex6y-d~zIJtHG6|mHBu$v&EsfMV7t+ zuxakRR+9?90gYGY%NAMga1enVo(uE6pE@Q26|I`UvS0&UHJ^`+r-A0qWU1 zbq$ILNWf^Emgq^wr{?j!=n2zz0wInbkyp|+Re`smVajUB8Kv3RP;mrYTym5#S2PRG3apLgY z)BGp#Xnmjj#<1iHH#(x?IKDf#1C5?j;|zTW2(Y_r8;>A@2t=jc=BE7icSiJQckv`R;V#M8U-&on*PPgQ!<>nOh9fxWp8P*>4UU?7Dr5NQ-x#_lRN*BH z7nmWA@g=C0=6VT=&;%(?2}h$h*=MCj2{p`0trO_1)D|G&6^!lil54h0j}u80Ps3wL z;bh*G&gs1SVO|4!ZzSgqtbd%7#lVYO|mQvhgl8PUe^8w7W`YMf&qgj;(Z>^VJ!##BsQgx5 zG-twfb@n2{j~D|*OY%ix3sZVgTbPFHN29W96MOKJdetx@=Jcw`3~Ey|0G$vsAgxGQ z-lxz(ILnhv0R8ltWjQQ=5Q&Rg@?QLhy{HL%hz^&436!QZ2t(QTQ(RGeho-$0U-goL zwBr;Z4wm^ZYeIP5(Q!CP;}7>D@da*!WU(J2|4%HPIC2O_V-Lat2{gMId_mccp*^7^ zrK7t0Lql)34I9D7gK~(6jcHN0E|$Q@BvTaPvJ-sK3i|e^}*Cr z1<+ChzzGh3B;Ui#urHMW5?{L5&ag=g3XCEO*&Pqrfn6*)BhSYE{PcJlSWbc23`e-|umbL2 z{0Nh9r?+gP<$TSZ#7SRUGC5x8L%YHkojnE}PkfSBpBlzV{XSLhgLH-)RHXR~A%+fy z{TiKDTSa6ay4?dw4Y@iG*(mPht_%XeI8 zh_W77+>uxFzyMVDSJ1|3i9bpD_F*K|lD@A&UHC~XlJs2%q45(2<)9s$^j!wPRY~6y z>E_?nZjz+0PCrWN*N-NBjr-B053Whl7uk;_eViYPsTUyfkCf2GBEzTessC|Jg~;w`EVqwz$ELs3mS(= z7FmC_bVA7mnLVH@UcR0G57flz)-vdes&wln)W++;aCEE#2lS^iQC+m-FZ<%5KjMC? zF_R1JFK68ib0cmObH%AvOn>UyyUG~Cb5b=k?CoGkTqkA-^95#1mV?v!OR+`<a|)7vHbi6NZN)2|ZpHm0Al!IDEzzq$f!1{8fJ% zJ0GWF;@Fw{^54Wf-=D_M%lgqY%3*lYqj$ibJbDL55Su=F$Cp8yIDq~dB!S27q#WuA zg-D{(zx&gu^lw}yMWtmU^Op1QEh~~q1)`bHEj>}>WsfMn_JA5pFbpccV1m9-8;35w zh=^Pbbx2iUS69dSrR!3DoLhQR8#G$ zTuUM$+4lxoyJUu!GZK<;NfjO0_nraNYW8BPUNrIERg?OT45-Osq-P*OcO>Kb8>P5g zb=;rS{7C34*$Yj$Zc&Vp7yO#>zYL(}@(b?M7ZNVTKq+=~MWbt3rBIg}ScB2j8dzmC zBzAl{fF@kf2I*6&4YF-0os(pgq+X;zN)xYxp~$QpoeQ#7!q_Dc$PQ|+f#fMz$kCuI zoa_P7bLpwfbXu3Jsq{j|x>g+@2jGAwewMHj++%ibJAeKJ_-XtY_@}gtJO;i8hMRZ3 z0c~<^UK@7#NtaRuk862UfXEM=}7ciED?WBN@13@6bva+TUy9``Ce&bSP$t zlK}}W>~nPGYY)ORMq_k1&>Fqs(72x5pc>WfD0FOkF0b8)Aqd$(>$hObkmj~E%+h;H+hi?1eGrH|FxGEuN2sr&j*RIx6y!$|0 zq2FM1vAZ>_5x$sj>lEhppLwLO-$fcoS$i@A8;i9EHM zjB^vRA+iU9f>1YM_E;lYvu1RC`1$Za_>5S?hN;|$D#>BL5uTyvZG(~V+1R@1O02b$ zV(MVnKUr+0g3Ir7so}TFpc{uFSE=R`Z zpw7zFZK0@bytO;JFr--e)_qC5wT-YFGa_lo$Ai(17)x&yxu=0w(oR{i)Y3u&`vc(Q zn*j@M!{-8KcW!J%hpyDAjq3HXc716(!`R!Y7-)?a%X(RpIN`iZ0(7?Xh{Q(caEUy1 zkPC@+*hbVv)31z6K|}jlJrLj5n&?v@;{gNu{G>3HTt2QDt+BPQwUuyFRs)hzQy=u< zwKJ`8g&@8H3~D4WE$nBVu6TrdrM02~F2xTx33A@Z*w`e;C%yLZBEI6X2F(-!Ltr!X zNRtSm?hsku%^X>f$omgL>yt*rcVGC5Iz*sDH*}^&3lAST2pHsjTI9U6(-tvznh2dX z9~LNvpztMQO^R+H9k;GNB+r5M`k)_OYk8r#A=Z5^*&r+m+kK%KN^bk1yO1N7-T$4j zIeg|2I;pI!{u1DDP>SowDAXXyn!%6~0z3mMY{Yr+h9Ss%sI`e;khhJyF5op>A~!AV z5OlaPI4IQ+bT10B4DV#?x56?x9X82;=<0MuGP;P73496&gC6UzK*N1dc_xgyM}P4FDCAjtFO;dV zb{77YwS$&kH?<@i@PRLB-u7?^DtdiHL)1xYjThV!Wdp;1$f(?!NXTkS+#2}Gcjy;> zehAL$prJZz522x)!@b|&y@&dVWHCi(CBGAOW*E7Hmqn280IL@dUNoU-gbi26d*WOj zWK6X-LglT;b*c#jSs0l<5uO^=rCI%Jc|Ze>)seO2U3ft*GWHhX{J9}0p@psQigc?- zA||10B_@1^FXt?``QaDZ@cDS#f861;lK}t7obeFrRc@}|$Q8ZgxVb%QTda>0hGA~@ z$zdi3?>SYbTiYnc!|z=k?$)k}Fj0Pg{nlZ5+*l&+vXXy70$v!0GeLupFA4o0d9L+0L6lPx?LW-3c5^ul#c1Gw;jM+wQ6ISy977@Ny`diBHCoK_H;&o9bL!^Rpr>hDyGz(EZmjDGCmug zDEy8~B+EmP7IhRySggT5f=iOjzA7${N#y^NL?Tk*Kk_?7cCtoYKZ!=CLXiAEu{>C^ zFj%GMI$toorb#a`J>&d>P?_wVXov|2aQ@;WM*vC48}Jl#QIet^ysaz4OadnShP48! zBmaZOU}xk}o@1LL^+A0TVMsMHA}Y>!oe7M-)>hS;HXaEJ6Aw+cPU3_dIg{`Kws4d; z)!Iw2$%|9Ck%@c`sBxM#>LmoFH)=%+)2b6FfHNBXIJTiPh5wf}wXnE)Wz7Ba>DIo^ zbqSnKGr(&Xzl?yyJWlwl*^{(B;jH$Q(J}4B*{@k2y93!$g1G0p@aPH$B9ZC$V0 zjxiEWO==&i_{|!EuAH*A7SAuRj&T?243!yC%3Ib%nH|=lM1DiX9dB846hef&uJIr& zWyG(5PH!tOCUQg!ML({v7CI9UITj%3mN+>9kpl>V-?3Vq2}lc@!+lLciktwq;De!z z&#~RY@29_k!WUY@(3*FxW1JZrzFg20#jUhP2)Wf6@X+F+mDadw1XX=AzB+*dT0{p! zJG5}7AQ(MXTO)e01o#Dv zm(=gG&YCP7cu9S9cb&D-O9)Ok@$Dm=sZKz|Cd2VXNR8K9H9|#o3M|9<_24_?1X9Y< zYJ+uwa9>UViApRgd+OuK|2Y&bH5LS5@~T->X28DG78|3AC)O^g@L@|Y-ft*MIBZRj zRgw(WhgK^7rQKB{avyWH#BHh+vGg6WeaJ4&;SF^6E?9wKYMpvA$tKs=S!k{h(wNw%yv@ znSj{TyzSNjLY|xe+r0ufQ}5{11fdxJ5xANO)hS?s>&`lB$V*x%*rInqLq4{46y{YY z;sMtWA45{<)QV#`t$Z25?h@-HX99=h+vEgx$-mnHW1xl>4$29LPXR~=c0wfJMBr$l zOio~L;r&mn{hbLMGF+7t*jpfTgHsE5ZjiaedejX2C*x!&foqP!k65FYmECYhHH0-giPza4uYBom6Z#PJx#yI*jYd4{_yuQO2 z8{lFmW9+%lnhfu=$g6|GdJBEzC0JJqsDpN(goe(Q!K~DRMqZidsYeR(KV;2vu1k7i z-63n3V38A$o&bWNQZNoDBv`Ssq1%Wm7T#psZSBQ_yw!%bq=-O#zY8KmyLJltZD~Fk z+K}Kxp@NhVK{AfgD_RPf$Jm!d*w%SrUfc^oM?y?S9kI>l);Vs%M{*HJ=&?s$LWKG# zg--5HbsdxYF*yNI8xWAm-HE_4xqmMwu(v=acPE0X$z5h^9jNZMrXcQCUWizJ%KDl@ zP^8vuRm~{D^AzH=P{|+{7x8JCwFf8Eky8=CX9{s$>o6ySs_er@AbaoTTne&A%s$*Q8{n2 zLMSu7mQ>6wRHN`QkNCsZUb6d=8s?-DN65bp4PA36Qyg{8`rK9USJrH-l6CtisUGj% zv@RAx<@FtU^ufiF9`RU!-*135ZPEXhwG$^q%4x`;?jkS2%Jji?>|tdPs{Gj+gqq*B z`a07R8;n>otdB5A&VUSyNG0O#K#=3q7{u__&Ll&m|VY2*3O9Nx3cQDU2h zh5dWhrTj_>!=gW|{e*SZIVx7$F~CVR4CVjyc0p6_L&UH{evbfItwhi7TSp4cRL;Xv z>f?8+p^Ey``VW6rLN)L&YYXAq7ucUyqEmlavwY6W?oj&|bq}q5IN?{>4ZJWe#hbq= zX}k&aB>9!oSGVzwRB2}%@W_M{IU*qFMb);I6i;GQuv+Y3WVIwwy`Xs<7`TB_L)8gV zU;r-WWm8#G+WsGOas)sBa-MsWXqV&Ju?|UJ?}JYn>|}hbttHRTP|>CT86Y7n{TpG`KNvne zrm$Ue*$A?tD?zphVUt{Hvf#f#Rl|aR38=$$WOQBmLL;bbGAubRY=iE#w>3kN_kVRq z2V8AI3~P^e5kmO2Dm1~(mLRN^V8J`uAS8wrGZzZ3F*Qg2C1nDPWjxnN0=w4BM}c-Rx6Sh<4IZp6=@Fa zL_ucs4b?02^Ps6OZOP8u391Rz2gYm}DoNX9C4%gDVb6efyrx30;~lSwD%p2aNZJk}72-pf()MES$@}qXjPFAHiEAvr4j4a(5^H$3P+J4^!;>>%^uvty zM&O%e>4Hv9KpIg=jhN(jNF#8EG%`+phcp6r@TOK3{`QAjj*0Cv0f#=Ls-Kd z!g`DR4q**V+Nl72=u^v96or<%~cC$9^hAJoM>WF9RM6}GamY*2wEYLH)Ro10Ku&Ju5hi7~BhTREYr zyxidd+Q^HE2Y`yy12|C-574uEWglF}9@Do*_fB37Kt=6r&751q9-&baOiPp_ZHK)8 z6eYB`JrXqXHXJxJ<;5g7(`eBjk+xRO1SDuD3+DxL0un0&!L6CLpcT=!+Rj8ow%6tL z?G2ocwsm)|OAwJPeEG`={NEfFzP|q8%M=(ar?W!V+PxysjM$Nf({; zgEbrC(5yQRLgbo-rKjnxQ-p8j{K#hAd0ax$IT#A?;JR5XiLs?|!aaFSvRQXqOE>Fo z%kSXLx)qbTwhC7reqf1fF1YJtwv6}cc!jQpInrN8_vw5v$Eq#jZ)_0(4q2oXCTN`O z)5#kr`*g`db9sZHPssQ2N05PnHd>!xL1+Z18V$xm7i)`(Lglg;) zohuUh>9Mv#=VFJ&D&_RJ3!N(x`d{6KcW^Eyy`TT@qzRiW^-$qggX%kz9?mf*^1o?m z?)i(BJ{7J=uyx^t1#*U@Q|IVtr_Pbzsp{0V_D(I9-zJ^9x~5Lu`d@Ua^t2!}PC8ZI zxYVho@&+B93KXPMsk1<}ctOG5seXfO{+!T6FB@>AQ$yix3V7K> z2$kO<+vgQB>RP?LRY%tX1?gHj1?^g=5~5m^WEb+-S2+Ut^Adl^n|5?A zkdY}-PKF5Z(rzi9sBmF!81H64DHHRX3LXZTJriH!VyIyPscm@41aePn3x6qaVqQ)N z$aLUTGE--3BRG}70)3(oDaQn2B|6tRYaG+xxrB7;ZqljTFKB%;7*yo_g6lAe;`a;A z(=zgYL7A4kXe{K)8zGZ(HZHMG&J}64FBCHj^z9mJUadKvmQ4c^Rwa2=_UGmspj#<0LOagk{^>a>6NjO}us` z9Fv!jvFNwL&EL7WYWgV$VM$DJRZc~O{i!Ble}9=tBtT3di#&s=k8;AYHrLkJ83u6) zBt7QSWEM$Wf)gnb58Ut96NjhE; zsE5{0vBflcA9sO#S)fSXCuCWGEN||WV+UuPh&~$X$+#Fm?9T%gOoJ7H-EzL<;aD`y zW)QxT7dw338F?}Gc?zgV7X+Lrh?lCUUYY+Y9lbsSrc$T6WC7qPt}9;vcmbE#^);I- zCp?lfbpYcsWcG@I@sHBcUxl{T&IBYNBr5;`asoVeV**$KXyZ&k1c{K>wKG5ex~+$E zU4n*W|1mEkAfb*Ee1e8#{{!U&b~KRv2cQ#gzzpIK!PZXRg68cArYzv%=mn^oW2!F{*Ni6gtY1$IoDQ8kL ze=Y*m;Jj}^2L0sxA4RsqoDiHT8wj{l;KSP$^KA`XRt>ZQLjDQ!g`% zYRvk&Xa4{DMN0bWWzv3;1CaV9Mc$~RUx0%2i=2Y?i&F{FE6L}bitYU(<@3X;wUOwo z3Bn`{!8+TEOwMfa=q616FX+2wyYzOZCr^7Vk(1)_>0F02U8)$|$GMo0uKzDchpx13 zb|xkHMIGF4y{gRO$ST`rUN|b}N6g{~HH#zi8zlE4s{!AGU69p)%kulgE-wC?UHnY# z!bd({rPTn(fR(=Nazru=IRwctp2%Bu7zR)f!;n)@!*D7giY;Gn>n}JJ6T={@0fAYv zL4w__b45b`XoD@-xftexCTK>oOC9uSbFUDgg`5fLU8mLvlgjtNdpi|Z`F!+zxsALk zR73lTJB9?jgtVv1rIj#5-gFK03x1FE_mMX(CFFjaAos|u#(=JlIyXlIIJ?O<*SVMo z5Pv||PAI6xq?#7}HDl|c_M59~kW9_{It16ntVeh#~Aju|#l2B7wId<#`PehWyj001xUwKOr`7L2j9=s2&Yn^ z_WIg9P~jMfQh6^2HB6VwL5(R3RJ?v5#am0o8C9I)a*#j0v42rCz~vwXg3Cb)1cPb_ z)d`)d;NWtQ0zrR%&%3X)w1C&>vL?EhgI8t@< z>K?M;^mvQV3#Z3h#V?#5Z%q#{Js!coJ8l{8Z+g7#gym&+lGYKY$Hxgom%M1!Gfj_M z^VAcT5dPpDO95W|X&wH^yX$j5?p#-O# z1qOl6?s+aw384h1gl^V2QBI&}$hyoR$vT;qAZsW=)=g-MU=7Q;DQ7MEU0cB3m~r~C zq2+^gb;eo8J=vtla)42a3*&u?2A$9^E}Kqs58Q^Vk76Ay%p zp{g5`tSJ;MdOb5B%GsYnfs>}i-pCrUZZe4S&avu_fxQFuG(vZc%}C(wN(O~o^BVobb6}D>fRDI(!YJ*($K`-p-c_S zeN4f^a@sqC_@^IQMyUj7xSRfPA78MnS8zl~sC_kJ$#tR7cL=ek(`&q;#OJfsjN-;58H5@Fn zRYO|hnWJWLyABSJt@;m^^AoGEjWTaE>Y`tr4Z<;5QQ`V9bPN>;HqsLn zP+*#%6ml4Hr;{4%whAfEJL`dI#k{ljV7ZB0;R{QwiG4~LqEj#HPQ6S^JWl<=?bIJ>F*^18 zKu-Pbe{gD)9D#svbSf3DJCzMl17CM4P@q#Og>P5q;_^?AwaGtnaCw{QpBm%4@dY{`7_=xjXY=N1fD~~KW>;=jbqduKNP^}kq zwG>>e2X0}F_D7KRt7WRn+)FL}+dxGvJZUQKNz%U7L+N89l%Oz89a!LSn~s}E_~USU zkI)Q<+k3??9B%I&iozZs3gJWuY=&kRnvJd?xo+_j&DaL1U1 z1z1^%kH^_#hsj?1L+@Lastps@iKOteLDm*UiwQb9xriRdV}h-TeiRrLY;79UN+^&u z#+J<5_(*IKNMbjXSSzTBAU<1lQVcKD$XdCW#h>ul0@gqL2*=uw6!iXTZ}HJ(cpzQI zQe7?uS!?+C8ymKjIy>2;|YbVZB+sckozYGROC0K+n47~+qc2|z|sNm>~(be65LgPaB1_Y zVepaSu|ZXMc42Etez17^2zG`thfA7JE`>Gt#=336l|(c59)8^fO>eQ zRjrR|3LVwg6bp-351QB=sum7CHy2Da=Yjs44;QV(9?@cq^q&H0{WtOTs!K(x5LfHd zh&#jhchS~Geq=5k-cBnvoUhxRk<4O-lg0>!O6IN&sr9rX!?A@G^J!rT!{M@pij1H_^*iJgklw2<$#=h(1G2^jarVzAMfjKDF>%_k;qsHRVPHf2r0n3Rf?>SF zD}zd`n%B50fAU$o()>w+72dWP&S$i+)nkik?L|EBBB9nMrVn{nxHE}&>l$3d4E+wz zDBhtpKTsoI_Bd40Iz*c@oM)A{)?{<2JmIRMy99*xp)-fe?~tYsm*4AbtYFP#Z&5lt z3s9&*!~86upf+z15#r;Usl(-mVyAh(NZ#d@L6NH}S-%bXFUaI&-?uhpcc^r1HQ&hS zE$S1!c@RAC5guA?hB+Fx1FBfl^6{w?>Um>0zuy+p$qgzWt)`9evf6dMO4oF&-G8A^ zD0ft|w&9&u_KV=ds##xRF@ngQ>7Ut%MMp?K35nE*PcFmvVxh@ac+@A!nibR(gvxun znp5#;#l|B7WZCv33@i2@SFkv@42;Q}(wKl6yl|WPW6A&7Jb@Ym*jL1nz zu@*HkC#At`kx|PUA2bQTJZj?Dcs7M1V9h23s@a?ocpaJ6WEkI8+nSW$$Z(F}BkEWy z_)%?OPg^OTaJgSp&_!;HnUFB>W}2N-m=1_K~w`YUEPrZ8duAaM&rtgC|;abJ!Y#67pxbJdZreZ zGE#O{DO3}*VJ`upF_f%Vu-ZsowuSXF8%0T;xnmk`aM#V31D^r+GHJ_3^7mR>Tkuo$ zNQ5m3WTy4ROdId?Gl;e>S&EKX?I8RWu2^@*gYy3gX_CMR{ND$twx6~#d-L=#-Ti_sJ+@V zN0|Bc$Pn&2RX&!%4d&IT0^K> zo=ac53?yLC5~tzQ@;zYWX}zrb*=WL(<2|u{q*yXW$ya%zf(P$o^C$`K2F@NO_aie$ z@sxzIR+jC9vEIOd9m{U)IK{wy!=nmDG+(fKP=A(7(NNTdQF7z)1}#AbPy$c;M6U`P zoXUQrrMRyNrMRyND&m%D#JWMFS+Iq;AK9BMl6_HHg6yG0u;-~S6t2w5(qiNf#cuw_ z#%2Ih;!#@H8@95A#)6*4MYkWyi*6~E+~MpCG58T&GvY2dy@ZGfAbIC}0yRTmvcqh|}D&U|T|VIk~!$`*GYvjtB=%+B!?`-vDWE*DvFU$0%j5AcwXV8>V-_U!N zQsDx6mVk&xCz=DE1|9fA!C*cnwQ&)iSvn$wZ@S%a9zSs4`zZeQNb9%k0Of#tLVGN@ zrp}x8kx2*V%|+cRA)y!&EXwW3C)^an;kn%L6u zNE0VHJpaHwm`)>m_dOR%Nv+>9FP%tu>Z?}HnVQDP3JfVnUvBeK5zO3yw z1a89YgbInBwe1!>(aBHW4h_y}>$2u*tb!Fx@9w9CDNPC7WU-cE3ADgt&IVT9oQ-I) zFef~kamfiwcBx-Qg!xX`T3G{`RR20A!7BIFs91eptuGbjv6%)GxUWVja9=HgLSRLv zK}15%tK+Q;P0U3(dF`xCq-DagK?0j^I9;=mA38k2xxkym3?#f}dQc~o0uNrUr^sR> zLLjgD>!T99+Zwn%TTu-O_WL8?ALq3{q#}#V9BYKA{;?~y0WRY4%xNQnav~qKpK4+s zP}aB_^Ny8&G0mF6-k~M9f$}AV;06ld;)0ZdixDCgr1GqCLFy5u^DIc2elnuOcTwtZ zFrQeIDrQti{}2#lap&*tTdE3rRb9tbs_xl@F~thWE!=G&CwG?hZMZem<{kc(c*YnA z|4Os~L*-zV!3SAW3KmhKRkF#zr38A5$19z?dQs@R!a#;+(kK~6IaB|IYsdz9QK1|R zca=cg=u9E-Xb)WJ#QWSF{Q{dnd3YrV{cc7|Dj%unv)Fo?&7qX2RLdpSCTuYUi^`h; zk>9#mDAo@J-ln$6n<7H6c3<7lj;H-#E5W~8YHfyacR!??FS7R5a_o>72i<&gZ2aOf zSZGi+g_b}vkbY*6))9t?;(E3nSyp&3(I&5F>tCQlJNAi}n9k;0s2X0+20x4*5{+SA z0n0q!QZPnBu8myJW<+o~xnp?&#ECxmeH-uosq=`?R)T_2vf1uea(OBFi6L2B43~3dm9>P4HS*?SuIH8uKfL5FU2W~lI#Dpnt><5^ zez#dIsG(kC7`UZyakm=jsC2tf_h1O-ys*}K#>8w?npgDdH#+)|@?|G8gZPg1)`@H~ zB}cDy+FPTLVKTgov%%Wii$LvDt4c%q%DcSL`We5lNRByh5Bbwg))s6Fm4bcd zT00+;ZJq1EMUCojhqa}n6c;rD-0hpKtqfdMnd*L&5xX?p^~$iIoQYekHQ-BF$`tjw zvenv*U7=vq>w=xzw^=u+1cq{VD1lI}kl^fYzm=5Tw27#%Jm)cuyR9M zj6=h^jsQc$mJZK2CT+&p3dJl&J8vje2AOz3h|1IlGA~j&U`@R*0}AZfDTO>Bq(E?3 zSYV&^BvZiXtb{L2a20_Ud$@{#LXdvtert;e3VzDHqH`2#@%)guSJaDo=}Pf#k+@=X zr9)mZ>PIS`IHyuM4ok~`G4)|7o(DJ zLAOD=cn6YwOOer;w*ooyf#FPi4)QC9JP`CbWdRepcJjrnPa&FwdgmP;_3%JY87GN5 zRIj9fxYZLMsPp?(?2lLqMZ|Bik|+cngx{wtV9(gQwNoE^x205A#g@(h@!H+#iB~a| zA>D5i8Lo<`>Phxjm;ymRRdvdWmvnk6j>lK%+>YN*vz6u7PFiP>lcGYs7a@p>(kFbve3H#CzmUhnCl}s$% z)dK|gdo#YJ(}Axzs=w^pE@83h(je3a?w}h2(>%e&wJOD>nz{pwT3wp-mwlcu~@;!J)Jbx*GdvF zAmIAiGu(g&8?w#$nYXV-@jp*zl;Z!s2S*;N6A^N`&5uw^H5V?oSR;`~1x(hujU7p#%I-_1Hvo`0sWzCNVA71C~OG{wM$E{B4lFT^dNXsE+n zms}}ZO-qmglz0~6UZbVBPzI&AQ0CSh7vfG+G-Us#&i*4>g6yHhSctn$i;+JREBxzz zVqMODp=iiIPv@^?kWfJWP=fqFxdJZ*MbTp955)@qudiB1vMLk}`B%)4=BZChkUx~* z2NJDm39^O~g>{c>*4NnpiiWKF>a1uffe79)QsR`}y}0xKvQ@?Wa+ z-%Lx8Ka?2P2^^rs$RCQ`{Nexlbpiq6+gJll2H*Isfl?~D23pKT)<6Y~Tm$_|Xp!vV zoxirG##R$xx&|6U{f=v({`SIe)0*I3NRE^P0AP7L^I#A!Hr{&C>$pZ z4+Vs-iTaT8p|@d7_1tK{dnlG5VN=wLQeOo1q0HIyUu!25i=?DD*sgHL+LOglFb=jO zN6Ujp3IVQR7+UJfrarR7%5686OnC||;|$7hnGh(YKHar{Z|$U|jplED4=h@GvnXn? z&_8%@J{remF--_6SmzO1CxajAY%k64-nCxkUwtZPK0*`1Mt|6)_tV+TxM%H7)YoOJ z>fv)Y*gHcV8H z>Vzgz5Rrc9AFyJgfU$+X`3Fpg+x-vpWB;^vRq4^h4?QN%0TcH!F+1gj)*UrQ#@;Ae zg0nSTNt{l>IPV7noHGje9=kw>u}jHh?A|Q)QV?GoX@dt=yhuB1K!p}p zsl-w^h3HG++X~s1vqO}JSKdsJj#o)>nfQ8?t*!Q-vAjZITUYjuHw)6Q=}Vb!Lv)F- zK0Q{xz>!+SHdwnpmTxO!v#_t8La#5>e(ptoxv1?A?a|nr3&m`jiT&~v2KtI?o)?4V zF}5XI=r~@kxNQ{+A4kGh*r13J{Q4U@1$g0-wiZHv7-i}Uwg!@3OKK=N&%|$B2I#W& zHk~u8q%G0J>QkW@TD8Z?(5g*KJSn;3I6WnIrp2OBo7xNI0_`vM8Ru!(xMgc-t*_mJ zSPW1qmIkPu$}<`_kYRvQGI@1C-YbnNnrDEzx8}Ahr`T33IGCnCP20ueaGAplLu@{y zN&E7}EdKPx7*_;Cj;s>1()66^Iq{!GDZ_5#a=a~vDPYm;O*N%o-Q$6tWo^4n{6eG+ zPXMA<-q5}B1~r4bs}1E(3cnbqT?QMs8di>7p*ZN8+^1FchT)o96>Jqrv(aD80omvp zDx13I7b?$i4UnO0D4Bk%1A9J{H%hd{YX!&ieu=j7ENnc9eM}J{<8ky-#OG)+ys2Eq zR#%Mg##FK;`qeh0Ohzd)h~<}Nh85#qRf4mhRjB~%5kWl_Z$)LmYx4ibYhMNMy8OTK zo>Z|_X6gSEUNApi6|6A&e?+!b1LWC(kTqfWHn27VNjQ{OsBVkXR*&bcs@ux3wUh?U zyeg2Hw|UImFDXD!VEilI#WA7+!Ke>Tw*Af&Fh+e2jrwH%UJctyc9m*~k$-8t?2j(d z5_jZR((aCzgN%E$7_0VOpsM}qshSvL6eVptMW&idd{AXuG(YoGP=N?4mIi(J1QM}E z(1W%Z^ps2n{j$ylDw^XbK!Z-9eUaBpA@MMfvC3}lt96O4qiV{8*GYl7SxSWH>q-at1~n-J3k zsokUr9_?^zAeUmJb{|pgjIDuhDI#tS+@cT+bKDwGz@D`EC)5y6IX$LSSi|1~)i8LX zQA4pcV5A`ZZ7f9;ZVf09bkhyJ;vX3m$E^W6jTc)3ea&?zhVbrZ8kfW$XP0OtwYn4K z*D*~el2FH3Z8R}JthSvf_XnVN-r3UDkc4V|*|h3IrMlSDd<$v=OV8T*dWlSxbjQrY z46=4k7rERB5LCE!?hsnz+PPEw!nJef#DHt(f3>y^^<6e^#J9JxO=t6{1&mem*#xYM zUoQ8j}I7tgOG=j`ulE1|I@LFHay#M$7_-EE1iE(MF(pq4aAes$tY0AHOn zVQu{pywtnG@AQKAA*}wWdiJ(;QmMo>JB^+#C|mK|ymBF| zDiw&=4Q%WKm6}b#Xf~|WA__*c&7Q;?yl7jZ5{T8+xLE=hU8Jw%Qc?P$)ZLUH_S8EB z1oH@l4lXjz?q|Eh&QnAjL7bf=M-XReN!AEQSzdE)utWQ5(yCiy62oaBcFfl&1?c4N zPLdsSrAZvOr9Yd5sYe4jI3!Z!U>a5+m@D!obc$mN7-#QzNnylf5{Q_P6-1=ZOt)pI z^w`~>0@cI}_svOSWigq*HOOXTwJ1NdNb+Q9kz`te7Rl{iGK#1F+&!45)~?XP++i{t z_&XGyz&cS(G*7$90nF2Tvd27G-A32aT1}Sb0ikFfMJSp_fnfFizFoQyzj3*JL7q0u zR)Q%ASkY=AM6W0gKaPWs#qeXtB$?p1f%C-hV;$8S!)CRBxWfjAAIG4QIQ%$Hk+G6T z16A^Eqmmc{2W2JMUdqBany?EDF4S8+*`pqgKORtYRPRTs9*#fGS#57>Ayas!%~pdI zoI+v)RScSfe(;?r72zqPV4?(3y3KB@6>Bsy{gCu?l-196+c>5`(9HkndjDwXjl&1p zKw-cO*ICeBl^7UPaeE;NQ*j6J3sZ52DFIXQZ!Vj~Un<^_VLQ!+P_2zrJdl8O@rx$r zKNZ7vRub4lLUT zZM}hpACnyS!8mGQU{24a7$O_kMB1j ziYH&}Q-JT@6U2C-akl#6i&V@YJEqDEvV%(TOm|LC)u%gWX)$(JZ%qx5Ha>)a5gT_S zrpk>wqmH3++y){se<&g`e<%>lA5&hjHD(IfIEHtZ>V)Z4L6r36J4W(m&F>cA8|Fd& zQ4ny_{P=%hGPOd-s(fI0%POi*Ky!!}Qq$r|@a6J7=jsy3EVc z)MaRi$JBLR)=gcH7NeBGJ?e1Wko|qzc&B2^m0j!krWa z(bx%5z?gYTf{xTUb(h(0vssiM8fUt0oawX#SBK&%HVNgf+MSB>0fRf1HgmAk=GvXg z@r{E!7Gi5CIvQ(bAY*Nn#xjq9m~J++W1XT#VCsn!bP(&K%vXoh4l$v;@z9R3<~Kon zzo8uy*jql5PXvMbI!04x` zUVi$F+rDRE)4YQ?WSR`(5L$wM0vjc2anty%-^P?>3A7mfQf69!Ag(&i6U2x99SKYH zhF|pi(Lg4yTT_|1gx8IwQaQ%z9Z;Y>DTUlBR3K>2GV5)9nF7Yi&p(sQMR<+njf45u z1qVm7zElueRw03_vPi&6`$}#){6zCsHA^oF3&?-q+S$(E}jd;IB*`rq!M5KSR%~sQgzAVQb;-CKqdi09I z7QM1Sdd0L^jCJ%^z&1iMh1r2=v4r8Z&kG<=iU~~ObkdYCjh7b?cN!NH7(Q=}tuQ|@ zH#mmxStCbV!Zp0~TAR4T=7qJkHkegmnbg#nAKGk-<>@7YtFii&HO^JCXTT|KKuFN0`qg0qzsdu{)+r4$iYOBYfI+80+#6|g6#ZJn;?wC$8i z)CtT6>NWQLe(}I`Pj7(dsEy7*zxhv*bmM@HGX*l^AZ#I@r{XXy6da?mDPZAN(>!9f z3DRtEvp2dJ-?gFxG&y0S!%E^%oG|?%6u}A8pW+uznEr$v%k$QX5q{47-aTSl;BUe- zJr&mTP9L?+WkoW9jeAsK%$gzzSl40Eyl0v#)V`|^Mn?m~m^%cJWXv7P10SEc1ErF4 zhtNzicMvpk?l4vSDxb-ly=hB}O%VT}bBAyNaN8YEVRmg{-Nt58Fis${U+kUF34~$s zNH(4FCC4taC|t}SVwVXBok94J@|LHeC2x8PrM_6_Lz#2ojO_~(+viOhX9(4Y^psSe zww$F@*vFg_5bWi(@pLMAnR=)4h!$fG`w0SX$IwQ;ODKPH z({5NonVKo5B!>R_2}+QN`ACt7<1Yn*9S4q+6|j+yO3ff)f*p^7XpFxUFy@o1U=xw$ zFK6at{4S26G^f1KE{$hMyELXHa_lws`DAT|ZkJxP812%1Mu4%`z!|c)^)-zCbu)-W z!zd!rFbV_>g9B3qY#8QcsuLPUK{O1bfYC4wKu2j9jw^&0Av(ko+$m_sjcu?*3QiXZ z_8=930k=;;+yRFxgdc*9aE0(Aii|dTKah<+BQ_Enl;`1|)`qUO>?~!0SvPN8#Sjzt zNNBfjhDSS0xj{2YWT18?0dZ?*Y*3b@h?sJtDa1&*3fR*+l4j~rP=iuo4Xe!z5Csiq zDp5fClwxwmu86`7N(F*$Dl$`6+%(fuaa>`fU4u-Rv@azlz=Y`%T4Tb@5Wg^CX3Pwj zFjrr^Jj>o~5$|6@zVrz&9nI`=RwX6nz zh_yyclLsgXrpY}sWt!YEQ>MvxeK5`$7)F{5AjwFR>wu3>nxs@RP0j#6dM<1cG%`*8 zB7Uu#$^ZPu)-rar_y>PsJ# zR8QbeN`*a^O+c{6g3vV*_yUTEiE8dFnW*N{5>FSkahBdiWz%BOMVZ!f)6IZK4|mU! z-PA8LWfnIop?6at7L%1C7L%0%F|xPqQR4+_c3Zjv$6fjO7pyHweXJ*wbtYyy9 zGx>eW4-IrzH_%;L;xUkEwr(KvY!bp~peIi=P{iy&2GS?$AQlazh(!Y_5F?u_V8cMw zs9tCw1<^2&0u~11C2zs1v~C{E=O$SALz9V#strCn6*+y>ESb{>P(c`j{RG4vgP7AN zfweHFPoc<|@g~d;kn!daONpF5X12`fPRc^$^i^jQLQJz|X6rZGqa3F6!xR~nJ3y5) zQu@0T5mWj(3Ncc;0``Q%b$2*?MX9iYp9iYoccg;i%#qPn^l7vr3RAiQ!IXYRulNC@ z;+WEDXCYHM?e~d)Fr~-M0SKn_c<~EUdiq_?;-2Pqq>Npea2?0Nf_WnQxwc_h27x zd(>qp+er~Huje}R^%xXvqNRwd6&tYalqE^&ITVg5{g{Bzltd4*nH!+b zDmGX4?px-_^k?X=Z}xyh?A;ZK*t;tbBmF60qj#@Gb;90VK{R@I1&lq$eXxm`tNk!X zPkntTFBmelh*{akeX9Oa1RNdB6X!>J%$2t4L9spkpVe(FJ1s_IjS6I}2?33z4|qT% z8cPw0#!?_gDpbIRu~t%@&{zthVJroV#_9$-N@K~1?RO9{V(R!QG*giTKY`9fOl;qy zA}|Qg3y3=iF$vxV8(|XsmLj8#ZUwT@17ahQ1izXqli=r+1x{@7nVwIUw34rQYK-}hb`&!I(N2=o9rNEjp)=;c`QjJm zzxl6V{$q3vSib*K1g@t~eLSpL3>NAAuA5~ryu2XZe+eF@zyA^p*y8<{=F0`^rQ**f zai_|9_+z~KA`0hY0eSBQk@0?4ArfDCDKbpF^3oz7w@yRXjSrSY4p485g$s=OiaR{< zK2V{QWtI3-OnlRh-^WiEXE^aZXkT$U15bK{igi6)x1AxTb~rVyA$rj|bL6Wp!Zg5n zC(oL$wdJW*E7xN0h^nKt&b&fieK|+KfFNRW(UANP4Di*L8(T8M`RRwBhx4d<_Se}} zB0_%8enmhqCk4swjcUOQJM6`HdYk7<@DcUx{qte#vmCCV3Tt3*#1y{W8rUZVeGj5} zMwY3#T6TKw=rN|9d}u@a^94iafp+2Z;FiCFL)sTHbk&o{-y-B=dAIWus}-mGb?aGx zfKTmIycEA#vvT{GGoqq?o@~vQr6rYs*(23#d;h+jB7Ga zWKLK{e|~Sg`6m#1=5V8wfs z!IGV7e>XzmE-w#!HO+p3O(s$~4xi5aJ1Re02oM*o`1&#SNJd5Fz_LbZjm@W0M8C>2 z-|QRAH@3Eq3R(eWd}HU0jo3gohJzB z@N51&zPGLY>j)}Ozxk?brHE{Hj7l(xtFD??j+m(d8MKRNzTpgW@Qd@cKIV6XYpa<{ zilPb~?Jd+A;AvwY;k4fRjSS(P>}lrPzzaWwxIK?s0KmSdTHr#?w@@|lIfKD7Ot#%y zPyk6f<_00rgTpcpqNlUHz1cip=Wm|R%O0>Nvx4*00PCy;&-do9-y&gxf~WH@%|~># zzoJ!|&-1$4Te2!XyesX{pt~J-@tzQwz6_Eh_Elhmy%}ij=JQKE?W6q=pv1BM2p;sZ zm-Rz{66Sgnm_ZfpTBjxPA9~ws`62szoz{-6_9hdG{n*D|*^59d#~D`CH{L-FoMRUdsvait0MHWB{>sPv4jDMOO=TN&z zi%1}Hbi-~KZtub>6Sy>NlM5RndHWIe6fI$av~Hs}v@zxeAVyd>QgCv|Z(JD9D`$@3 zK_|3me(jrqd+j@?eZ1bF{-7Q378J^Tixspc;=;& zVf0F zU`+8=eEPzkVf?{(`*pKvAymIya8+hrNZJE_#QsE7A7(M%MwZqh7RvWb4no$-YS**) zs!8^S`5PvpigDWG1^mlN_QvcnwYrG3BJn~Iy4drF?uJNsOY_$S@~w{dqs@21Ya1%_ z+JgzMl??{vlm(DA~2wP7>&Y%7fIp8X$dlb68K;shNu6{;#oynj2b|( zTZ6O5#>DV9=G!~5W)uziH`4icq9w>5N(}yeX)*GLVmE*MZi6kduViB=8uEAQ{AbV- zeu?3<6xlwoZ@KkQtsXc>V*`;)N(;Epa_ zcV1V-+>=!o4BTLx$doB+PSffw=4ak>RAUWjG0tG>EXMUzIfH4j7#D;Xs)3Kn?_9`# zc;8W{@iTS;>00&0#*Lz}J%t*yHdUxzGgjLlvwjqU^S;boET8kfbf)mfo$O_6?XPLf zqDYx%tH4tSI;ybI6pRxf%i^3LHrV0m289CWS)&9pR!09wg$jdl+2rU+Y#C*Ui-3y- z1iNzR-(aD91W(y)ubIC@R*m0`GcU|(x5Zw_#P(28xYWJ_&fAK2zIV_P{0j1~t@a(- zIdFRButavA7I)Xi4$;oi*tocu-`X{_b_9)$5T0GKaDDA9z`HiI410@mFoN$pJ^1d@ zVhp}-seqpY1Yb+R|JJ=>rBwc4`^tE}SA+WDTD4L?u~r>HA^J5;Kku|ZjwrswXeIja z5T@V7dCgt+KHOEsT#LmM3^`E5iFolPayYzgkNt?b#u7OkHUm4}znZx*qkM6+7-s~B zKA}}r!t0&h+|)Oe58P+3VD1Era3!f{I>iYn26SmGW?Vx?EwrmN{IcTrJ zmisVS;*Uv`9Ph#bVVw!|q55@BhGBH&sbS<35WNoDKZ!U*BrA1TXq^evI&%VBXN+zgkcpjxA``8nK%jQfIaANs%b1t~ z$DOFYAjWWha?a56_Rc1DlIn)_JFeI7I4!~Yo#~tKqIPAeyu#;GT8x#tOe=L$R!U96 zx$oJNw2$>VfgG%pA_wcFK+t1vyl)@L6tMAyV9+uWd+2cm5$S*V06eaM(Il^{CgEMC znIn0v3oym5LYbmjDlU_5uSiP_v!pGPW@$-_g;`9^`PsMau`3Qfj+4Wa&ddA^PlRRQ za}U65r-39ij3NmQqd>4?m1aYSt$^`^#|)|k+C;%Y(w}o-2BUys!?P@RoNQc`&7$)}lXh)2z5&nen-Id6 zUa@!A&Vb28a(|m5isWuO4!lE)(ShH1$;b=j zKn+A-idRG!DIV%uwzji$IF*2YRSbZ}eR$1oX9^esz5-wJxjluI$np+&1rg~RePOSt z(xY=WtIolr)*E49CANGQFP0mi3t%`S?)K>xrG-UQA)_5R_E`0K^+a>GtPq~C++2}8 zPjwNCJX)75`4ai`T`WH@k;9@(x|dX3+ffwJ_iZ-ie@dTH#bM*F73GTM{CdOwX|OgS zOD^k7fQsQG33lHc@wQPUa_<;i)6c9U;%he zTkZPReP{2Qk8v^RIacFYJnMUV8^)Na>(| z(B}uAQLyMFw2N6h;T}9%p%TEIG`wcbR{5E>p!yexPU2r{s-(OPa{NT?y1NS?6@MSG}=BFpcws&ngulxk^{52{ZJ@mJLP!CgtgCAa9#RviUnOt9%s@I_D3K{la z6mX1ZQz#t{x?Fz+H)FFX7_B;G1&;}JG*$_o*{TzzPNt?j?Pr*1yZe`BEr&tD{8RkyAd;2%dgissYC+Wr-M zUZf+L?WZ=uxVj-%`jrF)j%__l7WovN{Fe%=sv3||=(#NRLC z=*yRdI|_1M#IaqAc$GIR>Zs2Oy-H$3SkDyB%NBCT?H{;1Cp*~T6_Q}QVfi&zfmiv| zXh$L|K&1+E6{#j5)LcPAjTV~eRgt@PvgVZH84P7=;^C__)!d_fp=&JRIHwJNm2WEP zsLVzx5`|@k63dK6%M`67#+~}wE&Q5P@VV!B)Oc4_9`){L;r!oHj#BwFl_{Oy3UQQS z=~N4})NF4{^_Mm3`>JfZyK>D3*=kDmEaqBiFsD6Rs;zh!|7t^A;G-$D^blW6$R#8XT@-1S^5B)?LWRw&RYzy;EWgVsSX+-VBtFZ9o zD9=t%`Dn$j1cb)hF19vo6urFhmJ z5T4D&HXwAO#_#$#A&fWO-X@+;pHVrE=T>$^^QGI{bR_?*Ika6kj~_BI%3O722=8{? zoWiQFgc?7D4#}eTlcvi2Qi20+#)9Ki@Q!mzG4Gb1UD=VIPs^JtfLKgE@WT*`rf(-8 z)bwS!sRT6jxRpGks^eAm5~X;S*nJWCn&Nm~MPTpFAG!ZVM)LF}wSsxx&c2M>lN{A} zyV-5pieIhFvq107Pa@%T-*{IsHb+rK#4UW7>#^BVv{|4&pf11h4TSHqu08WRBQd`W z%B;U_PGIoE%ZQu3jR<$@2_sHeDSLs;i){w8*D2YvSn{y6!GVQb z)xwU?UX17a{SZa z;0AI>?LhHbl^=XQw+Qc?XAXOopw-e=@tRGc&rPGcpbLfx2z5a-thCm6m5kZ+C2dBs zNtEnaVs^a2oOGT$oT(0zmc5E+q&aG^Es8J^sT+yC_M*Mi0XgO+a7GZ%ZQ-b#&zRNL zuHu_oI+EC0Dj#ih%G*X*Wl?epO9zph6wDg%)(|Yr1LtzYD^NG1A zuiMdak+mcuRipMy{io+$h9ynL9S4uaQ(5bb1GAjKb7JqGA)g` zygU?6rF7u7k%gie#ggYy%7bzA^C)q}@hD1!IC-)~NR1S~cCQZML*s&4$2Jm9p}l2A z>Lt8lyk~#MNfSFvH8z$_-=JVzHa)zW|7W0Mo)>{ga~J&K{+;e<(JVaSd*%@l!oz{KMAI9dp_m*k^cgd^y&V;^Rq<=4*^q9j)^k-06t~zml0E z&xr0*TWbu6;wVRB$|9|htCx8gaK}&RM$jVMz8pfiXRMJs)(Ur?k=V4ZV2&3^D$H>o z>E@y67UuaNA?0G}M7GdJ!Yfq5N-2R+3B2%lXDbvS4vHuOAh+lTMZjued(dMwv4iCK z2X6i>b2x^0Ah=Q5t!_{)_7+u01TIUY5G4J`>1gXAHDdlEMGQdty~~lHlIDFKRE6Do zIw@#hg5S8%sfdRZH@Xr=J5t!sPbWo%FN}6n@Q~uBZh0`Q*ym1IBQI(_ImU65l~_x{ z6oaqWTDhNFjFyPa$DHQl9L!{{u~v`66pDaxn6#F}VdJ%dclc9!Moni6%J)`HXCL!v zP!(IvSs#vXQH~$_oe0Wfluq{HJ)ZKOEqVKkQeciz3ja5)a5gos2bPzX z7B9;-QkJ-ey_T}vxegz|QTyP{)iUCELaUHMn$THOjtScL>v-}$M;-P9C4}?m zuxVeXaCPktt>Z82c(;|X0!14)huK!oq6J0wiUT42wP29CafKt+k5t#z>cRZXaAyWw z-ZSu3;1;=_gsmYtY(0s#66^g)Ub@oJBhHUxW38bI?>@^>g;!bSh*UMfRycH(qZv!4 z(%lBb{XW&g;zJuBS=2l7h~5_NxX#*8o+!$-JE$J(Lcu7i&3fK=jiZepf^Msv6)1dfdy*!{p!%;tgdlv9S z4|JAtz%Jt6dcBLdPm8eoxl5V<4m!ZC^!~aCV2*8BVQvhnpmKQTN~s*?2L*!t`?p)6 z=_z0%Kg4e!?E>?If{66@wmD|1^q3!Zg7U-6x?~9WsqdSOkiI zL)6Y^(0rHP!GaWYiXz2u3RD%HqEr=~qCn87c!9J6HoD7W8)SERoGNQ{T1Sb-rnh<5 zmEc|)p4eNGenh`qNfCx^P=TONn?n7mPmdjN9ANjT8tBvSbf11lOH`kl%sg$UqY?|= zNCFI93QKF^K2&+Qu+YYUF8xP$=|5D6;nGSQb(dD5640d;sf5}apXySOf-Y607%qjH zqDz&UqDvJBx^(C3jx?r#aYBN#ihfi{Y-b80(!X`gQQV6j?)%XGtN0{o$EF;I#xf~C z^vTqXvYkz(C0;&REq$_DGK9Et=w&Jjmu@Bth(k+`GT0SZ{C;LmsP>s!@{IK_P&o^A!(rh{5$f7t#?p~Zt0&rb+ zfC&dtK!3n|PBw3L%hAe@Xq;dj$(AFA*ZoU9M5UhgC)I0$Wk@}9+fg%+)X#LOpZk}3 zl}f$+|4VA}9q8l~GmDt?HOov8aeru<2L4GqJ1UJJ=#J~04Tq73rJ37jhwvrA^_z0n zTksWmn{516D<}ASM})=_Haz8r8h4(PH;YaKQyQUs|{L)VjxQ5{ODq&jOW~?Yd(pi?4NZF4I zg_(`rS=g@7KL9gSsp8wpuxQ0IZ4vh1U4M2gV3!DE+!!={By61`-@$ty zsv;iU%Wj&Q%I;EB+)es+Gdw*6$8r)jwMt~a(_-9)f@0iK0xs~0d0$p&3#jfsn}ZC( zw+IH!Su8CdxNl{d}Cv^z4@Uth?Qi#Sq zsg7IV97^Bd&av-_n$SI|BeZJxVRX5_9EVsBsGJn)k*OIh>DdM1X;&;2i;WQwG^u{`=R#na z@mJF_41O3vAtbKMb0%x=Y~cweXBGAyEk%LUfe}<7tY6d*s#w&IL^-fl1meJ2 zf#B%!XU*AL)eI+)^R~#L(S$8>Xfzgd5o5RDt)vcUcTGUtcE{@?6SnHBCgmtHRw-^P zv0?SCXhW|m;qrf}t#aTNLpk8UE%%=)A*QWTIc=*)IUKjWNU>45-c&gZij+`i7IRQU zye!f}A!rzUc}M|!f@CIC5QAhkrNRnk(h4q-6=c+B7$mWV%k(!+6hTFqGmR+_^vMXl z+P#fx!)jU@qs>G1?schn8UX4JCM*pam@vrwL$;@sK(7Bm!C(UAP}pSnh8nUHF9B2_|@z{lDYzyHV)&#eYy)SG+yYOq&NTLhRsanEW&cr5B)s2r{W>7Gm{-3l> ze)Qr?fFEJ3@JH}bDF_FJ0LNLD($1P}J0-we0TO;K4Qtj40iLeywmdbTM+JI_FOx7% z3hFQWjVE&TwDBJTl4;|tfME9pzQHS(hsQbBu&*iNr9or6n6}Az-E~?3%Q9QFmBsnM zNVst`YJYSs^Kal5JVo#a#ly9kU$&9HPunhc#K4W7`~jPrc#0qo5XIt59%X^+(WSQQ zJ!_fmK<18c?08CSC)^Xa%br!?E;m+zIqruk%yI66rwE#X1l$iz^^wqoO6VXZFe*Ww zA`k+^=9MDASoFERP39)Wj+mPi2zJ4liO!Bp0b@US4O*#K+`I~nT0CFQD>?VEIaFWF z^E0+fkIkSZVj)f6XkQOiMOSU42|_cnVN z{8Ga#dv}q4B#$1VT-%$)nC)2D%po&Rm!k|ABMP#D@-7QN-=>ERN!h{ zNmN{oL8U<4K?PrT?+$Lwlrm!_ES>5ox+@hCncz3*D!o__N_gCvemI94*3V zJWZIx)JV8CLgkG^GvJM`PX^k=`5dWj$cI;WFd^v8Z`g zz*=ahpr00F;zm>o4m;}$h&!p^u(RvKDusAL=M<~C*ABRdCA)Mv)`yY_514xJ?#*B* zffw}++94+x)B`-Np)1+cRbc6lb9SUWJPVtqb^@gVKR7HnOsl^`4w6Az93(HHB&h8| zs%_y`&M(+jii6|hO%x)GzbgL4BHz1|u2d?-&f*NQjixT|@BB4IUOC)GR`pqlY< zmbXOu!-0){&E&~3>+o* zF)(iZ=8WOjyEzB2F%%6)??|f{6E+E-=ihSA4>3&zT>W)tjI1B1js*fmo!ZREfM@pkvyXhm=rJZOQxmB9_ZZcaTyCwVz;Ge z$iAh{z85V)_VCn!V4poPJdq8drN|!W-0b21`jKfr0c$C@lT2{td=XrYFMT&Eno(3a z!3hUXyC*m?#<}SQLNrcrmha-B1D%6o%L!A``Ar11t(f277vpQG&c+wxTPPT3H*0sv zFUA!DoO`(D*2?GH!)R!cj4Kjb!u7PSa^r$>HV$^4FtImjA@-OFQOUu4{!r%&=JUJs zU7>d=5~k*JyNpqnb_sl_UzA=Xa=F0ue6Py$Cfb`vTiy^rG?{*<9*E_!dGMICuY6<2 zR`%kBOo|HfChtS)mJ#2HBD?N#l*2< z`ffQ^Os6F46Da;m9tms3q(UicO)v{DA>Je+Bs0|7YGJWtC}z*#Ebq7=ZW06EhH{x}j< zls87AU^$!=D3-&Kr~<)}q&T_G6fllN@%Z}hR6iUJZ8Tqsf?QFWw_(+Dj=8M$|$o zX3ZWsv{oVtXWAeHXWEJoYz~K@3eX%XhWoHSKv};fSw~RT`iXYnjZZE8t{XSV2s-6)Sk!72KhDX}q&E zQ=E;>N`YWl;fZzyY)tTy_Ifu91rh1-M7sjUuEV{$0vfZ?)auikquqeUVV zO^XQgwbBQE!UgW8xHQKZ&CmQ4RDdb`G3L`dr&Kc^1|c|;JnAFlAQ5uPpQCXmi84xS zPz^l2{weUo)9cqLKm6+W3gPz+d`25;t)+Iz#^H6~hS8%a?TH?(%U;XXIm3HLv=y#oYcK@l9r%%^0qi%)UNH5>EH`m zjNbW_=>K1T-VxcrbeVVuM4@*SQRp27g4XT5-PwdGVDt{Y@?3Dgci1Y3NI&2;2wMe= zmO0^LnWUU6JDhM=Y!YRPmZ`E|#%&c^f|kkcUNVZO|J*&8r`E2}!rXd4oW4C2p1|5t zOw5@r_mkAzeZRk99^Rzee80R&7i6ZqKeBW62{`BuapB0q#L z`!{$nJ3>HQEQgr^iFbJdGYyK(u@SjvCWC_B1u{7wd7m=yG$ZXiweTnV{j?)7A1OxW zbMdkv?b$ESS}4-JrXxD4TFtD;rc>FkR2!U*JQ5K1bOhHcqlzW$Ge;fJhZ02&kf_Dr zh&(`oqs#$69UKlN!VbuN{1D2(vreg*4@d_++AkgSV82|WY){eUgh@b%hIMkXPB=db zVuL9r9>5#`JjDUb0ZNHDfO*C_poV!o)Tw)`zE#*nT8?!ZOY1aK*2z0Hrxi~mQ{3{> z9UzK-5EU{&m5HZ(`xFkT#V_u4;&=AA{s8(NJnMX3#l;i7HQsi%W4ozZg1{`e7bps+ zFqIk=KIdE(bPAB=Rdi=4nGsq z;a{A0ej9$r0ON5uGEvsgg;GL@v%{fqHT1jAt0wluAk|;cOMPGpGd~79Of4SLiop+K zH2^yf{GO@sK>`*X2T~N?+@YRI0W-7Cth_gJkl@N^%(umJ{-LwEN+N1bjHDmA`yE0= zbT_{okf#vWUU0r{V(q;+x8y>5t@S~^`(xOrY)y*=-y(+b^#_j9_~v8o6sYs&rE{Ib z&7%&6@LQc~mVpP4oOPIka)SEmf~*I5jZ4nX%u0)m1+h#D#()HZ#LF=KR0zB@(@$ZJ z@S|DyC(a&P_CcQhiStFaopSf+6tM-s?kjyN^D}$zR_0%=YZJ)?+_SFZB{EsIEjU|F zjRC8V`UyzGY`QyWJdqQSee^!24R}dBd<%UHo|>T@qudFg3yY+;!JU8~gr>L?a8LZg zoq&4>1MURe{LIPyEdBFS*PN4B&>>Q5T>5{GKy=BA)<4tIe-ECwE2uF4=iP!~{GXJj zgJS%MuoO8&7HbDzcUF~)wG>q@);>OHEY^}sE~*H%aj`b#5DX(4g$#@>B0zMt_8;nE zyyW8Mm(ENRYe=fhjk_q*C>X=D;US**HJpc12(Z=TneZNzK(u-d2&&w4ZdC~|H7~7L zGMN8+(>d8Z;!wz{<`bK+QIy)#ZJLJhvZp9u**0 z^MF6j8x{W0C&=3;*bd6pGlGCqIp8UMr*p+2*}|6{lB0+shrtpL^$YTF6j4Y(6x&?8 z=X{q{qKLTNS)M|0$ce`R6)@Hv#}thZ>tl+hlnlod4G)uXQrp9F5KSu&t3xoRh&6nz zFC#0We*4KeiYX8r?yKcvzM2{UyS7&eM3=m1`!jZJ3I96F@@ZvVMfv-`z%K9sB0_Fx?l~+s zG^rfIYGIt$Kt{ujnelYd&`NjeSjMM0<@a zsv@2T{PeMNsEO4Q^zP;XEvhsB@O<@rZh-aR;FC%bH3&=n#9dngk{whl0eK{vC0GDY z+~rnroneD1B2Hol9MMPev;-%yHRlF9%#)7jYiv_!Ikvh9M~qfyn!_`Gf}<=|1(tiK zOm0w&IU;ASAP6V-11KY$x+)Ot2i{0>y}%SO<|5oF*hT3@E>aPZ{>NljZ5517x!(~v zmVDueTxmZR?ApgZru@((ALu6efR-30`POZc?`S!iukd9Noe#;u2LM~xuS1{{@%uc|PdCra>ru1-6KN5e zdn{pYn#sFsF24~7yfNu1ygf-zgSw#?l)9l86bM@JR$-SVTD)_fCz_U0j^?Zlh7hwA zh%j6n=>?%Al1Hl}GI^vyfQZvfAJV#D)_hMu+zl2__}_6?{CkRw75^`-_@jUozj;(H z417r$c+$G|IXG1)_uf&Da(H+t{&f;gs9YHVaVv+zoexX6-ez?uBBu136oMARl&*k9 zO2^MY+P$uy@b5sWLpDJSMXYfWx{Q#39>qco;Po_YG zn@mZ_o_G5-xHLc5q(Zpqb&mxwx6>|G=62e@6I)-^?m8S{1j_gf~Gy_narlwo~I^w%<%3x-QS0?O`Gl#1n2`jo}A=ahBp)WnF{VNg_vn z%XM7ZEwxs+8ro5?nRw{)S~*u?UaGSe#Xl?ON@iDlh;mgT%-60Tnv{qpm~N9Ed0kD{ zMWs#`D!9UA;-+<%iF-Zh;U{tbCAeea){cq9Jv#P?fMBGU^jR-afMd>Xn&_%!VqwPs z@&7Dm8|-{g25JvnJ3y-7=m-?RZQk?|R4dK9X{heI$ioi^t?$kAjWlJ%fTVQz?i@ zkI7pB%dQo)6kTi0V={T)tKs^9y+-+=MYiY`*+NSUi=2`cIYUd)A}6UuJ_u})IC6R2(=8$;C{ya`V-0|ggbhb$j9H{xZeED!ng(hm0LxQGVj_i>&p}!t9 zcyv~)mm{-IEy@71jw|t{5K|5+3HOU%YVK;l(@VnXSx^)c&p1kfijJm=!l~JMd}(9X zf7k+wh>2$|g&2uP0n53h=kV-Cs3`92W>Z?M>3Ukz-2rL}r)Nuu%43(JKb@(_TiML@ z5>p`PwpqFbMynRU#6x=rnRqsmPKr1U6VEN7I3}KN#4k)d-{b^LJTIrY7IyJ-@9?5k z&p1_tXB!xQy@hKH`+A!R?QugNrMlgX?r4Q(hpww0PTiaXvbpSLx&$Utk|ViglG2?aF@)!K-l9c+IkHJM*sn zLwd03ggbt%9^*%zWg3|1yAfr0>ZD4A`PZe}_GT+79j+f_K_rOJ!MD;9JfaIFxPGws zxLCu`kJ<(hQ@GTyGTMvBjaV_mNm~979H-Q=S);$~8Ts;sj&g;x87Tw_*6h&5(IyMoeIR6E#hI$x?7Z$Hpgiw*N)+LCu0 zS~4s~LD!r5k6`YBuIX%oHwnAvamkny93B!c7Hk;@NPN&sxyALL`~Q;?!wU_9;rcvE z4jyjGbN`75)yAFRS%X|fwK*rmdyi>Fj2h^VE|1|^-CZ$S&^!hPZwc7*#t_IYc?2&E&G_hMWa z;@ae|)BAID$Qcq-^M)|nFGasvEZdFIsM%6!(Opt8K;OK`m8# zaB16ajog7WTqAcN4M4y9ED)pJglpRv8qJ=L;U$Bt*|D2NTX4W{5x;Q2Z&`E40bll~ zs6amJ#$)B>OJQGRT5F5iYyh7<-Wtqr-gwOK_cM-934-etsMl~hI6@$XSu>~q%jw{F zk|l&+T_0SYkDFjE$BX`WtcYDFdW?$}po5^;#!C?)}!(ko1x5Jlg2qnbi zz@W7Z=*2+`F)KZ@Eo3*lELouJBXvBPm0+AV-gB={IX*l`^fOkdyA$zSH{awJF z5l{4GSE!LV4__1zrNfR+wN7RM>j3Nydz^>MP>4AXD`4zD>t0sV62H#4~k# z?Z=&7UtuxMcm2dKobURryW@N}dZu;0?*gZJapzgqk2L0@&BrOtMj(djneY6|Db0Gj z<3q*uXIotwn@PmO0R?!Ga+>saa<9OO`V?q~xb-W4u5~$EPDybuwsajo@qrb-7!(m) znSD&rupX_&t%ri@>OiBdgg)Cq(V1JUa42dWFDsxm#gv8=<7-MAiuRb2yE3XV%cdw{%7R@%o$q9cwu|SCvnFz@ZZSPR zluAIWz~jrrL#mKs@FC^yr70BS=E3)Z2TbbNjLlWh;(DK@Lh(bMKsmlSsEEBssW4F9 z{(=O`=M-)F1ZXf&wo){j02mCE!xV$PbJsxGZwS0Z*-#*0Fi`%a7}F9rW9WMCK-vF= z3=}?bffbLeq7`?I5x20q-okXZ70oHe#|pmqWvNP7ZV?7xcs%08bl30@s$gb=`*2+& z>cOW-(a)o)1h=0tJSI>KhKIXvW>Acq#}ghr^)V=c^bg_Q0x^9Y_hPx9-z_S%?@<){g>s3O z00PX&VETVar<)c?HvZl+(*n!)>y2q6$&2THez%Zto6wMQQ4`uwgtrMSn_>kU+JQmQ zkD`F_?m_YWdLt-wOFik7LduWb3r@z6m_jl5m@vAe@7tzBM3;Difi0yhxYPwSE_K~c zmD8)OMeK7*hVihPYwtlvY^4a(3#%8b1mu2-L`zrySBAq`iV;4zdpMjzaeq=K?21#u z2&n&@3aZRPH~0++WaUL4m=3U<`mYfY)xB8$CL*L2%_)(Y1ZFGfp&_o`q-MKNw-d*I^1r>0GL5B7y!t@(>^hquR|Z0{r@)Q5d9CO`pK)w zvm*8Jj?b_SlnS2}>o$;Q#V(5WG+GB5oWs7SX!O`Rezn+|$1YRE-$sFc-jJtnBt4Do z2Mj(cqA12R#LbvQ(byB`HyEK%kD}0&yG;%rQ`%5A#guG{@inC%MSD!apm=fJKMM+B zgnV9z^@Q8mQ@jNW3j^X8e&9<6mx_&MXW1Y^4URQaj|}u zzy;5&TQI;jjqRiY(7CgBSx4PznWS&uC^p$``K~j;p?44~H`ycBd2O;cBm~#9T-q4K zuOtLNp7@MdVbTruD7xgt4fd`Q>j-w8nuj0Qy-gvQt_B+U%~F+Ed>F9uYxaBIjl&wh z2fLm^^}F?oz_ zDCIF;;#oJVW46X#re>q7=Yf&wAz2SF_+jo(Flbg#h=(|}R`6pgu}HF(H*E-$C5zc# z;_-{vSwsqGz)gJYmwZ*%krj()&FNT9V!E0Zm$~Qf2(hz16pEhi zv57~Yw?6GfFH-Dg?;CXhsw9YiGbZa}Hjc`Jv09sFPq3xwg`32Hg{xXWkBH=Lng*AP zvlBvafe|r5d|+Jr44yR6mX?GS95>t;!N%LnpqOVauUetFXrisG&R!+5WIinUsjU0; zH#hOw57=t3w<#JEcW3-4CIpUShpBy-2|FMl*z{*$TP?n)`tUVt4UPRmDKU3>b(73p zUZohE%Gz{}t)Yi}1+{KR3Jd#+gc+v3AXRwUNPX!QzLKd+>H#<|HDWe%<+Q9Q6^toY#Wb1r{I+8?FG6ukLxVm`0{O}_i{R84wN?yom0Ac{ z@E`A1<8$FZf6vMB1e#(M<~}dU2}?h}NuJ4T^KCzv?FI#3WrwLyjNkoV$@tw*F=qT; zGUE3NMPvM4AYT9dpTut!IfOwW#;>nJjNjNJ)zrVvkcv)o5_d-C3!?erc`nF!1d@b0@t@e7`Tr?gD`OWdMO+P3k$PV`Tknv z6}Utr2Ckyf3|s|_fvZ5wz*WGwQoOQlUZj02|dMXcNcFkrPvuh~EjM=@L<(#{ZqA_Mmh{KO<{s(ie@!23y zh%xJ{5M%bbJ7(AFHq0Di%>F`@>0ACjW=&lfv!K%xvk6;d%vRc>#_S+HehXQM*N|H7 z9=0AeM%enc3B$Ja79(ui_~>j!b$0gGGOxHLDlu*qm1f*3V2oP@V#cik#<-1(v2|c0 zsHGUP3ZW-t6{rYV{#uaiH~&mBT2(F)t#AOffvuVR8jpLbT^47%&fcJgV8kxjA|rMQ z#h4LWyhTRr=M;?#rPb8eo3{LekTqU{0gV{4z8W!Pk3jc|QY9Z%!3K(JUh!!oJFN?M{x_iiav=|}l+a?Uzs#}BPOk3SYWy)4_ zm}m`9rMleoiddpiL~H`p2+a{IL*tbYzHto{d?}t|=qeC1bQQ1&-T6z~2k?XxTL#Oa zR%0Y8#Gdd~pg7~^{XM6QVU-nPSbq&dN*v#J$M5Mm!GEx6RGx@m$k>#|+bXds6yXV9 z?KMa!*{ZO`6uWHW$b5}@{T-f}U~A&l(Oyej;}VD|TSe?v3a9c=n+RPEa!eI%Rr#(2 z61eNBrX7CzSHx{OGj83AxV4fEDxq6FVXK|y8>4L1*cqx@Oy~MZs^aQDh+4M}h)P`8 z`Z^DzHe{O-wPD-52TIU3Givn;+x$F2yuy~K#EDi>i9v`fTLp~KszA(WRlqpWKK^QO zE0#em#Yj~MJ&~$FaiaYwiA=N#8kORDRsrLBcJfwFkm4H^XJOAJPBa|YBu_MayGfpC z7*FlOecV_9aqr`D=GYdq*^~wkGt8tA+4coN(M$nj^lfO76Ux7j4@tHxhd$fbBDE@e zi}K=x{W|TlmD~L6Y%}{0ORijC#-ee*2-OF>@Pv?b)dcj!^lh@`W4C!)j;9*v_Ew&1 zph-*O13cAmR#=Uv8qSGdc&g#twmY6`n4V#q;d`o~1@GI`HkxVML4v#Bj)xj<5uX^E z|7Yj4maurL57W^Vwu|HBu{`Z4+-y{XqOs%vXe>-cZ6{~2{McAfcDqOqCu-nY?;}{T z{Af#C7+>7d7OZ#JE)HYG@T(hxA4n`K)X`k=AEfd4@){rbLWiNO8#U{Hb{MO=kp_B* zO2?~%&!6p(r?LJNjvCjo~N3`gkcdJ4{Zy6+>1kGxqPJ4=D}PlGB)o}fjH9N6c8Ng(2sc7 ztnTFwHueQ2#HHBhfEIUTeNHjx96YSF2MkNex1_LdDIUGEn|kM{Kkta+O65)ajZ`3L z#4}uqMx1sP2u?lqOFL9kO~Y~jDHttgyf?SYasT>uIpalr4Xt*MKJ-I`fGB=Q+a2s; z)hQuPdnpuRPJ0R%-O|4#xQV6t*YHyCJK+sjOUj0BY5Fw@ng_qepoyeG1BcvauVZyd z#~O)Zsw;?>-5Z?FD2QXzlfITskNDctbe!^NRLCihjy>W8obsL$X5*AMLHxogZ^GAi zobq1Dwk`BG<&EoRJHi%F7vPjPmp}~7|C4iE9L{l-{lm{3*fpLvuun2dz6Bzmx zLtyYX;0JeLqCmi4u{DNbOiSF1RVf-PoL3<9@BmjjMWHEoEw;KPK1kUVQy!)mUsG}^ z+G7eH#u!ae!j!um;1W*3iffd;Z9V8a^!Rit0j&a$W5JbE3_ju9z4Run93Y z6kLH3pM4N0@UQVcm7icOSpGFuT=!B5=;6CoTz7p#Dz5&;%8KhO7`yIgYOwLa1HQCd zNh~jEJ1UY7$h8Goe*Pwi&paEPo48y=9DSy~NJ9|oucLmmjADOMWAVyy_PeDvv+N*| zhtC=yltoyT<9||w{f!!dk6u}XtwgQB2O$<=t5Gl-2ZXW+t8kb_*k*voB5VuFibdFr z9c1Edzk?KE!Iz##HeAyoim(+0JAGyg#aM(@6yuDKFO2j9JQiX5dnq=GuziSj%TOT3 zBCKCEScFy7h>A_353P?gWf8VsADgELYx0E1bTZ{Jim)wz4(_3`MbvC`^+GTbi?Hy+ zoSe+a`G|r&99W4}II!Gg8e%-<=fk!WcG9tPi4;(SkK&3uWF@x3_25{I?I#qn61xX1 z!%8gt@Mqas3Ko{>dv?fjtis@BnDLa|`40?}<=BXwL_0blbf-~{g&(G2UV4mjY#@9$ z>+c*O%dw59Fx1lk^k6v_e%w)yQI4&9cRg5+_30fNCbAscOVQ*m#}1%S^mMPCvK*_@ zi-oGG#8`(@B}O@R5|w9`W5>him(_CY7(xgxFv7-vIo4Eclw(0LEype;vPe00#ZI~I zf1jdp3HJ_~W0Ye*1^zYhWbGPafdo)t*%fS+`e6)5Oe#cfHCOJtd(WAk+llk?H&5s zZW8)scl((DWY)^;t`s?lsXilXML}{4cW#$#`9X+KaSbiB9Jg?EzLZ-yy3`{+z%5({ zVKr{yI*MPoh3mK*w{VQUY%C=?XP9ktOX4%cakor|)dwQfj;*s`4 ztKkM^yXes|;!lQXdYZ=xUyEN=IsB<5{;XOCe?Ap|4!i+>@cYu?@4zqo5Zj;PPF}p_ z_r;Il7v4Denz+mbudw=A+zx_QlH@-o?k>Q)C&XLy*o)}t0*CPV7vc}TLViHJeu?jN zju7WW@jUBq+u#@G7-xMAzr@zaRj^nFz-Z`mPgL*|S&?-Eg~VK*dpIeDcbi!czRJ~R$Vq2j_#tCP16n|F1 zA8uuZ_q%f<3zuiJL}6+Hgl1;ZU1F~w2KZPPV&n3&A> zWQ^>Zct}9#c9XVBT+|T5XOtU0xOm!Iwudx!mh$8LI`-osaePNmU{pXU;P{-gc{SNJ zN`(*lE5-zCZRd*CZ$Tf14o~MV^ob1T54~$^QQkDdxG3Z7Zocqc+YDcQ3tk+OrN@@= zS6A3lS$qlDj@<_R0;@}7YgPg7c6)np3oevds+7R)cW6NGBtJULo5}0)WjD{&<#pb- zMXEZ*VvhL8Fi-IjZhJSqZ(|y3O$|c#-2A}SPR}agnIGC7WSuD*{nCM$s`o6x1&}2Y zOrDd~Gj73?BO6pMFzq&8-W5vRH>$Vc4L-1C@}!lv2vxb4-3F^-_p+6?`*k}QFvnht zIjL#l39DP-v`F^IHZ5oV%gEypd5w72oIxQGR6VS$+SeN7H`JH^yHNl16Wa_;Us%FZ zUUUqxECgq2OX4f>9|G*zeBf%^b^X63d~(|n8EhkMGj_)pv^%~52Lz64s7IFYNyWCl z>7%U~&GLq%jcAaOo0pL{cyL~NZsX>IbJH5;HfhwnS@Vp>gE9u?HOU<`q;ccsc^QM? zvU)M_c=Bhq!Isa#ijGNnX$*dtA&WbOqXI$);HM>gV-;8^eNw_p(;=p>&d3hsi$1r# z!AdE!7`^4^cr#xn%+*I_@>Pp%4f*j>JsKU-aa3o00Z^>9b=2oVv?ascNdqHe!s3*k zF&1ASo6pf3=klHFY%~0J)VzP!Q42n?b>LBRavSp|>uv9`7JGHwUJwu2XnU3opnUj_S6`mK z77pv-OUAwS$P{(=FKv&pF+QXYAJXpZMM@>|pV|x`su*7ImF;2nHYEk!J+(bDhq{^~ zFo*ia9{Fm@_RY3>Y!gM}>uZ3BGs)yMCUwdwQ)&EQeqLuielLIF8(TI@@D@?t6cH#_ z60v(_4yZgK1-&9>3mWdhBpC1ZMBZ+eP6*H(BFmVo>)%}# zxtL3s2p`aLKZ5m{&=Sa~SfJne&RSu0NL!5+80f$fp`rtKw+aOJd7HnrwPOkxx2G#o z9JSdBYNOcQ#!^Dk;*x!~550tlJ!-!6xjtww&;4U~{o+msZ2dL1n@UBWd}H|J8;TJ= z!5f56flEps)M~OHDH{E9g8Jo(^ow4)hv&>1QJ1*INGXCEbc><}-J(FyEginIr7#7I zn|{2BJK|ds{OA@+NZeBYi0!N@1REvRTq)nmHCI(C%d_UvD)TveZ7t=JtNyog$(3l1 zj|e$Fs9Kr8RUlaK+@|zFD&Kd^c8xtswTdp#dT{L!9LefI5!m8_zJ={H+;~E{II5|A(w4?%dNH9I>@t-ha#$Tz1a?`C zQ?}<-uyBCZO}G-jQCJR61mlUH0T6F^hsj=#+~-SgE*=1RmNKFCcFFL>jX=>AY>GX35wG8|Y8+(pfXgf@7xfJ~EfI-Dl1v&Hj_u zm<*ZUUw!Ouy z6zugdG7{4YC**vZ@~ac!gHW~i$?lBb=W&-v2*oKgx5rK3=SuNeYynjV)*H9*`R6I# zU20pcTb6@YH}s6uVmN z@~(Dzbn(iS5#_3gWjoY8hj&CRjKpH1B? zw0sGF97aN?KtQHDJV_dXlll$-52jo|6Q*2Rz7&0rDVG>?j}ou0gkhhRCKePKdwAxv zj-n9JW`ND$b0+B>dD7#Z>agF1McBY=`$%%&IspTL@XpL3_r6}n=7?j&I?-b z$Ke2;_PsNlZ|GE~7O!~ImVXbslT|luy(1JedHM(U_2JfAwweJE`$hI-juw}#P#6t) z=H43uTDuo_ziopX&8vV8kLPdzr3lD1a8(zhNJ5o!+^{y>FE^}>DKlt4Ri67;Jl5Y78*ksyZ+^tRkSHL1`0ZWh8XF%fvN2T*-!FJcZ#e!*7lI2M-;lvNE zG8p_Yy9l?di@m$3aKGHHI`-pswRlvhy`siGRv5)gVfdlGH580f@W=akWL0}CpZQ8> z#s^yLRrsWEd!)(_Q+QXl!yjuVvv*Z#(a0QcBR?WW!nRnqg6qT;fX82~5{8Z!JL5-z zNbHPz>?b>8Kiyvd;^l?L31#h5y@t@Ta(WAXwyb>*OFBS&XYOYc2-qD@^Z(U&!k#P7 zS8nYT%+ucL^f0SWRLH%oAEgEL;8VEbuHpmb>?>J2%7O<+A2=Yq&_!=~fTt%rhqJyu z6unFeIEU4pIVc70I!7xMF??XGy&s!F$xzjE2jsSOA;q8oz~Hx*-liDbu>uBntY97< z0_%HuKzFu*lA-8zhUi@sgQ5Y0qQ9pY6b%?f^oF?p53^q>8Hz47L|YD$en!!NF|S38 zrf8H8Xt#X$Rz)9Hhf<(`8VALh-UnDSiV;e+8hm4d{UOmQ=05!nor_ea5{0vQFqBhOQH6M`FqUmm zZ0(?!BzmayI4eBshZH9TMYOn~dclOids!a1=W85a5fMX)J|ju6u)6(z{f&eCP<4AI zdz0Yh#(t52plbr;YH8U)nIu?DNPz^`Pm2!9B*Bdu_SX8EgS=@?dt0`S5PHir_WYmy z1DU;Q*&FJ64)Phb>>2FayT~*W5WD|@%q6w$6WRH@$TB`@cjoT0+{uhYA7@d2^O8Q! z7@k+xK3oqv#7pYhpI{+}z;o`!7Y0zhnVlQ5vJ{MunUF(rzo{}na=*N(GF0|Yz#;i; zSlZeimJ}~x)C{w)jMLbSs5ac?qzMRiC`_`DU3sFoQUiN{&ax>bW)eCdlG&Bc6yy0Y zh-JtjWAiqY;&EFv7|e7({q2Y3<}Lpq&wH(S13++H9}CkOA)>K54#-qaccwn1HAR*QITGvL>H&iWGOI&x3bq!8NiR#_?7l+)7dS` z;MP#u%HCRKKn=6=LhG@zhe?3DHQ+}>)i$V+Y#bHaWkzzeSC8*`GNFUs5yD2i{nj1^ zhe&r7K4AY!V{NEP4ANGIWstU_7&AzFA2x!t55;4K;ZdG5!VWL4AB_mZW$NI=N}xg( z2kpo10+Rz(YDyA%kfPVl>P3K(Pc!}c(>O`}!_A2Wpz`3F2`Z>EBgfA_l~E!Zpn zfd9!3_R41&p)g{uf}I`Uf=(eXkHA|I`;-*;Rh@{WGEkZH*}~mA^UB`5dc!ghVfTz2Xt+ z^@3sVr>vv?F}u+fO91 zX%zFh?tp9TyB~-uPRX~Q)7hI8iPLDtP?!ckpkRE|y?KPM9%>({GT;Cj9p-4r`}CGm z3_j(6_Si7{Je5{F>&bd8$yiStjahtzrwq4uA*&37K(XG;!ai#{0h8>RuoM$X_OwGX zD_b9)o+8N}@I8qJT;6E{;$Gf~B)eYu`vp0U8sU@@$44U19B9WcM&jeIE?l*0Oif~) zrrjsdSuBR~;aR6P;~T+|W?(hG2AL1vBNMl{isOS|7be$xQ!cdY5o#AE*N@ojaN>R> zrG#8rwqhubj;o`rl z_uNYPaX=~j=&ZD{_C_i{j>W_OfOX)UfNml1U@?o5KQ1lh+mk%k2oq%jb0NeYE0l?--r8uzJ9ztGZw+dp2{sQ7?OJ; zV;{WV$k+!%3QT0|Tg3{o<>8MfV=o{iV=r(8m_-vg<$@6LCq`rk5)O!*dM;nI$QI9& z--(5cXr)R$SPMb#-qM)||C%0!uPQhoQ(t6nWXU=zVzS+Xyd-9PAZsc(a5#dQDIFmT zy5WuHiF~-07hV_abbl0T#UvL{rMOd?=I+syoXPxyH% z0lfboheLT}#>k5N_A~Yu*c_q<(*%jL1%##^s3|;p)W`~aV22*@JoQ=ofd7Be_@&ZS zRrv8|VH8ubkQ5O`gr^>OtQ@af-OhsM6)Xocu$I6mA^-1Gy92K1ZSX(du~&HXjO}-~p5PGHdm&+6onObfL^x(dl?hR&-`Ygxym`2Mpt2 zSeiV2NHrU$4;7H%;(zf03gHQUY@z)zZhfgrh2mlJ>}xdLdQ6setjDmdlgo= z62H2zdbEBM-PBP3I=U`cYQN{8fznpMz55S*XflqL57lWj?u-qNXOco7(JpkHxfK3YIDPm zk=mS};VW7#YIC`~<_i0Z5efyz+T5)b_A~5bujbX}c-Hho0tV;dl>73(n;FG?(2jc@ zHr+_{$O4@o%>^64(XDD!W|UMO*L@d;pQ!Y;SZ}WpEePdThEs*=O%OohknRQnrc=p# zG}f@ZhYx_p)Lwi5M2w5Mr>dLrB*l>v!e4BhAH>qBaD3@v)&_eAjkTk2JlfkyfB2c=G5vP=1bI;2gyx9sOX>+( zB|CpYCg4CkCg2qDn1xgzxb@#z?95{d7*hvkku3Qnw95TJ$NYgpjY7gcciL1T_~hUH zVCz8M{Y&Q{z41wT<|l&+!>4e=lcYgyPRge+G)O#!Nk14{CaM~Cw4t5qt7=8YLEj^L zS>JU+RDn|Y!gbD@Y#7yyosfT0c0xYIn4K{Bq|ph_P&{_RBx>`llWHf7KPfu_#A7EY z;wKk7>xNT#=sqm|D0J8dSo~QqZ}3rxfu4F!^5QTmt-yxIo++C+_FRo;|6osNXnFzR&B3=A=)88}NT5S$?{U529!3fQdErJg2(1QWyxA@XCL zP66XgS!{HdF;Czh69U_h$tj{T6%!fZDn$XtsaKifBIVuj_e%f&RX^JuHb7g*% z^*Q}V()Z}0+uj~xKYF54ROz%o1c7lv0kohy6fNiu1%jnI1%l;)G^!j|1_}+9>J$jJ zAC^95`|Gdn={T;jN2nxhf6kAxP?tk6#ZaglW??P@;yH7NbtTh~QJ1knnvk9RLgvO{nZ9is^&_9d^D`|4KL&B@2hHx6 z_mw{Z{(|Sh+f5=I8v9AnuX?D4e2)Bx9(sV?prodAeiUH0bF>SCQ@Nw8V<-zbL&6O% z0ItTvqa4*)6a}M$LeBVoN*Kxm&&W1{5ngRH2u{j2>c@WMC87CQIrp+NV(ePEQu9#XBhz@?rhAvopl8k->T%eqU+Vq z^2?PSby!Wx1}1REzSoUjveKJgH%K)!@7T=m;m8m?KwyJS_ zv9cotW5@zSY8A&kl|db%hUki*TdZ*7&vIK;$E*4iXZhqWhF9bpM)wcnjjB1S2aE#g z{G$erTFgrIV2_TV%@}7k=5*WwzE z9!%v@rW-?kzHEyY&r_lu>Ad^k6+z-9T(V{}8eM@ZfqN8M@u;5mAnmLiPh|vqGrn}> z;BYyzte-ZHRudf2Gpt=>X_|!u16FqT3k_J=Js^HzW%s~Yd8KQNNd01|6B3}@HmdOu zKB10diobMdR$a#`c7<9Ct6U=;dVzoq^)u)G`*dhsUim?1c^*~Y(U09GDr6?qkJ5r$ zXL)IT$3|A+9GKzWTw^{g>YPl1vU4)S=xX2?#OnEw)jB7Wmn|p;Q*D63G+-9RV44mv zm}+y2uJmSNZ&;YN8IU7W zd6ZP9^3I$ksXRd_Q+e-+UuDkmBTXFXiMNEqX)5m=?E#U>D~9SsWsR+(2IErB`lw?X z`<#Ns!`cFb{KIO652+luJ~itKJI~3w!fwir`>Aivk$LLyIk}&D@0`eJWb+T7kNr1A z8Z**@F^8zHf^a!II6*ZPX05J?7ldKdFCS7%G2Pgul_Nl7*C`zRvNFA}A-he%xQMxa zj)%5(3{V+_t|U{J@k#*I1(|~7Rm&Iu+}hF6qF4Tjw`}Wpo{y;Sz&!>|W%s2Prm+W{v>Pg6?VBOL!pZcUFjGzNk3a!#j}!&CJyIaJ+q*jEp(Lh&vG@Bw3~QQt z=SgUb-d72c-_q03Kn2fP2$!JkD%6u%>wmz1v!|nr%8&Y;k2spK?sw;pe$>&>4=c`& zgYM3XrsG#Wl&0fG?Fs!2W6x1}*o)7cm%aE5#b7TcM_6Ccmq7m%L6%_| zMPrs>5l=6G!6=U6%c1_>dAZ((uEey?!t-*yy^ylvdVAA(xWTheSoLs3snP@Y0G^SRabVMf9T=$TN2E1?d8i{#<;MhDb1+S86~GhXNG`ngCm$fKfB;D3HNYiFOmR4lbC?GO@b|PO-Y<0AuQFgF%j%DI(HO@>mDX$hG3BCMe@vy^xB`5dHhcC(KMN4@c=oj2d_1S~ z^!qy1;m?nA#HbdC$1_>j8LY|F0DS4J&UnY4?0pJ0Cx!n|Fs|#~zsP@l%CW=`1BU+= ze+;cBI=ZV2UbZF5E>&zZkE$nLlmW-UhsGGdv%qwrAkPBR)jSD3+%f(oEW;gRnM?2s zcZ_8&$+N(8$H>Ks4zmRT1A!RLGoQHkU9fq6xALvS;k>IhB%IIAsPZU(r=P1F z|7NbE0)P2A#{=2^)INL9YT>YM9AEO1vk71Sd|?pp_FQ3w;u+H%Z8X-GG*6z{>?0s_ zD@09^XEujll4mwYQf6^jU%O=78u>pmH8O%KH;!#8Afrd_{nALcYclxpxsGb`=w`w! zN1(3Hy(Eur&b?$D-TeOfDq;MU7YpxOaM%&Cz~PAJon9=A;B96*=2|`jqMI)k)@5s` zPw=?jr{FD-<}CTB4&0Fh9ulWDFVAr}|No~o6=Un^A7A2;FF6{qkEu>EwUj^T-H9Jv zlE>`cza&p=Ce3qv&n{3tabTy#|HzN^^m9a?IM(;>C3$l5XkbOgJ1!`!WcVzP!R9>Vqqw6!X1kO%3X#u$DVp)8n6oR%QGs4JGCgj>J8X)Bu}f`Ku@|XFX`NW zNnFwy&m$K)+Wl*UV+#NOUDhDEMef>;><;k=32mT(9NUfIqR~K)C%<5Fygl{rr+Q&Y;}m` z5WaGbGm0Nv;z;<{4$QyI7cF(PWckzvbl`LWp$;5mwn!gvS%&87WsaxVBFZZc2$Xx? zJEUJRd9?wGH)LG{Pmqd1EKZQV2=ORRkj}V7PLTSUNeVy>x3z7M%hxY=O!k_7#0gS( zA7ojpopF5en~qNWAn#F;v$xA#P zUlG~W?yL%>z#M0iLJ`9oXaU_>3rdE9GOoz{YZk?z0Kj1W^-+q!{3u{BKPr{S@Z&WS z`mxcJ3`Gw&L{Fd?6b%>@J%eIUG+^AK@yP2BL23Qja!Q7xml~o!rWh0r7!$YmnhuL9DhNAZyqR&zciUtgd{*_`-G+^AK;oN8}uT|68fki@^-!na;=rH&? z>!Be{SS5-<(SSkGbtp!N*24J4RgQ;ra5ZQ!Qqgw^B}_%v``nQpa2=wLXFguHGNYt2 z75)8XnTkICxubSDK`2wvy@h7@U#>OjR4?(GfYZs~7>#E@`Y_JN%cg${qg86l137{&UsX&VEYqxOM#aDp_J|fCvx=9eZDu z2OU4WDtGuG9(VYPc(KEepb+Df)+6sYYjH3%m~YtRm=$rHs5Ca2=Lkq{GLK)Cn@mbf zHkp4=2Ha!<18y=E2Ha#aScrS#TF_d6hA)24*_kQCxQ{aX#yEgV+k+R26d!Nngg0TU zQK?>uvci(Oz=@WeM}$$a_a4mswl{1~$4IpL~5 z%TtQgsW1%Ll%Gk+HuzZy*+esljC*+%)j2;wYF&X~Zf+|0nS@F4*GC+cb=I3|#t7;8 zvy70Q6k|q+^=Fx&wo^2Q$tY^_Q$MS5QUFL1Ct@=7xDDfE-p@vyETAZiletvlQcz6e z1SDddC=$&$fo5QwD9ymRl0rarvxwy4?x!5jYV0d&0EP=5N!~%hB2pp*p}V$JkK2Me z)#h7&bVREH;C$30>=@-0QKIlZ&vQqe@6V%7JL++3d~T#Fz$;dMHe%%$DiUMmDs|Is zHCD)kV|?Z9tDiYWh}v3_X*0vhAf=uvhFh4nYAgw!pq~E2(Qy6()B$TyFlO13uF0F1 zd>QcOrDpyZM)rrbmck$=!g8iv#%*2+FmpT7vFs);`KnM;Cf}&JLGaox+>n$5K|_On zz!k^qIvYlnW6)YY~uRGxSa34uqiFx}?QLAnbZD;|rcyw-90#ocWM-cb~N08zZaiX6QHO2s{_Pe8n zh5b$tG8JWZT}9bPiZHCo|8&!#Yb^K|5?5lqMxrsr4=dayFpI@dChh4Vt$5n$3h*WM z2g)TA_G{TCS?Jtzjl zBb%3AbB9O2U)1pE{EG~a&XfxW{mSJJ*U^75P6ZcIG=_)W6si^R=@ut6qF ziWS2tkJ+Qp0_;(x1^Db#Ao%R78sr?x6fjmf(|OKiNLsY1b0A#9^+qc173|!})>4h= zu42Po#T0`>YTan(YW-WoXZtA{eYVGJp8kWE&vqI<+ex|1A$0?UiXrtoMWfGtB|<7g)*bfQ>`=M1?r;{mQ?1%rD{UEk}#(B>dVgDh{drp)vJnuOkb{68ir>ND!+8p8~0P}pS3XBONh z{wnJqBY(yEhc;f?ho|P~HcZw)=q{32h&7NBp#WNu%UkD!hfB(&k$+S3jB*o&i?FoqCuv8{OBzB z4LmMhAHGtxQz+k@=v>9hUI%+%-CCvqmgXW?0JpN`ly@5M{{~-|N9c~X5ADoq`jA#t zN%2nL2Yjens#KUY>P4wUB0_sur809cqbVt7GlpN6*^CJkgV_wg$n*}y$n?&2nJ96$ z2y-yYDH(`v6Fgrpqm?NGSCcH$V)Uhko{uRR>H&<*^-zq=^_Y6hgv?<|hE3RSh(1d( zC>k&_*F!Nf*JFytG!njp9C^d5|MajMMy`iqP&8mbbS%I6Tu^mZhoVtFpfQ&OE;2Jd zcZec0GBoLk}eI(Jg|a6MqvV z@WA)n^?phK+|h~2pR%ty7PJ1;c1-?69le*Ee9hz!4y2uJf+AR7DjSnOpEPh@Wg{tA zCVvD3jddq~2nl_oxafv_qxflp$E8fU5grl7oxKxzZY+#Fv2utVPsl+NPO2krkhhBY z4f$5_LPF>**T{X$|A%t%&EgMl$Ty2u-bJqQw(|S`P%ge*y!9^fj5n1x{k=TT8^%7) zqdjLN-_BB%_<%YsAm~e&3%#>%`fnuMaK&-^hFqZCrh;(Akv27OlqKOeW20G#a^llG z{x|Y8uko9)Jh&mVZxw!%**6f6Pi;j!?kFk5fA*~dQE6n~vI$6L-#Yvzvu~7`WZwo- z2F$(z17_b82F$+My=g5#BeQP`vDjZFnw?{O&WW~%X5XHtQgNkf9uS`Qo6No~pftEW zolBj&^fx1waRb)fBKs!#3I}Fc`RE26GW+JM%*?)h21J;BTjQhcQ>yJNQ=81b`D&Bd zH$|D3Qw+ltr+Bh&+Mnhq(0{uj4}cx)=!64c*Qjk6pjUpA0eXdE%m5Al-3ZW#-$`g= zfQJ4~0yOS-H9!M@mjMdmF+dgZxU5tlxU9tY;1#e}eyk<60rO)DH3}Kq72bnaz!(%} zeyiK>GQX8Wg<(*1r48!)yAl*6AdN(Xq64!R3Iy|87h%|nt-kDp`h4_P_XQTS9On{^ zO`+ar1%BnQ*d(YR0hX1#gpzSFw9h42BJ+i5!2( zJb;U$F*j-hJ3J>yCjDU~0&d70`ba>E+~`QkW^QUZNEMsfg%pjm!h9Me+=ul^t z>Oc&RThEVa#ja4ZFsbg#A6fua7X@r4<+VRa%-w-MX}Ht71I5Ynv=f7qS^2y3LyO{g zt^CmPe)Nxu9h1ST-<==Z9~w92Ih7x$ub$v?GXzt4)+py5)|tw~2<-5ujKB^QgAs^X zl|g^X2pmk&7=Z)-BoR33Pc;Jj0a8R@KgwoC;8c(*B5)c-V+2kiQnk5!Sx+bAqG$ku zWDG#~7XmOv=2cewY2;Pj_YwFG75J$~Af{EoIvItEnPwD%Z!iiK-(VCf5bTBYLg!ef zfUy_aftj=yTw|PTc-T-U7Q?UuE*TwgiJFdCz|fo0_u)5b5Q^Ivf^QPv$J|tXZvmw6 zy@j&jjE=W4)W2!?ssY8L^XpQv%|W{{{4o;KEbeQ#~j94qhV8r6p+cR&8HKxFNG{ zSEvTu)Lax0_ofElvW}{d`~mN1cSiFEc2|t&!|gKMgcJA?yR((3E|lZ79nL0}-Z$Va z_+$M$X#srWc&B`Chn2ZS0?tgj-2%spQ|=dUdQ8Go?(HZ!n$$)>h)Io$Pfd6B)bu{L zc=H*~nyeq?0JHLia---miV|XH zD1;b_!7n)1tNfxzv>ag&Y{f+nw7KPJE}pTbJ58Biq=^dBaLg}$B#glP;!5!g^NTBQ z-7&xT@Eqq9fBD6vxy}#RcG@5_zqpxz4fQkc{R3j|w8(4h5RoghiheW|9K0p7ir>$3 zzRE6B7ECCfm!8B615!M z!t-upjY)e?;EY!WK^IQHiY}af6$p;qeXl#4sYc=0ohv&$>bX7TwA*-sr5Mgwb=BA} z)GBP*RikBBDF$1H2_E*Bv44*I3tZ(6Q*2KdFXa%X`0^Z{;*S5z(vo)tiZ$)5LpG`{1l@l#VHCVc#8lnEb2p_g+E z;}qwZ2_JYDK+Je%yrve*dk=r4=3%^^_)Ct&6BJ{{>n(S@{zdT^uYY&6I^FEny#lae9 z1J;fT!x(vh=Pq+kEM1{Zo8Ut#MtCHdon>5qNL3gY@KF$6zz3IS}7avHCIJurx>$4UMLeR53J3mc~tD^W4{Acd01Aj!+=zL%al0 z0h@`R_0$0D1BDRz@fJh{?9~T*jXu~%g<&6*kk%Z#V;>loAW{`ZA5f6&gV92wXH1Ai zF~2_5*-$RX#^06;vaz@2k~l^q5r&VpasuLhwBd`NX`0a&4Jk49MJkUj27f1uw?|rO z>ZqtAa9OMdW%4YDwW^Y{`>%l&Mg6XUW(87!vn5b5&Ze z+0tmlIE7h!WIc4R^SOwV1PmWfXxqLj->}zNot-Ce`FK33@iyN&doao&7H{;iWxN5Z z=rU*KG2Z<<#Oz<(3ea_y+zQYo9$8+Atw3B=9ikGq0@cJX+zP-iK6yP{G)2!E$oDOK z!?rvVKXpjV%O~c1OhlQuxF0C=K~ac&tO;G1wX<$cI)lKf&96QDg*eO z@0||6YF@f`H78Cx`xL)_)Co6IPa+NA8~csxnmAEF=-z;~k>_kFjNuc0aK6Q6QBH04 zxD?*~xU)B#Pr-9Oa<$^tT{A-Xd#83ksL#^5jgO7tMIDA!vDrJ@@R^e_w0`PA8h7oDjQZHc!8bM2k@ zG+y+R^VxfI#?!ngQ46sIfh7F!I{Yw4h3K>Lbp?cmkOfTG>}<-5BDFBR4BX_utT2@y zxZqsD9-z!3{>mToj=xr3%qgX=rt($7Te=i0UY^s=@MYWK!Mr=6VuF53=h|gwNBsx* zw_;%?9~c=E7U!p{3P73sJUYALT;O+@y?M`_DFSB%?>+`z7JcDVRw|!;)p?CgqwR$* zGG4u#LcriVoJal#nnUDA);W>RZSNH3#re@!umC!Aqdlgg_=rP1-*63j=L5=^Gv8H< zTXjoa{auT2YZv`Z=*jEwo#UdQL65S{KD1Hxw)Nn%4TQ8iJ4&hMG#>M?epsQ1;q7lY zUt+&fGE;G>g=fC#YQii5B;-&EU~>L+#w-XM)P_Y75B3a6ocXcqljR_ErmGbnrm@!nBQmgS%QRL0Kh?3x=Vz}no#y+HA zFHU1t_vKu#yKb^|lry(OVG6&ru75axuO}?+j-Sj;#P1TVeA1P`ODu!pxwYw_iu#rS zJ~6=6oo%DMVm9O}vjfw#uekOTY|F%KsP7Gs+pkER`t& zu9C@V9Q+}i;=$2Oem^fnx(koU!zG+^LdK*P&v@7My~f_6BF&C@-RPLtDcbCq^+v~R zplIxvb);jy_Uf3=NXLKz>=>%R=oku;9rJRm>uq+P>cWm0RngUg{Y=5wG3Nuh7VoO2 zGNi}5y0L&D5{0?z$9L6%_DH-dMWsdI*om=}!7co)`&`Xb2Cq&uUg=WWft@%k!F7nG zQkiBuGALN6iV%eEca-A!vMW2|d5uc0NIwGF{09a}mbU;sHHaUp1da36h>h#xkKx@! zS1&&ds9}UZh80P!_9}yyH;ltqzTSAUvTG@uLS>rXm`TCt_$fjBNV03V${>v?tr{iC`2iIHH$V5GINrZ%_ogf}gmL9=i zj^l~M2>9W7cm?y!TuThE+`!eGQ8-5*MEx9+YZ(Q6ldBAD!mNako2?$<)#`C-s}s#R zJweuA6+PB;*C16hj2bxnVzor^g0U?V*lem*44qiYZ-igLZCacc-kYDK&=}#l+Uj4cy;u6sVh^{jZrA-yrt01ki4^8h6UuRv*Cl- zelM-<_=YK37~3nrWM$EWH_CARYPl->Tq`$){Y=^LDFoMTD1mhoTr6_$5LlVbTrKqT z!904ss}eg;Ik5+m`-L^sD}?aw2gg)pu_0#1%MOfsEG$hc$F=${WATw3!ZZ66HnPCt zlkbV{o)B2?Z2|XJQjcILZ781-;R)r_C7F6P%xnuH)sP-WqBR8~kH5ANZ?of@i zeK)4FrJq||e<}`J-iHVr>ML;7{g6FZS`FPL-^-3YL!3bzp4r;f5k3+t97;Zgy%#*_ z?z^1N0{gIq)D#>K_z87jbQM@Gf2fOS)I2YQdo!YZsZEnA+3cI-WZSoTC=9X|~C1pUJw!?q5vJWw=<35twr!}1lB87I~m9bNCRa-pOhIJ7ss zTeS{LpkTDETqr-A1#^MI;HAoV@6uOQr{RU$SVJlkqrdx)eT`j6+Ni=<{#R#Lj@~Mi zPi|1yiM6KGp4m)q5o)Gk^sG>vz_Gv5n{|4)0?aee9L$+im6KV+P(FNAVOLeTFq=Hb z4N_vZoWfT=5fLt;qnS+pF9?yz|H}a!lPBGy^sqQ(zv78KNC8xe&tgx}Hk?>DrfaN! z^5Qw&TuXHQd1(5EF*$c2;tPAYvf`9&rJ+xx98TQVB0FxG(1!`=*TpYPK*KLgK*#nK z86kYx4O2MoQIh`B*o44Z1y4Z==c56+Z6%qpbpi$?8x0wv0j^Aav|gOk)AgVRKX_^Q z*t1<~6}%@Dy(9j7B;>yje_RDG1eYl@VV*=zN5nK&0le{qKqN*J1aZMz;*ZcS64e&* zV^2y*Gv1(=>mD-IW|lEt%~Qd`7WVOF3Z__i=`)2zA;K8|JI`&sU9;E^!hUQsd|w+g z(%%yZ;Di@uj%?r@X%+vCwq`WT80n-9)SYna=lQS)ZaGa3)d z7?jr}chHc=jhp9X41!mrL`IsIJm%WRE)&5rQ+!E4Fl3Fp!KrSM={3ID-J!4RiF->m zR(|R7C*k75-ElnmM`s0YbuiubYpuE%2! z6aN^FFSrC_UWDJJP$T>R0O5D8VH2UcA^h=_LIi}ALIkb=m5LS>+yuU73XP9=&l}|G zt@jDzxvxE$#@oCb9If{X?v`L^jQDDIh%g4CWCu_Axk8rwboW>x2SddkCPT$V zAb`VA8F9~{QVgMD(X#ysees@!@)J{CkMiRqTtWOwVQzVTAk>c{T1`8Jl zpwF;yafph*g#i{W_JxVUMJDe);@;*J!-0_zRKHQZP(VfmeD<$8Yc#+5RBoN(_M=>N zbjvMh8rCty52$10@~bO5kPnKRiMwuD*)91>M_eS&elE9yzpP%>aNgbSf^+BLB#iOt z8nwB4ns_@HY8(}I1i*9`YR?}sc=X8JVWZ|1GzbUHqTaD%RZcjcYd zD<6pWCjM27RBFmc z;e2tCtE(TqOM8x)VL1u{UJUNknf>6c7*o>v5vjSnPxSs2E|-aIgas|~;`kRoIa^mK z@TVWkr%JogYd)m!6R&ADGc@EcyoT}?JWXD5rSZy#>Q#j6_OOz69sc!H(p+Sw0#D#d z3l#CpDhav#muFo|{5}QVx#x+c7@~VETrEDF$m=fWdN<+uDn>U6a{K zFEXPZc+8N0m7-BTU_52H+Z2mB0ITSD=_S`h78gN!*=$4k2-$|}6pcCn13F@P(tKA< zexV4qv9Q2F`G8U6S6<*6&mN)zO!--c{5}+o@&Pl4yPC406oc{sT^1$o5uhEuZHnW*G??Os~o9=n9x%#jb z6zs)mEOUG~i=SNNg5dwmn>EQ)YE;`Ot5HJRLAh{Igqg1G0*v!cGln&^;^d{S$24}< zhaAgiry^vE`V>W*Rhqv54dpYwd!`H?m|F(xw;-%wN#Q#wheyiv(}D;rqv^j#@KqnU zI_jVVv&vM7kyWN3QbMa639WEXXqaBX5?Uh)#uP@~NLfPjWx({xgWe1lAQUCE>P+Fl zb*brf<8lMVIPB^#-gYf#k5i#uU2WXbsBoT`HWn_Wf^?>S90tr0*!3KRf6W5gL@Ez^ z61!$xr0kk;6m53R0ziw(=F1d~{WCA}4*m1GSO3h8l>Gymuz#o~qkkxfRMNhrO0j>i zlJ+$PWB+^^DJy9T16I<$r?i+`16r)4DYPgY`m$;`rFYB5LYhMF)sx1KRcQwHBo@+w z%6f+xHY1XPg{lZa2)jo3l1Q1Ew2sU9(tH_ELwkP= zSV&VCyu1-9lSRJXz(QI-D%0%L!4!;+?^jk9(i8?SRndM_nFC==qYy-QJI#mdv#<)>?9?=S>DDT~Q=9j2%~ zU$|Q9%gV|${xT{C*2P`)Z_3K?wUc6Sd~Lsz@PlQQ`OgAEx$U>=_c28}II0%!{$THV zU?dJZ+B##{QIH&VUvF?NVt-Owu!oNChtJkoqDUBH5B&+Ezl3FvF9U{Kj5mV?2<5b> zaCr5p@kw{ZI2=qnHn}#j`cxIfeG4Hemhp#CBH)UT2kLz}ZL8iaIN$rFGURR0gVO zv%er5zGGmPSt+Y5v%d0;s~%fGIo-Oj=P85H^Asd|e(?_1_w0Qt!|R!29EespOaG6y z=YWr*=$_jPEt!x82@ujCK!A`Q5(0rBNDn0RPC^qR9dfsKrvfS+1teimK|ryfqTr{V ziYUk@b^)|85Wm0ve#>QMUVm@Oo0;7tS0+z-y?SiOvjEA5 zG)rp1KYpjW9s4&$gyuLD$zzXJ4`+vHjW9=yJ|MP`(R?6M>iwxmvH97Q_xx>Jeebs8 z)uV0v!@|5sZ~C$72{yi_Q~hW@@crrxcABC@VqZk^^(R0lU(gz~$X6$-2eRL3u~3yA z8uDJt?n^d8pnHArCJcW~qRhSzB*wf|J;VrrBu5i z5M}RFS6Tp|iu+grOn$dI!3Y2>rSb>gt?tA|Q2@bGgiw!fylI>f%D?-velsI9@`vPR zQfME4xb0_9G6JAg+*SZ5-me~Jq+oIZZ72Ct-34HVpUuTL5fcI&#o&v2+>X_NVg7@g z=jT4G9%SR||80)jJy9}l_fSsJgyP@?J4}m(zHEE=vX`q{h12#lcHg1^pd#&z1d!2# zm%@@W5?@kKjaKrj)$t};cpl7v0?&gbd|K^be^3;r*iDjwWq`ykHSs@?1PhBM&W+ic ztkfxCy!zSGW&GIE$`(qqXg>KfI6V_b8{#M^PV^};(Lsj6lxUegfprpJ5I3+d33YA5 zFD#m$`y8^01{O@6wAR$Xyl(9Kql}$&*_YKbY^;oOil)HH?%-%?@xinjC%f>1fJk1l zZ*BvAazl1+`^;$A2cK?F;Saq@mLxNR3Az{rg>8Gg5Om)O)`lndX%L}IiIx*zpo;x4 zrKK)7Gn(7MDwg~tBZ{QCjDtIr8SU3V+=$1-f%ffr0|$N!+6M3q zAHY!;5v_3WLU|w7ea|oMrMwg_H|~!Jor*&j|246s9lH2l6qBWBKL7Ij>i3mMIFuxg zMnuOD=SH6uMs=0 zIl*X@#1MD6-quY48rYSZX$(@vK#f>`jSIj&Dj4=@61$juiesjBKq}{l2JB!W?KnjP zf|{sO9H&t1Ktveh`)J)5c#5Ky0Lk-Um>0N36cc0QdH?#g`bv9<7s~mhp76=5Cn+GR z>9Jsf9M%Py`B!yTQ@#K1Ap5g@AlZY1b7-?#5 zoP%hn_W)Dks?8Le<34tt;t^dd>BzLNy+eE4+Mj;_ZdlwO!w)^Ts+sq7#qpIbB%-Ov z@XvRou_*ZE&o`j#;Z4yEqXRO}@x0g1)%n7P@6>m6CiK#!=#885xCV}PtT9E2k`Eak zlE<=XF-|`K;FkuDwnhMq_dyLEMXWakz^(-VPBwImwE)1${YW!_9axc*dm~Rq^=j7S6$%^~42aW?`Q>xyv_tcad#F3W-k_Ftxl4`=>VG z-A|QeaC@xd1!iw%j2Lap;V+^vXdu{I_dg+jF3vHLMN^`9UB`l6Ra7%=-{EX?$D=Hb z!eCarWa8E7wq|n6A-S2@awuaZv=t_|yBk_G<5S}uQ`i{V43RCZNE68LX3Pv)i%3QQ z60wW0nATv19BME_E*WUXTiu;7h9@?3_;U2x2Eu9$_3ab^qJ|o=fxwsElaR`G(`v*I z)qeaU17CJFvUzNvaZp)$0=_BW2}r{2B3Q(j zfd8FxB5%^b{^VHxaBIiM>?6_Iko<)`%KrS=OB3t!zm8Xg@%1H+681T5g`Lnx&G?>F zI2UGs$N2#4H7;sc>#*UQq7UAzw6y;Mtl`GvZxjvALU88%6KK~m$6Y^G&YVF&y7Bm% z5QuJk0yEgb8JNM@S3uMEDGS*7j!bj(Y!KDYBC3~119e?!?=s1ZjF0Zpl$wLPFilq--& zn|MDXig9H4ttq)j#R?p~ZS2DUn3Jzh470_`_1C+!7S>yzd{IZ1MvKvs$#MLJ z?vCyj09nP3)~v`1U{A55j}gEWq1w$818*3i*Yt4Q$%av!$lIWvjvj0rEf&0mh=x3A z)S^@i5Uajzwi$ymEKYVO29hRbwUoPoS^r{Ac`H^f$KL=Gh>HtzuW-PICpS^j5~rKu zq&sY))tGpHI8IBv@1s>15x9KY6Bi_U_s7Z1`#a5K=6yGuz7YrLcT+qNd4rS_KwPY> z-l8?=aL0oo|3o^R0?2V?1xV$}%77j8rF}pQ*FDEZB$BO2gM%xcMd1GBm#ZU9#no1mH_@>4@$%y*jzX@2NvF8$C! zt1&H8M-E&Ho-ceT;zu=w8d-G$)9O7V$C+ z%d;H?eEdogLW7 z$<-=HUn78tly(ItkQCmFDWN!nPJ{=xloR2>v^9ni-pB9HQznCxi1!r+#?q*k`Z~ls z8BUf7f|8cGLs0~~Z-RKkZWfP7FO z0_@w?nGq-pTk^3B9b=3DCSJ8WJqBL=!;0-O5>mTlNGbbat|Q)wf{7l({MjE$TB7G^ z$`K}dtebPV))Vh-S?xGyQ(gx1!QGxD>z3fZxE@UBJKHERe7(~#ioHvzh}9gKY{_rad&FWhZRHO*u(w+Q zM;66RkrkoZO%W*2E-JMVgp$SMh{%c^hKGJpNmQ=J3UW~i8)M>3G_E=z9)dbvR1!bL zMWv$u!L5uYt&K;4AvoaFV!bLn%n0d|dR2clmDiSp%usuKt!>64Hd+XS3Mh}4S5Zcg z5~vd@SvN29AKqG0-VfuYwi#i3X}3W$dG{5LsY9&jT>cA*d3L&U0)J+Wqk(tmN=Jo_ zRTDAfos`9*gl0@A2QmkZw?LMI58*A4l@tX|QScVX4UmwBTOgPB`dvStO~NU8m=yI% zn_{VxM=xLZpp%K42Ua)X=hiLi=uKSbh*0bgf^>KhMEt;uAj!jehVt9iJNohg>m7TQ zH{$tk>mBLrO)9+@BQ}XQSRISUSuQ^}tV5LjV}U~4=t#VBP#90%=x|-ejSkABcB|u6 zz*};>e-VQpx_64sIp)0Ch%W&{zWWu*k$8ElW6mbWr>uS}0t|iMV7~r#$K~9M2;i`> zt(B1|_W1|DwTIdd3-z9AS?;}ZGi3il(t!tlJl&beGAK8Gb#??q;)P(S&carx&TRX4 zfMluXsyM#pME?Yy)zR6MADuKMif`EHxSacp0sJ>o+O^`VZ*}Cdc9bhr@IX;QqofU$ zHMx~kFnnVzoKO7qbRMs0*SHCv|8#NOs-^GioG@UuzDQ?7Xe%=Wu zcD6fO{!iRnS<;FR+u_J!ODI36{JTU6RsJ zD6K}+P=hke^nwf%X*J@9YJng0Eba0{VM<(3FC5o=E>ACUwzEfpPkuEkEt%1#av$wS z(Z7lJ(5)XnD3pIv;%pxct+g$*wSv9?w&f2$YaFbhTIL>aQY>V!QH(Kq8 zVmH%bToeCyzvET5ofeDMi9h>0J?+{Gh1B*P{zaj1Du!oF_KIShwEx_S>^m%Z#?j8k zUJro5gI_Nv$_(Gjv|7LA@C8(hT<}-48v2}R%4doCSgm}YD3d>H6Xng5lZi5?2iP&E zN3m;pURoscyz7QK*R$v(kijulf| zUZ-RSU!=R8I(hA;%%~*(=Sz+R3z|uzovW1_llc9kodeklYm(Pfk{cvR1xyJ;{-_u$ zTDY@nkk6zQjV-Db?oSmcFF=@!4D`RjJ76n>A9&62JbRY%hCYq{xj#wz=YCo(GtjYP z$MMK(A^lbs=4{2!iLVQ6(6dFo1f!6c|0!5^d;0=|n$@H)-&NW30l)o2Y4SGCD=N$!J=QZn7vvbCYUX<#!Wh zK?>=bm!uf|WKN3olQ|R?{e%NS;U~A!8sR5OEk$@&iof$fM*&3AQ4AP$M*%s|QK%f6 zqtGHbw%+}%<4N`cWdNN8uiG7^#b}imQsk{c0{{%hL)j_X7RO;|i?;?1ZS`UG0tN6P z$7_QI0F%kJ*FYNNz)2Hc8w_hroK=@2h87D_;i920El#fu1|qb^lCf8^{30|RpU~rhNwT$S!ln46wV3o4TI<4BU0iR{r9xm-&feq63oB1miw+^7KbP2C2H*rgYXBwVX$c% z?G1ymZHRkgKM~zV?|8)S?l+EnF+Slt1_2!RSq*IBI|d7V2#Y8}@ybH@3X4C_CN^|U z_;8kCzz3$&W*K{1%U*}-TJ}2O#v+_$um;X)jF!s@!nOv^GB%rXCA=nWtUQoG5sM+89d!hG#!p1h5Fl!zEO>Lx`+(fIt8y3PEdo&u@LAaQ=89 z0NHmG8GI=O46Dx{0UKJ@z@u(iP!EnLR6W{wLW|^hGCRt7hNYwuN5$>{KOo$J77N3M zh=$x&wRk`PNGa=P-8%N_#ccvw!%M}*i@8h9AWBNAJQZW$$kg9xJH`fC_I-16EZ@@1 zxtmR=IAt#?J|;Ye7Gu{oJ(d3z>)dDrFp<)Bbq%DjYb&$$Z2-Zyk* zE?bi-UABf+3%A<*dv-hfgQ;4N@er*>uev`~k8tIQRP5R0D$+BoO|(9P)An?0c!k~B zDx8v`_xE0>__5n9`3`P#0C)QRJqj!D@6BrEY{yPgP@FIT6#5wj(0UMK5%wSx62Jpmby$!d#PkJxkqOeo;Jp$>pT@YuYqybG}h z*1-NPazm=e&ziPHzK~$$xJ7Q=Tx^lI@`k25BkVD?3ckr&yd_fZKQC=7x0!>0u4(V= zA7{vHXL|d5;KMMc)^WooFw>_h`#e8_fb|8>1U4}VSdpkciOju(+Jc%~bDaW^yN#4%1 z8nHkPCV9Kj8cf1M4JLVkTRkB@!qiO5^pgE?KCMCgP-DQ4N$S<6W*YwG z8h%cz5kJ%*{<~=n;)fa^{_;ZSIQERGg@*bm4fU(E8c{VBDnPEX~v`jpI$5_R_{(Xe|no+Fs}= zmY-YMpe^e|tC0p&`)T;!Ar;KYcly)qhr#7B-Aht^uUeW74L{JEm0 zDl(ieYy5UgKCh@jR3gNXG4uWa2@U95#AjgY-hfr9Y$C~?aK9O6HEn_`&1bG~o@Fa& zF{bBrNG&~2A+_}UZ4?TJo4xkZurN`IUE=+1Np`;KN+-M*@UZ}piUy@&w)Qyu^Mo8H z?n>!vn%COw}c?UVFKySqNSrUYq_xdpS>ifr9Can)B`DqUHjv7Q-LM z!{y&m)PDd<@!oVWoLtpN+}f%J{=ijDvovjv5|;+z`+6#5G$xHOnv&*c6qhvskINbZ zqqwZ;>ciMA0Am5gcx50)ocKhab zXfe)nW~Ipu3IhPnEb&O@4HN)pmH>bo6oCLZ>#a2d*r6fEcWD_QKqTtm+6KiwVo_~_ zVh=@#K7j9p^a57gpm>tjqZ8oOtjDO54+GC)gF-j~xo4wCiMA84Crxfp1k%I}iVxD{ z2E|9#B;TVXzmy~uaB1A2u%d+<6b4!*t!Qjft&k0hhuX`DcC@EyxK#4AM|{IP&Ro7O=FZ5oBq7nj3)I7iTW z46(8fB*Z3JqSaQHfLpFE9qAMrOoa|2APx%so6H6PCJ{ET;XdT&|c=}S2;O>M035cYvKO#E#-2~Il)22JC zZ0wW?wYHS|Nb{4AXf>vte*ukS;^8-1g#m}X?oU+h^*hSM19(4fPh8M^;{t^R-x#DM zbd+i5q>jXI(JSIR60hjc(eG@Sb_PUzjUk*3l=($+Snr|!o7Lf_lv@8U_bGt?1yMAI?T(!7COwUv18!phAl#Lfnu}4>3?zAOiWL3kOFi#NEx`) zofXuBy&F}J=B%`cFQb!*Ilnp=s&$9(%03MI^7dP+p&xe>7cNX*^C_qK7?_P8|23 z^al!z;$|pQcxPQE8(bb)<;)i%VbC=0EMu`Tg~C|Gt*hM5 za+Q})tMxF?CYY6;olUDeiCYGwtue1$>4bNx^k|!?bk@{A)`WGMGLj&)P4%;i+6Q#F z&NHxz>pYJS;k5w>T@>NU0E8If284QqgSyemRNb1DjYVkXt=pZm7%i7m$ebO{4E8W( zON2!%Z3yP_t@V%YaHd)y!I{k=Gm_@kNTBa1pVKhWhz6w6W}@ee9>?q7<-CV|VZyCB z+^3*%F|YWPR-?ngtKP=i{{*#XpNVfBmSOg|--*_h&;x)EKbVMSuFoFZhR0HA2G3*=e_YE=Vp7z zF(~ItAA@QGyWse z_(~0=ApHA7hFqiHFSY$4m2xBYhmwLc*RrV{`SAzJV!RaxoJn?NMW(z3cf)E?j|o#^n1=(W4S@}556~_{k(G_KQMK8 z27l?W^8xk*)e%I3wn6kLDb}KK-9ICGn7u%7$_*k5Dpf~7Gq^!id&KEvA5j=c!fV?E z?`6s@owwlM#~rboTPF`-|Fv#<-q2LLTM?c`f=)-@=HRj&;nt_T*6D{{X zf)tV0FP4j9?8odALr`MT4^<^_OYV(;oKIZj?pt9=vC(e{;DFszAFysK$F_O8pb z^dZ2uiJyAFnQsT_;OHMdHq)_^V zvq83;a5l;YE08?^ZCfEETi2Hooh>JvLScAVO5)bG72prtR_K_m%{J3bD5oiD6kC^U zIZXjZak>fcINdZbiqp*@K8$4n7za~~;{q|_eA9qYpKpS`(acoenwgD7Xy$f@9f^#V z>)%0T*Hga4z!pmz68GNum$SDK2@YAf>rg|Hh#@N&l8--jW?3M?orrrb70IigIQtrr zpikg7!&9Jd+-7*1Vgpap?m;|3m3{=2F18tjQ~0+T607tMLEGh^8j%|gh7zQnByL1} zoGmvTPFfTHkP<&Fi3?{J8x9F|Q^ov}fvP@DKWvOtdGR48hXjQls1Z4``-z~{7^qnm z7auz1XmQ(_HpjT_kVB%YAjcmMxKeDJBLX)ej6$K7@ZQ5vAJ%eOk6247*725DwWLj3 zK!W!k7QqkfF{=ZxEuh$zNNjqS2~1*$0Vciszz|D52p{mtkr74}rA3&r2qO&dJVjNT|1NQB2?ZPPbp{d&S@$L;*oWq7eSEnI?KGv7VjQnC zi+wyG#6I4@EcWq>d>E?(FfO1Nmjq%&2Q*;x_wliR5DO5c<6`bt@NBV|`x*jGtT%<( z@GV1oDGM0(xZvC)imBPWZGLyQv?(v-@}7Uds^=vNhK75da(+VReDa?#&(ixUZ8zi) zg}?t#XJ=+8(KocerVVkpp$!R>INZ=W84Nhw{6>*rMA3!>2@W^3jzm;=9yIjL8tOu_ z!KU^$iUR|m_LiZfOGR?KqE-eVN#PyrYDboBM$(+Jtc&AINzht3VLNww+XmpD=n{J% zsK6y`Lu8QSOL7b|TE8P`E4=ZPyjbDI1#R9oE8csyzIv;o%*x}-4#F3(t()?5jnw{e zbn@+!Laa=1LAO*;#Rc6m@e3Dp@Jqf}#_Gf$%&^6aWkS-b7RGZHs$Ln3tuzbz@K1+J z*5bV~)8n=G%0Pkm0&&I~L1iU8{9hmzCFBdlqOJ-L7%qfk8u;oM@dLkcy>0!V%X}vU zzAmLny77uXTpoct!tj-2@qpt?^{Vhx6RdOURXv)CFOb2rA{)aWY{NfoqON1l2^MOxmO0udftN<9{gl`8o(^!VgBPcd`q#7`vo%i9Nz>dE#V5uY^}cExwJRp-c-=!Mv$W)wK!> z&nMF32aG?ARfn@?v{>hMNu1h43D1|@z8R;gO51$NZFs)7s<|3&Ga`f@7q&(}P(%33 zE!3#k#ssUT*s53a`1uxUkCcX@4o{*|F7;O6E_dg(2JPM_U53;xfpsg_#Q3ErQi7P3d7o!`s-yoKk~ zR95{A{ynj#N~3*+nRbq(UA0eO5VWfv5f3xr9G$Q@g&5CoOIF(`Ti}x#$!fN8T|WOl zSzWHY32OAN?#s(MsEIMUe`@c)Jf1HvBld5vu3?{1jzu(yAkxq}+(lQG^P z<6(o0{8R_EX}Ccu=_d&t)son2L|cr08en1sPUf3Bs$+r$v8|K3g;i6zj!(I+G@>4g4B=eX|^*Xkel*>dE-kG?e3;!ll&9VX;kfjb`+k%39 zpQZlHC^ar7;G1_gYG>cq1zx-INKAY7FA6Ot;COA}Vf3ORex&<@QT7AScEOv`gXPPLcW?x zBF|5mZ^~ESFwy~Qm6Ab6Yf5c_&gDJVMg6Z$`9t%rte(-y1gb!aW`KgoZN`^$RjY%! zL1>ZsCTm_m+#B7X87U!dP})tcZ)fdDB^rh=>7ibyWEJqgdZ@)LhgJ*2@V)J$vXqPh zx#&X6bT5FVLG9dZer{`2IREzNDWQDuk#{or!BdT*h|YRTk=hmT%_ZtUBVm)yw5F7C zzF#BKnQFSYNZK5A3u7Vzw;Y9}_|B+oQuHW5W}7l`#^ zYhLkKR3a~F>?$Pu50(^W7Vyda)HQs4Yc*7Xxf0G6V*9IO*anJQpDjGoU+t*?)B+CX zYdQetHambyRdq{I`0xO=s}WVRfoebYh&8GQ2dV{rRKE^XXDiR?sBRpjcJZS!DOBS< zKs&tb%jyvCfWhh-n{u*%M?JMTlYL5Q>n8u0=*j-grI~z)RN4Cle9}<$Fngb3HgTvm z4dBrGV43>0jWsGHUJd<6GC%n;d>i%HFg1xq(mK>TtAzib>bGQ@>$Y3hdKt^aFu<1I$2rnIMzeW(m z6~eGU)#yDY$Npqlh1yPGXDE*-#QgTKd#1tQYM_Q4PRew3B#Y}xfp+dZ zq6)uQa8|5fS)4RbS|F;lHk2Ixpn&Q!CFoT6UN-D^*>Pwd)|&u<%&%qCOB zqI1E4etcJA+F8&R#@u4=sZwK!(R7{cs#5OAJTG0Wwla4L2AtTC=Yez*H?ot8QP$QDt?U# zDC_fqsya_miuit2?ao4qh%f6T>lG2tXi}t2H0JM!j^}4jWv1{+5hoh)h<7vL zP1hFG`@3Py7fAhJ(%ky|5jPB?3`Ww*>(w<$)|m*O?@Z&T z?uzQnBMaRj{BZsE)A>y|z-;k$DzONzq%EXG4=x{;h4Ss$Q*Kn>WxGjB`3_;-R~Ac7 zDKd`VK>lR8x>b3)h+n%xb+KnC20d2muT@*H=V_fGOt-IuCBJFdHekXCEn21CtvpkN zxhi5Ihh*$2un-){jP(*Ky0kpGttXz}yBaY3w}=l}qh7;)A`MBmf~@{Y>kO<~%`gm@ ze4R-Vk2ugY-d^>eB0hVaI#~HBfN`f}{KCZ<_b$CjU8u0kZXl2^^N536(gt-KE1<>t z;KnwpYn04xayGqZqq<+|*G-OVP$=?!M2D(mrBk;$bqG1%afNhc)s2^IR&$IrP$Anl!tm2Uwne=u*l;)IR&_Rek#d3~-Seb` zxaWjX>ss-lx2gYTZ<7Y*7+fkDp*Un$c*pJPUiKMG}-hvBMNA-4fa4>b8egH=Q@Ewp$Na{|!Sm=aj-Kd5# z%+YFz7he9fJKp=k4z)_*Ma$*t5tG=H?o!iPK1Bcm!7GC93=dU0cjuqqr82CFEHr52togdt@y)p0h#QI@y+H^jLG}ea6(O2@aanXa6S=}+J z?47&AF8C(}iHOe*;>c9dgZHW#{N*)L2;>&CM8IwSeQG;DaTIS|&GsU8lZm)rJa58o zHNxhHFj-}#v`Q-i?Y;AU^^DE_C@@xDe@#prt)qp*ler^Pzeg2&W8N@}rOTWotwbt>H@Sy3pu-$Z?ET^~pnPD~Du zo&k&HpC4BzcbEt?DyysNi9~OlcxZtF^5SZ2qeM_vEarf%;7g)vr&w!i^T`L*Ikxw` zgPu@r_8tzwyGuN-5sxL}alLpf7mroqu~s~87LSeMQ6nB)JZ>xIYxb*s%d5p7s(5(B z<2vzJCLTA6$4c>7BOW)2#|H7(Bp#c^<5qY~Ul+Qt(mAzq{^D8l=E818F;96~jUj{6 zW+~jvOB62R{SZ(Pyt4~7Dbj|li`{f+T8uaMfLdP}{VzfDG4b$<$CKi*Upx+o$Ft&b zNIVXU$MfRxf_S_n9_~YFG?(dV_-*buFgT-Gv zsP0nE7V|}i)ZWqG3asbgF}(^2{18Ckpd@hek_mX9d`?YP@*DR6%uGBY#3Ncfnu$kq z@rW0XMDa+0heo_@53_Avki>t#1mYW?S1T_vqg~E-ewG-+Q;)!owe_A|L;h!*#4h~0 ztwo^ANakJC23Uy#I`_R@i-$EIbVrKruZ{mJc@)k1>!MJB(!IXN3_Va zMTp03VkyVP<9_kyz2fnzc)TPYnIc7zE*>MrW0-huO2Yyq|;Lp+CzN11rkib>=x z;&DR!c}zT>6NB-y@K_g`wzzunyoIwWXLqO>QngN$uNIGo#p6NoXdybmSn+5e9(M7_ z5!`2r$0G6PeDQcwJdSs7xTIcHOW`eX;?Y+;dc#BiUW-u9YE;ZBRLUw;$ZAx^YE;B( zRKjXhz$z3TT7rD5Q0P@C@G2B`70S5^Wn7K&t*Tlggj-!z3f!$ifmWd~t5A^DD8woh zU^Ozo3K?I8Jg-82$!BEHhPbhdHvCi=5^dN{7!qxWizBolZUIFUEP)!_20$ZXN&}6! z({IGX;ujjRov<1jaj@Tr3&k(AVVd8D1H~`2AuiF-hA)dUVZ$n%`hFrT{2n}JS2Y)f z(c&>gJo<|V8ubS83ype@_=QHrSsWVm4^f6j#XcL2TBR8kIv(*0Z5rjXX;pEtIGHe# zXTGXF#U=}P@GT2qn@+niG@*w${GDwFruezXid%4YRBbF`W$SBdEBggl^e+1`wlkme zDy)Uv)+n4B3XsMEMJFgd^ko0kMj}*UTj@c*af#B8?>?$t&u;T0tmz>SNpMmE{Nf>r zNtb^};*DeKBMRG1kjja`z7y&SwvQI;L-NWu)tSof9{l{9>a)tB9&#}Re`6NZfJ)4* z67LgOaCp_e18fk1M}3l*!Ebt7oxnb%O~-;bo6DXvlt}ZSW3F==(rE zs;AFqz&;{NZTpR9Kdu&v!x#0liXq5gqZ#*CefGuEW*;i^VYfEt=JAV^d zq(s*JL-h+*5P%G2%k3#;JJy3W3ZqRh^%MDhqe}arp5WQ7EAm+>t(UoYDbL4*9wo{GrVstBLx5OpI7WP~~d;I#)ru_IP>K$wj#f@2hXxHmk zwR@*a*Ig7(Nw(!D>jznk2A6ZZ`+D$weOJWuou||%*-ASJ;DbV4JRBzl&dJk=kodogW z-Jh!uvsWoAx(~mo*%^KKMcM|%{`Cv>Ap4jWqu2l?Y+&%;=SizTHsgr@eotD;wMu~h z){U&WL{|8<`T+|mApz!>%T|x?_(W|gL$e-jg8uv1X-Hi*FX6Fg)O^;GR{Pyoi7kbKs?v*LW$2m>WJ&%l!lrnKiTo>e>XZ@*JpD3eNfqjQj1uAmKdc_&bL zt2B8ru?)M;p?uJJHG>4Lc6w(-314?!9cCmhI(5>^YE1(?^r@bF{P$|9rB^3h+^f%h zuhuY5Wzns_iCBLVZGukS?gv<8?xw|Pm`&0!2B+RYS_L|F5^?GSq-7ANt{YjhM3(WR zdVn1bKqfk6+bcw@S7;M->g33b`uyhg-NxAe11@N5RGiB`r;T(|pQ5JzPBS$m<$Ff! zPOTlALXs`*KutRJEx)KESp8lk=1_O9NZqxjPHinN-~OxG zeuK0`ds0r49Z9|T^}nkHjDm@iO4J#(x3vB(XKZ_MXME)k^;TA9W@~d)P8fgW3$?8R zgFm`ngTK@o{?wO{q|&AXJmxF4hr#6r5G;W%*WT}P#R7%(=wCOAG>M|Q&2@~eBE%#z z;b?sJY1mN~9Ext_+!rJ2^Yw~rI&XSLjp7k}TPHr~j1&QUY8rp)jM^REdfPpUH~dD8 z;1AV@b8Y%@72TyeNIl-zOH3_V@O!_3U7-6zToruaw?rX#eXI5`4DNMH120j!l)>Gb z(!h1KjnDl~qa+0L?~n5a=RmNMscI|s8C3{M_b=!(QM%3N>k>{or}u6zzP7Pzte+5C zJu1|71q->t95m4Op-|V|dRuVSR=T=0jKo#N%&{5&D9p7e&M-yRCk1@%mre8eR_5x% z+EaR%5>F*1x@YmB;jSqxpVY~0xXwtnv>OGNJ5r;XxCSfzui(8>UGejOm8D%M_HME+Vc*KFktO~z+hx)^`2nJbz%2z{Ul?=-bZC|?)r zN@VX)PSJ>OYJ$IMs1Zgik8=$SIU^*srNqVYhxXbM`P`1j8f@D0NkjH6ZLW*_H5K_M zP2`DxWQ9ubQ7JPbo092Oz7!mIFxghCICgQty=PmvQf(}uw>bn`^yYKoUD>RKp~mQ) z*}c&_b7>R5Ft$v-)9&hq9fVTc+tN9^NCAMB5P*!aT17K~LcV!FHi(1bj$nXS8zK^S z1lK0I&aq0$sW?td))s}uERTT66yE9k7JYX))jBFQ3Qd$j2EQ4I`PPK*L`dNzA%EUhW_Rg+6HknrIqKv1a z%+f@8Im6X~h*B&?0YawEMyu!rDifz4f;gUtQ!<2W9!ug>4&k|3uJK7hhH!0BnJEvQ zyq^W~Y@zbtP+m()h~HnE?K)zApbzYi9#LBIiX2y7$Rp5_m+cuC#~;pdHDiwwKyei& zm*W__m#XF&R81w(igx;WPapo@Tvs*QLy?FHc3{)pnx5_sR}yK5i#> zab?@ThQFSTZQPNaA#jAxFDak@)lB$RT;pWIK_6+t&-(B)1+IbYGl~hPV>nX@yV4vi z0Pt6#YnutcD4mA;$61d$ZV;N<`C+ly|;Tz8R=9MTGMZ?0rXHDSO|sv|6V#lF*q7bd0^PPU6>|u35}L z0=0)-jfb=mGqE!d>ZnLMlv+hWBxe2zeHoOZz?7#}&&C5onptJ%GY& zI`(-K5pn zGl9V$aMqbN98w97QwGJXlX$dtj+sD8kG_6{Yh^HxP8#Xj7h7YwDI%{s-`7LimWI?l zTzCEwwxy2?9$puZR|MdT;*la2A&KH~ix>&ki^rY9>$i)?=i>3Hc)TRQj=)3T$Q9^T zBdS%1W;LQ%jo_;hbTxvluCDsALB0O)X_z!g9lIo?{+wU|spC2xspE72sbhzR)G^ov z^{TI~#LLhk{Sr~MqBXpCt_37;$GQfyUqv+r*$;i?Uf)lo1nO|FZ{}t1_3@p(SH$t+ z8{856Y@_!Z^N8`TIW{l9cD!q6!?=EczhytZpXizr`$M2Wq5)oSZ`4n-Hx!8L?P(nZt#06&I{>Xx^@TzN;y$dGX?_tMA+W`Sw<7 zL%y}YD^vj@I6ZU|vN~#>-FHP*Y>>M7b6gwPtprYv2EdSJPwg$Vn`sjf{ob{6UGr`1 zURsWaw$kRiHnE3kF=Q*X+x1}!Tnm(Y`|;}*xF#AwMZcP$Q*9}AWu&^$)jPIEAVxh> zfq41&HS>aq=*?W@+G7uYk8q?Vl^Z)@l6{Jllce%{{k&V8F4YzY*P8cqxiZ*=U~v3J zmuo4b3yKV)T|Tb-k{z0ThcE3Ziv` zW=W_k5hFi%LsnGm6{I=wRxy@)|G2@`(Z(uC3zm7ES7NZ`D zC(li1@6lq^!}0#S_$F5y3xJz$a=`-63gD?-?o=ZH&KUnNx3vQoJms1Py9b5{auYFt zM2?S}57)agjmSk`ld21@El;T6IAoOd%%IoOe#J6+2FYj}Awy{zwqVA6)X)%TbG~|14d~k{p z0;jrHnHwvm1NeuJy5}3)n$$EdFzyX%T2hCaCIrIDtLMKw?!L;VtS4$BhN%dI@=-jw z)m5NumlQLs3^V&8un*V4?fgJkE}|{S0K-G?aMiF^s8Vq>evy>uvB|@?yQ*S?)ZMh* zbu0Unz{y^3;@n+n>@+P#6GKCOYVNMlCJ5sIuv!X32};#JxzlyRrYHl2ht4GZ`&tVC z>}x0rT^?T~aOi8Ul?SN!xpv3ubMG$KFs02vzO19W6>opHYXD0gsDtrS%{(P6QIz0l z9zW3g;@z$%?MhD#V*75_aMqnd^m@Tlq_iE#7tCib<;$@+UMthwdO z9&yb|vaa(<@~}K5Tk(KvaWIR+ZHskmv0%YBPGgc`0V&Z%_2GSt+j_bylj?4?pP%~u zmIG`p!DOD19h4fQc+fplX-OdYBf5c-9mRl9iZqoP`-ub^k27pq#V-V2M*+ie!F1+dxpZJz3vaN*PR1JCcKq0exP^{ zOA;}eHUDU`i^z5i7z3HkzwVAxG$-`Sc-|!AS<0iy3(pMX{U3$BN5CO)u`=lBJIJ;5 zhu-qWn+DeBk*n5*dq+0jG^{>+S)gL8YgmF+_w$~#5gWwIKk>BU$Deg|W4doP9>j%m z;w}i4%3B8w2Z81>5`YKc&rQ}fnmc7)_2T)}i)*STfPQ~>Cz02(d?lptIUi22kM+2z zArCv`YLbCA)9Q44!Y`Z@yrH{K9a`%<$mkip1%E+2qXg=tXBZ4n*3ZkLH|@47Qvg>- z_oTKKiYm>pNp>bn902q65m$VOvS<+OEG`{p3DY$I=Gs?W>+Q-708_ZMJkg5Im3|nZ z-2|oeAii_9JB1i!f;5WKe~`I{DFG`2Ov7xUgFsUhi(;QCQtnw^+^rjwtgAn}a`AK( z-ojK*Tqvp2CZ+8)?iN5yz(CX1IubMsmX_H-+r-U}FJCuUIbbr_Q-Z4?2J=FlG#I{6 z>D4z}Un*~F@V-z!LE$l!pAArqS5wS*;f$JME-jwx?gdN7^L;nhHuYZrmTRfP{t%q_ z^eZNoWUIku4{JJDdf4Arqit{v zFzlr32X-|r7KUi68)CTyC@O~>Nd5Sf%ge5zNN}2eBQ3#M`!$34p#Qig8{5Kk6yIG} zKbxL5a}i+OQ1DCib)_qX_;7wY^E21SY%6KXk@7BDf|R!o=7&FbbuzZaX?ByYo)PSE zD*$mM)^GyOnkSz2rK@KL{WLOFvIXb7a_yj2Jh|s}%K0>@<}GCof-;5*?FIlUsPN+)Y!G>TYxgUzY4?>Ww-hGDEgffDPhkcWK^2(aD~V zmIQLK+bV3&PTt=}Nh~#nZM>i!r2A_Qb)@?{dKOLq6=?9jsLiAB7`4fz=BRC6T4&T+ ziy~*B)^$E*|4Vj%_jlbiSP7p*^r$7SW?z8B)eKTj5?7N-`EM6oTNnl7_%<9h0AT)2 zX<@cf-lWGSr?I_Q`jKAt2`h7nj1{j4Sr_7+^rP!-JKI3fW5^`++0=(^p~VO!t&Rj2>N)!JoFdXRy#AAfzw)a5>tLln@1MRNPl5S=ag8 z@gwPunRa(x2umSoaAiXLpM*b}B=k*`Q#N`s z8fU=tXxR@h z$)@VXk?$ft8vzF#+)a76`I~cijjd)F`&wAu7Z9ddWK)e_0}E6KC8A=z~RV5 z9A}Y{_bh3nG#KjlTU=P%hw9K~d)9+G-En!(SocvSNrU(KZ7T|oe%olM*>CF&{ong- z8zG^^%37Cns7cbnlvA<1j@DBM1Ei#lc&~?D&3%h(1-!6(OZNkfm8*whqSbQ!mads- z=1{Ss#`W8>#@A<(K$$*NE~?7~5%Z#Y0D#r8gzGDq_Bn@G!u7%>T*%DDb7o)K3a%}2 zp^K?(yGdnR%_<{Vms_REAnPK6J@Q((IS2`f+Irf{Wj8JRCI%G9PkHZ(=gDz3Exbpw z+@o#Y_tM<=*p&T1C|FnE(HMS05&$EFbaHREE3Xa>-W99}fN7QKj%JD-Vu7YcXzk9DN8g2zMk+J ziU51UnPujlu%b*xdywU{wJbP{2hp5Y?dfh|WB1gdx)YbFy7w$pw|UAdsmfuK<_=Q9 zMaN2di33K>ow~xk$fkT~(%gF#!GefQ-E0AhJ8;>GOR`p~&p|$y!*QFw?#;|z4yC@% z))Y9WFH?)>EVRfg!q=uM$>n|rT=2uqeZmCsApX}9>WKd}9^j6$DP1*qpZ|5C2+;pp zmz({sWqGiX*jh_+25DXA)8KC=34L=vF@tO;beBnX za5FM*Je0R*i1MZGs5oW(j5?Yr-cR|>C=$5dTSvJQY;gBErN28t@j2=r6#o40>$<^r zk4#%C8&hkwh!$>!zef^@U8#_-ahPq-$9NviRSCz0y@Yr zeIxEOe!k3|!^=jwBgkla$}gl3hp&^Ynd#nd`$LG^iqB74+S{_H9Ix5tSBVB@=X=BM z>c%6g++Qh8h6kTrR|de%zuLW1Nd%B!-Q!F@h@pEV6854d-*6?!L<0f&AGMHEkMv2FdPqE%?s#_v4k2A^dlzd!#+RtRX*^ zeSbUUs^L6x)OAHO=7Bh?tLDPky1GgLtgZ@yB*N+{Xb8`%L@k~G#sk1N3fVC~w_N{ofNH9B9kjw-Yw;o+-#*skbKri@DS}F#kO0oK#@dt|LUSna-A`EIy8vuXepS@Hy(s&;18NY!|%i^?eH9<+pL z1kS9s@_xS79SI575m2rfOuU0>1M5IbbVjFsSyL7+T7#;@?SL@_{NR^0`S+&Z{GasB334S6u@2k=xwY+t}ljjBbTz48OwFYI>D4FE{xb8g<#iCt#}5chyP zF90BgfA?ZVBfj(jcMjWN1r`3FJIw?IZkeikn0BO=S|7V5-;vt;Mw*Y*>ag08S_&(V z)PB0h{k5VzF_Ijq)uFT_wFW52_|gjl27f0I#0F77GR2p`kodYks#4W0!NP2 zUQLV9#L!S4sWn1?*+}2EMhZg-O4VQcjQcN}as#S8()aXjh0=iEOj%6<)jFHP_SMS#n-U8BtF z-L0eQT<=;-ArCrO*ZCxTfE=|A;t6Rlx^H9OQbVC9oF*ki1KBURt5U7&d^EjAt%}2Q zwoqRA1f))UDTIcj2}+YvL!eG770=E!dBuIy)<%N+RGLh|QKf9OS*0PPgROwAxkqhY zM6yG`m&INkN(hSyDq$IAQAkL3=}M)9fxC2}N8KkH+vW*O*?j4SwlNLDE#M7E2AauAE0dVK?YQb_R{gz81d#pNKx*yJvO&|&7C#BVt zy94}rTiqtR&1|x|x9TixR$5Qv?(`^`VzrJF&BCQUOb0If2Rv;p`T0X6*5k$mkM*Wd zr^mYY%D>#V+Th#M2=5E_EDDdo-fE0F*qe^2v;VY~L~aUL*ZHIzXeFgM4`-_|N;#2o zY3k9&8KpdH_jIt?Tt+D!6dt9V5+LQcF_+P$wXIvgQ6>R>3Auoz9&ysU#+Y|KcK9Xq z!TS<=XBv9?a3vJJUGqyLPa-cHP}_zlgnCl>wop$pf2u$H8Q#CPBY&`8Z6ZGq>WSqC z2Gq6|LEQUqgJsyB7D}xn;3sG<{-op=X3O&j)?Nko_B>5_t0tc2e8!;KB%adwns~lU z{A$KL2|Ts5wk@B|JS{?JR?fX@`h>~zXU*k@2G!nnnQ(c$EYi(vub~6*r6~_}vFv1~ z2Jrf4X>C!&)XKS2r_Y|ecwyz-MKh)^AA`YbiQLx;xr_KY*T<}A4cGs^rid_y@dZ>r>P%!`&>CEm&u-)C!G5E~K7jhEp3x=%Lz1lY z0gb0mg=(}H5Pd>0=^pKcB;j})OBqK5L^28EBwq=%2Kh>9?@4J`TV=P^t5=Vgz#lA^ zw(&tf-QF{l8KHs6rn=%D)R=5BPG&K!8HCtU3wUzHq)th7cLERKdHq=HM&1`Yc)qf+ z3d$%7^HC>H5t~Vik^hQud||pL(*ywgC+hrrEewAt6SV`T*v5$(p8Z`v&bZWFC;0Mp zJ*KW}HPq={m-o92&v2V^pN7EKcil}9;3Q__IP+9(^|=41soE<-PTvj&-o$x>>e@0;vtDnsWtN%!gh1ENXhQKqvXvm(N znOtLd%}^kkSG#x$S)=jh&^bGfPcHCm4Mw-66~KW)PpSm~-oZ(?0uXQQBhL7)r4Eeg zKpGl#j@G{xdA?>tDOHokzqE3Fe4)p@?w&RZn@C}CzvQ2@lC5P z-s;rNp5{uu3790dO2d@ewc~Vu2p(I7q*ZCxUdepnS5eiSZ|JkTA=57*evoIg9nvsxK9!F+Z@AN_nc2y_gGhI+OsRR980IZNBb z`-t+WY)_)ah2-87aQzXczv`f~Xxd7t3b0B?VBBDpzn@-dsIhZt>6K)08u9N03^Cm< zX*v=tJEh)(1WRsC;gqbrF4MB7be)zrZR(85tm)IHKIR{(qLln0Ls;PwNFG|VKH>aSWmo7iJKUFG1S!z zLl#5J#zU9%*R?=@`FKwsr9Get7DgpLH2l;++Qkuvt2facNT-9D?Mt)S{(d0&ZmL{b zAYJ(n0;zH$_JJgjCQ1DV38Wc0U8d)B&9BUX5X#HyGPQ7e?u;2TvZiMhW*6pUR1L+sb&%zP4#${!*ztq7>%WazHs3XF5UPm(>v~$GTjFfJWvh<1j@ZZ%(BaDmcJh-<$k+d$}+L)@5Tw4;w16~!#%x7 zn8Z&)R|pa&(;yb|yA*WEnL2Go-qfrvh1p%E=6A`@oiT03^ch_$waM8os!$Na%#@>%EIYg@^bUCatre1U@|S|BCRk=eyAf#xTb*ff<5afIV&w=ijsGO zS>-LWRsQynve9ppe-b6BeBWG8FA^mwlM$UolpPZLbX@YuDU?aCwo2^KgH`$2*QPJxg!hg$?r%H$v& zSt3Ya=*a$l;N0%F(WL~=qyHdqwxAtJ;M7X}2ML@RQwl1l<>$@F%FXSXH$4}oW4TkZ zv!`~+%bAihH7`pGoQt-?z}YuB`0(>gjEEOlGn^IeAH^s9wit-LFP#xg_piw_O&wa79< zwhvCZz}-)hA-g?@X||Y6^Y=sLbH8maB~<B~vhl}V1^$Cn!>1gtX1guwgt z7SDe|l(fp=cB`-5w`wbKfZfXP@Qkmobgc~Dv4sb~47tAcr4VHRzy#~rV*N1wp6!!@ zHECou&{~Lv<9|GvG_y);UBh z54=7_N%uzz87|)wDoI>DnN9aPOc}M7s=1M`X zF>vmP z5XdAD4_F`u+Sr|IY{avpT8nS5@zRRbBO-L2ir9dL@I-~rFW>A>;sCv2=dWBV3cbj zrd{b~BG>H$NO*bq{RRNNnBHzCP~=Pa?T42C)P!@VLzXVbtwINm4vjO?=|NVFn}svSP756}rpZZc5o zl!0158L36Z*Wb(3GKsHOY;Bw;?fBR70!Di0qvd-Uwo}kLW_weaS%)AIFxbEu+t9d8 z21lDdhttOe55{s)|Hj7ET4Q&%PtZD~JC@ev!d;CsG4!8U)(3!Aaov|Gz*Jmk_=s~M zd@#Z(`F~B5?C&$P@ZUxjo_R3-T8f7xT(Pil(x1LwzM0`>136P7V>r9>OR6L4ozpNP zO>#hVsQ5mWj)biR2GbExH6Goincp{-VRtDVsK?fJ(j5?ruM8S(Nj&&} z2pcEH5(PX1PYA z*4FJp#&aiaLaH}V;gxeI(&STc#EI#Tj=c{h01W9uv>3b~N4|{INA3qN)P7z5Hp5(s z;fA20hn99S{RG(C2=~WbksBCD=L=w$Sf6|6Q(#tleR0Tm?!8SYqe-S+l6;bmZqcM) z`m#x+Bg-uVM6J~EGG%sj2Xa<{x~7pwxMT*pG$C-hi; zM$giq%b%pMBn!5qts^tooUneu#)K&<5iGw+wUb&HaR5%ossh1uONka-EGn%qHp)ad@6?>IAZI}B1bJ(X> zWMz{U8*X*sBlhQ=C;hQ+1T|szhe^<5y8rmJu@7zkQTm3DbZR0U=OY7}Z0H+-gO6>O zbu>N2ax~S6m7&}sGmlM#jcOdt%(>;5b~NYCJOH{Y#X1rI*=U2QDIokJ%Yu(k;rT1t zqX$Rjv*>-9pL4trH@zas8)5i)zKR9Z?tEOiW1s>F8u}#BBi3n~-5zL{2@@pd>abxHwhApBi&3YyKQTJaLImmF+b+DZl79z5#JC|<8C^Uf=(YvemiD?i@ za-9kz1Ur2?8Z#-_PuCv|@%6?PiP7^3HvAfLxHcgH!G63jlJKT&4Br-N z4cbIPsn}WwrCyn?HyAJDd07~Y7vPy}Fi5V_TW^iL!LXky9jFH5Bf0}(FkYK(G#D>T z|NRZdjRq7?$6&OXVKf-IGgK5+c=hT*vp{4k+h>Ng!MIi7&BbyTs=-V)p zr-G4-(A-I@2IB;hTHD3!;2GBDB$(Fg)=*?AI|@Lo%*o6Yh?P0%F~ew1a%Y^GIk9Dy zw|R(v)7xGunjR5yxWzz_lyx!042#ZEpi42gfy9$%M{czFTwb@`;jr8NZm%og7Dcby z8FKms-X8V~VXw4*c_QR;P;2ZA(u1k@OxcepU)lgh<-Rz9Y9C#iL{sIzFYAYQ;=nB7wWqxGOi%Y z*sx6BaM<44#}BlZmi8~`4o8LrFYoYrLw=Vl5b*gN!JwOW+C{rJZC-52x*!yX^+a(lvlr_(JJ$19#;x&@tXyE7=lwItpZ47o%; z92Vg%0pGrWb4n9SJ4-|RR^H9nvJGAQVjXYGGAJr8tKiY&Wf7Y#6c(JGfZq)#3hgeh zGa!UrE+3qa62oFJx30CNgRCr8u)9$7AfN%ggdcQ9qHjLZkz}vpwovyNmZT7GOP=2*%TRX%Q7sgPe$y09S&Yqs!$kA1ePkd z&*2ippa@6wf?lWBL-Yjwf*^){pfv%zPn1q> zk37v31f4-&h!1(~_7Lw9>`pNp48kjd%Nr5}Kd;GR#xYU(rxm;{%aEuMR1WpOBXUuJ z81#l<_D)AA6mog|KA(%Xdz^5f3QUtHh$puwa$AlKPQ8b`-mn)`&+Za@4$%*m!Rzwd z;ZCB@;YGvx1F5IBuH3^ExCICA3wpf3+kh(|f=a?~ujul7J$^s$QSit*Fe-1XuxB0j zC}4}~+?CCdcTc3}0GHcKX_(;j@-REz<$(XejTX@IeiTt2%P-%O@h^Z2ru{5>4yv@wgT54vU7pe3;MAJgV%0KO9uw z4Bh=oq^O^pQZ%od7o84ASP<|QZB)GCUonZ6>K4-`SVT%(?BJj>02#Fqt5P)D(Ce=4@9$v|G3%NsK zJ_Ny~CkP(IE%^Lkbm0#t#AO1uOC6+dir2o)*fNjqLyP_qnOKlzU;**pSJ_eL_am)s zqJXWx&kJ#^;Nrs}!5bDl0VfxFD;Re7iE5UydEyodvW)FVN5OfZ zHpe5~3mo1s56bTKc>-`j*y9r5CO!C52wJ^%ps6U;Hyj2p5b_2>4iQ2a;6g9}w~2)? zsFmLhv7jLGQt?X=^4cIGhIr8q0XiJU_qiZ)gBy}yaNJ=W{RgC@OX|RpXB$!WY~jo! zqJy0)d!ksf;zCsTO{CWVINv;0iyi%t5&(`j!_{Y)3{V&T79lJU_yF9sv8x=)atlp~ z9kVdUsn@eWMyU)t^lxV&7?1TV^hH&kEek@Z@j!#()k1F3BltrhSc-|RZ~%N31gLg6 z01okrBj}f%dVvT_F#!y|6Cy~6Gwou??iF|l5n&MocF8UIZ;$L?Y#_j{VA$sZeS~FR zK=8r+B)8ozx#hJ zP*4zI6$OExU4%QgAqb~Z1|#1N@rjp*6{g?=kLL&L>IA6+0`Rap4WXUw6CLEh%jSnQ zs}T0_@XYQCf}rD-sYk@_!41(dHZ7gxC0PM@^GxIW(6Qrt_->g;W?rx2(TWzO1^F;_ zrzaeS8^b_rm(u}L1kVPx!Wn=Q`6%{OT?cIaZ^`KfK`@TQ{a_dUunq%sAX4-PLe7xG zX_sJvpk#vGBVz740uES_1w4W?3~O(P7Zz@Q-p^wf(MdXTv>xnA#&O*t*;a%D8Ak=f z_glReZN0FxXrOnXU7)ng3;xsx>t(2z&~Mmg;tRD78`i$_KHu- z!XiPhJz1nPpnp3H-GW%x{=6oVXUl??yiW6gFu}$IEEPQ%ce@W?uLuf$H&}RQ7}Sic zP6db0WfuZqg0jo5)?I+klqdiLW=m9}+_&gz4WW#F4Zx@0PcmW-KPFV8V z9dz`VWmqq|t$XQMTb5x-^w`TKD8VE4G2oscj`D?EZb5`a4k(Woa;)Hu8i{YSb8e0e zW+cEm$nSOrAXw(Zu;UZ-f@Oi%L*NNyOJF~^XD`HEyxR{^80?xr7#sp`ALfG)&w`l2 z;>jaB0bAxlJ!n`m!e<@yWF;)qPW3CjI3FCX9d?e~f>Ut1!k!RpJi)viK?j774wuCD zCId|CZhqW;{WIyaN$ic780sA?Cn8V|Spwk_2Lkt`W+Ic%{ zjrhUIOZ)GsAgj?#1L8mhJZygE5z&O)dLO#b4VvW}=yp*7Bbc&~!w(^RC=hmmITK)w z7JzUuEc!rtK(p}5tRU+(H%ha_%v|C1x{^gA4bfs2iaE&I?4gQ0TNbpmwOPIojH91- zd)?Rpz=c>5oS8EW@jQrVm`6K@Rd!GUMu;q&VCZ4_fD3j)1SL2{Sh&ES0bdZ}BbPMvcm*sr{dQ3dI$U;N7#x&801Kax z5B3dVgOzuPKI|zvD5Sf_+~+~5+3e?tsIN$i{9ym23_!ugWWX)wYfez`1(C?#i6F; zIOHX-DZwB${C>KmdjHfP_I$$3?ZoGSx^7R;WD|37Cgd@BmuXAOd* z8gPmVjz{?+udm;Zzm~&ME)RTLhYJvJ+zn3J1mN%n+$Oj(VMC|dypxOGu0>qC*YV(l ziue~?vk3eF!L7Ug8wdC5h(5gZKA}*=O#B9LD$&0y-y8aLMl5PINdj7&UZV(u_N=L>n2ARB6BH=A!nGj$XX7t27}HeV*Z3(=enW&y}HQ z6_-ahYZZAqg`?Bh)+MMs9DPv_im#h>gQ+V8n_J2F(c{a>3m#m*Y$WR}K@3#?j7tV^V;clRtH6%{Xiq2xk zm!K)LqoYhf-<%x{a3A3xI?tnz(e%Plo z5Y$-fB7HI^x{2ZHsW-^QS*o+Pa%ZbDb|wwIJ-UG5?xp)-P`sGiPq&a;i1(JDSqq}& z`gaWow3ElYE901O3vEd;#U=%79oO;gx+1zo!(SDa&Y&b}J+Pq&igg~Nr;KkrQDbzM zC-y%53cekQY8OY}! zq74kBxd%ud**|WdhwPVMSU{1buL&fiOt`Yo`joLTVZu0aDSn)M7YvuZXZ~P4IiEZP z-Hm*Gn{Ls#i})*-dQgv8bQTw&=|~LIRH}sn`85_T$+LJCEst*ImQiq2`T7dFnOswa z7v=KxdI-Ihx<rg+Rt^-zT9BCnHUAf zEEKB;d#VS}i`p8Qz4Y)zd5Y+k^jUAvvlkbzXZ^GESB`JR6SCm%WOzpLckKEoRD)cm zbf7YLG2H>VavYdth_ZtB8Ji6!Xcyzy>N2&Vi>^}{R`hcdhK@s1B zXELKm+N54PqT8FWKN}eJBi#XE)W>OzdT*B1-Eeb05FTZb!e=coWO_rOMp>Gkv#lku zJ3Lbm)Tfe=-o7(Bl4Y*KUCGJ8ip*T&OEPnY$=yICfA{X_I+nSP%zzZ6pi7xU8oX&3^nE(Kyn2}Mz$-1#lMhGdbMMl)Yb5!IZXR~cx^c{G z)cC=Yc4)#QO`GX~8gE`wh=wjrT*ZAy1Hn2$S%$nj8{uNARAl$x^m(ax4Sk{Ja<;{7 zWR-4;)2Vb=Io4<+MPF*pcD@ZUQq2ew*7Zz@;Mnt&5|MiGkXcduD-7939+KJ^(15#` z0Lu}ZzB|=+p@*J}-e3R#Y0KB<(A#PbJB^MK z7y9$MvUd8eh+EECUv?FD!CXpCiSPJ!4%)T8Y@Qx~Y~7Llw?Y~~8=LIE0Vvsj(@h)i z!BB3eQlaB99S*Qc?@704gizI?#$cLBFh5n?-h&-IH|2Z$xwThvSK}F4IG^G0jB-8) zHkGwv*`U$^c0NySAJvM6C$aq4xg?6DYPbwX^{+;|w&-1Av9319MP65%Ka)zgm9=2F z6*PHCN+HcmHJFi(?66(9qwFPaGYtwHE<&vu%KCAQbTi@b=DDc8p=^c$K+mQ|v$04% zF7xTDY}#$cCQ^s}MU72N-xXKssBBnrJquGNT#_I}c#C0=X$(5{+g=j}y`09NXEB4i zv6Dxk`r_rbf`}U?NH+G(r%yPOMFJnTa92bU35sp!=_RrCJcA@s?y40$102)cRj=J; zzcpnC%(He^Pb$3WJI~5p^_nNAY;A`JF{W&71z;IGS93gRbxCtpp6id4>;Q@;9$q7v z-$1fkjU=Ny*JPPw>7GkkwZDvM!j?i`Rx#Dr%AA(TeSquA>9_1k$tIOmGjhiIcl$4M ze4oxi$pXx~J_gL9+mfZm`RvwL0$}%mJDQ~h~T`V(<4a_sHS0`uY){RDP zow;ZGEX6M3*|KQCMTVkLB;np~e~A~dZy8wmMg~^?RbwU2Mg6yD%BD17PbwX#i-51_ z4u~OsCyhLRRmgMZb~p>3{Y~&B^<(mH*DHCK+YL%iNo*&0Cc7K5cQpL@vUM!$RXR}7 zbkQ9UGz)Gw`kPK@*aKxLq*d>gH8R{wn8T_`H8}OD%G~Gd`Nx!ecHXWP%qeCM+h=M(v)@_K7QT8$Bg%Ip2x zH)VZT>HBZWE@RmM^kL;Mwx{~AS^-@$-?#$0cz#B&t`o1~$+dHAbc-gj@Qdv7Jc#&b z;_cB@-<92AwVXNiUD*fRRv=AEw8;m=O;n54FBGiAo7mg~6fAsRjOP>}>DRJ>+>2B@A{&T9zh+vp2j-)pzm=`#XfOoZ zj<;~{qygZA52SxEzLTW^SN*7@!B?v zy~zESZqXK4J1*P1lKpl*lA6b+=%L65@e3AEe85qD6X+QguDM{n{$<1T{$e&g66VvC z*4PNYhJ3OO2<6~T`vI#q0=|J_2W9SZu>8rM}|1#VnnxKTlQM!dJq!ukecejrT`gh6Nn&Wni;Ofi(_I8NP z&80h6#C2p|Y%q5R4WWrEjPrh8Y`0+?{etbIv*hIG22K(`GPLH7BK9Q%JD4}cEipk%|O!sF`DctEU_XVEhEnBP?!+0=U zH}a9p6pmF&Kc(Q%lET^~;o0(_+Oj><$Yz510pa6}2qd=hn-GS^k?&=eR zLw37pSaK+958XnveAhzMqkrrb{kufVkK|Qf!aZdM@W_DJ3esu~^O z7csoO4TS+gUB@vW6LDbGqA|ztj4TxOeR!rRDw=JNea`$MgR55ezce^e(@z%~t?v5^ ztvi-)GchJW1Q!?5{i=P_Gr zbm+$&{p3IG_qA@e@vWUr8Nb|TO2thVHY3%J5#J+57B+_ZcY_xZ4X1W4i^!{13mck4 zM8R!zizZeo&x+;hvlk4G-Nmg2%A{19>_6W7x9Wb}M!K0O)apg(nZIr*Gyv%Jg(>&nz-7+9gbC z(?z>EQBOxnLI(U!M@i0GY*dn8QV6Q>YCX7*hsJg{V>>UlUKGt#IFkqG%R$k=m9-fe zD{Rj8U2MH*uStWsuQ;}<88djJ5WB|IdEJwOuyS7Q78^zTmopP>E3eXba?A(SiUGZp zG#l3Jhs=bNY{Y+kbLDW74T0LHg#HJwTa(hiM)x=ECG@lH}QX3oH0=Q9#34|aXPC_{0}HG%0f0~Ji#?( zTF1bIt>j}QMn*2pq<7EK-{!@7HeqJst}+DDgOpNS)8&4^`a~DTP!lF1lRH*;zsEF? z9-IcZdTFdx6J}+KO*UgaN-dHekpi|dMj@G3Z1EDKAddw8X877N0@I3S)S2(!t3@`K zFC_bhSv@u`vnD+_dE@&mcYq$kfwc=eAcU?BK9QrY6j37%|K7X1l?*@01{XK zf&!3On^F??86Moi0(}C{Gz%oHu8QTcY|c{IgsCQoSxR+4OwgBUocmd2J zH&cu|aH-LK_k-D+DiK;z8#~Va0g#0)chl|3xe9BplDn7ycM$rNug6!o-a~6v#x|j; zCChTrx|Ok=?2SvU9r|KpH)!vw*emRG0I}NCEK7k{DbcW{#^q?gr5Tk+FPgBaY2nh* zth(4yZUg4Is%i#dUvZWaTW{beaV4bu)I*BIsf)4)Xo)lI!tYJ-lf;{=*2N~VhYS+? zA`OovmU#r#m&WK5aeJ5k{sOxNBWUF_38Ft65dD#!sKIA`CnK8fGxJbvLo2r9GV;;e zbg8pT;ZS>YvL@CQt_;Fg99*)!mG)`BEq>%cQ&&b?bo>R6WOSO}WfrGz@yt~LIV-2R zjS{15bQ`@)A5~qwj50|E0T~X@Och>ge|M~~DLW1NviiuT7OxD-eSpV&d~fXUP1z;O ztkwHG<>IOa)AEs6v!>|IYFM$RmZYX}O~(|dPR%Jc`877NW0x8IKw0=fn^mahKnUTHh0EimrYx_8L1t=}C6t zqgv4&5g(qn+_(g9jmjVYy_eu)4XB=u55Hl#5!Gu`sH*Vlt}+}Fu4Lx|u2vd-t-_<( zau=`_b);j(3ATK>wL;&N1_I}UXR!4EV)fD7uYzC_n(Zx>EH^H|uO-xGOVUmT)3PI< z45nK&c~GS(j7ejj+o5XRy~}{2D$tqi##dfY0KRKm@Qi_>oI$^LOXy~D(olJKs9!dklrW!8v0hsw->0>DfonBBO7lWAo^?6)s37pE`_FfA@Y z1s5#KGhHh@uMh-|WcDvNUn{_!7)%lS;5BWjOC2|sX0#U9!L)SN;*4$g0cB=qVY}Ig z8x6FV4?z>IUv>d?#_$fAR_Ui=$$bvy=`2$EhBBikQc{ZVbTF@Fk$e8{Rr{F9zTO-);y)>czY0&=S9T#FYZU_jq_FFDxeGM}PDHH(j^ENi zNC>Wl^7(36*Y9J3UF)Cn=(}!jnat2U#34QzN1C?PO&DF(~n?R%$z5j#2-gI#0l+y1X?S8HYs!qf1m&1IW|xJ`y};>wr@!2Q zpDI(CEab~+GWkLZK^301kjzII)eJH@LSe~=ayM!O`X3CIK_RAlmtteFdoQs(YJJ(WfhxG5X|TLOLBiIDLw-GA+gn3a2@UAcvyR}0#@Box&gc`jL-i`JThnnqk->rCyDabOc z7k#s^Vmws*%{VTuxMfGH7ge;%hf-)zIFW}^$WR~=S2^`Ux=21GkP`D=sqwQtPz(-g zRYR3=ug~d+qLE?VFG4A9m>Cq2h9ajfSf@wSVsKC@)aih-^iZVU?}8GOP(;otdYx_& z3OGV>I0?$7J`HslGY*Q5y|TwqkaF4cLlME7${dF8xqP97qKfl%crVc9@$@5L^P3UxV_l zP#7AjCiUANz#~VbA?Gx=K`<-=r7+dCHd0Y&R@0M{?RR2 zb5z+yC>+Uq!%$Pz0RU?_nFqH6w0=4}?cG}!hS<94VVUAB;16CPIOhXOHU^wK1O2}C6)T;MccoNcUQ#KLQzb$5_mrtz_3$8eaUp9F zDmvSt*ryjN&IZC>9#hM$_^Se_uMFctrC}(;?Se|(B6uw)R567@>(H|YUL*RHWlTT% zdRpapTb40pU$ts_WvhIs2_Nuzye*2FS*rW^@LWK_DR@Pw z*ewJg%ds5_LHj(QU{DL)=?y>`VYgHqhXV24+ay#t_q5ai=d=-z?gS|a``?UAF$ZN0I;3%Rlbm2@Is+y9`hj(21$2> z9XtdJPORuY1a)uWFx^F!H|KPNl+(em+wBeUm_bev%1wLuAny*k!(pi0TnEY}yDIR! z8TjM#8`UMVJnG-hLN<@8T()e8fvHqV-Mc`U?7ZI_hKK{i2~0I$X@{U8%tQ6&fCNq$ zs?mA^?hr3Pd2TTCE~pakb3&mu9a+H?fU*Hi!C`^bbU~SQ@CkMh z`G7azfOsI}mgj*YAYueWEs|n>RUoIc)^Ow);0&!at{lRustlmFdo0? z3AkLLU?2dMtX2|C0>adB`K0TAM#FD0ISC+J`(=!XEs zC3?eR0PKIj2Zoe~^4K0!{8EKdF$Aq1t)a?Fj`vN+E7L z?-D#v0~;zqhg=Ym_*`Ih!PxN*D3Bf$aeoRX1zCoct8v>PAA6=@Whd5QJE$5sF&E{; z0v;zwWf00qLz(ap$WTahLR8@PL2TgmN&YKqzyyM0^@af{2M>saK`vb`XUO9ShkXuj z(B%~oRgF8GnMsByFPm8E6)g+d1gvN|1v@)J0p<)y+0ntymAz2iLy;T^3n2y<06TZk z6#yp%ObI*eAXZRL-R@LMZ-KyyZaXi8Mc@&HEl^B0ihY&2`3|=TCem;B z@n9}+`bo3s)-pV{4ihWw)$TA$-?>P)EtVLya8+aIx z+wSB=mjePM7udRh7YcXAR$o}zbD(3Oht@5Z9adOib3-9;r^J&wK}%uH>jXC(5U>%)YhgdE(;eWQ!6HHg zBD%c5Mz0+Jz_md=1-v{|eON}`7<^bZQPisztZ*|CER0oaVbS3{TNb1c2&GbcyW2&T zQ_t;coZHnnx2rL7!lWs3{q1wR8p=k>xm}HOyBc_>>D;bH!MR-xc`N1IuEx1tjdQyi z=XNznuEx1t4Rrza|Mjj0sXy8jj^k`wT5MWoWS!Yr5K@2isTV3E zR{6QczgYRlJo6Hla>Y@S7$H@0l>7zxWQ-L@TlFZ1drc1i!r+WK=tzeZ&hyN zmQq8>#Yq=YEieso`f^IST%44Br*a0vRa0*uk~eJ)-9ob4RF_LV-mARW@G`!{)D31Q zpQInHlq01_BuOTuzx|`KDZ}lidnK81a^rD5ocZ_Z(Wc!?MA($L1M=yU= zIb8oPNkI9t*}JcNTp8EDiz!^BQ8+(+Mns_CtQRSI*$y3@Rv}-ep~@p^@CE7ca(Sd* zKdp2(VR~S29^q1qT{WPhG$6G9qKd94|MFLQQ-v>02%lc~5>7*9oS;V@i|$K4Lr1r0 zY(*2ksJxH6t^!&V{gQ?L8mfhQd&ZZQZ*l?Z8A&lnaeRb4JPj_jotbFLGV?UjQ14%z z{yv#*5#56}0r-WloQ%Fse_ono(0^+B{XLjEJWA@kbgNZ(s{fT{XD26XVAoa{FRj#8$VXuNLdFGpGLovCZ|W)d zx_*u{1&^o{B#Ggq2Y;>nJHvdfNAU|ixPszaX(%2!YZUV=(D$+NyKUw|uS(HX=-95d z15m#v@dQ^`39UL6$fsITQdBM#fQHu+wc5hJi>11bTkR z;E`6y`bpD5PJ8rqi}+Elik=Fw;H&5sOob-!4!Y&-c0KejYu>BD6r%k8Nj3w7-hHhy znHX_j^i(yr!7pMs)`7%>t;gj7*#kgm2o|Z*fSSqPl-|ASCva&%_J|B?>La$L!^$+< zx+JlL`;f+0EAICh-K<@G{;*Om?x)RcXL|4I%t&#+QyFG?$Ry;{Fmlfz2UcJaOK zSPn|iN;T6dr+Ifem^vgW?$lz;T23@GGco0tTD|q=O$X^Th|R0jCdkP zTCy=QkmZ_H0fSXvLT)5z`cSUam!Z2l9et?AWK=8{laKs;;&M7gDzBL~$V!tzRE;H- zO$TZnP-|tagF2HAWE*y1Ts*>EMkA=%un}~#X2UM4A~vk2*8O^u?$!BWBgWj6Wqun? zI*={drN!~WwoHi(e_&I~zGy-)(VZH5iwUSYHnwIAq}@`9Yf77!OgcCn4jwgAgs-MN z(SzAcMpX0pwc3cuXdbk#gGZEAi-zLE+At5v;Hpu3Ko72jTwAM*QRTX-|0AoGV@Na) z8b1c^!egqYaC}ccNm|$6PT@$5+G|VViphkKSQ_RiRI#VKzm8ulxtDjqigff!kYLPJuI2% zS2gjgxueDoSSf+(YvcEmj#N4;D~i}=SW$dLLue}s0Q+)9e6xNWvWfB?4M4(mm?JKf zayjalj9-oFsxHXYgBi%u$BZoGf*nn$bk5riT127(QoyonwLZq{T%Ce8#&~(~NVbOL z#&fT_ct3`9EAXm0bm-v~TajOFG>3U`)f%U!wOC}EqeBcv7WutYR;4+<+6ObM(%7!( z=;td3qWtyoMcgzzZJjCwsg@K;@Fo~h*eg-_<;&Wmf(`NaxrNl5vXYe2EksEcR-;EZ z#xFN{ca7P*`%wHU{ksN17_JBcJxkdu(k zo^t8H<9G{aDqxw?Fk-Q%(=9|xrY5ETJ{iBv@UlVlU$hc^i}6wKXX5u}F$GsDrrU1G{mBO7VY-EoVows?{#?AT{$0(h z{>2R7=0C@$8USDyCtnkS4;0eTbCs7b_f7QcJg5fCo2c_&j(dGo0)qjQ(qUesi|Y!0}aj zc!lFjYm6M9hmJiK&p!)}_sooFp;_e#Qp)lv1E!DbXG*W3q!gwkGCJ~)cooCGX~6V# zJ-mYHGijJgsBk+h-O_83nIj_mL-S{Q-N`|L(vz@E5r6y8@cHXoq z7d6YRYMW}avkB->pZiQ>?wC}EWRYO9BA-%gj8;ZkF?R$Yr(1f$gX`FxDit|$bSMe4(?3)?*dV@m0*Hyksd%umZ$zhLYVB+*Nm`8qyf7yin zAp@V5rC_Z1bR>;WkAuQys7XQ_GI{5OZb|u?l;W+Bi-1ijKXktI=r8f>7_Mjq#czW< ztWzdT_jx)j-DqGbE;)*hzXyS|;s?>Ff5cldQ;bXIAutyz=9$b?J%g_M0Kk-h2R;3P z3MTsll=ES{13P#W>hfXy;@{N=?ED{mK=lfv4~WZ%S@-}SzUb@sdz?)ljH;*1*Jng6faQr&76kH6!`3GK)`T zcvwDy@gB*bDWAu$H3fLg2!Oy${{?OTS9~Is>`xoufcie#eed!D{v4l3Wxv!oV9!AVdj15}lHU7#1VyUap@HM8dU2=^L zc830jnyVy}hVnd*q$EKlZTbc$lc*Xby+5t0mO*u)t-0v@L{&fh_eO?20y3k|k*q1l zNoC5f$V0%&Vsx@fly03>bq9kA9*pIpn`^7C#ed(=u*X(euPt(v%*k3ue_vVU#51U1 z(8|@RpI^_b%4JVNM^)``C7 zn=_v5#9<4-3`E1OsnWjq_FJtt0My6O-tY)|OV^0E7gqhdB{Pb2WGo)3vDT>&@(-)6 zwBoAOa`8wz9>`b+t2cmUi2r-ydH+w^(qEY$CTKRIMfF5mZXg<^D@$ae85ZKYj{y?s z%44#cNT4PM8DF={Adc;N)6V_++$;_jmCaSX4WknaTd&dYNVi&}(<8+Z{*XqSuUG&6j_?g@r60#$rc4wTp!=`db5%?I zJn4cK0z5L+_wU=Th%>0o6?f&J1KX;eMs1QSa;1^&S1i&RUuJJwLmZ^(3gLZ)T{ocm zK$Ur|i)-XxXWvFZ-?KIq-bPqt8QN$r-q1wT2e%ZgFx!~55@Lfh`B`jmZQ zjkRxVlLq{2*A?ATBM(61@2v8(hoA>brc(z;Vg^cpbJv3iD5`s8-lz-PDsOszLECiNe8Z7dvgw? zq6@j~kS{Lebh#iQ9Q-LllEVNWbV33)$hb!G2p0q$IO~}RnT8=d7|v;H7kuH6$nycG z&*5{ac}_Eq>JJEXiFJLDG|CgO zy91D`(*=ol0ddF}3Tdt&A1NdxNhLPK=@~PRjfN#7yv>(o=#!9M4n-fUx)$(mvlo(TxWoVMxdQdO#KznIv|AOw2^V9=*sv*}RXtpa1ISpakG7#h; z&l}{}b*Iv6PR_B}J$wK%utGLH9_$3h}!r)h(P!yZUgE~hY)lb>cAo5`)`3$lzV$eFr9y)HtRzE?HG#)o+)WH|TX zPi>GY*yj&G{yK*M$rrtzphpoppW=N`erpha^GB)x#6ZxcSsja;}8AR0~Xl zeB_!`hxieRl05Tg(*6+%IDuSC0m{d2*U~NI>t<~oI&pPkhsnEln!P)0l<{4BSh*YE zCx6Qt-Cn-SOV3Nb#!@mm(TU{_q;>ja9s0-EgqZsz^@P52@>i-|zFdB!XKpK|PWp0O zVim*DVDja1Z7z8xb0O(L&M!{PWxuFHi{ZcASH=N0Zn+xzM~xen4Wkh2WYXD5+tNYk zwQCX%wrM?TGBM%dn%8TBLcX6~s7>c0xrZF&pO@g#fl7PZJcfFmVr@asCy6VyM*CmE-jooz zavA{cHC=+{zPh40>NYtsoCVO}^D=WIWj7C8=*dL;LTnN=tpC!8_975u|qMAWhm^ zQ_sK^O#qiu-%wOh($VOta{JONskApHJV;A>!?s>aHvK6Nu1)Mp3+~!XdM?qxM#~o$ zCx;WaaedcPW`P{Z=tZ@pOGYs|xOtD5pYfd=P-73a9eQ$q%|qO9>Pe%M-cY%QSwQF^UKDcT;Mb#f3>#%$%_1z*4&!7wVZb3eF-TYY1Qik27bfBF2;C9j- zkb0#n*BZC|<7?$_HWnB6%&{QHKJqM{HQb)qnMHVtW#L&dKQWGbgJuK~p1)Ep8U^4N zK29>a0x_2_^XHkrB)zx*ZcB4tP(8>wmNdatQlHT9GQ;SW^kM1LCr#E7b)r(AXx%HR zPxPGur@&5-)FKNREQ)5K?=O8Q|oDXqEoSTMx9!+E~8GFV)9c2rU=(0$6^;prBQ9O@|DsAL)J8#1G) zL>bxu-Ax8ixlDxplMGzG*aTRXc9JCHI~p-f+JB*&HEI7&miC_7-1?eyug;B#d)xIy ziA^Q2zX`A`@Aq#{bZ*BMt|#(N4QDrjP=_M|&u18JmI?6bFlvESP7HR!dPLvjbbT|M+VoeHy#=}(&;azx`wujPmhAz+NVW$W43TUPlV|`uL=$)iXM2dD%X`)~ zLwozw<{GA_N9|5C)QDfBk=3XUzlhb}W6%Pe5r&Oz&}(=po}GnXTL#ZG4VM-jN;nyI zv(ka;*f!D~5FM}BVASzt8!|dJb2Q{&(c;75yCU$CCBDw>~|JTF?4EG-zjEL4Z8_=QG6AyFW&`*fWH{A#` zQngoom!t{Qy=##9LyBO~gUd4i-W!SE7%rdgm5g&S-9ls$-r4xBzq{dGyazD5UF~8{ zpaHZ!fcxG~Ok(pl${Anu!(h+}?m zU+dpBP~sI!O5E0|_GJdW{8XYPJEBhB?yE=}V%bLY%|*5ErCu}i{+e0uL;-2aYcd7D zhyuQmu7G_INw;$EM!go^ji+Rxg}dOHriG~PqlCmhtH7&D_zVqClyJ{RqZ01gm{AGM zQIVC<;@MB1B&ss6*v3AMhP{>;hz@<4NOC{Ybk}0A|7=9}ABU}^|Im;3&!`@ocamzM z=riFn&{&ISM?XtEz;#COwyI6U)*pE+DR6~!GZC%M2o3#LVxS&?D2mhU-HT5ohU?!o z2+FsLpwN?(74nA@i3b^OG~Fwi*uWq63EV`wnM`anlBRu?fV|Z@2;xpcCNHDW-z2Wm zze{$k7Ms00@?^rMf7gJ;f6TBT;!fkGF$8`QasRKGxRazZJCI)7x8iwOi2EjZMv42Q z|4ux{N(#Ix?vK#$MBH~GqquKER&k{{7IIu^{!GQtm}Bj9;>u&l7&yP5!cc`zyKf;u zLYbeNpnkjJ!%$sL^(<+1ZuNMQTSiGJ^IxEd{xGTLlUgdt9)6I=fgJ=$C^K7ve%@&! zU%F_&H$Dab{b>`n)h6S?QmvzY#*U<~n^*tbjO_w2`lF;8%-9qRu{I`m*8YQx_bc8< z8RKG_Cvt`vxJYl@jSaa7e$8W_ReDmj^%=S+**R@8 zYU{2|zrVIt8E{p#Ez>ISi%mvt{XB)M3a{P^eLTN9zaxA8W@}Acp>T=aOy~s{J8tn* zztWM-+ib0LZ>PcR8dv>u9(yssSSej^GXN6S>t~~1?^P?@q>+=ib?BCKqO3ODe#V!=NV1+cK}4zSOx){uS{AB(1fEf< zotR(!7xrrfURCHXXn22Y-4Mv7IxoDe9XEIsbo>qMwwD~)Y^BfUILO}D{F%!1O$Pi_ zeaxiKowpeEId6-KpbD?*vu$zp(LC0%#af>m6wVBi`v84jxVpNuBYWi*YkmGA4QA%u z)lwci5n!zJd9ZSAa?lnleeS=-sLy#@2*uMcDbhqG?z1P|qDg{E=7iyPu}Y>iGLsw& z;p!o1!u888puRE1tSF4U2deAQ#|Nq}WcwGOxzAN!Bz=FNx{OV0`kFzVtCw@^HklWu zo5-sa8f{j2(d@iFR-@n#{8S7q*`ap zpT7`AU#s4VZhf&j7j1u~dZ2XVh3a>hvjptLpb2f3aqPYfqOi$0Wi?Rt*>~!`Uma=@Gs>az;qU&d;eD5kNY>>OziTBEojgi)pZ5{z3F^J z!HpjNbWG>t2AL=KX>s~5<|1@*Rdv@UB}#%9uH#k>9iwS!56>vudBI!Nvl*uXui8#K z4Nrohu3L@9v;EfJ-*`S}z|c~wDLJ@hs}aMSQW&c6s_~rgZuPW$W)a4A3YB(5TNu2g zux7s81=!9@{#)I|##9pmp%1Ux?KOq#rQF@q;QrJ(d3`#ZWM6PG0a%>9cVJGJ4`6SGkA*xWcW?{N>3Z4A*KK)iFd+*XSCsQs?3pP#JRiW;U97d2%*- zd|0w|8fY=slkSWdgjU;7?26<*{kw*B(s{}`Q+n32L7-bS992A;;#-8&K?|Jei5;^o z6-OQW`@X9PHWp#j2pKQsoCPW!o-AWS3b<+tM2wLNPfWqMZAMctVw;sIFrU91N0~oU zachHdJImB`Yqt@TK&25_BV($4B_oyrHXTSBz@Xg`^oSnb6NWo_UI#CDLLOfjZZvoW zhYPOwkQ7h_S>HOgdU+>y&o=Vq)l|_tDSW)yO@7Y)2PDjDSEz?FrIT_OV1EyeOO9pO z^S4{au6>L!sCq*3CAI^AScR_k6o{3@{c4-h;=X0Yh&POk$b5O&j1yf`oH!k|cN^4R zrE*&AOL@sL+l^jwFuBj{kJrOfH=`b$ty2DKa zuxN(F2P~MA3;oF+dCX;~xHy@X>qLvlW+*%wBKHGuk1tL>k;jZj$F5IKG>rfsP6PRB zb+Sbsq*;Wat}%57zotNR0#0EkVC*hIQ#USeORaOd?>I9d&odV+^sl><`>aB zx{;5>KDh&J*pr;hJxM>&_Ot&=Hxo?+fJgQww;BNS>##}92y zkbOJd%njyevL({}A5O01hSHrHRsy$}fs%U^w?2|w!cJ^JZ#@EHaUIb~7fGicP3~s7DD{TC zg-X7wCg^57`~dtS7zq;9H!WB&-M?V)`l|Kv?hM%;)c2&H*HmR*%Nh`&K>icSwd}42 z^w1N@#kqG;W06xi-O-ItCI@nRsdlQ^;$02s*ptaY91TY9!%%b(mGK&7Ih}i#X20$cJKY*U3L1l)=QZtkQ#eOVTu#?gv(3K+8b>N}_ zTyMIWOsin0JS`o7rZ9ue0LZlT0QzZ#lxcO{NhA@- zn)?*flB1;+bMz9ZICftb>Ohz(-f1RCs(&$=&u}I5lnv@QO_`VumIM-Xi>4&-i9<44Us|#|l@8P(VmsXd2_kBC8iR=P zow6gdIG%5ghJ4t=;@SOgC12-Wpoylj|24Xq$jb{m(UtEchnfHolX1)x;N4^&Jpi%Z z|2Dpxi)W#lmzK!({fJI_FoRugFykhe?PfF|Egh> zMzc{)LuPiT%S>N1Xz=PHc5tI!aJ+b;7J}n~XR_endmX6gSaLHvQGr+GW&#aQ5QJ{&s440Z@|zp_@ow9rH#f#)v^?ZD!jV&SpRX-+lAc-*jlZ9 zC51QZkZnR$dc4#g~Fewn}uC6uRy08+!2(jU)g;3MMH1u4M0@Q_` z6mE5m#|FQSpaZ(h`Ft$Nkvga*r=Q!pkzTZdbhi`7l2k#vSbtx9D+o}K*oS15fpPkd>+$ddaJu0w~(9~TsYM>$g^P7~nyKL@UuN@uRR$RNG)rQkyi4J?v z?sIs(cAqsULauk8Hy9E{srb~wQDrP?B79iBNTQwsZc1v_=<@WW5k`kLt4|t&*&t6( z4$9g%y_O{JvTKVh<+8rE$P!xVDi)a=a*7E2g~jF{ zUY(GQE^8OjhN=0cHoR5B|+mp?Zj?VXkHxFt9@$PxJdG(YbL_?9L=Zy$Ulke~NGT!GHd8vqw>^7GEYsa$?u zF+@u8^L_(QhRGsQ6TtJxhu5|6FY<+L7XC%PAgYajkq;^A;9ul6cU}C8Ty3leziQ`^ zGsyb5kc6}v;9qkVpi48xPOX6H1RljlsCY^BY#i!a@l^iF3cPg*VF;5@WaK|eC~^a; zy0fGb>a+h;1=P1VU#QWR%n@HF!oOe!De{YG+fp~V;}SW)h*>4cFS5HNf4y)&GZf7) zaxYT?v><+W$0ansh)Esf7il0pF$dTzjAEeaR|gZ=60)lpwM6ove0T^jxpBmxF|t0J z-z1Z5%YK&h0t-O`iXAUNsnQkWMxc?a_D(4i@fa0xaQ?^7Ur6LGTV2NomZ`_t*j5d@>UD+mNdbL*MTREY>rW|M9 z7{r!fUS#4nFeC{R_Q$C=`lIiEnNvjzOFm>6p@-%BFGa0a=TFnN#e8h4Qcz9~!cLJ4 z2wNkPdjp=TEy}->`J`S2mD6^xplsR=xP4y)+${3X^e|+17?DJqcEf-HNohB@b>{Qx zWU#m}XR{1C&I5Jz=XWJ8bkq9$(ZV|{3CRa>Z!#sSFrb_b`FU9;b&EFS9~Le!aP$n} z+)|oW;~ZO~(=zwdnX?^JEkn|3{J}zEb-<8SPRPMR%4sz!Eu(2QRKCe6IM}x2+_x!P zq9UtE{v#lUOx%v7)o8g)8-8iNOoh`2Cz?PVbfHbF(OK?5iND;!c0l4Ut(WQJFHM%E zjN2JwLE?56<2nVyiD>bK>>6mqgk=S0vNGs*kd@XMGiM8<@k|wcMr36aQ^Jtp(VZYG z#&y#7JM-V*G9xS?IzO{a4*GaEJepq0l+%Fy4<{;C=9VE}#<==MP=A!4*<5(h42tU* zZD<7bR)v`@1j!7FM5x&r(p*`pI7O(?vKUfvVoOwdD8w!XSpX$decbGu_!EHA^Aq|p zNk7!?=OuTqC@7K#;Y$i@sG*)qhNbI?B>ni6MOjUQaNUaxW7T8FUzgFOAL`5&B=@e& zjATKm`69u}aAtDC5SBzt(hsibJSl%DqotRb4@EK{I8qGtS28TU=D`l0#GhW}bYH5G zj30Nss9o=UF|8E0Pj}Ye3+mMMPBVTCli`)+K9q$g%f0`L`sLp1#gxn47z?`Gjq79y z>{Tr(<3}mWdUD>8;C8QC@)v(>pO-_&`k0Q2ps%KUVlHc|1LPPCpvV)E^lcQF$*Q3O zH#GpJqE0e?>}L^EMg3j2SQYjCFA`C2rgq=LpnGL*MAkn~F*hY$h=y{bwnuA8B;yD7 zGZuDcu-p`bVQJ^ep~dI(X{JU)V&oZga5=1cOegY?44@_Ye(?4tTr)DF@`9z%c)7XQ zTcC~+dz|tEE_~n#D=%3J9awN8^*1cnOTA^e*&zcWkY^_Lk#6hZYeBT4y@SEk^9!jmjwCMiFjSgyZg7+Rh(vd`i~BKu~>^kTV!r#N91 zo`YhSb!#}^KrrM?1lw6KvI~-y(@X>sTSHFU4zVRlTVf|QZLxEgt*#*@!-xhSa`?rT zs2o+eR;=S1PEo=AS1XX0@Qh6K@b^&12xO}&1;e>aRQwkeNO9$|9+g)x&_o`8PLjv( zWhR*%mKvi#v~X?Xy7K7UmYNMDnuWoEXP_{R(D+8S1k*SJ+K?;^+O~--482(Z&}DKa zMv{fWv4Un{NCqWY7y`ziBntz8k}M2bP@IKfJPSpn0NRo)44Sq&DOhA6g7ouul7&Gi zVtXZ&c!n(@;}oqRsTj0vb>k?xURdH29cZ$8&}$6}wwigpk%k3Fg*VuF5QldUQ^H8l z{_KKBvW)Abo|XbgXmgqYq(O*J*^*9omiFdW(0n$UraBzA%Cuz1V)NPj&UQ*p;=j+I zdApFYk`XpS(BD_kd^Riq8#t=PLa2cw03-QqSTLH;<^~pkNH4U}=d;m(=}_Blgj%PI z^MXwooK%{_FLJqJZ?dkCvuX5RsrB4?;u)G~jN74(YK-ciEo~uHXj5S2_=n^U6xS`t zc0iOy-<5hJcKb>*BgV3fnFujr#&rschjl1kju9(fso#tiCQwx1m6LM8TJRgkuLf+j zC*`Ul|3K!=3fc$6dJAj?!+C!DO8rr}3Uek2W~8&=V~#%nFxqS4D$Ioh46%K4n6Xn4%CF&@sUx$K6EXH+8zrWC&UKcwQ zKv=Op(+yQ6P1q8$yz8$*n{O(ZY0%bb)b`TN1tYX=b&K;4nKA4%soweamVzgFp%>dL z(K|(*D3mX&%N9T!WRksP(JHNac>zz%M7=x*b&NwYe*dBxXyl|;*Q2Tj z7CF!(-3zAjTjlN)W%DZA9Z@z*R_T?^bF0jhjWJThIca8GryyKahp?h7QhCguuF@-; za|whMc;#D%U3V0`$o~Pzn(3M>In%*!v#fJq()Bry#;N1Q0V- z(<}j!P&K+wgMMD6zmRi|(mVOnAeN-`Dh^ws%7sGY)>lE~UaR#)?y*{tDjkufCU+Ga z;`s1tb1K_q^aHdHpz@gh1)X?)TnZ|;Pl9PTpa5RQd@2Q*J11ZgWY&FFJY=>0v*I4B ziCF)0GT(j`WZsfQ=FO&L{_BB)K|Fsf1)2NE=pRmy`P|@wiW&STDaial5{y4k(1quJ zOhM+n1Wba=I%z(zS})C;|1C0)y$UinUZa<0%NjFj&V8)(63=x&m+mfLKO$D$hRHJa z9@+&6bMe_lLo>KLFh~RaN5m@J%p|yjn_?gG+yfNO=p$kkIGO-XkX$F(&NX_;wyYs- z%|4iCl?G8Gwb>FzjiC8$*EN_orr=|t08mbN8EX2V61JFF)PgnW*|7yfwE#LJ`HYMb zJ1UJLu_eqrWFLHiP5{W4QaF7Ox0O&*{Z9T>JTDU?`3mKp@}-pZgSk%bT^U}nibq&@ zVimWq(Obn=*8KZcafJ>;Md7Bhihm@D_-_de6?nx3A2_MtbbY?oT62ZHPUg+^YsoSJ z8Q=a)!Hfoc^R?z?v9g?GM1_$y7woCey8y<_F6Jd*5@L-t3Gy)q zvy7z)J@Q_`Dzp7~tM>~w2#*5p31KHL^&?CPBUwE@fc>~}opj)X0uL|DVgM0`Ln_u2 z1#^WMTTDVKyVotOh&pVEXQ4JH3qt&?wTOhhH1N7*bW$cEI|1S>qoU`AmyvEdRZz+c zE7_6A1pYiz5E0h1#cE9F#76~r{K~bc>)C>)2B34#7Q7(rGX|ahalzC~JQ_HlmNH)CY{+_US7bgD?6MDCg-=eu$kYKHFkG=nqnq)fhNYVW5VNd|mKFMSk`=a`MOAeOE2z&oZlK ztV?`_Kds{Qb~sVcfhP(&pdryGE7l%{``14K9t4QTCNwm&mp(jSWs9Bmi+pn3OSU}< zi7qlyl$uy*{IP=U#BqqErxf!w{w7K(gKuG9`K7>{!5;^zn{9r&$>~tuqkRB*NBv%~ zE`$GKow=63GYJO$U0})Je*qXXEk7gyqtWurDag@vXhXHB9pKi9E{E5q)I8as=Sk|1 z^NpBtvpl&+K-c0+EXGnU)hQd;g`ADwNjRi~uJ~MH1MS7pH;e0y1=V#F@o%l5(y-g3C%ag`&>g=uWhy5peHMxd9WH7Dxw1Q8DpXF}o^ zyrfl|^YQ2=9^Nxh$EeNWRU-HCk_@jX%S|jiQI?Bd(u?3TFa7(`ZFqb-@G`6r0bAJGn3cnrP9JpWQkjd>Q& z`5$S_S&RgwqnP_;t!y}6HkS<%>Zok6zOm5vHZ_48^3{tBKwD=&k%>a}B18DQpnEe# zD#_FAOS=U{+M|A?1IGtnHdmxO^)TqK29eGDXaF(Oqi-fa%=Bolml2U-@5_2A7J zViOqXeM2zP9Hc}}4D~}!`^1-2@dFf?kHsJE7BgB-zvNI+6Yr&(lQR{RDMXq{-Kp;3f9tYp$nDLQ!|s*E8@$ zQ2tC3l%HPy_to8b6Lm+B>?%)E$!^z)oup3F-3)brWU{9FpjD(nO@7FFb4~ZP%$Pyz z%{1Np>&fD&eJdVBuj+0FuoUa31D-UyemZsN2`{sjd3f~jVRPipvU{j)R6f8jvLHTX z1P4$wFb;%Tb9B@y)>Dh|4GSr?41B};J4b%Xmgo1ZH`jCX^c_hL42hi1z$ zxuSbmo?RM`@!64)F??p(4Gh!i-BY4+GMREFSwz_l=;-6%(y?GP&4qPzg1*(yfk zemXYd68f_}u;WUj$3W$%{fG=%m67i*s! zGqn?n7tG2*MH%@y(uujS1gY{5&5gXl@uN4IEC0dz-r!5FNaYNECcu~}|3?xqX3BpM z6Qvo_mM)7N;9JP>it1~| z!V}e3{T02eRD8uu^%)~TgP_KB3UbeytG?d+03DM3*-7fO-klW5WUaSgWn@+*e)KEm zT5pNWm{G5oX}w2Z!CJ3F79K;d^zH&X zKT!dFav)+88nJamm(<;aMjVXn(L#|Us6mG!2Lw9{04^B0Ry;og#z2j}&B)P$>HNf& zGELdpGDlxKk3<5z(3>5A#7q~t=aohGyc@|y^A|?cTVTC6p^x8E<03(qo@RR1qJ8M$3p%oY>Oa6<*NI_T1ekvb@RDpDnr z?LhXU2dzBNN_d6sLG`1b-GrK-jyz{D?suob?(Acsh^zoW$(cwqqj4^rflbASEQDs9 zF&{BiBTD^VsqOCV~l*b0vvubW;;EBz;ZnNHI{kqA_W*K8aZI*GJf?-FDp+UGo#q>6Lrb>jSD?3x2 z;pzl+&|k9QYI`9PslW#|n;Wj~GK+?6HZxpg3iM@Wz9RCR*3&+~d{zG`QnoDr#%6Q#H9rX^ z{=Z1SGW`1hV`jeACSc6W*E-4@{@Em}0H2SVabe-AVrS&zVpJo{+ zo1s~S$W@6%Rw44Qu?AU%zI_!ntrP9dKe~)$6)LwAK8%UBL)B;H*Fn4MMCqgu07hXku>PE$Kv%uCx>!I%&oobMA=GF3O!xU5(?W`NEH04{MVSe7D@L70% z9_}tb?=SekB0uk8h=1hgT_Cj>IzJEK*wjLQ!$0%L#}L;^@QY*_;P5Z<^%akQk&h}e z@Go-Ts|@}{PN2%-U&KQ!Uk?9#dJ|13gh>Q49!n$?y8YfKn`EFNtxi>zCO3|@;iSHn zXs8T4zH|Ps$c-qc>clK2%TVASWf`)NVbF&Ks7j66cv8eJ;a@PL6bXj3ZK<=-N|F-{ znN^YmLti$FP87N_L(v37otP4s2Jv26Nz$?A(eb=+CsRo>$~1(rs9V&tGW|e?g{PlG zlYBjXiRSAGvOTE!Y=}hj^{Cxt8gxhU_1xJy`nvEW+YMRLo!dlj6K1o;>XJU$ChFp! zlu-4y(Pmmuyd-O=gI1-3PLybhpK(lgDlHIB@gvpDjY^!bhG{{+LwkiSAxdwJgz~LX zyS6R)mSmq%+p%KQt8J^BWOX{p6=^3~Pm0>2>v-V{b{O(n??tu*CfS>ls6Ej%C3EDq zsKSW9jloTixT1G!dj)-eXJvzyr`^%_v@LbW)S$?Gni?6BgD8nj8d1Y>vZic_$}mMj z>%vmfBogYv&KL*d7;$b)C)SZ|g3UD=X?d`W)TcvqEhqG2D+yumV@t#z=k(^bpshDW zn`@gA!aicq^x2!EtF=u@uaga$;?yGArli-nLDLf*fnA!pVkR_I>De?HSE6_1j70p3 zEyP7<(rT!%&68niN}`jUqbE6D+JbZ8+={HXT%W^Gx0L}DxawV6&Vl@QcPrh>nS2ayJbkj)%`&N*e0OwtmWe2M`$snk1uRcUDCH@pM8#Hm z;GQVY3C}Q!=;J}rak}ENPIs=m7vk=Nx4f!vBm3&6 zeXtgig;`<7Gn9u_wkfLdz~bjSprXOiI};t&M{9>fo1oo;qSvFrfoKIhV#guTRp}Zu zq>pqq-bQxlhNCakkN%}EIwaBWy?CS-{n3Zik~##f&g~u_ByZDb2R0Z3cJvL6FkGRG zk=W4dJ}9cZP5(jBPgF9JKb2zzgM3PqMjY0GI2c5n6t<=ki|{rg7R+7o0Oe6q#ECxs z=CX%9R;Nq!d3`=dFy!((Y>+tG;|+yeLBG@K#E-w_ThcWmZ@*31Mhv4NH8)dgPTH0T zUFp7vo3KrvmL;%_tg~cl4grJ`Rs&x&@0jW?5pnx<()%)<4Q9*LQ~G5}X-31WrIbz% zRG~VLL{IZ?r&v$B>5TR%>q#0nEP4yapG>g`-qeAh+()C^`L9xl^dSWzA<{Y~?A@mS zEOD(F6ISV&(4H`%>@eTx(D>vVT}E8ks&L_QwEw$T&!{Hb^-CgWJ7K)xl6cZ$hxF`W zkbN7xe(11U{kEXb8V=b)I0?Pi=Aq9a+S0YcOM<%*jq*i1pmV(@);3%grzi=$+s&4R zYrB3~Ty~@k^9}+OAXj=4Tz9E2U1MDhD^#q6+gMQvl4nO!$w(H&HQ?R^JoO~o% zXA4`R(hYu*Bd>ZS{|D;$@W`^LU}m%t`eE7i0-8B9>JWOo#;iedp+&{OB4faIPPp5Q zElD1(nJ(cBNrd>&YuXJ*5D#p^Umw(gguwbEj&HfA!10|b!z+htlUaD;_zrzdPe$Ks zSK;`kL9U$v(nIUh|F4!x)=;KJV zI~tNVF%x|niT2}9QDJUw;3)RGF{vAL>{4b6Ioti$DP+1!0#YN>%-6lIqvE`%jeL{G zznsL76>w5>C32mH7u|KdAlX&cFJIP#Z@5D%ZS{7TOIvNIqtbQ>KaXWCiq7YGm)wCO zZg#cn3CkI>T=2N2F~??eoNwkafM5N3}Omr466 zr-5(R>p^)^ShZa}$=6bnGrq(sRk{vOtj#N77>LcacrJQ`-v@H0JqlLoG+dt2UfKmX z5|idfog9CBhq_~kAY{ZX; z68s=0C(GF6NS;N}Mx0P@C&PZ76`Pglh-pLabsKjHt%%W#7%3N$+tiKr3uwE!VL@N#m%6g_v=c8Z&0+N}>)XTt`P&MK_oQ|Gr)` z!G#X3jy@=?XQzWBfVI~#B`VX(qjevxt}ng1CR)k~JDDaV4w$Vj$c-}oY&X;)VoR8g zWl`FaEJ}B9r?paa0*`AVMIS&Nl_FxH(96+g z6;Aa#?1DKdhCt2R+=VSx&n-LbqIsLuF}_SOL6W!m-V_u3+5lLByvZpz;5QdA4eyY=gaM;9Vn|fV>i5ah`yHXkhV_fK2 zib>s{Uq&~ZNy6J-ML!ihcv{M;B4;U1rbL~xRPUQ;*)qbdOr>6j7Ry4*c0ifHmZ)9A zFJg|70TW2#m4Hk_F$f>QLi41;GP41eM)ks@VDR-NeIZ+|8vbhB|k1PvF@s6l)f{e(p`N8#ybgUN+{; zaJ!wTPqSEGec6+Sz~mlRZQT|LH}m1@E3{x&0OKF zyY;T{QYs6{QGb@*G^-<9k}TmrU;((wtzL~i`mJ8IJw()#w|b-QialxZ+dK}p=oRfY z7rf1C_XZq6m)qv{+5%pe+vO)aJxeHcD^z{h6LtAEd(1a_zslrnxyNjy*L;tDqgQQ@ zNwhc<5K8EvtJ>oIC{G8o;p$2K03|iE=I*1UHV7Awvc*7e?J-|Q8FB)l$7mO@jAHiK z@BHjN=4+s05(wvvJk)hOVtt<(AMVlf;mY@Reelt$7>gIA3P_9YBFH?dualiIxRHayhdF^kg4PoYqRhZLU_g)i4)ajF% zli?j57VTRHpT%}vRbD6$=^p5rhI_AxN^$^d|4b!WjpeMpN%>W)xVyEeV9ErX48+zIOsdBk&AzaBK#Wg&89< zNh7D4@no`|Cs(eX+9#NDIqK=I%T-SdPX9G!*dqc#9ZZz9GX{v~FBS z)Zm6#$JDJ`aoKnd9Z<&u6Y3bwA(%7=aOwuKy*4HYVYdz~Y!+*8SVg@%E*B35Uq09- zW--YG)eZNE#9$hEV}hOKU)P($OJ=fmr3!ZZl`2?fTE5ry9^QkLXUQHOQLONkeGsn4 z1VE449f6?VXSF-Ra3uhqat}NF4j)ylX{`M!&9i@g98L zM4t`>jQ*KEohVP|D)cEs>(!Ljj2>MFoMx!2Pyvxq#c-HBy}-8}C7G%KSx2@LB~ z?m`+FeXk5v<-S3bQRVhH z(-e8Zqz-P{fX@?Ar;QVGvec(du2dPl`VW-R)0Ezfmp6+N%S0JjUABta^8|%m_Li zQ2J-uXp20Zf2NI&B?L`zLavV_fe^2*@;-C9KCXb6 z$@PUKxjt;hh68#wT)6_;o8ZIcD4^3ryw z-Vv*f*7b;0%7B}EBr2D(0SVC!+-I)GK9R8=KxaaIyWS|;E7nmnD0o+NJYA5kW4flT z_+cOeW}AU;lZMB!x|Aq^hc`(xHHIEgCkOB*QNc5GM}A2RYG(TS@6)??x9l@=M4Qj&7Tr#pFJSk0$t~!o zbJDgV8ujgc<{GtvoWyY7J~NHFcc0$BTWdzpRe;hz)2O^WoqwiLe@$AgKbfvptWmk@ zNbDM89}iJC6gA`-Re4j{K@r!kG1|~Ac9-FPr*0Ai@#<_C0=`Iq`<{lPos$4F6Ya`x z>P7n}Gfw=d=fssORUyHO%TcP$blFfZXH5)!Q?FFRl*lGt9XH*aO>N}aPD!vS;nZ=Z z=De~cfnlGHTbhPf-UOH#uO=n&D*WcZq=s((2WsdzB{`#p-lGIFQA1vv&*lxe{T`Ry z;RrjOo^UwevIQJAQFM7+q8z?U+luTmFH)l7tLKIb;8vNa-@a+4ioSYNuZrF?BkEB= z>z}Ejj`DQ=nJTiprI+yrZ<#p`#JTglWp45NDN~|dC?UJMsTtKTLP?5gGf)d3E4QZxFErj+i-o~pGtSvghB8rn_+pe=; zt2k@zHE?%>xYvF|eY zS^LfX$m2;c53Y!D8T?#;F>@q8OTd`7MZK!?D$u5sf{@$x1(bVVx5@1sPzC@wkCT~pWp9tdfh=WYz;cR_K-gq_IMq3 zyDi}Jc>9aY^MD~o=}i)GPUq3vqL+8We0=9eEZ*E%hd206PYHi3T?K+Kd*y97vL zHRx^v#VPYT9-Uibo_A^m=F-UoGX{>;Gw{lHvGo%iq`TPE+RLGPbV5<`NtcD0<@BAN zyI-4eS30pTHjv}X9W-ZedwIGU2Z_Lejo9*5Y#+zhK4{LZ&PgyozZ2`k@izdB8M}HW zV9eNc9qP9Y)|vskzCJ);%=WV^h3VHuY>8?yP^&}W-`@*}Ce|qV7~*cGL~RYfhzEQh zTKw3mnrPPH*u7}$A5}8(ub>cOVDvkefikLWzf>03lH`$mI#~dhs5d_yc*j0RIt34G z;%!fYI>sBqFDLW!Wq8FKdWMB3-q6g0dT(gbK{Id27y;@H8P_Rf%+n!vIf4#H729H+ z7&W#xfuI7eI76M^kFBZ0pG_g?qhzj}rhR~*KlWX$dtLq_6?DU|oc8Hq)JN!~DK!c` z0$R|zP>`nADC{Kgf2WZCdU6)`(+AC@|AeXZ@1@*G_LNwHQ%{L4QN=`I!EoLC4hjuZ z>6&(^-#(4$ZgWUyx*Ivy+<-=AtlY6|KeQ{XUfO3%IaJ}}{a!S|8^1y51|SKM z9a*-Zsllzy&Ul<8L)+$%iJ@f?y&TlXUIprp6Vw^$8%a@5Hlos$=7qC3K6=Pp%}$mD zAfNUD(wE<&@H>uw;gGqSeJTm&VynWT9KQ}=%+zd20>(_xW>ZQVsM(SK7PaSJ1+_oY z3)-plg4P$^y%yx_KbIk2KN!l_sY59ZOOll3uqCQcD+_E{Ikfmfb`4b3R@fuU#0HJ9 z6%H3#9tIH2XIRad618Kgr@gQrFSwaXy|Gyb} z=Qehtx;*_7#WQJhB>j1FVaJNxNzxM2S(*u?f#!5#Ou&f@e1)NNGsNXCBddK$VV|%Sd^sqcWlxH^o6%-m|cO z2Hz6;GK-c}lkh^>Jh#0mOqk`F=a4mSjJrW_NAgSyTim~%`z5zY0!OzJ2>G8AJx9%NP2DN8 z?XIJVs|r|`GHbQ(QTp)7jd&2f9(4d%iext;o;15$uQ*)PlM$;FdX$i?Ku5Bt^dw_k z-IZ!W z(*PKqpHcXcQ2&_P9a58VG+~Pqhx1&0v^Z4Q5EXgrHDfYl>^JCLnHovR;Z8BNg9gAf z!}hM(h1JXRw;eOg%5l^HOc|48<+wM+nBN!xr?QEtC|m&l}e%2v*V|s5p~JkrKublzo>R-7%*e{JR7} z)$SyRN~)s#XA2(|>O;!}nb-&k(|?_J^>^a6Vct!#ZcaUt6Qz47tg!ot~{FouOC zzTBe4g*OY6*oN|&LLQYzh>vDbHTo#2&+qhtSH!GHCtVHLwUa9 zd*&{fTjob$Jwz5`6j?z$!zv(@# zdTjq5!xNo)bVD7L5k>nbTiMN<3TyNHebASg<272D6YT@&+)pZu@O<8T=04VxB$)o& z3jgHzi2!5fV?`1$W@9BLkH}ErsZ|uONVHtDfKGtw$cJCs*t}P6!RkFh` zvaaVL?B4II9M|9Y=?Qgo;S;~8adSAc3dcJRF6O09q`bEZdicnV4;iqV(RDRK%_w{~JF-1@{2FZR=71Z%u z;ZR{Ui>HZSwBmieUsU{lO3lKeNcicQo6yZyZh@#DiLivm-QOA^SzEC)v7k*|c^$MkaMf&|P+X3;CP{X1r6{U_}6mH{$ zG9NJG5m)JkuL|!F1h$yCN@YGkhrfc2NHPF<2sqma;NEWvKh*)~Tq}bqN*lf{?7|5y zwnJTJQHuc>U3Dz3DzV@fQQggSQb|@_?*G;`S{h{y} z#|@+K%CZ~E!jom!{{#K9>-B-zvNJ}4E<58o1wk7|(99Jhh(51H^jUVII#+BSMO0mV ziYwObr^2Nb;hi)fnTVzq&8kdZRy6#Ar-RI?%^#TgWE(%AKG_01n%*ay2Vf~oe<_~5 zvRJOlCvz~8l#X=YYDxE%o^(u9>~l)GA{k72-9@ z@pX@zyJYi{U`|(w4=&5M1Q;`yY*hlr%q8QG6V~wGC&}*TsrmA$o-bGKl1)x9hAiwX zW4TAqXT_fth5)4!Vt{pK&LE~lUCGKS*;A3c0oN>btGDo4Qv9lM$H~6K(62fuPgUC; z1ty`irzo(Y-*VIdnEF+_>&8zBh(Uyjr!8cQRlf=y*ZWoP8+0#=6(X^;S5nOFxB)Qr zspdC`53j_(4$`GO705VMrwqW9F-a`#y%b~qZ2(MFyOz9)c2;;=z7h~z?@-lm8E?w} z2y0Eg7j@~a16?a)(wV6CZ=nvlQ36dZTE#c>l|Q85ifXU$Ap=iT`+tw?Rr|Nc%~ZQF z5>&Mt*C_~A(;;{{9+mS$y+>t(*#m~EPS<%uFGzOIO@MOk6RNl&Rx#5dhqdLdOoB14ncaA?{wudX~ z5+5z>V<%7ik-bcbDtC&S>rDwF6?KhQ|v2|oicm4!g+dV!}3q@{z-P0p(7k<|RWa92Y*l_mt0d-K(Q}Owzpl7^L z+kuV~cN$HZ+-G2sNG8ev-Rgk7T5rDb$%HK@x4d$SOLrO*m~JZWWNU)A0$#7IsGYHe4k z)u?WyYh^vLm~Gi+N-TqIS^YurBOL!BbY&JPUnS4xecA^^%0~>2cj5UjPnvJbHYLHd z_QyAK{I39Gwk_M8fHB*a9XpBk1WP->{k~D*i8YwRoz1B^vQf{GE8mqZNw9?Y5x(g5^o3!aE=JvS( zFx{lJ9u^-_o-a9NxJmok08AN^Y|@sb7_)q3YF$}k(oGs$lFZYpm4V>;P1?FKuu0o< zN~_p+V%nMP(zZh#^rP(3wvCO)`BO5yqSrrU;fY><{gi&WY(HhD*Nw5Ddfm8Ak=i;s z3@>MwR_3&Rm&TnY7^+HLw@X_zF+NVH2aOUWVm(;qDnRYi`j9s~LX=8tv)Iv)H(R0( zsNmZOov*yChO~21yaU`I$1_q?zAj|j+)QM$j0ZqpJsB^~GS)AsXiEGcGY>3tYJ8e7 zf}IJ8q7P$AR5YYNr^YMt!epjW?{YchiL)KhD0ncgl&xn7>2@$pf>UYhG-z=1} z#l+=`oJQW+@$m)#XJ^On5MDF@kY+s<@2($08%B2-&5`(6?5tI^74F@rlU5QB@B3`L zDR&T^55&n8g$)yzcCC!3WwILIggSLKO4*TkDaUYeM)=rCn&=~RU}=ucghimP=#9?#>@A9LasG_p;k z66#YNhlfw@j_0D?#qnC`tK#_esAB(kTlB(Q_~%U}%0C}2iPuDR=h1)WqSx?0hbR77 ztrY(0DW$ayP|o~W&86|B@dy6jkF;w-GhRGn?l|5li(Bp)Gsm&@8R|IJ#w61_j#U9H zh2tn-7ARWesvO6j%yLL403n?K`1Jz7xQ~OW03^GQXy|jm`z6l-cj3ZoJ!E+ve)WDn zUI9J6G+wVL@|+rI*YokR=-rPdW}%Lb zr^=wIO91?bMe#C_8PkC}RR=IqzGo3FuYi{R^y2lU9qX5&#obe|Di?C0V)44#P4r^JQMxCBrg1)iC@(@v*t{j zGkFg6N|yjN&30GI6Vx=@T|Gfn7yr;1y@eW_T5mm|*IQSmzph62ZUeb zk^;O$04_CZ{8D_gFa%FXv4uL1`t5|UFl|VPGj;!mE7K6?g_q+)%$(@^*T)|crn7?+ zCwdA~qE1}0Ziqi!Mkrz`5sBnJDaD5g&#=WLlDm7|!iuQFmiV1~(MKr$&YXc}pzBhC zO7Si6m7D?Wp9kj*5%wm*jyxQxfYu$Fb1NS9{L|w*G4Dhkmubl&NS7d6qM{7H2(LcE zQVO=L%WLrgmasb*2-qBu^xori1nq90&Fv2b-C@7a;kEj$Xj;pIHKq0SW(+Ko?Fsn3 z{!kzg2)nF~kUQuVZSWvcAmsPh-9abXaA8s{Y4^!V5iZ*q2#9{WJ?ON#LXLnfj zkjv+D_&ip(h^FN%swIs)J^ciilV*53>esRO4om8h;ksMh8O>16l1Y`(x_$8q78uFq zwR%I2kk<*1iHJVP>JM**yFzxi2a-{vP6sB*I9UR2S19PU1zp~t%K@oj{Z^mV9dOzM zB0Mo3uu2;m7w_V7yrF>G;|bgBqA%zOIDqXNR zPq(HU5%tR~7SV#k@s<|9$KkVktih1eV{g}qr{CiX`5dmW z8{S@#Hmsb!lgmjnt`}9GI77@%JtFEiWx9yge;99`D>`z$&Y`2*JwB-M5E6aUUF1rh#7!2mL{p&*gO3Y*xq?4wDas ztstCM9}=s95EV_%k8o4caHla0+P&|6gM0Pq+U<_Psb$kDYFnqFn62@kHaRG3kPvqKL;k&kw#XT zyn?ehJyy}>vWo$y=mXtB$Rhge;eg*}vjsw^Q=7>(rIC{-tp>zXPas4D)slK>FGk3U z>RpP9*)X!*=7oh6hM~Q7zu)czUFZbjhC)sk>i6@ST2vKU9ClmK?sZv%L7(5@cYFNM z$Qu?tAwOP(9;x3u(|3VB@Y+K5P*@a$kZL+;bNfWE(-jE85_JXLPOAi1!*WYKYEVSy z|0))rTl~(TCmak~13u7lAP$;FnZ<;oyieIWBwHV|BVjpBFTBFy!((Y#^u}Zz$vn!b>Pl1WKf)R8)I%J~t%| zmt5Lalukmmt<#Xr3u_K->b$6Sc519p@zay!73Ttd=(mMj)-Z6>6@a(=Tw!~_3Q5>p z9$*b?2EypPt3)<(7M}-bDMxl-bgXE1C0KZF5f~B*J3Wqo+u;+f zez0?pN=}41+noWs1WRN@8B5S<5BuHzFo;{w0Y(ihyu;@6hg~87*<5rX4XH9?4QH_i z9N5Iciu1W0{$R*&4Z;!y<0*#RR-a$0ULVX`PMTp|)V`$~_SolO7+|?oM{m|BvRIs6 zKfIx64}z%K9d2vb>jd(_b6Y-8bf`ek( zkuS|Ca-;Vf6tyVpw7Q&j#5F9cXR$g#Z@IC3!x|Z^cEIcOxZED_D?;I*S6UpwNZIUG zSeCG|Y&L8Nt$te&)Mf}CKXYMgC>0kRT)<69!<(>f?W7h;UF$To3gDIYMto9MI)u>T z-3#eb%Mm?cN5~fT+rdS2*sQSBz4n{PI+-5m(~CEsI{7I2mzXkIXn_Mj++9U$nSm>_e%+u;HY<_q}QHI{B*V%{ui z#zl+YF0vBqmSYuzu7E4(1Q`SF*jzq%e%NR8foK7OphqwwBg$k0t~RF~PzzffkkJw9 zu{RU)g&kHAUNEf#K-zsi zQT8L#3=4+s!Yna6&8WryJ=hE>hOHKwa7)qkhsOafmmdtG%jyyR0S7i))Ebc8TaMKo5`9jW)8(?lE&=!o z8rINjJ<^v~X>nvDeq8IkuE(dr4e(+s@Odi`7So7v@MKlpgKsN<*|DSzY_$lWOIpK@DOA*n)w345YXFLw+DV=t1yJL?FIT1hTk7 zAqiY|SYDuN{6VWIg8hbc*I}M(j3-;#$ zdq{K!13}Rp4xxTuz#f+F%F+xQpav|>usE(G8!#}!PG=ZK6+`AjPYtXE69O?*5%%B+A|{Ian|fp~-It{s8TMrZav=obWLKKa?*+pJ!4WHX>pn3E zdLayZX?Tbbf@~plNm2tmsh_1r$5ukHn}$MCGRWq)Yu8R1`Lt?V`%_cRfnCL>3yW)K zr$h{_?u;Ti6e5O2D=^dJhusN0$Ou7Y59q%z1QxwMh$2uckQ0I^xYr6nB1li~aQOUi zHXzzz8vtGiI4gE#d**>~7U=sRs6E)lfp~=Bi8Y|5!{@R=K$?m%U2JK_4WfRp7WK|f zJuKaw5n~4C2{u4(*l2@O1i$@ZTfh(TD;ES|!oIN7i9a|J?0_4d?FHAx1I~&FVRSc` z-LNNQgSej0XDgs zW&zs^n=Vj(pwb*bXb;3xK{nkGPlD&_L>!v~OoCv|Y!IQrE6d}7=r5=}P#0jB46vGR*_hESW|~ovSap$@6HGm-2Z?_d-`mbD z=DJvK5fro)-0-kJ><)qMfZz)p`hd#~OD$-#LUf8~^E8hr#OG2lm-KHXJ8`&^g2s z;3-tt6@XH;!A8~!*K0_Ko1UbF1Hc3DeS_HDgu^zxyY+x*`h#K!g5gsyEmTg@(v3~6 zS-PRgcI>-XLH|P-7Q1}AiM}Zo5IjyWQbKs~Ac#;spv4W=yzL)5R zm^kbYJrGay(da5MX4xJbym7!eU;y^2a0=vsU>=-CVJ|KK&udR@d9ao=b#M`cBvTLX zr9tsD1A}Q6Wn+fjS)1Dqjt2M+SY0?=c!`Q`m(LwW9S?vur&~*)G#r?Tfe;WK9|wA@ z*fV#7+wTZ^#DE;x2d~}d4?$oH!i)|N1btmzh+Dyi89YvipUR4l98&mG4(*pu1=9@6 z#P^T-iW=v-b3Hc3gTx^nn1vhEC)QVfFZdPUC4&}$_?g2c$M@5|va9UPAikfPQ7)ZP zOD&}~z5AG&cy4Ue_WiLi%aR5yHNX!?mTnImVFTXac!8>fcZRL7MR0=t_6H!=CKWG) z6OQbl2tfkSqBc0jg)?hk0QTNC5sV%9lD3dUj?*kIa64h29=1ZT4}vKWX}7r{I*xT2 z?9V}*@di!yAkz#>)?7|F^|11h8a8VU{Sfzscv}d(Ebx+msSu)bdLcLtLK{TBcP3>? zeIqmAU=Yl$-(wBIDH=pETyW^%0%rz}9&F(3dR&ZUvp8Yf3Wo*oJVLWIasBF(`JPoCXQwKy>Ra!=!FJwOVJ4& zmqtx4*$I2*lp_+mmTo|1v01+lJP`F3VV@5x z1-vYu-5m~xr41V=!L|Tgs6Y^&GlGaY?1Nyx0vl|*6*Q?sba?}CsRM7qh=H>}3>n@7 zhX5D&DX`&y_$|aJoPM}^0{w|_=D2m191a0>kYZ$SSPt-~8Cs4cz*P})h7Cbe2yEKn z3Jf`|0ribHE ztF_GoV{BHqF$BsY*U9>r`2LOE>x4}V-oAj&boxSWkTJNo&3T$1tx3{j{-qPK*({0YK-QG&RHFoXx*0tMP zaN!lc4{*65{B46ctJ`($_SUuATiQ!w*KTh$PIJZS+U>1tx3{j{-b&mXyLNj^z4LbM z_SUuATi0%HDc@yZyS#}Ma*X3+3=~TWIuA6X| zbg5SKR;~v5*VyvcUDrEr9Q@6jd{=q2cva>JzWiAH> z4UKF2SZL&k$41Y|yX##j5{ALO`eAK{KK}Ta(BpIRdg8LysP7G@+Sd65UJZ;^88K$` zu%V&H5-m=_%aB`3EE&5_lt&w;pnAM?0=>Moq(!ZvkiIusIp}-rk+wsJ`yRr*`A(y6 zx0W<7Hwn_LMYB-VR;TJ@{PIzGba4u*J*E8F^4I0(&D@IruHK3NVvO?hY`gJaj7dJs z9{hPy6*Oy19s1#|M87J2nx z&^*GhDfRKs&$qyz2Kd)gFp<25_}99f@FyGpdIlzxXTiUmFsZy8{A(FZEUyv%b@xH| z(-{A14in64f`5@Gv76#w1^jJ+f0FFgE%7gsn6ZjV14qrpL;FEs%8U|9;z|~IpG9)CT#a(7w zP`4c=W4V0kwH+mIa5;nV0Dk-@g#SE@|2%^KJc|E}I*S(VD)HR*ApVCB{|Vwh58*#~ z_|I_sXC(Xy$(ep@V<9KmqWj`1oG7nn8Je22-&y!LZFB?Fci;HdSq6yZ^*xJ<_l>WQ z(C(6ZvTnrSGA-bj+$HKacuXh0?^&te-DA3O@B>|{JpRiTQ*qWvV-L%@%$bTEO_yNe3r)^6ye--9_#J+53 zZL`Wgj%7}C<+M2NBuF(nEqUpF%f=F zrXk=i3sOx=AQm6`T;BRQ6T9>EKSq7Op3_rkm^6TONoFO!?#F2H+=-o-?rIx!cgZrL zofJD&vV#}gY(F^vW({>Fbt$O*V^n;4Os6a>)0ClbPO~TKOD)cpG~t9kObe1_p6RSU z?X2fzbk_1*2|SPb5Zf7tX7&?Cuq7nf{6in3rk|8_*S5vu<|03hLP zmzHx9`oe(jy@}%O4J3z2oqK+Qn=gCLd{Uk07;HZz|EY;`K!1K$GDm214mc}oG^%E@ znG%)%C}%@{UY1GS)Qct03LXXy5e^M{bXg0bBU_AFngI>b`A3)CtAo($i2Err?0}TQ zYcG}j&hZ1z=_&kx0RWL_whI*>MV=o9;!cKat$&WM!^0~B+~h@f@Z-*D9~2mkQ6QEJ z)GdCyiODBF3hJN>oKOD888=9?ekmEvN#lPhiE(_9+>MfkImUKF(lAdrr_WD4>Kx5O zJg^50`9Ke}=Z}e&oJmgj2eB!}xR7TQEXH*To>OST;2~=!j5d>pLBE$og}p4huMF{q^4^A8}RD`EN=Z@(Qp=`h`gM3BRfUin_^z5gPybxT?lJn9;QV=q(C1g}jNM?cOQ$i*`7$FJQm#)fy zcc*}84bVu-)Gkep==B$!&`R13c&T5#IiY?T?lns5qg9@0+{^&1S4B<_H~GMXcm{sd zMdnc>=CSUCW$INc0Y?bUH|~iR51ZJCc~9s?${)V?yk7a_r)JJzz556Tl$ z^&6Ld%vmV7vc?;*;6x)=_=FIpa(gUfMCD$G_Eas+E^nfcjZq-$+(aizYnzt#=Y(7F z(2As!tnexituhbU&S@rdwnW7oevwS(E*-@>?VsHetzW*Z2^Y|kZ3v6EPBjinkc}9$ zJ}pXpJU1ypH}Tu$Ju0){glwTCvJJXgvZaxVTQQD`13Gwl^nPJ^3QG2oDY=wwgFR3* zrETep!X~zuxGW?44{s{G#ugJVbkis3Xl|)f3$XZ;34Mga`nDO+Dat*P*GStKyX@I2 zi~AW)3>?BN)gfe=O&=X#OOn|?5cNA!vnJ{zmJTyB@yEo{Uxn*FWkip>3GgR_gxW9i z*h?SDGOm-hLLDd6X1mf!@h}-hwgW1~Y>7G?{325Pn2{8JKX`i+-tno{FB9?TCaXUe z>X_C4n7ec+-%Ey9mVXZxp2)H5Q@u{i{WPURW{e75|HgGv%nMV=9L-P;++n;YANC*E zlTTvGnQisxPtouHnedFjg5hTh2ML8q0A!mC099@-9j67*3E?y&4vCr3T2l2}N=IY} z%h|5sP1$>em)H`rGM0aeZthaLN!ykzB}>=RuEK5ufO)@9Xj(s4e(t3O)v}tQbkm$+ zG)?j{nlXhHK`KZVq&rafhSF?aaiA{X*_jB?d8ktbNV?^=(%U$+z7;?#;`1j9ed&*A z2Q;L0X-(!W&-4Gb>>y8%(w{8s22Y*8f;D6rJ2D&I>+D%NOt4*mQpLy+XTBX%k_@{T zb+VWGEvXx=KpP6@)|CEpN9p&x(4FazzPhLCxV19h*zV{<$HV)U-p2{|vam#CI?fx~ zSs2U~6B)brf>d;8sYMT=)u=BSjYgz|9X}c8P03Qy5)JXc(1e?fHnd`VjTv}eCc-im z>KI}9vwvw-j$26K6>)i2^1B05_EwbQO-l9jd;Eku+_dir>c_gWabpnJ^<#;p{4Kf{Ibu;qicpc zsQ)FwTtB?DJkM_e812(y>Npj6YL1FNGuu7IK0}utdg2a7%a5ZJ=OFR##tAuDYOsbS zD)Haakhvxg62NSD6f(g9LvHz8ydf#*O#(v9X6`}~na`V&Irp*BOFaK)3NqK1(f?n9 z%*AIH4b9-Id~QzW#z`;-H^n~u|JZx)@TiLJahyA85N?1#2sI>u&|7*TAfPBURFNu> z3Iq@kkuF^b9ofwU*v)Q4K|0vhf&#CNE~qF13RozD6e)_d@05G@o;!Q*CV;;`e$VrH zpXbHR%$zf)&zX7=szqST#N5aY;}-L&qy-HCUw{9OBd4u^Vm`xF5)eZc1k{zW#}Iam zAnfgUZX^8Qi#buK=UKhmb~|hCwp~b_=C(Ncs5JqNP~cUs9R}f<*FJkz^x7_G|9h`( zA#hj{bF!Wlz4kRXhbp}4wYz4mm{JpMA>5jIZ5u_+8#xZ*wYwZE8ik>qUd-tY517nP zR;1TNUwbj9GCtgEH=cEyld$IT)>+YOU*l~{Y)&R)9d?~qF-m?wRjiIUEYG?NN;LB! zzu1^D4#)JIOrGICx#Er_2b}}JB+HJ%&ZPV}xiT!KqZrx83&4?=d?NG^!Z0>okp(1x z&$?iVCrML$^udSvLw?5oDAe+t-W^gXT_&SgBB=xJaQw^)E9$MltM1Sf!ZUYx;+*IX ziRaAR!I%j?h#A+ZGM`HsG+0=5GdfpbbQUC3SY4gY8P%_@(k*Dm?Q1&Mtl zN(?Nce$6Fju#EcVwG}}==o-Og7Ds+iNV?2%kiD?Hwl8vU&^n*Kds zLg>8tSoMpD$kqJzit7kfBrs-U)nzw~S#Y_?^@V=$u;Z`2E9-!$Ebm_%HPY5$x3-v* zhkN&d+dS?R-Cq$zRv^y)Zn!eB4EL5?s+kIO*{YJ2d@yyqDk=LtRB!Ge zG9VD0KW`qq!W0$#e%>s2{c@fMuS2vGF?fARz`O*n&nVH46}&=$2^AwTsV3fj6-@7| zT`;$eP*TTjLr8+~8P}Q`u81m@P#-Co;^P}ZhVzjZ71|OwGBQ|vT-~z9N9xGvz%wGZ zBpSj^6X9^p=98Pb_E)v^kz4~KEU%GesjDl<$Lil$O}l4xVlGK8cOnF*dGgA9aE+z%T`#-V%Fjd zIHjKDB?Ext^(<}Vbs|6z#Z2^kksi;hh}%4<9U;K#1KvtE)j$C3d`puCmQIqq6Lug3 zyVXXpbh)9J5MG35uGt)dKXK-FiGUMKO-ke!PcnV*nKi*p@fF*W7V;TL3d>M`1|^=r z>u{vs!Vp)@*Ym3&`3h)?wG8!dSj+@|d64HbG_{8%t$3sH;=g(LM`%1rYNt=fBsvdB*&X)Z?(hawUyhQRSH?UbG zKgiW2eJC35cJC_MBC6=&1G&FK+|L(yAU8IA*WMnM%}8!=5txJ!ALHGKKpPexjZvz; z)B+!Au_Xlm@yn9#W>fYHy)Dz_POxtl5Zi+if=!qfwZSppCxy6fJZJe7$$ddn5Dar*0&@Ag^oE{c9JqRfk zqT@c!sb_Rtm2N3t23@*np7st< z35!|$S$+vWGsm(^0MKW#r`&yA$!VdC>uz2|wuXdF#;ePWh z4TSdku6{Aoi!F8i(bIpL z2e^L~#XR|^S%7Q(Cr|wv(@w+)SBHRkiEt5=Xy_gfo%&q|hGQDxx}r&fv|bja1#-V} zTv`UX-?4m)Lqb-9%Pgcd^XGZrYa9p3`|cK4f)HB!r+M^B^njVQ+A>!{?-Llakk#4^ zV-~WOamC2|{>0YA46AvIB~hr>WxZS0puL;Ko~on{#Gd!w zAh9P&fmdTsJcMVlC*rcG(5jdJ``D91IW)_#7^g!8PO~7TLWY&jIrR)c&EB#^`lH#G z%>&SAMMX0%n+2e0mzB9E?L-Vf=>*J602)MzeyjjA446=HB6A5Mdsh@0h(g=B$PA*; z2iq+xCG;)9Wfq0jC^Q}BI7k%w{v8WB;PBgJ^C+~@1LnE+Ez>1*gTRM%M z*A4p6!)UjASIibsF%KVwY;Mi4C=~A9rFYPfQOVWoBg=eLxxp31EUe8+Nu}uj$fZpz zEt?#P_ov~Bav*votpI@{3ef2z^h$=LGq| z9tgNdgq@eWgIuJ_%cY&=ebSCM(puG*TNqy%luG{t89bE(xDF$`1wQ+tC0mm15ETuo6*Xd% ztI#VMTD)s^bZ}@sXbuF%Zi0XLrzKC4-vce!`Bs2Xxe30#gb15nwXBxpgAgG*^m-JQ zu=)JKt9Zs=mN)fnsc1px%5Cty#dpqbgnNiVCqX(|MZpqH7^*5gZiM??BUOr}^ZTA! zAIgnz1e>rOv;W<+RFdRMx^jBspav|VG68e=NiGZK)8WG&+^b;gC+&D_Ol_m&2dF#v%o{9InACm%||}zq)+j zU&}fP^}fchE^7eFi{ri>c0iNJD;QTo0j-DFZl*uy-!^ z*2r2Jp-(;J{=5MIGkA!?lgr`6`oOG^T^axBnr=c^_BdsMhgs@}vAFwllgop={|8b} z(9pb}UK`);Zyk)#Ed_}(D=d3mk0ceHx+YEvf4IgcJ|@=_jTvEU@5N|oigsT-+}K1k zrN```;EW&jC&dFL!5EX zCtzM?+;b`2sx=gQk(;G7N>ZZQzzR+t6|)z4{Vd4q1nt+0Ssdf&g!#7qUVNJ~lP~(> zejThqu6Ac8U-w1-l1|7Z99-|#r1%3T^KXuq%)xh@ojfg2DvN{5Si{(%9z(DTBG}b< z>$hd38XU}hl*dqUcU4%k>G%z>oR4|e+EFS!sI?YeZ+IK2?XapgHoDX`_7X+M zY2}2ACEA7M-b@hcMZK~;uX|T(H&U;8yKCl?)@hRT0>`0x`v^UbRMh9X=*DDZtD8X;UO9%3F9u9$hix6KwQ%>2);c&Wa7qULBydV7{-}es zE`Fu-lxq0T_SUCx_YT(FVw!y4(OOyBPNn8?=D}_7X_3mcnk}3nk`RsQWSx$4I$OhW zWYeiN@Q3B6l*LzrrZg#=IV}B!q1gimj~Px>Ax}xbb30pG;q)|XRle_6BWOxLJfI8t z^P!-lr2la@5zbd=G&b;{@qU`MJN~_!wFZ8z8{zJg@>7}xa`G5;qsvc;#ZrYSRq>Us zgpbx0rljKJuGY{JgOs19+-K$H{|zV5?Z@D}jYg$<&40^vGe_QdUA(qn13r!kvEfo>!Fw*3x7;}G8Fn8wC?Dlre8+hif2{qk5UG0W< zpAIGUz^7w+TboKKK|!KAb*vsqDynrubn4I>|GiVE30$g9>CLGJ-w>U8pqoP#UcFlr z+s|58LK6wI`o&*OKfM(FjORGWz;m*{b*Y5%ZitJ&8ccr=nBN9k%Svb^fzdDiYA_jY z7&9Y%@dh(e)S?qU+}c{d#HJYzP0MWxbjr)t8HeMU5mQ3&gTdC3@|RT0&``rQ0$(sN z)Bt>SZpEg!Lb`P*`tb&C8#Sp0wxnC*<)0yIwYHa4}jdQFcigDTr)SPvXYQ#grgwQZUAuI;AVFw2G4z*sEpM=E%=qy7} z{j(R)EWE}rIoEno9_*#J5o+S*rM0703i3Bc$v)|UkUVoU|vv)9}ls_<%OOhu7K+#_P zm0Q#h0pDNQ@}~8s{3--xTMbKK33KyT|HcE>S>M*T6-+^WEiU3V6T<>r8Y=u!Q}39I43VVF_EkTY@n++!BoITuU}t zTOs)-6e|m=hePMJl<&b}ChePlyOwUXk_U!$5KJ}Y{~;n!O-5;qbGKXb^=$>!gc(^t z2~<-@P>uF3LgGJqFO8#gc~MO?sRyd*{;u^l>ZagOowSP{M=DDGM|9HYf6S(D#**iG zg>ju~e@z7@)x5x4UNPw(F|T;p&7cae<`rQdSa%}y8e!IK`WCOKXDP=)vI=sgdA<)? z^N)E}@suabQD;*LcmI$qSvb#x2f04pW$hy2AO~3t^1k%Y%Z-#H1oZ7%)M9x(i! zlQ05fmeWme!??vPvGJKrYt zY9t}iCEg`=Sk`q8BxVY5{7Bde>_!l=z&Tpy*U;}vbL4%se-HZUrr{s4P0Q4?%P>~iS zk-H2m(TFC$Si1DB!P_kS*8j`@x_yRi7mlNJd2yOCq#iiU=G6S-_y?(20M=?17(yKHHvfF%)q8tf*syp27A zQT~qJM&vu*ZKMpT=QaZ4j(&@DM%VoP65k@7(>4EF2{k62m<6Ne%DvqN95G$0?w;S) z7qz@&KH9eMfXVNjzuE_NAuwj6?K5r|vtU&B4pSNmz9U+6nLA!J=1*k?HRk=CvXROf zDAYy$%UnL(yEpXU_wTn2$p0D1Ghpw+it2N2w17gI=Z6}1;Cf|nK9*$c1!cM!%DToV zNyjWq4K=u-kKPrmZ#(VSON81&>J{sw2V=4`^N%2OP(h-GsC{}Qspx$Vg>Jd?--oD| z1qN0B@HVdheMcPEZ)hAInp_2`Cy9t#1P;${FQE!|nJYEdUWM1<(o{t?L3f#y;B)?X z`-uEKcu4N#ApG8l{KF{nuDN&35z)BrkIW~#YcT|dc$d+sgS5pe3@+-IU(4WK`M|s4 zNPok-Q$cIuBRwo}o5@vNYfOHTJczLDc0s`jRvB3=-I2&YFvri;eQf?hNgnA1-ev^P zCd^!1GxY_wC?cIyn&*lmx=Tg|~6phE{ub0s43KG@q9eO0GXyIKJ z$H0p4Y^oi@Rd{1gN-{EUF?pUsX1!*)=Uz%T4D((uDjG%kZNQSe49x8EdhL698wtPX-9~DX zde4EGzB>2*ynMSKdV=6H8-xE<=t|`{2+Xm?`ThJ+w|nMMLRKzaYA_ot`Lq4dU;<+{ z28X+0%*Np4dwdLzx+jjoHSc*HgR4>TK;=CA7<}EW9X1A=4UtQ2`D-P41?-Qd{_L5d z)v%b&6;|BCCsU?=ECA?J|3pPD&lox%<5kxyh`iADqY#WI9W2KTrnUmQFs-~EP9 zS?RtT&T#SnKB1U<0Xx`vWL^FbQW!^}#>wh>6sgGnzG!Rr@0q0s#vJfCXamRn+36LFcKT^_H|rr2HrmNNPb5@w3pyB zONWA#yPscj9K_SR{gD5rA3FZPJRJ)6fEo2uJ~{z+90&&KWdfFmz*)$x;hQMjIl%Sb<~Ao%bvhRaAz zB&H)&RT2%j0s}Fdy9ZGjK?Oa0Jbc@&AskZ~XNDTvzH38eJWP`3!T#9vkiEgR6c)1} zGf%?*R>3a`0Q%|SO?S_L33jCR->P_#ybdB2qV3;Cplt2Rn0(cB?-@OA6TN0*l=QjY z_;%CIy@aPvNj(Tp-`BwHrJpzoH98&Fqew+xa^-@rsC+8n$OBHJeOO~QN)ny?1HAYd zV>Jl(2*6~yO!**K*+68#E4XeKKBN<2E4}z7X8UyOy36we;|DD$%Vb7N_bOdx+}j8Pp5ZT?VpK$d+wqyhcWu z?*tVdDMp2d+$v;CFIfr1)5$Ai%!5={|3i->Rr+3(&mr{Rd-@1(o=$m1VLtk#tGbVO zPp?Ai4L$utGS2fuDL&?T$V-ZP63GMH#(7Ab5BKtL+KK4pReZd9c?c!^vAleg+k6;{ zW}C-MY@-CRdwItVXy)a!xu(!)AC`@P0c)stHD!rvdU!Wq?begm zbmVj=Or9kE8TO|=VRaT3Yj;`L6IMSHp0>PRT@NbEes;NO09Hq{i*WBj=Sv*9uw5lw zkZnrGHUzDbNHyftss>{ic}zBd`<{UG*Z=2d{hX8 zLBT9veRxWzba_d-29bIao`|2ZP+Ye^PQ+FF<8El0f=G?Wll6#FQI4-@$%A~&0Z^LSFvXqWwx2p52dWcF@UnkR4P7`T_Ha@`GX7FBln@;S~X4_&oxd8q4OIIVC`wU zQGR&rjty0lqs4XBHEpL$P$7MD*p8@p7{pF3D5y)=;)9uH+}6 z2lkd7Q@8sqJNDPh0i{ZH&7DC0&7IaO02i$fI)=XU!xLY@38?m8>@B;11>~O6qKJ_= zeb4t10l5Rn<%vbz@bakRVYqqu<9@jNzVDj_HSJ&eD$5z{PB<~$9?hU z*yH7z>?W(v;xl(G$SREIGk4{E@wVx>2I}XB4^GG7=yN}Oc{;8-{X2p@FSj;%9wRT8 zQj^E1$YY)624xfEkwZg~=G@^bsJ_Mg*#wa4zxkw0_Ut+w;@7kiuM0{z7wu~E;%Kel6Wb3Dw=`S|=UZKC3!(64mh;H$bx9dzVO;tb!hgI&^ z?v^%ugTHp?iwonAS8)9_ACqO4?%|7qTs!9Ce!j@ppQW?l;k&HIdibse>j(d^0GF#I z`4ifhLC{}>F7e^Jh>Z5cP}mdJ`nG%k4DrW@;@-#&HL2?w|3)J2xCl>{n}LpSOmBNy zmByfi5W&XN5dYG6=8@JlaM#87J-H=lfp@>>h{VxLaF*N-79VM0PeU#J@s%YwRu3@t zpe-$oAmmHFoy&42V6S+4`wh+UmrL<7l>b&) zuh;bea#6U_$%QVkMZpq{KUJKSiLI!Y(%WDndzVNU=%W@V>|NL-_^`b)zK8KC`3qPa zIbcg4`5-J|8veo`uOd$X>f2HcM``+r@a%<|wE8UE@7#~Tvv3-0ZBJOd{ph+7{KIOz z1Knhh(K!0IkWd`mx?%+2t)2m7`lt=?eK-S2dcF@=Hh_7#A$7654~U1)I$(*WBfO;$ z+vNHIq(pIT=5e7>paiu3(1CN!>QbEed-(|f#|#*--7F>%2+-!Myw)h7w7HMX^n|5`T-o)+7F_d9RE2?p5CFQJ>j|oM+v7gzw!W8ccX+}w z*DgklpK}|D?_Ds<%tj9MF8D$2R+=bY4FXJRolE2U&lNl+cZM}Zch{tab^s-s;oxn3 z3XaG_K^+*1N5f(ksE3xuqxuyj>H&nI_yFe__Q!|f4*d%TqD7_g+WrMYX z@WBPc<(;rb5X%t*vBc!to8t*X3bvrrrE&fAg1+)E5W~#7>mu={^n&^F1z21(sIVP& zRNYh+M<1_P9se|>pb>VSTvXAA0OPR+;-Bc(Z$H+8j%Yatm!kF(k47}19qP&mDf{RO~&fAq>f;qE7tVF zN$8k@6kq8J4zG^Y|AX*stlmO+CC4kkdhpsZW~*qBAwUNmrt2ElDJoC%6`sscSHQeo zIJr_r%tkJ09D)lc`U_oSCKa?m*jZ;)kn5#M1y3SWI#66!bC1bsWtA5|`~sCR$dx*^ z;HEFC7N{+(ffarYFx&%h{mg=FU(}ERdMv4FfbniX-I7|_sR`=L5#g}|3S#iGP7BK5 zt7t0J%q6ZF&x`9&_{`FZQAc~K4ukCyQ?&s426FliKe4z1*iY`qCFr@IoSIkAxH1~g zyZh~!0zxTbG|T;;e6=yNoXNyMlrW#oX)s<`?Q^^)y&PnkSNC~}YH+q>IZ zdEG>2b8>ZBtsH#ns1Qb_`{?potH}!_)@!)DsIvl7rYC%76&1{s(M~T`dtRYyhudoB zZz)I%LWhg9TJ@C^4Y2Euf}t`xU7Xc6RBlx72sE?W4}pdRt6gKJA*ZMz?>kFIDpH>c z>QI_*AVWvcB9t11Iy}O4_9|t?C=^oG9cKmA?ao|@CERp$fyXOcnzeURMA?*MAqxK~ zdnGy2-4@5pEa+tzZ2EeD40(CgBuVN)K~nLu;K^0MAq}mo3^$xkYv34Jqq4Y1qqSW? z@DC|#;!aSqB?&RPRMrm@VgOllxXeL#naQQ)8D7R5E%>n_j-FZ25}!^pk;6De4y(%Q zwy{Bc!8qDM(tItd%rq0jD}<28iz*`(Y91k~01+MXFw6a>W|@7lAiJz|j#`Vy(K*dp z9w>DE<~Gc3e;2fqCGuE`nPG-2m$x+#*>`2i4}nPr~wu+05tD%m5d z_JFb&*kPBQeJ4da$`l@+_#Z`WoU`ntRW0$dcIyK` zU|DY_khO%I#|td)D>OYqU;!$;r3nk9E7wg8HKFy&g@LGi5HnP8)mxKQC*`i99Hb=K z0i_zwv`n)!$UW&g(rx7=KlX;U>u9%?GyPCRP%)Wzj)=|mb?=p915q>suXu=CHL=e#(r}z>2S%DebDLw1*EpPbp-TEQ2W&?rE3A{D)G$ z+5%-}aYY#9o-BbuGq^GhbCEX{a;K@vxcmT2_1{D>jw+~c6GrdjL>kV8K z$inFt3c@_61`iv2cA4B8FNyTY(L^GB5O&bqX4fcZHa7NMxAKd6QWDLD`qs*$6)_&-Zgbl z5xWN0u-U3Je$rlZO`TUk%r*78ho%;pXzF3Zid6d1r$~3Xz(HKO&86iTS4ciH3AwhV ztctCGPY(4KUED{P@R7Bu-O`IkV2~_Nt75SbUC7gOhPHc4wB3hZgYi)g06ju<0aQOj zsfI(~Q{~Nq%l$5^iUQH#^5%&*R?j4H93=6M>bt6bSv016F{#Tt9)O=`u9_E!W-vf6 z)1||1K(oZVA6JV(?(!7j#4rPIL&$&3xU$TxoRUnNE>d#`>)WPr1t`uXD(+oV#qr$b zuouPsrI35jtvI&yg3c7DxH}$zJa@TPoZ|e-@Lhs~#BJy`II^-y#{t0LK2ycLYo@q| z4KqPy9x}`T@}lte#8ohRiLAgJ$1}?KPgzyB1>V+lm6z-#R1rrDiq$xKNupIezJOU% zWCar=u0_bv8Sz60nDPouj~8HGU~&RE_6RN~&m0?*>?H%=IZE~?J`GrXwj$pA(aPb% z-=*_Ut&*jAB;j3UoZx&L$n9zl?}daQlHhc%FBRbJO;;H&A|@zjy4)a`q%i~C9e1ur zMq~o)WjV3Hjls;AXL98kM4=I83LGXX@ZqzY1Bx(8mfhSg`|wfdl&Ia4X4-Wvh*@3V zpRAdgs;-pGRd>^^y2Rwwi~UiV;9^FP>K=eCTd%(EhpI6^FQZ3-8_-N|f0^p-q?z6x zHVAc$c z(#X}H$Y@t_CR$!OAE^O$oV5CfvgmMeCK~Mq)R`#I$@*Y%r(%VfPL_!}dH9fW-mR39 zBsSfG9zLW5;A;a{*UWEGzY11+@E>NM36S=ZUI}0_ zHI@s|GtD`%eD%7L?(X*9CVG=dZEs_XveR!;yTwWj)Jzk_4r;zn{ zk>*pq%z)v3Y-+fJ)~&u6h<@;5xcQELw$iypYuR+3nhQ?7)u2*lRr zmBh8TCY8)$P}afKLH=aT?8wB`UiK14DRO+8;~{bA*0-z2`AcM)I&rmeemc(swb_1GeEH^|9DbErP{*zQ@C>8i4hLq2_;6X^)&4uO}QhW#6Du>|V z$GwG=F^U|HhnR(wA40^CvfE4!pAt$QFQg1t$a#d20@A&5xG2EpaR#GoWg`RCA1d8F z$u@~xP|IE&8-$W87ZYb{2J>*C0ajUR`&&jG z7@(Ip)5;B~vs%DZAuNmpW`N2^DzJT!tb?tERakTMdNP7pJ*V9)N@2u&t2Lhs9}bk{i%WbFUd{ zZfa%L-s}M-k4aPnEFroTPP)zs=2v9UTeeN%#@DC_5IelTkGDMimTg%T>@TA$($|TIHre8RwvhWqhxgBJSoUdh+ICUhoa~n}hhaJS)!OJ{WxQ;Y ztyBB!L@Rl@q#gN}Hpe%&R+XuENxjpEJUmtD%*Ntw0$M_1}!kVGt#ENv!DDtEM|8` zW&}@ck9vpVn2&AE^ib@A1i`8pin3Y^yJmORTz`CQ8%{p@2r;t`XMAFtCeMJyti$Pj z`pLN8r?yNqF_d4+(18lQDw8E$uk@Z|I4l7oP>3a&^P1qM_})(23-UTh0=*>|`UWV` zVDa%?w%jVlb>!aIzZ0vw#_zUG@|8aY;9RyfzO+r1zl6n%)pK9k2BJ?x@zY<~#u`BV z`ju^*e8K~2?H=18AJPX7E|^$N4<-mFijOalWEPyw7%acf<}5~V)wtm9?6b|01FI0> zCZMmT)buS#S&64W^ILvP1MOMM-mu%JLNLVAV*OIdb5#wu@Y?WPC z5865*xfKLuSB*4deZl~gUU+w%I&2#$$@j%P?C9Xo&Y(FE2)TS$-F4|(o7G323R*DMeI}CYl*``Ht`Kol30QXzNG|+YimP9YDxjsE_J0!{B zRe=r#BkXSg%AW6pbO5WFa?!R>ZUGT8ohDZ0u966A1Xs!S?8-%4WwVdm6GE{|a{I5^ zo|F5-VrCdUtKzmT*3~iq81wqR4)RDNfZV@`C3*Jn;4fk?uZRFXq#-`Muza`y#P8Q_ zUF2mV1R^vox?vLm0uM#utuL-^EWd69kbcwF-Jpk`Z`xwzcZ~o>+_E(?0I1L{w~_p% z2%s`i6%YB_=Fk2*GAl3yPy5?8L_Q|ArVJ$iPG1Sv0c=%j=$97VZt5yS+%M2}~8vgvAZIK)TYnaJQyKfsQH-g2C z3IfpXfvvp(z?gpa(sRNbv=`XNiXLUj#J)O8T4Ja&=MFLDjvg+L#HhYU4VU!8dSG9aITthS-0Xdr?Bv?2f%W zGXRz_2k%uK`-j?_>e~vkUjJya-l~^1y&R2Z39_CJWhJa0PbGDltX4=2iJ>5iZxOqMM zA$h77v0ZpHbGzapwk~L;AhCSNv0!N{^b+gX&~8O<3liH3;hDr%dq`}>e=RZBvc~qQ zNd6IujLlY3n%P&$XJ9cKgof-0se)THx96fCYv8rb$v~w86{6#P6KRJ-v(@a3{Gv%m z8hMKJ>yQv!A;!L=m@#=iACs%b+Ly=`Y65Rz?;!<&63<=&@%AOw5LaxRy)Tk$fu`)4 zm|E^hP@1gVP>1MTQlh;FlACw|D`x=4-#Pj@*?vQQ0)nzMu0>7Uw1qufZUJjp20G>R zf-Z7*Sj+|}0uYgEPxSzZ!V6RFt>p|OfVwU1z4QQ7XMo!ZMr6|m(Of>Q?A4Jp85dQv zhv8iXft>(UMI$KoyvYZxNoKkLl5%a_rXKQZu!M25pe7D(Yj2`&D-aiE)D3&Bt?Q0V zGQVnT&yY445~#j$0hP!n1P7nAzb(n1LVB1wKB|e2x3ee9AHf=?4mKq`42u~91mKsa z?a$}|m^1Z$#{R1O3j|=!)VRHU03uNH+uPsPLs7QtY8F_>fNd6nKWdFX=wR>D;KSDH zo1Pj_T>~J_ahHzvo{}682K;4g`;W;2OecGNxjZapf(Qu1-80Y`A|Zel zByM-HH$w!pVVXT&4=U)izA2p-((KP6xh2F;M?qkA4UJ%#+3oLc&qrOtaApttK)EYK z!ALCM)4mLK3d2P`?QH?R#zy$qss*$gEtcR(z3jU~jaOrF_h;=#<%zIs7L~?>5)Bji z#bRF)ZX35EBiwtVR!Zo4&K~R|F9rDc16^eP*-6H*nAzvjFkHF6JzWpL*0Sy&-WMmY zHv)(sU>~jrU>>k_XN$h_$3_6t1`>JeNpes44;lfqy=bqY2Vm*=DWkT-2HBVE+wP{w z8xFQNmaiEBY#nT$C;-sML|2viW?6NDw6dhIB@ z8N@QyAsO~Ms8KC_=2f54Ws-SCkvg7vvHA4KO#4`W^pt`?O}*Mc1T2wgQcFy|qH3|t zQZ%3wUfyF$c!c(1k1-o;epT1FPBq<3fko3RG2md#s9NGQGFM|!{~!t8KGMDwy-Enz zANAEDUaq2>*&GGgCH;Mry^2J>EMmArAHbOJSNg=P2U5 zTFZS-L%3-AXD#uZ#t&xV{Em8oI>Rsa#U!>3sqQ2yq4*Vr9I6K5bdPwJrB1kL;Su3( zCl)N+_0GN_xWasU89Zc;y*6$-k>utr!_5tTsX}22$4Csm*(Cc236akpxu4Pp7vD8W zTSY)z-?gtl?K%X{m|~xVo@W@mn7+-8!OZj%!$s4NAgmak&+>vZM)gocr_=DU?u zl4xA?7`}tKLV@pCxcH5ICn7ODB`zmACOI}cBO^X3Co?56K0YQVDLy7HJ3ApdJs!7Z z&QnaIGfSpaMcbHGjGbq_B7n`|X3n!ATy!3bnL?I}3h{aq%}5_>-1>MN=Q-`+JU?++ zdtL>>_h#Ggpxa)&DS?(&%{)>a~ zsn7O3L;sABs^j!g_L}rNV-e!VZ8Utl-T2HL=emdE{A8x5jW7#<+ZWn*O1V^w@T#CDyaNicLpcWW7Q(Uc8L2pWvHg-Xg-hR5 zk>7a0T9?@0lopmmX+OGQbxH$FW>F>$-&^S#!C=fzH^D)k4f~<6L>u;9O_$r-O7aJU zb9bm@x%kxFPG#_iEn8K>C$HIp@t)W04dq?170Ycuh`?P|*xMPvf3m{fM*hYKps&Rq zYXHEK|KE%Na;^66dH^AN{>^P{fD7`{?RF$Z;^iyp`a)zrwzrV|BS8Z;V_Sg9VCX}R z{*f75CCwtvDi|M7`77rV?gH900rL0xzb>%vLQ%XkpQuO@v)@neCO7oxE^U?lBUF!f z7ov4{PU)_8B!Ao1OINPLtL^h7pU$L@r2!o~$!Q*a+_2j}Mjbu+P+vrR+It`F_j8Mx zES@dNjPf#{QI2!kJCaiykW#h;Ffr0qA9hiDOv%x-~l60EGt23{kAUvCS?Tr*?Ub`cCI564CGt~pOwawWKAa#JvcXrvoMW~*FK;^RzM8NngUt8oep!TEkX%YZ&@)!0aW@|0s zU)tBneJNRL%FotXdV>;;XYz}!wWQ+ofWnM$?~U5x?W@V+=eJ+k-$(LjfDe;(%qaZf zKC(kN0TweK7)|7{p9EqZ09!M|`|Z8uJP5$TTgwCXr-Nu~B7F9GbvWL-#~zM1AF!7* zz;gb8y`Nm@f#pGVzxIgqef*$(gC2_c>N^mC(y8mQHFoZ`x510Qwg>591uuL?d0!C9 z%|#d8BzZsVKv=8mXaI&u>~H4Uf5d(P$)_PKTi0R7T+YK{Cfn1saqdwv(C7i^x(-{I z{nrR!!S`f4LkB>a0)f4=Lfy&QJh1-=uxOUU#t@g!G5e2_T(b_aHWuEt+_uN#D{JlA zNtK#)Fk9c!wPj0gzaF>8$%zmJptAI9~N{7Eh4d|1rHw4{z}&aZTfR0kpSuRcz@pxFLvxN?5CS0%|6?1=TR z32_&!V?=yV2lqW^kJLj6H1tqp@NNlc=zHFtEy=&Yes=%#Rvmolg1w&n2Q0ppxG4?& zQU{Ov!(K}d(01?}b?`@jkZqK^MgYthbv-bEg%=4xxhP=IY7e^$*H))JHM>MMlp>5; z?72jilyxWt5%f|-@N)@7Q2MexRg#~C{V+9*xnh4w?gWdO2A+)KR;U9Iy6>%Y|3s8m zE7YdRk3OU!KR2j@SpI%d6#o4d8DZ=q1e#5xLeDaK0=WJr09%a!Qvb0(r3YX%ZTomi zJ9)PeK&RXGBm)2%0_AUw09yTPkJ1AOit49GY`UVTsQ`1P$u&|SCgT+iqmb%=!JpqEs7WS97hyaagLv^h4Z5Gis z!ZO#wUu+9bTwX4GO5P>nL8Rl&a^ZLbO1lLWJ|}-;0FYmEMXN>Dq7HtBHCD~1CSx;PRPa)JR(-~o{y+&B! zI@G(K`QrNPif(#x6lA%ueXYXs63VGp%;Nfb5fFbJUbq;&%;4CPdLUjCQCJyVf7ccm zw!49KD@|bUrW;X^eoHB(p4Sy7C}+OH1WSN+`_?YJW8&|w;dKgABzX%p1~|hC-3Uqm zs^GeXBf^dAR0V&|CsteQ7G99#F8~_u{>+;X!eZwBU(~~s8x)Q)0I1Tiu)BOp1PG#- zRrf!Fha?o%z~>tley&F#xaD3&36KHqmas%)S7qsI!Dh)6yesmrulGow`Wm2UuBAww zVy^VO^xYEB%?ltEZB+xfk(C~PoCOnkLl>%w3k zG_HOzzFJy&W?TdO@yWuu2+d}IUiQB#xdC;43RoXVS%=TZKU3eUn?m7pH-#md&A58B zFHDe3{1n?d6z-HirWT@e(+@z2h6?ZNNXBL3I#=gTh3_Ew8vw|Ur#DV3+$Xw~$uH7{Ri;ZJ+imJR!+}4FKnC z5n=DMg^lHku$aY>zy`Qm@4_j103pYUQ#$i+z^4vA01FTz``Fz2=L)aN@t^^ZVqf+t z945Df#jN-E1~{*8VS7D*(0hW?dvvkAS9fjcSNI!}2f=<=g1K{GVFx(}7Sry1X+zwl zO6Mp&2wR_e@A<;h@6@f*bkvN3LncX zY%RYDiy6hVA*WaeqEYOEAdF&r18mPJT&nLwpg2XLc%^rW>4iFwwW)UDVZivMVTFt2 zpCKuN{iZ4%>OkoMv%0WEGcNLrC46m#uXM1*-R3QpL`ipW`jds)681l|bCZ1UCaD9X zOZO3lANrz@hWdOjxFJBmMwfdH#L?wu1GCY^m@kXC?O-YEs%2vy zEOhOKu7(+P#rzm{u1P`_;|qe7Ge3@AvoQ*dXsq|+VU#WtKh7p~z>lBxcLWEZISK-G z+B*{>V2(VhF>_?_*^z8qbgr&W@9#ZVq#pUvbacKu_TVXrLpM98#NXMN8VA# zaT9&j*xZp{Q*`q=M?oApv94pdgpM^X#*tTvfVgUX#|dWv-fElE-c78w38@2CyQ!L^WdQ1|AW*Hg14O{AHol2ywN3Ey+yAxI zel4(A6005GM6}u$H5QGmc1aU+t39gdW+6x6wpvG%VyyNj5s+K$RtD$AYR{^0RGLOs zJD;dVZ?!M7rvH`IUgm?3Pb0@aW~(#7jU8Xf=cq}j7R`>S{02%idn3Qtpmqbte4iBJ z%4*`+?7=YX~C}f5+^M2B<7xAS21Zz8{we! zWFi`c$A&nxiD+a~4Old*TBHtmPM>&3-7u*YhgTh^1%zjgQ?IF*x7BLOvj>y2RK}dJ zm>+6f=W5o=F&)WSv~N{ILd+Sae0~Zd;MeL>o^$+)BXk_ zWIsneJpfC_$)SUu@*+ck{*GRH070#}M%1x!9MY?4hWuhSHpZXm1LWgOt>wPjTvxBu zZImdJaJZS&0i{lV-qFinI-pnTUI@>W`kseUH#ap)#*JCvW4Ljh%HC3ey^=)3TTMl? zy{>U6M8mZ?j&rDdGxKPeujoZ?#)L$oVZ~vN0TPO6R!lT36an$s;f{kSn!$NV#@D&w z%#!gk&BSEz^JR`z#cA?$H(s|UPbDpA0MfB>Oq1>KkmLpML!Zw# z^E$VJDlCao_X|p8A!6G!M-)0l`hTgQCQ=vX_7l09MTtFbrP5boT%Ww+m?EKb3Ia9u z{{|7TFmbq<82k4S1(VA}1~(WXV}Ow0#`fXG*zNZMi>6@$Z1V);wxb;Ic0;-5?l>q! z33;~T3~JQeJW8BYR9&B=Aj@bc0_Uc>HqW63nA*IUQ1Pn=bo{H1uTWv0C%pUa#fuUA}4N6N3mVz%K$p2FybRyD`=WsYHbC~}!T5)WE4v5D-0P;BLn0DQRI zk!App_r}Bq@?Il=_g`})>jBul2czo*1klmd#Nvq4LovD*SsX3os}M?|%N1#L%$4*I zte<-aEJI`z4dl)G*?iFQjI@fXKMXHRsa_W>J^CmgqJ(0{>GM7gaXnGsSS$I|i!P0O z_${au3L8M3P2Zeb8IY(|xt6YS1ek7qZd&cwEVqP|GRsH-B^oNc$L1IjYEswH?sygT zj>bpqj%IQn0FN2(-3lFHkppQU_XD?)kh@Qw^HbQKP;YN^jF-giUau3_Z=K1^o^%8BlXvj=td; z*@+nmIoZ)UiSZfn*~uvx8R^j(nTZ*(*$J7k@hPsh=Vp!et(BP>osyECl9ZB?kr^Ky zn~{+j6PuL~6P=Wpla-u=iyX@0y+Nyte~NN`2=p7x0pQB1$l z_{?u}V`>%C?UA#yV(_4;2L|9;`yBq^$#LoAC4uae*!ax&_=N1Zl=#f7?CAL9l;rHV z?C2Cc^OI#Et~QGg4D_v)oE)E?l#!E^kdYD}8=aFJ9h;mUn-oL1%E?K}a1}khe3w)! zD?K?mGl%>?IXXTzJu@RQHZC?hodCq;#1Te*A|3XZwX`5EaCxB&EkDW@N`Fr4TJ8#m8l*$Hb;5Cz0#$T(8@z&mQ0# zo}8W>myn#1866W(+N30>XU7s5CB>wWS101LqFpl^9&kuu$)q+bF*!LQD<>g7IzB5s zCN4TLDLp=eJW7#`gJYM4y2hNCb&PnL_nxzvrxn*Jj(jjT8V|WOw@i3!TxN7O^O}sT z_!weuafu0uIWZZDnbAo}F|oL)?d%ZO;X1Pi`i3Rt5Pyt`kIRW6<{TTF9+#4no)wc3 z7nhNekd4p8IxBOB3r~nk&WTA%icXBn&Wz8=ic8MQNQq63$t14?5NXA6-<&ac*+!{W zF}?TLvn(be$7{zaN%%<7tZ3?VEpQjdfg1SGC5L}RQhahWi3#zE@o`z?Rf6c0^sHDC z4@sQJOpi&7PjD^I$s6q(7MB?lpF>|SNGJXoAD^C)o}HaZXwOMbiY0+z;(=XKI0-44 z$#U_#fmz5bC9h*p4%Zf>kk50*sjZR4-kuoDgalTsKA7^kK z>9gEH+|IkE67`Pc)kxEGa`df>5nD2KyC}!ubpktcg#ycXmWChT-}hoGnq$+Bmy|^Jz4pF5c9^*%-Z5 z5C7P~*$=%}AIEoejz+Z6 z2P0QBzShYZ6X>9X)XhwPp(!r<`};_o*4bHhI;Ad;5thg3$s=^ok{8KKXL-3j$<_C~ z+zI4zSYGa1e-lm5;n!7yFlX1U_H&5&1Kh z9l`pT{$gM0*+GAi=lG|W%Ke1?WUl~!N`JA}HFwfq?EQ;f^cTCZxSRfB*OmT9f3X`8 zpV43J9N6dd7duMw1^vZVmcOLG*t+6Z^cP#Y+CzWQ^?~uZh2*p@KK6Ee|A1MiJC5QeZYY|?)m!)sZcTz6J z@b6`r)UoenJ>A#Y9$%S5PTDr8c{mL(Ia4t}_K(&2<)6>;0VSl4?dD#JQ9hp))z5iF z4goFr>KgleR!vyUmUGzWvqEBV>;PvQJrrF(3L{wg5@BcM^I5QKOmA)ADg&JhQOj8T z#z5y%`3cwo-FB~}VNgDW)gqQ(#Yh58H758IKjQ~EQ}NXaJp%Bq^%a9ruUMQj$k|8k z4M5lv&j7pfk=Gt2z`lC0vn$Gt#TN%Vd&(od;8Z?OI;;dZYot5Jp_ybxlkS`#&-Q{- z`3&Lo;&Hlu&u}gxtNC7#DPQZ$_l}I-eE^zN^q|uu`z)0!+euz& zvX6B>Ey*n)D3g5bIA<4>7>8$!bB4)@u$t*>-#F(axf?8IP%B;{lIKvNrFoa+FW>Rb zMRJx$b%_YtBi7;F!HDzVX^?RdD$!&J%ddP7+u*{PkKW{qr2!6i0K9XgsD13dR+7 zJ{1VC#Tj6uj7CBH8tLR0-AL)&N`D52eQalMoPKZpOUi^2v&91wpOHFn0w?hm=dTF; zq99QB)=xnMY;S!(S!>eY=l(3t>$xgpF4#GoD#mrCaYXM)HSoDv&Z%a~o;2I}h8z-4 zxKl<+{?WZ)Py#q#G{>16Wn8C9a{yoP51#O9E7UliC5p|oL{S@AhHz!+k#SgQBW{IK zIV?yrHP|dqSbjh|d;S|Q%bfx3xG3*nw%iMrFc9Sb9a(DZtm&p)yuSZVoi!ykS`fj}V2<+K-ug8R-2>1aGSfUBVwRD4XsU$Z_AVsPNlgEW3 zapz6W+Hx!`W{(RsO2B(IIr|y_q-}OKlG_;pe6yJhhPobv!SGK5Lu3Q&>@Chc@<51_ z4R#Z^I!DP_u=v_m=h7hBkm!%S!`2S(+Tv_iUH@o^4ih_8A#Zm+Ay0ysn0pUQz;AAM zHr9775PijfXf`Yw3mWfy+vyB99x{pS*+|lxUvAm>j&rfRUL?xr&4kkAQJn8v;=}Jc z17z}-rm>>CYtq7B2PK+J8rIb>MkK?V6Ji{8PsFF>73a7Pe!P4qX~yGaN+L+W@4Vejd8{>VAl2bD_X2-FaA z4s$kOI!jA({X{~%>Q3x%@Zr#TE#>C0m>uw_pU8Ld zbpX`RfUMQeh`=8C@svZmo!Cci53#bd<=tQj>!*Dp4*Syik-jZ^dnsLP>qGG2$ff0@ z@JC;fIe-qkK$-fj5*=l1S09#WdL+Nt(UT@dR=~bzHD6JpdM0r$rOw0(W|2B>1?AMMBw^iWLBctFk;LEKe%jMr!tr*Fj$gtWPhTV818X1sKRf* zH?b;R_#p3X#7X$_H_iyxm=CQf{3nk1YH=m>8(~L(w@~xiR*Ki2;fM*M^pCA`B^N>T zuY_(U8lF_tz`J;YA9cQokY5r7^f=Y50Ssu@b2AAp4= znz^XD$bAG|BqWKth)q(BP1HrR7gtV*q8t89PWb#Gr93Q)!@B< zO>PrC01v#o_y>eW7box;Za}xdg~P`klBmEv4j;#o4d-9-!_4m1KuPng0+wj9$Bxeq zhT)7O)^+4#nWWeq3h8%W)_*szvHT`1rXk9QG{n2Uu}0`2gov|9VGMR#l5yuX!3fB> z(*+$MGEfdcZ=bxTU0F(!Nv!#jtBxFMXR#*ws5Jrou7Ilp&My$24LFC$Q5}7-`I4B4 z@$qNK5uk$|e>ScotynZ&E%2rqXyo!K%>tTrBIkP-bY#aSDzAhsLJ;k$Hz?%p?SWIDNiVt};eH+dA8SV*U{m$(p0NzhkZtT{Ft)5CWZ!oN zgY5fm>?He63}X#P$ujS~c^ioquN_4jLM0)gol2>GQ7XT4zH{%p-#zc%H&XwM=bZa} z=iASB_RVpjuZCsb(6nI!eQ7o&VNq+_F0hS$ZOUMYoU35K$jnhWv}ppaF>7&bc5z#{ zZV_+|$7P<4(Uu6f9_akEskH$H2uuEc7dlXZ!sY;}HqyQ!1a@T7%>l^yKt&TFIIOhj z7m(O#t*K7mf(FpzB}R9M(fUmZfytT6=u5bDn2kMvN{~vQnvyxbqJ5oVJkyjrjlje1 z_b#lUeH=Cw7{PrgnsA6Dr+6KTBy*=_*2<~QRN?P#y3-Pk)b2FCQJ6vG3_N#d3+80r(dw`pgQJ;!x~}V55)B9APee6lm)m*s zu*P#Ui?PX1xN?U^&dbcBX|37ev6($>yyk5RKXTf^&-5#;>hmFsI(M zdLy+rjc*jSH^nlSnIkSS4JGvYqRfFBUBvkgy9>-mwO^81o36ygVm>NPe8>$6=6tHN z9~Wg-XHR^aJHHhH+{fK+X=Ww*q>6wnYQ)}Nn)$O8K*>5ix;!w+;DhnF#AJd6mS={t z@y`h{@(KILAIse5G`ZeyMdnBJ15C2W6k^_Q6w5Mmp6yb&ikyPt|2KstGKTnLs05MK zIh}jAeI5S_lF0*#^}`(tcaAPg(tZ~vYaSH;O*G-)-M{cU>D@5JTYYtAIZZ3X3Qpwk zhIbcejNl7OzYSfmfF;tfj0S6p`i=QGNjNV&j*KAZ_8{N!?Q&$wI~@u5&a&n z-4YMa2vT-tJ`Yb0Zlz2UPz{Fygtt|rJBAOVO$CCC$p26>3EuQObzRSs?p=+XgU1mam9 zQlW`;D3K_da0n4)cpVB6E%#*h)JPK@fe|AbVg!%@RBWQkxl9wMAYspi3=;Nr28Ue~ z4(}!f7~KR?0JID=Lb7N^2UrH05+9VyK#wX4+Sx-X7l%3o%6}jzD<%T-VZBlMFo#uvQ*J(mt|UI=n(Xcim$7VZ$NZ>;==7v<+iTj8htbhwr)DMY0Z&w$K>?CT8X zMhfO^Vm~nXo5JKTm_{Y+|0J1=4EwFRyME6{3N?qYZ<~Wp)D=^pxziwtHkX5>TcQy) z;{VhvYQ+CR5dE(r{%B-4QKZ$bxz!|EH+MFP7Q9ZHgpqg}>%N{LuJ*ZDQe#&c=NZ#u zIzdAu79Q@WjCP&KMW%z0qlEiQ5r79Kx@zPiO93!Sxc@Z_XbJad-gKZ;5$VueO<7tr zkE)-hLQGgc|2)(i7crG@2BAJ7y2E~21HW_a|ndzEF zk46D4U;`RDJj<1X(9bX!bPDI2v&6ZsGbwuqI*4z7? zw|D8Omh9JdF8)@?$Y3d(Z#Yev3tcVghlrZc^@T3J=d3O!0+_Acf_<{sl|gIc8im=S zdgIkw$j%ERMlNN(poKMrKG?O?b(0Rj4T)iKVqa8Ze$9VDS6e(cyHUnnJ=A2mtBgjc zpl{{75sy8_yUJO(Sd4kJDeOuLcUf`W4iZsGos$WYlVal#9BP^!UedE3h!=HN1}r8Q z%)25kL0EJK62*NQ%GjO33sk-q)&S)Z)Hw86nY>O9P~yFtlvS>$W62>MfzfBZh7rJk zCC~!AAFY~Xf?#YG0`0kweU^Qlp`s=VheimEwi3BWzWfKCDUFRmQxC92;D z(MP!f3`-fN4ia#QX&Z*z2SDHqCGT*(rO{N*dN^z=k#nd{EJi1{vn}1_;#YAG!LTrq z0MAH{!NstrZAeSGcg_N!%-j#RYfPkd*>E#fUXJvk$8{#&A%MJ|<+|WBasQ0hb(ZeL z6hnO5){EExGhjZ+~$F-l(~9~r&-H|F8Qo`vG)-=leJje zux7wpD26N?mmn;9SX-<(J)eu|N(#5KMzs4|nSiRl$MHH8(Z=j|X_Qpg5f~AzDnF1E`__h32D7ssVw$1SyVz=lS8hnd{bE~=aA;t z^*TY~;2|yermJ5JS=1_gTQ zt!r4saw-&;Aj@cW-1SBDecCCL(|iHk6ipsn(8{1uw!8? z(y>~r1BJNOPUBm9UZi8$*BN|XQuw?p$KuQF{LvybH`g=FrvYya*Sw#&#%N?Rr<~8t z*=~UQ$}n?@1Mih#3of|M$rpH*TyR|>i&|&nYu~H5LZ5!>>ZpP6;$=_-+f#k|eqmtpBz8w3rO{zHVN zf8}WD_B=>Sx34q!OHlYTEHxr4Msu_^*_F@4z^?b40?68{aSn7Im2PD0cdj=HY2U`V z8>y&kr;WtHyODj@UCA+|Z<}mPyH*5X|DRm}LdF7Mly0Pv0mw2xcA-68h)tf8edbC* zgOY0St@BncENPh;;}TQ9hU__wzwnDT3*B_3$2)kz{+q7b+4MQ@$v)@zTdqxXkFX-c zUm&yew_)G@?rKH-xCVNgEL39B#-8}Yb%VUyhGpD#y-tszZ@{BaO4mh~;M+n>GNp?@ zg1$#MVS=~nU#`{~{TzM4;H1?Pf4geZYq%H)|GW(=f5-KJ0$`orNYP37tc`ezy%ziS zj%y^z-F9zEfR(G@`03YI$Tb6<(f`9A#K^|4yTH+Ihy-pnivJRyAE913d|=n zYO{o)gR5(w2{Yk?`XYlWQY;X0dc27`UN@wOwISmYE-_`ve?eU8r}!=O#PCqh!)>iS zaSy?ZLtN^{>rhWTMC(Y|C%N1&vxB+Zm&ha?lM$oFV@wdE`m|Mh<8E!8dSiQjWQ?+} zGk6`P@On2fYOBx=zQ%iVm{kK@?~GaVq+n6DBCp#+_H*K$7R$!#YTqmIh!B;}T|pyn zv^7H%djHN`fS9TR=4Mk^N^!O~&YjE#)OkFPx#Qd&$OnKYN@qMPjK|U$qky$r@NC}d zjW$ee=Efc6^ad8c?38hdX+ELP3b=D>G*>%bWQj7Q9cxz5okj_+5m6@Lj<6a1TN4rg z1y;O_Wo}Mci$y$YJi_ z!y@ixw38hG+@5P`LCKyU(=~y4gX>R1)#Kf<8X4b?)i36*OD9HPAJ-19m_~DbD|K9> z+Q|WU`{M50gs#AlB6uN)RXW5{4#6i`$)a{*??-KQp4Iq_BZhg_;TR>KXDt^xR?6K_ zBTu(uxyra3(r0iz2zz$~u^D>PKAqUoW!>`#eHTM2#IDsLj_Sm|VNa~_8AohSEGt^x z{XO}y9gC^pZb@(87J=3uB4}N&w|rHnwOo>WghunVH_cI9t=y zVh38a%I@nVxjnm4*e2f1J(0lpFd0TrN{_>gx}urVmd=Zm)2w4U9z;1QmR zNPSW#bv0K;MRzOBUP#7QoS{e|*>&26(^>)moJ${7-~B2*XJLVRFPfi!N~g#IdZpx^ zTA{Zea_80PH4F=d8C7x7TC_|UycAQnCkA9 zlPm+kD0#piVL(eBfH)k)m17;0x9K3uS%L7&`}?{-)98Mo zd?OtYVG5ah@jP;^5aW~SEiQ>{p==&*a%ed!{45!HUoqWd*z^?BeJ89~*lxaKpgWyE zPGg$x;oD0)(l0Rru-#J;w)>7QKZl3O?0EqLj<>H1og3nAs>S0dUVW;^-LnC^{Y&!(zmovF^@fWJmV)Se_zSpm;W! z#2SoqKS&p0D4>)BK*DDMP`Z%^bxMbJWE;+IZ&ef_F$F0vt2>zH9!_?3WEIA{M^hL2 zP|Y^#>Ns~~k8|t<_cZ!a6kPRmTrWh$rEH8cn6ii;IY~f^gBP)QS>WA!94a+yUbyJpxQ8u*;1N<4gB#p3Pz1`-alRJVehe7V2ybg6> zd8WBX<eH|I>5N5FDsH`^mFnvmt zYsKqy{;b^>Z((KR$oBE$2|pp%_U|`m9z7$_{-o?Mz>f4vUkHJ zrrr&mEXX5|W52w@{V9Dx2;S)2U?a%0s3a12Ssu7fr(*Q4R@tdtyuGG<5LP=Jl{_gL zbFk~u)hU*!4#d;ycdoF0j9Pz@7w&_Rhpox!rr-GP>a5_4!ZULTf@!_kN;ozcD3Jt#k9z*5tkq=dJYp*tPZlqnt$-_;utG8RdyGrzu>3P1T79; zb(Ghk-lFR^_tW{bFD1C)RaY=Pc-4u{YEU}b*~zQyd5~UZU&pD19PKN?pUt-0FrTDI z7wf>TU>Ai`%XVd7T@Uf32|m8vXP3&hN&V$Yg~h+$ZSqqTnV}Pdex#X&LhR9 zdF4=OzQn=h%wBQtjv*UB&QZ4Cc8CCc<)FJ*49Nt*C?myQVL+!ob&k{$8Qn!S-@(yU zG(c6+Ki_^^U#D&~576SIMRe02l2@LzNImNQ+F@iAN_orOgm09 zOpeem@47b`v=wiS~Wvs&`ku(nM; zOrVk-k2g8AX2+p#&$xdfWDO_WX>w# zz$h;33IjU1?1~hb22YPr!T6M@Q5DNtrop(xG!aAkd-BSJq|xW@z4S97VIw3%8hinj zL;^pXSEj+yzk1P5!KJFMOCq;T1w$TuOSI--!~c`oY1}DP@GEy;O)J7qf57{C!+HyL zMWA54e?^Gye~#8`&w#XE`#Rnl#DT*Kb9a*hH{aSwO4s20Jv~-m`p$hM-rmUAOW(Us(^Z06 zY*q0qP>G3{{r-b{1KHk{Wn6a;raRF$YS#II?myeQ%0mEK(YFYn*pR^9{n5RbJl~bg z{>i4c9^6aG)zYc!MvRS)i0jqYrgbp3R<=(Al5etq8q@ z+cCSQZ*Va*f2pgSv$6mv&3~jfe<6DF%A}AXKdcAYJ>wEnlKdAK`*~gEU*2@@b5MCG zc+0)maaObCAMS(MoRaJmD+5x>ZTB|%U^m{hVLGChkE_K?xTnA}QA0Mh$N5HV<)7|2 zD$2uOLjPSIl|-^~Sr1*&zxrD7Zk~d7CH=cud)_{xA&02dOOk8!ykGt8Zkdlv*AW=8 zYYIjHv1?#Awdd{CO**f`+_XI#5WwQ?>q4L8^SsHQnm|&L9`R0`rx$hMVz9<1yRn)1 zJ&)J`T#p&bZ;CJA=}w=wgF05gv(XBrOdA%{-xj5sbfp4lAZNfxp+@?GPeQi z7Vl|H-f6@#;yta&A%6Q&yyqd3(2V6R=INZP7(Z8-(VZ>Y;BLjPm-O6EHn(KY6!UZ< zYg)3~#XKqGjaIBvasIEX4SS-vr%&!D`Oc_}s$*G!B4?}c2ZpM4V--qx8j?~S+29hM zA*66ecCdtJE*a68H7V&CL%(l=2KD<+`!Kr94mMTE=5YMmx5%q^A^n zt^4_E98-yItbJ)t;rVVXqqL{TLcvyg8fS?A-N~;=PEVW5Z-Glsdxu~0lAd-5{w8qh z)6)bG>1mt!_WksEb2`P}Qhe$)J*_bO9l^(9>1hMt?|Gg< zr>B*JZv<)SX(blIf6j})Gtz$J&nL{^R3+_y{J&{%cKSE*3mzl5DSp9K+PB0nxO?$; z@e8g!`$PPK%a?A8UvTxwpW+uB?PJo z91;c zsjO2|PqGyX_P_X>dYaK>3;>jJfR@cX9xH$#w6IC2{s>T!RGGyVL`abMcothS^hR?} z1C6BWwD&|`O4>WM^h~1taWP~|;zJ`_y3Q647?8l$^&U`?Rd3-rz~Wka;;0psmA+z9 zklnJSuN1r0+Vdrulgd78S@w!=*+ zoFz(x{BU1(oGCij#ca_zJhS#>AJ6NAj>K((^$qR8p6la`&=o&KAv!41f z0MNw&V1I=Lxe}Kv+K?sYgp)9#raAIo5YDUd0Jgb3+c0ix71n~WI`lYhU8oMRURSp9 zF8U;tJi^mPqvuhP9OyiqBdZJj92ehM!$!YFfXCjmp2OpHg06MAr~Emh|fE z;0R9!d(AU2&I&Iikz~3~{YWYS6Tj#6@MC*23GE4sGU)Lt6ShGltV|R4kM-2nNNE7& z&xsCS)&R++i4D}SiNV%YH}UJ4Wu1){x`GC}avnKlm!F#b5UCS|vUZVp0Tc@!B00p3%lGD<$Ycdr3fY{Sy`MR|Sm?*eo`6P{+pA)j4HDr7@ffYiINQH= zPigl2G>@0=z(gyCKivUql08<#xn^_SqU8m6yzZ5r;rWenC04h|Ct5XfIc3`Y1zyK7 z@ac`I?Vhd@^3Cw@heSTo;f?8y(-;9vZ@ku1o!)q%XVmEpdnUy6Mp{`U6q8Vczm;-5 zY4U8(Du)!56_~@#Fjp@Q0f|4)2EhztP)Q`-wP1$v_8+mTIsK(VZ_f49(r6ikM%rJ( zd`}Xsh>K-U*9+dqv;n9;c3KjB$QGcHudEHgz6HF6jKHbv8)2f@0KYoE9zLOUsWl!r~=rNP1vhl3L=8QuS|38?P) zj^lMQI>E-^MY}w&@Wbai0wX$A#Rx=n(u%UwSsr{5b=<>IgOfcMa_-f>F7$)jvsj}& zMbk#m5#ChR6k6$f6k2&jXmCCMKCh<&nW9K;B4%8P>uDCru~&Vbhe9bu>u%3UJ|j2BG~eyHSel%9 z7-9n29Ahe|uCAaD_^W;Kd&`JNt;BVtXoLRAhvnv&S41P~=9uSnn@aA?^RI!O;DMg_ zdG(BBY@Z<7FpX?C$G|j=-bEXNs(}nBJCWt-ZFQ<#y*U<#Euns=t}ra;^4OZ{Lh|zgyij$jcvD!0BrC-4?n?D z5&)yvc4`>V$+mydZTpgH+h?QOHkLJP1t^9~OwGiywVy6575ea&=cGnE_u&o+p&Yn^VzzaLeu_+HvOrn!jfH;Af2P0SwUdS?k z_bg%(@~KFK0e06Q zQ{SL7ixJ4~1eZj%%OYcN7qYmoHMLkE8a6rwCH&6ibuzWMQZ(y0c~S>8`V)phVIE;= zUzkTgm$%Gi$u~V^b35#ku%};U>h^V^A;q&s<)DY7h|6myE^xf=WwNdjdNK-Zzz$Xn zTr1j>!(|8E-7}G*uF8fE-(G=yugLo<7MddOuX&x6H;j)~md{$Dk(~Xc!VIaCen?>; z_3IH*|N6g{`r(RM#Wbf0o9mUbF3^VrSw?RIPEi+?m?Rq#J0hLJ{-e-KRkEJYXjgt;P{1yJJp*G__twiwN&0_?ev zGXeH>28Z`$_mmfzt8h9CQz~L1+JQSmavC`k(6mL?8?l7-cb*Bzqra22slU@qz=r;E zCSbm3M4btk(O>$B!%V<5L9`L*?rA1~==2G3a%I{~o|wjQ%oG_Mp;9G=PRSgV5))e+ zl`Z#UyJWS@LB8QSw{E#NJ)wtA(w7nkA1u{O%{rZf+yXI2nFtse0XVC7R^=Qdc7V`N zl)=)>Frd?5>55bmIT2yKN26=#4OK(`d?uiCSVeLsz~1sh+Mukp8f`xSSpzsuT&E=Oy=oLZvzlugX!=@fB8iCk~47A!u!GX^9k)PK|`%t6N%w<`(i<8;| zoug52eXn2*iGxR@o7=P8CAF4YL`YHI7&gVdCIYPROIcTnYpEQp37b;K@;bB$b~Fs; z6p0=fC=dU{4^*u;|G=o)$tMJawe!y-Q8%5sy8R#$WfK7Y%)xFfFI0;C)cxc8L#sc^ z8c*nYOanxx46eqrS%1?ET%)8$T28sZv~nQ3a^h60c=V~Ma}=E#q>bm6*f~bG->))^ zeXY?$k(Tpr0#@#vkpYXrTW4=77I{d}=@7AA<8>%vWnIXs$hKa{YDPZQkr-j?97Y0R z>u7}HU*qO$e|q1Z;k!wo?iXx2Wdp!29b|RDJcAT=Q392V*KvH2aVyuQtncDTl|jx4 zR0&=A6$d#bP~`{71S-F1L=9QF21Rv>7(q04il`5v+Rf7!(&-bT&Q4?l6_NE*MTRn{ z-cn>X8C3tDvKq#cS%aKo)^eSu84?GNS;c?PIu=XPLC#S!sLTk!fyA2-OP&D0C>hl2 zVL+$2HBD-X3?8J$t=`d9)KgW_KaX1r!a9nUL9vU}8=@z0b1;NlSZ7cRdIlFm1V?AHC>EA$S=tV|AEv4aqeZq0we4AT!XQYJ+I>KB$dEl`9_E#loxYa@Jer^`4MY{+117#k=^a){dfcpZw` zLyLPuWR?zZ1ne0Y9s>5T!K%gg8!Us8!%U<-7jpK)zAp4`N$+w(cVclWmJz2v#Dx*u zjnE1!kJ!O*5MBj(Rgv6Fm~&+|@jzH|aL9Z|f_DrdXB5$WfDwS`UXKvnfx%9F9cB!v z5Y`9n>kM;FR9G~U#E2^d(M7v=*sve6N^faYxfp9Pc20g2xc@X+RffLjlDri)QhbPW z;I5=Aspt?eG#~kQa@gJC0 zYF15FvzGS&U5VR+&f0jSg=_ z!7nj9xbmqXY7~5DNYp53&jm!mY$QHb8r1NIy!@%V{6je>Muw*tl<|HfZc0wWFKXx| zgqDs1JNX{PKO<874>0k5iq?=7tm1|H?5_09Xp>L0?Vy1Nd7ab%MAHwNdMD=KThjsD z&_Fi~4;pAZRMo(PL!E*tmI$H)f~kES{|a)0#}xh?gDKh!HE*aow>;bA&>BQp!OwRT zWmVdGZ)uwa!}2~mbiJxMmd?;Mv{B;X!8EI#cZQ~U1zb;CkjGUJ_IL;HpV|Qd(U`3- zIH#EHcCT$=FiQ|c-NicA{Q++Zx{iJJP-*-QF%-*K#!y^hYQ&JbOPGA&(2maDb20P_ zp?U|C_c@tuV)O={&LuZXFYE3-p3S(QhsDe<%NAx!Q@t-I*l+JQ$UeeOU(Kk=R`u{M zpmDN?GhEaQ7WHRYcmpIV7 zKO4n8#|}TWwk9h($lH!)U>U<|YdR`1C15D-Q)#O|4)RWjp--Yu8C>nUa+tRtb>U*T z+BJE&w>^1s7@ITPTPYGMbYi%7zD7e4U{8+p{w}~^--x9#r91shw+2kRe0s3)Xz#a# zet=QK;m+>kyv^u&TnxvfJ{ZQXj`Q}l0>D6WQkr)a{T>6rkcR{G9PgcJ1rVa}-5l@L z=-(IsMA3et_Z7mSfF}!G3Qt!Yc?wSQjwQ6jaIO<0P=N58PxX$b<#6%-sor&Q;zMq% zu$3x_t()vkDrvoe-Ga${jiElq1k9Eg&gxC~j<@73 zH(@|O8omhw{%3a+Mu>C6FSDr8^L!6-UoZChB5zI7zOTFpBe@?dz1TZCS8i^>87)|& z-sfA9B}3Tp#onsq&QNx9v9||#Y#8gj#QPu_HJmM4;w?#@8P1+s;;p+-;7w1vkDqr) zPixP&qNJxK^9i=}G=VEUZ3#~=)6?$oel9)jF(?@Y-#;V%ns-OM$N!rK0y!pr!9k(p z;umbbIw5|+R+{(4FBm7B6u^4aqAATA>n71a!}-^YUZs@2jF4|@;yheLR-BxA^^&<30u9r=tMh!Qcrk4 zwE;+a(wjsVssM3<6eHyR%(`v%{>*l5^X9iAxFL>8eYVY8k8Z#SAkuOYTYk5_lobH_ zp#x9xX-l^qz~LRk9Uz301gOOwEMPQmiIgBw}R~?lWqOb zIHn8C!Z-}vbR>8xI$UPL>PO!(tUS#1na|4~<4XZh{V=czRY9(`S)i1|z&0X}JPbS{ z3T1U<8yF8LGai?i%4Dza@jg!%VgW)=Jr9+judh7qeS)q*b?9jCW?T#!TQgD~?X>_X z#@9;Hi`$dOs_8!OPyGEPdr7vjffC(!62@cF&3}QVcjqRHHblUw+v6j{1B?B0+-<-d zjqs(+w7$hT-4g%}J3Fs^uiq&lv6Yh=`Mg*_8;Twz}q zdhbPVA42IU-nQXOkaTu^#oLD#!NvPu@vb6t6l?mbw?ZU9Xw9qMavH6QAVQ0G`SNN4 z1d?J)8;UZ=-kVw`aiMargX5qnqrmIXaZuBL^t0Ah5lC1#4mwD}+Is9v8`6eV^T)HX zhrNtWMEo*14mtyuz~FhZGAFOC!XK+_s;qGo`>B^6WSvs_=pD-F&G?(?^X40Rpd;dop@T#z69Bv`4 zH(I@gG+}hqSuxC$oE5_*W`hQo{Dk<)B1PD`8{SP$UCXKSzWQwP&)!*dlAzd#OE8Tw z9+gBQugouB*jywu{TFZR9CRM~RPGM{;wE3kT!xE}A__weB!RHwx$!Tz#kD*`201z)Yz?IvaPzwMAw|9&6TS!AQ?r;>p*Z{mx%a=#90*Dx1 z)^zHsx>I8f$;p}{nlF{md}ELT^!M-kZp|EcXp?)k_ z(4A)t^TzvHQxC2&o#x=p(uqy9&inAh6^xoakj#roRZPDl-YE$I5F>GEL zUt{_kt^v;BtwlN(89s2%0fv?J^>+d&YX>kY!FSaP01i7hkxQ^P1^{L77QnzQBq@yc zv||+ZwYbXp4$=-7DG0ecE`eq`jFk_+TD~>MV#DnKeyPA6+yZc8`kqw!i27{||ArOZ zI;({BV}*%_9ho><(RZF^V5~|O(#H<;E*yN2or8;0*d#F4>fm0{x`Ttec^z`_AFB9f z5b}nOz;N(`7y+arySc=zsfc^5Qz~Nbz%muFujAyxg5h|D#k)yGz8|YD7+y75L<@!# zKVrqo)$)D9Ry~;!$12wHT_!olsT(oO4rZ#ZFES1!$7jY?)%GkE%p$3{(_uofVB zipM3$c&^p=?RH9+c0c6XP6u|9D{{qox9Jkaq{?vsE{P;QQ!!88#daZ2kFz>oP>|%{d_G<$ zoevIDo@?rxs*$&J1cvjyi4lPFJ;V98I-if*tNk`#dk&=Y+1DAWc~0T(Zk+F@ajNtE z5N6SUHy&0m+sb#AWja|sQobRj8vA5Qi!K)J4qIcGmXJwb~tYH?d z&#|{0DVb(EB3jBfJ?L-*;wEiOTFPMCOD!<){g9bVA>^_q|7GA1;fqe~BJ$OWyaZ(IL1LT%=DLo6y;}&-y*&0v)^f zn$f8kK;!~(0&v|^{v)lTunS+OY2bM_vc=b~a>ke|X)QTS zQy4VtKVs`3GM@`+s{ecvX3&5)uFbjL)5nip{~E=Ce$rKQL*n2))gKT0e$@D3h8s zJug@b(Y%DwoKKeveKEkdU!$$YBUgddkRyY9^=Kzt48GE8JnJ>sx5f&fSb2V(D+?iz zq~Q|NPWdm$V2UWmX~D|H$!cvX2vb~^18)=wsvK;5Jg-B&M6F@Gm(W&8aKpq`V0h>x zrjJ)UiSgtA`%a>a!k=NJ(Tx20@#G7$X3Sx-=^xfiM7SYJDOOAv(Tg5v*&N=z+=u8;LqjiI?FpwE@ezou?f7=g??aEXbM{{o|` zs|X!-8NnIt3KOiMGf}keU{+;#9WtvilYFH#(nLpK*i}P}0PL#b1l6v}OmMO*dp2b7 zw68N*Y_70)H+D68f*L$WgjqD;jo{gPnyi!I$o2;LZP3qA_#Tx4LsIP`bb^&0NtiM+n&?)Pm%~fa3`bSKN z>IGN>GN|GbWH@QFeY4~3J%s%}+jo|JBCN#X6K7C~i7C{4u5T-$U!zI}qwedt1cLI{ z6WF>1zFo?HP| zedp*XObe`q4o4*>qEPY*-!Vd`qe{7J$;@}B>9k=4GCtuF6D9uz@oARgydpk{gSpyz zmN}efS;l1|Yed@)(Pi!R{kqBbfc1N@$dg;Q?_>LN`f_gB;%iA`Ch;Z=*96l{d2;*9U%@GD#Tfat#4SeBARkvt=kD^jPX5o@vPjFzWLeot?9CF{q9L$ zSK0`-Dr8mcAyi_@DwdV{E~9kl=r&&kLOY;O?qlj+-GZA0eN#CXi zjvU^m2miC#rbl*6V$b<}&2oIFSP8!`j`SVJb|wdFk}Z?i$>d;V^1%eQ z*zYSzhD>DJ{k~@8%tUt4?`xZ@E*D!yMOJ1k)+uYgWZTPyyWoAdVb|E9r)k7va%Sjl@%{DNtz=fyARj$RPI zpwl`aenG?x@n6N$z;|C1gOo3k`t3)-5%|0 z8~rc$wWSR^AJ3XU@5^8KJ<&S&p!mh3T9kcl)^BBWyf{hjYjdcBeQhuFKKmg1B=-rP zjr2a-kbU;duH5X;bYEWb-Xyuh?NRU-m;bMq83;Xn=?9^+%Nu=;(*5Y;FhgMMGRb|Xp@6&{joe&-Z|>4M`liD_22TP(Al^Z;AF;R z`MirMBK^H0i-`8O57uJi-}b#pHcV!<-{GU$$1x_zhpxTDbCM@GxAXdE(XVhZG;=Yc8U2}{b5WZS&wZsP zMMwvWADkQSDWtJeoE=Ml2q!201Wdg1Y2T|f9v7QyS!HoCuoFK;&W2k6V7{YPmcJgY zZ3nRJL*D{x3&KYtHnjhYFGmdN3sALL?JtYO zv)auIB_hA96bZ$Y&4pG`g8@cvE}RsFGW`t{j7Rp=xCEIt-vRO>U4;b*3ET=)g1&CJ zi^=^!*CZa2HkC)!kS<7^|1mdQKN$t3~p5JFiiZ+_lZVcG-X@)23xi4P~`AJ7$ zWbZ#<1dzR-bycRUWXW*^sTQaA@y|x27?b`1i)azRA4ZsQK#JO_`AL&=V3_{&Ts%QDKt2HRGzQ9 zJY({_QxcgUj6x+Q-cbMRz7ZOI6jh?7xdpfw4Ej;dqIg`y0$>KC z^>zTMH+);H0MMf!`gL{_>cRlf5Aqg{cYY;J0k9?y3#8p+mSoDZ>lfdn8hsffg`OQo z3~%6K7%{v&mDTx;PZ2}{Ed7n=IA<{cqyhtO`sNT0b@rxjvlYsW;NN2?5E=(4|GTe> z6+npsi=0?G^Sh5f#~V8h=@aCXV;cMAHqTdb;2O^ChqNRv0kS=R`8E;`(BN-hdn~@VJaS9fzdY0Jm0$~X&%(OxN3&PS$tJlJ= z%ijF)2)tI*Q&8y;mby!N^|d1Q0`VVUe&Rn#ChJHH&z*pg2+!5}vZ>$s@P4N5)1pQu zdq!YdA>O_&#NYmz6GOL%rVYOo)5WNV)Zf^_@bW}@%{=}Id^ZvXHS?c6xLBB54F8ao z`#vVo3ZdAW{wj~9vAfVwrd3Dgrn3(fMV-PjR{D<0ZniO!p?>60<>r(-+v(V1dG{%=(3~q1i?KA8E9YAj>dL zGoTH@#SqZ?OqacY1;FeDrrH5qDemuXZ2=CS82CZNVP|4rL2H=@pqifK5 zb^!A#`;)BzU=HUi`=`fgewD1wOQx_s9Nd%038?s)#w5LrWpP;8+N`{ zNo!+@VZ3T%82t1_2AzqKnu=eDi$Q2JA7QK4``cInlvWPfv~m|Q_oy`_LCl4HfZj*K zo#U0qwzSD7h;rx~ckwzilXC_LitnXp)X`r% zo|KqjyW$syOL<=3gkb`HF~q;UD5(m75i5Qs;3j=uIp+)r>o)uR$P9Vk=k*8;eErCO z*%hQrXH)ylDH3{orvK3#v_F?XxEBE1?mdHzoaY}-d*d2#hFVR>Hlq`8u>p`V-~XTu z!2R9Qa@Q~p0M0MJQ6P!FWd{(q#6Q#q;Lz*CljtXQ040|C`C|gO23nXRc({g( z4cRta=C5J{kZ@#pclxI-z;gc#D}Wi!^FOMnTkFkcR^ol0g(CPKqIsgt_j4=z>sY^s zu>8&n9?a|D@6BM&Z3=?9r4=)nw~v4l!JKfY*H`gA$N~k@D*M*(x^y6hGNk2O?eAd& z0Q#6@2auKFZ)ODm6DhpLUzIMh17K_X)vW+P(obvred$I!0N68c;X(8iWzEm zXK?a9iH__1^Jowwg|@&EpT)(%e(+J*D_H=Pw&vNjCAy`GxOKB@;lE%Nc!_d-H}99a zXy;gJO~1JU{IQ_Op-cLZ*P$+{%;Ww6+RqZ4cSbEY1Uluz(9joMcvS6+K78~Z`l4(& zTW!M`NthY#Y@r`2J=2-B61+~bruIW?xA?2Zkj69BU9qO>H|pwWAaU@HsL7N54Kbw4 zOtT|G8(K`j?JA(T{XBGfr+=0fdS|DfUnn>b5Jl;j0%1f>9aFuTvSTVYQ&mTang6U~ zau_YM&Tju@nl3~fIUd=7i^0s(IUQnF$^u}HN8EM*2eY`BTL54#eWMPI-GVlE_x ze<%-$SRt%l`;wCTh0WigvXBJX}0g(K(e~+~V5MSQCylX0j0bsI0zE^%sc&z|55rNMhYA4Ow)hY4iuKke%R+9Puk65{rCGP(+(;Mb1f|8n2w?i zE9mkkvqMX37mRO-zBi>S7sUL}`iGN2vlxAjD`p7#5SCXF;^R8v0e6Qul={3smQTn= z!FWQ)I4ygOVo3{iD+ZGm5sLvZOKkT_j!@PCe{YSh$4#3`-HMArsq1I4-7osDDF9Zb zp0v{pbS{_%Q@H#WbS`HiL$G|viiMu=mx)O;wivxCsB-9FUgC9T2NU|@75_zzd@@Uh zabvu51|xtz=I|^vsJ%4HsgJQ|LT-SxuQM3DqA+N56wxM{$ZU0?;!c=F1Kt?$ocmf# zs+F4U+|hiitETvDW5jd+oBmORRG)2jG-&&{>222|0IwevqHO|zQ973Yg#n#9mZG!O zJ&t#z?6qXp^4Zym=p|e%UYPExh)YbXFa-XGB5)W+!+C_syiq*IJb??u$)?6hVevU; zMc}ObG5=h$I*PnC{RM@UhP+w%%9*2K@}iQVCK;ZR$Jz>+GqUp*o9C0 zaaIHhAEoU05YaUrb7ty;eR9sfoc6(OLmJTwm6%_Lj-U5m)95HvDKEzQ>Qg@6nTU%) zRioyx2^akxtpJBb$wIqtM}6jRX#G~^%{&3@6iL&vk1*O?s;Yw?BusCGhv8{N)(7#zBt z19Ma-+&jmq+p*_Cc02ZU27iqe{){LVt=qXdN9}fg4KrxKTf3e6=Q?*gt#r-A&9!zr z<>qG7?R1C$l-*7;07mI{jHhej<~jw!ymQrV=T|BF$ZiLVS9UwN#Iy-R;Jp-qhYgC1 zK{xpvs2C3Fd8S-%$Y0usL^v(_ZTeI>GTde zRtqR99+#M+4&}Wm221Y?=|^@*ALC+(a_`S&1Aq7bVFiGm00R5B7yx<#4lwoAU`rI7 zrt4f|g5Qe?f4Xt=LldTo8YxzTkBfoMPvhz^d=&8@#mcP-$$pVP7Jb~U;535=O=yX&rA5uL}m8BbC@$VDwQLF)*9bYfBIYh z5Zjb?m9CLAiGokMT#X5ExhxQJiFoSE>}EDbz{TYXRMoV#0;*z4ff_kT2OWXoxot55@Z9A2s^?an@8r4mT*$n_zRuwAO@%|l z1Eb{?)8?xlJlSLs-Q*!Ir+unKps+?(bIPqxhnjx%p01mf5(kH=TPiSLBTvoGCa*Z9 z0eaW)L+c`HbLh1S zfwP2`S-^{o(4tHLUbAyd*x8X-InY?6)i5l~X|1dhs6rpa#W2pRwjh+RYJgusW`(d$ zYJG3ln%LPL+Vwzy@1yCA(JGW)cY-}oBT!zW{i1;WWCwbs zR9cu#1TL;W>Skh^KPpg3P>{-Yl?mi$WOSgo{BORVfjCkh5Jm~j31Nhm(2O2sDZut8 z2I{aDr#2Vf(r{5Mx^nY(sk+DzjkO~~G%hhM%g|TS-RcXGHCm9>5=H0{Qn-MHwmdl2|kDRAVYlcccheDu=6JnU5OKtoo)SgULg?!O%aW|Q%qSTS;Nj?+^D2NUeaWt>9jwtit( z&iQMyi&Fz#=nuGUnE$zkN|2PdP77S4w^1FM{)t(Fn1@lv?ZvFsBY|hF0Lu8`Rh>25 zpj<9mG$YVLqopub7_t2DXrL~wfQzBAQcKvpnSnD_0HrbG(Q8X%u)c&#Onwbn9Z?LM z$I7l+C#H=El!i?WQ-9_AxCKQH!<06>&K#zMGUo&yiqZI!JpgY+$)OklM9D5o)F|0z ziBtY!&ww1E*w-1%ol=;)n<%+vi5ewWnJg;#%brDn0^$uv&aM44?pfUg+!6&3k}Z}5 zW@}mq1jH`4@jYZa{d-Y`V~YZO0CId;pd0&%1uBYP1GKj#mfB<2CBpT1w$r;`hEX|% z&0R~>u(?X=A~I}Zam#!LmzY*!DB$WnDBz}401BJGO6l2zP4?5F^LpGo*_y}AxTPiy z3=cX`LX($Yq_cBtD>UHvbxG8?iCL5b4qRe37&^QvFkPb!1Pu<6@}^x#xC<}+OOYdF zG~%IF^6*k?M$-dJK*?yj@jB$I%hm?A6EaChVEF2Ii~xMK&r;P_yDk0ieKn8rlI7j_ z>ei*IuWmM3wE8OJzFM7qx|uuBludz6WIt!YY3BBR{js~f5*fdOfO~Dv5C^WLkvEoR zli$QgK!fjc=&tNX7FbL^01QzwoibqzPA8@Dv|{6iDYDTYQ=6Ku#W8beZb0cb5Jq9?SCX2xQULf&>SLt*xs9d%TDk^%W_C!@|3)Lzh_{b`TbT;;{XB9dg(^y8@L7nXMx*9QIL+KsYRahVS%- z06sk2f7yTUudNgg@5W!<(oWC=^}9?C?fkW5kUPy!n*y<{=3+htX#BSbd6jeI+UOoFK-FNvld$dJ;MJjPfje$#-qDMAmkqXKXMV^qWE#|Fr1SgAK<#Q`tdHQ zs(xR@iwU|lV03agP~?R`SB=h(0z1hL7V-v+Et0rL6eEBzRNK8fNqdq_-5M~@#W2yh zLyEtN*U2aZr{kY}F_4>R&q;VA2<^l0kTpEDT+JFb>B@GvT*aOfndaNq87f$2$K|4p zi;}dVkP%B3J-OA-}7#_I%Jc7&f%bjAaJrj4s;6{Z($5@Nz z)+?~&-F!izd+#{~P`pii%+RHQL*FRJ-hVT&jWl`8IjlaR+h9Y9f{$gc9|`2tNXlc` zgwLHSAe-HF1tq>L*#S-5M13Ovbm#QVc|6l3k#NFxy4RIT9)J$VV48+y^e+&Og3z#t@R|Ln)c6_7Zh7Y*k9M{4--9@KC(!rs1iw717E@nzyM z)q)M#_;P#+>dsx*+@BFP_cchy`T2$j+l$8~rj>`9Ugpd9^nsPU$jJGW{V{*(%D@QZ z2oRT;DETiK0mkI!ITPBrpPaJp0TvhFQ#S2aT1S9wMC%T@S4&=pa<3=943r{dsE)u$ zz6N0gkd|~@sU}}7SN`{DNdbjLBc+J8$8Ob1b;@Q%m_-BL*t?&8HSn57+?;Z!pc5HI>b}qK-&}yocVG z&)t|E8WAWVvAkX?IF^+y9IU_=R0@_SpQL9K3${fde5O!vC;1lOqJ#oh7|s$3kPr?F zA=qx}+AY=e@3SV~-RcHKsN$>C^swkEhxG6*HfS%;3~TcGa2JKgCJ1Y_RqE_c(N+J^ zXl&m-FgZ{%i7c?6WYQ)zq5 znZHJ1?TY&SS1Nh;9#ryHgx$UAWOplA^>V@HY*#g&B7S-o#&<5l_}=`lIQ`%rIL*6S z?HhBi4)=|b;qN6clV%+N2D&$l+08q$jB*~6>L~FZgu&} z^N_cSf0SLwTd`UVc~e)*%*EkZIZIyFa`uyx6L5*yApZq}h+kQ`)olv1Ni~9FoF=Wm zs}X#znEgWy`m-D`una%T0spgmmg9}ptX6XHAu@a_Yxc+SdgQ`P`5ebP^W<|JhxqBQ z+sFA;7Xy~F;&p;Ga(}}&%Vaz>lKnI#P=jq7em-BWo_u~ZqZlhN;(Uo*FLU{2lweaw zoG-a>M%vok_uVHRo_&Ltrl$?(Hxj3(UEue=q^G^ZPfDey9fH3DIDC4Vz?+^XaHXdS zEa;t%GeRkF`s-uy3vR7GD}KSvP@jljaEk1l_yuPd&+}j9(_p{Q1yKmQiar&;U;*!< z_{DVq?{viKuVw6K8(am;ic>!M>IPqkB{#q)QqfZ#ga74I9g~^{OBcQ_+J8UHZJN_DrfWtl zOTD(K6YY*$5JzCNF05c;F!}LQUll~y^yfMvu_d(&4x=M5k`3*GW9WEXvj6uzT{*kr z6f8nnK#nCb$`HS^=P!*uhJoQuI1t8KTx<&Cu?+S}r(k_66n`x^i8bpSe2}^^6zuQf z0LMEAn^^&1L)iKzyX(>y>;T^C5^QV*5aKRmio2lXW(f#SULUj&AwpUg?`ecCr3BY# z^ep;9YCSlZYyA=~hAZ<*d>*81wV6qiJP(ZkjyY~tf*Y0Df%Y*tIO7=ns9qtuetI?Vmxv7ErxER{3 zxrXKI6P#}aP`K9L5_uro#^C~|(CEIwM2+^vO^K-}q|y>njJ>i)D6UyTmaW?YwruHo z$NLAT(`mR#c-49iE&+b0tzmx+;KpnDR`82jDHZ8IOx$@DIx{fn*XR=%DL4_>Rwgc1 zZ0m_NEI1^%+zP5lxr}UeGZM;1gi^KhxM9IgguaEofOT3VlpF? zfZ}IaOwjr^pyFAUVw>6<2ZSv0WW(M_Ok@`(29MAdm`^a|4!8sixy4#}T+s5Zd9`?d zJAmXV!4+2I!UmD3I!c-?bcqh#5Y@COqUsz8D@8SGTJVV&vP2aXYPMS>sG*_DPtAUa zY?RH`G;bF+#XLA0SXUI1heyJ~BxAf)0O7P^!mQv^^mQy**jMoiDv5N9DJ-)BDZIJX z=K1tE`VhTt^ARovH6GwE`$nYMLY-H!kJvo*U#d$KUJnUO#+U1LSv)*gy&2 zPQZ8~nOF==urC(`r_h}1kQa)`j?h2mfIcZY`V4MkXo*3azpD!=zs@pYn=e5$D-%4? z{o*OP(!7deFiyDaS{!W3uL9NK_4(R-4Xp5W*wBmxc}UVabzpR6?Rfx`$%envR{k94tIEsGy*WE$8<{^-;Lt@znkSgXZn=XCNmsxk zr)kg{vf27q1{SATn#$|dS(<{J8QWPJtO!`3%0~Rx{<-)UM2LT`qxe_(f<24XsWjn{ z#=r$ROW4Qyt zIYsI;v1zT`8}C(NuLXh=?5l^>T~nF(k-}{a<(Assr@>%~J;J8DgFUL)Q8Mn4TGIz@ zTE1!VrumyBx)-yvkUK`RVGFtR(5foPB{qPCv|&RVtitEf=iarMRK$i8jwVJpPRXJD zb33^jVJH7*@44fvD4zFSLcdT$=t<~3yk6)LumGVd3WD$|5~V4qK#*QS6QnF1LMN|4 zKzK^%s6djJ0E$Raz^~;iDuQ1di13@;yL-2H@9rfczyJJwe&PGv-I<-4otd58+nxPJ zO;3Vn0@?jF46{VOvl=#%wDSB@2@iy!4(`~UeMG}lTO!&hwRbN9R*L|^zEm?#f=J1KB3_9lDsz!1I=Hk-r20_J$gv9}i@AVYkQuVxS>f zvbTIKX%|_UAve39ZcJ8GfE{lqH5Qrfq(HY2o_9W(*@?-E!gQ(vaB#_??8Gw6B?3k* zo~S;%DS!!m_Pq~%-iXrYOA5eTTrVBBa9lt7qwJk*%sZ4qiQ6<4=wc%On0JuNgDk-(r~y9rb{`jX$4iFlMvS6bIzz^UiN6F2M;bp z!%s=U`KZSDjyxJ}^^RIpqasUk=Ucf>Ft?d9CQB05jDJV|srOZmOUcKN&8YA=+@s6Q zm}x^5hIyN^^F=lnsNKq$`;PqM$E)u|byPHWoTH*k!o#uv-7m`ijNOk(5FC}LSBL3h zqF4LhLCel&zbpk1_wLRp0py;`ZXpGbDdIa1sfa6OV+4xWllfhygTIKq!A0^@O zYs_PkEVDfI-_g#0y@Jjbf@{LpPpB{NBRGyl!zca&LdeuFXHHJG*>n!0S?6?^JUXMt zZm=44dW*s6F?uXktI1+QspS^VExy(DMU!f3W)U%>VO!Ril-9>-Kk_OLSHBChsWaBp z=`Bb@RS1Ehc+!0YC*#ONTOK?}f{Ew!S=%AC9b~gw5c|@&k*_QppZ*wp{7z5o4%^p! zo3KWN*FxV?7WeQ%-zN&4I`_0kR5RH|M%TO9A8HD*q#_PFi80w9JZbqH8raBK^k+7t zl)cJHcxwHCS#gbYUk-<~e`Rmg{1hITBze*TvCmTcQ*`;uB+Ddfa86-X+oNGCCRL$w zrR`B0p)GPLJ?l(&|M7P63R1#NMo`GSc_GU{mQ8rmF--BwnJnSK4LpMO< zz0Gu5uD-Ud%!{x+hXRJJfbkC0FnyL6v$m}v2Sn>*y%(5a1TM zOklf0+u+m+-u4=4WAbtBJJzdZ-=`tSQV_6@i}U(r0Mdm{3NI|0pOUDRNv2lhLg#}I zXEtK4fpDqRRuw+Sbg9g+P({r~i_Ua|v`1b`6;qBC=c2 zyoG65P?Cw~aT%AY#zQ%ThG@xse!`)AU-F7Dt1<%`Ro&}n+OJSIpr`pEvT-QDfHo$0 zliG_Y^ECx?N32 zIcv7Z2dB62x)^pErbwc0WPZw|i?O=J;fBB-`P#S{4QmOb`O=1waZ&^_^_%yQ>bF=n zAY?`%*@}1bY9p-K{GveR`j^cS%x|3q4{N5%9cIYZc*!9%JyLN=b5IGCr0`H4PZ&lrGar{}E=e|gV5M|w2;z>F zgS~e-Ho1@W&4QD` z%W)~O%q>f=eN+~W=nWIX-exc%R9h7mlGH@0siyKO`GjzH<=>kSR7?T&(r>MY#`g7Y zW;0^xFKVj7wE*rj+i?DtUYp;@Fg`Gs+w4O+}k}gDQU` za;U1x|A|oL(5gqG%E3c+?_`FpvYJw3nX_a0hc#)}hH_lqwM_lhsJGkeVjI%XWP3Ws z)}>OP1S=|`BL4oa#|uZcZ8RdH-1}Yv{Hb&?QEuC6G;V~qi4=gm^muZF zw-wu;1|Sb>0YI;G?_ena*iSqPL~AEm4JW z6TJ`Esx$ymg%gv!hZrDfdWLt46iPO*$x5sfZ%PY8lqUQlVL?;b>#^WXdok_TNFAvS z)1~Z4%}_--($}YW%f&L!^6-Kq?I(rj9cjlkaz|=j!#Ng}uPn-(a2r9&bpn^xNG@9- zlJ|7FjMXVpQt4x^8B$i4%=Y?Ym^Ct1S4!b|R%b@Anz2T0&QRv0BwRivP{q^FN@G^%2wz`aqyk#`ZPiuUu^Fh~9nMJA-*Z z;G%4Gy%2^Yio-PMfn>*^K7;X*?iC;Bl}zOr6fJ&kub3_g+ra|V;vMf-Y^$|UBE(?C z=#6vkiienM;r<;g%xYPjWx`rwZ)jibU7e0uJe2Y#fQU;>44Tah+6Kb+~tBZB2nH zQCm@mE#B=+8V@cgYj-I)udL>Ea%D|f_y06SZBgPBC!b^{ik#Me@J`qAQcCF=YMhkO z-?n=e%#}IKFOlfI!s!RLvOISj1G_8s@1w&R;SZx*W2wT zZ7}D@f-$j)F0l_}v zNvE?J3`VO%Yu7uRT7%uF1JqVCN)Ar19vpLd`e&NtVtUr1gSp;klI+EF-8XKD77ab< zt(<7GTCE8-(Ow>KQoB$>^2v)1Er*rGjNR(d>&;e+-k~)G`<_`m zIyOmXb!$POCZhr53upEncDLT>a2iZ5v&CxBq1610n!zy@CvOKX^d_yrY%^(17Ohe5 zaogNRv(saC*(~rsS~p7lc6xkp%;b!0O>up5Y!uVA4cRBoFryKL-o~wUR&CnjaH77! zqC-pjM>te*_Y%ru1`rz%1grs8FLTA63-WVM{?_Kn- zk^O6A{~DRZBpusP)5&4m8u%BvDq0i&B8PKp;a}twd2RfQ?1k0AzaRugS$k^@MI$%aYNO;G z302WG#@B~!jVpyH24dZ(koMV_QcR2W_zrOVz7xK(8m8TPJP2AerYBQn#l0C7Dqt8c zLS2BerfkxpBb#mI&=AH~zaJIXASkdVGUiV&(><}eFNzczaO zt4{Z_^uHsJzsdkLJCv!}%2V^dvc39BZNs!_r8PWh^DFrNV6MtZLup&R<#FZ_iKbLXE#iFa-%=NQoUe5ZSS~tJ z@y&*4*QP3!(Fbw9X}#`*i6XzSVlH^;ALb)^;ZoJ=(2wsTTVaD>Aa~Z2`(ZW8QYC^m zm)TGjjmfQCJJ_JIZ)hxAi)uplcI!a{PO3fl^tm(^vbBpg21msPD_8feVA!TKJXu?O zT*H^awxNs3+G0aZpECwpqnsO+;!&+yzO0x|@L#DdV`?!?H=xgI`IbtN;Uxz}Oj*uA zCB(pk)vMS+bXWM0evRj-5}NJ%J*(CZ#@6+XW!P7!CWsvUduc=POrozG!@fjU$%v@P z5kYsrZFalr`*t(zTpE;Mow*@6x{+@q@(!%#5`#z19TyEG;m56c+zSz?Du_opd7_7df=2feq+zPhODps!* ze51ARgBbQx3K1gTY2qe_8n9o`#pFB9ryJ1MI$v7_fS!6^3-*>0z*)VojsgJbf!PRj z6ZZ`!AoQ@p;5(uKFx}{D&&DeN1Z$amFKZMaa?Cy>+e85Zb+&*zlyEH@;=0^8(X!(d zr=Qi=KPIjtg|sZPFD;Xd&b9M3WS-iHesAY%%yy^h5u8$Qif*OHv}2xtM+G*l)UR;8 zSu5t5jR2TgwF9cr!B@MQXvB$J&mA|St{r?9mhOqmov@>WZyTE~>0m4*2iwrmH$Mhi zBVEmB6Hu2dPU>i1Dv|-1i`6Ersm; zF21f(WHRybOi08>&Th=@>O0K7OZO);kYokHlXNy;KjkYE6a4HcUvdm{d?Ob&Xupb2 zK-x}KlPL>ppo9R2ZtwK;y{2kraii_EkvYxRohiKmO;7VJ>7wBK)5TGWP48A!18Mfs zuVG&9!94L=`jcEqX>0_f1l3R0q@cIfwk^+;*$`aPWJ@-T0pixZ)H1&rJN!H2lvt{Lg0iN6Z0;(alZA zs4@wfxdcTffDge%7#-JH<;aU-ayhjaoePK@{9D|Iovz(r=)i>!ld^}fQ$5= zz78}60>br3-_Q3HQ#OkmtIj*iRAEYGVFS>iTm5`VAQM!jzfWHlOB%MB)`n97zTDq; zye!j@0Me-dhyBB;_wrz0VzuNfpp&>9{0sVxk_u4TmJ3V`SzIbWl{%6NaL9|kQEVF3 z5qJH&J5>TikT@66<(RglC#clv%v!%AE+l%!6!|Vpu$0Mf6Uq} z)a7O0@9cUS6Sn?pA_hLM+3GA*333_t zyz9Olol=sIEn?gxBagqVH=;|#5fs$65tyGOA6qAyWHK{|eYECuygl87L|s$f^uec< zG`f7r{;Wo*^UzVv*=OluBDJ(l$T88^MhZZ_HI8|9WFvOC5NFEdk%cY=@QWBn~ zQWpA_u>&_#K7}ko22drUg(KY}UwVQ{UCts{29BZNs7!Nj(#2$~$86>@%}D_8&{0bF z8pjLWAE&QZ7W?8EW-%{*l4(wq-gpHl;Aj{2chnG14CCP}$Y;BYZ_ zl5LoG!Z@|S20V3vafGJSTZ#nxHIlLwRkIg(3Yr&A)dp;^Px3f^>*kS?QS*+ zpm`G!lwc=E9u{>JneCR!%rBQ2bty5MlNmn5EIX-He~7BH1%n1YwLC8Pc#f|N zqbb0sBW`Gi<(F29znOfR!wbSM|E{lTDb44c@Xu77)PTC7EyC4^fXja0Hz`*0V|dW& zQx8vwa4o`cMDhx!w?5(qjM)>R%GuFJYrA~3+D5&ntu$Qsh2bI-N0PFI@MD|S9_1`r z9*>fn?u-p?KkDnKVe4`}M>KT0TL*GOVJDBc_#MyP9dcG0sqKnDWXz*4p&Xxj5 zY3Rw1EZrk_EtxJ6X%NKN4`+&ikOXG}gVYPR-=S@uN(m-%`y%Rcq3KyyCz?P6+e<3bQ&woBMXAtAFJvrRtR-QD^Svt8rp3M$XbBskt^DNMSc z>xX?2DNH&82f(U0;lcgprl`gx-?!{QnmLtg%>yvfwkUbzq;gbdk z91u0*`id|aFX89A`A1)-+L~hGP2UgfOqxc#rbwDbm52r?DAaY7YFO$_jN0Z~5_$w*yXD)Z zVb@SixrHa$WY|m>6Jf5|j=uTX=T-yQrvy;x7Z7G-4@CMWl>o~B>YFA7kV*dsUi!2Y zxV_yPzxl3d*sC;FanIyix|pzYbvw%a!#6* zpwt<65CfE;v3EPvjew|+4*u!u$<~uY#9`XOOvvT?=h2woeFM?Sd%id+0+~E+D#?S~ z;-MuJsVBcEIUbTltSi!dORX|fF_|ipZ%3%3Cf^1Rd>b@FIB;Rw9Yli@uk*wX`Lx?{ zN7RM8G9#RKRjw0^;em`vauyuttkKMqGC7B)Qs!J|N}2qa@z2q~1`3vuz~n|6oG`f{ zg2|Z=&18ZylR>tWe=1}W!F>x;F463~9l>u)`(re09$iIjff(L3x|oMRE6VyCNC9Mu zR*}DaOB2HxUe|K|ml*aB8mrjZV>{*K{jmc*&-!<&0aR52@W=T(NdaWk2|1@E)Rn*t z5@eTVv4)ToD7Ky99jxomFX&nUQ>9`B2B@Q~pnYY37p5N%E@((^8Xjwirrq+f{&*^Z zm|>?{>`C)PQ9<&yD%U|P5?v%=?i99K!dy-lxwOa4>%vZ<$&~H4WN>VX;is_Qva6cE zS~+I@PO=@WxNL96>&}{;YWplJcXInIGjS*KeU^-!QDf8zm}tQQip_ziuy9YSPw93s z5<4f8Sdm;})NV@wC$Y%gmSD?dKRkGG6NX%R8%SK3jpa%Dp2Gnb?nu{4Z*#uc#1(#CwhwD zDxE(~!%W~22(IFF8UdM19NF?2b5QpG(~P-O#wW@7u}x(0`s@{5E6@bWCd-Wy4)^~t zX^w_DB4h9%4Nn-{5y9Zb>__hn_sMuuU5Qiszu#rcZ}@!^=1_pg8`x|gcq6JgegmOd z`V~~Uoj)7RTfHC-#kceC(J&}S>yNUFn#(KY0}eIVdXM)0%Nk8J3|9}y@?44*dm;jE zLr4EWjiw<72N_5s5?zYG1z}({bH4}UCcXXl4X<`87h!3u zvO$@hUVKP9y($}H67k_wADWRwDB(S&blka$nGT0KVy3~dPxu=!@RTwhhj6qvg9at~ zJK8HZ(Ba`UT#&RP$SV_(#Jozl6WjG&tdJUj%=C@1wWR z-|^TfM=QNF_oOV|p=p!_D!*|oMg}Tfd;7JJ$>Oh>G}nK*9ty$U4H`VxG9NtXZQtL_#(7CmU+pi z9-}CBElVX`n0vIJmnHUCCb6O5=e3)K7xk*0Z z<4jUzaJpb2(S!5%qZA>T!L;opN-1WiW&zUmn;0Mbc$B}nhCM|0PEKz0v}Kss59wmE zR6FED2gmr+r2w+U&=OuswD;w{O*CwmksTcTs=uO!{hUTE^S(=!5a1|DWhsgVjq`t} zwv9D#yg!G1K=(#Ijo+h6DD|B1y8q<_<+|YA*ZuHPY=WN}C>i0|JmE9~j`O%K8TF8M zg8vBHCNry*DH1wm z_|I!JgE)90IDC$VC%(ChU#{rw{A#{gnF($uMY&F3@TiPI!C6Fn#h_xRnSOcfG%d`b z054=m`0rHzat+)&?-eah`iLK$)f^i5UdK%L|D_2a`*44M#qZ1o5fE?A@K4el#t`k$ z;wlqsD6UsO4uh$=8RVD8M$`OJ-3u*V5*sDaC8AOY0>4PS1B#jLf0z9p54zx*#68Xb z(Z$3$eD6n-=J-ug0GV_6LP^ER!NK-({dx^sDL{3GvBp#gltrDN$@r3mA0izxwAYl- zl0-_@`Tj37Y*QLml$`6 z1ucG@LdkPPCx9l*@w4dg4*yb>)@&w?1p5sRBq`h9SPDY!|BcP|-(%O%Amj?rA+LW2 z2RGISJLM9>ZsYUlyJ--ymuLO{=h1GSUYYY~#mk?T0~f8>0)7X3ipD4#x)dc)a=9RN zm%ne!0hn@!Wr!mha*svQPE%PuPD- zwWmfC-#`cs$?<=nVN36(^o{hz>_!=T{d-x4u95YnZt7HC^%w?cnZI&6rtM6wV9tcb?m!H)Rb&d_R!NAFA>ZyedP~xl&t*Nrb zv4Q%`54($*hujgU+jrY;Lu)?(teu~05{EW*+ujoYXDj9&!4+loJSvP!ZEiXbqbi-7 zj_|6fvTCMBj)b+1=@QX$1ciKEHeN&_E1vW>V%p?{&5p!9t#U+Y`FJ&lI??V>`*MNP z%u_tPV0PVTcoO&Ma^!JO^PESok<&1T3cFY$r*BF)?OK5*RJN7%D#vK#R#tq0-&T%U zmZQFvHIEAFO!Mf16>xf?-!P<&Sx?u5*aMl(*Ll%m{w*h5uO%O5Qy+ z4lPIujK}|N*D${l`A3N&zlgwgxqhG@CZm%k`1F_l^%_knR6id!`J->v-w5MTv)nJZ z!DR9xl>B(~kvt+F$-lqvRS+IZE_zgU0l}xg@&C@Swmp>Q$fBCNVMKHrNGTpH_|E^e zhJBKT6_;+k>0%OdJh>-0{s(_E83c*%@_vLhpq+*w@!k2G{@0l&_mC60RCsYui6R_c zBq=iqbd&G`5_)`Ui4Q)0%by9aZ_vFFve)m-)U%m%F(x|(8lr0nmFr3&NO-wM-k6!T zCphRAf2>B@Sd@xi4iSgx08=C9i-w|OKbNY3y8h-Lpn%}yZ~mv)BXR`LB})I@kE8%( zx3cnzg(mi_62P%P{4JyavM9JrDQ=}j3zXJEJ<+F zZm)c@v;y@YDFv5UmI+*Bp4qGJ3|sO;)0aa5t7lJ4pofNW?=2>3F~|W?i&B9j%yQ*hEwx%yKqF=cuNJ~y?p9hYWD(`2 zu+Yh~NF+izCR3}!G(4zPJNS;58jq?`hk`c2;3z+FMzcj}Cpi?PTqn4u-k3qP0EIAm zTgK>3nowDU@;%3BWQ1ah3$!c8#Jx)*6s5~oY0vY6T^>!-1o($A@%1a}wT&{kic2_J z?q-gF9Hz4kv1{RTLdtQUA`9vl-MOTQqPGa#dLXBuE*`( zjcTJ-EGT-I{(cOyIy_EMV~|&x2Cg#f9=bm=y&j-T@LU{++rR*k$#N<>(=;#@EovF4 zD1*g!tjXM0AVt z7p8B|V3~cCKuLVlbjJ4QFi9P>fyGiN*>rp=EJz9k*~lNL3v6Q8`ZQ{CkEz~1G{6{m ziLFQ15V{R?2?@-g?dzsMGih5fBL?j1v!Z{Jie<|feM8L~}X6S0C zK=au2UpALTM?YTHuoRphUr-BWwYpTN%pLf=y5MOSnlkyq)3NFOU_Y?RfVbMjq`$on zg*pYg4aW#FF(mx^7QkhuV~9-L68?P~*NQoCva#gIJ$NtvMQ%&`@Go-D!H<8DZ_NSx zi|ifm!oR?0qO*G%PKg1IQI(3$`mxR6s$MWTHE^h`<^&p>5a`#nH=LUy!Px@GKTKyc zzk49L+ELsukuClO_8TRgjkGNnA%47%OJ}1}N7C6;>KT~H{z!GirL(y~l>iYW-~@CV z_iwKoOiT;xh-LqzT5!p${?|9qi;dYY(u{I)A$iq1{Q~WoKlh=~Nobl%n&35A!)F6s8P-ad zAH(12cD=f-CEJ-UCZ=!QkLo=iI3fesx1l~NH!$E}`_TZfqtF)R3=Gs|0IKik+-3?; z8)v;8i?T=Owq;#3sL~M;)axlB#I0>5yxixvY5br5>ZikN<`TDk+59ehf_lXEW;co zpj6t8x*}-f2vAhNNgxp=cM3IWE?#U>(5qfV6?=h70f{8d#wSroG^l87f&%7rFrS~C zf%n*3w7l?Vr5Zm{C6s=nUhUqjy*y?4TkL(R0hLdqbP!te{(*c$X72Cj@@Yr_WTV-I zBbx4%$Q6CxCG-@N7{nvOWo<>3k8~+d8PAgnka+TFMB}0u;O#mMq)9DpQ}bpwM~7Yt zG-TQZr6Hyj^CP$#S%_(ZI=}-9Ke7fnJtFWq^9&C!EN%PJ@MLM*At(>tOhGPqQ~5ff z%n8}NFNVwKrCgHCA=6|GzC{x#V{np`!F?kGUo)#^3?dqyFgPQE!AbvL3_7I@;tUte zNg0D5&;-gDJQik<>`v?)8yLYb*JK=iP9q>3o{r$~*h6!eq&&Q!bz}k>%cVxSHa@UY z&4L%b9$3v*eUGv`a*eSPRU+!JfcFhFHoH=_;NCX^=`n0F)f9=Ixfxwd+L1u8S_F&ZmL`8D6VqB(n-E+*_8doTFn+`vGM z6hhWnhC*ldIl0y6JU%Y~f4kssH~i(m-yZne3xDsz-#+-;4}U@Udk_BJ4?aHcK%3n$ zH-SN%5;lRRUQ|VN`$l$pb&67|s&2d76YL$Tj#5j_`_ND_#oXc5QUXB6wVG1fu^n=s zkRq4KVV;5a6brSwW=J(IN#%a`x*?r(7GnXh@SICcaIj zi%D8q+xuv5YOYoaKsf48wP{6uay4Vy(H1>$C$7lf_$#G^|}%%s}L;)ev$cN4YDtIIUYTp zmOGM7I6xT+>9^_uRK6Dk`&H>0*|=;}>9=9jez!?yViHj8-nOM7)8T+LplXY|Rtcyq zP$vdd!MFP4*3vKocmzU3)t^Q{rsGZr|S!Q?Q5 z0=#hAF8!I@k<2O}8@`)13a{BAKoc1RAkV?b_y z<^TcX_9LmSW3jgn1Te{VBz5+9DU6ErGr{BoQMVvz!E<+)=n_gNc0aeWF05E{KVZMb zBP}?3(Nuq;i;27W_5hkODEDO<0ABfy^rdxVHMtZX0pQ&ia@$J*$XuN~IJY%hO$mTF zGzm#E!zofR9F-|9XGm@aYo&W4CTh$@b6?DD!W!usSx=W#dWx>W*Q6RuOcdt=Vut2Q zO>{8sTE#>MLY-)$=!))9}PX?YVLb9hm#bE%bXSe}aWYJE^oOS8kyj z!u$#Ff`uM%<+_<%AY08sZ*!ya9)|%ebhA4*#1!NfW1+vx!T#&X?ZI3jU{NfT&xQ70 zu9}7JgHe%M=mxHB8I_|(v^{bs=;;zsCEyqN1Rsm0B~OA+@FQ~H)v)CbQZ`0T9fX}p zS~)(228|BES;~~)nwN9CYhvmiqz_Zpr!f(0O+1L^kIcQoCek$|{2&`!2D(@PAbVPo z0OFojHzlY}V{#4BZm{LWi>y@OS*d-H%NSjQ!Xiotk@K2;$L4m_pqqno%QHv!aeE=d z4kdk>WD*tOwu) z7swo<;E4;oc~I^Gzd0Cn+lLl2jZA&fbmpyGq_#;gc}nhD)`F=LCRB2I+(?y(odoAj z%}r(4Zd4_=#kY7J=v*3IOzf@OAvAslgq0EivI5DQk=u$LN&}D;2mth*2}>3U0I}$T z3Wbf>u{40tL$g^BFiQYrswCvDqZoXH zjyLZjGk}K|TuFZ#o?Gu6mb((mVYO&VnG5dZw{jg&OQ!e7F^3ArScJGJT3j{bFo~vO;Vb<#12BXPla_Hd&OOMB5v3T@uz21pZ%PpK+e5>n= zCe>ut9wy&!sC$RvY}1_|?Nx`vXLen+K1-e+?{2&*zFF zdX}f@OAZHwu_+VQ&4DMu@~gq4LJJenu=I&>EB5CFnBRz?BTmDJsDu=*BC4jryl*w7 zk=>W4d>~hZb%e=Ki*K)jok-)`(`qC2k$j}0AAeCAs$Sut;*L&6x1Hhtz;-$xk=ss( zBg9Ln*p3CB6V`gnHn&|5&j9PJc8kgAuvwitgVo`5X>~|fF+Ve@xJKA?Gy}sUV^TDq z(Yz1_98oi${zv5I)8PnlO_Ao)7P$In<})HZCPY3D>8mn1c~j;yo0GS~fzH2XtQ&+) zp&UwnH?ILgNAq6SY{Ei|NIoNK@4IEZ5sL8X|O_LcEdir?Y4mh4jEJK!$Mnp9= z0#*8nyvuOXqXY&uCk(4*P-~CK4QjU9*v*iS-QPE;*TREW5`&T_G8X(=v`Of0#K8Wp zIKVu|t5r+gtxD8=A$q-V91mjTs+&n$%d}evI6}G8mse`V+AK?}oDrow%KKOW z;Qko^$W#JYa5m2<1&~E^*5NLYbnY(kQY3_pTB}o`yOqirjV~?^))* zJiHJ^eoe!ZDDv!4c@%jX^k4Zfv@#c56scS%i14tCLsi42w74qLd1R`>S*O;0xfmXgP5FN(nd~p~!LnV^LiK%_X-TNS8$NeTEnds(d|f z1B)~}Nm2mr5vD)$Oza6IfO=o$Js|~< zd5rT9X}OnWqfD+hF{T9=uV!2;I9c8(DzsbAI#2n|~13@mAuOJcA;J_Msn#P4ImTE%6CN#G z{^ZfUyz?4%42_lS!fEH_8`(GMVj`H%(N7QZ8nN>fpq7uyhl5;n zg^a?|N)(ds5eGH-U$UEMP}zQOStU?%=YVEc)FreiCVy6fa!8Dt$L8;1KcIUfq2)2E zM8pKYNN5?4rgehkbgokQtu^d9swub8ez7wLp|+io!!~%!y45!g+TUJk}%sr)tbXB8N`7iN=4NpUJ2(Qc`Rnetq5Zcx4}27s-vzm^50gA z*}j{Rzl7~UV4Wbn56_TDKmer0sslZkrKd>dHDt@fGlua!wZrY0=J1tmxyA4UnH{1 zk|m-fTOs)A21o;2zk0rSs3sSar4l;62X$2F`1r#7pEMUZa3OO1ga#*(}m%#$bO8$DftdPztuZ1l`Pi=H^toL>vrZ5Dz^Br}HB?ovX&_akTz2Fqu^XCf7$WIrmX9`O!l# zSrox!ff|!D(5Gu*IA2`_+gV?ifYUD{I4yWooE9l?T46Gr&shGSXEl#K_88=vD$@!! zl2W?Ek&Su1(6M_HlPDdoc1o_pl~1Ya@brzq^!sOk>E_TV?8Q`cxXCHG4p%-EH3*{h zmS}P^T_ScUX!2(<+`YiXoASpbEALAPO;^RB>zk{zMwKq@=*14j#0$X|neqlxC1O9p z@>{^Mu_LKU`8O{iAvKMFJ8rjkTmDUU3Jpp=5l%XV?rn!HqDgcOIVCt`M}7--30;g& z3C7`u;8IA2kiqEcw*0~9>7Dr%r3l31Y@2BWcp{Dg1Sq8_y}lGeHth)+rX_rki%93Q z^H(x#E{#^UkMtEkWlCVl=`p%Q6ss^MH)NVircC^b3iH{ia8yqM)bqG^l{xbqCm#q< z$^GL)yYhE2w|RJBQn^XP<4Hwx30+^5Pd|)y?v&b`qRa$0rzqD64E_%@s1S&kWNt}3 zEw{@$r)74@uV2V^OOoBFbGfV@yUuCT8IAY=jMiv3yG?GZ!>x0}*YI)+GmCAcn=z>t zV>zvUbnGra)W*|lXJ2%uy9;RQaN6F9Z>ad85VT>7BX z!Tbx%)YIxq^;kZk-((I2Y`1i;Gr!&TVejW_m?cEMQMOykM*v?ukRN2S2waradX+Go zWbs2e`-!LJhg)3H2gfcSoWH-`xgQ=K5=JK|n@sShL-}oBqtuWrU;uy|1D5n4bDMQ}k<(0Zs|C75 zbY6mIXdp{a3$Cvh(=1Qw^%`T6RJ>jTr~_RWyxw;o<6El``bkP~{8jLny6GG(a=UXrqT;8gzm%p4h;Gii9j=9maJU&?#* z-rXeQP}Qqb`$Rct?7sYnLmdH*OL1q4tQxLe%AbKQ6y$r@lQf5Putj110Aub#ZEpuS zmLtCm(__l57w8gEr_z-;ls>tWh>U>T-1CiyijSyK@$QMaKG_#Z+E1NH$WPW~B z+&)=SZYgqX`SQ8i3`~G|DvfBpPk_@07l6~=CE&DY1gGYFm31IJiX|D>J`cg^r||LqdtmZH37GsOg2}`AQKO@X zL9IcTh?Y_o)p=@G9W>_i{Ov4L0Oi8+Mi#3Nf5-`IBo-lrF$q`mpHH%z9X7keWwu&P z7Nf=Dbhw-btIcXRnj99VPK&ZqXT%40*Pii0Y@$)?b{Gsso55|+8f`YW&0;VaEjo+U zZ1Y$RsPFl=s|Sx>%$To9)>~a3yTRgdnYC`X31PD7Tn@X{Wj0wXTAfYn2-@e)_+FD- zOy3@K=GB6<bBVQTDwPQbeoJ?z0skw*zIPW#pbjdtR90YSpV6{hcw0Z-S^aD zU6Q$&ZkPO0&=_r;RbWc7q!?@?#iq5W>cPrGX3Wz}DQ2W}4WQ$v`wSeK*1K!Boo2_Q6-t2HYbPl)CWO3L*6i$l)E;Q&p9=FF4yk7d9 z(XsVh4xQQMa60X9y}@PF+VnaYb(2Z!G&pVg<;N!T7M7%S8{A-HcC!W0=}l&bM{m@b zwMO_J@A9}zD5u-x_~4k!(?8P`+cUAUBx}^3jVSBr1}#cnQBW>ft9OFN*-RGj8%C$z z309`lx=k*#&FrywETBz}#b9bivmTBJSao){&gijPt!AfFuXEYlCbQk+cAA2>*JT{m zBv~B>w+;a9ZZI&H-eNOYj4m)ySn=9CHap6SpB*2pJTlX(NsQhtN}ZdbOEgFA74?}i zU61XoE$X?Upm8$rV7IuPR;$5dFar4wy%CtQY3)`E_ywmUc>DE?QL#xTvmTs<#btF_ zEk={aX*D(*%bnz@rAo z)&|d!R7|&=<0!6I!O-fX(K`z2wA4|iW^rmg4yV>-*Ey_4gWGBY4{X!hKwWGGoz)!d z`)vlOTGVScXvw7w3A~XNGe#Dv*%YIhMrmt(F%rW@Rvk!5DuyQn;%HhQtlg}2x^;T3 z!|c(zz$=<;27F3IYqlG04x<$vtFz9=yWcN(QSaq0wyS<-Rbne z3}rP1VdC4MN%FuQKDz<1fod2%7N^yvcbE(?ojGB8(^(NrD`7i}+Ox-wdbBR+mu!mO ztp}aWn%)Ne(7K>vBG@&!bf?1uQyX|pP*9821;zvuo5O4h-gcXUj~=M#kqoE6=!%gNjj?@gy3|V3>K$R3r1^z z`(bWScDoTcatDEFFtg&i*2FE*C3~XxYC%i#3S3%?#hjw2XERU(a2`rS z=shl{3p}7rYYS%0$3C!_6Hy4ljq1V0H6~|jrs$)Oj%+3?MlH=!BP&K+9&BXe8x<~rR)`vnZo5GX(XZ1JoOTGDeq!_<(d}FL z`s8Bzvts9Fx#yjh=JV}H|OJ%wH^;d!g?@j z14JEqkJS!wK7<=qh&)_Qh&sqDoTzs=Au5M>P-ifMmjj*FgK@!xXRteUcDM$yv3gLu zWieP@^j@{-`nO(PV)RZ?)~Ur7G;BvfB{b&8g6gfTR%+^ki_#XoS#P&FT`;>s9E5#< z(d98ZoooK)8^S|pce&6HY^%%bh{C7R} z?|O`EfB(B4`*%GSu?YNkJtkjelJ(dNEADt>t1V|9!v`-Pdu;MDE=N|a0&=2C=`oeG z*CId2aR>LGD!0?Ec5t8ltzeZ}j>FvF3W7D19~R;te<++J8v8x0$MscxBWaJYLxYbP2#|3d4S z^ns^Poj;*vN&!m!v!Lk|j4d;rg`AX`={iV*mzi!RB^X0yI)=>z82oPvE*D>g>I?sw zPM&S)hkucWBKqTBBsJtS_!l|)`7HiLF0Ky1zsS9!=kPCbhP3SS_$PcjB~L|_#qxm< znptScrYeaHTufPUuOL*Gd4(G&>ba=&fB0O~odIvwL9I%M;;N0n1dy+?_!o2@<;^H* zTkZ@{MgjL`lu8|WGiqI#&|-E4)e-k*)KaPhh#*Ib03CTV>b&3I z(jDMW)m23d6*=R{L)Sma*P){`v{e#LQq3twaH3hXqe`eX^Pd8Ap-M={eoZwbM|LT^ zl{mcDONjTIsv!e&w*b|t7P7GSqTsE};r*k8c(w7NE=-jW8Xq5elC2gJm4nb*O_W0w z4sWFpmuxqh3|3bU{m8b6f~zWrt4VZRIOQ!hl>9(QhLYSc-@R<&(=ktkAid{}W!>4Q z=zhsr=dK}?)o)!Yg({xwUxJEgMUPsc3Cyq%`lwc@3+tdUkyC)?+Mz}42)dXY(Q|~j zBYF}5tcaBO61Xm3c>3u%my97YidkwDl zInjHU37jWcA|-sDx)EI>5)Z$K$<+g=B6)A^1WhuzFeYkM6OOQ>bQ3bDgX)LYvS;aH z8DujBNHT_N8iX#ydRnWT5xQB$&cJsiY8I?B*!)S zwQ1;ROuIrle|P7I9y%0qJa?dL!aSqA%AxYihypG{HneTsA~cFsZp*r86og#>c(P?k zs{pX2WvD0nx)Q+HgW0K+TWrbkYeV+qA>Q4p^ISVvxpnAr47)M{@iLyxWpquLO*We@ zCZq#Ey_H`{%7)NT1t_9Hg-QU)zk{cZ?B;D_s2O`r382Ow zFqK3CkeuT`D*^mu3iXfzkYpvXMRN57rvEU9T++5gN|_dz!V{DLWaHI}8?VL1kC)85 z@QNL2`I~H^)vTHB8e4uN(PyMYT0V(n;9MAaG_Y%tJkAwiwUMy+al7il@7jfyGt4st zlzKFfnly|Gp!Sj3U8n~G6&z#agMsN$$a|dYgT_P7ao3FjJ; zV&pc_45~zgK}SdWxIh!fL)?*P>TX3o)B`U5 z{Ce=UCqiBgbAw01A7HQWtrSVynDmc}rgyPGaleNaS25Vcr4JA85gBqA^ zDLRujMQ<>t*sQIR^+v6>tx50Jw$<6;S6d65A=x-MWQ>!vL!UcB!H!Lak83vs4k_7E zY{nkR$*o(bwC?eEo7a-z|3-{T)~0BUuaz&ahx2@`webI9$H~p~`V}cDLM46r%9C{Vi zcs5ii#R$Bl(UcgJ2r+1ro8gw|fhS}^^PdfsLlbMwiqrfnDA|Zb2&y{M`V6^`WE9^` z63u-iXYMUHwUn#j@>0mYeo_i5KR8q&Jo=n6K8p$lgvvjpMqVErDs1xzRInSGIxsZ6 z#Clk1P{<+bArSE_)TOdxSq8c5hHuj|Q7gHFe- ztjnZyNBC}Yg}1;bM~jpuiJ`>LGnxjAMu%RAVOH}aL6ny_=*GAoBrZAEY}qQHQRL4-I1rFp>NYD7P_rnko_d4DNb8R3VnV zL{%d3CC@MEV)Bm8r8CGiDYR4yKo(xVNZL-_(MxlNdnHr_cj>bfg#vE!N~i=td?mCd z4M6t1fEfE*p&<&yw4mGG=7`Cd68crz6hGe^Tt78bTEn)d0m$y_FVjK?V=*Zx+3%`> z@~4LyN}&nYeQ7wpBi{^IMoX9=tIg6gVSmP{(0=~RP-lhqf6NT&+3^bPXUz(=m$sKJ zRlehlksfifz-mO7h&lkj$b#b*SP(2PtGqG0gJuPq_DZNdY&}m*NKu+#evRxxSe|OU zsDoyk2v2>A5&gxTNO&%lL*4#;%AtX~Le-IHZs(xKr-qVN68PE71Mve0j)FbZkHvuZRjcO{xGi5Tg$cv)Uv@$uA# z=cFDp`CJ%Uacx-->Va6{ZnSqXe6ncI;}9ICO^O2?X5({mhgt7j)VtBj>~IcKrEc(% zjrD?GzY~hpu){F{f-}syG#kV|^9W(P>#NKNr zjg2Vf>~rYe3fR}0P1kT6bnU&d=tTtEh->IN0qQ#x8leP*UqkaN0o-2+&nrv1!R*9~ z>@?=tnSPG@v@wOk5*}jM5{1t@L1uMmn}+?EYQb%l7M(*eYeUblMRW~eXXaXPVPDh5 zgcATrS{Lf706=yQe^&yiv>rCAB|XSEX~J`oADt8OmS80Rw6<{Bg(q(a#w%Jph)PE>2fnwUH)Qg=&hL7p$qaIVZt`p9G(=>#hV|as@p?vvv1NhvQZJRv?_EU%s_ZT zWq#m*6{msr3c%cK+GiK+aNnaDBU57RCzP_2De>ONWJ;tarp^jT1&Z*T$khBqePViC z0hrr@Hti2R84J(PJjn))`$NyfGR+7YYB!pq;Ryva0u5wxv__%9qX1lz$#GQV7{dw`F5>Q;Gz}>Qcm5o%=*>e_Yv1aJgNS z>jV$7LB^)A#~tk*sygRMjtpv#yyp3^Iu=f>!SJ6ISbB3Z`1#pT%~&*V^#V3{?QE!i ztj36r1ws+;jEO#LD?f5N4mq6cnf+<#P+3hk4A%~I&Yw_|dW}kiI}rhQ@M_3VR`V=| zYmdh0Cnivj!HIBRhT+sU8B)&6HyLW4kGjb~%bsj9B+(^dwZSJ*h(CV}<*eBrAAIm# z=$?k14t$5hN@5Z7=n`T*)6a9(BWWv|+gc@ng+GS6OOeY=rWBukn5LC`-a6xEXm~01 zNCej0^Qhx5u>8!WYq&rZhm>dOVxYT2o#h{v;m*}@xs{(U`xyTQC14_-kRvgPpOF6} zLzl(?6#0Z42}J_IUzGsJC*()~@(H=jrmlAco`YD=SzOYS1``+vwrM53uC^X92;uqP; z+tn2=$ZY|T_h8`(WGOKifLL2SC4dHTg;S*fGN)6MA0wK5q>JC`YD5t%$^(88kC`M( z=})#A$b)lTFGxeGR7_TsX}TlSb0HN;K6$HB;RI#~k3)#421#+iEd9g=j=>csO)ojN z;GyI~Kf@#vz=)iZVtn&l7*H*~`5i_~`U3HFUYS*%uA(JR;+q7zL{#tKkj8~$7`De} zP$X=Wky+tx)51nRJ62S2m zg(fKg1Wvu#Or^H|mW5VnTk;&${Z@tLP-3gXR_q!jsI9FEn=3<^Sicg$qSl2?qyRE6 z>3TRzPnX#Mwsi95#wVXio#a_eRTL*#2=$_q4902;8)}#zc_f0Dydgybmi{S>r}Tlp z!p|PPrH_+wDOhT>wM>nRa!ZfDD6lBN3zq(;p-|USqeV;V77kT3&58WzwB}I2iX~@g zVNP>RDh8>Gu65mRQG_eU=T#NqE@u`#Xrk%M!D(N5J6V0%vMLO$X4Wk(%FR0dV$@{| zZNyQ{I<%*7D#N}7>`Kjg;yy6z*>o{6>$fg)W*rI8bw2>CR0IfYsx9q7W)iLWTQ?G- zao|v0F>pYkU||+}knW0vUGGyRVH+q2e*Ruz4Go(|RgyFP)ZSe?zJD44w+}>@h$!I~ ziCj`;nGDJ7OJ14ymJE9gq=}g=7QdEcLVAv!!tE1+N@#9U6vYN>B|x%V0b@+(4~fV1JqYJ z?p5Z3Gd$%wfy1^k4h6%BW_X6na>LUC^&E==ykK~~W<_@@Fg-7;8(wFAY@X(DfZ??; z6#wsbFlMS=rN z>$l6qw5ajDiO08YO!^BEFG(wN!kLzGoxtUPWLyfS5zVwLSLCK;1nQMdiv*fXpDXI7 z^&LMxX&eqA(q!yciZQJ>OLJ;30+LXZ8A|}8%xPuI2!LvHT6Y*6>6~V~5_L|ah0GZj zT@oJJ0yOe!QKp)4vDb=LsqL`kUMupmIao<(!%GTKC1Nka;I2b`lOK;qzg#c6&K{v! za(8K&{}c^mPtwKYG?({3MctSqSGdVk0wpu74ZIj>8o0@nE)l6h_r5CHsmkBiMJH+~ zUn1u3hm$BMd_SE0m%{yU=8r3=(sxB2n2*kIcf<2PPkSvmu!7a`t zGVST&(ti~-K|6mes>?i07f+0X*3E7fRbvLw#X~DY@%o$4+DRApDg(vW%AP5Ov_BVB zX2#HUeSU)umj6^#lbK8xFJu9v@~xs2W*%MK{tsx~Yx9{#45EwQyjj!~yoN6h{rJtx*)RiKRhr6_^duz*=$|_qk^P)AQai5xBu2DTuCagA>^`Du(BflkH7M_| zo!#khDtg`Mdaio5a6RM8L}Y~C*cpcp2J&CDO+v=-w>m{jluN6A!Ahd zM*;Lyr@mOKdCXngHcfw2T^m0hG|io8dq&Ub7Kn`5HS7&-FXlBw*{ z&H$Qu4TO+>!(-(Sq7Bdgs*hiCZKWEXj1-DsEw(f~Ies3@optQ#w$A=fUilqA2@l5$ z_3?i|&z{Cl7=~4NzXBfk8&77|xF=2)@NUENfuexSoM?FRMLj&35Q&ENq~S>|(ZE>s z^9@htM0;qZh9})bD-}J=_LkwvQ!0lvg#-K320`Voci@Zy%;@QL_(=w_9*%X?qG^1!e6}pS_tPNR?Gx zw7qKfg!3rV+Kq1n)8+*H>6bY(=!g&K;w!}w)e6nQmZig-So+wySm$&=?|xe;^FrDclEWk(=)ymhHTU3Mgg?> z(Ovy)J@njR7XbY+#oo?Vq-VSpj6aKhdRLxpq@I!202v`mcJ;Q66B*rTK|_1wtaoc- zHk~CLuxgR&B2sUO)NLZQPNeP>sdq%`9+7%qq>4rAXOVhJq&A4u3nJwfDH5sgMe1#l zdQqg-i_`&;+9XmWQrq^;Lb6OGt3~E}B6U=xo)xLTL~55v-4LmlMJi-JQY%F&UZg%3 zsU{-zib%BN}BoK%|x+HEY~sBDq6k&Jn2(L~4;p9T%zB zMam&kYeec(k@`fWz80x(L~650?G&kRMe0|P`cB~mpGAhlMc!bNK5 zERjqS$>k!IAyS7$s=Y{U5viUcRVq>gMCv7x8YNPzMe1RZvWwI-k@`rao)M|-BK49; zJugx#Md}Zc`cR~fiPYx@0%t+rBKf_@JSkExk=i6u2Sutxq)v;}3nFz{q{>Aq;2=_m zL@HXOmWouWNPQwwEk)`lk?Jf``$ejcNX-+eJ4EVZk(zb4NPZ)d6GiHOBK4$5tq`gC zBDGDVN=0goNWCRePLV1XsozEFYmr(YQa_8-5s}&@Qk5e0n@GJXQs+eK8Iig!QYUK8 zLNfRelD~+|IFWi?q#BFV9+AossSib}yGVT|QvF2gHIW)BQdJ^#zev3;Qjxppf=K-+ zQrcmpz7(l2k@`WT5=E*)q?(D;dXZ`;QU^t<`$U6{e$A=@xq|J}csmpz?Hkl=k#tu|cPO$Q*&-@Z3z~ zOtL3axV^GJW^j)+wQtbg(Eo}`2HAo%{K2YOZ{}$;WM+78M*z8ssyb+Mk>jjvMP$(6~FC3MNMl4X{Qg=yQ0wG)SFOGkTwg8PF7hMroFM2bVMJQOKR1M(CQzt zKqgbs5?fs={;XBt%VVwv($3JzAg$gJqm<|)yran^*p=wW%uiF_n8Q$Nqpe$wZrArnM?`6w;$@?CrF-RoW_*R<_;| zLPc#cY_BjOU-uy+Cff5f)E+m3@6={6_MDTO!GPY5cJ%&f%z2KmuvS!@XOE%sD^A(& z4l+$`cl(et$=k)Pb3J9gP$LW%u-Id@aufR_GaLPQBz2$gVHBO6 zURhT=q6#>u2q^6ge)(W~5{>z$Mm(MEVGq*wnFQ>y6i`3`x7u4`e(UIB&)4c6RocbG z@Ty?icdNa-Er=>Q+6xsE5pdL0d5#h&V)F^O&^!M*9z>7)RvBjNs#80u)S`~AU>aF~ z!iN8_E0_w7V(QS|$ZuojLm5xOYvW=*pa6X@Khaeqf>x%YMfAJf9z?bd_8hWzwkIp) z9&5=hpnLk-b8Mu;mnv}j^Xe|oV!?wg&#zkh9M+fP)tvYLq%JRHq|2$ zuRYpoSodk6G}>MqN!#yohuF^P)H5oz!8Yhq^xo17H6k(C6o=5B)6nTjql{x_8J_Zn z(!S1<>e7J$_7E-kcb}cGB~Yt;In=SK@Vr72l@GKh+T!>zD>>LV@WqJSe9gaOkE90Y zDpRz+D!q?C-BXjf*e5T<8oBviF5695CAOQFbPK4DsW5(=xkqO5nEJ(zN{=yZtH(b} zxfs%z&fi&?W_ww{J19-1#X3hnurbj|69-oaxD7kk)9bbt_%Td34z9XeNlnp8@>q%m;IM%kO#y6dDa zM$*CL$`D%iKu{7L>>n7Y7ZzWV^9SS8V+Qbm`A|jH4ZPlmV=zq|uhSng(npWAx3=AH z#NA`Y-ECiNdrgPn#J2y-W`{wa50ABn(X7sIHKon>Anfz9ky&D5atqjhue}qZV64S& zjo`3CfGh5Uf-8);uT8iu_uF5vo!3ccjii+C;O1}fh(ctkIfP3pmr3dvqrVkPd76xY!O?|}P$o7PeovLEj&T)lM(8tvcsA8Qf zLVL_aoa7_22|ynez1_A@H2V(3oqcmH-GPSZGyw5SXhCtEsMFeb7dM1D%f*gwjT{plo@^8=+$jH69H%9aeHlB7xD9at>baM zu7QZ4yQkROYVD32y{^{B{jFMFOLRM|!35I0sffYeuaoXkNn9)4r@<>9W5SI@sSdo6 zrwu#qwQ1Z-o}3N^zO2JaR9JR%RPpec_Bfio9i#t`p_LKZLKF4{GnReM{bE6A(F{a7 zD|GhPD!Xu|J&m?ILW60oD>RTMUWOa=g^B%{naw`jtl6s*DeG3K^R&)BVPNNJf0$su znPIxYorQ^8eHvSgQH*Cw^-d^tVu37`W%ID)k36UO?AIAKz-ugc15 zM%JaQxsKYl-ioN6)}m(Z4h^NVWss6*lG4pA#iNrzEZK#JBFq+Eo9y)<>M=#sBvsUy zdG;RK1QYvVGkg6!G`jWA*yC-h6xa$CcJL+C1}(Z--HL#=|zzNx~u%7sm$M*`8-swox3`KD&|AT6-CHJRS4VIwHI&)!r3k1 z@l>&MR~=dx5EekiDb676f=Se0W>KCVREPE~u-DU?o>UyN#HN>Yz@U1)X^t^_IL_! z5fM)d*Te--%k~gdXcW}XS`fdMtptMRs)A-21@R#IQ6k=`B04OFlKMnME7G1cN}Fyi z&DTp>y@c@0I#uL{Mv-W3oow$J$?yJu~G+-iV{%iJz^uhD`;H1qtOxK>b_tkoxq5%~M93b3qXlfNM zOk8GfqCIN*anS4bEZan@f{eE+$m!RCe91()?+y4Qi@g-ZS$&Z+`HFUJ{weQx#BbI7 zNJNT!t4~bGKbG4w;j())q#)P*2lB{`h(w93HX+Bav^TR=S|OeONUn9fKUXjm>5u_8 z4*#vJ8(EK+Ww18d>NR_BLS07C#8n9F)udH#+B+ykwQ+{-$AQCKRIuRM(sh|*?r9lJ z2`3#LYlz4Y>qTtJfG5xJ(>F`!_H*XUv*nARpjg zDelX?r}eg05#$IK4TYr_y^B$DzV~Ml^!>Z|?X$+;K7CpVq<&AYueO)l%ETXap^{m3 zYHN7T>YIJq+YTAu>^&&zCk4OyJ$noI?&1xVqJ8U+pZ7lS2NgV?d+?sxVf^)-_GVh0 zGfG#(t4GjmyS=4W`-~X2RTr}c=j3T9B-afT!gYf$b_kvBzSFDIw#vSjJc$pVfb}zW zUb~sdozGaPcGEiHulatRPJPM|u^ego^)UHsPwbdq?U6Z=jb+=Otuj7a56|~06XOZ_ zZd1>A+b?5=eG?faF>k4u4ja(5%S@Q%H^J;a9qBPi->aDSKDMW7-^0He^z==UFtg4I!tq+T9ZNlm_jy?)e4n)n~2 z{>P&tCFxa_^!C@-f$)lvggq%xmbGqr-iY!T*DqC4!e;D2*=Qtv;!ooKg?5epm2W%a zZxdEm*weKWDzfPoSpPqmpcsYU{wE+A6hvD#!W4HOYLBGnzOiS(HZS?quhMIZ zjC>7?FMjr7(YF}6GL>J4ZiP9@F#bB-^lJ>N?I`X$Y+5N&e*M~a2;25I{u*0_tz^Ib zJ!DT-ex3FMWEUHM{fOz;JfLU&h+kvZiTAzN{Rr7F8-HD5`ZZgDFMdKq3=?thulxKA zZ|hUzuQyo#dh|8~3$Ov%`|Dq~p@GBC^4B@ME35bJw=6oH3V+0;?uHi;O%HFkx3XO| zA}(1YrtiSM#fImUuKw>1)OrIN@Pl8iCvt|~oQ+k_L&dEsW9f%zb@@g{cYg*qWVA%t zkomu`_Bb>CHd~0GQc08$|{V&a1l^k z-2ujP9xGQGrA$8&?HL(@|565Gyl@2Vs>a4QY$pE6@2zY7XFCdeIwF`io6_8E_C9p-dBksO{bKK^XnNRR{v-7I zi-^(%U5$>F#&Rx~J)0v1wppSut$1}AtO&xi8r%F1W4)yBegpxZyMC6*k&wP&h8l#LlX~SM{)c~2yyv@D2^=d5B~|)Q<1|H$4N(E za%$tIEE!S37R^a6RZl6uL5%NR#{Y)#_!j;f<2u4MuLQ1P=#FcA%3t20f@3&IkH!Sh z>qRl)a(iEQMR*s3@YxSxa=h+OR8rQkm^xJV57bmgpKzruv^($L>o~4Z^B9J;hkSf` zW|uw3*91z=*_#P)lwLXa_`r(Z#)w>fIK^(K^+)Z=?B;VEr_l*?g;Je=b_514Kh>2c zSB22&6ZWpQ5=-(zYx4TP?RQf5Qy7-M^_3ifIX^ZPm!pY=p0@YKMnmz$SQ%gV!4K_0 z?7SV}AL$raR7yQrA6FHrmkd%p_0hrhk58&n3+JtB;g_?hg(fuT5Sm}3zx8+>cR}dC zzn1d1F*cX0Ql9(`h@;5}}pD`V@l;d&(z>xBG$OzesqHf%_sBx;@~N2(=yO->iCXvt zVfLlztV;vV!!`4n&9WLXSUHV~jp9uxaNY1chf4O_la#g)aDnz;wcoCR&-MVXV6+F0 zYxc3)ZK|kVim0+sbbeT#SUP(hOIO&8Xdxx{g1=&Ty=!Hc@LF{422OQMR^i18ys#t8 z+N@|CPdIx=Y$Rn5i`66Bwnuzo6f1yjjpscrsA;i(W* zp*cF)mRXX?pUkSlOBq!)DGIw|7T3f%hTW>9Dn*hfWSN5zz)DqigQ~dj_tti#X*K@w z)wXuaKS~!(-iFd=2RORfuKAdXUM?%8|JR_G8G(+*TDFSLRM8W{VIKDeI}+&f2WUxQ z&RE2aHN?>~{xMpF^nY}V012!gyvfbjc$IBcMIP@W?N-Hf888SrQ_g{Zy zm)CPNvCY%ra}01Q`vRLlX2v;s(~kjo1+nWz!OpP8vK4j3I}&Z{8B5RAakSR=1n}$g z&Jy&g7~tzg(U?8EYEkWBjtDwb$1G!wwG18*uO(p{*qH=JJ==c0{Ckb%4==>JDq{Zm zwrcT6)k)=lgK<*eqR%Lx(kF#M3tawn6;R6dAK-Wvt*nu3MqeXy%zL!6Gxb~Srzb= zBH(%sHq-5!3;38{cXg!PKOzF?dbl$P@c^rO_|#e&c3cjk`xew|6w$QV(r1*9Z=6!) z9#`a+H*<`&{cb7ah_wvf8rYz@BSVY7q_n44nv>#bPent}NH>M+?J17N_l*mq@X~sL z^mXK}AX}99OH<1V7rDNc#l{S_gN<9NuZMFt*_r8Rs0~!57b?;{T_lN4$JI-q@Gg+q z&r;@XicH90nXr38CXQdQh2f=u8=ts3^P)|Ns~xS)c0_ASSa>TMbIPTMn{A7jWmq^` zaLIqT$vfdJqN1ncLgjF_L4AjE{T=LET}kxDIRwbZyd0NIb?3#SqvT-u@FUB2cwc#k z)t9`MkiCn*Ht;SEsxtN&Wu$x@kxc)zL5JUCiQi?7=gC-XJ4aKk?qy#i(1I@Oo(A3_ zlDf}FE68r=Os3rD;zQ|1aeRz#c~T3wZ1f3gUeZ3-jQrHaYO@oZeD zpQ5V5XnGgNo7!Ui5A`q!{)XFdvCQPS$h(vEF?miY8q^P;#CwAOTckV=Mm3n?*)qS$ zB>790tZrTIa@b2bqU!3P)D>uvG`bZwX=2SC+u9Gs*QN66_+VO?D<%^rh3&cQ-*kCD z+q^tS6RqwQUyWm?u)YJjJ=1w?#SB)`i}M}L5c9=zQ)jP+_on2GEDHW`WMi*a)F8;nxTgtp^^%eiB;Js_dZg=EpdnCO0(NU2KdPN?Vy-EbA9+owd`6;OHKXpsacmriMP^9zJrGgTXR^2bLy zvbB6wMt4<4i^m-;sCX~BMaf7ZRnHggfCg0B{qm`tPZb%ba? zsqpVr_@xD?NY;9{b17l^_NZ@75-NNW*zCb*@BsI==#O%F049p_p3(DoD02RUcJJ9| zz_%?#Ul;W+t=++Wa7o2_x-EiI?{cWiBB9s)4PH@co5Th>eEfo zaEj(3$7tNJC^C8erBrL?bqh#nDg)>pf6PEFZk16S?e|6id94ZQ%k`&^zN_do9)-TQ$Wj8Y28%XL!KJ#0Jg~?R8bwtEw#Di{P(STUq#^*;c+}md3Ta@G02JA5|IOsWLp1 z#vT;*ShBR0-nJ#`aqz+6X5v8nPg z1SuNOp-12zazpWyb~p~MBiG~<#?ydFrVd(2z2*Ujt&;-RPr=!Tqr()>O=>~qw>l$i zw^=d~eU?l%im)$i05e2Omqrn0jPru^JT{7*&nQMw+%lMc%wxveY5^EG&`S(lVjs9Sx^|jpj3W&`*-nKb=x3(=t$`AsFT(x-nM-E>o6p#nqI}pv}%E;in$l%UQcSx`|4@X^U^pe17{p< zc@h0msbCJ6y|^6aqd+42D#$%aqGN7LYQjzyu8Dy(Q)kBqUJOt)>H+lgN!()AGLzw+~%8j`85*yjspF9f-|FZdvd03LEf}vuJkfnIh=VKdf|Yd z9C^4VMs$6BijQ8;c}w&>wjc|Nm8d&kOLbnO0ryq)qPr%!8w;koC-pRyp=&F((2TpC11LDzcI%& zD&BKsY6DejulF5ov_ccLpQLvD0Mv&h6`39?tKu|XNT>ok_3<|P{+j$3l`Yo%+N-6w zdJ8Mlw6{%?R@Lygn0(@4<<$C7x+D2h4Q{g9_xN`kW=fz$(DjCnBpiHlbke@9L8+S^ z>XH148va&te>vvh=WCGT!^(W@gz!Pi);sRePMdyzq6V$s57qqvjgMqXE3-kx8lwQakO$HJllkK&5p?= zGnp4~KKmTw$7)G_TP5S#0ZIPzZ!Ckb@36{L;uU5tpIh4dC01VDlIu{p_NE>#G*L^R zXYDX?x0$)T`KHDuN2-Q34Kc)N*Hv!CCdX*)iV1Yl4C28&vjQEnr4F(+r_{C5Dd2{A zw!(1_rK~lZ(8l5q1* z!2cG}RC`|1pE1+#wav9;VuzJgu{j36{d>gVKatG;8JNDxs+f`-K&_%=-wsXgNtpI8~JXjt((j8mvMfv_`9lTMk^lF z#ceL}3Qj=-rcFghmNyu#~ z1T)?Y%C@J(QFg7=U_B_;%gF43JzK)^}T$;$0o!VGwXZB#U9wy}sG01_>pHD># zQuXPrZLlSMz)k6el)ex2=Olfefj;6pUE|5@aZ<(4W^*+2rtAVj$)jj?cxnihZ$*6Z zV<~05K?>esjBPDv8ujvlHJ#1p>xW=I4@u^J19S9Y$1ri27?omVR$DWocKyieh!+@bg5ILf-VD^Pbv@3zE`_Q$iege_w07+YKThI!}< zML%Jx{I70U*%=-AfHIxsn@u+a>b9~%tT=Ocubk0_HI0w3HH2Ib+rFc3PB}Khm|E6_4rQNeVdXf@6Yhf+gI+nuKTSrAzfx9s%H+)Y>mBw4p^HeUByfB61AsZiZQi9uDToP7D~N=r~#^h ztrR*Sn~Mm&Ik5>rhvz(OSraCh*WkmtRr*YbxKSCyBgE>Fw=wyc{js$gAn591L;(!H z8Xfz%t~19rMKAGWy~ITWD$@zUiB>dgYomr#6$;0p*jVgG^kR<-_RgGHoe%vUS6??n zZ|Tf3h3VPua=UFg;}pBI%uuXT5zZFye^`+}D+p}ENu-jUX-%o3G%bX}mtws7!C1Df zdfB#sDNPPLpJ~y~3|M_;{;M!Kw3u>FS^{OqI1_At8*%4MI1UvJjdf;g=^^@XnnrUp zXK#YyBgy9;71eWgv89Sfrr|VM%}Jd$5aM1oXFUgdxlX}ibXkJ z*Wb`N&o$Pkp0*~mKGm6TYeaLB&}C67-xjR4H0zzla&Du`zoX!2~8S`0~W85wU zhv|4Oo!!@1hH2cEOoufSJ)?-e?}(lj5T?66HSw=-eXP0I z$wUND@zSnaW4QyL?hLcrMq*n@EH@9fyUZ@z*f>mC4{fOnP?}xyFx{$iBOI95s+rAmNKMp4Zc(s(JnyUaWeD~;J^uSsKaoypoF$y*?KWA=I!(cp=+3-?+e_N-sYO;6$A7fxg{_DLg_=V`fV>FIjtYdRB^);mRHNaHgZbl-%kfR^<~ z=lxIzzh?x)K46AlBfX(7_Kp*oqPz)0sjWRYp2H^cA7inTqvs8R_=_ zpvMk$w$W;1zqp7xeEVCl!N$K+V-wI#)iP=@+H-4( z&Q{QbXu@zUQ->)XE+azk!2bFzr1^KkI*gH^kt#?JB#A;UYMHEfW!f+ke268yfVSQR zpJ}0lzo5c7(#RSJe<5A&^LWn0ooD8>Dn=>c1z(Zyq&akfzL$TSiM zi`<)Z2HcDgNMDVGb+k=+)`>gToNBo}DG*RE|_PWHss^CRPxAXyLTikzPrIwfc!E8lgiP&Od z?}z9pJgJ1`N(F z#G)wOfb@?dM-$gwBF^xjc6*dD@X?zQyqAv~cAa8pTkQ$?+o|%mh#O^4(d$h!eL*hm zF%xQ1lx`~~0IAyI5s$*2t(M5QB{FlebF#L|#8`0?#z&7iJ85poK&Y8tX~$=1xn*xQ z?Mi1$u-|isiMGv5<1G@wQ=RQ>HcFj_anu*xQ@vN;S!?3is zYLSzp4yd+BdT*ddZ@##FQ~nd@E9}0xkc;!2+3=K?JPLjYt(uGyri^jHYZAKv0ngF5k|2OP+0S3V`rBlW0akhd z4?OwLf!Rf8;=(U4GwgY1vAFfipVZo)#5N1vRM-uzr_wmYcJas;;O9K9lOI*cdtY!q zBrb#VCy!Ui3JI2DFb#PTdVNzTtyD<9_)SUI-8e~6gpm2e3o&zl!$^P4pU&lmh2nB! z8p(^W@9tIZ;S!%9!=_TGS`XCOPk78K&M`un2D7?1#@{>T7Poz1MpND3)ri%H&RfVf z*9VNGDNCG>;?%NGh2q#;j`6yq%=zUd7?@0n$S@$@df7P>%XxxpY)NS%H(yJQ_3yM< zuVBhQOyY(Za5YPvPa!~Qg&SbTiE3U%&hFZC5;xC)8~3X7Y1L()hILmCW_+TLGWk)Vrl)9 zW;y!i_#WCT68e$>`i!>NRL&P~0$Q^Mt@RrT+N^?}d(Syf`^p6R{3f8d51d`KKPBjt z3i|5<=Pd2G3H19-K&XpuT4J2udE+VKRrtMJ#Y5LR=h|XLhRNHF61mdbHP-F1btu?v zlGjV&eN*n7h1(;nc)8X*b`}c%=WM4pxn}_&i2jwIse~7XVc6WGm$3%f)YvH#))j@ro17dMMcBjMv7+>sI3d{41;m5RrtBkBD74&gR;^@kU!QmO1PR zBj7q-TPgudR6y~Ih_muB+lqZH>Ql-VXRNl!gjx`Et0!Q<<35U zQ?Fs)!YqGjqHQ$OIOz3T4UAXQw&Fa&8HqfpB0b0Raf$7b7LhdSJ1pBBGr^Dg;9OVG z0uR`u;=M~*yv6R{xA3|Ren4a2`>}I@u1$}Gp7iG@&LYK%CD->KTrd2HsqKLJTp`qZ zqq8*~>{x}5nf7odVMQ<#rnqfpAPqZ+*z>B5W|y!J&G{P6G2fwoE6}P+v0_=9(zhY7 zHdFFfePQG`r_3ts-YGeZgYj6a`Pw{+KW;6-wjB$sALv}MR)b&itx?>T5cSHU*?lwX z(aJWUfAt&aSh=yFziCb9wmM@cie4>6;4TI~5n1za@b=`ume`mkv=lxpvlgw}24M$( zhcK+>uPOD)jcDlDOwUb_*e=SOVM1w11#B?CB(E!Q z39_eVHloHZIDWx2={rAPDloynx)a=3MedxSZ-DW!u=0~wTIuYDO)vtW`20DvWDClc z1&&&`@b_D456V*Ar<6*N2n9-`l2ac=Rfd8JmEnjG;PO}mUYzsJ=ql&!9XCqtsov931H`S6D~FB>ma~m70f9m> z)8FzK?lRrTETs>8f<;PoQG?sWihHs=mqYE7rQgG)2d40%J(CXB&ZugDAXz|F(OQW2sWgDAW~R>&(2T#?#g6Z3$X$?cFe~YK{>(I02@$!i=xpTRNNh&*bRMqm z48re6)NqAp`BGAMk0hwHJ#{HX8*Ee46+s1mnR{eMOE7VIx6KNm+}e1Fhs0l)UgCaBSsqQ*rjY@z`q~>($ZJwaX{swx+<1qJ zJpx_La6hIM`Q`tIJiZRdKS}cUCNk=`F7*p?CE}7yD?F~tRJ6}V2i=zqj6*Rxuo0Yx z6A8i)@gi!Y6`p%NGCtBb!eTk~cGTg38`2G|*dVvTYV}i$vyyr#UK`AA$9^FWDNx4_;VRYtDRw|7ObHUjy`f7^g0nx7U^k5Co z4~E6KTGOpH{bYUZlf^2=%L{dC(I~$d3YVAc^4%6Nx}K|^Rx4E>1z%sXD-9cq*2lWk z4KO!+${QQ!x=%|_)jMJu?v`@NmFFuj?TVyzb6gE+M|L&7gBI_Kz~(YMSA7@sZjkkv zLqe)|M#GB8n$z1egbbGa0Y?6qi*`&)v#S$mb^`bX%vWq~-&AicGjn>fD?ytnX)}y8 z)^(TWt^|6gNlqATdkt4iO*0nxaiz$Q0F5*BJ2yeckmi12L%JO=})I26v{o zlC^5dt2FXvxLk2m(yBU~WQK zN;_iHg8kLj^{8(nfNvPVxOvuFYl;d6bOCrmnk!z*kzi~d6JTx%=uyFx)!5Y@H~b^l zWWt(>T;K0`z8=foAu)6!57ofR*BDacyiHM6O9G z<?_PL}ZY zCOE7QKF65jiiK~>WgcgM!vHm*hezb#cF5KkJs+^--fQCWJ!_?HTzo@-&;=Q|{2{!r zXQAO0w}tejmfR&KF3!OYqF39wVz3RE%e&Q|`^tC(6oz-ixSah!PA!_!9`(1ylD)~q z=I*zwgDW0;f|-5JpDnKQ?1}T`R@HXWL}LxS*$Em*rS;j+Kr&5g?q}P5DtNLpRFEK^ ztF}E(WSBcNia8#Jnwfo#OplR3f9?vOp^uSyt2L85*?Zma>N9jOthRq)eTHJ)jGVd@ z^fPAsj~nTaTGLsvYjZ(gqtju-{pnoniFvSXZyK2^t(iPI`#9g#5MN1RRls`tGr1Qh zzJT?N@*ZfpJB{F9tify=e(VX;pf%R5JIuE~na7RdlAK0VaVyAIjpR%JL9Xrva+V~w zpmS|J=G_C0q|Lq2*PDq)CUe$=4z=~S4&2O+^@05zA)!MRsIQ5Aa~qo2otDr+CMa8l z-*0!tYV#%ZSp~`_VRK*f!KW>GvrRm%ti%0W_{O#5ZBTezQ77A1hnJ4*5J3C;L#5@G z;EzmTZnqZ-FgP5M;DZX-GdLvBM<2r-X)&cN$hOatyvIc5W{`LX#;TeFpoj1#dRwSV zwtQ*Q+4gd+<$l{Xk^1lG7C@u6q44-}H2%hD*aJkapJB%d>_HSd$d#mZmGn-2^k|wr z$%T1wM-0q=Zq5m$tAk+8+FR1wnCPAtNYTbg`U8ISNaC_rY|n|Nitlp*DCADi@3o|l zHPKn|&4;+sG?MhC20G#+xU)AKj?kEn_#o?|R$NZKVnJh8f0dhyUE?%7IJHu$?Ll%M!t4zu(nQ z`^5zN$qd`F#8E`eA9S^W8_uw+Dhz>k_7~uW9LuRgv$fXoRCO;RDVI#_f6Q!8h1aF! z|Fp+QaL$*E#Ax4)#*XkC?NR->$}y;QZMvUdWZ zxaADK(+HjzUWKc#OI-Lk>+zgeDu`|!p^r<0j3SEuL&V(~9nvWAepd*-^TTqUHp=0( znl54J(A@aS;#x;h*+g`?*+w}}S<8XFD~6lS^m2bXo4TGjj9^g8`_?4Z`^3TSK-%-D zt3DibX72N6;u0z0$M5V~9Zp4m<_73)z#gNBUDhI4??)fQU|knOvA6*$hz^bS4A$@j zn^1#p)paR0&K-f>SmUaUhx{!*o3x8lVA48CXnO_fGiMXKwvH47VX!5)m5Gbk zc_Ce&=8DlCklcF}ZuxXqC)-#{)F?AbOm43}0e@ntL@m)#{xg_^r5(d4yH9J3*E7%r z7Fp64nCPt4YfnM}TO@sxQc#bNnPdCXlDE;sWAEqsOqBMFH}mGX9@HL}s7Do)sbcEUyG_w3CmNaKy-bc_A+*CWEQF2t|AEqnp3=T~D%ck3ms0#QbX_@;o57Py*dVa@f55hny?#Q^Kr>S|VcpkpwF=2geh z2QOj@Tu1zYT1U7L;91m>LgnW8(?WPLT_m-m9~EJH?DF5}s!L_fJEc&~li(QSiha~oXKkSkc*KpfCM$Q;(&ZQ;j)}@m3vLDBuj2QQf7cpe-cnR~&5+iY; ziOBus{LAPs-%4VIAFx6_#Zjw87y)_r3?4E068}agPd~2Q9Y#Z! zdzSTYZ=pBKiu#@f$KFgbEI9UT;ccHBu9$D^+(q5D3 zQU#4IHgK|L?ygT`&UdOqcdbBUec6Bxd(n*Mi00R|JICpz{$8TLRnWZ4rYs7j&fd`} zhDMjU8sURl{-tiV$c%=^IaNE~g0BNpaJVTXn8v*6>W@#_`$O_d{2=nneY`f4yrol& z{O(f39#AgGoNVvfpC@t$NiHmRICpuk{&-U<1+-)>duEwV4WdOK zL)0jfsNqu7m=-i=JZ{p1CBm{0IiE)(<2ANM`NFXW{CHAP)VwS!)U<>7iR3!@6rY`07 z!GIioxN88N{0g(l_$*^}#b)`x?^-*8F28+^aMf)(wU?LbdFccyf4X}F?bw8gS`QO2 zH%qVot^ljAe|od4G48(Ll1}zA*Z+Va@8Dmq#&nEs(xOkg*WvXaZMliL%*^D0<8(EA{uW!&Fzq^cyB7=(XQcMCiS&b+#N))o z2JRrbzPo!M6?~6L-8CI|*^4Xb?X4)>upNXeUM6FWGzj*gnNnq@WZBI$_#-{>L{W z@Kr2>b}Fp}@T_IWN%X+50lBsPwpawW(yJR^$oj_B+qMO}AJEeKV)b;L3$`8~#`;DM z;)<=;M6S7(Cmly6RJ-9Ib&ynSnPjTIt7zw5^v8{rumL%i*eq)-_o4E1%&NLh%f;@d zDpbOKQUJD3>H-`muy+Q5$pk97qgxOqPwy5=GutC>JK9pzi2o3U?wY2(Bn2$?2(XQS zV#;^9;wiVJdoXo>37v5Gctk#5v=lJkS^%pkYY8eaVJ}`|vy}3cM+)!0f}T2yPhnYT z9-qRp(!9^id@v7h-(sIDOgkmn$31M=nml^r*=~_EE~7?}_zsqZ0DK3_N`TM$bUEOP z*5Y&YRufCBcKVEj*ZPz$=vIrgpRo%K=cji1_1|!jVQxCU7wC5gafDuy-@}i8?vQIF zmP-x9uGT~jxBht@ZT{r3+(-(GaffJ=CAQcP3)^rv7D^3RYyz|@C=AWxTAAc5_v84+ z9D6}^D1Ck_`p+^WmaMS@{s4Bn#QuD9Y)$MH$g4wT$6#K5FtWE=v(e_pYnLVaUq7~| zxizNQh1J-keF8nn zl6-cRZASL@Fx+WV$|AKHCh#;fn1`GOXY;}-bO6TUwL1L+FWu7|dTMJ-xOdDr-YJ#y zw<{e-&G?P0y`QkB}~7v7^#dO zEyQ2{ml1g33i|EZ%P8~RQov}B0JdV81FM56a~O&{!pI)_AM6cRUD?`VCZZhQo+SF>=uO&zx2RevqHmNE!mySY(7yQfJjlXWIy6zZ`puoo{ydKMUQ%vb+`vY z*jVPmR&J}pKVTBT$2PCjad+01O99I~0%i|I;r9o*hhn#j6>O;q=4lU6)F7pMZ5kEg z4%2>?*dIJtPs`B%hF6caTJkX~w9@p0q3#yizmkftXX`bV66U_ocFvM>#>CmO1gGU* z4o8ipiTEe3ZArzyVjK7tD&GXPB2qR;OKxX0-wEwZ=7z18BhsC&4Unh;FN)1q%3VFv zX>DUHE$ofLs%Bpkw2v9;(IZU3%${-xpDFP(ym%fD#^k}ZFZ?qXdxEhoM4M*9KW@fz zgN?1{ZlZl4p=-QQ_V(;C?sy6xQXRtULE1Ye=$kiz@`SH@ygOd2mgGt=neA7>>pk(c z$olRG4Tk~@fsUPKC`aH56Wj?}t@e5s#op5pw=m&|yf>q>LvR$UW1>4!yJ5mzHRF7< zntr*r2GfKjxJEe=jH#gnizzR_sZ^AVb%z!r-(&@viCnWG_jrrR+GvR#;lZ+x2y8qJ zPjSa$`O^w}r!|(H=lQAbDD6dwo$tZ=9GMvtx`)u^AKwVVlBX5>X=}E}n+YOn1U-H( z1$-(6=+hz*04SLP?_c;eSoE~wms|6Bar3pt?uOd$5`9FXY0gG$&u0t9ZpiaZ+zD9x zv|{hGW^+Tv1Sv&}>Yz7|aB?)zn}@8$cv{ia9gp3K$T!tukjVAbB8S%yzu|QwM4LR} zAr_--Kkg@m-R2RtVh);2$*?|=)TlX{OfR1>B>>!`gEuh5yG)Z>xa(!3%;4z=-&A3IIl5m_pm+F(5^VQY(@ORn#ii{kmHWlu1Vr$Nql8ER)c~& zyBkncYj-qGFI!Rnwx;s7%8qT^F}VEQk?zjd(yxJ^k7;Ms)jXF2a9i!!#pjl=pzw_Ps7+da>(7U}*e(99Z-htGw2O7v|6K{l>$5pa$ zAdJyVlDF8)^G&#Ndmx;1{i9x3lI(5|(q1%?=bOpg?Vj$1D%dQ^UwO$sdr|f??8SuM zDDLMb=%+V7rACb=BuAx64pb3#Zb%s?kEIBtcYFzgIG}DZmdm~#9|L|Ng}+yS#NtDi{Qv{9sAkYEAU)J!+_3mc)NOM7ANrhqxQjh&$X#2#;75^qe)8 zmmr_J6P|i{7riBsmxZmQFj;tY_LkiPyxc;v?b?HT#`|5&-=|1ZP3lQrQ0{Nzw>+ zs_%3-j@EgoEEY-i(#XItI1>U$+nWAlxZ?TErf`GvBs0u8lhsWL)O(jR+ zOjH~Sls9C5Y55Wxm6!MgGNcE<*AG7xP?e;ekupvyGI+T@yyI<+Xx5S`5`WARf7BX} z_B};Q=&ILh9Gx5J?yZ*|2k3+8%cJ>qsVM)p%+k232(htfpt*)4MXs!2l$(_e9SLRQ z)FIbCA0s|A71L7Bhb9(JtBlr$b=4JqdanFvItF9GGAMj7LZfo6eaU_9iQ1F0wA1y{ z;@6SHN5z5`+?GI1Pr=8UVp-D3N=b_WRoCBoymniI(g)qOwf7|bU4@QqRrmz^gYIlB zsadI@%o^-#9#s=uf+8x_VXC?D_cf~7Po z-eqeZ+vH1+xVvlFl9#FQcwg+n3GP1Pn>%;A)i_ML_%m7MP$XA5YKg0SfG&%xJZPwJ)J@-dvzjs=b#Jgeqe~N8Z?Fqy)jjTfYVs-Un!cL)tXSWt&bvimjl%2;HQvA{yc zdpIB#HyiyDyYGcQHmPKMaxjF>&UW|JzBIu$nqin`6w+;T;WcBky?~umVcE@HA+-2^ z*oa{Nq;DKGe;U*GVsoB3Ar9Cd+1SjE56Ex>#cAmJ{lPM46H#6i6_Rb zFOifw1I0nraYsXY;DIa*RVfCtJ4X3-anS4<_J-Q9T)mSOK7dvsF1e>UQ%s#~Fjk<{ zk8cz|@6OSlltrA*1>qtB!gtb-gi*CB-DJIV#lXpi#bbNIq`UBiyzxhaDe$N}RC`Z? zanZBq-5Ff=1uSd9W_KuMKhi&m7O&e4L%RSL92Y=asG`gyjMe+gi*QY?eY^ zpPE*xet-){1x8#N#qHU4ov5 zFQ^M%rpn_lk&Ijo6^z;2zd3Dq!5*PypE2Pj@NjW}*mH=w52bKtK}&k~4M4;4jdc~0Pk9gR%E!iOPnY1T+PWNu zDqp8|SE+o?ppO|q~a9?q168^3;?Aj{>wMzN6_N&N9tfUhT|tc>HP1l z__+9KTy-59_YPG0t%+Y@=8LmJ^WQ}${YR&sSE-&(b-Q-f#5!$aarJz>21cY&58Xzm z(E4R?b@`?qu6aISBAQd_=LJw%K0ZEG@gD3{lK4Z_H>yvzpV7|4H-`l)4Zx%ArdpAd z(BCM*qv&3A;8ln=?0@WSiRD4I#z*i7pOV-oj99h<#hVKeDLsm4M-=11pY z20erQxdUW-#m%21W-YzH&YhusDup3*?5`z{IZvR%y9yKZ-93mT`)lYUU0Cs*b%VWZ zjx{*eBPMi2vJpD=XXC&m4g)qRh@=C{3Ioag5|&L7N%m*&@n_4ezZ}NexwkMvc5SQ$ zu7OEH82N4ZU3$+J7Z|X)$UZPOk~V$p7H1{#T%XY@k>3F9+LZ$**Xl=~xSPTsV`6)S z$o@0ju{uZgmx`%3fy5}y%v+t2o$}m+5ciW0S=(ZK#W50gcYpa8_Atnif zbqTnN+tW#T0qFJb+={K2~A8 z?U^;P;(-Af_#SeBbOzR&*lVRK4&>>oz!=U>Yz4+&;l#fEwth2pgcD1QO1x{?fk7qC*bU@-d@OUp!P5B6ilR6eT(rcrk7q__$h8@>kr3$$1ms}D=4Iwx6?7K-VS#YT;IsBd=d=@W0~^z;udR;ME}IJauPo@_zMA2&?y+i&b(uOAafvxQV7~>GMz^ZOp^6~`@d2EQ z-SKo_*uVffaA#Ezc6H&8OkqiUBX5VtoIpk@VL0fe>!K1OMUy;|FF)fmwW=MpcMnXJ zH{Ol!tvAfgJ*v_Wj}RO~OyPbU{_rTIB_@^s`xUdlMY7Nf^g`p;5yZ`D!2x$#>EVGP z^v*sUy?x%YxX&oXoeR1gA$WoMyZ!ED?Mq4DsL(Nmk10JcFoD(`aHrzC09JbVpEa1R zNY*oh0_f;LtiT+T^rH&>%-fYA)co7sIpjVBt9{rKf4~~gqy72A80};F=>AqDZP_K; zn$S#Xa#aX@@a^svwC@PuVf@tS5`~D|d`-~@cit<%LEHI~+uff_;eWehsN}-HL^}J` zAY1`(6!b2Z^o}MvM`G*#0oycD(#IRA@sZzVA<|i&%I7#aqOA|Up9a_Vmk4a@9TluRBfKFPVFNOky8<%oJ?Hx%VPeP;KPlmNEm6 zqlh1e-8iYE`<+|)*MFHreNpt?5lVMTyBgKFrAOsjXr_FAod!TdyMUQ^)Ai(1~)_G+%uCe-j!<{i+ zA-`J+IbssRZ?UHq-XgxQ-aul>alzBG`3RFz)g@V=J?@879~?YgkBJWn*ovgngjGie{o@ zi1G7SP>W_iQC*W7RAE%O8dn*qtuYCB$0UFoPhx#Ep1o2)wJN~VdQjWj5kvn6$IZ1$ z6V_?MawSJ6RyNTB`|CAR3;%j+=ByJwfBA+XICy^yjVLs!GDy?<8!PID3CsPbNizCR zTZzq4v7Qc;rDd65Ef5*=uXS!c%~H^M?v}98Doogo(p0RSjWDr>TC;K*g0)z(UQ}6U z|BtWh4v6CD;@+Z269fxZ#NHKqS1|U9y(_3#)`Gn&mKZ@%u|7>=i&0|%v1=@dCML#) zDo1b8~n2@y8BR-f!N#dGn_1+qp8(#3T=#4?J-A3b{!+jJ;DP z&IuXEG5D%0M?Js}d4TbSTH^}ngy$yMQyGlky)yffJ!pM}zM)j3BD(fl57dVqsJv@C zRf1p&cQLxQ07B8Ve6EjHdg1D>8hu0Prx2{Bs^7ffr{wSA-oN>}IJ$(dR1N*7TYiUeOE3Q!z4_i*MU-A$jMZw3U5n>DZ$p6J2jc+O*#G6s<5GL;|C{eH4<3EETxyU|Zg@NF_Uzo5tg zUWCF9^u;^Wni!LtJ!<&Kvj#5WO%WLN*G=%NGB}nyZInwMKwrNb$cH|!Z7r$1G0|ST zq2V``pW;_|ch`Xm{Oy7JhbJz^VKiN=Yb}ZI)?+JJ@!fh?Lj?@UGOM(rp0%D*zN>qm zm+5MpjkwD*E?+-fNn76m95-&fL)xtxw5sRvZAX&>vu+a zC)%%Ozd-Y3^PCeP`JC1KWyg%J?rM3etKopKlr}cC9#V8(APPSsk=YO;JYiN+<(_ds z!1>G0n^i#Zi#Aj~=vU+IRY6)+)9QXYkUN^`yyNv%63z#%wSb+h7HzDEDl;1M%>!`w zEzAD2bXmVJN?p*;hfYRX{cu4S3dvtLmF1w$_gK55Fh}+_A$zz(LNEPkZcA$uOEd?P z7P6gLf_cu`0ig1Z51<7oT<_;cce`Z;DT_?_`R{@+Md9=DfzzD!Sk29KuV~i)spwH_ zjHq-IKGhwb-<+7%##$FY49>(Lalb2P&~0t4bCr80-fefhQkP&~`v2Ik0tGeipO<1< zgI&!X`Bu)z+{9GsWDTK*-}cK(RXbRN6hFAYup3+QQmsfU+&Wk%M(U6_>Zn2aJEH$` zvn-E8DGOOzSk3#gCD}XbsH8M7k?Xo6|1h+FMJm-9i@RDmBG+(5=Hm@2!^c}Vbl{|P zKg{W*eqrWO*95b^aZ-JJC6m(CS`Qy3iAG4TM@DuNCVfG7>j(jBsqFVcXWPrNKk)kGc2gx zFkAm0%zguaIorUTkt1f!fxw*VjycJRi7VKGyFjz~Pa%zD|F=}2c3cf%4*6ojVhk{T z^wlNcru2&=ZLim-e4Pe3?(Mn2SmBcZe!Y_a=m;(uf<)X7Vb69KddNl7Joiw1vR7wE zs5uopGg|b-*&@K4igz`T>=jtOAqvH z2w8fYJquLsBO5-<+5rcdNS8Y^fF+pbR>opKlQ?}i_JS=9z!olma1$>{CGOhsOEe?! z`yuS*5Kb}Opc~~3%|Di0J;GWW?gO0Hu`bZu`*!R|YbzXWy8({K8IUht&y2EGfcpRk z+~@+xf3+idw6!6eLEQk?$+;K27dt#c)45hLSyrdLEG!~=$da0VjDJ8+zTR|?bVJb^&L1_cb3+~wKi13BjfLt)a8^_!-{pHWQ*KiiXFyDmn zSKZG{IXp4s0Ll=8XH8ES*_`D1fa4iIAad7Dt|^PHwUt7>jc&sYm$T4q9M7Sr#pvPV z*h;kT1IQKKI?J=$0%+m_N4EjquoPyiPaCcFYasn%GIv_<+1n85+1929GsYQ%k>uK? zHT`kR#XQoLplGhN{i}Lan^NaP9sA z^jBt^>zspP?4>S$t)AdDEYw`s9WfdKmuNnL9^{Zx3-MJf3GvlVj*dbGsc-+|hARs72;Yow(p&v1W7 zP?)6}>t&o4mtJh;J{A15s8mr~X{D11+ujwHUazsX#@k+2mpkNpIYM%W&@St+rJ89% z!WUNNl=$6-C3eAJOeh|Q%NN#TVwjv`4RD9dfoZ84p%mRntXD}VBPdksqY z5JTunj^J?Yc~@)pe`Kws%CY)F*lpB!=F&EvpT3iL6CCbWiY9)7WpDfpiY z&G+*GH|tY;NLE3mMPFlRK1_6I9_;N}k6FA?U!i?m3Xirx3b7`mgBgXn{RU15;W|iv z+P@Wxx&bUpp11pQbYnj41yI@Szu9K3p{y`Lm$`#-Z$e|phm7!{`$b1ope=i``AKxI zV{y)P@a4y~+}#3bT-A|56#fa$w@#Y19e1w{+lnrD|L?~A!JLu#>Z`*}?0=q_crezk zB5^2tYi71xMe-nLP(Fa-Kf?ej)X!nD9}z4z7cuvg}*_OS`NQ3Bc2@}Fx36`%gbfD0>bele%y>F<7)rt}w+A)ku)n}HffaN<>^KUW2% zB0_d54HW)*-=F@eiQkXTcNE8l<@&pyOP200MlWx6?qg_nPZO$}48>0>09BGAetVXe zPUlha2+{nME*_vA`x}Ry?Eo&1;vy$-5VgPrpC^O!Hy9ndsbMre0Jt5aZuu#*J#c4u z;&O%cI1z6fZXAP$agIPr-j7vUBfxNkud`qcmW)1$wBbIO-5*@_P^25b?EJ#5a+pu}%5hQa?wy&hZM*nSq` z+3wS62un3jw1Nf**>b;$>zP;>w=>b<-{*ope>BjGRUCsW^3P!p-ZDpYd@7X_I@hpY z&O^hdn&^|<(EAU<7dK05j)4~tThzzrh(0!FbiTZJ{v}=@ePW_-aYNsE!CDV{Vb&mb zd2GxXlN%i@X!FodUs~mn^W2(`{D*yu zpFI8!9>kpavMk?YHxCHKur@>}c9%R_|3J95P=PdF;-BHJ@nX>p|$@QLy@Il-HOJLlAMvuhzY`sme9x@nr zoE>o6T3?Cw0PE}l2F?aii}Cvk(2JSYe99sdalVYmy)nUFDT+DgRg{|sWv&PItemjB zP~O&AHI#G{HdTgAc?n~~VPEXdUBrOhW5DwHH~I=L-r%2(It9t^6fgA{uNAuf6JLn( zKg99*Jrn%44BoX5#_NBNtz}uk!s{PlTdsMqe9MDn2Y!9UZ?G{`^U|tYj&(2ZEC+H2 zysSW$yzaHo^&5_Tmy>6{B+d8%{wCM{eC|i%z5~w$gwK)=3HgCi5=^X?d-?+n_l?xtMqtW{J}1M80_AV0q;WZLc=;{)mB!S zRjhEY0>-SHWtlr%VvcYQxl~lnm~bcE;T+Ovg5S?}ll74tG5LBrsKtyx8j>|3gs$|x z6`*9B=+E5IxBO)7WO?Ea{5VHo2eW~6ugS!G^q^Z-fKp_Lp(2GS_^!j+I5;jv>D{wR z;Jh1I9)>)KrRHQL22rV(*yXl1fm=$zyz}<{1clCOIld6Z_kycriyYCLI?o`01s|X+$tQ z^4Yh&cn8I#?8a%fJE!6Zik+1Ff<09J7{f9H$n*ogeQi>C=TWDUFUH+Q`^8YyU|UQ4 zphWDaSaG5b&hRsd;rK&_Xz>FZ^|3>rgkL)-kmYUZ&gx|EZ2YO>e{j@S&zW*ox^l1v z(nQrM#+aYKb)BETxaT=PU%F?^&y_AC(_?;4L88n2Jj!GoH894N_A!RHKxow)#t$Sr z&amKtXYyN^9#*}9-;wj+axG}3lTG*raH*b%Axpy>eTjRbsj-vg&v%Y`2=q{lvF%6) zn7Qo`r&pHe2I5l_5uRB7G$_D^U(Z<-(6=7251p`l@dRFZdsmt5eYnDC zFaOkopPW>RHBV0V-Y~VQJU#l;L#xWfibgxHCFu}rn1{BEqBiCKm&=J~g?V zbS+6MmM>2uQM?M{_@p_IO$qRMzfUP!8D+YOJViq0UZ`FDY>n~b7H)XsbH?M_0rIzn zDW95n+ax@PSnK20E!^-va^hK0{F#kyG(F;0~{LyaYrf{-ntfQ0u+`E4EWTXAlj{ws0%O0dN~kK~-<2PDy##QSU7cQRnUvS~3eV~Rd&7XWe8L*p z5*Ie|ww^t0^|zQ!Z8Fw<#1$K6Nd3PhrhX`)!`ww*aF}SXbGS7fW~fCYgiI{}5JLRb zen6P5nNok4d!y?NGa4NMG>;)R+C&>6qp?#nG#cwpnpT|NOfX-5DZ@OlV?43>aGO{f z!)?8Zy++1n&Ndu7V*E58TJh!d{QUbNCB*}Kxq*#46EX`zb-3?9LH2)3B^XU%E@JH` zX(cKDbnJ-mj)S{lH4Bq`7 zs7?hm>vs>Bm(G4F{l->{!pmrRsCtPRjx)s2;Z(Y!tx#^YUfx`k@@eCe^rqYlZ@7O% z6~)CsZ!2iQ3W~r)NAMdrz;Ot9SHjn21*ukHTSIBv6-*0?LBj&cQrgB}c5rq_RYjwJ znP~I2v}KUoH-b3Is>-%H_?00yl&Bn0wpFop!3!ldASF<&- zEO!G4sBUY9izM6tmbe4FtZti#AJ%oP{8SBF3v7+uX!p3&^0mgEjWY_Eo*g&cU^+$E z!g2S98_X36#{3SN3$yw8wT{ok|8xU7S<6<^@*4+|)+;ZDvr$@#n%1^;u5jCR(K4`^ zr_qeTug&04p*po_fVEO=GsALTuIsRztBk{hd~wvGj;*^p;*=Z^KQ<7Hd#Rh6(Tlpa zE(JHUQht+Srw@yrFlBW7__ZvDTWDuJ>;G>(2Xm-LLp^7AW#OJA-K@v%Q4ez5>iO*d z*7IWy_53E<^SfEkA0G7}$E}{TF7@Ph>j@v)R>xMrs~qmehwve)^34rVRcP)mD&)99 zevfLv?8$zgebJT>JxUd(Hm+x`8YmrP`u5<)9TgEb7`O1T{FFs{o7fY0_EurENnsLk)q4{prDR%uY2WkQOH(l4>3xmEQpp*x6PayGSP!A9hCHb@ia{j?RN#?whQyY_%-ENc01~#@P7LgZ}wD=&$s$4aVV3 zRA+ZHe3lB<=(deql~_l>R;6dGj=_RbYNcyv$h9mO@kC~9Aoi8y|Ikj#r|)p ze2Uhvaro%Ar0Cg&vLdNaJ6k(Cdj}sN4W~{!~k18d_>6`fbzlAcA%}P zlH|d~2WA5nf!-7#_lcRdVqPfSpMu`oFM()-Y~_`G9w56tLHG#ixH6%Ix%Rjrp-dlQ z>!e)sK)L)5l&vwg9+p2O`cAR7{>rZ&^uN4={$9T|)o5!+&08rl+Az~xAc4bdjo8hd zJzW-(xU>@q#P%rEE!xp9Bia8gmEjN)>zCH!q^~Ge6wc~Q=ewg{o=wDIEx%T-TMw7c z>44iAW$lh1VQZwso6LX;>b)2%y0Pxej5e5=aN6Rhi~xqL;bEh|&}tdu^HH{TmX+=p z$^QrA!Dw{E5gB8#YU^$}=#H`f|6uf6`+i;e#V5;K(Pf}^V{MTZt2@w*{{z(j?ECf1 zIqM^t)Kq*KS)L5ZSP?d5gmuY%cXlH-qv0zuDTE2BC0Ws;?m6F=;_Ev z9Ju4ZmK5K|=1qV0wAC?w4)Rem;}b3z%=*+KG^+`P4MBIejda{LaDIqOcSqy5rR44| zG}%@k=9TjUq7d3K8Dnvh2S4!!Kgw9p%UykDimifWgGAqas;!!`&VzpSJLua^v$e;M zb35z*VVbS0a@>Rdh$sD{>9#?*EZs@EVFqT+J07IECnA*}|HM}Z`9cANZ zu@qidE38or(+AIuSfXi&SSU#VH)yf+W?APzW9=?%bhpnGu3&j0VuK2aa(2zM8 zWC)%3zs|woM~yM=YEX5IVZ$Y{bKZaa2p8vp_u+2Xv14fSJj^Bto!B)KFdPSaU=Mf) z_NL98cbYXH%&&E=z-!eQU%LERVol1Q|Ax1+O0H(~0tj`5M>Wfw)$oZK8ptQ+&d?-g zu>STp4|3Up?p|nCAB&CE*_JXYIZjz_dVpq)aaChxwP1%GbcWGVe)XXH#bCzrBfXp| zt+|=O!bL=@rWw_S5VKcGA$SwGu9HK?8l%#UoY^W%6N#LeDy5+ZdA*#;g_&Y6y95i& zN+|<8F#5_E<{p@@mb7KIhWMQ^C&1`rbo)FHfZ1b(=rDrnQ_ON(Mawpc?$L4#qs<<4 z9~l)Z8$d5xfCtxXxYlEbEew}{IV&HSWvi@YdVrj9f^cO3GfWxKAGH>u3$WpbgP`F% zd<9(${lMYGf*V;4*qWN#xD7?{KHg`XF)A+>@ zZaELa(&LQb76t&hjrZ5unkyY-fT`|O#Fnq8qAN~__5JHmu*Fr zqT_`M1k>0n@Ek5Y-dzt0jCVVz=WY0V3yg0gnSAM1$hE!)d7bfwHbn58C_I)XRC_ssv9$1oq!$?(y`+V3262?7{jUV zK$Bd7xcS2X;nIS=;c*x)9)AlP%jU+Ux6)$%Y`U!!URiPp_~v%J^FHBT&rw5qL0Q%= z<&#`bItJbMJn5Kx-#F=*a-W@a6bwcYmxGQQh7gZ(A>wzZAkwB3Sx9;rf4pc`e=MT5 zZAY)BhdPd5aD_0NJf(B+H;l1H;&6nWaiISkWac(H+#r*7k0mDn=8NZ)}BZ_1RB={u6ljH&NffK}G z&$vMnn?USmoty*>!(quj50KpugE_YeV`#Z1(T%$TV|v+x?xI=w1s5?I#tT2;?yUFW zRlHyP#e@FG9O=2WDXSEkXc%L3e+coZKV+i2F%Ft&^rI^S^@t53b8pp9hM98?Kc7+0 z1E#hUh7TFYgAW;~2a8+Y7>fGZP}Ehoq~EVt_GoPb%D)ZU!8v9{F;?8EHPfSxX%mHv zc?a?1xR0DPSU+g+>;*k$8)BSzi5k59?A60 zPhb~V;~QJ7a^C~wjsS6J8IFA#QJzoMRimr7mU4 znRRio@)b;zev{=ct4F7GXnK8_yptWT4C}a)M|hC4jcV?0K0diM@aYC zq#gg*pE8PLv~M=K|G4&b(^2Ll4lGSF8_;T5Xy4qo)mKhC=|ZUg16xze=k5$0GZ;GT zCbVXkK5jy353x*sC}RXZvbBYOhnwJU=ZsO+e^V!#_ZS#nQ;g07{~>h$v8^wbG>rNid6WFTBA78*pL$)5>eS&uw?VS)I^!`&@Pxd9A zD+l-p4+%L!thlhLmf8JdWuP|C@S1tFI}pCXc^6R3bK5Xwr3_K>2is^&y>7fDy&Ga! zwr#$0P=;vsqwNDsylxQt-VL$!Cm2hs3&hVj#Jb@QaqZm@%m3Z1niBb22CDwTHVwxZ zZoK^ZZlL)up;5)j@&Icut{$a-wRKjCOm(;9g{IQ=GMJd$I=AC*5MOhNJn(m0ywb#j zykXAdVIOa)ODq1sqCQr_`1ud~s>g;$F2{}oat27-wI#wlgnwT`_~#iJm z?Vnd0qTDvw4Dr!M;Qk;tHg6ehUYFPeR!(dxRhzT^3a;CD?c%iy*3MmPyd@5p?(8`) z-|4<|Ao7!r{C$tbmrB3Ipyk~cH*qlOvQ{) z+GybiYmi{9i$Si?ghg;0K4GMXNcQq-`s66veCpSS6h^Md4pi zYhlT7=L;UbLKG*A|G6jt=n#d=*hp(drwVCRmEUDPUrwing|#TSj5xdOW?`)(yto;< z#Ao(&W=qU#!nkaG+R)tcT5#b7G-T+FSwk)#PPOhKtYir6w1*J8rE9iIbPL6}5eMM{&KNi^dh2={kG;UP1+t5`&XL^2OpL?eNl2PGQh^5k`|iq=V)W0Ehds`aqUbSIzYO3tTZaN+1A z9>+Lv3@-;f9}V@oa#Qf6I!wXml92Kpo9UZt>QFTsU*sh>qKVyaH>RaEv;gIpEP)p_ zV2cjB^K(Gr2a^Y{A9Fgd{}AS8wlR3`Z|NNEeu=D5$HWc{ETF&x{LrCNJ&zWqX zjj8ap>-pJ;Xde%E>bcQARQjMTuHrmodVJ2%4~Z_vY4F>q1mSZdgjyJv@J>XBG^MO( zl0SNhy4ql+)hyPJZ73q!;g250$t6yk&vNa5{;Fe`lg@m7kB>l5t)v8l3_kel`~JX? zm7skCt&#G9jB&RCjO!wIjQRfu#V8Qr+LW$zWV_t-lh48`Mns1$i`{oNq#8xCyy^GqT5aw#jBX8~IZ;|GrHKK{ zKP`-^sTrU4EA9}RT54sKSed7y?_nV~*n_75vngr~UKF^sDXf)NS9xEi-`7gRLfeCW zo}lMVf{t-LZDPoa`$|3`$^EBjSXLY5lnETU%i-sC!UOo|Y*%f~+fHk)+;I|y(3W-( zhwedWHKdG9R%T-@%=uQ}YC(O-*FUV8xiEdzv$O+wZ>t+rBnj?vBFJY;<* ztsz#@tD3oJSn)ZoeE--9JrO05(WuT^CG4aaoixhuP8bP>sci@hd6bRoqIFeUWcGC%z(%K zD;e-3y|orf@LZ$Qn*0X?9!RS?yL6cUT-Oe3duLZ;+W$UQ9}!NvU|jC4wY5~|#8R(S z;R!~sRRoaSYh2-a$QW_`@vDKjjuQ+>7`T!Z2?E2#u5qQCCS&9u2w_j=B$8d5FgK^= zUa1uj^Lm+j^9rrHWsN%rDNYXX4xMwuW*xkjSJtb zvNKLZbI!dR4cFe%o80NOxB^$oIDiU$3>_L zPvAne+-1#Zi50&~Yh3#3%z@pGl~e;6{JY^=4d#{O3SLVB$L|j@aDOMb1;2{HI+Po~ zkl!V3yfQ-TsKlH2Nq?Gdc5s+u_)>3qW&=;d%dzQKLm1yvl|BuoB zxWvyzk#3LHB9sF%#Bf!sPYwFH^w?fpap3X+ET3FEkAdmEF4I04qqVYJb+7)CS^Wz9 zfEsHNU%JqSIIR(xEV;-$c^u~KTnR>3LK-2|U_9pR|J-5!F=35XIUuk|G{3|d8xIRx zHi0jhz0yC#mjDL~gq!R5DhX_PZ7i8tnNkz9dMxB?WnO361lZQ_1oyeRWP&kQFP>*u z8FUGs*x**pTE_cUFN>cb#j9uM=SgQW%I2kvNmv+_JALe;M&7gr(nKa zX3`{7#!=FVDOg!0dJrx)__8b{%TUeviO{vFS`8)5NneoqO~cA&uLu2SX1f+980`Wh z+x!ks1Mzm!{37CnQwG02#q(k7KNfFx+wZv zvo-&^wuZg0RVvBk?1inRJSUedXBn2z(&9$#|I5CfX7_iLF=o!yVsJGsV?n}b?T*15 zpOx`4A@h8;jBzI%PLIU|spw>;?#scf&oi8{$?< zW~1ODtWz$Um^D%`iCy4ulEpYb-&LQSlh`vSojHl&zAsl1;Fd4O%L8D@^H;=LXpsK` zqZ3hWFrE4Urzk!P+{a2D+$GA^wE%FjIehY}Ap@|030v?@XLh3)1lUtSb%i_en-_Z-x#gvW^RSf@-8&2(BKi$ z*9rlvpnk8-x?)CZ<&?iYs`1s^IdmCO%U zm1Z(EQx!{Ncj^Wbwfs>Y*OVBUo@okx_nOr%sd@i}LQy#DjgDWf!FNi^e3{rvS`*wz z@5cHpqjH=N%B=si0lSFpGWF7pIIr94PQ58d>S0~4m7_Po(2xrT`AIz=( zSM=$Uo6YFiW-T;fCypXIT2!3{-s*N11gZO35TNd0fuinVL9qG_3v#K4Sddq}#R7|Z zfd#qMWEO;~4~!VGps2c)1%c{r7UWSsV1Y%|Sdd#?!-8DumIM|Cswa$K9SicQD_G#A z-Z5fJSP-Os$pVYI%1FG*f@w|bWa`PA=N zkV`$4I{{%HHH}3ZsMlDKSKY({i~5)a`P57nbXC{0Ah)`j1>Wi|7Fg6Q7AR^e3-YR0 zSm2{3vLICbo&|Z-XDskipRgdWn$3c6^^6fq$(<{K!7OD_i<-`YAoY=vxt0Za)FUkL zQZKT=qTV-R@3TNr4;zW+Sddpe$O1ogGYj&lmssGVvWu-icY_7_)u${_)MZA%F4agt zrj%tWaP2$C_DP9`&i(uvhUqcWTO$Yu^^B7sS&fYAg}rf z3w+gkEbvj6vmiue-eaJ<$$~z9>Pi;+sUNZ+w|blfLFyMQ2vylZ8H%1{fuH&n3w+hH zMsR=y-s(dZc&Q&7vCmoHqi&a3d zWl4wYBI!H!c#21y`T=$RM2o;gJns{&MG2;PK>78p1t?6%5V!V3n#FyVp%1AKvo!ggyRv%`-i3b;!-iyACEYdbqh=S zl3iKy6?6OSal|Q%wdlN_E^Xsi<{+5$fYK- zf0wbx3iepV9&6cy*yBU?IK&=D*y9*`oM4ZW>~WesGT0-NJZpDO?3s0*F~J&Or)S z2uD&1R|pnkvgKNm!qtN%F&me{^~4zqnQO_#V$8;+s0CeOh2>a+T11Yj#av<~Tw-A^ zv2d4I=w+iqW+_wDqAs!GF0qm>v4m1C(b6ulvM#amF0qO(u|6)bel9UKOqeX#s7c}D zgvHpfNm2hvnC8VZnTbtN|838Pv7f+Um@!DyT2+z*%=5Qzc*M6jp!A#Q#5MdQ;7PTDY12 z;T328ub1%uZ2w~OHY69)*j1oi)B{LKQV-$@@WYGfr!=j4pt&ds=8F=Y zD(%-QKphL zH8=t`KOv}&g-onUl$5S5#s($mfY!ybn+6}yN(Zu83MF!5Mu}B5T7Ez);ltMBNZUdO zSiuwP58}Cwx*pWp`>;6%S$AkFVwNTJ!$GZwkGUpVMXe7Z@hYu61cJSEpT(Y2rNby< zu9S|_BE&4GNjt2K#JOsVBidMOclI68O5m{d;t|xqR^zDQ6cszFmA0fEYkE{GZh`Z8 z-(y}9JN^V z=fRMx(X(?yhN*P(gjN-o*S|f1#a$vre6G#5d{6s6N2Sl`>gQT0Zbp3iIf`Uc$Vu%p z%NaU(Qma}B%XEgzj6Z_k`s4w8I=w!LRz0Gwr%-+^%{~Q?Bedz17TSi*1SnV##y^&? z*cl&d5mPg6;ZfxdTlldhJWDV75J}m1#PNv$bb*)&V%2l>4?{jk6;5mA6F#T|eNC)~ zN4FMu9Ba)MglwhHAg*^r(#=kI{MZYRl>_iN9K%YmO#mxVAPz|bM&nUv0v_{cu;^?& z{O04aa3LNMix~l%Z@|K9+-4ig#}cESnV5hAlb7L9j_^3R4G(QQ9s!@>@ozdFDF^Y` zbOevzj^WY&I38zC;Gunv$GB5?y!slC72mLGo|=4V*N{}}I!j`E5>{Eb6-k9{EXi01 ztH2 zBH3ifdI!^AEZ^QR;5U_0!g3pa$8xN*VO;*&&+-x0PM5{8i(g{allIE^W&NnJ{ z@{qWRQ)Ah^Kt>5>os#%*KoUO0*`y`c_j3jO`lK8eh ziSO!@)U`bQ2%CnJ_?|wA8;B%s4wAS9NK)B;9AF z@_l5n1tIY@0VH&5$XJ;_MV8)ms)!f`Tk2SN z<`8%)TlX0Kv4cij)|#7Tcd)Vw)}&a9k6<5E3D zjcjIr)h_icG_IyzjH3T5SjNYZLl3SC)gY>KLue||q8nNdvz0{mZfLDVr1DLzi-^p- zsdW&MYd5u?B2wj+L^JJ{M04quL{q{l(M-2$jRe3ktJb=J)Px#s(4_C$SZM2Yttwq9 zP&mNZjxIK>u^?P&6Ut9?*QT`Q}7{O&A**PVoWv{mgc4r5ks&ex?%f@>SwcK4t_3{-~9un7cwYTWQ-}ZI~eQzo+#P zkn!V9=n*u(&-Ho^mE1yAztbiQx*c+))Dvx-$XWA58zUllo=Q10 zpK9Yp&d*Ophn%2%-)pgE&Iy|Oy`VWk&%f8kikyMZL<3Kd^_ezRdjO%@OTnvirHf3JVEQTg=kNZ*N@sL8D-f~pjeJk z%1f;lO;}j6x~T}fir{0&T!M$uVFQjMYAC`wvizhCHMw3#>Q92vb@bpTZJb#JTQ8~y z&Va0Y2Bm+;s<>3LJUvXgj?aD_NLPNAh+F=m#S7TuzX&m{qgpSdoLw)p(V|%BOKqfx zEPg5F{P9v#%?5E+UVj5tdR=lfsLU17pgXkqSFNv!a)-YARU0lMU4IiJ^A4T(O^Egm zRr+02c850nF4iu0=-=P9A*K-SFd?kdQI)@)V*Z8@!mYu@=|NU0Us1)yKcxEV{;9=@ zoUL*s;4f{6*&dGC^gd7%zIC+c_IIK^OK8u)Va{bI~4UQ{hCLDj`5aqE_v$?dw-Q8^6HL$xk_vDN;!Y$)g5!hRT}Ig zxZX>1eRRj7V=rkwqS#()=Bp1j#j=-)Wq4zVrF|2HUi=EN#QaviFa;ITLrgL5rF?$6 zV`T91yPBeWKyym>*YQczz4G}SmSHd5_R}3YvzMCqi)Qbo9sasQh4)fGfbJN%dudF7 zL~}VnA0R5Okx#VeId#sb_cL>zQ))ilG2ot4(LjB;*&eoiRsZP>{yyrWkcGA9Y@l94 z(ESl8bmcjX3ep{0m*;djNJ4CwUw6!H&uK?~-C?<&Q=wpegj`pFo~SFW7knN6MqLqJ z6-&^{m>YQ=5_wL~gGFyWr!FBv2+!$Ai0+tqo>R#JqNzuzX#t_cM=7;{?wCN2Qk8^5VO{b(HB{||OA`lCv4H@4_LqMi>!b%(h>O8JZEj+y=_B^D6_=qOo=>W-=SC`~S^ zk1~xQuVU#8R8e7$Vw#DWa}hP>meXV@rcW^|J56(n>GMUztGJ#ZB1y&d#UfIuguYBf zc9qaqi%4Wi3E-=e`a+Qt9i}f4k^6FFY$^Q%k@KpQzCc74gzN8%NI+?QfhksYM50z- zhgN*Op0<_IN700XH@t;yIZcJj=raZ3`ZA*1PE(Px`W#a<9Oc)KfzoM{Lo`jc2+?HF z=Vf)r?l*({%IS`sLk0~gr*}8;Gw66Z-LYHDp#0^f$f)vSq-D@WIZ~y97+e{&w1V!~ zh-T3D6@(edps0$%4rS1%awKmhF>W(xbR{voGwAC|5(nXxB@PzIk;j!K4q8@`IM`A} z42|=2ri$)38#qt7tLl!e{dwwERqtR*k{wg27fwQS<4-#zx$}&W2;H%zPow)0LNsX+nnw?9k^9y?AQlkoc(Sd<_7(J|AJCNo#yXo&R(P@-gPj4X;6b=P}rHDx| zA*D8cLA$;%foasdzQp>9`jS-c%8@D!BzEH)NDVpEKmzb;=xi=^Y$%~^Y$y~njh@Ll z^&9DqlY%r_(nxP*8Y14_UuuEy~ei<^kynMO~V2s42GZYrT|XsSC--qI+$sTjp5)=X*)HIrz*Z>G1A z#b32M`1k2yia)$sogkXftd5_vzY?13P2~pf>kWco1F2@DUV|dq)yXTh)xU+Ljon&E zJ+i)q)FZcB=#EpyG%6PRV=J*Lc}$&Ki-qfBI@(&Qx?~$Mi$A87ZNzf! zF}-RdtSzs)+^49z$qp}bO3$+kQDaKaq|t4KY%=L&TitQ4l}Ux$39)BVOgp`!*{#ew zPz^r|jKEBK*+p+a56<2UbZ9^(`L!1U&csa!y5mTN(=|N@x>cAe8bJTEb~i5)7&~FY z*qM$+P9`ZGBBN#%PAHG~X$ ziiLV6{o7MQ>)lHjpG-Q`ODJ_F73-~cmpf~XFWzPE^M^}e7abR@MzshMqb8HC^p^Uf zTpy_+Gy4czkVzN%NGesduhcQ4`$|GS-d7T`cR$_n$|95c_mcop`w7zo_3tMEMD`am zZ6+=2FJ^&Ey4zpOhnZA%fW+H`0b)gxNv8(rEoG%HTm_9^SA~N6>D4K+c$?f*rOB=Q zPF?IbP$J$jP^$gKKrwHj^9SjU^JP8&3fBj$Rv(u&cfbIMd=EdY_9nHB*cN4yZIIqh z=HpN;5VWa7j|S)mC#jtxQAvk~Wi{jxSbG_cn#ah9D) z(}#%NdL~(iNE%u_M%df+)G0>ftfx<7L@%$WS222Da~-vw4cnaqP+!$SxlKEFcrc=d zom)-QW5v>GH64o;eYl!l$LbACOTU^L4HdJ{YMM7xOtP!#_E5c|SsC-XP#?_%y7O4~ zALevnRF*{*hl#P5Me~P=#-!4gVFEIhUOS7WQqSQ+lBu+3xLBm8l5d>o%v5}Uptm&H zU_RLDW}L*SOEx;%JP>cwO*XF3i8wJduTZHGV$@%uStG=7zCyQ0h*5uqnv4{ka)maI z6dlSuz11JK1O2@nbasp$j??+h`KkISJ=A1}m*{~PR%%PU!!!<=LLn-XokUVci6N0l zCr61&6RE^#v1Uo6gweumB+`$gh1p1?HmVfastQIE>5VE3N+JyyBNaO`Ms!6Y1&$Rm zNTktYg;*2mz*sSQ66xQuqLUJ-?>J!z5^3K!sR{p$lj`X+UV=V2ULS1^ghVE{%Xnw6 z9=OHiR-O8XbSq}+TO!Sl7e*(MF2?J_%vW8Utif%NjlSm)X7l?(nBOzh%zL>hvn<{M z$4}6!nbq-(r+87Oc6>xbhU>*C|MPB@&Fa3VzbA+}{d;OTQHmr_6k7U>_D&RSenxL6 z3LE!~x=a$qp3t~Sy5p7J6ULD88|uCHJ8Nd~^$)rir~MC{=IRNRm@Lfo6Pi3(Nb(7N zF;?rid||O{J#l4b4Vo)8MIMsAtpWsRBBiew!-n za5gobCi*{{7ETk5%%P~p=W+E2W3;A=@R7T>0$+$ zP2Ww|+sY^()&NTTn)GHiP$Isu7ZeQ7m?4p!nIV>>*<_h1Y*RM1n5jG7{bkeqnNo{0 zW=a5gW{DXtn|jTXq_lCCSUYFa<5?1P_-vsH*`&^vI7po>LH{O48qJZ|nKei1nlI-_ zlFc_)^xzq)HdoAjXK3bJF|D4V%X38soT1|Lq=-6CaD9dj&68-n6ZGk`BSm(B<`3`NOu!)R*)Cb;nVdVtwA=~QRFXi7TGo-Y<# z>2zhjn0(Wz=mIGcw?HtQj^8C0Ln)nPD0Z>fCp=cIY$+iivBxCvz7|eeU83fDyErp zRC<}{m~%93nb6pCbWzR;OBM|~NX?SP^nZ}nB#Txaq!-CTH4akS<)WU0v~{`OtEkaH z<~pTjN5BhkD*Pt-tZ>8R@nnZRKewk_{+%b&_e=hUMG_si=mzvd)=B?Cg7nhQ5up*xi zHz|^<(aV}@1&a9XnU85LIbn7$l$tky{8RK=a{3D9>bX6Jx}@lJOg?z|L-77o_c>72 zrp*1*sPq!!BP5PeQ}p_#w0Kf~<{X+E&Y_01&MKR0J6g>13o^gacwC{iO0Q&cz|#|$ zD`|(>G<%g^)oho6$Gl5l!Ecn~CFAP2qho8!gucx4bmBtFvs!OfOyX?;b4D!%XHNV1d9JF@;I}HNYdXH7Fx$=i!qpjx-n1IlU}%cipy)64&UB&DQbq2bc%!1$e+3OjgJ3G2<(*FB$JG z6Wf@F?CV_=wpMRtikcJqGpE^XIL#WOe(-R76NjjI{%d5Z2?y!oTCuC==_{FwZSoP8 zUXQ}RwH7Wax9#I2$bAgoTZT$O(}R3Qggwn z*=VYjxi=fY%;Q<@`xEWjs5dpWk|*6|&d=BXaPV4-UPet0qb(bz6?A9{FZKu})O5H< zGhkY|YDz6fy*&FYa@2D0mp1CH45w+c-o>omISTFiP;X=E6;GPSihP1Amp{}inO5ir zR@B0rs)cOTNPmIEzA9DM_)(9K#6cQQs?R*IpBJR`kMu}W$UOZOoU+wo@WnQG`h|IA z8?yL_Ax$ctmnM$FxGp|^S~W*odCs3DP;vu2w|QG@Ri56=p>aI>Gv?F1xhAWxA~}A9 zI52Ba~qUH8;o3RDbdika6NTqADb_-agYBdxVIW*cTff*2}Xo_5zc~F zq?1lr3>J-C;VbCO<*tH8uESMOqFvY%?h!^VcP;CSxRdts7?HcXDdTu85}er|I4c%D zU(Q_a@?}74v}&_x&KBD5jGUk=h?s5T?lnKbRmcg#y=x4RJB)$D_yp0J%Y9Xh+$Ah` zEPPj-x!l*o$mPBkt>OFO%)K0n_f1Cb)i6dp7zxhosdYs5URtn4aQ~dPI3q{tG$JPV z{AKAYyh3(@@OPy~9T}{OGn?hyQHMnp{Pm#yketik4jQ!l4O}SR7CMpn3pgY96z6_F z-pDz#`O7pTJB=o8)kDi05#|oB)*tC56D!{-5*=Sc8>pLEZWb2TPL}w>*2v}S!VoNUow+H_I>qYH znadZ42JMXm294FBM9h~KMz*=MkaPJm!pIfN2q*MqXT@TT;LPPq1cTPGL`aY-<`cb< zZBF!Z#e6z8a?R;DuSEO3vtls=J1gcBut951zz*8N?7Ip+s~XwntV$Jk=wX$OZ-vAs z8f6;hWiw#1|Aa~LBjfominA|=*e4s~cjzUQRmqgRLyrquo9vqlA4{r#G1;z9UcXZh zFT53LqZ+7gSyX?gUQXGOOapi7gK(AS=R5UgzHAmn(cQ`Gcj>VfordnxI|b=RVj5tU ztW6$83wG&M>EUO3CH~n5N>%zypN-(t&-4!vRNbwwLU4Mwz8pcvJ$e^>X!+qDy@@ZI zXMuh?neOk=J0P|4UcDcJMSH=)c6zi|ABLdyK79a!ZTs|*2z*oZ5eO!v>M;ner}D%` zX}XHor)fMf&wf1)u`&C3?DBqIq;9(26km2uOxG(1v9BFa?|q{Ib!fpsy)32ZJ|QUZ zAYJc@pymO+6@n=T^v(#*9nf1N@Hq&m{czR+@P4{}P#=n*?ji8JpH>{=CGQ?W^Y&BK z!`N=`Ks9QFP76LRR)E$V*30;^6jb~wna&(W@f{R!M32Um=7Wyt^?cdl0hzg$Q}t7N z5z0EIR|{Orzx7ggJ_ORvhcx7v-r9F;?9jNelaWJBj_LzJz2PXtwTJ#Rf}Y3pzKEq7 zf%kEJ2x23S>%9@2KF$e>oq%BX(7Y2myHDMILLY;m_2-b`9!me5m&|*T2ZK-Q!+j31 zcC0CAp}-foI_Kw;dVOD(1YXN?UfbY@qo$nFMyha{!Q7?h)IT!W!5m>&_ z6A&!;MxTM;pKtV;2F>Ey7V5SgpzAK-XolA_x(>x;Ld%0M>-8-UsNH3~jz3$Eq7y%6 zVvBwZmD_w-FN)0Lm-UtiUSHND5j4Gm#u0sbMQ`ZO)~KM^!zi|WgQb3Yp+bT5@Ty+h zYc0NVj=YMN6YakWO(H6G4FVuqc@0t_`tzC|k6=U=l#S>{mOd3hyX!n}|8<^M>ITnS zc7x|xZbCbW2Hw<11oE{p{|1FUT&u1~#c%2PP_ptZeLy~5a#vfdjPan%0rqf;w!t#l zZ|Mb4=BHcwC9y`hKscBk+BuA3$*O880&OxxNpvfwdTeQh&IG^1sq+@~YPVgjr?_4g3kMKY=eC^k}~m21kv- zQFtkE6!}8$2WC#t`JeS|0W4Q-z6&tTcTupHy(Y`=k3&PQI#w;_1-QvU>BbJ+9H)?ax-%ir`*kpAR1{bK|Rf7d^x@chUw{fE8*37`L=Z$i-b zPcXKQ?)<4o2eGXfMAgp__aeIfm%a#$t)sJl0csr$`y0UPD97lPDRJTdwoeR}}k z#6@O;>Bvk9eu1_>yj9YV9=z7OQI`mNFp7WlTHlVK#lQL{s#yelJo;DPiiCy#fsb_* z{zl)7AmfeR12;1jc&kVIt}xh{wt~jK)q5f}^Q}I|vSM9x`!36!bx-YmecA313@=zg zmby$(UiO~g_YPgL*gwXvvEXaMj}Ul!*|#Ftuc-Yq#3~l!MXbf_ zb8s6(_u}>repiheAECyx0Z`(U; FCG8QE@I~1&sQN0^DPd1U=~E@_YY}uSXo7+o4txk`VP z=CLJZAn>bjTei2y#S}x!+S~Z2w3&S; zg0s!++YtPWDrXeb>o89(R=V=xpy0lwt6++>l8OUEf!&GPUSxXH!cC1$D8{K*_`| zQ1}eW)73s3!2%=rudDri#1=;LAa6JOBE*(;v(HBm(A_>4!P4&b4-f?R;K7C-=!y&q z>-j&r&O5G(}NqCoQSv4#kWWBH-Kfi1N0#XSqJJP;T({T&mg@eHq9C|NU!O6T;`N6+qI3T zH3-km`v&QOP{J=TUqlho+6>#Gj5tBkvJC!r3_y&Jlby&SSd9D$g{| zxbrAhIXp_&5TSeohZaWQJnRvFM{t^RBnG@aVnZal z)*j&S7SMce&bknVH{559uW}DIjo4*2g&;Km(06* z$;Rvw!;RT5hHH2{hHK#y%e9ymtB1)bGl!yyW)786nvLfu9mYfVhS)w{Z&`pQA#rL4 zEy0)>j#FD`0{YAi(QSf0)}1FP`;SHf{YQ(j^B|2HuX_sL3A+D^yN$dGvguAOVhj{; z5F38bz5k~n;vP0C?yTJqKTbrEZV2Z|=%F`6*d(OCR&1H1H!HAK<{gQ=Gv*=g&B=Nx z(QCh6NO&G>T+WqkglL%c6rn#iE-7Ls>-9WoA{51qS;9l+WZ^$U?=M0gH!h3eAyd?! zqEB(>xzfjzQK-k0MeexpYuFApGy$1K%WG-eF2xwKaQQ1ah$4UA?GoE zA*=kp&@A19Or-iCQw&8?n?ScG1kEP5>F1gPM@Z6OwdM{(mTg!0Dl11m`7`*YZ=5o%V^9q(- zTESIqAvlKv0v9MOnf<^`wCo42VcwNoi|CbHi;R_eG*o)7VwFp)aJ46k8mn1l^=e#; z$s+$6Zp8^}_}r$h;WVvlae*d_q_wy}lZD?pPG#9Tf-BZ@v{CEz5wf|XYha!_x`tT4 zPVZeHYFyZ`@S!owU}3Gr20mwr8#p(=jqEJmhXkUY0BZH*?!=+l(5l5M_SSyBDCDessOsOI^juO>{kN)=LV{U6>ZxFjV&tg^G79 zCYC(zP!#%4f5ItQA%<Io_ zo_KfQY}^!!c3=qFEne-=+Z3RWNReY7M0|BjFBu=}DN=Ug26I}smVnNVooI{Q!f%&8 z&YizE4a5HlTG(97+3BSm%2BRKbS#mgh;lXJzO zqa5$SQI62#n7-JZ3MD`Cyn0InC+R)-BTwiKIF1fKS6n`>&y!00NoNB?B%a1-8+8hA zEDKNQ)nStD$O*lJC;c0P%I%hw>n}o+I{S*io89t@lBe{_I1AkhVW`a!V@~2y%Mn>8 zaUtZ0PNcnS{^xx#WF6FMpr>Lr|3;%iK!y6&;!fbJ>=_wHU1>Ewznr*Xb= zgV{Y-d40++y__QN#Xaa-pFZ*==ZB$-*t&hx=OrQ z`Kw;lX}&o7s~+S!U(%ZK;`6T<(e{aEcl1EdeUjFW=gX;;aB9)FwCM6cFE66*>iKYv z_le{?Xu5r(++B8N-NotHCsOWW(B3D4@1cJC#Cqv`zK3e=6MgRE^4}*;N~gjDJj(79 zvmT(6?!#0Z+H;@iqO-GK*E_?Ypp&6D@uWZY&>%fzNqVm6+`p7)@es3gr`PuPMnN*f zCIdq^K4E-_xEW&NL$p$c$bN_ioD9+C5qffl*!~C?#xmiXqPKUY{{oTVuy`>h1()$M z@hk<84X|67idWxd!Zi)gBFn_mGz{SfMc#D1ttb6Ih!|sK5i}9>Af&q3@efAtCFy!y zXq`{TsB%zr&pvGq7ao~tl2kD^QxA4cmDChy3b1)p8 z5zl3)f364tSP}{8zF!7vbAF*5L08 z=>UykN~5(q3;83%Lv2ODmwG)H$Dg9&>z8_ER|=rS$K%E1mwL-0EaMLh>42nKi5V|3 z(A0R7>+a4%{;u$OPvQNW-bNDsqHs}v5%HVeP7?l>@aMteR0p$By1$tIS8fsf z>vcRSfD%8Fr?iFef1{Tbf4$brx!fkTiF1YjA9^EC3P3GGs~7Kjsn=-u6!@JE8>g(&kz?=J z1*8Ki)|;|vF9z;@P(%v(J3u->I9Lk*OuJhOAMJ5m{v41F{}cCF{-BJ^kG}?7b5N{# zhnoSb^T&UGBvAt-Un_H^S66{U6K{_+xMWT#QeS9!pmanUwT{16KdmoH}VUoAIBCIkN(ojdlEuR z=S|=dw~I&?qrF6-&13V4XPFpj0^aLxqU8s@tUJf$Z}|dmiBTW)K$iuSTG15o;|IOA zD+Q2_TY^aWp!acS8GpLxbx#EUjq8N}hrgR5rv0rqaHRmWizJ8>f9oAx9RI=#kB@pS zPYOU*LYAn9Nc^Zb5vN0=OL?$`MU6k(+ny$Ne$;!qb4>nF?@5ku|D<5jX`6Y<;pY+~ESjHde(E+FJn}pYiB}CcJdJ7Mh@t1ixh|~{wW(Kb&_AQMm)W&Ft=9gsvbk=Qr3itziQmvG^)25J`&&A;HDK><0Xj1$kdu3R_8H%Tv)q7b8#w< z5hw!@^~L)bV~h-(Y9fy3GrGz^@us3^exte! z1T+_I^BV&#yQoyx+qsBQjqcSK>G_Sau7psMkqN@$Vg$RehHoo@RC>$6;x=NBi_uyJ z*0&XZxfm^EAhWG#ZZT86fK)%Xn^aTWv>3sbB+95#dlyl--=s34uB%bilMphRD!ced zk?diV5wC_$au*4%Mr+GTiV@MnMP!Vc)JBvmVAS*^gcvhrj1K>bQLTniUd9;pwTlQk zHo1bhQ^5FIqKr;1V(G_tKhe(3m?4o7-sFytd&fu6h`SWX@gOjmj>ZbJeb5 zn!7Pust-dk3O1ZvTQv1Bx?1j0$+m^Nh?d>%RTU>ZjB2igP_pF-_?OeLNsa7Yj*^5I zG&;L*qywSiWIK3` z(SQs6j6o8G?Q%3=d4FTDL>Y%%#H|JQYKs&8Mkks2w+BR_a>j5O&<}}e<&4oT3#oW{ zQ^o6YMsHUNpm>&LqFZ^RyOhm5BFmHlVqxvAC($N7 z{P+W^u8%lV#b_gy4&aNJTg4Yw5_PMZ*$#gwMpZR>O8KCNF8DUmn`$)QD)RV+0?lS%_`7Gkg#^treyV}T`wNHMQ=f7ngBS0!O_{t()RW`gt zTpgpnMDBSlvdmd^j2060LQ;co-770P*2VS3NhRkK)9M;SEUTy*zk6B4$-B5hO4Tze zdJ;lqX3I0zMl7yt_=(Z=j7m~@r;9~6eZ)w3wjO#R$8Tk^h=SWibb8fIWp#c{D&L*{N8vmyD9Eq9P?8(i@4QcT_aJvnK4bG)Edxw zb#J(EYHoZZ(c}P&SX6y|GqI|!^W1|e5Z!CtlHrC0QCmN$l&iB!ZZSV}lF}<6M!)=UnQeLdNMFec$ z*i-0jF%+^qK3Le=85d+=ey}Lr-Z&!zr&@`u_Qn+%Sl>n*>|pGY$%r-mlAFH@#Gak#p z{Z2_oIvc)DGUVGODYc8?;Yes^XNy>zxv7(A(ajhx(=Lv7{q4o3R^nARBf^nF7crze zYIlj+eNI<#ue(vjlLBaQ*X2A;MTx?F(A#VFz*TsR)D8^6>8rc>8}YCQE)7CZE-00) zQaRuoqo!2;+ykeleO!P@_yz+o$8XceBBo8=93aZ{L|0@~tFPGE)41ivfxMx(G+K+9 z?nY7ZqT7}raitd?Iap*5K`$@5rLt(<+ZZRKei|%}_cpf5z^qWA^)Vu4V8{@W-p2@& zfn7sHr@qEA%SEce>M<5^Dt=2-QK6p^=t>ARsI^=S?1wumYlIEAh@jnD>WWYOj6p7p zdWMV8{>B_BPe2NZ_G48<$pN^_vHV5^id{^M-8#0Im^#3SmWl<STqNTic4J15I=r zDDwNQ?ZoGSMlYFm&=}EgkSSk)vMdRm!c)a*D)6ZVqFabD(31ivs#P}jHzFhy z*Fsu|QP;w1A!9Kv#ADavZi9`UGUkH`RO)&nb1>SFmCjFf6up0_(NdxlKpw_6Z*eLV zCz-YS%(jU10^4he_CwHX8D-BDyN4K4q`WKSPUE(h5!Ru2U}t&K0*g4FxZOu24#i`A zB9;5Abr$iW`Ho=Gdl+682%+>Nmkawa+!9!0*kX&=FmXo}@ot#WN}|~bsKuNel|)z= zMjzIi1{8c~M_utc%oyszXzeP|f4H&9lLLRPMcX&2Tg$Jf`0c~ahNA5VBhr#iC3v&} zjp=u@qsTWBZxMu0f~m6oI*P7F5kE0-Bwln_dEa`mc%%^{W8Oi`#npCIC^QEfRcye1 zY^;)hy(7K3;do-{bl6>FtI!i`9y8r-Aw!yVf3~frL3D= zwTLdA_B0TSMjPc_2_fsX%f->rh{PJMhaFv_>lh5bjB1_0MfCms%HqNpJn=HBgLJq% zH;+UC867)o5esi$_7y)y;=*Or>VicK%6lb9)QG~lV$|yriuv7@a^i<5qp3vIkrsER zzoLvVw~bWHcaLy-8i^&xw|I$ZV~wt!L{ZWtc^>+RbumUIv8bO>Q7j7D;c4MWXKy-6 z9y1Ql9*i#CbCmp#ap)P0wmou`d|Wi1Nf{kC#Fc1cp$x2fC_-Y4SyFielta7R@Dl}M z(LSvF66p8z8-Zd~tWnIq6yLaObQsy-d+cjMq19%H)&Xd8F#u#gQHTz#xs5_wsH(K@ z1H?N`z~bE$dO%|A`#|D-CSdV?Gi29lxwHI$Nwf=|3=S#qpa_G*3gq!-a8v=>2?BW> zC$Nb!rKNcQr%b?^CYvE`S5+3DjdR70iyXmd#N@nEDbbn1MFr}2W$=pvle#mwqQK3b z46Z3~bpV4K3UnUC;Fbax?UMYeNS(o~a#w*)p$zUTkU4~bu7KAh1`ie3F_}RMfxXlm zlV&hTHvuIW#$ya;R5Rv@M*gL(_;`r|yX z=!5cTs8kk6AmB(1EdC&kR!B>Np)%izNK1kyGMivZgVup)@4Yxm8xwHB+fnEOiT}ZL ztsO~rGzrVCW=Ok<9cUr$tN@mV5a_1BdkMZF5JyQ)s>2$+O~5JkHA7nWnk*imK!Dww zNr)mHd>IT;!0N{!Oo8BX3`YJBRAewpfhCm~j8ULsvI8%*5%tE_aa{QrBb(^46 zzLsU3tiV`Vw`mGsX$nP|p+M~b2D22nD#7;(V6h5CnWun#nIv(F*n(JPu>y$_#4F%c zn>CgzfaNTd>JJKp%c8C(kVfZ)Hei9qIur1D*?^E;%9lv_CR47RmSBqle@L*MfS@F? zX$vK}%LJU{9y6rPZph+&3Vhv&!F~lc6R=~$K}E8q${__hHRd>n75FT{Q3ZN8VU6Po z2nkLq@I`{t3e;=HQO+>1Q&kpAa!#q-kl=y>4VrVDOA6338`S=?0&gX_ssOFAfyQ+O z>RTDyRG?HRdHvm1~}X9o8apanS4c%T5SxdAW~Xw#j+BLyn=V30~+168-r zHw-ce*op9|%Tn?ly;%NO0a}5BJaQCBmEfrYQ~I*Ta|PlDF?gxKr4R8qVNP1+I=@@IisZkqka6ur!>(X9C-)_GAPAiGGE(Apqw6 z&shc`rwwEv&#OSq@eJ}SFm)0Givr1$85AJ!J>^jYdsZS14-;?$ds2uN0%?b^0Sv@N z0PIXyUQEen*jZj&ftA=*1`$dsK>Nr5lqSH-b+A;3fR70{#j<8d>#=~v{t8@L!l1le zkwr_HR8(MAJcG&#OiW-oG zNgJi|L4tM)wBNxR9TX_Flfl;tEZW7OlLE9q4YKL1z@Ik0q8?uIn|zQP5|^b0oQ(@8PfJ0W^srD0Vf%R60ma$v_B0}7^+l;oMI5B zz*nakj8K5qNFhqN0<+IB7^Oh{vkXQnka3Pdqym*MJMr`HSVfjzW|e3KK1vWvU=dYz z^i|fFXacV6WHY4Ik>Y6zgh?=iz(Y!aY;d69vm`LD|Jh1D0XyPAK1YFicNol5;G+Z! z6gYgBH5Mumdym0l1^m6O3#?&%b9WxRm5%V+G)X3Ani~nIX;M#Nx{eJj&1DsscZ{Fu1OOurRo( zz++biw;cfE&lfi)ca+LocLw(qSYMFA0|g!zWnd^k8}Xpvj}&O;#UNFIvl65e;1z0Q zFoy2GStjAiJ~l%dzVf6ba})^lX7E&jnH3m3S0GPC1}_yjAi*mIx>sV2*9zD_Rc7)= z5n4HieBUWhqZ)%h6}VrW!3PC=f*5>MV2%Wz6?iSd7Xmx^!f3})ob6cN!-Q*}*9>Ww zVL%6^$gjYLMYZI~I4@zE90onlw zpt1rxJ2R-N04)iGMs)>VNKjLOUR_xuK!LBiJM;TzkRn5Ruu2^TXg46FT2FzKJsC7m zAa8#LjT9IoK@$ZYNYISHEozSR0UV_z0Xq?Hk5&{sMDof5S>DzJEN*XxwD*Hp+);u1 z!x>l==sSXeO@RU<8FW!#n*`k!)B7i9I#Oem9!h07b`?U^dn!Qt2m$m~;Pq$*eHCaB z&7i*m8zmU1fLjb}geYL2B}u3vw3iUF8A{*;HOF9Vo&zx41l$}W&5(9|8jCdrK7Y?3 zLIL|624fTmoXa2zz%KK+F3C8hVxPwLy@V=Smj#X{yPO8tYGlH0={b)%vE5n1oIW3J&2G-9D!5Rq-048 zVDVyl{v^U}x>U(mZ(wU^)QZ|JEz= zSgLGPpwo7avq^z;34T&w|1Q?psz9~f47MwHvF_AwV(<%S~fB)FwO@yo37s{(H& zxU0aDE39!}fxxQ_bOp9Z@K6EoYpjvN_dl*1?U97Sr;*BWsw~-L1IRQ1S2o)WX?Je1 z_=y5d?=#3%phPBvXA01sNl5Vp0bc8R`nkORUYUgRcx{HX$uBv=8wEUmWAILacfT|E zQ-O{7@=zWh6rlCA03Q{|kl-@|zW>c}VU;gR<%NZT6BbQ63R-|c9tDoMG03ODR1XF& z3iuXe;Hp49PX=xZ#1_iK*Pn+Xg9@{XrvkKI7lkjZKx$D2MHQgUn4saMK)2!yN+>|P zFF~V}0)K?>0BOsHNR1y0ssP)~s_^%yizV01$UjR5TO z{Kht8(nP6*2Qz48n&|88lMu zuRw_~1_KG)pz(nWF+yXo3HaI_VurNO;Vd4e0IiFL{%{3o9W>p4Mk>-gl2tSX3Pmx9 zP+&zYgE0iwQYi|;#v{@gYXUAsG=(xrOeP>99&Z8`Pc%bXs2$6mA)l-Wt%C-bsz7<{ z0R=E!0ooJ_V1@!diy3^Yz?vTze5XLORSdpY;KgbNbC=Nb9~YDsO(WI$N~Q6725|~} z*}!0t0<=XGqAVehOqC^zmH-JR;L0vHL)xirEEWp$q!IIBSL0|w{$`r}h~;31QXN@edO2EQm!Hqd0&ezaOztYO`2~af3Pk_LKvy8|?+hL)Q0@(b6a}jN#UM=q-%kuO6u9t( zK^FIaZUE;zd8sy!mCBBM4004Wn4iJZ{{ahw=L*oqQ%K{b0>|ALyiy?Doxy7b?AJV) zyip{yFoSmrR4KvWPX(HnV(>wMfn^wcR3Oot!Dj{P`!e{Vz}2!0oa|VqD=&dIeoXQx zaJ&+Od7TRcN%rCeeP9pr`_)s@Y;0*z|2hK~ZYKNX_b%PP{V4ikR`{;JEMyaKcd7UEP?AV-4A z3efIV(5R}wutp53D^RI1gPIP2``?8&Oahe3A}fO+0xS3)^9_T#Cg5?kz8TW~?#beY z3JmGXps@n9$rXy#lt4VCIBOt-7E5XTC&EbvD|uQ7%UdgOb})ms3TzE!&|ZOxV;OW* zU~vosD}k+)srO_ColQV_+O@6}By$7WG%4?40xnZeGo*Ey!qIyZ*iHKT@$igV^)mtM z4=_Vomsu4N@X1z?wepZ|s^GGPI$3{#*izSBmW;R?{+SpXvmT%%H?tzQdi>zPC=l{*_5#47L{3q25Lf&#Ru7QiG0Xb&xb zDFo(HvB-Kez;qLEv1XVdtscI`f%sd#{y9P;Eb9jOPN{TAWbnNLz4tSitH5x4I)W(k z71(irL7W0tFt-GaMGB0-sw#jbCa`PtH8x1RNwjGaEK|Vg2y3iRz#H>Ii1LF1MNTtV zrGUU2I5gHM@Dv|Y1FTcv$OQ)W4T>zk!sJH8 zWPKYN$4$UloHRpPX-vvOe42orBMf=L(cC?Ak3w2IR>{nf^-PIhBG8fdkw#0hi)|8PbjxVX>h=F)s#> z6mTlRAXR}Lr5U6vVBhJ(BvX;9Wf^2Eu*Z+V69x8_W00!=?e&FnK2u;%Wd<)4AR80V z_)UR_H5vTQ*B@7ww)%p~A4;Wf0E4#*lni9>UV#!p4E|E!?>Y?rR^Vkl2A>od(15`| zCa`P$8#4I{tGH#WFKo=fS%JGv8RS)9S2G6r6`0kWfklCDTQDe~z=@U&+!Ywyih;eL zB2!y4DWt#;Z5R|$;6OVD#T01XoeL;O6LUhO`}1S=?8FXA2ngSHNQhgMkWcUd=)ZF5H7pG6TlNO{mU1IQ!V1e`~#8PfcY zvUq|5z84rwBJc|(P#aqfAc3hS-~^^qD3!#t69L3CO#m_SpJfKMli0cd^4SW|=4b$O z2z*B=XxQisV7>`BgE%v!J;Uy45HBKtM*)0@^o+q$6R>^)h0c(8;WInSSD1w5KbRq{ z)=L(zQlQaW25S^J`kui$1wMY2pNgg0&-uekhKy3T>&yy0`QQ) zHY!#LY(oK%Y631+I)&K&2JKP-ah3^K{MZa>WS#`#93KBT0+}ZPd8$;%ItjpY1;{uF zz)J4N1;`2sz-tA_?g+pe1<2qCz&i!(WN-xJPesVy2*3vg$lM6PM+L~*2*76r z$k+(L7X`@72!Ioob<5@;10w)=2t1+YgMASK`Ryj*2C|qTjckZOUVy*?icmC#frklL z-_s0fWJLt}g$ZPl{#~hG%ml1ooI-a=j19j?zZBf z4|e(jC}#prpn@6F$aV^zXI`%84Ob3R1*fF3KVb3U|0gp|8uzlWWFOvWfv6`Mn9Z5hNWkSW1L1z-V$=l`b=!Fwqi0HI8$n}9Q&K_R^4v$YQr&oTjvXPY4n zraeeJR{=gyDY|{bxfWqmG`n(F9~4AfGl>f#_uNJa{gh4v{9Q`{7wNF>Y!5m zr2x!y5csG7jCAn$^N%91(LpLsSobb7{q_KZyb9=2!-c>?DpuZ9)+k^CE|$9)(qBJ z2u0?+=OiN)AiEq$VVnZ6#z7!ffs>zEW1<2u#X%ZV6o4TP0@D?M84dz7C7}7&WJ$gw zvWiL$OB|#!#{^vRd1gq1?F|ygDFDkG1QsihD-(z(fZ0rGdjlHFO~5G%gzQodiyM@} zDpRh(-UflS3c%V1fei}4)&_x13Un#QV2c7V6&Y+-;HOFqb}6vGvYp9ZMPPJ;Qr)ir zOl}Z3r~nLZ5IC#=%xw@jrT~m>5ICv88dx3yNG5QOngixG2%K9+_kSYX9v74x7B)!! zivnxvF}SM0#D)xRC~&?pgWC$g)&`|BrE$;fs!3r<97mfZV#B?pg3=o%3v#l_X_lp;DZA9JF&(m z1$<>TUlf2j4ocJ+E8bYK4f&#N-l*$SWk;ST} zz%B`D61YeOhxrZ4CC~(1@Y)F3r5wgLNM6sBYdymmG*kfQH%Oz20%Uvxpt%CDyFnmW z0a)E2&_;nEnMQjB7L1YC-`7O&9|oD-;5arDa6!A6Ar0m>D9P>$Jiwm006i6etqlTw z6o91-0{s<$oectm81Vc@_Dm+BN(H7iNM)D;FtkBn1Ofal#AY@KXeQutMwlV&P{R6= z3c$h!>5o$Y_B9CDV-;DuipfL;$ixOzrVvP{Okr7rz;qLErZdctwr@R)XDI-C8l?Xp zQyK*3k_P^l!_=PrAge4e2`9PG3~4Z+{Db+`KskecPEALFD5ycrGvjCDTF`e z$iW|cD-r2f^^o?rGXgu^N}3zGpd4fapNpNVI z2o6pDz@a%EI5ZCfho)EH&@>7hnhSwL69sT+dH@c+u)|qNOK9QJyE2^3(xEp@gy`@qbeMF?t?=QYjEhM42N!saOlN3pN|<0T<77uF2?@n3q{m%!#|k8T9F2>FvRK2FiBHfA#vFG>SQ$!=?g84c8wWqI1IY6l&D$gsZrA z5+#yhF?jJNPGQBr)#^E26m3r#&GJ#JE)f~mjDRA@WxYd(8hJ@vIT4&}lt~CVXQ2|T zHDzLKGO`It=2W-*A9|K4=5D!>PD|aiQ%ryQFAX`(bo0MyxrDUyoZI&Q(vS;G*Zr3c zKErhDziCa^9sdqi5Lc27S656G|F1w5iqxwWHD<&zRO-inbN_@iSt-mk|4-XLVbFOl z=!XALvEd9(U!sF0io$1&QsO$X-Tz?;H!gDQz5k{aT`@DwS#ZSC38yb{P4@hU7ONW) zXciPx+yB;2m+i6ZKU8F$HHzk=OJRvva^3KwvxeDhj(1e}pNGB60S_;8X)ve#Z~P@y z;#2>|zT)?1m$_Ic|BD}d*3TJ)n-?*mz+$!97 zThn1L9hT8y6&;4rVLTnC(qS$gj?v*V9d6PgnhvAsaF7m5>F|gS{poO+4#{-bPlxSv z7(|D)ba+gMZ&%TOzg@`NhYlm?5JiVcbl60Po^-I&A&m~R>ClM|8|biw4murj=@3eX zYjo&Nhjw(xq=QC>MRYhxhhOO+aHyhPB<}(`+@r%TI&{Qg$&fggVG+Z_Bf_x1Yx7k} zc`q65ot##RPQMs6^V3KlS6&2rz@)Mq@Z)?E){iJ5qWVqG4?<&qTm|7*$|x>26gLV2 z(S0Bew!2FiRS=@vL0mNol@^{S;|qu`mkmpPx;w=Ah>Qw`54K47RZP2VRLW0xhd6H$ zTnmN)cLy1+h)s8jIA5`(uHg&4!{WEgM(O-?!+^cnsP={*;4u+&#i*E{hPJq>!l}Pe z74c|DgUaAgR!J25t{7#V)`*)|4FCLe+dwumzux1Ummb8#%U%{+9D=|=yjL^P&2(p_}jC~u}q?WQ7jyI^=@t7F5(;waNb@#?za zQ-PG^OAED^oK&Yq;Kr2GFM3ko4I`(mbGy}|l-I)={BTO8Gf_v6{QbS6{^}6N~5NFP&7n_(PXl z)hSi$V_OL%8dtb=L|EvUPGO8 zkF7{v(elryH7SK%xesf$r>AVq-V_J+Erk^}>DveMYtrZ5!}PuBZTkL(r8Oy?27_wa zra693A13)deRR%~QhxH+qblSU`tYbUxivmKs_4PBe{E|(wXY~b{U22<=csaHCtE+F z1;e6a!bgY3goS94;bXW##7`e&Qa6wUCH4NpquS(F`}>g(xgGz0)Pmgke?MwikgGo{ zkF9=Qq*g=pt$ZPM2f2HK=A5tiAc?;}jINnbnIVw1irFv2E%e;+Y@PhfRT zvPm$vCVg*VXN`Tau3Dpc)OF%YU=st65=rAfJ!)Eli*GG!t$~{P3I9E*CB<>)^a9Pe zRRgRofC~)^jR_5j9vK=H5jH7g)R-}{d9AIiv$-CckB%(pMJihR+ThOVzpzl5q=v;( z@JDhc`brTx9+wfY&JK!{+_y1&z^DBIN5%~UciwT3l-<9VENpX%2c_Nb5nfD zO`V$(K(6P!lv?Drn3uwRwZFriFfXNYe|p{25`V{meN8@^J||}wL1Q*Bujz}*Z~Df= z3Y?Vw0P}Cs=jLYmhIx=L>BGE~7ChoKK9j;-c*>cSmQ<1xXHx2x<b(=t*Dc&IG-6)XqHQ?J0~+cbxsx{#S#AJT#6rM;CDWy0=ezZr<5mm z)cF*?;Mbo|;W6cx^C{eFc`nFs%?l~j+tXXVW`(UTX)5d=nBbDW(alX?3e00k=4>;4 z^SU~GvD(LmPG7}{!P^TJ6Mp_BiX~mVkaD&)u`p~U zeTQJtO8Tn7a+UO@Dc?>QuhKRoH7=N1sfMiVY->C!>nnmAr(IlvUQO zIl?cnun%8k&l;yzsK)#cTPd@U?Z+cgnt9Pyz>t;nJ%j}-ZF^F~CaK|FWJW!$Q;|_c z@w!iH8PT0s#-{!gWDvLwQF{v|fWkv5quYw4*Id#-THYj|q=4 zd!zN0^^ZAXa8T(oT*&zgWRo6TAWvQPg4DXy)TQH6>yX7)`d;fP z$&0X9B`s*x7vxLIzc%~kAnPWKcyN2XgSJNJjFe9g3cnA)lUH{va8CWg+IQ*L<+Jv_Y-DII;7+FhQj zx3)6oxr&D6DrunVM_8MZz8Aa5msI~z>J1Ny=<-aa-sG8Vuc&9K4ZLW&O#22#o1}@T zb+Fc?El)cBEOjlnQ-ANYrWALjcN(|faql#~s&c*4_-ZfclU7l7sGZiI=ZKaC{A7n3 z;FHFe(KMg5>Q!kHPHSKwW%jLwc_Qh10=q=g_hTCQl5Y8=6{;qis;#vLnyQXS?2uM; zMTfLA28HMfp(rRDJY^gTf8`WZ8p~|LjSrzgn{g9Y;-A{If3f5Y+1vW!U>>C0DIMSEif_zDFzos>) zB^xW)+83=+R*ZdIshC()%hyBV{?=8%wIwd(obD&_8tZQ0b`p1WPOmHRUh8q-x=LF< zl~H9cIj1{@&|B6I&FKflRp*HNB0-HA4)1;Sq(sjO&E>%hAuKBr2dR^Jo=hIjrHs&qm zEBaPVuOi`k>s`RHd8+v|=keiz%?a7?J*7qnQlxonZFsop+oPGjwJ_fzrJG^5MLU%A zq-uIZGkLzcSO?7!g$Gs-4_(z*8?otEx+EQsZcuMGCXs98+x&c1Eon%1129w=>&jDo=wX^rJK z<>dkIC}E5{ZlvVftdVA{#MW;9X&8L|D?J z>FM>kYQ@&2^J`Aib?N-%*LR()^5k{td=0H%m(Fil7vOr)-xS(O*eH?aO+1d1@6h`6 zC!X@A?PQbGjzHs-FowPglB=>F0&!=GO@tz)dw(8w1vE;QVHXPaPs z0AvXp9~u#h7h*>XKC-@dT%;v5Z0UW2vK|jB2hukO<_)B;FU%T9Up3e>kiLB|WWc^y z?JgptYg8K=krW=B(S8BdW&hO-?g96&X7H`9;I#}MWt&{f;G0v#wG6&HtiG1fP);-a z>0~R0iHfv|%_?zEceyTOHou<1xBRi!GyLT>oak)3>Wu36il9>&Ma4g-GTiVPET6?2 z&bH?yat|328Z&fc$k35tLq|Dezd76ffs6;GWY|EEb_;sI_<{6&fz<=)+W>P1(pP$o z=^Fz329o^%!v@l~W25OS+#I$H7{+SFuqGcTf9ansDka6-$Y|Ms+T&3E%%<}Eb+mRu z0qcm<`7^7Pz|Ac@CTz5Nvh2)H7Xd6R`mYxtYfo!0#Hvh!%3_mCCO_m~b;;yD|H37c zCokMAnLPK@$dc*Zn0s8N^*2N-NIj13qfw#bLPtAZfUR$>|Cln|d;cX9i!GU6KHQ#o zDeFwbr5mN7b~HzQk}a8hNn~0w%lD^$6}4hJ&;slm1tSd7w;I+Lq;Ikxw(w@@`WmLs zxu)q0Q@-N0O}3*B`H}{^W|raS%W0)DOUvOinA$=FTZi)O!l_c3epR`$VOD%{#i!S+ z7vipzK%LKA%~Q-&bsF z^VlvxPxk9jR5G3_-eRoj+a5!{q~7Nu5dG!wB zp&*M_*!Y%Hy?0g>d0}0%{)uw zJ6=5<=WDPN&n9t0=39$wuTeph@sG6h-NGA}^!>Kc^yR}er(}6Cw=R97F(;;7OVave z*#=P?9yyf7m*)eA`{$u7o^l9E$_kKAG$X9zM4_ZCFR@sAP=KP$OUeo^!L7T@s-vJT z(UHSrM$6$`WF%$rwdZ^|i=P>4I@~^ov-o9bio;!ZIEx=@QVwTTmSgNNC)*lKV(mN9 z)tATEz#~#4@<?q7=$<^e~e>Qy{_#{9w?<1zKKAyNF>wuYl>Fask^u5A|7?O3t z|M}9l9QP{e8;oxQq_4tLe7GUKOYzNz^mR8JzF5r}fBeUYrIQ|biS)#7g_G(X%gXCX zjosvOmbdI$6|D`>wYoj+R(dEmeq(D}RIdOgk84Mc2^$&dctf#vu=XY{S#8JMGNvQ@ zS;HL{qEkBZ&7uAin_>D=GEHBR$K*>&y_}U)lu95{vnp`ATdh{RBZYc+9+%6pr@kOx zl98I#miw{4b2fMV=FZurWG9KRCZIenqeG*jF#Av4S&VVcF2QHSdcc|iVZou15s~9U zqQl0-gpC<$J^^Ig^3rusI0O@?5uroF{*T<|;$$mtUb{YTPz)6OE6wuG^m%+ReU(3% zzIXqazWfugB3pZ$R3K0GrLhCKMa#;m<)IPbQ6Xa@!=uAO#^HIyyd|}=4nt|&SW1s7 zN-)AY9|Ct~Awy{pG@~rE9)-kTyuP59ugO`Qu$~1eO82|bnDIq}$YE6_cENfJ3dO{X z7d^ck^CEYwZ-L4P|DwJ4Qav3jn>ZyloA0onW3&0v@*AHmU*K%9PPVCN8Id0UjhtGD zfQZiJB8R(gd^RU_V|;c=;!nqCmytKs2TrzhYIV2h*dcW1RgTzkmXU@h zwZ@p((f@4;n{c*mgIElH_l*}12=yjr%bTg~ptCKRVtNdZ9Ebl!q&@mefn%Xso4|zVlc^FMVyWcwYK0 zVb#3!UB+^G>3fWI@(w8$$V=bXSQ#&U_V+bS??J4EmoNs4;H9s2py>+=GJVDBnm(8M zrf)n}xXT#h8=AhYSkf-pE39Rgz6)5yE`4jUdL6#DxH8sY-MWPLv0z>LCS#?#^sT@O zb?MuWrRmal18dTyuMZZZYtIi&&iVBbt{)O*ri`l%s};X;krD$ncZHdeI8Q^;&p1j(qhAo zer}Eo?xTVPD>|;*bW9g7iz_W|9qK0^OLpAK=DDFqw`6{OHn54Io9;m8fW?*#hAWGEKiql^$B^& zJLmV?*-5|Oe*8F;28H`|pVTMUv)&Va2yImF3BT+_)O#YE$rhi-7MPbVwXTDBcsW(? z2|uQ0Ib4_ePxu9^d3~AE`1((3@`RA>n|!uu_{T9$gzkRqFNcu3^`G$DNWlhA_}RB{ zgD2ct5e=U3Y)^axS&ZWio&Yk$lT%ur7b7sIQhB~!)2y#h ziY8DTrC@fxVP~su{70DA%74+tbmjk|wI3BlMoopT? z_7$)H$SEcQhU63|kThmgYQzds!r$$i03*!JUSLk@tw_Pb+&yg{+^y&n&&-jvz={osT+85uN*QYaw4V{!a~i?x4_vJFPhqO%gE=e z<<7QMr0pIO6%mQYvak_hV}?2MS?g>&E@i{Ug--bIBikuw+f9ux9-UnxRKR+eFNQLwz!a4!f$ftwN^2zZPCO3T;V^lckp`i6!& ze6iYaTT-5PxuwJCc2Tz9Q+~Prx}W^IX;QzZJl!PvJ>~oGk$zA4qI=%&DbEv??fGcz^u@rCh4ht&0SoC{4a*hM*AXTw*cYpH(emSB zTk$B;JLzu!r*l_2cP}SG-aRc}l`^z0u^zyQ{r`OdW<6wUg?E1S0ocWNPq~?&y^|Fy z_5NuMIXh6o*;W}dup28xd5JE6N6PP9roVq$Pl|gw+y3A3fYR`FV0XX01P|r%eSZmV zyI6dG32uqx1Nn(7fXmDwM@K9IF7Jl0oPmp#A*@W`nrR5D3%E`i!qNmTMTW4HHp5uI zfX7`x#1w(+fWfR%;5~+*z?km1vQT=v@Q;_1!O^lpUY z$*(ljz-FF-511bYo-YA8iGtw;lvSFg_i#E-$}2UdZRQxI;rx!`y6tn)Umb;(a_ci+ zedL_pCi0wbyFdv@lxy}AuIaKfUYT!KDtmR7dHeUuSJEtun$!0)-I zeY1foz}f#B@YD;)@fg2>$6Y{Jw}9tdfLXvo{{txKdsKjvK#6%diQ8l5zWO2z2~2tK zQb=IV1fFsM-iA>OT=}^@d*drH14wR`_ntESz{lqj5PwVu_qfHUf4KEZTod9F@9(}! zFoy*408If7_W;M0MWL+HtZBU4-#&b`2wX8vlz1&40gjZJzNTpYFUikNh0c8VS}^)1 j!weOa1sXLQWawewK^G9#Nqs1*G^<;V9q7B#(j+|qFu12> diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index 9bfc3386535c5dba422b46210bb14de4e9a1a8bf..0a7f0dccdb2c0b004d6f98928898875b8fefaf47 100644 GIT binary patch delta 64 zcmca`is`~BrVTBOhG~`s$w`HIiTb7%spcl;mT4BICTYe778aHk=H|wUi3W*g7ReTd U21&-I#%V?d#s-_WGR|HM0KtS5Z2$lO delta 64 zcmca`is`~BrVTBOhWQ0%7RhM_Ir?Ua$!5tZDanS3CYC9Q21bdd#+DYAW+q8yNy(;( UNogh~mdR#GsfL@kGR|HM0Nvsh)c^nh diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 6eace2d6d82b0e6827a19862d7f355ae9700aad5..258f87aad1a08ae010a4846a6b82d1e520736c37 100644 GIT binary patch delta 63 zcmV~$(G7qw2nE0}kyMJjjzb&yGDc_W0uIApm+>wa<)Uv=iS~^C-o7bLtJVTqLl|f< S6LG}b^OB3+` delta 63 zcmV~$(G9>L2nE0}QKiKf83(X_nY25(K!@SaWqOyJa?`hnpBF7gnFlNoy$73Uw{lv& Rv?;{lQO$>l2K?rgG=JDZ6\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, @@ -2489,10 +2505,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.241500Z", - "iopub.status.busy": "2024-04-06T04:32:59.241175Z", - "iopub.status.idle": "2024-04-06T04:32:59.438012Z", - "shell.execute_reply": "2024-04-06T04:32:59.437410Z" + "iopub.execute_input": "2024-04-08T19:11:35.515941Z", + "iopub.status.busy": "2024-04-08T19:11:35.515782Z", + "iopub.status.idle": "2024-04-08T19:11:35.714780Z", + "shell.execute_reply": "2024-04-08T19:11:35.714213Z" } }, "outputs": [ @@ -2532,10 +2548,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.440559Z", - "iopub.status.busy": "2024-04-06T04:32:59.440204Z", - "iopub.status.idle": "2024-04-06T04:32:59.444637Z", - "shell.execute_reply": "2024-04-06T04:32:59.444201Z" + "iopub.execute_input": "2024-04-08T19:11:35.716901Z", + "iopub.status.busy": "2024-04-08T19:11:35.716713Z", + "iopub.status.idle": "2024-04-08T19:11:35.721189Z", + "shell.execute_reply": "2024-04-08T19:11:35.720773Z" }, "nbsphinx": "hidden" }, @@ -2572,60 +2588,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a761c23187462489caed4c9ca92b4a": { - "model_module": "@jupyter-widgets/base", + "0211903c89794ee8b6917c76a058c50e": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "01d6c6d5c4544426af39a2a43bf4efd5": { + "051beaa23ca4413db29dc6c87c6eb69c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2678,60 +2659,49 @@ "width": null } }, - "052ba7dd44914624bc95867853cefd91": { - "model_module": "@jupyter-widgets/base", + "08fafba5db2c49c199f47a5f187bd97d": { + "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2d19bf757fc54a04bea20bd6a7bfe3bd", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0ba9b8ac89734f8aa6f7b25158ce1c26", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "0ba9b8ac89734f8aa6f7b25158ce1c26": { + "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": "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": "" } }, - "062e939e5df64531b55a3a30c9808fb8": { + "0eb8f33ec6a4418f82faef40016d8087": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2746,16 +2716,39 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_69040f6c457a458dad3f64ae864e2abc", - "IPY_MODEL_856c1d7d86824b298f7b89334a62b627", - "IPY_MODEL_e36a0c3800fd4b46a397695a9464376d" + "IPY_MODEL_5ce79bcbda6944e0a843326c3542493d", + "IPY_MODEL_e19c6ab20a44441b82013b108be2dfe5", + "IPY_MODEL_f070a30dde5346f9a173c14652288a61" ], - "layout": "IPY_MODEL_18c03d0fbf7e486fa980759c0f1d203c", + "layout": "IPY_MODEL_f229cd5bb5604bc2bbb59bbf7c81d777", "tabbable": null, "tooltip": null } }, - "07e1a5656606425a8fa445a2a98b32db": { + "1489dff3fa9f4025b92de6f3b08f875e": { + "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_2f250b135ef04e06b65dc22fa2cbe7ea", + "placeholder": "​", + "style": "IPY_MODEL_608e038942dc41a99c6a0a85532e4742", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "1518031392e944988beab26f0ec70ea0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2771,56 +2764,84 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_01a761c23187462489caed4c9ca92b4a", + "layout": "IPY_MODEL_34b68eb2ae824e30b199e73d4a24f9d7", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_44469fd7a84845fabbb26081bc4f71e6", + "style": "IPY_MODEL_934292ec1d1f47d7bfc3a64e97e22164", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "0898a61434e8435fb6d38cda818ecf42": { + "1595a1d05b1744eeb8405298fae08359": { "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_fb2af0144f1742c1a26396a9b5fe4c73", + "placeholder": "​", + "style": "IPY_MODEL_b809fa6c8639462db220a761b405d58d", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "0c49ec751aa74bdeb0da999541e70abf": { + "1a475bdd7b6b4cffa0aa39c22e228634": { "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_716dbef584c74a10be95d49134e35c36", - "placeholder": "​", - "style": "IPY_MODEL_d53fb7bf594d407c9e842abf6174a266", + "layout": "IPY_MODEL_72ef0108d5814322b3ca06bd5220584b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee8869c02a164612932f45b84d319cd2", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": 40.0 + } + }, + "1bf8c6f4bc3e4633b0c368f433ba214d": { + "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 } }, - "0ffda52749f54b41b5d0955c9e1f987d": { + "1bf974982e4e4b2481c1302de4d0bb3b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2873,7 +2894,7 @@ "width": null } }, - "10d5b8ef6dd44439b5792f3b9229a7b3": { + "1d72b73af5474a01bc6184c102001f9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2926,83 +2947,69 @@ "width": null } }, - "154d43bd379742748defa9f93f6dec4c": { + "1feb2d3d69ac43679423195bacfa218a": { "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_59b99f3d2363470f95240494b60426e7", - "placeholder": "​", - "style": "IPY_MODEL_e630ccc4aafc46e1994dc57c453efa4c", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 250653.12 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "18c03d0fbf7e486fa980759c0f1d203c": { - "model_module": "@jupyter-widgets/base", + "2021e36f762d452aa27e529207b4d1fa": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "228bd7d078cf45699f3b012c267f6eb0": { + "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_eafbc6640e3840d6acb39b13445654da", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7c1f0a8a503e4e499f398654a3f934e5", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "18f4e2fb304e4207a8582f3b68e46f1b": { + "248aafd9fcd240928857d2c42d281abd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3055,57 +3062,7 @@ "width": null } }, - "19d828f4e1d84ef2ac773bc529b40946": { - "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": "" - } - }, - "19dc464653b042e1b8ba1ff4aa4f3231": { - "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": "" - } - }, - "19ea4bc4cf5345209bfc0f43ac43a390": { - "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 - } - }, - "1aaabe82fd3748eeb69937660d87bde7": { + "27d17868cbd6451ebcf8d7c7951e2a77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3120,15 +3077,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bcf53273c9a94359bf468708176a7fb9", + "layout": "IPY_MODEL_61971c6d7922459a9b1ed0c9c414cdca", "placeholder": "​", - "style": "IPY_MODEL_eda57d1ce07e4fb08048f276454d81c4", + "style": "IPY_MODEL_49cb378542644b368738ecd332b0a762", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 67.82it/s]" + "value": " 40/40 [00:00<00:00, 69.64it/s]" } }, - "1bc46611a5134fa6bf3a2e1d7329d9e1": { + "28e8e7e456ea491db9b2409a1bb6ee44": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3143,38 +3100,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0ffda52749f54b41b5d0955c9e1f987d", + "layout": "IPY_MODEL_dd462dfa7f9949738f0551ce01718b0d", "placeholder": "​", - "style": "IPY_MODEL_19ea4bc4cf5345209bfc0f43ac43a390", + "style": "IPY_MODEL_512b4394b0e84a1d9765ebc108a8dbdf", "tabbable": null, "tooltip": null, "value": "100%" } }, - "1d0f443e388d4ac3aa919649a0bd9f6d": { - "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_d6430b8f25744f0fbc6b7c7b994b06d4", - "placeholder": "​", - "style": "IPY_MODEL_868c933ad87744c3aa05cb4cd929090f", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.96it/s]" - } - }, - "1f1036a8b18842dfa18443fdf59a95dd": { + "2c4e61dacdec449ab2e9fa6b6459f6bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3227,7 +3161,7 @@ "width": null } }, - "21a93b6159174e5c99baaca057c26509": { + "2d19bf757fc54a04bea20bd6a7bfe3bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3280,25 +3214,7 @@ "width": null } }, - "224bb80ea8ed446ebd6daabd22c3e867": { - "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 - } - }, - "230d8fb552244430b2da0fe2e463b928": { + "2dcb1da8fc8746cb8d2bcbb0b5fbca34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3313,57 +3229,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0c49ec751aa74bdeb0da999541e70abf", - "IPY_MODEL_a8de67324e69481aa6f71eebd240332f", - "IPY_MODEL_154d43bd379742748defa9f93f6dec4c" + "IPY_MODEL_3b1df04f38894cdbb06719a2c258a28e", + "IPY_MODEL_bbab2e26c4fd423dacd41b5823f1e144", + "IPY_MODEL_ed9f2e8995144260afc1a776c3c728c6" ], - "layout": "IPY_MODEL_052ba7dd44914624bc95867853cefd91", + "layout": "IPY_MODEL_1bf974982e4e4b2481c1302de4d0bb3b", "tabbable": null, "tooltip": null } }, - "23a4c60ebe91433cabd5be6f58108e1f": { - "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_677944caba4f4f68bedc7737bb773d16", - "placeholder": "​", - "style": "IPY_MODEL_baa12a5e11064c04934b7f2251564311", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.99it/s]" - } - }, - "2a60a0c90b1b4f46a02f1662dd4c96e4": { - "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 - } - }, - "2b93072870f04a1daa0f16d389336ebc": { + "2e43d2f9905b4786bd2429abe71f9c11": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3416,7 +3291,7 @@ "width": null } }, - "318a2a3cc2dd449d87d92d835336d9d3": { + "2f250b135ef04e06b65dc22fa2cbe7ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3469,43 +3344,30 @@ "width": null } }, - "36f95cffa25d460ba500ec7de91933d4": { - "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 - } - }, - "374d6a83e7564f3a99a8e43b41595fa9": { + "2f426faf084c41288d432aad9a0cc67b": { "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_56fb1e127c0d4778aa58bf70adf9e292", + "placeholder": "​", + "style": "IPY_MODEL_6e2675fc6ccc4cd59366cae5f0c9e353", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" } }, - "37a89191786645eba770cde499fad762": { + "340e7303a6ad4cd192d14fd90e0362a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3520,16 +3382,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_1bc46611a5134fa6bf3a2e1d7329d9e1", - "IPY_MODEL_c0d6bc39047b4081aa9475148f266798", - "IPY_MODEL_1d0f443e388d4ac3aa919649a0bd9f6d" + "IPY_MODEL_c09a89ed151f484cbc846804d8c80a5e", + "IPY_MODEL_f44238836eba40dd8365f2683b775574", + "IPY_MODEL_45ba61488f3e42d9b1983a76067096be" ], - "layout": "IPY_MODEL_dcee1ad305e747b396286c15af30cc5f", + "layout": "IPY_MODEL_c4823f24e46c4225a1be6685c385e2de", "tabbable": null, "tooltip": null } }, - "391330f212e94180b7c1d3f8b79364a2": { + "34b68eb2ae824e30b199e73d4a24f9d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3582,25 +3444,30 @@ "width": null } }, - "3cb626be1259490498bdc43c229957d5": { + "3b1df04f38894cdbb06719a2c258a28e": { "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_3ffe5eadabde4ba7bc1bf7d6584058f9", + "placeholder": "​", + "style": "IPY_MODEL_2021e36f762d452aa27e529207b4d1fa", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "432d3960ddab4b78b5489f18eb6be184": { + "3d509074899549a5988580659a594acd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3618,41 +3485,101 @@ "text_color": null } }, - "44469fd7a84845fabbb26081bc4f71e6": { - "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": "" - } - }, - "45f3fe0616ef4b6d9187942fe63d092e": { - "model_module": "@jupyter-widgets/controls", + "3ffe5eadabde4ba7bc1bf7d6584058f9": { + "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", + "_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 + } + }, + "4488e3c1bbcf4f18b659b04ab234f3ec": { + "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 } }, - "494d4dec9fa34cdba034f2e400df4b4a": { + "45ba61488f3e42d9b1983a76067096be": { + "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_051beaa23ca4413db29dc6c87c6eb69c", + "placeholder": "​", + "style": "IPY_MODEL_0211903c89794ee8b6917c76a058c50e", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 584.25it/s]" + } + }, + "4776167486a64076a84a01784b59af15": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3667,16 +3594,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4ec9881e872043c7af778c7f3fe8b2b3", - "IPY_MODEL_79d4ea75e0b24546ab4c3e15dcd17fe0", - "IPY_MODEL_b37c27a865bd41238ca6e848152182c4" + "IPY_MODEL_1595a1d05b1744eeb8405298fae08359", + "IPY_MODEL_bf0f538039594aa7aba3502ccbb1866a", + "IPY_MODEL_b413bfe75faf4e83809cfbd3e9c0f441" ], - "layout": "IPY_MODEL_2b93072870f04a1daa0f16d389336ebc", + "layout": "IPY_MODEL_5b71bc4833f84223a9bfc19a48c54d73", "tabbable": null, "tooltip": null } }, - "4df2995557604f368c17b7801a8aa7a3": { + "49cb378542644b368738ecd332b0a762": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3694,76 +3621,43 @@ "text_color": null } }, - "4ec9881e872043c7af778c7f3fe8b2b3": { - "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_61c55f2451eb4c4e864fb94997319e79", - "placeholder": "​", - "style": "IPY_MODEL_c2f27d13f7af40c4938b2ff919248bc7", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "567dbef175174a0bb698e0c117bdefd8": { + "4ce7b5e8a49e4daf8752a8cfb2c049a6": { "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_bb928bde160d418985c159d23435d896", - "placeholder": "​", - "style": "IPY_MODEL_224bb80ea8ed446ebd6daabd22c3e867", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "58016631c51a4c2bab081a3fcb222ce6": { + "512b4394b0e84a1d9765ebc108a8dbdf": { "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_8665b261d85149c79feba0ad3dc5a0fe", - "placeholder": "​", - "style": "IPY_MODEL_374d6a83e7564f3a99a8e43b41595fa9", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "58b8b6bed5f84d05ade00f2d3773b9e5": { + "5299f330a8b6448cbae432e24bc2bbd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3816,7 +3710,7 @@ "width": null } }, - "59b99f3d2363470f95240494b60426e7": { + "56fb1e127c0d4778aa58bf70adf9e292": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3869,33 +3763,7 @@ "width": null } }, - "5cc1b33d526e4c2ea671e6aa4a807101": { - "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_f7dcc2439e854d37a27bed7fba490b03", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8cb9becccf604e7d912917ceb837d524", - "tabbable": null, - "tooltip": null, - "value": 2.0 - } - }, - "61c55f2451eb4c4e864fb94997319e79": { + "57413d25cf8b43a59d135e9b7f386c8b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3948,57 +3816,56 @@ "width": null } }, - "61e1bb7f02a4489dbe279ece48634ba8": { + "57501a6ea66843e69eb94c16fd71dd26": { "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_b87839e9f74c433ab8102575e86bcd7b", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fd142fc752ad437dac5e121ea0a962e7", + "layout": "IPY_MODEL_f430353dd08d430fba9b508f111e2cba", + "placeholder": "​", + "style": "IPY_MODEL_3d509074899549a5988580659a594acd", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 60000/60000 [00:11<00:00, 7101.30 examples/s]" } }, - "63f80561a9aa4de6b4236289ee6db555": { + "5b300800d44b4998a13101625e6be790": { "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_58016631c51a4c2bab081a3fcb222ce6", - "IPY_MODEL_8dd48fae4b9146fc9977d90131836405", - "IPY_MODEL_a92e3ef11d2c4d27a1956ce5946d7509" - ], - "layout": "IPY_MODEL_b5d8fdd0cad64e94955d5536ac4dad32", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8458fd5694574fd397fa3ed659cf9d21", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_822bcec6c7dc47839c1fd0d48be8aafa", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "677944caba4f4f68bedc7737bb773d16": { + "5b71bc4833f84223a9bfc19a48c54d73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4051,7 +3918,7 @@ "width": null } }, - "69040f6c457a458dad3f64ae864e2abc": { + "5ce79bcbda6944e0a843326c3542493d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4066,15 +3933,67 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cf59e27052914f59bf2cb3f866f7faf0", + "layout": "IPY_MODEL_713a34eee7ad43ca9e8faa69d5fedcd6", "placeholder": "​", - "style": "IPY_MODEL_b8708f41587841e587390f8e235ba687", + "style": "IPY_MODEL_4ce7b5e8a49e4daf8752a8cfb2c049a6", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Generating train split: 100%" + } + }, + "5df8c0f13e114da5a54feb67563465d2": { + "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 + } + }, + "608e038942dc41a99c6a0a85532e4742": { + "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 + } + }, + "60b1f65ee807482daeb5f7b6893651bb": { + "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": "" } }, - "698bb2e60fd546c7a86f3c097d682c43": { + "61971c6d7922459a9b1ed0c9c414cdca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4127,7 +4046,7 @@ "width": null } }, - "6aa2909e4e954f21a2e9b886e37872f9": { + "61ffaf9c2a1245668b4f2c0c3cec4cf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4180,60 +4099,47 @@ "width": null } }, - "716dbef584c74a10be95d49134e35c36": { - "model_module": "@jupyter-widgets/base", + "655fc2039c2f44018f7440a3d2e07a6e": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_28e8e7e456ea491db9b2409a1bb6ee44", + "IPY_MODEL_1518031392e944988beab26f0ec70ea0", + "IPY_MODEL_cb4e85dad70c4bb69fa58d8fa9da1fd9" + ], + "layout": "IPY_MODEL_5299f330a8b6448cbae432e24bc2bbd2", + "tabbable": null, + "tooltip": null + } + }, + "6718ecc2d5994895aff73d3a70d4daa4": { + "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": "" } }, - "758003aac78a4e89a0dd59c12bfd4499": { + "68df804c48394616884c2cfd1f17c6d3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4248,15 +4154,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_75938d156efb49d681d43a2189e81adf", + "layout": "IPY_MODEL_1d72b73af5474a01bc6184c102001f9f", "placeholder": "​", - "style": "IPY_MODEL_45f3fe0616ef4b6d9187942fe63d092e", + "style": "IPY_MODEL_bb9b4b065b90414cab93989f1cf870db", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 63.71it/s]" + } + }, + "6e2675fc6ccc4cd59366cae5f0c9e353": { + "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 } }, - "75938d156efb49d681d43a2189e81adf": { + "6efaaef2d3fa4de3b05d6ab4ce2b1b80": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4309,49 +4233,7 @@ "width": null } }, - "75c5b3367f7243e08df5201fed89d767": { - "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": "" - } - }, - "79d4ea75e0b24546ab4c3e15dcd17fe0": { - "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_bc9ac33beb964c04b7e19f6a1e1a6eec", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_841bd0118c39439da9678bf5209d9393", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "7b9c395b1818449285ae7e333c19aed1": { + "7134548633584aebb32846399e998599": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4404,25 +4286,7 @@ "width": null } }, - "7edd891321db4e908b5ed0d68bdddab0": { - "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 - } - }, - "801329626f1a493bb35bed548fee56d1": { + "713a34eee7ad43ca9e8faa69d5fedcd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4475,23 +4339,30 @@ "width": null } }, - "841bd0118c39439da9678bf5209d9393": { + "72e848187eb646c9ae2eb5c3c13a1b5b": { "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_6efaaef2d3fa4de3b05d6ab4ce2b1b80", + "placeholder": "​", + "style": "IPY_MODEL_9376be5b85774e909a6da2348e1e3ac7", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "84941543352247e1ba0307848d584119": { + "72ef0108d5814322b3ca06bd5220584b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4544,33 +4415,7 @@ "width": null } }, - "856c1d7d86824b298f7b89334a62b627": { - "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_1f1036a8b18842dfa18443fdf59a95dd", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ea07905d53eb44ab85413ddd0eb1103b", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "8665b261d85149c79feba0ad3dc5a0fe": { + "753491adc5dc4a15b482f01444260ff6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4623,25 +4468,7 @@ "width": null } }, - "868c933ad87744c3aa05cb4cd929090f": { - "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 - } - }, - "89b15967dfb6452597bba424994c0aed": { + "7692ec62386c4b5ea0e3da9a9d6aaab3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4694,7 +4521,7 @@ "width": null } }, - "8a36d05bb1a644cf890db7ec5c1979a1": { + "76fe935e94da4fe49c9fff42053d9f08": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4747,76 +4574,41 @@ "width": null } }, - "8cb9becccf604e7d912917ceb837d524": { + "7859aae2967f4d8982962ef94210548f": { "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 } }, - "8cf1e91b3283461d9e7f008fa37fff9f": { - "model_module": "@jupyter-widgets/base", + "7c1f0a8a503e4e499f398654a3f934e5": { + "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": "" } }, - "8dd48fae4b9146fc9977d90131836405": { + "7cb2476470d344aebe942210b6aee3ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4832,40 +4624,53 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a3362f365ba145b7a2e7a9bab08c112e", - "max": 60000.0, + "layout": "IPY_MODEL_cab2229300b043cf885deb986c9d5f5d", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_75c5b3367f7243e08df5201fed89d767", + "style": "IPY_MODEL_a79b0ad4d31c46fab5f329886f96aab7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 40.0 } }, - "8fc744a4ac8745b8846e23dbc6bcb863": { + "7df899c21e114a00beea9fabc687baff": { "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_21a93b6159174e5c99baaca057c26509", - "placeholder": "​", - "style": "IPY_MODEL_4df2995557604f368c17b7801a8aa7a3", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7fca1639267a4153bd7fb80d23c652ea": { + "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 } }, - "903744741edd4db481eac7c207012baf": { + "8040c639d3ba48d7baa187303a949a5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4880,104 +4685,206 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_318a2a3cc2dd449d87d92d835336d9d3", + "layout": "IPY_MODEL_9ca9e07ed9664701a1f4fc752659aba7", "placeholder": "​", - "style": "IPY_MODEL_36f95cffa25d460ba500ec7de91933d4", + "style": "IPY_MODEL_d5de18c08b9a4f0fb3bae9ffcb6e2608", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 68.31it/s]" } }, - "915b6ef632714224897e7967dd8fed40": { + "822bcec6c7dc47839c1fd0d48be8aafa": { "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": "" } }, - "92a29e7b86344f558ffc62c421168612": { - "model_module": "@jupyter-widgets/controls", + "8458fd5694574fd397fa3ed659cf9d21": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_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_567dbef175174a0bb698e0c117bdefd8", - "IPY_MODEL_61e1bb7f02a4489dbe279ece48634ba8", - "IPY_MODEL_b52404a02e14446b9eb8d9c9687553ab" - ], - "layout": "IPY_MODEL_58b8b6bed5f84d05ade00f2d3773b9e5", - "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 } }, - "99b1fdbd6a654220895926ef07fb0a65": { - "model_module": "@jupyter-widgets/controls", + "853c7b5eeb0944a5903419f543447327": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_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_9ec788f231ad4c5da7b9ca1152c1d44b", - "IPY_MODEL_5cc1b33d526e4c2ea671e6aa4a807101", - "IPY_MODEL_ec8926923d6642cc965b8aea46f7cdb2" - ], - "layout": "IPY_MODEL_10d5b8ef6dd44439b5792f3b9229a7b3", - "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 + } + }, + "87d3d0fca9104cfab810183eee2ef94c": { + "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 } }, - "9c6eb45703d9488c8455e7918665d719": { + "934292ec1d1f47d7bfc3a64e97e22164": { "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_c977511679bc4e89a67b20d915a938ff", - "placeholder": "​", - "style": "IPY_MODEL_915b6ef632714224897e7967dd8fed40", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:48<00:00, 1364.59it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "9e2f25346ff041f0a43792feaf401889": { + "9376be5b85774e909a6da2348e1e3ac7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4995,46 +4902,104 @@ "text_color": null } }, - "9ec788f231ad4c5da7b9ca1152c1d44b": { + "9ad282371d5e4b848fe0fbcf04f86eb4": { "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_6aa2909e4e954f21a2e9b886e37872f9", - "placeholder": "​", - "style": "IPY_MODEL_c76ed92d69d6444fb0a07c53c18b4c0d", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9ca9e07ed9664701a1f4fc752659aba7": { + "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 } }, - "a0876f2d03024b3bb5761a0df2937cfb": { + "9cb7ee51c974488f82e9774c7de42d77": { "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_cca4bec16de941d8a1f0eeab09f1309a", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6718ecc2d5994895aff73d3a70d4daa4", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "a3362f365ba145b7a2e7a9bab08c112e": { + "9d34e8136fdb41cba0282f20fe3254b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5087,49 +5052,49 @@ "width": null } }, - "a45cdcaa39ac4e7783e3300484169c30": { + "a30eaea1846c404db6f70144bebe00f7": { "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_a7573684d96b4a809e222e1e9c978b20", + "IPY_MODEL_9cb7ee51c974488f82e9774c7de42d77", + "IPY_MODEL_57501a6ea66843e69eb94c16fd71dd26" + ], + "layout": "IPY_MODEL_87d3d0fca9104cfab810183eee2ef94c", + "tabbable": null, + "tooltip": null } }, - "a8de67324e69481aa6f71eebd240332f": { + "a5fbf7ed0a8945eba908a5b839852cc3": { "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_698bb2e60fd546c7a86f3c097d682c43", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0898a61434e8435fb6d38cda818ecf42", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "a92e3ef11d2c4d27a1956ce5946d7509": { + "a7573684d96b4a809e222e1e9c978b20": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5144,39 +5109,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_801329626f1a493bb35bed548fee56d1", + "layout": "IPY_MODEL_753491adc5dc4a15b482f01444260ff6", "placeholder": "​", - "style": "IPY_MODEL_432d3960ddab4b78b5489f18eb6be184", + "style": "IPY_MODEL_7df899c21e114a00beea9fabc687baff", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 287662.14 examples/s]" + "value": "Map (num_proc=4): 100%" } }, - "ab2472f1e90442db812aa71752ac3895": { + "a79b0ad4d31c46fab5f329886f96aab7": { "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_8fc744a4ac8745b8846e23dbc6bcb863", - "IPY_MODEL_fcd3307eda42417c8c3f83f6bd1ece28", - "IPY_MODEL_1aaabe82fd3748eeb69937660d87bde7" - ], - "layout": "IPY_MODEL_cf898ee7a0764e2b9f7a0b8a17fee462", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "abb6597d1f0d493ebdf894332caa8c19": { + "ae20b62666184b608438ba88eb80b458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5191,16 +5148,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_903744741edd4db481eac7c207012baf", - "IPY_MODEL_fa5a714c695a458288a525b0b07ff15d", - "IPY_MODEL_9c6eb45703d9488c8455e7918665d719" + "IPY_MODEL_2f426faf084c41288d432aad9a0cc67b", + "IPY_MODEL_08fafba5db2c49c199f47a5f187bd97d", + "IPY_MODEL_cbf43ef383f248dbaf25bd28be2369f3" ], - "layout": "IPY_MODEL_7b9c395b1818449285ae7e333c19aed1", + "layout": "IPY_MODEL_2c4e61dacdec449ab2e9fa6b6459f6bb", "tabbable": null, "tooltip": null } }, - "b37c27a865bd41238ca6e848152182c4": { + "b413bfe75faf4e83809cfbd3e9c0f441": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5215,15 +5172,49 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ba458d4944864736a9229851605bc597", + "layout": "IPY_MODEL_dd125613a65d44acb8f52566b6d32b7b", "placeholder": "​", - "style": "IPY_MODEL_f3aa86d829fc4ca5bb1496d9254e9cac", + "style": "IPY_MODEL_1feb2d3d69ac43679423195bacfa218a", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.56it/s]" + "value": " 60000/60000 [00:47<00:00, 1395.51it/s]" } }, - "b52404a02e14446b9eb8d9c9687553ab": { + "b809fa6c8639462db220a761b405d58d": { + "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 + } + }, + "b907020aafd94e9eb016e8a6f09cd29d": { + "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": "" + } + }, + "bb1d38afd1504d6fa78a843fce81e967": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5238,68 +5229,59 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_391330f212e94180b7c1d3f8b79364a2", + "layout": "IPY_MODEL_c0e1beb3ae784339ae2d251c3b206de1", "placeholder": "​", - "style": "IPY_MODEL_2a60a0c90b1b4f46a02f1662dd4c96e4", + "style": "IPY_MODEL_7859aae2967f4d8982962ef94210548f", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 69.41it/s]" + "value": "100%" } }, - "b5d8fdd0cad64e94955d5536ac4dad32": { - "model_module": "@jupyter-widgets/base", + "bb9b4b065b90414cab93989f1cf870db": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "bbab2e26c4fd423dacd41b5823f1e144": { + "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_853c7b5eeb0944a5903419f543447327", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d25bc5458cd94e56a86b43c849fcf730", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "b84a380de582459fa124b9a70e1b5359": { + "be3b3d55d750477a8208878dd73e24dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5352,25 +5334,56 @@ "width": null } }, - "b8708f41587841e587390f8e235ba687": { + "bf0f538039594aa7aba3502ccbb1866a": { "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": "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_e24f52db1e9d4d00904f83f3b85ef467", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_60b1f65ee807482daeb5f7b6893651bb", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "c09a89ed151f484cbc846804d8c80a5e": { + "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": "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_f26ae4423ac24cafa84c3645e576b7d8", + "placeholder": "​", + "style": "IPY_MODEL_5df8c0f13e114da5a54feb67563465d2", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" } }, - "b87839e9f74c433ab8102575e86bcd7b": { + "c0cef619c3e34a9aa9fe3fbde95dd0f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5423,7 +5436,7 @@ "width": null } }, - "ba458d4944864736a9229851605bc597": { + "c0e1beb3ae784339ae2d251c3b206de1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5476,25 +5489,7 @@ "width": null } }, - "baa12a5e11064c04934b7f2251564311": { - "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 - } - }, - "bb928bde160d418985c159d23435d896": { + "c14d4f58688049a48600554f3cec71e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5547,7 +5542,7 @@ "width": null } }, - "bc9ac33beb964c04b7e19f6a1e1a6eec": { + "c4823f24e46c4225a1be6685c385e2de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5600,7 +5595,7 @@ "width": null } }, - "bcf53273c9a94359bf468708176a7fb9": { + "cab2229300b043cf885deb986c9d5f5d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5653,69 +5648,53 @@ "width": null } }, - "c0d6bc39047b4081aa9475148f266798": { + "cb4e85dad70c4bb69fa58d8fa9da1fd9": { "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_89b15967dfb6452597bba424994c0aed", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_19dc464653b042e1b8ba1ff4aa4f3231", + "layout": "IPY_MODEL_2e43d2f9905b4786bd2429abe71f9c11", + "placeholder": "​", + "style": "IPY_MODEL_7fca1639267a4153bd7fb80d23c652ea", "tabbable": null, "tooltip": null, - "value": 40.0 - } - }, - "c2f27d13f7af40c4938b2ff919248bc7": { - "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": " 40/40 [00:00<00:00, 63.09it/s]" } }, - "c76ed92d69d6444fb0a07c53c18b4c0d": { + "cbf43ef383f248dbaf25bd28be2369f3": { "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_9d34e8136fdb41cba0282f20fe3254b5", + "placeholder": "​", + "style": "IPY_MODEL_d962cb9310644741b92e8a219b2e7104", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 246927.12 examples/s]" } }, - "c977511679bc4e89a67b20d915a938ff": { + "cca4bec16de941d8a1f0eeab09f1309a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5768,54 +5747,106 @@ "width": null } }, - "cb078ccfa5bd45de92c15a832146aa12": { + "d17335f81c08473e83f57bcb91f327b6": { "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_8a36d05bb1a644cf890db7ec5c1979a1", - "placeholder": "​", - "style": "IPY_MODEL_7edd891321db4e908b5ed0d68bdddab0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1489dff3fa9f4025b92de6f3b08f875e", + "IPY_MODEL_1a475bdd7b6b4cffa0aa39c22e228634", + "IPY_MODEL_68df804c48394616884c2cfd1f17c6d3" + ], + "layout": "IPY_MODEL_76fe935e94da4fe49c9fff42053d9f08", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 6806.11 examples/s]" + "tooltip": null } }, - "cb88b48c32274a769a3b6b2f97e0d5f7": { + "d25bc5458cd94e56a86b43c849fcf730": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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": "" + } + }, + "d5de18c08b9a4f0fb3bae9ffcb6e2608": { + "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 + } + }, + "d962cb9310644741b92e8a219b2e7104": { + "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 + } + }, + "dcc296059a18404489cb96f85970a3f1": { + "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": "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_f15c402cc4324dd7b2cf7f6985c8d4ed", - "IPY_MODEL_dfeea2e3d11f410baa4ffc746955acbf", - "IPY_MODEL_cb078ccfa5bd45de92c15a832146aa12" - ], - "layout": "IPY_MODEL_ee99b4bb138247288c3fc9cb7044cdb8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f3adb5d571d446d89e42b148a0e2e0dc", + "placeholder": "​", + "style": "IPY_MODEL_a5fbf7ed0a8945eba908a5b839852cc3", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "cf59e27052914f59bf2cb3f866f7faf0": { + "dd125613a65d44acb8f52566b6d32b7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5868,7 +5899,7 @@ "width": null } }, - "cf898ee7a0764e2b9f7a0b8a17fee462": { + "dd462dfa7f9949738f0551ce01718b0d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5921,25 +5952,33 @@ "width": null } }, - "d53fb7bf594d407c9e842abf6174a266": { + "e19c6ab20a44441b82013b108be2dfe5": { "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_be3b3d55d750477a8208878dd73e24dc", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f4ad6c92501549ec9b25fa93f87e6348", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "d6430b8f25744f0fbc6b7c7b994b06d4": { + "e24f52db1e9d4d00904f83f3b85ef467": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5992,60 +6031,78 @@ "width": null } }, - "d8b68b042b5241cf86f858b91a7ab195": { - "model_module": "@jupyter-widgets/base", + "e284f57ce3ea4f358e133be317a427f8": { + "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_c0cef619c3e34a9aa9fe3fbde95dd0f4", + "placeholder": "​", + "style": "IPY_MODEL_4488e3c1bbcf4f18b659b04ab234f3ec", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 55.22it/s]" + } + }, + "e3f2da6ea95d4ffea5b61ce1470cc963": { + "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_bb1d38afd1504d6fa78a843fce81e967", + "IPY_MODEL_228bd7d078cf45699f3b012c267f6eb0", + "IPY_MODEL_27d17868cbd6451ebcf8d7c7951e2a77" + ], + "layout": "IPY_MODEL_61ffaf9c2a1245668b4f2c0c3cec4cf7", + "tabbable": null, + "tooltip": null + } + }, + "e9e0602cde8548a3b201f1e9d8487610": { + "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_72e848187eb646c9ae2eb5c3c13a1b5b", + "IPY_MODEL_5b300800d44b4998a13101625e6be790", + "IPY_MODEL_8040c639d3ba48d7baa187303a949a5f" + ], + "layout": "IPY_MODEL_248aafd9fcd240928857d2c42d281abd", + "tabbable": null, + "tooltip": null } }, - "dcee1ad305e747b396286c15af30cc5f": { + "eafbc6640e3840d6acb39b13445654da": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6098,51 +6155,31 @@ "width": null } }, - "dd33a2c7592548889d520f9105c23bb8": { - "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 - } - }, - "dfeea2e3d11f410baa4ffc746955acbf": { + "eb19d31ef8f4468b9f0d6aabf630dc05": { "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_18f4e2fb304e4207a8582f3b68e46f1b", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a0876f2d03024b3bb5761a0df2937cfb", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_dcc296059a18404489cb96f85970a3f1", + "IPY_MODEL_7cb2476470d344aebe942210b6aee3ab", + "IPY_MODEL_e284f57ce3ea4f358e133be317a427f8" + ], + "layout": "IPY_MODEL_c14d4f58688049a48600554f3cec71e9", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "e36a0c3800fd4b46a397695a9464376d": { + "ed9f2e8995144260afc1a776c3c728c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6157,33 +6194,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f989133be39d4b8585b24a17aa6b8612", + "layout": "IPY_MODEL_7692ec62386c4b5ea0e3da9a9d6aaab3", "placeholder": "​", - "style": "IPY_MODEL_dd33a2c7592548889d520f9105c23bb8", + "style": "IPY_MODEL_1bf8c6f4bc3e4633b0c368f433ba214d", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 71.69it/s]" - } - }, - "e630ccc4aafc46e1994dc57c453efa4c": { - "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": " 40/40 [00:00<00:00, 67.07it/s]" } }, - "ea07905d53eb44ab85413ddd0eb1103b": { + "ee8869c02a164612932f45b84d319cd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -6199,31 +6218,7 @@ "description_width": "" } }, - "eb284719132643f39c4d672792c9676b": { - "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_758003aac78a4e89a0dd59c12bfd4499", - "IPY_MODEL_07e1a5656606425a8fa445a2a98b32db", - "IPY_MODEL_23a4c60ebe91433cabd5be6f58108e1f" - ], - "layout": "IPY_MODEL_84941543352247e1ba0307848d584119", - "tabbable": null, - "tooltip": null - } - }, - "ec8926923d6642cc965b8aea46f7cdb2": { + "f070a30dde5346f9a173c14652288a61": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6238,33 +6233,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b84a380de582459fa124b9a70e1b5359", + "layout": "IPY_MODEL_57413d25cf8b43a59d135e9b7f386c8b", "placeholder": "​", - "style": "IPY_MODEL_3cb626be1259490498bdc43c229957d5", + "style": "IPY_MODEL_9ad282371d5e4b848fe0fbcf04f86eb4", "tabbable": null, "tooltip": null, - "value": " 2/2 [00:00<00:00, 514.95it/s]" - } - }, - "eda57d1ce07e4fb08048f276454d81c4": { - "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": " 60000/60000 [00:00<00:00, 300464.24 examples/s]" } }, - "ee99b4bb138247288c3fc9cb7044cdb8": { + "f229cd5bb5604bc2bbb59bbf7c81d777": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6317,48 +6294,60 @@ "width": null } }, - "f15c402cc4324dd7b2cf7f6985c8d4ed": { - "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_01d6c6d5c4544426af39a2a43bf4efd5", - "placeholder": "​", - "style": "IPY_MODEL_9e2f25346ff041f0a43792feaf401889", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "f3aa86d829fc4ca5bb1496d9254e9cac": { - "model_module": "@jupyter-widgets/controls", + "f26ae4423ac24cafa84c3645e576b7d8": { + "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 } }, - "f7dcc2439e854d37a27bed7fba490b03": { + "f3adb5d571d446d89e42b148a0e2e0dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6411,7 +6400,7 @@ "width": null } }, - "f989133be39d4b8585b24a17aa6b8612": { + "f430353dd08d430fba9b508f111e2cba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6464,7 +6453,7 @@ "width": null } }, - "fa5a714c695a458288a525b0b07ff15d": { + "f44238836eba40dd8365f2683b775574": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6480,56 +6469,83 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8cf1e91b3283461d9e7f008fa37fff9f", - "max": 60000.0, + "layout": "IPY_MODEL_7134548633584aebb32846399e998599", + "max": 2.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a45cdcaa39ac4e7783e3300484169c30", + "style": "IPY_MODEL_b907020aafd94e9eb016e8a6f09cd29d", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 2.0 } }, - "fcd3307eda42417c8c3f83f6bd1ece28": { + "f4ad6c92501549ec9b25fa93f87e6348": { "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_d8b68b042b5241cf86f858b91a7ab195", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_19d828f4e1d84ef2ac773bc529b40946", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "fd142fc752ad437dac5e121ea0a962e7": { - "model_module": "@jupyter-widgets/controls", + "fb2af0144f1742c1a26396a9b5fe4c73": { + "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 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 43decdf02..763870823 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:02.881954Z", - "iopub.status.busy": "2024-04-06T04:33:02.881761Z", - "iopub.status.idle": "2024-04-06T04:33:03.953480Z", - "shell.execute_reply": "2024-04-06T04:33:03.952937Z" + "iopub.execute_input": "2024-04-08T19:11:39.427663Z", + "iopub.status.busy": "2024-04-08T19:11:39.427246Z", + "iopub.status.idle": "2024-04-08T19:11:40.493201Z", + "shell.execute_reply": "2024-04-08T19:11:40.492649Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:03.956075Z", - "iopub.status.busy": "2024-04-06T04:33:03.955587Z", - "iopub.status.idle": "2024-04-06T04:33:03.973883Z", - "shell.execute_reply": "2024-04-06T04:33:03.973490Z" + "iopub.execute_input": "2024-04-08T19:11:40.495661Z", + "iopub.status.busy": "2024-04-08T19:11:40.495382Z", + "iopub.status.idle": "2024-04-08T19:11:40.513938Z", + "shell.execute_reply": "2024-04-08T19:11:40.513525Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:03.975942Z", - "iopub.status.busy": "2024-04-06T04:33:03.975699Z", - "iopub.status.idle": "2024-04-06T04:33:04.012978Z", - "shell.execute_reply": "2024-04-06T04:33:04.012510Z" + "iopub.execute_input": "2024-04-08T19:11:40.515940Z", + "iopub.status.busy": "2024-04-08T19:11:40.515700Z", + "iopub.status.idle": "2024-04-08T19:11:40.560958Z", + "shell.execute_reply": "2024-04-08T19:11:40.560524Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.014896Z", - "iopub.status.busy": "2024-04-06T04:33:04.014722Z", - "iopub.status.idle": "2024-04-06T04:33:04.018157Z", - "shell.execute_reply": "2024-04-06T04:33:04.017691Z" + "iopub.execute_input": "2024-04-08T19:11:40.562932Z", + "iopub.status.busy": "2024-04-08T19:11:40.562610Z", + "iopub.status.idle": "2024-04-08T19:11:40.566002Z", + "shell.execute_reply": "2024-04-08T19:11:40.565577Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.020151Z", - "iopub.status.busy": "2024-04-06T04:33:04.019837Z", - "iopub.status.idle": "2024-04-06T04:33:04.027381Z", - "shell.execute_reply": "2024-04-06T04:33:04.026969Z" + "iopub.execute_input": "2024-04-08T19:11:40.567900Z", + "iopub.status.busy": "2024-04-08T19:11:40.567583Z", + "iopub.status.idle": "2024-04-08T19:11:40.574730Z", + "shell.execute_reply": "2024-04-08T19:11:40.574279Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.029320Z", - "iopub.status.busy": "2024-04-06T04:33:04.029148Z", - "iopub.status.idle": "2024-04-06T04:33:04.031565Z", - "shell.execute_reply": "2024-04-06T04:33:04.031152Z" + "iopub.execute_input": "2024-04-08T19:11:40.576667Z", + "iopub.status.busy": "2024-04-08T19:11:40.576404Z", + "iopub.status.idle": "2024-04-08T19:11:40.578794Z", + "shell.execute_reply": "2024-04-08T19:11:40.578358Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.033457Z", - "iopub.status.busy": "2024-04-06T04:33:04.033286Z", - "iopub.status.idle": "2024-04-06T04:33:07.020218Z", - "shell.execute_reply": "2024-04-06T04:33:07.019691Z" + "iopub.execute_input": "2024-04-08T19:11:40.580843Z", + "iopub.status.busy": "2024-04-08T19:11:40.580536Z", + "iopub.status.idle": "2024-04-08T19:11:43.565419Z", + "shell.execute_reply": "2024-04-08T19:11:43.564907Z" } }, "outputs": [], @@ -402,10 +402,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:07.022814Z", - "iopub.status.busy": "2024-04-06T04:33:07.022610Z", - "iopub.status.idle": "2024-04-06T04:33:07.032179Z", - "shell.execute_reply": "2024-04-06T04:33:07.031775Z" + "iopub.execute_input": "2024-04-08T19:11:43.568043Z", + "iopub.status.busy": "2024-04-08T19:11:43.567842Z", + "iopub.status.idle": "2024-04-08T19:11:43.577436Z", + "shell.execute_reply": "2024-04-08T19:11:43.577042Z" } }, "outputs": [], @@ -437,10 +437,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:07.034095Z", - "iopub.status.busy": "2024-04-06T04:33:07.033903Z", - "iopub.status.idle": "2024-04-06T04:33:08.789065Z", - "shell.execute_reply": "2024-04-06T04:33:08.788481Z" + "iopub.execute_input": "2024-04-08T19:11:43.579441Z", + "iopub.status.busy": "2024-04-08T19:11:43.579134Z", + "iopub.status.idle": "2024-04-08T19:11:45.292514Z", + "shell.execute_reply": "2024-04-08T19:11:45.291914Z" } }, "outputs": [ @@ -485,10 +485,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.792211Z", - "iopub.status.busy": "2024-04-06T04:33:08.791532Z", - "iopub.status.idle": "2024-04-06T04:33:08.814502Z", - "shell.execute_reply": "2024-04-06T04:33:08.814015Z" + "iopub.execute_input": "2024-04-08T19:11:45.296728Z", + "iopub.status.busy": "2024-04-08T19:11:45.295425Z", + "iopub.status.idle": "2024-04-08T19:11:45.320295Z", + "shell.execute_reply": "2024-04-08T19:11:45.319812Z" }, "scrolled": true }, @@ -613,10 +613,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.817077Z", - "iopub.status.busy": "2024-04-06T04:33:08.816765Z", - "iopub.status.idle": "2024-04-06T04:33:08.825617Z", - "shell.execute_reply": "2024-04-06T04:33:08.825158Z" + "iopub.execute_input": "2024-04-08T19:11:45.323669Z", + "iopub.status.busy": "2024-04-08T19:11:45.322766Z", + "iopub.status.idle": "2024-04-08T19:11:45.333647Z", + "shell.execute_reply": "2024-04-08T19:11:45.333187Z" } }, "outputs": [ @@ -720,10 +720,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.828222Z", - "iopub.status.busy": "2024-04-06T04:33:08.827849Z", - "iopub.status.idle": "2024-04-06T04:33:08.838568Z", - "shell.execute_reply": "2024-04-06T04:33:08.838097Z" + "iopub.execute_input": "2024-04-08T19:11:45.336997Z", + "iopub.status.busy": "2024-04-08T19:11:45.336094Z", + "iopub.status.idle": "2024-04-08T19:11:45.348729Z", + "shell.execute_reply": "2024-04-08T19:11:45.348256Z" } }, "outputs": [ @@ -852,10 +852,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.841680Z", - "iopub.status.busy": "2024-04-06T04:33:08.840763Z", - "iopub.status.idle": "2024-04-06T04:33:08.851889Z", - "shell.execute_reply": "2024-04-06T04:33:08.851420Z" + "iopub.execute_input": "2024-04-08T19:11:45.352087Z", + "iopub.status.busy": "2024-04-08T19:11:45.351199Z", + "iopub.status.idle": "2024-04-08T19:11:45.362031Z", + "shell.execute_reply": "2024-04-08T19:11:45.361569Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.855383Z", - "iopub.status.busy": "2024-04-06T04:33:08.854470Z", - "iopub.status.idle": "2024-04-06T04:33:08.866911Z", - "shell.execute_reply": "2024-04-06T04:33:08.866438Z" + "iopub.execute_input": "2024-04-08T19:11:45.365401Z", + "iopub.status.busy": "2024-04-08T19:11:45.364508Z", + "iopub.status.idle": "2024-04-08T19:11:45.376129Z", + "shell.execute_reply": "2024-04-08T19:11:45.375592Z" } }, "outputs": [ @@ -1083,10 +1083,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.869543Z", - "iopub.status.busy": "2024-04-06T04:33:08.869360Z", - "iopub.status.idle": "2024-04-06T04:33:08.876491Z", - "shell.execute_reply": "2024-04-06T04:33:08.875865Z" + "iopub.execute_input": "2024-04-08T19:11:45.378395Z", + "iopub.status.busy": "2024-04-08T19:11:45.378081Z", + "iopub.status.idle": "2024-04-08T19:11:45.384257Z", + "shell.execute_reply": "2024-04-08T19:11:45.383732Z" } }, "outputs": [ @@ -1170,10 +1170,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.878704Z", - "iopub.status.busy": "2024-04-06T04:33:08.878368Z", - "iopub.status.idle": "2024-04-06T04:33:08.884874Z", - "shell.execute_reply": "2024-04-06T04:33:08.884343Z" + "iopub.execute_input": "2024-04-08T19:11:45.386145Z", + "iopub.status.busy": "2024-04-08T19:11:45.385969Z", + "iopub.status.idle": "2024-04-08T19:11:45.392100Z", + "shell.execute_reply": "2024-04-08T19:11:45.391633Z" } }, "outputs": [ @@ -1266,10 +1266,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.887114Z", - "iopub.status.busy": "2024-04-06T04:33:08.886669Z", - "iopub.status.idle": "2024-04-06T04:33:08.893228Z", - "shell.execute_reply": "2024-04-06T04:33:08.892752Z" + "iopub.execute_input": "2024-04-08T19:11:45.394135Z", + "iopub.status.busy": "2024-04-08T19:11:45.393818Z", + "iopub.status.idle": "2024-04-08T19:11:45.399861Z", + "shell.execute_reply": "2024-04-08T19:11:45.399452Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index a6257a523..cdfc50478 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": "2024-04-06T04:33:11.681681Z", - "iopub.status.busy": "2024-04-06T04:33:11.681132Z", - "iopub.status.idle": "2024-04-06T04:33:14.408684Z", - "shell.execute_reply": "2024-04-06T04:33:14.408170Z" + "iopub.execute_input": "2024-04-08T19:11:47.873795Z", + "iopub.status.busy": "2024-04-08T19:11:47.873616Z", + "iopub.status.idle": "2024-04-08T19:11:50.509546Z", + "shell.execute_reply": "2024-04-08T19:11:50.508929Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:33:14.411372Z", - "iopub.status.busy": "2024-04-06T04:33:14.410870Z", - "iopub.status.idle": "2024-04-06T04:33:14.414126Z", - "shell.execute_reply": "2024-04-06T04:33:14.413639Z" + "iopub.execute_input": "2024-04-08T19:11:50.512202Z", + "iopub.status.busy": "2024-04-08T19:11:50.511881Z", + "iopub.status.idle": "2024-04-08T19:11:50.515413Z", + "shell.execute_reply": "2024-04-08T19:11:50.514847Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.416093Z", - "iopub.status.busy": "2024-04-06T04:33:14.415817Z", - "iopub.status.idle": "2024-04-06T04:33:14.419148Z", - "shell.execute_reply": "2024-04-06T04:33:14.418621Z" + "iopub.execute_input": "2024-04-08T19:11:50.517389Z", + "iopub.status.busy": "2024-04-08T19:11:50.517121Z", + "iopub.status.idle": "2024-04-08T19:11:50.520136Z", + "shell.execute_reply": "2024-04-08T19:11:50.519721Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.421094Z", - "iopub.status.busy": "2024-04-06T04:33:14.420828Z", - "iopub.status.idle": "2024-04-06T04:33:14.445821Z", - "shell.execute_reply": "2024-04-06T04:33:14.445234Z" + "iopub.execute_input": "2024-04-08T19:11:50.522130Z", + "iopub.status.busy": "2024-04-08T19:11:50.521805Z", + "iopub.status.idle": "2024-04-08T19:11:50.573099Z", + "shell.execute_reply": "2024-04-08T19:11:50.572633Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.448095Z", - "iopub.status.busy": "2024-04-06T04:33:14.447753Z", - "iopub.status.idle": "2024-04-06T04:33:14.451521Z", - "shell.execute_reply": "2024-04-06T04:33:14.451033Z" + "iopub.execute_input": "2024-04-08T19:11:50.575235Z", + "iopub.status.busy": "2024-04-08T19:11:50.574826Z", + "iopub.status.idle": "2024-04-08T19:11:50.578661Z", + "shell.execute_reply": "2024-04-08T19:11:50.578198Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'getting_spare_card', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer', 'lost_or_stolen_phone', 'supported_cards_and_currencies'}\n" + "Classes: {'visa_or_mastercard', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.453654Z", - "iopub.status.busy": "2024-04-06T04:33:14.453334Z", - "iopub.status.idle": "2024-04-06T04:33:14.456651Z", - "shell.execute_reply": "2024-04-06T04:33:14.456195Z" + "iopub.execute_input": "2024-04-08T19:11:50.580716Z", + "iopub.status.busy": "2024-04-08T19:11:50.580386Z", + "iopub.status.idle": "2024-04-08T19:11:50.583329Z", + "shell.execute_reply": "2024-04-08T19:11:50.582809Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.458570Z", - "iopub.status.busy": "2024-04-06T04:33:14.458385Z", - "iopub.status.idle": "2024-04-06T04:33:18.310859Z", - "shell.execute_reply": "2024-04-06T04:33:18.310235Z" + "iopub.execute_input": "2024-04-08T19:11:50.585157Z", + "iopub.status.busy": "2024-04-08T19:11:50.584978Z", + "iopub.status.idle": "2024-04-08T19:11:54.999343Z", + "shell.execute_reply": "2024-04-08T19:11:54.998804Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:18.313664Z", - "iopub.status.busy": "2024-04-06T04:33:18.313302Z", - "iopub.status.idle": "2024-04-06T04:33:19.193930Z", - "shell.execute_reply": "2024-04-06T04:33:19.193370Z" + "iopub.execute_input": "2024-04-08T19:11:55.002005Z", + "iopub.status.busy": "2024-04-08T19:11:55.001591Z", + "iopub.status.idle": "2024-04-08T19:11:55.890538Z", + "shell.execute_reply": "2024-04-08T19:11:55.889962Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:19.196805Z", - "iopub.status.busy": "2024-04-06T04:33:19.196442Z", - "iopub.status.idle": "2024-04-06T04:33:19.199261Z", - "shell.execute_reply": "2024-04-06T04:33:19.198798Z" + "iopub.execute_input": "2024-04-08T19:11:55.893249Z", + "iopub.status.busy": "2024-04-08T19:11:55.892862Z", + "iopub.status.idle": "2024-04-08T19:11:55.895882Z", + "shell.execute_reply": "2024-04-08T19:11:55.895415Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:19.201572Z", - "iopub.status.busy": "2024-04-06T04:33:19.201213Z", - "iopub.status.idle": "2024-04-06T04:33:20.771182Z", - "shell.execute_reply": "2024-04-06T04:33:20.770550Z" + "iopub.execute_input": "2024-04-08T19:11:55.898149Z", + "iopub.status.busy": "2024-04-08T19:11:55.897768Z", + "iopub.status.idle": "2024-04-08T19:11:57.484712Z", + "shell.execute_reply": "2024-04-08T19:11:57.482845Z" }, "scrolled": true }, @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.774433Z", - "iopub.status.busy": "2024-04-06T04:33:20.773600Z", - "iopub.status.idle": "2024-04-06T04:33:20.799139Z", - "shell.execute_reply": "2024-04-06T04:33:20.798597Z" + "iopub.execute_input": "2024-04-08T19:11:57.489057Z", + "iopub.status.busy": "2024-04-08T19:11:57.487744Z", + "iopub.status.idle": "2024-04-08T19:11:57.513599Z", + "shell.execute_reply": "2024-04-08T19:11:57.513105Z" }, "scrolled": true }, @@ -666,10 +666,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.801783Z", - "iopub.status.busy": "2024-04-06T04:33:20.801390Z", - "iopub.status.idle": "2024-04-06T04:33:20.811382Z", - "shell.execute_reply": "2024-04-06T04:33:20.810884Z" + "iopub.execute_input": "2024-04-08T19:11:57.517153Z", + "iopub.status.busy": "2024-04-08T19:11:57.516242Z", + "iopub.status.idle": "2024-04-08T19:11:57.527834Z", + "shell.execute_reply": "2024-04-08T19:11:57.527359Z" }, "scrolled": true }, @@ -779,10 +779,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.813909Z", - "iopub.status.busy": "2024-04-06T04:33:20.813527Z", - "iopub.status.idle": "2024-04-06T04:33:20.818371Z", - "shell.execute_reply": "2024-04-06T04:33:20.817869Z" + "iopub.execute_input": "2024-04-08T19:11:57.531248Z", + "iopub.status.busy": "2024-04-08T19:11:57.530335Z", + "iopub.status.idle": "2024-04-08T19:11:57.536787Z", + "shell.execute_reply": "2024-04-08T19:11:57.536230Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.820606Z", - "iopub.status.busy": "2024-04-06T04:33:20.820299Z", - "iopub.status.idle": "2024-04-06T04:33:20.826482Z", - "shell.execute_reply": "2024-04-06T04:33:20.826090Z" + "iopub.execute_input": "2024-04-08T19:11:57.538876Z", + "iopub.status.busy": "2024-04-08T19:11:57.538699Z", + "iopub.status.idle": "2024-04-08T19:11:57.546063Z", + "shell.execute_reply": "2024-04-08T19:11:57.545305Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.828437Z", - "iopub.status.busy": "2024-04-06T04:33:20.828137Z", - "iopub.status.idle": "2024-04-06T04:33:20.834167Z", - "shell.execute_reply": "2024-04-06T04:33:20.833652Z" + "iopub.execute_input": "2024-04-08T19:11:57.548261Z", + "iopub.status.busy": "2024-04-08T19:11:57.547854Z", + "iopub.status.idle": "2024-04-08T19:11:57.554234Z", + "shell.execute_reply": "2024-04-08T19:11:57.553695Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.836059Z", - "iopub.status.busy": "2024-04-06T04:33:20.835877Z", - "iopub.status.idle": "2024-04-06T04:33:20.841929Z", - "shell.execute_reply": "2024-04-06T04:33:20.841349Z" + "iopub.execute_input": "2024-04-08T19:11:57.556102Z", + "iopub.status.busy": "2024-04-08T19:11:57.555808Z", + "iopub.status.idle": "2024-04-08T19:11:57.561366Z", + "shell.execute_reply": "2024-04-08T19:11:57.560849Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.843988Z", - "iopub.status.busy": "2024-04-06T04:33:20.843684Z", - "iopub.status.idle": "2024-04-06T04:33:20.852453Z", - "shell.execute_reply": "2024-04-06T04:33:20.851982Z" + "iopub.execute_input": "2024-04-08T19:11:57.563375Z", + "iopub.status.busy": "2024-04-08T19:11:57.563066Z", + "iopub.status.idle": "2024-04-08T19:11:57.571545Z", + "shell.execute_reply": "2024-04-08T19:11:57.571096Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.854597Z", - "iopub.status.busy": "2024-04-06T04:33:20.854199Z", - "iopub.status.idle": "2024-04-06T04:33:20.859815Z", - "shell.execute_reply": "2024-04-06T04:33:20.859258Z" + "iopub.execute_input": "2024-04-08T19:11:57.573469Z", + "iopub.status.busy": "2024-04-08T19:11:57.573151Z", + "iopub.status.idle": "2024-04-08T19:11:57.578415Z", + "shell.execute_reply": "2024-04-08T19:11:57.577995Z" } }, "outputs": [ @@ -1322,10 +1322,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.861773Z", - "iopub.status.busy": "2024-04-06T04:33:20.861471Z", - "iopub.status.idle": "2024-04-06T04:33:20.866885Z", - "shell.execute_reply": "2024-04-06T04:33:20.866352Z" + "iopub.execute_input": "2024-04-08T19:11:57.580309Z", + "iopub.status.busy": "2024-04-08T19:11:57.579985Z", + "iopub.status.idle": "2024-04-08T19:11:57.585107Z", + "shell.execute_reply": "2024-04-08T19:11:57.584704Z" } }, "outputs": [ @@ -1404,10 +1404,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.869013Z", - "iopub.status.busy": "2024-04-06T04:33:20.868609Z", - "iopub.status.idle": "2024-04-06T04:33:20.872412Z", - "shell.execute_reply": "2024-04-06T04:33:20.871871Z" + "iopub.execute_input": "2024-04-08T19:11:57.587089Z", + "iopub.status.busy": "2024-04-08T19:11:57.586774Z", + "iopub.status.idle": "2024-04-08T19:11:57.590241Z", + "shell.execute_reply": "2024-04-08T19:11:57.589704Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.874578Z", - "iopub.status.busy": "2024-04-06T04:33:20.874128Z", - "iopub.status.idle": "2024-04-06T04:33:20.879644Z", - "shell.execute_reply": "2024-04-06T04:33:20.879101Z" + "iopub.execute_input": "2024-04-08T19:11:57.592302Z", + "iopub.status.busy": "2024-04-08T19:11:57.591981Z", + "iopub.status.idle": "2024-04-08T19:11:57.597076Z", + "shell.execute_reply": "2024-04-08T19:11:57.596530Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 31a8923c7..8386c499c 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:24.564833Z", - "iopub.status.busy": "2024-04-06T04:33:24.564645Z", - "iopub.status.idle": "2024-04-06T04:33:25.678241Z", - "shell.execute_reply": "2024-04-06T04:33:25.677637Z" + "iopub.execute_input": "2024-04-08T19:12:00.906624Z", + "iopub.status.busy": "2024-04-08T19:12:00.906269Z", + "iopub.status.idle": "2024-04-08T19:12:02.013278Z", + "shell.execute_reply": "2024-04-08T19:12:02.012738Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.681005Z", - "iopub.status.busy": "2024-04-06T04:33:25.680432Z", - "iopub.status.idle": "2024-04-06T04:33:25.683479Z", - "shell.execute_reply": "2024-04-06T04:33:25.683004Z" + "iopub.execute_input": "2024-04-08T19:12:02.015823Z", + "iopub.status.busy": "2024-04-08T19:12:02.015525Z", + "iopub.status.idle": "2024-04-08T19:12:02.018326Z", + "shell.execute_reply": "2024-04-08T19:12:02.017864Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.685643Z", - "iopub.status.busy": "2024-04-06T04:33:25.685458Z", - "iopub.status.idle": "2024-04-06T04:33:25.698037Z", - "shell.execute_reply": "2024-04-06T04:33:25.697552Z" + "iopub.execute_input": "2024-04-08T19:12:02.020260Z", + "iopub.status.busy": "2024-04-08T19:12:02.020087Z", + "iopub.status.idle": "2024-04-08T19:12:02.032329Z", + "shell.execute_reply": "2024-04-08T19:12:02.031881Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.700120Z", - "iopub.status.busy": "2024-04-06T04:33:25.699931Z", - "iopub.status.idle": "2024-04-06T04:33:30.316432Z", - "shell.execute_reply": "2024-04-06T04:33:30.315931Z" + "iopub.execute_input": "2024-04-08T19:12:02.034317Z", + "iopub.status.busy": "2024-04-08T19:12:02.034142Z", + "iopub.status.idle": "2024-04-08T19:12:10.633860Z", + "shell.execute_reply": "2024-04-08T19:12:10.633305Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 71792b57a..35b51f794 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": "2024-04-06T04:33:32.453926Z", - "iopub.status.busy": "2024-04-06T04:33:32.453487Z", - "iopub.status.idle": "2024-04-06T04:33:33.577711Z", - "shell.execute_reply": "2024-04-06T04:33:33.577162Z" + "iopub.execute_input": "2024-04-08T19:12:12.681561Z", + "iopub.status.busy": "2024-04-08T19:12:12.681389Z", + "iopub.status.idle": "2024-04-08T19:12:13.734405Z", + "shell.execute_reply": "2024-04-08T19:12:13.733868Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:33.580468Z", - "iopub.status.busy": "2024-04-06T04:33:33.579978Z", - "iopub.status.idle": "2024-04-06T04:33:33.583331Z", - "shell.execute_reply": "2024-04-06T04:33:33.582894Z" + "iopub.execute_input": "2024-04-08T19:12:13.737231Z", + "iopub.status.busy": "2024-04-08T19:12:13.736791Z", + "iopub.status.idle": "2024-04-08T19:12:13.740148Z", + "shell.execute_reply": "2024-04-08T19:12:13.739710Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:33.585545Z", - "iopub.status.busy": "2024-04-06T04:33:33.585109Z", - "iopub.status.idle": "2024-04-06T04:33:36.718652Z", - "shell.execute_reply": "2024-04-06T04:33:36.718005Z" + "iopub.execute_input": "2024-04-08T19:12:13.742187Z", + "iopub.status.busy": "2024-04-08T19:12:13.741855Z", + "iopub.status.idle": "2024-04-08T19:12:16.687217Z", + "shell.execute_reply": "2024-04-08T19:12:16.686507Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.721727Z", - "iopub.status.busy": "2024-04-06T04:33:36.721060Z", - "iopub.status.idle": "2024-04-06T04:33:36.760399Z", - "shell.execute_reply": "2024-04-06T04:33:36.759784Z" + "iopub.execute_input": "2024-04-08T19:12:16.690229Z", + "iopub.status.busy": "2024-04-08T19:12:16.689558Z", + "iopub.status.idle": "2024-04-08T19:12:16.723065Z", + "shell.execute_reply": "2024-04-08T19:12:16.722493Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.763173Z", - "iopub.status.busy": "2024-04-06T04:33:36.762842Z", - "iopub.status.idle": "2024-04-06T04:33:36.801368Z", - "shell.execute_reply": "2024-04-06T04:33:36.800735Z" + "iopub.execute_input": "2024-04-08T19:12:16.725574Z", + "iopub.status.busy": "2024-04-08T19:12:16.725213Z", + "iopub.status.idle": "2024-04-08T19:12:16.748633Z", + "shell.execute_reply": "2024-04-08T19:12:16.748076Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.804245Z", - "iopub.status.busy": "2024-04-06T04:33:36.803821Z", - "iopub.status.idle": "2024-04-06T04:33:36.807084Z", - "shell.execute_reply": "2024-04-06T04:33:36.806596Z" + "iopub.execute_input": "2024-04-08T19:12:16.751185Z", + "iopub.status.busy": "2024-04-08T19:12:16.750822Z", + "iopub.status.idle": "2024-04-08T19:12:16.753746Z", + "shell.execute_reply": "2024-04-08T19:12:16.753306Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.809090Z", - "iopub.status.busy": "2024-04-06T04:33:36.808779Z", - "iopub.status.idle": "2024-04-06T04:33:36.811544Z", - "shell.execute_reply": "2024-04-06T04:33:36.811006Z" + "iopub.execute_input": "2024-04-08T19:12:16.755833Z", + "iopub.status.busy": "2024-04-08T19:12:16.755526Z", + "iopub.status.idle": "2024-04-08T19:12:16.758525Z", + "shell.execute_reply": "2024-04-08T19:12:16.758102Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.813573Z", - "iopub.status.busy": "2024-04-06T04:33:36.813305Z", - "iopub.status.idle": "2024-04-06T04:33:36.837656Z", - "shell.execute_reply": "2024-04-06T04:33:36.837105Z" + "iopub.execute_input": "2024-04-08T19:12:16.760530Z", + "iopub.status.busy": "2024-04-08T19:12:16.760254Z", + "iopub.status.idle": "2024-04-08T19:12:16.783193Z", + "shell.execute_reply": "2024-04-08T19:12:16.782688Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ad7ffe9f7e104f438570b96387ce328e", + "model_id": "6a6240bb0ab443d38a48eadee74f3ae2", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a93f0182ebb47fc96441f7413ee50a4", + "model_id": "f9a5120ba56d4977aa0d368fb7c66d40", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.843747Z", - "iopub.status.busy": "2024-04-06T04:33:36.843506Z", - "iopub.status.idle": "2024-04-06T04:33:36.850771Z", - "shell.execute_reply": "2024-04-06T04:33:36.850304Z" + "iopub.execute_input": "2024-04-08T19:12:16.789722Z", + "iopub.status.busy": "2024-04-08T19:12:16.789232Z", + "iopub.status.idle": "2024-04-08T19:12:16.795676Z", + "shell.execute_reply": "2024-04-08T19:12:16.795154Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.853060Z", - "iopub.status.busy": "2024-04-06T04:33:36.852662Z", - "iopub.status.idle": "2024-04-06T04:33:36.856158Z", - "shell.execute_reply": "2024-04-06T04:33:36.855726Z" + "iopub.execute_input": "2024-04-08T19:12:16.797734Z", + "iopub.status.busy": "2024-04-08T19:12:16.797436Z", + "iopub.status.idle": "2024-04-08T19:12:16.800819Z", + "shell.execute_reply": "2024-04-08T19:12:16.800306Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.858276Z", - "iopub.status.busy": "2024-04-06T04:33:36.858000Z", - "iopub.status.idle": "2024-04-06T04:33:36.864594Z", - "shell.execute_reply": "2024-04-06T04:33:36.864108Z" + "iopub.execute_input": "2024-04-08T19:12:16.802799Z", + "iopub.status.busy": "2024-04-08T19:12:16.802383Z", + "iopub.status.idle": "2024-04-08T19:12:16.808583Z", + "shell.execute_reply": "2024-04-08T19:12:16.808080Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.866698Z", - "iopub.status.busy": "2024-04-06T04:33:36.866352Z", - "iopub.status.idle": "2024-04-06T04:33:36.905959Z", - "shell.execute_reply": "2024-04-06T04:33:36.905317Z" + "iopub.execute_input": "2024-04-08T19:12:16.810430Z", + "iopub.status.busy": "2024-04-08T19:12:16.810131Z", + "iopub.status.idle": "2024-04-08T19:12:16.843764Z", + "shell.execute_reply": "2024-04-08T19:12:16.843069Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.908640Z", - "iopub.status.busy": "2024-04-06T04:33:36.908384Z", - "iopub.status.idle": "2024-04-06T04:33:36.948839Z", - "shell.execute_reply": "2024-04-06T04:33:36.948221Z" + "iopub.execute_input": "2024-04-08T19:12:16.846251Z", + "iopub.status.busy": "2024-04-08T19:12:16.846029Z", + "iopub.status.idle": "2024-04-08T19:12:16.876055Z", + "shell.execute_reply": "2024-04-08T19:12:16.875395Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.951895Z", - "iopub.status.busy": "2024-04-06T04:33:36.951511Z", - "iopub.status.idle": "2024-04-06T04:33:37.080581Z", - "shell.execute_reply": "2024-04-06T04:33:37.079922Z" + "iopub.execute_input": "2024-04-08T19:12:16.878797Z", + "iopub.status.busy": "2024-04-08T19:12:16.878362Z", + "iopub.status.idle": "2024-04-08T19:12:16.997690Z", + "shell.execute_reply": "2024-04-08T19:12:16.997074Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:37.083569Z", - "iopub.status.busy": "2024-04-06T04:33:37.082731Z", - "iopub.status.idle": "2024-04-06T04:33:40.126106Z", - "shell.execute_reply": "2024-04-06T04:33:40.125422Z" + "iopub.execute_input": "2024-04-08T19:12:17.000317Z", + "iopub.status.busy": "2024-04-08T19:12:16.999797Z", + "iopub.status.idle": "2024-04-08T19:12:20.051499Z", + "shell.execute_reply": "2024-04-08T19:12:20.050857Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.128582Z", - "iopub.status.busy": "2024-04-06T04:33:40.128353Z", - "iopub.status.idle": "2024-04-06T04:33:40.189416Z", - "shell.execute_reply": "2024-04-06T04:33:40.188788Z" + "iopub.execute_input": "2024-04-08T19:12:20.054045Z", + "iopub.status.busy": "2024-04-08T19:12:20.053678Z", + "iopub.status.idle": "2024-04-08T19:12:20.108066Z", + "shell.execute_reply": "2024-04-08T19:12:20.107454Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.191652Z", - "iopub.status.busy": "2024-04-06T04:33:40.191314Z", - "iopub.status.idle": "2024-04-06T04:33:40.231110Z", - "shell.execute_reply": "2024-04-06T04:33:40.230569Z" + "iopub.execute_input": "2024-04-08T19:12:20.110315Z", + "iopub.status.busy": "2024-04-08T19:12:20.109981Z", + "iopub.status.idle": "2024-04-08T19:12:20.147390Z", + "shell.execute_reply": "2024-04-08T19:12:20.146954Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "7997ced4", + "id": "9da437a7", "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": "57a8d119", + "id": "fce848ae", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1340,13 +1340,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "9fb93000", + "id": "0fe990fa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.233390Z", - "iopub.status.busy": "2024-04-06T04:33:40.233191Z", - "iopub.status.idle": "2024-04-06T04:33:40.327660Z", - "shell.execute_reply": "2024-04-06T04:33:40.327127Z" + "iopub.execute_input": "2024-04-08T19:12:20.149376Z", + "iopub.status.busy": "2024-04-08T19:12:20.149051Z", + "iopub.status.idle": "2024-04-08T19:12:20.266660Z", + "shell.execute_reply": "2024-04-08T19:12:20.266055Z" } }, "outputs": [ @@ -1354,7 +1354,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ...\n", + "Finding underperforming_group issues ...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1387,7 +1393,7 @@ }, { "cell_type": "markdown", - "id": "27082dba", + "id": "e1f798da", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1396,13 +1402,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "5a3f0b1c", + "id": "35842b9a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.330424Z", - "iopub.status.busy": "2024-04-06T04:33:40.330165Z", - "iopub.status.idle": "2024-04-06T04:33:40.412901Z", - "shell.execute_reply": "2024-04-06T04:33:40.412405Z" + "iopub.execute_input": "2024-04-08T19:12:20.269272Z", + "iopub.status.busy": "2024-04-08T19:12:20.269030Z", + "iopub.status.idle": "2024-04-08T19:12:20.330497Z", + "shell.execute_reply": "2024-04-08T19:12:20.329977Z" } }, "outputs": [ @@ -1410,14 +1416,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ..." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Finding underperforming_group issues ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1445,7 +1444,7 @@ }, { "cell_type": "markdown", - "id": "bb4c5299", + "id": "798d7822", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1456,13 +1455,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "0a847975", + "id": "fdfd0a78", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.415545Z", - "iopub.status.busy": "2024-04-06T04:33:40.415364Z", - "iopub.status.idle": "2024-04-06T04:33:40.424747Z", - "shell.execute_reply": "2024-04-06T04:33:40.424323Z" + "iopub.execute_input": "2024-04-08T19:12:20.332905Z", + "iopub.status.busy": "2024-04-08T19:12:20.332706Z", + "iopub.status.idle": "2024-04-08T19:12:20.340139Z", + "shell.execute_reply": "2024-04-08T19:12:20.339592Z" } }, "outputs": [], @@ -1564,7 +1563,7 @@ }, { "cell_type": "markdown", - "id": "f6c74243", + "id": "623406db", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1579,13 +1578,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "665cd26e", + "id": "78a115a5", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.427036Z", - "iopub.status.busy": "2024-04-06T04:33:40.426714Z", - "iopub.status.idle": "2024-04-06T04:33:40.447448Z", - "shell.execute_reply": "2024-04-06T04:33:40.446876Z" + "iopub.execute_input": "2024-04-08T19:12:20.342036Z", + "iopub.status.busy": "2024-04-08T19:12:20.341739Z", + "iopub.status.idle": "2024-04-08T19:12:20.360239Z", + "shell.execute_reply": "2024-04-08T19:12:20.359697Z" } }, "outputs": [ @@ -1602,7 +1601,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7516/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_7838/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1636,13 +1635,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "1a0ba0a1", + "id": "40dae4e0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.449833Z", - "iopub.status.busy": "2024-04-06T04:33:40.449476Z", - "iopub.status.idle": "2024-04-06T04:33:40.452685Z", - "shell.execute_reply": "2024-04-06T04:33:40.452130Z" + "iopub.execute_input": "2024-04-08T19:12:20.362253Z", + "iopub.status.busy": "2024-04-08T19:12:20.361948Z", + "iopub.status.idle": "2024-04-08T19:12:20.365026Z", + "shell.execute_reply": "2024-04-08T19:12:20.364516Z" } }, "outputs": [ @@ -1737,7 +1736,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "14b2e46a058f49b7877f1e0a8fc3b5b6": { + "12810a0d31ae4f278489cceb3717deb2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1790,7 +1789,7 @@ "width": null } }, - "23c0ea245fbd417183656f1b6c07712f": { + "21b83733b12e4ab9a8e1fc932a77b365": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1843,30 +1842,65 @@ "width": null } }, - "245c87c4c2aa416db47130538b929d58": { + "6566ff0f4c9545e0a8d4ac6514750721": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "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": "" + } + }, + "68c12f7c9aa14eb596e0d4b9f346ded1": { + "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 + } + }, + "6a6240bb0ab443d38a48eadee74f3ae2": { + "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": "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_7480a084643d44109ad2e89da0ca1645", - "placeholder": "​", - "style": "IPY_MODEL_d3c14fbb47a44544abaeec177f87256f", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b66e8c48cfaa46939a45c8b4440cd4a6", + "IPY_MODEL_bf86d89ad3204f18be960f57e96ccf59", + "IPY_MODEL_b765b2a3ff1d4b41a66c68404700508b" + ], + "layout": "IPY_MODEL_cfc2c60ce9954a939a633840b1c6d1f7", "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1599963.38it/s]" + "tooltip": null } }, - "26607dc2026d44d18409e2097833bbb6": { + "995b23ebdb8b4621ae697068c80e4d01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1919,7 +1953,60 @@ "width": null } }, - "538f177063ec4fd38db9281cbb4e4736": { + "9a847008c2cc4912b7568541c8506397": { + "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 + } + }, + "9cca6ed55b464af5a62dfafe2ab723a7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1934,15 +2021,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_23c0ea245fbd417183656f1b6c07712f", + "layout": "IPY_MODEL_9a847008c2cc4912b7568541c8506397", "placeholder": "​", - "style": "IPY_MODEL_f7f940143f124c22a39fad1b33b95e97", + "style": "IPY_MODEL_b0998f9686d34540aef7e956282bc228", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " + "value": " 10000/? [00:00<00:00, 1657172.66it/s]" + } + }, + "b0998f9686d34540aef7e956282bc228": { + "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 + } + }, + "b37c8cc149634a1fa49852024830088d": { + "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 } }, - "5d3a89a90e3b4986a627cb78ab15f855": { + "b66e8c48cfaa46939a45c8b4440cd4a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1957,39 +2080,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c9e7c9ca5279402f833cd03f267b90b0", + "layout": "IPY_MODEL_12810a0d31ae4f278489cceb3717deb2", "placeholder": "​", - "style": "IPY_MODEL_7a5ac41ff90446bda080db1a245f6076", + "style": "IPY_MODEL_c9626b33f28a41cbb8e7ab7eaa6d20db", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: " } }, - "6a93f0182ebb47fc96441f7413ee50a4": { + "b765b2a3ff1d4b41a66c68404700508b": { "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_538f177063ec4fd38db9281cbb4e4736", - "IPY_MODEL_8147d6885df5404da13067a4b04feaff", - "IPY_MODEL_245c87c4c2aa416db47130538b929d58" - ], - "layout": "IPY_MODEL_26607dc2026d44d18409e2097833bbb6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_21b83733b12e4ab9a8e1fc932a77b365", + "placeholder": "​", + "style": "IPY_MODEL_b37c8cc149634a1fa49852024830088d", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1108870.85it/s]" } }, - "7480a084643d44109ad2e89da0ca1645": { + "ba6196a4a8f546aabd03b97f4e409a2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2042,25 +2164,7 @@ "width": null } }, - "7a5ac41ff90446bda080db1a245f6076": { - "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 - } - }, - "8147d6885df5404da13067a4b04feaff": { + "bf86d89ad3204f18be960f57e96ccf59": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2076,17 +2180,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b88c297a26be479180743ea12a8782ee", + "layout": "IPY_MODEL_c1cb2e0cc26b4999826ac84dc19ced5c", "max": 50.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_8a8358bc01474398ae2c3b719fe94d03", + "style": "IPY_MODEL_6566ff0f4c9545e0a8d4ac6514750721", "tabbable": null, "tooltip": null, "value": 50.0 } }, - "83a387e924c445e394cde04898e3e09e": { + "c1cb2e0cc26b4999826ac84dc19ced5c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2139,47 +2243,30 @@ "width": null } }, - "8a8358bc01474398ae2c3b719fe94d03": { + "c5076875c8174d66894ad735da25ceb0": { "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": "" - } - }, - "ad7ffe9f7e104f438570b96387ce328e": { - "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_5d3a89a90e3b4986a627cb78ab15f855", - "IPY_MODEL_f6ab57c77c6546aeb601051c57a99435", - "IPY_MODEL_cda88cf88c8042acb6232ea9423f355f" - ], - "layout": "IPY_MODEL_14b2e46a058f49b7877f1e0a8fc3b5b6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0bb2759b4794473a6c806fa37369c3f", + "placeholder": "​", + "style": "IPY_MODEL_68c12f7c9aa14eb596e0d4b9f346ded1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "b884a7bf75b9420f8028a27180dff7b2": { + "c9626b33f28a41cbb8e7ab7eaa6d20db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2197,60 +2284,49 @@ "text_color": null } }, - "b88c297a26be479180743ea12a8782ee": { - "model_module": "@jupyter-widgets/base", + "cabf93bc271f433bbdf492408e032d42": { + "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": "" + } + }, + "cc473ba1779244319b406315a2ad79b3": { + "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_995b23ebdb8b4621ae697068c80e4d01", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cabf93bc271f433bbdf492408e032d42", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "be6b5fe0f64b4e89bba0bc6b2e5c249c": { + "cfc2c60ce9954a939a633840b1c6d1f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2303,7 +2379,7 @@ "width": null } }, - "c9e7c9ca5279402f833cd03f267b90b0": { + "d0bb2759b4794473a6c806fa37369c3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2356,105 +2432,28 @@ "width": null } }, - "cc099a9799bc402bbc12d51076fd879a": { - "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": "" - } - }, - "cda88cf88c8042acb6232ea9423f355f": { - "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_83a387e924c445e394cde04898e3e09e", - "placeholder": "​", - "style": "IPY_MODEL_b884a7bf75b9420f8028a27180dff7b2", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1022876.23it/s]" - } - }, - "d3c14fbb47a44544abaeec177f87256f": { - "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 - } - }, - "f6ab57c77c6546aeb601051c57a99435": { + "f9a5120ba56d4977aa0d368fb7c66d40": { "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_be6b5fe0f64b4e89bba0bc6b2e5c249c", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cc099a9799bc402bbc12d51076fd879a", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c5076875c8174d66894ad735da25ceb0", + "IPY_MODEL_cc473ba1779244319b406315a2ad79b3", + "IPY_MODEL_9cca6ed55b464af5a62dfafe2ab723a7" + ], + "layout": "IPY_MODEL_ba6196a4a8f546aabd03b97f4e409a2f", "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "f7f940143f124c22a39fad1b33b95e97": { - "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 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 09d453fd1..319d2d3ff 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": "2024-04-06T04:33:43.785678Z", - "iopub.status.busy": "2024-04-06T04:33:43.785475Z", - "iopub.status.idle": "2024-04-06T04:33:44.953788Z", - "shell.execute_reply": "2024-04-06T04:33:44.953182Z" + "iopub.execute_input": "2024-04-08T19:12:23.385502Z", + "iopub.status.busy": "2024-04-08T19:12:23.385324Z", + "iopub.status.idle": "2024-04-08T19:12:24.500994Z", + "shell.execute_reply": "2024-04-08T19:12:24.500451Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:33:44.956257Z", - "iopub.status.busy": "2024-04-06T04:33:44.955968Z", - "iopub.status.idle": "2024-04-06T04:33:45.136559Z", - "shell.execute_reply": "2024-04-06T04:33:45.135941Z" + "iopub.execute_input": "2024-04-08T19:12:24.503635Z", + "iopub.status.busy": "2024-04-08T19:12:24.503134Z", + "iopub.status.idle": "2024-04-08T19:12:24.674963Z", + "shell.execute_reply": "2024-04-08T19:12:24.674378Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.139194Z", - "iopub.status.busy": "2024-04-06T04:33:45.138996Z", - "iopub.status.idle": "2024-04-06T04:33:45.151534Z", - "shell.execute_reply": "2024-04-06T04:33:45.150954Z" + "iopub.execute_input": "2024-04-08T19:12:24.677405Z", + "iopub.status.busy": "2024-04-08T19:12:24.677010Z", + "iopub.status.idle": "2024-04-08T19:12:24.688933Z", + "shell.execute_reply": "2024-04-08T19:12:24.688406Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.153835Z", - "iopub.status.busy": "2024-04-06T04:33:45.153455Z", - "iopub.status.idle": "2024-04-06T04:33:45.364208Z", - "shell.execute_reply": "2024-04-06T04:33:45.363567Z" + "iopub.execute_input": "2024-04-08T19:12:24.690846Z", + "iopub.status.busy": "2024-04-08T19:12:24.690671Z", + "iopub.status.idle": "2024-04-08T19:12:24.894029Z", + "shell.execute_reply": "2024-04-08T19:12:24.893464Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.366694Z", - "iopub.status.busy": "2024-04-06T04:33:45.366209Z", - "iopub.status.idle": "2024-04-06T04:33:45.393157Z", - "shell.execute_reply": "2024-04-06T04:33:45.392663Z" + "iopub.execute_input": "2024-04-08T19:12:24.896362Z", + "iopub.status.busy": "2024-04-08T19:12:24.896017Z", + "iopub.status.idle": "2024-04-08T19:12:24.922340Z", + "shell.execute_reply": "2024-04-08T19:12:24.921893Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.395608Z", - "iopub.status.busy": "2024-04-06T04:33:45.395250Z", - "iopub.status.idle": "2024-04-06T04:33:47.125309Z", - "shell.execute_reply": "2024-04-06T04:33:47.124671Z" + "iopub.execute_input": "2024-04-08T19:12:24.924554Z", + "iopub.status.busy": "2024-04-08T19:12:24.924219Z", + "iopub.status.idle": "2024-04-08T19:12:26.591119Z", + "shell.execute_reply": "2024-04-08T19:12:26.590421Z" } }, "outputs": [ @@ -483,10 +483,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:47.127865Z", - "iopub.status.busy": "2024-04-06T04:33:47.127360Z", - "iopub.status.idle": "2024-04-06T04:33:47.146064Z", - "shell.execute_reply": "2024-04-06T04:33:47.145478Z" + "iopub.execute_input": "2024-04-08T19:12:26.593843Z", + "iopub.status.busy": "2024-04-08T19:12:26.593202Z", + "iopub.status.idle": "2024-04-08T19:12:26.611348Z", + "shell.execute_reply": "2024-04-08T19:12:26.610866Z" }, "scrolled": true }, @@ -611,10 +611,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:47.148206Z", - "iopub.status.busy": "2024-04-06T04:33:47.148010Z", - "iopub.status.idle": "2024-04-06T04:33:48.575713Z", - "shell.execute_reply": "2024-04-06T04:33:48.575123Z" + "iopub.execute_input": "2024-04-08T19:12:26.613273Z", + "iopub.status.busy": "2024-04-08T19:12:26.613008Z", + "iopub.status.idle": "2024-04-08T19:12:27.994069Z", + "shell.execute_reply": "2024-04-08T19:12:27.993485Z" }, "id": "AaHC5MRKjruT" }, @@ -733,10 +733,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.578373Z", - "iopub.status.busy": "2024-04-06T04:33:48.577728Z", - "iopub.status.idle": "2024-04-06T04:33:48.591925Z", - "shell.execute_reply": "2024-04-06T04:33:48.591473Z" + "iopub.execute_input": "2024-04-08T19:12:27.996963Z", + "iopub.status.busy": "2024-04-08T19:12:27.996205Z", + "iopub.status.idle": "2024-04-08T19:12:28.010313Z", + "shell.execute_reply": "2024-04-08T19:12:28.009892Z" }, "id": "Wy27rvyhjruU" }, @@ -785,10 +785,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.594180Z", - "iopub.status.busy": "2024-04-06T04:33:48.593840Z", - "iopub.status.idle": "2024-04-06T04:33:48.670108Z", - "shell.execute_reply": "2024-04-06T04:33:48.669540Z" + "iopub.execute_input": "2024-04-08T19:12:28.012483Z", + "iopub.status.busy": "2024-04-08T19:12:28.012148Z", + "iopub.status.idle": "2024-04-08T19:12:28.092332Z", + "shell.execute_reply": "2024-04-08T19:12:28.091737Z" }, "id": "Db8YHnyVjruU" }, @@ -895,10 +895,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.672461Z", - "iopub.status.busy": "2024-04-06T04:33:48.672082Z", - "iopub.status.idle": "2024-04-06T04:33:48.894054Z", - "shell.execute_reply": "2024-04-06T04:33:48.893460Z" + "iopub.execute_input": "2024-04-08T19:12:28.094848Z", + "iopub.status.busy": "2024-04-08T19:12:28.094388Z", + "iopub.status.idle": "2024-04-08T19:12:28.305015Z", + "shell.execute_reply": "2024-04-08T19:12:28.304459Z" }, "id": "iJqAHuS2jruV" }, @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.896310Z", - "iopub.status.busy": "2024-04-06T04:33:48.895957Z", - "iopub.status.idle": "2024-04-06T04:33:48.912992Z", - "shell.execute_reply": "2024-04-06T04:33:48.912438Z" + "iopub.execute_input": "2024-04-08T19:12:28.307155Z", + "iopub.status.busy": "2024-04-08T19:12:28.306977Z", + "iopub.status.idle": "2024-04-08T19:12:28.324108Z", + "shell.execute_reply": "2024-04-08T19:12:28.323676Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1404,10 +1404,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.915370Z", - "iopub.status.busy": "2024-04-06T04:33:48.914978Z", - "iopub.status.idle": "2024-04-06T04:33:48.925166Z", - "shell.execute_reply": "2024-04-06T04:33:48.924650Z" + "iopub.execute_input": "2024-04-08T19:12:28.326002Z", + "iopub.status.busy": "2024-04-08T19:12:28.325829Z", + "iopub.status.idle": "2024-04-08T19:12:28.335620Z", + "shell.execute_reply": "2024-04-08T19:12:28.335205Z" }, "id": "0lonvOYvjruV" }, @@ -1554,10 +1554,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.927196Z", - "iopub.status.busy": "2024-04-06T04:33:48.927015Z", - "iopub.status.idle": "2024-04-06T04:33:49.014441Z", - "shell.execute_reply": "2024-04-06T04:33:49.013806Z" + "iopub.execute_input": "2024-04-08T19:12:28.337624Z", + "iopub.status.busy": "2024-04-08T19:12:28.337213Z", + "iopub.status.idle": "2024-04-08T19:12:28.422599Z", + "shell.execute_reply": "2024-04-08T19:12:28.421980Z" }, "id": "MfqTCa3kjruV" }, @@ -1638,10 +1638,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.016777Z", - "iopub.status.busy": "2024-04-06T04:33:49.016537Z", - "iopub.status.idle": "2024-04-06T04:33:49.145893Z", - "shell.execute_reply": "2024-04-06T04:33:49.145286Z" + "iopub.execute_input": "2024-04-08T19:12:28.424970Z", + "iopub.status.busy": "2024-04-08T19:12:28.424722Z", + "iopub.status.idle": "2024-04-08T19:12:28.549007Z", + "shell.execute_reply": "2024-04-08T19:12:28.548406Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1701,10 +1701,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.148236Z", - "iopub.status.busy": "2024-04-06T04:33:49.148006Z", - "iopub.status.idle": "2024-04-06T04:33:49.151659Z", - "shell.execute_reply": "2024-04-06T04:33:49.151136Z" + "iopub.execute_input": "2024-04-08T19:12:28.551383Z", + "iopub.status.busy": "2024-04-08T19:12:28.551092Z", + "iopub.status.idle": "2024-04-08T19:12:28.554976Z", + "shell.execute_reply": "2024-04-08T19:12:28.554255Z" }, "id": "0rXP3ZPWjruW" }, @@ -1742,10 +1742,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.153711Z", - "iopub.status.busy": "2024-04-06T04:33:49.153349Z", - "iopub.status.idle": "2024-04-06T04:33:49.157155Z", - "shell.execute_reply": "2024-04-06T04:33:49.156622Z" + "iopub.execute_input": "2024-04-08T19:12:28.557035Z", + "iopub.status.busy": "2024-04-08T19:12:28.556717Z", + "iopub.status.idle": "2024-04-08T19:12:28.560298Z", + "shell.execute_reply": "2024-04-08T19:12:28.559774Z" }, "id": "-iRPe8KXjruW" }, @@ -1800,10 +1800,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.159176Z", - "iopub.status.busy": "2024-04-06T04:33:49.158878Z", - "iopub.status.idle": "2024-04-06T04:33:49.196839Z", - "shell.execute_reply": "2024-04-06T04:33:49.196263Z" + "iopub.execute_input": "2024-04-08T19:12:28.562193Z", + "iopub.status.busy": "2024-04-08T19:12:28.561944Z", + "iopub.status.idle": "2024-04-08T19:12:28.599077Z", + "shell.execute_reply": "2024-04-08T19:12:28.598539Z" }, "id": "ZpipUliyjruW" }, @@ -1854,10 +1854,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.198950Z", - "iopub.status.busy": "2024-04-06T04:33:49.198645Z", - "iopub.status.idle": "2024-04-06T04:33:49.242193Z", - "shell.execute_reply": "2024-04-06T04:33:49.241610Z" + "iopub.execute_input": "2024-04-08T19:12:28.601134Z", + "iopub.status.busy": "2024-04-08T19:12:28.600813Z", + "iopub.status.idle": "2024-04-08T19:12:28.642167Z", + "shell.execute_reply": "2024-04-08T19:12:28.641727Z" }, "id": "SLq-3q4xjruX" }, @@ -1926,10 +1926,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.244487Z", - "iopub.status.busy": "2024-04-06T04:33:49.244090Z", - "iopub.status.idle": "2024-04-06T04:33:49.337248Z", - "shell.execute_reply": "2024-04-06T04:33:49.336579Z" + "iopub.execute_input": "2024-04-08T19:12:28.644151Z", + "iopub.status.busy": "2024-04-08T19:12:28.643835Z", + "iopub.status.idle": "2024-04-08T19:12:28.738961Z", + "shell.execute_reply": "2024-04-08T19:12:28.738341Z" }, "id": "g5LHhhuqFbXK" }, @@ -1961,10 +1961,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.339846Z", - "iopub.status.busy": "2024-04-06T04:33:49.339620Z", - "iopub.status.idle": "2024-04-06T04:33:49.430742Z", - "shell.execute_reply": "2024-04-06T04:33:49.430143Z" + "iopub.execute_input": "2024-04-08T19:12:28.741750Z", + "iopub.status.busy": "2024-04-08T19:12:28.741263Z", + "iopub.status.idle": "2024-04-08T19:12:28.827551Z", + "shell.execute_reply": "2024-04-08T19:12:28.826947Z" }, "id": "p7w8F8ezBcet" }, @@ -2021,10 +2021,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.432983Z", - "iopub.status.busy": "2024-04-06T04:33:49.432697Z", - "iopub.status.idle": "2024-04-06T04:33:49.645127Z", - "shell.execute_reply": "2024-04-06T04:33:49.644551Z" + "iopub.execute_input": "2024-04-08T19:12:28.829915Z", + "iopub.status.busy": "2024-04-08T19:12:28.829683Z", + "iopub.status.idle": "2024-04-08T19:12:29.038522Z", + "shell.execute_reply": "2024-04-08T19:12:29.037949Z" }, "id": "WETRL74tE_sU" }, @@ -2059,10 +2059,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.647536Z", - "iopub.status.busy": "2024-04-06T04:33:49.647110Z", - "iopub.status.idle": "2024-04-06T04:33:49.836451Z", - "shell.execute_reply": "2024-04-06T04:33:49.835806Z" + "iopub.execute_input": "2024-04-08T19:12:29.040910Z", + "iopub.status.busy": "2024-04-08T19:12:29.040732Z", + "iopub.status.idle": "2024-04-08T19:12:29.214576Z", + "shell.execute_reply": "2024-04-08T19:12:29.213963Z" }, "id": "kCfdx2gOLmXS" }, @@ -2224,10 +2224,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.838935Z", - "iopub.status.busy": "2024-04-06T04:33:49.838446Z", - "iopub.status.idle": "2024-04-06T04:33:49.845067Z", - "shell.execute_reply": "2024-04-06T04:33:49.844540Z" + "iopub.execute_input": "2024-04-08T19:12:29.217044Z", + "iopub.status.busy": "2024-04-08T19:12:29.216667Z", + "iopub.status.idle": "2024-04-08T19:12:29.222938Z", + "shell.execute_reply": "2024-04-08T19:12:29.222502Z" }, "id": "-uogYRWFYnuu" }, @@ -2281,10 +2281,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.847230Z", - "iopub.status.busy": "2024-04-06T04:33:49.846825Z", - "iopub.status.idle": "2024-04-06T04:33:50.065771Z", - "shell.execute_reply": "2024-04-06T04:33:50.065168Z" + "iopub.execute_input": "2024-04-08T19:12:29.224909Z", + "iopub.status.busy": "2024-04-08T19:12:29.224587Z", + "iopub.status.idle": "2024-04-08T19:12:29.437683Z", + "shell.execute_reply": "2024-04-08T19:12:29.437115Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2331,10 +2331,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:50.068226Z", - "iopub.status.busy": "2024-04-06T04:33:50.067840Z", - "iopub.status.idle": "2024-04-06T04:33:51.143014Z", - "shell.execute_reply": "2024-04-06T04:33:51.142387Z" + "iopub.execute_input": "2024-04-08T19:12:29.439974Z", + "iopub.status.busy": "2024-04-08T19:12:29.439567Z", + "iopub.status.idle": "2024-04-08T19:12:30.486127Z", + "shell.execute_reply": "2024-04-08T19:12:30.485508Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index f2ec4a55c..a709c4ddc 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:54.655001Z", - "iopub.status.busy": "2024-04-06T04:33:54.654839Z", - "iopub.status.idle": "2024-04-06T04:33:55.737154Z", - "shell.execute_reply": "2024-04-06T04:33:55.736607Z" + "iopub.execute_input": "2024-04-08T19:12:33.752421Z", + "iopub.status.busy": "2024-04-08T19:12:33.752248Z", + "iopub.status.idle": "2024-04-08T19:12:34.830539Z", + "shell.execute_reply": "2024-04-08T19:12:34.829972Z" }, "nbsphinx": "hidden" }, @@ -102,7 +102,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -136,10 +136,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.739856Z", - "iopub.status.busy": "2024-04-06T04:33:55.739430Z", - "iopub.status.idle": "2024-04-06T04:33:55.742481Z", - "shell.execute_reply": "2024-04-06T04:33:55.741958Z" + "iopub.execute_input": "2024-04-08T19:12:34.833064Z", + "iopub.status.busy": "2024-04-08T19:12:34.832801Z", + "iopub.status.idle": "2024-04-08T19:12:34.835936Z", + "shell.execute_reply": "2024-04-08T19:12:34.835405Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.744755Z", - "iopub.status.busy": "2024-04-06T04:33:55.744422Z", - "iopub.status.idle": "2024-04-06T04:33:55.752051Z", - "shell.execute_reply": "2024-04-06T04:33:55.751620Z" + "iopub.execute_input": "2024-04-08T19:12:34.837888Z", + "iopub.status.busy": "2024-04-08T19:12:34.837708Z", + "iopub.status.idle": "2024-04-08T19:12:34.845722Z", + "shell.execute_reply": "2024-04-08T19:12:34.845317Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.754050Z", - "iopub.status.busy": "2024-04-06T04:33:55.753666Z", - "iopub.status.idle": "2024-04-06T04:33:55.808130Z", - "shell.execute_reply": "2024-04-06T04:33:55.807549Z" + "iopub.execute_input": "2024-04-08T19:12:34.847573Z", + "iopub.status.busy": "2024-04-08T19:12:34.847397Z", + "iopub.status.idle": "2024-04-08T19:12:34.894104Z", + "shell.execute_reply": "2024-04-08T19:12:34.893588Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.810525Z", - "iopub.status.busy": "2024-04-06T04:33:55.810206Z", - "iopub.status.idle": "2024-04-06T04:33:55.827426Z", - "shell.execute_reply": "2024-04-06T04:33:55.826967Z" + "iopub.execute_input": "2024-04-08T19:12:34.896019Z", + "iopub.status.busy": "2024-04-08T19:12:34.895834Z", + "iopub.status.idle": "2024-04-08T19:12:34.912597Z", + "shell.execute_reply": "2024-04-08T19:12:34.912094Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.829293Z", - "iopub.status.busy": "2024-04-06T04:33:55.829117Z", - "iopub.status.idle": "2024-04-06T04:33:55.833052Z", - "shell.execute_reply": "2024-04-06T04:33:55.832518Z" + "iopub.execute_input": "2024-04-08T19:12:34.914647Z", + "iopub.status.busy": "2024-04-08T19:12:34.914307Z", + "iopub.status.idle": "2024-04-08T19:12:34.917956Z", + "shell.execute_reply": "2024-04-08T19:12:34.917438Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.835165Z", - "iopub.status.busy": "2024-04-06T04:33:55.834833Z", - "iopub.status.idle": "2024-04-06T04:33:55.865218Z", - "shell.execute_reply": "2024-04-06T04:33:55.864706Z" + "iopub.execute_input": "2024-04-08T19:12:34.919974Z", + "iopub.status.busy": "2024-04-08T19:12:34.919671Z", + "iopub.status.idle": "2024-04-08T19:12:34.946169Z", + "shell.execute_reply": "2024-04-08T19:12:34.945655Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.867653Z", - "iopub.status.busy": "2024-04-06T04:33:55.867231Z", - "iopub.status.idle": "2024-04-06T04:33:55.894195Z", - "shell.execute_reply": "2024-04-06T04:33:55.893624Z" + "iopub.execute_input": "2024-04-08T19:12:34.948155Z", + "iopub.status.busy": "2024-04-08T19:12:34.947833Z", + "iopub.status.idle": "2024-04-08T19:12:34.973985Z", + "shell.execute_reply": "2024-04-08T19:12:34.973459Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.896247Z", - "iopub.status.busy": "2024-04-06T04:33:55.896066Z", - "iopub.status.idle": "2024-04-06T04:33:57.627098Z", - "shell.execute_reply": "2024-04-06T04:33:57.626566Z" + "iopub.execute_input": "2024-04-08T19:12:34.976074Z", + "iopub.status.busy": "2024-04-08T19:12:34.975781Z", + "iopub.status.idle": "2024-04-08T19:12:36.687655Z", + "shell.execute_reply": "2024-04-08T19:12:36.687110Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.629758Z", - "iopub.status.busy": "2024-04-06T04:33:57.629236Z", - "iopub.status.idle": "2024-04-06T04:33:57.636079Z", - "shell.execute_reply": "2024-04-06T04:33:57.635555Z" + "iopub.execute_input": "2024-04-08T19:12:36.690165Z", + "iopub.status.busy": "2024-04-08T19:12:36.689693Z", + "iopub.status.idle": "2024-04-08T19:12:36.696336Z", + "shell.execute_reply": "2024-04-08T19:12:36.695815Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.638210Z", - "iopub.status.busy": "2024-04-06T04:33:57.637876Z", - "iopub.status.idle": "2024-04-06T04:33:57.650276Z", - "shell.execute_reply": "2024-04-06T04:33:57.649820Z" + "iopub.execute_input": "2024-04-08T19:12:36.698324Z", + "iopub.status.busy": "2024-04-08T19:12:36.698034Z", + "iopub.status.idle": "2024-04-08T19:12:36.710339Z", + "shell.execute_reply": "2024-04-08T19:12:36.709902Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.652252Z", - "iopub.status.busy": "2024-04-06T04:33:57.651928Z", - "iopub.status.idle": "2024-04-06T04:33:57.658292Z", - "shell.execute_reply": "2024-04-06T04:33:57.657737Z" + "iopub.execute_input": "2024-04-08T19:12:36.712346Z", + "iopub.status.busy": "2024-04-08T19:12:36.711929Z", + "iopub.status.idle": "2024-04-08T19:12:36.718208Z", + "shell.execute_reply": "2024-04-08T19:12:36.717694Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.660348Z", - "iopub.status.busy": "2024-04-06T04:33:57.660033Z", - "iopub.status.idle": "2024-04-06T04:33:57.662546Z", - "shell.execute_reply": "2024-04-06T04:33:57.662096Z" + "iopub.execute_input": "2024-04-08T19:12:36.720257Z", + "iopub.status.busy": "2024-04-08T19:12:36.719972Z", + "iopub.status.idle": "2024-04-08T19:12:36.722551Z", + "shell.execute_reply": "2024-04-08T19:12:36.722114Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.664568Z", - "iopub.status.busy": "2024-04-06T04:33:57.664236Z", - "iopub.status.idle": "2024-04-06T04:33:57.667775Z", - "shell.execute_reply": "2024-04-06T04:33:57.667336Z" + "iopub.execute_input": "2024-04-08T19:12:36.724389Z", + "iopub.status.busy": "2024-04-08T19:12:36.724098Z", + "iopub.status.idle": "2024-04-08T19:12:36.727537Z", + "shell.execute_reply": "2024-04-08T19:12:36.727025Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.669724Z", - "iopub.status.busy": "2024-04-06T04:33:57.669426Z", - "iopub.status.idle": "2024-04-06T04:33:57.672060Z", - "shell.execute_reply": "2024-04-06T04:33:57.671546Z" + "iopub.execute_input": "2024-04-08T19:12:36.729415Z", + "iopub.status.busy": "2024-04-08T19:12:36.729242Z", + "iopub.status.idle": "2024-04-08T19:12:36.731642Z", + "shell.execute_reply": "2024-04-08T19:12:36.731237Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.673964Z", - "iopub.status.busy": "2024-04-06T04:33:57.673653Z", - "iopub.status.idle": "2024-04-06T04:33:57.677802Z", - "shell.execute_reply": "2024-04-06T04:33:57.677364Z" + "iopub.execute_input": "2024-04-08T19:12:36.733573Z", + "iopub.status.busy": "2024-04-08T19:12:36.733257Z", + "iopub.status.idle": "2024-04-08T19:12:36.737118Z", + "shell.execute_reply": "2024-04-08T19:12:36.736613Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.679746Z", - "iopub.status.busy": "2024-04-06T04:33:57.679562Z", - "iopub.status.idle": "2024-04-06T04:33:57.708692Z", - "shell.execute_reply": "2024-04-06T04:33:57.708184Z" + "iopub.execute_input": "2024-04-08T19:12:36.739150Z", + "iopub.status.busy": "2024-04-08T19:12:36.738829Z", + "iopub.status.idle": "2024-04-08T19:12:36.767384Z", + "shell.execute_reply": "2024-04-08T19:12:36.766867Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.711548Z", - "iopub.status.busy": "2024-04-06T04:33:57.711062Z", - "iopub.status.idle": "2024-04-06T04:33:57.716161Z", - "shell.execute_reply": "2024-04-06T04:33:57.715701Z" + "iopub.execute_input": "2024-04-08T19:12:36.769379Z", + "iopub.status.busy": "2024-04-08T19:12:36.769213Z", + "iopub.status.idle": "2024-04-08T19:12:36.773887Z", + "shell.execute_reply": "2024-04-08T19:12:36.773364Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index e4f3da5a6..93017979b 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": "2024-04-06T04:34:00.530023Z", - "iopub.status.busy": "2024-04-06T04:34:00.529838Z", - "iopub.status.idle": "2024-04-06T04:34:01.665208Z", - "shell.execute_reply": "2024-04-06T04:34:01.664664Z" + "iopub.execute_input": "2024-04-08T19:12:39.372434Z", + "iopub.status.busy": "2024-04-08T19:12:39.372031Z", + "iopub.status.idle": "2024-04-08T19:12:40.491618Z", + "shell.execute_reply": "2024-04-08T19:12:40.491005Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:01.667949Z", - "iopub.status.busy": "2024-04-06T04:34:01.667372Z", - "iopub.status.idle": "2024-04-06T04:34:01.860713Z", - "shell.execute_reply": "2024-04-06T04:34:01.860104Z" + "iopub.execute_input": "2024-04-08T19:12:40.494221Z", + "iopub.status.busy": "2024-04-08T19:12:40.493824Z", + "iopub.status.idle": "2024-04-08T19:12:40.685279Z", + "shell.execute_reply": "2024-04-08T19:12:40.684689Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:01.863387Z", - "iopub.status.busy": "2024-04-06T04:34:01.863099Z", - "iopub.status.idle": "2024-04-06T04:34:01.876408Z", - "shell.execute_reply": "2024-04-06T04:34:01.875857Z" + "iopub.execute_input": "2024-04-08T19:12:40.688253Z", + "iopub.status.busy": "2024-04-08T19:12:40.687653Z", + "iopub.status.idle": "2024-04-08T19:12:40.701124Z", + "shell.execute_reply": "2024-04-08T19:12:40.700676Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:01.878382Z", - "iopub.status.busy": "2024-04-06T04:34:01.878075Z", - "iopub.status.idle": "2024-04-06T04:34:04.553375Z", - "shell.execute_reply": "2024-04-06T04:34:04.552763Z" + "iopub.execute_input": "2024-04-08T19:12:40.703173Z", + "iopub.status.busy": "2024-04-08T19:12:40.702854Z", + "iopub.status.idle": "2024-04-08T19:12:43.329249Z", + "shell.execute_reply": "2024-04-08T19:12:43.328755Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:04.555866Z", - "iopub.status.busy": "2024-04-06T04:34:04.555447Z", - "iopub.status.idle": "2024-04-06T04:34:05.899176Z", - "shell.execute_reply": "2024-04-06T04:34:05.898628Z" + "iopub.execute_input": "2024-04-08T19:12:43.331514Z", + "iopub.status.busy": "2024-04-08T19:12:43.331169Z", + "iopub.status.idle": "2024-04-08T19:12:44.670891Z", + "shell.execute_reply": "2024-04-08T19:12:44.670276Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:05.901446Z", - "iopub.status.busy": "2024-04-06T04:34:05.901252Z", - "iopub.status.idle": "2024-04-06T04:34:05.905303Z", - "shell.execute_reply": "2024-04-06T04:34:05.904832Z" + "iopub.execute_input": "2024-04-08T19:12:44.673413Z", + "iopub.status.busy": "2024-04-08T19:12:44.673216Z", + "iopub.status.idle": "2024-04-08T19:12:44.677262Z", + "shell.execute_reply": "2024-04-08T19:12:44.676816Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:05.907229Z", - "iopub.status.busy": "2024-04-06T04:34:05.906935Z", - "iopub.status.idle": "2024-04-06T04:34:07.727455Z", - "shell.execute_reply": "2024-04-06T04:34:07.726870Z" + "iopub.execute_input": "2024-04-08T19:12:44.679281Z", + "iopub.status.busy": "2024-04-08T19:12:44.678982Z", + "iopub.status.idle": "2024-04-08T19:12:46.437869Z", + "shell.execute_reply": "2024-04-08T19:12:46.437260Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:07.730219Z", - "iopub.status.busy": "2024-04-06T04:34:07.729486Z", - "iopub.status.idle": "2024-04-06T04:34:07.737826Z", - "shell.execute_reply": "2024-04-06T04:34:07.737345Z" + "iopub.execute_input": "2024-04-08T19:12:46.440643Z", + "iopub.status.busy": "2024-04-08T19:12:46.440072Z", + "iopub.status.idle": "2024-04-08T19:12:46.448250Z", + "shell.execute_reply": "2024-04-08T19:12:46.447724Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:07.739895Z", - "iopub.status.busy": "2024-04-06T04:34:07.739580Z", - "iopub.status.idle": "2024-04-06T04:34:10.345477Z", - "shell.execute_reply": "2024-04-06T04:34:10.344972Z" + "iopub.execute_input": "2024-04-08T19:12:46.450615Z", + "iopub.status.busy": "2024-04-08T19:12:46.450220Z", + "iopub.status.idle": "2024-04-08T19:12:49.029942Z", + "shell.execute_reply": "2024-04-08T19:12:49.029325Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.347724Z", - "iopub.status.busy": "2024-04-06T04:34:10.347360Z", - "iopub.status.idle": "2024-04-06T04:34:10.351001Z", - "shell.execute_reply": "2024-04-06T04:34:10.350556Z" + "iopub.execute_input": "2024-04-08T19:12:49.032160Z", + "iopub.status.busy": "2024-04-08T19:12:49.031822Z", + "iopub.status.idle": "2024-04-08T19:12:49.035518Z", + "shell.execute_reply": "2024-04-08T19:12:49.035071Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.352909Z", - "iopub.status.busy": "2024-04-06T04:34:10.352732Z", - "iopub.status.idle": "2024-04-06T04:34:10.357176Z", - "shell.execute_reply": "2024-04-06T04:34:10.356760Z" + "iopub.execute_input": "2024-04-08T19:12:49.037498Z", + "iopub.status.busy": "2024-04-08T19:12:49.037171Z", + "iopub.status.idle": "2024-04-08T19:12:49.041048Z", + "shell.execute_reply": "2024-04-08T19:12:49.040619Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.359140Z", - "iopub.status.busy": "2024-04-06T04:34:10.358816Z", - "iopub.status.idle": "2024-04-06T04:34:10.361865Z", - "shell.execute_reply": "2024-04-06T04:34:10.361423Z" + "iopub.execute_input": "2024-04-08T19:12:49.042924Z", + "iopub.status.busy": "2024-04-08T19:12:49.042604Z", + "iopub.status.idle": "2024-04-08T19:12:49.045672Z", + "shell.execute_reply": "2024-04-08T19:12:49.045228Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index a41b44c5c..b290d6163 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": "2024-04-06T04:34:12.844775Z", - "iopub.status.busy": "2024-04-06T04:34:12.844311Z", - "iopub.status.idle": "2024-04-06T04:34:13.980776Z", - "shell.execute_reply": "2024-04-06T04:34:13.980176Z" + "iopub.execute_input": "2024-04-08T19:12:51.506697Z", + "iopub.status.busy": "2024-04-08T19:12:51.506534Z", + "iopub.status.idle": "2024-04-08T19:12:52.637000Z", + "shell.execute_reply": "2024-04-08T19:12:52.636397Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:13.983263Z", - "iopub.status.busy": "2024-04-06T04:34:13.983016Z", - "iopub.status.idle": "2024-04-06T04:34:15.579622Z", - "shell.execute_reply": "2024-04-06T04:34:15.579010Z" + "iopub.execute_input": "2024-04-08T19:12:52.639569Z", + "iopub.status.busy": "2024-04-08T19:12:52.639309Z", + "iopub.status.idle": "2024-04-08T19:12:55.104415Z", + "shell.execute_reply": "2024-04-08T19:12:55.103670Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.582324Z", - "iopub.status.busy": "2024-04-06T04:34:15.581949Z", - "iopub.status.idle": "2024-04-06T04:34:15.585226Z", - "shell.execute_reply": "2024-04-06T04:34:15.584699Z" + "iopub.execute_input": "2024-04-08T19:12:55.107140Z", + "iopub.status.busy": "2024-04-08T19:12:55.106931Z", + "iopub.status.idle": "2024-04-08T19:12:55.110341Z", + "shell.execute_reply": "2024-04-08T19:12:55.109801Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.587256Z", - "iopub.status.busy": "2024-04-06T04:34:15.586885Z", - "iopub.status.idle": "2024-04-06T04:34:15.593670Z", - "shell.execute_reply": "2024-04-06T04:34:15.593228Z" + "iopub.execute_input": "2024-04-08T19:12:55.112430Z", + "iopub.status.busy": "2024-04-08T19:12:55.112060Z", + "iopub.status.idle": "2024-04-08T19:12:55.118161Z", + "shell.execute_reply": "2024-04-08T19:12:55.117642Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.595551Z", - "iopub.status.busy": "2024-04-06T04:34:15.595372Z", - "iopub.status.idle": "2024-04-06T04:34:16.077823Z", - "shell.execute_reply": "2024-04-06T04:34:16.077255Z" + "iopub.execute_input": "2024-04-08T19:12:55.120250Z", + "iopub.status.busy": "2024-04-08T19:12:55.119951Z", + "iopub.status.idle": "2024-04-08T19:12:55.604414Z", + "shell.execute_reply": "2024-04-08T19:12:55.603862Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.079945Z", - "iopub.status.busy": "2024-04-06T04:34:16.079765Z", - "iopub.status.idle": "2024-04-06T04:34:16.085000Z", - "shell.execute_reply": "2024-04-06T04:34:16.084559Z" + "iopub.execute_input": "2024-04-08T19:12:55.607305Z", + "iopub.status.busy": "2024-04-08T19:12:55.606952Z", + "iopub.status.idle": "2024-04-08T19:12:55.612116Z", + "shell.execute_reply": "2024-04-08T19:12:55.611686Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.086986Z", - "iopub.status.busy": "2024-04-06T04:34:16.086699Z", - "iopub.status.idle": "2024-04-06T04:34:16.090564Z", - "shell.execute_reply": "2024-04-06T04:34:16.090132Z" + "iopub.execute_input": "2024-04-08T19:12:55.614134Z", + "iopub.status.busy": "2024-04-08T19:12:55.613824Z", + "iopub.status.idle": "2024-04-08T19:12:55.617419Z", + "shell.execute_reply": "2024-04-08T19:12:55.617014Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.092402Z", - "iopub.status.busy": "2024-04-06T04:34:16.092226Z", - "iopub.status.idle": "2024-04-06T04:34:16.742313Z", - "shell.execute_reply": "2024-04-06T04:34:16.741698Z" + "iopub.execute_input": "2024-04-08T19:12:55.619403Z", + "iopub.status.busy": "2024-04-08T19:12:55.619145Z", + "iopub.status.idle": "2024-04-08T19:12:56.292272Z", + "shell.execute_reply": "2024-04-08T19:12:56.291640Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.744425Z", - "iopub.status.busy": "2024-04-06T04:34:16.744233Z", - "iopub.status.idle": "2024-04-06T04:34:16.915555Z", - "shell.execute_reply": "2024-04-06T04:34:16.915036Z" + "iopub.execute_input": "2024-04-08T19:12:56.294743Z", + "iopub.status.busy": "2024-04-08T19:12:56.294368Z", + "iopub.status.idle": "2024-04-08T19:12:56.451834Z", + "shell.execute_reply": "2024-04-08T19:12:56.451237Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.917406Z", - "iopub.status.busy": "2024-04-06T04:34:16.917231Z", - "iopub.status.idle": "2024-04-06T04:34:16.921449Z", - "shell.execute_reply": "2024-04-06T04:34:16.921026Z" + "iopub.execute_input": "2024-04-08T19:12:56.454163Z", + "iopub.status.busy": "2024-04-08T19:12:56.453784Z", + "iopub.status.idle": "2024-04-08T19:12:56.458257Z", + "shell.execute_reply": "2024-04-08T19:12:56.457717Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.923486Z", - "iopub.status.busy": "2024-04-06T04:34:16.923119Z", - "iopub.status.idle": "2024-04-06T04:34:17.368354Z", - "shell.execute_reply": "2024-04-06T04:34:17.367768Z" + "iopub.execute_input": "2024-04-08T19:12:56.460284Z", + "iopub.status.busy": "2024-04-08T19:12:56.459945Z", + "iopub.status.idle": "2024-04-08T19:12:56.918547Z", + "shell.execute_reply": "2024-04-08T19:12:56.917913Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:17.371163Z", - "iopub.status.busy": "2024-04-06T04:34:17.370822Z", - "iopub.status.idle": "2024-04-06T04:34:17.674268Z", - "shell.execute_reply": "2024-04-06T04:34:17.673692Z" + "iopub.execute_input": "2024-04-08T19:12:56.921651Z", + "iopub.status.busy": "2024-04-08T19:12:56.921292Z", + "iopub.status.idle": "2024-04-08T19:12:57.253473Z", + "shell.execute_reply": "2024-04-08T19:12:57.252856Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:17.676624Z", - "iopub.status.busy": "2024-04-06T04:34:17.676303Z", - "iopub.status.idle": "2024-04-06T04:34:18.037637Z", - "shell.execute_reply": "2024-04-06T04:34:18.037134Z" + "iopub.execute_input": "2024-04-08T19:12:57.255657Z", + "iopub.status.busy": "2024-04-08T19:12:57.255478Z", + "iopub.status.idle": "2024-04-08T19:12:57.619053Z", + "shell.execute_reply": "2024-04-08T19:12:57.618466Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.040616Z", - "iopub.status.busy": "2024-04-06T04:34:18.040298Z", - "iopub.status.idle": "2024-04-06T04:34:18.480221Z", - "shell.execute_reply": "2024-04-06T04:34:18.479710Z" + "iopub.execute_input": "2024-04-08T19:12:57.621976Z", + "iopub.status.busy": "2024-04-08T19:12:57.621623Z", + "iopub.status.idle": "2024-04-08T19:12:58.060261Z", + "shell.execute_reply": "2024-04-08T19:12:58.059741Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.484224Z", - "iopub.status.busy": "2024-04-06T04:34:18.483951Z", - "iopub.status.idle": "2024-04-06T04:34:18.910308Z", - "shell.execute_reply": "2024-04-06T04:34:18.909828Z" + "iopub.execute_input": "2024-04-08T19:12:58.064403Z", + "iopub.status.busy": "2024-04-08T19:12:58.064187Z", + "iopub.status.idle": "2024-04-08T19:12:58.481716Z", + "shell.execute_reply": "2024-04-08T19:12:58.481166Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.912281Z", - "iopub.status.busy": "2024-04-06T04:34:18.912098Z", - "iopub.status.idle": "2024-04-06T04:34:19.127034Z", - "shell.execute_reply": "2024-04-06T04:34:19.126447Z" + "iopub.execute_input": "2024-04-08T19:12:58.484504Z", + "iopub.status.busy": "2024-04-08T19:12:58.484329Z", + "iopub.status.idle": "2024-04-08T19:12:58.698454Z", + "shell.execute_reply": "2024-04-08T19:12:58.697889Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.129044Z", - "iopub.status.busy": "2024-04-06T04:34:19.128856Z", - "iopub.status.idle": "2024-04-06T04:34:19.327498Z", - "shell.execute_reply": "2024-04-06T04:34:19.327017Z" + "iopub.execute_input": "2024-04-08T19:12:58.700764Z", + "iopub.status.busy": "2024-04-08T19:12:58.700331Z", + "iopub.status.idle": "2024-04-08T19:12:58.897447Z", + "shell.execute_reply": "2024-04-08T19:12:58.896906Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.329747Z", - "iopub.status.busy": "2024-04-06T04:34:19.329569Z", - "iopub.status.idle": "2024-04-06T04:34:19.332430Z", - "shell.execute_reply": "2024-04-06T04:34:19.332000Z" + "iopub.execute_input": "2024-04-08T19:12:58.899675Z", + "iopub.status.busy": "2024-04-08T19:12:58.899273Z", + "iopub.status.idle": "2024-04-08T19:12:58.902127Z", + "shell.execute_reply": "2024-04-08T19:12:58.901613Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.334383Z", - "iopub.status.busy": "2024-04-06T04:34:19.334059Z", - "iopub.status.idle": "2024-04-06T04:34:20.209133Z", - "shell.execute_reply": "2024-04-06T04:34:20.208555Z" + "iopub.execute_input": "2024-04-08T19:12:58.904091Z", + "iopub.status.busy": "2024-04-08T19:12:58.903780Z", + "iopub.status.idle": "2024-04-08T19:12:59.779761Z", + "shell.execute_reply": "2024-04-08T19:12:59.779165Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.211448Z", - "iopub.status.busy": "2024-04-06T04:34:20.211008Z", - "iopub.status.idle": "2024-04-06T04:34:20.342519Z", - "shell.execute_reply": "2024-04-06T04:34:20.342095Z" + "iopub.execute_input": "2024-04-08T19:12:59.782264Z", + "iopub.status.busy": "2024-04-08T19:12:59.781937Z", + "iopub.status.idle": "2024-04-08T19:12:59.964112Z", + "shell.execute_reply": "2024-04-08T19:12:59.963525Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.344524Z", - "iopub.status.busy": "2024-04-06T04:34:20.344193Z", - "iopub.status.idle": "2024-04-06T04:34:20.458465Z", - "shell.execute_reply": "2024-04-06T04:34:20.457952Z" + "iopub.execute_input": "2024-04-08T19:12:59.966448Z", + "iopub.status.busy": "2024-04-08T19:12:59.965968Z", + "iopub.status.idle": "2024-04-08T19:13:00.154653Z", + "shell.execute_reply": "2024-04-08T19:13:00.154036Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.460533Z", - "iopub.status.busy": "2024-04-06T04:34:20.460222Z", - "iopub.status.idle": "2024-04-06T04:34:21.196312Z", - "shell.execute_reply": "2024-04-06T04:34:21.195737Z" + "iopub.execute_input": "2024-04-08T19:13:00.156712Z", + "iopub.status.busy": "2024-04-08T19:13:00.156532Z", + "iopub.status.idle": "2024-04-08T19:13:00.829599Z", + "shell.execute_reply": "2024-04-08T19:13:00.829059Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:21.198485Z", - "iopub.status.busy": "2024-04-06T04:34:21.198170Z", - "iopub.status.idle": "2024-04-06T04:34:21.201764Z", - "shell.execute_reply": "2024-04-06T04:34:21.201234Z" + "iopub.execute_input": "2024-04-08T19:13:00.832154Z", + "iopub.status.busy": "2024-04-08T19:13:00.831662Z", + "iopub.status.idle": "2024-04-08T19:13:00.835999Z", + "shell.execute_reply": "2024-04-08T19:13:00.835484Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index dff88146d..c8a250110 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": "2024-04-06T04:34:23.301443Z", - "iopub.status.busy": "2024-04-06T04:34:23.301280Z", - "iopub.status.idle": "2024-04-06T04:34:25.945799Z", - "shell.execute_reply": "2024-04-06T04:34:25.945183Z" + "iopub.execute_input": "2024-04-08T19:13:03.168340Z", + "iopub.status.busy": "2024-04-08T19:13:03.168171Z", + "iopub.status.idle": "2024-04-08T19:13:05.872246Z", + "shell.execute_reply": "2024-04-08T19:13:05.871721Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:25.948528Z", - "iopub.status.busy": "2024-04-06T04:34:25.948218Z", - "iopub.status.idle": "2024-04-06T04:34:26.266936Z", - "shell.execute_reply": "2024-04-06T04:34:26.266392Z" + "iopub.execute_input": "2024-04-08T19:13:05.874860Z", + "iopub.status.busy": "2024-04-08T19:13:05.874355Z", + "iopub.status.idle": "2024-04-08T19:13:06.204418Z", + "shell.execute_reply": "2024-04-08T19:13:06.203821Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:26.269348Z", - "iopub.status.busy": "2024-04-06T04:34:26.269036Z", - "iopub.status.idle": "2024-04-06T04:34:26.272997Z", - "shell.execute_reply": "2024-04-06T04:34:26.272583Z" + "iopub.execute_input": "2024-04-08T19:13:06.206962Z", + "iopub.status.busy": "2024-04-08T19:13:06.206657Z", + "iopub.status.idle": "2024-04-08T19:13:06.210651Z", + "shell.execute_reply": "2024-04-08T19:13:06.210217Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:26.275074Z", - "iopub.status.busy": "2024-04-06T04:34:26.274739Z", - "iopub.status.idle": "2024-04-06T04:34:31.314624Z", - "shell.execute_reply": "2024-04-06T04:34:31.314110Z" + "iopub.execute_input": "2024-04-08T19:13:06.212643Z", + "iopub.status.busy": "2024-04-08T19:13:06.212236Z", + "iopub.status.idle": "2024-04-08T19:13:14.211316Z", + "shell.execute_reply": "2024-04-08T19:13:14.210735Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1769472/170498071 [00:00<00:09, 17538639.93it/s]" + " 0%| | 32768/170498071 [00:00<11:46, 241421.69it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 8192000/170498071 [00:00<00:03, 44831466.83it/s]" + " 0%| | 229376/170498071 [00:00<03:01, 939950.18it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13041664/170498071 [00:00<00:03, 46433907.51it/s]" + " 1%| | 884736/170498071 [00:00<01:03, 2688209.96it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 19791872/170498071 [00:00<00:02, 54704480.34it/s]" + " 2%|▏ | 3538944/170498071 [00:00<00:18, 9239543.74it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 25788416/170498071 [00:00<00:02, 56333002.76it/s]" + " 6%|▌ | 9633792/170498071 [00:00<00:07, 21711409.29it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 31424512/170498071 [00:00<00:02, 55036228.43it/s]" + " 9%|▉ | 15695872/170498071 [00:00<00:04, 32236184.47it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37978112/170498071 [00:00<00:02, 58347764.86it/s]" + " 11%|█▏ | 19202048/170498071 [00:00<00:04, 31213118.98it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 43843584/170498071 [00:00<00:02, 56723331.51it/s]" + " 15%|█▍ | 25165824/170498071 [00:01<00:03, 36748958.36it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49676288/170498071 [00:00<00:02, 57186066.18it/s]" + " 17%|█▋ | 29196288/170498071 [00:01<00:03, 37692490.20it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 56197120/170498071 [00:01<00:01, 59520956.47it/s]" + " 20%|██ | 34471936/170498071 [00:01<00:03, 41145514.19it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 62193664/170498071 [00:01<00:01, 55914267.41it/s]" + " 23%|██▎ | 38699008/170498071 [00:01<00:03, 40166399.16it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68943872/170498071 [00:01<00:01, 59219350.19it/s]" + " 26%|██▌ | 43909120/170498071 [00:01<00:02, 43337775.10it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 77758464/170498071 [00:01<00:01, 67611548.92it/s]" + " 28%|██▊ | 48332800/170498071 [00:01<00:02, 41475797.12it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 87556096/170498071 [00:01<00:01, 76485308.02it/s]" + " 31%|███ | 52822016/170498071 [00:01<00:02, 42394898.27it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 98697216/170498071 [00:01<00:00, 86783379.22it/s]" + " 33%|███▎ | 57114624/170498071 [00:01<00:02, 40475978.10it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 107642880/170498071 [00:01<00:00, 87414535.88it/s]" + " 36%|███▌ | 61571072/170498071 [00:01<00:02, 41609620.80it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 117768192/170498071 [00:01<00:00, 91407747.36it/s]" + " 39%|███▊ | 65863680/170498071 [00:02<00:02, 39729349.49it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 127565824/170498071 [00:01<00:00, 93045985.07it/s]" + " 41%|████▏ | 70418432/170498071 [00:02<00:02, 41333024.63it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 136904704/170498071 [00:01<00:00, 88471190.33it/s]" + " 44%|████▍ | 74809344/170498071 [00:02<00:02, 42035778.22it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 146505728/170498071 [00:02<00:00, 90636821.57it/s]" + " 46%|████▋ | 79069184/170498071 [00:02<00:02, 40305702.80it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 155648000/170498071 [00:02<00:00, 86904824.23it/s]" + " 49%|████▉ | 83427328/170498071 [00:02<00:02, 41196757.42it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 165478400/170498071 [00:02<00:00, 90092503.62it/s]" + " 51%|█████▏ | 87588864/170498071 [00:02<00:02, 39637138.62it/s]" ] }, { @@ -428,7 +428,151 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 72776359.59it/s]" + " 54%|█████▍ | 91881472/170498071 [00:02<00:01, 40536471.23it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|█████▋ | 95977472/170498071 [00:02<00:01, 39252273.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 59%|█████▉ | 100466688/170498071 [00:02<00:01, 40454041.31it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 62%|██████▏ | 104955904/170498071 [00:02<00:01, 39445045.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 64%|██████▍ | 109510656/170498071 [00:03<00:01, 41083281.40it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 113803264/170498071 [00:03<00:01, 41583370.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 69%|██████▉ | 117997568/170498071 [00:03<00:01, 40073702.55it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 72%|███████▏ | 122290176/170498071 [00:03<00:01, 40828624.13it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 74%|███████▍ | 126418944/170498071 [00:03<00:01, 39461562.96it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130744320/170498071 [00:03<00:00, 40509967.30it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 79%|███████▉ | 135266304/170498071 [00:03<00:00, 41845498.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 139493376/170498071 [00:03<00:00, 40939532.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▍ | 144211968/170498071 [00:03<00:00, 42696106.22it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 148504576/170498071 [00:04<00:00, 41635041.16it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|████████▉ | 152961024/170498071 [00:04<00:00, 42468713.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 157253632/170498071 [00:04<00:00, 42241843.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▍| 161939456/170498071 [00:04<00:00, 43462412.29it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 166297600/170498071 [00:04<00:00, 43386976.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:04<00:00, 37601665.95it/s]" ] }, { @@ -546,10 +690,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.316844Z", - "iopub.status.busy": "2024-04-06T04:34:31.316485Z", - "iopub.status.idle": "2024-04-06T04:34:31.321190Z", - "shell.execute_reply": "2024-04-06T04:34:31.320736Z" + "iopub.execute_input": "2024-04-08T19:13:14.213408Z", + "iopub.status.busy": "2024-04-08T19:13:14.213222Z", + "iopub.status.idle": "2024-04-08T19:13:14.217828Z", + "shell.execute_reply": "2024-04-08T19:13:14.217410Z" }, "nbsphinx": "hidden" }, @@ -600,10 +744,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.323414Z", - "iopub.status.busy": "2024-04-06T04:34:31.323024Z", - "iopub.status.idle": "2024-04-06T04:34:31.843073Z", - "shell.execute_reply": "2024-04-06T04:34:31.842461Z" + "iopub.execute_input": "2024-04-08T19:13:14.219646Z", + "iopub.status.busy": "2024-04-08T19:13:14.219474Z", + "iopub.status.idle": "2024-04-08T19:13:14.735288Z", + "shell.execute_reply": "2024-04-08T19:13:14.734716Z" } }, "outputs": [ @@ -636,10 +780,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.845476Z", - "iopub.status.busy": "2024-04-06T04:34:31.845122Z", - "iopub.status.idle": "2024-04-06T04:34:32.343468Z", - "shell.execute_reply": "2024-04-06T04:34:32.342863Z" + "iopub.execute_input": "2024-04-08T19:13:14.737482Z", + "iopub.status.busy": "2024-04-08T19:13:14.737170Z", + "iopub.status.idle": "2024-04-08T19:13:15.227922Z", + "shell.execute_reply": "2024-04-08T19:13:15.227323Z" } }, "outputs": [ @@ -677,10 +821,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:32.345737Z", - "iopub.status.busy": "2024-04-06T04:34:32.345520Z", - "iopub.status.idle": "2024-04-06T04:34:32.349079Z", - "shell.execute_reply": "2024-04-06T04:34:32.348636Z" + "iopub.execute_input": "2024-04-08T19:13:15.229967Z", + "iopub.status.busy": "2024-04-08T19:13:15.229777Z", + "iopub.status.idle": "2024-04-08T19:13:15.233685Z", + "shell.execute_reply": "2024-04-08T19:13:15.233276Z" } }, "outputs": [], @@ -703,17 +847,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:32.351161Z", - "iopub.status.busy": "2024-04-06T04:34:32.350840Z", - "iopub.status.idle": "2024-04-06T04:34:45.259522Z", - "shell.execute_reply": "2024-04-06T04:34:45.258934Z" + "iopub.execute_input": "2024-04-08T19:13:15.235578Z", + "iopub.status.busy": "2024-04-08T19:13:15.235253Z", + "iopub.status.idle": "2024-04-08T19:13:27.791114Z", + "shell.execute_reply": "2024-04-08T19:13:27.790500Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "991b461cb5f14fa38412734f4f788575", + "model_id": "2bb5503dd8b443508a98689b99426ed1", "version_major": 2, "version_minor": 0 }, @@ -772,10 +916,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:45.261911Z", - "iopub.status.busy": "2024-04-06T04:34:45.261529Z", - "iopub.status.idle": "2024-04-06T04:34:46.966878Z", - "shell.execute_reply": "2024-04-06T04:34:46.966282Z" + "iopub.execute_input": "2024-04-08T19:13:27.793604Z", + "iopub.status.busy": "2024-04-08T19:13:27.793211Z", + "iopub.status.idle": "2024-04-08T19:13:29.587802Z", + "shell.execute_reply": "2024-04-08T19:13:29.587253Z" } }, "outputs": [ @@ -819,10 +963,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:46.969590Z", - "iopub.status.busy": "2024-04-06T04:34:46.969163Z", - "iopub.status.idle": "2024-04-06T04:34:47.194956Z", - "shell.execute_reply": "2024-04-06T04:34:47.194388Z" + "iopub.execute_input": "2024-04-08T19:13:29.590598Z", + "iopub.status.busy": "2024-04-08T19:13:29.590127Z", + "iopub.status.idle": "2024-04-08T19:13:29.858111Z", + "shell.execute_reply": "2024-04-08T19:13:29.857584Z" } }, "outputs": [ @@ -858,10 +1002,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:47.197286Z", - "iopub.status.busy": "2024-04-06T04:34:47.197100Z", - "iopub.status.idle": "2024-04-06T04:34:47.844542Z", - "shell.execute_reply": "2024-04-06T04:34:47.843965Z" + "iopub.execute_input": "2024-04-08T19:13:29.861034Z", + "iopub.status.busy": "2024-04-08T19:13:29.860632Z", + "iopub.status.idle": "2024-04-08T19:13:30.577484Z", + "shell.execute_reply": "2024-04-08T19:13:30.576958Z" } }, "outputs": [ @@ -911,10 +1055,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:47.847025Z", - "iopub.status.busy": "2024-04-06T04:34:47.846663Z", - "iopub.status.idle": "2024-04-06T04:34:48.133586Z", - "shell.execute_reply": "2024-04-06T04:34:48.133164Z" + "iopub.execute_input": "2024-04-08T19:13:30.580265Z", + "iopub.status.busy": "2024-04-08T19:13:30.579691Z", + "iopub.status.idle": "2024-04-08T19:13:30.924605Z", + "shell.execute_reply": "2024-04-08T19:13:30.924026Z" } }, "outputs": [ @@ -962,10 +1106,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.135743Z", - "iopub.status.busy": "2024-04-06T04:34:48.135451Z", - "iopub.status.idle": "2024-04-06T04:34:48.362823Z", - "shell.execute_reply": "2024-04-06T04:34:48.362258Z" + "iopub.execute_input": "2024-04-08T19:13:30.926999Z", + "iopub.status.busy": "2024-04-08T19:13:30.926574Z", + "iopub.status.idle": "2024-04-08T19:13:31.175317Z", + "shell.execute_reply": "2024-04-08T19:13:31.174782Z" } }, "outputs": [ @@ -1021,10 +1165,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.365290Z", - "iopub.status.busy": "2024-04-06T04:34:48.364817Z", - "iopub.status.idle": "2024-04-06T04:34:48.441430Z", - "shell.execute_reply": "2024-04-06T04:34:48.440837Z" + "iopub.execute_input": "2024-04-08T19:13:31.177937Z", + "iopub.status.busy": "2024-04-08T19:13:31.177576Z", + "iopub.status.idle": "2024-04-08T19:13:31.272978Z", + "shell.execute_reply": "2024-04-08T19:13:31.272473Z" } }, "outputs": [], @@ -1045,10 +1189,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.444056Z", - "iopub.status.busy": "2024-04-06T04:34:48.443776Z", - "iopub.status.idle": "2024-04-06T04:34:58.624130Z", - "shell.execute_reply": "2024-04-06T04:34:58.623554Z" + "iopub.execute_input": "2024-04-08T19:13:31.275519Z", + "iopub.status.busy": "2024-04-08T19:13:31.275167Z", + "iopub.status.idle": "2024-04-08T19:13:41.679014Z", + "shell.execute_reply": "2024-04-08T19:13:41.678397Z" } }, "outputs": [ @@ -1085,10 +1229,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:58.626457Z", - "iopub.status.busy": "2024-04-06T04:34:58.626142Z", - "iopub.status.idle": "2024-04-06T04:35:00.411515Z", - "shell.execute_reply": "2024-04-06T04:35:00.411019Z" + "iopub.execute_input": "2024-04-08T19:13:41.681464Z", + "iopub.status.busy": "2024-04-08T19:13:41.681014Z", + "iopub.status.idle": "2024-04-08T19:13:43.393278Z", + "shell.execute_reply": "2024-04-08T19:13:43.392676Z" } }, "outputs": [ @@ -1119,10 +1263,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.414250Z", - "iopub.status.busy": "2024-04-06T04:35:00.413656Z", - "iopub.status.idle": "2024-04-06T04:35:00.626834Z", - "shell.execute_reply": "2024-04-06T04:35:00.626355Z" + "iopub.execute_input": "2024-04-08T19:13:43.395878Z", + "iopub.status.busy": "2024-04-08T19:13:43.395510Z", + "iopub.status.idle": "2024-04-08T19:13:43.601564Z", + "shell.execute_reply": "2024-04-08T19:13:43.600964Z" } }, "outputs": [], @@ -1136,10 +1280,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.629326Z", - "iopub.status.busy": "2024-04-06T04:35:00.628902Z", - "iopub.status.idle": "2024-04-06T04:35:00.632039Z", - "shell.execute_reply": "2024-04-06T04:35:00.631618Z" + "iopub.execute_input": "2024-04-08T19:13:43.604043Z", + "iopub.status.busy": "2024-04-08T19:13:43.603730Z", + "iopub.status.idle": "2024-04-08T19:13:43.606880Z", + "shell.execute_reply": "2024-04-08T19:13:43.606367Z" } }, "outputs": [], @@ -1161,10 +1305,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.634061Z", - "iopub.status.busy": "2024-04-06T04:35:00.633729Z", - "iopub.status.idle": "2024-04-06T04:35:00.641703Z", - "shell.execute_reply": "2024-04-06T04:35:00.641295Z" + "iopub.execute_input": "2024-04-08T19:13:43.609039Z", + "iopub.status.busy": "2024-04-08T19:13:43.608752Z", + "iopub.status.idle": "2024-04-08T19:13:43.617066Z", + "shell.execute_reply": "2024-04-08T19:13:43.616668Z" }, "nbsphinx": "hidden" }, @@ -1209,7 +1353,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "15c8db426b2d442dafc5fec0ada46d26": { + "007c6ddc44eb433e853f88ed09044f49": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,7 +1406,7 @@ "width": null } }, - "18e0c03543334359bae24bc35d678719": { + "086fdb340ddc44499e840c6359ce1479": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1280,7 +1424,30 @@ "text_color": null } }, - "5888f59c5c7747d284d2a1179b08220a": { + "0c6902059f6d43049f050a70f2c4d5ed": { + "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_007c6ddc44eb433e853f88ed09044f49", + "placeholder": "​", + "style": "IPY_MODEL_8e6e75da45e94500ac3664d6571c19a5", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 261MB/s]" + } + }, + "0c87bf09ff7545318077176d0bc67dc5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1333,53 +1500,31 @@ "width": null } }, - "62503695057042fe9e46cf6d976cf0ec": { + "2bb5503dd8b443508a98689b99426ed1": { "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_dc69440eba354ce18f5a8f226872b05a", - "placeholder": "​", - "style": "IPY_MODEL_18e0c03543334359bae24bc35d678719", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 180MB/s]" - } - }, - "66a60ffdadfe43c49835a3149977dd23": { - "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_5888f59c5c7747d284d2a1179b08220a", - "placeholder": "​", - "style": "IPY_MODEL_a901c8deef634fefa5bf50b380005288", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b99b680885934cdfa31bc3a843e20724", + "IPY_MODEL_f15ac1823a7f4e549da71d08245aa9b2", + "IPY_MODEL_0c6902059f6d43049f050a70f2c4d5ed" + ], + "layout": "IPY_MODEL_0c87bf09ff7545318077176d0bc67dc5", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "710b50fb237d4bfa800dd8ccca2aa500": { + "3897024bcca245b1bc58655ded2b9bc5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1432,57 +1577,7 @@ "width": null } }, - "721bf251193348b0a2bc03a41fa88621": { - "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_710b50fb237d4bfa800dd8ccca2aa500", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ad6af0ebf6a84194902f8859297785ed", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "991b461cb5f14fa38412734f4f788575": { - "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_66a60ffdadfe43c49835a3149977dd23", - "IPY_MODEL_721bf251193348b0a2bc03a41fa88621", - "IPY_MODEL_62503695057042fe9e46cf6d976cf0ec" - ], - "layout": "IPY_MODEL_15c8db426b2d442dafc5fec0ada46d26", - "tabbable": null, - "tooltip": null - } - }, - "a901c8deef634fefa5bf50b380005288": { + "8e6e75da45e94500ac3664d6571c19a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1500,23 +1595,30 @@ "text_color": null } }, - "ad6af0ebf6a84194902f8859297785ed": { + "b99b680885934cdfa31bc3a843e20724": { "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_3897024bcca245b1bc58655ded2b9bc5", + "placeholder": "​", + "style": "IPY_MODEL_086fdb340ddc44499e840c6359ce1479", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" } }, - "dc69440eba354ce18f5a8f226872b05a": { + "c8047222b06d47abb1cddbdcb8b6aaff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1568,6 +1670,48 @@ "visibility": null, "width": null } + }, + "f15ac1823a7f4e549da71d08245aa9b2": { + "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_c8047222b06d47abb1cddbdcb8b6aaff", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fb1f241a35b74a80a9334872055927bc", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "fb1f241a35b74a80a9334872055927bc": { + "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/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index bac3e263c..673215d3c 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": "2024-04-06T04:35:04.945916Z", - "iopub.status.busy": "2024-04-06T04:35:04.945744Z", - "iopub.status.idle": "2024-04-06T04:35:06.052331Z", - "shell.execute_reply": "2024-04-06T04:35:06.051744Z" + "iopub.execute_input": "2024-04-08T19:13:47.803397Z", + "iopub.status.busy": "2024-04-08T19:13:47.802938Z", + "iopub.status.idle": "2024-04-08T19:13:48.925278Z", + "shell.execute_reply": "2024-04-08T19:13:48.924752Z" }, "nbsphinx": "hidden" }, @@ -117,7 +117,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.054800Z", - "iopub.status.busy": "2024-04-06T04:35:06.054557Z", - "iopub.status.idle": "2024-04-06T04:35:06.072120Z", - "shell.execute_reply": "2024-04-06T04:35:06.071716Z" + "iopub.execute_input": "2024-04-08T19:13:48.927915Z", + "iopub.status.busy": "2024-04-08T19:13:48.927470Z", + "iopub.status.idle": "2024-04-08T19:13:48.945021Z", + "shell.execute_reply": "2024-04-08T19:13:48.944602Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.074202Z", - "iopub.status.busy": "2024-04-06T04:35:06.073811Z", - "iopub.status.idle": "2024-04-06T04:35:06.076794Z", - "shell.execute_reply": "2024-04-06T04:35:06.076351Z" + "iopub.execute_input": "2024-04-08T19:13:48.947242Z", + "iopub.status.busy": "2024-04-08T19:13:48.946736Z", + "iopub.status.idle": "2024-04-08T19:13:48.949732Z", + "shell.execute_reply": "2024-04-08T19:13:48.949294Z" }, "nbsphinx": "hidden" }, @@ -199,10 +199,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.078868Z", - "iopub.status.busy": "2024-04-06T04:35:06.078492Z", - "iopub.status.idle": "2024-04-06T04:35:06.208916Z", - "shell.execute_reply": "2024-04-06T04:35:06.208494Z" + "iopub.execute_input": "2024-04-08T19:13:48.951557Z", + "iopub.status.busy": "2024-04-08T19:13:48.951388Z", + "iopub.status.idle": "2024-04-08T19:13:49.150115Z", + "shell.execute_reply": "2024-04-08T19:13:49.149617Z" } }, "outputs": [ @@ -375,10 +375,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.211100Z", - "iopub.status.busy": "2024-04-06T04:35:06.210666Z", - "iopub.status.idle": "2024-04-06T04:35:06.392965Z", - "shell.execute_reply": "2024-04-06T04:35:06.392412Z" + "iopub.execute_input": "2024-04-08T19:13:49.152258Z", + "iopub.status.busy": "2024-04-08T19:13:49.151925Z", + "iopub.status.idle": "2024-04-08T19:13:49.328521Z", + "shell.execute_reply": "2024-04-08T19:13:49.328018Z" }, "nbsphinx": "hidden" }, @@ -418,10 +418,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.395403Z", - "iopub.status.busy": "2024-04-06T04:35:06.395013Z", - "iopub.status.idle": "2024-04-06T04:35:06.638949Z", - "shell.execute_reply": "2024-04-06T04:35:06.638348Z" + "iopub.execute_input": "2024-04-08T19:13:49.330913Z", + "iopub.status.busy": "2024-04-08T19:13:49.330551Z", + "iopub.status.idle": "2024-04-08T19:13:49.538644Z", + "shell.execute_reply": "2024-04-08T19:13:49.538041Z" } }, "outputs": [ @@ -457,10 +457,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.641297Z", - "iopub.status.busy": "2024-04-06T04:35:06.640953Z", - "iopub.status.idle": "2024-04-06T04:35:06.645580Z", - "shell.execute_reply": "2024-04-06T04:35:06.645032Z" + "iopub.execute_input": "2024-04-08T19:13:49.540725Z", + "iopub.status.busy": "2024-04-08T19:13:49.540437Z", + "iopub.status.idle": "2024-04-08T19:13:49.544691Z", + "shell.execute_reply": "2024-04-08T19:13:49.544278Z" } }, "outputs": [], @@ -478,10 +478,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.647781Z", - "iopub.status.busy": "2024-04-06T04:35:06.647427Z", - "iopub.status.idle": "2024-04-06T04:35:06.654351Z", - "shell.execute_reply": "2024-04-06T04:35:06.653847Z" + "iopub.execute_input": "2024-04-08T19:13:49.546581Z", + "iopub.status.busy": "2024-04-08T19:13:49.546300Z", + "iopub.status.idle": "2024-04-08T19:13:49.552461Z", + "shell.execute_reply": "2024-04-08T19:13:49.552021Z" } }, "outputs": [], @@ -528,10 +528,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.656526Z", - "iopub.status.busy": "2024-04-06T04:35:06.656127Z", - "iopub.status.idle": "2024-04-06T04:35:06.658766Z", - "shell.execute_reply": "2024-04-06T04:35:06.658318Z" + "iopub.execute_input": "2024-04-08T19:13:49.554456Z", + "iopub.status.busy": "2024-04-08T19:13:49.554124Z", + "iopub.status.idle": "2024-04-08T19:13:49.556701Z", + "shell.execute_reply": "2024-04-08T19:13:49.556278Z" } }, "outputs": [], @@ -546,10 +546,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.660791Z", - "iopub.status.busy": "2024-04-06T04:35:06.660469Z", - "iopub.status.idle": "2024-04-06T04:35:14.877273Z", - "shell.execute_reply": "2024-04-06T04:35:14.876740Z" + "iopub.execute_input": "2024-04-08T19:13:49.558519Z", + "iopub.status.busy": "2024-04-08T19:13:49.558220Z", + "iopub.status.idle": "2024-04-08T19:13:57.783852Z", + "shell.execute_reply": "2024-04-08T19:13:57.783248Z" } }, "outputs": [], @@ -573,10 +573,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.880136Z", - "iopub.status.busy": "2024-04-06T04:35:14.879546Z", - "iopub.status.idle": "2024-04-06T04:35:14.886452Z", - "shell.execute_reply": "2024-04-06T04:35:14.885981Z" + "iopub.execute_input": "2024-04-08T19:13:57.787203Z", + "iopub.status.busy": "2024-04-08T19:13:57.786649Z", + "iopub.status.idle": "2024-04-08T19:13:57.794488Z", + "shell.execute_reply": "2024-04-08T19:13:57.794042Z" } }, "outputs": [ @@ -679,10 +679,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.888384Z", - "iopub.status.busy": "2024-04-06T04:35:14.888208Z", - "iopub.status.idle": "2024-04-06T04:35:14.891854Z", - "shell.execute_reply": "2024-04-06T04:35:14.891406Z" + "iopub.execute_input": "2024-04-08T19:13:57.796488Z", + "iopub.status.busy": "2024-04-08T19:13:57.796215Z", + "iopub.status.idle": "2024-04-08T19:13:57.799641Z", + "shell.execute_reply": "2024-04-08T19:13:57.799235Z" } }, "outputs": [], @@ -697,10 +697,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.893970Z", - "iopub.status.busy": "2024-04-06T04:35:14.893580Z", - "iopub.status.idle": "2024-04-06T04:35:14.896696Z", - "shell.execute_reply": "2024-04-06T04:35:14.896194Z" + "iopub.execute_input": "2024-04-08T19:13:57.801520Z", + "iopub.status.busy": "2024-04-08T19:13:57.801263Z", + "iopub.status.idle": "2024-04-08T19:13:57.804571Z", + "shell.execute_reply": "2024-04-08T19:13:57.804133Z" } }, "outputs": [ @@ -735,10 +735,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.898553Z", - "iopub.status.busy": "2024-04-06T04:35:14.898382Z", - "iopub.status.idle": "2024-04-06T04:35:14.901388Z", - "shell.execute_reply": "2024-04-06T04:35:14.900951Z" + "iopub.execute_input": "2024-04-08T19:13:57.806527Z", + "iopub.status.busy": "2024-04-08T19:13:57.806223Z", + "iopub.status.idle": "2024-04-08T19:13:57.809258Z", + "shell.execute_reply": "2024-04-08T19:13:57.808725Z" } }, "outputs": [], @@ -757,10 +757,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.903150Z", - "iopub.status.busy": "2024-04-06T04:35:14.902982Z", - "iopub.status.idle": "2024-04-06T04:35:14.910845Z", - "shell.execute_reply": "2024-04-06T04:35:14.910300Z" + "iopub.execute_input": "2024-04-08T19:13:57.811263Z", + "iopub.status.busy": "2024-04-08T19:13:57.810959Z", + "iopub.status.idle": "2024-04-08T19:13:57.818786Z", + "shell.execute_reply": "2024-04-08T19:13:57.818238Z" } }, "outputs": [ @@ -884,10 +884,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.912783Z", - "iopub.status.busy": "2024-04-06T04:35:14.912607Z", - "iopub.status.idle": "2024-04-06T04:35:14.915272Z", - "shell.execute_reply": "2024-04-06T04:35:14.914817Z" + "iopub.execute_input": "2024-04-08T19:13:57.820889Z", + "iopub.status.busy": "2024-04-08T19:13:57.820509Z", + "iopub.status.idle": "2024-04-08T19:13:57.823238Z", + "shell.execute_reply": "2024-04-08T19:13:57.822711Z" }, "nbsphinx": "hidden" }, @@ -922,10 +922,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.917066Z", - "iopub.status.busy": "2024-04-06T04:35:14.916896Z", - "iopub.status.idle": "2024-04-06T04:35:15.039512Z", - "shell.execute_reply": "2024-04-06T04:35:15.038973Z" + "iopub.execute_input": "2024-04-08T19:13:57.825125Z", + "iopub.status.busy": "2024-04-08T19:13:57.824848Z", + "iopub.status.idle": "2024-04-08T19:13:57.944411Z", + "shell.execute_reply": "2024-04-08T19:13:57.943830Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.041688Z", - "iopub.status.busy": "2024-04-06T04:35:15.041372Z", - "iopub.status.idle": "2024-04-06T04:35:15.143758Z", - "shell.execute_reply": "2024-04-06T04:35:15.143187Z" + "iopub.execute_input": "2024-04-08T19:13:57.946652Z", + "iopub.status.busy": "2024-04-08T19:13:57.946416Z", + "iopub.status.idle": "2024-04-08T19:13:58.050381Z", + "shell.execute_reply": "2024-04-08T19:13:58.049796Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.146009Z", - "iopub.status.busy": "2024-04-06T04:35:15.145689Z", - "iopub.status.idle": "2024-04-06T04:35:15.632674Z", - "shell.execute_reply": "2024-04-06T04:35:15.632055Z" + "iopub.execute_input": "2024-04-08T19:13:58.052902Z", + "iopub.status.busy": "2024-04-08T19:13:58.052525Z", + "iopub.status.idle": "2024-04-08T19:13:58.541282Z", + "shell.execute_reply": "2024-04-08T19:13:58.540644Z" } }, "outputs": [], @@ -1042,10 +1042,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.635337Z", - "iopub.status.busy": "2024-04-06T04:35:15.634992Z", - "iopub.status.idle": "2024-04-06T04:35:15.743506Z", - "shell.execute_reply": "2024-04-06T04:35:15.742910Z" + "iopub.execute_input": "2024-04-08T19:13:58.544024Z", + "iopub.status.busy": "2024-04-08T19:13:58.543626Z", + "iopub.status.idle": "2024-04-08T19:13:58.648994Z", + "shell.execute_reply": "2024-04-08T19:13:58.648352Z" } }, "outputs": [ @@ -1080,10 +1080,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.745851Z", - "iopub.status.busy": "2024-04-06T04:35:15.745490Z", - "iopub.status.idle": "2024-04-06T04:35:15.753696Z", - "shell.execute_reply": "2024-04-06T04:35:15.753263Z" + "iopub.execute_input": "2024-04-08T19:13:58.651502Z", + "iopub.status.busy": "2024-04-08T19:13:58.651135Z", + "iopub.status.idle": "2024-04-08T19:13:58.659988Z", + "shell.execute_reply": "2024-04-08T19:13:58.659533Z" } }, "outputs": [ @@ -1190,10 +1190,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.755695Z", - "iopub.status.busy": "2024-04-06T04:35:15.755367Z", - "iopub.status.idle": "2024-04-06T04:35:15.758042Z", - "shell.execute_reply": "2024-04-06T04:35:15.757595Z" + "iopub.execute_input": "2024-04-08T19:13:58.661959Z", + "iopub.status.busy": "2024-04-08T19:13:58.661702Z", + "iopub.status.idle": "2024-04-08T19:13:58.664433Z", + "shell.execute_reply": "2024-04-08T19:13:58.663997Z" }, "nbsphinx": "hidden" }, @@ -1218,10 +1218,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.760007Z", - "iopub.status.busy": "2024-04-06T04:35:15.759679Z", - "iopub.status.idle": "2024-04-06T04:35:21.169503Z", - "shell.execute_reply": "2024-04-06T04:35:21.168860Z" + "iopub.execute_input": "2024-04-08T19:13:58.666308Z", + "iopub.status.busy": "2024-04-08T19:13:58.666060Z", + "iopub.status.idle": "2024-04-08T19:14:04.108369Z", + "shell.execute_reply": "2024-04-08T19:14:04.107772Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:21.172001Z", - "iopub.status.busy": "2024-04-06T04:35:21.171800Z", - "iopub.status.idle": "2024-04-06T04:35:21.180339Z", - "shell.execute_reply": "2024-04-06T04:35:21.179868Z" + "iopub.execute_input": "2024-04-08T19:14:04.110851Z", + "iopub.status.busy": "2024-04-08T19:14:04.110406Z", + "iopub.status.idle": "2024-04-08T19:14:04.119270Z", + "shell.execute_reply": "2024-04-08T19:14:04.118848Z" } }, "outputs": [ @@ -1377,10 +1377,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:21.182362Z", - "iopub.status.busy": "2024-04-06T04:35:21.182047Z", - "iopub.status.idle": "2024-04-06T04:35:21.256060Z", - "shell.execute_reply": "2024-04-06T04:35:21.255579Z" + "iopub.execute_input": "2024-04-08T19:14:04.121410Z", + "iopub.status.busy": "2024-04-08T19:14:04.120984Z", + "iopub.status.idle": "2024-04-08T19:14:04.185861Z", + "shell.execute_reply": "2024-04-08T19:14:04.185384Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 89e2cb219..7512e088c 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": "2024-04-06T04:35:24.243928Z", - "iopub.status.busy": "2024-04-06T04:35:24.243468Z", - "iopub.status.idle": "2024-04-06T04:35:26.047952Z", - "shell.execute_reply": "2024-04-06T04:35:26.047292Z" + "iopub.execute_input": "2024-04-08T19:14:07.395028Z", + "iopub.status.busy": "2024-04-08T19:14:07.394566Z", + "iopub.status.idle": "2024-04-08T19:14:11.485319Z", + "shell.execute_reply": "2024-04-08T19:14:11.484630Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:26.050495Z", - "iopub.status.busy": "2024-04-06T04:35:26.050118Z", - "iopub.status.idle": "2024-04-06T04:36:08.935704Z", - "shell.execute_reply": "2024-04-06T04:36:08.935125Z" + "iopub.execute_input": "2024-04-08T19:14:11.488001Z", + "iopub.status.busy": "2024-04-08T19:14:11.487586Z", + "iopub.status.idle": "2024-04-08T19:15:03.035425Z", + "shell.execute_reply": "2024-04-08T19:15:03.034793Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:08.938332Z", - "iopub.status.busy": "2024-04-06T04:36:08.937887Z", - "iopub.status.idle": "2024-04-06T04:36:09.999880Z", - "shell.execute_reply": "2024-04-06T04:36:09.999323Z" + "iopub.execute_input": "2024-04-08T19:15:03.037988Z", + "iopub.status.busy": "2024-04-08T19:15:03.037617Z", + "iopub.status.idle": "2024-04-08T19:15:04.144423Z", + "shell.execute_reply": "2024-04-08T19:15:04.143898Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:36:10.002451Z", - "iopub.status.busy": "2024-04-06T04:36:10.002049Z", - "iopub.status.idle": "2024-04-06T04:36:10.005300Z", - "shell.execute_reply": "2024-04-06T04:36:10.004764Z" + "iopub.execute_input": "2024-04-08T19:15:04.146910Z", + "iopub.status.busy": "2024-04-08T19:15:04.146510Z", + "iopub.status.idle": "2024-04-08T19:15:04.149732Z", + "shell.execute_reply": "2024-04-08T19:15:04.149284Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.007484Z", - "iopub.status.busy": "2024-04-06T04:36:10.007053Z", - "iopub.status.idle": "2024-04-06T04:36:10.010737Z", - "shell.execute_reply": "2024-04-06T04:36:10.010232Z" + "iopub.execute_input": "2024-04-08T19:15:04.151905Z", + "iopub.status.busy": "2024-04-08T19:15:04.151503Z", + "iopub.status.idle": "2024-04-08T19:15:04.155404Z", + "shell.execute_reply": "2024-04-08T19:15:04.154966Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.012726Z", - "iopub.status.busy": "2024-04-06T04:36:10.012460Z", - "iopub.status.idle": "2024-04-06T04:36:10.016097Z", - "shell.execute_reply": "2024-04-06T04:36:10.015646Z" + "iopub.execute_input": "2024-04-08T19:15:04.157319Z", + "iopub.status.busy": "2024-04-08T19:15:04.157012Z", + "iopub.status.idle": "2024-04-08T19:15:04.160392Z", + "shell.execute_reply": "2024-04-08T19:15:04.159984Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.018111Z", - "iopub.status.busy": "2024-04-06T04:36:10.017712Z", - "iopub.status.idle": "2024-04-06T04:36:10.020470Z", - "shell.execute_reply": "2024-04-06T04:36:10.020044Z" + "iopub.execute_input": "2024-04-08T19:15:04.162271Z", + "iopub.status.busy": "2024-04-08T19:15:04.161951Z", + "iopub.status.idle": "2024-04-08T19:15:04.164604Z", + "shell.execute_reply": "2024-04-08T19:15:04.164202Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.022477Z", - "iopub.status.busy": "2024-04-06T04:36:10.022151Z", - "iopub.status.idle": "2024-04-06T04:37:25.589281Z", - "shell.execute_reply": "2024-04-06T04:37:25.588682Z" + "iopub.execute_input": "2024-04-08T19:15:04.166521Z", + "iopub.status.busy": "2024-04-08T19:15:04.166174Z", + "iopub.status.idle": "2024-04-08T19:16:20.073084Z", + "shell.execute_reply": "2024-04-08T19:16:20.072471Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "430f85b602e34595b215cff777f2e22c", + "model_id": "f80951daaff1439bae07b22f26431578", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72840f69ea214918a754b98c138bcd01", + "model_id": "4414546b77b44486a511d3a262f3937f", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:25.591755Z", - "iopub.status.busy": "2024-04-06T04:37:25.591547Z", - "iopub.status.idle": "2024-04-06T04:37:26.256442Z", - "shell.execute_reply": "2024-04-06T04:37:26.255866Z" + "iopub.execute_input": "2024-04-08T19:16:20.075744Z", + "iopub.status.busy": "2024-04-08T19:16:20.075337Z", + "iopub.status.idle": "2024-04-08T19:16:20.750666Z", + "shell.execute_reply": "2024-04-08T19:16:20.750121Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:26.258907Z", - "iopub.status.busy": "2024-04-06T04:37:26.258403Z", - "iopub.status.idle": "2024-04-06T04:37:28.986847Z", - "shell.execute_reply": "2024-04-06T04:37:28.986249Z" + "iopub.execute_input": "2024-04-08T19:16:20.753014Z", + "iopub.status.busy": "2024-04-08T19:16:20.752584Z", + "iopub.status.idle": "2024-04-08T19:16:23.452100Z", + "shell.execute_reply": "2024-04-08T19:16:23.451576Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:28.989011Z", - "iopub.status.busy": "2024-04-06T04:37:28.988663Z", - "iopub.status.idle": "2024-04-06T04:38:01.556933Z", - "shell.execute_reply": "2024-04-06T04:38:01.556488Z" + "iopub.execute_input": "2024-04-08T19:16:23.454306Z", + "iopub.status.busy": "2024-04-08T19:16:23.454030Z", + "iopub.status.idle": "2024-04-08T19:16:56.036315Z", + "shell.execute_reply": "2024-04-08T19:16:56.035779Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3ec0bdaf05d45038d515229edd1fce4", + "model_id": "71098e13b4334a47bbac4b75032ea150", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:01.559018Z", - "iopub.status.busy": "2024-04-06T04:38:01.558830Z", - "iopub.status.idle": "2024-04-06T04:38:15.949783Z", - "shell.execute_reply": "2024-04-06T04:38:15.949244Z" + "iopub.execute_input": "2024-04-08T19:16:56.038307Z", + "iopub.status.busy": "2024-04-08T19:16:56.038128Z", + "iopub.status.idle": "2024-04-08T19:17:10.772843Z", + "shell.execute_reply": "2024-04-08T19:17:10.772275Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:15.952245Z", - "iopub.status.busy": "2024-04-06T04:38:15.951882Z", - "iopub.status.idle": "2024-04-06T04:38:19.737339Z", - "shell.execute_reply": "2024-04-06T04:38:19.736758Z" + "iopub.execute_input": "2024-04-08T19:17:10.775162Z", + "iopub.status.busy": "2024-04-08T19:17:10.774934Z", + "iopub.status.idle": "2024-04-08T19:17:14.602622Z", + "shell.execute_reply": "2024-04-08T19:17:14.602118Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:19.739665Z", - "iopub.status.busy": "2024-04-06T04:38:19.739350Z", - "iopub.status.idle": "2024-04-06T04:38:21.124441Z", - "shell.execute_reply": "2024-04-06T04:38:21.123826Z" + "iopub.execute_input": "2024-04-08T19:17:14.604704Z", + "iopub.status.busy": "2024-04-08T19:17:14.604405Z", + "iopub.status.idle": "2024-04-08T19:17:16.041428Z", + "shell.execute_reply": "2024-04-08T19:17:16.040893Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a41dd8fa1c914418a704a6e5e8be1e2e", + "model_id": "abbf1d8d4dc4498dbba438256d734fad", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:21.126758Z", - "iopub.status.busy": "2024-04-06T04:38:21.126415Z", - "iopub.status.idle": "2024-04-06T04:38:21.155079Z", - "shell.execute_reply": "2024-04-06T04:38:21.154584Z" + "iopub.execute_input": "2024-04-08T19:17:16.043794Z", + "iopub.status.busy": "2024-04-08T19:17:16.043524Z", + "iopub.status.idle": "2024-04-08T19:17:16.075824Z", + "shell.execute_reply": "2024-04-08T19:17:16.075325Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:21.157615Z", - "iopub.status.busy": "2024-04-06T04:38:21.157258Z", - "iopub.status.idle": "2024-04-06T04:38:27.224964Z", - "shell.execute_reply": "2024-04-06T04:38:27.224445Z" + "iopub.execute_input": "2024-04-08T19:17:16.078301Z", + "iopub.status.busy": "2024-04-08T19:17:16.077923Z", + "iopub.status.idle": "2024-04-08T19:17:22.245581Z", + "shell.execute_reply": "2024-04-08T19:17:22.245090Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:27.227144Z", - "iopub.status.busy": "2024-04-06T04:38:27.226803Z", - "iopub.status.idle": "2024-04-06T04:38:27.282607Z", - "shell.execute_reply": "2024-04-06T04:38:27.282076Z" + "iopub.execute_input": "2024-04-08T19:17:22.247739Z", + "iopub.status.busy": "2024-04-08T19:17:22.247405Z", + "iopub.status.idle": "2024-04-08T19:17:22.303831Z", + "shell.execute_reply": "2024-04-08T19:17:22.303283Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02c8e49579ab4c2cb5010e5d696f7f75": { + "001fb5816a074024996ee285412c72e2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1091,7 +1091,7 @@ "width": null } }, - "06ef42058bc7433ca11271d89630261f": { + "010891f081ec41c1aabdc9984ea3e880": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1144,56 +1144,7 @@ "width": null } }, - "07b675adb016454a9854b4ea300e4cfe": { - "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_70deac36097f491e8666a288db33cf71", - "placeholder": "​", - "style": "IPY_MODEL_f73f43e9ac32458989fecfd81a100778", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "0e3edfaa67be4256bf241604d1fdfd54": { - "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_686826beb1be457bab28c73bcd2ffefb", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8db16dbcca284d40921261550c3c9241", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "0ffcaa24253e452f8a885fef873bdcfb": { + "1983a07fcf9348578a07d2aee3240441": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1211,7 +1162,7 @@ "text_color": null } }, - "182b3bcc537c404793b98eb84a4a9fef": { + "26332d6a8cc44c2e97dbf9c486f38f25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1226,15 +1177,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ea8cdf6af87544368e97f412bed093e3", + "layout": "IPY_MODEL_7388d890f96b440d9df5bb2bcf630681", "placeholder": "​", - "style": "IPY_MODEL_ff45dd3e934e4e0c9f7fa5da7043dbd7", + "style": "IPY_MODEL_99f6441d531f4710baa405c0d7109b5f", "tabbable": null, "tooltip": null, - "value": " 4997817/4997817 [00:32<00:00, 154776.29it/s]" + "value": " 4997817/4997817 [00:32<00:00, 154803.51it/s]" } }, - "20149c21eb1a4d47b708a9f402b4f051": { + "2644b4fc5fda48acb706485e94aabc6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1249,67 +1200,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_42508acbff6b4c069678d14fb3bb650d", + "layout": "IPY_MODEL_2c287de703a14c5cb0941da5922a6c41", "placeholder": "​", - "style": "IPY_MODEL_d2dd7b61c9974c56af1e4ae1413f64cf", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "232cb3f4436c4a29aa37462ded23ed0a": { - "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_2a73fcdba5f1491486544ab0e5fd82f6", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_83ed95add600447dbcd4112a82b755ac", + "style": "IPY_MODEL_1983a07fcf9348578a07d2aee3240441", "tabbable": null, "tooltip": null, - "value": 30.0 + "value": "images processed using softmin: 100%" } }, - "28dfa37fd60e430591dba8d65770190b": { - "model_module": "@jupyter-widgets/controls", + "2c287de703a14c5cb0941da5922a6c41": { + "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_a6b9abd68857477cbdc3898a45b7c10f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40eb33493e30499e97354717869f83be", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_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 } }, - "2a73fcdba5f1491486544ab0e5fd82f6": { + "301a3fcd99094c40a1fb9f975619c430": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1362,7 +1314,7 @@ "width": null } }, - "2bc90b79c1364f709f2cc5d8d3ac1a01": { + "328b4d1e22fb41b9be4bc667200a34d2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1377,54 +1329,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9efde9d20e674f91930d36a8fffde7e0", + "layout": "IPY_MODEL_001fb5816a074024996ee285412c72e2", "placeholder": "​", - "style": "IPY_MODEL_0ffcaa24253e452f8a885fef873bdcfb", + "style": "IPY_MODEL_dcb3aba2d5ee498da09596d6b1189652", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.88it/s]" + "value": " 30/30 [00:01<00:00, 21.04it/s]" } }, - "2d5ba316556d415b8f9eceaac5bda45b": { + "332840cfdb9a451ebd2a374dae54e637": { "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_957a7ec883f34696b2479a90a87bdbcb", - "placeholder": "​", - "style": "IPY_MODEL_562e407d8d8745fc83b37ab7c94d1b60", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.41it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "40eb33493e30499e97354717869f83be": { + "365979231dd54217a8aac4a279204067": { "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_b85f1eb5f0e64530870f6fbf949cda91", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d7bd991e20f14b0c98dd8d679d84939c", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "42508acbff6b4c069678d14fb3bb650d": { + "380290952081433398d72252f55f3512": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1477,31 +1432,7 @@ "width": null } }, - "430f85b602e34595b215cff777f2e22c": { - "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_621c7249a2054bf59226e64a4b8f7081", - "IPY_MODEL_28dfa37fd60e430591dba8d65770190b", - "IPY_MODEL_7e7920af14e54024b17aa6179ca38f35" - ], - "layout": "IPY_MODEL_4ad7bb286eca4c09948bb75a92a76c29", - "tabbable": null, - "tooltip": null - } - }, - "4ad7bb286eca4c09948bb75a92a76c29": { + "40897686e955404f91514151f240c0d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1554,25 +1485,31 @@ "width": null } }, - "562e407d8d8745fc83b37ab7c94d1b60": { + "4414546b77b44486a511d3a262f3937f": { "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_d2ea6fbb7475458087e30a3aee7c2421", + "IPY_MODEL_83945befc2ec4271af67b93797cadd10", + "IPY_MODEL_93bd352e7edc4aac8660f540a733e80f" + ], + "layout": "IPY_MODEL_e5304a7b2a15495792b3302840dc6164", + "tabbable": null, + "tooltip": null } }, - "5b27262a9cd94dcca7fa54df4cceb26a": { + "5abd50f2ff82441194222375826e37c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1625,23 +1562,30 @@ "width": null } }, - "5bb6200f11d74e33b58d64accf19085f": { + "5ac2298939434623ab7427ecd71bb845": { "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_f618acb94c794f1ba33254b732f3da99", + "placeholder": "​", + "style": "IPY_MODEL_bdd0d707ff2c40a783c34c22c644bff2", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "60b778a0ef8e44b39608af38b47ca860": { + "6249a51dbc1145c985fed7ad172f3f6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1659,7 +1603,7 @@ "text_color": null } }, - "60ce120b6f0d4b35939edd06a49b48fa": { + "68c0ed1d2b074054bc0c083b8ccd8a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1712,7 +1656,7 @@ "width": null } }, - "621c7249a2054bf59226e64a4b8f7081": { + "6b76fce815c1461d9f75cd9b932f9d87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1727,33 +1671,39 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7f6b7ffd00bb4a2caab3b50683296936", + "layout": "IPY_MODEL_68c0ed1d2b074054bc0c083b8ccd8a71", "placeholder": "​", - "style": "IPY_MODEL_cbbaae80c54e40cabbdb2edd591551d4", + "style": "IPY_MODEL_6249a51dbc1145c985fed7ad172f3f6f", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: 100%" } }, - "646252281ae14f8886cc21d9e56afec5": { + "71098e13b4334a47bbac4b75032ea150": { "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_5ac2298939434623ab7427ecd71bb845", + "IPY_MODEL_e05c95cfe29749928b6771d970a5fc81", + "IPY_MODEL_26332d6a8cc44c2e97dbf9c486f38f25" + ], + "layout": "IPY_MODEL_40897686e955404f91514151f240c0d0", + "tabbable": null, + "tooltip": null } }, - "686826beb1be457bab28c73bcd2ffefb": { + "7388d890f96b440d9df5bb2bcf630681": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,28 +1756,95 @@ "width": null } }, - "70deac36097f491e8666a288db33cf71": { - "model_module": "@jupyter-widgets/base", + "773d8ea77b7443f2ade5b4c99a303fcb": { + "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, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "83945befc2ec4271af67b93797cadd10": { + "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_010891f081ec41c1aabdc9984ea3e880", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_beb83a56568c469189a5939e700e69a4", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "93bd352e7edc4aac8660f540a733e80f": { + "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_ffe9b9351b8a4b129d0904a8757b9a46", + "placeholder": "​", + "style": "IPY_MODEL_fc53622cc52741d8bde9b20fe8c9a3c0", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.44it/s]" + } + }, + "99e35d66f5634eb28a74eb5bfa102bde": { + "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, @@ -1859,7 +1876,25 @@ "width": null } }, - "72840f69ea214918a754b98c138bcd01": { + "99f6441d531f4710baa405c0d7109b5f": { + "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 + } + }, + "abbf1d8d4dc4498dbba438256d734fad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1874,16 +1909,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8b3e196faee64d279374daae7049f9a7", - "IPY_MODEL_0e3edfaa67be4256bf241604d1fdfd54", - "IPY_MODEL_2d5ba316556d415b8f9eceaac5bda45b" + "IPY_MODEL_2644b4fc5fda48acb706485e94aabc6a", + "IPY_MODEL_c68ef40a35e04fc39427a9f284523b28", + "IPY_MODEL_328b4d1e22fb41b9be4bc667200a34d2" ], - "layout": "IPY_MODEL_7c0fbc0da1b843e89059929faa931860", + "layout": "IPY_MODEL_ef987a4e9c5445f2a1965215131e2de0", "tabbable": null, "tooltip": null } }, - "7c0fbc0da1b843e89059929faa931860": { + "b2e6852040e34be68af10fb4a3c2b9a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1936,30 +1971,7 @@ "width": null } }, - "7e7920af14e54024b17aa6179ca38f35": { - "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_06ef42058bc7433ca11271d89630261f", - "placeholder": "​", - "style": "IPY_MODEL_60b778a0ef8e44b39608af38b47ca860", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 407.39it/s]" - } - }, - "7f6b7ffd00bb4a2caab3b50683296936": { + "b85f1eb5f0e64530870f6fbf949cda91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2012,7 +2024,41 @@ "width": null } }, - "83ed95add600447dbcd4112a82b755ac": { + "bdd0d707ff2c40a783c34c22c644bff2": { + "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 + } + }, + "beb83a56568c469189a5939e700e69a4": { + "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": "" + } + }, + "c158f6f873284cbaa7be7bbaebbd7763": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2028,7 +2074,51 @@ "description_width": "" } }, - "8b3e196faee64d279374daae7049f9a7": { + "c4adc9fb2d8c49ab9d863ff53317708d": { + "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 + } + }, + "c68ef40a35e04fc39427a9f284523b28": { + "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_301a3fcd99094c40a1fb9f975619c430", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_332840cfdb9a451ebd2a374dae54e637", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "ce77d0d3de7c4b089021754738d5ceb4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2043,15 +2133,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_02c8e49579ab4c2cb5010e5d696f7f75", + "layout": "IPY_MODEL_99e35d66f5634eb28a74eb5bfa102bde", "placeholder": "​", - "style": "IPY_MODEL_646252281ae14f8886cc21d9e56afec5", + "style": "IPY_MODEL_773d8ea77b7443f2ade5b4c99a303fcb", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 405.54it/s]" + } + }, + "d2ea6fbb7475458087e30a3aee7c2421": { + "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_b2e6852040e34be68af10fb4a3c2b9a7", + "placeholder": "​", + "style": "IPY_MODEL_c4adc9fb2d8c49ab9d863ff53317708d", "tabbable": null, "tooltip": null, "value": "number of examples processed for checking labels: 100%" } }, - "8db16dbcca284d40921261550c3c9241": { + "d7bd991e20f14b0c98dd8d679d84939c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2067,60 +2180,51 @@ "description_width": "" } }, - "957a7ec883f34696b2479a90a87bdbcb": { - "model_module": "@jupyter-widgets/base", + "dcb3aba2d5ee498da09596d6b1189652": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9efde9d20e674f91930d36a8fffde7e0": { + "e05c95cfe29749928b6771d970a5fc81": { + "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_380290952081433398d72252f55f3512", + "max": 4997817.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c158f6f873284cbaa7be7bbaebbd7763", + "tabbable": null, + "tooltip": null, + "value": 4997817.0 + } + }, + "e5304a7b2a15495792b3302840dc6164": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2173,31 +2277,7 @@ "width": null } }, - "a41dd8fa1c914418a704a6e5e8be1e2e": { - "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_07b675adb016454a9854b4ea300e4cfe", - "IPY_MODEL_232cb3f4436c4a29aa37462ded23ed0a", - "IPY_MODEL_2bc90b79c1364f709f2cc5d8d3ac1a01" - ], - "layout": "IPY_MODEL_5b27262a9cd94dcca7fa54df4cceb26a", - "tabbable": null, - "tooltip": null - } - }, - "a6b9abd68857477cbdc3898a45b7c10f": { + "ef987a4e9c5445f2a1965215131e2de0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2250,25 +2330,7 @@ "width": null } }, - "cbbaae80c54e40cabbdb2edd591551d4": { - "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 - } - }, - "d2ba7ead109f40f88a0f2fdcd7f79091": { + "f618acb94c794f1ba33254b732f3da99": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2321,25 +2383,7 @@ "width": null } }, - "d2dd7b61c9974c56af1e4ae1413f64cf": { - "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 - } - }, - "d3ec0bdaf05d45038d515229edd1fce4": { + "f80951daaff1439bae07b22f26431578": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2354,16 +2398,34 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_20149c21eb1a4d47b708a9f402b4f051", - "IPY_MODEL_f5b6a3b6f2474f7c8d2b531dbc37f186", - "IPY_MODEL_182b3bcc537c404793b98eb84a4a9fef" + "IPY_MODEL_6b76fce815c1461d9f75cd9b932f9d87", + "IPY_MODEL_365979231dd54217a8aac4a279204067", + "IPY_MODEL_ce77d0d3de7c4b089021754738d5ceb4" ], - "layout": "IPY_MODEL_d2ba7ead109f40f88a0f2fdcd7f79091", + "layout": "IPY_MODEL_5abd50f2ff82441194222375826e37c9", "tabbable": null, "tooltip": null } }, - "ea8cdf6af87544368e97f412bed093e3": { + "fc53622cc52741d8bde9b20fe8c9a3c0": { + "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 + } + }, + "ffe9b9351b8a4b129d0904a8757b9a46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2415,68 +2477,6 @@ "visibility": null, "width": null } - }, - "f5b6a3b6f2474f7c8d2b531dbc37f186": { - "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_60ce120b6f0d4b35939edd06a49b48fa", - "max": 4997817.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5bb6200f11d74e33b58d64accf19085f", - "tabbable": null, - "tooltip": null, - "value": 4997817.0 - } - }, - "f73f43e9ac32458989fecfd81a100778": { - "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 - } - }, - "ff45dd3e934e4e0c9f7fa5da7043dbd7": { - "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 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index e868c19b5..1733ede8c 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": "2024-04-06T04:38:29.398070Z", - "iopub.status.busy": "2024-04-06T04:38:29.397578Z", - "iopub.status.idle": "2024-04-06T04:38:30.762030Z", - "shell.execute_reply": "2024-04-06T04:38:30.761463Z" + "iopub.execute_input": "2024-04-08T19:17:24.524829Z", + "iopub.status.busy": "2024-04-08T19:17:24.524651Z", + "iopub.status.idle": "2024-04-08T19:17:26.451617Z", + "shell.execute_reply": "2024-04-08T19:17:26.450937Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-04-06 04:38:29-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-04-08 19:17:24-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.98, 2400:52e0:1a00::718:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "143.244.49.177, 2400:52e0:1a01::994:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.49.177|:443... 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 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.04s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.49MB/s in 0.2s \r\n", "\r\n", - "2024-04-06 04:38:29 (22.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-04-08 19:17:24 (5.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,9 +138,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-04-06 04:38:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.84.148, 52.216.129.163, 52.217.231.17, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.84.148|:443... " + "--2024-04-08 19:17:25-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.130.187, 54.231.165.233, 52.216.62.161, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.130.187|:443... " ] }, { @@ -167,7 +174,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 14%[=> ] 2.33M 11.7MB/s " + "pred_probs.npz 1%[ ] 211.53K 926KB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 22%[===> ] 3.71M 8.12MB/s " ] }, { @@ -175,9 +190,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 46.9MB/s in 0.3s \r\n", + "pred_probs.npz 94%[=================> ] 15.37M 22.6MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 23.5MB/s in 0.7s \r\n", "\r\n", - "2024-04-06 04:38:30 (46.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-04-08 19:17:26 (23.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -194,10 +210,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:30.764412Z", - "iopub.status.busy": "2024-04-06T04:38:30.764032Z", - "iopub.status.idle": "2024-04-06T04:38:31.972111Z", - "shell.execute_reply": "2024-04-06T04:38:31.971535Z" + "iopub.execute_input": "2024-04-08T19:17:26.454458Z", + "iopub.status.busy": "2024-04-08T19:17:26.454223Z", + "iopub.status.idle": "2024-04-08T19:17:27.676181Z", + "shell.execute_reply": "2024-04-08T19:17:27.675698Z" }, "nbsphinx": "hidden" }, @@ -208,7 +224,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -234,10 +250,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.974580Z", - "iopub.status.busy": "2024-04-06T04:38:31.974308Z", - "iopub.status.idle": "2024-04-06T04:38:31.977556Z", - "shell.execute_reply": "2024-04-06T04:38:31.977128Z" + "iopub.execute_input": "2024-04-08T19:17:27.678806Z", + "iopub.status.busy": "2024-04-08T19:17:27.678375Z", + "iopub.status.idle": "2024-04-08T19:17:27.681955Z", + "shell.execute_reply": "2024-04-08T19:17:27.681515Z" } }, "outputs": [], @@ -287,10 +303,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.979700Z", - "iopub.status.busy": "2024-04-06T04:38:31.979317Z", - "iopub.status.idle": "2024-04-06T04:38:31.982377Z", - "shell.execute_reply": "2024-04-06T04:38:31.981830Z" + "iopub.execute_input": "2024-04-08T19:17:27.683962Z", + "iopub.status.busy": "2024-04-08T19:17:27.683699Z", + "iopub.status.idle": "2024-04-08T19:17:27.686524Z", + "shell.execute_reply": "2024-04-08T19:17:27.686095Z" }, "nbsphinx": "hidden" }, @@ -308,10 +324,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.984377Z", - "iopub.status.busy": "2024-04-06T04:38:31.984017Z", - "iopub.status.idle": "2024-04-06T04:38:41.053110Z", - "shell.execute_reply": "2024-04-06T04:38:41.052521Z" + "iopub.execute_input": "2024-04-08T19:17:27.688377Z", + "iopub.status.busy": "2024-04-08T19:17:27.688200Z", + "iopub.status.idle": "2024-04-08T19:17:36.852616Z", + "shell.execute_reply": "2024-04-08T19:17:36.852071Z" } }, "outputs": [], @@ -385,10 +401,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.055687Z", - "iopub.status.busy": "2024-04-06T04:38:41.055498Z", - "iopub.status.idle": "2024-04-06T04:38:41.061081Z", - "shell.execute_reply": "2024-04-06T04:38:41.060531Z" + "iopub.execute_input": "2024-04-08T19:17:36.855120Z", + "iopub.status.busy": "2024-04-08T19:17:36.854821Z", + "iopub.status.idle": "2024-04-08T19:17:36.860286Z", + "shell.execute_reply": "2024-04-08T19:17:36.859865Z" }, "nbsphinx": "hidden" }, @@ -428,10 +444,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.063230Z", - "iopub.status.busy": "2024-04-06T04:38:41.062809Z", - "iopub.status.idle": "2024-04-06T04:38:41.426124Z", - "shell.execute_reply": "2024-04-06T04:38:41.425590Z" + "iopub.execute_input": "2024-04-08T19:17:36.862236Z", + "iopub.status.busy": "2024-04-08T19:17:36.861904Z", + "iopub.status.idle": "2024-04-08T19:17:37.207147Z", + "shell.execute_reply": "2024-04-08T19:17:37.206565Z" } }, "outputs": [], @@ -468,10 +484,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.428511Z", - "iopub.status.busy": "2024-04-06T04:38:41.428316Z", - "iopub.status.idle": "2024-04-06T04:38:41.432566Z", - "shell.execute_reply": "2024-04-06T04:38:41.432029Z" + "iopub.execute_input": "2024-04-08T19:17:37.209618Z", + "iopub.status.busy": "2024-04-08T19:17:37.209283Z", + "iopub.status.idle": "2024-04-08T19:17:37.213376Z", + "shell.execute_reply": "2024-04-08T19:17:37.212864Z" } }, "outputs": [ @@ -543,10 +559,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.434828Z", - "iopub.status.busy": "2024-04-06T04:38:41.434438Z", - "iopub.status.idle": "2024-04-06T04:38:43.797032Z", - "shell.execute_reply": "2024-04-06T04:38:43.796336Z" + "iopub.execute_input": "2024-04-08T19:17:37.215394Z", + "iopub.status.busy": "2024-04-08T19:17:37.215083Z", + "iopub.status.idle": "2024-04-08T19:17:39.552115Z", + "shell.execute_reply": "2024-04-08T19:17:39.551400Z" } }, "outputs": [], @@ -568,10 +584,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.800002Z", - "iopub.status.busy": "2024-04-06T04:38:43.799357Z", - "iopub.status.idle": "2024-04-06T04:38:43.803395Z", - "shell.execute_reply": "2024-04-06T04:38:43.802849Z" + "iopub.execute_input": "2024-04-08T19:17:39.555424Z", + "iopub.status.busy": "2024-04-08T19:17:39.554614Z", + "iopub.status.idle": "2024-04-08T19:17:39.558938Z", + "shell.execute_reply": "2024-04-08T19:17:39.558474Z" } }, "outputs": [ @@ -607,10 +623,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.805438Z", - "iopub.status.busy": "2024-04-06T04:38:43.805041Z", - "iopub.status.idle": "2024-04-06T04:38:43.810204Z", - "shell.execute_reply": "2024-04-06T04:38:43.809632Z" + "iopub.execute_input": "2024-04-08T19:17:39.560894Z", + "iopub.status.busy": "2024-04-08T19:17:39.560573Z", + "iopub.status.idle": "2024-04-08T19:17:39.565814Z", + "shell.execute_reply": "2024-04-08T19:17:39.565368Z" } }, "outputs": [ @@ -788,10 +804,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.812097Z", - "iopub.status.busy": "2024-04-06T04:38:43.811923Z", - "iopub.status.idle": "2024-04-06T04:38:43.837570Z", - "shell.execute_reply": "2024-04-06T04:38:43.837054Z" + "iopub.execute_input": "2024-04-08T19:17:39.567759Z", + "iopub.status.busy": "2024-04-08T19:17:39.567433Z", + "iopub.status.idle": "2024-04-08T19:17:39.593200Z", + "shell.execute_reply": "2024-04-08T19:17:39.592668Z" } }, "outputs": [ @@ -893,10 +909,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.839685Z", - "iopub.status.busy": "2024-04-06T04:38:43.839262Z", - "iopub.status.idle": "2024-04-06T04:38:43.843573Z", - "shell.execute_reply": "2024-04-06T04:38:43.843046Z" + "iopub.execute_input": "2024-04-08T19:17:39.595168Z", + "iopub.status.busy": "2024-04-08T19:17:39.594990Z", + "iopub.status.idle": "2024-04-08T19:17:39.599302Z", + "shell.execute_reply": "2024-04-08T19:17:39.598861Z" } }, "outputs": [ @@ -970,10 +986,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.845456Z", - "iopub.status.busy": "2024-04-06T04:38:43.845286Z", - "iopub.status.idle": "2024-04-06T04:38:45.262927Z", - "shell.execute_reply": "2024-04-06T04:38:45.262416Z" + "iopub.execute_input": "2024-04-08T19:17:39.601340Z", + "iopub.status.busy": "2024-04-08T19:17:39.600975Z", + "iopub.status.idle": "2024-04-08T19:17:41.028748Z", + "shell.execute_reply": "2024-04-08T19:17:41.028272Z" } }, "outputs": [ @@ -1145,10 +1161,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:45.265138Z", - "iopub.status.busy": "2024-04-06T04:38:45.264818Z", - "iopub.status.idle": "2024-04-06T04:38:45.268799Z", - "shell.execute_reply": "2024-04-06T04:38:45.268374Z" + "iopub.execute_input": "2024-04-08T19:17:41.030867Z", + "iopub.status.busy": "2024-04-08T19:17:41.030672Z", + "iopub.status.idle": "2024-04-08T19:17:41.034715Z", + "shell.execute_reply": "2024-04-08T19:17:41.034283Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/clean_learning/index.doctree b/master/.doctrees/tutorials/clean_learning/index.doctree index 1aa75447749d9d38754b10317eaec8bcf5928002..30047ba4ca43f7f9df79819cb001b3e26ace9e6c 100644 GIT binary patch delta 62 zcmX>tep-A(E~8etep-A(E~8<7ftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu=6Q^|TmX9J6D$A# diff --git a/master/.doctrees/tutorials/clean_learning/tabular.doctree b/master/.doctrees/tutorials/clean_learning/tabular.doctree index 302ccf789c59eeb728d3e5813456c817ae220f04..c9831851ab64a6a1a0d983940ba05418c22857f0 100644 GIT binary patch delta 64 zcmZ2>lX=-q<_%{!4bvMaP11}FEG#T7%*~Ax6Acp0ERrn@ U4U&vajnj+_j14yblX=-q<_%{!4f6}kERxdsg3U+5*P3^g)Xe z;sgvymMuL$If1V{=Lik zeCP7Mxc3wP(fS|k`c3~^dsp_qy!X=nzTHduzuw(lvN_#-_ibA?Z`gR(R<}KE*cjaQ z*uQV`*fn$IZ176?fp#6xnAbgJ4Gc>FtV(+U>q{?7Xvj$rz3UQO=?k z$y;ZsiqY8|T~0KJ>FtV(+U+)-c<0QXOg2#+6E{W+m4$aSdy$Duf~g?V@a{^B+U?f7 za`{<3?lJ)axlKX}!l==i2CR_+QU)h=_R%Mg${<#0=BSA_ zNv0D`gKY=uE%SOwQ)wBCDOe}6q{c_XQ4=ZPPcmU}aAEyuHN{b2mSh-bYk`bWtUycxtL$ z^^(C~k5r$Vcd6wjd*)2!4)u%zB+6#ujf|A3!PS4OUaY1(XPN}dlg5JuMse3tqx3zE zg6rmnZJniqy)V^=s$$^Rzf#lg*pOB?^nsc-j~=PI-E1S%MrFcn7Mfe9d1R3j>a+1$ zG}W6kd30SO*=uJFb4+jr#^e`@3SVhnEX z>cTbY6}PPJHS@2mmvzVH!);rbS2PDc+?n5tAt$K_+K48=Dte`+#%9&BCNMh#E^aAH z%o9q{m#NY=P$kjAhz}NR(vQI9jP#aB39u)jl=LY2z`aU3T1K1W>*}S0<6p0TR~6$n zyN^}ZbqiseSMl1-@?TZ&>nfg;&c+4>2q>$J?KmXweo%8hrH0>ZesHV)M|6ij^X^ z4{=RTFrArV-co~Gqro!5l6HnhB7%uw)Zj9@1&!8+CxzhicUtF6AspB~Q`ngXckU;fb&+ zgpjDpF-gsnALUdcq^ugPy{jn`kXh8BqKCxmJujg-eA>|=GCv&%LZG% zUENdl@Ou(66Os@qlr#qYH3Za&WL!Zs4XSmWH>zGCcy8W$1Kd>zo`>A^)isesqM9}Q4 zpjsILscMrG0kL&RWti?*?2ih(3~re!+HT}l|mptA+&%|kq9LsAY=W3m=Q@z~`Y3W1|cg@?w3 zMo5sLG$&>v1~k)h!jh4CEX8epPwE?bt`K*~aXVVH$CWx zi01LBnX#sqV^A7r!gFK1H5}dohEs_X!f8?<_o0Z?7K*N&c%pCuQWQTz5Vzza+kfm^ z$UP4p92=L8L|7qYbM*)7>(DX5%0}{_DHWI=Z3LYe9du9zrr1m+=9sB#y%7#bNkOe< z&=P2s9BmL6Fry;d3DmS|1tXEk$--sBRHjp65?wS5F8e#qKQ%sPtAQzQY<$EasqJ1@ zuUxCv5H<^35D#eAm;?#*0UEXs3M~WUro`_fnMtW+bd11NdXl+tDOBTWEQnEQ>~)<4 zS1F}a;OZI)1H}-p6QQsVG8iqx;7}2r8?*Ni4?|)jC@~)y6q;*`5-tlx%jeLGCQxrr zC$_G_TXSXcmL!2Y0NbQ8=hl+Ic$C9VI~>#sr%=tJ^_^>bg#(Pp?X4T@1zlh}Xt-!v zNkGCH%b+NU!JL>4%#r5t3pyh!q=(j;plA&G3)}`0y^@f>mJ^0wg(OC`3MB_7Z;Lv% zG9WrikUh*GMI1bxFv8mSxMqm8Qpo1WhWe&1B&$s5c~E-rHYoJ21P@sa5hx{rx6!t$ z;nQUF5~Qn9w`~sA;dG?!nwA(YO`Go0=IZZP=M4`0uzCd64%9yeI`0SrTes>S9+wQ1 zfaob{cb*Z(g+r(zU6;}&ZV+KYjFdl|COQ7)xoI7gTKtmlg zg53DntT}cM*sZO#adC(2cH0;0vwJ>qo)o4OSlU7{2D8&++;W=f$O3_meGW7|g>NSe z1Pmk0!c)*5DiX5KV z+xcEqJV0Bjv=o2Zh1|HOzPO7)%yEXvjpJFOWS&VzG^B?Q5+>_l>w#8v2pGXok&=RS z@oouBg4L*BU=NLTx+xBZq#gXnD*1Dk5Vbb7Q;4Dh@FR= zck2W7$~AP2*0VSdZBXL#htO0T!5A)5DT=^Dql{7f+(EkQp1I?}dlzgGK*+)O#kD|HSg2SpE~se`5JhEdOyIo+g(6 z#PXk5{_pkQSSFVLX&>22p6bN%e~(WMGY1dM`M3YG{{PqV*ZmJYwP5hjuBtw=h_<>zKip zcJ&9nn^zCMz3aq*xcTG%@n?Uy``3eWZ&|hG#;bR_&5PIV*t~eH+j)K35!{Zr;jCsu z`^Q|JJoxRNb%S&Fo;+B8%c+BXd)5zbxaGtZSGlcMrp=4nuHHI0dhaRq=H2aSq&j|Z z&)zNn%^UUy8*aVr-*$KQzLN&`-L|IJE%4cG)tY6arB;_!tH$5mds%ft5C1p#<-T>N zUzx7I{_3q)F7DjscBI9(HWo(f@0LIM?EY)3RfBe)`X7}#@Z@a=&S;2dJS!@*^hRXP zjnkSKl2c@+`x-p^^L@pO4s3e#$fFw0j3+`x>TMJ;WJgl;oN^WjnlH1#_CMd&FI}_g z(bWfzdE%vaHJPcztaJ?b5kv)(c+Qz5VyIz+#nHlw7j?IL?it)J;xkfd#X`&?5M`(e zQc0d;PFcC}K8qK1xBL9TEX{ z7p2IAb1M?ZuDPc)D8W1nf{brhyr{d~#uwKe)vyqpx0z<#Q79F?B2?)>qEc4r$+Ih7 z)ZOj_f4T0-#tJP7V}v-VMUdWW$rE>mlI%>@Vz_;Oy>(gNTPF---ea%`PNj*QD(Pe3 z#B!zEdl%|e?Mq*;j~{NmuezaHuwE1Hf^#V+!83DOXlrFs-r-z_)&139t77eT`jiyZNc1H%c^y*?+OkMxdc zaI67y*h$K8pp?4EN%nyO$k8SG7S!-Lso^Xltxq{8bUV^B$3ftlh}bjJ8>fvG_3sQcQpzh50U-2bEM8&yB@ zXcJAE5Iht^xX&@-S|fDw#?h&6EiIsZT=ZyDsPDTCUC{SYCv1bWl#)=+T0x28n64OF zDvnmUYSN>io1}HtfGH8iGVd@&v~UExYMCBZuhq{~ry53WR2t_TMDPGEzSmwjt(>F7 z<4pZ@)f9^QcFX3P_GgzAanK^Ra{azCm?V&JR#B@ljtK);2?jFP&?b_G*fHBv)xzw0 zV{pwZXLamOgS318UY*hx9VsgrePR++)sX;HMg|XcV}W$Io*=BT(mUo2$PZ6fDryv@ zj*>V{+{BX!L-#330#a5g8*r`){>O8!yrhv3bbfos!FtVb%dq+m$b5EjKW6Dt-7(Z| z|7W%SOgRNKEve^>W#gq0-YIKAb`gMgKICXR(t2xWB~?4TV{`AD)!xzxEyYf$BbQ!X zcxwC*w-vT!rjV9IGRjy9sg(pYhST0l0SgMQ3y#~#sNtCS!SbXaX*dQUPK|`*6*B?) za`c?0kk$`Lrc4u5r-~pEX{^Jj#Sa|H&E)ZD8gpA|0qtcg>q{FxdjX>zZEF9qvVKqF zO*i?`!GRk=b22&`6*z=ErA%@iCHSlYyon zbe4)UA)|&*&pBWcWrm(z(yFD(qgL(DESy98_yzU)_SlKDx4w5lu|6km#^LWi=YSC<~yA>@^|HH|#!m2oM-v3ZdL_zT*Vz!XBZu;}8rPtnc* z4MYfstc3a`J`rsw)FKQM29+G%v7)}aYS7CfW-EYE*uh{dJg+3gOW&>Cg$cK%Lecj3 zq`stBv)jd{_9j}d>}#o97CvEC2$rJp@LbS;#3L8LhKALTJ0*z}LQ_~!W5Ih4gO;P` zuy+donz$Y}X5Ycmnrtb4K}1BScfM8mjw2-W@etD(?hYJ2vt)-dTB_Kd{h|7zK9aPL%})%NCU38P=R>*gHoZ9g#HbXQ6l+<7;+VGsf4R^Xl~<)EkMJ0+-IU|dt?OYD7Ba<&iYy(^ zQuvfwa_QBDXT}b3S!qX8TxKyc9hF*kG=QYlgVK10#7D0}2Tt<41?UTofe$kz9eNm2 zMkQy1Hqk(swfhe)@oNiAag5~{kvStnfeD6x>--=gJSW$la}L6E{%f)wcuq05AdVIvpd^XEZe^PP!}r9EILY6NxQhWKByLjCqxr3NM)?<<(la_Qbfd_f82&^E8jntSan3ob0#zBXSLshSEc*!tm z6)rG@kZOYPkyS}5oWa~X?Qhl7)xzwGV@@j+9AoUNFW2igaVbGb`kTx&9y7a^j0j;pDQOG$9B)c>7cJIqsm{NY72PyJ`So|&iq-5c$B>OX$ZFi-vGssH%TA`8L$;jV3-`cJ=V sn5X`S{ywslkqaAzr01S{&;44MLD7X^m}nM8beuW&3qK$j+zCO& zg@_Ar^_GGQ3Aivput3OQ5_O@VX65X3CBeyNcA^`9)%9w+D`4M$prC8hysGog$M55u zm)|^i`OSk@esh^x{ANxsz983asGfan!E!Sygc!UxB1&AyfmAZqG_8#K{{728xPN(p z&m*h*mzU*NqiD(NXYsL|ot2ohmnvobQ&!RH6aQVlFpLz~rs8cd-mtI>ntW_BIJS{ji#x+>i?J7Yk{g4%c5&S4=wQ&(^M6?$ z9i}W=m10?GX3Z(8kS(FM9xuo=z4(*iVD;Uv4__V*Yp|{*mDkNkR-yDpT5KvcG3?9g z=*p$Li;;zVBhz9LJXVavir$DzXct;v=QKKT&1o8k?&6r!D>nu|ef8e*qbF>`H$Hj& zTeprk-D#u(qa!*-HaSVP_zPN^wcZpxkDHCgU|w~U%Ivv{@m`UWSORCOX+p^lR(Fnv zA1=PrYtJ!;;xo^F@%A$}KlSO)-tG$YdU&l_}HjoG)`B_ z)n-QJLkWe^Hj?O6F+69_8K_0CB~s*qpM#o92^?7!om}@v9tN zsZLBYeg<1F^7Qeym(PvezyOV%F6cUv2e{sLU`+CNx{J z!WcL9DJO1PYvWT$8N75JW`ttk2gzGq80m-r#kP`1&pB2-&u1_2^ww_&^P!7^+UZpw zar)bX|gDE{InG)=-Yo7&|@qH|FfG_m*E8Mz3@U3a^HOvob*}RbZ}F zt~%zJw=e_9d}W1ak#r3y=HhgzL1(8@+@{e9>`r0Ujk(UmPw)I^`S^)dC+?Xupm{U| zq+68npl1ZODM|~{B0EWbHH24%T`-+C@)qHg})q zPT?M6t&>{VV9g65|D1phFpctJO^#l~mg;M>TdetvhFg$ebD100*mN_bT8%zS1Jj%d z+aY-sH0r1u2M&&sHDc77tDM)xol^4j%ik@puKxbs;f=-Edxh!sM~9nZ_YKwxH{HH6 zTp10xpDYxbY-8s+fJh?c!U0nV!k_sAGF6=-RV%pGL#!KUREGjxt0@GUCzUjpQjA7n zK(ZJ}L+qT1Ysgujwz}2}x|!5o%<1U$;R}Zu;6f-A7*0S3hOSx-aaKhtsB1Ho8@QR| zn^NpRkD9&aNa#|n!AXz=&E6z&i2oBgP62bvF;iljVQH$6!&JLp-x=;Mx?%6!7-;MT zoam3ktrM7r^Kzz*2J@?uq3fu!IPFtv#pV`@ignwN7`1qvg@SPOF?u|Zh!~1&l2Yg8 ztk;!Y&~!%9>^|KoR(l9B*U4)Gu`I!G_DKOg+@eaNqHE%iyC-F~R!hN%!CqkyGfWrk z6Uc_F2T3MHI<4Sje!3WeIqM+!m}`X*36VtUB;aH!I>j~%Suf`F^i#ugV<)7UfT^cP z$Wkc~B~b~}m{M}tMinhGfXym0YnV1ML{NDn@=%2m4Z#g22a=%F;zlDjR#wQ0P8r@LeLrnX)3(WT8C9SH56>%egk^F6|i2=4K4Iy zuCusx!v4gZ(=M0La@(N&+H(Tw!d0Y7wV+K+Z+LbVWe`CN*fbJkmP^wKUxS3PHfx#; z`8=|E&Dq6DZytA_?iK9}Sr_MKX7M8AT)l@UEL>rbK`pQ-wfQPQPuube+%O0a>Ba`> zj)03#9_TYQ5h^mBiPuO~I|BJSo%r3*byKPw6jf3NNLK+D6 zTa@*Jo+fbEiMig!!c^1n;p=n~`8?7}^wlL!grz=VwZ%#g{e+c=cv@6_v;DUSD!^Ka zGzu4_-CBeDh_IJf+;%PE#T}}47dO4B+-+p@#*f@h97s&5OWFk2ax>% z^wYx*wurpmsXGd4}XEArT_o{ delta 8772 zcmeHMO^9Sy6{b?Jg~^Bn9HVZg-ZW}ZGu`*++FK{I@*kroMUVQ}(%(tP{nOlu6D;j-JFzc1aph%wDSg{=G{-xOZuR z&xh9cE-lNiu6i9}Anj8sjZ$-xYa)D|M7x?M_15xz>%ZPzeq|Uvab%rKVpXYC&px#h zLT1vUw3g{p|60Cx{mAc@KVFQ6T!^k|;jF1C$Ewtt$wp!_+R<9y9quj0i-X%R{o(h^ zoBhBqUms3CZWQSg^}-8}kH;!HmZSr5tt2(4fSTyam3%_7ag8^9eprbsx`b#GezoZ& zJ*O+{h6Q6j}kuue+0R7zYgo*iCUjJ=psx-qEHcVbR2 zVb)&*;d_tts6N#eO-6#US+WU@oP!Ytk+;bbhD6+@9- z7At0Aksz&!Bd1kNl9TbtrK)@=lYVP?Wm11y4(mt0JN#nNjlN5#7krbBpT2f?`Phlm z^o>uSe&^QlwnNQV9h%DCs9sM$6(pbcPN*V>Bnq$7qja!ICY>q7#JW~5ODuGl+@AefXBfXIU3FPM7UxXwuH z1)bjb?VwkkkUOCL?VaU^R{;YC3PTEs2uzVEIIf|(gx09Kg~pBoSE)ue?2>jCT(Tjv zKrK@O$JA2Vdc1t}o5d)JZ1TlsRRvY4QM7N-8J{)R*3KBC6E|%0xO#EKN#9+D>E1t= zkDc0h5}*7J!U|$;_$*hgF+GJj77v;Lt%Fsvn>lcUFU(16itqu>R$!>AORLeKh35g* zYtKGpdK0<-bgyLh0oF@v3$QUMWFn;+u@JMS7^D#szN6-<0m5ePbWRbF1kPtt%83pY zbJK#}RLkutnxue=su(yD2?i6oW`hlpAi(TpMp!TM^xDJ2%~5)RH;OjhzC2uB6%XDE zAuzj0+UaDHkKjdPgfLyx_x=F}+zGT;4w4#ihTy9dV)VdWb=8LK3tGMtG|+!-Pyomw zP^z-zNLGc$*%AJBnqk%nY-R=M#GJ0ZHhgJiV@$-wltSo7g}(aCjx9_J#MPgJ*vLgW zKnzx8XnyPSf?@n z4VjAva{uXGz3v07m)8zp1UxC7LW3E>41u+gqk^4i+IW}!%r4ng%^@prK4peZ1g;=K z(c+tNaXNF1KUrLLs3znKyov5@^Z|Cx<&X{hK|Ry2UfAi0>%-Hd?}Y`ro*0cXxv^=g zF+%txJ`>fFGhy%`XLAa$gJ8#vJ;v-diB(5Z5R8n;E%E+1CJWHjlE9}Vp-Be&=Lizj z)v>2}9D6Zmlh}#5)2_RI7$HcZLTM`eQ-eu_>n&Aa4-9n5JH2xfE35;?WQGnvuZTlT z1tM%IHs@>cvur6%>^YFZSQ3m$Bq#{zO&f+!7M^qHgyd}wy^uTI`umMxt6OwVx3aew z!D4lajw*(xiiIO^5x&4hwQUQWA>=ePq+~SGY8XW#TfKq0nouand0e$lbM}z};=gbd z_n+<+ZG)?0KfHg{NoxbEkF7$28HXEeu26<5p&iXAjFRH2?3v}ql!{ZDo1i`LD?+YD zmtnX}af$tXSYZ)Ma3(A;B90wcUO@JlN@E4ChkHMhtfH-!q^QAV5#Y=PYv%L zjT7);Qc;5-DjQK^&_-*G4HY}&23^l#1rzbM;D_K1@>RuVLbZTT@u)=GsM`nDH$FJr zS*$A0*#yMTEE0A$$JDsUkFYc^b7l$>|wRatTaRoi`4rLcIuto%u%fXN` zoA<*qINNk>i-8RWsU=OhMC@$=E;y(LXA5&YFJRc-K$p-dmn&Jvsljgsmg)mmqcDB@ zXT$yic5!f?-Z(nat4{0v4GO;kS<^e=e)E^Zj}|Xdr$PI;>CNT-)1BhA`|8z8YXh^7Os{@@ z`03&BaX5S&4j=z}ncD~6;qY!+W^l1I?zXANvu)+WU diff --git a/master/.doctrees/tutorials/datalab/data_monitor.doctree b/master/.doctrees/tutorials/datalab/data_monitor.doctree index 01be91344f7844bd97ad9ad8e24e697b95eb3252..def02f773141edf13cac76cb00677c89d8c6e2be 100644 GIT binary patch delta 3854 zcmeHJzl&Z~5algfB#0mw5|F??SC9<`Gxy%PcjnrtpoNe{Ei6PO%>C7MC@P|E@exvKE2_4Mv&8&Wm3*c3cwPf~-iiG7yFRGoYG#oyoT zf_CowWBcTY4IGgmX`dKtl+cnC@2l58R>}Ln>VXy~?;7$uW8mmZvs8UZ=%^*I0gUo) zfGV0@e0^;=zdp87g0rpwgo+78A{8y2LX5&K*hyh`FCO`Dczf80&2ohd#c)IYN+$6H z)(F(>^JM-Rj!oA;8jde6?+)J#-4>=lKOVOG9hlz+9!(KS4MVjRu^N+-7%ZVkHQq4f zC7tB04>h_}At8C2Qt~xf&QwDR&CPTk{&Dzp7>(r;+nz{lFoz&6Mnq2~I+Z-bjJ^yg9b$SgVs^sk*;VmZY%eRIQ6T z%ZAl9DMt6%i4`o8Og%`dlp-!UEE&ZK(f_ehzIUUB90(wMj= z5yDbqKtUGAj!`F-Oe=xwl1a}iGZQ$q_SW!vZ#W+uyos*n^$ zD|#)wKnjj#0ru@SHcvhhL$R(RQBI~f3^`HM-FMdPV-NbR*TeMQ_ruw-+ky`C%oaTe z7cT7a0v2D$CTk)(AP(5DRp<3$6lynD)L4;*lAInq3;|h5>tO}4u{d9O=C$-)keF2+ z$0?&yGtDJMaRQrnE3SF~4t_FcF7P2WAUp;wJ-HqAJV4*?XLB5dM5%??vOh>EHzzLFQXrC^5M>g*FKRgm%xc$zlsX-R znG*G?E2S{Kd~Lun#bj;DIYJS1sQagPzQziHGzf_n^QG_Q#@yr5dz}#-c2jRr52Jfp z{pY<7uRZU5)(x2@`&G`Za_&|!yUMv$&h>(3c}BWPv;SOP<(xj5S2_3pZ-U#(x#t$& KJ+Su59sdHV{)18g delta 3840 zcmeH}zl&W}5XZU876}p%lq^VKuPev~gE>Fu{1_V}Xdz@#3kwkmGv~}{jIRGc5G=J( zEN-QcLKF?6Zt)1Iv@%)B8cXP~{d^1ob3qVeRnM*qusj|t~ zgsm+e9YQ#aoo;_PoL=0xIeayAYncA{XxQtQV1FI*ShJBt#l##lwC2z`vR0cOYjEPi ziU%cfOejSx1QP86vJJs_uuWQ=V{d;y ze5~x9^wSeJ1~YcEzoSvp*&l`nruV-X9viba)n&&CZPOtkxyU(rD25S9X|(n>ZBz0bgKm+9KDty0OpG1knGp|HvjwMCqqN-M5HXQW>8C6evtR-((= zNvSIPkWpnsB3YYhwEb+z8-sPpp4H{Kjx?+s_?+_>}fvoBoQd*;cfE?(+M z^LTXWbZO_MbJOQP4O`nCtm*2{!^010A=%4vawwcEBtkN(QbZjZkr<*CKLu0tmC+Xf zX!>W8VmMfFnQ9}P`De44B#2;h@Du@50a+3Y8yAVP!C9O~&DN`7dhgre;@B-g=XriN zJ!-C8Q7$ML3lY_-wueT!>V_)S+Ui(J@GCA8ZPC^2QLJtes1z^<$4xh4Ftz2G>(F%D zU{i3drKG(8c80Y-phomD=~k!d>Zy(U7qdWn*=NsnlmFwn=SSPD&!*yXHA7Rom0-~2 zoG`^2@5;0$OU0My$s?!^QEJF7tH2|O&qr}aAB=S&ux&;y>pXkijV{Iz)xOcz8U2uF zyOVyppne;>$rUF~uiP47R2di`DmK^(*<{AZ2!%LNYwTb(o!-HiOPpQTnNB+Hqn<{O zNB3H_a^2}J*V1;^y2oWjzdms51NSf2b$#I02X1}f)(7s$=g0r&z`eNm=8=v4d;SJe C*ofu; diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index d6d7edfe6ee01d06735f1a137dd3cc73f24ddb16..bab2bfd57f1b875faa3e00432dc9b3e05f9a3763 100644 GIT binary patch delta 2199 zcmeHHJ4lvM80MS=$%=x!ER`P`1u1&&mm+A$A?QjpSjO+Uf*5N4HFuk82pZx*4MBqq z4YqyJ5Y*Dp66q9{RDwEpZ*P3`YCVp6%-QoJ zrkATAnbc8n7AaCnD=Dcl$Oz>WMwW6K38Ls4XevPy7(1EpAZXdzUF_>BKHE?6an#OgVL-m2$a_c~Cs9wJqc6S%Mf88v;mL`Y6-82`#rgAod zQy|UV2aRbYfQi+2_rPMP{uA&oye0A(?gL1hhr#9ls@*bPXz5(Q+(gPFb3q!3ZS*Wi z#T?c|rP;UOYGqY<0q3eNUJEY)hq~A?s*UIf$b(J6|B`rQ-+w&ON0^V0_6!qi zjB+|__acvgnUpm*mxHh1Lr_>PvQaek)<;IQvB+SacWHqV72s@1 z(&Pf{Y9tbg9zxRl1=v;?5d%dsv-W{e8y}P8?Vyrc?HeolW*`kxKy>S zj_#u4(DDA-kz)t@4%hICX2s;s>Z-wYob67pr(kQPuElxmOxwOgnYw1+iAwLU1#m5Z V|Na8j0{HvSwYmV7Ie?Eh`~dyPqN4x+ delta 2167 zcmeHHJxEqj80MS=sf;4TAVMz|2q}7g-g|z6whV%Pi3TJ2Jij1HUuez!O*I4!a-asG z!G;FgZZrh7G`L7Qg=H3PLP3K|Q1{Ccqwv=5cn{Ax&-1*```r17midX6$Fo~${gO~F zlF>e4oN(SdYCwBKn8~?TT7VVS<;F$;?Su{VCfZh@ZRpb31Vd;y?#a^VAlh3T9YUW_ zx>G~9|2Rim_Sdgm9vu#27i#0KJ{G!qvaaFr3$?Y=;qmb+X?g_RPwxWSQ7lGu4yAVw zP;9x7QQ?;;S|2ijwX8ILtS zS1LuMndkUqrYl4w^AR6v_Ys&8g4oiR25YDxDmracxm45^rG|P_rnT^*e2#&q5MiyJ8|kr z|M42#cvInbbk*QG2Yb@1Ioww1t1*uQY2P<2Q}+TsQ@Qt><=ZUZU#h=ZzIA`C|0`cn ImhZ{-?@-5{GXMYp diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index dd3806a42cf00eca728ec38938771e0111b6300a..78ad7e090621f587a0e0ac607d6530a367daba14 100644 GIT binary patch delta 72 zcmbQ<$2q-^bHf!*!!*l+RC5z^%QOp9lQd%k3kypNb93XwM1w>#i)0H! bgCt{9<1`}!V}oW^u69-~#_gi{vze9DTFIWV7Uylw`w16U&rD1EWM!V@nH5Gm|8m=-t1H z`s4D)<9qjMU3FaJ7Zvmcp**u zywa-a^KQTNxHBuqNpi{IVJZkBqE}fHDuhoy5t6(2E3KM7@49&YwLQLc__fVT_kMN9 zKoPZc=<#!x{^jKzOSgaRJ!`)?a_I5Xdn+HEe)7KSZ+0I|`*&AjvH88DPrm1~&-}{L zZ#{9*tTjJe`EuFXj-^*#JL_ky;XQUNJ^1>@BS-({&-#^;G6#5w4J->ma48ypI38Id z+NsnYVQJNL%NPIssV7y*QyV!=+2C=?c$-O7&iSl3iGj%Wd8Jj;=biV>D~=y%DshOX zE#3L;odewN$|1h*+q>W7JGS)2?>xKe^KGlovibGtS$=Z%ic6L@eD}UpOO_3{=lSM_ z+Y|lT_i&=gCZY%nF#(juanA_NG}&l`(TbAp1PZH;?D+oKCsmPZ&Sf?Hz^o0I#^^`9Mi@b$mKX(2vE_?fdZiky>^UeS=K&?E$_cz0TbRm~13wBLgR zvKTy#&g5V;VC+p~I51DV%sx0S+6R_aO&_@Jl-|cLu7t*|DJ_h0N)WA_5spxefFrHT zwDaiFs^)>8{Lods$%n4$+q(YMWBr}=^~ZY0*S~PAzfj+Btp7`O_2b^@^+U({r_|?t zqjyIA+_C1QKjEwlBGZ}fh?-t$6#bGz^ZFJR%XKi@yIzN`CeYyH3r{ZpII7Mmqo z>(`sVKDWQWp?=>-`_<&{p6~DLz15aad8xN^a@mXhPxR|&e$e06ud4c~|LAWSZIm3r zpm(r$ZhhTL{S)fjU&3xreYt;PO@6<3a=q*2{t3g4R@(Z-PxNsr2YRQ~fAA{S9e#Dq zx-b6-pB??tn$MnV)@}H4zj|ve-tb@j2kXU``e)T&{z?Cg|MLy@{;0RT{=J{{^;>B3 z=*Hf--ug=4zojR8+Fc(${<^z9yy@yodvAMN)&BREzQE*^*ZL2hHrA92M0ACSj1Z8# z=L|%FTbryk@YKVHhMOmQ?i*Y?9D7SNVZ^x{tw2KXU^vM*m5B`zSU$8c*gQF=2Y2); zq)En&;VOA2NR0SNW~L1HT1%-#z3JA$X_Eto2T%8^!W-C{o-w$rDj1LTrl$wH>VH0C zu%Ut>mzo!Ffr;QUVq4%!$RxG$I-1F*y9fJ+V;QpvBvYO!712otwwb)rQgNYVtPh?$ z*fx1648GK>0XUPfhlX16=$VZ{AqNB;tg`jPH{!&nms1f&sU_|U}TzD<)=ION^d;dZfG}BGTCfI)hB;xSRv0ZGWn64 zcm3{lJ8!-9{AymARJE@+|#SVu}p z<7VKUHks_4vQg#~L#&VfTN^d@4HrVvULs6j6F?bWUovMx_AC%#-2^MY3V*{Y3q}Ld zWQ9lskma1Iz@=cq@+PzZiw^*c3kA!E$%s~Bwo%nW?CsAEH`Tv($)Fk|A^}}YhHSjx zPTEMhOxEkn4d6EEJ=u7pb&0sYEEWCJ`-p86QJQe^~56jL!Ki8zw2 z1KSN05rr{>U`e}JQ84MJ5G$Fi_dYt(^)LQ(w58sE#o!$qI3Yp-YV{NUFx-Z4#1NRJ z_fiqz1M~RFdY?6l=%}@x9Qr)AwS8v!T0e2haO)$V9)7xaTj7r4THur%bM5NVp{>BI zw5Phab#(3+al&GsQs*GSYf}jG*2NDr8@S4(KZ^XrH6S6MS)N+DWi6MPuWQY(DFQw&51(#$d!6q*2LpO}NjJANf zm?`WDkvRwo&jgH_bJ03fDC%=hj|bCnHbcBHqr&?7_g;4^R2*D=ZL%)r$KR=iT|=8`LoTop#gkn zmf6^Z2G!=U&o2$$aXipX7dfdPUK*UfV07e>WDpEWM8JVV{-uV4c(9ah{m_krEgk6aZ>bhEFBtVLPLH`lH%9GzN5*?9J@M1A;{(b;EDc`}Cp4B?l#WI&cV0bRGC#8EgV zvWSd~mZfXzAt_n5mHhM9y0K@t*P3iw8kq##hCQSVhk;ny*&g3Z`6tb)xJWxL)v z+E}q9z!9+lf;gd#LZZR3JCA&nK?y+YnzFJ2=WrASG*Yn>RB|76hH@WK#DN1QfUzmW zkh+isWpojepumeGnn)8tGi*dSjT6+}NL?kHEyPMD8;D)K$QOxSRf0(d62m#DHqw zDb*KVKit}(D$KSTr>Lq*R-4=0RZn4+xr<&#iO`I`M1({km`8Q*2;w7IT~CfdR>2xm z#C=;xAxIHGmGp)~!7a@1U$S+K&+*yRn-nWJKQ(iKC`t{AN}j$mdQ^)HT+EPw$in%h1X1{ zmQ4$n?RRE|WVi1d&b05fmejM?gMmP1@j0F(rIO%~}$ zMF@8So|hm*>MrbSlrh4fY=nLX?aQJzNFK6RfUH41dIwLAHg}bmnr)o4vA<-u*$koLl6V3YJwF-o)I}fk7+odce<^-po=C7VH90XKnG-!BQw~X1ClFPvFVa9{F(%$ zxrnb(dVvKaT1>?{BzS>4RXVF>bXA2t#Ws22RZ*mX>q|qC zvYCefM?goQFhT4UUDXK<*eDwa0~-;FBH5wBgnu^K46;zG8?x?t z3U)iKeu^$gYRqFyB}=>zda0-lkv>6Q874t8#E7=*;K5cwa24K*2#WRuL=J0EID>?M zK(qniyMHl!u(zNLDnOJy0$~w1h+HB93mGII%D$>E{2io*R-`G}ZZ}gf?WZu?#TWHz zm}!-hZNyO2?C6g`{6Z{%ctlWdVhqA`a?R|Pep5(yI4fPx+ALP`%(bg$A6Q(GImU%e zeQxXMUE>77FCb8o#KD3P_JikWmw@3(B}ME+4vl;TM|6^)pMui?r3QU+NdS%4YIT%g8uKNfXJ|PBeEXqbiXY5B|rbuLTL_h?fB!&$8(;j7EkfdSl9j<2I zjM8h7GTR^hy8Kl-td;y#+Eu;j-+?U2L(>vM{}?g^#3#Z9wFsMlmc{^37f}$oA>Koa zZ<@^#11iW(Xm1LeaXh@GUTymF9_%07B!Fh3>)Xh&5R7HhVT4{My7blZ!-DNLdjTM` zY?C+OHRhP^k;z4eGDUzxL9IZU1(u7L0sgzHeO@v2OA};o4JAaN3Feqis8$)#ULdA- z!a#-YU2IxPXrDuPix~2Oo@tQ|nS=mtrXAYHdPT#%M?tJgCYz143UrK{L7WB`5RqWY zp=C+M!KzN$C|$~0bd?-(?drm5t;mK|VMnbvzW@a%OBN9yEtCra?G?Q(j(mg8dInRv z3ap`>=TP8X846o6jVg?RHB=(#fGLB%5e5KSD$+p=g@EKx4>7zXoYN>5xWqUhX3(fn z%B>dZXTN~bZJ)Vd+_!yZw29H9f)|1E2nH`=C^=LkZ-a`UsA|0u@>WOz*>lrGO1AB zf!HG4VRjGKgNW}{L|%I2NmMBn#o!5w%&4pjf-6jLFpzYNYC!U4)GUVv^vgIJm}v32 z4D}HAfm+%}js^qd5wR@H%(kf8)^gEi6C0+w{ML)~IXB*X0U^5yIe*_RptI=yTe&D# z(?sbt7%!MA1{5HEaa<8V-zXml;?Sw57gd}(?9f)YDZ3UwzTlhdRu|52C6^U;#G6{| zocf;Qj+6DD-Z8vpK1ekmq?!*>t)B5)i@W(C)w%($`5@JNkm^l`-PX-+%m=C7e28m4 zNHrg%TKg~K=7Uu8L8^5FT=PMy`5@JNkZL|iH6Nr}{^u|AL8_JUtyMC9VU}stKg^jA zQq2dc-k9#24^qt<-f96hAEcTOQoS+ZHXo$whJg!HT=PMy`5@KL{=ZS?gH-cDs&xZg w^FgZlAk}=3YCcFcb9`$)NQJSrmC=s*Ak}=3YCcHC<^Q8WstE?E_P_mq0G;A}OaK4? delta 21595 zcmeHPYpi8ud3N810TCErE?nm9btZ*K8P0t7%Ybzt&<5o;DYcY~_+5ocfk~kuHUx$y zwTkT+j$xq|$|P7xNeYu`+>t0Z?KO>MlA2PZrH0rxfeB!w2nE|`t+mg|-bdW)pQOp= z2h7>$+v{E5_q~_rdES?~`{6BjKfL9!$B(2p-K)*YTUM33FYjzR7Y`hsy1O~yAF6GCv}8O|UlmW1NoGE+V8 z4~8v^mKT4x@!*{ot~rd zEBgOfSgO8v=$Uiw zdH&ayKl1c>v(~)0`pXq-i_34meag>T!+R_)Kl0O~4j%f}pLUDnnGu>=%9#>8a~nid znrX{|Ra*IG%WbRbEua5)#~od8?Tp3av{6dPpLgbWE;*uS z6~`f-u)Obk+j`QN)kFN?_jbO+@7VH}zyHF-=UXPvvif>`mYESD5Pu%%G zyMF$P3zpw}>!CFdtxoPY)_!*Ik)J-=Eu3dUvrLmELa^u!WzK1;C3ArrJI)9#tLls} zqTLQ0&@#tt(%vX8M3&Oz925(JQL2ohV+U+oRUO%A%dwr?&c_>iC9RG$lPD}tK4xPD z=PWp90=3P3wymlj_^FRy-Wh%T@~$n*55L?U-upY)LEcDI!u`+9e&yzk}i#`R>0 zC~dVY&;43gmoMR?a^oxAy_?_<#nq+9(NeO{rc*U|M?1j{NuyT;#12H zexh59{`PS9>dr5~*s-s7wv8@&wfo6#dERT?joqTCHn!!Iqa-Ji6x07Sdo0P1yQh|S zzK)lE^!4ug@~&IDo61+N@AL;pS!qSNx9prSuuKtJF22z{x!nI2RzCX{R{rRd=}End ziWb|V-0)m)NBNH@_0|_K!z{5$Br8$ps6)j9j8chfgT!t}8@|-Lu|Eu2rD$1HHYz~1 z?1>{fNFFE58R zWOV5F`cHI*DHxZD2l{Mf%xp$$rW%Srz^!~ilYPi`+o84+BsyyZ^ z{bC0dOIBWg%Z}f@X4~zzZ!fk{z*cWkRuQKMuk#YXE?3Z)VSm1Hw^#nFcXfYgJ&k;EbW;pca|q^c@w_Bh-?1);mv7wSfcnoPqdx@T~sT{jD-cgpbl$ zBQz5+F-t~hPSMz?OY-PoV-skF!8w}j65NC0K7+Lyfk`N*hATm2x#9NS3FAm>v7gEV zFAXj$^i11Imy3r7XAc3UrDuvpBPj@-wi(EpxX2?&9?A7yzPfIJYt(@6&hVtA5}7+| zok@awL8X=1*~Ui;X$Vz{(;&ec1FH{(I-Y}PjD@5|8~&*GjZV>GHjVwW#by&}?|Y%Y zq5RMVycuDX0^~`9c z*b*(D{`>xxp_ibeF_Per@RtA?dQQsDjVvJK%aFLAQGJ6%d3DFr` zZ^-4-$M#pE&KXDrnlq7<6^1&8OiB}gI1>aoJWobl%M0*w*GGH17iO9kCH6zg=Revz zy3iEDM9R{L07ZZjk17WiK=IOC2ChbX?;G6IA4;CR_8A@*1Gs6vdaAQsby2 zK~(t@G3T`KF?kXZpeUO(X@cdIpFYu9m~FCr^~pXR#@QCjbD!#;S1!EJ9}cDU;NCn! zXyHPWY$Z+&?9&5PAi@@l}2d;7?E^Z=oU<8o`w1QHukl&F>N4syY9MKWvqs$`V1jmEiDmBzLC=bkXji>^E8iU3nBHf}U z@IcQFfIh+*shpbWI zMQj9HWs+lOnFug%{Jugi$z?&PF?O{3ulr9TRPY!AjD@T%V$`06tTR%83?_ki<{~9)QE)xwK*X9ENTL8c!E1;#z_4Zo)X&ySOA6%l)lc_7-MJ&rzNt7J zI&H2wy}IS#R&nF7Wmo0?TL-6|R+lkCq5jeU9%2v>44@;p1V)1YSOyJmbl^jSoBIoh zl^hX?!U%vXkIJWrp+1PJJ^@7C-g}@kBsx(il~js=58y_TPlbfbqf`RkG$GlLvo^Ek z{)NHCLxjyNyH)Nd$MX?NJ z0YuYiD-djoM`CNBj3^4duXGkP;0WQ)fu%W=ZHvqjr45w%jHFy{_$+Gn=e|351eA2P zKr~Owa&Hl;WfViW~=K z%#2B#fZ((W$$BFXcZM7_O_79h4gkXOVDO}*Q0FNziPn`Eh*8ZpTbJSjiF1}!Aj4Dp zS8lgkWiwbng~fGp|*2N%~~Ao85E0`Lk<0-#G<(*VQbKYITPXqunvS) z(PEoc9=W5Ats%)dItdhHvxKYv0QXOhAjr0nA-9F!BIm{6m-mR zR#HQdA=#r*r~?!ifI|WvRtrou4+9Z2!E)}R(O4Uh0f-FHZJ_8t`yqL}jfky*Ky!oY z3;7<`LD7mtmhjPLwOWpmNLoxkh3wF3v%cCaO@CPFKQgmfDrJ0}gV0C{9aRfRNH z09PWiYdNZ=0aX>-D!M+?xnuU#tdTI<9CA7o>Fo202(F?W+}7JzwC$>fhy~yh1xIA=t!siA01d}#|5<+My_ ztOBknknq40l`%xBQ8FfpSDW#2cNknUoNb#o@pU-BJE2X;468K3C~6Jl3=cwWHAhV8 zRap@*5v>`Xb%hMUued?KHpA~ww0)=$$hdY51zK{(&;(VIK()gcH1L+LY9GE+YhhbP zAA1>PuFZCX6~k=PdLq)0Nh zTNEqk9t6YXgZC4ymCJq5E4CCLG}l5O{|XwTnS{ zsba~1KB7|sC4{1gF>quvW?%_7Owe^EP_IzZ6$&(FKqNo~%9UQJUtj7YL(DeK>I^ZA zv&~j~;|la=meeFRL|!Jq-PvcIkS-vwpoasDjUbDRa2#e`z*;5)#`;Fv%}fVOi%;t5 z)h(x0Rj*pEAGYkMimxIX2pJXL^N^<~*tE*HP^j!qI}bT`wBesCdBY+*RyqiTaEJvM z5di#024x_t1h+KW|GEC3b%p`w4wxG&Y>AFpMP3a&(np#?!cfqJNZ)3+VO)TPv+PbV zF7&^kXe+>6NPh`v5DfQ6RV76?jH}XP02U$65P({ME2^PxgZ>#vgKmT_Alh7} zQey;~cmX67Z5#t(4qc@RreRP?RfT#*-Dor0(5=~4>*7nw8XEcvuQJU6D1Z^F6d_a< zm{co=hEPRNK!1UMQF}weSyv4`Ln`2{0qsRJ6m_5y=Z660&L>D!kPU@_YO5@yD3D=b zNob%WBsyT*7K64axwz6<7Dc(ofod5xfd?yL239pPbCr#}1yTU$MVf&8CDf!M-!f#f z(gJI_mNg|Sw>aZ;Otl=AUWuuN+1n~Fdwg(uBglKCZ*-fnZJ;P*K@8Xs0Dg(&>_gOJ z*)T^6!jA;W(04_9*#)p@Mp09M4q0ldQG*E;&?IPg#;76SjDkFy`FiBKE zDQFikpjT7%^G<5?j6#BpLf*%?QKi%qO!~lC2-<^{Yvu@@tL_}Nm~C_bQ27~F(aEbD z&UJY4s%{Z9%h4;xHV9}JMa?u`TeK0~gi{WgQ|Ocj3dw?i&f}AO+vVoJPcr7@I z@i&N5z#SkBkX|7&NRQbNJd0v3Y#%y^jiS+FwdpV8Av3Ku{blH!5F|N9Q|jglQgbz{ zVYmdOk2cnvuRTBBL_}6tl9MR0nv3C9f{s)~1W8f4 zq~TAPr?F6~8$EjAv{MKlZ^Kq)58ReRfQ_aVzW)HqiSGoB()qI?)8sM6bQ_aVz=HpbW z{aZ|~&&R3O3~{Z}w~gg|oT?rYn~zh?$EoJyRMUWRK29|`$~7OSnvYXWPr9veLqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu=1GiR+yH@36IcKM diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index 7582d8fc2d00b5f74ec384ec78919a0725f5df7a..8a8827416102892bdcb8a674c3462de94ff4ff6f 100644 GIT binary patch delta 68 zcmZo!%HFb+eZxLZ!!*l+RC5z^%QOp9lQd%k3kypNb93XwM1w>#i)0H! YgCt{9<1`}!V}s`RoZH`XGEO}K0PmRi{vze9DTFIWV7Uylw`w16U&rD1EWM!V@nH5Gm|87aAOHXW diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index 3c1f5948e5a64d05ef518a1bbb9a10b11fb2117b..697bbba89d8778248a3d2f820b17d11c28c31654 100644 GIT binary patch delta 399 zcmZqN%GtP;bHff!!!*l+RC5z^%QOp9lQd%k3kypNb93XwM1w>#i)0H! zgCt{9<1`}!V}s2vIWI>}4o^{?UR=#6HrX^qX>wJH^kn7~xygHyl_yV4ah@!dqA>YJ zN(x~`5oyn+FKlC!WKq{qP@nAgPg)toNX|&iOHYl7FUZUTa})EDQ*&bCONtWniqley zCRe1(Ourb!C_VXgy5r=D>C#LwwUZMsiA_F#fqk-fMj_Qql%IS$bLRBI21fD8@3Pn? yFUgkTf;bK2vdMxu;*i{vze9DTFIWV7Uylw`w16U&rD1EWM!V@nH5Gm|8}4oXp;URcd2F!P2QR=KiTh}^yHc8GLuCzB!qMn)RQw3^U_me;tMkK)PdaT3)>haML?|N z#JuFxoS68MqQt!7wA7->7t$S>VrnNRUJ{#p{sQ}C?~Fn!nI}K_bmq*-U$dnrFUppg ud_P+Z>`gwfBfzc$Ic{=7mLf06MFMaP11}FEG#T7%*~Ax6Acp0ERrn@ h4U&vajnj+_j18LKa<;$aWCUU+AZFhFmXpQjH2_=08yElp delta 80 zcmdn_N_gih;SGB@4f6}kERxdp!F&Ns8U zF}Jxfw|;e27XN+>7thnOaoK|P#Th7CIOm_HF4$l$LiwANF@RpOyI5Mdy__2f{n;e#N?>K zMdHfgPdC=ujkVFmAI46*mfor!jJDtXGHQIP*1C@kf3eXXe7mYftABpKulco9FYkE1 z@^II!QFG?q>gnx6ZjIXG?^P>eA<7t37BMOe;JtSWDLP0|`k0ex?|fNZn5oeqmLdv7 zZ@^?hE&)X}608wc$wvNFJ=xrQd_3Qt{IU9IREOd)IZsI|0Y$oyJ&Iy1zr%N~F+pTK~J6X})?;?Qc&V8ee8&h}lJOu@C`z?FmeDEF2J#Yl1y7 z-u2yR5J>aI^-2umU}mCO->j%sGB|Cu4pL>H;K^lKqSuP74H`|y?83}~w$a5X!J`I} zg}^(bgaW4p8zSvN%%Ta3bSW}H07-}u9SE>O7~%SIv^}vQ933$^2!nZ@){+rfam<-_ zK3x9b?9!!6N9(0nqKYVD_N+GQYK&SllprG}S(=w_R)^a41LHF+%mMHA?KpU&`fT*^ zQ0tcX>eo)adG5r?S6)4SZb-qqRu5Ef>Fm*_+BcrB2e{dWtMcq~lQJ;-pmYu?u)a{S zn$cU)jWLlS3E6eK(=mnOb%@H!0<4XY3V0r9E(@&^sJ`8gcr!+@I*_!Mmw}c(fIuw` zDN5D{-Pdg5&3Xt!bML3>#KHiC=60vxi8m9^S!E4hX+1CuLS}=7Sk>sGE?bKNi9 z9u0)jBLTT$0Et2pI(SUKd;D+^J! z7$S&*!9rPBO);s`3D&8^LJWc+@BTE>!u0qCyVLCc`mO1FVw=<3jvh?K1Xcyzcw@iY6N85e^=+h}bx8Vu%OxZqkuZP9yB9lym6 z^?xjler;+9y5$?}l{$dySJ-NI^#)sPqt7<_h+0}tSc_9CCa#J!Hh3O*Pgu_{v=%i+ zYEAJRzyul;$5Rj}K^H^B21;B1vW4#JJM7W^GfwD8Jajn@5@Rq@$&7S>DoX1z+td%dYA~j}L=^?mIz`=) z&YGiAz4`v?d#6_}TsYFM2%y=ck>T1E@KjoBi^v>Zi7Mz|rYxy_HOUT$4_;-THXl}+ zrdQ~-FQ0tv%<&U1zI5zNWpwXb9V?YvIenyK2S$h68n2Ej^31cd5(Mgl^cE$#6j)EK zv!F5xiO!O-%o zWO^CPPM2q@Y-H*bqIH5DOKcormm1c&^#)mO%cIFZmp1Ir?15pQmp`01kJgW zsC>aBf>Ocj?Cn76K3TR}xgxCPM&$@4DVKtmPDJuxs-POgJe}VknsEikAj>L@U0FWg zee>jKd2GRPau6_}5*a@Cm?XI=xk^M~peMg08%-(-DAQm-QZN7p%sCS9;;f` diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index 5769a4136df79e9202656e12e65747ffd80e03bd..bafd7558740fe337b3311467a60b257130fa1fc5 100644 GIT binary patch delta 76 zcmZoa&f9*Rcf$cr!!*l+RC5z^%QOp9lQd%k3kypNb93XwM1w>#i)0H! egCt{9<1`}!V}s_8ob4Yu8G)E-`$taZX^{ZHBN(j! delta 76 zcmZoa&f9*Rcf$cr!~6m>i{vze9DTFIWV7Uylw`w16U&rD1EWM!V@nH5Gm|8saadwQAfsWLWkGUMVP2xXsYR-}iMeH(g{euJv4Mq!rG>esaadwQAfsV^ftf{enn8}fSz@wTa!N|FVWNp;N}_>LqN%Z^g{7HEl37x+X<|~E RiHT*hSyHOu<|f92TmW(k6M6su diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index fd4348e968f7b177b10a24fe590e8726e0c06ae0..fe9a30b0f3abe299557e3c9769aa8439fc55222e 100644 GIT binary patch delta 72 zcmZ3mkz>I|jtzS_4bvMaP11}FEG#T7%*~Ax6Acp0ERrn@ c4U&vajnj+_j18LKa<;$aWZeFilW8*_09}|C=Kufz delta 72 zcmZ3mkz>I|jtzS_4f6}kERxdMaP11}FEG#T7%*~Ax6Acp0ERrn@ k4U&vajnj+_j1BZt@{^18i}Op1l2a!aGHY$NMaP11}FEG#T7%*~Ax6Acp0ERrn@ c4U&vajnj+_j18JUa<+fuWZeFdlWCD80Ascn0{{R3 delta 72 zcmbPyoMZBFjtvJm4f6}kERxdEyA9jZ1Q8=FZHW7{Sj%$*PDVA%rlWK?osY8o@PaCl(4@ zJW`}kNg;#~OtC21_(xQ*5u5x1^<_0+F!D;9l#hGn-E;0aXJx%_Wxa1>qn~HH5d58a zH>xNZG&YmMEP=?9G1573ngVg~Qpqep!sh4xx3`Az#N6U3fH8n(A&myEwHmRi^V&`R zbJp-*hH(7vN4FO{-&c#yc$gd9YutGk-t0Yi94Gc1Y<`U|#|z=TQXpd_k0=$8v!uy- zYf zRNkN?a8^nrBS0xDj1RxC6M;QpMdr!{PC`19F)6gs86JTID(9>hJ#6_cv7v5}*aucm zV=CFGj(y^ pjjp?4W4c*x-DE>Wt-|U)YxULG@92I<_dEI@J37@p{g8b+`V$%1V5a~8 delta 2160 zcmeHHJ8P9e5M@Dxpom~hI{mnajUk!coqfd!K4PKds)!;XgfROOA!xuf7D2RA3x!); zQUrXELI@$4;-YBdKkyf*U?Vp91?nayNXV5B`&3)Z8P1$Db9P~Q@51ulm6aZTXGYtQ zVR}eY&>_aa30))*q@=S>qt`wt@)^+u?Gs=9+Vk#4zc@ZUdlFz6V4yO>7%i0>RIc@g z#{XGs_-}n;WM|W@+1Ag+sx=a(d%Kle@59^fg-3C$Yhm?!d^J)z$31EuyviIiNsC+< zi5au8RvFb=dhxIpw0h~~@O))Bcm>%DX(IYe79d6&LLfn;0NQ76vqPP=CY#t(QPh+n z`K+lhI&>g%;d8*{?Q1XIWCtr{t6^ua>g~cd{bK^W%A%M2YbHIa*DP>)} zOqrC=3cM_qfD+cOWBc2uFR}xjXII%gtF$H!IY){)p$zD(R3tgN;zx`^c1ptsRzJg$ z83q;Av2Q6GtYXj(u`Gh5!jO1TNHzAza8=gf zQD`qBqKK4($|(RvLLG%(pMCQ-kJdz@{b`i-cAihL$LxOH+8TxW!j(&Z!6N%i-UG ChhF&r diff --git a/master/.doctrees/tutorials/pred_probs_cross_val.doctree b/master/.doctrees/tutorials/pred_probs_cross_val.doctree index 4caf02d368e068239291186c13a937421e136141..6485d4ffc298547e6fd3a7266835022882676dbc 100644 GIT binary patch delta 64 zcmbQ&&N#1~af213VVY$@a#CSlqQ0p`s=0}|WtxSlNt&^Ng@vVsxw&y-qCujWMY4sV TL6Whlahj2VvBBnSMq?)cb)6C< delta 64 zcmbQ&&N#1~af213VSa&`MRJ-!j=ou9vRQIUO0r?1iDgQnfl;EVv89EjnMsmaQnG1c TQksd0WwKdPs^R8rMq?)ck^2*j diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index 91bb58abe0638b1673788c5998488e9b5c138e5e..a307056b427ca6ccf7eaa76e50ba2d99a8a134ff 100644 GIT binary patch delta 68 zcmaEInC;17whdc24bvMaP11}FEG#T7%*~Ax6Acp0ERrn@ Y4U&vajnj+_j18Kfa&CXh$@qUd04^XF%>V!Z delta 68 zcmaEInC;17whdc24f6}kERxdHStLjdll#Qk#2I`s zi!rDRN!&O=^1G63L{Y)4ED0zX+>6=BY-Vo@!US|HepS^SszdC%a#xE(zi!=g&*S^P zb9(>I_5C~7U;o3Vto~|>*Io#F?20Apt7EEKIOm}8j4E6wQ3w8>&4Lc2c5cYA{ zCtzo0`8LlHJARh1zv*}Cz`S9gBUK6Ufyol~>_JhAiEiGfHG zlZni(wYL<7(=3HS!h*)WU;)gd1~>z z6XVYAPp2_fnO&xwh`h_VkhD{5vei^WUS1elr{)*_{#^IZho;eSl_hc&#So*wR#a1C z_2g3ye!TgYZfiDN>Ym*gvW?k$S(HGoqeyU%ml*4_Bd>j$O@7w>d@^Xm-g>2F&P+<$ z-~xgU3cnL1j2&-X?H-s7-|cQshK6@JP9N_tHXV;|J>9t}`cafDvx>r!V2Vj3;dFE< zWf$x+lDpTtuWj^-Jj$A5lBhM};99Z`Sz6S%n(^)1i21cYcfXko=mS$zagviHBeSoT zQ})qoRS~zH-F&J0BaR*KTaIsjy^}4s@)w;Pe|)n4+<5N!?vcU!qB$E=*=Q3klL7#hCgyfAy?g)7~|dwibh%D48u`P}Y}8|Q{y z4l%Q+s#d3fCFw~ir7b&Uinhtmu8IB|8{M(!9+_~{zuXNi(E3$b`K6Q4H1R+raS%s4&kxVimDJYzy z5_EA+98@@#=yYJi=rS^1Iy5ivR;&PKeOQ!j-1|qsiC3TLHixK+aXiO)(G>B0s8ZBOK_?Y`u*--B&;bIi zQDsErLj;nHu9dZx)(V$Lpz8EN(`GOw2npSdR%Hi*Dp~{`3Yu7vnt`DH1R`lTUiNF# z@1kE{KG{D%)LP~!XBl0fD3#B`3kuFMav8`h8+4jj8>LcWWeL0tSo#QlEG_|mNvs6Y zdmXc8RcrxwgA$!FqB4nKHL5+{z1^K!f^XXJJzl%q$)UDfA7XEN{QGu)=8;wO6%aiI z2qiR#JcF5|)579Sm zMTSm2y9A$hLU{}i3h;wEYp9u3idAKI7piI(>z=$g*wjr#De6yoes8=yxnL!7EN7{NsLxpIsurfRe>hq;`OBvbDzLXerY ztb{QN^?~#!1dG8d@`{pAl|gQ@2^a?mu@`SMrksQcuWE-Bti;uLz(UPM)AvHoAG^}s zGd||~^U?`@p5=-J63E{gXNqUA097wp609o1;F1s6zdb^NE4Nlvuu(uhimXQCz6-UD zB=R8+hgNVaDyW4F7*sG?3iL7BQ`YFKw`?TJqYv|d>Vcn9&2#!?jI3D&Z7GMsRE)L< zKupG!ic2&Xik`kwgSg@9` zS1Oo9D`WIjdZ;oH4 zBidK7!3HWmF%e^4mh-m)cN(wD<*?n_E7Uu%yZC7TJq(}6*Xk(h8eQs+o^B-%s2j$A zztWu=N+Y-h?8d#++wuFiyC>J%-+KF7Z-4LkUy7p$TyKBJ?4RrHZ@vAkx4-rF_paS@ jz5R{r?eD!;+x7Oh-u~9x-~aj_#s7Evn_>HV; z5FbYDAU=ZlDB=#p3B>%&#}IcS?n2y+xCe1B;^T-GnP8%lCe`YktEwoK#wUNd_v7}dS1#?Hp8fC9y|+#bPI(g5 zhQdlxke;2P7<`i2$CQPffAOWhacG7QzIt)Ux+D=ZS79<4p?x;eC>2W1vZg}I+ci%u zZg*nb+y6yRGG|{|rXY#tmlMRcnBviaqRR4v+&VS?;GfQS|M~R5HU^H_dT$&#mts!Z z2hU`pOH$O?^{d^tb_Nn9YE3~GEhvN%oHpd7^EOnkW0_5U)%|)hD4#29SF;rboiRb? z$Ua-+oRC4p5nsE1HhjOkF==KV`$wjaH4~3-UF!Vcs*SGJEJTdNDltbLjj&#;V1u@+ zDpHKh#fxO9kVqV3jtafNJ&lo>##dhM&dgr_`t-Mx-YS`#qQFX^|J4MgY_Y@?eRaZ* zFFZayJG=73?l+TW_VLYUJ25nqqmqO1r*}`kFrL5CJv<0oDj_+h&XLxMrBqI4Ku-&0 z<*GBi7124*qEugqq7GJgu9#JFiM<`)nM_a3Uj2Lbd)%|QpafS*#gI@GrDL|zg~Zxd zZDuI{uajotWjT$!V&0g8@#1scLkI4H4$pn(;M>paU%PgG*eA{|M&)fuOi6@5K1OFa zD0VifupYSs2)Gvdb<5* z&X4(z-I=~o+x%4?cyRFy)?2SYoKeO?#zn@HB&WeKs@78D@)>%cqqI4u#Gsr^A}ERO z8zYiVLDBfm4ZOm^_qyHLy`PzW7u_qm0EBEEKC6t4RgtuUKsdQ(Lz@t6U115q#tq~W zfU@#7JLP;gSPl{r)4Ys=K%&M!FtO5gi$wgB2-rJOv%%K>|wIs6v!OllrB>tQW zfR~jE+EE68!Fi#Mf7_Yv&fd1u-=XG4?$yM$Qm+AcWk4D1O7dP;O{gF$LR~?ycc6^4 z?v1euJmRv?-jS&ZOsE}$I0K$LzKgxrCVe)MwY0|AOwxcuLP>==8K?ueb_Kmw-en<; zwC_T1cl_7x^xVU81AH!t8k5K@qbFT_07(ZGFfc*OHC+skcTey14I!K};OR~L-L_qU z-El;<=Jfb!)p)--{{H*(Zg*(Zw|JX107WWNiD;|HAWjqXT$hD~&xr($T}`Zs(JCRT zl-W_y&=m+8aY_}6=1pBmUxf)_jgMhesJi5{ibg~K67+EXvK!K88=1#He0KU&-$*=O zzubxOiO)^%8Gv(;eu@<`2u8)gI-dbAh+wovRT)lwRN z_HuW2rG84l`v90=CkE@d2+8A6p&F53W;WHYm3%R?&DQ^1y`VNF5VHY(;Vi}cO+kY!s;W6zZH?4IByVthZ=2M~Ftv zUe&c}^U$+kIKxv4A_qW$L8W5!(;35QZ9O$KM`Ih&Ho*c0h7Sed=aLmZAb1mht;`GE z>l=y3e_ZN9??f(~v=#ID46=A@EtDFX3CmX94D$T^&QMV)*f!v#A_ISoiA$(bGDaTu zZA0}20f@p4e>pHx^2cfvbO9iTy=3}X(Jc9Fo;@VuoZBxVO&)8jO782#lyVhRO3c5y^0C89}ZJ$3s%Iq zD?ku}sTm8U*yNGM4a5?9&E#v_d1n0g)6-L*_kKhCupK&O6^E3e(6CfusxhgzoWA`F$|K~pid$IE@gPmz_@f)$lg<5WNwLiZYz@Ed=J!5( z=W=GZo8NZx3){`FwSGa@RzJRMH^296oJ%IzZhr6CoR9QxyZLQ5zwPGte%tzX^E>*d X!*=sqEu7oU?*q2^&9M3X<<9>D)DF_? diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index e71b299c157e88e43c363489e3dc135b0da41801..fff5d790377b882a4e43818ac9876d853accecc7 100644 GIT binary patch delta 1779 zcmX^8gzMrHE|vz?sSF!gj&K^LSr#NG73L-Cn_8rro0wasS(uun85>wwSX!8y8z&|j zB$`_~ zCL4N(s_W_LDHxg<>lvAt=$TmR8Je5xC>WU-7+9Gar5acnCK?!8Sy@_|Ob(1O;YCq4 z`GUhSJyW0_Unl)y1t7@GQ!vmoD(2$CVwHjhNaf^-L4Ua5_M1*V?X*n8OwZ8RK+n(u zXsxMUgnBZpe4#^7@<}&h%&`|^Q#2u`2tf{GBYxOK$b3$ P&Jb%AW!x&tWVQ(a#wV^z delta 1701 zcmcckgzNMZE|vz?sf-(0j&K_07noTjry1nvn3NV3mpX`69WS)Q=?P^E5k$}%gWr)VsgKO2``GW$rl`s>Fet1>3Tcq7b^fkW}bq9 zo`Fd*7nhNNk%_JW5FxBl&@eL6GoAd==MUU&V}r?kam&=r^(;*E3{8O6ni}aD8Jg)C z8UbBnW(?(<16^gPXJ|e-CO(z}rWRPfK*Q2 znP5Iy-&J5VARqz3Il0*@n{n#oi2+vdm;tAX(8*eXT;!#U8zG^S-vm}to;qA7-wRGB zI)&thBq37BjgV`kri?qG{?qrmGO~|`1}LB030)_t#mc~-tBWN&lV55igde6nT~JhN y)RCGlb|zRTWU$9h$q_`*t|0#X#Qk_3USource code for cleanlab.object_detection.summary

[docs]def plot_class_size_distributions( - labels, predictions, class_names=None, class_to_show=MAX_CLASS_TO_SHOW + labels, predictions, class_names=None, class_to_show=MAX_CLASS_TO_SHOW, **kwargs ): """ Plots the size distributions for bounding boxes for each class. @@ -845,6 +845,9 @@

Source code for cleanlab.object_detection.summary

class_to_show: optional The number of classes to show in the plots. Classes over `class_to_show` are hidden. If this argument is provided, then the classes are sorted by the number of instances in the dataset. Defaults to `MAX_CLASS_TO_SHOW` which is set to 10. + + kwargs: + Additional keyword arguments to pass to `plt.show()`. """ try: import matplotlib.pyplot as plt @@ -871,10 +874,10 @@

Source code for cleanlab.object_detection.summary

axs[i].set_ylabel("count") axs[i].set_title("annotated" if i == 0 else "predicted") - plt.show()
+ plt.show(**kwargs)
-
[docs]def plot_class_distribution(labels, predictions, class_names=None): +
[docs]def plot_class_distribution(labels, predictions, class_names=None, **kwargs): """ Plots the distribution of class labels associated with all annotated bounding boxes and predicted bounding boxes in the dataset. @@ -892,6 +895,9 @@

Source code for cleanlab.object_detection.summary

class_names: optional Optional dictionary mapping one-hot-encoded class labels back to their original class names in the format ``{"integer-label": "original-class-name"}``. + + kwargs: + Additional keyword arguments to pass to `plt.show()` (matplotlib.pyplot.show). """ try: import matplotlib.pyplot as plt @@ -907,7 +913,7 @@

Source code for cleanlab.object_detection.summary

axs[i].pie(d.values(), labels=d.keys(), autopct="%1.1f%%") axs[i].set_title("Annotated" if i == 0 else "Predicted") - plt.show()
+ plt.show(**kwargs)
[docs]def visualize( @@ -920,6 +926,7 @@

Source code for cleanlab.object_detection.summary

class_names: Optional[Dict[Any, Any]] = None, figsize: Optional[Tuple[int, int]] = None, save_path: Optional[str] = None, + **kwargs, ) -> None: """Display the annotated bounding boxes (given labels) and predicted bounding boxes (model predictions) for a particular image. Given labels are shown in red, model predictions in blue. @@ -960,6 +967,9 @@

Source code for cleanlab.object_detection.summary

figsize: Optional figure size for plotting the image. Corresponds to ``matplotlib.figure.figsize``. + + kwargs: + Additional keyword arguments to pass to `plt.show()` (matplotlib.pyplot.show). """ try: import matplotlib.pyplot as plt @@ -1031,7 +1041,7 @@

Source code for cleanlab.object_detection.summary

transparent=True, pad_inches=0.5, ) - plt.show()
+ plt.show(**kwargs)
def _get_per_class_confusion_matrix_dict_( diff --git a/master/_modules/cleanlab/segmentation/summary.html b/master/_modules/cleanlab/segmentation/summary.html index 8c82757a4..0ea8e10e1 100644 --- a/master/_modules/cleanlab/segmentation/summary.html +++ b/master/_modules/cleanlab/segmentation/summary.html @@ -615,6 +615,7 @@

Source code for cleanlab.segmentation.summary

class_names: Optional[List[str]] = None, exclude: Optional[List[int]] = None, top: Optional[int] = None, + **kwargs, # Accepting additional kwargs for plt.show() ) -> None: """ Display semantic segmentation label issues, showing images with problematic pixels highlighted. @@ -667,6 +668,8 @@

Source code for cleanlab.segmentation.summary

exclude: Optional list of label classes that can be ignored in the errors, each element must be 0, 1, ..., K-1 + kwargs + Additional keyword arguments to pass to `plt.show()` (matplotlib.pyplot.show). """ class_names, exclude, top = _get_summary_optional_params(class_names, exclude, top) if labels is None and len(exclude) > 0: @@ -680,7 +683,7 @@

Source code for cleanlab.segmentation.summary

import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.colors import ListedColormap - except: + except ImportError: raise ImportError('try "pip install matplotlib"') output_plots = (pred_probs is not None) + (labels is not None) + 1 @@ -708,7 +711,7 @@

Source code for cleanlab.segmentation.summary

handles=patches, loc="center", ncol=len(class_names), facecolor="white", fontsize=20 ) # adjust fontsize for larger text plt.axis("off") - plt.show() + plt.show(**kwargs) for i in correct_ordering: # Show images @@ -738,7 +741,7 @@

Source code for cleanlab.segmentation.summary

mask = ~np.isin(labels[i], exclude) ax.imshow(issues[i] & mask, cmap=error_cmap, vmin=0, vmax=1) ax.set_title(f"Image {i}: Suggested Errors (in Red)") - plt.show() + plt.show(**kwargs) return None
diff --git a/master/_sources/tutorials/clean_learning/tabular.ipynb b/master/_sources/tutorials/clean_learning/tabular.ipynb index 8ff05f0bd..c255f7133 100644 --- a/master/_sources/tutorials/clean_learning/tabular.ipynb +++ b/master/_sources/tutorials/clean_learning/tabular.ipynb @@ -121,7 +121,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/clean_learning/text.ipynb b/master/_sources/tutorials/clean_learning/text.ipynb index 70fb68efa..1d858207b 100644 --- a/master/_sources/tutorials/clean_learning/text.ipynb +++ b/master/_sources/tutorials/clean_learning/text.ipynb @@ -129,7 +129,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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/audio.ipynb b/master/_sources/tutorials/datalab/audio.ipynb index ccfe3cd2c..013c3246b 100644 --- a/master/_sources/tutorials/datalab/audio.ipynb +++ b/master/_sources/tutorials/datalab/audio.ipynb @@ -91,7 +91,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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/data_monitor.ipynb b/master/_sources/tutorials/datalab/data_monitor.ipynb index ba69d4f43..34eeb7fc9 100644 --- a/master/_sources/tutorials/datalab/data_monitor.ipynb +++ b/master/_sources/tutorials/datalab/data_monitor.ipynb @@ -71,7 +71,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb index 926d8c660..c495a5c38 100644 --- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb +++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb @@ -87,7 +87,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb index 810e242fb..805ad6294 100644 --- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb @@ -85,7 +85,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index e06e46265..c42d393d2 100644 --- a/master/_sources/tutorials/datalab/tabular.ipynb +++ b/master/_sources/tutorials/datalab/tabular.ipynb @@ -81,7 +81,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 90c91f3c1..32a6d6409 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 7a9484bfb..fadf9b2d9 100644 --- a/master/_sources/tutorials/dataset_health.ipynb +++ b/master/_sources/tutorials/dataset_health.ipynb @@ -77,7 +77,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 f4f769e7f..e155f282b 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 fa1c91842..42cb17a4c 100644 --- a/master/_sources/tutorials/multiannotator.ipynb +++ b/master/_sources/tutorials/multiannotator.ipynb @@ -96,7 +96,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 285c7eeb2..3aea75fd6 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 786c68a2b..f84474830 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 5f552451b..230e275e7 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 ebea59ea4..b7f58c0c3 100644 --- a/master/_sources/tutorials/regression.ipynb +++ b/master/_sources/tutorials/regression.ipynb @@ -111,7 +111,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 6392c5c2c..757e388f8 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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 9bc09c00e..564a5b06e 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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/cleanlab/object_detection/summary.html b/master/cleanlab/object_detection/summary.html index 50a2b5b89..96359b8f8 100644 --- a/master/cleanlab/object_detection/summary.html +++ b/master/cleanlab/object_detection/summary.html @@ -744,7 +744,7 @@

summary
-cleanlab.object_detection.summary.plot_class_size_distributions(labels, predictions, class_names=None, class_to_show=10)[source]#
+cleanlab.object_detection.summary.plot_class_size_distributions(labels, predictions, class_names=None, class_to_show=10, **kwargs)[source]#

Plots the size distributions for bounding boxes for each class.

This plot can help you find annotated/predicted boxes for a particular class that are abnormally big/small.

@@ -758,6 +758,7 @@

summary
-cleanlab.object_detection.summary.plot_class_distribution(labels, predictions, class_names=None)[source]#
+cleanlab.object_detection.summary.plot_class_distribution(labels, predictions, class_names=None, **kwargs)[source]#

Plots the distribution of class labels associated with all annotated bounding boxes and predicted bounding boxes in the dataset.

This plot can help you understand which classes are rare or over/under-predicted by the model overall.

@@ -776,6 +777,7 @@

summaryobject_counts_per_image for further details.

  • class_names (optional) – Optional dictionary mapping one-hot-encoded class labels back to their original class names in the format {"integer-label": "original-class-name"}.

    • +

  • kwargs – Additional keyword arguments to pass to plt.show() (matplotlib.pyplot.show).

    • @@ -783,7 +785,7 @@

    summary
    -cleanlab.object_detection.summary.visualize(image, *, label=None, prediction=None, prediction_threshold=None, overlay=True, class_names=None, figsize=None, save_path=None)[source]#
    +cleanlab.object_detection.summary.visualize(image, *, label=None, prediction=None, prediction_threshold=None, overlay=True, class_names=None, figsize=None, save_path=None, **kwargs)[source]#

    Display the annotated bounding boxes (given labels) and predicted bounding boxes (model predictions) for a particular image. Given labels are shown in red, model predictions in blue.

    @@ -807,6 +809,7 @@

    summaryOptional[str]) – Path to save figure at. If a path is provided, the figure is saved. To save in a specific image format, add desired file extension to the end of save_path. Allowed file extensions are: ‘png’, ‘pdf’, ‘ps’, ‘eps’, and ‘svg’.

  • figsize (Optional[Tuple[int, int]]) – Optional figure size for plotting the image. Corresponds to matplotlib.figure.figsize.

    • +

  • kwargs – Additional keyword arguments to pass to plt.show() (matplotlib.pyplot.show).

    • Return type:
    diff --git a/master/cleanlab/segmentation/summary.html b/master/cleanlab/segmentation/summary.html index d55e44d1c..3957f85d1 100644 --- a/master/cleanlab/segmentation/summary.html +++ b/master/cleanlab/segmentation/summary.html @@ -608,7 +608,7 @@

    -cleanlab.segmentation.summary.display_issues(issues, *, labels=None, pred_probs=None, class_names=None, exclude=None, top=None)[source]#
    +cleanlab.segmentation.summary.display_issues(issues, *, labels=None, pred_probs=None, class_names=None, exclude=None, top=None, **kwargs)[source]#

    Display semantic segmentation label issues, showing images with problematic pixels highlighted.

    Can also show given and predicted masks for each image identified to have label issue.

    @@ -646,6 +646,7 @@

  • top (Optional[int]) – Optional maximum number of issues to be printed. If not provided, a good default is used.

  • exclude (Optional[List[int]]) – Optional list of label classes that can be ignored in the errors, each element must be 0, 1, …, K-1

    • +

  • kwargs – Additional keyword arguments to pass to plt.show() (matplotlib.pyplot.show).

    • Return type:
    diff --git a/master/searchindex.js b/master/searchindex.js index e969baeb5..d67072555 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/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/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/data_monitor", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "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/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/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/data_monitor.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/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", "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", "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 Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Audio Classification with SpeechBrain and Cleanlab", "DataMonitor: Leverage statistics from Datalab to audit new data", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Image Classification with PyTorch and Cleanlab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "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, 80, 85, 86, 87, 94, 96, 97], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 85, 86, 87, 94, 96, 97], "generate_noise_matrix_from_trac": [0, 1, 85, 86, 87, 94, 96, 97], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 16, 40, 45, 47, 48, 49, 50, 51, 52, 64, 88, 92, 103], "method": [1, 2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102, 103], "ar": [1, 2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 26, 29, 30, 32, 34, 36, 37, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "us": [1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 79, 80, 86, 92, 100], "benchmark": [1, 37, 79, 80, 85, 86, 87, 94, 96, 97], "cleanlab": [1, 2, 3, 4, 5, 7, 11, 12, 13, 14, 15, 16, 18, 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, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 85, 86, 87, 92, 95, 100], "": [1, 2, 3, 4, 10, 18, 32, 36, 37, 41, 45, 48, 50, 52, 57, 58, 62, 64, 65, 66, 67, 69, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "core": [1, 40, 43, 71, 73], "algorithm": [1, 2, 8, 10, 31, 38, 42, 50, 52, 57, 66, 75, 77, 79, 93, 94, 96, 103], "These": [1, 2, 3, 4, 5, 8, 10, 21, 37, 39, 41, 42, 43, 44, 55, 57, 58, 61, 65, 66, 70, 74, 75, 77, 78, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "introduc": [1, 84, 93, 94], "synthet": [1, 96, 97, 102], "nois": [1, 2, 3, 36, 43, 46, 52, 58, 85, 86, 87, 92, 96, 101], "label": [1, 2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 20, 21, 22, 24, 29, 31, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 85, 86, 92, 95, 99, 100], "classif": [1, 3, 4, 5, 7, 10, 12, 14, 16, 32, 34, 36, 40, 42, 43, 46, 48, 49, 52, 57, 58, 59, 60, 61, 66, 67, 75, 76, 77, 78, 79, 80, 81, 85, 86, 87, 95, 96, 99, 100, 101, 102], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 25, 26, 27, 28, 30, 31, 39, 40, 41, 42, 43, 46, 48, 52, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 84, 86, 90, 95, 96, 100], "specif": [1, 3, 5, 9, 14, 15, 16, 27, 33, 34, 39, 55, 59, 62, 65, 74, 78, 88, 90, 91, 94, 98, 103], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "modul": [1, 3, 13, 14, 15, 16, 21, 24, 29, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 48, 50, 52, 55, 57, 62, 65, 66, 67, 79, 88, 93, 97], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 16, 18, 23, 30, 34, 36, 37, 38, 40, 41, 43, 46, 50, 52, 56, 57, 58, 59, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 99, 100, 101, 102, 103], "gener": [1, 2, 3, 7, 10, 18, 23, 25, 33, 36, 48, 52, 53, 66, 67, 69, 74, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 99, 100, 102, 103], "valid": [1, 2, 3, 5, 10, 12, 32, 34, 36, 43, 44, 46, 47, 48, 50, 52, 57, 59, 62, 65, 67, 69, 70, 78, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 95, 97, 98, 101, 102, 103], "matric": [1, 3, 46, 93], "which": [1, 2, 3, 5, 7, 10, 12, 13, 14, 16, 18, 22, 26, 32, 33, 34, 36, 37, 41, 42, 43, 46, 48, 51, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 73, 74, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "learn": [1, 2, 3, 5, 9, 10, 14, 16, 22, 30, 33, 38, 39, 40, 41, 43, 45, 47, 52, 55, 57, 59, 66, 68, 70, 73, 77, 79, 83, 84, 86, 88, 90, 91, 92, 96, 97, 101], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101, 102, 103], "possibl": [1, 2, 3, 7, 10, 36, 37, 41, 43, 45, 46, 48, 59, 60, 61, 62, 64, 65, 66, 67, 69, 75, 77, 78, 85, 87, 93, 94, 96, 97, 98, 101, 102, 103], "noisi": [1, 2, 3, 10, 36, 38, 41, 43, 46, 52, 58, 59, 61, 67, 69, 70, 71, 73, 74, 80, 85, 86, 87, 90, 91, 93, 95, 96], "given": [1, 2, 3, 5, 10, 14, 30, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 51, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "matrix": [1, 2, 3, 5, 10, 16, 18, 31, 36, 43, 45, 46, 49, 52, 53, 59, 62, 64, 65, 66, 67, 90, 98, 99], "trace": [1, 85, 86, 87, 94, 96, 97], "valu": [1, 2, 3, 4, 5, 10, 12, 13, 16, 18, 22, 26, 27, 32, 34, 36, 37, 38, 40, 41, 43, 45, 46, 48, 50, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "more": [1, 2, 3, 5, 7, 9, 10, 13, 14, 16, 18, 26, 36, 37, 40, 41, 42, 45, 48, 50, 52, 57, 58, 59, 60, 61, 62, 64, 65, 67, 69, 70, 73, 74, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 96, 97, 98, 99, 102, 103], "function": [1, 2, 3, 4, 5, 7, 10, 13, 14, 16, 23, 26, 30, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 102, 103], "noise_matrix": [1, 2, 3, 10, 46, 52, 85, 86, 87, 94, 96, 97], "py": [1, 3, 33, 37, 38, 43, 46, 48, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97], "verbos": [1, 2, 5, 7, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 40, 43, 57, 58, 59, 64, 66, 67, 69, 71, 73, 74, 78, 86, 94, 96], "fals": [1, 2, 3, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 47, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 98, 99, 101, 102], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 85], "prior": [1, 2, 3, 36, 43, 46, 48], "repres": [1, 2, 3, 7, 10, 12, 16, 18, 26, 32, 34, 36, 40, 43, 46, 49, 50, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 103], "p": [1, 2, 3, 5, 10, 36, 43, 45, 46, 50, 52, 57, 65, 66, 67, 71, 90, 91, 94, 96, 103], "true_label": [1, 2, 3, 36, 46, 52, 94, 96], "k": [1, 2, 3, 4, 5, 8, 10, 12, 16, 18, 19, 23, 26, 28, 31, 36, 40, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 60, 61, 62, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 82, 84, 85, 86, 87, 93, 94, 96, 97, 98, 99, 102, 103], "check": [1, 2, 5, 6, 9, 10, 12, 16, 27, 34, 37, 40, 41, 47, 53, 56, 62, 65, 69, 79, 82, 83, 84, 85, 86, 87, 88, 93, 94, 96, 97, 101], "learnabl": 1, "mean": [1, 2, 7, 8, 12, 13, 22, 26, 38, 41, 46, 48, 50, 64, 69, 83, 87, 91, 93, 94, 96, 97, 98, 99, 101], "achiev": [1, 2, 37, 38, 41, 69, 93, 96, 103], "better": [1, 5, 43, 57, 59, 67, 69, 70, 79, 83, 84, 87, 90, 91, 93, 94, 97, 98, 99, 103], "than": [1, 2, 3, 4, 7, 9, 10, 26, 28, 31, 36, 43, 52, 56, 57, 62, 64, 66, 67, 69, 73, 77, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "random": [1, 2, 3, 7, 10, 18, 31, 40, 48, 57, 67, 69, 82, 84, 85, 86, 87, 88, 90, 93, 94, 96, 97, 99], "perform": [1, 2, 4, 7, 10, 26, 28, 31, 37, 41, 48, 65, 69, 79, 82, 83, 86, 93, 94, 96, 97, 100, 101], "averag": [1, 3, 5, 10, 22, 28, 36, 37, 41, 48, 50, 57, 58, 65, 66, 67, 93, 96, 99], "amount": [1, 3, 88], "paramet": [1, 2, 3, 4, 5, 9, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 87, 88, 91], "np": [1, 2, 3, 4, 5, 7, 16, 18, 31, 36, 38, 40, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "ndarrai": [1, 2, 3, 4, 5, 16, 23, 25, 26, 30, 31, 32, 36, 38, 40, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 103], "an": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 50, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 100, 101, 102, 103], "arrai": [1, 2, 3, 4, 5, 7, 10, 12, 16, 18, 26, 32, 36, 38, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "shape": [1, 2, 3, 4, 5, 16, 18, 36, 38, 40, 42, 43, 45, 46, 47, 48, 50, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 84, 92, 93, 94, 97, 98, 99, 102, 103], "condit": [1, 2, 3, 46, 51, 52, 67, 88, 94, 103], "probabl": [1, 2, 3, 5, 8, 10, 16, 23, 25, 28, 32, 36, 40, 41, 42, 43, 45, 46, 48, 49, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 92, 93, 94, 95, 97, 98, 99, 102, 103], "k_": [1, 2, 3, 46, 52], "k_y": [1, 2, 3, 46, 52], "contain": [1, 2, 3, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 43, 45, 46, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 73, 74, 75, 77, 78, 80, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102], "fraction": [1, 2, 3, 10, 20, 38, 46, 52, 57, 69, 90, 93], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 96, 97, 98, 100, 101, 102, 103], "everi": [1, 2, 3, 4, 5, 16, 37, 41, 43, 46, 51, 52, 59, 67, 69, 70, 82, 84, 85, 86, 87, 88, 90, 91, 93, 96, 98, 100, 102, 103], "class": [1, 2, 3, 4, 5, 7, 9, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 52, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "other": [1, 2, 3, 5, 10, 16, 22, 27, 36, 37, 39, 40, 41, 43, 46, 49, 52, 53, 55, 57, 58, 61, 65, 66, 67, 69, 74, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 99, 102, 103], "assum": [1, 2, 3, 12, 43, 46, 51, 52, 67, 71, 74, 93, 97, 99, 101, 102, 103], "column": [1, 2, 3, 5, 10, 12, 13, 30, 36, 40, 43, 46, 48, 49, 51, 52, 57, 58, 59, 61, 62, 65, 66, 67, 69, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 101, 102, 103], "sum": [1, 2, 3, 26, 31, 32, 36, 46, 48, 52, 58, 59, 61, 64, 69, 85, 86, 87, 88, 93, 94, 96, 97, 102, 103], "1": [1, 2, 3, 4, 5, 7, 10, 12, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 92, 93, 100], "each": [1, 2, 3, 4, 5, 7, 8, 9, 12, 13, 14, 16, 20, 22, 23, 25, 26, 31, 32, 33, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "true": [1, 2, 3, 5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 43, 46, 48, 51, 52, 53, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "return": [1, 2, 3, 4, 5, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "type": [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 93, 97, 98, 101, 102, 103], "bool": [1, 2, 3, 5, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 48, 51, 52, 57, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78], "is_valid": 1, "whether": [1, 3, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 37, 40, 41, 43, 52, 57, 58, 59, 61, 62, 78, 83, 84, 87, 88, 90, 91, 92, 93, 94, 101, 103], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 22, 23, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 46, 48, 49, 50, 51, 52, 57, 59, 61, 64, 65, 66, 67, 69, 70, 75, 77, 78, 79, 84, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 102, 103], "perfect": [1, 2, 36, 69, 94, 98], "exactli": [1, 3, 10, 36, 37, 41, 43, 60, 66, 86, 87, 88, 90, 91, 94], "yield": [1, 37, 41, 85], "between": [1, 5, 10, 15, 16, 21, 22, 24, 26, 29, 32, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 50, 55, 57, 58, 61, 64, 66, 67, 69, 70, 73, 77, 78, 80, 83, 84, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "below": [1, 3, 4, 5, 10, 36, 37, 40, 41, 43, 45, 48, 50, 57, 58, 59, 64, 65, 73, 77, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "we": [1, 2, 3, 5, 7, 10, 13, 22, 37, 40, 41, 43, 48, 52, 53, 56, 57, 64, 65, 67, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "loop": [1, 3, 46, 52, 88, 98], "implement": [1, 2, 3, 9, 14, 22, 37, 38, 40, 41, 46, 52, 69, 79, 82, 84, 86, 90, 99, 100], "what": [1, 5, 9, 10, 16, 33, 36, 38, 40, 43, 57, 58, 62, 64, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "doe": [1, 2, 3, 7, 10, 40, 41, 43, 48, 50, 53, 64, 65, 69, 71, 73, 77, 83, 84, 86, 87, 88, 90, 91, 97, 101, 102], "do": [1, 2, 5, 9, 10, 36, 40, 41, 52, 53, 66, 67, 71, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "fast": 1, "explain": [1, 10], "python": [1, 2, 41, 56, 69, 83, 84, 86, 87, 88, 90, 91, 92, 94, 99], "pseudocod": [1, 100], "happen": [1, 10, 43, 59, 85, 91, 96, 102], "n": [1, 2, 3, 5, 7, 36, 37, 40, 41, 43, 45, 46, 47, 48, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 82, 83, 84, 85, 88, 91, 92, 93, 96, 97, 98, 101, 102, 103], "without": [1, 2, 5, 9, 10, 12, 14, 20, 37, 41, 61, 69, 79, 83, 84, 85, 91, 93, 94, 98, 99], "ani": [1, 2, 3, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 40, 41, 43, 45, 47, 50, 51, 52, 56, 57, 59, 61, 62, 64, 65, 67, 69, 71, 73, 74, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102], "distinct": [1, 18, 52, 103], "natur": [1, 10, 96, 99], "number": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 78, 80, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 102, 103], "0": [1, 2, 3, 4, 5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "count_joint": 1, "len": [1, 2, 3, 7, 36, 40, 46, 51, 52, 53, 66, 67, 69, 82, 83, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "y": [1, 2, 3, 5, 8, 18, 30, 31, 41, 46, 48, 52, 53, 56, 65, 69, 70, 83, 84, 85, 86, 87, 90, 93, 94, 96, 97, 99, 101], "round": [1, 40, 43, 52, 69, 93, 101], "astyp": [1, 96], "int": [1, 2, 3, 4, 5, 7, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 37, 38, 40, 41, 43, 48, 49, 50, 51, 52, 53, 58, 59, 61, 65, 66, 67, 69, 71, 73, 74, 75, 78, 84, 86, 88, 98, 99], "rang": [1, 3, 5, 7, 12, 46, 48, 50, 52, 65, 69, 70, 85, 88, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 12, 13, 16, 22, 36, 40, 43, 46, 47, 48, 49, 50, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "pragma": 1, "cover": [1, 3, 80, 92, 93], "choic": [1, 8, 43, 50, 88, 93, 97, 99], "replac": [1, 51, 56, 67, 82, 83, 85, 86, 87, 88, 91, 92, 93, 96, 99], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 67, 84, 85, 86, 87], "05": [1, 10, 26, 30, 51, 65, 69, 75, 77, 90, 92, 93, 94, 98], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 85, 86, 87, 94, 96, 97], "none": [1, 2, 3, 4, 5, 7, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 85, 86, 87, 88, 93, 94, 96, 97, 102], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 26, 39, 41, 48, 69, 82, 84, 85, 86, 87, 90, 92, 94, 96, 97], "max_it": [1, 83, 84, 91, 99], "10000": [1, 40, 92, 93], "x": [1, 2, 3, 5, 10, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 37, 38, 41, 43, 45, 46, 48, 51, 52, 53, 56, 57, 59, 65, 66, 67, 69, 71, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 96, 97, 99, 101], "diagon": [1, 3, 5, 43, 46, 52], "equal": [1, 3, 10, 12, 59, 64, 74, 100], "creat": [1, 2, 9, 16, 18, 37, 40, 41, 43, 52, 69, 79, 83, 84, 88, 90, 91, 93, 102, 103], "impli": [1, 10, 36, 58, 65], "float": [1, 2, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 39, 40, 41, 43, 45, 47, 48, 50, 51, 52, 57, 58, 59, 61, 64, 65, 69, 73, 77, 84, 85, 86, 87, 94, 96, 97], "entri": [1, 3, 5, 36, 37, 41, 43, 45, 49, 50, 52, 57, 58, 59, 62, 82, 83, 90, 91, 94, 97, 98, 101], "maximum": [1, 10, 66, 74, 78, 102], "minimum": [1, 8, 10, 20, 43, 45, 59, 64, 77], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 16, 26, 37, 41, 43, 64, 69, 86, 93, 94, 96, 98, 99], "default": [1, 2, 3, 4, 5, 7, 10, 14, 16, 28, 30, 33, 36, 37, 38, 40, 41, 43, 45, 46, 48, 50, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 86, 88, 93, 101, 102], "If": [1, 2, 3, 4, 5, 10, 12, 13, 16, 26, 28, 34, 36, 37, 40, 41, 43, 45, 46, 48, 51, 52, 56, 57, 58, 59, 62, 64, 65, 66, 69, 70, 71, 73, 74, 77, 78, 79, 80, 82, 83, 84, 86, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "have": [1, 2, 3, 4, 5, 7, 9, 10, 16, 21, 24, 26, 29, 36, 37, 39, 40, 41, 43, 46, 48, 52, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 74, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 14, 16, 22, 33, 36, 37, 40, 41, 42, 43, 46, 48, 49, 51, 52, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "necessari": [1, 2, 3, 4, 7, 10, 12, 51, 85, 86], "In": [1, 2, 3, 5, 10, 36, 37, 40, 41, 56, 57, 58, 60, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 99, 100, 101, 102, 103], "particular": [1, 5, 6, 10, 13, 14, 16, 19, 20, 22, 26, 27, 28, 31, 37, 41, 52, 57, 61, 65, 69, 74, 78, 79, 82, 83, 84, 85, 87, 91, 93, 96, 97, 99, 101], "satisfi": [1, 3, 36], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 30, 35, 37, 38, 39, 40, 41, 43, 46, 52, 55, 56, 59, 66, 67, 69, 71, 79, 80, 84, 92, 93, 94, 100], "argument": [1, 2, 3, 5, 10, 16, 23, 27, 30, 31, 32, 37, 40, 41, 42, 43, 48, 53, 56, 57, 58, 59, 61, 64, 65, 66, 67, 69, 73, 74, 75, 77, 83, 85, 87, 88, 91, 92, 93, 97, 98, 101, 103], "when": [1, 2, 3, 5, 10, 12, 14, 23, 26, 37, 41, 43, 46, 48, 50, 52, 56, 59, 61, 62, 64, 66, 67, 69, 70, 82, 83, 85, 86, 87, 88, 90, 91, 96, 100, 101, 102, 103], "The": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 74, 75, 77, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103], "rate": [1, 2, 3, 10, 38, 52, 84, 103], "set": [1, 2, 3, 5, 9, 10, 12, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 37, 40, 41, 43, 47, 48, 50, 52, 56, 57, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 82, 83, 85, 86, 87, 90, 91, 93, 96, 97, 99, 100, 101, 102, 103], "note": [1, 2, 3, 7, 8, 10, 12, 27, 31, 34, 37, 40, 41, 42, 43, 48, 52, 56, 57, 62, 64, 65, 66, 67, 69, 70, 74, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "you": [1, 2, 3, 5, 7, 9, 10, 14, 16, 36, 37, 39, 40, 41, 43, 48, 55, 56, 57, 59, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "high": [1, 2, 16, 40, 43, 52, 64, 67, 69, 82, 83, 85, 86, 87, 88, 92, 94, 98, 101, 102, 103], "mai": [1, 2, 3, 4, 5, 10, 13, 21, 22, 24, 29, 32, 36, 37, 39, 40, 41, 43, 46, 48, 52, 57, 58, 62, 64, 65, 66, 67, 69, 71, 74, 78, 80, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 100, 101, 102, 103], "imposs": [1, 10, 94], "also": [1, 2, 3, 5, 7, 9, 10, 22, 34, 36, 37, 40, 41, 43, 48, 51, 56, 57, 66, 69, 74, 77, 78, 79, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 100, 101, 102, 103], "low": [1, 10, 52, 57, 79, 85, 86, 87, 91, 94, 98, 102], "zero": [1, 3, 5, 37, 41, 45, 52, 53, 86, 88, 97, 98, 99], "forc": [1, 2, 3, 5, 41, 86, 103], "instead": [1, 2, 3, 10, 13, 16, 33, 36, 37, 40, 41, 43, 46, 52, 56, 57, 59, 61, 65, 66, 67, 69, 70, 73, 75, 77, 80, 82, 83, 84, 88, 90, 91, 93, 94, 97, 98, 99, 101, 102, 103], "onli": [1, 2, 3, 4, 5, 7, 10, 16, 23, 26, 30, 36, 37, 40, 41, 42, 43, 45, 46, 50, 51, 52, 53, 56, 57, 66, 67, 69, 71, 73, 77, 78, 79, 83, 84, 86, 87, 88, 91, 96, 97, 98, 99, 100, 101, 102, 103], "guarante": [1, 3, 5, 15, 21, 24, 29, 37, 39, 41, 44, 46, 55, 80], "produc": [1, 2, 5, 9, 10, 16, 48, 57, 67, 69, 71, 73, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 102, 103], "higher": [1, 5, 10, 36, 43, 45, 46, 48, 50, 56, 57, 58, 69, 87, 91, 93, 98], "opposit": [1, 103], "occur": [1, 3, 10, 36, 51, 64, 86, 87, 88, 93, 99], "small": [1, 3, 10, 36, 40, 48, 50, 52, 58, 65, 83, 88, 91, 92, 97, 99], "numpi": [1, 3, 4, 5, 7, 10, 12, 18, 31, 32, 40, 41, 42, 48, 50, 51, 53, 56, 61, 64, 69, 70, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "max": [1, 43, 66, 67, 87, 88, 99], "tri": [1, 37, 41, 100], "befor": [1, 2, 3, 37, 41, 50, 52, 66, 69, 74, 82, 83, 85, 91, 93, 94, 96, 99, 101], "option": [1, 2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 23, 26, 30, 36, 37, 40, 41, 43, 46, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 82, 84, 86, 87, 88, 90, 93, 94, 97, 101, 102], "left": [1, 2, 43, 45, 50, 52, 59, 62, 65, 85, 86, 87, 97, 98, 99, 102], "stochast": 1, "exceed": 1, "m": [1, 5, 37, 41, 47, 48, 57, 62, 64, 65, 66, 85, 86, 87, 92, 96, 97, 98, 103], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 37, 41, 56, 93, 94, 102], "length": [1, 5, 12, 26, 27, 36, 38, 43, 52, 59, 62, 66, 67, 69, 71, 74, 78, 82, 84, 97, 99, 102, 103], "must": [1, 2, 3, 4, 5, 7, 16, 36, 37, 38, 39, 41, 43, 46, 48, 49, 50, 52, 55, 56, 57, 58, 59, 66, 67, 69, 71, 73, 74, 75, 77, 78, 84, 96, 100, 102, 103], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 12, 36, 40, 43, 49, 52, 53, 57, 59, 65, 71, 73, 74, 75, 77, 78, 82, 83, 84, 93, 96, 97, 98, 102, 103], "ball": [1, 92], "bin": [1, 3, 59, 85, 86, 87, 99], "ensur": [1, 2, 10, 37, 41, 50, 52, 53, 56, 64, 67, 69, 82, 83, 84, 86, 87, 88, 91, 93, 94, 99, 100, 101], "most": [1, 3, 5, 7, 10, 16, 36, 40, 43, 48, 56, 57, 58, 59, 62, 64, 65, 66, 67, 70, 73, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102], "least": [1, 4, 10, 18, 31, 36, 40, 57, 58, 64, 67, 77, 87, 88, 93, 96, 99, 102], "int_arrai": [1, 52], "can": [2, 3, 5, 7, 8, 9, 13, 14, 16, 33, 34, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 78, 79, 80, 82, 83, 84, 86, 88, 90, 91, 97, 98, 99, 100, 101, 102, 103], "model": [2, 3, 4, 5, 9, 10, 16, 18, 30, 32, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 51, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 85, 86, 87, 92, 95, 100, 102, 103], "For": [2, 3, 5, 7, 9, 10, 11, 16, 22, 35, 36, 37, 40, 41, 43, 46, 48, 50, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 75, 77, 78, 79, 82, 83, 84, 85, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103], "regular": [2, 3, 40, 56], "multi": [2, 3, 4, 10, 32, 36, 37, 40, 41, 43, 47, 48, 49, 52, 53, 58, 59, 60, 61, 66, 67, 79, 93, 94, 95], "task": [2, 5, 7, 10, 11, 12, 14, 15, 16, 25, 30, 33, 36, 40, 46, 48, 49, 50, 52, 57, 59, 67, 69, 79, 83, 84, 85, 91, 92, 93, 94, 97, 99, 101, 102, 103], "cleanlearn": [2, 3, 10, 23, 30, 37, 52, 56, 68, 69, 70, 79, 80, 82, 83, 101], "wrap": [2, 37, 41, 56, 66, 69, 79, 82, 83, 85, 86, 87, 90, 91, 94, 101], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 41, 48, 56, 65, 66, 69, 74, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 98], "sklearn": [2, 3, 4, 5, 8, 10, 18, 31, 36, 41, 48, 52, 56, 66, 69, 70, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 100, 101], "classifi": [2, 3, 41, 48, 52, 57, 60, 66, 67, 79, 80, 82, 83, 84, 90, 91, 93, 96, 97, 99, 100, 102, 103], "adher": [2, 41, 69], "estim": [2, 3, 4, 5, 9, 13, 22, 36, 40, 41, 43, 46, 52, 57, 58, 59, 64, 66, 69, 71, 73, 77, 79, 80, 84, 85, 86, 87, 88, 90, 91, 93, 95, 98, 99, 100, 101, 102, 103], "api": [2, 3, 14, 56, 62, 65, 66, 69, 80, 93, 101], "defin": [2, 3, 5, 7, 10, 14, 22, 36, 37, 38, 40, 41, 43, 67, 69, 71, 84, 86, 87, 90, 93, 96, 99, 103], "four": [2, 10, 92, 94, 103], "clf": [2, 3, 5, 48, 69, 79, 82, 90, 93, 94, 97], "fit": [2, 3, 5, 8, 10, 18, 39, 41, 55, 56, 66, 68, 69, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 100, 101, 103], "sample_weight": [2, 41, 69, 94], "predict_proba": [2, 5, 36, 39, 41, 48, 55, 56, 82, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 99], "predict": [2, 3, 5, 8, 9, 10, 16, 22, 23, 25, 28, 30, 32, 34, 36, 39, 40, 41, 42, 43, 45, 46, 48, 49, 51, 52, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 83, 92, 93, 94, 95, 99, 101, 102, 103], "score": [2, 3, 4, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 40, 42, 43, 45, 48, 50, 57, 58, 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 73, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 99, 101], "data": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 36, 38, 39, 40, 41, 42, 43, 48, 49, 52, 55, 56, 57, 58, 59, 60, 64, 66, 67, 68, 69, 74, 75, 76, 77, 78, 80, 83, 88, 89, 95, 100], "e": [2, 3, 5, 10, 12, 22, 32, 36, 37, 40, 41, 43, 46, 48, 49, 52, 53, 57, 58, 59, 60, 62, 65, 66, 67, 69, 71, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101], "featur": [2, 3, 4, 5, 8, 10, 16, 18, 19, 23, 26, 27, 28, 30, 31, 48, 52, 66, 69, 79, 82, 85, 86, 87, 90, 91, 93, 94, 96, 97, 101], "element": [2, 3, 5, 36, 42, 43, 45, 52, 57, 59, 67, 74, 75, 77, 83, 84, 91, 93, 103], "first": [2, 5, 10, 17, 26, 27, 36, 40, 48, 52, 57, 58, 62, 65, 67, 69, 82, 83, 84, 86, 88, 90, 93, 96, 97, 98, 99, 101, 102, 103], "index": [2, 10, 26, 36, 43, 51, 52, 53, 58, 67, 69, 74, 77, 78, 83, 84, 86, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "should": [2, 3, 5, 7, 10, 14, 22, 26, 31, 32, 36, 37, 40, 41, 43, 45, 46, 48, 50, 51, 52, 56, 57, 58, 61, 62, 64, 65, 66, 67, 69, 70, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 100, 101, 102, 103], "correspond": [2, 3, 5, 10, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 34, 36, 37, 40, 41, 42, 43, 45, 46, 48, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "differ": [2, 5, 7, 10, 13, 15, 21, 24, 26, 27, 29, 36, 37, 39, 40, 41, 43, 44, 48, 50, 52, 53, 55, 57, 62, 64, 66, 69, 82, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 99, 100, 101], "sampl": [2, 3, 5, 8, 10, 16, 20, 43, 45, 48, 59, 62, 65, 67, 69, 70, 79, 80, 83, 92, 93, 94, 95, 97, 98, 101, 102, 103], "size": [2, 10, 31, 37, 40, 41, 43, 48, 59, 64, 65, 69, 71, 73, 83, 85, 88, 90, 93, 94, 96, 97, 98, 100, 102], "here": [2, 5, 7, 10, 14, 40, 43, 46, 56, 57, 58, 59, 61, 62, 65, 66, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "re": [2, 5, 37, 41, 51, 57, 69, 79, 82, 83, 84, 85, 86, 90, 91, 93, 101, 102, 103], "weight": [2, 10, 37, 38, 41, 48, 57, 64, 67, 69, 83, 84, 85, 86, 87, 91], "loss": [2, 38, 56, 67, 69, 88], "while": [2, 3, 10, 37, 40, 41, 47, 48, 52, 69, 79, 88, 93, 94, 96, 97, 101], "train": [2, 3, 4, 5, 9, 10, 16, 18, 32, 37, 38, 39, 41, 48, 52, 56, 57, 62, 65, 66, 69, 70, 80, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 100, 102, 103], "support": [2, 3, 4, 5, 12, 14, 33, 34, 40, 42, 48, 52, 53, 56, 66, 67, 77, 79, 80, 84, 85, 86, 87, 88, 93], "your": [2, 3, 5, 9, 10, 16, 36, 37, 39, 40, 41, 43, 48, 52, 55, 56, 57, 58, 59, 61, 66, 67, 69, 70, 71, 73, 74, 80, 82, 83, 84, 85, 88, 90, 92, 96, 97, 98, 99, 100, 101, 102, 103], "recommend": [2, 5, 7, 10, 13, 16, 40, 43, 57, 86, 87, 88, 93, 100, 101], "furthermor": 2, "correctli": [2, 3, 10, 36, 37, 41, 43, 46, 53, 58, 59, 64, 65, 69, 71, 83, 91, 93, 97, 98, 101, 102], "clonabl": [2, 69], "via": [2, 5, 7, 10, 13, 16, 18, 22, 36, 38, 40, 41, 48, 52, 57, 62, 65, 66, 67, 69, 70, 73, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 97, 98, 99, 100, 101, 102, 103], "base": [2, 3, 4, 5, 7, 10, 12, 13, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 37, 40, 41, 42, 43, 46, 47, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 64, 66, 67, 69, 70, 73, 75, 77, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "clone": [2, 69, 97], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 40, 45, 46, 47, 48, 49, 50, 51, 52, 53, 61, 65, 69, 75, 80, 84, 86, 93, 94, 96, 97, 98, 99, 101, 103], "multipl": [2, 3, 5, 12, 13, 34, 36, 43, 50, 51, 57, 58, 59, 61, 64, 65, 69, 79, 86, 87, 88, 93, 95, 97, 98, 101], "g": [2, 3, 5, 10, 12, 22, 32, 36, 37, 41, 43, 49, 52, 59, 60, 62, 65, 66, 67, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101], "manual": [2, 69, 82, 83, 84, 93, 99, 100, 101, 103], "pytorch": [2, 37, 38, 41, 69, 79, 84, 93, 95, 97, 102], "call": [2, 3, 5, 6, 10, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 48, 52, 56, 66, 69, 83, 84, 85, 86, 87, 91, 93, 94, 97, 99, 100, 101, 102, 103], "__init__": [2, 38, 69, 88], "independ": [2, 3, 10, 58, 69, 91, 100, 101, 103], "compat": [2, 37, 40, 41, 56, 69, 70, 73, 77, 79, 82, 83, 93, 100, 101], "neural": [2, 38, 56, 66, 69, 84, 88, 93, 97, 99, 101], "network": [2, 37, 38, 41, 56, 66, 69, 83, 84, 88, 91, 93, 97, 99, 101], "typic": [2, 37, 41, 66, 69, 82, 83, 84, 87, 88, 90, 91, 99, 100], "initi": [2, 3, 13, 18, 37, 41, 57, 69, 82, 91, 93], "insid": [2, 41, 69, 93, 94], "There": [2, 3, 7, 79, 94, 96], "two": [2, 3, 10, 18, 26, 36, 37, 40, 41, 49, 52, 62, 64, 65, 80, 83, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101, 102, 103], "new": [2, 7, 9, 10, 14, 22, 37, 40, 41, 47, 51, 52, 57, 69, 83, 84, 86, 91, 92, 93, 99, 100, 103], "notion": 2, "confid": [2, 3, 10, 22, 36, 40, 43, 46, 48, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 73, 77, 79, 82, 88, 90, 91, 94, 96, 97, 98, 100, 102, 103], "packag": [2, 5, 7, 9, 10, 11, 15, 35, 39, 43, 44, 52, 55, 56, 62, 65, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "prune": [2, 3, 43, 59, 69, 80, 98], "everyth": [2, 65, 94], "els": [2, 65, 85, 86, 88, 92, 93, 96, 97, 98], "mathemat": [2, 3, 10, 46, 97], "keep": [2, 13, 14, 52, 79, 85, 86, 92, 93, 102], "belong": [2, 3, 10, 36, 43, 45, 46, 58, 59, 60, 61, 66, 67, 71, 75, 77, 78, 87, 88, 94, 97, 99, 102, 103], "2": [2, 3, 4, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 56, 58, 59, 61, 62, 65, 66, 67, 69, 70, 74, 75, 77, 78, 92, 93, 100], "error": [2, 3, 5, 10, 37, 41, 42, 43, 45, 46, 52, 58, 59, 61, 62, 64, 65, 67, 69, 71, 73, 74, 77, 80, 82, 84, 85, 86, 87, 90, 91, 92, 95], "erron": [2, 3, 36, 43, 46, 52, 58, 59, 67, 69, 70, 71, 99, 101], "import": [2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 40, 42, 48, 50, 51, 57, 61, 64, 69, 70, 75, 77, 78, 79, 82, 83, 90, 91, 93, 97, 98, 99, 101, 102, 103], "linear_model": [2, 5, 36, 52, 69, 79, 83, 84, 85, 86, 87, 91, 93, 94, 96, 99], "logisticregress": [2, 3, 5, 36, 52, 79, 83, 84, 85, 86, 87, 91, 93, 94, 96, 99], "logreg": 2, "cl": [2, 14, 30, 69, 79, 82, 83, 93, 94, 101], "pass": [2, 3, 5, 8, 10, 12, 13, 14, 16, 23, 30, 33, 37, 40, 41, 43, 47, 48, 52, 56, 57, 59, 66, 67, 69, 75, 79, 83, 84, 85, 86, 87, 91, 92, 93, 94, 96, 98, 99, 101], "x_train": [2, 82, 85, 86, 87, 94, 96, 97, 101], "labels_maybe_with_error": 2, "had": [2, 3, 69, 98], "issu": [2, 3, 4, 5, 6, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 36, 37, 39, 40, 41, 42, 43, 55, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 83, 89, 95, 96, 100, 101], "pred": [2, 43, 52, 82, 83, 100, 101], "x_test": [2, 82, 85, 86, 87, 94, 97, 101], "might": [2, 5, 57, 69, 74, 82, 83, 86, 87, 88, 93, 98], "case": [2, 3, 10, 13, 36, 48, 57, 69, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 99, 101, 103], "standard": [2, 3, 5, 30, 36, 43, 56, 58, 59, 61, 67, 69, 79, 82, 86, 87, 90, 92, 94, 98], "adapt": [2, 37, 39, 52, 55, 69, 99], "skorch": [2, 69, 79, 93], "kera": [2, 55, 62, 65, 69, 79, 93, 98], "scikera": [2, 56, 69, 93], "open": [2, 40, 92, 98, 103], "doesn": [2, 69, 79], "t": [2, 3, 4, 7, 10, 17, 27, 37, 38, 40, 41, 42, 43, 48, 50, 51, 61, 66, 67, 69, 75, 77, 78, 79, 86, 87, 88, 90, 91, 92, 94, 97, 98, 101, 103], "alreadi": [2, 5, 10, 16, 37, 40, 41, 46, 56, 57, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101], "exist": [2, 5, 10, 12, 18, 37, 40, 41, 51, 56, 62, 64, 66, 69, 79, 80, 82, 83, 86, 87, 91, 96, 103], "made": [2, 5, 16, 37, 41, 69, 82, 83, 88, 91, 93, 96, 98, 100, 101], "easi": [2, 11, 46, 69, 86, 87, 92, 93, 94, 97], "inherit": [2, 7, 38, 69], "baseestim": [2, 41, 69], "yourmodel": [2, 69], "def": [2, 7, 14, 37, 41, 56, 69, 83, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "self": [2, 3, 5, 7, 10, 12, 13, 14, 16, 31, 37, 38, 40, 41, 43, 48, 66, 67, 69, 82, 83, 86, 88, 91, 92, 97, 102, 103], "refer": [2, 10, 16, 37, 41, 42, 58, 59, 61, 62, 64, 65, 69, 73, 74, 86, 87, 88, 90, 91, 93, 94, 97, 100, 101], "origin": [2, 5, 10, 41, 42, 43, 51, 52, 56, 58, 59, 62, 65, 66, 69, 70, 73, 75, 77, 82, 83, 86, 88, 90, 91, 93, 94, 98, 99, 101, 103], "total": [2, 3, 4, 36, 40, 52, 58, 78, 85, 88, 93, 102], "state": [2, 3, 5, 37, 38, 41, 47, 69, 94, 97, 98, 103], "art": [2, 38, 94, 97], "northcutt": [2, 3, 36, 66, 67], "et": [2, 3, 36, 38, 66, 67], "al": [2, 3, 36, 38, 66, 67], "2021": [2, 3, 36, 66, 67], "weak": [2, 65], "supervis": [2, 10, 86, 87, 93, 96], "find": [2, 5, 9, 10, 13, 14, 16, 19, 20, 22, 23, 25, 26, 27, 28, 31, 32, 36, 37, 39, 40, 41, 42, 43, 47, 51, 52, 55, 62, 65, 66, 67, 69, 71, 75, 77, 80, 86, 95, 100], "uncertainti": [2, 10, 45, 66, 69, 93, 99, 101], "It": [2, 3, 5, 7, 10, 12, 13, 16, 22, 27, 30, 32, 33, 34, 37, 41, 43, 46, 48, 50, 57, 64, 65, 69, 79, 83, 86, 87, 88, 91, 93, 94, 97, 100], "work": [2, 3, 7, 10, 12, 30, 36, 37, 40, 41, 43, 46, 51, 52, 53, 56, 57, 67, 69, 79, 80, 83, 85, 86, 87, 92, 99, 101], "includ": [2, 3, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 37, 39, 40, 41, 51, 52, 55, 57, 58, 61, 62, 66, 67, 69, 73, 74, 75, 77, 79, 80, 86, 87, 88, 90, 91, 93, 94, 97, 98, 99, 103], "deep": [2, 39, 41, 55, 56, 69, 91], "see": [2, 3, 5, 7, 10, 13, 14, 33, 36, 37, 40, 41, 42, 43, 48, 52, 56, 58, 59, 61, 62, 65, 66, 67, 69, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "subfield": 2, "theori": [2, 94], "machin": [2, 5, 9, 10, 14, 16, 33, 39, 50, 55, 69, 82, 83, 86, 87, 92, 96], "across": [2, 3, 5, 7, 10, 13, 22, 36, 40, 48, 58, 65, 66, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 100, 101], "varieti": [2, 82, 83, 93], "like": [2, 3, 5, 6, 7, 10, 14, 32, 36, 37, 40, 41, 43, 46, 52, 56, 57, 58, 61, 62, 64, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "pu": [2, 52], "input": [2, 3, 5, 10, 16, 26, 36, 37, 40, 41, 46, 48, 51, 52, 53, 56, 65, 69, 79, 80, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 101, 102, 103], "discret": [2, 34, 43, 46, 52, 66, 67, 71, 73, 74], "vector": [2, 3, 4, 5, 10, 16, 43, 46, 48, 49, 52, 66, 67, 79, 83, 84, 86, 87, 88, 90, 91, 94, 97, 98, 99, 102, 103], "would": [2, 3, 5, 37, 40, 41, 43, 52, 59, 69, 79, 83, 85, 86, 88, 93, 94, 99, 101, 103], "obtain": [2, 5, 8, 10, 16, 43, 57, 59, 62, 65, 67, 70, 84, 87, 91, 93, 96, 98, 100, 102, 103], "been": [2, 4, 36, 43, 46, 51, 52, 57, 58, 62, 64, 66, 67, 69, 84, 86, 90, 93, 94, 96, 97, 98, 99, 102, 103], "dure": [2, 10, 16, 66, 69, 82, 83, 84, 85, 90, 91, 93, 94, 97, 100, 101, 103], "denot": [2, 3, 46, 48, 52, 59, 66, 67, 77], "tild": 2, "paper": [2, 4, 10, 57, 66, 75, 77, 92, 94, 96, 99, 101, 103], "cv_n_fold": [2, 3, 69, 83], "5": [2, 3, 4, 5, 8, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 41, 43, 45, 47, 48, 52, 57, 58, 61, 62, 65, 69, 70, 77, 83, 86, 91, 92, 93, 97, 98, 99, 100, 102, 103], "converge_latent_estim": [2, 3], "pulearn": [2, 52], "find_label_issues_kwarg": [2, 10, 69, 80, 93, 94], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 59, 75, 93], "clean": [2, 64, 67, 69, 70, 79, 82, 83, 85, 86, 87, 92, 101], "even": [2, 3, 7, 9, 10, 36, 40, 45, 46, 52, 69, 84, 93, 94, 96, 97, 98], "messi": [2, 69, 94], "ridden": [2, 69], "autom": [2, 9, 10, 69, 79, 87, 92, 93], "robust": [2, 46, 69, 87, 93], "prone": [2, 69], "out": [2, 3, 5, 10, 16, 28, 37, 41, 43, 48, 56, 59, 60, 62, 65, 66, 67, 69, 70, 78, 79, 80, 83, 92, 93, 94, 95, 97, 98, 99, 101, 102, 103], "current": [2, 3, 5, 7, 10, 13, 14, 22, 37, 41, 42, 43, 48, 57, 64, 69, 85, 86, 87, 93, 96, 98], "intend": [2, 13, 14, 15, 16, 32, 33, 34, 44, 57, 73, 77, 84, 86, 87, 91, 94], "A": [2, 3, 4, 5, 7, 10, 12, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 36, 37, 38, 41, 43, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 61, 64, 65, 66, 67, 69, 71, 73, 74, 78, 80, 82, 83, 84, 86, 88, 90, 91, 92, 93, 94, 96, 98, 100, 103], "follow": [2, 3, 10, 14, 30, 34, 36, 37, 40, 41, 48, 50, 57, 58, 62, 64, 65, 66, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "experiment": [2, 37, 38, 40, 41, 42, 59, 80, 85, 93], "wrapper": [2, 56, 82, 83, 84, 101], "around": [2, 64, 85, 86, 87, 98, 99, 103], "fasttext": [2, 55], "store": [2, 4, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 66, 69, 82, 83, 85, 90, 91, 92, 93, 102, 103], "along": [2, 48, 59, 77, 85, 86, 87, 88, 93, 99], "dimens": [2, 52, 71, 74, 88, 93, 99, 102], "select": [2, 9, 10, 26, 57, 67, 88, 93, 96, 99], "split": [2, 3, 5, 10, 12, 40, 48, 51, 52, 69, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 97, 100, 103], "cross": [2, 3, 10, 36, 43, 46, 47, 48, 59, 62, 65, 67, 69, 70, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 95, 97, 98, 101, 102, 103], "fold": [2, 3, 36, 43, 46, 69, 82, 84, 90, 92, 93, 98, 102], "By": [2, 36, 58, 59, 69, 86, 93, 102], "need": [2, 3, 10, 36, 37, 40, 41, 43, 58, 59, 61, 66, 69, 79, 83, 84, 86, 87, 91, 93, 94, 96, 97, 98, 102], "holdout": [2, 3, 69], "comput": [2, 3, 4, 5, 7, 8, 10, 19, 20, 22, 23, 26, 27, 28, 31, 36, 37, 38, 40, 41, 43, 45, 46, 47, 48, 52, 57, 58, 59, 61, 64, 65, 66, 67, 69, 70, 71, 73, 79, 80, 83, 86, 87, 92, 94, 95, 98, 99, 101, 102], "them": [2, 3, 5, 7, 9, 10, 11, 12, 27, 32, 35, 37, 39, 40, 41, 43, 55, 57, 66, 69, 80, 82, 83, 85, 86, 87, 88, 90, 91, 93, 96, 97, 99, 101, 102, 103], "numer": [2, 3, 5, 10, 13, 22, 30, 34, 48, 64, 66, 69, 74, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 94, 96, 97, 99, 101], "consist": [2, 3, 37, 41, 52, 57, 102, 103], "latent": [2, 3, 46], "thei": [2, 3, 5, 15, 21, 24, 26, 29, 37, 38, 39, 41, 43, 44, 50, 52, 56, 59, 64, 67, 69, 70, 73, 77, 79, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 99, 101, 103], "relat": [2, 3, 13, 19, 20, 26, 27, 28, 31, 46, 52, 58, 69, 87, 91], "close": [2, 3, 10, 40, 46, 66, 84, 86, 87, 88, 90, 91, 93, 94, 98], "form": [2, 3, 10, 37, 38, 41, 46, 51, 52, 67, 69, 93], "equival": [2, 3, 37, 41, 46, 66, 99, 101], "iter": [2, 3, 36, 37, 41, 43, 52, 58, 59, 69, 85, 93, 96, 102], "enforc": [2, 37, 41, 52], "perfectli": [2, 36, 58, 94], "certain": [2, 3, 5, 37, 41, 56, 65, 69, 85, 86, 87, 92, 98, 99], "dict": [2, 3, 5, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 40, 41, 43, 47, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 77, 85, 86, 87, 88, 93, 103], "keyword": [2, 3, 5, 10, 16, 23, 27, 30, 37, 40, 41, 43, 45, 48, 51, 56, 57, 59, 66, 67, 69, 75, 77, 86], "filter": [2, 3, 10, 40, 42, 51, 58, 60, 61, 63, 65, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 87, 88, 91, 92, 93, 97, 98, 101, 102, 103], "find_label_issu": [2, 3, 10, 30, 39, 40, 42, 43, 58, 59, 60, 61, 62, 63, 64, 65, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 93, 97, 98, 101, 102, 103], "particularli": [2, 79, 96, 99], "filter_bi": [2, 3, 40, 43, 59, 80, 93], "frac_nois": [2, 43, 59, 75, 93], "min_examples_per_class": [2, 43, 59, 87, 93, 94], "impact": [2, 4, 10, 86, 87, 88], "ml": [2, 4, 5, 9, 10, 15, 69, 79, 82, 83, 86, 87, 88, 90, 91, 96, 97, 101], "accuraci": [2, 38, 67, 82, 83, 84, 88, 93, 94, 96, 99, 101, 102], "n_job": [2, 40, 43, 59, 71, 73, 75, 93, 99, 102], "disabl": [2, 37, 41, 43, 99], "process": [2, 3, 7, 13, 16, 32, 37, 40, 41, 43, 51, 57, 59, 65, 71, 73, 75, 83, 84, 85, 86, 93, 96, 100], "caus": [2, 43, 48, 86, 87, 93], "rank": [2, 3, 10, 36, 40, 42, 43, 48, 58, 59, 60, 62, 63, 65, 66, 68, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 87, 92, 93, 97, 98, 99, 102, 103], "get_label_quality_scor": [2, 39, 40, 42, 43, 44, 48, 57, 59, 60, 61, 62, 63, 64, 67, 68, 70, 72, 73, 75, 76, 77, 80, 93, 94, 97, 98, 102, 103], "adjust_pred_prob": [2, 10, 61, 66, 67, 94], "control": [2, 5, 9, 10, 16, 40, 43, 57, 65, 66, 69, 75, 77, 86, 87, 92, 93], "how": [2, 3, 5, 10, 12, 13, 14, 16, 22, 36, 37, 38, 40, 41, 46, 52, 57, 58, 61, 62, 64, 66, 67, 69, 73, 77, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 98, 99, 100, 101, 102], "much": [2, 10, 36, 40, 43, 69, 85, 93, 94, 96, 99], "output": [2, 3, 5, 10, 16, 32, 37, 38, 41, 46, 52, 56, 57, 58, 62, 64, 65, 66, 69, 73, 74, 77, 78, 79, 80, 83, 84, 86, 88, 91, 92, 93, 98, 99, 100, 101], "print": [2, 5, 7, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 52, 57, 58, 59, 64, 66, 67, 69, 71, 73, 74, 78, 80, 82, 83, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "suppress": [2, 40, 57, 64, 66, 67, 69, 71, 73, 74, 102, 103], "statement": [2, 40, 57, 64, 66, 67, 69, 71, 73, 74], "big": [2, 40, 59, 65, 69, 94], "limit": [2, 5, 16, 40, 59, 85, 98, 102, 103], "memori": [2, 37, 40, 41, 59, 65, 71, 73, 85, 86, 102], "label_issues_batch": [2, 39, 59, 93], "find_label_issues_batch": [2, 39, 40, 59, 93], "pred_prob": [2, 3, 5, 8, 10, 16, 23, 25, 26, 28, 31, 32, 36, 40, 42, 43, 45, 46, 47, 48, 49, 52, 53, 57, 58, 59, 61, 62, 65, 66, 67, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 101], "threshold": [2, 3, 4, 7, 10, 18, 19, 20, 22, 28, 30, 31, 40, 50, 64, 65, 66, 67, 73, 77, 86, 98, 99, 102, 103], "inverse_noise_matrix": [2, 3, 10, 46, 52, 80, 94], "label_issu": [2, 40, 43, 59, 62, 69, 71, 80, 82, 83, 84, 88, 91, 93, 94, 97, 101], "clf_kwarg": [2, 3, 10, 69], "clf_final_kwarg": [2, 69], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 36, 40, 43, 45, 57, 58, 59, 61, 62, 64, 65, 67, 69, 70, 73, 77, 79, 84, 88, 90, 91, 94, 96, 98, 100, 101], "result": [2, 3, 9, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 40, 41, 43, 45, 50, 52, 59, 61, 62, 65, 67, 69, 70, 71, 73, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 101, 102, 103], "identifi": [2, 3, 5, 7, 9, 10, 12, 16, 27, 33, 36, 40, 42, 43, 59, 62, 65, 67, 69, 70, 71, 74, 75, 77, 78, 79, 82, 83, 84, 86, 87, 88, 90, 91, 92, 94, 97, 99, 101, 102, 103], "final": [2, 10, 69, 82, 90, 98, 100, 101], "remain": [2, 69, 80, 82, 83, 88, 97, 101, 103], "datasetlik": [2, 52, 69], "beyond": [2, 5, 7, 9, 10, 11, 35, 79, 82, 83, 101, 102], "pd": [2, 3, 5, 7, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 47, 56, 57, 58, 69, 77, 82, 83, 84, 86, 87, 90, 91, 93, 94, 96, 101, 103], "datafram": [2, 3, 5, 7, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 40, 47, 52, 53, 56, 57, 58, 69, 74, 78, 80, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 101, 102, 103], "scipi": [2, 5, 13, 52], "spars": [2, 4, 5, 10, 13, 16, 18, 31, 52, 53, 87, 88, 90, 91, 94], "csr_matrix": [2, 4, 5, 13, 16, 18, 31], "torch": [2, 37, 38, 41, 83, 84, 88, 91, 92, 99], "util": [2, 5, 10, 16, 33, 37, 38, 41, 44, 56, 57, 62, 65, 69, 79, 80, 84, 86, 87, 88, 93, 94, 99], "tensorflow": [2, 52, 56, 79, 84, 93], "object": [2, 5, 10, 12, 13, 16, 32, 33, 37, 38, 40, 41, 48, 52, 53, 56, 59, 62, 63, 64, 65, 66, 69, 77, 79, 83, 84, 87, 88, 90, 93, 94, 95, 97, 101], "list": [2, 3, 5, 12, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 40, 41, 42, 43, 49, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 73, 74, 75, 77, 78, 80, 83, 84, 85, 86, 87, 88, 92, 93, 94, 97, 98, 101, 103], "index_list": 2, "subset": [2, 3, 5, 16, 36, 40, 43, 52, 67, 74, 78, 82, 83, 84, 88, 90, 91, 93, 97, 98, 99, 100, 101, 103], "wa": [2, 3, 12, 14, 40, 50, 52, 57, 58, 64, 66, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 100, 102, 103], "abl": [2, 3, 10, 69, 84, 93, 94, 96, 97], "format": [2, 3, 5, 10, 12, 32, 37, 40, 41, 43, 46, 47, 48, 49, 52, 53, 56, 57, 58, 59, 62, 65, 66, 67, 69, 71, 73, 74, 77, 78, 82, 84, 86, 87, 88, 90, 92, 96, 101, 102, 103], "make": [2, 3, 5, 18, 37, 40, 41, 48, 56, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "sure": [2, 5, 40, 43, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101], "shuffl": [2, 10, 52, 84, 88, 91, 97, 99], "ha": [2, 3, 5, 6, 10, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 42, 46, 48, 51, 52, 57, 62, 64, 69, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 90, 91, 94, 96, 97, 98, 99, 100, 101, 103], "batch": [2, 40, 52, 56, 57, 71, 73, 85, 88, 93, 99], "order": [2, 5, 10, 34, 36, 37, 41, 42, 43, 46, 47, 48, 50, 52, 57, 58, 59, 62, 65, 66, 67, 71, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103], "destroi": [2, 52], "oper": [2, 37, 40, 41, 52, 56, 67, 79, 82, 83, 91, 93, 99], "eg": [2, 5, 10, 52, 62, 65, 86, 87, 93], "repeat": [2, 52, 57, 96, 99], "appli": [2, 34, 37, 39, 41, 43, 48, 49, 51, 52, 61, 66, 75, 82, 83, 84, 85, 86, 87, 88, 90, 93, 96, 97, 99, 100, 101, 102], "array_lik": [2, 3, 36, 43, 52, 59, 66, 70], "some": [2, 3, 5, 10, 14, 22, 36, 37, 39, 41, 43, 46, 51, 52, 55, 57, 58, 59, 61, 62, 65, 66, 67, 69, 71, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 100, 101, 102, 103], "seri": [2, 3, 40, 52, 53, 69, 77, 93], "row": [2, 3, 5, 10, 13, 27, 32, 36, 40, 43, 45, 46, 52, 57, 58, 59, 61, 66, 67, 69, 74, 75, 77, 78, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 99, 103], "rather": [2, 3, 5, 26, 36, 52, 56, 57, 64, 73, 77, 83, 96, 100, 101, 102, 103], "leav": [2, 43], "per": [2, 3, 5, 7, 13, 36, 40, 43, 48, 51, 57, 58, 59, 61, 64, 65, 67, 70, 71, 73, 77, 87, 93, 98, 103], "determin": [2, 3, 10, 12, 16, 22, 26, 30, 36, 40, 43, 48, 52, 57, 59, 62, 64, 67, 73, 77, 86, 93, 96, 98, 99, 101], "cutoff": [2, 3, 99], "consid": [2, 3, 4, 5, 10, 13, 16, 23, 26, 28, 31, 36, 37, 41, 43, 52, 57, 64, 66, 67, 70, 73, 77, 82, 83, 84, 88, 90, 91, 93, 94, 98, 99, 100, 101, 102], "section": [2, 3, 7, 10, 80, 88, 90, 93, 98], "3": [2, 3, 4, 5, 7, 10, 34, 36, 37, 41, 43, 46, 47, 48, 49, 50, 51, 52, 56, 59, 66, 67, 69, 70, 75, 77, 92, 93, 100], "equat": [2, 3, 46], "advanc": [2, 3, 5, 9, 10, 16, 64, 66, 77, 80, 87, 89, 93, 94], "user": [2, 3, 5, 9, 10, 14, 16, 27, 32, 33, 34, 37, 41, 43, 56, 64, 66, 67, 69, 73, 77, 85, 94], "specifi": [2, 3, 4, 5, 8, 10, 13, 14, 16, 18, 31, 33, 37, 40, 41, 43, 48, 51, 56, 57, 58, 59, 62, 64, 66, 67, 69, 70, 78, 80, 83, 84, 87, 88, 91, 96, 98, 101], "automat": [2, 3, 5, 26, 36, 79, 82, 83, 88, 90, 91, 92, 93, 96, 97, 98, 101, 102, 103], "greater": [2, 3, 5, 7, 9, 10, 28, 40, 52, 64, 87, 92, 93, 103], "count": [2, 22, 26, 36, 40, 43, 46, 52, 58, 59, 65, 80, 88, 93, 98], "observ": [2, 3, 46, 84, 85, 86, 87, 96, 99, 101], "mislabel": [2, 10, 36, 40, 42, 43, 46, 57, 58, 59, 62, 64, 67, 73, 75, 77, 79, 82, 83, 84, 88, 90, 91, 93, 94, 98, 101], "one": [2, 3, 5, 7, 10, 26, 36, 37, 40, 41, 42, 43, 48, 50, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 99, 100, 101, 103], "get_label_issu": [2, 39, 40, 68, 69, 82, 83, 94, 101], "either": [2, 3, 4, 7, 10, 37, 40, 41, 43, 57, 59, 64, 66, 67, 71, 73, 85, 87, 93, 97, 98], "boolean": [2, 7, 10, 22, 40, 43, 51, 57, 59, 62, 67, 69, 71, 73, 74, 79, 83, 84, 87, 88, 91, 93, 98, 101, 102], "label_issues_mask": [2, 43, 67, 69, 80], "indic": [2, 3, 4, 5, 7, 10, 13, 22, 36, 40, 41, 42, 43, 45, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 73, 75, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "its": [2, 5, 7, 9, 10, 16, 37, 40, 41, 43, 50, 51, 59, 62, 65, 66, 67, 69, 71, 75, 77, 79, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 100, 101, 102, 103], "return_indices_ranked_bi": [2, 40, 43, 59, 75, 80, 82, 83, 93, 94], "significantli": [2, 88, 94, 96, 100], "reduc": [2, 40, 43, 52, 84, 93], "time": [2, 10, 37, 40, 41, 52, 57, 80, 82, 83, 85, 86, 88, 92, 93, 94, 98, 99, 101, 102, 103], "take": [2, 5, 10, 36, 37, 41, 47, 48, 52, 56, 67, 82, 85, 88, 90, 96, 97, 98, 103], "run": [2, 5, 6, 7, 9, 10, 11, 14, 16, 26, 27, 32, 35, 37, 40, 41, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 103], "skip": [2, 10, 37, 41, 69, 84, 93, 97, 103], "slow": [2, 3], "step": [2, 7, 26, 48, 65, 85, 88, 94, 96, 100], "caution": [2, 5, 93], "previous": [2, 5, 13, 52, 66, 69, 80, 82, 84, 86, 90, 91, 96, 100], "assign": [2, 7, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 37, 41, 47, 48, 52, 69, 82, 86, 88, 90, 93, 101, 102, 103], "individu": [2, 4, 7, 10, 13, 26, 37, 41, 42, 57, 61, 64, 67, 69, 75, 77, 80, 82, 87, 90, 93, 96, 97, 98, 103], "still": [2, 40, 41, 52, 66, 82, 84, 88, 93, 99], "extra": [2, 37, 41, 52, 56, 57, 58, 69, 88, 91, 93, 96, 99], "receiv": [2, 10, 37, 41, 42, 58, 61, 62, 69, 71, 75, 87, 98], "overwritten": [2, 69], "callabl": [2, 3, 37, 41, 48, 51, 56, 61, 93], "x_val": 2, "y_val": 2, "map": [2, 3, 12, 40, 41, 44, 47, 51, 52, 65, 67, 69, 74, 84, 85, 86, 87, 88, 93, 94, 97, 103], "appropri": [2, 10, 16, 34, 59, 67, 86, 90, 97, 98], "earli": [2, 88], "stop": [2, 88], "x_valid": 2, "y_valid": 2, "could": [2, 4, 7, 10, 22, 36, 52, 66, 82, 86, 88, 90, 97, 101, 103], "f": [2, 7, 82, 83, 84, 85, 86, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "ignor": [2, 37, 41, 51, 56, 69, 74, 78, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "allow": [2, 4, 36, 37, 40, 41, 45, 52, 57, 65, 66, 69, 71, 73, 83, 84, 85, 88, 93, 100, 102], "access": [2, 10, 13, 37, 41, 69, 83, 87, 88, 91, 97], "hyperparamet": [2, 61, 66, 88], "purpos": [2, 86, 87, 93, 97, 101], "want": [2, 5, 10, 36, 40, 53, 57, 59, 69, 83, 85, 86, 88, 91, 92, 96, 98, 99, 100, 102, 103], "explicitli": [2, 8, 10, 41, 69, 93], "yourself": [2, 5, 40, 87], "altern": [2, 7, 10, 48, 52, 56, 57, 67, 80, 83, 84, 88, 90, 91, 93, 94, 96, 97, 99, 101], "same": [2, 3, 5, 7, 9, 10, 12, 14, 16, 26, 30, 37, 40, 41, 43, 52, 56, 57, 59, 66, 67, 69, 73, 74, 77, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 97, 98, 99, 100, 101, 102], "effect": [2, 10, 27, 37, 41, 57, 66, 69, 88, 90, 91, 93, 99], "offer": [2, 5, 9, 10, 83, 84, 86, 87, 91, 93, 94, 97], "after": [2, 3, 5, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 57, 69, 83, 86, 88, 91, 93, 94, 96, 98, 99, 100, 101, 102], "attribut": [2, 5, 7, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 37, 40, 41, 48, 66, 69, 82, 86], "label_issues_df": [2, 69, 88], "similar": [2, 10, 36, 37, 41, 52, 57, 61, 62, 64, 66, 69, 73, 77, 85, 86, 87, 88, 90, 91, 93, 94, 98, 99, 102], "document": [2, 3, 5, 14, 16, 36, 37, 40, 41, 42, 43, 48, 51, 56, 58, 59, 61, 64, 65, 66, 69, 73, 74, 75, 77, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "descript": [2, 5, 7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 36, 42, 52, 62, 69, 86, 87], "were": [2, 3, 5, 36, 41, 58, 64, 77, 82, 84, 90, 93, 94, 96, 98, 100, 102], "present": [2, 3, 5, 10, 12, 13, 20, 36, 52, 66, 74, 79, 88, 90, 93, 99], "actual": [2, 3, 5, 36, 57, 58, 67, 87, 93, 94, 103], "num_class": [2, 36, 40, 52, 56, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99], "uniqu": [2, 31, 52, 74, 86, 93, 97, 99], "given_label": [2, 5, 25, 30, 36, 46, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 94, 101, 102, 103], "normal": [2, 3, 18, 26, 31, 43, 45, 48, 50, 51, 52, 67, 93, 94, 99], "trick": [2, 93], "distribut": [2, 3, 5, 10, 26, 28, 36, 41, 43, 47, 50, 57, 65, 66, 67, 79, 85, 86, 87, 88, 90, 91, 98, 99], "account": [2, 36, 57, 61, 66, 67, 83, 91, 93, 94, 96, 97, 99, 101], "word": [2, 3, 51, 77, 78, 93], "remov": [2, 10, 31, 36, 37, 41, 43, 69, 79, 82, 83, 87, 88, 90, 91, 92, 93, 94, 97, 99, 101], "so": [2, 3, 5, 6, 7, 10, 14, 26, 34, 36, 37, 40, 41, 43, 52, 57, 58, 64, 67, 69, 73, 77, 84, 86, 87, 88, 91, 94, 97, 99, 102], "proportion": [2, 10, 43], "just": [2, 3, 5, 10, 13, 32, 36, 38, 40, 52, 56, 67, 69, 71, 79, 80, 82, 83, 84, 87, 88, 90, 91, 93, 94, 97, 98, 99, 100, 101, 102], "procedur": 2, "get": [2, 3, 5, 8, 13, 31, 37, 38, 41, 43, 48, 50, 51, 52, 57, 59, 61, 66, 67, 69, 70, 71, 79, 82, 83, 84, 88, 91, 92, 93, 94, 99, 100, 101], "detect": [2, 5, 7, 9, 13, 14, 16, 18, 22, 28, 42, 50, 60, 62, 63, 64, 65, 66, 67, 68, 69, 72, 76, 79, 82, 83, 85, 86, 88, 89, 95, 97, 101, 102, 103], "arg": [2, 12, 22, 27, 31, 37, 38, 41, 48, 52, 67, 69], "kwarg": [2, 7, 10, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 42, 48, 56, 69, 71, 73, 75, 93], "test": [2, 5, 10, 26, 41, 48, 56, 69, 79, 82, 83, 85, 86, 87, 88, 90, 91, 100, 101, 103], "expect": [2, 3, 37, 41, 43, 48, 57, 66, 67, 69, 82, 83, 93, 94, 96, 97, 98, 101, 103], "class_predict": 2, "evalu": [2, 10, 37, 38, 39, 40, 41, 65, 69, 82, 83, 84, 86, 87, 88, 93, 94, 96, 100, 101, 102], "simpli": [2, 10, 36, 67, 83, 86, 87, 90, 91, 93, 94, 97, 101, 102, 103], "quantifi": [2, 5, 7, 10, 13, 43, 61, 66, 69, 79, 87, 88, 90, 91, 94, 98], "save_spac": [2, 10, 68, 69], "potenti": [2, 10, 36, 43, 51, 59, 62, 65, 67, 69, 71, 73, 80, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 102, 103], "cach": [2, 83, 91], "panda": [2, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 52, 53, 56, 57, 58, 80, 82, 83, 84, 86, 87, 90, 91, 92, 93, 94, 96, 101, 102], "unlik": [2, 10, 43, 45, 48, 56, 58, 59, 61, 77, 86, 96, 97, 99, 101], "both": [2, 5, 10, 16, 26, 36, 37, 41, 43, 52, 57, 59, 67, 71, 73, 78, 79, 86, 88, 93, 94, 96, 103], "mask": [2, 40, 43, 51, 52, 59, 62, 67, 69, 71, 73, 74, 79, 85, 92, 93, 96, 98, 102, 103], "prefer": [2, 67, 75, 97], "plan": 2, "subsequ": [2, 3, 37, 41, 83, 91, 93, 94, 98], "invok": [2, 37, 41, 94, 100], "scratch": [2, 69], "To": [2, 5, 7, 9, 10, 11, 13, 16, 26, 35, 37, 40, 41, 42, 43, 56, 57, 59, 61, 65, 66, 67, 69, 70, 71, 73, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102, 103], "share": [2, 67, 69], "mostli": [2, 52, 64, 69, 97, 101], "longer": [2, 34, 47, 48, 51, 69, 80, 83, 91, 93, 98], "info": [2, 5, 7, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 58, 69, 77, 86, 87, 92, 103], "about": [2, 3, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 38, 40, 45, 57, 58, 61, 65, 69, 74, 77, 84, 86, 88, 90, 91, 92, 93, 94, 96, 99], "docstr": [2, 36, 37, 41, 52, 69, 92, 94], "unless": [2, 37, 41, 69, 93], "our": [2, 3, 10, 56, 57, 67, 69, 79, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "is_label_issu": [2, 30, 69, 83, 84, 85, 86, 87, 88, 90, 91, 94, 97, 101], "entir": [2, 10, 26, 40, 43, 46, 58, 59, 64, 67, 69, 71, 73, 74, 79, 85, 86, 87, 93, 98, 99, 100, 102, 103], "accur": [2, 3, 5, 9, 10, 16, 36, 40, 43, 57, 58, 59, 62, 65, 67, 69, 70, 71, 73, 74, 80, 87, 88, 90, 91, 93, 96, 101], "label_qu": [2, 57, 69, 83, 94, 96, 101], "measur": [2, 5, 36, 57, 58, 69, 79, 82, 92, 93, 94, 96, 97, 101, 102, 103], "qualiti": [2, 3, 5, 7, 9, 10, 13, 30, 31, 36, 40, 42, 43, 45, 48, 57, 58, 59, 61, 62, 64, 67, 69, 70, 73, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 101], "lower": [2, 4, 5, 7, 10, 13, 28, 40, 48, 50, 57, 58, 61, 64, 65, 67, 69, 70, 73, 77, 83, 84, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "eas": 2, "comparison": [2, 37, 41, 65, 94, 96], "against": [2, 37, 41, 86, 90, 93, 96, 97], "predicted_label": [2, 5, 25, 30, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 94, 101, 102], "ad": [2, 37, 41, 85, 87, 96, 101], "precis": [2, 50, 59, 62, 65, 93, 94, 102, 103], "definit": [2, 7, 34, 48, 69, 82, 90], "accessor": [2, 69], "describ": [2, 10, 18, 57, 66, 67, 69, 75, 77, 94, 96, 97, 98, 100, 103], "precomput": [2, 4, 5, 46, 69, 87, 88, 90, 91, 92, 94], "clear": [2, 37, 41, 69, 83, 91, 101], "save": [2, 5, 16, 37, 40, 41, 65, 69, 93, 98, 102, 103], "space": [2, 5, 10, 66, 69, 88, 90, 92], "place": [2, 37, 41, 52, 69, 82, 96], "larg": [2, 9, 10, 40, 69, 88, 90, 91, 93, 98, 99, 102, 103], "deploi": [2, 9, 10, 69, 88, 90, 91, 93], "care": [2, 10, 37, 41, 69, 91, 93, 94], "avail": [2, 4, 5, 7, 10, 12, 14, 33, 41, 69, 93, 94, 96, 98, 101], "cannot": [2, 5, 12, 14, 52, 100, 103], "anymor": 2, "classmethod": [2, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 34, 41, 48, 69], "__init_subclass__": [2, 39, 41, 68, 69], "set_": [2, 41, 69], "_request": [2, 41, 69], "pep": [2, 41, 69], "487": [2, 41, 69], "look": [2, 5, 7, 16, 37, 41, 52, 69, 74, 82, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 102, 103], "inform": [2, 5, 7, 10, 13, 16, 33, 37, 41, 52, 57, 58, 62, 65, 69, 74, 77, 78, 79, 84, 85, 86, 90, 91, 94, 96, 99, 102, 103], "__metadata_request__": [2, 41, 69], "infer": [2, 41, 52, 69, 74, 78, 82, 83, 88, 96, 97], "signatur": [2, 37, 41, 69], "accept": [2, 37, 41, 50, 67, 69, 86, 87, 93], "metadata": [2, 41, 69, 88, 90, 91, 103], "through": [2, 5, 7, 41, 69, 83, 84, 85, 87, 91, 92, 93, 96, 98, 99], "develop": [2, 9, 41, 69, 93, 94, 103], "request": [2, 41, 69, 82, 83, 87, 91, 92, 97, 103], "those": [2, 3, 4, 10, 40, 41, 43, 56, 57, 59, 65, 69, 73, 77, 78, 79, 84, 88, 93, 98, 102], "http": [2, 4, 5, 7, 9, 10, 11, 18, 35, 37, 38, 40, 41, 45, 52, 62, 65, 66, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "www": [2, 41, 69, 99], "org": [2, 4, 18, 37, 38, 41, 52, 66, 69, 93, 94, 103], "dev": [2, 41, 69], "0487": [2, 41, 69], "get_metadata_rout": [2, 39, 41, 68, 69], "rout": [2, 41, 69], "pleas": [2, 37, 41, 56, 69, 79, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 99, 101, 103], "guid": [2, 7, 10, 41, 69, 80, 88, 89], "mechan": [2, 37, 41, 69], "metadatarequest": [2, 41, 69], "encapsul": [2, 16, 41, 64, 69], "get_param": [2, 39, 41, 55, 56, 68, 69], "subobject": [2, 41, 69], "param": [2, 10, 37, 41, 56, 66, 69, 93], "name": [2, 5, 6, 7, 10, 12, 13, 32, 34, 36, 37, 41, 47, 48, 52, 56, 57, 58, 65, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 101, 102, 103], "set_fit_request": [2, 39, 41, 68, 69], "str": [2, 3, 4, 5, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 46, 48, 50, 51, 52, 56, 57, 58, 62, 64, 65, 67, 69, 74, 78, 84, 85, 86, 93, 96, 97, 98, 103], "unchang": [2, 37, 41, 69, 103], "relev": [2, 16, 26, 41, 69, 88], "enable_metadata_rout": [2, 41, 69], "set_config": [2, 41, 69], "meta": [2, 41, 69], "rais": [2, 4, 5, 12, 13, 34, 37, 41, 45, 48, 50, 69, 84, 93], "alia": [2, 37, 41, 69], "metadata_rout": [2, 41, 69], "retain": [2, 41, 52, 69], "chang": [2, 32, 34, 37, 40, 41, 45, 69, 77, 82, 83, 84, 86, 91, 93, 98, 99, 103], "version": [2, 5, 7, 9, 10, 11, 15, 21, 24, 29, 35, 37, 39, 41, 44, 45, 52, 55, 56, 67, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "sub": [2, 41, 64, 69], "pipelin": [2, 41, 69, 101], "otherwis": [2, 4, 7, 10, 34, 36, 37, 40, 41, 43, 49, 50, 51, 52, 59, 66, 69, 71, 73, 74, 78, 83, 91, 93], "updat": [2, 13, 37, 40, 41, 56, 69, 80, 85, 86, 88], "set_param": [2, 39, 41, 55, 56, 68, 69], "simpl": [2, 37, 41, 43, 57, 67, 69, 82, 83, 85, 86, 87, 88, 90, 91, 96, 99, 101], "well": [2, 3, 9, 10, 37, 41, 45, 46, 57, 59, 65, 67, 69, 74, 77, 78, 80, 86, 87, 88, 90, 91, 93, 94, 96, 98, 99], "nest": [2, 37, 41, 42, 53, 69, 75, 77, 78, 103], "latter": [2, 37, 41, 69, 99], "compon": [2, 41, 69], "__": [2, 41, 69], "set_score_request": [2, 68, 69], "structur": [3, 66, 82, 85, 90, 93], "unobserv": 3, "less": [3, 4, 5, 10, 31, 40, 48, 57, 66, 67, 71, 73, 77, 87, 88, 90, 92, 93, 94, 98, 103], "channel": [3, 84, 94], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 36, 46, 52, 58, 83, 87, 92], "inv": 3, "confident_joint": [3, 22, 36, 43, 52, 58, 59, 80, 93, 94], "un": 3, "under": [3, 10, 37, 41, 58, 65, 66, 87, 99], "joint": [3, 36, 43, 46, 52, 58, 59, 92], "num_label_issu": [3, 40, 43, 59, 74, 78, 80], "estimation_method": [3, 40], "off_diagon": 3, "multi_label": [3, 36, 43, 52, 53, 59, 97], "don": [3, 79, 87, 88, 90, 91, 94, 98, 101], "statis": 3, "compute_confident_joint": [3, 36, 43, 52, 59, 94], "off": [3, 43, 52, 64, 88, 94, 98, 99], "j": [3, 5, 36, 37, 41, 42, 43, 59, 62, 65, 66, 75, 77, 78, 85, 86, 87, 94, 102, 103], "confident_learn": [3, 43, 59, 94], "off_diagonal_calibr": 3, "calibr": [3, 4, 43, 52, 57, 96], "cj": [3, 46, 52], "axi": [3, 31, 46, 48, 50, 71, 74, 84, 85, 86, 87, 88, 93, 94, 96, 97, 99, 101, 102], "bincount": [3, 85, 86, 87, 94, 96, 97], "alwai": [3, 10, 37, 41, 52, 82, 83, 84, 94, 101], "estimate_issu": 3, "over": [3, 5, 10, 37, 40, 41, 64, 65, 71, 73, 82, 87, 88, 90, 92, 93, 94, 99, 101], "As": [3, 7, 79, 86, 87, 91, 94, 101, 103], "add": [3, 5, 7, 12, 13, 37, 41, 56, 65, 83, 84, 85, 86, 87, 88, 91, 93, 94, 97], "approach": [3, 4, 36, 40, 43, 56, 82, 85, 90, 94, 97, 99, 101], "custom": [3, 7, 10, 11, 30, 37, 40, 41, 48, 51, 67, 83, 87, 91, 94, 101], "know": [3, 10, 86, 87, 88, 90, 91, 93, 94, 96, 101], "cut": [3, 64, 79, 94], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 32, 98, 99, 103], "underestim": 3, "few": [3, 9, 10, 65, 79, 87, 93, 96, 97, 98, 99, 103], "4": [3, 4, 5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 47, 48, 51, 61, 62, 64, 65, 67, 70, 77, 92, 93, 97, 102, 103], "detail": [3, 4, 5, 10, 14, 16, 33, 36, 37, 41, 42, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 73, 74, 75, 79, 80, 84, 93, 97, 99, 103], "num_issu": [3, 7, 40, 84, 85, 86, 87, 88, 90, 91, 94], "calibrate_confident_joint": 3, "up": [3, 7, 10, 17, 26, 27, 30, 43, 48, 56, 57, 83, 85, 92, 93, 98, 101, 103], "p_": [3, 36, 43], "pair": [3, 5, 10, 36, 43, 94], "v": [3, 10, 40, 58, 59, 61, 67, 85, 86, 87, 97, 98, 99, 100], "rest": [3, 5, 7, 9, 10, 11, 35, 58, 59, 61, 69, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94, 96, 101], "fashion": [3, 5, 71, 82], "2x2": 3, "incorrectli": [3, 36, 58, 59, 62, 90, 103], "calibrated_cj": 3, "c": [3, 10, 50, 51, 59, 67, 79, 82, 84, 86, 87, 90, 91, 93, 94, 97, 98, 99, 100, 101], "whose": [3, 4, 5, 10, 28, 37, 41, 46, 51, 57, 61, 64, 70, 73, 77, 78, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 99, 102, 103], "truli": [3, 99, 102], "estimate_joint": [3, 36, 94], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 59, 65, 94, 98, 100, 102, 103], "return_indices_of_off_diagon": 3, "frequenc": [3, 26, 57, 58, 65, 74, 98, 99], "done": [3, 10, 56, 69, 86, 93, 94, 97, 99, 100], "overfit": [3, 10, 62, 65, 82, 84, 85, 86, 87, 88, 90, 91, 100], "classifict": 3, "singl": [3, 5, 9, 10, 12, 26, 36, 37, 41, 42, 48, 49, 52, 57, 58, 64, 65, 66, 67, 77, 82, 84, 85, 86, 93, 94, 97, 98], "baselin": [3, 37, 43, 83, 99, 101], "proxi": 3, "union": [3, 5, 12, 48, 52, 53, 59, 65, 69, 77, 93], "tupl": [3, 31, 37, 41, 42, 46, 47, 49, 51, 52, 57, 59, 65, 73, 75, 77, 78, 84, 103], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 5, 10, 40, 46, 57, 71, 73, 79, 83, 85, 88, 93, 102], "practic": [3, 82, 83, 87, 88, 94, 99, 101], "complet": [3, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 101], "gist": 3, "cj_ish": 3, "guess": [3, 46, 94, 96], "8": [3, 5, 7, 8, 47, 48, 49, 51, 61, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "parallel": [3, 43, 65, 75, 92], "again": [3, 56, 82, 93, 99], "simplifi": [3, 14, 93], "understand": [3, 9, 36, 58, 65, 87, 94, 101, 102, 103], "100": [3, 37, 41, 50, 67, 82, 83, 86, 87, 88, 90, 92, 93, 94, 97, 98, 99, 103], "optim": [3, 37, 38, 41, 56, 88, 96], "speed": [3, 43, 83, 92, 93, 101], "dtype": [3, 23, 25, 26, 31, 37, 41, 51, 52, 61, 77, 84, 98], "enumer": [3, 37, 41, 84, 85, 86, 87, 88, 103], "s_label": 3, "confident_bin": 3, "6": [3, 5, 41, 48, 52, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "num_confident_bin": 3, "argmax": [3, 43, 67, 71, 74, 84, 93, 94, 98, 99, 102], "elif": 3, "estimate_lat": 3, "py_method": [3, 46], "cnt": [3, 46], "1d": [3, 5, 12, 16, 32, 40, 43, 48, 49, 52, 53, 61, 70, 82, 84], "eqn": [3, 46], "margin": [3, 43, 46, 48, 67], "marginal_p": [3, 46], "shorthand": [3, 13], "proport": [3, 10, 36, 58, 94, 100], "poorli": [3, 46, 82], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 94], "variabl": [3, 7, 14, 27, 52, 69, 70, 84, 86, 90, 94, 97, 101], "exact": [3, 46, 82, 85, 86, 87, 88, 90], "within": [3, 4, 5, 10, 15, 32, 37, 38, 41, 42, 44, 59, 64, 73, 75, 77, 86, 87, 88, 93, 98, 102], "percent": 3, "often": [3, 36, 46, 58, 93, 94, 100, 102], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 52, 53, 65, 82, 83, 84, 85, 86, 88, 90, 91, 93, 97, 98, 99, 101], "wai": [3, 5, 56, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 100], "pro": 3, "con": 3, "pred_proba": [3, 100], "combin": [3, 36, 86, 88, 92, 93, 94, 100, 101], "becaus": [3, 46, 52, 64, 91, 93, 94, 96, 98], "littl": [3, 40, 92, 98, 103], "uniform": [3, 67, 92, 93, 94], "20": [3, 7, 42, 78, 84, 88, 91, 92, 93, 94, 98, 101, 102, 103], "Such": [3, 88, 99], "bound": [3, 23, 25, 37, 41, 51, 61, 62, 64, 65, 98], "reason": [3, 22, 37, 41], "comment": [3, 51, 103], "end": [3, 5, 37, 41, 65], "file": [3, 5, 12, 39, 40, 55, 65, 82, 84, 86, 90, 91, 92, 93, 98, 99, 102, 103], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 94], "handl": [3, 5, 7, 10, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 80, 82, 85, 86, 87, 88, 90, 91, 94, 102, 103], "five": [3, 62, 65, 94, 98], "estimate_cv_predicted_prob": [3, 94], "estimate_noise_matric": 3, "get_confident_threshold": [3, 39, 40], "amongst": [3, 10, 98], "confident_threshold": [3, 10, 22, 23, 40, 66], "valuat": [4, 9, 18], "assess": [4, 98], "point": [4, 5, 7, 9, 10, 18, 26, 37, 41, 85, 86, 87, 88, 90, 91, 93, 94, 96], "contribut": [4, 10, 18, 98], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 16, 18, 19, 26, 28, 31, 90], "metric": [4, 5, 10, 18, 19, 26, 31, 50, 52, 56, 65, 66, 82, 83, 84, 88, 90, 91, 94, 101], "10": [4, 10, 18, 19, 23, 26, 31, 37, 38, 65, 66, 67, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "shaplei": [4, 10, 18], "nearest": [4, 5, 10, 16, 23, 26, 28, 50, 66, 87, 91, 99], "neighbor": [4, 5, 10, 16, 18, 23, 26, 28, 50, 66, 86, 87, 88, 90, 91, 93, 94, 99], "knn": [4, 10, 13, 18, 26, 31, 50, 66, 90, 99], "graph": [4, 5, 10, 13, 16, 18, 26, 31], "calcul": [4, 10, 18, 26, 40, 48, 50, 57, 61, 62, 64, 65, 66, 69, 73, 88, 92], "directli": [4, 5, 14, 16, 33, 34, 40, 56, 57, 83, 87, 91, 93, 97, 98, 101], "lowest": [4, 10, 57, 65, 87, 88, 93, 96, 97, 98, 102], "fall": [4, 10, 64, 73, 77, 94, 99], "flag": [4, 10, 22, 26, 43, 48, 58, 59, 62, 69, 79, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 98, 99, 101, 102], "approxim": [4, 10, 18, 40, 66, 96], "top": [4, 5, 10, 36, 40, 42, 43, 52, 59, 62, 65, 67, 74, 78, 79, 83, 84, 86, 87, 90, 91, 92, 93, 94, 98, 99, 101, 103], "found": [4, 5, 7, 10, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 97, 99, 101, 103], "arxiv": [4, 18, 94], "ab": [4, 18, 94, 98], "1908": 4, "08619": 4, "1911": [4, 18], "07128": [4, 18], "embed": [4, 5, 10, 16, 66, 79, 83, 84, 86, 87, 90, 91, 94, 97, 101], "represent": [4, 5, 10, 16, 34, 37, 41, 49, 59, 79, 83, 84, 86, 87, 88, 91, 93, 94, 99], "suppli": [4, 97, 98, 101], "2d": [4, 5, 16, 32, 40, 48, 49, 51, 52, 57, 82, 84, 97], "num_exampl": [4, 5, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 58, 84, 85, 86, 87, 88, 90, 91, 94], "num_featur": [4, 5, 16, 37, 41, 56], "distanc": [4, 5, 10, 16, 18, 26, 28, 31, 50, 64, 66, 90, 99], "construct": [4, 5, 7, 10, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 41, 48, 56], "nearestneighbor": [4, 5, 10, 18, 66, 90, 99], "cosin": [4, 10, 50, 66, 99], "dim": [4, 66, 88, 102], "euclidean": [4, 5, 10, 50, 64, 66, 90], "dimension": [4, 26, 52, 84, 94, 99], "exce": [4, 85, 88], "transform": [4, 10, 32, 48, 50, 52, 66, 67, 82, 83, 87, 88, 91, 99, 103], "rel": [4, 10, 36, 57, 58, 66, 86, 87, 88, 90, 91, 94, 99], "adjust": [4, 38, 43, 61, 66, 67, 79, 94], "closer": [4, 10, 64, 98], "highli": [4, 87, 88], "influenti": 4, "posit": [4, 5, 37, 41, 50, 52, 65, 92, 99], "convers": 4, "neg": [4, 10, 64, 65, 86, 87, 92], "valueerror": [4, 5, 12, 13, 34, 45, 48, 50, 93], "neither": [4, 5, 10, 14, 98], "nor": [4, 5, 10, 14], "larger": [4, 18, 69, 71, 73, 85, 88, 91, 92, 93], "55": [4, 51, 92, 98, 101], "525": 4, "unifi": 5, "audit": [5, 9, 12, 13, 16, 84, 88, 89, 90, 91, 93, 94, 97, 98, 101], "kind": [5, 6, 7, 10, 84, 85, 86, 88, 90, 91, 92, 94], "addit": [5, 7, 9, 11, 13, 33, 35, 37, 41, 48, 53, 57, 65, 75, 82, 83, 84, 86, 90, 91, 94, 96, 99, 100], "depend": [5, 7, 9, 11, 12, 13, 35, 39, 43, 45, 52, 55, 59, 66, 69, 70, 79], "instal": [5, 7, 9, 11, 35, 37, 39, 40, 41, 43, 55, 56, 71, 73], "pip": [5, 7, 9, 11, 35, 56, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "development": [5, 7, 9, 11, 35], "git": [5, 7, 9, 11, 35, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "github": [5, 7, 9, 11, 35, 37, 38, 52, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "com": [5, 7, 9, 11, 35, 37, 38, 40, 45, 52, 66, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "egg": [5, 7, 9, 11, 35, 79, 92], "label_nam": [5, 7, 8, 10, 12, 18, 31, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 101], "image_kei": [5, 10, 88], "interfac": [5, 9, 10, 79, 93, 94], "librari": [5, 10, 41, 62, 65, 66, 79, 83, 86, 91, 92, 93], "goal": [5, 101], "track": [5, 7, 13, 14, 79, 85, 86, 92, 93, 94], "intermedi": [5, 9, 87], "statist": [5, 10, 13, 22, 26, 36, 57, 58, 65, 87, 90, 91, 94], "convert": [5, 10, 12, 34, 37, 41, 49, 50, 53, 57, 64, 73, 77, 80, 83, 84, 88, 91, 92, 93, 96, 97, 98], "hug": [5, 10, 12, 88], "face": [5, 10, 12, 16, 88, 92, 97], "kei": [5, 7, 10, 12, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 41, 48, 57, 58, 64, 66, 85, 86, 87, 88, 91, 93, 94, 96, 98], "string": [5, 10, 12, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 41, 52, 57, 58, 70, 74, 77, 78, 83, 90, 91, 93, 96, 97, 103], "dictionari": [5, 7, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 41, 47, 52, 57, 58, 61, 62, 64, 65, 86, 87, 90, 91, 94, 96, 97, 98], "path": [5, 12, 37, 40, 41, 65, 84, 86, 93, 98], "local": [5, 7, 10, 12, 37, 38, 41, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "text": [5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 42, 48, 66, 75, 77, 78, 79, 81, 86, 87, 89, 92, 93, 94, 95, 96, 99], "txt": [5, 12, 103], "csv": [5, 12, 82, 83, 90, 91, 101], "json": [5, 12], "hub": [5, 12], "multiclass": [5, 12, 15, 48, 52, 57, 97], "regress": [5, 7, 10, 12, 14, 16, 21, 30, 32, 34, 83, 85, 86, 87, 91, 95, 96, 99], "multilabel": [5, 12, 14, 15, 21, 25, 32, 34, 49, 97], "imag": [5, 9, 36, 41, 62, 64, 65, 66, 71, 73, 74, 79, 86, 87, 89, 92, 93, 95, 96, 97, 98, 100, 102], "field": [5, 10, 37, 41], "themselv": [5, 82, 83, 101], "pil": [5, 88], "cleanvis": [5, 10], "level": [5, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 51, 75, 77, 87, 88, 93, 95, 97, 102], "load_dataset": [5, 12, 88], "glue": 5, "sst2": 5, "properti": [5, 12, 13, 34, 37, 41, 85], "has_label": [5, 12], "class_nam": [5, 12, 20, 36, 42, 58, 65, 74, 78, 79, 92, 94, 98, 102, 103], "empti": [5, 12, 46, 57, 87, 93, 97], "find_issu": [5, 6, 7, 8, 10, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101], "issue_typ": [5, 6, 7, 8, 10, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101], "sort": [5, 16, 40, 43, 48, 57, 59, 62, 64, 65, 67, 73, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 101, 102, 103], "common": [5, 13, 16, 87, 89, 92, 93, 94, 97, 98, 102], "real": [5, 16, 79, 86, 87, 93, 94, 96, 101, 102], "world": [5, 16, 79, 86, 87, 93, 94, 96, 101, 102], "interact": [5, 16, 91, 93], "thereof": [5, 16], "insight": [5, 16, 65, 96], "best": [5, 9, 10, 16, 47, 57, 67, 82, 83, 86, 87, 88, 90, 91, 93, 96, 97, 99, 101, 103], "properli": [5, 10, 40, 47, 52, 53, 71, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 99, 101, 102], "respect": [5, 37, 41, 62, 65, 84, 85, 86, 87, 88, 90, 91, 94, 97, 98], "lexicograph": [5, 47, 52, 84, 85, 86, 87, 88, 90, 91, 94, 97], "squar": [5, 52, 69, 92, 101], "csr": 5, "evenli": 5, "omit": [5, 64, 65, 88, 98], "itself": [5, 32, 37, 41, 98], "three": [5, 10, 36, 57, 58, 69, 74, 82, 84, 85, 86, 87, 90, 92, 94, 96, 100, 101, 102, 103], "indptr": 5, "wise": 5, "start": [5, 7, 10, 34, 37, 38, 41, 48, 79, 90, 97, 103], "th": [5, 42, 47, 51, 52, 57, 59, 62, 64, 65, 66, 75, 77, 78, 91, 97, 98, 103], "ascend": [5, 36, 58, 88, 94], "segment": [5, 71, 73, 74, 95], "reflect": [5, 82, 83, 90, 91, 96, 98, 99, 101], "maintain": [5, 56], "kneighbors_graph": [5, 18, 90], "illustr": 5, "todens": 5, "second": [5, 48, 52, 65, 67, 86, 93, 94, 103], "duplic": [5, 9, 21, 22, 37, 41, 79, 85, 86, 94, 101], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 57, 93, 96, 103], "unspecifi": [5, 16, 43, 59], "interest": [5, 16, 22, 74, 78, 82, 83, 91, 94, 101, 102, 103], "constructor": [5, 10, 16, 23, 30], "issuemanag": [5, 9, 13, 14, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 33], "respons": [5, 16, 22, 69, 70, 92, 101, 103], "random_st": [5, 82, 84, 85, 86, 87, 88, 94, 97, 99], "lab": [5, 6, 8, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 40, 79, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 101], "comprehens": [5, 79, 88, 97, 101], "nbr": 5, "n_neighbor": [5, 10, 18, 66], "mode": [5, 11, 18, 37, 40, 41, 99], "4x4": 5, "float64": [5, 26, 37, 41, 77], "compress": [5, 10, 52, 71, 73], "toarrai": 5, "NOT": [5, 40, 91], "23606798": 5, "41421356": 5, "configur": [5, 16, 48, 87], "suppos": [5, 10, 62, 82, 83, 99, 101], "who": [5, 64, 82, 90, 94, 103], "manag": [5, 8, 9, 10, 13, 14, 15, 16, 17, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 56, 86, 93], "clean_learning_kwarg": [5, 10, 23, 30, 93, 101], "labelissuemanag": [5, 10, 14, 21, 23], "prune_method": [5, 80], "prune_by_noise_r": [5, 43, 59, 94], "report": [5, 7, 11, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 58, 78, 79, 84, 85, 86, 87, 90, 91, 93, 94, 97, 101, 103], "include_descript": [5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33], "show_summary_scor": [5, 33], "show_all_issu": [5, 33], "summari": [5, 7, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 42, 55, 56, 58, 63, 72, 73, 75, 76, 77, 80, 84, 85, 86, 87, 88, 90, 91, 92, 94, 98, 101, 102, 103], "show": [5, 7, 26, 37, 41, 47, 52, 65, 74, 78, 82, 85, 87, 88, 90, 91, 92, 93, 94, 96, 99, 101, 102, 103], "suffer": [5, 10, 13, 22, 59, 67, 78, 103], "onc": [5, 22, 36, 37, 41, 82, 85, 86, 93, 94, 97, 98], "familiar": 5, "overal": [5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 42, 48, 57, 58, 61, 64, 65, 69, 73, 74, 75, 77, 79, 80, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 98, 103], "sever": [5, 7, 10, 12, 13, 22, 37, 40, 41, 43, 61, 64, 66, 67, 73, 77, 79, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 98, 99, 103], "compar": [5, 57, 66, 77, 86, 87, 90, 94, 98], "issue_summari": [5, 7, 10, 13, 85, 86], "With": [5, 9, 10, 40, 83, 91, 93, 94, 96, 101, 102, 103], "usag": [5, 40, 56], "usual": [5, 12, 32, 33, 88, 96, 101], "ti": [5, 57], "exhibit": [5, 7, 10, 13, 74, 87, 88, 90, 91, 94, 98], "ie": [5, 69], "likelihood": [5, 10, 40, 42, 43, 59, 64, 66, 67, 71, 75], "wherea": [5, 52, 59, 100], "outlier": [5, 9, 14, 21, 22, 31, 44, 67, 79, 85, 86, 87, 94, 95, 101], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 94, 101], "global": [5, 7, 22, 37, 41], "non_iid": [5, 10, 14, 26, 87, 88, 90, 91, 94], "hypothesi": 5, "iid": [5, 7, 9, 26, 90, 94], "never": [5, 84, 94, 97, 99, 100], "someth": [5, 7, 37, 41, 67, 98], "123": [5, 85, 86, 87], "456": [5, 82, 83, 84], "nearest_neighbor": 5, "7": [5, 48, 49, 56, 75, 77, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "9": [5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 42, 48, 49, 61, 75, 77, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "distance_to_nearest_neighbor": [5, 86, 87, 88, 90, 91, 94], "789": 5, "get_issu": [5, 10, 13, 84, 85, 87, 88, 90, 91, 93, 97, 101], "issue_nam": [5, 6, 7, 10, 13, 14, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86, 87], "focu": [5, 13, 91, 102, 103], "full": [5, 10, 13, 40, 56, 65, 85, 88, 103], "summar": [5, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 58, 74, 78, 79, 102], "specific_issu": [5, 13], "lie": [5, 10, 66, 67, 83, 84, 86, 87, 88, 90, 91, 94], "get_issue_summari": [5, 13, 85, 87], "get_info": [5, 13, 87, 91], "yet": [5, 17, 27, 56, 92, 96], "list_possible_issue_typ": [5, 14, 15], "regist": [5, 7, 14, 15, 17, 27, 37, 41, 86], "rtype": [5, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41], "registri": [5, 14, 15], "list_default_issue_typ": [5, 14, 15], "folder": [5, 84, 86, 88], "load": [5, 12, 40, 65, 88, 92, 93, 94, 98, 99, 102, 103], "futur": [5, 10, 22, 37, 41, 57, 79, 83, 84, 86, 91, 93], "overwrit": [5, 86], "separ": [5, 36, 48, 61, 86, 87, 88, 93, 98, 100], "static": 5, "rememb": [5, 91, 93, 94], "part": [5, 10, 37, 41, 43, 62, 64, 65, 84, 86, 92, 102, 103], "ident": [5, 10, 22, 52, 85, 91], "datalab": [6, 8, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 79, 82, 83, 84, 88, 93, 96, 101], "walk": 7, "alongsid": [7, 37, 41, 86, 93], "pre": [7, 8, 10, 37, 41, 86, 87, 101], "runtim": [7, 37, 40, 41, 69, 71, 73, 84, 88, 93], "issue_manager_factori": [7, 14, 86], "myissuemanag": [7, 14], "myissuemanagerforregress": 7, "decor": [7, 14], "ll": [7, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "thing": [7, 41, 83, 94, 101], "next": [7, 57, 79, 82, 83, 84, 85, 90, 91, 93, 96, 98, 101, 103], "dummi": 7, "randint": [7, 31, 48, 85, 86, 87, 93], "mark": [7, 10, 80, 98, 99, 101], "regard": [7, 87, 94], "rand": [7, 48, 85, 86, 87], "is_": [7, 10, 86], "_issu": [7, 10, 86], "issue_score_kei": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86], "whole": [7, 26, 37, 41, 87], "make_summari": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86], "popul": [7, 87, 91], "verbosity_level": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "std": [7, 98], "raw_scor": 7, "bit": 7, "involv": [7, 40, 74, 78, 85, 93, 97], "intermediate_arg": 7, "min": [7, 48, 64, 77, 86, 93, 99], "sin_filt": 7, "sin": 7, "arang": 7, "kernel": 7, "affect": [7, 10, 37, 41, 71, 77, 91, 93], "easili": [7, 46, 80, 82, 83, 84, 85, 87, 90, 91, 94, 96, 97, 99, 100, 101, 102], "hard": [7, 41, 92, 99], "sai": [7, 10, 37, 41, 97, 102], "anoth": [7, 22, 36, 40, 51, 64, 67, 83, 90, 91, 93, 94, 96, 99], "try": [7, 9, 10, 40, 43, 56, 57, 71, 73, 79, 85, 87, 88, 90, 91, 93, 94, 102], "won": [7, 37, 41, 86, 87, 93, 97], "issue_manag": [7, 10, 11, 13, 15, 18, 19, 20, 23, 25, 26, 27, 28, 30, 31, 86], "instanti": [7, 16, 40, 56, 66, 83, 84, 87, 90], "477762": 7, "286455": 7, "term": [7, 10, 46, 52, 65, 84, 85, 86, 87, 88, 90, 91, 94], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 19, 28, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 98, 99, 101, 102, 103], "003042": 7, "058117": 7, "11": [7, 56, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "121908": 7, "15": [7, 50, 56, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "169312": 7, "17": [7, 83, 84, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 86, 87, 92, 94], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 31], "group": [8, 9, 26, 31, 92, 98, 103], "dbscan": [8, 10, 31, 93], "hdbscan": [8, 93], "etc": [8, 10, 22, 32, 37, 41, 46, 56, 57, 75, 79, 86, 87, 90, 91, 93, 94, 97, 101], "sensit": [8, 10, 50], "ep": [8, 31, 65], "radiu": 8, "min_sampl": [8, 31], "kmean": [8, 93], "your_data": 8, "get_pred_prob": [8, 85], "n_cluster": [8, 31, 93], "cluster_id": [8, 10, 31, 93], "labels_": 8, "underperforming_group": [8, 10, 14, 21, 87, 88, 90, 91, 93, 94], "search": [9, 10, 20, 26, 27, 51, 69, 93, 100], "nondefault": 9, "Near": [9, 93], "imbal": [9, 21, 61, 66, 67, 87], "null": [9, 14, 21, 87, 88, 91, 94], "togeth": [9, 10, 46, 83, 86, 87, 88, 90, 91, 94, 101, 103], "built": [9, 48], "own": [9, 37, 39, 41, 55, 61, 62, 65, 71, 75, 82, 83, 84, 87, 88, 90, 91, 93, 96, 97, 101, 102, 103], "prerequisit": 9, "basic": [9, 41, 56, 90, 91, 99], "fulli": [9, 10, 37, 41, 56, 93], "platform": [9, 10, 79, 88, 90, 91, 93], "write": [9, 10], "code": [9, 10, 37, 41, 46, 52, 56, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "being": [9, 10, 13, 36, 37, 41, 43, 48, 51, 52, 67, 82, 90, 93, 94, 101, 102], "100x": [9, 10], "faster": [9, 10, 40, 66, 69, 71, 73, 93, 94], "intellig": [9, 10], "quickli": [9, 10, 38, 82, 84, 88, 90, 91, 93, 97, 99, 102, 103], "fix": [9, 10, 57, 83, 85, 91, 94, 101], "scientist": [9, 10], "million": [9, 10, 103], "thank": [9, 10], "ai": [9, 10, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103], "suggest": [9, 10, 36, 57, 58, 64, 83, 88, 91, 93, 101], "power": [9, 10, 88, 90, 91, 92, 94, 103], "automl": [9, 10, 79, 93], "system": [9, 10, 84, 85, 88, 90, 91, 102], "foundat": [9, 10, 79], "improv": [9, 10, 57, 82, 83, 87, 88, 92, 93, 94, 101, 102], "click": [9, 10, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "tune": [9, 10, 83, 84, 91, 92, 99], "serv": [9, 10, 13, 16, 96], "auto": [9, 10, 82, 83, 85, 87, 92, 93, 101], "free": [9, 10, 79, 84, 87, 88, 90, 91, 93, 94], "page": [10, 87, 93, 94], "variou": [10, 13, 30, 39, 53, 55, 79, 82, 86, 87, 90, 91, 92, 93, 94, 96, 98], "why": [10, 85, 91], "matter": [10, 36, 58, 83, 91], "didn": 10, "plu": [10, 101], "_score": 10, "badli": [10, 64, 82, 83, 103], "issue_scor": 10, "outlier_scor": [10, 28, 86, 87, 88, 90, 91, 94, 99], "atyp": [10, 66, 86, 87, 88, 90, 91, 94, 99], "datapoint": [10, 31, 43, 48, 52, 67, 70, 79, 82, 83, 84, 86, 87, 90, 91, 93, 100, 101], "is_issu": [10, 22], "is_outlier_issu": [10, 86, 87, 88, 90, 91, 94], "annot": [10, 36, 47, 57, 58, 59, 61, 62, 64, 65, 74, 77, 78, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 95, 98, 102], "dissimilar": [10, 90, 91], "preced": 10, "incorrect": [10, 64, 67, 70, 82, 84, 85, 86, 87, 88, 90, 91, 94, 98, 101], "due": [10, 40, 43, 67, 71, 73, 84, 85, 86, 87, 88, 90, 91, 94, 101], "appear": [10, 36, 47, 58, 59, 62, 70, 87, 88, 90, 91, 101, 102], "now": [10, 40, 80, 82, 83, 84, 85, 87, 96, 98, 99, 101, 103], "token": [10, 42, 51, 73, 74, 75, 76, 77, 78, 93, 94, 95], "hamper": [10, 88, 92], "analyt": [10, 79, 93, 96], "lead": [10, 64, 67, 88, 98], "draw": [10, 85, 86, 87], "conclus": [10, 91], "veri": [10, 36, 58, 62, 64, 83, 86, 87, 88, 90, 91, 93, 94, 96, 99, 101], "rare": [10, 43, 65, 86, 87, 88, 90, 91, 93, 94], "anomal": [10, 67, 86, 87, 88, 90, 91, 94], "articl": [10, 40, 93], "blog": 10, "unexpect": [10, 37, 41, 91], "consequ": 10, "inspect": [10, 83, 84, 85, 87, 88, 94, 98, 101], "extrem": [10, 86, 87, 88, 90, 91, 93, 94], "record": [10, 37, 41, 84, 90, 101], "abbrevi": 10, "misspel": 10, "typo": [10, 78], "resolut": 10, "video": [10, 92], "audio": [10, 86, 87, 89, 93], "minor": [10, 51], "variat": 10, "translat": 10, "d": [10, 50, 82, 90, 91, 93, 94, 97, 101, 103], "constant": [10, 31, 69], "median": [10, 30, 50], "question": [10, 22, 79, 94], "nearli": [10, 22, 87, 88, 90, 91], "awar": [10, 80, 94], "presenc": [10, 94], "near_dupl": [10, 14, 19, 85, 86, 87, 88, 90, 91, 93, 94], "signific": [10, 90, 91, 94], "violat": [10, 90, 91, 94], "assumpt": [10, 90, 91, 94], "changepoint": [10, 90, 91, 94], "shift": [10, 90, 91, 94], "drift": [10, 87, 90, 94], "autocorrel": [10, 90, 91, 94], "almost": [10, 90, 91, 94], "adjac": [10, 90, 91, 94], "tend": [10, 36, 46, 90, 91, 94, 102, 103], "sequenti": [10, 37, 41, 56, 88], "gap": 10, "b": [10, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 51, 52, 77, 82, 90, 91, 92, 93, 94, 100, 103], "x1": [10, 62, 65, 98], "x2": [10, 62, 65, 98], "10th": 10, "100th": 10, "90": [10, 77, 82, 90, 94, 100, 101], "similarli": [10, 37, 41, 86, 88, 90, 93, 98], "math": [10, 88], "behind": [10, 66, 94], "proper": [10, 52, 57, 62, 65, 82, 88, 91, 93, 96, 98], "scenario": [10, 67, 85, 86, 87], "underli": [10, 42, 66, 75, 77, 103], "stem": [10, 66, 99], "evolv": 10, "influenc": 10, "act": [10, 64, 86], "accordingli": [10, 32], "emploi": [10, 97, 99], "partit": [10, 100], "ahead": 10, "good": [10, 37, 41, 50, 56, 58, 64, 67, 71, 73, 74, 79, 85, 88, 90, 91], "problem": [10, 32, 40, 48, 74, 79, 86, 87, 88, 91, 93], "deploy": [10, 82, 83, 94, 101], "overlook": [10, 64, 98], "fact": 10, "thu": [10, 36, 41, 58, 82, 84, 90, 91, 94, 100, 103], "diagnos": [10, 87, 93], "rarest": [10, 87], "q": [10, 98], "subpar": 10, "special": [10, 51], "techniqu": [10, 98], "smote": 10, "asymmetr": [10, 36], "class_imbal": [10, 14, 20, 87, 88, 90, 91, 94], "excess": [10, 88], "dark": [10, 102], "bright": [10, 103], "blurri": [10, 88], "lack": [10, 56], "unusu": [10, 98, 99], "cluster": [10, 18, 31], "slice": 10, "poor": 10, "subpopul": 10, "faq": [10, 79, 87, 88, 90, 91, 95], "get_self_confidence_for_each_label": [10, 48, 67], "r": [10, 40, 69, 85, 86, 87, 101, 102], "tabular": [10, 79, 81, 86, 87, 89, 93, 96], "categor": [10, 66, 81, 82, 86, 87, 89, 93, 101], "encod": [10, 49, 65, 71, 74, 82, 83, 90, 91, 93, 101, 102], "miss": [10, 27, 37, 41, 52, 62, 64, 85, 90, 93, 98, 101], "pattern": 10, "isn": [10, 17, 27], "scalabl": 10, "sacrific": 10, "One": [10, 52, 66, 93], "quantif": 10, "data_valu": [10, 14, 21], "exert": [10, 87], "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, 21, 23, 30], "health_summari": [10, 23, 36, 79, 92], "health_summary_kwarg": 10, "tandem": [10, 92], "view": [10, 37, 41, 42, 43, 73, 75, 77, 79, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 96, 97, 98, 99, 100, 101, 103], "ood_kwarg": 10, "outofdistribut": [10, 28, 66, 99], "outsid": [10, 93, 97], "outlierissuemanag": [10, 14, 21, 28, 86], "nearduplicateissuemanag": [10, 14, 19, 21], "noniidissuemanag": [10, 14, 21, 26], "num_permut": [10, 26], "permut": [10, 26], "significance_threshold": [10, 26], "signic": 10, "noniid": [10, 21], "classimbalanceissuemanag": [10, 14, 20, 21], "underperforminggroupissuemanag": [10, 14, 21, 31], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 31], "filter_cluster_id": [10, 21, 31], "clustering_kwarg": [10, 31], "nullissuemanag": [10, 14, 21, 27], "datavaluationissuemanag": [10, 14, 18, 21], "codeblock": 10, "demonstr": [10, 40, 86, 87, 88, 91, 93, 94, 96, 97, 98, 101, 102], "howev": [10, 37, 41, 52, 82, 83, 84, 88, 90, 91, 96, 100, 102], "mandatori": 10, "image_issue_types_kwarg": 10, "32": [10, 84, 86, 92, 96, 98], "fewer": [10, 43, 52, 98], "vice": [10, 58], "versa": [10, 58], "light": [10, 88, 92, 98, 102], "29": [10, 85, 88, 92, 96, 97, 98, 102, 103], "low_inform": [10, 88], "odd_aspect_ratio": [10, 88], "35": [10, 86, 92, 96, 97, 98], "odd_siz": [10, 88], "doc": [10, 37, 41, 79, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "studio": [11, 79, 87, 88, 90, 91, 93], "data_issu": [11, 15, 16, 33, 86], "issue_find": [11, 15], "factori": [11, 15, 16], "model_output": [11, 15], "except": [12, 37, 41, 56, 67, 85, 86, 87, 88, 96], "dataformaterror": [12, 15], "add_not": 12, "with_traceback": 12, "tb": 12, "__traceback__": 12, "datasetdicterror": [12, 15], "datasetdict": 12, "datasetloaderror": [12, 15], "dataset_typ": 12, "fail": 12, "hold": 12, "associ": [12, 16, 32, 34, 37, 41, 65, 96], "sublist": 12, "map_to_int": 12, "abc": [12, 22, 32], "is_avail": [12, 88], "dataissu": [13, 15, 16, 33], "central": [13, 103], "repositori": 13, "strategi": [13, 48, 93], "_infostrategi": 13, "basi": 13, "collect_statist": 13, "reus": [13, 22], "avoid": [13, 37, 40, 41, 43, 52, 59, 62, 65, 69, 71, 73, 85, 86, 87, 93], "recomput": [13, 83], "weighted_knn_graph": 13, "issue_manager_that_computes_knn_graph": 13, "collect_issues_from_issue_manag": 13, "collect_issues_from_imagelab": 13, "imagelab": 13, "set_health_scor": 13, "health": [13, 23, 36, 58, 79], "get_data_statist": [13, 15], "concret": 14, "subclass": [14, 37, 41, 66, 86], "regressionlabelissuemanag": [14, 21, 29, 30], "multilabelissuemanag": [14, 21, 24, 25], "from_str": [14, 34, 44, 48], "my_issu": 14, "logic": [14, 34, 40, 43, 71, 73], "stabl": [15, 21, 24, 29, 39, 44, 52, 55, 66, 80], "issuefind": [15, 16, 33], "modeloutput": [15, 32], "multiclasspredprob": [15, 32], "regressionpredict": [15, 32], "multilabelpredprob": [15, 32], "instati": 16, "public": [16, 94, 98, 102, 103], "creation": [16, 41], "execut": [16, 37, 41, 86, 93, 98], "coordin": [16, 62, 64, 65, 98, 103], "behavior": [16, 36, 37, 41, 65, 85, 93], "At": [16, 65, 93], "get_available_issue_typ": 16, "direct": [17, 27, 37, 41, 56], "valuabl": 18, "vstack": [18, 52, 88, 92, 93, 94, 96, 97], "25": [18, 26, 37, 48, 50, 85, 87, 88, 92, 94, 96, 97, 98, 103], "classvar": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "short": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 51, 52], "data_valuation_scor": 18, "item": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 85, 86, 87, 88, 93, 94, 96, 97], "some_info_kei": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "additional_info_kei": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "default_threshold": [18, 21, 28], "collect_info": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "info_to_omit": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "compos": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 37, 41, 83, 91, 99], "is_x_issu": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "x_score": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_a": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_b1": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_b2": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "report_str": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33], "_": [19, 20, 22, 23, 25, 26, 27, 30, 31, 48, 51, 52, 82, 84, 86, 88, 92, 94, 97], "near_duplicate_set": [19, 21, 86, 87, 88, 90, 91, 93, 94], "occurr": [19, 20, 22, 26, 27, 28, 31, 51], "median_nn_dist": 19, "near_duplicate_scor": [19, 86, 87, 88, 90, 91, 93, 94], "class_imbalance_scor": [20, 87], "bleed": [21, 24, 29, 39], "edg": [21, 24, 29, 39, 64, 79, 94, 103], "sharp": [21, 24, 29, 39], "get_health_summari": [21, 23], "ood": [21, 28, 66, 67, 86, 87, 88, 91, 94, 99], "simplified_kolmogorov_smirnov_test": [21, 26], "outlier_cluster_label": [21, 31], "no_underperforming_cluster_id": [21, 31], "set_knn_graph": [21, 31], "perform_clust": [21, 31], "get_worst_clust": [21, 31], "find_issues_with_predict": [21, 29, 30], "find_issues_with_featur": [21, 29, 30], "believ": [22, 102], "priori": [22, 94], "abstract": [22, 32], "applic": [23, 57, 93, 94, 96, 103], "typevar": [23, 25, 37, 41, 51, 61, 64, 65], "scalartyp": [23, 25], "covari": [23, 25, 69, 101], "summary_dict": 23, "label_scor": [23, 25, 30, 84, 85, 86, 87, 88, 90, 91, 94, 97, 101], "neighbor_histogram": 26, "non_neighbor_histogram": 26, "kolmogorov": 26, "smirnov": 26, "largest": [26, 40, 48, 67, 71, 73, 98, 102], "empir": [26, 47, 57], "cumul": 26, "ecdf": 26, "histogram": [26, 90, 101], "absolut": [26, 30], "trial": 26, "non_iid_scor": [26, 87, 90, 91, 94], "null_track": 27, "extend": [27, 49, 56, 88, 98, 99, 103], "superclass": 27, "arbitrari": [27, 36, 73, 77, 86, 99, 101], "prompt": 27, "address": [27, 83, 86, 87, 91, 93], "enabl": [27, 41], "null_scor": [27, 87], "37037": 28, "q3_avg_dist": 28, "iqr_avg_dist": 28, "median_outlier_scor": 28, "multipli": [30, 50], "deleg": 30, "confus": [31, 32, 36, 37, 41, 43, 52, 65, 83, 103], "50": [31, 41, 85, 88, 93, 94, 96, 98, 99, 101], "keepdim": [31, 93], "signifi": 31, "absenc": 31, "find_issues_kwarg": 31, "int64": [31, 84, 96], "npt": 31, "int_": 31, "id": [31, 57, 86, 88, 93, 96], "unique_cluster_id": 31, "_description_": 31, "performed_clust": 31, "worst_cluster_id": 31, "underperforming_group_scor": [31, 87], "convent": [32, 34], "subject": [32, 34], "meant": [32, 34], "Not": 32, "mainli": [32, 99, 103], "content": [32, 66, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "fetch": [32, 40, 84, 87, 93], "datset": 33, "exclud": [33, 42, 74, 78, 86, 93, 103], "get_report": 33, "enum": [34, 48], "qualnam": [34, 48], "boundari": [34, 48, 85, 86, 87], "continu": [34, 56, 82, 83, 88, 91, 93, 96, 98, 101, 103], "binari": [34, 48, 52, 59, 61, 94, 103], "simultan": [34, 101], "task_str": 34, "is_classif": 34, "__contains__": [34, 44, 48], "member": [34, 37, 41, 48, 86, 87], "typeerror": [34, 48], "12": [34, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "__getitem__": [34, 44, 48], "match": [34, 36, 37, 41, 43, 48, 57, 58, 67, 85, 86, 87, 88, 92, 98, 100, 102], "__iter__": [34, 44, 48], "__len__": [34, 44, 48], "alias": [34, 48], "is_regress": 34, "is_multilabel": 34, "overview": [36, 82, 83, 84, 87, 88, 90, 91, 96, 98, 99, 101, 103], "modifi": [36, 37, 40, 41, 52, 93, 94], "help": [36, 37, 41, 65, 79, 80, 82, 83, 84, 86, 88, 90, 91, 92, 93, 96, 97, 101, 102, 103], "rank_classes_by_label_qu": [36, 87], "merg": [36, 51, 79, 92, 93, 103], "find_overlapping_class": [36, 93, 94], "problemat": [36, 58, 74, 78, 84, 98, 103], "unnorm": [36, 58, 94], "abov": [36, 37, 40, 41, 52, 57, 64, 65, 67, 73, 77, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 100, 101, 102, 103], "model_select": [36, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 101], "cross_val_predict": [36, 41, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 100, 101], "get_data_labels_from_dataset": 36, "yourfavoritemodel": [36, 94], "cv": [36, 48, 82, 84, 85, 86, 87, 90, 94, 96], "df": [36, 52, 78, 84, 93], "overall_label_qu": [36, 58], "col": 36, "prob": [36, 51, 94, 100], "divid": [36, 58, 67], "label_nois": [36, 58], "human": [36, 92, 102, 103], "clearli": [36, 67, 88, 98, 102], "num": [36, 58, 92, 94], "overlap": [36, 79, 92, 93, 94], "ontolog": 36, "publish": [36, 103], "therefor": [36, 67], "vehicl": [36, 92], "truck": [36, 92, 99, 102], "intuit": [36, 58, 85], "car": [36, 92, 98, 102], "frequent": [36, 57, 90, 93, 101], "characterist": 36, "l": [36, 37, 41, 62, 64, 65], "class1": 36, "class2": 36, "relationship": 36, "dog": [36, 52, 58, 60, 74, 92, 93, 99, 100, 103], "cat": [36, 52, 58, 60, 92, 93, 99, 100], "captur": [36, 84, 98, 99, 102], "co": [36, 37, 38], "noisy_label": [36, 85, 86, 87, 97], "overlapping_class": 36, "descend": [36, 37, 41, 48, 58, 65], "overall_label_health_scor": [36, 58, 94], "half": [36, 37, 39, 41, 58, 92, 103], "health_scor": [36, 58], "classes_by_label_qu": [36, 87], "cnn": [37, 39, 41, 88], "cifar": [37, 38, 92, 99], "teach": [37, 38], "bhanml": 37, "blob": 37, "master": [37, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "call_bn": [37, 39], "bn": 37, "input_channel": 37, "n_output": 37, "dropout_r": 37, "top_bn": 37, "architectur": [37, 41], "shown": [37, 65, 85, 86, 93, 96, 99, 100, 102, 103], "forward": [37, 38, 39, 41, 88, 96], "overridden": [37, 41], "although": [37, 41, 66, 82, 90], "recip": [37, 41], "afterward": [37, 41], "sinc": [37, 41, 45, 53, 58, 65, 73, 77, 93, 96, 97, 98, 100, 103], "former": [37, 41], "hook": [37, 41, 92], "silent": [37, 40, 41], "t_destin": [37, 39, 41], "__call__": [37, 39, 41, 44, 48], "add_modul": [37, 39, 41], "child": [37, 41], "fn": [37, 41, 65], "recurs": [37, 41, 48], "submodul": [37, 41], "children": [37, 39, 41, 103], "nn": [37, 38, 41, 88], "init": [37, 41, 94], "no_grad": [37, 41, 88, 99], "init_weight": [37, 41], "linear": [37, 41, 83, 88, 91], "fill_": [37, 41], "net": [37, 41, 84, 88, 92], "in_featur": [37, 41], "out_featur": [37, 41], "bia": [37, 41, 88], "tensor": [37, 38, 41, 83, 84, 88, 91, 99], "requires_grad": [37, 41], "bfloat16": [37, 39, 41], "cast": [37, 41, 84], "buffer": [37, 39, 41], "datatyp": [37, 41], "xdoctest": [37, 41], "undefin": [37, 41], "var": [37, 41], "buf": [37, 41], "20l": [37, 41], "1l": [37, 41], "5l": [37, 41], "call_super_init": [37, 39, 41], "immedi": [37, 41, 99], "compil": [37, 39, 41, 56], "cpu": [37, 39, 41, 43, 84, 88], "move": [37, 41, 48, 80, 92], "cuda": [37, 39, 41, 84, 88], "devic": [37, 41, 84, 88], "gpu": [37, 41, 83, 84, 91], "live": [37, 41], "copi": [37, 41, 69, 82, 84, 85, 86, 87, 90, 93, 97, 100, 101], "doubl": [37, 39, 41], "dump_patch": [37, 39, 41], "eval": [37, 39, 41, 88, 97, 99], "dropout": [37, 41], "batchnorm": [37, 41], "grad": [37, 41], "extra_repr": [37, 39, 41], "line": [37, 41, 79, 86, 92, 96, 99, 103], "get_buff": [37, 39, 41], "target": [37, 38, 41, 69, 70, 99, 101], "throw": [37, 41], "get_submodul": [37, 39, 41], "explan": [37, 41], "qualifi": [37, 41], "referenc": [37, 41], "attributeerror": [37, 41], "invalid": [37, 41, 91], "resolv": [37, 41, 85, 103], "get_extra_st": [37, 39, 41], "state_dict": [37, 39, 41], "set_extra_st": [37, 39, 41], "build": [37, 41, 88, 102], "picklabl": [37, 41], "serial": [37, 41], "backward": [37, 41, 88], "break": [37, 41, 88, 98], "pickl": [37, 41, 98], "get_paramet": [37, 39, 41], "let": [37, 41, 66, 67, 82, 83, 84, 85, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "net_b": [37, 41], "net_c": [37, 41], "conv": [37, 41], "conv2d": [37, 41, 88], "16": [37, 41, 48, 56, 73, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 102, 103], "33": [37, 41, 92, 98], "kernel_s": [37, 41], "stride": [37, 41], "200": [37, 41, 67, 85, 92, 98, 103], "diagram": [37, 41, 100], "degre": [37, 41], "queri": [37, 41, 87, 88, 93, 97], "named_modul": [37, 39, 41], "o": [37, 41, 50, 51, 84, 85, 86, 87, 92, 93, 94, 97, 98, 103], "transit": [37, 41], "ipu": [37, 39, 41], "load_state_dict": [37, 39, 41], "strict": [37, 41, 48], "persist": [37, 41], "strictli": [37, 41], "inplac": [37, 41, 96], "preserv": [37, 41, 52], "namedtupl": [37, 41], "missing_kei": [37, 41], "unexpected_kei": [37, 41], "runtimeerror": [37, 41], "idx": [37, 41, 52, 53, 65, 86, 88, 93, 94, 96, 98, 99], "named_buff": [37, 39, 41], "prefix": [37, 41, 84, 103], "remove_dupl": [37, 41], "prepend": [37, 41], "running_var": [37, 41], "named_children": [37, 39, 41], "conv4": [37, 41], "conv5": [37, 41], "memo": [37, 41], "named_paramet": [37, 39, 41], "register_backward_hook": [37, 39, 41], "deprec": [37, 41, 45, 83, 84, 91, 93], "favor": [37, 41], "register_full_backward_hook": [37, 39, 41], "removablehandl": [37, 41], "register_buff": [37, 39, 41], "running_mean": [37, 41], "register_forward_hook": [37, 39, 41], "with_kwarg": [37, 41], "always_cal": [37, 41], "possibli": [37, 41, 82, 90], "fire": [37, 41, 92], "register_module_forward_hook": [37, 41], "regardless": [37, 41, 86, 87], "register_forward_pre_hook": [37, 39, 41], "And": [37, 41], "forward_pr": [37, 41], "register_module_forward_pre_hook": [37, 41], "gradient": [37, 41, 88, 90, 101], "grad_input": [37, 41], "grad_output": [37, 41], "technic": [37, 41], "caller": [37, 41], "register_module_full_backward_hook": [37, 41], "register_full_backward_pre_hook": [37, 39, 41], "backward_pr": [37, 41], "register_module_full_backward_pre_hook": [37, 41], "register_load_state_dict_post_hook": [37, 39, 41], "post": [37, 41], "incompatible_kei": [37, 41], "modif": [37, 41], "thrown": [37, 41], "register_modul": [37, 39, 41], "register_paramet": [37, 39, 41], "register_state_dict_pre_hook": [37, 39, 41], "keep_var": [37, 41], "requires_grad_": [37, 39, 41], "autograd": [37, 41], "freez": [37, 41, 83, 84, 91], "finetun": [37, 41], "gan": [37, 41], "share_memori": [37, 39, 41], "share_memory_": [37, 41], "destin": [37, 41], "shallow": [37, 41], "releas": [37, 41, 56, 80, 84, 93], "design": [37, 41, 85], "ordereddict": [37, 41], "detach": [37, 41, 88], "non_block": [37, 41], "memory_format": [37, 41], "channels_last": [37, 41], "Its": [37, 41, 48, 58, 64], "complex": [37, 41, 84], "integr": [37, 41, 79, 93], "asynchron": [37, 41], "host": [37, 41], "pin": [37, 41, 83, 91, 92], "desir": [37, 41, 51, 65], "4d": [37, 41], "ignore_w": [37, 41], "determinist": [37, 41, 84], "1913": [37, 41], "3420": [37, 41], "5113": [37, 41], "2325": [37, 41], "env": [37, 41], "torch_doctest_cuda1": [37, 41], "gpu1": [37, 41], "1914": [37, 41], "5112": [37, 41], "2324": [37, 41], "float16": [37, 41], "cdoubl": [37, 41], "3741": [37, 41], "2382": [37, 41], "5593": [37, 41], "4443": [37, 41], "complex128": [37, 41], "6122": [37, 41], "1150": [37, 41], "to_empti": [37, 39, 41], "storag": [37, 41, 83, 91], "dst_type": [37, 41], "xpu": [37, 39, 41], "zero_grad": [37, 39, 41, 88], "set_to_non": [37, 41], "reset": [37, 41], "context": [37, 41, 98], "noisili": [38, 94], "han": 38, "2018": 38, "cifar_cnn": [38, 39], "loss_coteach": [38, 39], "y_1": 38, "y_2": 38, "forget_r": 38, "class_weight": 38, "logit": [38, 56, 88], "decim": [38, 52], "forget": [38, 48, 103], "rate_schedul": 38, "epoch": [38, 39, 41, 88, 93], "initialize_lr_schedul": [38, 39], "lr": [38, 39, 41], "001": [38, 67, 93], "250": [38, 86, 87, 94, 98], "epoch_decay_start": 38, "80": [38, 82, 90, 97, 101], "schedul": 38, "beta": 38, "adam": 38, "adjust_learning_r": [38, 39], "alpha_plan": 38, "beta1_plan": 38, "forget_rate_schedul": [38, 39], "num_gradu": 38, "expon": 38, "tell": [38, 83, 88, 91, 94], "train_load": [38, 41], "model1": [38, 94], "optimizer1": 38, "model2": [38, 94], "optimizer2": 38, "dataload": [38, 88, 99], "parser": 38, "parse_arg": 38, "num_iter_per_epoch": 38, "print_freq": 38, "topk": 38, "top1": 38, "top5": 38, "test_load": 38, "offici": [39, 55, 103], "wish": [39, 55, 99, 102, 103], "adj_confident_thresholds_shar": [39, 40], "labels_shar": [39, 40], "pred_probs_shar": [39, 40], "labelinspector": [39, 40, 93], "get_num_issu": [39, 40], "get_quality_scor": [39, 40], "update_confident_threshold": [39, 40], "score_label_qu": [39, 40], "split_arr": [39, 40], "span_classif": 39, "display_issu": [39, 42, 72, 73, 74, 75, 76, 77, 78, 102, 103], "mnist_pytorch": 39, "get_mnist_dataset": [39, 41], "get_sklearn_digits_dataset": [39, 41], "simplenet": [39, 41], "batch_siz": [39, 40, 41, 71, 73, 85, 88, 93, 99, 102], "log_interv": [39, 41], "momentum": [39, 41], "no_cuda": [39, 41], "test_batch_s": [39, 41, 88], "loader": [39, 41, 88], "set_predict_proba_request": [39, 41], "set_predict_request": [39, 41], "coteach": [39, 80], "mini": [40, 71, 73, 93], "low_self_confid": [40, 43, 59], "self_confid": [40, 43, 44, 48, 59, 61, 67, 75, 77, 82, 83, 93, 94], "conveni": [40, 83, 84, 91], "script": 40, "labels_fil": [40, 93], "pred_probs_fil": [40, 93], "quality_score_kwarg": 40, "num_issue_kwarg": 40, "return_mask": 40, "variant": [40, 57, 102], "read": [40, 45, 87, 93, 94, 99, 103], "zarr": [40, 93], "memmap": [40, 102], "pythonspe": 40, "mmap": [40, 93], "hdf5": 40, "further": [40, 42, 58, 59, 61, 64, 65, 73, 74, 84, 93], "yourfil": 40, "npy": [40, 92, 93, 102], "mmap_mod": [40, 102], "tip": [40, 43, 56, 93], "save_arrai": 40, "your_arrai": 40, "disk": [40, 92, 93], "npz": [40, 103], "maxim": [40, 57, 71, 73, 102], "multiprocess": [40, 43, 59, 71, 73, 88, 93], "linux": [40, 71, 73], "physic": [40, 43, 71, 73, 98], "psutil": [40, 43, 71, 73], "labels_arrai": [40, 53], "predprob": 40, "pred_probs_arrai": 40, "back": [40, 65, 86, 93, 98, 99], "store_result": 40, "becom": [40, 99], "verifi": [40, 93, 96, 99], "long": [40, 57, 66, 96], "enough": [40, 52, 93], "chunk": [40, 100], "ram": [40, 92], "end_index": 40, "labels_batch": 40, "pred_probs_batch": 40, "batch_result": 40, "indices_of_examples_with_issu": [40, 93], "shortcut": 40, "encount": [40, 43, 71], "1000": [40, 84, 91, 93, 99], "aggreg": [40, 44, 48, 57, 61, 64, 67, 77, 93, 94, 96], "seen": [40, 85, 93, 99, 103], "far": [40, 57], "label_quality_scor": [40, 61, 64, 67, 70, 94, 98], "method1": 40, "method2": 40, "normalized_margin": [40, 43, 44, 48, 59, 61, 67, 75, 77], "low_normalized_margin": [40, 43, 59], "issue_indic": [40, 64, 88], "update_num_issu": 40, "arr": [40, 93], "chunksiz": 40, "convnet": 41, "bespok": [41, 56], "download": [41, 84, 88, 93, 99], "mnist": [41, 79, 84, 92], "handwritten": 41, "digit": [41, 84, 92], "last": [41, 48, 62, 65, 85, 86, 87, 93, 96, 98, 103], "sklearn_digits_test_s": 41, "64": [41, 82, 88, 90, 94, 98], "01": [41, 67, 69, 84, 94, 97, 98], "templat": 41, "flexibli": 41, "among": [41, 57, 94], "test_set": 41, "Be": 41, "overrid": 41, "train_idx": [41, 52, 99], "train_label": [41, 83, 99], "scikit": [41, 52, 66, 79, 83, 84, 85, 86, 87, 90, 91, 93, 101], "span": 42, "sentenc": [42, 51, 75, 77, 78, 83, 91], "token_classif": [42, 51, 75, 77, 78, 93], "encourag": [43, 59, 67, 70], "multilabel_classif": [43, 58, 59, 61, 67, 93, 97], "pred_probs_by_class": 43, "prune_count_matrix_col": 43, "rank_by_kwarg": [43, 59, 67, 94], "num_to_remove_per_class": [43, 59], "bad": [43, 59, 64, 67, 91, 93], "seem": [43, 94, 97], "aren": 43, "confidence_weighted_entropi": [43, 44, 48, 59, 61, 67, 75, 77], "label_issues_idx": [43, 67], "entropi": [43, 45, 47, 48, 66, 67], "prune_by_class": [43, 59, 94], "predicted_neq_given": [43, 59, 94], "prune_counts_matrix": 43, "smallest": [43, 67], "unus": 43, "number_of_mislabeled_examples_in_class_k": 43, "delet": [43, 79, 83, 93], "too": [43, 48, 66, 87, 88, 93, 98], "thread": [43, 59], "window": [43, 84, 92], "shorter": [43, 62], "find_predicted_neq_given": 43, "find_label_issues_using_argmax_confusion_matrix": 43, "remove_noise_from_class": [44, 52], "clip_noise_r": [44, 52], "clip_valu": [44, 52], "value_count": [44, 52, 93], "value_counts_fill_missing_class": [44, 52], "get_missing_class": [44, 52], "round_preserving_sum": [44, 52], "round_preserving_row_tot": [44, 52], "estimate_pu_f1": [44, 52], "confusion_matrix": [44, 52], "print_square_matrix": [44, 52], "print_noise_matrix": [44, 52, 94], "print_inverse_noise_matrix": [44, 52], "print_joint_matrix": [44, 52, 94], "compress_int_arrai": [44, 52], "train_val_split": [44, 52], "subset_x_i": [44, 52], "subset_label": [44, 52], "subset_data": [44, 52], "extract_indices_tf": [44, 52], "unshuffle_tensorflow_dataset": [44, 52], "is_torch_dataset": [44, 52], "is_tensorflow_dataset": [44, 52], "csr_vstack": [44, 52], "append_extra_datapoint": [44, 52], "get_num_class": [44, 52], "num_unique_class": [44, 52], "get_unique_class": [44, 52], "format_label": [44, 52], "smart_display_datafram": [44, 52], "force_two_dimens": [44, 52], "latent_algebra": [44, 80], "compute_ps_py_inv_noise_matrix": [44, 46], "compute_py_inv_noise_matrix": [44, 46], "compute_inv_noise_matrix": [44, 46], "compute_noise_matrix_from_invers": [44, 46], "compute_pi": [44, 46], "compute_pyx": [44, 46], "label_quality_util": 44, "get_normalized_entropi": [44, 45], "multilabel_util": [44, 97], "stack_compl": [44, 49], "get_onehot_num_class": [44, 49], "int2onehot": [44, 49, 97], "onehot2int": [44, 49, 97], "multilabel_scor": [44, 61], "classlabelscor": [44, 48], "exponential_moving_averag": [44, 48, 61], "softmin": [44, 48, 61, 64, 73, 77], "possible_method": [44, 48], "multilabelscor": [44, 48], "get_class_label_quality_scor": [44, 48], "multilabel_pi": [44, 48], "get_cross_validated_multilabel_pred_prob": [44, 48], "transform_distances_to_scor": [44, 50], "correct_precision_error": [44, 50], "token_classification_util": [44, 103], "get_sent": [44, 51, 103], "filter_sent": [44, 51, 103], "process_token": [44, 51], "merge_prob": [44, 51], "color_sent": [44, 51], "assert_valid_input": [44, 53], "assert_valid_class_label": [44, 53], "assert_nonempty_input": [44, 53], "assert_indexing_work": [44, 53], "labels_to_arrai": [44, 53], "labels_to_list_multilabel": [44, 53], "min_allowed_prob": 45, "wikipedia": 45, "activ": [45, 47, 56, 57, 79, 96], "towardsdatasci": 45, "cheatsheet": 45, "ec57bc067c0b": 45, "clip": [45, 52, 84], "behav": 45, "unnecessari": [45, 93], "slightli": [45, 82, 83, 85], "interv": [45, 48, 99], "herein": 46, "inexact": 46, "cours": 46, "propag": 46, "throughout": [46, 52, 69, 84, 96, 102, 103], "increas": [46, 50, 64, 66, 67, 84, 86, 93, 96, 97, 103], "dot": [46, 77, 93], "true_labels_class_count": 46, "pyx": 46, "multiannot": 47, "assert_valid_inputs_multiannot": 47, "labels_multiannot": [47, 57], "ensembl": [47, 48, 57, 67, 82, 90, 93, 97, 99, 101], "allow_single_label": 47, "annotator_id": 47, "assert_valid_pred_prob": 47, "pred_probs_unlabel": [47, 57], "format_multiannotator_label": [47, 57, 96], "formatted_label": [47, 52], "old": [47, 52, 80, 84, 92], "check_consensus_label_class": 47, "consensus_label": [47, 57, 96], "consensus_method": [47, 57], "consensu": [47, 57, 79, 95, 103], "establish": [47, 56, 83, 101], "compute_soft_cross_entropi": 47, "soft": [47, 92], "find_best_temp_scal": 47, "coarse_search_rang": [47, 69, 93], "fine_search_s": [47, 69, 93], "temperatur": [47, 48, 64, 73, 77], "scale": [47, 50, 82, 92, 93, 99, 102], "factor": [47, 48, 50, 71, 73], "minim": [47, 64, 99], "temp_scale_pred_prob": 47, "temp": 47, "sharpen": [47, 92], "smoothen": 47, "get_normalized_margin_for_each_label": [48, 67], "get_confidence_weighted_entropy_for_each_label": [48, 67], "75": [48, 85, 86, 87, 92, 96, 97, 98, 101, 103], "scorer": 48, "alpha": [48, 61, 64, 85, 86, 87, 94, 97, 101], "exponenti": 48, "ema": 48, "s_1": 48, "s_k": 48, "ema_k": 48, "accord": [48, 59, 90, 91, 94, 103], "formula": [48, 50], "_t": 48, "cdot": 48, "s_t": 48, "qquad": 48, "leq": 48, "_1": 48, "give": [48, 67, 94, 96, 102], "recent": [48, 103], "success": 48, "previou": [48, 88, 93, 98], "discount": 48, "s_ema": 48, "175": [48, 88, 94, 98], "underflow": 48, "nan": [48, 57, 82, 90, 96, 101], "aggregated_scor": 48, "base_scor": 48, "base_scorer_kwarg": 48, "aggregator_kwarg": [48, 61], "n_sampl": 48, "n_label": 48, "worst": [48, 96], "class_label_quality_scor": 48, "42": [48, 91, 92, 98, 103], "452": 48, "new_scor": 48, "575": 48, "get_label_quality_scores_per_class": [48, 60, 61], "ml_scorer": 48, "binar": [48, 49], "reformat": [48, 84], "wider": 48, "splitter": 48, "kfold": [48, 88], "onevsrestclassifi": [48, 97], "randomforestclassifi": [48, 94, 97], "n_split": [48, 87, 88, 97], "pred_prob_slic": 49, "onehot": 49, "hot": [49, 59, 65, 71, 74, 82, 90, 92, 93, 101, 102], "onehot_matrix": 49, "avg_dist": 50, "scaling_factor": 50, "exp": [50, 66, 67, 86], "dt": 50, "right": [50, 62, 65, 83, 91, 97, 98, 99], "strength": [50, 65], "pronounc": 50, "differenti": 50, "ly": 50, "rule": [50, 51, 92], "thumb": 50, "ood_features_scor": [50, 66, 99], "88988177": 50, "80519832": 50, "toler": 50, "minkowski": 50, "noth": 50, "epsilon": 50, "sensibl": 50, "fixed_scor": 50, "readabl": 51, "lambda": [51, 84, 86, 93, 96], "long_sent": 51, "headlin": 51, "charact": [51, 52], "s1": 51, "s2": 51, "processed_token": 51, "alecnlcb": 51, "entiti": [51, 79, 93, 103], "mapped_ent": 51, "unique_ident": 51, "loc": [51, 85, 86, 87, 88, 103], "nbitbas": [51, 61], "probs_merg": 51, "0125": [51, 77], "0375": 51, "075": 51, "025": 51, "color": [51, 74, 85, 86, 87, 90, 94, 97, 99, 101, 102], "red": [51, 65, 85, 86, 87, 92, 94, 97, 98, 99, 102], "colored_sent": 51, "termcolor": 51, "31msentenc": 51, "0m": 51, "ancillari": 52, "class_without_nois": 52, "any_other_class": 52, "choos": [52, 67, 82, 90, 93, 94, 101], "tradition": 52, "new_sum": 52, "fill": 52, "come": [52, 85, 86, 87, 93, 102], "major": [52, 57, 80, 85, 88, 99], "versu": [52, 94], "obviou": 52, "cgdeboer": 52, "iteround": 52, "reach": 52, "prob_s_eq_1": 52, "claesen": 52, "f1": [52, 65, 91, 94], "BE": 52, "left_nam": 52, "top_nam": 52, "titl": [52, 85, 86, 87, 94, 97, 99], "short_titl": 52, "round_plac": 52, "pretti": [52, 85, 94], "joint_matrix": 52, "num_possible_valu": 52, "holdout_idx": 52, "extract": [52, 66, 83, 84, 91, 96, 99, 102], "allow_shuffl": 52, "turn": [52, 79, 98], "shuffledataset": 52, "histori": 52, "pre_x": 52, "buffer_s": 52, "csr_matric": 52, "append": [52, 84, 85, 88, 92, 93, 94, 96, 97, 98, 99, 103], "bottom": [52, 62, 65, 98], "to_data": 52, "from_data": 52, "taken": 52, "label_matrix": 52, "canon": 52, "displai": [52, 65, 74, 78, 83, 84, 85, 91, 94, 103], "jupyt": [52, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "notebook": [52, 57, 84, 85, 87, 92, 93, 94, 96, 97, 98, 102, 103], "consol": 52, "html": [52, 62, 65, 66, 90, 93, 94], "allow_missing_class": 53, "allow_one_class": 53, "length_x": 53, "labellik": 53, "labels_list": [53, 59], "keraswrappermodel": [55, 56, 79], "keraswrappersequenti": [55, 56], "tf": [56, 84], "legaci": 56, "newer": 56, "interim": 56, "advis": [56, 97], "stabil": 56, "until": 56, "accommod": 56, "keraswrapp": 56, "huggingface_keras_imdb": 56, "unit": [56, 103], "model_kwarg": [56, 69], "compile_kwarg": 56, "sparsecategoricalcrossentropi": 56, "layer": [56, 83, 84, 91, 99], "dens": 56, "my_keras_model": 56, "from_logit": 56, "declar": 56, "apply_softmax": 56, "analysi": 57, "analyz": [57, 79, 94, 96, 97], "get_label_quality_multiannot": [57, 96], "vote": 57, "crowdsourc": [57, 79, 96], "dawid": [57, 96], "skene": [57, 96], "analog": [57, 85, 92, 96], "chosen": [57, 67, 93, 96], "crowdlab": [57, 96], "unlabel": [57, 88, 90, 91, 96, 99, 102], "decid": [57, 83, 91, 92, 96, 101, 103], "get_active_learning_scor": [57, 96], "activelab": [57, 96], "priorit": [57, 64, 98, 102, 103], "showcas": 57, "main": [57, 85], "best_qual": 57, "quality_method": 57, "calibrate_prob": 57, "return_detailed_qu": 57, "return_annotator_stat": 57, "return_weight": 57, "label_quality_score_kwarg": 57, "necessarili": [57, 65, 91, 94], "did": [57, 58, 82, 83, 84, 90, 94, 96, 101], "majority_vot": 57, "broken": [57, 65, 92, 101], "highest": [57, 65, 86, 88, 100], "0th": 57, "consensus_quality_scor": [57, 96], "annotator_agr": [57, 96], "reman": 57, "1st": 57, "2nd": [57, 71], "3rd": 57, "consensus_label_suffix": 57, "consensus_quality_score_suffix": 57, "suffix": 57, "emsembl": 57, "weigh": [57, 92], "agreement": [57, 96], "agre": 57, "prevent": [57, 93], "overconfid": [57, 100], "wrong": [57, 62, 64, 80, 83, 85, 86, 87, 91, 93, 94, 98], "detailed_label_qu": [57, 96], "annotator_stat": [57, 96], "model_weight": 57, "annotator_weight": 57, "warn": [57, 86, 87, 88, 90, 91, 93, 94], "labels_info": 57, "num_annot": [57, 96], "deriv": [57, 96], "quality_annotator_1": 57, "quality_annotator_2": 57, "quality_annotator_m": 57, "annotator_qu": [57, 96], "num_examples_label": [57, 96], "agreement_with_consensu": [57, 96], "worst_class": [57, 96], "trustworthi": [57, 96, 101], "get_label_quality_multiannotator_ensembl": 57, "weigtht": 57, "budget": 57, "retrain": [57, 83, 101], "active_learning_scor": 57, "active_learning_scores_unlabel": 57, "get_active_learning_scores_ensembl": 57, "henc": [57, 84, 86, 96], "get_majority_vote_label": [57, 96], "event": 57, "lastli": [57, 90], "convert_long_to_wide_dataset": 57, "labels_multiannotator_long": 57, "wide": [57, 82, 83, 84], "suitabl": [57, 82, 90], "labels_multiannotator_wid": 57, "common_multilabel_issu": [58, 60], "mutual": [58, 97], "exclus": [58, 97], "rank_classes_by_multilabel_qu": [58, 60], "overall_multilabel_health_scor": [58, 60], "multilabel_health_summari": [58, 60], "classes_by_multilabel_qu": 58, "inner": [59, 73], "find_multilabel_issues_per_class": [59, 60], "per_class_label_issu": 59, "label_issues_list": 59, "pred_probs_list": [59, 67, 88, 94], "anim": [60, 99], "rat": 60, "predat": 60, "pet": 60, "reptil": 60, "manner": [61, 82, 83, 96, 101], "box": [62, 64, 65, 92, 98], "object_detect": [62, 64, 65, 98], "return_indices_ranked_by_scor": [62, 98], "overlapping_label_check": [62, 64], "suboptim": [62, 64], "locat": [62, 64, 98, 102, 103], "bbox": [62, 65, 98], "image_nam": [62, 65], "y1": [62, 65, 98], "y2": [62, 65, 98], "later": [62, 65, 66, 83, 103], "corner": [62, 65, 98], "xyxi": [62, 65, 98], "io": [62, 65, 84, 92], "keras_cv": [62, 65], "bounding_box": [62, 65, 98], "detectron": [62, 65, 98], "detectron2": [62, 65, 98], "readthedoc": [62, 65], "en": [62, 65], "latest": [62, 65], "visual": [62, 63, 65, 82, 85, 86, 87, 88, 101, 103], "draw_box": [62, 65], "mmdetect": [62, 65, 98], "swap": [62, 64, 74, 78], "penal": [62, 64], "concern": [62, 64, 79, 87], "issues_from_scor": [63, 64, 72, 73, 74, 76, 77, 78, 98, 102, 103], "compute_overlooked_box_scor": [63, 64], "compute_badloc_box_scor": [63, 64], "compute_swap_box_scor": [63, 64], "pool_box_scores_per_imag": [63, 64], "object_counts_per_imag": [63, 65, 98], "bounding_box_size_distribut": [63, 65, 98], "class_label_distribut": [63, 65, 98], "get_sorted_bbox_count_idx": [63, 65], "plot_class_size_distribut": [63, 65], "plot_class_distribut": [63, 65], "get_average_per_class_confusion_matrix": [63, 65], "calculate_per_class_metr": [63, 65], "aggregation_weight": 64, "imperfect": [64, 93], "chose": [64, 96, 98], "imperfectli": [64, 98], "dirti": [64, 67, 70, 101], "subtyp": 64, "badloc": 64, "nonneg": 64, "high_probability_threshold": 64, "auxiliary_input": [64, 65], "vari": [64, 87], "iou": [64, 65], "heavili": 64, "auxiliarytypesdict": 64, "pred_label": [64, 83], "pred_label_prob": 64, "pred_bbox": 64, "lab_label": 64, "lab_bbox": 64, "similarity_matrix": 64, "min_possible_similar": 64, "scores_overlook": 64, "low_probability_threshold": 64, "scores_badloc": 64, "accident": [64, 83, 90, 91, 93], "scores_swap": 64, "box_scor": 64, "image_scor": [64, 73, 102], "discov": [65, 87, 103], "abnorm": [65, 88, 98], "auxiliari": [65, 99, 102], "_get_valid_inputs_for_compute_scor": 65, "object_count": 65, "down": 65, "bbox_siz": 65, "class_distribut": 65, "plot": [65, 85, 86, 87, 94, 97, 99, 101, 102], "sorted_idx": [65, 99], "class_to_show": 65, "hidden": [65, 85, 99], "max_class_to_show": 65, "prediction_threshold": 65, "overlai": [65, 98], "figsiz": [65, 85, 86, 87, 88, 94, 97, 99], "save_path": [65, 98], "blue": [65, 92, 94, 98], "overlaid": 65, "side": [65, 92, 98], "figur": [65, 94, 97, 99, 101], "extens": [65, 94, 96], "png": [65, 98], "pdf": [65, 66], "svg": 65, "matplotlib": [65, 85, 86, 87, 88, 94, 97, 98, 99, 101], "num_proc": [65, 88], "intersect": [65, 93], "tp": 65, "fp": 65, "ground": [65, 92, 94, 96, 101], "truth": [65, 94, 96, 101], "bias": 65, "avg_metr": 65, "distionari": 65, "95": [65, 75, 77, 90, 92, 94, 101], "per_class_metr": 65, "Of": 66, "li": 66, "smaller": [66, 85, 97, 98], "find_top_issu": [66, 67, 99], "reli": [66, 83, 84, 85, 86, 87, 91, 98, 99, 101], "dist_metr": 66, "subtract": [66, 67], "renorm": [66, 67, 93], "least_confid": 66, "sum_": 66, "log": [66, 67, 80], "softmax": [66, 73, 77, 88], "literatur": 66, "gen": 66, "liu": 66, "lochman": 66, "zach": 66, "openaccess": 66, "thecvf": 66, "cvpr2023": 66, "liu_gen_pushing_the_limits_of_softmax": 66, "based_out": 66, "distribution_detection_cvpr_2023_pap": 66, "fit_scor": [66, 99], "ood_predictions_scor": 66, "pretrain": [66, 83, 84, 91, 99], "adjust_confident_threshold": 66, "probabilist": [66, 82, 84, 86, 87, 90, 91, 99, 100], "order_label_issu": [67, 80], "whichev": [67, 100], "argsort": [67, 83, 88, 91, 94, 98, 99, 101], "max_": 67, "get_label_quality_ensemble_scor": [67, 93, 94], "weight_ensemble_members_bi": 67, "custom_weight": 67, "log_loss_search_t_valu": 67, "0001": [67, 92], "scheme": 67, "log_loss_search": 67, "log_loss": [67, 91], "1e0": 67, "1e1": 67, "1e2": 67, "2e2": 67, "quality_scor": [67, 99], "forth": 67, "top_issue_indic": 67, "rank_bi": [67, 80], "weird": [67, 78], "minu": 67, "prob_label": 67, "max_prob_not_label": 67, "idea": [67, 85, 98], "AND": [67, 91], "get_epistemic_uncertainti": [68, 69], "get_aleatoric_uncertainti": [68, 69], "corrupt": [69, 101], "linearregress": [69, 93, 101], "y_with_nois": 69, "n_boot": [69, 93], "include_aleatoric_uncertainti": [69, 93], "sole": [69, 82, 86, 96, 99], "bootstrap": [69, 93, 101], "resampl": [69, 84, 93], "epistem": [69, 93, 99, 101], "aleator": [69, 93, 101], "model_final_kwarg": 69, "coars": 69, "thorough": [69, 93], "fine": [69, 83, 84, 91, 99], "grain": 69, "grid": 69, "varianc": [69, 94], "epistemic_uncertainti": 69, "residu": [69, 70, 93], "deviat": [69, 98, 101], "aleatoric_uncertainti": 69, "outr": 70, "contin": 70, "raw": [70, 79, 80, 87, 88, 92, 93, 96, 98, 99, 101], "aka": [70, 84, 94, 98, 101, 103], "00323821": 70, "33692597": 70, "00191686": 70, "semant": [71, 73, 74, 95], "pixel": [71, 73, 74, 99, 102], "h": [71, 73, 74, 102], "height": [71, 73, 74, 102], "w": [71, 73, 74, 102], "width": [71, 73, 74, 102], "labels_one_hot": [71, 74, 102], "stream": [71, 85, 99, 103], "downsampl": [71, 73, 102], "shrink": [71, 73], "divis": [71, 73, 86], "common_label_issu": [72, 74, 76, 78, 102, 103], "filter_by_class": [72, 74, 102], "segmant": [73, 74], "num_pixel_issu": [73, 102], "product": [73, 88, 93], "pixel_scor": [73, 102], "highlight": [74, 78, 85, 86, 87, 90, 102], "enter": 74, "legend": [74, 85, 86, 87, 97, 98, 101, 102], "colormap": 74, "background": 74, "person": [74, 93, 98, 102, 103], "ambigu": [74, 78, 83, 84, 91, 92, 94, 103], "systemat": [74, 78, 96], "misunderstood": [74, 78], "issues_df": [74, 88], "class_index": 74, "issues_subset": [74, 78], "filter_by_token": [76, 78, 103], "token_score_method": 77, "sentence_score_method": 77, "sentence_score_kwarg": 77, "compris": [77, 78], "token_scor": [77, 103], "converg": 77, "toward": 77, "_softmin_sentence_scor": 77, "sentence_scor": [77, 103], "token_info": 77, "70": [77, 90], "02": [77, 86, 87, 94, 98, 99], "03": [77, 90, 92, 94, 98, 103], "04": [77, 90, 98, 103], "08": [77, 94, 98, 101, 103], "commonli": [78, 80, 86, 87, 97, 103], "But": [78, 91, 94, 101, 103], "restrict": [78, 93], "reliabl": [79, 82, 84, 93, 96, 102], "thousand": 79, "imagenet": [79, 92], "popular": [79, 96, 98], "centric": [79, 88, 90, 91, 95], "capabl": 79, "minut": [79, 82, 83, 84, 90, 91, 92, 96, 97, 98, 101, 102, 103], "conda": 79, "feature_embed": [79, 99], "Then": [79, 82, 83, 88, 93], "your_dataset": [79, 84, 86, 87, 88, 90, 91, 93], "column_name_of_label": [79, 84, 86, 87, 88, 90, 91], "plagu": [79, 87], "untrain": 79, "\u30c4": 79, "label_issues_info": [79, 87], "sklearn_compatible_model": 79, "framework": [79, 97, 98], "complianc": 79, "tag": [79, 97, 103], "sequenc": 79, "recognit": [79, 84, 93, 103], "train_data": [79, 82, 83, 99, 101], "gotten": 79, "test_data": [79, 82, 83, 85, 94, 97, 99, 101], "deal": [79, 87], "tutori": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "feel": [79, 84, 87, 93], "ask": [79, 93], "slack": [79, 93], "project": [79, 101], "welcom": 79, "commun": [79, 93], "guidelin": [79, 98], "piec": 79, "smart": [79, 88, 90, 91, 93], "edit": [79, 93], "easier": [79, 94], "unreli": [79, 82, 84, 90, 91], "link": [79, 84, 92, 98], "older": 80, "outlin": 80, "substitut": 80, "v2": [80, 82, 90], "get_noise_indic": 80, "psx": 80, "sorted_index_method": 80, "order_label_error": 80, "label_errors_bool": 80, "latent_estim": 80, "num_label_error": 80, "learningwithnoisylabel": 80, "neatli": 80, "organ": [80, 82, 90, 92, 103], "reorgan": 80, "baseline_method": 80, "incorpor": [80, 94], "research": [80, 94], "polyplex": 80, "terminologi": 80, "label_error": 80, "quickstart": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101, 102, 103], "sql": [82, 90], "databas": [82, 90], "excel": [82, 90], "parquet": [82, 90], "student": [82, 90, 101, 103], "grade": [82, 90, 101], "900": [82, 90, 101], "exam": [82, 90, 101], "letter": [82, 90, 103], "hundr": [82, 90], "mistak": [82, 83, 88, 90, 91], "No": [82, 83, 91, 93], "extratreesclassifi": 82, "extratre": 82, "ranked_label_issu": [82, 83], "branch": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "preprocess": [82, 83, 87, 90, 99, 101], "standardscal": [82, 90, 99], "labelencod": [82, 83], "train_test_split": [82, 83, 85, 86, 87, 99], "accuracy_scor": [82, 83, 84, 91, 94], "grades_data": [82, 90], "read_csv": [82, 83, 90, 91, 101], "demo": [82, 87, 90, 97], "head": [82, 83, 84, 87, 88, 90, 91, 92, 94, 96, 101], "stud_id": [82, 90], "exam_1": [82, 90, 101], "exam_2": [82, 90, 101], "exam_3": [82, 90, 101], "letter_grad": [82, 90], "f48f73": [82, 90], "53": [82, 85, 86, 87, 90, 92, 97, 98], "00": [82, 86, 87, 88, 90, 92, 99], "77": [82, 86, 87, 90, 98], "0bd4e7": [82, 90], "81": [82, 90, 91, 98, 101, 103], "great": [82, 85, 90, 92], "particip": [82, 90], "cb9d7a": [82, 90], "61": [82, 90, 94, 98, 101], "94": [82, 90, 92, 94, 98, 101], "78": [82, 90, 92, 94, 98, 101], "9acca4": [82, 90], "48": [82, 90, 92, 94, 98], "x_raw": [82, 90], "labels_raw": 82, "interg": [82, 83], "categorical_featur": [82, 101], "x_encod": [82, 90], "get_dummi": [82, 90, 101], "drop_first": [82, 90], "numeric_featur": [82, 90], "scaler": [82, 90, 99], "x_process": [82, 90], "fit_transform": [82, 90], "bring": [82, 83, 88, 90, 91, 96, 101], "byod": [82, 83, 88, 90, 91, 96, 101], "decis": [82, 85, 86, 87], "tress": 82, "held": [82, 84, 90, 91, 92, 98, 99, 100], "straightforward": [82, 84, 90], "benefit": [82, 84, 100, 102], "num_crossval_fold": [82, 84, 90, 96], "u": [82, 83, 84, 86, 88, 90, 93, 94, 96, 97, 100, 101, 102, 103], "tabl": [82, 90, 92, 96], "212": [82, 94], "review": [82, 83, 87, 90, 91, 92, 93, 94, 98, 101, 102, 103], "iloc": [82, 83, 84, 90, 91, 101], "58": [82, 87, 90, 92, 94, 98], "92": [82, 86, 94, 98], "93": [82, 92, 98, 101], "827": 82, "99": [82, 92, 94], "86": [82, 87, 88, 90, 94, 98, 101], "74": [82, 98, 101], "637": [82, 90], "79": [82, 92, 98], "65": [82, 86, 98], "cheat": 82, "0pt": 82, "120": [82, 86, 87], "97": [82, 92, 93, 94, 98, 101, 103], "233": 82, "68": [82, 92, 94, 98], "83": [82, 94, 98, 101, 103], "76": [82, 85, 94, 97, 98, 101], "suspici": [82, 90], "carefulli": [82, 88, 90, 91], "examin": [82, 85, 86, 87, 90, 98], "labels_train": 82, "labels_test": 82, "test_siz": [82, 83, 85, 86, 87], "acc_og": [82, 83], "783068783068783": 82, "robustli": [82, 83, 101], "14": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "acc_cl": [82, 83], "8095238095238095": 82, "especi": [82, 83, 85, 88, 93, 101], "blindli": [82, 83, 84, 93, 101], "trust": [82, 83, 84, 93, 94, 96, 100, 101], "effort": [82, 83, 101], "intent": [83, 91], "servic": [83, 91, 93], "onlin": [83, 91], "bank": [83, 91, 92], "banking77": [83, 91], "oo": [83, 91], "000": [83, 88, 91, 92, 103], "categori": [83, 88, 91], "shortlist": [83, 91, 101], "scope": [83, 91], "logist": [83, 85, 86, 87, 91, 96, 99], "probabilit": [83, 84], "drop": [83, 93, 96, 101], "earlier": [83, 103], "sentence_transform": [83, 91], "sentencetransform": [83, 91], "payment": [83, 91], "cancel_transf": [83, 91], "transfer": [83, 91], "fund": [83, 91], "cancel": [83, 91], "transact": [83, 91], "my": [83, 91], "revert": [83, 91], "morn": [83, 91], "realis": [83, 91], "yesterdai": [83, 91], "rent": [83, 91], "realli": [83, 91, 96, 102], "tomorrow": [83, 91], "raw_text": [83, 91], "raw_label": 83, "raw_train_text": 83, "raw_test_text": 83, "raw_train_label": 83, "raw_test_label": 83, "39": [83, 84, 86, 88, 91, 92, 93, 98, 101, 102, 103], "visa_or_mastercard": [83, 91], "apple_pay_or_google_pai": [83, 91], "card_payment_fee_charg": [83, 91], "lost_or_stolen_phon": [83, 91], "beneficiary_not_allow": [83, 91], "supported_cards_and_curr": [83, 91], "getting_spare_card": [83, 91], "change_pin": [83, 91], "card_about_to_expir": [83, 91], "card": [83, 91, 92], "utter": [83, 91], "encond": 83, "test_label": [83, 94, 97, 99], "suit": [83, 91, 92, 93], "electra": [83, 91], "discrimin": [83, 91], "googl": [83, 85, 91], "train_text": 83, "test_text": 83, "home": [83, 86, 87, 91, 92], "runner": [83, 86, 87, 91], "google_electra": [83, 91], "pool": [83, 91, 93, 99], "opt": [83, 84, 87, 88, 90, 91, 94], "hostedtoolcach": [83, 84, 87, 88, 90, 91, 94], "x64": [83, 84, 87, 88, 90, 91, 94], "lib": [83, 84, 87, 88, 90, 91, 94], "python3": [83, 84, 87, 88, 90, 91, 94], "site": [83, 84, 87, 88, 90, 91, 94], "_util": [83, 91], "831": [83, 91], "userwarn": [83, 84, 86, 87, 91], "typedstorag": [83, 91], "untypedstorag": [83, 91], "untyped_storag": [83, 91], "fget": [83, 91], "__get__": [83, 91], "owner": [83, 91], "leverag": [83, 84, 91, 93, 94, 96], "computation": [83, 84, 91], "intens": [83, 84, 91], "400": [83, 85, 91], "858371": 83, "547274": 83, "826228": 83, "966008": 83, "792449": 83, "identified_issu": [83, 101], "lowest_quality_label": [83, 84, 91, 94, 101], "to_numpi": [83, 91, 93, 101], "44": [83, 92, 97, 98], "646": 83, "390": 83, "628": 83, "121": [83, 85, 94], "702": 83, "863": [83, 84], "135": 83, "337": [83, 98], "735": 83, "print_as_df": 83, "inverse_transform": 83, "charg": [83, 91], "cash": [83, 91], "holidai": [83, 91], "sent": [83, 91, 103], "mine": [83, 91], "expir": [83, 91], "fight": 83, "hors": [83, 92, 99], "duck": [83, 92], "me": [83, 91], "whoever": [83, 91], "consum": [83, 101], "18": [83, 84, 91, 92, 93, 94, 98, 99, 101, 102], "baseline_model": [83, 101], "87": [83, 87, 88, 98, 101], "acceler": [83, 101], "19": [83, 84, 88, 91, 92, 93, 94, 98, 99, 101, 102], "89": [83, 85, 86, 90, 98, 101], "spoken": 84, "500": [84, 99, 103], "english": [84, 92], "pronunci": 84, "wav": 84, "huggingfac": [84, 86, 87, 88, 93], "voxceleb": 84, "speech": [84, 103], "your_pred_prob": [84, 85, 86, 87, 90, 91], "tensorflow_io": 84, "huggingface_hub": 84, "reproduc": [84, 90, 94, 96], "command": 84, "wget": [84, 98, 102, 103], "navig": 84, "browser": 84, "jakobovski": 84, "archiv": [84, 103], "v1": 84, "tar": [84, 99], "gz": [84, 99], "mkdir": [84, 103], "spoken_digit": 84, "xf": 84, "6_nicolas_32": 84, "data_path": 84, "listdir": 84, "nondeterminist": 84, "file_nam": 84, "endswith": 84, "file_path": 84, "join": [84, 85, 88, 93], "7_george_26": 84, "0_nicolas_24": 84, "0_nicolas_6": 84, "listen": 84, "display_exampl": 84, "expand": [84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "pulldown": [84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "colab": [84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "tfio": 84, "pathlib": 84, "ipython": 84, "load_wav_16k_mono": 84, "filenam": 84, "khz": 84, "file_cont": 84, "read_fil": 84, "sample_r": 84, "decode_wav": 84, "desired_channel": 84, "squeez": 84, "rate_in": 84, "rate_out": 84, "16000": 84, "wav_file_nam": 84, "audio_r": 84, "wav_file_exampl": 84, "plai": [84, 92, 93], "button": 84, "wav_file_name_exampl": 84, "7_jackson_43": 84, "hear": 84, "extractor": 84, "encoderclassifi": 84, "spkrec": 84, "xvect": 84, "feature_extractor": 84, "from_hparam": 84, "run_opt": 84, "uncom": 84, "ffmpeg": 84, "backend": 84, "wav_audio_file_path": 84, "torchaudio": 84, "extract_audio_embed": 84, "emb": [84, 88], "signal": 84, "encode_batch": 84, "embeddings_list": [84, 88], "embeddings_arrai": 84, "650": 84, "stft": 84, "return_complex": 84, "view_as_r": 84, "recov": 84, "trigger": 84, "aten": 84, "src": 84, "nativ": 84, "spectralop": 84, "cpp": 84, "_vf": 84, "n_fft": 84, "hop_length": 84, "win_length": 84, "attr": 84, "512": [84, 88], "196311": 84, "319459": 84, "478975": 84, "2890875": 84, "8170238": 84, "89265": 84, "24": [84, 92, 94, 96, 98, 101], "898056": 84, "256195": 84, "559641": 84, "559721": 84, "62067": 84, "285245": 84, "21": [84, 86, 92, 93, 94, 98, 101, 103], "709627": 84, "5033693": 84, "913803": 84, "819831": 84, "1831515": 84, "208763": 84, "084257": 84, "3210397": 84, "005453": 84, "216152": 84, "478235": 84, "6821785": 84, "053807": 84, "242471": 84, "091424": 84, "78334856": 84, "03954": 84, "23": [84, 88, 92, 94, 98, 101], "569176": 84, "761097": 84, "1258295": 84, "753237": 84, "3508866": 84, "598274": 84, "23712": 84, "2500": 84, "tol": 84, "decreas": [84, 93], "cv_accuraci": 84, "9708": 84, "9976": 84, "986": 84, "002161": 84, "176": [84, 92, 94, 97], "002483": 84, "2318": 84, "004411": 84, "1005": 84, "004857": 84, "1871": 84, "007494": 84, "investig": [84, 85], "040587": 84, "999207": 84, "999377": 84, "975220": 84, "999367": 84, "identified_label_issu": [84, 91], "sort_valu": [84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 101], "516": 84, "1946": 84, "469": 84, "2132": 84, "worth": [84, 94], "6_yweweler_25": 84, "7_nicolas_43": 84, "6_theo_27": 84, "6_yweweler_36": 84, "6_yweweler_14": 84, "6_yweweler_35": 84, "6_nicolas_8": 84, "sound": 84, "quit": [84, 99], "22": [84, 86, 88, 92, 93, 94, 97, 98, 103], "ve": [85, 92, 93, 94, 96, 98], "prove": 85, "monitor": [85, 92], "ran": 85, "data_monitor": 85, "your_datalab": 85, "new_data_batch": 85, "your_label": 85, "get_your_label": 85, "websit": [85, 92], "todo": 85, "get_ipython": 85, "e0b7615c1169c6d8fcae15be6477bd7327e82e00": 85, "cmd": 85, "dep": 85, "dependencies_test": 85, "missing_depend": 85, "__import__": 85, "importerror": 85, "sep": [85, 103], "npleas": 85, "toi": [85, 86, 87, 88, 92, 94, 96], "mid": [85, 86, 87], "workflow": [85, 95, 101], "unseen": 85, "inf": [85, 86, 87], "bins_map": [85, 86, 87], "create_data": [85, 86, 87], "800": 85, "y_bin": [85, 86, 87], "y_i": [85, 86, 87], "y_bin_idx": [85, 86, 87], "y_train": [85, 86, 87, 94, 101], "y_test": [85, 86, 87, 94, 101], "y_train_idx": [85, 86, 87], "y_test_idx": [85, 86, 87], "slide": [85, 86, 87, 92], "frame": [85, 86, 87], "x_out": [85, 86, 87], "tini": [85, 86, 87], "concaten": [85, 86, 87, 93, 100], "y_out": [85, 86, 87], "y_out_bin": [85, 86, 87], "y_out_bin_idx": [85, 86, 87], "exact_duplicate_idx": [85, 86, 87], "x_duplic": [85, 86, 87], "y_duplic": [85, 86, 87], "y_duplicate_idx": [85, 86, 87], "noisy_labels_idx": [85, 86, 87, 97], "train_x": 85, "test_x": 85, "train_y_tru": 85, "test_y_tru": 85, "train_i": 85, "test_i": 85, "train_y_idx": 85, "test_y_idx": 85, "scatter": [85, 86, 87, 94, 97, 101], "black": [85, 86, 87, 92, 101], "cyan": [85, 86, 87], "pyplot": [85, 86, 87, 88, 94, 97, 99, 101], "plt": [85, 86, 87, 88, 94, 97, 99, 101], "plot_data": [85, 86, 87, 94, 97, 101], "fig": [85, 86, 87, 88, 92, 99, 101], "ax": [85, 86, 87, 88, 99, 101], "subplot": [85, 86, 87, 88, 99], "set_titl": [85, 86, 87, 88, 99], "set_xlabel": [85, 86, 87], "x_1": [85, 86, 87], "fontsiz": [85, 86, 87, 88, 94, 97], "set_ylabel": [85, 86, 87], "x_2": [85, 86, 87], "set_xlim": [85, 86, 87], "set_ylim": [85, 86, 87], "linestyl": [85, 86, 87], "circl": [85, 86, 87, 94, 97], "misclassifi": [85, 86, 87], "zip": [85, 86, 87, 88, 98, 103], "label_err": [85, 86, 87], "180": [85, 86, 87, 98], "marker": [85, 86, 87], "facecolor": [85, 86, 87], "edgecolor": [85, 86, 87], "linewidth": [85, 86, 87, 99], "title_fontproperti": [85, 86, 87], "semibold": [85, 86, 87], "first_legend": [85, 86, 87], "align": [85, 86, 87], "markerscal": 85, "second_legend": [85, 86, 87], "46": [85, 90, 92, 94, 98, 103], "gca": [85, 86, 87], "add_artist": [85, 86, 87], "tight_layout": [85, 86, 87], "ideal": [85, 86, 87], "simplic": [85, 97], "327": [85, 98], "9297": 85, "000124": 85, "259": 85, "000725": 85, "269": 85, "000794": 85, "002061": 85, "125": [85, 86], "002908": 85, "fly": [85, 92], "feed": [85, 93], "simul": 85, "tqdm": [85, 88], "sleep": [85, 92], "generate_stream": 85, "sleep_tim": 85, "desc": 85, "singleton_stream": 85, "seamless": [85, 93], "singleton": 85, "batched_stream": 85, "processed_singleton": 85, "suggested_label": [85, 91], "250997": 85, "285757": 85, "43": [85, 86, 92, 94, 98], "120906": 85, "principl": 85, "processed_batch": 85, "51": [85, 86, 87, 90, 92, 94, 98], "002748": 85, "189996": 85, "093505": 85, "037250": 85, "149": [85, 98], "076397": 85, "154": 85, "294010": 85, "160": [85, 91, 101], "073622": 85, "166": [85, 88], "140832": 85, "167": [85, 92, 94, 98], "041743": 85, "181": 85, "169429": 85, "127304": 85, "235": [85, 98], "090310": 85, "254": [85, 92, 98], "183343": 85, "256": [85, 92, 93, 98], "048720": 85, "263": [85, 97, 98], "138820": 85, "292": 85, "239609": 85, "295": [85, 98], "022075": 85, "306": 85, "103040": 85, "343": 85, "234755": 85, "354": 85, "001612": 85, "359": 85, "068359": 85, "367": [85, 101], "015793": 85, "368": 85, "029022": 85, "391": 85, "106761": 85, "troublesom": 85, "623844": 85, "812647": 85, "816854": 85, "661968": 85, "632244": 85, "395": 85, "474599": 85, "396": 85, "653901": 85, "397": 85, "584554": 85, "398": 85, "817287": 85, "399": 85, "881545": 85, "183": 85, "937927": 85, "309": 85, "939505": 85, "133": 85, "947290": 85, "177": 85, "952187": 85, "314": [85, 98], "997293": 85, "27": [85, 90, 92, 94, 98, 103], "655501": 85, "underneath": 86, "hood": [86, 93], "alert": 86, "introduct": 86, "mayb": [86, 87, 91], "your_feature_matrix": [86, 87], "dup": [86, 87], "45": [86, 87, 92, 94, 98], "remaind": 86, "modal": [86, 87, 93, 96], "132": [86, 87, 94, 98], "9318": 86, "006940": 86, "007830": 86, "40": [86, 87, 91, 92], "014828": 86, "107": [86, 87, 94, 97], "021241": 86, "026407": 86, "notic": [86, 94, 96, 98], "3558": [86, 87], "126": [86, 87, 94, 98], "006636": [86, 87], "130": [86, 87], "012571": [86, 87], "129": [86, 87, 103], "127": [86, 87], "014909": [86, 87], "128": [86, 87, 88], "017443": [86, 87], "6160": [86, 87], "is_near_duplicate_issu": [86, 87, 88, 90, 91, 93, 94], "131": [86, 87, 102], "000000e": [86, 87], "000002": [86, 87], "463180e": [86, 87], "07": [86, 87, 88, 90, 94, 98, 101], "161148": [86, 87], "859087e": [86, 87], "30": [86, 87, 88, 92, 93, 97, 102, 103], "3453": 86, "029542": 86, "031182": 86, "057961": 86, "058244": 86, "348": 86, "378": 86, "357": 86, "34": [86, 92, 94, 96, 98, 103], "54": [86, 92, 94, 98], "039122": 86, "044598": 86, "105": [86, 98], "105196": 86, "133654": 86, "168033": 86, "101107": 86, "37": [86, 92], "183382": 86, "109": [86, 92, 98], "209259": 86, "211042": 86, "221316": 86, "average_ood_scor": 86, "34530442089193386": 86, "52": [86, 92, 98, 103], "169820": 86, "087324e": 86, "259024": 86, "583757e": 86, "91": [86, 98], "346458": 86, "341292e": 86, "specfi": 86, "new_lab": 86, "scoring_funct": 86, "div": 86, "rem": 86, "inv_scal": 86, "49": [86, 92, 94, 98], "superstitionissuemanag": 86, "unlucki": 86, "superstit": 86, "to_seri": 86, "issues_mask": 86, "summary_scor": 86, "9242": 86, "is_superstition_issu": 86, "superstition_scor": 86, "26": [86, 88, 92, 94, 96, 98], "047581": 86, "090635": 86, "129591": 86, "164840": 86, "lurk": [87, 88, 94], "_split": 87, "737": 87, "thoroughli": 87, "904": 87, "_base": [87, 88, 90, 91, 94], "246": [87, 88, 90, 91, 94, 98], "efficiencywarn": [87, 88, 90, 91, 94], "sort_graph_by_row_valu": [87, 88, 90, 91, 94], "warn_when_not_sort": [87, 88, 90, 91, 94], "8561": 87, "001908": 87, "003564": 87, "007331": 87, "008963": 87, "009664": 87, "0227": 87, "is_class_imbalance_issu": 87, "022727": 87, "conceptu": 87, "856061": 87, "355772": 87, "616034": 87, "821750": 87, "901562": 87, "betweeen": 87, "is_null_issu": 87, "is_non_iid_issu": [87, 90, 91, 94], "is_underperforming_group_issu": 87, "859131": 87, "417707": 87, "664083": 87, "970324": 87, "816953": 87, "375317": 87, "641516": 87, "890575": 87, "531021": 87, "460593": 87, "601188": 87, "826147": 87, "752808": 87, "321635": 87, "562539": 87, "948362": 87, "090243": 87, "472909": 87, "746763": 87, "878267": 87, "examples_w_issu": [87, 93], "013445": 87, "025184": 87, "026376": 87, "inde": [87, 91], "miscellan": [87, 103], "428571": 87, "111111": 87, "571429": 87, "407407": 87, "592593": 87, "337838": 87, "092593": 87, "662162": 87, "333333": [87, 92], "952381": 87, "666667": 87, "portion": 87, "huge": [87, 94], "worri": [87, 91], "critic": 87, "60": [88, 94, 101], "torchvis": [88, 99], "tensordataset": 88, "stratifiedkfold": [88, 97], "autonotebook": 88, "fashion_mnist": 88, "9m": 88, "lt": [88, 90, 91, 92, 96, 99], "7mb": 88, "18m": 88, "57": [88, 92, 94], "num_row": 88, "60000": 88, "transformed_dataset": 88, "with_format": 88, "255": [88, 92], "unsqueez": 88, "cpu_count": 88, "torch_dataset": 88, "quick": [88, 97], "super": [88, 90, 91], "relu": 88, "batchnorm2d": 88, "maxpool2d": 88, "lazylinear": 88, "flatten": 88, "get_test_accuraci": 88, "testload": [88, 99], "energi": 88, "trainload": [88, 99], "n_epoch": 88, "patienc": 88, "criterion": 88, "crossentropyloss": 88, "adamw": 88, "best_test_accuraci": 88, "start_epoch": 88, "running_loss": 88, "best_epoch": 88, "end_epoch": 88, "3f": [88, 101], "acc": [88, 94], "time_taken": 88, "compute_embed": 88, "compute_pred_prob": 88, "train_batch_s": 88, "num_work": 88, "worker": [88, 103], "train_id_list": 88, "test_id_list": 88, "train_id": 88, "test_id": 88, "embeddings_model": 88, "ntrain": 88, "trainset": 88, "testset": 88, "pin_memori": 88, "fold_embed": 88, "fold_pred_prob": 88, "finish": 88, "482": 88, "720": 88, "940": 88, "329": [88, 98], "88": [88, 92, 93, 94, 97, 98, 101], "195": 88, "775": 88, "493": 88, "060": 88, "963": 88, "330": [88, 98], "505": 88, "588": 88, "476": 88, "340": 88, "770": 88, "328": [88, 98], "310": 88, "552": 88, "reorder": 88, "hstack": [88, 93, 94, 96], "vision": 88, "grayscal": 88, "max_preval": 88, "7714": 88, "3772": 88, "3585": 88, "3651": 88, "27080": 88, "873833e": 88, "40378": 88, "915575e": 88, "25316": 88, "390277e": 88, "06": [88, 94, 98, 103], "2090": 88, "751164e": 88, "14999": 88, "881301e": 88, "9569": 88, "11262": 88, "000003": 88, "coat": [88, 92], "shirt": [88, 92], "19228": 88, "000010": 88, "dress": 88, "32657": 88, "000013": 88, "bag": [88, 92, 99, 100], "21282": 88, "000016": 88, "53564": 88, "000018": 88, "pullov": 88, "6321": 88, "30968": 88, "001267": 88, "30659": 88, "000022": [88, 103], "47824": 88, "001454": 88, "3370": 88, "000026": 88, "54565": 88, "001854": 88, "9762": 88, "258": 88, "47139": 88, "000033": 88, "166980": 88, "986195": 88, "997205": 88, "sandal": [88, 92], "948781": 88, "999358": 88, "54078": 88, "17371": 88, "000025": 88, "plot_label_issue_exampl": 88, "ncol": [88, 99], "nrow": [88, 99], "ceil": 88, "axes_list": 88, "label_issue_indic": 88, "gl": 88, "sl": 88, "fontdict": 88, "imshow": [88, 99], "cmap": [88, 101], "grai": 88, "subplots_adjust": 88, "hspace": 88, "outsiz": 88, "outlier_issu": [88, 91], "outlier_issues_df": 88, "depict": [88, 97, 98, 99, 100, 102], "plot_outlier_issues_exampl": 88, "n_comparison_imag": 88, "sample_from_class": 88, "number_of_sampl": 88, "non_outlier_indic": 88, "isnul": 88, "non_outlier_indices_excluding_curr": 88, "sampled_indic": 88, "label_scores_of_sampl": 88, "top_score_indic": 88, "top_label_indic": 88, "sampled_imag": 88, "get_image_given_label_and_sampl": 88, "image_from_dataset": 88, "corresponding_label": 88, "comparison_imag": 88, "images_to_plot": 88, "idlist": 88, "iterrow": 88, "near_duplicate_issu": [88, 93], "closest": 88, "counterpart": 88, "near_duplicate_issues_df": 88, "plot_near_duplicate_issue_exampl": 88, "seen_id_pair": 88, "get_image_and_given_label_and_predicted_label": 88, "duplicate_imag": 88, "nd_set": 88, "challeng": 88, "dark_issu": 88, "reveal": [88, 98, 102], "dark_scor": 88, "dark_issues_df": 88, "is_dark_issu": 88, "34848": 88, "203922": 88, "50270": 88, "204588": 88, "3936": 88, "213098": 88, "733": 88, "217686": 88, "8094": 88, "230118": 88, "plot_image_issue_exampl": 88, "28": [88, 91, 92, 94, 96, 103], "difficult": 88, "disproportion": 88, "lowinfo_issu": 88, "low_information_scor": 88, "lowinfo_issues_df": 88, "is_low_information_issu": 88, "53050": 88, "067975": 88, "40875": 88, "089929": 88, "9594": 88, "092601": 88, "34825": 88, "107744": 88, "37530": 88, "108516": 88, "lot": 88, "histgradientboostingclassifi": 90, "cat_featur": 90, "boost": [90, 93, 96, 101], "xgboost": [90, 93, 101], "think": [90, 91, 93, 97, 102, 103], "nonzero": 90, "358": 90, "294": [90, 98], "941": 90, "7109": 90, "000005": [90, 91], "886": 90, "000059": 90, "709": 90, "000104": 90, "723": 90, "000169": 90, "689": 90, "000181": 90, "3590": 90, "051882e": 90, "683133e": 90, "536582e": 90, "406589e": 90, "324246e": 90, "6165": 90, "582": 90, "185": [90, 92, 98], "187": [90, 92], "898": 90, "0014": [90, 92], "595": 90, "702427": 90, "147": [90, 94, 98], "711186": 90, "157": [90, 94], "721394": 90, "771": 90, "731979": 90, "740335": 90, "0014153602099278074": 90, "issue_result": 90, "000842": 90, "555944": 90, "004374": 90, "sorted_issu": 90, "73": [90, 92, 97, 98, 101], "deserv": 90, "outlier_result": 90, "sorted_outli": 90, "56": [90, 92, 101], "96": [90, 92, 94, 97, 98, 101], "style": [90, 102], "font": 90, "18px": 90, "ff00ff": 90, "bac": 90, "unintend": [90, 91], "duplicate_result": 90, "690": 90, "perhap": [90, 94, 96], "twice": 90, "67": [90, 92, 98, 101], "wari": [90, 91, 93], "dive": 91, "your_featur": 91, "text_embed": 91, "data_dict": [91, 94, 96], "85": [91, 98], "38": [91, 92, 98, 103], "9710": 91, "981": 91, "974": 91, "000146": 91, "982": [91, 92], "000224": 91, "971": 91, "000507": 91, "980": [91, 92], "000960": 91, "3584": 91, "994": 91, "009642": 91, "999": 91, "013067": 91, "013841": 91, "433": 91, "014722": 91, "989": 91, "018224": 91, "6070": 91, "095724": 91, "148": [91, 103], "006237": 91, "546": 91, "099341": 91, "514": 91, "006485": 91, "481": 91, "123418": 91, "008165": 91, "0000": [91, 92, 94], "313": [91, 98], "564102": 91, "572258": 91, "574915": 91, "31": [91, 92, 94, 96, 98], "575507": 91, "575874": 91, "792090": 91, "257611": 91, "698710": 91, "182121": 91, "771619": 91, "data_with_suggested_label": 91, "withdraw": 91, "monei": 91, "lowest_quality_outli": 91, "OR": 91, "636c65616e6c616220697320617765736f6d6521": 91, "phone": [91, 92], "gone": 91, "gt": [91, 96, 103], "samp": 91, "br": 91, "press": [91, 103], "nonsens": 91, "sens": 91, "detriment": 91, "duplicate_issu": 91, "fee": 91, "pai": 91, "go": [91, 92, 94], "strongli": 91, "p_valu": 91, "benign": 91, "curat": [91, 95], "mnist_test_set": 92, "imagenet_val_set": 92, "tench": 92, "goldfish": 92, "white": [92, 103], "shark": 92, "tiger": 92, "hammerhead": 92, "electr": 92, "rai": 92, "stingrai": 92, "cock": 92, "hen": 92, "ostrich": 92, "brambl": 92, "goldfinch": 92, "hous": 92, "finch": 92, "junco": 92, "indigo": 92, "bunt": 92, "american": [92, 103], "robin": 92, "bulbul": 92, "jai": 92, "magpi": 92, "chickade": 92, "dipper": 92, "kite": 92, "bald": 92, "eagl": 92, "vultur": 92, "grei": 92, "owl": 92, "salamand": 92, "smooth": 92, "newt": 92, "spot": [92, 93, 98], "axolotl": 92, "bullfrog": 92, "tree": 92, "frog": [92, 99], "tail": 92, "loggerhead": 92, "sea": 92, "turtl": 92, "leatherback": 92, "mud": 92, "terrapin": 92, "band": 92, "gecko": 92, "green": [92, 103], "iguana": 92, "carolina": 92, "anol": 92, "desert": 92, "grassland": 92, "whiptail": 92, "lizard": 92, "agama": 92, "frill": 92, "neck": 92, "allig": 92, "gila": 92, "monster": 92, "european": 92, "chameleon": 92, "komodo": 92, "dragon": 92, "nile": 92, "crocodil": 92, "triceratop": 92, "worm": 92, "snake": 92, "ring": 92, "eastern": 92, "hog": 92, "nose": 92, "kingsnak": 92, "garter": 92, "water": 92, "vine": 92, "night": 92, "boa": 92, "constrictor": 92, "african": 92, "rock": 92, "indian": 92, "cobra": 92, "mamba": 92, "saharan": 92, "horn": 92, "viper": 92, "diamondback": 92, "rattlesnak": 92, "sidewind": 92, "trilobit": 92, "harvestman": 92, "scorpion": 92, "yellow": 92, "garden": 92, "spider": 92, "barn": 92, "southern": 92, "widow": 92, "tarantula": 92, "wolf": 92, "tick": 92, "centiped": 92, "grous": 92, "ptarmigan": 92, "ruf": 92, "prairi": 92, "peacock": 92, "quail": 92, "partridg": 92, "parrot": 92, "macaw": 92, "sulphur": 92, "crest": 92, "cockatoo": 92, "lorikeet": 92, "coucal": 92, "bee": 92, "eater": 92, "hornbil": 92, "hummingbird": 92, "jacamar": 92, "toucan": 92, "breast": 92, "mergans": 92, "goos": 92, "swan": 92, "tusker": 92, "echidna": 92, "platypu": 92, "wallabi": 92, "koala": 92, "wombat": 92, "jellyfish": 92, "anemon": 92, "brain": 92, "coral": 92, "flatworm": 92, "nematod": 92, "conch": 92, "snail": 92, "slug": 92, "chiton": 92, "chamber": 92, "nautilu": 92, "dung": 92, "crab": 92, "fiddler": 92, "king": 92, "lobster": 92, "spini": 92, "crayfish": 92, "hermit": 92, "isopod": 92, "stork": 92, "spoonbil": 92, "flamingo": 92, "heron": 92, "egret": 92, "bittern": 92, "crane": 92, "bird": [92, 99], "limpkin": 92, "gallinul": 92, "coot": 92, "bustard": 92, "ruddi": 92, "turnston": 92, "dunlin": 92, "redshank": 92, "dowitch": 92, "oystercatch": 92, "pelican": 92, "penguin": 92, "albatross": 92, "whale": 92, "killer": 92, "dugong": 92, "lion": 92, "chihuahua": 92, "japanes": 92, "chin": 92, "maltes": 92, "pekinges": 92, "shih": 92, "tzu": 92, "charl": 92, "spaniel": 92, "papillon": 92, "terrier": 92, "rhodesian": 92, "ridgeback": 92, "afghan": [92, 103], "hound": 92, "basset": 92, "beagl": 92, "bloodhound": 92, "bluetick": 92, "coonhound": 92, "tan": 92, "walker": 92, "foxhound": 92, "redbon": 92, "borzoi": 92, "irish": 92, "wolfhound": 92, "italian": 92, "greyhound": 92, "whippet": 92, "ibizan": 92, "norwegian": 92, "elkhound": 92, "otterhound": 92, "saluki": 92, "scottish": 92, "deerhound": 92, "weimaran": 92, "staffordshir": 92, "bull": 92, "bedlington": 92, "border": 92, "kerri": 92, "norfolk": 92, "norwich": 92, "yorkshir": 92, "wire": 92, "fox": 92, "lakeland": 92, "sealyham": 92, "airedal": 92, "cairn": 92, "australian": 92, "dandi": 92, "dinmont": 92, "boston": 92, "miniatur": 92, "schnauzer": 92, "giant": 92, "tibetan": 92, "silki": 92, "wheaten": 92, "west": 92, "highland": 92, "lhasa": 92, "apso": 92, "flat": 92, "retriev": 92, "curli": 92, "golden": 92, "labrador": 92, "chesapeak": 92, "bai": 92, "german": [92, 103], "shorthair": 92, "pointer": 92, "vizsla": 92, "setter": 92, "gordon": 92, "brittani": 92, "clumber": 92, "springer": 92, "welsh": 92, "cocker": 92, "sussex": 92, "kuvasz": 92, "schipperk": 92, "groenendael": 92, "malinoi": 92, "briard": 92, "kelpi": 92, "komondor": 92, "sheepdog": 92, "shetland": 92, "colli": 92, "bouvier": 92, "de": 92, "flandr": 92, "rottweil": 92, "shepherd": 92, "dobermann": 92, "pinscher": 92, "swiss": [92, 103], "mountain": 92, "bernes": 92, "appenzel": 92, "sennenhund": 92, "entlebuch": 92, "boxer": 92, "bullmastiff": 92, "mastiff": 92, "french": 92, "bulldog": 92, "dane": 92, "st": 92, "bernard": 92, "huski": 92, "alaskan": 92, "malamut": 92, "siberian": 92, "dalmatian": 92, "affenpinsch": 92, "basenji": 92, "pug": 92, "leonberg": 92, "newfoundland": 92, "pyrenean": 92, "samoi": 92, "pomeranian": 92, "chow": 92, "keeshond": 92, "griffon": 92, "bruxelloi": 92, "pembrok": 92, "corgi": 92, "cardigan": 92, "poodl": 92, "mexican": 92, "hairless": 92, "tundra": 92, "coyot": 92, "dingo": 92, "dhole": 92, "wild": 92, "hyena": 92, "kit": 92, "arctic": 92, "tabbi": 92, "persian": 92, "siames": 92, "egyptian": 92, "mau": 92, "cougar": 92, "lynx": 92, "leopard": 92, "snow": 92, "jaguar": 92, "cheetah": 92, "brown": [92, 102], "bear": 92, "polar": 92, "sloth": 92, "mongoos": 92, "meerkat": 92, "beetl": 92, "ladybug": 92, "longhorn": 92, "leaf": 92, "rhinocero": 92, "weevil": 92, "ant": 92, "grasshopp": 92, "cricket": 92, "stick": 92, "insect": 92, "cockroach": 92, "manti": 92, "cicada": 92, "leafhopp": 92, "lacew": 92, "dragonfli": 92, "damselfli": 92, "admir": 92, "ringlet": 92, "monarch": 92, "butterfli": 92, "gossam": 92, "wing": 92, "starfish": 92, "urchin": 92, "cucumb": 92, "cottontail": 92, "rabbit": 92, "hare": 92, "angora": 92, "hamster": 92, "porcupin": 92, "squirrel": 92, "marmot": 92, "beaver": 92, "guinea": 92, "pig": 92, "sorrel": 92, "zebra": 92, "boar": 92, "warthog": 92, "hippopotamu": 92, "ox": 92, "buffalo": 92, "bison": 92, "bighorn": 92, "sheep": 92, "alpin": 92, "ibex": 92, "hartebeest": 92, "impala": 92, "gazel": 92, "dromedari": 92, "llama": 92, "weasel": 92, "mink": 92, "polecat": 92, "foot": 92, "ferret": 92, "otter": 92, "skunk": 92, "badger": 92, "armadillo": 92, "toed": 92, "orangutan": 92, "gorilla": 92, "chimpanze": 92, "gibbon": 92, "siamang": 92, "guenon": 92, "pata": 92, "monkei": 92, "baboon": 92, "macaqu": 92, "langur": 92, "colobu": 92, "probosci": 92, "marmoset": 92, "capuchin": 92, "howler": 92, "titi": 92, "geoffroi": 92, "lemur": 92, "indri": 92, "asian": 92, "eleph": 92, "bush": 92, "snoek": 92, "eel": 92, "coho": 92, "salmon": 92, "beauti": 92, "clownfish": 92, "sturgeon": 92, "garfish": 92, "lionfish": 92, "pufferfish": 92, "abacu": 92, "abaya": 92, "academ": 92, "gown": 92, "accordion": 92, "acoust": 92, "guitar": 92, "aircraft": 92, "carrier": 92, "airlin": 92, "airship": 92, "altar": 92, "ambul": 92, "amphibi": 92, "clock": [92, 103], "apiari": 92, "apron": 92, "wast": 92, "assault": 92, "rifl": 92, "backpack": 92, "bakeri": 92, "balanc": 92, "beam": 92, "balloon": 92, "ballpoint": 92, "pen": 92, "aid": 92, "banjo": 92, "balust": 92, "barbel": 92, "barber": 92, "chair": [92, 98], "barbershop": 92, "baromet": 92, "barrel": 92, "wheelbarrow": 92, "basebal": 92, "basketbal": 92, "bassinet": 92, "bassoon": 92, "swim": 92, "cap": 92, "bath": 92, "towel": 92, "bathtub": 92, "station": 92, "wagon": 92, "lighthous": 92, "beaker": 92, "militari": 92, "beer": 92, "bottl": 92, "glass": 92, "bell": 92, "cot": 92, "bib": 92, "bicycl": [92, 102], "bikini": 92, "binder": 92, "binocular": 92, "birdhous": 92, "boathous": 92, "bobsleigh": 92, "bolo": 92, "tie": 92, "poke": 92, "bonnet": 92, "bookcas": 92, "bookstor": 92, "bow": 92, "brass": 92, "bra": 92, "breakwat": 92, "breastplat": 92, "broom": 92, "bucket": 92, "buckl": 92, "bulletproof": 92, "vest": 92, "butcher": 92, "shop": 92, "taxicab": 92, "cauldron": 92, "candl": 92, "cannon": 92, "cano": 92, "mirror": [92, 98], "carousel": 92, "tool": [92, 94, 96], "carton": 92, "wheel": 92, "teller": 92, "cassett": 92, "player": 92, "castl": 92, "catamaran": 92, "cd": 92, "cello": 92, "mobil": [92, 103], "chain": 92, "fenc": [92, 102], "mail": 92, "chainsaw": 92, "chest": 92, "chiffoni": 92, "chime": 92, "china": 92, "cabinet": 92, "christma": 92, "stock": 92, "church": 92, "movi": 92, "theater": 92, "cleaver": 92, "cliff": 92, "dwell": 92, "cloak": 92, "clog": 92, "cocktail": 92, "shaker": 92, "coffe": 92, "mug": 92, "coffeemak": 92, "coil": 92, "lock": 92, "keyboard": 92, "confectioneri": 92, "ship": [92, 99], "corkscrew": 92, "cornet": 92, "cowboi": 92, "boot": 92, "hat": 92, "cradl": 92, "crash": 92, "helmet": 92, "crate": 92, "infant": 92, "bed": 92, "crock": 92, "pot": 92, "croquet": 92, "crutch": 92, "cuirass": 92, "dam": 92, "desk": 92, "desktop": 92, "rotari": 92, "dial": 92, "telephon": 92, "diaper": 92, "watch": 92, "dine": 92, "dishcloth": 92, "dishwash": 92, "disc": 92, "brake": 92, "dock": 92, "sled": 92, "dome": 92, "doormat": 92, "drill": 92, "rig": 92, "drum": 92, "drumstick": 92, "dumbbel": 92, "dutch": 92, "oven": 92, "fan": 92, "locomot": 92, "entertain": 92, "center": 92, "envelop": 92, "espresso": 92, "powder": 92, "feather": 92, "fireboat": 92, "engin": [92, 102], "screen": 92, "sheet": 92, "flagpol": 92, "flute": 92, "footbal": 92, "forklift": 92, "fountain": 92, "poster": 92, "freight": 92, "fry": 92, "pan": 92, "fur": 92, "garbag": 92, "ga": 92, "pump": 92, "goblet": 92, "kart": 92, "golf": 92, "cart": 92, "gondola": 92, "gong": 92, "grand": 92, "piano": 92, "greenhous": 92, "grill": 92, "groceri": 92, "guillotin": 92, "barrett": 92, "hair": 92, "sprai": 92, "hammer": 92, "dryer": 92, "hand": [92, 94], "handkerchief": 92, "drive": 92, "harmonica": 92, "harp": 92, "harvest": 92, "hatchet": 92, "holster": 92, "honeycomb": 92, "hoop": 92, "skirt": 92, "horizont": 92, "bar": 92, "drawn": 92, "hourglass": 92, "ipod": 92, "cloth": 92, "iron": 92, "jack": 92, "lantern": 92, "jean": 92, "jeep": 92, "jigsaw": 92, "puzzl": 92, "pull": 92, "rickshaw": 92, "joystick": 92, "kimono": 92, "knee": 92, "pad": 92, "knot": 92, "ladl": 92, "lampshad": 92, "laptop": 92, "lawn": 92, "mower": 92, "knife": 92, "lifeboat": 92, "lighter": 92, "limousin": 92, "ocean": 92, "liner": 92, "lipstick": 92, "slip": 92, "shoe": 92, "lotion": 92, "speaker": 92, "loup": 92, "sawmil": 92, "magnet": 92, "compass": 92, "mailbox": 92, "tight": 92, "tank": 92, "manhol": 92, "maraca": 92, "marimba": 92, "maypol": 92, "maze": 92, "cup": [92, 98], "medicin": 92, "megalith": 92, "microphon": 92, "microwav": 92, "milk": 92, "minibu": 92, "miniskirt": 92, "minivan": 92, "missil": 92, "mitten": [92, 93], "mix": 92, "bowl": 92, "modem": 92, "monasteri": 92, "mope": 92, "mortar": 92, "mosqu": 92, "mosquito": 92, "scooter": 92, "bike": 92, "tent": 92, "mous": [92, 93], "mousetrap": 92, "van": 92, "muzzl": 92, "nail": 92, "brace": 92, "necklac": 92, "nippl": 92, "obelisk": 92, "obo": 92, "ocarina": 92, "odomet": 92, "oil": 92, "oscilloscop": 92, "overskirt": 92, "bullock": 92, "oxygen": 92, "packet": 92, "paddl": 92, "padlock": 92, "paintbrush": 92, "pajama": 92, "palac": [92, 103], "parachut": 92, "park": 92, "bench": 92, "meter": 92, "passeng": 92, "patio": 92, "payphon": 92, "pedest": 92, "pencil": 92, "perfum": 92, "petri": 92, "dish": 92, "photocopi": 92, "plectrum": 92, "pickelhaub": 92, "picket": 92, "pickup": 92, "pier": 92, "piggi": 92, "pill": 92, "pillow": 92, "ping": 92, "pong": 92, "pinwheel": 92, "pirat": 92, "pitcher": 92, "plane": 92, "planetarium": 92, "plastic": 92, "plate": 92, "rack": 92, "plow": 92, "plunger": 92, "polaroid": 92, "camera": 92, "pole": [92, 102], "polic": 92, "poncho": 92, "billiard": 92, "soda": 92, "potter": 92, "prayer": 92, "rug": 92, "printer": 92, "prison": 92, "projectil": 92, "projector": 92, "hockei": 92, "puck": 92, "punch": 92, "purs": 92, "quill": 92, "quilt": 92, "race": 92, "racket": 92, "radiat": 92, "radio": 92, "telescop": 92, "rain": 92, "recreat": 92, "reel": 92, "reflex": 92, "refriger": 92, "remot": 92, "restaur": 92, "revolv": 92, "rotisseri": 92, "eras": 92, "rugbi": 92, "ruler": 92, "safe": 92, "safeti": 92, "salt": 92, "sarong": 92, "saxophon": 92, "scabbard": 92, "school": 92, "bu": [92, 102], "schooner": 92, "scoreboard": 92, "crt": 92, "screw": 92, "screwdriv": 92, "seat": 92, "belt": 92, "sew": 92, "shield": 92, "shoji": 92, "basket": 92, "shovel": 92, "shower": 92, "curtain": 92, "ski": 92, "door": 92, "slot": 92, "snorkel": 92, "snowmobil": 92, "snowplow": 92, "soap": 92, "dispens": 92, "soccer": [92, 103], "sock": [92, 93], "solar": 92, "thermal": 92, "collector": 92, "sombrero": 92, "soup": 92, "heater": 92, "shuttl": 92, "spatula": 92, "motorboat": 92, "web": 92, "spindl": 92, "sport": [92, 103], "spotlight": 92, "stage": 92, "steam": 92, "arch": 92, "bridg": 92, "steel": 92, "stethoscop": 92, "scarf": 92, "stone": 92, "wall": [92, 102], "stopwatch": 92, "stove": 92, "strainer": 92, "tram": 92, "stretcher": 92, "couch": 92, "stupa": 92, "submarin": 92, "sundial": 92, "sunglass": 92, "sunscreen": 92, "suspens": 92, "mop": 92, "sweatshirt": 92, "swimsuit": 92, "swing": 92, "switch": 92, "syring": 92, "lamp": 92, "tape": 92, "teapot": 92, "teddi": 92, "televis": [92, 103], "tenni": 92, "thatch": 92, "roof": 92, "front": 92, "thimbl": 92, "thresh": 92, "throne": 92, "tile": 92, "toaster": 92, "tobacco": 92, "toilet": 92, "totem": 92, "tow": 92, "tractor": 92, "semi": 92, "trailer": 92, "trai": 92, "trench": 92, "tricycl": 92, "trimaran": 92, "tripod": 92, "triumphal": 92, "trolleybu": 92, "trombon": 92, "tub": 92, "turnstil": 92, "typewrit": 92, "umbrella": 92, "unicycl": 92, "upright": 92, "vacuum": 92, "cleaner": 92, "vase": 92, "vault": 92, "velvet": 92, "vend": 92, "vestment": 92, "viaduct": 92, "violin": 92, "volleybal": 92, "waffl": 92, "wallet": 92, "wardrob": 92, "sink": 92, "wash": 92, "jug": 92, "tower": 92, "whiskei": 92, "whistl": 92, "wig": 92, "shade": [92, 102], "windsor": 92, "wine": 92, "wok": 92, "wooden": 92, "spoon": 92, "wool": 92, "rail": 92, "shipwreck": 92, "yawl": 92, "yurt": 92, "comic": 92, "book": 92, "crossword": 92, "traffic": [92, 98, 102], "sign": [92, 102, 103], "dust": 92, "jacket": [92, 98], "menu": 92, "guacamol": 92, "consomm": 92, "trifl": 92, "ic": 92, "cream": 92, "pop": 92, "baguett": 92, "bagel": 92, "pretzel": 92, "cheeseburg": 92, "mash": 92, "potato": 92, "cabbag": 92, "broccoli": 92, "cauliflow": 92, "zucchini": 92, "spaghetti": 92, "squash": 92, "acorn": 92, "butternut": 92, "artichok": 92, "pepper": [92, 93], "cardoon": 92, "mushroom": 92, "granni": 92, "smith": 92, "strawberri": 92, "orang": 92, "lemon": 92, "pineappl": 92, "banana": 92, "jackfruit": 92, "custard": 92, "appl": 92, "pomegran": 92, "hai": 92, "carbonara": 92, "chocol": 92, "syrup": 92, "dough": 92, "meatloaf": 92, "pizza": 92, "pie": 92, "burrito": 92, "eggnog": 92, "alp": 92, "bubbl": 92, "reef": 92, "geyser": 92, "lakeshor": 92, "promontori": 92, "shoal": 92, "seashor": 92, "vallei": 92, "volcano": 92, "bridegroom": 92, "scuba": 92, "diver": 92, "rapese": 92, "daisi": 92, "ladi": 92, "slipper": 92, "corn": 92, "rose": 92, "hip": 92, "chestnut": 92, "fungu": 92, "agar": 92, "gyromitra": 92, "stinkhorn": 92, "earth": 92, "star": 92, "wood": 92, "bolet": 92, "ear": 92, "cifar10_test_set": 92, "airplan": [92, 99], "automobil": [92, 99], "deer": [92, 99], "cifar100_test_set": 92, "aquarium_fish": 92, "babi": 92, "boi": 92, "camel": 92, "caterpillar": 92, "cattl": [92, 103], "cloud": 92, "dinosaur": 92, "dolphin": 92, "flatfish": 92, "forest": 92, "girl": 92, "kangaroo": 92, "lawn_mow": 92, "man": 92, "maple_tre": 92, "motorcycl": [92, 102], "oak_tre": 92, "orchid": 92, "palm_tre": 92, "pear": 92, "pickup_truck": 92, "pine_tre": 92, "plain": 92, "poppi": 92, "possum": 92, "raccoon": 92, "road": [92, 102], "rocket": 92, "seal": 92, "shrew": 92, "skyscrap": 92, "streetcar": 92, "sunflow": 92, "sweet_pepp": 92, "trout": 92, "tulip": 92, "willow_tre": 92, "woman": [92, 98], "caltech256": 92, "ak47": 92, "bat": 92, "glove": 92, "birdbath": 92, "blimp": 92, "bonsai": 92, "boom": 92, "breadmak": 92, "buddha": 92, "bulldoz": 92, "cactu": 92, "cake": 92, "tire": 92, "cartman": 92, "cereal": 92, "chandeli": 92, "chess": 92, "board": 92, "chimp": 92, "chopstick": 92, "coffin": 92, "coin": 92, "comet": 92, "cormor": 92, "globe": 92, "diamond": 92, "dice": 92, "doorknob": 92, "drink": 92, "straw": 92, "dumb": 92, "eiffel": 92, "elk": 92, "ewer": 92, "eyeglass": 92, "fern": 92, "fighter": 92, "jet": [92, 101], "extinguish": 92, "hydrant": 92, "firework": 92, "flashlight": 92, "floppi": 92, "fri": 92, "frisbe": 92, "galaxi": 92, "giraff": 92, "goat": 92, "gate": 92, "grape": 92, "pick": [92, 93], "hamburg": 92, "hammock": 92, "harpsichord": 92, "hawksbil": 92, "helicopt": 92, "hibiscu": 92, "homer": 92, "simpson": 92, "horsesho": 92, "air": 92, "skeleton": 92, "ibi": 92, "cone": 92, "iri": 92, "jesu": 92, "christ": 92, "joi": 92, "kayak": 92, "ketch": 92, "ladder": 92, "lath": 92, "licens": 92, "lightbulb": 92, "lightn": 92, "mandolin": 92, "mar": 92, "mattress": 92, "megaphon": 92, "menorah": 92, "microscop": 92, "minaret": 92, "minotaur": 92, "motorbik": 92, "mussel": 92, "neckti": 92, "octopu": 92, "palm": 92, "pilot": 92, "paperclip": 92, "shredder": 92, "pci": 92, "peopl": [92, 98], "pez": 92, "picnic": 92, "pram": 92, "prai": 92, "pyramid": 92, "rainbow": 92, "roulett": 92, "saddl": 92, "saturn": 92, "segwai": 92, "propel": 92, "sextant": 92, "music": 92, "skateboard": 92, "smokestack": 92, "sneaker": 92, "boat": 92, "stain": 92, "steer": 92, "stirrup": 92, "superman": 92, "sushi": 92, "armi": [92, 103], "sword": 92, "tambourin": 92, "teepe": 92, "court": 92, "theodolit": 92, "tomato": 92, "tombston": 92, "tour": 92, "pisa": 92, "treadmil": 92, "fork": 92, "tweezer": 92, "unicorn": 92, "vcr": 92, "waterfal": 92, "watermelon": 92, "weld": 92, "windmil": 92, "xylophon": 92, "yarmulk": 92, "yo": 92, "toad": 92, "twenty_news_test_set": 92, "alt": 92, "atheism": 92, "comp": 92, "graphic": [92, 102], "misc": [92, 103], "sy": 92, "ibm": 92, "pc": 92, "hardwar": 92, "mac": 92, "forsal": 92, "rec": 92, "sci": 92, "crypt": 92, "electron": 92, "med": 92, "soc": 92, "religion": 92, "christian": [92, 103], "talk": [92, 103], "polit": 92, "gun": 92, "mideast": 92, "amazon": 92, "neutral": 92, "imdb_test_set": 92, "all_class": 92, "20news_test_set": 92, "_load_classes_predprobs_label": 92, "dataset_nam": 92, "labelerror": 92, "url_bas": 92, "5392f6c71473055060be3044becdde1cbc18284d": 92, "url_label": 92, "original_test_label": 92, "_original_label": 92, "url_prob": 92, "cross_validated_predicted_prob": 92, "_pyx": 92, "num_part": 92, "datatset": 92, "bytesio": 92, "allow_pickl": 92, "pred_probs_part": 92, "url": 92, "_of_": 92, "nload": 92, "imdb": 92, "interpret": [92, 93, 94, 97, 101], "capit": 92, "29780": 92, "780": 92, "medic": [92, 103], "doctor": 92, "359223": 92, "640777": 92, "184": [92, 94], "258427": 92, "341176": 92, "263158": 92, "658824": 92, "337349": 92, "246575": 92, "662651": 92, "248": 92, "330000": 92, "355769": 92, "670000": 92, "251": [92, 98], "252": 92, "112": 92, "253": [92, 98], "022989": 92, "049505": 92, "190": [92, 94, 98], "66": 92, "002216": 92, "000974": 92, "59": [92, 98], "000873": 92, "000739": 92, "32635": 92, "32636": 92, "47": [92, 98], "32637": 92, "32638": 92, "32639": 92, "32640": 92, "051": 92, "002242": 92, "997758": 92, "002088": 92, "001045": 92, "997912": 92, "002053": 92, "997947": 92, "001980": 92, "000991": 92, "998020": 92, "001946": 92, "002915": 92, "998054": 92, "001938": 92, "002904": 92, "998062": 92, "001020": 92, "998980": 92, "001018": 92, "002035": 92, "998982": 92, "999009": 92, "0003": 92, "0002": 92, "36": [92, 103], "41": [92, 98, 101], "71": [92, 94, 98, 101], "071": 92, "067269": 92, "929": 92, "046": 92, "058243": 92, "954": 92, "035": 92, "032096": 92, "965": 92, "031": 92, "012232": 92, "969": 92, "022": 92, "025896": 92, "978": 92, "020": [92, 94], "013092": 92, "018": 92, "013065": 92, "016": 92, "030542": 92, "984": 92, "013": 92, "020833": 92, "987": 92, "012": 92, "010020": 92, "988": 92, "0073": 92, "0020": 92, "0016": 92, "0015": 92, "0013": 92, "0012": 92, "0010": 92, "0008": 92, "0007": 92, "0006": 92, "0005": 92, "0004": 92, "244": [92, 98], "98": [92, 93, 101, 103], "452381": 92, "459770": 92, "72": [92, 94, 97, 101], "523364": 92, "460784": 92, "446602": 92, "103774": 92, "030612": 92, "110092": 92, "049020": 92, "0034": 92, "0032": 92, "0026": 92, "0025": 92, "4945": 92, "4946": 92, "4947": 92, "4948": 92, "4949": 92, "4950": 92, "846": 92, "82": [92, 94, 98, 101], "7532": 92, "532": 92, "034483": 92, "009646": 92, "965517": 92, "030457": 92, "020513": 92, "969543": 92, "028061": 92, "035443": 92, "971939": 92, "025316": 92, "005168": 92, "974684": 92, "049751": 92, "979487": 92, "019920": 92, "042802": 92, "980080": 92, "017677": 92, "005115": 92, "982323": 92, "012987": 92, "005236": 92, "987013": 92, "012723": 92, "025126": 92, "987277": 92, "010989": 92, "008264": 92, "989011": 92, "010283": 92, "027778": 92, "989717": 92, "009677": 92, "990323": 92, "007614": 92, "010127": 92, "992386": 92, "005051": 92, "994949": 92, "005025": 92, "994975": 92, "005013": 92, "994987": 92, "001859": 92, "001328": 92, "000929": 92, "000664": 92, "186": [92, 94], "188": [92, 94, 97], "189": [92, 94], "snippet": 93, "nlp": [93, 103], "mind": [93, 94], "alphanumer": 93, "facilit": 93, "classlabel": 93, "guidanc": 93, "labels_str": 93, "datalab_str": 93, "labels_int": 93, "remap": 93, "datalab_int": 93, "my_dict": 93, "pet_nam": 93, "rover": 93, "rocki": 93, "speci": 93, "from_dict": 93, "datalab_dataset": 93, "number_of_class": 93, "total_number_of_data_point": 93, "alphabet": 93, "labels_proper_format": 93, "your_classifi": 93, "issues_datafram": 93, "class_predicted_for_flagged_exampl": 93, "class_predicted_for_all_exampl": 93, "grant": 93, "On": [93, 94, 98], "merged_dataset": 93, "label_column_nam": 93, "datataset": 93, "fair": [93, 94], "game": 93, "speedup": [93, 99], "flexibl": 93, "tempfil": 93, "mkdtemp": 93, "sped": 93, "anywai": 93, "pred_probs_merg": 93, "merge_rare_class": 93, "count_threshold": 93, "class_mapping_orig2new": 93, "heath_summari": 93, "num_examples_per_class": 93, "rare_class": 93, "num_classes_merg": 93, "other_class": 93, "labels_merg": 93, "new_c": 93, "merged_prob": 93, "new_class": 93, "original_class": 93, "num_check": 93, "ones_array_ref": 93, "isclos": 93, "though": [93, 94, 103], "successfulli": 93, "meaning": [93, 99], "virtuou": [93, 96], "cycl": [93, 96], "jointli": 93, "junk": 93, "clutter": 93, "unknown": 93, "caltech": 93, "combined_boolean_mask": 93, "mask1": 93, "mask2": 93, "gradientboostingclassifi": [93, 94], "true_error": [93, 94, 97], "101": [93, 98], "102": [93, 97, 98], "104": [93, 94, 98], "model_to_find_error": 93, "model_to_return": 93, "cl0": 93, "randomizedsearchcv": 93, "expens": 93, "param_distribut": 93, "learning_r": [93, 94], "max_depth": [93, 94], "magnitud": 93, "coeffici": [93, 101], "optin": 93, "environ": [93, 94], "rerun": [93, 94], "cell": [93, 94], "unabl": [93, 94], "render": [93, 94], "nbviewer": [93, 94], "nbsp": [93, 94], "cleanlearninginot": [93, 94], "fittedcleanlearn": [93, 94], "linearregressionlinearregress": 93, "n_init": 93, "fit_predict": 93, "continuous_column": 93, "categorical_column": 93, "data_df": 93, "feature_a": 93, "feature_b": 93, "unexpectedli": 93, "emphas": 93, "crucial": 93, "merge_duplicate_set": 93, "merge_kei": 93, "construct_group_kei": 93, "merged_set": 93, "consolidate_set": 93, "tolist": [93, 97], "issubset": 93, "frozenset": 93, "sets_list": 93, "mutabl": 93, "new_set": 93, "current_set": 93, "intersecting_set": 93, "lowest_score_strategi": 93, "sub_df": 93, "idxmin": 93, "filter_near_dupl": 93, "strategy_fn": 93, "strategy_kwarg": 93, "duplicate_row": 93, "group_kei": 93, "to_keep_indic": 93, "groupbi": 93, "explod": 93, "to_remov": 93, "isin": [93, 99], "kept": 93, "ids_to_remove_seri": 93, "tmp": 93, "ipykernel_7516": 93, "1995098996": 93, "deprecationwarn": 93, "dataframegroupbi": 93, "include_group": 93, "silenc": 93, "assist": 93, "streamlin": 93, "ux": 93, "agpl": 93, "compani": 93, "commerci": 93, "alter": 93, "email": 93, "team": 93, "discuss": 93, "anywher": 93, "profession": 93, "expert": 93, "depth": 94, "survei": [94, 103], "focus": [94, 96, 97, 101], "scienc": 94, "multivariate_norm": [94, 96, 97], "make_data": [94, 96], "cov": [94, 96, 97], "avg_trac": [94, 97], "py_tru": 94, "noise_matrix_tru": 94, "noise_marix": 94, "s_test": 94, "noisy_test_label": 94, "purpl": 94, "val": 94, "namespac": 94, "exec": 94, "markerfacecolor": [94, 97], "markeredgecolor": [94, 97, 101], "markers": [94, 97, 101], "markeredgewidth": [94, 97, 101], "realist": 94, "7560": 94, "637318e": 94, "896262e": 94, "548391e": 94, "923417e": 94, "375075e": 94, "3454": 94, "014051": 94, "020451": 94, "249": [94, 98], "042594": 94, "043859": 94, "045954": 94, "6120": 94, "023714": 94, "007136": 94, "119": [94, 98], "107266": 94, "103": [94, 98], "033738": 94, "238": [94, 98], "119505": 94, "236": [94, 98, 103], "037843": 94, "222": 94, "614915": 94, "122": [94, 98], "624422": 94, "625965": 94, "626079": 94, "118": 94, "627675": 94, "695223": 94, "323529": 94, "523015": 94, "013720": 94, "675727": 94, "646521": 94, "anyth": 94, "enhanc": [94, 96, 98], "magic": 94, "liter": 94, "identif": 94, "x27": 94, "logisticregressionlogisticregress": 94, "ever": 94, "092": 94, "040": 94, "024": 94, "004": 94, "surpris": 94, "1705": 94, "01936": 94, "ton": 94, "yourfavoritemodel1": 94, "merged_label": 94, "merged_test_label": 94, "newli": [94, 96], "yourfavoritemodel2": 94, "yourfavoritemodel3": 94, "cl3": 94, "takeawai": 94, "That": [94, 97], "randomli": 94, "my_test_pred_prob": 94, "my_test_pr": 94, "issues_test": 94, "corrected_test_label": 94, "pretend": 94, "cl_test_pr": 94, "69": [94, 101], "fairli": 94, "label_acc": 94, "percentag": 94, "offset": 94, "nquestion": 94, "overestim": 94, "answer": 94, "experienc": 94, "knowledg": 94, "prioiri": 94, "known": 94, "versatil": 94, "label_issues_indic": 94, "213": [94, 98], "218": [94, 98], "152": 94, "197": [94, 98], "196": [94, 98], "170": 94, "214": 94, "164": [94, 97], "198": [94, 98], "191": [94, 98], "63": [94, 98, 101], "117": [94, 101], "62": [94, 98, 101], "206": [94, 98], "115": [94, 98], "193": 94, "194": 94, "201": [94, 98], "174": 94, "163": [94, 103], "150": [94, 96, 98, 103], "169": [94, 103], "151": [94, 98], "168": 94, "precision_scor": 94, "recall_scor": 94, "f1_score": 94, "true_label_issu": 94, "filter_by_list": 94, "718750": [94, 96], "807018": 94, "912": 94, "733333": 94, "800000": 94, "721311": 94, "792793": 94, "908": 94, "676923": 94, "765217": 94, "892": 94, "567901": 94, "702290": 94, "844": 94, "gaug": 94, "label_issues_count": 94, "155": [94, 98], "156": 94, "172": [94, 97], "easiest": 94, "modular": 94, "penalti": 94, "l2": 94, "model3": 94, "n_estim": 94, "cv_pred_probs_1": 94, "cv_pred_probs_2": 94, "cv_pred_probs_3": 94, "label_quality_scores_best": 94, "cv_pred_probs_ensembl": 94, "label_quality_scores_bett": 94, "superior": [94, 100], "timm": 95, "glad": 96, "multiannotator_label": 96, "300": [96, 103], "noisier": 96, "111": [96, 101], "local_data": [96, 97], "true_labels_train": [96, 97], "noise_matrix_bett": 96, "noise_matrix_wors": 96, "transpos": [96, 99], "dropna": 96, "zfill": 96, "row_na_check": 96, "notna": 96, "reset_index": 96, "a0001": 96, "a0002": 96, "a0003": 96, "a0004": 96, "a0005": 96, "a0006": 96, "a0007": 96, "a0008": 96, "a0009": 96, "a0010": 96, "a0041": 96, "a0042": 96, "a0043": 96, "a0044": 96, "a0045": 96, "a0046": 96, "a0047": 96, "a0048": 96, "a0049": 96, "a0050": 96, "na": 96, "60856743": 96, "41693214": 96, "40908785": 96, "87147629": 96, "64941785": 96, "10774851": 96, "0524466": 96, "71853246": 96, "37169848": 96, "66031048": 96, "multiannotator_util": 96, "crude": 96, "straight": 96, "majority_vote_label": 96, "736118": 96, "757751": 96, "782232": 96, "715565": 96, "824256": 96, "quality_annotator_a0001": 96, "quality_annotator_a0002": 96, "quality_annotator_a0003": 96, "quality_annotator_a0004": 96, "quality_annotator_a0005": 96, "quality_annotator_a0006": 96, "quality_annotator_a0007": 96, "quality_annotator_a0008": 96, "quality_annotator_a0009": 96, "quality_annotator_a0010": 96, "quality_annotator_a0041": 96, "quality_annotator_a0042": 96, "quality_annotator_a0043": 96, "quality_annotator_a0044": 96, "quality_annotator_a0045": 96, "quality_annotator_a0046": 96, "quality_annotator_a0047": 96, "quality_annotator_a0048": 96, "quality_annotator_a0049": 96, "quality_annotator_a0050": 96, "070564": 96, "216078": 96, "119188": 96, "alongisd": 96, "244981": 96, "208333": 96, "295979": 96, "294118": 96, "324197": 96, "310345": 96, "355316": 96, "346154": 96, "439732": 96, "480000": 96, "a0031": 96, "523205": 96, "580645": 96, "a0034": 96, "535313": 96, "607143": 96, "a0021": 96, "606999": 96, "a0015": 96, "609526": 96, "678571": 96, "a0011": 96, "621103": 96, "692308": 96, "wors": 96, "improved_consensus_label": 96, "majority_vote_accuraci": 96, "cleanlab_label_accuraci": 96, "8581081081081081": 96, "9797297297297297": 96, "besid": 96, "sorted_consensus_quality_scor": 96, "worst_qual": 96, "better_qu": 96, "worst_quality_accuraci": 96, "better_quality_accuraci": 96, "9893238434163701": 96, "improved_pred_prob": 96, "treat": [96, 97, 101, 103], "analzi": 96, "copyright": 97, "advertis": 97, "violenc": 97, "nsfw": 97, "celeba": 97, "make_multilabel_data": 97, "boxes_coordin": 97, "box_multilabel": 97, "make_multi": 97, "bx1": 97, "by1": 97, "bx2": 97, "by2": 97, "label_list": 97, "ur": 97, "upper": 97, "inidx": 97, "logical_and": 97, "inv_d": 97, "labels_idx": 97, "true_labels_test": 97, "dict_unique_label": 97, "get_color_arrai": 97, "dcolor": 97, "aa4400": 97, "55227f": 97, "55a100": 97, "00ff00": 97, "007f7f": 97, "386b55": 97, "0000ff": 97, "y_onehot": 97, "single_class_label": 97, "stratifi": [97, 100], "kf": 97, "train_index": 97, "test_index": 97, "clf_cv": 97, "x_train_cv": 97, "x_test_cv": 97, "y_train_cv": 97, "y_test_cv": 97, "y_pred_cv": 97, "saw": 97, "num_to_displai": 97, "09": [97, 98, 101], "275": 97, "267": 97, "225": 97, "171": 97, "234": 97, "165": 97, "227": [97, 98], "262": [97, 98], "266": [97, 98], "139": 97, "143": [97, 98], "216": [97, 98, 103], "265": 97, "159": [97, 98], "despit": [97, 103], "suspect": 97, "888": 97, "8224": 97, "9632": 97, "968": 97, "6512": 97, "0444": 97, "774": 97, "labels_binary_format": 97, "labels_list_format": 97, "surround": 98, "scene": 98, "coco": 98, "everydai": 98, "has_label_issu": 98, "insal": 98, "nc": [98, 102, 103], "s3": [98, 102, 103], "amazonaw": [98, 102, 103], "objectdetectionbenchmark": 98, "tutorial_obj": 98, "pkl": 98, "example_imag": 98, "unzip": [98, 103], "_separate_label": 98, "_separate_predict": 98, "begin": 98, "image_path": 98, "rb": 98, "image_to_visu": 98, "seg_map": 98, "334": 98, "float32": 98, "bboxes_ignor": 98, "290": 98, "286": 98, "285": 98, "224": 98, "231": [98, 103], "293": 98, "289": 98, "282": 98, "281": 98, "271": 98, "280": 98, "277": 98, "279": 98, "287": 98, "299": 98, "276": 98, "307": 98, "321": 98, "326": 98, "333": 98, "261": 98, "319": 98, "257": 98, "283": 98, "243": 98, "303": 98, "316": 98, "247": 98, "323": 98, "226": 98, "228": 98, "232": 98, "219": 98, "239": 98, "240": 98, "209": 98, "242": 98, "202": 98, "230": 98, "215": 98, "220": 98, "229": 98, "217": [98, 103], "237": 98, "207": 98, "204": 98, "84": [98, 101, 103], "205": 98, "223": 98, "153": 98, "140": 98, "124": 98, "268": 98, "273": 98, "108": 98, "284": 98, "110": 98, "136": 98, "145": 98, "173": 98, "297": 98, "317": 98, "192": 98, "332": 98, "324": 98, "203": 98, "320": 98, "199": 98, "291": 98, "000000481413": 98, "jpg": 98, "42398": 98, "44503": 98, "29968": 98, "336": 98, "21005": 98, "9978472": 98, "forgot": 98, "drew": 98, "label_issue_idx": 98, "num_examples_to_show": 98, "138": 98, "candid": 98, "97489622": 98, "70610878": 98, "98764951": 98, "88899237": 98, "99085805": 98, "issue_idx": 98, "95569726e": 98, "03354841e": 98, "57510169e": 98, "58447666e": 98, "39755858e": 98, "issue_to_visu": 98, "000000009483": 98, "95569726168054e": 98, "addition": [98, 102], "visibl": 98, "missmatch": 98, "likelei": 98, "agnost": 98, "vaidat": 98, "inconsist": 98, "000000395701": 98, "033548411774308e": 98, "armchair": 98, "tv": 98, "000000154004": 98, "38300759625496356": 98, "foreground": 98, "000000448410": 98, "0008575101690203273": 98, "crowd": 98, "alon": 98, "explor": [98, 99], "resembl": [98, 99], "000000499768": 98, "9748962231208227": 98, "000000521141": 98, "8889923658893665": 98, "000000143931": 98, "9876495074395956": 98, "bonu": 98, "uncov": 98, "irregular": 98, "anomali": 98, "object_detection_util": 98, "calculate_bounding_box_area": 98, "num_imgs_to_show": 98, "lab_object_count": 98, "pred_object_count": 98, "000000430073": 98, "000000183709": 98, "000000189475": 98, "studi": 98, "label_norm": 98, "pred_norm": 98, "area": [98, 102], "lab_area": 98, "pred_area": 98, "lab_area_mean": 98, "lab_area_std": 98, "max_deviation_valu": 98, "max_deviation_class": 98, "deviation_valu": 98, "deviation_class": 98, "mean_area": 98, "std_area": 98, "class_area": 98, "deviations_awai": 98, "max_deviation_index": 98, "num_imgs_to_show_per_class": 98, "class_num": 98, "sorted_indic": 98, "000000422886": 98, "000000341828": 98, "000000461009": 98, "train_feature_embed": 99, "ood_train_feature_scor": 99, "test_feature_embed": 99, "ood_test_feature_scor": 99, "ood_train_predictions_scor": 99, "train_pred_prob": 99, "ood_test_predictions_scor": 99, "test_pred_prob": 99, "pylab": 99, "rcparam": 99, "baggingclassifi": 99, "therebi": 99, "rescal": 99, "transform_norm": 99, "totensor": 99, "root": 99, "animal_class": 99, "non_animal_class": 99, "animal_idx": 99, "test_idx": 99, "toronto": 99, "edu": 99, "kriz": 99, "170498071": 99, "72776359": 99, "59it": 99, "5000": 99, "plot_imag": 99, "visualize_outli": 99, "txt_class": 99, "img": [99, 101], "npimg": 99, "show_label": 99, "data_subset": 99, "resnet50": 99, "corpu": 99, "2048": 99, "embed_imag": 99, "create_model": 99, "strang": 99, "odd": 99, "train_ood_features_scor": 99, "top_train_ood_features_idx": 99, "fun": 99, "negat": 99, "homogen": 99, "bottom_train_ood_features_idx": 99, "test_ood_features_scor": 99, "top_ood_features_idx": 99, "inevit": 99, "trade": 99, "5th": 99, "percentil": 99, "fifth_percentil": 99, "plt_rang": 99, "hist": 99, "train_outlier_scor": 99, "ylabel": 99, "axvlin": 99, "test_outlier_scor": 99, "ood_features_indic": 99, "revisit": 99, "return_invers": 99, "train_feature_embeddings_sc": 99, "test_feature_embeddings_sc": 99, "train_pred_label": 99, "9702": 99, "train_ood_predictions_scor": 99, "test_ood_predictions_scor": 99, "lost": 99, "unsuit": 100, "ok": [100, 103], "convention": 100, "aforement": 100, "hypothet": 100, "contrast": 100, "tradit": 100, "disjoint": 100, "out_of_sample_pred_probs_for_a": 100, "out_of_sample_pred_probs_for_b": 100, "out_of_sample_pred_probs_for_c": 100, "out_of_sample_pred_prob": 100, "price": 101, "incom": 101, "ag": 101, "sensor": 101, "histgradientboostingregressor": 101, "r2_score": 101, "student_grades_r": 101, "final_scor": 101, "true_final_scor": 101, "homework": 101, "3d": 101, "hue": 101, "mpl_toolkit": 101, "mplot3d": 101, "axes3d": 101, "errors_idx": 101, "add_subplot": 101, "z": 101, "colorbar": 101, "errors_mask": 101, "feature_column": 101, "predicted_column": 101, "x_train_raw": 101, "x_test_raw": 101, "randomforestregressor": 101, "385101": 101, "499503": 101, "698255": 101, "776647": 101, "109373": 101, "170547": 101, "481096": 101, "984759": 101, "645270": 101, "795928": 101, "141": 101, "659": 101, "318": 101, "305": 101, "560": 101, "657": 101, "688": 101, "view_datapoint": 101, "concat": 101, "preds_og": 101, "r2_og": 101, "838": 101, "found_label_issu": 101, "preds_cl": 101, "r2_cl": 101, "926": 101, "favorit": 101, "968627e": 101, "228799": 101, "646674e": 101, "402962": 101, "323818e": 101, "952758": 101, "422144e": 101, "456908": 101, "465815e": 101, "753968": 101, "791186e": 101, "110719": 101, "485156e": 101, "670640": 101, "225300e": 101, "749976": 101, "499679e": 101, "947007": 101, "067882e": 101, "648396": 101, "synthia": 102, "imagesegment": 102, "given_mask": 102, "predicted_mask": 102, "set_printopt": [102, 103], "sky": 102, "sidewalk": 102, "veget": 102, "terrain": 102, "rider": 102, "pred_probs_filepath": 102, "1088": 102, "1920": 102, "label_filepath": 102, "synthia_class": 102, "maunal": 102, "100000": 102, "244800": 102, "leftmost": 102, "middl": [102, 103], "infact": 102, "rightmost": 102, "discrep": 102, "3263230": 102, "783379": 102, "275110": 102, "255792": 102, "78225": 102, "55990": 102, "54427": 102, "33591": 102, "24645": 102, "21308": 102, "15045": 102, "14171": 102, "13832": 102, "13498": 102, "11490": 102, "9164": 102, "8769": 102, "6999": 102, "6031": 102, "5011": 102, "mistakenli": 102, "class_issu": 102, "aim": [102, 103], "domin": 102, "bunch": 103, "conll": 103, "2003": 103, "love": 103, "n_i": 103, "optional_list_of_ordered_class_nam": 103, "deepai": 103, "conll2003": 103, "rm": 103, "tokenclassif": 103, "2024": 103, "2400": 103, "52e0": 103, "1a00": 103, "718": 103, "connect": 103, "443": 103, "await": 103, "982975": 103, "960k": 103, "959": 103, "94k": 103, "kb": 103, "mb": 103, "directori": 103, "inflat": 103, "17045998": 103, "16m": 103, "octet": 103, "26m": 103, "9mb": 103, "bert": 103, "read_npz": 103, "filepath": 103, "corrsespond": 103, "iob2": 103, "given_ent": 103, "entity_map": 103, "readfil": 103, "startswith": 103, "docstart": 103, "isalpha": 103, "isupp": 103, "indices_to_preview": 103, "nsentenc": 103, "eu": 103, "reject": 103, "boycott": 103, "british": 103, "lamb": 103, "00030412": 103, "00023826": 103, "99936208": 103, "00007009": 103, "00002545": 103, "99998795": 103, "00000401": 103, "00000218": 103, "00000455": 103, "00000131": 103, "00000749": 103, "99996115": 103, "00001371": 103, "0000087": 103, "00000895": 103, "99998936": 103, "00000382": 103, "00000178": 103, "00000366": 103, "00000137": 103, "99999101": 103, "00000266": 103, "00000174": 103, "0000035": 103, "00000109": 103, "99998768": 103, "00000482": 103, "00000202": 103, "00000438": 103, "0000011": 103, "00000465": 103, "99996392": 103, "00001105": 103, "0000116": 103, "00000878": 103, "99998671": 103, "00000364": 103, "00000213": 103, "00000472": 103, "00000281": 103, "99999073": 103, "00000211": 103, "00000159": 103, "00000442": 103, "00000115": 103, "peter": 103, "blackburn": 103, "00000358": 103, "00000529": 103, "99995623": 103, "0000129": 103, "0000024": 103, "00001812": 103, "99994141": 103, "00001645": 103, "00002162": 103, "brussel": 103, "1996": 103, "00001172": 103, "00000821": 103, "00004661": 103, "0000618": 103, "99987167": 103, "99999061": 103, "00000201": 103, "00000195": 103, "00000408": 103, "00000135": 103, "2254": 103, "2907": 103, "19392": 103, "9962": 103, "8904": 103, "19303": 103, "12918": 103, "9256": 103, "11855": 103, "18392": 103, "20426": 103, "19402": 103, "14744": 103, "19371": 103, "4645": 103, "10331": 103, "9430": 103, "6143": 103, "18367": 103, "12914": 103, "todai": 103, "weather": 103, "march": 103, "scalfaro": 103, "northern": 103, "himself": 103, "said": 103, "germani": 103, "nastja": 103, "rysich": 103, "north": 103, "spla": 103, "fought": 103, "khartoum": 103, "govern": 103, "south": 103, "1983": 103, "autonomi": 103, "animist": 103, "region": 103, "moslem": 103, "arabis": 103, "mayor": 103, "antonio": 103, "gonzalez": 103, "garcia": 103, "revolutionari": 103, "parti": 103, "wednesdai": 103, "troop": 103, "raid": 103, "farm": 103, "stole": 103, "rape": 103, "women": 103, "spring": 103, "chg": 103, "hrw": 103, "12pct": 103, "princ": 103, "photo": 103, "moment": 103, "spokeswoman": 103, "rainier": 103, "told": 103, "reuter": 103, "danila": 103, "carib": 103, "w224": 103, "equip": 103, "radiomet": 103, "earn": 103, "19996": 103, "london": 103, "denom": 103, "sale": 103, "uk": 103, "jp": 103, "fr": 103, "maccabi": 103, "hapoel": 103, "haifa": 103, "tel": 103, "aviv": 103, "hospit": 103, "rever": 103, "roman": 103, "cathol": 103, "nun": 103, "admit": 103, "calcutta": 103, "week": 103, "ago": 103, "fever": 103, "vomit": 103, "allianc": 103, "embattl": 103, "kabul": 103, "salang": 103, "highwai": 103, "mondai": 103, "tuesdai": 103, "suprem": 103, "council": 103, "led": 103, "jumbish": 103, "milli": 103, "movement": 103, "warlord": 103, "abdul": 103, "rashid": 103, "dostum": 103, "dollar": 103, "exchang": 103, "3570": 103, "12049": 103, "born": 103, "1937": 103, "provinc": 103, "anhui": 103, "dai": 103, "came": 103, "shanghai": 103, "citi": 103, "prolif": 103, "author": 103, "teacher": 103, "chines": 103, "16764": 103, "1990": 103, "historian": 103, "alan": 103, "john": 103, "percival": 103, "taylor": 103, "di": 103, "20446": 103, "pace": 103, "bowler": 103, "ian": 103, "harvei": 103, "claim": 103, "victoria": 103, "15514": 103, "cotti": 103, "osc": 103, "foreign": 103, "minist": 103, "7525": 103, "sultan": 103, "specter": 103, "met": 103, "crown": 103, "abdullah": 103, "defenc": 103, "aviat": 103, "jeddah": 103, "saudi": 103, "agenc": 103, "2288": 103, "hi": 103, "customari": 103, "outfit": 103, "champion": 103, "damp": 103, "scalp": 103, "canada": 103, "reign": 103, "olymp": 103, "donovan": 103, "bailei": 103, "1992": 103, "linford": 103, "christi": 103, "britain": 103, "1984": 103, "1988": 103, "carl": 103, "lewi": 103, "ambigi": 103, "punctuat": 103, "chicago": 103, "digest": 103, "philadelphia": 103, "usda": 103, "york": 103, "token_issu": 103, "471": 103, "kean": 103, "year": 103, "contract": 103, "manchest": 103, "19072": 103, "societi": 103, "bite": 103, "deliv": 103, "19910": 103, "father": 103, "clarenc": 103, "woolmer": 103, "renam": 103, "uttar": 103, "pradesh": 103, "india": 103, "ranji": 103, "trophi": 103, "nation": 103, "championship": 103, "captain": 103, "1949": 103, "15658": 103, "19879": 103, "iii": 103, "brian": 103, "shimer": 103, "randi": 103, "jone": 103, "19104": 103}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [11, 0, 0, "-", "datalab"], [36, 0, 0, "-", "dataset"], [39, 0, 0, "-", "experimental"], [43, 0, 0, "-", "filter"], [44, 0, 0, "-", "internal"], [55, 0, 0, "-", "models"], [57, 0, 0, "-", "multiannotator"], [60, 0, 0, "-", "multilabel_classification"], [63, 0, 0, "-", "object_detection"], [66, 0, 0, "-", "outlier"], [67, 0, 0, "-", "rank"], [68, 0, 0, "-", "regression"], [72, 0, 0, "-", "segmentation"], [76, 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"], [15, 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": [[12, 0, 0, "-", "data"], [13, 0, 0, "-", "data_issues"], [16, 0, 0, "-", "issue_finder"], [14, 0, 0, "-", "issue_manager_factory"], [32, 0, 0, "-", "model_outputs"], [33, 0, 0, "-", "report"], [34, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[12, 2, 1, "", "Data"], [12, 5, 1, "", "DataFormatError"], [12, 5, 1, "", "DatasetDictError"], [12, 5, 1, "", "DatasetLoadError"], [12, 2, 1, "", "Label"], [12, 2, 1, "", "MultiClass"], [12, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[13, 2, 1, "", "DataIssues"], [13, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 6, 1, "", "info"], [13, 6, 1, "", "issue_summary"], [13, 6, 1, "", "issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[16, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[16, 3, 1, "", "find_issues"], [16, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[18, 0, 0, "-", "data_valuation"], [19, 0, 0, "-", "duplicate"], [20, 0, 0, "-", "imbalance"], [22, 0, 0, "-", "issue_manager"], [23, 0, 0, "-", "label"], [26, 0, 0, "-", "noniid"], [27, 0, 0, "-", "null"], [28, 0, 0, "-", "outlier"], [31, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[18, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[18, 6, 1, "", "DEFAULT_THRESHOLD"], [18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[19, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[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, 6, 1, "", "near_duplicate_sets"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[20, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[22, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[23, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 3, 1, "", "get_health_summary"], [23, 6, 1, "", "health_summary_parameters"], [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.multilabel": [[25, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[25, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[26, 2, 1, "", "NonIIDIssueManager"], [26, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[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.null": [[27, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[28, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[28, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[30, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[30, 2, 1, "", "RegressionLabelIssueManager"], [30, 1, 1, "", "find_issues_with_features"], [30, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[31, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[31, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [31, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "filter_cluster_ids"], [31, 3, 1, "", "find_issues"], [31, 3, 1, "", "get_worst_cluster"], [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, "", "perform_clustering"], [31, 3, 1, "", "report"], [31, 3, 1, "", "set_knn_graph"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[14, 7, 1, "", "REGISTRY"], [14, 1, 1, "", "list_default_issue_types"], [14, 1, 1, "", "list_possible_issue_types"], [14, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[32, 2, 1, "", "ModelOutput"], [32, 2, 1, "", "MultiClassPredProbs"], [32, 2, 1, "", "MultiLabelPredProbs"], [32, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[33, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[33, 3, 1, "", "get_report"], [33, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[34, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[34, 6, 1, "", "CLASSIFICATION"], [34, 6, 1, "", "MULTILABEL"], [34, 6, 1, "", "REGRESSION"], [34, 3, 1, "", "__contains__"], [34, 3, 1, "", "__getitem__"], [34, 3, 1, "", "__iter__"], [34, 3, 1, "", "__len__"], [34, 3, 1, "", "from_str"], [34, 4, 1, "", "is_classification"], [34, 4, 1, "", "is_multilabel"], [34, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[36, 1, 1, "", "find_overlapping_classes"], [36, 1, 1, "", "health_summary"], [36, 1, 1, "", "overall_label_health_score"], [36, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[37, 0, 0, "-", "cifar_cnn"], [38, 0, 0, "-", "coteaching"], [40, 0, 0, "-", "label_issues_batched"], [41, 0, 0, "-", "mnist_pytorch"], [42, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[37, 2, 1, "", "CNN"], [37, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[37, 6, 1, "", "T_destination"], [37, 3, 1, "", "__call__"], [37, 3, 1, "", "add_module"], [37, 3, 1, "", "apply"], [37, 3, 1, "", "bfloat16"], [37, 3, 1, "", "buffers"], [37, 6, 1, "", "call_super_init"], [37, 3, 1, "", "children"], [37, 3, 1, "", "compile"], [37, 3, 1, "", "cpu"], [37, 3, 1, "", "cuda"], [37, 3, 1, "", "double"], [37, 6, 1, "", "dump_patches"], [37, 3, 1, "", "eval"], [37, 3, 1, "", "extra_repr"], [37, 3, 1, "", "float"], [37, 3, 1, "id0", "forward"], [37, 3, 1, "", "get_buffer"], [37, 3, 1, "", "get_extra_state"], [37, 3, 1, "", "get_parameter"], [37, 3, 1, "", "get_submodule"], [37, 3, 1, "", "half"], [37, 3, 1, "", "ipu"], [37, 3, 1, "", "load_state_dict"], [37, 3, 1, "", "modules"], [37, 3, 1, "", "named_buffers"], [37, 3, 1, "", "named_children"], [37, 3, 1, "", "named_modules"], [37, 3, 1, "", "named_parameters"], [37, 3, 1, "", "parameters"], [37, 3, 1, "", "register_backward_hook"], [37, 3, 1, "", "register_buffer"], [37, 3, 1, "", "register_forward_hook"], [37, 3, 1, "", "register_forward_pre_hook"], [37, 3, 1, "", "register_full_backward_hook"], [37, 3, 1, "", "register_full_backward_pre_hook"], [37, 3, 1, "", "register_load_state_dict_post_hook"], [37, 3, 1, "", "register_module"], [37, 3, 1, "", "register_parameter"], [37, 3, 1, "", "register_state_dict_pre_hook"], [37, 3, 1, "", "requires_grad_"], [37, 3, 1, "", "set_extra_state"], [37, 3, 1, "", "share_memory"], [37, 3, 1, "", "state_dict"], [37, 3, 1, "", "to"], [37, 3, 1, "", "to_empty"], [37, 3, 1, "", "train"], [37, 6, 1, "", "training"], [37, 3, 1, "", "type"], [37, 3, 1, "", "xpu"], [37, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[38, 1, 1, "", "adjust_learning_rate"], [38, 1, 1, "", "evaluate"], [38, 1, 1, "", "forget_rate_scheduler"], [38, 1, 1, "", "initialize_lr_scheduler"], [38, 1, 1, "", "loss_coteaching"], [38, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[40, 2, 1, "", "LabelInspector"], [40, 7, 1, "", "adj_confident_thresholds_shared"], [40, 1, 1, "", "find_label_issues_batched"], [40, 7, 1, "", "labels_shared"], [40, 7, 1, "", "pred_probs_shared"], [40, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[40, 3, 1, "", "get_confident_thresholds"], [40, 3, 1, "", "get_label_issues"], [40, 3, 1, "", "get_num_issues"], [40, 3, 1, "", "get_quality_scores"], [40, 3, 1, "", "score_label_quality"], [40, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[41, 2, 1, "", "CNN"], [41, 2, 1, "", "SimpleNet"], [41, 1, 1, "", "get_mnist_dataset"], [41, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[41, 3, 1, "", "__init_subclass__"], [41, 6, 1, "", "batch_size"], [41, 6, 1, "", "dataset"], [41, 6, 1, "", "epochs"], [41, 3, 1, "id0", "fit"], [41, 3, 1, "", "get_metadata_routing"], [41, 3, 1, "", "get_params"], [41, 6, 1, "", "loader"], [41, 6, 1, "", "log_interval"], [41, 6, 1, "", "lr"], [41, 6, 1, "", "momentum"], [41, 6, 1, "", "no_cuda"], [41, 3, 1, "id1", "predict"], [41, 3, 1, "id4", "predict_proba"], [41, 6, 1, "", "seed"], [41, 3, 1, "", "set_fit_request"], [41, 3, 1, "", "set_params"], [41, 3, 1, "", "set_predict_proba_request"], [41, 3, 1, "", "set_predict_request"], [41, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[41, 6, 1, "", "T_destination"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "add_module"], [41, 3, 1, "", "apply"], [41, 3, 1, "", "bfloat16"], [41, 3, 1, "", "buffers"], [41, 6, 1, "", "call_super_init"], [41, 3, 1, "", "children"], [41, 3, 1, "", "compile"], [41, 3, 1, "", "cpu"], [41, 3, 1, "", "cuda"], [41, 3, 1, "", "double"], [41, 6, 1, "", "dump_patches"], [41, 3, 1, "", "eval"], [41, 3, 1, "", "extra_repr"], [41, 3, 1, "", "float"], [41, 3, 1, "", "forward"], [41, 3, 1, "", "get_buffer"], [41, 3, 1, "", "get_extra_state"], [41, 3, 1, "", "get_parameter"], [41, 3, 1, "", "get_submodule"], [41, 3, 1, "", "half"], [41, 3, 1, "", "ipu"], [41, 3, 1, "", "load_state_dict"], [41, 3, 1, "", "modules"], [41, 3, 1, "", "named_buffers"], [41, 3, 1, "", "named_children"], [41, 3, 1, "", "named_modules"], [41, 3, 1, "", "named_parameters"], [41, 3, 1, "", "parameters"], [41, 3, 1, "", "register_backward_hook"], [41, 3, 1, "", "register_buffer"], [41, 3, 1, "", "register_forward_hook"], [41, 3, 1, "", "register_forward_pre_hook"], [41, 3, 1, "", "register_full_backward_hook"], [41, 3, 1, "", "register_full_backward_pre_hook"], [41, 3, 1, "", "register_load_state_dict_post_hook"], [41, 3, 1, "", "register_module"], [41, 3, 1, "", "register_parameter"], [41, 3, 1, "", "register_state_dict_pre_hook"], [41, 3, 1, "", "requires_grad_"], [41, 3, 1, "", "set_extra_state"], [41, 3, 1, "", "share_memory"], [41, 3, 1, "", "state_dict"], [41, 3, 1, "", "to"], [41, 3, 1, "", "to_empty"], [41, 3, 1, "", "train"], [41, 6, 1, "", "training"], [41, 3, 1, "", "type"], [41, 3, 1, "", "xpu"], [41, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[42, 1, 1, "", "display_issues"], [42, 1, 1, "", "find_label_issues"], [42, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [43, 1, 1, "", "find_predicted_neq_given"], [43, 7, 1, "", "pred_probs_by_class"], [43, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[45, 0, 0, "-", "label_quality_utils"], [46, 0, 0, "-", "latent_algebra"], [47, 0, 0, "-", "multiannotator_utils"], [48, 0, 0, "-", "multilabel_scorer"], [49, 0, 0, "-", "multilabel_utils"], [50, 0, 0, "-", "outlier"], [51, 0, 0, "-", "token_classification_utils"], [52, 0, 0, "-", "util"], [53, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[45, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[46, 1, 1, "", "compute_inv_noise_matrix"], [46, 1, 1, "", "compute_noise_matrix_from_inverse"], [46, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [46, 1, 1, "", "compute_py"], [46, 1, 1, "", "compute_py_inv_noise_matrix"], [46, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[47, 1, 1, "", "assert_valid_inputs_multiannotator"], [47, 1, 1, "", "assert_valid_pred_probs"], [47, 1, 1, "", "check_consensus_label_classes"], [47, 1, 1, "", "compute_soft_cross_entropy"], [47, 1, 1, "", "find_best_temp_scaler"], [47, 1, 1, "", "format_multiannotator_labels"], [47, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[48, 2, 1, "", "Aggregator"], [48, 2, 1, "", "ClassLabelScorer"], [48, 2, 1, "", "MultilabelScorer"], [48, 1, 1, "", "exponential_moving_average"], [48, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [48, 1, 1, "", "get_label_quality_scores"], [48, 1, 1, "", "multilabel_py"], [48, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[48, 3, 1, "", "__call__"], [48, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[48, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [48, 6, 1, "", "NORMALIZED_MARGIN"], [48, 6, 1, "", "SELF_CONFIDENCE"], [48, 3, 1, "", "__call__"], [48, 3, 1, "", "__contains__"], [48, 3, 1, "", "__getitem__"], [48, 3, 1, "", "__iter__"], [48, 3, 1, "", "__len__"], [48, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[48, 3, 1, "", "__call__"], [48, 3, 1, "", "aggregate"], [48, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[49, 1, 1, "", "get_onehot_num_classes"], [49, 1, 1, "", "int2onehot"], [49, 1, 1, "", "onehot2int"], [49, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[50, 1, 1, "", "correct_precision_errors"], [50, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, 1, 1, "", "color_sentence"], [51, 1, 1, "", "filter_sentence"], [51, 1, 1, "", "get_sentence"], [51, 1, 1, "", "mapping"], [51, 1, 1, "", "merge_probs"], [51, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[52, 1, 1, "", "append_extra_datapoint"], [52, 1, 1, "", "clip_noise_rates"], [52, 1, 1, "", "clip_values"], [52, 1, 1, "", "compress_int_array"], [52, 1, 1, "", "confusion_matrix"], [52, 1, 1, "", "csr_vstack"], [52, 1, 1, "", "estimate_pu_f1"], [52, 1, 1, "", "extract_indices_tf"], [52, 1, 1, "", "force_two_dimensions"], [52, 1, 1, "", "format_labels"], [52, 1, 1, "", "get_missing_classes"], [52, 1, 1, "", "get_num_classes"], [52, 1, 1, "", "get_unique_classes"], [52, 1, 1, "", "is_tensorflow_dataset"], [52, 1, 1, "", "is_torch_dataset"], [52, 1, 1, "", "num_unique_classes"], [52, 1, 1, "", "print_inverse_noise_matrix"], [52, 1, 1, "", "print_joint_matrix"], [52, 1, 1, "", "print_noise_matrix"], [52, 1, 1, "", "print_square_matrix"], [52, 1, 1, "", "remove_noise_from_class"], [52, 1, 1, "", "round_preserving_row_totals"], [52, 1, 1, "", "round_preserving_sum"], [52, 1, 1, "", "smart_display_dataframe"], [52, 1, 1, "", "subset_X_y"], [52, 1, 1, "", "subset_data"], [52, 1, 1, "", "subset_labels"], [52, 1, 1, "", "train_val_split"], [52, 1, 1, "", "unshuffle_tensorflow_dataset"], [52, 1, 1, "", "value_counts"], [52, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[53, 1, 1, "", "assert_indexing_works"], [53, 1, 1, "", "assert_nonempty_input"], [53, 1, 1, "", "assert_valid_class_labels"], [53, 1, 1, "", "assert_valid_inputs"], [53, 1, 1, "", "labels_to_array"], [53, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[56, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[56, 2, 1, "", "KerasWrapperModel"], [56, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[56, 3, 1, "", "fit"], [56, 3, 1, "", "get_params"], [56, 3, 1, "", "predict"], [56, 3, 1, "", "predict_proba"], [56, 3, 1, "", "set_params"], [56, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[56, 3, 1, "", "fit"], [56, 3, 1, "", "get_params"], [56, 3, 1, "", "predict"], [56, 3, 1, "", "predict_proba"], [56, 3, 1, "", "set_params"], [56, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[57, 1, 1, "", "convert_long_to_wide_dataset"], [57, 1, 1, "", "get_active_learning_scores"], [57, 1, 1, "", "get_active_learning_scores_ensemble"], [57, 1, 1, "", "get_label_quality_multiannotator"], [57, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [57, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[58, 0, 0, "-", "dataset"], [59, 0, 0, "-", "filter"], [61, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[58, 1, 1, "", "common_multilabel_issues"], [58, 1, 1, "", "multilabel_health_summary"], [58, 1, 1, "", "overall_multilabel_health_score"], [58, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, 1, 1, "", "find_label_issues"], [59, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[61, 1, 1, "", "get_label_quality_scores"], [61, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[62, 0, 0, "-", "filter"], [64, 0, 0, "-", "rank"], [65, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[62, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[64, 1, 1, "", "compute_badloc_box_scores"], [64, 1, 1, "", "compute_overlooked_box_scores"], [64, 1, 1, "", "compute_swap_box_scores"], [64, 1, 1, "", "get_label_quality_scores"], [64, 1, 1, "", "issues_from_scores"], [64, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[65, 1, 1, "", "bounding_box_size_distribution"], [65, 1, 1, "", "calculate_per_class_metrics"], [65, 1, 1, "", "class_label_distribution"], [65, 1, 1, "", "get_average_per_class_confusion_matrix"], [65, 1, 1, "", "get_sorted_bbox_count_idxs"], [65, 1, 1, "", "object_counts_per_image"], [65, 1, 1, "", "plot_class_distribution"], [65, 1, 1, "", "plot_class_size_distributions"], [65, 1, 1, "", "visualize"]], "cleanlab.outlier": [[66, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[66, 3, 1, "", "fit"], [66, 3, 1, "", "fit_score"], [66, 3, 1, "", "score"]], "cleanlab.rank": [[67, 1, 1, "", "find_top_issues"], [67, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [67, 1, 1, "", "get_label_quality_ensemble_scores"], [67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_normalized_margin_for_each_label"], [67, 1, 1, "", "get_self_confidence_for_each_label"], [67, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[69, 0, 0, "-", "learn"], [70, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[69, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[69, 3, 1, "", "__init_subclass__"], [69, 3, 1, "", "find_label_issues"], [69, 3, 1, "", "fit"], [69, 3, 1, "", "get_aleatoric_uncertainty"], [69, 3, 1, "", "get_epistemic_uncertainty"], [69, 3, 1, "", "get_label_issues"], [69, 3, 1, "", "get_metadata_routing"], [69, 3, 1, "", "get_params"], [69, 3, 1, "", "predict"], [69, 3, 1, "", "save_space"], [69, 3, 1, "", "score"], [69, 3, 1, "", "set_fit_request"], [69, 3, 1, "", "set_params"], [69, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[70, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[71, 0, 0, "-", "filter"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[71, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[74, 1, 1, "", "common_label_issues"], [74, 1, 1, "", "display_issues"], [74, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[75, 0, 0, "-", "filter"], [77, 0, 0, "-", "rank"], [78, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[75, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[77, 1, 1, "", "get_label_quality_scores"], [77, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[78, 1, 1, "", "common_label_issues"], [78, 1, 1, "", "display_issues"], [78, 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, 82, 83, 84, 88, 90, 91, 93, 94, 97, 103], "count": [3, 94], "data_valu": [4, 18], "datalab": [5, 7, 9, 10, 11, 85, 86, 87, 89, 90, 91, 94, 97], "creat": [7, 85, 86, 87, 94, 96], "your": [7, 79, 86, 87, 91, 93, 94], "own": 7, "issu": [7, 9, 10, 21, 30, 79, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 102, 103], "manag": [7, 21], "prerequisit": 7, "implement": 7, "issuemanag": [7, 86], "basic": 7, "check": 7, "intermedi": 7, "advanc": [7, 86], "us": [7, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "gener": 8, "cluster": [8, 93], "id": 8, "guid": [9, 11], "type": [9, 10, 94], "custom": [9, 86], "cleanlab": [9, 10, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "studio": [9, 10], "easi": [9, 10, 79, 88, 90, 91], "mode": [9, 10, 79, 88, 90, 91], "can": [10, 85, 87, 92, 93, 94, 96], "detect": [10, 87, 90, 91, 93, 94, 98, 99], "estim": [10, 94, 96, 97], "each": 10, "label": [10, 23, 25, 30, 79, 82, 83, 84, 87, 88, 90, 91, 93, 94, 96, 97, 98, 101, 102, 103], "outlier": [10, 28, 50, 66, 88, 90, 91, 97, 99], "Near": [10, 87, 88, 90, 91], "duplic": [10, 19, 87, 88, 90, 91, 93, 97], "non": [10, 91], "iid": [10, 91], "class": [10, 80, 94, 102], "imbal": [10, 20], "imag": [10, 88, 99], "specif": [10, 21, 102], "underperform": [10, 93], "group": [10, 93], "null": [10, 27], "data": [10, 12, 79, 82, 84, 85, 86, 87, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "valuat": 10, "option": 10, "paramet": [10, 94], "get": [11, 85, 86, 87, 96, 97, 98, 102, 103], "start": [11, 92], "api": 11, "refer": 11, "data_issu": 13, "factori": 14, "intern": [15, 44], "issue_find": 16, "issue_manag": [21, 22], "regist": 21, "ml": [21, 93, 94], "task": [21, 34], "multilabel": 24, "noniid": 26, "regress": [29, 68, 69, 70, 93, 101], "prioriti": 30, "order": 30, "find": [30, 79, 82, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "underperforming_group": 31, "model_output": 32, "report": [33, 88], "dataset": [36, 58, 79, 83, 85, 87, 88, 91, 92, 93, 94, 97, 98, 99, 101, 102, 103], "cifar_cnn": 37, "coteach": 38, "experiment": 39, "label_issues_batch": 40, "mnist_pytorch": 41, "span_classif": 42, "filter": [43, 59, 62, 71, 75, 94], "label_quality_util": 45, "latent_algebra": 46, "multiannotator_util": 47, "multilabel_scor": 48, "multilabel_util": 49, "token_classification_util": 51, "util": 52, "valid": [53, 88, 100], "fasttext": 54, "model": [55, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101], "kera": 56, "multiannot": [57, 96], "multilabel_classif": 60, "rank": [61, 64, 67, 70, 73, 77, 94], "object_detect": 63, "summari": [65, 74, 78], "learn": [69, 82, 85, 87, 93, 94], "segment": [72, 102], "token_classif": [76, 103], "open": [79, 93], "sourc": [79, 93], "document": 79, "quickstart": 79, "1": [79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "instal": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "2": [79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "common": [79, 80, 103], "3": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "handl": [79, 93], "error": [79, 83, 88, 93, 94, 96, 97, 98, 101, 102, 103], "train": [79, 82, 83, 84, 93, 99, 101], "robust": [79, 82, 83, 94, 101], "noisi": [79, 82, 83, 94, 101], "4": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 98, 99, 101], "curat": [79, 92], "fix": [79, 93], "level": [79, 92, 94, 103], "5": [79, 82, 84, 85, 87, 88, 90, 94, 96, 101], "improv": [79, 96], "via": [79, 94, 96], "mani": [79, 94], "other": [79, 96, 98, 101], "techniqu": 79, "contribut": 79, "how": [80, 93, 94, 96, 97, 103], "migrat": 80, "version": 80, "0": 80, "from": [80, 82, 83, 85, 86, 87, 94, 101], "pre": [80, 84, 93, 99], "function": [80, 86], "name": 80, "chang": 80, "modul": [80, 94], "new": [80, 85], "remov": 80, "argument": [80, 86], "variabl": 80, "cleanlearn": [81, 93, 94], "tutori": [81, 89, 92, 95], "tabular": [82, 90], "scikit": 82, "requir": [82, 83, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "depend": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "load": [82, 83, 84, 85, 86, 87, 90, 91, 101], "process": [82, 90, 99, 101], "select": [82, 90], "comput": [82, 84, 88, 90, 91, 93, 96, 100], "out": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "sampl": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "predict": [82, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 100], "probabl": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "more": [82, 83, 85, 87, 94, 101], "text": [83, 91, 103], "format": [83, 91, 93, 97, 98], "defin": [83, 88, 91, 101], "potenti": [83, 96, 101], "audio": 84, "speechbrain": 84, "import": [84, 85, 86, 87, 88, 92, 94, 96], "them": [84, 92, 94], "featur": [84, 88, 99], "fit": 84, "linear": 84, "datamonitor": 85, "leverag": 85, "statist": [85, 96], "audit": [85, 86, 87], "skip": [85, 87, 92, 94, 96], "detail": [85, 87, 92, 94, 96], "classifi": [85, 86, 87], "6": [85, 94], "about": [85, 87], "addit": [85, 87], "workflow": [86, 94], "instanti": 86, "object": [86, 98], "increment": 86, "search": 86, "specifi": [86, 93], "nondefault": 86, "save": 86, "ad": 86, "A": 87, "unifi": 87, "all": [87, 94], "kind": [87, 98], "inform": [87, 88], "pytorch": [88, 99], "fetch": [88, 92], "normal": 88, "fashion": 88, "mnist": 88, "prepar": 88, "k": [88, 90, 100], "fold": [88, 100], "cross": [88, 100], "embed": [88, 99], "7": [88, 94], "view": 88, "most": [88, 103], "like": 88, "exampl": [88, 93, 94, 99], "sever": 88, "set": [88, 94], "dark": 88, "top": [88, 102], "low": 88, "numer": 90, "categor": 90, "column": 90, "construct": 90, "nearest": 90, "neighbour": 90, "graph": 90, "drift": [91, 97], "evalu": 92, "health": [92, 94], "8": [92, 94], "popular": 92, "faq": 93, "what": [93, 94, 100], "do": [93, 94], "i": [93, 94, 100], "infer": 93, "correct": 93, "ha": 93, "flag": 93, "should": 93, "v": 93, "test": [93, 94, 99], "big": 93, "limit": 93, "memori": 93, "why": 93, "isn": 93, "t": 93, "work": [93, 94, 96, 103], "me": 93, "differ": [93, 98], "clean": [93, 94], "final": 93, "hyperparamet": 93, "tune": 93, "onli": 93, "one": [93, 94, 97, 102], "doe": [93, 96, 103], "take": 93, "so": 93, "long": 93, "slice": 93, "when": [93, 94], "identifi": [93, 98], "run": 93, "licens": 93, "under": 93, "an": 93, "answer": 93, "question": 93, "The": 94, "centric": 94, "ai": 94, "machin": 94, "find_label_issu": 94, "line": 94, "code": 94, "visual": [94, 98, 99, 102], "twenti": 94, "lowest": 94, "qualiti": [94, 96, 97, 98, 102, 103], "see": 94, "now": 94, "let": 94, "": 94, "happen": 94, "we": 94, "merg": 94, "seafoam": 94, "green": 94, "yellow": 94, "too": 94, "you": 94, "re": 94, "One": 94, "score": [94, 96, 97, 98, 102, 103], "rule": 94, "overal": [94, 102], "accur": 94, "thi": 94, "directli": 94, "fulli": 94, "character": 94, "nois": 94, "matrix": [94, 97], "joint": 94, "prior": 94, "true": 94, "distribut": 94, "flip": 94, "rate": 94, "ani": 94, "again": 94, "support": 94, "lot": 94, "method": 94, "filter_bi": 94, "automat": 94, "everi": 94, "uniqu": 94, "num_label_issu": 94, "threshold": 94, "found": 94, "Not": 94, "sure": 94, "ensembl": 94, "multipl": [94, 96], "predictor": 94, "consensu": 96, "annot": 96, "initi": 96, "major": 96, "vote": 96, "better": 96, "compar": 96, "inspect": 96, "retrain": 96, "further": 96, "multi": 97, "beyond": 97, "mislabel": [97, 102, 103], "given": 97, "hot": 97, "binari": 97, "without": 97, "applic": 97, "real": 97, "download": [98, 102, 103], "objectlab": 98, "exploratori": 98, "analysi": 98, "timm": 99, "cifar10": 99, "some": 99, "pred_prob": [99, 102, 103], "wai": 101, "semant": 102, "which": 102, "ar": 102, "commonli": 102, "focus": 102, "token": 103, "word": 103, "sentenc": 103, "contain": 103, "particular": 103}, "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"], [18, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [11, "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"]], "Label Issue": [[10, "label-issue"]], "Outlier Issue": [[10, "outlier-issue"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "Non-IID Issue": [[10, "non-iid-issue"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "Null Issue": [[10, "null-issue"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "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": [[11, "getting-started"]], "Guides": [[11, "guides"]], "API Reference": [[11, "api-reference"]], "data": [[12, "module-cleanlab.datalab.internal.data"]], "data_issues": [[13, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[14, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[15, "internal"], [44, "internal"]], "issue_finder": [[16, "issue-finder"]], "duplicate": [[19, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[20, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[21, "issue-manager"], [22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[21, "registered-issue-managers"]], "ML task-specific issue managers": [[21, "ml-task-specific-issue-managers"]], "label": [[23, "module-cleanlab.datalab.internal.issue_manager.label"], [25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [30, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[24, "multilabel"]], "noniid": [[26, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[27, "null"]], "outlier": [[28, "module-cleanlab.datalab.internal.issue_manager.outlier"], [50, "module-cleanlab.internal.outlier"], [66, "module-cleanlab.outlier"]], "regression": [[29, "regression"], [68, "regression"]], "Priority Order for finding issues:": [[30, null]], "underperforming_group": [[31, "underperforming-group"]], "model_outputs": [[32, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[33, "report"]], "task": [[34, "task"]], "dataset": [[36, "module-cleanlab.dataset"], [58, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "experimental": [[39, "experimental"]], "label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "filter": [[43, "module-cleanlab.filter"], [59, "module-cleanlab.multilabel_classification.filter"], [62, "filter"], [71, "filter"], [75, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "util": [[52, "module-cleanlab.internal.util"]], "validation": [[53, "module-cleanlab.internal.validation"]], "fasttext": [[54, "fasttext"]], "models": [[55, "models"]], "keras": [[56, "module-cleanlab.models.keras"]], "multiannotator": [[57, "module-cleanlab.multiannotator"]], "multilabel_classification": [[60, "multilabel-classification"]], "rank": [[61, "module-cleanlab.multilabel_classification.rank"], [64, "module-cleanlab.object_detection.rank"], [67, "module-cleanlab.rank"], [73, "module-cleanlab.segmentation.rank"], [77, "module-cleanlab.token_classification.rank"]], "object_detection": [[63, "object-detection"]], "summary": [[65, "summary"], [74, "module-cleanlab.segmentation.summary"], [78, "module-cleanlab.token_classification.summary"]], "regression.learn": [[69, "module-cleanlab.regression.learn"]], "regression.rank": [[70, "module-cleanlab.regression.rank"]], "segmentation": [[72, "segmentation"]], "token_classification": [[76, "token-classification"]], "cleanlab open-source documentation": [[79, "cleanlab-open-source-documentation"]], "Quickstart": [[79, "quickstart"]], "1. Install cleanlab": [[79, "install-cleanlab"]], "2. Find common issues in your data": [[79, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[79, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[79, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[79, "improve-your-data-via-many-other-techniques"]], "Contributing": [[79, "contributing"]], "Easy Mode": [[79, "easy-mode"], [88, "Easy-Mode"], [90, "Easy-Mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[80, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[80, "function-and-class-name-changes"]], "Module name changes": [[80, "module-name-changes"]], "New modules": [[80, "new-modules"]], "Removed modules": [[80, "removed-modules"]], "Common argument and variable name changes": [[80, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[81, "cleanlearning-tutorials"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[82, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "1. Install required dependencies": [[82, "1.-Install-required-dependencies"], [83, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [101, "1.-Install-required-dependencies"]], "2. Load and process the data": [[82, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [101, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[82, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [90, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[82, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[82, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[83, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[83, "2.-Load-and-format-the-text-dataset"], [91, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[83, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[83, "4.-Train-a-more-robust-model-from-noisy-labels"], [101, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Audio Classification with SpeechBrain and Cleanlab": [[84, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[84, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[84, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[84, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[84, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[84, "5.-Use-cleanlab-to-find-label-issues"], [90, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[85, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[85, "1.-Install-and-import-required-dependencies"], [87, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [96, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[85, "2.-Create-and-load-the-data-(can-skip-these-details)"], [87, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[85, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"], [87, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[85, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [87, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[85, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[85, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "Datalab: Advanced workflows to audit your data": [[86, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[86, "Install-and-import-required-dependencies"]], "Create and load the data": [[86, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[86, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[86, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[86, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[86, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[86, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[86, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[87, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "5. Learn more about the issues in your dataset": [[87, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[87, "Get-additional-information"]], "Near duplicate issues": [[87, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "Label issues": [[88, "Label-issues"], [90, "Label-issues"], [91, "Label-issues"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[88, "Outlier-issues"], [90, "Outlier-issues"], [91, "Outlier-issues"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "Datalab Tutorials": [[89, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[90, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[90, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[91, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[91, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[92, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "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?": [[93, "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?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "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.": [[94, "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": [[94, "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": [[94, "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!": [[94, "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": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "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)": [[94, "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:": [[94, "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": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "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": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[97, "3.-Use-cleanlab-to-find-label-issues"], [98, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[97, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[97, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[97, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[97, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[97, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[98, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "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"], [11, "module-cleanlab.datalab"], [12, "module-cleanlab.datalab.internal.data"], [13, "module-cleanlab.datalab.internal.data_issues"], [14, "module-cleanlab.datalab.internal.issue_manager_factory"], [15, "module-cleanlab.datalab.internal"], [16, "module-cleanlab.datalab.internal.issue_finder"], [18, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [19, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [20, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [23, "module-cleanlab.datalab.internal.issue_manager.label"], [25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [26, "module-cleanlab.datalab.internal.issue_manager.noniid"], [27, "module-cleanlab.datalab.internal.issue_manager.null"], [28, "module-cleanlab.datalab.internal.issue_manager.outlier"], [30, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [31, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [32, "module-cleanlab.datalab.internal.model_outputs"], [33, "module-cleanlab.datalab.internal.report"], [34, "module-cleanlab.datalab.internal.task"], [36, "module-cleanlab.dataset"], [37, "module-cleanlab.experimental.cifar_cnn"], [38, "module-cleanlab.experimental.coteaching"], [39, "module-cleanlab.experimental"], [40, "module-cleanlab.experimental.label_issues_batched"], [41, "module-cleanlab.experimental.mnist_pytorch"], [42, "module-cleanlab.experimental.span_classification"], [43, "module-cleanlab.filter"], [44, "module-cleanlab.internal"], [45, "module-cleanlab.internal.label_quality_utils"], [46, "module-cleanlab.internal.latent_algebra"], [47, "module-cleanlab.internal.multiannotator_utils"], [48, "module-cleanlab.internal.multilabel_scorer"], [49, "module-cleanlab.internal.multilabel_utils"], [50, "module-cleanlab.internal.outlier"], [51, "module-cleanlab.internal.token_classification_utils"], [52, "module-cleanlab.internal.util"], [53, "module-cleanlab.internal.validation"], [55, "module-cleanlab.models"], [56, "module-cleanlab.models.keras"], [57, "module-cleanlab.multiannotator"], [58, "module-cleanlab.multilabel_classification.dataset"], [59, "module-cleanlab.multilabel_classification.filter"], [60, "module-cleanlab.multilabel_classification"], [61, "module-cleanlab.multilabel_classification.rank"], [62, "module-cleanlab.object_detection.filter"], [63, "module-cleanlab.object_detection"], [64, "module-cleanlab.object_detection.rank"], [65, "module-cleanlab.object_detection.summary"], [66, "module-cleanlab.outlier"], [67, "module-cleanlab.rank"], [68, "module-cleanlab.regression"], [69, "module-cleanlab.regression.learn"], [70, "module-cleanlab.regression.rank"], [71, "module-cleanlab.segmentation.filter"], [72, "module-cleanlab.segmentation"], [73, "module-cleanlab.segmentation.rank"], [74, "module-cleanlab.segmentation.summary"], [75, "module-cleanlab.token_classification.filter"], [76, "module-cleanlab.token_classification"], [77, "module-cleanlab.token_classification.rank"], [78, "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": [[11, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[12, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[12, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[12, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[12, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[12, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[12, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[12, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[12, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[12, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[12, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[12, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[12, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[12, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[12, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[12, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[13, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[13, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[14, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[15, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[16, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[18, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[19, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[20, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[23, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[26, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[27, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[28, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[30, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[31, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "set_knn_graph() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.set_knn_graph"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[32, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[33, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[33, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[33, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[33, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[34, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[34, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[36, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[37, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[37, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.forward"], [37, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[39, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [41, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [41, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [41, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[43, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[43, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[43, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[44, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[45, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[50, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[52, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[53, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[55, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[56, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[57, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[58, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[60, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[61, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[62, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[62, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[63, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[64, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[65, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[66, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[66, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[67, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[68, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[69, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[69, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[69, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[70, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[70, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[71, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[71, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[72, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[73, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[74, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[75, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[75, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[76, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[77, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[78, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[78, "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/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/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/data_monitor", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/image", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "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/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/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/data_monitor.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/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", "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", "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 Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Audio Classification with SpeechBrain and Cleanlab", "DataMonitor: Leverage statistics from Datalab to audit new data", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Image Classification with PyTorch and Cleanlab", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "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, 80, 85, 86, 87, 94, 96, 97], "noise_matrix_is_valid": [0, 1], "generate_noisy_label": [0, 1, 85, 86, 87, 94, 96, 97], "generate_noise_matrix_from_trac": [0, 1, 85, 86, 87, 94, 96, 97], "generate_n_rand_probabilities_that_sum_to_m": [0, 1], "randomly_distribute_n_balls_into_k_bin": [0, 1], "helper": [1, 16, 40, 45, 47, 48, 49, 50, 51, 52, 64, 88, 92, 103], "method": [1, 2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102, 103], "ar": [1, 2, 3, 4, 5, 7, 10, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 26, 29, 30, 32, 34, 36, 37, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "us": [1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 79, 80, 86, 92, 100], "benchmark": [1, 37, 79, 80, 85, 86, 87, 94, 96, 97], "cleanlab": [1, 2, 3, 4, 5, 7, 11, 12, 13, 14, 15, 16, 18, 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, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 85, 86, 87, 92, 95, 100], "": [1, 2, 3, 4, 10, 18, 32, 36, 37, 41, 45, 48, 50, 52, 57, 58, 62, 64, 65, 66, 67, 69, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "core": [1, 40, 43, 71, 73], "algorithm": [1, 2, 8, 10, 31, 38, 42, 50, 52, 57, 66, 75, 77, 79, 93, 94, 96, 103], "These": [1, 2, 3, 4, 5, 8, 10, 21, 37, 39, 41, 42, 43, 44, 55, 57, 58, 61, 65, 66, 70, 74, 75, 77, 78, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "introduc": [1, 84, 93, 94], "synthet": [1, 96, 97, 102], "nois": [1, 2, 3, 36, 43, 46, 52, 58, 85, 86, 87, 92, 96, 101], "label": [1, 2, 3, 4, 5, 7, 8, 9, 12, 14, 15, 16, 20, 21, 22, 24, 29, 31, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 85, 86, 92, 95, 99, 100], "classif": [1, 3, 4, 5, 7, 10, 12, 14, 16, 32, 34, 36, 40, 42, 43, 46, 48, 49, 52, 57, 58, 59, 60, 61, 66, 67, 75, 76, 77, 78, 79, 80, 81, 85, 86, 87, 95, 96, 99, 100, 101, 102], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 25, 26, 27, 28, 30, 31, 39, 40, 41, 42, 43, 46, 48, 52, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 82, 84, 86, 90, 95, 96, 100], "specif": [1, 3, 5, 9, 14, 15, 16, 27, 33, 34, 39, 55, 59, 62, 65, 74, 78, 88, 90, 91, 94, 98, 103], "thi": [1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "modul": [1, 3, 13, 14, 15, 16, 21, 24, 29, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 48, 50, 52, 55, 57, 62, 65, 66, 67, 79, 88, 93, 97], "provid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 16, 18, 23, 30, 34, 36, 37, 38, 40, 41, 43, 46, 50, 52, 56, 57, 58, 59, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 99, 100, 101, 102, 103], "gener": [1, 2, 3, 7, 10, 18, 23, 25, 33, 36, 48, 52, 53, 66, 67, 69, 74, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 99, 100, 102, 103], "valid": [1, 2, 3, 5, 10, 12, 32, 34, 36, 43, 44, 46, 47, 48, 50, 52, 57, 59, 62, 65, 67, 69, 70, 78, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 95, 97, 98, 101, 102, 103], "matric": [1, 3, 46, 93], "which": [1, 2, 3, 5, 7, 10, 12, 13, 14, 16, 18, 22, 26, 32, 33, 34, 36, 37, 41, 42, 43, 46, 48, 51, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 73, 74, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "learn": [1, 2, 3, 5, 9, 10, 14, 16, 22, 30, 33, 38, 39, 40, 41, 43, 45, 47, 52, 55, 57, 59, 66, 68, 70, 73, 77, 79, 83, 84, 86, 88, 90, 91, 92, 96, 97, 101], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101, 102, 103], "possibl": [1, 2, 3, 7, 10, 36, 37, 41, 43, 45, 46, 48, 59, 60, 61, 62, 64, 65, 66, 67, 69, 75, 77, 78, 85, 87, 93, 94, 96, 97, 98, 101, 102, 103], "noisi": [1, 2, 3, 10, 36, 38, 41, 43, 46, 52, 58, 59, 61, 67, 69, 70, 71, 73, 74, 80, 85, 86, 87, 90, 91, 93, 95, 96], "given": [1, 2, 3, 5, 10, 14, 30, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 51, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "matrix": [1, 2, 3, 5, 10, 16, 18, 31, 36, 43, 45, 46, 49, 52, 53, 59, 62, 64, 65, 66, 67, 90, 98, 99], "trace": [1, 85, 86, 87, 94, 96, 97], "valu": [1, 2, 3, 4, 5, 10, 12, 13, 16, 18, 22, 26, 27, 32, 34, 36, 37, 38, 40, 41, 43, 45, 46, 48, 50, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "more": [1, 2, 3, 5, 7, 9, 10, 13, 14, 16, 18, 26, 36, 37, 40, 41, 42, 45, 48, 50, 52, 57, 58, 59, 60, 61, 62, 64, 65, 67, 69, 70, 73, 74, 75, 77, 79, 84, 86, 88, 90, 91, 92, 93, 96, 97, 98, 99, 102, 103], "function": [1, 2, 3, 4, 5, 7, 10, 13, 14, 16, 23, 26, 30, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 102, 103], "noise_matrix": [1, 2, 3, 10, 46, 52, 85, 86, 87, 94, 96, 97], "py": [1, 3, 33, 37, 38, 43, 46, 48, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97], "verbos": [1, 2, 5, 7, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 40, 43, 57, 58, 59, 64, 66, 67, 69, 71, 73, 74, 78, 86, 94, 96], "fals": [1, 2, 3, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 47, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 98, 99, 101, 102], "sourc": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 85], "prior": [1, 2, 3, 36, 43, 46, 48], "repres": [1, 2, 3, 7, 10, 12, 16, 18, 26, 32, 34, 36, 40, 43, 46, 49, 50, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 103], "p": [1, 2, 3, 5, 10, 36, 43, 45, 46, 50, 52, 57, 65, 66, 67, 71, 90, 91, 94, 96, 103], "true_label": [1, 2, 3, 36, 46, 52, 94, 96], "k": [1, 2, 3, 4, 5, 8, 10, 12, 16, 18, 19, 23, 26, 28, 31, 36, 40, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 60, 61, 62, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 82, 84, 85, 86, 87, 93, 94, 96, 97, 98, 99, 102, 103], "check": [1, 2, 5, 6, 9, 10, 12, 16, 27, 34, 37, 40, 41, 47, 53, 56, 62, 65, 69, 79, 82, 83, 84, 85, 86, 87, 88, 93, 94, 96, 97, 101], "learnabl": 1, "mean": [1, 2, 7, 8, 12, 13, 22, 26, 38, 41, 46, 48, 50, 64, 69, 83, 87, 91, 93, 94, 96, 97, 98, 99, 101], "achiev": [1, 2, 37, 38, 41, 69, 93, 96, 103], "better": [1, 5, 43, 57, 59, 67, 69, 70, 79, 83, 84, 87, 90, 91, 93, 94, 97, 98, 99, 103], "than": [1, 2, 3, 4, 7, 9, 10, 26, 28, 31, 36, 43, 52, 56, 57, 62, 64, 66, 67, 69, 73, 77, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "random": [1, 2, 3, 7, 10, 18, 31, 40, 48, 57, 67, 69, 82, 84, 85, 86, 87, 88, 90, 93, 94, 96, 97, 99], "perform": [1, 2, 4, 7, 10, 26, 28, 31, 37, 41, 48, 65, 69, 79, 82, 83, 86, 93, 94, 96, 97, 100, 101], "averag": [1, 3, 5, 10, 22, 28, 36, 37, 41, 48, 50, 57, 58, 65, 66, 67, 93, 96, 99], "amount": [1, 3, 88], "paramet": [1, 2, 3, 4, 5, 9, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 87, 88, 91], "np": [1, 2, 3, 4, 5, 7, 16, 18, 31, 36, 38, 40, 42, 43, 45, 46, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "ndarrai": [1, 2, 3, 4, 5, 16, 23, 25, 26, 30, 31, 32, 36, 38, 40, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 103], "an": [1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 50, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 100, 101, 102, 103], "arrai": [1, 2, 3, 4, 5, 7, 10, 12, 16, 18, 26, 32, 36, 38, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "shape": [1, 2, 3, 4, 5, 16, 18, 36, 38, 40, 42, 43, 45, 46, 47, 48, 50, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 84, 92, 93, 94, 97, 98, 99, 102, 103], "condit": [1, 2, 3, 46, 51, 52, 67, 88, 94, 103], "probabl": [1, 2, 3, 5, 8, 10, 16, 23, 25, 28, 32, 36, 40, 41, 42, 43, 45, 46, 48, 49, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 92, 93, 94, 95, 97, 98, 99, 102, 103], "k_": [1, 2, 3, 46, 52], "k_y": [1, 2, 3, 46, 52], "contain": [1, 2, 3, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 43, 45, 46, 51, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 73, 74, 75, 77, 78, 80, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102], "fraction": [1, 2, 3, 10, 20, 38, 46, 52, 57, 69, 90, 93], "exampl": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 51, 52, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 96, 97, 98, 100, 101, 102, 103], "everi": [1, 2, 3, 4, 5, 16, 37, 41, 43, 46, 51, 52, 59, 67, 69, 70, 82, 84, 85, 86, 87, 88, 90, 91, 93, 96, 98, 100, 102, 103], "class": [1, 2, 3, 4, 5, 7, 9, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 51, 52, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 103], "other": [1, 2, 3, 5, 10, 16, 22, 27, 36, 37, 39, 40, 41, 43, 46, 49, 52, 53, 55, 57, 58, 61, 65, 66, 67, 69, 74, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 99, 102, 103], "assum": [1, 2, 3, 12, 43, 46, 51, 52, 67, 71, 74, 93, 97, 99, 101, 102, 103], "column": [1, 2, 3, 5, 10, 12, 13, 30, 36, 40, 43, 46, 48, 49, 51, 52, 57, 58, 59, 61, 62, 65, 66, 67, 69, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 101, 102, 103], "sum": [1, 2, 3, 26, 31, 32, 36, 46, 48, 52, 58, 59, 61, 64, 69, 85, 86, 87, 88, 93, 94, 96, 97, 102, 103], "1": [1, 2, 3, 4, 5, 7, 10, 12, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 92, 93, 100], "each": [1, 2, 3, 4, 5, 7, 8, 9, 12, 13, 14, 16, 20, 22, 23, 25, 26, 31, 32, 33, 36, 37, 38, 40, 41, 42, 43, 45, 46, 48, 49, 50, 52, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "true": [1, 2, 3, 5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 43, 46, 48, 51, 52, 53, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "return": [1, 2, 3, 4, 5, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "type": [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 93, 97, 98, 101, 102, 103], "bool": [1, 2, 3, 5, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 48, 51, 52, 57, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 78], "is_valid": 1, "whether": [1, 3, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 37, 40, 41, 43, 52, 57, 58, 59, 61, 62, 78, 83, 84, 87, 88, 90, 91, 92, 93, 94, 101, 103], "from": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 22, 23, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 46, 48, 49, 50, 51, 52, 57, 59, 61, 64, 65, 66, 67, 69, 70, 75, 77, 78, 79, 84, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 102, 103], "perfect": [1, 2, 36, 69, 94, 98], "exactli": [1, 3, 10, 36, 37, 41, 43, 60, 66, 86, 87, 88, 90, 91, 94], "yield": [1, 37, 41, 85], "between": [1, 5, 10, 15, 16, 21, 22, 24, 26, 29, 32, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 50, 55, 57, 58, 61, 64, 66, 67, 69, 70, 73, 77, 78, 80, 83, 84, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "below": [1, 3, 4, 5, 10, 36, 37, 40, 41, 43, 45, 48, 50, 57, 58, 59, 64, 65, 73, 77, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "we": [1, 2, 3, 5, 7, 10, 13, 22, 37, 40, 41, 43, 48, 52, 53, 56, 57, 64, 65, 67, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "loop": [1, 3, 46, 52, 88, 98], "implement": [1, 2, 3, 9, 14, 22, 37, 38, 40, 41, 46, 52, 69, 79, 82, 84, 86, 90, 99, 100], "what": [1, 5, 9, 10, 16, 33, 36, 38, 40, 43, 57, 58, 62, 64, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "doe": [1, 2, 3, 7, 10, 40, 41, 43, 48, 50, 53, 64, 65, 69, 71, 73, 77, 83, 84, 86, 87, 88, 90, 91, 97, 101, 102], "do": [1, 2, 5, 9, 10, 36, 40, 41, 52, 53, 66, 67, 71, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "fast": 1, "explain": [1, 10], "python": [1, 2, 41, 56, 69, 83, 84, 86, 87, 88, 90, 91, 92, 94, 99], "pseudocod": [1, 100], "happen": [1, 10, 43, 59, 85, 91, 96, 102], "n": [1, 2, 3, 5, 7, 36, 37, 40, 41, 43, 45, 46, 47, 48, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 82, 83, 84, 85, 88, 91, 92, 93, 96, 97, 98, 101, 102, 103], "without": [1, 2, 5, 9, 10, 12, 14, 20, 37, 41, 61, 69, 79, 83, 84, 85, 91, 93, 94, 98, 99], "ani": [1, 2, 3, 5, 7, 9, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 40, 41, 43, 45, 47, 50, 51, 52, 56, 57, 59, 61, 62, 64, 65, 67, 69, 71, 73, 74, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102], "distinct": [1, 18, 52, 103], "natur": [1, 10, 96, 99], "number": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 77, 78, 80, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 102, 103], "0": [1, 2, 3, 4, 5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "count_joint": 1, "len": [1, 2, 3, 7, 36, 40, 46, 51, 52, 53, 66, 67, 69, 82, 83, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "y": [1, 2, 3, 5, 8, 18, 30, 31, 41, 46, 48, 52, 53, 56, 65, 69, 70, 83, 84, 85, 86, 87, 90, 93, 94, 96, 97, 99, 101], "round": [1, 40, 43, 52, 69, 93, 101], "astyp": [1, 96], "int": [1, 2, 3, 4, 5, 7, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 37, 38, 40, 41, 43, 48, 49, 50, 51, 52, 53, 58, 59, 61, 65, 66, 67, 69, 71, 73, 74, 75, 78, 84, 86, 88, 98, 99], "rang": [1, 3, 5, 7, 12, 46, 48, 50, 52, 65, 69, 70, 85, 88, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "idx_flip": 1, "where": [1, 2, 3, 5, 7, 10, 12, 13, 16, 22, 36, 40, 43, 46, 47, 48, 49, 50, 51, 52, 53, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "pragma": 1, "cover": [1, 3, 80, 92, 93], "choic": [1, 8, 43, 50, 88, 93, 97, 99], "replac": [1, 51, 56, 67, 82, 83, 85, 86, 87, 88, 91, 92, 93, 96, 99], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 67, 84, 85, 86, 87], "05": [1, 10, 26, 30, 51, 65, 69, 75, 77, 90, 92, 93, 94, 98], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 85, 86, 87, 94, 96, 97], "none": [1, 2, 3, 4, 5, 7, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 85, 86, 87, 88, 93, 94, 96, 97, 102], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 10, 26, 39, 41, 48, 69, 82, 84, 85, 86, 87, 90, 92, 94, 96, 97], "max_it": [1, 83, 84, 91, 99], "10000": [1, 40, 92, 93], "x": [1, 2, 3, 5, 10, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 37, 38, 41, 43, 45, 46, 48, 51, 52, 53, 56, 57, 59, 65, 66, 67, 69, 71, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 96, 97, 99, 101], "diagon": [1, 3, 5, 43, 46, 52], "equal": [1, 3, 10, 12, 59, 64, 74, 100], "creat": [1, 2, 9, 16, 18, 37, 40, 41, 43, 52, 69, 79, 83, 84, 88, 90, 91, 93, 102, 103], "impli": [1, 10, 36, 58, 65], "float": [1, 2, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 39, 40, 41, 43, 45, 47, 48, 50, 51, 52, 57, 58, 59, 61, 64, 65, 69, 73, 77, 84, 85, 86, 87, 94, 96, 97], "entri": [1, 3, 5, 36, 37, 41, 43, 45, 49, 50, 52, 57, 58, 59, 62, 82, 83, 90, 91, 94, 97, 98, 101], "maximum": [1, 10, 66, 74, 78, 102], "minimum": [1, 8, 10, 20, 43, 45, 59, 64, 77], "noise_r": 1, "non": [1, 2, 3, 5, 7, 9, 16, 26, 37, 41, 43, 64, 69, 86, 93, 94, 96, 98, 99], "default": [1, 2, 3, 4, 5, 7, 10, 14, 16, 28, 30, 33, 36, 37, 38, 40, 41, 43, 45, 46, 48, 50, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 86, 88, 93, 101, 102], "If": [1, 2, 3, 4, 5, 10, 12, 13, 16, 26, 28, 34, 36, 37, 40, 41, 43, 45, 46, 48, 51, 52, 56, 57, 58, 59, 62, 64, 65, 66, 69, 70, 71, 73, 74, 77, 78, 79, 80, 82, 83, 84, 86, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "have": [1, 2, 3, 4, 5, 7, 9, 10, 16, 21, 24, 26, 29, 36, 37, 39, 40, 41, 43, 46, 48, 52, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 70, 74, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "all": [1, 2, 3, 5, 7, 8, 9, 10, 13, 14, 16, 22, 33, 36, 37, 40, 41, 42, 43, 46, 48, 49, 51, 52, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "necessari": [1, 2, 3, 4, 7, 10, 12, 51, 85, 86], "In": [1, 2, 3, 5, 10, 36, 37, 40, 41, 56, 57, 58, 60, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 99, 100, 101, 102, 103], "particular": [1, 5, 6, 10, 13, 14, 16, 19, 20, 22, 26, 27, 28, 31, 37, 41, 52, 57, 61, 65, 69, 74, 78, 79, 82, 83, 84, 85, 87, 91, 93, 96, 97, 99, 101], "satisfi": [1, 3, 36], "requir": [1, 2, 5, 7, 8, 9, 10, 11, 12, 30, 35, 37, 38, 39, 40, 41, 43, 46, 52, 55, 56, 59, 66, 67, 69, 71, 79, 80, 84, 92, 93, 94, 100], "argument": [1, 2, 3, 5, 10, 16, 23, 27, 30, 31, 32, 37, 40, 41, 42, 43, 48, 53, 56, 57, 58, 59, 61, 64, 65, 66, 67, 69, 73, 74, 75, 77, 83, 85, 87, 88, 91, 92, 93, 97, 98, 101, 103], "when": [1, 2, 3, 5, 10, 12, 14, 23, 26, 37, 41, 43, 46, 48, 50, 52, 56, 59, 61, 62, 64, 66, 67, 69, 70, 82, 83, 85, 86, 87, 88, 90, 91, 96, 100, 101, 102, 103], "The": [1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 52, 56, 57, 58, 59, 62, 64, 65, 66, 67, 69, 71, 74, 75, 77, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103], "rate": [1, 2, 3, 10, 38, 52, 84, 103], "set": [1, 2, 3, 5, 9, 10, 12, 13, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 37, 40, 41, 43, 47, 48, 50, 52, 56, 57, 59, 62, 64, 65, 66, 67, 69, 71, 73, 74, 82, 83, 85, 86, 87, 90, 91, 93, 96, 97, 99, 100, 101, 102, 103], "note": [1, 2, 3, 7, 8, 10, 12, 27, 31, 34, 37, 40, 41, 42, 43, 48, 52, 56, 57, 62, 64, 65, 66, 67, 69, 70, 74, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "you": [1, 2, 3, 5, 7, 9, 10, 14, 16, 36, 37, 39, 40, 41, 43, 48, 55, 56, 57, 59, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103], "high": [1, 2, 16, 40, 43, 52, 64, 67, 69, 82, 83, 85, 86, 87, 88, 92, 94, 98, 101, 102, 103], "mai": [1, 2, 3, 4, 5, 10, 13, 21, 22, 24, 29, 32, 36, 37, 39, 40, 41, 43, 46, 48, 52, 57, 58, 62, 64, 65, 66, 67, 69, 71, 74, 78, 80, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 100, 101, 102, 103], "imposs": [1, 10, 94], "also": [1, 2, 3, 5, 7, 9, 10, 22, 34, 36, 37, 40, 41, 43, 48, 51, 56, 57, 66, 69, 74, 77, 78, 79, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 100, 101, 102, 103], "low": [1, 10, 52, 57, 79, 85, 86, 87, 91, 94, 98, 102], "zero": [1, 3, 5, 37, 41, 45, 52, 53, 86, 88, 97, 98, 99], "forc": [1, 2, 3, 5, 41, 86, 103], "instead": [1, 2, 3, 10, 13, 16, 33, 36, 37, 40, 41, 43, 46, 52, 56, 57, 59, 61, 65, 66, 67, 69, 70, 73, 75, 77, 80, 82, 83, 84, 88, 90, 91, 93, 94, 97, 98, 99, 101, 102, 103], "onli": [1, 2, 3, 4, 5, 7, 10, 16, 23, 26, 30, 36, 37, 40, 41, 42, 43, 45, 46, 50, 51, 52, 53, 56, 57, 66, 67, 69, 71, 73, 77, 78, 79, 83, 84, 86, 87, 88, 91, 96, 97, 98, 99, 100, 101, 102, 103], "guarante": [1, 3, 5, 15, 21, 24, 29, 37, 39, 41, 44, 46, 55, 80], "produc": [1, 2, 5, 9, 10, 16, 48, 57, 67, 69, 71, 73, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 102, 103], "higher": [1, 5, 10, 36, 43, 45, 46, 48, 50, 56, 57, 58, 69, 87, 91, 93, 98], "opposit": [1, 103], "occur": [1, 3, 10, 36, 51, 64, 86, 87, 88, 93, 99], "small": [1, 3, 10, 36, 40, 48, 50, 52, 58, 65, 83, 88, 91, 92, 97, 99], "numpi": [1, 3, 4, 5, 7, 10, 12, 18, 31, 32, 40, 41, 42, 48, 50, 51, 53, 56, 61, 64, 69, 70, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "max": [1, 43, 66, 67, 87, 88, 99], "tri": [1, 37, 41, 100], "befor": [1, 2, 3, 37, 41, 50, 52, 66, 69, 74, 82, 83, 85, 91, 93, 94, 96, 99, 101], "option": [1, 2, 3, 4, 5, 7, 8, 9, 12, 13, 16, 23, 26, 30, 36, 37, 40, 41, 43, 46, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 82, 84, 86, 87, 88, 90, 93, 94, 97, 101, 102], "left": [1, 2, 43, 45, 50, 52, 59, 62, 65, 85, 86, 87, 97, 98, 99, 102], "stochast": 1, "exceed": 1, "m": [1, 5, 37, 41, 47, 48, 57, 62, 64, 65, 66, 85, 86, 87, 92, 96, 97, 98, 103], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 37, 41, 56, 93, 94, 102], "length": [1, 5, 12, 26, 27, 36, 38, 43, 52, 59, 62, 66, 67, 69, 71, 74, 78, 82, 84, 97, 99, 102, 103], "must": [1, 2, 3, 4, 5, 7, 16, 36, 37, 38, 39, 41, 43, 46, 48, 49, 50, 52, 55, 56, 57, 58, 59, 66, 67, 69, 71, 73, 74, 75, 77, 78, 84, 96, 100, 102, 103], "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 10, 12, 36, 40, 43, 49, 52, 53, 57, 59, 65, 71, 73, 74, 75, 77, 78, 82, 83, 84, 93, 96, 97, 98, 102, 103], "ball": [1, 92], "bin": [1, 3, 59, 85, 86, 87, 99], "ensur": [1, 2, 10, 37, 41, 50, 52, 53, 56, 64, 67, 69, 82, 83, 84, 86, 87, 88, 91, 93, 94, 99, 100, 101], "most": [1, 3, 5, 7, 10, 16, 36, 40, 43, 48, 56, 57, 58, 59, 62, 64, 65, 66, 67, 70, 73, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102], "least": [1, 4, 10, 18, 31, 36, 40, 57, 58, 64, 67, 77, 87, 88, 93, 96, 99, 102], "int_arrai": [1, 52], "can": [2, 3, 5, 7, 8, 9, 13, 14, 16, 33, 34, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 69, 70, 71, 74, 75, 78, 79, 80, 82, 83, 84, 86, 88, 90, 91, 97, 98, 99, 100, 101, 102, 103], "model": [2, 3, 4, 5, 9, 10, 16, 18, 30, 32, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 51, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 85, 86, 87, 92, 95, 100, 102, 103], "For": [2, 3, 5, 7, 9, 10, 11, 16, 22, 35, 36, 37, 40, 41, 43, 46, 48, 50, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 73, 75, 77, 78, 79, 82, 83, 84, 85, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 100, 102, 103], "regular": [2, 3, 40, 56], "multi": [2, 3, 4, 10, 32, 36, 37, 40, 41, 43, 47, 48, 49, 52, 53, 58, 59, 60, 61, 66, 67, 79, 93, 94, 95], "task": [2, 5, 7, 10, 11, 12, 14, 15, 16, 25, 30, 33, 36, 40, 46, 48, 49, 50, 52, 57, 59, 67, 69, 79, 83, 84, 85, 91, 92, 93, 94, 97, 99, 101, 102, 103], "cleanlearn": [2, 3, 10, 23, 30, 37, 52, 56, 68, 69, 70, 79, 80, 82, 83, 101], "wrap": [2, 37, 41, 56, 66, 69, 79, 82, 83, 85, 86, 87, 90, 91, 94, 101], "instanc": [2, 3, 5, 6, 7, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 41, 48, 56, 65, 66, 69, 74, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 98], "sklearn": [2, 3, 4, 5, 8, 10, 18, 31, 36, 41, 48, 52, 56, 66, 69, 70, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 100, 101], "classifi": [2, 3, 41, 48, 52, 57, 60, 66, 67, 79, 80, 82, 83, 84, 90, 91, 93, 96, 97, 99, 100, 102, 103], "adher": [2, 41, 69], "estim": [2, 3, 4, 5, 9, 13, 22, 36, 40, 41, 43, 46, 52, 57, 58, 59, 64, 66, 69, 71, 73, 77, 79, 80, 84, 85, 86, 87, 88, 90, 91, 93, 95, 98, 99, 100, 101, 102, 103], "api": [2, 3, 14, 56, 62, 65, 66, 69, 80, 93, 101], "defin": [2, 3, 5, 7, 10, 14, 22, 36, 37, 38, 40, 41, 43, 67, 69, 71, 84, 86, 87, 90, 93, 96, 99, 103], "four": [2, 10, 92, 94, 103], "clf": [2, 3, 5, 48, 69, 79, 82, 90, 93, 94, 97], "fit": [2, 3, 5, 8, 10, 18, 39, 41, 55, 56, 66, 68, 69, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 100, 101, 103], "sample_weight": [2, 41, 69, 94], "predict_proba": [2, 5, 36, 39, 41, 48, 55, 56, 82, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 99], "predict": [2, 3, 5, 8, 9, 10, 16, 22, 23, 25, 28, 30, 32, 34, 36, 39, 40, 41, 42, 43, 45, 46, 48, 49, 51, 52, 55, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 83, 92, 93, 94, 95, 99, 101, 102, 103], "score": [2, 3, 4, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 40, 42, 43, 45, 48, 50, 57, 58, 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 73, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 99, 101], "data": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 36, 38, 39, 40, 41, 42, 43, 48, 49, 52, 55, 56, 57, 58, 59, 60, 64, 66, 67, 68, 69, 74, 75, 76, 77, 78, 80, 83, 88, 89, 95, 100], "e": [2, 3, 5, 10, 12, 22, 32, 36, 37, 40, 41, 43, 46, 48, 49, 52, 53, 57, 58, 59, 60, 62, 65, 66, 67, 69, 71, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101], "featur": [2, 3, 4, 5, 8, 10, 16, 18, 19, 23, 26, 27, 28, 30, 31, 48, 52, 66, 69, 79, 82, 85, 86, 87, 90, 91, 93, 94, 96, 97, 101], "element": [2, 3, 5, 36, 42, 43, 45, 52, 57, 59, 67, 74, 75, 77, 83, 84, 91, 93, 103], "first": [2, 5, 10, 17, 26, 27, 36, 40, 48, 52, 57, 58, 62, 65, 67, 69, 82, 83, 84, 86, 88, 90, 93, 96, 97, 98, 99, 101, 102, 103], "index": [2, 10, 26, 36, 43, 51, 52, 53, 58, 67, 69, 74, 77, 78, 83, 84, 86, 87, 88, 90, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "should": [2, 3, 5, 7, 10, 14, 22, 26, 31, 32, 36, 37, 40, 41, 43, 45, 46, 48, 50, 51, 52, 56, 57, 58, 61, 62, 64, 65, 66, 67, 69, 70, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 100, 101, 102, 103], "correspond": [2, 3, 5, 10, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 34, 36, 37, 40, 41, 42, 43, 45, 46, 48, 51, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 71, 74, 75, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "differ": [2, 5, 7, 10, 13, 15, 21, 24, 26, 27, 29, 36, 37, 39, 40, 41, 43, 44, 48, 50, 52, 53, 55, 57, 62, 64, 66, 69, 82, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 99, 100, 101], "sampl": [2, 3, 5, 8, 10, 16, 20, 43, 45, 48, 59, 62, 65, 67, 69, 70, 79, 80, 83, 92, 93, 94, 95, 97, 98, 101, 102, 103], "size": [2, 10, 31, 37, 40, 41, 43, 48, 59, 64, 65, 69, 71, 73, 83, 85, 88, 90, 93, 94, 96, 97, 98, 100, 102], "here": [2, 5, 7, 10, 14, 40, 43, 46, 56, 57, 58, 59, 61, 62, 65, 66, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "re": [2, 5, 37, 41, 51, 57, 69, 79, 82, 83, 84, 85, 86, 90, 91, 93, 101, 102, 103], "weight": [2, 10, 37, 38, 41, 48, 57, 64, 67, 69, 83, 84, 85, 86, 87, 91], "loss": [2, 38, 56, 67, 69, 88], "while": [2, 3, 10, 37, 40, 41, 47, 48, 52, 69, 79, 88, 93, 94, 96, 97, 101], "train": [2, 3, 4, 5, 9, 10, 16, 18, 32, 37, 38, 39, 41, 48, 52, 56, 57, 62, 65, 66, 69, 70, 80, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 100, 102, 103], "support": [2, 3, 4, 5, 12, 14, 33, 34, 40, 42, 48, 52, 53, 56, 66, 67, 77, 79, 80, 84, 85, 86, 87, 88, 93], "your": [2, 3, 5, 9, 10, 16, 36, 37, 39, 40, 41, 43, 48, 52, 55, 56, 57, 58, 59, 61, 66, 67, 69, 70, 71, 73, 74, 80, 82, 83, 84, 85, 88, 90, 92, 96, 97, 98, 99, 100, 101, 102, 103], "recommend": [2, 5, 7, 10, 13, 16, 40, 43, 57, 86, 87, 88, 93, 100, 101], "furthermor": 2, "correctli": [2, 3, 10, 36, 37, 41, 43, 46, 53, 58, 59, 64, 65, 69, 71, 83, 91, 93, 97, 98, 101, 102], "clonabl": [2, 69], "via": [2, 5, 7, 10, 13, 16, 18, 22, 36, 38, 40, 41, 48, 52, 57, 62, 65, 66, 67, 69, 70, 73, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 97, 98, 99, 100, 101, 102, 103], "base": [2, 3, 4, 5, 7, 10, 12, 13, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 37, 40, 41, 42, 43, 46, 47, 48, 50, 51, 52, 53, 56, 57, 58, 59, 61, 64, 66, 67, 69, 70, 73, 75, 77, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "clone": [2, 69, 97], "intern": [2, 3, 7, 10, 11, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 40, 45, 46, 47, 48, 49, 50, 51, 52, 53, 61, 65, 69, 75, 80, 84, 86, 93, 94, 96, 97, 98, 99, 101, 103], "multipl": [2, 3, 5, 12, 13, 34, 36, 43, 50, 51, 57, 58, 59, 61, 64, 65, 69, 79, 86, 87, 88, 93, 95, 97, 98, 101], "g": [2, 3, 5, 10, 12, 22, 32, 36, 37, 41, 43, 49, 52, 59, 60, 62, 65, 66, 67, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101], "manual": [2, 69, 82, 83, 84, 93, 99, 100, 101, 103], "pytorch": [2, 37, 38, 41, 69, 79, 84, 93, 95, 97, 102], "call": [2, 3, 5, 6, 10, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 48, 52, 56, 66, 69, 83, 84, 85, 86, 87, 91, 93, 94, 97, 99, 100, 101, 102, 103], "__init__": [2, 38, 69, 88], "independ": [2, 3, 10, 58, 69, 91, 100, 101, 103], "compat": [2, 37, 40, 41, 56, 69, 70, 73, 77, 79, 82, 83, 93, 100, 101], "neural": [2, 38, 56, 66, 69, 84, 88, 93, 97, 99, 101], "network": [2, 37, 38, 41, 56, 66, 69, 83, 84, 88, 91, 93, 97, 99, 101], "typic": [2, 37, 41, 66, 69, 82, 83, 84, 87, 88, 90, 91, 99, 100], "initi": [2, 3, 13, 18, 37, 41, 57, 69, 82, 91, 93], "insid": [2, 41, 69, 93, 94], "There": [2, 3, 7, 79, 94, 96], "two": [2, 3, 10, 18, 26, 36, 37, 40, 41, 49, 52, 62, 64, 65, 80, 83, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101, 102, 103], "new": [2, 7, 9, 10, 14, 22, 37, 40, 41, 47, 51, 52, 57, 69, 83, 84, 86, 91, 92, 93, 99, 100, 103], "notion": 2, "confid": [2, 3, 10, 22, 36, 40, 43, 46, 48, 52, 57, 58, 59, 62, 64, 65, 66, 67, 69, 73, 77, 79, 82, 88, 90, 91, 94, 96, 97, 98, 100, 102, 103], "packag": [2, 5, 7, 9, 10, 11, 15, 35, 39, 43, 44, 52, 55, 56, 62, 65, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "prune": [2, 3, 43, 59, 69, 80, 98], "everyth": [2, 65, 94], "els": [2, 65, 85, 86, 88, 92, 93, 96, 97, 98], "mathemat": [2, 3, 10, 46, 97], "keep": [2, 13, 14, 52, 79, 85, 86, 92, 93, 102], "belong": [2, 3, 10, 36, 43, 45, 46, 58, 59, 60, 61, 66, 67, 71, 75, 77, 78, 87, 88, 94, 97, 99, 102, 103], "2": [2, 3, 4, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 40, 41, 43, 45, 46, 47, 48, 49, 50, 51, 52, 56, 58, 59, 61, 62, 65, 66, 67, 69, 70, 74, 75, 77, 78, 92, 93, 100], "error": [2, 3, 5, 10, 37, 41, 42, 43, 45, 46, 52, 58, 59, 61, 62, 64, 65, 67, 69, 71, 73, 74, 77, 80, 82, 84, 85, 86, 87, 90, 91, 92, 95], "erron": [2, 3, 36, 43, 46, 52, 58, 59, 67, 69, 70, 71, 99, 101], "import": [2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 40, 42, 48, 50, 51, 57, 61, 64, 69, 70, 75, 77, 78, 79, 82, 83, 90, 91, 93, 97, 98, 99, 101, 102, 103], "linear_model": [2, 5, 36, 52, 69, 79, 83, 84, 85, 86, 87, 91, 93, 94, 96, 99], "logisticregress": [2, 3, 5, 36, 52, 79, 83, 84, 85, 86, 87, 91, 93, 94, 96, 99], "logreg": 2, "cl": [2, 14, 30, 69, 79, 82, 83, 93, 94, 101], "pass": [2, 3, 5, 8, 10, 12, 13, 14, 16, 23, 30, 33, 37, 40, 41, 43, 47, 48, 52, 56, 57, 59, 65, 66, 67, 69, 74, 75, 79, 83, 84, 85, 86, 87, 91, 92, 93, 94, 96, 98, 99, 101], "x_train": [2, 82, 85, 86, 87, 94, 96, 97, 101], "labels_maybe_with_error": 2, "had": [2, 3, 69, 98], "issu": [2, 3, 4, 5, 6, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 36, 37, 39, 40, 41, 42, 43, 55, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 83, 89, 95, 96, 100, 101], "pred": [2, 43, 52, 82, 83, 100, 101], "x_test": [2, 82, 85, 86, 87, 94, 97, 101], "might": [2, 5, 57, 69, 74, 82, 83, 86, 87, 88, 93, 98], "case": [2, 3, 10, 13, 36, 48, 57, 69, 82, 83, 84, 86, 87, 88, 90, 92, 93, 94, 99, 101, 103], "standard": [2, 3, 5, 30, 36, 43, 56, 58, 59, 61, 67, 69, 79, 82, 86, 87, 90, 92, 94, 98], "adapt": [2, 37, 39, 52, 55, 69, 99], "skorch": [2, 69, 79, 93], "kera": [2, 55, 62, 65, 69, 79, 93, 98], "scikera": [2, 56, 69, 93], "open": [2, 40, 92, 98, 103], "doesn": [2, 69, 79], "t": [2, 3, 4, 7, 10, 17, 27, 37, 38, 40, 41, 42, 43, 48, 50, 51, 61, 66, 67, 69, 75, 77, 78, 79, 86, 87, 88, 90, 91, 92, 94, 97, 98, 101, 103], "alreadi": [2, 5, 10, 16, 37, 40, 41, 46, 56, 57, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101], "exist": [2, 5, 10, 12, 18, 37, 40, 41, 51, 56, 62, 64, 66, 69, 79, 80, 82, 83, 86, 87, 91, 96, 103], "made": [2, 5, 16, 37, 41, 69, 82, 83, 88, 91, 93, 96, 98, 100, 101], "easi": [2, 11, 46, 69, 86, 87, 92, 93, 94, 97], "inherit": [2, 7, 38, 69], "baseestim": [2, 41, 69], "yourmodel": [2, 69], "def": [2, 7, 14, 37, 41, 56, 69, 83, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "self": [2, 3, 5, 7, 10, 12, 13, 14, 16, 31, 37, 38, 40, 41, 43, 48, 66, 67, 69, 82, 83, 86, 88, 91, 92, 97, 102, 103], "refer": [2, 10, 16, 37, 41, 42, 58, 59, 61, 62, 64, 65, 69, 73, 74, 86, 87, 88, 90, 91, 93, 94, 97, 100, 101], "origin": [2, 5, 10, 41, 42, 43, 51, 52, 56, 58, 59, 62, 65, 66, 69, 70, 73, 75, 77, 82, 83, 86, 88, 90, 91, 93, 94, 98, 99, 101, 103], "total": [2, 3, 4, 36, 40, 52, 58, 78, 85, 88, 93, 102], "state": [2, 3, 5, 37, 38, 41, 47, 69, 94, 97, 98, 103], "art": [2, 38, 94, 97], "northcutt": [2, 3, 36, 66, 67], "et": [2, 3, 36, 38, 66, 67], "al": [2, 3, 36, 38, 66, 67], "2021": [2, 3, 36, 66, 67], "weak": [2, 65], "supervis": [2, 10, 86, 87, 93, 96], "find": [2, 5, 9, 10, 13, 14, 16, 19, 20, 22, 23, 25, 26, 27, 28, 31, 32, 36, 37, 39, 40, 41, 42, 43, 47, 51, 52, 55, 62, 65, 66, 67, 69, 71, 75, 77, 80, 86, 95, 100], "uncertainti": [2, 10, 45, 66, 69, 93, 99, 101], "It": [2, 3, 5, 7, 10, 12, 13, 16, 22, 27, 30, 32, 33, 34, 37, 41, 43, 46, 48, 50, 57, 64, 65, 69, 79, 83, 86, 87, 88, 91, 93, 94, 97, 100], "work": [2, 3, 7, 10, 12, 30, 36, 37, 40, 41, 43, 46, 51, 52, 53, 56, 57, 67, 69, 79, 80, 83, 85, 86, 87, 92, 99, 101], "includ": [2, 3, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 37, 39, 40, 41, 51, 52, 55, 57, 58, 61, 62, 66, 67, 69, 73, 74, 75, 77, 79, 80, 86, 87, 88, 90, 91, 93, 94, 97, 98, 99, 103], "deep": [2, 39, 41, 55, 56, 69, 91], "see": [2, 3, 5, 7, 10, 13, 14, 33, 36, 37, 40, 41, 42, 43, 48, 52, 56, 58, 59, 61, 62, 65, 66, 67, 69, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "subfield": 2, "theori": [2, 94], "machin": [2, 5, 9, 10, 14, 16, 33, 39, 50, 55, 69, 82, 83, 86, 87, 92, 96], "across": [2, 3, 5, 7, 10, 13, 22, 36, 40, 48, 58, 65, 66, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 100, 101], "varieti": [2, 82, 83, 93], "like": [2, 3, 5, 6, 7, 10, 14, 32, 36, 37, 40, 41, 43, 46, 52, 56, 57, 58, 61, 62, 64, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "pu": [2, 52], "input": [2, 3, 5, 10, 16, 26, 36, 37, 40, 41, 46, 48, 51, 52, 53, 56, 65, 69, 79, 80, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 101, 102, 103], "discret": [2, 34, 43, 46, 52, 66, 67, 71, 73, 74], "vector": [2, 3, 4, 5, 10, 16, 43, 46, 48, 49, 52, 66, 67, 79, 83, 84, 86, 87, 88, 90, 91, 94, 97, 98, 99, 102, 103], "would": [2, 3, 5, 37, 40, 41, 43, 52, 59, 69, 79, 83, 85, 86, 88, 93, 94, 99, 101, 103], "obtain": [2, 5, 8, 10, 16, 43, 57, 59, 62, 65, 67, 70, 84, 87, 91, 93, 96, 98, 100, 102, 103], "been": [2, 4, 36, 43, 46, 51, 52, 57, 58, 62, 64, 66, 67, 69, 84, 86, 90, 93, 94, 96, 97, 98, 99, 102, 103], "dure": [2, 10, 16, 66, 69, 82, 83, 84, 85, 90, 91, 93, 94, 97, 100, 101, 103], "denot": [2, 3, 46, 48, 52, 59, 66, 67, 77], "tild": 2, "paper": [2, 4, 10, 57, 66, 75, 77, 92, 94, 96, 99, 101, 103], "cv_n_fold": [2, 3, 69, 83], "5": [2, 3, 4, 5, 8, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 41, 43, 45, 47, 48, 52, 57, 58, 61, 62, 65, 69, 70, 77, 83, 86, 91, 92, 93, 97, 98, 99, 100, 102, 103], "converge_latent_estim": [2, 3], "pulearn": [2, 52], "find_label_issues_kwarg": [2, 10, 69, 80, 93, 94], "label_quality_scores_kwarg": [2, 10], "low_memori": [2, 59, 75, 93], "clean": [2, 64, 67, 69, 70, 79, 82, 83, 85, 86, 87, 92, 101], "even": [2, 3, 7, 9, 10, 36, 40, 45, 46, 52, 69, 84, 93, 94, 96, 97, 98], "messi": [2, 69, 94], "ridden": [2, 69], "autom": [2, 9, 10, 69, 79, 87, 92, 93], "robust": [2, 46, 69, 87, 93], "prone": [2, 69], "out": [2, 3, 5, 10, 16, 28, 37, 41, 43, 48, 56, 59, 60, 62, 65, 66, 67, 69, 70, 78, 79, 80, 83, 92, 93, 94, 95, 97, 98, 99, 101, 102, 103], "current": [2, 3, 5, 7, 10, 13, 14, 22, 37, 41, 42, 43, 48, 57, 64, 69, 85, 86, 87, 93, 96, 98], "intend": [2, 13, 14, 15, 16, 32, 33, 34, 44, 57, 73, 77, 84, 86, 87, 91, 94], "A": [2, 3, 4, 5, 7, 10, 12, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 36, 37, 38, 41, 43, 46, 47, 48, 49, 50, 51, 52, 56, 57, 58, 61, 64, 65, 66, 67, 69, 71, 73, 74, 78, 80, 82, 83, 84, 86, 88, 90, 91, 92, 93, 94, 96, 98, 100, 103], "follow": [2, 3, 10, 14, 30, 34, 36, 37, 40, 41, 48, 50, 57, 58, 62, 64, 65, 66, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "experiment": [2, 37, 38, 40, 41, 42, 59, 80, 85, 93], "wrapper": [2, 56, 82, 83, 84, 101], "around": [2, 64, 85, 86, 87, 98, 99, 103], "fasttext": [2, 55], "store": [2, 4, 5, 10, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 66, 69, 82, 83, 85, 90, 91, 92, 93, 102, 103], "along": [2, 48, 59, 77, 85, 86, 87, 88, 93, 99], "dimens": [2, 52, 71, 74, 88, 93, 99, 102], "select": [2, 9, 10, 26, 57, 67, 88, 93, 96, 99], "split": [2, 3, 5, 10, 12, 40, 48, 51, 52, 69, 82, 84, 85, 86, 87, 88, 90, 91, 92, 94, 97, 100, 103], "cross": [2, 3, 10, 36, 43, 46, 47, 48, 59, 62, 65, 67, 69, 70, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 95, 97, 98, 101, 102, 103], "fold": [2, 3, 36, 43, 46, 69, 82, 84, 90, 92, 93, 98, 102], "By": [2, 36, 58, 59, 69, 86, 93, 102], "need": [2, 3, 10, 36, 37, 40, 41, 43, 58, 59, 61, 66, 69, 79, 83, 84, 86, 87, 91, 93, 94, 96, 97, 98, 102], "holdout": [2, 3, 69], "comput": [2, 3, 4, 5, 7, 8, 10, 19, 20, 22, 23, 26, 27, 28, 31, 36, 37, 38, 40, 41, 43, 45, 46, 47, 48, 52, 57, 58, 59, 61, 64, 65, 66, 67, 69, 70, 71, 73, 79, 80, 83, 86, 87, 92, 94, 95, 98, 99, 101, 102], "them": [2, 3, 5, 7, 9, 10, 11, 12, 27, 32, 35, 37, 39, 40, 41, 43, 55, 57, 66, 69, 80, 82, 83, 85, 86, 87, 88, 90, 91, 93, 96, 97, 99, 101, 102, 103], "numer": [2, 3, 5, 10, 13, 22, 30, 34, 48, 64, 66, 69, 74, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 94, 96, 97, 99, 101], "consist": [2, 3, 37, 41, 52, 57, 102, 103], "latent": [2, 3, 46], "thei": [2, 3, 5, 15, 21, 24, 26, 29, 37, 38, 39, 41, 43, 44, 50, 52, 56, 59, 64, 67, 69, 70, 73, 77, 79, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 99, 101, 103], "relat": [2, 3, 13, 19, 20, 26, 27, 28, 31, 46, 52, 58, 69, 87, 91], "close": [2, 3, 10, 40, 46, 66, 84, 86, 87, 88, 90, 91, 93, 94, 98], "form": [2, 3, 10, 37, 38, 41, 46, 51, 52, 67, 69, 93], "equival": [2, 3, 37, 41, 46, 66, 99, 101], "iter": [2, 3, 36, 37, 41, 43, 52, 58, 59, 69, 85, 93, 96, 102], "enforc": [2, 37, 41, 52], "perfectli": [2, 36, 58, 94], "certain": [2, 3, 5, 37, 41, 56, 65, 69, 85, 86, 87, 92, 98, 99], "dict": [2, 3, 5, 10, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 40, 41, 43, 47, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 77, 85, 86, 87, 88, 93, 103], "keyword": [2, 3, 5, 10, 16, 23, 27, 30, 37, 40, 41, 43, 45, 48, 51, 56, 57, 59, 65, 66, 67, 69, 74, 75, 77, 86], "filter": [2, 3, 10, 40, 42, 51, 58, 60, 61, 63, 65, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 87, 88, 91, 92, 93, 97, 98, 101, 102, 103], "find_label_issu": [2, 3, 10, 30, 39, 40, 42, 43, 58, 59, 60, 61, 62, 63, 64, 65, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 93, 97, 98, 101, 102, 103], "particularli": [2, 79, 96, 99], "filter_bi": [2, 3, 40, 43, 59, 80, 93], "frac_nois": [2, 43, 59, 75, 93], "min_examples_per_class": [2, 43, 59, 87, 93, 94], "impact": [2, 4, 10, 86, 87, 88], "ml": [2, 4, 5, 9, 10, 15, 69, 79, 82, 83, 86, 87, 88, 90, 91, 96, 97, 101], "accuraci": [2, 38, 67, 82, 83, 84, 88, 93, 94, 96, 99, 101, 102], "n_job": [2, 40, 43, 59, 71, 73, 75, 93, 99, 102], "disabl": [2, 37, 41, 43, 99], "process": [2, 3, 7, 13, 16, 32, 37, 40, 41, 43, 51, 57, 59, 65, 71, 73, 75, 83, 84, 85, 86, 93, 96, 100], "caus": [2, 43, 48, 86, 87, 93], "rank": [2, 3, 10, 36, 40, 42, 43, 48, 58, 59, 60, 62, 63, 65, 66, 68, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 87, 92, 93, 97, 98, 99, 102, 103], "get_label_quality_scor": [2, 39, 40, 42, 43, 44, 48, 57, 59, 60, 61, 62, 63, 64, 67, 68, 70, 72, 73, 75, 76, 77, 80, 93, 94, 97, 98, 102, 103], "adjust_pred_prob": [2, 10, 61, 66, 67, 94], "control": [2, 5, 9, 10, 16, 40, 43, 57, 65, 66, 69, 75, 77, 86, 87, 92, 93], "how": [2, 3, 5, 10, 12, 13, 14, 16, 22, 36, 37, 38, 40, 41, 46, 52, 57, 58, 61, 62, 64, 66, 67, 69, 73, 77, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 98, 99, 100, 101, 102], "much": [2, 10, 36, 40, 43, 69, 85, 93, 94, 96, 99], "output": [2, 3, 5, 10, 16, 32, 37, 38, 41, 46, 52, 56, 57, 58, 62, 64, 65, 66, 69, 73, 74, 77, 78, 79, 80, 83, 84, 86, 88, 91, 92, 93, 98, 99, 100, 101], "print": [2, 5, 7, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 52, 57, 58, 59, 64, 66, 67, 69, 71, 73, 74, 78, 80, 82, 83, 84, 85, 87, 88, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "suppress": [2, 40, 57, 64, 66, 67, 69, 71, 73, 74, 102, 103], "statement": [2, 40, 57, 64, 66, 67, 69, 71, 73, 74], "big": [2, 40, 59, 65, 69, 94], "limit": [2, 5, 16, 40, 59, 85, 98, 102, 103], "memori": [2, 37, 40, 41, 59, 65, 71, 73, 85, 86, 102], "label_issues_batch": [2, 39, 59, 93], "find_label_issues_batch": [2, 39, 40, 59, 93], "pred_prob": [2, 3, 5, 8, 10, 16, 23, 25, 26, 28, 31, 32, 36, 40, 42, 43, 45, 46, 47, 48, 49, 52, 53, 57, 58, 59, 61, 62, 65, 66, 67, 71, 73, 74, 75, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 101], "threshold": [2, 3, 4, 7, 10, 18, 19, 20, 22, 28, 30, 31, 40, 50, 64, 65, 66, 67, 73, 77, 86, 98, 99, 102, 103], "inverse_noise_matrix": [2, 3, 10, 46, 52, 80, 94], "label_issu": [2, 40, 43, 59, 62, 69, 71, 80, 82, 83, 84, 88, 91, 93, 94, 97, 101], "clf_kwarg": [2, 3, 10, 69], "clf_final_kwarg": [2, 69], "validation_func": [2, 3, 10], "correct": [2, 5, 9, 10, 36, 40, 43, 45, 57, 58, 59, 61, 62, 64, 65, 67, 69, 70, 73, 77, 79, 84, 88, 90, 91, 94, 96, 98, 100, 101], "result": [2, 3, 9, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 40, 41, 43, 45, 50, 52, 59, 61, 62, 65, 67, 69, 70, 71, 73, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 101, 102, 103], "identifi": [2, 3, 5, 7, 9, 10, 12, 16, 27, 33, 36, 40, 42, 43, 59, 62, 65, 67, 69, 70, 71, 74, 75, 77, 78, 79, 82, 83, 84, 86, 87, 88, 90, 91, 92, 94, 97, 99, 101, 102, 103], "final": [2, 10, 69, 82, 90, 98, 100, 101], "remain": [2, 69, 80, 82, 83, 88, 97, 101, 103], "datasetlik": [2, 52, 69], "beyond": [2, 5, 7, 9, 10, 11, 35, 79, 82, 83, 101, 102], "pd": [2, 3, 5, 7, 13, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 47, 56, 57, 58, 69, 77, 82, 83, 84, 86, 87, 90, 91, 93, 94, 96, 101, 103], "datafram": [2, 3, 5, 7, 12, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 40, 47, 52, 53, 56, 57, 58, 69, 74, 78, 80, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 101, 102, 103], "scipi": [2, 5, 13, 52], "spars": [2, 4, 5, 10, 13, 16, 18, 31, 52, 53, 87, 88, 90, 91, 94], "csr_matrix": [2, 4, 5, 13, 16, 18, 31], "torch": [2, 37, 38, 41, 83, 84, 88, 91, 92, 99], "util": [2, 5, 10, 16, 33, 37, 38, 41, 44, 56, 57, 62, 65, 69, 79, 80, 84, 86, 87, 88, 93, 94, 99], "tensorflow": [2, 52, 56, 79, 84, 93], "object": [2, 5, 10, 12, 13, 16, 32, 33, 37, 38, 40, 41, 48, 52, 53, 56, 59, 62, 63, 64, 65, 66, 69, 77, 79, 83, 84, 87, 88, 90, 93, 94, 95, 97, 101], "list": [2, 3, 5, 12, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 38, 40, 41, 42, 43, 49, 51, 52, 53, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 73, 74, 75, 77, 78, 80, 83, 84, 85, 86, 87, 88, 92, 93, 94, 97, 98, 101, 103], "index_list": 2, "subset": [2, 3, 5, 16, 36, 40, 43, 52, 67, 74, 78, 82, 83, 84, 88, 90, 91, 93, 97, 98, 99, 100, 101, 103], "wa": [2, 3, 12, 14, 40, 50, 52, 57, 58, 64, 66, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 100, 102, 103], "abl": [2, 3, 10, 69, 84, 93, 94, 96, 97], "format": [2, 3, 5, 10, 12, 32, 37, 40, 41, 43, 46, 47, 48, 49, 52, 53, 56, 57, 58, 59, 62, 65, 66, 67, 69, 71, 73, 74, 77, 78, 82, 84, 86, 87, 88, 90, 92, 96, 101, 102, 103], "make": [2, 3, 5, 18, 37, 40, 41, 48, 56, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "sure": [2, 5, 40, 43, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101], "shuffl": [2, 10, 52, 84, 88, 91, 97, 99], "ha": [2, 3, 5, 6, 10, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 42, 46, 48, 51, 52, 57, 62, 64, 69, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 90, 91, 94, 96, 97, 98, 99, 100, 101, 103], "batch": [2, 40, 52, 56, 57, 71, 73, 85, 88, 93, 99], "order": [2, 5, 10, 34, 36, 37, 41, 42, 43, 46, 47, 48, 50, 52, 57, 58, 59, 62, 65, 66, 67, 71, 74, 75, 77, 78, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 101, 102, 103], "destroi": [2, 52], "oper": [2, 37, 40, 41, 52, 56, 67, 79, 82, 83, 91, 93, 99], "eg": [2, 5, 10, 52, 62, 65, 86, 87, 93], "repeat": [2, 52, 57, 96, 99], "appli": [2, 34, 37, 39, 41, 43, 48, 49, 51, 52, 61, 66, 75, 82, 83, 84, 85, 86, 87, 88, 90, 93, 96, 97, 99, 100, 101, 102], "array_lik": [2, 3, 36, 43, 52, 59, 66, 70], "some": [2, 3, 5, 10, 14, 22, 36, 37, 39, 41, 43, 46, 51, 52, 55, 57, 58, 59, 61, 62, 65, 66, 67, 69, 71, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 100, 101, 102, 103], "seri": [2, 3, 40, 52, 53, 69, 77, 93], "row": [2, 3, 5, 10, 13, 27, 32, 36, 40, 43, 45, 46, 52, 57, 58, 59, 61, 66, 67, 69, 74, 75, 77, 78, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 99, 103], "rather": [2, 3, 5, 26, 36, 52, 56, 57, 64, 73, 77, 83, 96, 100, 101, 102, 103], "leav": [2, 43], "per": [2, 3, 5, 7, 13, 36, 40, 43, 48, 51, 57, 58, 59, 61, 64, 65, 67, 70, 71, 73, 77, 87, 93, 98, 103], "determin": [2, 3, 10, 12, 16, 22, 26, 30, 36, 40, 43, 48, 52, 57, 59, 62, 64, 67, 73, 77, 86, 93, 96, 98, 99, 101], "cutoff": [2, 3, 99], "consid": [2, 3, 4, 5, 10, 13, 16, 23, 26, 28, 31, 36, 37, 41, 43, 52, 57, 64, 66, 67, 70, 73, 77, 82, 83, 84, 88, 90, 91, 93, 94, 98, 99, 100, 101, 102], "section": [2, 3, 7, 10, 80, 88, 90, 93, 98], "3": [2, 3, 4, 5, 7, 10, 34, 36, 37, 41, 43, 46, 47, 48, 49, 50, 51, 52, 56, 59, 66, 67, 69, 70, 75, 77, 92, 93, 100], "equat": [2, 3, 46], "advanc": [2, 3, 5, 9, 10, 16, 64, 66, 77, 80, 87, 89, 93, 94], "user": [2, 3, 5, 9, 10, 14, 16, 27, 32, 33, 34, 37, 41, 43, 56, 64, 66, 67, 69, 73, 77, 85, 94], "specifi": [2, 3, 4, 5, 8, 10, 13, 14, 16, 18, 31, 33, 37, 40, 41, 43, 48, 51, 56, 57, 58, 59, 62, 64, 66, 67, 69, 70, 78, 80, 83, 84, 87, 88, 91, 96, 98, 101], "automat": [2, 3, 5, 26, 36, 79, 82, 83, 88, 90, 91, 92, 93, 96, 97, 98, 101, 102, 103], "greater": [2, 3, 5, 7, 9, 10, 28, 40, 52, 64, 87, 92, 93, 103], "count": [2, 22, 26, 36, 40, 43, 46, 52, 58, 59, 65, 80, 88, 93, 98], "observ": [2, 3, 46, 84, 85, 86, 87, 96, 99, 101], "mislabel": [2, 10, 36, 40, 42, 43, 46, 57, 58, 59, 62, 64, 67, 73, 75, 77, 79, 82, 83, 84, 88, 90, 91, 93, 94, 98, 101], "one": [2, 3, 5, 7, 10, 26, 36, 37, 40, 41, 42, 43, 48, 50, 52, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 96, 99, 100, 101, 103], "get_label_issu": [2, 39, 40, 68, 69, 82, 83, 94, 101], "either": [2, 3, 4, 7, 10, 37, 40, 41, 43, 57, 59, 64, 66, 67, 71, 73, 85, 87, 93, 97, 98], "boolean": [2, 7, 10, 22, 40, 43, 51, 57, 59, 62, 67, 69, 71, 73, 74, 79, 83, 84, 87, 88, 91, 93, 98, 101, 102], "label_issues_mask": [2, 43, 67, 69, 80], "indic": [2, 3, 4, 5, 7, 10, 13, 22, 36, 40, 41, 42, 43, 45, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 67, 69, 70, 73, 75, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "its": [2, 5, 7, 9, 10, 16, 37, 40, 41, 43, 50, 51, 59, 62, 65, 66, 67, 69, 71, 75, 77, 79, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 100, 101, 102, 103], "return_indices_ranked_bi": [2, 40, 43, 59, 75, 80, 82, 83, 93, 94], "significantli": [2, 88, 94, 96, 100], "reduc": [2, 40, 43, 52, 84, 93], "time": [2, 10, 37, 40, 41, 52, 57, 80, 82, 83, 85, 86, 88, 92, 93, 94, 98, 99, 101, 102, 103], "take": [2, 5, 10, 36, 37, 41, 47, 48, 52, 56, 67, 82, 85, 88, 90, 96, 97, 98, 103], "run": [2, 5, 6, 7, 9, 10, 11, 14, 16, 26, 27, 32, 35, 37, 40, 41, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 103], "skip": [2, 10, 37, 41, 69, 84, 93, 97, 103], "slow": [2, 3], "step": [2, 7, 26, 48, 65, 85, 88, 94, 96, 100], "caution": [2, 5, 93], "previous": [2, 5, 13, 52, 66, 69, 80, 82, 84, 86, 90, 91, 96, 100], "assign": [2, 7, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 37, 41, 47, 48, 52, 69, 82, 86, 88, 90, 93, 101, 102, 103], "individu": [2, 4, 7, 10, 13, 26, 37, 41, 42, 57, 61, 64, 67, 69, 75, 77, 80, 82, 87, 90, 93, 96, 97, 98, 103], "still": [2, 40, 41, 52, 66, 82, 84, 88, 93, 99], "extra": [2, 37, 41, 52, 56, 57, 58, 69, 88, 91, 93, 96, 99], "receiv": [2, 10, 37, 41, 42, 58, 61, 62, 69, 71, 75, 87, 98], "overwritten": [2, 69], "callabl": [2, 3, 37, 41, 48, 51, 56, 61, 93], "x_val": 2, "y_val": 2, "map": [2, 3, 12, 40, 41, 44, 47, 51, 52, 65, 67, 69, 74, 84, 85, 86, 87, 88, 93, 94, 97, 103], "appropri": [2, 10, 16, 34, 59, 67, 86, 90, 97, 98], "earli": [2, 88], "stop": [2, 88], "x_valid": 2, "y_valid": 2, "could": [2, 4, 7, 10, 22, 36, 52, 66, 82, 86, 88, 90, 97, 101, 103], "f": [2, 7, 82, 83, 84, 85, 86, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "ignor": [2, 37, 41, 51, 56, 69, 74, 78, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "allow": [2, 4, 36, 37, 40, 41, 45, 52, 57, 65, 66, 69, 71, 73, 83, 84, 85, 88, 93, 100, 102], "access": [2, 10, 13, 37, 41, 69, 83, 87, 88, 91, 97], "hyperparamet": [2, 61, 66, 88], "purpos": [2, 86, 87, 93, 97, 101], "want": [2, 5, 10, 36, 40, 53, 57, 59, 69, 83, 85, 86, 88, 91, 92, 96, 98, 99, 100, 102, 103], "explicitli": [2, 8, 10, 41, 69, 93], "yourself": [2, 5, 40, 87], "altern": [2, 7, 10, 48, 52, 56, 57, 67, 80, 83, 84, 88, 90, 91, 93, 94, 96, 97, 99, 101], "same": [2, 3, 5, 7, 9, 10, 12, 14, 16, 26, 30, 37, 40, 41, 43, 52, 56, 57, 59, 66, 67, 69, 73, 74, 77, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 97, 98, 99, 100, 101, 102], "effect": [2, 10, 27, 37, 41, 57, 66, 69, 88, 90, 91, 93, 99], "offer": [2, 5, 9, 10, 83, 84, 86, 87, 91, 93, 94, 97], "after": [2, 3, 5, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 57, 69, 83, 86, 88, 91, 93, 94, 96, 98, 99, 100, 101, 102], "attribut": [2, 5, 7, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 37, 40, 41, 48, 66, 69, 82, 86], "label_issues_df": [2, 69, 88], "similar": [2, 10, 36, 37, 41, 52, 57, 61, 62, 64, 66, 69, 73, 77, 85, 86, 87, 88, 90, 91, 93, 94, 98, 99, 102], "document": [2, 3, 5, 14, 16, 36, 37, 40, 41, 42, 43, 48, 51, 56, 58, 59, 61, 64, 65, 66, 69, 73, 74, 75, 77, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "descript": [2, 5, 7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 36, 42, 52, 62, 69, 86, 87], "were": [2, 3, 5, 36, 41, 58, 64, 77, 82, 84, 90, 93, 94, 96, 98, 100, 102], "present": [2, 3, 5, 10, 12, 13, 20, 36, 52, 66, 74, 79, 88, 90, 93, 99], "actual": [2, 3, 5, 36, 57, 58, 67, 87, 93, 94, 103], "num_class": [2, 36, 40, 52, 56, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99], "uniqu": [2, 31, 52, 74, 86, 93, 97, 99], "given_label": [2, 5, 25, 30, 36, 46, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 94, 101, 102, 103], "normal": [2, 3, 18, 26, 31, 43, 45, 48, 50, 51, 52, 67, 93, 94, 99], "trick": [2, 93], "distribut": [2, 3, 5, 10, 26, 28, 36, 41, 43, 47, 50, 57, 65, 66, 67, 79, 85, 86, 87, 88, 90, 91, 98, 99], "account": [2, 36, 57, 61, 66, 67, 83, 91, 93, 94, 96, 97, 99, 101], "word": [2, 3, 51, 77, 78, 93], "remov": [2, 10, 31, 36, 37, 41, 43, 69, 79, 82, 83, 87, 88, 90, 91, 92, 93, 94, 97, 99, 101], "so": [2, 3, 5, 6, 7, 10, 14, 26, 34, 36, 37, 40, 41, 43, 52, 57, 58, 64, 67, 69, 73, 77, 84, 86, 87, 88, 91, 94, 97, 99, 102], "proportion": [2, 10, 43], "just": [2, 3, 5, 10, 13, 32, 36, 38, 40, 52, 56, 67, 69, 71, 79, 80, 82, 83, 84, 87, 88, 90, 91, 93, 94, 97, 98, 99, 100, 101, 102], "procedur": 2, "get": [2, 3, 5, 8, 13, 31, 37, 38, 41, 43, 48, 50, 51, 52, 57, 59, 61, 66, 67, 69, 70, 71, 79, 82, 83, 84, 88, 91, 92, 93, 94, 99, 100, 101], "detect": [2, 5, 7, 9, 13, 14, 16, 18, 22, 28, 42, 50, 60, 62, 63, 64, 65, 66, 67, 68, 69, 72, 76, 79, 82, 83, 85, 86, 88, 89, 95, 97, 101, 102, 103], "arg": [2, 12, 22, 27, 31, 37, 38, 41, 48, 52, 67, 69], "kwarg": [2, 7, 10, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 42, 48, 56, 65, 69, 71, 73, 74, 75, 93], "test": [2, 5, 10, 26, 41, 48, 56, 69, 79, 82, 83, 85, 86, 87, 88, 90, 91, 100, 101, 103], "expect": [2, 3, 37, 41, 43, 48, 57, 66, 67, 69, 82, 83, 93, 94, 96, 97, 98, 101, 103], "class_predict": 2, "evalu": [2, 10, 37, 38, 39, 40, 41, 65, 69, 82, 83, 84, 86, 87, 88, 93, 94, 96, 100, 101, 102], "simpli": [2, 10, 36, 67, 83, 86, 87, 90, 91, 93, 94, 97, 101, 102, 103], "quantifi": [2, 5, 7, 10, 13, 43, 61, 66, 69, 79, 87, 88, 90, 91, 94, 98], "save_spac": [2, 10, 68, 69], "potenti": [2, 10, 36, 43, 51, 59, 62, 65, 67, 69, 71, 73, 80, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 102, 103], "cach": [2, 83, 91], "panda": [2, 5, 7, 12, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 52, 53, 56, 57, 58, 80, 82, 83, 84, 86, 87, 90, 91, 92, 93, 94, 96, 101, 102], "unlik": [2, 10, 43, 45, 48, 56, 58, 59, 61, 77, 86, 96, 97, 99, 101], "both": [2, 5, 10, 16, 26, 36, 37, 41, 43, 52, 57, 59, 67, 71, 73, 78, 79, 86, 88, 93, 94, 96, 103], "mask": [2, 40, 43, 51, 52, 59, 62, 67, 69, 71, 73, 74, 79, 85, 92, 93, 96, 98, 102, 103], "prefer": [2, 67, 75, 97], "plan": 2, "subsequ": [2, 3, 37, 41, 83, 91, 93, 94, 98], "invok": [2, 37, 41, 94, 100], "scratch": [2, 69], "To": [2, 5, 7, 9, 10, 11, 13, 16, 26, 35, 37, 40, 41, 42, 43, 56, 57, 59, 61, 65, 66, 67, 69, 70, 71, 73, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102, 103], "share": [2, 67, 69], "mostli": [2, 52, 64, 69, 97, 101], "longer": [2, 34, 47, 48, 51, 69, 80, 83, 91, 93, 98], "info": [2, 5, 7, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 58, 69, 77, 86, 87, 92, 103], "about": [2, 3, 5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 38, 40, 45, 57, 58, 61, 65, 69, 74, 77, 84, 86, 88, 90, 91, 92, 93, 94, 96, 99], "docstr": [2, 36, 37, 41, 52, 69, 92, 94], "unless": [2, 37, 41, 69, 93], "our": [2, 3, 10, 56, 57, 67, 69, 79, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "is_label_issu": [2, 30, 69, 83, 84, 85, 86, 87, 88, 90, 91, 94, 97, 101], "entir": [2, 10, 26, 40, 43, 46, 58, 59, 64, 67, 69, 71, 73, 74, 79, 85, 86, 87, 93, 98, 99, 100, 102, 103], "accur": [2, 3, 5, 9, 10, 16, 36, 40, 43, 57, 58, 59, 62, 65, 67, 69, 70, 71, 73, 74, 80, 87, 88, 90, 91, 93, 96, 101], "label_qu": [2, 57, 69, 83, 94, 96, 101], "measur": [2, 5, 36, 57, 58, 69, 79, 82, 92, 93, 94, 96, 97, 101, 102, 103], "qualiti": [2, 3, 5, 7, 9, 10, 13, 30, 31, 36, 40, 42, 43, 45, 48, 57, 58, 59, 61, 62, 64, 67, 69, 70, 73, 75, 77, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 101], "lower": [2, 4, 5, 7, 10, 13, 28, 40, 48, 50, 57, 58, 61, 64, 65, 67, 69, 70, 73, 77, 83, 84, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "eas": 2, "comparison": [2, 37, 41, 65, 94, 96], "against": [2, 37, 41, 86, 90, 93, 96, 97], "predicted_label": [2, 5, 25, 30, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 94, 101, 102], "ad": [2, 37, 41, 85, 87, 96, 101], "precis": [2, 50, 59, 62, 65, 93, 94, 102, 103], "definit": [2, 7, 34, 48, 69, 82, 90], "accessor": [2, 69], "describ": [2, 10, 18, 57, 66, 67, 69, 75, 77, 94, 96, 97, 98, 100, 103], "precomput": [2, 4, 5, 46, 69, 87, 88, 90, 91, 92, 94], "clear": [2, 37, 41, 69, 83, 91, 101], "save": [2, 5, 16, 37, 40, 41, 65, 69, 93, 98, 102, 103], "space": [2, 5, 10, 66, 69, 88, 90, 92], "place": [2, 37, 41, 52, 69, 82, 96], "larg": [2, 9, 10, 40, 69, 88, 90, 91, 93, 98, 99, 102, 103], "deploi": [2, 9, 10, 69, 88, 90, 91, 93], "care": [2, 10, 37, 41, 69, 91, 93, 94], "avail": [2, 4, 5, 7, 10, 12, 14, 33, 41, 69, 93, 94, 96, 98, 101], "cannot": [2, 5, 12, 14, 52, 100, 103], "anymor": 2, "classmethod": [2, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 34, 41, 48, 69], "__init_subclass__": [2, 39, 41, 68, 69], "set_": [2, 41, 69], "_request": [2, 41, 69], "pep": [2, 41, 69], "487": [2, 41, 69], "look": [2, 5, 7, 16, 37, 41, 52, 69, 74, 82, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 99, 102, 103], "inform": [2, 5, 7, 10, 13, 16, 33, 37, 41, 52, 57, 58, 62, 65, 69, 74, 77, 78, 79, 84, 85, 86, 90, 91, 94, 96, 99, 102, 103], "__metadata_request__": [2, 41, 69], "infer": [2, 41, 52, 69, 74, 78, 82, 83, 88, 96, 97], "signatur": [2, 37, 41, 69], "accept": [2, 37, 41, 50, 67, 69, 86, 87, 93], "metadata": [2, 41, 69, 88, 90, 91, 103], "through": [2, 5, 7, 41, 69, 83, 84, 85, 87, 91, 92, 93, 96, 98, 99], "develop": [2, 9, 41, 69, 93, 94, 103], "request": [2, 41, 69, 82, 83, 87, 91, 92, 97, 103], "those": [2, 3, 4, 10, 40, 41, 43, 56, 57, 59, 65, 69, 73, 77, 78, 79, 84, 88, 93, 98, 102], "http": [2, 4, 5, 7, 9, 10, 11, 18, 35, 37, 38, 40, 41, 45, 52, 62, 65, 66, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "www": [2, 41, 69, 99], "org": [2, 4, 18, 37, 38, 41, 52, 66, 69, 93, 94, 103], "dev": [2, 41, 69], "0487": [2, 41, 69], "get_metadata_rout": [2, 39, 41, 68, 69], "rout": [2, 41, 69], "pleas": [2, 37, 41, 56, 69, 79, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 96, 97, 99, 101, 103], "guid": [2, 7, 10, 41, 69, 80, 88, 89], "mechan": [2, 37, 41, 69], "metadatarequest": [2, 41, 69], "encapsul": [2, 16, 41, 64, 69], "get_param": [2, 39, 41, 55, 56, 68, 69], "subobject": [2, 41, 69], "param": [2, 10, 37, 41, 56, 66, 69, 93], "name": [2, 5, 6, 7, 10, 12, 13, 32, 34, 36, 37, 41, 47, 48, 52, 56, 57, 58, 65, 69, 74, 78, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 101, 102, 103], "set_fit_request": [2, 39, 41, 68, 69], "str": [2, 3, 4, 5, 12, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 40, 41, 43, 46, 48, 50, 51, 52, 56, 57, 58, 62, 64, 65, 67, 69, 74, 78, 84, 85, 86, 93, 96, 97, 98, 103], "unchang": [2, 37, 41, 69, 103], "relev": [2, 16, 26, 41, 69, 88], "enable_metadata_rout": [2, 41, 69], "set_config": [2, 41, 69], "meta": [2, 41, 69], "rais": [2, 4, 5, 12, 13, 34, 37, 41, 45, 48, 50, 69, 84, 93], "alia": [2, 37, 41, 69], "metadata_rout": [2, 41, 69], "retain": [2, 41, 52, 69], "chang": [2, 32, 34, 37, 40, 41, 45, 69, 77, 82, 83, 84, 86, 91, 93, 98, 99, 103], "version": [2, 5, 7, 9, 10, 11, 15, 21, 24, 29, 35, 37, 39, 41, 44, 45, 52, 55, 56, 67, 69, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103], "sub": [2, 41, 64, 69], "pipelin": [2, 41, 69, 101], "otherwis": [2, 4, 7, 10, 34, 36, 37, 40, 41, 43, 49, 50, 51, 52, 59, 66, 69, 71, 73, 74, 78, 83, 91, 93], "updat": [2, 13, 37, 40, 41, 56, 69, 80, 85, 86, 88], "set_param": [2, 39, 41, 55, 56, 68, 69], "simpl": [2, 37, 41, 43, 57, 67, 69, 82, 83, 85, 86, 87, 88, 90, 91, 96, 99, 101], "well": [2, 3, 9, 10, 37, 41, 45, 46, 57, 59, 65, 67, 69, 74, 77, 78, 80, 86, 87, 88, 90, 91, 93, 94, 96, 98, 99], "nest": [2, 37, 41, 42, 53, 69, 75, 77, 78, 103], "latter": [2, 37, 41, 69, 99], "compon": [2, 41, 69], "__": [2, 41, 69], "set_score_request": [2, 68, 69], "structur": [3, 66, 82, 85, 90, 93], "unobserv": 3, "less": [3, 4, 5, 10, 31, 40, 48, 57, 66, 67, 71, 73, 77, 87, 88, 90, 92, 93, 94, 98, 103], "channel": [3, 84, 94], "character": 3, "flip": 3, "nm": 3, "invers": [3, 10, 36, 46, 52, 58, 83, 87, 92], "inv": 3, "confident_joint": [3, 22, 36, 43, 52, 58, 59, 80, 93, 94], "un": 3, "under": [3, 10, 37, 41, 58, 65, 66, 87, 99], "joint": [3, 36, 43, 46, 52, 58, 59, 92], "num_label_issu": [3, 40, 43, 59, 74, 78, 80], "estimation_method": [3, 40], "off_diagon": 3, "multi_label": [3, 36, 43, 52, 53, 59, 97], "don": [3, 79, 87, 88, 90, 91, 94, 98, 101], "statis": 3, "compute_confident_joint": [3, 36, 43, 52, 59, 94], "off": [3, 43, 52, 64, 88, 94, 98, 99], "j": [3, 5, 36, 37, 41, 42, 43, 59, 62, 65, 66, 75, 77, 78, 85, 86, 87, 94, 102, 103], "confident_learn": [3, 43, 59, 94], "off_diagonal_calibr": 3, "calibr": [3, 4, 43, 52, 57, 96], "cj": [3, 46, 52], "axi": [3, 31, 46, 48, 50, 71, 74, 84, 85, 86, 87, 88, 93, 94, 96, 97, 99, 101, 102], "bincount": [3, 85, 86, 87, 94, 96, 97], "alwai": [3, 10, 37, 41, 52, 82, 83, 84, 94, 101], "estimate_issu": 3, "over": [3, 5, 10, 37, 40, 41, 64, 65, 71, 73, 82, 87, 88, 90, 92, 93, 94, 99, 101], "As": [3, 7, 79, 86, 87, 91, 94, 101, 103], "add": [3, 5, 7, 12, 13, 37, 41, 56, 65, 83, 84, 85, 86, 87, 88, 91, 93, 94, 97], "approach": [3, 4, 36, 40, 43, 56, 82, 85, 90, 94, 97, 99, 101], "custom": [3, 7, 10, 11, 30, 37, 40, 41, 48, 51, 67, 83, 87, 91, 94, 101], "know": [3, 10, 86, 87, 88, 90, 91, 93, 94, 96, 101], "cut": [3, 64, 79, 94], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 32, 98, 99, 103], "underestim": 3, "few": [3, 9, 10, 65, 79, 87, 93, 96, 97, 98, 99, 103], "4": [3, 4, 5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 47, 48, 51, 61, 62, 64, 65, 67, 70, 77, 92, 93, 97, 102, 103], "detail": [3, 4, 5, 10, 14, 16, 33, 36, 37, 41, 42, 48, 52, 56, 57, 58, 59, 61, 62, 64, 65, 66, 73, 74, 75, 79, 80, 84, 93, 97, 99, 103], "num_issu": [3, 7, 40, 84, 85, 86, 87, 88, 90, 91, 94], "calibrate_confident_joint": 3, "up": [3, 7, 10, 17, 26, 27, 30, 43, 48, 56, 57, 83, 85, 92, 93, 98, 101, 103], "p_": [3, 36, 43], "pair": [3, 5, 10, 36, 43, 94], "v": [3, 10, 40, 58, 59, 61, 67, 85, 86, 87, 97, 98, 99, 100], "rest": [3, 5, 7, 9, 10, 11, 35, 58, 59, 61, 69, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94, 96, 101], "fashion": [3, 5, 71, 82], "2x2": 3, "incorrectli": [3, 36, 58, 59, 62, 90, 103], "calibrated_cj": 3, "c": [3, 10, 50, 51, 59, 67, 79, 82, 84, 86, 87, 90, 91, 93, 94, 97, 98, 99, 100, 101], "whose": [3, 4, 5, 10, 28, 37, 41, 46, 51, 57, 61, 64, 70, 73, 77, 78, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 99, 102, 103], "truli": [3, 99, 102], "estimate_joint": [3, 36, 94], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 59, 65, 94, 98, 100, 102, 103], "return_indices_of_off_diagon": 3, "frequenc": [3, 26, 57, 58, 65, 74, 98, 99], "done": [3, 10, 56, 69, 86, 93, 94, 97, 99, 100], "overfit": [3, 10, 62, 65, 82, 84, 85, 86, 87, 88, 90, 91, 100], "classifict": 3, "singl": [3, 5, 9, 10, 12, 26, 36, 37, 41, 42, 48, 49, 52, 57, 58, 64, 65, 66, 67, 77, 82, 84, 85, 86, 93, 94, 97, 98], "baselin": [3, 37, 43, 83, 99, 101], "proxi": 3, "union": [3, 5, 12, 48, 52, 53, 59, 65, 69, 77, 93], "tupl": [3, 31, 37, 41, 42, 46, 47, 49, 51, 52, 57, 59, 65, 73, 75, 77, 78, 84, 103], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 5, 10, 40, 46, 57, 71, 73, 79, 83, 85, 88, 93, 102], "practic": [3, 82, 83, 87, 88, 94, 99, 101], "complet": [3, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 101], "gist": 3, "cj_ish": 3, "guess": [3, 46, 94, 96], "8": [3, 5, 7, 8, 47, 48, 49, 51, 61, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "parallel": [3, 43, 65, 75, 92], "again": [3, 56, 82, 93, 99], "simplifi": [3, 14, 93], "understand": [3, 9, 36, 58, 65, 87, 94, 101, 102, 103], "100": [3, 37, 41, 50, 67, 82, 83, 86, 87, 88, 90, 92, 93, 94, 97, 98, 99, 103], "optim": [3, 37, 38, 41, 56, 88, 96], "speed": [3, 43, 83, 92, 93, 101], "dtype": [3, 23, 25, 26, 31, 37, 41, 51, 52, 61, 77, 84, 98], "enumer": [3, 37, 41, 84, 85, 86, 87, 88, 103], "s_label": 3, "confident_bin": 3, "6": [3, 5, 41, 48, 52, 77, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "num_confident_bin": 3, "argmax": [3, 43, 67, 71, 74, 84, 93, 94, 98, 99, 102], "elif": 3, "estimate_lat": 3, "py_method": [3, 46], "cnt": [3, 46], "1d": [3, 5, 12, 16, 32, 40, 43, 48, 49, 52, 53, 61, 70, 82, 84], "eqn": [3, 46], "margin": [3, 43, 46, 48, 67], "marginal_p": [3, 46], "shorthand": [3, 13], "proport": [3, 10, 36, 58, 94, 100], "poorli": [3, 46, 82], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 94], "variabl": [3, 7, 14, 27, 52, 69, 70, 84, 86, 90, 94, 97, 101], "exact": [3, 46, 82, 85, 86, 87, 88, 90], "within": [3, 4, 5, 10, 15, 32, 37, 38, 41, 42, 44, 59, 64, 73, 75, 77, 86, 87, 88, 93, 98, 102], "percent": 3, "often": [3, 36, 46, 58, 93, 94, 100, 102], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 9, 10, 52, 53, 65, 82, 83, 84, 85, 86, 88, 90, 91, 93, 97, 98, 99, 101], "wai": [3, 5, 56, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 97, 98, 100], "pro": 3, "con": 3, "pred_proba": [3, 100], "combin": [3, 36, 86, 88, 92, 93, 94, 100, 101], "becaus": [3, 46, 52, 64, 91, 93, 94, 96, 98], "littl": [3, 40, 92, 98, 103], "uniform": [3, 67, 92, 93, 94], "20": [3, 7, 42, 78, 84, 88, 91, 92, 93, 94, 98, 101, 102, 103], "Such": [3, 88, 99], "bound": [3, 23, 25, 37, 41, 51, 61, 62, 64, 65, 98], "reason": [3, 22, 37, 41], "comment": [3, 51, 103], "end": [3, 5, 37, 41, 65], "file": [3, 5, 12, 39, 40, 55, 65, 82, 84, 86, 90, 91, 92, 93, 98, 99, 102, 103], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 94], "handl": [3, 5, 7, 10, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 40, 41, 80, 82, 85, 86, 87, 88, 90, 91, 94, 102, 103], "five": [3, 62, 65, 94, 98], "estimate_cv_predicted_prob": [3, 94], "estimate_noise_matric": 3, "get_confident_threshold": [3, 39, 40], "amongst": [3, 10, 98], "confident_threshold": [3, 10, 22, 23, 40, 66], "valuat": [4, 9, 18], "assess": [4, 98], "point": [4, 5, 7, 9, 10, 18, 26, 37, 41, 85, 86, 87, 88, 90, 91, 93, 94, 96], "contribut": [4, 10, 18, 98], "data_shapley_knn": 4, "knn_graph": [4, 5, 10, 16, 18, 19, 26, 28, 31, 90], "metric": [4, 5, 10, 18, 19, 26, 31, 50, 52, 56, 65, 66, 82, 83, 84, 88, 90, 91, 94, 101], "10": [4, 10, 18, 19, 23, 26, 31, 37, 38, 65, 66, 67, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103], "shaplei": [4, 10, 18], "nearest": [4, 5, 10, 16, 23, 26, 28, 50, 66, 87, 91, 99], "neighbor": [4, 5, 10, 16, 18, 23, 26, 28, 50, 66, 86, 87, 88, 90, 91, 93, 94, 99], "knn": [4, 10, 13, 18, 26, 31, 50, 66, 90, 99], "graph": [4, 5, 10, 13, 16, 18, 26, 31], "calcul": [4, 10, 18, 26, 40, 48, 50, 57, 61, 62, 64, 65, 66, 69, 73, 88, 92], "directli": [4, 5, 14, 16, 33, 34, 40, 56, 57, 83, 87, 91, 93, 97, 98, 101], "lowest": [4, 10, 57, 65, 87, 88, 93, 96, 97, 98, 102], "fall": [4, 10, 64, 73, 77, 94, 99], "flag": [4, 10, 22, 26, 43, 48, 58, 59, 62, 69, 79, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 98, 99, 101, 102], "approxim": [4, 10, 18, 40, 66, 96], "top": [4, 5, 10, 36, 40, 42, 43, 52, 59, 62, 65, 67, 74, 78, 79, 83, 84, 86, 87, 90, 91, 92, 93, 94, 98, 99, 101, 103], "found": [4, 5, 7, 10, 13, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 97, 99, 101, 103], "arxiv": [4, 18, 94], "ab": [4, 18, 94, 98], "1908": 4, "08619": 4, "1911": [4, 18], "07128": [4, 18], "embed": [4, 5, 10, 16, 66, 79, 83, 84, 86, 87, 90, 91, 94, 97, 101], "represent": [4, 5, 10, 16, 34, 37, 41, 49, 59, 79, 83, 84, 86, 87, 88, 91, 93, 94, 99], "suppli": [4, 97, 98, 101], "2d": [4, 5, 16, 32, 40, 48, 49, 51, 52, 57, 82, 84, 97], "num_exampl": [4, 5, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33, 36, 58, 84, 85, 86, 87, 88, 90, 91, 94], "num_featur": [4, 5, 16, 37, 41, 56], "distanc": [4, 5, 10, 16, 18, 26, 28, 31, 50, 64, 66, 90, 99], "construct": [4, 5, 7, 10, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 37, 41, 48, 56], "nearestneighbor": [4, 5, 10, 18, 66, 90, 99], "cosin": [4, 10, 50, 66, 99], "dim": [4, 66, 88, 102], "euclidean": [4, 5, 10, 50, 64, 66, 90], "dimension": [4, 26, 52, 84, 94, 99], "exce": [4, 85, 88], "transform": [4, 10, 32, 48, 50, 52, 66, 67, 82, 83, 87, 88, 91, 99, 103], "rel": [4, 10, 36, 57, 58, 66, 86, 87, 88, 90, 91, 94, 99], "adjust": [4, 38, 43, 61, 66, 67, 79, 94], "closer": [4, 10, 64, 98], "highli": [4, 87, 88], "influenti": 4, "posit": [4, 5, 37, 41, 50, 52, 65, 92, 99], "convers": 4, "neg": [4, 10, 64, 65, 86, 87, 92], "valueerror": [4, 5, 12, 13, 34, 45, 48, 50, 93], "neither": [4, 5, 10, 14, 98], "nor": [4, 5, 10, 14], "larger": [4, 18, 69, 71, 73, 85, 88, 91, 92, 93], "55": [4, 51, 92, 98, 101], "525": 4, "unifi": 5, "audit": [5, 9, 12, 13, 16, 84, 88, 89, 90, 91, 93, 94, 97, 98, 101], "kind": [5, 6, 7, 10, 84, 85, 86, 88, 90, 91, 92, 94], "addit": [5, 7, 9, 11, 13, 33, 35, 37, 41, 48, 53, 57, 65, 74, 75, 82, 83, 84, 86, 90, 91, 94, 96, 99, 100], "depend": [5, 7, 9, 11, 12, 13, 35, 39, 43, 45, 52, 55, 59, 66, 69, 70, 79], "instal": [5, 7, 9, 11, 35, 37, 39, 40, 41, 43, 55, 56, 71, 73], "pip": [5, 7, 9, 11, 35, 56, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "development": [5, 7, 9, 11, 35], "git": [5, 7, 9, 11, 35, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "github": [5, 7, 9, 11, 35, 37, 38, 52, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101], "com": [5, 7, 9, 11, 35, 37, 38, 40, 45, 52, 66, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "egg": [5, 7, 9, 11, 35, 79, 92], "label_nam": [5, 7, 8, 10, 12, 18, 31, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 98, 101], "image_kei": [5, 10, 88], "interfac": [5, 9, 10, 79, 93, 94], "librari": [5, 10, 41, 62, 65, 66, 79, 83, 86, 91, 92, 93], "goal": [5, 101], "track": [5, 7, 13, 14, 79, 85, 86, 92, 93, 94], "intermedi": [5, 9, 87], "statist": [5, 10, 13, 22, 26, 36, 57, 58, 65, 87, 90, 91, 94], "convert": [5, 10, 12, 34, 37, 41, 49, 50, 53, 57, 64, 73, 77, 80, 83, 84, 88, 91, 92, 93, 96, 97, 98], "hug": [5, 10, 12, 88], "face": [5, 10, 12, 16, 88, 92, 97], "kei": [5, 7, 10, 12, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 41, 48, 57, 58, 64, 66, 85, 86, 87, 88, 91, 93, 94, 96, 98], "string": [5, 10, 12, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 36, 37, 41, 52, 57, 58, 70, 74, 77, 78, 83, 90, 91, 93, 96, 97, 103], "dictionari": [5, 7, 10, 12, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 37, 41, 47, 52, 57, 58, 61, 62, 64, 65, 86, 87, 90, 91, 94, 96, 97, 98], "path": [5, 12, 37, 40, 41, 65, 84, 86, 93, 98], "local": [5, 7, 10, 12, 37, 38, 41, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "text": [5, 7, 10, 12, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 42, 48, 66, 75, 77, 78, 79, 81, 86, 87, 89, 92, 93, 94, 95, 96, 99], "txt": [5, 12, 103], "csv": [5, 12, 82, 83, 90, 91, 101], "json": [5, 12], "hub": [5, 12], "multiclass": [5, 12, 15, 48, 52, 57, 97], "regress": [5, 7, 10, 12, 14, 16, 21, 30, 32, 34, 83, 85, 86, 87, 91, 95, 96, 99], "multilabel": [5, 12, 14, 15, 21, 25, 32, 34, 49, 97], "imag": [5, 9, 36, 41, 62, 64, 65, 66, 71, 73, 74, 79, 86, 87, 89, 92, 93, 95, 96, 97, 98, 100, 102], "field": [5, 10, 37, 41], "themselv": [5, 82, 83, 101], "pil": [5, 88], "cleanvis": [5, 10], "level": [5, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 36, 51, 75, 77, 87, 88, 93, 95, 97, 102], "load_dataset": [5, 12, 88], "glue": 5, "sst2": 5, "properti": [5, 12, 13, 34, 37, 41, 85], "has_label": [5, 12], "class_nam": [5, 12, 20, 36, 42, 58, 65, 74, 78, 79, 92, 94, 98, 102, 103], "empti": [5, 12, 46, 57, 87, 93, 97], "find_issu": [5, 6, 7, 8, 10, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101], "issue_typ": [5, 6, 7, 8, 10, 13, 14, 16, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 101], "sort": [5, 16, 40, 43, 48, 57, 59, 62, 64, 65, 67, 73, 75, 77, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 101, 102, 103], "common": [5, 13, 16, 87, 89, 92, 93, 94, 97, 98, 102], "real": [5, 16, 79, 86, 87, 93, 94, 96, 101, 102], "world": [5, 16, 79, 86, 87, 93, 94, 96, 101, 102], "interact": [5, 16, 91, 93], "thereof": [5, 16], "insight": [5, 16, 65, 96], "best": [5, 9, 10, 16, 47, 57, 67, 82, 83, 86, 87, 88, 90, 91, 93, 96, 97, 99, 101, 103], "properli": [5, 10, 40, 47, 52, 53, 71, 84, 85, 86, 87, 88, 90, 91, 93, 94, 97, 99, 101, 102], "respect": [5, 37, 41, 62, 65, 84, 85, 86, 87, 88, 90, 91, 94, 97, 98], "lexicograph": [5, 47, 52, 84, 85, 86, 87, 88, 90, 91, 94, 97], "squar": [5, 52, 69, 92, 101], "csr": 5, "evenli": 5, "omit": [5, 64, 65, 88, 98], "itself": [5, 32, 37, 41, 98], "three": [5, 10, 36, 57, 58, 69, 74, 82, 84, 85, 86, 87, 90, 92, 94, 96, 100, 101, 102, 103], "indptr": 5, "wise": 5, "start": [5, 7, 10, 34, 37, 38, 41, 48, 79, 90, 97, 103], "th": [5, 42, 47, 51, 52, 57, 59, 62, 64, 65, 66, 75, 77, 78, 91, 97, 98, 103], "ascend": [5, 36, 58, 88, 94], "segment": [5, 71, 73, 74, 95], "reflect": [5, 82, 83, 90, 91, 96, 98, 99, 101], "maintain": [5, 56], "kneighbors_graph": [5, 18, 90], "illustr": 5, "todens": 5, "second": [5, 48, 52, 65, 67, 86, 93, 94, 103], "duplic": [5, 9, 21, 22, 37, 41, 79, 85, 86, 94, 101], "explicit": 5, "precend": 5, "collect": [5, 10, 13, 16, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 32, 57, 93, 96, 103], "unspecifi": [5, 16, 43, 59], "interest": [5, 16, 22, 74, 78, 82, 83, 91, 94, 101, 102, 103], "constructor": [5, 10, 16, 23, 30], "issuemanag": [5, 9, 13, 14, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 33], "respons": [5, 16, 22, 69, 70, 92, 101, 103], "random_st": [5, 82, 84, 85, 86, 87, 88, 94, 97, 99], "lab": [5, 6, 8, 10, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 40, 79, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 101], "comprehens": [5, 79, 88, 97, 101], "nbr": 5, "n_neighbor": [5, 10, 18, 66], "mode": [5, 11, 18, 37, 40, 41, 99], "4x4": 5, "float64": [5, 26, 37, 41, 77], "compress": [5, 10, 52, 71, 73], "toarrai": 5, "NOT": [5, 40, 91], "23606798": 5, "41421356": 5, "configur": [5, 16, 48, 87], "suppos": [5, 10, 62, 82, 83, 99, 101], "who": [5, 64, 82, 90, 94, 103], "manag": [5, 8, 9, 10, 13, 14, 15, 16, 17, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 56, 86, 93], "clean_learning_kwarg": [5, 10, 23, 30, 93, 101], "labelissuemanag": [5, 10, 14, 21, 23], "prune_method": [5, 80], "prune_by_noise_r": [5, 43, 59, 94], "report": [5, 7, 11, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 58, 78, 79, 84, 85, 86, 87, 90, 91, 93, 94, 97, 101, 103], "include_descript": [5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 33], "show_summary_scor": [5, 33], "show_all_issu": [5, 33], "summari": [5, 7, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 42, 55, 56, 58, 63, 72, 73, 75, 76, 77, 80, 84, 85, 86, 87, 88, 90, 91, 92, 94, 98, 101, 102, 103], "show": [5, 7, 26, 37, 41, 47, 52, 65, 74, 78, 82, 85, 87, 88, 90, 91, 92, 93, 94, 96, 99, 101, 102, 103], "suffer": [5, 10, 13, 22, 59, 67, 78, 103], "onc": [5, 22, 36, 37, 41, 82, 85, 86, 93, 94, 97, 98], "familiar": 5, "overal": [5, 7, 10, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 42, 48, 57, 58, 61, 64, 65, 69, 73, 74, 75, 77, 79, 80, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 98, 103], "sever": [5, 7, 10, 12, 13, 22, 37, 40, 41, 43, 61, 64, 66, 67, 73, 77, 79, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 98, 99, 103], "compar": [5, 57, 66, 77, 86, 87, 90, 94, 98], "issue_summari": [5, 7, 10, 13, 85, 86], "With": [5, 9, 10, 40, 83, 91, 93, 94, 96, 101, 102, 103], "usag": [5, 40, 56], "usual": [5, 12, 32, 33, 88, 96, 101], "ti": [5, 57], "exhibit": [5, 7, 10, 13, 74, 87, 88, 90, 91, 94, 98], "ie": [5, 69], "likelihood": [5, 10, 40, 42, 43, 59, 64, 66, 67, 71, 75], "wherea": [5, 52, 59, 100], "outlier": [5, 9, 14, 21, 22, 31, 44, 67, 79, 85, 86, 87, 94, 95, 101], "fundament": [5, 10], "incompar": 5, "quantiti": [5, 94, 101], "global": [5, 7, 22, 37, 41], "non_iid": [5, 10, 14, 26, 87, 88, 90, 91, 94], "hypothesi": 5, "iid": [5, 7, 9, 26, 90, 94], "never": [5, 84, 94, 97, 99, 100], "someth": [5, 7, 37, 41, 67, 98], "123": [5, 85, 86, 87], "456": [5, 82, 83, 84], "nearest_neighbor": 5, "7": [5, 48, 49, 56, 75, 77, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "9": [5, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 42, 48, 49, 61, 75, 77, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "distance_to_nearest_neighbor": [5, 86, 87, 88, 90, 91, 94], "789": 5, "get_issu": [5, 10, 13, 84, 85, 87, 88, 90, 91, 93, 97, 101], "issue_nam": [5, 6, 7, 10, 13, 14, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86, 87], "focu": [5, 13, 91, 102, 103], "full": [5, 10, 13, 40, 56, 65, 85, 88, 103], "summar": [5, 13, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 36, 58, 74, 78, 79, 102], "specific_issu": [5, 13], "lie": [5, 10, 66, 67, 83, 84, 86, 87, 88, 90, 91, 94], "get_issue_summari": [5, 13, 85, 87], "get_info": [5, 13, 87, 91], "yet": [5, 17, 27, 56, 92, 96], "list_possible_issue_typ": [5, 14, 15], "regist": [5, 7, 14, 15, 17, 27, 37, 41, 86], "rtype": [5, 14, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41], "registri": [5, 14, 15], "list_default_issue_typ": [5, 14, 15], "folder": [5, 84, 86, 88], "load": [5, 12, 40, 65, 88, 92, 93, 94, 98, 99, 102, 103], "futur": [5, 10, 22, 37, 41, 57, 79, 83, 84, 86, 91, 93], "overwrit": [5, 86], "separ": [5, 36, 48, 61, 86, 87, 88, 93, 98, 100], "static": 5, "rememb": [5, 91, 93, 94], "part": [5, 10, 37, 41, 43, 62, 64, 65, 84, 86, 92, 102, 103], "ident": [5, 10, 22, 52, 85, 91], "datalab": [6, 8, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 79, 82, 83, 84, 88, 93, 96, 101], "walk": 7, "alongsid": [7, 37, 41, 86, 93], "pre": [7, 8, 10, 37, 41, 86, 87, 101], "runtim": [7, 37, 40, 41, 69, 71, 73, 84, 88, 93], "issue_manager_factori": [7, 14, 86], "myissuemanag": [7, 14], "myissuemanagerforregress": 7, "decor": [7, 14], "ll": [7, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 100, 101, 103], "thing": [7, 41, 83, 94, 101], "next": [7, 57, 79, 82, 83, 84, 85, 90, 91, 93, 96, 98, 101, 103], "dummi": 7, "randint": [7, 31, 48, 85, 86, 87, 93], "mark": [7, 10, 80, 98, 99, 101], "regard": [7, 87, 94], "rand": [7, 48, 85, 86, 87], "is_": [7, 10, 86], "_issu": [7, 10, 86], "issue_score_kei": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86], "whole": [7, 26, 37, 41, 87], "make_summari": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 86], "popul": [7, 87, 91], "verbosity_level": [7, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "std": [7, 98], "raw_scor": 7, "bit": 7, "involv": [7, 40, 74, 78, 85, 93, 97], "intermediate_arg": 7, "min": [7, 48, 64, 77, 86, 93, 99], "sin_filt": 7, "sin": 7, "arang": 7, "kernel": 7, "affect": [7, 10, 37, 41, 71, 77, 91, 93], "easili": [7, 46, 80, 82, 83, 84, 85, 87, 90, 91, 94, 96, 97, 99, 100, 101, 102], "hard": [7, 41, 92, 99], "sai": [7, 10, 37, 41, 97, 102], "anoth": [7, 22, 36, 40, 51, 64, 67, 83, 90, 91, 93, 94, 96, 99], "try": [7, 9, 10, 40, 43, 56, 57, 71, 73, 79, 85, 87, 88, 90, 91, 93, 94, 102], "won": [7, 37, 41, 86, 87, 93, 97], "issue_manag": [7, 10, 11, 13, 15, 18, 19, 20, 23, 25, 26, 27, 28, 30, 31, 86], "instanti": [7, 16, 40, 56, 66, 83, 84, 87, 90], "477762": 7, "286455": 7, "term": [7, 10, 46, 52, 65, 84, 85, 86, 87, 88, 90, 91, 94], "4778": 7, "is_basic_issu": 7, "basic_scor": 7, "13": [7, 19, 28, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 98, 99, 101, 102, 103], "003042": 7, "058117": 7, "11": [7, 56, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "121908": 7, "15": [7, 50, 56, 69, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "169312": 7, "17": [7, 83, 84, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "229044": 7, "2865": 7, "is_intermediate_issu": 7, "intermediate_scor": 7, "000000": [7, 86, 87, 92, 94], "007059": 7, "009967": 7, "010995": 7, "087332": 7, "016296": 7, "03947": 7, "019459": 7, "794251": 7, "underperform": [8, 9, 31], "group": [8, 9, 26, 31, 92, 98, 103], "dbscan": [8, 10, 31, 93], "hdbscan": [8, 93], "etc": [8, 10, 22, 32, 37, 41, 46, 56, 57, 75, 79, 86, 87, 90, 91, 93, 94, 97, 101], "sensit": [8, 10, 50], "ep": [8, 31, 65], "radiu": 8, "min_sampl": [8, 31], "kmean": [8, 93], "your_data": 8, "get_pred_prob": [8, 85], "n_cluster": [8, 31, 93], "cluster_id": [8, 10, 31, 93], "labels_": 8, "underperforming_group": [8, 10, 14, 21, 87, 88, 90, 91, 93, 94], "search": [9, 10, 20, 26, 27, 51, 69, 93, 100], "nondefault": 9, "Near": [9, 93], "imbal": [9, 21, 61, 66, 67, 87], "null": [9, 14, 21, 87, 88, 91, 94], "togeth": [9, 10, 46, 83, 86, 87, 88, 90, 91, 94, 101, 103], "built": [9, 48], "own": [9, 37, 39, 41, 55, 61, 62, 65, 71, 75, 82, 83, 84, 87, 88, 90, 91, 93, 96, 97, 101, 102, 103], "prerequisit": 9, "basic": [9, 41, 56, 90, 91, 99], "fulli": [9, 10, 37, 41, 56, 93], "platform": [9, 10, 79, 88, 90, 91, 93], "write": [9, 10], "code": [9, 10, 37, 41, 46, 52, 56, 79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 101, 102, 103], "being": [9, 10, 13, 36, 37, 41, 43, 48, 51, 52, 67, 82, 90, 93, 94, 101, 102], "100x": [9, 10], "faster": [9, 10, 40, 66, 69, 71, 73, 93, 94], "intellig": [9, 10], "quickli": [9, 10, 38, 82, 84, 88, 90, 91, 93, 97, 99, 102, 103], "fix": [9, 10, 57, 83, 85, 91, 94, 101], "scientist": [9, 10], "million": [9, 10, 103], "thank": [9, 10], "ai": [9, 10, 79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 95, 96, 97, 99, 101, 103], "suggest": [9, 10, 36, 57, 58, 64, 83, 88, 91, 93, 101], "power": [9, 10, 88, 90, 91, 92, 94, 103], "automl": [9, 10, 79, 93], "system": [9, 10, 84, 85, 88, 90, 91, 102], "foundat": [9, 10, 79], "improv": [9, 10, 57, 82, 83, 87, 88, 92, 93, 94, 101, 102], "click": [9, 10, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "tune": [9, 10, 83, 84, 91, 92, 99], "serv": [9, 10, 13, 16, 96], "auto": [9, 10, 82, 83, 85, 87, 92, 93, 101], "free": [9, 10, 79, 84, 87, 88, 90, 91, 93, 94], "page": [10, 87, 93, 94], "variou": [10, 13, 30, 39, 53, 55, 79, 82, 86, 87, 90, 91, 92, 93, 94, 96, 98], "why": [10, 85, 91], "matter": [10, 36, 58, 83, 91], "didn": 10, "plu": [10, 101], "_score": 10, "badli": [10, 64, 82, 83, 103], "issue_scor": 10, "outlier_scor": [10, 28, 86, 87, 88, 90, 91, 94, 99], "atyp": [10, 66, 86, 87, 88, 90, 91, 94, 99], "datapoint": [10, 31, 43, 48, 52, 67, 70, 79, 82, 83, 84, 86, 87, 90, 91, 93, 100, 101], "is_issu": [10, 22], "is_outlier_issu": [10, 86, 87, 88, 90, 91, 94], "annot": [10, 36, 47, 57, 58, 59, 61, 62, 64, 65, 74, 77, 78, 79, 84, 85, 86, 87, 88, 90, 91, 93, 94, 95, 98, 102], "dissimilar": [10, 90, 91], "preced": 10, "incorrect": [10, 64, 67, 70, 82, 84, 85, 86, 87, 88, 90, 91, 94, 98, 101], "due": [10, 40, 43, 67, 71, 73, 84, 85, 86, 87, 88, 90, 91, 94, 101], "appear": [10, 36, 47, 58, 59, 62, 70, 87, 88, 90, 91, 101, 102], "now": [10, 40, 80, 82, 83, 84, 85, 87, 96, 98, 99, 101, 103], "token": [10, 42, 51, 73, 74, 75, 76, 77, 78, 93, 94, 95], "hamper": [10, 88, 92], "analyt": [10, 79, 93, 96], "lead": [10, 64, 67, 88, 98], "draw": [10, 85, 86, 87], "conclus": [10, 91], "veri": [10, 36, 58, 62, 64, 83, 86, 87, 88, 90, 91, 93, 94, 96, 99, 101], "rare": [10, 43, 65, 86, 87, 88, 90, 91, 93, 94], "anomal": [10, 67, 86, 87, 88, 90, 91, 94], "articl": [10, 40, 93], "blog": 10, "unexpect": [10, 37, 41, 91], "consequ": 10, "inspect": [10, 83, 84, 85, 87, 88, 94, 98, 101], "extrem": [10, 86, 87, 88, 90, 91, 93, 94], "record": [10, 37, 41, 84, 90, 101], "abbrevi": 10, "misspel": 10, "typo": [10, 78], "resolut": 10, "video": [10, 92], "audio": [10, 86, 87, 89, 93], "minor": [10, 51], "variat": 10, "translat": 10, "d": [10, 50, 82, 90, 91, 93, 94, 97, 101, 103], "constant": [10, 31, 69], "median": [10, 30, 50], "question": [10, 22, 79, 94], "nearli": [10, 22, 87, 88, 90, 91], "awar": [10, 80, 94], "presenc": [10, 94], "near_dupl": [10, 14, 19, 85, 86, 87, 88, 90, 91, 93, 94], "signific": [10, 90, 91, 94], "violat": [10, 90, 91, 94], "assumpt": [10, 90, 91, 94], "changepoint": [10, 90, 91, 94], "shift": [10, 90, 91, 94], "drift": [10, 87, 90, 94], "autocorrel": [10, 90, 91, 94], "almost": [10, 90, 91, 94], "adjac": [10, 90, 91, 94], "tend": [10, 36, 46, 90, 91, 94, 102, 103], "sequenti": [10, 37, 41, 56, 88], "gap": 10, "b": [10, 18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 36, 51, 52, 77, 82, 90, 91, 92, 93, 94, 100, 103], "x1": [10, 62, 65, 98], "x2": [10, 62, 65, 98], "10th": 10, "100th": 10, "90": [10, 77, 82, 90, 94, 100, 101], "similarli": [10, 37, 41, 86, 88, 90, 93, 98], "math": [10, 88], "behind": [10, 66, 94], "proper": [10, 52, 57, 62, 65, 82, 88, 91, 93, 96, 98], "scenario": [10, 67, 85, 86, 87], "underli": [10, 42, 66, 75, 77, 103], "stem": [10, 66, 99], "evolv": 10, "influenc": 10, "act": [10, 64, 86], "accordingli": [10, 32], "emploi": [10, 97, 99], "partit": [10, 100], "ahead": 10, "good": [10, 37, 41, 50, 56, 58, 64, 67, 71, 73, 74, 79, 85, 88, 90, 91], "problem": [10, 32, 40, 48, 74, 79, 86, 87, 88, 91, 93], "deploy": [10, 82, 83, 94, 101], "overlook": [10, 64, 98], "fact": 10, "thu": [10, 36, 41, 58, 82, 84, 90, 91, 94, 100, 103], "diagnos": [10, 87, 93], "rarest": [10, 87], "q": [10, 98], "subpar": 10, "special": [10, 51], "techniqu": [10, 98], "smote": 10, "asymmetr": [10, 36], "class_imbal": [10, 14, 20, 87, 88, 90, 91, 94], "excess": [10, 88], "dark": [10, 102], "bright": [10, 103], "blurri": [10, 88], "lack": [10, 56], "unusu": [10, 98, 99], "cluster": [10, 18, 31], "slice": 10, "poor": 10, "subpopul": 10, "faq": [10, 79, 87, 88, 90, 91, 95], "get_self_confidence_for_each_label": [10, 48, 67], "r": [10, 40, 69, 85, 86, 87, 101, 102], "tabular": [10, 79, 81, 86, 87, 89, 93, 96], "categor": [10, 66, 81, 82, 86, 87, 89, 93, 101], "encod": [10, 49, 65, 71, 74, 82, 83, 90, 91, 93, 101, 102], "miss": [10, 27, 37, 41, 52, 62, 64, 85, 90, 93, 98, 101], "pattern": 10, "isn": [10, 17, 27], "scalabl": 10, "sacrific": 10, "One": [10, 52, 66, 93], "quantif": 10, "data_valu": [10, 14, 21], "exert": [10, 87], "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, 21, 23, 30], "health_summari": [10, 23, 36, 79, 92], "health_summary_kwarg": 10, "tandem": [10, 92], "view": [10, 37, 41, 42, 43, 73, 75, 77, 79, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 96, 97, 98, 99, 100, 101, 103], "ood_kwarg": 10, "outofdistribut": [10, 28, 66, 99], "outsid": [10, 93, 97], "outlierissuemanag": [10, 14, 21, 28, 86], "nearduplicateissuemanag": [10, 14, 19, 21], "noniidissuemanag": [10, 14, 21, 26], "num_permut": [10, 26], "permut": [10, 26], "significance_threshold": [10, 26], "signic": 10, "noniid": [10, 21], "classimbalanceissuemanag": [10, 14, 20, 21], "underperforminggroupissuemanag": [10, 14, 21, 31], "determinin": 10, "neighbour": 10, "min_cluster_sampl": [10, 31], "filter_cluster_id": [10, 21, 31], "clustering_kwarg": [10, 31], "nullissuemanag": [10, 14, 21, 27], "datavaluationissuemanag": [10, 14, 18, 21], "codeblock": 10, "demonstr": [10, 40, 86, 87, 88, 91, 93, 94, 96, 97, 98, 101, 102], "howev": [10, 37, 41, 52, 82, 83, 84, 88, 90, 91, 96, 100, 102], "mandatori": 10, "image_issue_types_kwarg": 10, "32": [10, 84, 86, 92, 96, 98], "fewer": [10, 43, 52, 98], "vice": [10, 58], "versa": [10, 58], "light": [10, 88, 92, 98, 102], "29": [10, 85, 88, 92, 96, 97, 98, 102, 103], "low_inform": [10, 88], "odd_aspect_ratio": [10, 88], "35": [10, 86, 92, 96, 97, 98], "odd_siz": [10, 88], "doc": [10, 37, 41, 79, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "studio": [11, 79, 87, 88, 90, 91, 93], "data_issu": [11, 15, 16, 33, 86], "issue_find": [11, 15], "factori": [11, 15, 16], "model_output": [11, 15], "except": [12, 37, 41, 56, 67, 85, 86, 87, 88, 96], "dataformaterror": [12, 15], "add_not": 12, "with_traceback": 12, "tb": 12, "__traceback__": 12, "datasetdicterror": [12, 15], "datasetdict": 12, "datasetloaderror": [12, 15], "dataset_typ": 12, "fail": 12, "hold": 12, "associ": [12, 16, 32, 34, 37, 41, 65, 96], "sublist": 12, "map_to_int": 12, "abc": [12, 22, 32], "is_avail": [12, 88], "dataissu": [13, 15, 16, 33], "central": [13, 103], "repositori": 13, "strategi": [13, 48, 93], "_infostrategi": 13, "basi": 13, "collect_statist": 13, "reus": [13, 22], "avoid": [13, 37, 40, 41, 43, 52, 59, 62, 65, 69, 71, 73, 85, 86, 87, 93], "recomput": [13, 83], "weighted_knn_graph": 13, "issue_manager_that_computes_knn_graph": 13, "collect_issues_from_issue_manag": 13, "collect_issues_from_imagelab": 13, "imagelab": 13, "set_health_scor": 13, "health": [13, 23, 36, 58, 79], "get_data_statist": [13, 15], "concret": 14, "subclass": [14, 37, 41, 66, 86], "regressionlabelissuemanag": [14, 21, 29, 30], "multilabelissuemanag": [14, 21, 24, 25], "from_str": [14, 34, 44, 48], "my_issu": 14, "logic": [14, 34, 40, 43, 71, 73], "stabl": [15, 21, 24, 29, 39, 44, 52, 55, 66, 80], "issuefind": [15, 16, 33], "modeloutput": [15, 32], "multiclasspredprob": [15, 32], "regressionpredict": [15, 32], "multilabelpredprob": [15, 32], "instati": 16, "public": [16, 94, 98, 102, 103], "creation": [16, 41], "execut": [16, 37, 41, 86, 93, 98], "coordin": [16, 62, 64, 65, 98, 103], "behavior": [16, 36, 37, 41, 65, 85, 93], "At": [16, 65, 93], "get_available_issue_typ": 16, "direct": [17, 27, 37, 41, 56], "valuabl": 18, "vstack": [18, 52, 88, 92, 93, 94, 96, 97], "25": [18, 26, 37, 48, 50, 85, 87, 88, 92, 94, 96, 97, 98, 103], "classvar": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "short": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 51, 52], "data_valuation_scor": 18, "item": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 37, 41, 52, 85, 86, 87, 88, 93, 94, 96, 97], "some_info_kei": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "additional_info_kei": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31], "default_threshold": [18, 21, 28], "collect_info": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], "info_to_omit": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "compos": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31, 37, 41, 83, 91, 99], "is_x_issu": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "x_score": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_a": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_b1": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "val_b2": [18, 19, 20, 22, 23, 25, 26, 28, 30, 31], "report_str": [18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33], "_": [19, 20, 22, 23, 25, 26, 27, 30, 31, 48, 51, 52, 82, 84, 86, 88, 92, 94, 97], "near_duplicate_set": [19, 21, 86, 87, 88, 90, 91, 93, 94], "occurr": [19, 20, 22, 26, 27, 28, 31, 51], "median_nn_dist": 19, "near_duplicate_scor": [19, 86, 87, 88, 90, 91, 93, 94], "class_imbalance_scor": [20, 87], "bleed": [21, 24, 29, 39], "edg": [21, 24, 29, 39, 64, 79, 94, 103], "sharp": [21, 24, 29, 39], "get_health_summari": [21, 23], "ood": [21, 28, 66, 67, 86, 87, 88, 91, 94, 99], "simplified_kolmogorov_smirnov_test": [21, 26], "outlier_cluster_label": [21, 31], "no_underperforming_cluster_id": [21, 31], "set_knn_graph": [21, 31], "perform_clust": [21, 31], "get_worst_clust": [21, 31], "find_issues_with_predict": [21, 29, 30], "find_issues_with_featur": [21, 29, 30], "believ": [22, 102], "priori": [22, 94], "abstract": [22, 32], "applic": [23, 57, 93, 94, 96, 103], "typevar": [23, 25, 37, 41, 51, 61, 64, 65], "scalartyp": [23, 25], "covari": [23, 25, 69, 101], "summary_dict": 23, "label_scor": [23, 25, 30, 84, 85, 86, 87, 88, 90, 91, 94, 97, 101], "neighbor_histogram": 26, "non_neighbor_histogram": 26, "kolmogorov": 26, "smirnov": 26, "largest": [26, 40, 48, 67, 71, 73, 98, 102], "empir": [26, 47, 57], "cumul": 26, "ecdf": 26, "histogram": [26, 90, 101], "absolut": [26, 30], "trial": 26, "non_iid_scor": [26, 87, 90, 91, 94], "null_track": 27, "extend": [27, 49, 56, 88, 98, 99, 103], "superclass": 27, "arbitrari": [27, 36, 73, 77, 86, 99, 101], "prompt": 27, "address": [27, 83, 86, 87, 91, 93], "enabl": [27, 41], "null_scor": [27, 87], "37037": 28, "q3_avg_dist": 28, "iqr_avg_dist": 28, "median_outlier_scor": 28, "multipli": [30, 50], "deleg": 30, "confus": [31, 32, 36, 37, 41, 43, 52, 65, 83, 103], "50": [31, 41, 85, 93, 94, 96, 98, 99, 101], "keepdim": [31, 93], "signifi": 31, "absenc": 31, "find_issues_kwarg": 31, "int64": [31, 84, 96], "npt": 31, "int_": 31, "id": [31, 57, 86, 88, 93, 96], "unique_cluster_id": 31, "_description_": 31, "performed_clust": 31, "worst_cluster_id": 31, "underperforming_group_scor": [31, 87], "convent": [32, 34], "subject": [32, 34], "meant": [32, 34], "Not": 32, "mainli": [32, 99, 103], "content": [32, 66, 84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "fetch": [32, 40, 84, 87, 93], "datset": 33, "exclud": [33, 42, 74, 78, 86, 93, 103], "get_report": 33, "enum": [34, 48], "qualnam": [34, 48], "boundari": [34, 48, 85, 86, 87], "continu": [34, 56, 82, 83, 88, 91, 93, 96, 98, 101, 103], "binari": [34, 48, 52, 59, 61, 94, 103], "simultan": [34, 101], "task_str": 34, "is_classif": 34, "__contains__": [34, 44, 48], "member": [34, 37, 41, 48, 86, 87], "typeerror": [34, 48], "12": [34, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 101, 102, 103], "__getitem__": [34, 44, 48], "match": [34, 36, 37, 41, 43, 48, 57, 58, 67, 85, 86, 87, 88, 92, 98, 100, 102], "__iter__": [34, 44, 48], "__len__": [34, 44, 48], "alias": [34, 48], "is_regress": 34, "is_multilabel": 34, "overview": [36, 82, 83, 84, 87, 88, 90, 91, 96, 98, 99, 101, 103], "modifi": [36, 37, 40, 41, 52, 93, 94], "help": [36, 37, 41, 65, 79, 80, 82, 83, 84, 86, 88, 90, 91, 92, 93, 96, 97, 101, 102, 103], "rank_classes_by_label_qu": [36, 87], "merg": [36, 51, 79, 92, 93, 103], "find_overlapping_class": [36, 93, 94], "problemat": [36, 58, 74, 78, 84, 98, 103], "unnorm": [36, 58, 94], "abov": [36, 37, 40, 41, 52, 57, 64, 65, 67, 73, 77, 82, 83, 84, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 100, 101, 102, 103], "model_select": [36, 48, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 99, 101], "cross_val_predict": [36, 41, 82, 83, 84, 85, 86, 87, 90, 91, 93, 94, 96, 100, 101], "get_data_labels_from_dataset": 36, "yourfavoritemodel": [36, 94], "cv": [36, 48, 82, 84, 85, 86, 87, 90, 94, 96], "df": [36, 52, 78, 84, 93], "overall_label_qu": [36, 58], "col": 36, "prob": [36, 51, 94, 100], "divid": [36, 58, 67], "label_nois": [36, 58], "human": [36, 92, 102, 103], "clearli": [36, 67, 88, 98, 102], "num": [36, 58, 92, 94], "overlap": [36, 79, 92, 93, 94], "ontolog": 36, "publish": [36, 103], "therefor": [36, 67], "vehicl": [36, 92], "truck": [36, 92, 99, 102], "intuit": [36, 58, 85], "car": [36, 92, 98, 102], "frequent": [36, 57, 90, 93, 101], "characterist": 36, "l": [36, 37, 41, 62, 64, 65], "class1": 36, "class2": 36, "relationship": 36, "dog": [36, 52, 58, 60, 74, 92, 93, 99, 100, 103], "cat": [36, 52, 58, 60, 92, 93, 99, 100], "captur": [36, 84, 98, 99, 102], "co": [36, 37, 38], "noisy_label": [36, 85, 86, 87, 97], "overlapping_class": 36, "descend": [36, 37, 41, 48, 58, 65], "overall_label_health_scor": [36, 58, 94], "half": [36, 37, 39, 41, 58, 92, 103], "health_scor": [36, 58], "classes_by_label_qu": [36, 87], "cnn": [37, 39, 41, 88], "cifar": [37, 38, 92, 99], "teach": [37, 38], "bhanml": 37, "blob": 37, "master": [37, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "call_bn": [37, 39], "bn": 37, "input_channel": 37, "n_output": 37, "dropout_r": 37, "top_bn": 37, "architectur": [37, 41], "shown": [37, 65, 85, 86, 93, 96, 99, 100, 102, 103], "forward": [37, 38, 39, 41, 88, 96], "overridden": [37, 41], "although": [37, 41, 66, 82, 90], "recip": [37, 41], "afterward": [37, 41], "sinc": [37, 41, 45, 53, 58, 65, 73, 77, 93, 96, 97, 98, 100, 103], "former": [37, 41], "hook": [37, 41, 92], "silent": [37, 40, 41], "t_destin": [37, 39, 41], "__call__": [37, 39, 41, 44, 48], "add_modul": [37, 39, 41], "child": [37, 41], "fn": [37, 41, 65], "recurs": [37, 41, 48], "submodul": [37, 41], "children": [37, 39, 41, 103], "nn": [37, 38, 41, 88], "init": [37, 41, 94], "no_grad": [37, 41, 88, 99], "init_weight": [37, 41], "linear": [37, 41, 83, 88, 91], "fill_": [37, 41], "net": [37, 41, 84, 88, 92], "in_featur": [37, 41], "out_featur": [37, 41], "bia": [37, 41, 88], "tensor": [37, 38, 41, 83, 84, 88, 91, 99], "requires_grad": [37, 41], "bfloat16": [37, 39, 41], "cast": [37, 41, 84], "buffer": [37, 39, 41], "datatyp": [37, 41], "xdoctest": [37, 41], "undefin": [37, 41], "var": [37, 41], "buf": [37, 41], "20l": [37, 41], "1l": [37, 41], "5l": [37, 41], "call_super_init": [37, 39, 41], "immedi": [37, 41, 99], "compil": [37, 39, 41, 56], "cpu": [37, 39, 41, 43, 84, 88], "move": [37, 41, 48, 80, 92], "cuda": [37, 39, 41, 84, 88], "devic": [37, 41, 84, 88], "gpu": [37, 41, 83, 84, 91], "live": [37, 41], "copi": [37, 41, 69, 82, 84, 85, 86, 87, 90, 93, 97, 100, 101], "doubl": [37, 39, 41], "dump_patch": [37, 39, 41], "eval": [37, 39, 41, 88, 97, 99], "dropout": [37, 41], "batchnorm": [37, 41], "grad": [37, 41], "extra_repr": [37, 39, 41], "line": [37, 41, 79, 86, 92, 96, 99, 103], "get_buff": [37, 39, 41], "target": [37, 38, 41, 69, 70, 99, 101], "throw": [37, 41], "get_submodul": [37, 39, 41], "explan": [37, 41], "qualifi": [37, 41], "referenc": [37, 41], "attributeerror": [37, 41], "invalid": [37, 41, 91], "resolv": [37, 41, 85, 103], "get_extra_st": [37, 39, 41], "state_dict": [37, 39, 41], "set_extra_st": [37, 39, 41], "build": [37, 41, 88, 102], "picklabl": [37, 41], "serial": [37, 41], "backward": [37, 41, 88], "break": [37, 41, 88, 98], "pickl": [37, 41, 98], "get_paramet": [37, 39, 41], "let": [37, 41, 66, 67, 82, 83, 84, 85, 87, 88, 90, 91, 93, 96, 97, 98, 99, 101, 102, 103], "net_b": [37, 41], "net_c": [37, 41], "conv": [37, 41], "conv2d": [37, 41, 88], "16": [37, 41, 48, 56, 73, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 98, 99, 102, 103], "33": [37, 41, 92, 98], "kernel_s": [37, 41], "stride": [37, 41], "200": [37, 41, 67, 85, 92, 98, 103], "diagram": [37, 41, 100], "degre": [37, 41], "queri": [37, 41, 87, 88, 93, 97], "named_modul": [37, 39, 41], "o": [37, 41, 50, 51, 84, 85, 86, 87, 92, 93, 94, 97, 98, 103], "transit": [37, 41], "ipu": [37, 39, 41], "load_state_dict": [37, 39, 41], "strict": [37, 41, 48], "persist": [37, 41], "strictli": [37, 41], "inplac": [37, 41, 96], "preserv": [37, 41, 52], "namedtupl": [37, 41], "missing_kei": [37, 41], "unexpected_kei": [37, 41], "runtimeerror": [37, 41], "idx": [37, 41, 52, 53, 65, 86, 88, 93, 94, 96, 98, 99], "named_buff": [37, 39, 41], "prefix": [37, 41, 84, 103], "remove_dupl": [37, 41], "prepend": [37, 41], "running_var": [37, 41], "named_children": [37, 39, 41], "conv4": [37, 41], "conv5": [37, 41], "memo": [37, 41], "named_paramet": [37, 39, 41], "register_backward_hook": [37, 39, 41], "deprec": [37, 41, 45, 83, 84, 91, 93], "favor": [37, 41], "register_full_backward_hook": [37, 39, 41], "removablehandl": [37, 41], "register_buff": [37, 39, 41], "running_mean": [37, 41], "register_forward_hook": [37, 39, 41], "with_kwarg": [37, 41], "always_cal": [37, 41], "possibli": [37, 41, 82, 90], "fire": [37, 41, 92], "register_module_forward_hook": [37, 41], "regardless": [37, 41, 86, 87], "register_forward_pre_hook": [37, 39, 41], "And": [37, 41], "forward_pr": [37, 41], "register_module_forward_pre_hook": [37, 41], "gradient": [37, 41, 88, 90, 101], "grad_input": [37, 41], "grad_output": [37, 41], "technic": [37, 41], "caller": [37, 41], "register_module_full_backward_hook": [37, 41], "register_full_backward_pre_hook": [37, 39, 41], "backward_pr": [37, 41], "register_module_full_backward_pre_hook": [37, 41], "register_load_state_dict_post_hook": [37, 39, 41], "post": [37, 41], "incompatible_kei": [37, 41], "modif": [37, 41], "thrown": [37, 41], "register_modul": [37, 39, 41], "register_paramet": [37, 39, 41], "register_state_dict_pre_hook": [37, 39, 41], "keep_var": [37, 41], "requires_grad_": [37, 39, 41], "autograd": [37, 41], "freez": [37, 41, 83, 84, 91], "finetun": [37, 41], "gan": [37, 41], "share_memori": [37, 39, 41], "share_memory_": [37, 41], "destin": [37, 41], "shallow": [37, 41], "releas": [37, 41, 56, 80, 84, 93], "design": [37, 41, 85], "ordereddict": [37, 41], "detach": [37, 41, 88], "non_block": [37, 41], "memory_format": [37, 41], "channels_last": [37, 41], "Its": [37, 41, 48, 58, 64], "complex": [37, 41, 84], "integr": [37, 41, 79, 93], "asynchron": [37, 41], "host": [37, 41], "pin": [37, 41, 83, 91, 92], "desir": [37, 41, 51, 65], "4d": [37, 41], "ignore_w": [37, 41], "determinist": [37, 41, 84], "1913": [37, 41], "3420": [37, 41], "5113": [37, 41], "2325": [37, 41], "env": [37, 41], "torch_doctest_cuda1": [37, 41], "gpu1": [37, 41], "1914": [37, 41], "5112": [37, 41], "2324": [37, 41], "float16": [37, 41], "cdoubl": [37, 41], "3741": [37, 41], "2382": [37, 41], "5593": [37, 41], "4443": [37, 41], "complex128": [37, 41], "6122": [37, 41], "1150": [37, 41], "to_empti": [37, 39, 41], "storag": [37, 41, 83, 91], "dst_type": [37, 41], "xpu": [37, 39, 41], "zero_grad": [37, 39, 41, 88], "set_to_non": [37, 41], "reset": [37, 41], "context": [37, 41, 98], "noisili": [38, 94], "han": 38, "2018": 38, "cifar_cnn": [38, 39], "loss_coteach": [38, 39], "y_1": 38, "y_2": 38, "forget_r": 38, "class_weight": 38, "logit": [38, 56, 88], "decim": [38, 52], "forget": [38, 48, 103], "rate_schedul": 38, "epoch": [38, 39, 41, 88, 93], "initialize_lr_schedul": [38, 39], "lr": [38, 39, 41], "001": [38, 67, 93], "250": [38, 86, 87, 94, 98], "epoch_decay_start": 38, "80": [38, 82, 90, 97, 101], "schedul": 38, "beta": 38, "adam": 38, "adjust_learning_r": [38, 39], "alpha_plan": 38, "beta1_plan": 38, "forget_rate_schedul": [38, 39], "num_gradu": 38, "expon": 38, "tell": [38, 83, 88, 91, 94], "train_load": [38, 41], "model1": [38, 94], "optimizer1": 38, "model2": [38, 94], "optimizer2": 38, "dataload": [38, 88, 99], "parser": 38, "parse_arg": 38, "num_iter_per_epoch": 38, "print_freq": 38, "topk": 38, "top1": 38, "top5": 38, "test_load": 38, "offici": [39, 55, 103], "wish": [39, 55, 99, 102, 103], "adj_confident_thresholds_shar": [39, 40], "labels_shar": [39, 40], "pred_probs_shar": [39, 40], "labelinspector": [39, 40, 93], "get_num_issu": [39, 40], "get_quality_scor": [39, 40], "update_confident_threshold": [39, 40], "score_label_qu": [39, 40], "split_arr": [39, 40], "span_classif": 39, "display_issu": [39, 42, 72, 73, 74, 75, 76, 77, 78, 102, 103], "mnist_pytorch": 39, "get_mnist_dataset": [39, 41], "get_sklearn_digits_dataset": [39, 41], "simplenet": [39, 41], "batch_siz": [39, 40, 41, 71, 73, 85, 88, 93, 99, 102], "log_interv": [39, 41], "momentum": [39, 41], "no_cuda": [39, 41], "test_batch_s": [39, 41, 88], "loader": [39, 41, 88], "set_predict_proba_request": [39, 41], "set_predict_request": [39, 41], "coteach": [39, 80], "mini": [40, 71, 73, 93], "low_self_confid": [40, 43, 59], "self_confid": [40, 43, 44, 48, 59, 61, 67, 75, 77, 82, 83, 93, 94], "conveni": [40, 83, 84, 91], "script": 40, "labels_fil": [40, 93], "pred_probs_fil": [40, 93], "quality_score_kwarg": 40, "num_issue_kwarg": 40, "return_mask": 40, "variant": [40, 57, 102], "read": [40, 45, 87, 93, 94, 99, 103], "zarr": [40, 93], "memmap": [40, 102], "pythonspe": 40, "mmap": [40, 93], "hdf5": 40, "further": [40, 42, 58, 59, 61, 64, 65, 73, 74, 84, 93], "yourfil": 40, "npy": [40, 92, 93, 102], "mmap_mod": [40, 102], "tip": [40, 43, 56, 93], "save_arrai": 40, "your_arrai": 40, "disk": [40, 92, 93], "npz": [40, 103], "maxim": [40, 57, 71, 73, 102], "multiprocess": [40, 43, 59, 71, 73, 88, 93], "linux": [40, 71, 73], "physic": [40, 43, 71, 73, 98], "psutil": [40, 43, 71, 73], "labels_arrai": [40, 53], "predprob": 40, "pred_probs_arrai": 40, "back": [40, 65, 86, 93, 98, 99], "store_result": 40, "becom": [40, 99], "verifi": [40, 93, 96, 99], "long": [40, 57, 66, 96], "enough": [40, 52, 93], "chunk": [40, 100], "ram": [40, 92], "end_index": 40, "labels_batch": 40, "pred_probs_batch": 40, "batch_result": 40, "indices_of_examples_with_issu": [40, 93], "shortcut": 40, "encount": [40, 43, 71], "1000": [40, 84, 91, 93, 99], "aggreg": [40, 44, 48, 57, 61, 64, 67, 77, 93, 94, 96], "seen": [40, 85, 93, 99, 103], "far": [40, 57], "label_quality_scor": [40, 61, 64, 67, 70, 94, 98], "method1": 40, "method2": 40, "normalized_margin": [40, 43, 44, 48, 59, 61, 67, 75, 77], "low_normalized_margin": [40, 43, 59], "issue_indic": [40, 64, 88], "update_num_issu": 40, "arr": [40, 93], "chunksiz": 40, "convnet": 41, "bespok": [41, 56], "download": [41, 84, 88, 93, 99], "mnist": [41, 79, 84, 92], "handwritten": 41, "digit": [41, 84, 92], "last": [41, 48, 62, 65, 85, 86, 87, 93, 96, 98, 103], "sklearn_digits_test_s": 41, "64": [41, 82, 88, 90, 94, 98], "01": [41, 67, 69, 84, 94, 97, 98], "templat": 41, "flexibli": 41, "among": [41, 57, 94], "test_set": 41, "Be": 41, "overrid": 41, "train_idx": [41, 52, 99], "train_label": [41, 83, 99], "scikit": [41, 52, 66, 79, 83, 84, 85, 86, 87, 90, 91, 93, 101], "span": 42, "sentenc": [42, 51, 75, 77, 78, 83, 91], "token_classif": [42, 51, 75, 77, 78, 93], "encourag": [43, 59, 67, 70], "multilabel_classif": [43, 58, 59, 61, 67, 93, 97], "pred_probs_by_class": 43, "prune_count_matrix_col": 43, "rank_by_kwarg": [43, 59, 67, 94], "num_to_remove_per_class": [43, 59], "bad": [43, 59, 64, 67, 91, 93], "seem": [43, 94, 97], "aren": 43, "confidence_weighted_entropi": [43, 44, 48, 59, 61, 67, 75, 77], "label_issues_idx": [43, 67], "entropi": [43, 45, 47, 48, 66, 67], "prune_by_class": [43, 59, 94], "predicted_neq_given": [43, 59, 94], "prune_counts_matrix": 43, "smallest": [43, 67], "unus": 43, "number_of_mislabeled_examples_in_class_k": 43, "delet": [43, 79, 83, 93], "too": [43, 48, 66, 87, 88, 93, 98], "thread": [43, 59], "window": [43, 84, 92], "shorter": [43, 62], "find_predicted_neq_given": 43, "find_label_issues_using_argmax_confusion_matrix": 43, "remove_noise_from_class": [44, 52], "clip_noise_r": [44, 52], "clip_valu": [44, 52], "value_count": [44, 52, 93], "value_counts_fill_missing_class": [44, 52], "get_missing_class": [44, 52], "round_preserving_sum": [44, 52], "round_preserving_row_tot": [44, 52], "estimate_pu_f1": [44, 52], "confusion_matrix": [44, 52], "print_square_matrix": [44, 52], "print_noise_matrix": [44, 52, 94], "print_inverse_noise_matrix": [44, 52], "print_joint_matrix": [44, 52, 94], "compress_int_arrai": [44, 52], "train_val_split": [44, 52], "subset_x_i": [44, 52], "subset_label": [44, 52], "subset_data": [44, 52], "extract_indices_tf": [44, 52], "unshuffle_tensorflow_dataset": [44, 52], "is_torch_dataset": [44, 52], "is_tensorflow_dataset": [44, 52], "csr_vstack": [44, 52], "append_extra_datapoint": [44, 52], "get_num_class": [44, 52], "num_unique_class": [44, 52], "get_unique_class": [44, 52], "format_label": [44, 52], "smart_display_datafram": [44, 52], "force_two_dimens": [44, 52], "latent_algebra": [44, 80], "compute_ps_py_inv_noise_matrix": [44, 46], "compute_py_inv_noise_matrix": [44, 46], "compute_inv_noise_matrix": [44, 46], "compute_noise_matrix_from_invers": [44, 46], "compute_pi": [44, 46], "compute_pyx": [44, 46], "label_quality_util": 44, "get_normalized_entropi": [44, 45], "multilabel_util": [44, 97], "stack_compl": [44, 49], "get_onehot_num_class": [44, 49], "int2onehot": [44, 49, 97], "onehot2int": [44, 49, 97], "multilabel_scor": [44, 61], "classlabelscor": [44, 48], "exponential_moving_averag": [44, 48, 61], "softmin": [44, 48, 61, 64, 73, 77], "possible_method": [44, 48], "multilabelscor": [44, 48], "get_class_label_quality_scor": [44, 48], "multilabel_pi": [44, 48], "get_cross_validated_multilabel_pred_prob": [44, 48], "transform_distances_to_scor": [44, 50], "correct_precision_error": [44, 50], "token_classification_util": [44, 103], "get_sent": [44, 51, 103], "filter_sent": [44, 51, 103], "process_token": [44, 51], "merge_prob": [44, 51], "color_sent": [44, 51], "assert_valid_input": [44, 53], "assert_valid_class_label": [44, 53], "assert_nonempty_input": [44, 53], "assert_indexing_work": [44, 53], "labels_to_arrai": [44, 53], "labels_to_list_multilabel": [44, 53], "min_allowed_prob": 45, "wikipedia": 45, "activ": [45, 47, 56, 57, 79, 96], "towardsdatasci": 45, "cheatsheet": 45, "ec57bc067c0b": 45, "clip": [45, 52, 84], "behav": 45, "unnecessari": [45, 93], "slightli": [45, 82, 83, 85], "interv": [45, 48, 99], "herein": 46, "inexact": 46, "cours": 46, "propag": 46, "throughout": [46, 52, 69, 84, 96, 102, 103], "increas": [46, 50, 64, 66, 67, 84, 86, 93, 96, 97, 103], "dot": [46, 77, 93], "true_labels_class_count": 46, "pyx": 46, "multiannot": 47, "assert_valid_inputs_multiannot": 47, "labels_multiannot": [47, 57], "ensembl": [47, 48, 57, 67, 82, 90, 93, 97, 99, 101], "allow_single_label": 47, "annotator_id": 47, "assert_valid_pred_prob": 47, "pred_probs_unlabel": [47, 57], "format_multiannotator_label": [47, 57, 96], "formatted_label": [47, 52], "old": [47, 52, 80, 84, 92], "check_consensus_label_class": 47, "consensus_label": [47, 57, 96], "consensus_method": [47, 57], "consensu": [47, 57, 79, 95, 103], "establish": [47, 56, 83, 101], "compute_soft_cross_entropi": 47, "soft": [47, 92], "find_best_temp_scal": 47, "coarse_search_rang": [47, 69, 93], "fine_search_s": [47, 69, 93], "temperatur": [47, 48, 64, 73, 77], "scale": [47, 50, 82, 92, 93, 99, 102], "factor": [47, 48, 50, 71, 73], "minim": [47, 64, 99], "temp_scale_pred_prob": 47, "temp": 47, "sharpen": [47, 92], "smoothen": 47, "get_normalized_margin_for_each_label": [48, 67], "get_confidence_weighted_entropy_for_each_label": [48, 67], "75": [48, 85, 86, 87, 92, 96, 97, 98, 101, 103], "scorer": 48, "alpha": [48, 61, 64, 85, 86, 87, 94, 97, 101], "exponenti": 48, "ema": 48, "s_1": 48, "s_k": 48, "ema_k": 48, "accord": [48, 59, 90, 91, 94, 103], "formula": [48, 50], "_t": 48, "cdot": 48, "s_t": 48, "qquad": 48, "leq": 48, "_1": 48, "give": [48, 67, 94, 96, 102], "recent": [48, 103], "success": 48, "previou": [48, 88, 93, 98], "discount": 48, "s_ema": 48, "175": [48, 88, 94, 98], "underflow": 48, "nan": [48, 57, 82, 90, 96, 101], "aggregated_scor": 48, "base_scor": 48, "base_scorer_kwarg": 48, "aggregator_kwarg": [48, 61], "n_sampl": 48, "n_label": 48, "worst": [48, 96], "class_label_quality_scor": 48, "42": [48, 91, 92, 98, 103], "452": 48, "new_scor": 48, "575": 48, "get_label_quality_scores_per_class": [48, 60, 61], "ml_scorer": 48, "binar": [48, 49], "reformat": [48, 84], "wider": 48, "splitter": 48, "kfold": [48, 88], "onevsrestclassifi": [48, 97], "randomforestclassifi": [48, 94, 97], "n_split": [48, 87, 88, 97], "pred_prob_slic": 49, "onehot": 49, "hot": [49, 59, 65, 71, 74, 82, 90, 92, 93, 101, 102], "onehot_matrix": 49, "avg_dist": 50, "scaling_factor": 50, "exp": [50, 66, 67, 86], "dt": 50, "right": [50, 62, 65, 83, 91, 97, 98, 99], "strength": [50, 65], "pronounc": 50, "differenti": 50, "ly": 50, "rule": [50, 51, 92], "thumb": 50, "ood_features_scor": [50, 66, 99], "88988177": 50, "80519832": 50, "toler": 50, "minkowski": 50, "noth": 50, "epsilon": 50, "sensibl": 50, "fixed_scor": 50, "readabl": 51, "lambda": [51, 84, 86, 93, 96], "long_sent": 51, "headlin": 51, "charact": [51, 52], "s1": 51, "s2": 51, "processed_token": 51, "alecnlcb": 51, "entiti": [51, 79, 93, 103], "mapped_ent": 51, "unique_ident": 51, "loc": [51, 85, 86, 87, 88, 103], "nbitbas": [51, 61], "probs_merg": 51, "0125": [51, 77], "0375": 51, "075": 51, "025": 51, "color": [51, 74, 85, 86, 87, 90, 94, 97, 99, 101, 102], "red": [51, 65, 85, 86, 87, 92, 94, 97, 98, 99, 102], "colored_sent": 51, "termcolor": 51, "31msentenc": 51, "0m": 51, "ancillari": 52, "class_without_nois": 52, "any_other_class": 52, "choos": [52, 67, 82, 90, 93, 94, 101], "tradition": 52, "new_sum": 52, "fill": 52, "come": [52, 85, 86, 87, 93, 102], "major": [52, 57, 80, 85, 88, 99], "versu": [52, 94], "obviou": 52, "cgdeboer": 52, "iteround": 52, "reach": 52, "prob_s_eq_1": 52, "claesen": 52, "f1": [52, 65, 91, 94], "BE": 52, "left_nam": 52, "top_nam": 52, "titl": [52, 85, 86, 87, 94, 97, 99], "short_titl": 52, "round_plac": 52, "pretti": [52, 85, 94], "joint_matrix": 52, "num_possible_valu": 52, "holdout_idx": 52, "extract": [52, 66, 83, 84, 91, 96, 99, 102], "allow_shuffl": 52, "turn": [52, 79, 98], "shuffledataset": 52, "histori": 52, "pre_x": 52, "buffer_s": 52, "csr_matric": 52, "append": [52, 84, 85, 88, 92, 93, 94, 96, 97, 98, 99, 103], "bottom": [52, 62, 65, 98], "to_data": 52, "from_data": 52, "taken": 52, "label_matrix": 52, "canon": 52, "displai": [52, 65, 74, 78, 83, 84, 85, 91, 94, 103], "jupyt": [52, 84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "notebook": [52, 57, 84, 85, 87, 92, 93, 94, 96, 97, 98, 102, 103], "consol": 52, "html": [52, 62, 65, 66, 90, 93, 94], "allow_missing_class": 53, "allow_one_class": 53, "length_x": 53, "labellik": 53, "labels_list": [53, 59], "keraswrappermodel": [55, 56, 79], "keraswrappersequenti": [55, 56], "tf": [56, 84], "legaci": 56, "newer": 56, "interim": 56, "advis": [56, 97], "stabil": 56, "until": 56, "accommod": 56, "keraswrapp": 56, "huggingface_keras_imdb": 56, "unit": [56, 103], "model_kwarg": [56, 69], "compile_kwarg": 56, "sparsecategoricalcrossentropi": 56, "layer": [56, 83, 84, 91, 99], "dens": 56, "my_keras_model": 56, "from_logit": 56, "declar": 56, "apply_softmax": 56, "analysi": 57, "analyz": [57, 79, 94, 96, 97], "get_label_quality_multiannot": [57, 96], "vote": 57, "crowdsourc": [57, 79, 96], "dawid": [57, 96], "skene": [57, 96], "analog": [57, 85, 92, 96], "chosen": [57, 67, 93, 96], "crowdlab": [57, 96], "unlabel": [57, 88, 90, 91, 96, 99, 102], "decid": [57, 83, 91, 92, 96, 101, 103], "get_active_learning_scor": [57, 96], "activelab": [57, 96], "priorit": [57, 64, 98, 102, 103], "showcas": 57, "main": [57, 85], "best_qual": 57, "quality_method": 57, "calibrate_prob": 57, "return_detailed_qu": 57, "return_annotator_stat": 57, "return_weight": 57, "label_quality_score_kwarg": 57, "necessarili": [57, 65, 91, 94], "did": [57, 58, 82, 83, 84, 90, 94, 96, 101], "majority_vot": 57, "broken": [57, 65, 92, 101], "highest": [57, 65, 86, 88, 100], "0th": 57, "consensus_quality_scor": [57, 96], "annotator_agr": [57, 96], "reman": 57, "1st": 57, "2nd": [57, 71], "3rd": 57, "consensus_label_suffix": 57, "consensus_quality_score_suffix": 57, "suffix": 57, "emsembl": 57, "weigh": [57, 92], "agreement": [57, 96], "agre": 57, "prevent": [57, 93], "overconfid": [57, 100], "wrong": [57, 62, 64, 80, 83, 85, 86, 87, 91, 93, 94, 98], "detailed_label_qu": [57, 96], "annotator_stat": [57, 96], "model_weight": 57, "annotator_weight": 57, "warn": [57, 86, 87, 88, 90, 91, 93, 94], "labels_info": 57, "num_annot": [57, 96], "deriv": [57, 96], "quality_annotator_1": 57, "quality_annotator_2": 57, "quality_annotator_m": 57, "annotator_qu": [57, 96], "num_examples_label": [57, 96], "agreement_with_consensu": [57, 96], "worst_class": [57, 96], "trustworthi": [57, 96, 101], "get_label_quality_multiannotator_ensembl": 57, "weigtht": 57, "budget": 57, "retrain": [57, 83, 101], "active_learning_scor": 57, "active_learning_scores_unlabel": 57, "get_active_learning_scores_ensembl": 57, "henc": [57, 84, 86, 96], "get_majority_vote_label": [57, 96], "event": 57, "lastli": [57, 90], "convert_long_to_wide_dataset": 57, "labels_multiannotator_long": 57, "wide": [57, 82, 83, 84], "suitabl": [57, 82, 90], "labels_multiannotator_wid": 57, "common_multilabel_issu": [58, 60], "mutual": [58, 97], "exclus": [58, 97], "rank_classes_by_multilabel_qu": [58, 60], "overall_multilabel_health_scor": [58, 60], "multilabel_health_summari": [58, 60], "classes_by_multilabel_qu": 58, "inner": [59, 73], "find_multilabel_issues_per_class": [59, 60], "per_class_label_issu": 59, "label_issues_list": 59, "pred_probs_list": [59, 67, 88, 94], "anim": [60, 99], "rat": 60, "predat": 60, "pet": 60, "reptil": 60, "manner": [61, 82, 83, 96, 101], "box": [62, 64, 65, 92, 98], "object_detect": [62, 64, 65, 98], "return_indices_ranked_by_scor": [62, 98], "overlapping_label_check": [62, 64], "suboptim": [62, 64], "locat": [62, 64, 98, 102, 103], "bbox": [62, 65, 98], "image_nam": [62, 65], "y1": [62, 65, 98], "y2": [62, 65, 98], "later": [62, 65, 66, 83, 103], "corner": [62, 65, 98], "xyxi": [62, 65, 98], "io": [62, 65, 84, 92], "keras_cv": [62, 65], "bounding_box": [62, 65, 98], "detectron": [62, 65, 98], "detectron2": [62, 65, 98], "readthedoc": [62, 65], "en": [62, 65], "latest": [62, 65], "visual": [62, 63, 65, 82, 85, 86, 87, 88, 101, 103], "draw_box": [62, 65], "mmdetect": [62, 65, 98], "swap": [62, 64, 74, 78], "penal": [62, 64], "concern": [62, 64, 79, 87], "issues_from_scor": [63, 64, 72, 73, 74, 76, 77, 78, 98, 102, 103], "compute_overlooked_box_scor": [63, 64], "compute_badloc_box_scor": [63, 64], "compute_swap_box_scor": [63, 64], "pool_box_scores_per_imag": [63, 64], "object_counts_per_imag": [63, 65, 98], "bounding_box_size_distribut": [63, 65, 98], "class_label_distribut": [63, 65, 98], "get_sorted_bbox_count_idx": [63, 65], "plot_class_size_distribut": [63, 65], "plot_class_distribut": [63, 65], "get_average_per_class_confusion_matrix": [63, 65], "calculate_per_class_metr": [63, 65], "aggregation_weight": 64, "imperfect": [64, 93], "chose": [64, 96, 98], "imperfectli": [64, 98], "dirti": [64, 67, 70, 101], "subtyp": 64, "badloc": 64, "nonneg": 64, "high_probability_threshold": 64, "auxiliary_input": [64, 65], "vari": [64, 87], "iou": [64, 65], "heavili": 64, "auxiliarytypesdict": 64, "pred_label": [64, 83], "pred_label_prob": 64, "pred_bbox": 64, "lab_label": 64, "lab_bbox": 64, "similarity_matrix": 64, "min_possible_similar": 64, "scores_overlook": 64, "low_probability_threshold": 64, "scores_badloc": 64, "accident": [64, 83, 90, 91, 93], "scores_swap": 64, "box_scor": 64, "image_scor": [64, 73, 102], "discov": [65, 87, 103], "abnorm": [65, 88, 98], "auxiliari": [65, 99, 102], "_get_valid_inputs_for_compute_scor": 65, "object_count": 65, "down": 65, "bbox_siz": 65, "class_distribut": 65, "plot": [65, 85, 86, 87, 94, 97, 99, 101, 102], "sorted_idx": [65, 99], "class_to_show": 65, "hidden": [65, 85, 99], "max_class_to_show": 65, "plt": [65, 74, 85, 86, 87, 88, 94, 97, 99, 101], "matplotlib": [65, 74, 85, 86, 87, 88, 94, 97, 98, 99, 101], "pyplot": [65, 74, 85, 86, 87, 88, 94, 97, 99, 101], "prediction_threshold": 65, "overlai": [65, 98], "figsiz": [65, 85, 86, 87, 88, 94, 97, 99], "save_path": [65, 98], "blue": [65, 92, 94, 98], "overlaid": 65, "side": [65, 92, 98], "figur": [65, 94, 97, 99, 101], "extens": [65, 94, 96], "png": [65, 98], "pdf": [65, 66], "svg": 65, "num_proc": [65, 88], "intersect": [65, 93], "tp": 65, "fp": 65, "ground": [65, 92, 94, 96, 101], "truth": [65, 94, 96, 101], "bias": 65, "avg_metr": 65, "distionari": 65, "95": [65, 75, 77, 90, 92, 94, 101], "per_class_metr": 65, "Of": 66, "li": 66, "smaller": [66, 85, 97, 98], "find_top_issu": [66, 67, 99], "reli": [66, 83, 84, 85, 86, 87, 91, 98, 99, 101], "dist_metr": 66, "subtract": [66, 67], "renorm": [66, 67, 93], "least_confid": 66, "sum_": 66, "log": [66, 67, 80], "softmax": [66, 73, 77, 88], "literatur": 66, "gen": 66, "liu": 66, "lochman": 66, "zach": 66, "openaccess": 66, "thecvf": 66, "cvpr2023": 66, "liu_gen_pushing_the_limits_of_softmax": 66, "based_out": 66, "distribution_detection_cvpr_2023_pap": 66, "fit_scor": [66, 99], "ood_predictions_scor": 66, "pretrain": [66, 83, 84, 91, 99], "adjust_confident_threshold": 66, "probabilist": [66, 82, 84, 86, 87, 90, 91, 99, 100], "order_label_issu": [67, 80], "whichev": [67, 100], "argsort": [67, 83, 88, 91, 94, 98, 99, 101], "max_": 67, "get_label_quality_ensemble_scor": [67, 93, 94], "weight_ensemble_members_bi": 67, "custom_weight": 67, "log_loss_search_t_valu": 67, "0001": [67, 92], "scheme": 67, "log_loss_search": 67, "log_loss": [67, 91], "1e0": 67, "1e1": 67, "1e2": 67, "2e2": 67, "quality_scor": [67, 99], "forth": 67, "top_issue_indic": 67, "rank_bi": [67, 80], "weird": [67, 78], "minu": 67, "prob_label": 67, "max_prob_not_label": 67, "idea": [67, 85, 98], "AND": [67, 91], "get_epistemic_uncertainti": [68, 69], "get_aleatoric_uncertainti": [68, 69], "corrupt": [69, 101], "linearregress": [69, 93, 101], "y_with_nois": 69, "n_boot": [69, 93], "include_aleatoric_uncertainti": [69, 93], "sole": [69, 82, 86, 96, 99], "bootstrap": [69, 93, 101], "resampl": [69, 84, 93], "epistem": [69, 93, 99, 101], "aleator": [69, 93, 101], "model_final_kwarg": 69, "coars": 69, "thorough": [69, 93], "fine": [69, 83, 84, 91, 99], "grain": 69, "grid": 69, "varianc": [69, 94], "epistemic_uncertainti": 69, "residu": [69, 70, 93], "deviat": [69, 98, 101], "aleatoric_uncertainti": 69, "outr": 70, "contin": 70, "raw": [70, 79, 80, 87, 88, 92, 93, 96, 98, 99, 101], "aka": [70, 84, 94, 98, 101, 103], "00323821": 70, "33692597": 70, "00191686": 70, "semant": [71, 73, 74, 95], "pixel": [71, 73, 74, 99, 102], "h": [71, 73, 74, 102], "height": [71, 73, 74, 102], "w": [71, 73, 74, 102], "width": [71, 73, 74, 102], "labels_one_hot": [71, 74, 102], "stream": [71, 85, 99, 103], "downsampl": [71, 73, 102], "shrink": [71, 73], "divis": [71, 73, 86], "common_label_issu": [72, 74, 76, 78, 102, 103], "filter_by_class": [72, 74, 102], "segmant": [73, 74], "num_pixel_issu": [73, 102], "product": [73, 88, 93], "pixel_scor": [73, 102], "highlight": [74, 78, 85, 86, 87, 90, 102], "enter": 74, "legend": [74, 85, 86, 87, 97, 98, 101, 102], "colormap": 74, "background": 74, "person": [74, 93, 98, 102, 103], "ambigu": [74, 78, 83, 84, 91, 92, 94, 103], "systemat": [74, 78, 96], "misunderstood": [74, 78], "issues_df": [74, 88], "class_index": 74, "issues_subset": [74, 78], "filter_by_token": [76, 78, 103], "token_score_method": 77, "sentence_score_method": 77, "sentence_score_kwarg": 77, "compris": [77, 78], "token_scor": [77, 103], "converg": 77, "toward": 77, "_softmin_sentence_scor": 77, "sentence_scor": [77, 103], "token_info": 77, "70": [77, 90], "02": [77, 86, 87, 94, 98], "03": [77, 90, 92, 94, 98, 103], "04": [77, 90, 98, 99, 103], "08": [77, 94, 98, 101, 103], "commonli": [78, 80, 86, 87, 97, 103], "But": [78, 91, 94, 101, 103], "restrict": [78, 93], "reliabl": [79, 82, 84, 93, 96, 102], "thousand": 79, "imagenet": [79, 92], "popular": [79, 96, 98], "centric": [79, 88, 90, 91, 95], "capabl": 79, "minut": [79, 82, 83, 84, 90, 91, 92, 96, 97, 98, 101, 102, 103], "conda": 79, "feature_embed": [79, 99], "Then": [79, 82, 83, 88, 93], "your_dataset": [79, 84, 86, 87, 88, 90, 91, 93], "column_name_of_label": [79, 84, 86, 87, 88, 90, 91], "plagu": [79, 87], "untrain": 79, "\u30c4": 79, "label_issues_info": [79, 87], "sklearn_compatible_model": 79, "framework": [79, 97, 98], "complianc": 79, "tag": [79, 97, 103], "sequenc": 79, "recognit": [79, 84, 93, 103], "train_data": [79, 82, 83, 99, 101], "gotten": 79, "test_data": [79, 82, 83, 85, 94, 97, 99, 101], "deal": [79, 87], "tutori": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "feel": [79, 84, 87, 93], "ask": [79, 93], "slack": [79, 93], "project": [79, 101], "welcom": 79, "commun": [79, 93], "guidelin": [79, 98], "piec": 79, "smart": [79, 88, 90, 91, 93], "edit": [79, 93], "easier": [79, 94], "unreli": [79, 82, 84, 90, 91], "link": [79, 84, 92, 98], "older": 80, "outlin": 80, "substitut": 80, "v2": [80, 82, 90], "get_noise_indic": 80, "psx": 80, "sorted_index_method": 80, "order_label_error": 80, "label_errors_bool": 80, "latent_estim": 80, "num_label_error": 80, "learningwithnoisylabel": 80, "neatli": 80, "organ": [80, 82, 90, 92, 103], "reorgan": 80, "baseline_method": 80, "incorpor": [80, 94], "research": [80, 94], "polyplex": 80, "terminologi": 80, "label_error": 80, "quickstart": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 96, 97, 98, 99, 101, 102, 103], "sql": [82, 90], "databas": [82, 90], "excel": [82, 90], "parquet": [82, 90], "student": [82, 90, 101, 103], "grade": [82, 90, 101], "900": [82, 90, 101], "exam": [82, 90, 101], "letter": [82, 90, 103], "hundr": [82, 90], "mistak": [82, 83, 88, 90, 91], "No": [82, 83, 91, 93], "extratreesclassifi": 82, "extratre": 82, "ranked_label_issu": [82, 83], "branch": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101], "preprocess": [82, 83, 87, 90, 99, 101], "standardscal": [82, 90, 99], "labelencod": [82, 83], "train_test_split": [82, 83, 85, 86, 87, 99], "accuracy_scor": [82, 83, 84, 91, 94], "grades_data": [82, 90], "read_csv": [82, 83, 90, 91, 101], "demo": [82, 87, 90, 97], "head": [82, 83, 84, 87, 88, 90, 91, 92, 94, 96, 101], "stud_id": [82, 90], "exam_1": [82, 90, 101], "exam_2": [82, 90, 101], "exam_3": [82, 90, 101], "letter_grad": [82, 90], "f48f73": [82, 90], "53": [82, 85, 86, 87, 90, 92, 97, 98], "00": [82, 86, 87, 88, 90, 92, 99], "77": [82, 86, 87, 90, 98], "0bd4e7": [82, 90], "81": [82, 90, 91, 98, 101, 103], "great": [82, 85, 90, 92], "particip": [82, 90], "cb9d7a": [82, 90], "61": [82, 90, 94, 98, 101], "94": [82, 90, 92, 94, 98, 101], "78": [82, 90, 92, 94, 98, 101], "9acca4": [82, 90], "48": [82, 90, 92, 94, 98], "x_raw": [82, 90], "labels_raw": 82, "interg": [82, 83], "categorical_featur": [82, 101], "x_encod": [82, 90], "get_dummi": [82, 90, 101], "drop_first": [82, 90], "numeric_featur": [82, 90], "scaler": [82, 90, 99], "x_process": [82, 90], "fit_transform": [82, 90], "bring": [82, 83, 88, 90, 91, 96, 101], "byod": [82, 83, 88, 90, 91, 96, 101], "decis": [82, 85, 86, 87], "tress": 82, "held": [82, 84, 90, 91, 92, 98, 99, 100], "straightforward": [82, 84, 90], "benefit": [82, 84, 100, 102], "num_crossval_fold": [82, 84, 90, 96], "u": [82, 83, 84, 86, 88, 90, 93, 94, 96, 97, 100, 101, 102, 103], "tabl": [82, 90, 92, 96], "212": [82, 94], "review": [82, 83, 87, 90, 91, 92, 93, 94, 98, 101, 102, 103], "iloc": [82, 83, 84, 90, 91, 101], "58": [82, 87, 90, 92, 94, 98], "92": [82, 86, 94, 98], "93": [82, 92, 98, 101], "827": 82, "99": [82, 92, 94], "86": [82, 87, 88, 90, 94, 98, 101], "74": [82, 98, 101], "637": [82, 90], "79": [82, 92, 98], "65": [82, 86, 98], "cheat": 82, "0pt": 82, "120": [82, 86, 87], "97": [82, 92, 93, 94, 98, 101, 103], "233": [82, 103], "68": [82, 92, 94, 98], "83": [82, 94, 98, 101, 103], "76": [82, 85, 94, 97, 98, 101], "suspici": [82, 90], "carefulli": [82, 88, 90, 91], "examin": [82, 85, 86, 87, 90, 98], "labels_train": 82, "labels_test": 82, "test_siz": [82, 83, 85, 86, 87], "acc_og": [82, 83], "783068783068783": 82, "robustli": [82, 83, 101], "14": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "acc_cl": [82, 83], "8095238095238095": 82, "especi": [82, 83, 85, 88, 93, 101], "blindli": [82, 83, 84, 93, 101], "trust": [82, 83, 84, 93, 94, 96, 100, 101], "effort": [82, 83, 101], "intent": [83, 91], "servic": [83, 91, 93], "onlin": [83, 91], "bank": [83, 91, 92], "banking77": [83, 91], "oo": [83, 91], "000": [83, 88, 91, 92, 103], "categori": [83, 88, 91], "shortlist": [83, 91, 101], "scope": [83, 91], "logist": [83, 85, 86, 87, 91, 96, 99], "probabilit": [83, 84], "drop": [83, 93, 96, 101], "earlier": [83, 103], "sentence_transform": [83, 91], "sentencetransform": [83, 91], "payment": [83, 91], "cancel_transf": [83, 91], "transfer": [83, 91], "fund": [83, 91], "cancel": [83, 91], "transact": [83, 91], "my": [83, 91], "revert": [83, 91], "morn": [83, 91], "realis": [83, 91], "yesterdai": [83, 91], "rent": [83, 91], "realli": [83, 91, 96, 102], "tomorrow": [83, 91], "raw_text": [83, 91], "raw_label": 83, "raw_train_text": 83, "raw_test_text": 83, "raw_train_label": 83, "raw_test_label": 83, "39": [83, 84, 86, 88, 91, 92, 93, 98, 101, 102, 103], "supported_cards_and_curr": [83, 91], "beneficiary_not_allow": [83, 91], "card_payment_fee_charg": [83, 91], "getting_spare_card": [83, 91], "card_about_to_expir": [83, 91], "visa_or_mastercard": [83, 91], "lost_or_stolen_phon": [83, 91], "change_pin": [83, 91], "apple_pay_or_google_pai": [83, 91], "card": [83, 91, 92], "utter": [83, 91], "encond": 83, "test_label": [83, 94, 97, 99], "suit": [83, 91, 92, 93], "electra": [83, 91], "discrimin": [83, 91], "googl": [83, 85, 91], "train_text": 83, "test_text": 83, "home": [83, 86, 87, 91, 92], "runner": [83, 86, 87, 91], "google_electra": [83, 91], "pool": [83, 91, 93, 99], "opt": [83, 84, 87, 88, 90, 91, 94], "hostedtoolcach": [83, 84, 87, 88, 90, 91, 94], "x64": [83, 84, 87, 88, 90, 91, 94], "lib": [83, 84, 87, 88, 90, 91, 94], "python3": [83, 84, 87, 88, 90, 91, 94], "site": [83, 84, 87, 88, 90, 91, 94], "_util": [83, 91], "831": [83, 91], "userwarn": [83, 84, 86, 87, 91], "typedstorag": [83, 91], "untypedstorag": [83, 91], "untyped_storag": [83, 91], "fget": [83, 91], "__get__": [83, 91], "owner": [83, 91], "leverag": [83, 84, 91, 93, 94, 96], "computation": [83, 84, 91], "intens": [83, 84, 91], "400": [83, 85, 91], "858371": 83, "547274": 83, "826228": 83, "966008": 83, "792449": 83, "identified_issu": [83, 101], "lowest_quality_label": [83, 84, 91, 94, 101], "to_numpi": [83, 91, 93, 101], "44": [83, 88, 92, 97, 98], "646": 83, "390": 83, "628": 83, "121": [83, 85, 94], "702": 83, "863": [83, 84], "135": 83, "337": [83, 98], "735": 83, "print_as_df": 83, "inverse_transform": 83, "charg": [83, 91], "cash": [83, 91], "holidai": [83, 91], "sent": [83, 91, 103], "mine": [83, 91], "expir": [83, 91], "fight": 83, "hors": [83, 92, 99], "duck": [83, 92], "me": [83, 91], "whoever": [83, 91], "consum": [83, 101], "18": [83, 84, 91, 92, 93, 94, 98, 99, 101, 102], "baseline_model": [83, 101], "87": [83, 87, 88, 98, 101], "acceler": [83, 101], "19": [83, 84, 88, 91, 92, 93, 94, 98, 99, 101, 102, 103], "89": [83, 85, 86, 90, 98, 101], "spoken": 84, "500": [84, 99, 103], "english": [84, 92], "pronunci": 84, "wav": 84, "huggingfac": [84, 86, 87, 88, 93], "voxceleb": 84, "speech": [84, 103], "your_pred_prob": [84, 85, 86, 87, 90, 91], "tensorflow_io": 84, "huggingface_hub": 84, "reproduc": [84, 90, 94, 96], "command": 84, "wget": [84, 98, 102, 103], "navig": 84, "browser": 84, "jakobovski": 84, "archiv": [84, 103], "v1": 84, "tar": [84, 99], "gz": [84, 99], "mkdir": [84, 103], "spoken_digit": 84, "xf": 84, "6_nicolas_32": 84, "data_path": 84, "listdir": 84, "nondeterminist": 84, "file_nam": 84, "endswith": 84, "file_path": 84, "join": [84, 85, 88, 93], "7_george_26": 84, "0_nicolas_24": 84, "0_nicolas_6": 84, "listen": 84, "display_exampl": 84, "expand": [84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "pulldown": [84, 85, 86, 87, 88, 92, 94, 96, 97, 99, 101, 103], "colab": [84, 85, 86, 87, 88, 92, 93, 94, 96, 97, 99, 101, 103], "tfio": 84, "pathlib": 84, "ipython": 84, "load_wav_16k_mono": 84, "filenam": 84, "khz": 84, "file_cont": 84, "read_fil": 84, "sample_r": 84, "decode_wav": 84, "desired_channel": 84, "squeez": 84, "rate_in": 84, "rate_out": 84, "16000": 84, "wav_file_nam": 84, "audio_r": 84, "wav_file_exampl": 84, "plai": [84, 92, 93], "button": 84, "wav_file_name_exampl": 84, "7_jackson_43": 84, "hear": 84, "extractor": 84, "encoderclassifi": 84, "spkrec": 84, "xvect": 84, "feature_extractor": 84, "from_hparam": 84, "run_opt": 84, "uncom": 84, "ffmpeg": 84, "backend": 84, "wav_audio_file_path": 84, "torchaudio": 84, "extract_audio_embed": 84, "emb": [84, 88], "signal": 84, "encode_batch": 84, "embeddings_list": [84, 88], "embeddings_arrai": 84, "650": 84, "stft": 84, "return_complex": 84, "view_as_r": 84, "recov": 84, "trigger": 84, "aten": 84, "src": 84, "nativ": 84, "spectralop": 84, "cpp": 84, "_vf": 84, "n_fft": 84, "hop_length": 84, "win_length": 84, "attr": 84, "512": [84, 88], "196311": 84, "319459": 84, "478975": 84, "2890875": 84, "8170238": 84, "89265": 84, "24": [84, 88, 92, 94, 96, 98, 101, 103], "898056": 84, "256195": 84, "559641": 84, "559721": 84, "62067": 84, "285245": 84, "21": [84, 86, 92, 93, 94, 98, 101, 103], "709627": 84, "5033693": 84, "913803": 84, "819831": 84, "1831515": 84, "208763": 84, "084257": 84, "3210397": 84, "005453": 84, "216152": 84, "478235": 84, "6821785": 84, "053807": 84, "242471": 84, "091424": 84, "78334856": 84, "03954": 84, "23": [84, 88, 92, 94, 98, 101, 103], "569176": 84, "761097": 84, "1258295": 84, "753237": 84, "3508866": 84, "598274": 84, "23712": 84, "2500": 84, "tol": 84, "decreas": [84, 93], "cv_accuraci": 84, "9708": 84, "9976": 84, "986": 84, "002161": 84, "176": [84, 92, 94, 97], "002483": 84, "2318": 84, "004411": 84, "1005": 84, "004857": 84, "1871": 84, "007494": 84, "investig": [84, 85], "040587": 84, "999207": 84, "999377": 84, "975220": 84, "999367": 84, "identified_label_issu": [84, 91], "sort_valu": [84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 101], "516": 84, "1946": 84, "469": 84, "2132": 84, "worth": [84, 94], "6_yweweler_25": 84, "7_nicolas_43": 84, "6_theo_27": 84, "6_yweweler_36": 84, "6_yweweler_14": 84, "6_yweweler_35": 84, "6_nicolas_8": 84, "sound": 84, "quit": [84, 99], "22": [84, 86, 88, 92, 93, 94, 97, 98, 103], "ve": [85, 92, 93, 94, 96, 98], "prove": 85, "monitor": [85, 92], "ran": 85, "data_monitor": 85, "your_datalab": 85, "new_data_batch": 85, "your_label": 85, "get_your_label": 85, "websit": [85, 92], "todo": 85, "get_ipython": 85, "cc319efea07da004d1544c0577402d71f309fa06": 85, "cmd": 85, "dep": 85, "dependencies_test": 85, "missing_depend": 85, "__import__": 85, "importerror": 85, "sep": [85, 103], "npleas": 85, "toi": [85, 86, 87, 88, 92, 94, 96], "mid": [85, 86, 87], "workflow": [85, 95, 101], "unseen": 85, "inf": [85, 86, 87], "bins_map": [85, 86, 87], "create_data": [85, 86, 87], "800": 85, "y_bin": [85, 86, 87], "y_i": [85, 86, 87], "y_bin_idx": [85, 86, 87], "y_train": [85, 86, 87, 94, 101], "y_test": [85, 86, 87, 94, 101], "y_train_idx": [85, 86, 87], "y_test_idx": [85, 86, 87], "slide": [85, 86, 87, 92], "frame": [85, 86, 87], "x_out": [85, 86, 87], "tini": [85, 86, 87], "concaten": [85, 86, 87, 93, 100], "y_out": [85, 86, 87], "y_out_bin": [85, 86, 87], "y_out_bin_idx": [85, 86, 87], "exact_duplicate_idx": [85, 86, 87], "x_duplic": [85, 86, 87], "y_duplic": [85, 86, 87], "y_duplicate_idx": [85, 86, 87], "noisy_labels_idx": [85, 86, 87, 97], "train_x": 85, "test_x": 85, "train_y_tru": 85, "test_y_tru": 85, "train_i": 85, "test_i": 85, "train_y_idx": 85, "test_y_idx": 85, "scatter": [85, 86, 87, 94, 97, 101], "black": [85, 86, 87, 92, 101], "cyan": [85, 86, 87], "plot_data": [85, 86, 87, 94, 97, 101], "fig": [85, 86, 87, 88, 92, 99, 101], "ax": [85, 86, 87, 88, 99, 101], "subplot": [85, 86, 87, 88, 99], "set_titl": [85, 86, 87, 88, 99], "set_xlabel": [85, 86, 87], "x_1": [85, 86, 87], "fontsiz": [85, 86, 87, 88, 94, 97], "set_ylabel": [85, 86, 87], "x_2": [85, 86, 87], "set_xlim": [85, 86, 87], "set_ylim": [85, 86, 87], "linestyl": [85, 86, 87], "circl": [85, 86, 87, 94, 97], "misclassifi": [85, 86, 87], "zip": [85, 86, 87, 88, 98, 103], "label_err": [85, 86, 87], "180": [85, 86, 87, 98], "marker": [85, 86, 87], "facecolor": [85, 86, 87], "edgecolor": [85, 86, 87], "linewidth": [85, 86, 87, 99], "title_fontproperti": [85, 86, 87], "semibold": [85, 86, 87], "first_legend": [85, 86, 87], "align": [85, 86, 87], "markerscal": 85, "second_legend": [85, 86, 87], "46": [85, 90, 92, 94, 98], "gca": [85, 86, 87], "add_artist": [85, 86, 87], "tight_layout": [85, 86, 87], "ideal": [85, 86, 87], "simplic": [85, 97], "327": [85, 98], "9297": 85, "000124": 85, "259": 85, "000725": 85, "269": 85, "000794": 85, "002061": 85, "125": [85, 86], "002908": 85, "fly": [85, 92], "feed": [85, 93], "simul": 85, "tqdm": [85, 88], "sleep": [85, 92], "generate_stream": 85, "sleep_tim": 85, "desc": 85, "singleton_stream": 85, "seamless": [85, 93], "singleton": 85, "batched_stream": 85, "processed_singleton": 85, "suggested_label": [85, 91], "250997": 85, "285757": 85, "43": [85, 86, 92, 94, 98], "120906": 85, "principl": 85, "processed_batch": 85, "51": [85, 86, 87, 90, 92, 94, 98], "002748": 85, "189996": 85, "093505": 85, "037250": 85, "149": [85, 98], "076397": 85, "154": 85, "294010": 85, "160": [85, 91, 101], "073622": 85, "166": [85, 88], "140832": 85, "167": [85, 92, 94, 98], "041743": 85, "181": 85, "169429": 85, "127304": 85, "235": [85, 98], "090310": 85, "254": [85, 92, 98], "183343": 85, "256": [85, 92, 93, 98], "048720": 85, "263": [85, 97, 98], "138820": 85, "292": 85, "239609": 85, "295": [85, 98], "022075": 85, "306": 85, "103040": 85, "343": 85, "234755": 85, "354": 85, "001612": 85, "359": 85, "068359": 85, "367": [85, 101], "015793": 85, "368": 85, "029022": 85, "391": 85, "106761": 85, "troublesom": 85, "623844": 85, "812647": 85, "816854": 85, "661968": 85, "632244": 85, "395": 85, "474599": 85, "396": 85, "653901": 85, "397": 85, "584554": 85, "398": 85, "817287": 85, "399": 85, "881545": 85, "183": 85, "937927": 85, "309": 85, "939505": 85, "133": 85, "947290": 85, "177": [85, 103], "952187": 85, "314": [85, 98], "997293": 85, "27": [85, 90, 92, 94, 98, 103], "655501": 85, "underneath": 86, "hood": [86, 93], "alert": 86, "introduct": 86, "mayb": [86, 87, 91], "your_feature_matrix": [86, 87], "dup": [86, 87], "45": [86, 87, 92, 94, 98], "remaind": 86, "modal": [86, 87, 93, 96], "132": [86, 87, 94, 98], "9318": 86, "006940": 86, "007830": 86, "40": [86, 87, 91, 92], "014828": 86, "107": [86, 87, 94, 97], "021241": 86, "026407": 86, "notic": [86, 94, 96, 98], "3558": [86, 87], "126": [86, 87, 94, 98], "006636": [86, 87], "130": [86, 87, 103], "012571": [86, 87], "129": [86, 87], "127": [86, 87], "014909": [86, 87], "128": [86, 87, 88], "017443": [86, 87], "6160": [86, 87], "is_near_duplicate_issu": [86, 87, 88, 90, 91, 93, 94], "131": [86, 87, 102], "000000e": [86, 87], "000002": [86, 87], "463180e": [86, 87], "07": [86, 87, 88, 90, 94, 98, 101], "161148": [86, 87], "859087e": [86, 87], "30": [86, 87, 88, 92, 93, 97, 102, 103], "3453": 86, "029542": 86, "031182": 86, "057961": 86, "058244": 86, "348": 86, "378": 86, "357": 86, "34": [86, 92, 94, 96, 98, 103], "54": [86, 92, 94, 98, 103], "039122": 86, "044598": 86, "105": [86, 98], "105196": 86, "133654": 86, "168033": 86, "101107": 86, "37": [86, 92], "183382": 86, "109": [86, 92, 98], "209259": 86, "211042": 86, "221316": 86, "average_ood_scor": 86, "34530442089193386": 86, "52": [86, 92, 98, 103], "169820": 86, "087324e": 86, "259024": 86, "583757e": 86, "91": [86, 98], "346458": 86, "341292e": 86, "specfi": 86, "new_lab": 86, "scoring_funct": 86, "div": 86, "rem": 86, "inv_scal": 86, "49": [86, 92, 94, 98, 103], "superstitionissuemanag": 86, "unlucki": 86, "superstit": 86, "to_seri": 86, "issues_mask": 86, "summary_scor": 86, "9242": 86, "is_superstition_issu": 86, "superstition_scor": 86, "26": [86, 88, 92, 94, 96, 98, 103], "047581": 86, "090635": 86, "129591": 86, "164840": 86, "lurk": [87, 88, 94], "_split": 87, "737": 87, "thoroughli": 87, "904": 87, "_base": [87, 88, 90, 91, 94], "246": [87, 88, 90, 91, 94, 98], "efficiencywarn": [87, 88, 90, 91, 94], "sort_graph_by_row_valu": [87, 88, 90, 91, 94], "warn_when_not_sort": [87, 88, 90, 91, 94], "8561": 87, "001908": 87, "003564": 87, "007331": 87, "008963": 87, "009664": 87, "0227": 87, "is_class_imbalance_issu": 87, "022727": 87, "conceptu": 87, "856061": 87, "355772": 87, "616034": 87, "821750": 87, "901562": 87, "betweeen": 87, "is_null_issu": 87, "is_non_iid_issu": [87, 90, 91, 94], "is_underperforming_group_issu": 87, "859131": 87, "417707": 87, "664083": 87, "970324": 87, "816953": 87, "375317": 87, "641516": 87, "890575": 87, "531021": 87, "460593": 87, "601188": 87, "826147": 87, "752808": 87, "321635": 87, "562539": 87, "948362": 87, "090243": 87, "472909": 87, "746763": 87, "878267": 87, "examples_w_issu": [87, 93], "013445": 87, "025184": 87, "026376": 87, "inde": [87, 91], "miscellan": [87, 103], "428571": 87, "111111": 87, "571429": 87, "407407": 87, "592593": 87, "337838": 87, "092593": 87, "662162": 87, "333333": [87, 92], "952381": 87, "666667": 87, "portion": 87, "huge": [87, 94], "worri": [87, 91], "critic": 87, "60": [88, 94, 101], "torchvis": [88, 99], "tensordataset": 88, "stratifiedkfold": [88, 97], "autonotebook": 88, "fashion_mnist": 88, "9m": 88, "lt": [88, 90, 91, 92, 96, 99], "8mb": 88, "18m": 88, "7mb": 88, "num_row": 88, "60000": 88, "transformed_dataset": 88, "with_format": 88, "255": [88, 92], "unsqueez": 88, "cpu_count": 88, "torch_dataset": 88, "quick": [88, 97], "super": [88, 90, 91], "relu": 88, "batchnorm2d": 88, "maxpool2d": 88, "lazylinear": 88, "flatten": 88, "get_test_accuraci": 88, "testload": [88, 99], "energi": 88, "trainload": [88, 99], "n_epoch": 88, "patienc": 88, "criterion": 88, "crossentropyloss": 88, "adamw": 88, "best_test_accuraci": 88, "start_epoch": 88, "running_loss": 88, "best_epoch": 88, "end_epoch": 88, "3f": [88, 101], "acc": [88, 94], "time_taken": 88, "compute_embed": 88, "compute_pred_prob": 88, "train_batch_s": 88, "num_work": 88, "worker": [88, 103], "train_id_list": 88, "test_id_list": 88, "train_id": 88, "test_id": 88, "embeddings_model": 88, "ntrain": 88, "trainset": 88, "testset": 88, "pin_memori": 88, "fold_embed": 88, "fold_pred_prob": 88, "finish": 88, "482": 88, "720": 88, "943": 88, "329": [88, 98], "88": [88, 92, 93, 94, 97, 98, 101], "195": 88, "606": 88, "493": 88, "060": 88, "715": 88, "330": [88, 98], "505": 88, "438": 88, "476": 88, "340": 88, "569": 88, "328": [88, 98], "310": 88, "479": 88, "reorder": 88, "hstack": [88, 93, 94, 96], "vision": 88, "grayscal": 88, "max_preval": 88, "7714": 88, "3772": 88, "3585": 88, "3651": 88, "27080": 88, "873833e": 88, "40378": 88, "915575e": 88, "25316": 88, "390277e": 88, "06": [88, 94, 98, 103], "2090": 88, "751164e": 88, "14999": 88, "881301e": 88, "9569": 88, "11262": 88, "000003": 88, "coat": [88, 92], "shirt": [88, 92], "19228": 88, "000010": 88, "dress": 88, "32657": 88, "000013": 88, "bag": [88, 92, 99, 100], "21282": 88, "000016": 88, "53564": 88, "000018": 88, "pullov": 88, "6321": 88, "30968": 88, "001267": 88, "30659": 88, "000022": [88, 103], "47824": 88, "001454": 88, "3370": 88, "000026": 88, "54565": 88, "001854": 88, "9762": 88, "258": 88, "47139": 88, "000033": 88, "166980": 88, "986195": 88, "997205": 88, "sandal": [88, 92], "948781": 88, "999358": 88, "54078": 88, "17371": 88, "000025": 88, "plot_label_issue_exampl": 88, "ncol": [88, 99], "nrow": [88, 99], "ceil": 88, "axes_list": 88, "label_issue_indic": 88, "gl": 88, "sl": 88, "fontdict": 88, "imshow": [88, 99], "cmap": [88, 101], "grai": 88, "subplots_adjust": 88, "hspace": 88, "outsiz": 88, "outlier_issu": [88, 91], "outlier_issues_df": 88, "depict": [88, 97, 98, 99, 100, 102], "plot_outlier_issues_exampl": 88, "n_comparison_imag": 88, "sample_from_class": 88, "number_of_sampl": 88, "non_outlier_indic": 88, "isnul": 88, "non_outlier_indices_excluding_curr": 88, "sampled_indic": 88, "label_scores_of_sampl": 88, "top_score_indic": 88, "top_label_indic": 88, "sampled_imag": 88, "get_image_given_label_and_sampl": 88, "image_from_dataset": 88, "corresponding_label": 88, "comparison_imag": 88, "images_to_plot": 88, "idlist": 88, "iterrow": 88, "near_duplicate_issu": [88, 93], "closest": 88, "counterpart": 88, "near_duplicate_issues_df": 88, "plot_near_duplicate_issue_exampl": 88, "seen_id_pair": 88, "get_image_and_given_label_and_predicted_label": 88, "duplicate_imag": 88, "nd_set": 88, "challeng": 88, "dark_issu": 88, "reveal": [88, 98, 102], "dark_scor": 88, "dark_issues_df": 88, "is_dark_issu": 88, "34848": 88, "203922": 88, "50270": 88, "204588": 88, "3936": 88, "213098": 88, "733": 88, "217686": 88, "8094": 88, "230118": 88, "plot_image_issue_exampl": 88, "28": [88, 91, 92, 94, 96, 103], "difficult": 88, "disproportion": 88, "lowinfo_issu": 88, "low_information_scor": 88, "lowinfo_issues_df": 88, "is_low_information_issu": 88, "53050": 88, "067975": 88, "40875": 88, "089929": 88, "9594": 88, "092601": 88, "34825": 88, "107744": 88, "37530": 88, "108516": 88, "lot": 88, "histgradientboostingclassifi": 90, "cat_featur": 90, "boost": [90, 93, 96, 101], "xgboost": [90, 93, 101], "think": [90, 91, 93, 97, 102, 103], "nonzero": 90, "358": 90, "294": [90, 98], "941": 90, "7109": 90, "000005": [90, 91], "886": 90, "000059": 90, "709": 90, "000104": 90, "723": 90, "000169": 90, "689": 90, "000181": 90, "3590": 90, "051882e": 90, "683133e": 90, "536582e": 90, "406589e": 90, "324246e": 90, "6165": 90, "582": 90, "185": [90, 92, 98], "187": [90, 92, 103], "898": 90, "0014": [90, 92], "595": 90, "702427": 90, "147": [90, 94, 98], "711186": 90, "157": [90, 94], "721394": 90, "771": 90, "731979": 90, "740335": 90, "0014153602099278074": 90, "issue_result": 90, "000842": 90, "555944": 90, "004374": 90, "sorted_issu": 90, "73": [90, 92, 97, 98, 101], "deserv": 90, "outlier_result": 90, "sorted_outli": 90, "56": [90, 92, 101], "96": [90, 92, 94, 97, 98, 101], "style": [90, 102], "font": 90, "18px": 90, "ff00ff": 90, "bac": 90, "unintend": [90, 91], "duplicate_result": 90, "690": 90, "perhap": [90, 94, 96], "twice": 90, "67": [90, 92, 98, 101], "wari": [90, 91, 93], "dive": 91, "your_featur": 91, "text_embed": 91, "data_dict": [91, 94, 96], "85": [91, 98], "38": [91, 92, 98], "9710": 91, "981": 91, "974": 91, "000146": 91, "982": [91, 92], "000224": 91, "971": 91, "000507": 91, "980": [91, 92], "000960": 91, "3584": 91, "994": [91, 103], "009642": 91, "999": 91, "013067": 91, "013841": 91, "433": 91, "014722": 91, "989": 91, "018224": 91, "6070": 91, "095724": 91, "148": 91, "006237": 91, "546": 91, "099341": 91, "514": 91, "006485": 91, "481": 91, "123418": 91, "008165": 91, "0000": [91, 92, 94], "313": [91, 98], "564102": 91, "572258": 91, "574915": 91, "31": [91, 92, 94, 96, 98], "575507": 91, "575874": 91, "792090": 91, "257611": 91, "698710": 91, "182121": 91, "771619": 91, "data_with_suggested_label": 91, "withdraw": 91, "monei": 91, "lowest_quality_outli": 91, "OR": 91, "636c65616e6c616220697320617765736f6d6521": 91, "phone": [91, 92], "gone": 91, "gt": [91, 96, 103], "samp": 91, "br": 91, "press": [91, 103], "nonsens": 91, "sens": 91, "detriment": 91, "duplicate_issu": 91, "fee": 91, "pai": 91, "go": [91, 92, 94], "strongli": 91, "p_valu": 91, "benign": 91, "curat": [91, 95], "mnist_test_set": 92, "imagenet_val_set": 92, "tench": 92, "goldfish": 92, "white": [92, 103], "shark": 92, "tiger": 92, "hammerhead": 92, "electr": 92, "rai": 92, "stingrai": 92, "cock": 92, "hen": 92, "ostrich": 92, "brambl": 92, "goldfinch": 92, "hous": 92, "finch": 92, "junco": 92, "indigo": 92, "bunt": 92, "american": [92, 103], "robin": 92, "bulbul": 92, "jai": 92, "magpi": 92, "chickade": 92, "dipper": 92, "kite": 92, "bald": 92, "eagl": 92, "vultur": 92, "grei": 92, "owl": 92, "salamand": 92, "smooth": 92, "newt": 92, "spot": [92, 93, 98], "axolotl": 92, "bullfrog": 92, "tree": 92, "frog": [92, 99], "tail": 92, "loggerhead": 92, "sea": 92, "turtl": 92, "leatherback": 92, "mud": 92, "terrapin": 92, "band": 92, "gecko": 92, "green": [92, 103], "iguana": 92, "carolina": 92, "anol": 92, "desert": 92, "grassland": 92, "whiptail": 92, "lizard": 92, "agama": 92, "frill": 92, "neck": 92, "allig": 92, "gila": 92, "monster": 92, "european": 92, "chameleon": 92, "komodo": 92, "dragon": 92, "nile": 92, "crocodil": 92, "triceratop": 92, "worm": 92, "snake": 92, "ring": 92, "eastern": 92, "hog": 92, "nose": 92, "kingsnak": 92, "garter": 92, "water": 92, "vine": 92, "night": 92, "boa": 92, "constrictor": 92, "african": 92, "rock": 92, "indian": 92, "cobra": 92, "mamba": 92, "saharan": 92, "horn": 92, "viper": 92, "diamondback": 92, "rattlesnak": 92, "sidewind": 92, "trilobit": 92, "harvestman": 92, "scorpion": 92, "yellow": 92, "garden": 92, "spider": 92, "barn": 92, "southern": 92, "widow": 92, "tarantula": 92, "wolf": 92, "tick": 92, "centiped": 92, "grous": 92, "ptarmigan": 92, "ruf": 92, "prairi": 92, "peacock": 92, "quail": 92, "partridg": 92, "parrot": 92, "macaw": 92, "sulphur": 92, "crest": 92, "cockatoo": 92, "lorikeet": 92, "coucal": 92, "bee": 92, "eater": 92, "hornbil": 92, "hummingbird": 92, "jacamar": 92, "toucan": 92, "breast": 92, "mergans": 92, "goos": 92, "swan": 92, "tusker": 92, "echidna": 92, "platypu": 92, "wallabi": 92, "koala": 92, "wombat": 92, "jellyfish": 92, "anemon": 92, "brain": 92, "coral": 92, "flatworm": 92, "nematod": 92, "conch": 92, "snail": 92, "slug": 92, "chiton": 92, "chamber": 92, "nautilu": 92, "dung": 92, "crab": 92, "fiddler": 92, "king": 92, "lobster": 92, "spini": 92, "crayfish": 92, "hermit": 92, "isopod": 92, "stork": 92, "spoonbil": 92, "flamingo": 92, "heron": 92, "egret": 92, "bittern": 92, "crane": 92, "bird": [92, 99], "limpkin": 92, "gallinul": 92, "coot": 92, "bustard": 92, "ruddi": 92, "turnston": 92, "dunlin": 92, "redshank": 92, "dowitch": 92, "oystercatch": 92, "pelican": 92, "penguin": 92, "albatross": 92, "whale": 92, "killer": 92, "dugong": 92, "lion": 92, "chihuahua": 92, "japanes": 92, "chin": 92, "maltes": 92, "pekinges": 92, "shih": 92, "tzu": 92, "charl": 92, "spaniel": 92, "papillon": 92, "terrier": 92, "rhodesian": 92, "ridgeback": 92, "afghan": [92, 103], "hound": 92, "basset": 92, "beagl": 92, "bloodhound": 92, "bluetick": 92, "coonhound": 92, "tan": 92, "walker": 92, "foxhound": 92, "redbon": 92, "borzoi": 92, "irish": 92, "wolfhound": 92, "italian": 92, "greyhound": 92, "whippet": 92, "ibizan": 92, "norwegian": 92, "elkhound": 92, "otterhound": 92, "saluki": 92, "scottish": 92, "deerhound": 92, "weimaran": 92, "staffordshir": 92, "bull": 92, "bedlington": 92, "border": 92, "kerri": 92, "norfolk": 92, "norwich": 92, "yorkshir": 92, "wire": 92, "fox": 92, "lakeland": 92, "sealyham": 92, "airedal": 92, "cairn": 92, "australian": 92, "dandi": 92, "dinmont": 92, "boston": 92, "miniatur": 92, "schnauzer": 92, "giant": 92, "tibetan": 92, "silki": 92, "wheaten": 92, "west": 92, "highland": 92, "lhasa": 92, "apso": 92, "flat": 92, "retriev": 92, "curli": 92, "golden": 92, "labrador": 92, "chesapeak": 92, "bai": 92, "german": [92, 103], "shorthair": 92, "pointer": 92, "vizsla": 92, "setter": 92, "gordon": 92, "brittani": 92, "clumber": 92, "springer": 92, "welsh": 92, "cocker": 92, "sussex": 92, "kuvasz": 92, "schipperk": 92, "groenendael": 92, "malinoi": 92, "briard": 92, "kelpi": 92, "komondor": 92, "sheepdog": 92, "shetland": 92, "colli": 92, "bouvier": 92, "de": 92, "flandr": 92, "rottweil": 92, "shepherd": 92, "dobermann": 92, "pinscher": 92, "swiss": [92, 103], "mountain": 92, "bernes": 92, "appenzel": 92, "sennenhund": 92, "entlebuch": 92, "boxer": 92, "bullmastiff": 92, "mastiff": 92, "french": 92, "bulldog": 92, "dane": 92, "st": 92, "bernard": 92, "huski": 92, "alaskan": 92, "malamut": 92, "siberian": 92, "dalmatian": 92, "affenpinsch": 92, "basenji": 92, "pug": 92, "leonberg": 92, "newfoundland": 92, "pyrenean": 92, "samoi": 92, "pomeranian": 92, "chow": 92, "keeshond": 92, "griffon": 92, "bruxelloi": 92, "pembrok": 92, "corgi": 92, "cardigan": 92, "poodl": 92, "mexican": 92, "hairless": 92, "tundra": 92, "coyot": 92, "dingo": 92, "dhole": 92, "wild": 92, "hyena": 92, "kit": 92, "arctic": 92, "tabbi": 92, "persian": 92, "siames": 92, "egyptian": 92, "mau": 92, "cougar": 92, "lynx": 92, "leopard": 92, "snow": 92, "jaguar": 92, "cheetah": 92, "brown": [92, 102], "bear": 92, "polar": 92, "sloth": 92, "mongoos": 92, "meerkat": 92, "beetl": 92, "ladybug": 92, "longhorn": 92, "leaf": 92, "rhinocero": 92, "weevil": 92, "ant": 92, "grasshopp": 92, "cricket": 92, "stick": 92, "insect": 92, "cockroach": 92, "manti": 92, "cicada": 92, "leafhopp": 92, "lacew": 92, "dragonfli": 92, "damselfli": 92, "admir": 92, "ringlet": 92, "monarch": 92, "butterfli": 92, "gossam": 92, "wing": 92, "starfish": 92, "urchin": 92, "cucumb": 92, "cottontail": 92, "rabbit": 92, "hare": 92, "angora": 92, "hamster": 92, "porcupin": 92, "squirrel": 92, "marmot": 92, "beaver": 92, "guinea": 92, "pig": 92, "sorrel": 92, "zebra": 92, "boar": 92, "warthog": 92, "hippopotamu": 92, "ox": 92, "buffalo": 92, "bison": 92, "bighorn": 92, "sheep": 92, "alpin": 92, "ibex": 92, "hartebeest": 92, "impala": 92, "gazel": 92, "dromedari": 92, "llama": 92, "weasel": 92, "mink": 92, "polecat": 92, "foot": 92, "ferret": 92, "otter": 92, "skunk": 92, "badger": 92, "armadillo": 92, "toed": 92, "orangutan": 92, "gorilla": 92, "chimpanze": 92, "gibbon": 92, "siamang": 92, "guenon": 92, "pata": 92, "monkei": 92, "baboon": 92, "macaqu": 92, "langur": 92, "colobu": 92, "probosci": 92, "marmoset": 92, "capuchin": 92, "howler": 92, "titi": 92, "geoffroi": 92, "lemur": 92, "indri": 92, "asian": 92, "eleph": 92, "bush": 92, "snoek": 92, "eel": 92, "coho": 92, "salmon": 92, "beauti": 92, "clownfish": 92, "sturgeon": 92, "garfish": 92, "lionfish": 92, "pufferfish": 92, "abacu": 92, "abaya": 92, "academ": 92, "gown": 92, "accordion": 92, "acoust": 92, "guitar": 92, "aircraft": 92, "carrier": 92, "airlin": 92, "airship": 92, "altar": 92, "ambul": 92, "amphibi": 92, "clock": [92, 103], "apiari": 92, "apron": 92, "wast": 92, "assault": 92, "rifl": 92, "backpack": 92, "bakeri": 92, "balanc": 92, "beam": 92, "balloon": 92, "ballpoint": 92, "pen": 92, "aid": 92, "banjo": 92, "balust": 92, "barbel": 92, "barber": 92, "chair": [92, 98], "barbershop": 92, "baromet": 92, "barrel": 92, "wheelbarrow": 92, "basebal": 92, "basketbal": 92, "bassinet": 92, "bassoon": 92, "swim": 92, "cap": 92, "bath": 92, "towel": 92, "bathtub": 92, "station": 92, "wagon": 92, "lighthous": 92, "beaker": 92, "militari": 92, "beer": 92, "bottl": 92, "glass": 92, "bell": 92, "cot": 92, "bib": 92, "bicycl": [92, 102], "bikini": 92, "binder": 92, "binocular": 92, "birdhous": 92, "boathous": 92, "bobsleigh": 92, "bolo": 92, "tie": 92, "poke": 92, "bonnet": 92, "bookcas": 92, "bookstor": 92, "bow": 92, "brass": 92, "bra": 92, "breakwat": 92, "breastplat": 92, "broom": 92, "bucket": 92, "buckl": 92, "bulletproof": 92, "vest": 92, "butcher": 92, "shop": 92, "taxicab": 92, "cauldron": 92, "candl": 92, "cannon": 92, "cano": 92, "mirror": [92, 98], "carousel": 92, "tool": [92, 94, 96], "carton": 92, "wheel": 92, "teller": 92, "cassett": 92, "player": 92, "castl": 92, "catamaran": 92, "cd": 92, "cello": 92, "mobil": [92, 103], "chain": 92, "fenc": [92, 102], "mail": 92, "chainsaw": 92, "chest": 92, "chiffoni": 92, "chime": 92, "china": 92, "cabinet": 92, "christma": 92, "stock": 92, "church": 92, "movi": 92, "theater": 92, "cleaver": 92, "cliff": 92, "dwell": 92, "cloak": 92, "clog": 92, "cocktail": 92, "shaker": 92, "coffe": 92, "mug": 92, "coffeemak": 92, "coil": 92, "lock": 92, "keyboard": 92, "confectioneri": 92, "ship": [92, 99], "corkscrew": 92, "cornet": 92, "cowboi": 92, "boot": 92, "hat": 92, "cradl": 92, "crash": 92, "helmet": 92, "crate": 92, "infant": 92, "bed": 92, "crock": 92, "pot": 92, "croquet": 92, "crutch": 92, "cuirass": 92, "dam": 92, "desk": 92, "desktop": 92, "rotari": 92, "dial": 92, "telephon": 92, "diaper": 92, "watch": 92, "dine": 92, "dishcloth": 92, "dishwash": 92, "disc": 92, "brake": 92, "dock": 92, "sled": 92, "dome": 92, "doormat": 92, "drill": 92, "rig": 92, "drum": 92, "drumstick": 92, "dumbbel": 92, "dutch": 92, "oven": 92, "fan": 92, "locomot": 92, "entertain": 92, "center": 92, "envelop": 92, "espresso": 92, "powder": 92, "feather": 92, "fireboat": 92, "engin": [92, 102], "screen": 92, "sheet": 92, "flagpol": 92, "flute": 92, "footbal": 92, "forklift": 92, "fountain": 92, "poster": 92, "freight": 92, "fry": 92, "pan": 92, "fur": 92, "garbag": 92, "ga": 92, "pump": 92, "goblet": 92, "kart": 92, "golf": 92, "cart": 92, "gondola": 92, "gong": 92, "grand": 92, "piano": 92, "greenhous": 92, "grill": 92, "groceri": 92, "guillotin": 92, "barrett": 92, "hair": 92, "sprai": 92, "hammer": 92, "dryer": 92, "hand": [92, 94], "handkerchief": 92, "drive": 92, "harmonica": 92, "harp": 92, "harvest": 92, "hatchet": 92, "holster": 92, "honeycomb": 92, "hoop": 92, "skirt": 92, "horizont": 92, "bar": 92, "drawn": 92, "hourglass": 92, "ipod": 92, "cloth": 92, "iron": 92, "jack": 92, "lantern": 92, "jean": 92, "jeep": 92, "jigsaw": 92, "puzzl": 92, "pull": 92, "rickshaw": 92, "joystick": 92, "kimono": 92, "knee": 92, "pad": 92, "knot": 92, "ladl": 92, "lampshad": 92, "laptop": 92, "lawn": 92, "mower": 92, "knife": 92, "lifeboat": 92, "lighter": 92, "limousin": 92, "ocean": 92, "liner": 92, "lipstick": 92, "slip": 92, "shoe": 92, "lotion": 92, "speaker": 92, "loup": 92, "sawmil": 92, "magnet": 92, "compass": 92, "mailbox": 92, "tight": 92, "tank": 92, "manhol": 92, "maraca": 92, "marimba": 92, "maypol": 92, "maze": 92, "cup": [92, 98], "medicin": 92, "megalith": 92, "microphon": 92, "microwav": 92, "milk": 92, "minibu": 92, "miniskirt": 92, "minivan": 92, "missil": 92, "mitten": [92, 93], "mix": 92, "bowl": 92, "modem": 92, "monasteri": 92, "mope": 92, "mortar": 92, "mosqu": 92, "mosquito": 92, "scooter": 92, "bike": 92, "tent": 92, "mous": [92, 93], "mousetrap": 92, "van": 92, "muzzl": 92, "nail": 92, "brace": 92, "necklac": 92, "nippl": 92, "obelisk": 92, "obo": 92, "ocarina": 92, "odomet": 92, "oil": 92, "oscilloscop": 92, "overskirt": 92, "bullock": 92, "oxygen": 92, "packet": 92, "paddl": 92, "padlock": 92, "paintbrush": 92, "pajama": 92, "palac": [92, 103], "parachut": 92, "park": 92, "bench": 92, "meter": 92, "passeng": 92, "patio": 92, "payphon": 92, "pedest": 92, "pencil": 92, "perfum": 92, "petri": 92, "dish": 92, "photocopi": 92, "plectrum": 92, "pickelhaub": 92, "picket": 92, "pickup": 92, "pier": 92, "piggi": 92, "pill": 92, "pillow": 92, "ping": 92, "pong": 92, "pinwheel": 92, "pirat": 92, "pitcher": 92, "plane": 92, "planetarium": 92, "plastic": 92, "plate": 92, "rack": 92, "plow": 92, "plunger": 92, "polaroid": 92, "camera": 92, "pole": [92, 102], "polic": 92, "poncho": 92, "billiard": 92, "soda": 92, "potter": 92, "prayer": 92, "rug": 92, "printer": 92, "prison": 92, "projectil": 92, "projector": 92, "hockei": 92, "puck": 92, "punch": 92, "purs": 92, "quill": 92, "quilt": 92, "race": 92, "racket": 92, "radiat": 92, "radio": 92, "telescop": 92, "rain": 92, "recreat": 92, "reel": 92, "reflex": 92, "refriger": 92, "remot": 92, "restaur": 92, "revolv": 92, "rotisseri": 92, "eras": 92, "rugbi": 92, "ruler": 92, "safe": 92, "safeti": 92, "salt": 92, "sarong": 92, "saxophon": 92, "scabbard": 92, "school": 92, "bu": [92, 102], "schooner": 92, "scoreboard": 92, "crt": 92, "screw": 92, "screwdriv": 92, "seat": 92, "belt": 92, "sew": 92, "shield": 92, "shoji": 92, "basket": 92, "shovel": 92, "shower": 92, "curtain": 92, "ski": 92, "door": 92, "slot": 92, "snorkel": 92, "snowmobil": 92, "snowplow": 92, "soap": 92, "dispens": 92, "soccer": [92, 103], "sock": [92, 93], "solar": 92, "thermal": 92, "collector": 92, "sombrero": 92, "soup": 92, "heater": 92, "shuttl": 92, "spatula": 92, "motorboat": 92, "web": 92, "spindl": 92, "sport": [92, 103], "spotlight": 92, "stage": 92, "steam": 92, "arch": 92, "bridg": 92, "steel": 92, "stethoscop": 92, "scarf": 92, "stone": 92, "wall": [92, 102], "stopwatch": 92, "stove": 92, "strainer": 92, "tram": 92, "stretcher": 92, "couch": 92, "stupa": 92, "submarin": 92, "sundial": 92, "sunglass": 92, "sunscreen": 92, "suspens": 92, "mop": 92, "sweatshirt": 92, "swimsuit": 92, "swing": 92, "switch": 92, "syring": 92, "lamp": 92, "tape": 92, "teapot": 92, "teddi": 92, "televis": [92, 103], "tenni": 92, "thatch": 92, "roof": 92, "front": 92, "thimbl": 92, "thresh": 92, "throne": 92, "tile": 92, "toaster": 92, "tobacco": 92, "toilet": 92, "totem": 92, "tow": 92, "tractor": 92, "semi": 92, "trailer": 92, "trai": 92, "trench": 92, "tricycl": 92, "trimaran": 92, "tripod": 92, "triumphal": 92, "trolleybu": 92, "trombon": 92, "tub": 92, "turnstil": 92, "typewrit": 92, "umbrella": 92, "unicycl": 92, "upright": 92, "vacuum": 92, "cleaner": 92, "vase": 92, "vault": 92, "velvet": 92, "vend": 92, "vestment": 92, "viaduct": 92, "violin": 92, "volleybal": 92, "waffl": 92, "wallet": 92, "wardrob": 92, "sink": 92, "wash": 92, "jug": 92, "tower": 92, "whiskei": 92, "whistl": 92, "wig": 92, "shade": [92, 102], "windsor": 92, "wine": 92, "wok": 92, "wooden": 92, "spoon": 92, "wool": 92, "rail": 92, "shipwreck": 92, "yawl": 92, "yurt": 92, "comic": 92, "book": 92, "crossword": 92, "traffic": [92, 98, 102], "sign": [92, 102, 103], "dust": 92, "jacket": [92, 98], "menu": 92, "guacamol": 92, "consomm": 92, "trifl": 92, "ic": 92, "cream": 92, "pop": 92, "baguett": 92, "bagel": 92, "pretzel": 92, "cheeseburg": 92, "mash": 92, "potato": 92, "cabbag": 92, "broccoli": 92, "cauliflow": 92, "zucchini": 92, "spaghetti": 92, "squash": 92, "acorn": 92, "butternut": 92, "artichok": 92, "pepper": [92, 93], "cardoon": 92, "mushroom": 92, "granni": 92, "smith": 92, "strawberri": 92, "orang": 92, "lemon": 92, "pineappl": 92, "banana": 92, "jackfruit": 92, "custard": 92, "appl": 92, "pomegran": 92, "hai": 92, "carbonara": 92, "chocol": 92, "syrup": 92, "dough": 92, "meatloaf": 92, "pizza": 92, "pie": 92, "burrito": 92, "eggnog": 92, "alp": 92, "bubbl": 92, "reef": 92, "geyser": 92, "lakeshor": 92, "promontori": 92, "shoal": 92, "seashor": 92, "vallei": 92, "volcano": 92, "bridegroom": 92, "scuba": 92, "diver": 92, "rapese": 92, "daisi": 92, "ladi": 92, "slipper": 92, "corn": 92, "rose": 92, "hip": 92, "chestnut": 92, "fungu": 92, "agar": 92, "gyromitra": 92, "stinkhorn": 92, "earth": 92, "star": 92, "wood": 92, "bolet": 92, "ear": 92, "cifar10_test_set": 92, "airplan": [92, 99], "automobil": [92, 99], "deer": [92, 99], "cifar100_test_set": 92, "aquarium_fish": 92, "babi": 92, "boi": 92, "camel": 92, "caterpillar": 92, "cattl": [92, 103], "cloud": 92, "dinosaur": 92, "dolphin": 92, "flatfish": 92, "forest": 92, "girl": 92, "kangaroo": 92, "lawn_mow": 92, "man": 92, "maple_tre": 92, "motorcycl": [92, 102], "oak_tre": 92, "orchid": 92, "palm_tre": 92, "pear": 92, "pickup_truck": 92, "pine_tre": 92, "plain": 92, "poppi": 92, "possum": 92, "raccoon": 92, "road": [92, 102], "rocket": 92, "seal": 92, "shrew": 92, "skyscrap": 92, "streetcar": 92, "sunflow": 92, "sweet_pepp": 92, "trout": 92, "tulip": 92, "willow_tre": 92, "woman": [92, 98], "caltech256": 92, "ak47": 92, "bat": 92, "glove": 92, "birdbath": 92, "blimp": 92, "bonsai": 92, "boom": 92, "breadmak": 92, "buddha": 92, "bulldoz": 92, "cactu": 92, "cake": 92, "tire": 92, "cartman": 92, "cereal": 92, "chandeli": 92, "chess": 92, "board": 92, "chimp": 92, "chopstick": 92, "coffin": 92, "coin": 92, "comet": 92, "cormor": 92, "globe": 92, "diamond": 92, "dice": 92, "doorknob": 92, "drink": 92, "straw": 92, "dumb": 92, "eiffel": 92, "elk": 92, "ewer": 92, "eyeglass": 92, "fern": 92, "fighter": 92, "jet": [92, 101], "extinguish": 92, "hydrant": 92, "firework": 92, "flashlight": 92, "floppi": 92, "fri": 92, "frisbe": 92, "galaxi": 92, "giraff": 92, "goat": 92, "gate": 92, "grape": 92, "pick": [92, 93], "hamburg": 92, "hammock": 92, "harpsichord": 92, "hawksbil": 92, "helicopt": 92, "hibiscu": 92, "homer": 92, "simpson": 92, "horsesho": 92, "air": 92, "skeleton": 92, "ibi": 92, "cone": 92, "iri": 92, "jesu": 92, "christ": 92, "joi": 92, "kayak": 92, "ketch": 92, "ladder": 92, "lath": 92, "licens": 92, "lightbulb": 92, "lightn": 92, "mandolin": 92, "mar": 92, "mattress": 92, "megaphon": 92, "menorah": 92, "microscop": 92, "minaret": 92, "minotaur": 92, "motorbik": 92, "mussel": 92, "neckti": 92, "octopu": 92, "palm": 92, "pilot": 92, "paperclip": 92, "shredder": 92, "pci": 92, "peopl": [92, 98], "pez": 92, "picnic": 92, "pram": 92, "prai": 92, "pyramid": 92, "rainbow": 92, "roulett": 92, "saddl": 92, "saturn": 92, "segwai": 92, "propel": 92, "sextant": 92, "music": 92, "skateboard": 92, "smokestack": 92, "sneaker": 92, "boat": 92, "stain": 92, "steer": 92, "stirrup": 92, "superman": 92, "sushi": 92, "armi": [92, 103], "sword": 92, "tambourin": 92, "teepe": 92, "court": 92, "theodolit": 92, "tomato": 92, "tombston": 92, "tour": 92, "pisa": 92, "treadmil": 92, "fork": 92, "tweezer": 92, "unicorn": 92, "vcr": 92, "waterfal": 92, "watermelon": 92, "weld": 92, "windmil": 92, "xylophon": 92, "yarmulk": 92, "yo": 92, "toad": 92, "twenty_news_test_set": 92, "alt": 92, "atheism": 92, "comp": 92, "graphic": [92, 102], "misc": [92, 103], "sy": 92, "ibm": 92, "pc": 92, "hardwar": 92, "mac": 92, "forsal": 92, "rec": 92, "sci": 92, "crypt": 92, "electron": 92, "med": 92, "soc": 92, "religion": 92, "christian": [92, 103], "talk": [92, 103], "polit": 92, "gun": 92, "mideast": 92, "amazon": 92, "neutral": 92, "imdb_test_set": 92, "all_class": 92, "20news_test_set": 92, "_load_classes_predprobs_label": 92, "dataset_nam": 92, "labelerror": 92, "url_bas": 92, "5392f6c71473055060be3044becdde1cbc18284d": 92, "url_label": 92, "original_test_label": 92, "_original_label": 92, "url_prob": 92, "cross_validated_predicted_prob": 92, "_pyx": 92, "num_part": 92, "datatset": 92, "bytesio": 92, "allow_pickl": 92, "pred_probs_part": 92, "url": 92, "_of_": 92, "nload": 92, "imdb": 92, "interpret": [92, 93, 94, 97, 101], "capit": 92, "29780": 92, "780": 92, "medic": [92, 103], "doctor": 92, "359223": 92, "640777": 92, "184": [92, 94], "258427": 92, "341176": 92, "263158": 92, "658824": 92, "337349": 92, "246575": 92, "662651": 92, "248": 92, "330000": 92, "355769": 92, "670000": 92, "251": [92, 98], "252": 92, "112": 92, "253": [92, 98], "022989": 92, "049505": 92, "190": [92, 94, 98], "66": 92, "002216": 92, "000974": 92, "59": [92, 98], "000873": 92, "000739": 92, "32635": 92, "32636": 92, "47": [92, 98], "32637": 92, "32638": 92, "32639": 92, "32640": 92, "051": 92, "002242": 92, "997758": 92, "002088": 92, "001045": 92, "997912": 92, "002053": 92, "997947": 92, "001980": 92, "000991": 92, "998020": 92, "001946": 92, "002915": 92, "998054": 92, "001938": 92, "002904": 92, "998062": 92, "001020": 92, "998980": 92, "001018": 92, "002035": 92, "998982": 92, "999009": 92, "0003": 92, "0002": 92, "36": [92, 103], "41": [92, 98, 101], "71": [92, 94, 98, 101], "071": 92, "067269": 92, "929": 92, "046": 92, "058243": 92, "954": 92, "035": 92, "032096": 92, "965": 92, "031": 92, "012232": 92, "969": 92, "022": 92, "025896": 92, "978": 92, "020": [92, 94], "013092": 92, "018": 92, "013065": 92, "016": 92, "030542": 92, "984": 92, "013": 92, "020833": 92, "987": 92, "012": 92, "010020": 92, "988": 92, "0073": 92, "0020": 92, "0016": 92, "0015": 92, "0013": 92, "0012": 92, "0010": 92, "0008": 92, "0007": 92, "0006": 92, "0005": 92, "0004": 92, "244": [92, 98, 103], "98": [92, 93, 101], "452381": 92, "459770": 92, "72": [92, 94, 97, 101], "523364": 92, "460784": 92, "446602": 92, "57": [92, 94], "103774": 92, "030612": 92, "110092": 92, "049020": 92, "0034": 92, "0032": 92, "0026": 92, "0025": 92, "4945": 92, "4946": 92, "4947": 92, "4948": 92, "4949": 92, "4950": 92, "846": 92, "82": [92, 94, 98, 101], "7532": 92, "532": 92, "034483": 92, "009646": 92, "965517": 92, "030457": 92, "020513": 92, "969543": 92, "028061": 92, "035443": 92, "971939": 92, "025316": 92, "005168": 92, "974684": 92, "049751": 92, "979487": 92, "019920": 92, "042802": 92, "980080": 92, "017677": 92, "005115": 92, "982323": 92, "012987": 92, "005236": 92, "987013": 92, "012723": 92, "025126": 92, "987277": 92, "010989": 92, "008264": 92, "989011": 92, "010283": 92, "027778": 92, "989717": 92, "009677": 92, "990323": 92, "007614": 92, "010127": 92, "992386": 92, "005051": 92, "994949": 92, "005025": 92, "994975": 92, "005013": 92, "994987": 92, "001859": 92, "001328": 92, "000929": 92, "000664": 92, "186": [92, 94], "188": [92, 94, 97], "189": [92, 94], "snippet": 93, "nlp": [93, 103], "mind": [93, 94], "alphanumer": 93, "facilit": 93, "classlabel": 93, "guidanc": 93, "labels_str": 93, "datalab_str": 93, "labels_int": 93, "remap": 93, "datalab_int": 93, "my_dict": 93, "pet_nam": 93, "rover": 93, "rocki": 93, "speci": 93, "from_dict": 93, "datalab_dataset": 93, "number_of_class": 93, "total_number_of_data_point": 93, "alphabet": 93, "labels_proper_format": 93, "your_classifi": 93, "issues_datafram": 93, "class_predicted_for_flagged_exampl": 93, "class_predicted_for_all_exampl": 93, "grant": 93, "On": [93, 94, 98], "merged_dataset": 93, "label_column_nam": 93, "datataset": 93, "fair": [93, 94], "game": 93, "speedup": [93, 99], "flexibl": 93, "tempfil": 93, "mkdtemp": 93, "sped": 93, "anywai": 93, "pred_probs_merg": 93, "merge_rare_class": 93, "count_threshold": 93, "class_mapping_orig2new": 93, "heath_summari": 93, "num_examples_per_class": 93, "rare_class": 93, "num_classes_merg": 93, "other_class": 93, "labels_merg": 93, "new_c": 93, "merged_prob": 93, "new_class": 93, "original_class": 93, "num_check": 93, "ones_array_ref": 93, "isclos": 93, "though": [93, 94, 103], "successfulli": 93, "meaning": [93, 99], "virtuou": [93, 96], "cycl": [93, 96], "jointli": 93, "junk": 93, "clutter": 93, "unknown": 93, "caltech": 93, "combined_boolean_mask": 93, "mask1": 93, "mask2": 93, "gradientboostingclassifi": [93, 94], "true_error": [93, 94, 97], "101": [93, 98], "102": [93, 97, 98], "104": [93, 94, 98], "model_to_find_error": 93, "model_to_return": 93, "cl0": 93, "randomizedsearchcv": 93, "expens": 93, "param_distribut": 93, "learning_r": [93, 94], "max_depth": [93, 94], "magnitud": 93, "coeffici": [93, 101], "optin": 93, "environ": [93, 94], "rerun": [93, 94], "cell": [93, 94], "unabl": [93, 94], "render": [93, 94], "nbviewer": [93, 94], "nbsp": [93, 94], "cleanlearninginot": [93, 94], "fittedcleanlearn": [93, 94], "linearregressionlinearregress": 93, "n_init": 93, "fit_predict": 93, "continuous_column": 93, "categorical_column": 93, "data_df": 93, "feature_a": 93, "feature_b": 93, "unexpectedli": 93, "emphas": 93, "crucial": 93, "merge_duplicate_set": 93, "merge_kei": 93, "construct_group_kei": 93, "merged_set": 93, "consolidate_set": 93, "tolist": [93, 97], "issubset": 93, "frozenset": 93, "sets_list": 93, "mutabl": 93, "new_set": 93, "current_set": 93, "intersecting_set": 93, "lowest_score_strategi": 93, "sub_df": 93, "idxmin": 93, "filter_near_dupl": 93, "strategy_fn": 93, "strategy_kwarg": 93, "duplicate_row": 93, "group_kei": 93, "to_keep_indic": 93, "groupbi": 93, "explod": 93, "to_remov": 93, "isin": [93, 99], "kept": 93, "ids_to_remove_seri": 93, "tmp": 93, "ipykernel_7838": 93, "1995098996": 93, "deprecationwarn": 93, "dataframegroupbi": 93, "include_group": 93, "silenc": 93, "assist": 93, "streamlin": 93, "ux": 93, "agpl": 93, "compani": 93, "commerci": 93, "alter": 93, "email": 93, "team": 93, "discuss": 93, "anywher": 93, "profession": 93, "expert": 93, "depth": 94, "survei": [94, 103], "focus": [94, 96, 97, 101], "scienc": 94, "multivariate_norm": [94, 96, 97], "make_data": [94, 96], "cov": [94, 96, 97], "avg_trac": [94, 97], "py_tru": 94, "noise_matrix_tru": 94, "noise_marix": 94, "s_test": 94, "noisy_test_label": 94, "purpl": 94, "val": 94, "namespac": 94, "exec": 94, "markerfacecolor": [94, 97], "markeredgecolor": [94, 97, 101], "markers": [94, 97, 101], "markeredgewidth": [94, 97, 101], "realist": 94, "7560": 94, "637318e": 94, "896262e": 94, "548391e": 94, "923417e": 94, "375075e": 94, "3454": 94, "014051": 94, "020451": 94, "249": [94, 98], "042594": 94, "043859": 94, "045954": 94, "6120": 94, "023714": 94, "007136": 94, "119": [94, 98], "107266": 94, "103": [94, 98], "033738": 94, "238": [94, 98], "119505": 94, "236": [94, 98], "037843": 94, "222": 94, "614915": 94, "122": [94, 98], "624422": 94, "625965": 94, "626079": 94, "118": 94, "627675": 94, "695223": 94, "323529": 94, "523015": 94, "013720": 94, "675727": 94, "646521": 94, "anyth": 94, "enhanc": [94, 96, 98], "magic": 94, "liter": 94, "identif": 94, "x27": 94, "logisticregressionlogisticregress": 94, "ever": 94, "092": 94, "040": 94, "024": 94, "004": 94, "surpris": 94, "1705": 94, "01936": 94, "ton": 94, "yourfavoritemodel1": 94, "merged_label": 94, "merged_test_label": 94, "newli": [94, 96], "yourfavoritemodel2": 94, "yourfavoritemodel3": 94, "cl3": 94, "takeawai": 94, "That": [94, 97], "randomli": 94, "my_test_pred_prob": 94, "my_test_pr": 94, "issues_test": 94, "corrected_test_label": 94, "pretend": 94, "cl_test_pr": 94, "69": [94, 101], "fairli": 94, "label_acc": 94, "percentag": 94, "offset": 94, "nquestion": 94, "overestim": 94, "answer": 94, "experienc": 94, "knowledg": 94, "prioiri": 94, "known": 94, "versatil": 94, "label_issues_indic": 94, "213": [94, 98], "218": [94, 98], "152": 94, "197": [94, 98], "196": [94, 98], "170": 94, "214": 94, "164": [94, 97], "198": [94, 98], "191": [94, 98], "63": [94, 98, 101], "117": [94, 101], "62": [94, 98, 101, 103], "206": [94, 98], "115": [94, 98], "193": 94, "194": 94, "201": [94, 98], "174": 94, "163": 94, "150": [94, 96, 98], "169": 94, "151": [94, 98], "168": 94, "precision_scor": 94, "recall_scor": 94, "f1_score": 94, "true_label_issu": 94, "filter_by_list": 94, "718750": [94, 96], "807018": 94, "912": 94, "733333": 94, "800000": 94, "721311": 94, "792793": 94, "908": 94, "676923": 94, "765217": 94, "892": 94, "567901": 94, "702290": 94, "844": 94, "gaug": 94, "label_issues_count": 94, "155": [94, 98], "156": 94, "172": [94, 97], "easiest": 94, "modular": 94, "penalti": 94, "l2": 94, "model3": 94, "n_estim": 94, "cv_pred_probs_1": 94, "cv_pred_probs_2": 94, "cv_pred_probs_3": 94, "label_quality_scores_best": 94, "cv_pred_probs_ensembl": 94, "label_quality_scores_bett": 94, "superior": [94, 100], "timm": 95, "glad": 96, "multiannotator_label": 96, "300": [96, 103], "noisier": 96, "111": [96, 101], "local_data": [96, 97], "true_labels_train": [96, 97], "noise_matrix_bett": 96, "noise_matrix_wors": 96, "transpos": [96, 99], "dropna": 96, "zfill": 96, "row_na_check": 96, "notna": 96, "reset_index": 96, "a0001": 96, "a0002": 96, "a0003": 96, "a0004": 96, "a0005": 96, "a0006": 96, "a0007": 96, "a0008": 96, "a0009": 96, "a0010": 96, "a0041": 96, "a0042": 96, "a0043": 96, "a0044": 96, "a0045": 96, "a0046": 96, "a0047": 96, "a0048": 96, "a0049": 96, "a0050": 96, "na": 96, "60856743": 96, "41693214": 96, "40908785": 96, "87147629": 96, "64941785": 96, "10774851": 96, "0524466": 96, "71853246": 96, "37169848": 96, "66031048": 96, "multiannotator_util": 96, "crude": 96, "straight": 96, "majority_vote_label": 96, "736118": 96, "757751": 96, "782232": 96, "715565": 96, "824256": 96, "quality_annotator_a0001": 96, "quality_annotator_a0002": 96, "quality_annotator_a0003": 96, "quality_annotator_a0004": 96, "quality_annotator_a0005": 96, "quality_annotator_a0006": 96, "quality_annotator_a0007": 96, "quality_annotator_a0008": 96, "quality_annotator_a0009": 96, "quality_annotator_a0010": 96, "quality_annotator_a0041": 96, "quality_annotator_a0042": 96, "quality_annotator_a0043": 96, "quality_annotator_a0044": 96, "quality_annotator_a0045": 96, "quality_annotator_a0046": 96, "quality_annotator_a0047": 96, "quality_annotator_a0048": 96, "quality_annotator_a0049": 96, "quality_annotator_a0050": 96, "070564": 96, "216078": 96, "119188": 96, "alongisd": 96, "244981": 96, "208333": 96, "295979": 96, "294118": 96, "324197": 96, "310345": 96, "355316": 96, "346154": 96, "439732": 96, "480000": 96, "a0031": 96, "523205": 96, "580645": 96, "a0034": 96, "535313": 96, "607143": 96, "a0021": 96, "606999": 96, "a0015": 96, "609526": 96, "678571": 96, "a0011": 96, "621103": 96, "692308": 96, "wors": 96, "improved_consensus_label": 96, "majority_vote_accuraci": 96, "cleanlab_label_accuraci": 96, "8581081081081081": 96, "9797297297297297": 96, "besid": 96, "sorted_consensus_quality_scor": 96, "worst_qual": 96, "better_qu": 96, "worst_quality_accuraci": 96, "better_quality_accuraci": 96, "9893238434163701": 96, "improved_pred_prob": 96, "treat": [96, 97, 101, 103], "analzi": 96, "copyright": 97, "advertis": 97, "violenc": 97, "nsfw": 97, "celeba": 97, "make_multilabel_data": 97, "boxes_coordin": 97, "box_multilabel": 97, "make_multi": 97, "bx1": 97, "by1": 97, "bx2": 97, "by2": 97, "label_list": 97, "ur": 97, "upper": 97, "inidx": 97, "logical_and": 97, "inv_d": 97, "labels_idx": 97, "true_labels_test": 97, "dict_unique_label": 97, "get_color_arrai": 97, "dcolor": 97, "aa4400": 97, "55227f": 97, "55a100": 97, "00ff00": 97, "007f7f": 97, "386b55": 97, "0000ff": 97, "y_onehot": 97, "single_class_label": 97, "stratifi": [97, 100], "kf": 97, "train_index": 97, "test_index": 97, "clf_cv": 97, "x_train_cv": 97, "x_test_cv": 97, "y_train_cv": 97, "y_test_cv": 97, "y_pred_cv": 97, "saw": 97, "num_to_displai": 97, "09": [97, 98, 101], "275": 97, "267": 97, "225": 97, "171": 97, "234": 97, "165": [97, 103], "227": [97, 98], "262": [97, 98], "266": [97, 98], "139": 97, "143": [97, 98, 103], "216": [97, 98, 103], "265": 97, "159": [97, 98], "despit": [97, 103], "suspect": 97, "888": 97, "8224": 97, "9632": 97, "968": 97, "6512": 97, "0444": 97, "774": 97, "labels_binary_format": 97, "labels_list_format": 97, "surround": 98, "scene": 98, "coco": 98, "everydai": 98, "has_label_issu": 98, "insal": 98, "nc": [98, 102, 103], "s3": [98, 102, 103], "amazonaw": [98, 102, 103], "objectdetectionbenchmark": 98, "tutorial_obj": 98, "pkl": 98, "example_imag": 98, "unzip": [98, 103], "_separate_label": 98, "_separate_predict": 98, "begin": 98, "image_path": 98, "rb": 98, "image_to_visu": 98, "seg_map": 98, "334": 98, "float32": 98, "bboxes_ignor": 98, "290": 98, "286": 98, "285": 98, "224": 98, "231": [98, 103], "293": 98, "289": 98, "282": 98, "281": 98, "271": 98, "280": 98, "277": 98, "279": 98, "287": 98, "299": 98, "276": 98, "307": 98, "321": 98, "326": 98, "333": 98, "261": 98, "319": 98, "257": 98, "283": 98, "243": 98, "303": 98, "316": 98, "247": 98, "323": 98, "226": 98, "228": 98, "232": 98, "219": 98, "239": 98, "240": 98, "209": 98, "242": 98, "202": 98, "230": 98, "215": 98, "220": 98, "229": 98, "217": 98, "237": 98, "207": 98, "204": 98, "84": [98, 101], "205": 98, "223": 98, "153": 98, "140": 98, "124": 98, "268": 98, "273": 98, "108": 98, "284": 98, "110": 98, "136": 98, "145": 98, "173": 98, "297": 98, "317": 98, "192": 98, "332": 98, "324": 98, "203": 98, "320": 98, "199": 98, "291": 98, "000000481413": 98, "jpg": 98, "42398": 98, "44503": 98, "29968": 98, "336": 98, "21005": 98, "9978472": 98, "forgot": 98, "drew": 98, "label_issue_idx": 98, "num_examples_to_show": 98, "138": 98, "candid": 98, "97489622": 98, "70610878": 98, "98764951": 98, "88899237": 98, "99085805": 98, "issue_idx": 98, "95569726e": 98, "03354841e": 98, "57510169e": 98, "58447666e": 98, "39755858e": 98, "issue_to_visu": 98, "000000009483": 98, "95569726168054e": 98, "addition": [98, 102], "visibl": 98, "missmatch": 98, "likelei": 98, "agnost": 98, "vaidat": 98, "inconsist": 98, "000000395701": 98, "033548411774308e": 98, "armchair": 98, "tv": 98, "000000154004": 98, "38300759625496356": 98, "foreground": 98, "000000448410": 98, "0008575101690203273": 98, "crowd": 98, "alon": 98, "explor": [98, 99], "resembl": [98, 99], "000000499768": 98, "9748962231208227": 98, "000000521141": 98, "8889923658893665": 98, "000000143931": 98, "9876495074395956": 98, "bonu": 98, "uncov": 98, "irregular": 98, "anomali": 98, "object_detection_util": 98, "calculate_bounding_box_area": 98, "num_imgs_to_show": 98, "lab_object_count": 98, "pred_object_count": 98, "000000430073": 98, "000000183709": 98, "000000189475": 98, "studi": 98, "label_norm": 98, "pred_norm": 98, "area": [98, 102], "lab_area": 98, "pred_area": 98, "lab_area_mean": 98, "lab_area_std": 98, "max_deviation_valu": 98, "max_deviation_class": 98, "deviation_valu": 98, "deviation_class": 98, "mean_area": 98, "std_area": 98, "class_area": 98, "deviations_awai": 98, "max_deviation_index": 98, "num_imgs_to_show_per_class": 98, "class_num": 98, "sorted_indic": 98, "000000422886": 98, "000000341828": 98, "000000461009": 98, "train_feature_embed": 99, "ood_train_feature_scor": 99, "test_feature_embed": 99, "ood_test_feature_scor": 99, "ood_train_predictions_scor": 99, "train_pred_prob": 99, "ood_test_predictions_scor": 99, "test_pred_prob": 99, "pylab": 99, "rcparam": 99, "baggingclassifi": 99, "therebi": 99, "rescal": 99, "transform_norm": 99, "totensor": 99, "root": 99, "animal_class": 99, "non_animal_class": 99, "animal_idx": 99, "test_idx": 99, "toronto": 99, "edu": 99, "kriz": 99, "170498071": 99, "37601665": 99, "95it": 99, "5000": 99, "plot_imag": 99, "visualize_outli": 99, "txt_class": 99, "img": [99, 101], "npimg": 99, "show_label": 99, "data_subset": 99, "resnet50": 99, "corpu": 99, "2048": 99, "embed_imag": 99, "create_model": 99, "strang": 99, "odd": 99, "train_ood_features_scor": 99, "top_train_ood_features_idx": 99, "fun": 99, "negat": 99, "homogen": 99, "bottom_train_ood_features_idx": 99, "test_ood_features_scor": 99, "top_ood_features_idx": 99, "inevit": 99, "trade": 99, "5th": 99, "percentil": 99, "fifth_percentil": 99, "plt_rang": 99, "hist": 99, "train_outlier_scor": 99, "ylabel": 99, "axvlin": 99, "test_outlier_scor": 99, "ood_features_indic": 99, "revisit": 99, "return_invers": 99, "train_feature_embeddings_sc": 99, "test_feature_embeddings_sc": 99, "train_pred_label": 99, "9702": 99, "train_ood_predictions_scor": 99, "test_ood_predictions_scor": 99, "lost": 99, "unsuit": 100, "ok": [100, 103], "convention": 100, "aforement": 100, "hypothet": 100, "contrast": 100, "tradit": 100, "disjoint": 100, "out_of_sample_pred_probs_for_a": 100, "out_of_sample_pred_probs_for_b": 100, "out_of_sample_pred_probs_for_c": 100, "out_of_sample_pred_prob": 100, "price": 101, "incom": 101, "ag": 101, "sensor": 101, "histgradientboostingregressor": 101, "r2_score": 101, "student_grades_r": 101, "final_scor": 101, "true_final_scor": 101, "homework": 101, "3d": 101, "hue": 101, "mpl_toolkit": 101, "mplot3d": 101, "axes3d": 101, "errors_idx": 101, "add_subplot": 101, "z": 101, "colorbar": 101, "errors_mask": 101, "feature_column": 101, "predicted_column": 101, "x_train_raw": 101, "x_test_raw": 101, "randomforestregressor": 101, "385101": 101, "499503": 101, "698255": 101, "776647": 101, "109373": 101, "170547": 101, "481096": 101, "984759": 101, "645270": 101, "795928": 101, "141": 101, "659": 101, "318": 101, "305": 101, "560": 101, "657": 101, "688": 101, "view_datapoint": 101, "concat": 101, "preds_og": 101, "r2_og": 101, "838": 101, "found_label_issu": 101, "preds_cl": 101, "r2_cl": 101, "926": 101, "favorit": 101, "968627e": 101, "228799": 101, "646674e": 101, "402962": 101, "323818e": 101, "952758": 101, "422144e": 101, "456908": 101, "465815e": 101, "753968": 101, "791186e": 101, "110719": 101, "485156e": 101, "670640": 101, "225300e": 101, "749976": 101, "499679e": 101, "947007": 101, "067882e": 101, "648396": 101, "synthia": 102, "imagesegment": 102, "given_mask": 102, "predicted_mask": 102, "set_printopt": [102, 103], "sky": 102, "sidewalk": 102, "veget": 102, "terrain": 102, "rider": 102, "pred_probs_filepath": 102, "1088": 102, "1920": 102, "label_filepath": 102, "synthia_class": 102, "maunal": 102, "100000": 102, "244800": 102, "leftmost": 102, "middl": [102, 103], "infact": 102, "rightmost": 102, "discrep": 102, "3263230": 102, "783379": 102, "275110": 102, "255792": 102, "78225": 102, "55990": 102, "54427": 102, "33591": 102, "24645": 102, "21308": 102, "15045": 102, "14171": 102, "13832": 102, "13498": 102, "11490": 102, "9164": 102, "8769": 102, "6999": 102, "6031": 102, "5011": 102, "mistakenli": 102, "class_issu": 102, "aim": [102, 103], "domin": 102, "bunch": 103, "conll": 103, "2003": 103, "love": 103, "n_i": 103, "optional_list_of_ordered_class_nam": 103, "deepai": 103, "conll2003": 103, "rm": 103, "tokenclassif": 103, "2024": 103, "2400": 103, "52e0": 103, "1a01": 103, "connect": 103, "443": 103, "await": 103, "982975": 103, "960k": 103, "959": 103, "94k": 103, "49mb": 103, "mb": 103, "directori": 103, "inflat": 103, "161": 103, "17045998": 103, "16m": 103, "octet": 103, "26m": 103, "5mb": 103, "bert": 103, "read_npz": 103, "filepath": 103, "corrsespond": 103, "iob2": 103, "given_ent": 103, "entity_map": 103, "readfil": 103, "startswith": 103, "docstart": 103, "isalpha": 103, "isupp": 103, "indices_to_preview": 103, "nsentenc": 103, "eu": 103, "reject": 103, "boycott": 103, "british": 103, "lamb": 103, "00030412": 103, "00023826": 103, "99936208": 103, "00007009": 103, "00002545": 103, "99998795": 103, "00000401": 103, "00000218": 103, "00000455": 103, "00000131": 103, "00000749": 103, "99996115": 103, "00001371": 103, "0000087": 103, "00000895": 103, "99998936": 103, "00000382": 103, "00000178": 103, "00000366": 103, "00000137": 103, "99999101": 103, "00000266": 103, "00000174": 103, "0000035": 103, "00000109": 103, "99998768": 103, "00000482": 103, "00000202": 103, "00000438": 103, "0000011": 103, "00000465": 103, "99996392": 103, "00001105": 103, "0000116": 103, "00000878": 103, "99998671": 103, "00000364": 103, "00000213": 103, "00000472": 103, "00000281": 103, "99999073": 103, "00000211": 103, "00000159": 103, "00000442": 103, "00000115": 103, "peter": 103, "blackburn": 103, "00000358": 103, "00000529": 103, "99995623": 103, "0000129": 103, "0000024": 103, "00001812": 103, "99994141": 103, "00001645": 103, "00002162": 103, "brussel": 103, "1996": 103, "00001172": 103, "00000821": 103, "00004661": 103, "0000618": 103, "99987167": 103, "99999061": 103, "00000201": 103, "00000195": 103, "00000408": 103, "00000135": 103, "2254": 103, "2907": 103, "19392": 103, "9962": 103, "8904": 103, "19303": 103, "12918": 103, "9256": 103, "11855": 103, "18392": 103, "20426": 103, "19402": 103, "14744": 103, "19371": 103, "4645": 103, "10331": 103, "9430": 103, "6143": 103, "18367": 103, "12914": 103, "todai": 103, "weather": 103, "march": 103, "scalfaro": 103, "northern": 103, "himself": 103, "said": 103, "germani": 103, "nastja": 103, "rysich": 103, "north": 103, "spla": 103, "fought": 103, "khartoum": 103, "govern": 103, "south": 103, "1983": 103, "autonomi": 103, "animist": 103, "region": 103, "moslem": 103, "arabis": 103, "mayor": 103, "antonio": 103, "gonzalez": 103, "garcia": 103, "revolutionari": 103, "parti": 103, "wednesdai": 103, "troop": 103, "raid": 103, "farm": 103, "stole": 103, "rape": 103, "women": 103, "spring": 103, "chg": 103, "hrw": 103, "12pct": 103, "princ": 103, "photo": 103, "moment": 103, "spokeswoman": 103, "rainier": 103, "told": 103, "reuter": 103, "danila": 103, "carib": 103, "w224": 103, "equip": 103, "radiomet": 103, "earn": 103, "19996": 103, "london": 103, "denom": 103, "sale": 103, "uk": 103, "jp": 103, "fr": 103, "maccabi": 103, "hapoel": 103, "haifa": 103, "tel": 103, "aviv": 103, "hospit": 103, "rever": 103, "roman": 103, "cathol": 103, "nun": 103, "admit": 103, "calcutta": 103, "week": 103, "ago": 103, "fever": 103, "vomit": 103, "allianc": 103, "embattl": 103, "kabul": 103, "salang": 103, "highwai": 103, "mondai": 103, "tuesdai": 103, "suprem": 103, "council": 103, "led": 103, "jumbish": 103, "milli": 103, "movement": 103, "warlord": 103, "abdul": 103, "rashid": 103, "dostum": 103, "dollar": 103, "exchang": 103, "3570": 103, "12049": 103, "born": 103, "1937": 103, "provinc": 103, "anhui": 103, "dai": 103, "came": 103, "shanghai": 103, "citi": 103, "prolif": 103, "author": 103, "teacher": 103, "chines": 103, "16764": 103, "1990": 103, "historian": 103, "alan": 103, "john": 103, "percival": 103, "taylor": 103, "di": 103, "20446": 103, "pace": 103, "bowler": 103, "ian": 103, "harvei": 103, "claim": 103, "victoria": 103, "15514": 103, "cotti": 103, "osc": 103, "foreign": 103, "minist": 103, "7525": 103, "sultan": 103, "specter": 103, "met": 103, "crown": 103, "abdullah": 103, "defenc": 103, "aviat": 103, "jeddah": 103, "saudi": 103, "agenc": 103, "2288": 103, "hi": 103, "customari": 103, "outfit": 103, "champion": 103, "damp": 103, "scalp": 103, "canada": 103, "reign": 103, "olymp": 103, "donovan": 103, "bailei": 103, "1992": 103, "linford": 103, "christi": 103, "britain": 103, "1984": 103, "1988": 103, "carl": 103, "lewi": 103, "ambigi": 103, "punctuat": 103, "chicago": 103, "digest": 103, "philadelphia": 103, "usda": 103, "york": 103, "token_issu": 103, "471": 103, "kean": 103, "year": 103, "contract": 103, "manchest": 103, "19072": 103, "societi": 103, "bite": 103, "deliv": 103, "19910": 103, "father": 103, "clarenc": 103, "woolmer": 103, "renam": 103, "uttar": 103, "pradesh": 103, "india": 103, "ranji": 103, "trophi": 103, "nation": 103, "championship": 103, "captain": 103, "1949": 103, "15658": 103, "19879": 103, "iii": 103, "brian": 103, "shimer": 103, "randi": 103, "jone": 103, "19104": 103}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [4, 0, 0, "-", "data_valuation"], [11, 0, 0, "-", "datalab"], [36, 0, 0, "-", "dataset"], [39, 0, 0, "-", "experimental"], [43, 0, 0, "-", "filter"], [44, 0, 0, "-", "internal"], [55, 0, 0, "-", "models"], [57, 0, 0, "-", "multiannotator"], [60, 0, 0, "-", "multilabel_classification"], [63, 0, 0, "-", "object_detection"], [66, 0, 0, "-", "outlier"], [67, 0, 0, "-", "rank"], [68, 0, 0, "-", "regression"], [72, 0, 0, "-", "segmentation"], [76, 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"], [15, 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": [[12, 0, 0, "-", "data"], [13, 0, 0, "-", "data_issues"], [16, 0, 0, "-", "issue_finder"], [14, 0, 0, "-", "issue_manager_factory"], [32, 0, 0, "-", "model_outputs"], [33, 0, 0, "-", "report"], [34, 0, 0, "-", "task"]], "cleanlab.datalab.internal.data": [[12, 2, 1, "", "Data"], [12, 5, 1, "", "DataFormatError"], [12, 5, 1, "", "DatasetDictError"], [12, 5, 1, "", "DatasetLoadError"], [12, 2, 1, "", "Label"], [12, 2, 1, "", "MultiClass"], [12, 2, 1, "", "MultiLabel"]], "cleanlab.datalab.internal.data.Data": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[12, 3, 1, "", "add_note"], [12, 6, 1, "", "args"], [12, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiClass": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data.MultiLabel": [[12, 4, 1, "", "class_names"], [12, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[13, 2, 1, "", "DataIssues"], [13, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[13, 3, 1, "", "collect_issues_from_imagelab"], [13, 3, 1, "", "collect_issues_from_issue_manager"], [13, 3, 1, "", "collect_statistics"], [13, 3, 1, "", "get_info"], [13, 3, 1, "", "get_issue_summary"], [13, 3, 1, "", "get_issues"], [13, 6, 1, "", "info"], [13, 6, 1, "", "issue_summary"], [13, 6, 1, "", "issues"], [13, 3, 1, "", "set_health_score"], [13, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[16, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[16, 3, 1, "", "find_issues"], [16, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[18, 0, 0, "-", "data_valuation"], [19, 0, 0, "-", "duplicate"], [20, 0, 0, "-", "imbalance"], [22, 0, 0, "-", "issue_manager"], [23, 0, 0, "-", "label"], [26, 0, 0, "-", "noniid"], [27, 0, 0, "-", "null"], [28, 0, 0, "-", "outlier"], [31, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[18, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[18, 6, 1, "", "DEFAULT_THRESHOLD"], [18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[19, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[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, 6, 1, "", "near_duplicate_sets"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[20, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[22, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[23, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 3, 1, "", "get_health_summary"], [23, 6, 1, "", "health_summary_parameters"], [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.multilabel": [[25, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[25, 2, 1, "", "MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager": [[25, 3, 1, "", "collect_info"], [25, 6, 1, "", "description"], [25, 3, 1, "", "find_issues"], [25, 6, 1, "", "info"], [25, 6, 1, "", "issue_name"], [25, 6, 1, "", "issue_score_key"], [25, 6, 1, "", "issues"], [25, 3, 1, "", "make_summary"], [25, 3, 1, "", "report"], [25, 6, 1, "", "summary"], [25, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[26, 2, 1, "", "NonIIDIssueManager"], [26, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[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.null": [[27, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[28, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[28, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [28, 3, 1, "", "report"], [28, 6, 1, "", "summary"], [28, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[30, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[30, 2, 1, "", "RegressionLabelIssueManager"], [30, 1, 1, "", "find_issues_with_features"], [30, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[30, 3, 1, "", "collect_info"], [30, 6, 1, "", "description"], [30, 3, 1, "", "find_issues"], [30, 6, 1, "", "info"], [30, 6, 1, "", "issue_name"], [30, 6, 1, "", "issue_score_key"], [30, 6, 1, "", "issues"], [30, 3, 1, "", "make_summary"], [30, 3, 1, "", "report"], [30, 6, 1, "", "summary"], [30, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[31, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[31, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [31, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [31, 3, 1, "", "collect_info"], [31, 6, 1, "", "description"], [31, 3, 1, "", "filter_cluster_ids"], [31, 3, 1, "", "find_issues"], [31, 3, 1, "", "get_worst_cluster"], [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, "", "perform_clustering"], [31, 3, 1, "", "report"], [31, 3, 1, "", "set_knn_graph"], [31, 6, 1, "", "summary"], [31, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[14, 7, 1, "", "REGISTRY"], [14, 1, 1, "", "list_default_issue_types"], [14, 1, 1, "", "list_possible_issue_types"], [14, 1, 1, "", "register"]], "cleanlab.datalab.internal.model_outputs": [[32, 2, 1, "", "ModelOutput"], [32, 2, 1, "", "MultiClassPredProbs"], [32, 2, 1, "", "MultiLabelPredProbs"], [32, 2, 1, "", "RegressionPredictions"]], "cleanlab.datalab.internal.model_outputs.ModelOutput": [[32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.model_outputs.RegressionPredictions": [[32, 6, 1, "", "argument"], [32, 3, 1, "", "collect"], [32, 6, 1, "", "data"], [32, 3, 1, "", "validate"]], "cleanlab.datalab.internal.report": [[33, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[33, 3, 1, "", "get_report"], [33, 3, 1, "", "report"]], "cleanlab.datalab.internal.task": [[34, 2, 1, "", "Task"]], "cleanlab.datalab.internal.task.Task": [[34, 6, 1, "", "CLASSIFICATION"], [34, 6, 1, "", "MULTILABEL"], [34, 6, 1, "", "REGRESSION"], [34, 3, 1, "", "__contains__"], [34, 3, 1, "", "__getitem__"], [34, 3, 1, "", "__iter__"], [34, 3, 1, "", "__len__"], [34, 3, 1, "", "from_str"], [34, 4, 1, "", "is_classification"], [34, 4, 1, "", "is_multilabel"], [34, 4, 1, "", "is_regression"]], "cleanlab.dataset": [[36, 1, 1, "", "find_overlapping_classes"], [36, 1, 1, "", "health_summary"], [36, 1, 1, "", "overall_label_health_score"], [36, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[37, 0, 0, "-", "cifar_cnn"], [38, 0, 0, "-", "coteaching"], [40, 0, 0, "-", "label_issues_batched"], [41, 0, 0, "-", "mnist_pytorch"], [42, 0, 0, "-", "span_classification"]], "cleanlab.experimental.cifar_cnn": [[37, 2, 1, "", "CNN"], [37, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[37, 6, 1, "", "T_destination"], [37, 3, 1, "", "__call__"], [37, 3, 1, "", "add_module"], [37, 3, 1, "", "apply"], [37, 3, 1, "", "bfloat16"], [37, 3, 1, "", "buffers"], [37, 6, 1, "", "call_super_init"], [37, 3, 1, "", "children"], [37, 3, 1, "", "compile"], [37, 3, 1, "", "cpu"], [37, 3, 1, "", "cuda"], [37, 3, 1, "", "double"], [37, 6, 1, "", "dump_patches"], [37, 3, 1, "", "eval"], [37, 3, 1, "", "extra_repr"], [37, 3, 1, "", "float"], [37, 3, 1, "id0", "forward"], [37, 3, 1, "", "get_buffer"], [37, 3, 1, "", "get_extra_state"], [37, 3, 1, "", "get_parameter"], [37, 3, 1, "", "get_submodule"], [37, 3, 1, "", "half"], [37, 3, 1, "", "ipu"], [37, 3, 1, "", "load_state_dict"], [37, 3, 1, "", "modules"], [37, 3, 1, "", "named_buffers"], [37, 3, 1, "", "named_children"], [37, 3, 1, "", "named_modules"], [37, 3, 1, "", "named_parameters"], [37, 3, 1, "", "parameters"], [37, 3, 1, "", "register_backward_hook"], [37, 3, 1, "", "register_buffer"], [37, 3, 1, "", "register_forward_hook"], [37, 3, 1, "", "register_forward_pre_hook"], [37, 3, 1, "", "register_full_backward_hook"], [37, 3, 1, "", "register_full_backward_pre_hook"], [37, 3, 1, "", "register_load_state_dict_post_hook"], [37, 3, 1, "", "register_module"], [37, 3, 1, "", "register_parameter"], [37, 3, 1, "", "register_state_dict_pre_hook"], [37, 3, 1, "", "requires_grad_"], [37, 3, 1, "", "set_extra_state"], [37, 3, 1, "", "share_memory"], [37, 3, 1, "", "state_dict"], [37, 3, 1, "", "to"], [37, 3, 1, "", "to_empty"], [37, 3, 1, "", "train"], [37, 6, 1, "", "training"], [37, 3, 1, "", "type"], [37, 3, 1, "", "xpu"], [37, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[38, 1, 1, "", "adjust_learning_rate"], [38, 1, 1, "", "evaluate"], [38, 1, 1, "", "forget_rate_scheduler"], [38, 1, 1, "", "initialize_lr_scheduler"], [38, 1, 1, "", "loss_coteaching"], [38, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[40, 2, 1, "", "LabelInspector"], [40, 7, 1, "", "adj_confident_thresholds_shared"], [40, 1, 1, "", "find_label_issues_batched"], [40, 7, 1, "", "labels_shared"], [40, 7, 1, "", "pred_probs_shared"], [40, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[40, 3, 1, "", "get_confident_thresholds"], [40, 3, 1, "", "get_label_issues"], [40, 3, 1, "", "get_num_issues"], [40, 3, 1, "", "get_quality_scores"], [40, 3, 1, "", "score_label_quality"], [40, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[41, 2, 1, "", "CNN"], [41, 2, 1, "", "SimpleNet"], [41, 1, 1, "", "get_mnist_dataset"], [41, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[41, 3, 1, "", "__init_subclass__"], [41, 6, 1, "", "batch_size"], [41, 6, 1, "", "dataset"], [41, 6, 1, "", "epochs"], [41, 3, 1, "id0", "fit"], [41, 3, 1, "", "get_metadata_routing"], [41, 3, 1, "", "get_params"], [41, 6, 1, "", "loader"], [41, 6, 1, "", "log_interval"], [41, 6, 1, "", "lr"], [41, 6, 1, "", "momentum"], [41, 6, 1, "", "no_cuda"], [41, 3, 1, "id1", "predict"], [41, 3, 1, "id4", "predict_proba"], [41, 6, 1, "", "seed"], [41, 3, 1, "", "set_fit_request"], [41, 3, 1, "", "set_params"], [41, 3, 1, "", "set_predict_proba_request"], [41, 3, 1, "", "set_predict_request"], [41, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[41, 6, 1, "", "T_destination"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "add_module"], [41, 3, 1, "", "apply"], [41, 3, 1, "", "bfloat16"], [41, 3, 1, "", "buffers"], [41, 6, 1, "", "call_super_init"], [41, 3, 1, "", "children"], [41, 3, 1, "", "compile"], [41, 3, 1, "", "cpu"], [41, 3, 1, "", "cuda"], [41, 3, 1, "", "double"], [41, 6, 1, "", "dump_patches"], [41, 3, 1, "", "eval"], [41, 3, 1, "", "extra_repr"], [41, 3, 1, "", "float"], [41, 3, 1, "", "forward"], [41, 3, 1, "", "get_buffer"], [41, 3, 1, "", "get_extra_state"], [41, 3, 1, "", "get_parameter"], [41, 3, 1, "", "get_submodule"], [41, 3, 1, "", "half"], [41, 3, 1, "", "ipu"], [41, 3, 1, "", "load_state_dict"], [41, 3, 1, "", "modules"], [41, 3, 1, "", "named_buffers"], [41, 3, 1, "", "named_children"], [41, 3, 1, "", "named_modules"], [41, 3, 1, "", "named_parameters"], [41, 3, 1, "", "parameters"], [41, 3, 1, "", "register_backward_hook"], [41, 3, 1, "", "register_buffer"], [41, 3, 1, "", "register_forward_hook"], [41, 3, 1, "", "register_forward_pre_hook"], [41, 3, 1, "", "register_full_backward_hook"], [41, 3, 1, "", "register_full_backward_pre_hook"], [41, 3, 1, "", "register_load_state_dict_post_hook"], [41, 3, 1, "", "register_module"], [41, 3, 1, "", "register_parameter"], [41, 3, 1, "", "register_state_dict_pre_hook"], [41, 3, 1, "", "requires_grad_"], [41, 3, 1, "", "set_extra_state"], [41, 3, 1, "", "share_memory"], [41, 3, 1, "", "state_dict"], [41, 3, 1, "", "to"], [41, 3, 1, "", "to_empty"], [41, 3, 1, "", "train"], [41, 6, 1, "", "training"], [41, 3, 1, "", "type"], [41, 3, 1, "", "xpu"], [41, 3, 1, "", "zero_grad"]], "cleanlab.experimental.span_classification": [[42, 1, 1, "", "display_issues"], [42, 1, 1, "", "find_label_issues"], [42, 1, 1, "", "get_label_quality_scores"]], "cleanlab.filter": [[43, 1, 1, "", "find_label_issues"], [43, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [43, 1, 1, "", "find_predicted_neq_given"], [43, 7, 1, "", "pred_probs_by_class"], [43, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[45, 0, 0, "-", "label_quality_utils"], [46, 0, 0, "-", "latent_algebra"], [47, 0, 0, "-", "multiannotator_utils"], [48, 0, 0, "-", "multilabel_scorer"], [49, 0, 0, "-", "multilabel_utils"], [50, 0, 0, "-", "outlier"], [51, 0, 0, "-", "token_classification_utils"], [52, 0, 0, "-", "util"], [53, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[45, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[46, 1, 1, "", "compute_inv_noise_matrix"], [46, 1, 1, "", "compute_noise_matrix_from_inverse"], [46, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [46, 1, 1, "", "compute_py"], [46, 1, 1, "", "compute_py_inv_noise_matrix"], [46, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[47, 1, 1, "", "assert_valid_inputs_multiannotator"], [47, 1, 1, "", "assert_valid_pred_probs"], [47, 1, 1, "", "check_consensus_label_classes"], [47, 1, 1, "", "compute_soft_cross_entropy"], [47, 1, 1, "", "find_best_temp_scaler"], [47, 1, 1, "", "format_multiannotator_labels"], [47, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[48, 2, 1, "", "Aggregator"], [48, 2, 1, "", "ClassLabelScorer"], [48, 2, 1, "", "MultilabelScorer"], [48, 1, 1, "", "exponential_moving_average"], [48, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [48, 1, 1, "", "get_label_quality_scores"], [48, 1, 1, "", "multilabel_py"], [48, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[48, 3, 1, "", "__call__"], [48, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[48, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [48, 6, 1, "", "NORMALIZED_MARGIN"], [48, 6, 1, "", "SELF_CONFIDENCE"], [48, 3, 1, "", "__call__"], [48, 3, 1, "", "__contains__"], [48, 3, 1, "", "__getitem__"], [48, 3, 1, "", "__iter__"], [48, 3, 1, "", "__len__"], [48, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[48, 3, 1, "", "__call__"], [48, 3, 1, "", "aggregate"], [48, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[49, 1, 1, "", "get_onehot_num_classes"], [49, 1, 1, "", "int2onehot"], [49, 1, 1, "", "onehot2int"], [49, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[50, 1, 1, "", "correct_precision_errors"], [50, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, 1, 1, "", "color_sentence"], [51, 1, 1, "", "filter_sentence"], [51, 1, 1, "", "get_sentence"], [51, 1, 1, "", "mapping"], [51, 1, 1, "", "merge_probs"], [51, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[52, 1, 1, "", "append_extra_datapoint"], [52, 1, 1, "", "clip_noise_rates"], [52, 1, 1, "", "clip_values"], [52, 1, 1, "", "compress_int_array"], [52, 1, 1, "", "confusion_matrix"], [52, 1, 1, "", "csr_vstack"], [52, 1, 1, "", "estimate_pu_f1"], [52, 1, 1, "", "extract_indices_tf"], [52, 1, 1, "", "force_two_dimensions"], [52, 1, 1, "", "format_labels"], [52, 1, 1, "", "get_missing_classes"], [52, 1, 1, "", "get_num_classes"], [52, 1, 1, "", "get_unique_classes"], [52, 1, 1, "", "is_tensorflow_dataset"], [52, 1, 1, "", "is_torch_dataset"], [52, 1, 1, "", "num_unique_classes"], [52, 1, 1, "", "print_inverse_noise_matrix"], [52, 1, 1, "", "print_joint_matrix"], [52, 1, 1, "", "print_noise_matrix"], [52, 1, 1, "", "print_square_matrix"], [52, 1, 1, "", "remove_noise_from_class"], [52, 1, 1, "", "round_preserving_row_totals"], [52, 1, 1, "", "round_preserving_sum"], [52, 1, 1, "", "smart_display_dataframe"], [52, 1, 1, "", "subset_X_y"], [52, 1, 1, "", "subset_data"], [52, 1, 1, "", "subset_labels"], [52, 1, 1, "", "train_val_split"], [52, 1, 1, "", "unshuffle_tensorflow_dataset"], [52, 1, 1, "", "value_counts"], [52, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[53, 1, 1, "", "assert_indexing_works"], [53, 1, 1, "", "assert_nonempty_input"], [53, 1, 1, "", "assert_valid_class_labels"], [53, 1, 1, "", "assert_valid_inputs"], [53, 1, 1, "", "labels_to_array"], [53, 1, 1, "", "labels_to_list_multilabel"]], "cleanlab.models": [[56, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[56, 2, 1, "", "KerasWrapperModel"], [56, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[56, 3, 1, "", "fit"], [56, 3, 1, "", "get_params"], [56, 3, 1, "", "predict"], [56, 3, 1, "", "predict_proba"], [56, 3, 1, "", "set_params"], [56, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[56, 3, 1, "", "fit"], [56, 3, 1, "", "get_params"], [56, 3, 1, "", "predict"], [56, 3, 1, "", "predict_proba"], [56, 3, 1, "", "set_params"], [56, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[57, 1, 1, "", "convert_long_to_wide_dataset"], [57, 1, 1, "", "get_active_learning_scores"], [57, 1, 1, "", "get_active_learning_scores_ensemble"], [57, 1, 1, "", "get_label_quality_multiannotator"], [57, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [57, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[58, 0, 0, "-", "dataset"], [59, 0, 0, "-", "filter"], [61, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[58, 1, 1, "", "common_multilabel_issues"], [58, 1, 1, "", "multilabel_health_summary"], [58, 1, 1, "", "overall_multilabel_health_score"], [58, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, 1, 1, "", "find_label_issues"], [59, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[61, 1, 1, "", "get_label_quality_scores"], [61, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[62, 0, 0, "-", "filter"], [64, 0, 0, "-", "rank"], [65, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[62, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[64, 1, 1, "", "compute_badloc_box_scores"], [64, 1, 1, "", "compute_overlooked_box_scores"], [64, 1, 1, "", "compute_swap_box_scores"], [64, 1, 1, "", "get_label_quality_scores"], [64, 1, 1, "", "issues_from_scores"], [64, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[65, 1, 1, "", "bounding_box_size_distribution"], [65, 1, 1, "", "calculate_per_class_metrics"], [65, 1, 1, "", "class_label_distribution"], [65, 1, 1, "", "get_average_per_class_confusion_matrix"], [65, 1, 1, "", "get_sorted_bbox_count_idxs"], [65, 1, 1, "", "object_counts_per_image"], [65, 1, 1, "", "plot_class_distribution"], [65, 1, 1, "", "plot_class_size_distributions"], [65, 1, 1, "", "visualize"]], "cleanlab.outlier": [[66, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[66, 3, 1, "", "fit"], [66, 3, 1, "", "fit_score"], [66, 3, 1, "", "score"]], "cleanlab.rank": [[67, 1, 1, "", "find_top_issues"], [67, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [67, 1, 1, "", "get_label_quality_ensemble_scores"], [67, 1, 1, "", "get_label_quality_scores"], [67, 1, 1, "", "get_normalized_margin_for_each_label"], [67, 1, 1, "", "get_self_confidence_for_each_label"], [67, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[69, 0, 0, "-", "learn"], [70, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[69, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[69, 3, 1, "", "__init_subclass__"], [69, 3, 1, "", "find_label_issues"], [69, 3, 1, "", "fit"], [69, 3, 1, "", "get_aleatoric_uncertainty"], [69, 3, 1, "", "get_epistemic_uncertainty"], [69, 3, 1, "", "get_label_issues"], [69, 3, 1, "", "get_metadata_routing"], [69, 3, 1, "", "get_params"], [69, 3, 1, "", "predict"], [69, 3, 1, "", "save_space"], [69, 3, 1, "", "score"], [69, 3, 1, "", "set_fit_request"], [69, 3, 1, "", "set_params"], [69, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[70, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[71, 0, 0, "-", "filter"], [73, 0, 0, "-", "rank"], [74, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[71, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[73, 1, 1, "", "get_label_quality_scores"], [73, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[74, 1, 1, "", "common_label_issues"], [74, 1, 1, "", "display_issues"], [74, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[75, 0, 0, "-", "filter"], [77, 0, 0, "-", "rank"], [78, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[75, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[77, 1, 1, "", "get_label_quality_scores"], [77, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[78, 1, 1, "", "common_label_issues"], [78, 1, 1, "", "display_issues"], [78, 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, 82, 83, 84, 88, 90, 91, 93, 94, 97, 103], "count": [3, 94], "data_valu": [4, 18], "datalab": [5, 7, 9, 10, 11, 85, 86, 87, 89, 90, 91, 94, 97], "creat": [7, 85, 86, 87, 94, 96], "your": [7, 79, 86, 87, 91, 93, 94], "own": 7, "issu": [7, 9, 10, 21, 30, 79, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 97, 98, 102, 103], "manag": [7, 21], "prerequisit": 7, "implement": 7, "issuemanag": [7, 86], "basic": 7, "check": 7, "intermedi": 7, "advanc": [7, 86], "us": [7, 82, 83, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "gener": 8, "cluster": [8, 93], "id": 8, "guid": [9, 11], "type": [9, 10, 94], "custom": [9, 86], "cleanlab": [9, 10, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101, 102, 103], "studio": [9, 10], "easi": [9, 10, 79, 88, 90, 91], "mode": [9, 10, 79, 88, 90, 91], "can": [10, 85, 87, 92, 93, 94, 96], "detect": [10, 87, 90, 91, 93, 94, 98, 99], "estim": [10, 94, 96, 97], "each": 10, "label": [10, 23, 25, 30, 79, 82, 83, 84, 87, 88, 90, 91, 93, 94, 96, 97, 98, 101, 102, 103], "outlier": [10, 28, 50, 66, 88, 90, 91, 97, 99], "Near": [10, 87, 88, 90, 91], "duplic": [10, 19, 87, 88, 90, 91, 93, 97], "non": [10, 91], "iid": [10, 91], "class": [10, 80, 94, 102], "imbal": [10, 20], "imag": [10, 88, 99], "specif": [10, 21, 102], "underperform": [10, 93], "group": [10, 93], "null": [10, 27], "data": [10, 12, 79, 82, 84, 85, 86, 87, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "valuat": 10, "option": 10, "paramet": [10, 94], "get": [11, 85, 86, 87, 96, 97, 98, 102, 103], "start": [11, 92], "api": 11, "refer": 11, "data_issu": 13, "factori": 14, "intern": [15, 44], "issue_find": 16, "issue_manag": [21, 22], "regist": 21, "ml": [21, 93, 94], "task": [21, 34], "multilabel": 24, "noniid": 26, "regress": [29, 68, 69, 70, 93, 101], "prioriti": 30, "order": 30, "find": [30, 79, 82, 83, 84, 85, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 102, 103], "underperforming_group": 31, "model_output": 32, "report": [33, 88], "dataset": [36, 58, 79, 83, 85, 87, 88, 91, 92, 93, 94, 97, 98, 99, 101, 102, 103], "cifar_cnn": 37, "coteach": 38, "experiment": 39, "label_issues_batch": 40, "mnist_pytorch": 41, "span_classif": 42, "filter": [43, 59, 62, 71, 75, 94], "label_quality_util": 45, "latent_algebra": 46, "multiannotator_util": 47, "multilabel_scor": 48, "multilabel_util": 49, "token_classification_util": 51, "util": 52, "valid": [53, 88, 100], "fasttext": 54, "model": [55, 79, 82, 83, 84, 88, 90, 91, 93, 94, 96, 97, 98, 99, 101], "kera": 56, "multiannot": [57, 96], "multilabel_classif": 60, "rank": [61, 64, 67, 70, 73, 77, 94], "object_detect": 63, "summari": [65, 74, 78], "learn": [69, 82, 85, 87, 93, 94], "segment": [72, 102], "token_classif": [76, 103], "open": [79, 93], "sourc": [79, 93], "document": 79, "quickstart": 79, "1": [79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "instal": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "2": [79, 80, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "common": [79, 80, 103], "3": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 97, 98, 99, 101, 102, 103], "handl": [79, 93], "error": [79, 83, 88, 93, 94, 96, 97, 98, 101, 102, 103], "train": [79, 82, 83, 84, 93, 99, 101], "robust": [79, 82, 83, 94, 101], "noisi": [79, 82, 83, 94, 101], "4": [79, 82, 83, 84, 85, 86, 87, 88, 90, 91, 94, 96, 98, 99, 101], "curat": [79, 92], "fix": [79, 93], "level": [79, 92, 94, 103], "5": [79, 82, 84, 85, 87, 88, 90, 94, 96, 101], "improv": [79, 96], "via": [79, 94, 96], "mani": [79, 94], "other": [79, 96, 98, 101], "techniqu": 79, "contribut": 79, "how": [80, 93, 94, 96, 97, 103], "migrat": 80, "version": 80, "0": 80, "from": [80, 82, 83, 85, 86, 87, 94, 101], "pre": [80, 84, 93, 99], "function": [80, 86], "name": 80, "chang": 80, "modul": [80, 94], "new": [80, 85], "remov": 80, "argument": [80, 86], "variabl": 80, "cleanlearn": [81, 93, 94], "tutori": [81, 89, 92, 95], "tabular": [82, 90], "scikit": 82, "requir": [82, 83, 85, 86, 87, 88, 90, 91, 96, 97, 98, 99, 101, 102, 103], "depend": [82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97, 98, 99, 101, 102, 103], "load": [82, 83, 84, 85, 86, 87, 90, 91, 101], "process": [82, 90, 99, 101], "select": [82, 90], "comput": [82, 84, 88, 90, 91, 93, 96, 100], "out": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "sampl": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "predict": [82, 84, 85, 86, 87, 88, 90, 91, 96, 97, 98, 100], "probabl": [82, 84, 85, 86, 87, 88, 90, 91, 96, 100], "more": [82, 83, 85, 87, 94, 101], "text": [83, 91, 103], "format": [83, 91, 93, 97, 98], "defin": [83, 88, 91, 101], "potenti": [83, 96, 101], "audio": 84, "speechbrain": 84, "import": [84, 85, 86, 87, 88, 92, 94, 96], "them": [84, 92, 94], "featur": [84, 88, 99], "fit": 84, "linear": 84, "datamonitor": 85, "leverag": 85, "statist": [85, 96], "audit": [85, 86, 87], "skip": [85, 87, 92, 94, 96], "detail": [85, 87, 92, 94, 96], "classifi": [85, 86, 87], "6": [85, 94], "about": [85, 87], "addit": [85, 87], "workflow": [86, 94], "instanti": 86, "object": [86, 98], "increment": 86, "search": 86, "specifi": [86, 93], "nondefault": 86, "save": 86, "ad": 86, "A": 87, "unifi": 87, "all": [87, 94], "kind": [87, 98], "inform": [87, 88], "pytorch": [88, 99], "fetch": [88, 92], "normal": 88, "fashion": 88, "mnist": 88, "prepar": 88, "k": [88, 90, 100], "fold": [88, 100], "cross": [88, 100], "embed": [88, 99], "7": [88, 94], "view": 88, "most": [88, 103], "like": 88, "exampl": [88, 93, 94, 99], "sever": 88, "set": [88, 94], "dark": 88, "top": [88, 102], "low": 88, "numer": 90, "categor": 90, "column": 90, "construct": 90, "nearest": 90, "neighbour": 90, "graph": 90, "drift": [91, 97], "evalu": 92, "health": [92, 94], "8": [92, 94], "popular": 92, "faq": 93, "what": [93, 94, 100], "do": [93, 94], "i": [93, 94, 100], "infer": 93, "correct": 93, "ha": 93, "flag": 93, "should": 93, "v": 93, "test": [93, 94, 99], "big": 93, "limit": 93, "memori": 93, "why": 93, "isn": 93, "t": 93, "work": [93, 94, 96, 103], "me": 93, "differ": [93, 98], "clean": [93, 94], "final": 93, "hyperparamet": 93, "tune": 93, "onli": 93, "one": [93, 94, 97, 102], "doe": [93, 96, 103], "take": 93, "so": 93, "long": 93, "slice": 93, "when": [93, 94], "identifi": [93, 98], "run": 93, "licens": 93, "under": 93, "an": 93, "answer": 93, "question": 93, "The": 94, "centric": 94, "ai": 94, "machin": 94, "find_label_issu": 94, "line": 94, "code": 94, "visual": [94, 98, 99, 102], "twenti": 94, "lowest": 94, "qualiti": [94, 96, 97, 98, 102, 103], "see": 94, "now": 94, "let": 94, "": 94, "happen": 94, "we": 94, "merg": 94, "seafoam": 94, "green": 94, "yellow": 94, "too": 94, "you": 94, "re": 94, "One": 94, "score": [94, 96, 97, 98, 102, 103], "rule": 94, "overal": [94, 102], "accur": 94, "thi": 94, "directli": 94, "fulli": 94, "character": 94, "nois": 94, "matrix": [94, 97], "joint": 94, "prior": 94, "true": 94, "distribut": 94, "flip": 94, "rate": 94, "ani": 94, "again": 94, "support": 94, "lot": 94, "method": 94, "filter_bi": 94, "automat": 94, "everi": 94, "uniqu": 94, "num_label_issu": 94, "threshold": 94, "found": 94, "Not": 94, "sure": 94, "ensembl": 94, "multipl": [94, 96], "predictor": 94, "consensu": 96, "annot": 96, "initi": 96, "major": 96, "vote": 96, "better": 96, "compar": 96, "inspect": 96, "retrain": 96, "further": 96, "multi": 97, "beyond": 97, "mislabel": [97, 102, 103], "given": 97, "hot": 97, "binari": 97, "without": 97, "applic": 97, "real": 97, "download": [98, 102, 103], "objectlab": 98, "exploratori": 98, "analysi": 98, "timm": 99, "cifar10": 99, "some": 99, "pred_prob": [99, 102, 103], "wai": 101, "semant": 102, "which": 102, "ar": 102, "commonli": 102, "focus": 102, "token": 103, "word": 103, "sentenc": 103, "contain": 103, "particular": 103}, "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"], [18, "data-valuation"]], "datalab": [[5, "module-cleanlab.datalab.datalab"], [11, "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"]], "Label Issue": [[10, "label-issue"]], "Outlier Issue": [[10, "outlier-issue"]], "(Near) Duplicate Issue": [[10, "near-duplicate-issue"]], "Non-IID Issue": [[10, "non-iid-issue"]], "Class Imbalance Issue": [[10, "class-imbalance-issue"]], "Image-specific Issues": [[10, "image-specific-issues"]], "Underperforming Group Issue": [[10, "underperforming-group-issue"]], "Null Issue": [[10, "null-issue"]], "Data Valuation Issue": [[10, "data-valuation-issue"]], "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": [[11, "getting-started"]], "Guides": [[11, "guides"]], "API Reference": [[11, "api-reference"]], "data": [[12, "module-cleanlab.datalab.internal.data"]], "data_issues": [[13, "module-cleanlab.datalab.internal.data_issues"]], "factory": [[14, "module-cleanlab.datalab.internal.issue_manager_factory"]], "internal": [[15, "internal"], [44, "internal"]], "issue_finder": [[16, "issue-finder"]], "duplicate": [[19, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "imbalance": [[20, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "issue_manager": [[21, "issue-manager"], [22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "Registered issue managers": [[21, "registered-issue-managers"]], "ML task-specific issue managers": [[21, "ml-task-specific-issue-managers"]], "label": [[23, "module-cleanlab.datalab.internal.issue_manager.label"], [25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [30, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "multilabel": [[24, "multilabel"]], "noniid": [[26, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "null": [[27, "null"]], "outlier": [[28, "module-cleanlab.datalab.internal.issue_manager.outlier"], [50, "module-cleanlab.internal.outlier"], [66, "module-cleanlab.outlier"]], "regression": [[29, "regression"], [68, "regression"]], "Priority Order for finding issues:": [[30, null]], "underperforming_group": [[31, "underperforming-group"]], "model_outputs": [[32, "module-cleanlab.datalab.internal.model_outputs"]], "report": [[33, "report"]], "task": [[34, "task"]], "dataset": [[36, "module-cleanlab.dataset"], [58, "module-cleanlab.multilabel_classification.dataset"]], "cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "experimental": [[39, "experimental"]], "label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "filter": [[43, "module-cleanlab.filter"], [59, "module-cleanlab.multilabel_classification.filter"], [62, "filter"], [71, "filter"], [75, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "util": [[52, "module-cleanlab.internal.util"]], "validation": [[53, "module-cleanlab.internal.validation"]], "fasttext": [[54, "fasttext"]], "models": [[55, "models"]], "keras": [[56, "module-cleanlab.models.keras"]], "multiannotator": [[57, "module-cleanlab.multiannotator"]], "multilabel_classification": [[60, "multilabel-classification"]], "rank": [[61, "module-cleanlab.multilabel_classification.rank"], [64, "module-cleanlab.object_detection.rank"], [67, "module-cleanlab.rank"], [73, "module-cleanlab.segmentation.rank"], [77, "module-cleanlab.token_classification.rank"]], "object_detection": [[63, "object-detection"]], "summary": [[65, "summary"], [74, "module-cleanlab.segmentation.summary"], [78, "module-cleanlab.token_classification.summary"]], "regression.learn": [[69, "module-cleanlab.regression.learn"]], "regression.rank": [[70, "module-cleanlab.regression.rank"]], "segmentation": [[72, "segmentation"]], "token_classification": [[76, "token-classification"]], "cleanlab open-source documentation": [[79, "cleanlab-open-source-documentation"]], "Quickstart": [[79, "quickstart"]], "1. Install cleanlab": [[79, "install-cleanlab"]], "2. Find common issues in your data": [[79, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[79, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[79, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[79, "improve-your-data-via-many-other-techniques"]], "Contributing": [[79, "contributing"]], "Easy Mode": [[79, "easy-mode"], [88, "Easy-Mode"], [90, "Easy-Mode"], [91, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[80, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[80, "function-and-class-name-changes"]], "Module name changes": [[80, "module-name-changes"]], "New modules": [[80, "new-modules"]], "Removed modules": [[80, "removed-modules"]], "Common argument and variable name changes": [[80, "common-argument-and-variable-name-changes"]], "CleanLearning Tutorials": [[81, "cleanlearning-tutorials"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[82, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "1. Install required dependencies": [[82, "1.-Install-required-dependencies"], [83, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [101, "1.-Install-required-dependencies"]], "2. Load and process the data": [[82, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [101, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[82, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [90, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find label issues": [[82, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[82, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[83, "Text-Classification-with-Noisy-Labels"]], "2. Load and format the text dataset": [[83, "2.-Load-and-format-the-text-dataset"], [91, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and use cleanlab to find potential label errors": [[83, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[83, "4.-Train-a-more-robust-model-from-noisy-labels"], [101, "4.-Train-a-more-robust-model-from-noisy-labels"]], "Audio Classification with SpeechBrain and Cleanlab": [[84, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[84, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[84, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[84, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[84, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[84, "5.-Use-cleanlab-to-find-label-issues"], [90, "5.-Use-cleanlab-to-find-label-issues"]], "DataMonitor: Leverage statistics from Datalab to audit new data": [[85, "DataMonitor:-Leverage-statistics-from-Datalab-to-audit-new-data"]], "1. Install and import required dependencies": [[85, "1.-Install-and-import-required-dependencies"], [87, "1.-Install-and-import-required-dependencies"], [88, "1.-Install-and-import-required-dependencies"], [96, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[85, "2.-Create-and-load-the-data-(can-skip-these-details)"], [87, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[85, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"], [87, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[85, "4.-Use-Datalab-to-find-issues-in-the-dataset"], [87, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Use DataMonitor to find issues in new data": [[85, "5.-Use-DataMonitor-to-find-issues-in-new-data"]], "6. Learn more about the issues in the additional data": [[85, "6.-Learn-more-about-the-issues-in-the-additional-data"]], "Datalab: Advanced workflows to audit your data": [[86, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[86, "Install-and-import-required-dependencies"]], "Create and load the data": [[86, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[86, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[86, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[86, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[86, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[86, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[86, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[87, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "5. Learn more about the issues in your dataset": [[87, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[87, "Get-additional-information"]], "Near duplicate issues": [[87, "Near-duplicate-issues"], [88, "Near-duplicate-issues"]], "Image Classification with PyTorch and Cleanlab": [[88, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[88, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[88, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[88, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[88, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[88, "7.-Use-cleanlab-to-find-issues"]], "View report": [[88, "View-report"]], "Label issues": [[88, "Label-issues"], [90, "Label-issues"], [91, "Label-issues"]], "View most likely examples with label errors": [[88, "View-most-likely-examples-with-label-errors"]], "Outlier issues": [[88, "Outlier-issues"], [90, "Outlier-issues"], [91, "Outlier-issues"]], "View most severe outliers": [[88, "View-most-severe-outliers"]], "View sets of near duplicate images": [[88, "View-sets-of-near-duplicate-images"]], "Dark images": [[88, "Dark-images"]], "View top examples of dark images": [[88, "View-top-examples-of-dark-images"]], "Low information images": [[88, "Low-information-images"]], "Datalab Tutorials": [[89, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[90, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "4. Construct K nearest neighbours graph": [[90, "4.-Construct-K-nearest-neighbours-graph"]], "Near-duplicate issues": [[90, "Near-duplicate-issues"], [91, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[91, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[91, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[91, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[91, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[92, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[92, "Install-dependencies-and-import-them"], [94, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[92, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[92, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[93, "FAQ"]], "What data can cleanlab detect issues in?": [[93, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[93, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[93, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[93, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[93, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[93, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[93, "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?": [[93, "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?": [[93, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[93, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[93, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[93, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[93, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[93, "Can't-find-an-answer-to-your-question?"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[94, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[94, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[94, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[94, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[94, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[94, "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.": [[94, "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": [[94, "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": [[94, "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!": [[94, "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": [[94, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[94, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[94, "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)": [[94, "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:": [[94, "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": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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.": [[94, "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?": [[94, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[94, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[95, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[96, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[96, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[96, "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": [[96, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[96, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[96, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[96, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[96, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[96, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[97, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[97, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[97, "2.-Format-data,-labels,-and-model-predictions"], [98, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[97, "3.-Use-cleanlab-to-find-label-issues"], [98, "3.-Use-cleanlab-to-find-label-issues"], [102, "3.-Use-cleanlab-to-find-label-issues"], [103, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[97, "Label-quality-scores"]], "Data issues beyond mislabeling (outliers, duplicates, drift, \u2026)": [[97, "Data-issues-beyond-mislabeling-(outliers,-duplicates,-drift,-...)"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[97, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Estimate label issues without Datalab": [[97, "Estimate-label-issues-without-Datalab"]], "Application to Real Data": [[97, "Application-to-Real-Data"]], "Finding Label Errors in Object Detection Datasets": [[98, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[98, "1.-Install-required-dependencies-and-download-data"], [102, "1.-Install-required-dependencies-and-download-data"], [103, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[98, "Get-label-quality-scores"], [102, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[98, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[98, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[98, "Other-uses-of-visualize"]], "Exploratory data analysis": [[98, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[99, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[99, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[99, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[99, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[99, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[99, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[100, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[100, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[100, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[101, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[101, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "5. Other ways to find noisy labels in regression datasets": [[101, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[102, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[102, "2.-Get-data,-labels,-and-pred_probs"], [103, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[102, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[102, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[102, "Focusing-on-one-specific-class"]], "Find Label Errors in Token Classification (Text) Datasets": [[103, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[103, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[103, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[103, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[103, "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"], [11, "module-cleanlab.datalab"], [12, "module-cleanlab.datalab.internal.data"], [13, "module-cleanlab.datalab.internal.data_issues"], [14, "module-cleanlab.datalab.internal.issue_manager_factory"], [15, "module-cleanlab.datalab.internal"], [16, "module-cleanlab.datalab.internal.issue_finder"], [18, "module-cleanlab.datalab.internal.issue_manager.data_valuation"], [19, "module-cleanlab.datalab.internal.issue_manager.duplicate"], [20, "module-cleanlab.datalab.internal.issue_manager.imbalance"], [22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"], [23, "module-cleanlab.datalab.internal.issue_manager.label"], [25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"], [26, "module-cleanlab.datalab.internal.issue_manager.noniid"], [27, "module-cleanlab.datalab.internal.issue_manager.null"], [28, "module-cleanlab.datalab.internal.issue_manager.outlier"], [30, "module-cleanlab.datalab.internal.issue_manager.regression.label"], [31, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"], [32, "module-cleanlab.datalab.internal.model_outputs"], [33, "module-cleanlab.datalab.internal.report"], [34, "module-cleanlab.datalab.internal.task"], [36, "module-cleanlab.dataset"], [37, "module-cleanlab.experimental.cifar_cnn"], [38, "module-cleanlab.experimental.coteaching"], [39, "module-cleanlab.experimental"], [40, "module-cleanlab.experimental.label_issues_batched"], [41, "module-cleanlab.experimental.mnist_pytorch"], [42, "module-cleanlab.experimental.span_classification"], [43, "module-cleanlab.filter"], [44, "module-cleanlab.internal"], [45, "module-cleanlab.internal.label_quality_utils"], [46, "module-cleanlab.internal.latent_algebra"], [47, "module-cleanlab.internal.multiannotator_utils"], [48, "module-cleanlab.internal.multilabel_scorer"], [49, "module-cleanlab.internal.multilabel_utils"], [50, "module-cleanlab.internal.outlier"], [51, "module-cleanlab.internal.token_classification_utils"], [52, "module-cleanlab.internal.util"], [53, "module-cleanlab.internal.validation"], [55, "module-cleanlab.models"], [56, "module-cleanlab.models.keras"], [57, "module-cleanlab.multiannotator"], [58, "module-cleanlab.multilabel_classification.dataset"], [59, "module-cleanlab.multilabel_classification.filter"], [60, "module-cleanlab.multilabel_classification"], [61, "module-cleanlab.multilabel_classification.rank"], [62, "module-cleanlab.object_detection.filter"], [63, "module-cleanlab.object_detection"], [64, "module-cleanlab.object_detection.rank"], [65, "module-cleanlab.object_detection.summary"], [66, "module-cleanlab.outlier"], [67, "module-cleanlab.rank"], [68, "module-cleanlab.regression"], [69, "module-cleanlab.regression.learn"], [70, "module-cleanlab.regression.rank"], [71, "module-cleanlab.segmentation.filter"], [72, "module-cleanlab.segmentation"], [73, "module-cleanlab.segmentation.rank"], [74, "module-cleanlab.segmentation.summary"], [75, "module-cleanlab.token_classification.filter"], [76, "module-cleanlab.token_classification"], [77, "module-cleanlab.token_classification.rank"], [78, "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": [[11, "module-cleanlab.datalab"]], "data (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.Data"]], "dataformaterror": [[12, "cleanlab.datalab.internal.data.DataFormatError"]], "datasetdicterror": [[12, "cleanlab.datalab.internal.data.DatasetDictError"]], "datasetloaderror": [[12, "cleanlab.datalab.internal.data.DatasetLoadError"]], "label (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.Label"]], "multiclass (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.MultiClass"]], "multilabel (class in cleanlab.datalab.internal.data)": [[12, "cleanlab.datalab.internal.data.MultiLabel"]], "add_note() (cleanlab.datalab.internal.data.dataformaterror method)": [[12, "cleanlab.datalab.internal.data.DataFormatError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetdicterror method)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.add_note"]], "add_note() (cleanlab.datalab.internal.data.datasetloaderror method)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.add_note"]], "args (cleanlab.datalab.internal.data.dataformaterror attribute)": [[12, "cleanlab.datalab.internal.data.DataFormatError.args"]], "args (cleanlab.datalab.internal.data.datasetdicterror attribute)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.args"]], "args (cleanlab.datalab.internal.data.datasetloaderror attribute)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.args"]], "class_names (cleanlab.datalab.internal.data.data property)": [[12, "cleanlab.datalab.internal.data.Data.class_names"]], "class_names (cleanlab.datalab.internal.data.label property)": [[12, "cleanlab.datalab.internal.data.Label.class_names"]], "class_names (cleanlab.datalab.internal.data.multiclass property)": [[12, "cleanlab.datalab.internal.data.MultiClass.class_names"]], "class_names (cleanlab.datalab.internal.data.multilabel property)": [[12, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[12, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[12, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[12, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[12, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[12, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[12, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[12, "cleanlab.datalab.internal.data.DatasetDictError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetloaderror method)": [[12, "cleanlab.datalab.internal.data.DatasetLoadError.with_traceback"]], "dataissues (class in cleanlab.datalab.internal.data_issues)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues"]], "cleanlab.datalab.internal.data_issues": [[13, "module-cleanlab.datalab.internal.data_issues"]], "collect_issues_from_imagelab() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_imagelab"]], "collect_issues_from_issue_manager() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_issues_from_issue_manager"]], "collect_statistics() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.collect_statistics"]], "get_data_statistics() (in module cleanlab.datalab.internal.data_issues)": [[13, "cleanlab.datalab.internal.data_issues.get_data_statistics"]], "get_info() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_info"]], "get_issue_summary() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_issue_summary"]], "get_issues() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.get_issues"]], "info (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.info"]], "issue_summary (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.issue_summary"]], "issues (cleanlab.datalab.internal.data_issues.dataissues attribute)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.issues"]], "set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[13, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[14, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[14, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[15, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[16, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[16, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[18, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[19, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[19, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.verbosity_levels"]], "classimbalanceissuemanager (class in cleanlab.datalab.internal.issue_manager.imbalance)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[20, "module-cleanlab.datalab.internal.issue_manager.imbalance"]], "collect_info() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[22, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[22, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.verbosity_levels"]], "labelissuemanager (class in cleanlab.datalab.internal.issue_manager.label)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label": [[23, "module-cleanlab.datalab.internal.issue_manager.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.find_issues"]], "get_health_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.get_health_summary"]], "health_summary_parameters (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.health_summary_parameters"]], "info (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.label.labelissuemanager class method)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.label.labelissuemanager attribute)": [[23, "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager.verbosity_levels"]], "multilabelissuemanager (class in cleanlab.datalab.internal.issue_manager.multilabel.label)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.multilabel.label": [[25, "module-cleanlab.datalab.internal.issue_manager.multilabel.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager class method)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.multilabel.label.multilabelissuemanager attribute)": [[25, "cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager.verbosity_levels"]], "noniidissuemanager (class in cleanlab.datalab.internal.issue_manager.noniid)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager"]], "cleanlab.datalab.internal.issue_manager.noniid": [[26, "module-cleanlab.datalab.internal.issue_manager.noniid"]], "collect_info() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager class method)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.report"]], "simplified_kolmogorov_smirnov_test() (in module cleanlab.datalab.internal.issue_manager.noniid)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.simplified_kolmogorov_smirnov_test"]], "summary (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.noniid.noniidissuemanager attribute)": [[26, "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager.verbosity_levels"]], "nullissuemanager (class in cleanlab.datalab.internal.issue_manager.null)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null": [[27, "module-cleanlab.datalab.internal.issue_manager.null"]], "collect_info() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.null.nullissuemanager class method)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.null.nullissuemanager attribute)": [[27, "cleanlab.datalab.internal.issue_manager.null.NullIssueManager.verbosity_levels"]], "default_thresholds (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.DEFAULT_THRESHOLDS"]], "outlierissuemanager (class in cleanlab.datalab.internal.issue_manager.outlier)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier": [[28, "module-cleanlab.datalab.internal.issue_manager.outlier"]], "collect_info() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.make_summary"]], "ood (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.ood"]], "report() (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager class method)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.outlier.outlierissuemanager attribute)": [[28, "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager.verbosity_levels"]], "regressionlabelissuemanager (class in cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[30, "module-cleanlab.datalab.internal.issue_manager.regression.label"]], "collect_info() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.find_issues"]], "find_issues_with_features() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_features"]], "find_issues_with_predictions() (in module cleanlab.datalab.internal.issue_manager.regression.label)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.find_issues_with_predictions"]], "info (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager class method)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.regression.label.regressionlabelissuemanager attribute)": [[30, "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager.verbosity_levels"]], "no_underperforming_cluster_id (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.NO_UNDERPERFORMING_CLUSTER_ID"]], "outlier_cluster_labels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.OUTLIER_CLUSTER_LABELS"]], "underperforminggroupissuemanager (class in cleanlab.datalab.internal.issue_manager.underperforming_group)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[31, "module-cleanlab.datalab.internal.issue_manager.underperforming_group"]], "collect_info() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.description"]], "filter_cluster_ids() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.filter_cluster_ids"]], "find_issues() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.find_issues"]], "get_worst_cluster() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.get_worst_cluster"]], "info (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.make_summary"]], "perform_clustering() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.perform_clustering"]], "report() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager class method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.report"]], "set_knn_graph() (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager method)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.set_knn_graph"]], "summary (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.underperforming_group.underperforminggroupissuemanager attribute)": [[31, "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager.verbosity_levels"]], "modeloutput (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput"]], "multiclasspredprobs (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs"]], "multilabelpredprobs (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs"]], "regressionpredictions (class in cleanlab.datalab.internal.model_outputs)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions"]], "argument (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.argument"]], "argument (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.argument"]], "cleanlab.datalab.internal.model_outputs": [[32, "module-cleanlab.datalab.internal.model_outputs"]], "collect() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.collect"]], "collect() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.collect"]], "data (cleanlab.datalab.internal.model_outputs.modeloutput attribute)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.data"]], "data (cleanlab.datalab.internal.model_outputs.multiclasspredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.multilabelpredprobs attribute)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.data"]], "data (cleanlab.datalab.internal.model_outputs.regressionpredictions attribute)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.data"]], "validate() (cleanlab.datalab.internal.model_outputs.modeloutput method)": [[32, "cleanlab.datalab.internal.model_outputs.ModelOutput.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multiclasspredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiClassPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.multilabelpredprobs method)": [[32, "cleanlab.datalab.internal.model_outputs.MultiLabelPredProbs.validate"]], "validate() (cleanlab.datalab.internal.model_outputs.regressionpredictions method)": [[32, "cleanlab.datalab.internal.model_outputs.RegressionPredictions.validate"]], "reporter (class in cleanlab.datalab.internal.report)": [[33, "cleanlab.datalab.internal.report.Reporter"]], "cleanlab.datalab.internal.report": [[33, "module-cleanlab.datalab.internal.report"]], "get_report() (cleanlab.datalab.internal.report.reporter method)": [[33, "cleanlab.datalab.internal.report.Reporter.get_report"]], "report() (cleanlab.datalab.internal.report.reporter method)": [[33, "cleanlab.datalab.internal.report.Reporter.report"]], "classification (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.CLASSIFICATION"]], "multilabel (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.MULTILABEL"]], "regression (cleanlab.datalab.internal.task.task attribute)": [[34, "cleanlab.datalab.internal.task.Task.REGRESSION"]], "task (class in cleanlab.datalab.internal.task)": [[34, "cleanlab.datalab.internal.task.Task"]], "__contains__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__contains__"]], "__getitem__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__getitem__"]], "__iter__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__iter__"]], "__len__() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.__len__"]], "cleanlab.datalab.internal.task": [[34, "module-cleanlab.datalab.internal.task"]], "from_str() (cleanlab.datalab.internal.task.task class method)": [[34, "cleanlab.datalab.internal.task.Task.from_str"]], "is_classification (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_classification"]], "is_multilabel (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_multilabel"]], "is_regression (cleanlab.datalab.internal.task.task property)": [[34, "cleanlab.datalab.internal.task.Task.is_regression"]], "cleanlab.dataset": [[36, "module-cleanlab.dataset"]], "find_overlapping_classes() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.find_overlapping_classes"]], "health_summary() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.health_summary"]], "overall_label_health_score() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.overall_label_health_score"]], "rank_classes_by_label_quality() (in module cleanlab.dataset)": [[36, "cleanlab.dataset.rank_classes_by_label_quality"]], "cnn (class in cleanlab.experimental.cifar_cnn)": [[37, "cleanlab.experimental.cifar_cnn.CNN"]], "t_destination (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.T_destination"]], "__call__() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.__call__"]], "add_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.add_module"]], "apply() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.apply"]], "bfloat16() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.bfloat16"]], "buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.buffers"]], "call_bn() (in module cleanlab.experimental.cifar_cnn)": [[37, "cleanlab.experimental.cifar_cnn.call_bn"]], "call_super_init (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.call_super_init"]], "children() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.children"]], "cleanlab.experimental.cifar_cnn": [[37, "module-cleanlab.experimental.cifar_cnn"]], "compile() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.compile"]], "cpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.cpu"]], "cuda() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.cuda"]], "double() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.double"]], "dump_patches (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.dump_patches"]], "eval() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.eval"]], "extra_repr() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.extra_repr"]], "float() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.float"]], "forward() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.forward"], [37, "id0"]], "get_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_buffer"]], "get_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_extra_state"]], "get_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_parameter"]], "get_submodule() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.get_submodule"]], "half() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.half"]], "ipu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.ipu"]], "load_state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.load_state_dict"]], "modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.modules"]], "named_buffers() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_buffers"]], "named_children() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_children"]], "named_modules() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_modules"]], "named_parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.named_parameters"]], "parameters() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.parameters"]], "register_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_backward_hook"]], "register_buffer() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_buffer"]], "register_forward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_module"]], "register_parameter() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.requires_grad_"]], "set_extra_state() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.set_extra_state"]], "share_memory() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.share_memory"]], "state_dict() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.state_dict"]], "to() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.to"]], "to_empty() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.to_empty"]], "train() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.train"]], "training (cleanlab.experimental.cifar_cnn.cnn attribute)": [[37, "cleanlab.experimental.cifar_cnn.CNN.training"]], "type() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.type"]], "xpu() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.xpu"]], "zero_grad() (cleanlab.experimental.cifar_cnn.cnn method)": [[37, "cleanlab.experimental.cifar_cnn.CNN.zero_grad"]], "adjust_learning_rate() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.adjust_learning_rate"]], "cleanlab.experimental.coteaching": [[38, "module-cleanlab.experimental.coteaching"]], "evaluate() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.evaluate"]], "forget_rate_scheduler() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.forget_rate_scheduler"]], "initialize_lr_scheduler() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.initialize_lr_scheduler"]], "loss_coteaching() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.loss_coteaching"]], "train() (in module cleanlab.experimental.coteaching)": [[38, "cleanlab.experimental.coteaching.train"]], "cleanlab.experimental": [[39, "module-cleanlab.experimental"]], "labelinspector (class in cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector"]], "adj_confident_thresholds_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.adj_confident_thresholds_shared"]], "cleanlab.experimental.label_issues_batched": [[40, "module-cleanlab.experimental.label_issues_batched"]], "find_label_issues_batched() (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.find_label_issues_batched"]], "get_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_confident_thresholds"]], "get_label_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_label_issues"]], "get_num_issues() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_num_issues"]], "get_quality_scores() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.get_quality_scores"]], "labels_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.labels_shared"]], "pred_probs_shared (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.pred_probs_shared"]], "score_label_quality() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.score_label_quality"]], "split_arr() (in module cleanlab.experimental.label_issues_batched)": [[40, "cleanlab.experimental.label_issues_batched.split_arr"]], "update_confident_thresholds() (cleanlab.experimental.label_issues_batched.labelinspector method)": [[40, "cleanlab.experimental.label_issues_batched.LabelInspector.update_confident_thresholds"]], "cnn (class in cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.CNN"]], "simplenet (class in cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet"]], "t_destination (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.T_destination"]], "__call__() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.__call__"]], "__init_subclass__() (cleanlab.experimental.mnist_pytorch.cnn class method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.__init_subclass__"]], "add_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.add_module"]], "apply() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.apply"]], "batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.batch_size"]], "bfloat16() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.bfloat16"]], "buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.buffers"]], "call_super_init (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.call_super_init"]], "children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.children"]], "cleanlab.experimental.mnist_pytorch": [[41, "module-cleanlab.experimental.mnist_pytorch"]], "compile() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.compile"]], "cpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.cpu"]], "cuda() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.cuda"]], "dataset (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.dataset"]], "double() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.double"]], "dump_patches (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.dump_patches"]], "epochs (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.epochs"]], "eval() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.eval"]], "extra_repr() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.extra_repr"]], "fit() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.fit"], [41, "id0"]], "float() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.float"]], "forward() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.forward"]], "get_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_buffer"]], "get_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_extra_state"]], "get_metadata_routing() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.get_metadata_routing"]], "get_mnist_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.get_mnist_dataset"]], "get_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_parameter"]], "get_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.get_params"]], "get_sklearn_digits_dataset() (in module cleanlab.experimental.mnist_pytorch)": [[41, "cleanlab.experimental.mnist_pytorch.get_sklearn_digits_dataset"]], "get_submodule() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.get_submodule"]], "half() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.half"]], "ipu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.ipu"]], "load_state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.load_state_dict"]], "loader (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.loader"]], "log_interval (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.log_interval"]], "lr (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.lr"]], "modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.modules"]], "momentum (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.momentum"]], "named_buffers() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_buffers"]], "named_children() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_children"]], "named_modules() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_modules"]], "named_parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.named_parameters"]], "no_cuda (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.no_cuda"]], "parameters() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.parameters"]], "predict() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.predict"], [41, "id1"]], "predict_proba() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.predict_proba"], [41, "id4"]], "register_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_backward_hook"]], "register_buffer() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_buffer"]], "register_forward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_hook"]], "register_forward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_forward_pre_hook"]], "register_full_backward_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_hook"]], "register_full_backward_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_full_backward_pre_hook"]], "register_load_state_dict_post_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_load_state_dict_post_hook"]], "register_module() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_module"]], "register_parameter() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_parameter"]], "register_state_dict_pre_hook() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.register_state_dict_pre_hook"]], "requires_grad_() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.requires_grad_"]], "seed (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.seed"]], "set_extra_state() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.set_extra_state"]], "set_fit_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_fit_request"]], "set_params() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_params"]], "set_predict_proba_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_proba_request"]], "set_predict_request() (cleanlab.experimental.mnist_pytorch.cnn method)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.set_predict_request"]], "share_memory() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.share_memory"]], "state_dict() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.state_dict"]], "test_batch_size (cleanlab.experimental.mnist_pytorch.cnn attribute)": [[41, "cleanlab.experimental.mnist_pytorch.CNN.test_batch_size"]], "to() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.to"]], "to_empty() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.to_empty"]], "train() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.train"]], "training (cleanlab.experimental.mnist_pytorch.simplenet attribute)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.training"]], "type() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.type"]], "xpu() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.xpu"]], "zero_grad() (cleanlab.experimental.mnist_pytorch.simplenet method)": [[41, "cleanlab.experimental.mnist_pytorch.SimpleNet.zero_grad"]], "cleanlab.experimental.span_classification": [[42, "module-cleanlab.experimental.span_classification"]], "display_issues() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.display_issues"]], "find_label_issues() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.find_label_issues"]], "get_label_quality_scores() (in module cleanlab.experimental.span_classification)": [[42, "cleanlab.experimental.span_classification.get_label_quality_scores"]], "cleanlab.filter": [[43, "module-cleanlab.filter"]], "find_label_issues() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_label_issues"]], "find_label_issues_using_argmax_confusion_matrix() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_label_issues_using_argmax_confusion_matrix"]], "find_predicted_neq_given() (in module cleanlab.filter)": [[43, "cleanlab.filter.find_predicted_neq_given"]], "pred_probs_by_class (in module cleanlab.filter)": [[43, "cleanlab.filter.pred_probs_by_class"]], "prune_count_matrix_cols (in module cleanlab.filter)": [[43, "cleanlab.filter.prune_count_matrix_cols"]], "cleanlab.internal": [[44, "module-cleanlab.internal"]], "cleanlab.internal.label_quality_utils": [[45, "module-cleanlab.internal.label_quality_utils"]], "get_normalized_entropy() (in module cleanlab.internal.label_quality_utils)": [[45, "cleanlab.internal.label_quality_utils.get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[46, "module-cleanlab.internal.latent_algebra"]], "compute_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_inv_noise_matrix"]], "compute_noise_matrix_from_inverse() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_noise_matrix_from_inverse"]], "compute_ps_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_ps_py_inv_noise_matrix"]], "compute_py() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_py"]], "compute_py_inv_noise_matrix() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_py_inv_noise_matrix"]], "compute_pyx() (in module cleanlab.internal.latent_algebra)": [[46, "cleanlab.internal.latent_algebra.compute_pyx"]], "assert_valid_inputs_multiannotator() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.assert_valid_inputs_multiannotator"]], "assert_valid_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.assert_valid_pred_probs"]], "check_consensus_label_classes() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.check_consensus_label_classes"]], "cleanlab.internal.multiannotator_utils": [[47, "module-cleanlab.internal.multiannotator_utils"]], "compute_soft_cross_entropy() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.compute_soft_cross_entropy"]], "find_best_temp_scaler() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.find_best_temp_scaler"]], "format_multiannotator_labels() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.format_multiannotator_labels"]], "temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)": [[47, "cleanlab.internal.multiannotator_utils.temp_scale_pred_probs"]], "aggregator (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator"]], "confidence_weighted_entropy (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY"]], "classlabelscorer (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer"]], "multilabelscorer (class in cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer"]], "normalized_margin (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.NORMALIZED_MARGIN"]], "self_confidence (cleanlab.internal.multilabel_scorer.classlabelscorer attribute)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.SELF_CONFIDENCE"]], "__call__() (cleanlab.internal.multilabel_scorer.aggregator method)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.classlabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__call__"]], "__call__() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.__call__"]], "__contains__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__contains__"]], "__getitem__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__getitem__"]], "__iter__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__iter__"]], "__len__() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.__len__"]], "aggregate() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.aggregate"]], "cleanlab.internal.multilabel_scorer": [[48, "module-cleanlab.internal.multilabel_scorer"]], "exponential_moving_average() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.exponential_moving_average"]], "from_str() (cleanlab.internal.multilabel_scorer.classlabelscorer class method)": [[48, "cleanlab.internal.multilabel_scorer.ClassLabelScorer.from_str"]], "get_class_label_quality_scores() (cleanlab.internal.multilabel_scorer.multilabelscorer method)": [[48, "cleanlab.internal.multilabel_scorer.MultilabelScorer.get_class_label_quality_scores"]], "get_cross_validated_multilabel_pred_probs() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_cross_validated_multilabel_pred_probs"]], "get_label_quality_scores() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.get_label_quality_scores"]], "multilabel_py() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[48, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[48, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[49, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[49, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[50, "module-cleanlab.internal.outlier"]], "correct_precision_errors() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.correct_precision_errors"]], "transform_distances_to_scores() (in module cleanlab.internal.outlier)": [[50, "cleanlab.internal.outlier.transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[51, "module-cleanlab.internal.token_classification_utils"]], "color_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.color_sentence"]], "filter_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.filter_sentence"]], "get_sentence() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.get_sentence"]], "mapping() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.mapping"]], "merge_probs() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.merge_probs"]], "process_token() (in module cleanlab.internal.token_classification_utils)": [[51, "cleanlab.internal.token_classification_utils.process_token"]], "append_extra_datapoint() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.append_extra_datapoint"]], "cleanlab.internal.util": [[52, "module-cleanlab.internal.util"]], "clip_noise_rates() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_noise_rates"]], "clip_values() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.clip_values"]], "compress_int_array() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.compress_int_array"]], "confusion_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.confusion_matrix"]], "csr_vstack() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.csr_vstack"]], "estimate_pu_f1() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.estimate_pu_f1"]], "extract_indices_tf() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.extract_indices_tf"]], "force_two_dimensions() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.force_two_dimensions"]], "format_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.format_labels"]], "get_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_missing_classes"]], "get_num_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_num_classes"]], "get_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.get_unique_classes"]], "is_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_tensorflow_dataset"]], "is_torch_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.is_torch_dataset"]], "num_unique_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.num_unique_classes"]], "print_inverse_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_inverse_noise_matrix"]], "print_joint_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_joint_matrix"]], "print_noise_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_noise_matrix"]], "print_square_matrix() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.print_square_matrix"]], "remove_noise_from_class() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.remove_noise_from_class"]], "round_preserving_row_totals() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_row_totals"]], "round_preserving_sum() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.round_preserving_sum"]], "smart_display_dataframe() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.smart_display_dataframe"]], "subset_x_y() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_X_y"]], "subset_data() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_data"]], "subset_labels() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.subset_labels"]], "train_val_split() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.train_val_split"]], "unshuffle_tensorflow_dataset() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.unshuffle_tensorflow_dataset"]], "value_counts() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts"]], "value_counts_fill_missing_classes() (in module cleanlab.internal.util)": [[52, "cleanlab.internal.util.value_counts_fill_missing_classes"]], "assert_indexing_works() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_indexing_works"]], "assert_nonempty_input() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_nonempty_input"]], "assert_valid_class_labels() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_class_labels"]], "assert_valid_inputs() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.assert_valid_inputs"]], "cleanlab.internal.validation": [[53, "module-cleanlab.internal.validation"]], "labels_to_array() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_array"]], "labels_to_list_multilabel() (in module cleanlab.internal.validation)": [[53, "cleanlab.internal.validation.labels_to_list_multilabel"]], "cleanlab.models": [[55, "module-cleanlab.models"]], "keraswrappermodel (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperModel"]], "keraswrappersequential (class in cleanlab.models.keras)": [[56, "cleanlab.models.keras.KerasWrapperSequential"]], "cleanlab.models.keras": [[56, "module-cleanlab.models.keras"]], "fit() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.fit"]], "fit() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.fit"]], "get_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.get_params"]], "get_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.get_params"]], "predict() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict"]], "predict() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict"]], "predict_proba() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.predict_proba"]], "predict_proba() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.predict_proba"]], "set_params() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.set_params"]], "set_params() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.set_params"]], "summary() (cleanlab.models.keras.keraswrappermodel method)": [[56, "cleanlab.models.keras.KerasWrapperModel.summary"]], "summary() (cleanlab.models.keras.keraswrappersequential method)": [[56, "cleanlab.models.keras.KerasWrapperSequential.summary"]], "cleanlab.multiannotator": [[57, "module-cleanlab.multiannotator"]], "convert_long_to_wide_dataset() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.convert_long_to_wide_dataset"]], "get_active_learning_scores() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores"]], "get_active_learning_scores_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_active_learning_scores_ensemble"]], "get_label_quality_multiannotator() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator"]], "get_label_quality_multiannotator_ensemble() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_label_quality_multiannotator_ensemble"]], "get_majority_vote_label() (in module cleanlab.multiannotator)": [[57, "cleanlab.multiannotator.get_majority_vote_label"]], "cleanlab.multilabel_classification.dataset": [[58, "module-cleanlab.multilabel_classification.dataset"]], "common_multilabel_issues() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.common_multilabel_issues"]], "multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.multilabel_health_summary"]], "overall_multilabel_health_score() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.overall_multilabel_health_score"]], "rank_classes_by_multilabel_quality() (in module cleanlab.multilabel_classification.dataset)": [[58, "cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[59, "module-cleanlab.multilabel_classification.filter"]], "find_label_issues() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_label_issues"]], "find_multilabel_issues_per_class() (in module cleanlab.multilabel_classification.filter)": [[59, "cleanlab.multilabel_classification.filter.find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification": [[60, "module-cleanlab.multilabel_classification"]], "cleanlab.multilabel_classification.rank": [[61, "module-cleanlab.multilabel_classification.rank"]], "get_label_quality_scores() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores"]], "get_label_quality_scores_per_class() (in module cleanlab.multilabel_classification.rank)": [[61, "cleanlab.multilabel_classification.rank.get_label_quality_scores_per_class"]], "cleanlab.object_detection.filter": [[62, "module-cleanlab.object_detection.filter"]], "find_label_issues() (in module cleanlab.object_detection.filter)": [[62, "cleanlab.object_detection.filter.find_label_issues"]], "cleanlab.object_detection": [[63, "module-cleanlab.object_detection"]], "cleanlab.object_detection.rank": [[64, "module-cleanlab.object_detection.rank"]], "compute_badloc_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_badloc_box_scores"]], "compute_overlooked_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_overlooked_box_scores"]], "compute_swap_box_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.compute_swap_box_scores"]], "get_label_quality_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.issues_from_scores"]], "pool_box_scores_per_image() (in module cleanlab.object_detection.rank)": [[64, "cleanlab.object_detection.rank.pool_box_scores_per_image"]], "bounding_box_size_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.bounding_box_size_distribution"]], "calculate_per_class_metrics() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.calculate_per_class_metrics"]], "class_label_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.class_label_distribution"]], "cleanlab.object_detection.summary": [[65, "module-cleanlab.object_detection.summary"]], "get_average_per_class_confusion_matrix() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_average_per_class_confusion_matrix"]], "get_sorted_bbox_count_idxs() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.get_sorted_bbox_count_idxs"]], "object_counts_per_image() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.object_counts_per_image"]], "plot_class_distribution() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[65, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[66, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[66, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[66, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[67, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.find_top_issues"]], "get_confidence_weighted_entropy_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_confidence_weighted_entropy_for_each_label"]], "get_label_quality_ensemble_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_ensemble_scores"]], "get_label_quality_scores() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_label_quality_scores"]], "get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[67, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[67, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[68, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[69, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[69, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[69, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.fit"]], "get_aleatoric_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_aleatoric_uncertainty"]], "get_epistemic_uncertainty() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_epistemic_uncertainty"]], "get_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_label_issues"]], "get_metadata_routing() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_metadata_routing"]], "get_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.get_params"]], "predict() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.predict"]], "save_space() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.save_space"]], "score() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.score"]], "set_fit_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_fit_request"]], "set_params() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_params"]], "set_score_request() (cleanlab.regression.learn.cleanlearning method)": [[69, "cleanlab.regression.learn.CleanLearning.set_score_request"]], "cleanlab.regression.rank": [[70, "module-cleanlab.regression.rank"]], "get_label_quality_scores() (in module cleanlab.regression.rank)": [[70, "cleanlab.regression.rank.get_label_quality_scores"]], "cleanlab.segmentation.filter": [[71, "module-cleanlab.segmentation.filter"]], "find_label_issues() (in module cleanlab.segmentation.filter)": [[71, "cleanlab.segmentation.filter.find_label_issues"]], "cleanlab.segmentation": [[72, "module-cleanlab.segmentation"]], "cleanlab.segmentation.rank": [[73, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[73, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[74, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[74, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[75, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[75, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[76, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[77, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[77, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[78, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[78, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[78, "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 0b55b5e74..c84eb4e52 100644 --- a/master/tutorials/clean_learning/tabular.ipynb +++ b/master/tutorials/clean_learning/tabular.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:50.769823Z", - "iopub.status.busy": "2024-04-06T04:26:50.769660Z", - "iopub.status.idle": "2024-04-06T04:26:51.904390Z", - "shell.execute_reply": "2024-04-06T04:26:51.903868Z" + "iopub.execute_input": "2024-04-08T19:04:20.808965Z", + "iopub.status.busy": "2024-04-08T19:04:20.808791Z", + "iopub.status.idle": "2024-04-08T19:04:21.997144Z", + "shell.execute_reply": "2024-04-08T19:04:21.996577Z" }, "nbsphinx": "hidden" }, @@ -127,7 +127,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -152,10 +152,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:51.906925Z", - "iopub.status.busy": "2024-04-06T04:26:51.906505Z", - "iopub.status.idle": "2024-04-06T04:26:51.925092Z", - "shell.execute_reply": "2024-04-06T04:26:51.924644Z" + "iopub.execute_input": "2024-04-08T19:04:21.999784Z", + "iopub.status.busy": "2024-04-08T19:04:21.999468Z", + "iopub.status.idle": "2024-04-08T19:04:22.020020Z", + "shell.execute_reply": "2024-04-08T19:04:22.019546Z" } }, "outputs": [], @@ -196,10 +196,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:51.927250Z", - "iopub.status.busy": "2024-04-06T04:26:51.926936Z", - "iopub.status.idle": "2024-04-06T04:26:52.095031Z", - "shell.execute_reply": "2024-04-06T04:26:52.094456Z" + "iopub.execute_input": "2024-04-08T19:04:22.022733Z", + "iopub.status.busy": "2024-04-08T19:04:22.022190Z", + "iopub.status.idle": "2024-04-08T19:04:22.250382Z", + "shell.execute_reply": "2024-04-08T19:04:22.249811Z" } }, "outputs": [ @@ -306,10 +306,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:52.125272Z", - "iopub.status.busy": "2024-04-06T04:26:52.125095Z", - "iopub.status.idle": "2024-04-06T04:26:52.128526Z", - "shell.execute_reply": "2024-04-06T04:26:52.128074Z" + "iopub.execute_input": "2024-04-08T19:04:22.288376Z", + "iopub.status.busy": "2024-04-08T19:04:22.287864Z", + "iopub.status.idle": "2024-04-08T19:04:22.292293Z", + "shell.execute_reply": "2024-04-08T19:04:22.291761Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:52.130584Z", - "iopub.status.busy": "2024-04-06T04:26:52.130251Z", - "iopub.status.idle": "2024-04-06T04:26:52.138357Z", - "shell.execute_reply": "2024-04-06T04:26:52.137794Z" + "iopub.execute_input": "2024-04-08T19:04:22.294486Z", + "iopub.status.busy": "2024-04-08T19:04:22.294124Z", + "iopub.status.idle": "2024-04-08T19:04:22.302832Z", + "shell.execute_reply": "2024-04-08T19:04:22.302384Z" } }, "outputs": [], @@ -385,10 +385,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:52.140399Z", - "iopub.status.busy": "2024-04-06T04:26:52.140214Z", - "iopub.status.idle": "2024-04-06T04:26:52.142650Z", - "shell.execute_reply": "2024-04-06T04:26:52.142235Z" + "iopub.execute_input": "2024-04-08T19:04:22.305016Z", + "iopub.status.busy": "2024-04-08T19:04:22.304694Z", + "iopub.status.idle": "2024-04-08T19:04:22.307358Z", + "shell.execute_reply": "2024-04-08T19:04:22.306925Z" } }, "outputs": [], @@ -410,10 +410,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:52.144478Z", - "iopub.status.busy": "2024-04-06T04:26:52.144308Z", - "iopub.status.idle": "2024-04-06T04:26:52.655201Z", - "shell.execute_reply": "2024-04-06T04:26:52.654687Z" + "iopub.execute_input": "2024-04-08T19:04:22.309357Z", + "iopub.status.busy": "2024-04-08T19:04:22.308992Z", + "iopub.status.idle": "2024-04-08T19:04:22.826772Z", + "shell.execute_reply": "2024-04-08T19:04:22.826102Z" } }, "outputs": [], @@ -447,10 +447,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:52.657171Z", - "iopub.status.busy": "2024-04-06T04:26:52.656995Z", - "iopub.status.idle": "2024-04-06T04:26:54.226322Z", - "shell.execute_reply": "2024-04-06T04:26:54.225696Z" + "iopub.execute_input": "2024-04-08T19:04:22.829195Z", + "iopub.status.busy": "2024-04-08T19:04:22.829001Z", + "iopub.status.idle": "2024-04-08T19:04:24.584334Z", + "shell.execute_reply": "2024-04-08T19:04:24.583696Z" } }, "outputs": [ @@ -482,10 +482,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.229079Z", - "iopub.status.busy": "2024-04-06T04:26:54.228374Z", - "iopub.status.idle": "2024-04-06T04:26:54.238321Z", - "shell.execute_reply": "2024-04-06T04:26:54.237839Z" + "iopub.execute_input": "2024-04-08T19:04:24.587018Z", + "iopub.status.busy": "2024-04-08T19:04:24.586424Z", + "iopub.status.idle": "2024-04-08T19:04:24.596789Z", + "shell.execute_reply": "2024-04-08T19:04:24.596333Z" } }, "outputs": [ @@ -606,10 +606,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.240174Z", - "iopub.status.busy": "2024-04-06T04:26:54.239999Z", - "iopub.status.idle": "2024-04-06T04:26:54.244001Z", - "shell.execute_reply": "2024-04-06T04:26:54.243600Z" + "iopub.execute_input": "2024-04-08T19:04:24.598784Z", + "iopub.status.busy": "2024-04-08T19:04:24.598605Z", + "iopub.status.idle": "2024-04-08T19:04:24.603028Z", + "shell.execute_reply": "2024-04-08T19:04:24.602574Z" } }, "outputs": [], @@ -634,10 +634,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.245815Z", - "iopub.status.busy": "2024-04-06T04:26:54.245645Z", - "iopub.status.idle": "2024-04-06T04:26:54.252428Z", - "shell.execute_reply": "2024-04-06T04:26:54.252032Z" + "iopub.execute_input": "2024-04-08T19:04:24.604932Z", + "iopub.status.busy": "2024-04-08T19:04:24.604757Z", + "iopub.status.idle": "2024-04-08T19:04:24.612374Z", + "shell.execute_reply": "2024-04-08T19:04:24.611848Z" } }, "outputs": [], @@ -659,10 +659,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.254565Z", - "iopub.status.busy": "2024-04-06T04:26:54.254191Z", - "iopub.status.idle": "2024-04-06T04:26:54.363811Z", - "shell.execute_reply": "2024-04-06T04:26:54.363293Z" + "iopub.execute_input": "2024-04-08T19:04:24.614464Z", + "iopub.status.busy": "2024-04-08T19:04:24.614057Z", + "iopub.status.idle": "2024-04-08T19:04:24.725724Z", + "shell.execute_reply": "2024-04-08T19:04:24.725123Z" } }, "outputs": [ @@ -692,10 +692,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.365703Z", - "iopub.status.busy": "2024-04-06T04:26:54.365531Z", - "iopub.status.idle": "2024-04-06T04:26:54.368039Z", - "shell.execute_reply": "2024-04-06T04:26:54.367639Z" + "iopub.execute_input": "2024-04-08T19:04:24.728264Z", + "iopub.status.busy": "2024-04-08T19:04:24.727792Z", + "iopub.status.idle": "2024-04-08T19:04:24.730921Z", + "shell.execute_reply": "2024-04-08T19:04:24.730473Z" } }, "outputs": [], @@ -716,10 +716,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:54.369822Z", - "iopub.status.busy": "2024-04-06T04:26:54.369654Z", - "iopub.status.idle": "2024-04-06T04:26:56.249703Z", - "shell.execute_reply": "2024-04-06T04:26:56.248990Z" + "iopub.execute_input": "2024-04-08T19:04:24.732880Z", + "iopub.status.busy": "2024-04-08T19:04:24.732594Z", + "iopub.status.idle": "2024-04-08T19:04:26.886972Z", + "shell.execute_reply": "2024-04-08T19:04:26.886312Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:56.252825Z", - "iopub.status.busy": "2024-04-06T04:26:56.252085Z", - "iopub.status.idle": "2024-04-06T04:26:56.262987Z", - "shell.execute_reply": "2024-04-06T04:26:56.262497Z" + "iopub.execute_input": "2024-04-08T19:04:26.889971Z", + "iopub.status.busy": "2024-04-08T19:04:26.889228Z", + "iopub.status.idle": "2024-04-08T19:04:26.900494Z", + "shell.execute_reply": "2024-04-08T19:04:26.899943Z" } }, "outputs": [ @@ -772,10 +772,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:26:56.265020Z", - "iopub.status.busy": "2024-04-06T04:26:56.264831Z", - "iopub.status.idle": "2024-04-06T04:26:56.313621Z", - "shell.execute_reply": "2024-04-06T04:26:56.313208Z" + "iopub.execute_input": "2024-04-08T19:04:26.902458Z", + "iopub.status.busy": "2024-04-08T19:04:26.902143Z", + "iopub.status.idle": "2024-04-08T19:04:27.012965Z", + "shell.execute_reply": "2024-04-08T19:04:27.012399Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/clean_learning/text.html b/master/tutorials/clean_learning/text.html index bac36fac4..19d19deae 100644 --- a/master/tutorials/clean_learning/text.html +++ b/master/tutorials/clean_learning/text.html @@ -783,7 +783,7 @@

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

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

    @@ -846,43 +846,43 @@

    2. Load and format the text dataset

    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    -
    +
    @@ -1181,7 +1181,7 @@

    4. Train a more robust model from noisy labels -{"state": {"947502200a574c5bb753e3a810fa8f74": {"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}}, "b9a4822bbbc543158d3165bdc309367e": {"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": ""}}, "9b703acaaefe43f2a747995e6baf26ca": {"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_947502200a574c5bb753e3a810fa8f74", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b9a4822bbbc543158d3165bdc309367e", "tabbable": null, "tooltip": null, "value": 391.0}}, "fb7871277ea44cc992dd5130b07e281f": {"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}}, "83a784ca18dd40209d7df691456fa335": {"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}}, "4d3503dbfed14de4b1ffb6918b31358f": {"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_fb7871277ea44cc992dd5130b07e281f", "placeholder": "\u200b", "style": "IPY_MODEL_83a784ca18dd40209d7df691456fa335", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "ef836ca5c2e0473c8f44942bbfbde97c": {"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}}, "c3b7fa05cbd34b3e8440577d3268b3a2": {"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}}, "795e0495ac274840b84a9b2585fcb45b": {"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_ef836ca5c2e0473c8f44942bbfbde97c", "placeholder": "\u200b", "style": "IPY_MODEL_c3b7fa05cbd34b3e8440577d3268b3a2", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200769.3kB/s]"}}, "57a3d879c99546208499a10cab8e04c7": {"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}}, "0b23f615f5b84f338a77080fed288888": {"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_4d3503dbfed14de4b1ffb6918b31358f", "IPY_MODEL_9b703acaaefe43f2a747995e6baf26ca", "IPY_MODEL_795e0495ac274840b84a9b2585fcb45b"], "layout": "IPY_MODEL_57a3d879c99546208499a10cab8e04c7", "tabbable": null, "tooltip": null}}, "f83c84fa51244911ad30fbc24f0dae14": {"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}}, "ae1d1e4addb14d72ad47d043144ef5b2": {"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": ""}}, "7de6723f9beb43dbbad71ef72bf3d394": {"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_f83c84fa51244911ad30fbc24f0dae14", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ae1d1e4addb14d72ad47d043144ef5b2", "tabbable": null, "tooltip": null, "value": 2211.0}}, "ef220577d59248bdbc40d7374238082a": {"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}}, "04b7c5dc6e7948f7b6da88bf9de4c09b": {"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}}, "d3d9e1e700e14803bfdd9de847eb8a1b": {"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_ef220577d59248bdbc40d7374238082a", "placeholder": "\u200b", "style": "IPY_MODEL_04b7c5dc6e7948f7b6da88bf9de4c09b", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "bd7e59232eee46a9941063b605e66d9b": {"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}}, "202e77f783604892ba40274a0d289276": {"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}}, "58219d6711ac4cbe976717b4a76aa16b": {"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_bd7e59232eee46a9941063b605e66d9b", "placeholder": "\u200b", "style": "IPY_MODEL_202e77f783604892ba40274a0d289276", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007457kB/s]"}}, "83718921424246c8b3bb82278f13e7dd": {"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}}, "328b046d1b9d4dcfa0edb3132c01a4f2": {"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_d3d9e1e700e14803bfdd9de847eb8a1b", "IPY_MODEL_7de6723f9beb43dbbad71ef72bf3d394", "IPY_MODEL_58219d6711ac4cbe976717b4a76aa16b"], "layout": "IPY_MODEL_83718921424246c8b3bb82278f13e7dd", "tabbable": null, "tooltip": null}}, "f76bdeeb99374127a9a34c37ef45c330": {"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}}, "a0c9bb1d94eb48e1945c490bd1d43922": {"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": ""}}, "d9f750fd331d4370b4ead712d6fe626d": {"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_f76bdeeb99374127a9a34c37ef45c330", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a0c9bb1d94eb48e1945c490bd1d43922", "tabbable": null, "tooltip": null, "value": 665.0}}, "385b07eec10c4e1780987426b0fb1ffa": {"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}}, "6942d17c55104716b38548fde85fe97d": {"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}}, "250cfa9619754602808099a37a2351cd": {"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_385b07eec10c4e1780987426b0fb1ffa", "placeholder": "\u200b", "style": "IPY_MODEL_6942d17c55104716b38548fde85fe97d", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "4a44c65dbe25405084910ffb25948f0b": {"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}}, "95ba2b873c0b456186466656bd4a4b05": {"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}}, "10e4288569c34c62a5b336b51d001893": {"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_4a44c65dbe25405084910ffb25948f0b", "placeholder": "\u200b", "style": "IPY_MODEL_95ba2b873c0b456186466656bd4a4b05", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007125kB/s]"}}, "751b322f8b584ba699d87d767abcfd88": {"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}}, "f26e295a2fa0406c8e3fff0e0818202d": {"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_250cfa9619754602808099a37a2351cd", "IPY_MODEL_d9f750fd331d4370b4ead712d6fe626d", "IPY_MODEL_10e4288569c34c62a5b336b51d001893"], "layout": "IPY_MODEL_751b322f8b584ba699d87d767abcfd88", "tabbable": null, "tooltip": null}}, "e4e868950b0e4748a7bb54abf124ca39": {"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}}, "33e4cc93e6f44376a34c0ceb628ca488": {"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": ""}}, "ce5ef1ebbb6d4b7589aa40b6d38dd8f2": {"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_e4e868950b0e4748a7bb54abf124ca39", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_33e4cc93e6f44376a34c0ceb628ca488", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "b805e697dc3841fe87f86c354f112531": {"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}}, "64fdd33a9fa24c04b2d980484a3dffca": {"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}}, "5c1ec47f563245d7989ffc7eff2b3393": {"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_b805e697dc3841fe87f86c354f112531", "placeholder": "\u200b", "style": "IPY_MODEL_64fdd33a9fa24c04b2d980484a3dffca", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "27f1cfa796cd4d64880fb8ad1470589b": {"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}}, "fe7ce2b57aca4dc1b98fc34a52588b14": {"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}}, "18d5e009472c494c99ff042d5f4315c1": {"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_27f1cfa796cd4d64880fb8ad1470589b", "placeholder": "\u200b", "style": "IPY_MODEL_fe7ce2b57aca4dc1b98fc34a52588b14", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007167MB/s]"}}, "aa772cc8b00945c1aa7b22081cb39031": {"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}}, "d20256762cdf4c06816c5653fdfef6a8": {"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_5c1ec47f563245d7989ffc7eff2b3393", "IPY_MODEL_ce5ef1ebbb6d4b7589aa40b6d38dd8f2", "IPY_MODEL_18d5e009472c494c99ff042d5f4315c1"], "layout": "IPY_MODEL_aa772cc8b00945c1aa7b22081cb39031", "tabbable": null, "tooltip": null}}, "e5b68aee810b4fc48e7950601e662dad": {"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}}, "b8d38e973ddb4490be0781dbb8717675": {"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": ""}}, "dd34df9493e74cc89434500b5b18d5ac": {"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_e5b68aee810b4fc48e7950601e662dad", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b8d38e973ddb4490be0781dbb8717675", "tabbable": null, "tooltip": null, "value": 466062.0}}, "f756af4beca342ed8bed100e0bfffe79": {"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}}, "032871bbc89742aba639ffaee1200e37": {"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}}, "a4758f91aee04d48822bb6b9c3453db8": {"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_f756af4beca342ed8bed100e0bfffe79", "placeholder": "\u200b", "style": "IPY_MODEL_032871bbc89742aba639ffaee1200e37", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "d1036ff8e3b44432a630841123cd665e": {"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}}, "fcdc6f83ca124a68b2ab9d6ace3e2308": {"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}}, "806be8102c2840c683f53e27268a1a1d": {"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_d1036ff8e3b44432a630841123cd665e", "placeholder": "\u200b", "style": "IPY_MODEL_fcdc6f83ca124a68b2ab9d6ace3e2308", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u20077.08MB/s]"}}, "73369ea4b442452ba79dde0f11ccccf7": {"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}}, "d2370f99c49d43b39ac2ea8e64340375": {"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_a4758f91aee04d48822bb6b9c3453db8", "IPY_MODEL_dd34df9493e74cc89434500b5b18d5ac", "IPY_MODEL_806be8102c2840c683f53e27268a1a1d"], "layout": "IPY_MODEL_73369ea4b442452ba79dde0f11ccccf7", "tabbable": null, "tooltip": null}}, "6475f269b762452097c0dd8a50483fa7": {"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}}, "829408c009e84267b620439ac80aa762": {"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": ""}}, "623c296ec27847a08d197e0f9349902d": {"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_6475f269b762452097c0dd8a50483fa7", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_829408c009e84267b620439ac80aa762", "tabbable": null, "tooltip": null, "value": 48.0}}, "2b3304b6189e458ba99e1273f6dc3065": {"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}}, "1978592a53c7411fbb7d77958baefc3c": {"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}}, "b015ab05281b4ee48af1da0ccdc94161": {"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_2b3304b6189e458ba99e1273f6dc3065", "placeholder": "\u200b", "style": "IPY_MODEL_1978592a53c7411fbb7d77958baefc3c", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "d330efe583834f6e9a4aef2d210f2448": {"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}}, "5108e22e56ca44b8a5f0abeba3da954d": {"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}}, "29d77952655945c5975ed010d32ac352": {"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_d330efe583834f6e9a4aef2d210f2448", "placeholder": "\u200b", "style": "IPY_MODEL_5108e22e56ca44b8a5f0abeba3da954d", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20079.54kB/s]"}}, "974e2a874ecf49ff8142c67afb939afd": {"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}}, "fb7ad54f03394e35a23b11c642b2c405": {"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_b015ab05281b4ee48af1da0ccdc94161", "IPY_MODEL_623c296ec27847a08d197e0f9349902d", "IPY_MODEL_29d77952655945c5975ed010d32ac352"], "layout": "IPY_MODEL_974e2a874ecf49ff8142c67afb939afd", "tabbable": null, "tooltip": null}}, "9123a1575ca34375b0b43f4289e4f1ef": {"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}}, "431300eb075a4588806ea9c78d8531a3": {"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": ""}}, "263444dd6e2144b3b9f73741003fea9f": {"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_9123a1575ca34375b0b43f4289e4f1ef", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_431300eb075a4588806ea9c78d8531a3", "tabbable": null, "tooltip": null, "value": 231508.0}}, "2ea5a0c2da3a4d83b5610c2c9d2d542c": {"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}}, "a0bbd2409f4d4acfb2e91ffc5b12b95e": {"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}}, "964aed80ab284dd48a31d9d5ad28fd30": {"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_2ea5a0c2da3a4d83b5610c2c9d2d542c", "placeholder": "\u200b", "style": "IPY_MODEL_a0bbd2409f4d4acfb2e91ffc5b12b95e", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "533ead7294bf4d068190ca8e90d78e80": {"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}}, "fade1033c9ad433a857f6d209599eaad": {"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}}, "b6313e82b4f34189bbf42e2bf514a82d": {"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_533ead7294bf4d068190ca8e90d78e80", "placeholder": "\u200b", "style": "IPY_MODEL_fade1033c9ad433a857f6d209599eaad", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u200736.7MB/s]"}}, "4a315905dbb7402f9b17e6768c6aa8eb": {"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}}, "2ccab9f1f4f0466db60167c0d669672d": {"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_964aed80ab284dd48a31d9d5ad28fd30", "IPY_MODEL_263444dd6e2144b3b9f73741003fea9f", "IPY_MODEL_b6313e82b4f34189bbf42e2bf514a82d"], "layout": "IPY_MODEL_4a315905dbb7402f9b17e6768c6aa8eb", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"55ca57fbdbeb4dd09672a18d9e527e1e": {"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}}, "e715922dec9a4f518bd83655ec5ece8c": {"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": ""}}, "34f290f5d2fb480c8484034ab6752fca": {"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_55ca57fbdbeb4dd09672a18d9e527e1e", "max": 391.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e715922dec9a4f518bd83655ec5ece8c", "tabbable": null, "tooltip": null, "value": 391.0}}, "34051f0f4e6242febbd69d5ad6e263e0": {"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}}, "938fb2a8d3a54d448069f0b85d12697d": {"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}}, "b3270c93e7874c21a39571fdb1d43780": {"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_34051f0f4e6242febbd69d5ad6e263e0", "placeholder": "\u200b", "style": "IPY_MODEL_938fb2a8d3a54d448069f0b85d12697d", "tabbable": null, "tooltip": null, "value": ".gitattributes:\u2007100%"}}, "f9d2786f039f4739a73d2d301bf8b745": {"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}}, "ae0fb7a982a2436ea7ad32f01526eea2": {"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}}, "9a1995f42694404abb75d6338d1bddc7": {"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_f9d2786f039f4739a73d2d301bf8b745", "placeholder": "\u200b", "style": "IPY_MODEL_ae0fb7a982a2436ea7ad32f01526eea2", "tabbable": null, "tooltip": null, "value": "\u2007391/391\u2007[00:00<00:00,\u200769.6kB/s]"}}, "acdced823e2a4d6f950e27bd4802b639": {"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}}, "414486671bbc4579b154f2d4dd8df463": {"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_b3270c93e7874c21a39571fdb1d43780", "IPY_MODEL_34f290f5d2fb480c8484034ab6752fca", "IPY_MODEL_9a1995f42694404abb75d6338d1bddc7"], "layout": "IPY_MODEL_acdced823e2a4d6f950e27bd4802b639", "tabbable": null, "tooltip": null}}, "dcfe560d74704020ba7189f6b35342ae": {"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}}, "3912e2b011df41679c1659d40bc9fefb": {"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": ""}}, "a85b90552a3e4665b51fc3b6e7d921f7": {"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_dcfe560d74704020ba7189f6b35342ae", "max": 2211.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_3912e2b011df41679c1659d40bc9fefb", "tabbable": null, "tooltip": null, "value": 2211.0}}, "63fe7f89c61d469689356c8afe8f7d0a": {"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}}, "068617b1c6e7410ea8a61aaa3bf35a59": {"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}}, "161f5f17377e4e528cc2e973474950ee": {"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_63fe7f89c61d469689356c8afe8f7d0a", "placeholder": "\u200b", "style": "IPY_MODEL_068617b1c6e7410ea8a61aaa3bf35a59", "tabbable": null, "tooltip": null, "value": "README.md:\u2007100%"}}, "427f262b918e461c9202e7a81d04c8dc": {"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}}, "fad61d9bcd544d64a2896b7a4fafeca3": {"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}}, "4ba657e74f59423f9bed6c70b030fcfa": {"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_427f262b918e461c9202e7a81d04c8dc", "placeholder": "\u200b", "style": "IPY_MODEL_fad61d9bcd544d64a2896b7a4fafeca3", "tabbable": null, "tooltip": null, "value": "\u20072.21k/2.21k\u2007[00:00<00:00,\u2007355kB/s]"}}, "eb1b16cb3c634b0496ccce16fde573eb": {"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}}, "596554d1fd004a229dc0e9d5610bace9": {"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_161f5f17377e4e528cc2e973474950ee", "IPY_MODEL_a85b90552a3e4665b51fc3b6e7d921f7", "IPY_MODEL_4ba657e74f59423f9bed6c70b030fcfa"], "layout": "IPY_MODEL_eb1b16cb3c634b0496ccce16fde573eb", "tabbable": null, "tooltip": null}}, "7ba8b4d37b90455ea0856a76fd53349f": {"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}}, "5a8a165febd74ab8a0e847c8a208e73f": {"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": ""}}, "a200cf245005465880fbcc7a852362d4": {"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_7ba8b4d37b90455ea0856a76fd53349f", "max": 665.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5a8a165febd74ab8a0e847c8a208e73f", "tabbable": null, "tooltip": null, "value": 665.0}}, "5385ad8ba67c426daa3e219dd49f7c1b": {"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}}, "3a50e9ac92bb47ef8a4dd27183e221e7": {"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}}, "c4431e7dcf5a41e1934fd352a0e18cc6": {"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_5385ad8ba67c426daa3e219dd49f7c1b", "placeholder": "\u200b", "style": "IPY_MODEL_3a50e9ac92bb47ef8a4dd27183e221e7", "tabbable": null, "tooltip": null, "value": "config.json:\u2007100%"}}, "cbfa8e31e7cb4fd5b0f2827be2777ef3": {"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}}, "16dad8a9c9074f5ab4ed24858cf4f896": {"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}}, "5de73fab2f284817999b4f68149d32b9": {"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_cbfa8e31e7cb4fd5b0f2827be2777ef3", "placeholder": "\u200b", "style": "IPY_MODEL_16dad8a9c9074f5ab4ed24858cf4f896", "tabbable": null, "tooltip": null, "value": "\u2007665/665\u2007[00:00<00:00,\u2007127kB/s]"}}, "3041fa94176846a99ea4c32b7fa1cade": {"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}}, "4a26fef448554f36a9cb66bea78f484a": {"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_c4431e7dcf5a41e1934fd352a0e18cc6", "IPY_MODEL_a200cf245005465880fbcc7a852362d4", "IPY_MODEL_5de73fab2f284817999b4f68149d32b9"], "layout": "IPY_MODEL_3041fa94176846a99ea4c32b7fa1cade", "tabbable": null, "tooltip": null}}, "8cfe5b4829a843b499e9d9b1aad10794": {"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}}, "8c3149008147411a9172b8a0627cbbdf": {"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": ""}}, "60cb8f29c0a04f8a9b8775d425f7c1d4": {"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_8cfe5b4829a843b499e9d9b1aad10794", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8c3149008147411a9172b8a0627cbbdf", "tabbable": null, "tooltip": null, "value": 54245363.0}}, "e93f36ea176647cd9b1069f0297aadef": {"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}}, "2dcd6e4805fb403ba8b6c1ce8e5ff117": {"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}}, "9f72c96cd44c40618e8826a9fc735324": {"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_e93f36ea176647cd9b1069f0297aadef", "placeholder": "\u200b", "style": "IPY_MODEL_2dcd6e4805fb403ba8b6c1ce8e5ff117", "tabbable": null, "tooltip": null, "value": "pytorch_model.bin:\u2007100%"}}, "99f0d4542e8e423b9018be6c83282910": {"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}}, "107f3b6a64fb41898b51c78312996d47": {"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}}, "6b70c1af64654cf0bfc8e515cbe2644e": {"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_99f0d4542e8e423b9018be6c83282910", "placeholder": "\u200b", "style": "IPY_MODEL_107f3b6a64fb41898b51c78312996d47", "tabbable": null, "tooltip": null, "value": "\u200754.2M/54.2M\u2007[00:00<00:00,\u2007230MB/s]"}}, "3c2b01854c664a3a834ebb6315a06d7a": {"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}}, "c18cde9a3b464ad1a69d4fbf65c4287b": {"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_9f72c96cd44c40618e8826a9fc735324", "IPY_MODEL_60cb8f29c0a04f8a9b8775d425f7c1d4", "IPY_MODEL_6b70c1af64654cf0bfc8e515cbe2644e"], "layout": "IPY_MODEL_3c2b01854c664a3a834ebb6315a06d7a", "tabbable": null, "tooltip": null}}, "7167dd43cb39496d8971354c7dcf8fb3": {"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}}, "a13aee655acd48a2bc16f80efcd736a4": {"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": ""}}, "33cce396c4bf413ebe5341fe28d03486": {"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_7167dd43cb39496d8971354c7dcf8fb3", "max": 466062.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a13aee655acd48a2bc16f80efcd736a4", "tabbable": null, "tooltip": null, "value": 466062.0}}, "fc16653aacb543948c8bcdbebbe18041": {"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}}, "fdc67e28664e4583ace57f50340b3649": {"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}}, "792627ad3d4a4356926466777e5e286b": {"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_fc16653aacb543948c8bcdbebbe18041", "placeholder": "\u200b", "style": "IPY_MODEL_fdc67e28664e4583ace57f50340b3649", "tabbable": null, "tooltip": null, "value": "tokenizer.json:\u2007100%"}}, "30e21853c71148d39d7be87c7c68889a": {"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}}, "fdf1adc0e36d4541972658ad13062aac": {"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}}, "42a8d833492a48fe958eb757ee25c31c": {"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_30e21853c71148d39d7be87c7c68889a", "placeholder": "\u200b", "style": "IPY_MODEL_fdf1adc0e36d4541972658ad13062aac", "tabbable": null, "tooltip": null, "value": "\u2007466k/466k\u2007[00:00<00:00,\u20076.02MB/s]"}}, "d27490d630934411bc6291d305749d0a": {"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}}, "e8839cb132d74eb9a916dda9fdafe1c4": {"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_792627ad3d4a4356926466777e5e286b", "IPY_MODEL_33cce396c4bf413ebe5341fe28d03486", "IPY_MODEL_42a8d833492a48fe958eb757ee25c31c"], "layout": "IPY_MODEL_d27490d630934411bc6291d305749d0a", "tabbable": null, "tooltip": null}}, "859e480b7e01405fb1af9226d6a94736": {"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}}, "0435bd2a67ae4580b11f72300c8c1b9f": {"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": ""}}, "6062369e250e403897e6f33990c23d57": {"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_859e480b7e01405fb1af9226d6a94736", "max": 48.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_0435bd2a67ae4580b11f72300c8c1b9f", "tabbable": null, "tooltip": null, "value": 48.0}}, "f41c778f7656469892daa08fc3f86248": {"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}}, "2b0e68483cbf470db65245930283cce4": {"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}}, "a8e917adc0db482da10f7259260a52fc": {"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_f41c778f7656469892daa08fc3f86248", "placeholder": "\u200b", "style": "IPY_MODEL_2b0e68483cbf470db65245930283cce4", "tabbable": null, "tooltip": null, "value": "tokenizer_config.json:\u2007100%"}}, "41e5eb399b424312bb565497b2f550a6": {"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}}, "f8c8778791df4709aa10067dd9083588": {"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}}, "7b208fafba0244c7a857e15c10805106": {"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_41e5eb399b424312bb565497b2f550a6", "placeholder": "\u200b", "style": "IPY_MODEL_f8c8778791df4709aa10067dd9083588", "tabbable": null, "tooltip": null, "value": "\u200748.0/48.0\u2007[00:00<00:00,\u20078.81kB/s]"}}, "b93f1aea04264f86afd5ca6e5f3deb9a": {"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}}, "5e9e17de388746f4ba1fb4f05e426c4e": {"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_a8e917adc0db482da10f7259260a52fc", "IPY_MODEL_6062369e250e403897e6f33990c23d57", "IPY_MODEL_7b208fafba0244c7a857e15c10805106"], "layout": "IPY_MODEL_b93f1aea04264f86afd5ca6e5f3deb9a", "tabbable": null, "tooltip": null}}, "82f22504089445d980458c94bb3f0b22": {"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}}, "b2f57f17657e430c89e1a52e7f245309": {"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": ""}}, "3eea5b07c46a4588903f301a31822500": {"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_82f22504089445d980458c94bb3f0b22", "max": 231508.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b2f57f17657e430c89e1a52e7f245309", "tabbable": null, "tooltip": null, "value": 231508.0}}, "be33e673f6fe4622ab6bfd3a727d2c04": {"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}}, "00882e39d8294fa7bf11a9929411c91d": {"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}}, "b3a0081d105e4d6193af250cc9cef948": {"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_be33e673f6fe4622ab6bfd3a727d2c04", "placeholder": "\u200b", "style": "IPY_MODEL_00882e39d8294fa7bf11a9929411c91d", "tabbable": null, "tooltip": null, "value": "vocab.txt:\u2007100%"}}, "ab4a32cb43804d52b25207713bc5f0f4": {"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}}, "4c9a189517774bbe90d04de5fdd9e48f": {"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}}, "5194d575ab3e4498bcd728fe5c7d9a3d": {"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_ab4a32cb43804d52b25207713bc5f0f4", "placeholder": "\u200b", "style": "IPY_MODEL_4c9a189517774bbe90d04de5fdd9e48f", "tabbable": null, "tooltip": null, "value": "\u2007232k/232k\u2007[00:00<00:00,\u20071.77MB/s]"}}, "c3f5dd847178422f94661624f9ecf612": {"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}}, "3af073a39e45402187f042a3cf90b160": {"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_b3a0081d105e4d6193af250cc9cef948", "IPY_MODEL_3eea5b07c46a4588903f301a31822500", "IPY_MODEL_5194d575ab3e4498bcd728fe5c7d9a3d"], "layout": "IPY_MODEL_c3f5dd847178422f94661624f9ecf612", "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 c1d11beeb..cf6ae4311 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": "2024-04-06T04:26:58.967747Z", - "iopub.status.busy": "2024-04-06T04:26:58.967347Z", - "iopub.status.idle": "2024-04-06T04:27:01.569874Z", - "shell.execute_reply": "2024-04-06T04:27:01.569267Z" + "iopub.execute_input": "2024-04-08T19:04:29.937745Z", + "iopub.status.busy": "2024-04-08T19:04:29.937583Z", + "iopub.status.idle": "2024-04-08T19:04:33.047637Z", + "shell.execute_reply": "2024-04-08T19:04:33.046998Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:27:01.572409Z", - "iopub.status.busy": "2024-04-06T04:27:01.572137Z", - "iopub.status.idle": "2024-04-06T04:27:01.575389Z", - "shell.execute_reply": "2024-04-06T04:27:01.574976Z" + "iopub.execute_input": "2024-04-08T19:04:33.050116Z", + "iopub.status.busy": "2024-04-08T19:04:33.049809Z", + "iopub.status.idle": "2024-04-08T19:04:33.053073Z", + "shell.execute_reply": "2024-04-08T19:04:33.052649Z" } }, "outputs": [], @@ -185,10 +185,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.577325Z", - "iopub.status.busy": "2024-04-06T04:27:01.576995Z", - "iopub.status.idle": "2024-04-06T04:27:01.580088Z", - "shell.execute_reply": "2024-04-06T04:27:01.579648Z" + "iopub.execute_input": "2024-04-08T19:04:33.054962Z", + "iopub.status.busy": "2024-04-08T19:04:33.054682Z", + "iopub.status.idle": "2024-04-08T19:04:33.057634Z", + "shell.execute_reply": "2024-04-08T19:04:33.057206Z" }, "nbsphinx": "hidden" }, @@ -219,10 +219,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.581930Z", - "iopub.status.busy": "2024-04-06T04:27:01.581670Z", - "iopub.status.idle": "2024-04-06T04:27:01.658694Z", - "shell.execute_reply": "2024-04-06T04:27:01.658172Z" + "iopub.execute_input": "2024-04-08T19:04:33.059556Z", + "iopub.status.busy": "2024-04-08T19:04:33.059236Z", + "iopub.status.idle": "2024-04-08T19:04:33.304635Z", + "shell.execute_reply": "2024-04-08T19:04:33.304093Z" } }, "outputs": [ @@ -312,10 +312,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.660795Z", - "iopub.status.busy": "2024-04-06T04:27:01.660465Z", - "iopub.status.idle": "2024-04-06T04:27:01.663899Z", - "shell.execute_reply": "2024-04-06T04:27:01.663501Z" + "iopub.execute_input": "2024-04-08T19:04:33.306822Z", + "iopub.status.busy": "2024-04-08T19:04:33.306485Z", + "iopub.status.idle": "2024-04-08T19:04:33.309981Z", + "shell.execute_reply": "2024-04-08T19:04:33.309578Z" } }, "outputs": [], @@ -330,10 +330,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.665856Z", - "iopub.status.busy": "2024-04-06T04:27:01.665521Z", - "iopub.status.idle": "2024-04-06T04:27:01.668705Z", - "shell.execute_reply": "2024-04-06T04:27:01.668216Z" + "iopub.execute_input": "2024-04-08T19:04:33.311977Z", + "iopub.status.busy": "2024-04-08T19:04:33.311602Z", + "iopub.status.idle": "2024-04-08T19:04:33.314907Z", + "shell.execute_reply": "2024-04-08T19:04:33.314372Z" } }, "outputs": [ @@ -342,7 +342,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'getting_spare_card', 'change_pin', 'card_about_to_expire'}\n" + "Classes: {'supported_cards_and_currencies', 'beneficiary_not_allowed', 'cancel_transfer', 'card_payment_fee_charged', 'getting_spare_card', 'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'change_pin', 'apple_pay_or_google_pay'}\n" ] } ], @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.670676Z", - "iopub.status.busy": "2024-04-06T04:27:01.670295Z", - "iopub.status.idle": "2024-04-06T04:27:01.673371Z", - "shell.execute_reply": "2024-04-06T04:27:01.672860Z" + "iopub.execute_input": "2024-04-08T19:04:33.316831Z", + "iopub.status.busy": "2024-04-08T19:04:33.316579Z", + "iopub.status.idle": "2024-04-08T19:04:33.319694Z", + "shell.execute_reply": "2024-04-08T19:04:33.319261Z" } }, "outputs": [ @@ -409,10 +409,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.675367Z", - "iopub.status.busy": "2024-04-06T04:27:01.675041Z", - "iopub.status.idle": "2024-04-06T04:27:01.678078Z", - "shell.execute_reply": "2024-04-06T04:27:01.677656Z" + "iopub.execute_input": "2024-04-08T19:04:33.321534Z", + "iopub.status.busy": "2024-04-08T19:04:33.321217Z", + "iopub.status.idle": "2024-04-08T19:04:33.324298Z", + "shell.execute_reply": "2024-04-08T19:04:33.323874Z" } }, "outputs": [], @@ -453,17 +453,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:01.679941Z", - "iopub.status.busy": "2024-04-06T04:27:01.679685Z", - "iopub.status.idle": "2024-04-06T04:27:06.350529Z", - "shell.execute_reply": "2024-04-06T04:27:06.349987Z" + "iopub.execute_input": "2024-04-08T19:04:33.326161Z", + "iopub.status.busy": "2024-04-08T19:04:33.325901Z", + "iopub.status.idle": "2024-04-08T19:04:39.132517Z", + "shell.execute_reply": "2024-04-08T19:04:39.131899Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b23f615f5b84f338a77080fed288888", + "model_id": "414486671bbc4579b154f2d4dd8df463", "version_major": 2, "version_minor": 0 }, @@ -477,7 +477,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "328b046d1b9d4dcfa0edb3132c01a4f2", + "model_id": "596554d1fd004a229dc0e9d5610bace9", "version_major": 2, "version_minor": 0 }, @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f26e295a2fa0406c8e3fff0e0818202d", + "model_id": "4a26fef448554f36a9cb66bea78f484a", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d20256762cdf4c06816c5653fdfef6a8", + "model_id": "c18cde9a3b464ad1a69d4fbf65c4287b", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2370f99c49d43b39ac2ea8e64340375", + "model_id": "e8839cb132d74eb9a916dda9fdafe1c4", "version_major": 2, "version_minor": 0 }, @@ -533,7 +533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb7ad54f03394e35a23b11c642b2c405", + "model_id": "5e9e17de388746f4ba1fb4f05e426c4e", "version_major": 2, "version_minor": 0 }, @@ -547,7 +547,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ccab9f1f4f0466db60167c0d669672d", + "model_id": "3af073a39e45402187f042a3cf90b160", "version_major": 2, "version_minor": 0 }, @@ -609,10 +609,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:06.353364Z", - "iopub.status.busy": "2024-04-06T04:27:06.352933Z", - "iopub.status.idle": "2024-04-06T04:27:06.356122Z", - "shell.execute_reply": "2024-04-06T04:27:06.355669Z" + "iopub.execute_input": "2024-04-08T19:04:39.135354Z", + "iopub.status.busy": "2024-04-08T19:04:39.134961Z", + "iopub.status.idle": "2024-04-08T19:04:39.137900Z", + "shell.execute_reply": "2024-04-08T19:04:39.137434Z" } }, "outputs": [], @@ -634,10 +634,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:06.358024Z", - "iopub.status.busy": "2024-04-06T04:27:06.357833Z", - "iopub.status.idle": "2024-04-06T04:27:06.360554Z", - "shell.execute_reply": "2024-04-06T04:27:06.360115Z" + "iopub.execute_input": "2024-04-08T19:04:39.139840Z", + "iopub.status.busy": "2024-04-08T19:04:39.139530Z", + "iopub.status.idle": "2024-04-08T19:04:39.141949Z", + "shell.execute_reply": "2024-04-08T19:04:39.141547Z" } }, "outputs": [], @@ -652,10 +652,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:06.362646Z", - "iopub.status.busy": "2024-04-06T04:27:06.362219Z", - "iopub.status.idle": "2024-04-06T04:27:08.620143Z", - "shell.execute_reply": "2024-04-06T04:27:08.619410Z" + "iopub.execute_input": "2024-04-08T19:04:39.143931Z", + "iopub.status.busy": "2024-04-08T19:04:39.143626Z", + "iopub.status.idle": "2024-04-08T19:04:41.418071Z", + "shell.execute_reply": "2024-04-08T19:04:41.417473Z" }, "scrolled": true }, @@ -678,10 +678,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.623393Z", - "iopub.status.busy": "2024-04-06T04:27:08.622654Z", - "iopub.status.idle": "2024-04-06T04:27:08.630521Z", - "shell.execute_reply": "2024-04-06T04:27:08.629847Z" + "iopub.execute_input": "2024-04-08T19:04:41.421050Z", + "iopub.status.busy": "2024-04-08T19:04:41.420335Z", + "iopub.status.idle": "2024-04-08T19:04:41.427937Z", + "shell.execute_reply": "2024-04-08T19:04:41.427499Z" } }, "outputs": [ @@ -782,10 +782,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.632688Z", - "iopub.status.busy": "2024-04-06T04:27:08.632254Z", - "iopub.status.idle": "2024-04-06T04:27:08.636058Z", - "shell.execute_reply": "2024-04-06T04:27:08.635598Z" + "iopub.execute_input": "2024-04-08T19:04:41.429872Z", + "iopub.status.busy": "2024-04-08T19:04:41.429566Z", + "iopub.status.idle": "2024-04-08T19:04:41.433444Z", + "shell.execute_reply": "2024-04-08T19:04:41.433009Z" } }, "outputs": [], @@ -799,10 +799,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.638183Z", - "iopub.status.busy": "2024-04-06T04:27:08.637783Z", - "iopub.status.idle": "2024-04-06T04:27:08.641176Z", - "shell.execute_reply": "2024-04-06T04:27:08.640663Z" + "iopub.execute_input": "2024-04-08T19:04:41.435332Z", + "iopub.status.busy": "2024-04-08T19:04:41.435016Z", + "iopub.status.idle": "2024-04-08T19:04:41.437872Z", + "shell.execute_reply": "2024-04-08T19:04:41.437382Z" } }, "outputs": [ @@ -837,10 +837,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.643224Z", - "iopub.status.busy": "2024-04-06T04:27:08.642931Z", - "iopub.status.idle": "2024-04-06T04:27:08.645892Z", - "shell.execute_reply": "2024-04-06T04:27:08.645427Z" + "iopub.execute_input": "2024-04-08T19:04:41.439975Z", + "iopub.status.busy": "2024-04-08T19:04:41.439662Z", + "iopub.status.idle": "2024-04-08T19:04:41.442421Z", + "shell.execute_reply": "2024-04-08T19:04:41.442004Z" } }, "outputs": [], @@ -860,10 +860,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.647921Z", - "iopub.status.busy": "2024-04-06T04:27:08.647631Z", - "iopub.status.idle": "2024-04-06T04:27:08.654408Z", - "shell.execute_reply": "2024-04-06T04:27:08.653855Z" + "iopub.execute_input": "2024-04-08T19:04:41.444405Z", + "iopub.status.busy": "2024-04-08T19:04:41.444102Z", + "iopub.status.idle": "2024-04-08T19:04:41.450660Z", + "shell.execute_reply": "2024-04-08T19:04:41.450205Z" } }, "outputs": [ @@ -988,10 +988,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.656381Z", - "iopub.status.busy": "2024-04-06T04:27:08.656204Z", - "iopub.status.idle": "2024-04-06T04:27:08.882696Z", - "shell.execute_reply": "2024-04-06T04:27:08.882060Z" + "iopub.execute_input": "2024-04-08T19:04:41.452688Z", + "iopub.status.busy": "2024-04-08T19:04:41.452371Z", + "iopub.status.idle": "2024-04-08T19:04:41.707871Z", + "shell.execute_reply": "2024-04-08T19:04:41.707294Z" }, "scrolled": true }, @@ -1030,10 +1030,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:08.886463Z", - "iopub.status.busy": "2024-04-06T04:27:08.885511Z", - "iopub.status.idle": "2024-04-06T04:27:09.066323Z", - "shell.execute_reply": "2024-04-06T04:27:09.065793Z" + "iopub.execute_input": "2024-04-08T19:04:41.710541Z", + "iopub.status.busy": "2024-04-08T19:04:41.710134Z", + "iopub.status.idle": "2024-04-08T19:04:41.886669Z", + "shell.execute_reply": "2024-04-08T19:04:41.886149Z" }, "scrolled": true }, @@ -1066,10 +1066,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:09.070042Z", - "iopub.status.busy": "2024-04-06T04:27:09.069102Z", - "iopub.status.idle": "2024-04-06T04:27:09.074062Z", - "shell.execute_reply": "2024-04-06T04:27:09.073560Z" + "iopub.execute_input": "2024-04-08T19:04:41.889178Z", + "iopub.status.busy": "2024-04-08T19:04:41.888817Z", + "iopub.status.idle": "2024-04-08T19:04:41.892522Z", + "shell.execute_reply": "2024-04-08T19:04:41.892069Z" }, "nbsphinx": "hidden" }, @@ -1113,7 +1113,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "032871bbc89742aba639ffaee1200e37": { + "00882e39d8294fa7bf11a9929411c91d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1131,72 +1131,59 @@ "text_color": null } }, - "04b7c5dc6e7948f7b6da88bf9de4c09b": { + "0435bd2a67ae4580b11f72300c8c1b9f": { "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": "" } }, - "0b23f615f5b84f338a77080fed288888": { + "068617b1c6e7410ea8a61aaa3bf35a59": { "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_4d3503dbfed14de4b1ffb6918b31358f", - "IPY_MODEL_9b703acaaefe43f2a747995e6baf26ca", - "IPY_MODEL_795e0495ac274840b84a9b2585fcb45b" - ], - "layout": "IPY_MODEL_57a3d879c99546208499a10cab8e04c7", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "10e4288569c34c62a5b336b51d001893": { + "107f3b6a64fb41898b51c78312996d47": { "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_4a44c65dbe25405084910ffb25948f0b", - "placeholder": "​", - "style": "IPY_MODEL_95ba2b873c0b456186466656bd4a4b05", - "tabbable": null, - "tooltip": null, - "value": " 665/665 [00:00<00:00, 125kB/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "18d5e009472c494c99ff042d5f4315c1": { + "161f5f17377e4e528cc2e973474950ee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1211,15 +1198,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_27f1cfa796cd4d64880fb8ad1470589b", + "layout": "IPY_MODEL_63fe7f89c61d469689356c8afe8f7d0a", "placeholder": "​", - "style": "IPY_MODEL_fe7ce2b57aca4dc1b98fc34a52588b14", + "style": "IPY_MODEL_068617b1c6e7410ea8a61aaa3bf35a59", "tabbable": null, "tooltip": null, - "value": " 54.2M/54.2M [00:00<00:00, 167MB/s]" + "value": "README.md: 100%" } }, - "1978592a53c7411fbb7d77958baefc3c": { + "16dad8a9c9074f5ab4ed24858cf4f896": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1237,7 +1224,7 @@ "text_color": null } }, - "202e77f783604892ba40274a0d289276": { + "2b0e68483cbf470db65245930283cce4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1255,56 +1242,25 @@ "text_color": null } }, - "250cfa9619754602808099a37a2351cd": { - "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_385b07eec10c4e1780987426b0fb1ffa", - "placeholder": "​", - "style": "IPY_MODEL_6942d17c55104716b38548fde85fe97d", - "tabbable": null, - "tooltip": null, - "value": "config.json: 100%" - } - }, - "263444dd6e2144b3b9f73741003fea9f": { + "2dcd6e4805fb403ba8b6c1ce8e5ff117": { "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_9123a1575ca34375b0b43f4289e4f1ef", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_431300eb075a4588806ea9c78d8531a3", - "tabbable": null, - "tooltip": null, - "value": 231508.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "27f1cfa796cd4d64880fb8ad1470589b": { + "3041fa94176846a99ea4c32b7fa1cade": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1357,30 +1313,7 @@ "width": null } }, - "29d77952655945c5975ed010d32ac352": { - "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_d330efe583834f6e9a4aef2d210f2448", - "placeholder": "​", - "style": "IPY_MODEL_5108e22e56ca44b8a5f0abeba3da954d", - "tabbable": null, - "tooltip": null, - "value": " 48.0/48.0 [00:00<00:00, 9.54kB/s]" - } - }, - "2b3304b6189e458ba99e1273f6dc3065": { + "30e21853c71148d39d7be87c7c68889a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1433,31 +1366,33 @@ "width": null } }, - "2ccab9f1f4f0466db60167c0d669672d": { + "33cce396c4bf413ebe5341fe28d03486": { "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_964aed80ab284dd48a31d9d5ad28fd30", - "IPY_MODEL_263444dd6e2144b3b9f73741003fea9f", - "IPY_MODEL_b6313e82b4f34189bbf42e2bf514a82d" - ], - "layout": "IPY_MODEL_4a315905dbb7402f9b17e6768c6aa8eb", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_7167dd43cb39496d8971354c7dcf8fb3", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a13aee655acd48a2bc16f80efcd736a4", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 466062.0 } }, - "2ea5a0c2da3a4d83b5610c2c9d2d542c": { + "34051f0f4e6242febbd69d5ad6e263e0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1510,31 +1445,33 @@ "width": null } }, - "328b046d1b9d4dcfa0edb3132c01a4f2": { + "34f290f5d2fb480c8484034ab6752fca": { "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_d3d9e1e700e14803bfdd9de847eb8a1b", - "IPY_MODEL_7de6723f9beb43dbbad71ef72bf3d394", - "IPY_MODEL_58219d6711ac4cbe976717b4a76aa16b" - ], - "layout": "IPY_MODEL_83718921424246c8b3bb82278f13e7dd", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_55ca57fbdbeb4dd09672a18d9e527e1e", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e715922dec9a4f518bd83655ec5ece8c", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 391.0 } }, - "33e4cc93e6f44376a34c0ceb628ca488": { + "3912e2b011df41679c1659d40bc9fefb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1550,7 +1487,49 @@ "description_width": "" } }, - "385b07eec10c4e1780987426b0fb1ffa": { + "3a50e9ac92bb47ef8a4dd27183e221e7": { + "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 + } + }, + "3af073a39e45402187f042a3cf90b160": { + "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_b3a0081d105e4d6193af250cc9cef948", + "IPY_MODEL_3eea5b07c46a4588903f301a31822500", + "IPY_MODEL_5194d575ab3e4498bcd728fe5c7d9a3d" + ], + "layout": "IPY_MODEL_c3f5dd847178422f94661624f9ecf612", + "tabbable": null, + "tooltip": null + } + }, + "3c2b01854c664a3a834ebb6315a06d7a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1603,23 +1582,57 @@ "width": null } }, - "431300eb075a4588806ea9c78d8531a3": { + "3eea5b07c46a4588903f301a31822500": { "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_82f22504089445d980458c94bb3f0b22", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b2f57f17657e430c89e1a52e7f245309", + "tabbable": null, + "tooltip": null, + "value": 231508.0 + } + }, + "414486671bbc4579b154f2d4dd8df463": { + "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_b3270c93e7874c21a39571fdb1d43780", + "IPY_MODEL_34f290f5d2fb480c8484034ab6752fca", + "IPY_MODEL_9a1995f42694404abb75d6338d1bddc7" + ], + "layout": "IPY_MODEL_acdced823e2a4d6f950e27bd4802b639", + "tabbable": null, + "tooltip": null } }, - "4a315905dbb7402f9b17e6768c6aa8eb": { + "41e5eb399b424312bb565497b2f550a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1672,7 +1685,7 @@ "width": null } }, - "4a44c65dbe25405084910ffb25948f0b": { + "427f262b918e461c9202e7a81d04c8dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1725,7 +1738,7 @@ "width": null } }, - "4d3503dbfed14de4b1ffb6918b31358f": { + "42a8d833492a48fe958eb757ee25c31c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1740,50 +1753,120 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_fb7871277ea44cc992dd5130b07e281f", + "layout": "IPY_MODEL_30e21853c71148d39d7be87c7c68889a", "placeholder": "​", - "style": "IPY_MODEL_83a784ca18dd40209d7df691456fa335", + "style": "IPY_MODEL_fdf1adc0e36d4541972658ad13062aac", "tabbable": null, "tooltip": null, - "value": ".gitattributes: 100%" + "value": " 466k/466k [00:00<00:00, 6.02MB/s]" } }, - "5108e22e56ca44b8a5f0abeba3da954d": { + "4a26fef448554f36a9cb66bea78f484a": { "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_c4431e7dcf5a41e1934fd352a0e18cc6", + "IPY_MODEL_a200cf245005465880fbcc7a852362d4", + "IPY_MODEL_5de73fab2f284817999b4f68149d32b9" + ], + "layout": "IPY_MODEL_3041fa94176846a99ea4c32b7fa1cade", + "tabbable": null, + "tooltip": null } }, - "533ead7294bf4d068190ca8e90d78e80": { - "model_module": "@jupyter-widgets/base", + "4ba657e74f59423f9bed6c70b030fcfa": { + "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, + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_427f262b918e461c9202e7a81d04c8dc", + "placeholder": "​", + "style": "IPY_MODEL_fad61d9bcd544d64a2896b7a4fafeca3", + "tabbable": null, + "tooltip": null, + "value": " 2.21k/2.21k [00:00<00:00, 355kB/s]" + } + }, + "4c9a189517774bbe90d04de5fdd9e48f": { + "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 + } + }, + "5194d575ab3e4498bcd728fe5c7d9a3d": { + "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_ab4a32cb43804d52b25207713bc5f0f4", + "placeholder": "​", + "style": "IPY_MODEL_4c9a189517774bbe90d04de5fdd9e48f", + "tabbable": null, + "tooltip": null, + "value": " 232k/232k [00:00<00:00, 1.77MB/s]" + } + }, + "5385ad8ba67c426daa3e219dd49f7c1b": { + "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, @@ -1819,7 +1902,7 @@ "width": null } }, - "57a3d879c99546208499a10cab8e04c7": { + "55ca57fbdbeb4dd09672a18d9e527e1e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1872,7 +1955,47 @@ "width": null } }, - "58219d6711ac4cbe976717b4a76aa16b": { + "596554d1fd004a229dc0e9d5610bace9": { + "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_161f5f17377e4e528cc2e973474950ee", + "IPY_MODEL_a85b90552a3e4665b51fc3b6e7d921f7", + "IPY_MODEL_4ba657e74f59423f9bed6c70b030fcfa" + ], + "layout": "IPY_MODEL_eb1b16cb3c634b0496ccce16fde573eb", + "tabbable": null, + "tooltip": null + } + }, + "5a8a165febd74ab8a0e847c8a208e73f": { + "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": "" + } + }, + "5de73fab2f284817999b4f68149d32b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1887,38 +2010,65 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bd7e59232eee46a9941063b605e66d9b", + "layout": "IPY_MODEL_cbfa8e31e7cb4fd5b0f2827be2777ef3", "placeholder": "​", - "style": "IPY_MODEL_202e77f783604892ba40274a0d289276", + "style": "IPY_MODEL_16dad8a9c9074f5ab4ed24858cf4f896", "tabbable": null, "tooltip": null, - "value": " 2.21k/2.21k [00:00<00:00, 457kB/s]" + "value": " 665/665 [00:00<00:00, 127kB/s]" } }, - "5c1ec47f563245d7989ffc7eff2b3393": { + "5e9e17de388746f4ba1fb4f05e426c4e": { "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a8e917adc0db482da10f7259260a52fc", + "IPY_MODEL_6062369e250e403897e6f33990c23d57", + "IPY_MODEL_7b208fafba0244c7a857e15c10805106" + ], + "layout": "IPY_MODEL_b93f1aea04264f86afd5ca6e5f3deb9a", + "tabbable": null, + "tooltip": null + } + }, + "6062369e250e403897e6f33990c23d57": { + "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_b805e697dc3841fe87f86c354f112531", - "placeholder": "​", - "style": "IPY_MODEL_64fdd33a9fa24c04b2d980484a3dffca", + "layout": "IPY_MODEL_859e480b7e01405fb1af9226d6a94736", + "max": 48.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0435bd2a67ae4580b11f72300c8c1b9f", "tabbable": null, "tooltip": null, - "value": "pytorch_model.bin: 100%" + "value": 48.0 } }, - "623c296ec27847a08d197e0f9349902d": { + "60cb8f29c0a04f8a9b8775d425f7c1d4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1934,17 +2084,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_6475f269b762452097c0dd8a50483fa7", - "max": 48.0, + "layout": "IPY_MODEL_8cfe5b4829a843b499e9d9b1aad10794", + "max": 54245363.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_829408c009e84267b620439ac80aa762", + "style": "IPY_MODEL_8c3149008147411a9172b8a0627cbbdf", "tabbable": null, "tooltip": null, - "value": 48.0 + "value": 54245363.0 } }, - "6475f269b762452097c0dd8a50483fa7": { + "63fe7f89c61d469689356c8afe8f7d0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1997,43 +2147,30 @@ "width": null } }, - "64fdd33a9fa24c04b2d980484a3dffca": { - "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 - } - }, - "6942d17c55104716b38548fde85fe97d": { + "6b70c1af64654cf0bfc8e515cbe2644e": { "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_99f0d4542e8e423b9018be6c83282910", + "placeholder": "​", + "style": "IPY_MODEL_107f3b6a64fb41898b51c78312996d47", + "tabbable": null, + "tooltip": null, + "value": " 54.2M/54.2M [00:00<00:00, 230MB/s]" } }, - "73369ea4b442452ba79dde0f11ccccf7": { + "7167dd43cb39496d8971354c7dcf8fb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2086,7 +2223,53 @@ "width": null } }, - "751b322f8b584ba699d87d767abcfd88": { + "792627ad3d4a4356926466777e5e286b": { + "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_fc16653aacb543948c8bcdbebbe18041", + "placeholder": "​", + "style": "IPY_MODEL_fdc67e28664e4583ace57f50340b3649", + "tabbable": null, + "tooltip": null, + "value": "tokenizer.json: 100%" + } + }, + "7b208fafba0244c7a857e15c10805106": { + "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_41e5eb399b424312bb565497b2f550a6", + "placeholder": "​", + "style": "IPY_MODEL_f8c8778791df4709aa10067dd9083588", + "tabbable": null, + "tooltip": null, + "value": " 48.0/48.0 [00:00<00:00, 8.81kB/s]" + } + }, + "7ba8b4d37b90455ea0856a76fd53349f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2139,95 +2322,7 @@ "width": null } }, - "795e0495ac274840b84a9b2585fcb45b": { - "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_ef836ca5c2e0473c8f44942bbfbde97c", - "placeholder": "​", - "style": "IPY_MODEL_c3b7fa05cbd34b3e8440577d3268b3a2", - "tabbable": null, - "tooltip": null, - "value": " 391/391 [00:00<00:00, 69.3kB/s]" - } - }, - "7de6723f9beb43dbbad71ef72bf3d394": { - "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_f83c84fa51244911ad30fbc24f0dae14", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ae1d1e4addb14d72ad47d043144ef5b2", - "tabbable": null, - "tooltip": null, - "value": 2211.0 - } - }, - "806be8102c2840c683f53e27268a1a1d": { - "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_d1036ff8e3b44432a630841123cd665e", - "placeholder": "​", - "style": "IPY_MODEL_fcdc6f83ca124a68b2ab9d6ace3e2308", - "tabbable": null, - "tooltip": null, - "value": " 466k/466k [00:00<00:00, 7.08MB/s]" - } - }, - "829408c009e84267b620439ac80aa762": { - "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": "" - } - }, - "83718921424246c8b3bb82278f13e7dd": { + "82f22504089445d980458c94bb3f0b22": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2280,25 +2375,7 @@ "width": null } }, - "83a784ca18dd40209d7df691456fa335": { - "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 - } - }, - "9123a1575ca34375b0b43f4289e4f1ef": { + "859e480b7e01405fb1af9226d6a94736": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2351,7 +2428,23 @@ "width": null } }, - "947502200a574c5bb753e3a810fa8f74": { + "8c3149008147411a9172b8a0627cbbdf": { + "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": "" + } + }, + "8cfe5b4829a843b499e9d9b1aad10794": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2404,7 +2497,7 @@ "width": null } }, - "95ba2b873c0b456186466656bd4a4b05": { + "938fb2a8d3a54d448069f0b85d12697d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2422,30 +2515,7 @@ "text_color": null } }, - "964aed80ab284dd48a31d9d5ad28fd30": { - "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_2ea5a0c2da3a4d83b5610c2c9d2d542c", - "placeholder": "​", - "style": "IPY_MODEL_a0bbd2409f4d4acfb2e91ffc5b12b95e", - "tabbable": null, - "tooltip": null, - "value": "vocab.txt: 100%" - } - }, - "974e2a874ecf49ff8142c67afb939afd": { + "99f0d4542e8e423b9018be6c83282910": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2498,51 +2568,53 @@ "width": null } }, - "9b703acaaefe43f2a747995e6baf26ca": { + "9a1995f42694404abb75d6338d1bddc7": { "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_947502200a574c5bb753e3a810fa8f74", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b9a4822bbbc543158d3165bdc309367e", + "layout": "IPY_MODEL_f9d2786f039f4739a73d2d301bf8b745", + "placeholder": "​", + "style": "IPY_MODEL_ae0fb7a982a2436ea7ad32f01526eea2", "tabbable": null, "tooltip": null, - "value": 391.0 + "value": " 391/391 [00:00<00:00, 69.6kB/s]" } }, - "a0bbd2409f4d4acfb2e91ffc5b12b95e": { + "9f72c96cd44c40618e8826a9fc735324": { "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_e93f36ea176647cd9b1069f0297aadef", + "placeholder": "​", + "style": "IPY_MODEL_2dcd6e4805fb403ba8b6c1ce8e5ff117", + "tabbable": null, + "tooltip": null, + "value": "pytorch_model.bin: 100%" } }, - "a0c9bb1d94eb48e1945c490bd1d43922": { + "a13aee655acd48a2bc16f80efcd736a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2558,7 +2630,59 @@ "description_width": "" } }, - "a4758f91aee04d48822bb6b9c3453db8": { + "a200cf245005465880fbcc7a852362d4": { + "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_7ba8b4d37b90455ea0856a76fd53349f", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a8a165febd74ab8a0e847c8a208e73f", + "tabbable": null, + "tooltip": null, + "value": 665.0 + } + }, + "a85b90552a3e4665b51fc3b6e7d921f7": { + "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_dcfe560d74704020ba7189f6b35342ae", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3912e2b011df41679c1659d40bc9fefb", + "tabbable": null, + "tooltip": null, + "value": 2211.0 + } + }, + "a8e917adc0db482da10f7259260a52fc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2573,15 +2697,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f756af4beca342ed8bed100e0bfffe79", + "layout": "IPY_MODEL_f41c778f7656469892daa08fc3f86248", "placeholder": "​", - "style": "IPY_MODEL_032871bbc89742aba639ffaee1200e37", + "style": "IPY_MODEL_2b0e68483cbf470db65245930283cce4", "tabbable": null, "tooltip": null, - "value": "tokenizer.json: 100%" + "value": "tokenizer_config.json: 100%" } }, - "aa772cc8b00945c1aa7b22081cb39031": { + "ab4a32cb43804d52b25207713bc5f0f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2622,81 +2746,19 @@ "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 - } - }, - "ae1d1e4addb14d72ad47d043144ef5b2": { - "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": "" - } - }, - "b015ab05281b4ee48af1da0ccdc94161": { - "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_2b3304b6189e458ba99e1273f6dc3065", - "placeholder": "​", - "style": "IPY_MODEL_1978592a53c7411fbb7d77958baefc3c", - "tabbable": null, - "tooltip": null, - "value": "tokenizer_config.json: 100%" - } - }, - "b6313e82b4f34189bbf42e2bf514a82d": { - "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_533ead7294bf4d068190ca8e90d78e80", - "placeholder": "​", - "style": "IPY_MODEL_fade1033c9ad433a857f6d209599eaad", - "tabbable": null, - "tooltip": null, - "value": " 232k/232k [00:00<00:00, 36.7MB/s]" + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "b805e697dc3841fe87f86c354f112531": { + "acdced823e2a4d6f950e27bd4802b639": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2749,23 +2811,25 @@ "width": null } }, - "b8d38e973ddb4490be0781dbb8717675": { + "ae0fb7a982a2436ea7ad32f01526eea2": { "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 } }, - "b9a4822bbbc543158d3165bdc309367e": { + "b2f57f17657e430c89e1a52e7f245309": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2781,7 +2845,53 @@ "description_width": "" } }, - "bd7e59232eee46a9941063b605e66d9b": { + "b3270c93e7874c21a39571fdb1d43780": { + "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_34051f0f4e6242febbd69d5ad6e263e0", + "placeholder": "​", + "style": "IPY_MODEL_938fb2a8d3a54d448069f0b85d12697d", + "tabbable": null, + "tooltip": null, + "value": ".gitattributes: 100%" + } + }, + "b3a0081d105e4d6193af250cc9cef948": { + "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_be33e673f6fe4622ab6bfd3a727d2c04", + "placeholder": "​", + "style": "IPY_MODEL_00882e39d8294fa7bf11a9929411c91d", + "tabbable": null, + "tooltip": null, + "value": "vocab.txt: 100%" + } + }, + "b93f1aea04264f86afd5ca6e5f3deb9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2834,51 +2944,7 @@ "width": null } }, - "c3b7fa05cbd34b3e8440577d3268b3a2": { - "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 - } - }, - "ce5ef1ebbb6d4b7589aa40b6d38dd8f2": { - "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_e4e868950b0e4748a7bb54abf124ca39", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_33e4cc93e6f44376a34c0ceb628ca488", - "tabbable": null, - "tooltip": null, - "value": 54245363.0 - } - }, - "d1036ff8e3b44432a630841123cd665e": { + "be33e673f6fe4622ab6bfd3a727d2c04": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2931,31 +2997,7 @@ "width": null } }, - "d20256762cdf4c06816c5653fdfef6a8": { - "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_5c1ec47f563245d7989ffc7eff2b3393", - "IPY_MODEL_ce5ef1ebbb6d4b7589aa40b6d38dd8f2", - "IPY_MODEL_18d5e009472c494c99ff042d5f4315c1" - ], - "layout": "IPY_MODEL_aa772cc8b00945c1aa7b22081cb39031", - "tabbable": null, - "tooltip": null - } - }, - "d2370f99c49d43b39ac2ea8e64340375": { + "c18cde9a3b464ad1a69d4fbf65c4287b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2970,16 +3012,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_a4758f91aee04d48822bb6b9c3453db8", - "IPY_MODEL_dd34df9493e74cc89434500b5b18d5ac", - "IPY_MODEL_806be8102c2840c683f53e27268a1a1d" + "IPY_MODEL_9f72c96cd44c40618e8826a9fc735324", + "IPY_MODEL_60cb8f29c0a04f8a9b8775d425f7c1d4", + "IPY_MODEL_6b70c1af64654cf0bfc8e515cbe2644e" ], - "layout": "IPY_MODEL_73369ea4b442452ba79dde0f11ccccf7", + "layout": "IPY_MODEL_3c2b01854c664a3a834ebb6315a06d7a", "tabbable": null, "tooltip": null } }, - "d330efe583834f6e9a4aef2d210f2448": { + "c3f5dd847178422f94661624f9ecf612": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3032,7 +3074,7 @@ "width": null } }, - "d3d9e1e700e14803bfdd9de847eb8a1b": { + "c4431e7dcf5a41e1934fd352a0e18cc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3047,67 +3089,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ef220577d59248bdbc40d7374238082a", + "layout": "IPY_MODEL_5385ad8ba67c426daa3e219dd49f7c1b", "placeholder": "​", - "style": "IPY_MODEL_04b7c5dc6e7948f7b6da88bf9de4c09b", - "tabbable": null, - "tooltip": null, - "value": "README.md: 100%" - } - }, - "d9f750fd331d4370b4ead712d6fe626d": { - "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_f76bdeeb99374127a9a34c37ef45c330", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a0c9bb1d94eb48e1945c490bd1d43922", - "tabbable": null, - "tooltip": null, - "value": 665.0 - } - }, - "dd34df9493e74cc89434500b5b18d5ac": { - "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_e5b68aee810b4fc48e7950601e662dad", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b8d38e973ddb4490be0781dbb8717675", + "style": "IPY_MODEL_3a50e9ac92bb47ef8a4dd27183e221e7", "tabbable": null, "tooltip": null, - "value": 466062.0 + "value": "config.json: 100%" } }, - "e4e868950b0e4748a7bb54abf124ca39": { + "cbfa8e31e7cb4fd5b0f2827be2777ef3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3160,7 +3150,7 @@ "width": null } }, - "e5b68aee810b4fc48e7950601e662dad": { + "d27490d630934411bc6291d305749d0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3213,7 +3203,7 @@ "width": null } }, - "ef220577d59248bdbc40d7374238082a": { + "dcfe560d74704020ba7189f6b35342ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3266,7 +3256,47 @@ "width": null } }, - "ef836ca5c2e0473c8f44942bbfbde97c": { + "e715922dec9a4f518bd83655ec5ece8c": { + "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": "" + } + }, + "e8839cb132d74eb9a916dda9fdafe1c4": { + "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_792627ad3d4a4356926466777e5e286b", + "IPY_MODEL_33cce396c4bf413ebe5341fe28d03486", + "IPY_MODEL_42a8d833492a48fe958eb757ee25c31c" + ], + "layout": "IPY_MODEL_d27490d630934411bc6291d305749d0a", + "tabbable": null, + "tooltip": null + } + }, + "e93f36ea176647cd9b1069f0297aadef": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3319,31 +3349,7 @@ "width": null } }, - "f26e295a2fa0406c8e3fff0e0818202d": { - "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_250cfa9619754602808099a37a2351cd", - "IPY_MODEL_d9f750fd331d4370b4ead712d6fe626d", - "IPY_MODEL_10e4288569c34c62a5b336b51d001893" - ], - "layout": "IPY_MODEL_751b322f8b584ba699d87d767abcfd88", - "tabbable": null, - "tooltip": null - } - }, - "f756af4beca342ed8bed100e0bfffe79": { + "eb1b16cb3c634b0496ccce16fde573eb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3396,7 +3402,7 @@ "width": null } }, - "f76bdeeb99374127a9a34c37ef45c330": { + "f41c778f7656469892daa08fc3f86248": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3449,7 +3455,25 @@ "width": null } }, - "f83c84fa51244911ad30fbc24f0dae14": { + "f8c8778791df4709aa10067dd9083588": { + "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 + } + }, + "f9d2786f039f4739a73d2d301bf8b745": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3502,7 +3526,7 @@ "width": null } }, - "fade1033c9ad433a857f6d209599eaad": { + "fad61d9bcd544d64a2896b7a4fafeca3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3520,7 +3544,7 @@ "text_color": null } }, - "fb7871277ea44cc992dd5130b07e281f": { + "fc16653aacb543948c8bcdbebbe18041": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3573,31 +3597,7 @@ "width": null } }, - "fb7ad54f03394e35a23b11c642b2c405": { - "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_b015ab05281b4ee48af1da0ccdc94161", - "IPY_MODEL_623c296ec27847a08d197e0f9349902d", - "IPY_MODEL_29d77952655945c5975ed010d32ac352" - ], - "layout": "IPY_MODEL_974e2a874ecf49ff8142c67afb939afd", - "tabbable": null, - "tooltip": null - } - }, - "fcdc6f83ca124a68b2ab9d6ace3e2308": { + "fdc67e28664e4583ace57f50340b3649": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3615,7 +3615,7 @@ "text_color": null } }, - "fe7ce2b57aca4dc1b98fc34a52588b14": { + "fdf1adc0e36d4541972658ad13062aac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", diff --git a/master/tutorials/datalab/audio.html b/master/tutorials/datalab/audio.html index 4120a32c1..ffde9c051 100644 --- a/master/tutorials/datalab/audio.html +++ b/master/tutorials/datalab/audio.html @@ -1318,7 +1318,7 @@

    5. Use cleanlab to find label issues -{"state": {"61797416aadf4af5bcea4adfab15ea37": {"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}}, "648981cda80e4fe686afd77c841b011b": {"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": ""}}, "245795f1b8c14f0580deec4394842154": {"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_61797416aadf4af5bcea4adfab15ea37", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_648981cda80e4fe686afd77c841b011b", "tabbable": null, "tooltip": null, "value": 2041.0}}, "0958ba1748ee4beca7da01e8e46a9749": {"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}}, "6b5d1d793a204f1ab4608428b5ba64b9": {"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}}, "68b320632eea4620bbd3b735bdc34dd9": {"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_0958ba1748ee4beca7da01e8e46a9749", "placeholder": "\u200b", "style": "IPY_MODEL_6b5d1d793a204f1ab4608428b5ba64b9", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "9f88f31de9d74eb0928335732d663561": {"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}}, "b74e55a2c5174b88b66ecaa2389897db": {"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}}, "8848bb7bffb545109abca26a5ae067d7": {"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_9f88f31de9d74eb0928335732d663561", "placeholder": "\u200b", "style": "IPY_MODEL_b74e55a2c5174b88b66ecaa2389897db", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007514kB/s]"}}, "f6e17f0c62ca4e53a8d555529912ee89": {"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}}, "bd5cd533ee9f44c9a03ecc0071f84776": {"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_68b320632eea4620bbd3b735bdc34dd9", "IPY_MODEL_245795f1b8c14f0580deec4394842154", "IPY_MODEL_8848bb7bffb545109abca26a5ae067d7"], "layout": "IPY_MODEL_f6e17f0c62ca4e53a8d555529912ee89", "tabbable": null, "tooltip": null}}, "87a12f7b60404561827c31d53af0eaa8": {"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}}, "51ff76b0c91049ceab3634eafef03d03": {"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": ""}}, "38cedb80c4884a7aa9c8360e66697385": {"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_87a12f7b60404561827c31d53af0eaa8", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_51ff76b0c91049ceab3634eafef03d03", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "841d6e99393d4eacb2171cd893b4a9ae": {"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}}, "ad0c26113b5641988b98da2e1367adc3": {"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}}, "4cb73865490e4acab5b8bd74d5733f8e": {"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_841d6e99393d4eacb2171cd893b4a9ae", "placeholder": "\u200b", "style": "IPY_MODEL_ad0c26113b5641988b98da2e1367adc3", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "753341b93da447daa4758e7e5a5610e7": {"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}}, "ae98285a153e46948474fc4abeceddfd": {"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}}, "ba43a6d3c0eb4eae9b16f9526c18eff9": {"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_753341b93da447daa4758e7e5a5610e7", "placeholder": "\u200b", "style": "IPY_MODEL_ae98285a153e46948474fc4abeceddfd", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200739.9MB/s]"}}, "d16f6084ce1149cd889d6a2cb15f31c5": {"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}}, "65cd560eff044447950b5de1dfe924a9": {"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_4cb73865490e4acab5b8bd74d5733f8e", "IPY_MODEL_38cedb80c4884a7aa9c8360e66697385", "IPY_MODEL_ba43a6d3c0eb4eae9b16f9526c18eff9"], "layout": "IPY_MODEL_d16f6084ce1149cd889d6a2cb15f31c5", "tabbable": null, "tooltip": null}}, "a0795bde2b3a4af08cc09a2b34f5e2aa": {"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}}, "b5504d0328b84005b17ed0cf71889d5b": {"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": ""}}, "ba716f0978944bb8b14f373794cf086b": {"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_a0795bde2b3a4af08cc09a2b34f5e2aa", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b5504d0328b84005b17ed0cf71889d5b", "tabbable": null, "tooltip": null, "value": 3201.0}}, "d64c977788594151a2a69bd522b4b4e2": {"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}}, "b387baf48cbf40a79960c2e5e37c6dc5": {"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}}, "451ef1098e4b4a0cbc94307f8c54a69a": {"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_d64c977788594151a2a69bd522b4b4e2", "placeholder": "\u200b", "style": "IPY_MODEL_b387baf48cbf40a79960c2e5e37c6dc5", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "399a48d2dd4a4aeba66c8583c6cb3267": {"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}}, "8d05285dc1f6426fba9ead2f8bc7365b": {"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}}, "ffab753038714d3ca6e89b271ede7406": {"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_399a48d2dd4a4aeba66c8583c6cb3267", "placeholder": "\u200b", "style": "IPY_MODEL_8d05285dc1f6426fba9ead2f8bc7365b", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007894kB/s]"}}, "9e087829122b4cf38c2f2193a3ae9b93": {"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}}, "16bedf557e1f48c3b23c71d15d5ff3ae": {"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_451ef1098e4b4a0cbc94307f8c54a69a", "IPY_MODEL_ba716f0978944bb8b14f373794cf086b", "IPY_MODEL_ffab753038714d3ca6e89b271ede7406"], "layout": "IPY_MODEL_9e087829122b4cf38c2f2193a3ae9b93", "tabbable": null, "tooltip": null}}, "4c0901a7fd664a0386f5506a5f1265c6": {"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}}, "6c5e187c01984c899378ec18b1b6f259": {"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": ""}}, "9d58780c4a6d406b98d0415c7c2a4ee5": {"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_4c0901a7fd664a0386f5506a5f1265c6", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6c5e187c01984c899378ec18b1b6f259", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "202cf82f0f8a4e6ab6c44edcc4f643d4": {"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}}, "68b5eff6aadd4afb9e1940334ccb3d9b": {"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}}, "fa3cbfeda5cd4b7fbc3c0c9e921e5864": {"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_202cf82f0f8a4e6ab6c44edcc4f643d4", "placeholder": "\u200b", "style": "IPY_MODEL_68b5eff6aadd4afb9e1940334ccb3d9b", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "ae7e6d3ea8df4eb8badebf0329826518": {"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}}, "2a522c81494744879ad988349f56ed6d": {"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}}, "0148687423ae4404946f694c128d1db8": {"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_ae7e6d3ea8df4eb8badebf0329826518", "placeholder": "\u200b", "style": "IPY_MODEL_2a522c81494744879ad988349f56ed6d", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200739.4MB/s]"}}, "c639e0d31a954ec89eb3549596545904": {"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}}, "9527fc8121814f01a443e62ff927d47b": {"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_fa3cbfeda5cd4b7fbc3c0c9e921e5864", "IPY_MODEL_9d58780c4a6d406b98d0415c7c2a4ee5", "IPY_MODEL_0148687423ae4404946f694c128d1db8"], "layout": "IPY_MODEL_c639e0d31a954ec89eb3549596545904", "tabbable": null, "tooltip": null}}, "77a067827f5d4c28b687f203c008e031": {"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}}, "4edb5018fb694daab45e95ca9a2d5a86": {"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": ""}}, "aece8b0eb585430bbc9c4df8054ab74b": {"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_77a067827f5d4c28b687f203c008e031", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_4edb5018fb694daab45e95ca9a2d5a86", "tabbable": null, "tooltip": null, "value": 128619.0}}, "25b2f2a091664e82b0b254933237ecda": {"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}}, "bbb63db0bf1246e09f27e3fbcd6067f2": {"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}}, "45c7d5509d354e7db551d2ee42562f4a": {"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_25b2f2a091664e82b0b254933237ecda", "placeholder": "\u200b", "style": "IPY_MODEL_bbb63db0bf1246e09f27e3fbcd6067f2", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "9b265031b424474386c724023fbceea8": {"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}}, "13d241d8b1a14cd99f8717170fdc36e9": {"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}}, "283a7fca65ce4358af3da78be675147f": {"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_9b265031b424474386c724023fbceea8", "placeholder": "\u200b", "style": "IPY_MODEL_13d241d8b1a14cd99f8717170fdc36e9", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20072.64MB/s]"}}, "4055580cee714e89b235223a231f6c86": {"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}}, "f6af1b2285f641a2b47091704e8edf17": {"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_45c7d5509d354e7db551d2ee42562f4a", "IPY_MODEL_aece8b0eb585430bbc9c4df8054ab74b", "IPY_MODEL_283a7fca65ce4358af3da78be675147f"], "layout": "IPY_MODEL_4055580cee714e89b235223a231f6c86", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"a2ac773a77354c65a9b27aead854f501": {"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}}, "1845bd7583734a468217bf0a33a4a04e": {"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": ""}}, "9dcd19b1766a4f0f9e62b19feae944e0": {"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_a2ac773a77354c65a9b27aead854f501", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1845bd7583734a468217bf0a33a4a04e", "tabbable": null, "tooltip": null, "value": 2041.0}}, "6add2113ed1541dfbdb3c42fbbce25fc": {"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}}, "fc4736ca693444da8e5b28edfdc65bc5": {"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}}, "03e98d7846af44088b8a646b91364594": {"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_6add2113ed1541dfbdb3c42fbbce25fc", "placeholder": "\u200b", "style": "IPY_MODEL_fc4736ca693444da8e5b28edfdc65bc5", "tabbable": null, "tooltip": null, "value": "hyperparams.yaml:\u2007100%"}}, "bca20dd54024424b908c519dc7c65413": {"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}}, "6e856fb0c33a4b509b13af9f0bfa347d": {"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}}, "4a17f13f9daa4bd3a9e0f9a6bdcd54ce": {"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_bca20dd54024424b908c519dc7c65413", "placeholder": "\u200b", "style": "IPY_MODEL_6e856fb0c33a4b509b13af9f0bfa347d", "tabbable": null, "tooltip": null, "value": "\u20072.04k/2.04k\u2007[00:00<00:00,\u2007458kB/s]"}}, "6f1084b3c29943cbae5ae582af5573d2": {"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}}, "593320329ae4474ea377016a9d84c521": {"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_03e98d7846af44088b8a646b91364594", "IPY_MODEL_9dcd19b1766a4f0f9e62b19feae944e0", "IPY_MODEL_4a17f13f9daa4bd3a9e0f9a6bdcd54ce"], "layout": "IPY_MODEL_6f1084b3c29943cbae5ae582af5573d2", "tabbable": null, "tooltip": null}}, "099362e1ce5c4a6b8d8d4c331a960d34": {"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}}, "4c8c8519a91e4cf59efc5ee43887ceef": {"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": ""}}, "605873f7b6924ba3a2b8d496de6a3f37": {"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_099362e1ce5c4a6b8d8d4c331a960d34", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_4c8c8519a91e4cf59efc5ee43887ceef", "tabbable": null, "tooltip": null, "value": 16887676.0}}, "358c21f56ec2458089c2e7d7e5e7611d": {"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}}, "b38f5dc0540f47bcb4f22237b8bc10b5": {"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}}, "1d8d1f51752d4b69aa1135e17d8449e2": {"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_358c21f56ec2458089c2e7d7e5e7611d", "placeholder": "\u200b", "style": "IPY_MODEL_b38f5dc0540f47bcb4f22237b8bc10b5", "tabbable": null, "tooltip": null, "value": "embedding_model.ckpt:\u2007100%"}}, "6b84bee63e5341e68a70a1df0644c356": {"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}}, "702d80d4b5874038a51e0c3fe4e29c9c": {"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}}, "47f17e40d80540e8b9cd62de82c60b92": {"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_6b84bee63e5341e68a70a1df0644c356", "placeholder": "\u200b", "style": "IPY_MODEL_702d80d4b5874038a51e0c3fe4e29c9c", "tabbable": null, "tooltip": null, "value": "\u200716.9M/16.9M\u2007[00:00<00:00,\u200750.6MB/s]"}}, "789cb19d03704e0b95b9af8afa82d248": {"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}}, "69f610d6cd3c4ac58e8089ac7c27c111": {"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_1d8d1f51752d4b69aa1135e17d8449e2", "IPY_MODEL_605873f7b6924ba3a2b8d496de6a3f37", "IPY_MODEL_47f17e40d80540e8b9cd62de82c60b92"], "layout": "IPY_MODEL_789cb19d03704e0b95b9af8afa82d248", "tabbable": null, "tooltip": null}}, "7a1b19cb87394307a53c97aa5de7ef54": {"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}}, "7c70a56bdf6c4f4e9f2f310fe97caf21": {"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": ""}}, "170825b92f484861b53a305933672d63": {"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_7a1b19cb87394307a53c97aa5de7ef54", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_7c70a56bdf6c4f4e9f2f310fe97caf21", "tabbable": null, "tooltip": null, "value": 3201.0}}, "37c6159b215f46748ce591cd48fe49f0": {"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}}, "af4e6010ee0847468fae428d5bceb881": {"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}}, "124ddd3253c4476b83146639e17d57b4": {"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_37c6159b215f46748ce591cd48fe49f0", "placeholder": "\u200b", "style": "IPY_MODEL_af4e6010ee0847468fae428d5bceb881", "tabbable": null, "tooltip": null, "value": "mean_var_norm_emb.ckpt:\u2007100%"}}, "c0a147631094428498d5242f0dae3a28": {"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}}, "3b7f0046c33247249156bee86a8ab344": {"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}}, "4911164b858e49c1b45c2acea166106f": {"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_c0a147631094428498d5242f0dae3a28", "placeholder": "\u200b", "style": "IPY_MODEL_3b7f0046c33247249156bee86a8ab344", "tabbable": null, "tooltip": null, "value": "\u20073.20k/3.20k\u2007[00:00<00:00,\u2007736kB/s]"}}, "6232f100dcfc4faea627bbfd5cf8d09e": {"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}}, "a992a72ca18a4979975cfb11417f3b00": {"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_124ddd3253c4476b83146639e17d57b4", "IPY_MODEL_170825b92f484861b53a305933672d63", "IPY_MODEL_4911164b858e49c1b45c2acea166106f"], "layout": "IPY_MODEL_6232f100dcfc4faea627bbfd5cf8d09e", "tabbable": null, "tooltip": null}}, "ca834c7b06bd4f90ba096fa6f8d7103c": {"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}}, "01548e5746cc4788b0d61577e4b012b3": {"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": ""}}, "10f5629a4670476891a02ba1e61d2b9f": {"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_ca834c7b06bd4f90ba096fa6f8d7103c", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_01548e5746cc4788b0d61577e4b012b3", "tabbable": null, "tooltip": null, "value": 15856877.0}}, "c05dd7bca0b442bbb6c59095a6fdc7da": {"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}}, "2398940ef11744cd931d78b60fe1bf96": {"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}}, "d7cff1a5c1c84b53a0f820ae0ae64f22": {"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_c05dd7bca0b442bbb6c59095a6fdc7da", "placeholder": "\u200b", "style": "IPY_MODEL_2398940ef11744cd931d78b60fe1bf96", "tabbable": null, "tooltip": null, "value": "classifier.ckpt:\u2007100%"}}, "fa4f7d539024400ead56c0975b429f2c": {"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}}, "49d50cf65e2243fe85ea4efa439383a4": {"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}}, "8227ef3e4a1d4b60bb8d49436af87b02": {"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_fa4f7d539024400ead56c0975b429f2c", "placeholder": "\u200b", "style": "IPY_MODEL_49d50cf65e2243fe85ea4efa439383a4", "tabbable": null, "tooltip": null, "value": "\u200715.9M/15.9M\u2007[00:00<00:00,\u200747.3MB/s]"}}, "6b1d25f5e1274b7087e5be1edb44f263": {"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}}, "344f8176bb9c4df2b5f5c5842359b28c": {"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_d7cff1a5c1c84b53a0f820ae0ae64f22", "IPY_MODEL_10f5629a4670476891a02ba1e61d2b9f", "IPY_MODEL_8227ef3e4a1d4b60bb8d49436af87b02"], "layout": "IPY_MODEL_6b1d25f5e1274b7087e5be1edb44f263", "tabbable": null, "tooltip": null}}, "7187ee77d34d4ee58aea3e5ef7ea0d6f": {"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}}, "a84cb458429f4ac9ba17b78d73eb1b9c": {"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": ""}}, "1e261881c01a4c82af0950354ddbbf67": {"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_7187ee77d34d4ee58aea3e5ef7ea0d6f", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a84cb458429f4ac9ba17b78d73eb1b9c", "tabbable": null, "tooltip": null, "value": 128619.0}}, "3656dfd1293341618ec097a837ccf824": {"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}}, "153a557fc0794ff2b1d801bc73e89764": {"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}}, "f98ecf4b23e04508a03cf3042be644e5": {"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_3656dfd1293341618ec097a837ccf824", "placeholder": "\u200b", "style": "IPY_MODEL_153a557fc0794ff2b1d801bc73e89764", "tabbable": null, "tooltip": null, "value": "label_encoder.txt:\u2007100%"}}, "15e61f73e248421da683941c01a6ba8a": {"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}}, "770aeb28530d4716aeceac624b99192d": {"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}}, "3f10333ce59449f8893619a6a1200e72": {"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_15e61f73e248421da683941c01a6ba8a", "placeholder": "\u200b", "style": "IPY_MODEL_770aeb28530d4716aeceac624b99192d", "tabbable": null, "tooltip": null, "value": "\u2007129k/129k\u2007[00:00<00:00,\u20071.94MB/s]"}}, "6d97e6b4b14a4b088f0a5c2c2e610564": {"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}}, "f91d49b1c62d4d839fff74899d0af511": {"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_f98ecf4b23e04508a03cf3042be644e5", "IPY_MODEL_1e261881c01a4c82af0950354ddbbf67", "IPY_MODEL_3f10333ce59449f8893619a6a1200e72"], "layout": "IPY_MODEL_6d97e6b4b14a4b088f0a5c2c2e610564", "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 661651aab..28ea897d0 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": "2024-04-06T04:27:12.140596Z", - "iopub.status.busy": "2024-04-06T04:27:12.140418Z", - "iopub.status.idle": "2024-04-06T04:27:16.566489Z", - "shell.execute_reply": "2024-04-06T04:27:16.565924Z" + "iopub.execute_input": "2024-04-08T19:04:46.105517Z", + "iopub.status.busy": "2024-04-08T19:04:46.104987Z", + "iopub.status.idle": "2024-04-08T19:04:51.038814Z", + "shell.execute_reply": "2024-04-08T19:04:51.038258Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:27:16.569088Z", - "iopub.status.busy": "2024-04-06T04:27:16.568539Z", - "iopub.status.idle": "2024-04-06T04:27:16.571617Z", - "shell.execute_reply": "2024-04-06T04:27:16.571189Z" + "iopub.execute_input": "2024-04-08T19:04:51.041604Z", + "iopub.status.busy": "2024-04-08T19:04:51.041029Z", + "iopub.status.idle": "2024-04-08T19:04:51.044345Z", + "shell.execute_reply": "2024-04-08T19:04:51.043904Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:16.573587Z", - "iopub.status.busy": "2024-04-06T04:27:16.573414Z", - "iopub.status.idle": "2024-04-06T04:27:16.578000Z", - "shell.execute_reply": "2024-04-06T04:27:16.577578Z" + "iopub.execute_input": "2024-04-08T19:04:51.046287Z", + "iopub.status.busy": "2024-04-08T19:04:51.045963Z", + "iopub.status.idle": "2024-04-08T19:04:51.050303Z", + "shell.execute_reply": "2024-04-08T19:04:51.049883Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:16.580077Z", - "iopub.status.busy": "2024-04-06T04:27:16.579751Z", - "iopub.status.idle": "2024-04-06T04:27:18.276513Z", - "shell.execute_reply": "2024-04-06T04:27:18.275051Z" + "iopub.execute_input": "2024-04-08T19:04:51.052312Z", + "iopub.status.busy": "2024-04-08T19:04:51.051991Z", + "iopub.status.idle": "2024-04-08T19:04:52.964225Z", + "shell.execute_reply": "2024-04-08T19:04:52.963597Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:18.282470Z", - "iopub.status.busy": "2024-04-06T04:27:18.281603Z", - "iopub.status.idle": "2024-04-06T04:27:18.299325Z", - "shell.execute_reply": "2024-04-06T04:27:18.298235Z" + "iopub.execute_input": "2024-04-08T19:04:52.967053Z", + "iopub.status.busy": "2024-04-08T19:04:52.966626Z", + "iopub.status.idle": "2024-04-08T19:04:52.977284Z", + "shell.execute_reply": "2024-04-08T19:04:52.976855Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:18.302849Z", - "iopub.status.busy": "2024-04-06T04:27:18.302356Z", - "iopub.status.idle": "2024-04-06T04:27:18.311115Z", - "shell.execute_reply": "2024-04-06T04:27:18.310622Z" + "iopub.execute_input": "2024-04-08T19:04:52.979356Z", + "iopub.status.busy": "2024-04-08T19:04:52.979056Z", + "iopub.status.idle": "2024-04-08T19:04:52.984474Z", + "shell.execute_reply": "2024-04-08T19:04:52.984027Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:18.313433Z", - "iopub.status.busy": "2024-04-06T04:27:18.313090Z", - "iopub.status.idle": "2024-04-06T04:27:18.744781Z", - "shell.execute_reply": "2024-04-06T04:27:18.744269Z" + "iopub.execute_input": "2024-04-08T19:04:52.986467Z", + "iopub.status.busy": "2024-04-08T19:04:52.986184Z", + "iopub.status.idle": "2024-04-08T19:04:53.470988Z", + "shell.execute_reply": "2024-04-08T19:04:53.470375Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:18.747037Z", - "iopub.status.busy": "2024-04-06T04:27:18.746633Z", - "iopub.status.idle": "2024-04-06T04:27:20.503082Z", - "shell.execute_reply": "2024-04-06T04:27:20.502599Z" + "iopub.execute_input": "2024-04-08T19:04:53.473214Z", + "iopub.status.busy": "2024-04-08T19:04:53.472775Z", + "iopub.status.idle": "2024-04-08T19:04:55.493272Z", + "shell.execute_reply": "2024-04-08T19:04:55.492735Z" }, "id": "vL9lkiKsHvKr" }, @@ -474,10 +474,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:20.505556Z", - "iopub.status.busy": "2024-04-06T04:27:20.505108Z", - "iopub.status.idle": "2024-04-06T04:27:20.523191Z", - "shell.execute_reply": "2024-04-06T04:27:20.522660Z" + "iopub.execute_input": "2024-04-08T19:04:55.495706Z", + "iopub.status.busy": "2024-04-08T19:04:55.495509Z", + "iopub.status.idle": "2024-04-08T19:04:55.513796Z", + "shell.execute_reply": "2024-04-08T19:04:55.513240Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -557,10 +557,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:20.525153Z", - "iopub.status.busy": "2024-04-06T04:27:20.524840Z", - "iopub.status.idle": "2024-04-06T04:27:20.527990Z", - "shell.execute_reply": "2024-04-06T04:27:20.527457Z" + "iopub.execute_input": "2024-04-08T19:04:55.515946Z", + "iopub.status.busy": "2024-04-08T19:04:55.515624Z", + "iopub.status.idle": "2024-04-08T19:04:55.519151Z", + "shell.execute_reply": "2024-04-08T19:04:55.518745Z" }, "id": "I8JqhOZgi94g" }, @@ -582,10 +582,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:20.530026Z", - "iopub.status.busy": "2024-04-06T04:27:20.529694Z", - "iopub.status.idle": "2024-04-06T04:27:34.456164Z", - "shell.execute_reply": "2024-04-06T04:27:34.455636Z" + "iopub.execute_input": "2024-04-08T19:04:55.521090Z", + "iopub.status.busy": "2024-04-08T19:04:55.520779Z", + "iopub.status.idle": "2024-04-08T19:05:10.361560Z", + "shell.execute_reply": "2024-04-08T19:05:10.361009Z" }, "id": "2FSQ2GR9R_YA" }, @@ -627,10 +627,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:34.458814Z", - "iopub.status.busy": "2024-04-06T04:27:34.458439Z", - "iopub.status.idle": "2024-04-06T04:27:34.462230Z", - "shell.execute_reply": "2024-04-06T04:27:34.461692Z" + "iopub.execute_input": "2024-04-08T19:05:10.364429Z", + "iopub.status.busy": "2024-04-08T19:05:10.364034Z", + "iopub.status.idle": "2024-04-08T19:05:10.367863Z", + "shell.execute_reply": "2024-04-08T19:05:10.367337Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:34.464292Z", - "iopub.status.busy": "2024-04-06T04:27:34.463976Z", - "iopub.status.idle": "2024-04-06T04:27:35.183273Z", - "shell.execute_reply": "2024-04-06T04:27:35.182717Z" + "iopub.execute_input": "2024-04-08T19:05:10.370078Z", + "iopub.status.busy": "2024-04-08T19:05:10.369656Z", + "iopub.status.idle": "2024-04-08T19:05:11.087473Z", + "shell.execute_reply": "2024-04-08T19:05:11.086872Z" }, "id": "i_drkY9YOcw4" }, @@ -727,10 +727,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.186021Z", - "iopub.status.busy": "2024-04-06T04:27:35.185608Z", - "iopub.status.idle": "2024-04-06T04:27:35.190731Z", - "shell.execute_reply": "2024-04-06T04:27:35.190237Z" + "iopub.execute_input": "2024-04-08T19:05:11.090530Z", + "iopub.status.busy": "2024-04-08T19:05:11.089968Z", + "iopub.status.idle": "2024-04-08T19:05:11.095044Z", + "shell.execute_reply": "2024-04-08T19:05:11.094507Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -777,10 +777,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.193080Z", - "iopub.status.busy": "2024-04-06T04:27:35.192698Z", - "iopub.status.idle": "2024-04-06T04:27:35.302175Z", - "shell.execute_reply": "2024-04-06T04:27:35.301475Z" + "iopub.execute_input": "2024-04-08T19:05:11.098295Z", + "iopub.status.busy": "2024-04-08T19:05:11.097250Z", + "iopub.status.idle": "2024-04-08T19:05:11.200881Z", + "shell.execute_reply": "2024-04-08T19:05:11.200284Z" } }, "outputs": [ @@ -817,10 +817,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.304615Z", - "iopub.status.busy": "2024-04-06T04:27:35.304237Z", - "iopub.status.idle": "2024-04-06T04:27:35.316277Z", - "shell.execute_reply": "2024-04-06T04:27:35.315829Z" + "iopub.execute_input": "2024-04-08T19:05:11.203369Z", + "iopub.status.busy": "2024-04-08T19:05:11.202937Z", + "iopub.status.idle": "2024-04-08T19:05:11.215358Z", + "shell.execute_reply": "2024-04-08T19:05:11.214779Z" }, "scrolled": true }, @@ -875,10 +875,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.318303Z", - "iopub.status.busy": "2024-04-06T04:27:35.317968Z", - "iopub.status.idle": "2024-04-06T04:27:35.325434Z", - "shell.execute_reply": "2024-04-06T04:27:35.324937Z" + "iopub.execute_input": "2024-04-08T19:05:11.217681Z", + "iopub.status.busy": "2024-04-08T19:05:11.217301Z", + "iopub.status.idle": "2024-04-08T19:05:11.225343Z", + "shell.execute_reply": "2024-04-08T19:05:11.224791Z" } }, "outputs": [ @@ -982,10 +982,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.327556Z", - "iopub.status.busy": "2024-04-06T04:27:35.327202Z", - "iopub.status.idle": "2024-04-06T04:27:35.331288Z", - "shell.execute_reply": "2024-04-06T04:27:35.330846Z" + "iopub.execute_input": "2024-04-08T19:05:11.227551Z", + "iopub.status.busy": "2024-04-08T19:05:11.227260Z", + "iopub.status.idle": "2024-04-08T19:05:11.231569Z", + "shell.execute_reply": "2024-04-08T19:05:11.231018Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.333294Z", - "iopub.status.busy": "2024-04-06T04:27:35.332989Z", - "iopub.status.idle": "2024-04-06T04:27:35.338557Z", - "shell.execute_reply": "2024-04-06T04:27:35.337997Z" + "iopub.execute_input": "2024-04-08T19:05:11.233584Z", + "iopub.status.busy": "2024-04-08T19:05:11.233223Z", + "iopub.status.idle": "2024-04-08T19:05:11.238833Z", + "shell.execute_reply": "2024-04-08T19:05:11.238283Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1153,10 +1153,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.340521Z", - "iopub.status.busy": "2024-04-06T04:27:35.340225Z", - "iopub.status.idle": "2024-04-06T04:27:35.658582Z", - "shell.execute_reply": "2024-04-06T04:27:35.657998Z" + "iopub.execute_input": "2024-04-08T19:05:11.240707Z", + "iopub.status.busy": "2024-04-08T19:05:11.240530Z", + "iopub.status.idle": "2024-04-08T19:05:11.595733Z", + "shell.execute_reply": "2024-04-08T19:05:11.595243Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1210,10 +1210,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.660829Z", - "iopub.status.busy": "2024-04-06T04:27:35.660413Z", - "iopub.status.idle": "2024-04-06T04:27:35.769702Z", - "shell.execute_reply": "2024-04-06T04:27:35.769175Z" + "iopub.execute_input": "2024-04-08T19:05:11.597887Z", + "iopub.status.busy": "2024-04-08T19:05:11.597533Z", + "iopub.status.idle": "2024-04-08T19:05:11.705498Z", + "shell.execute_reply": "2024-04-08T19:05:11.704961Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1258,10 +1258,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.771757Z", - "iopub.status.busy": "2024-04-06T04:27:35.771495Z", - "iopub.status.idle": "2024-04-06T04:27:35.872918Z", - "shell.execute_reply": "2024-04-06T04:27:35.872364Z" + "iopub.execute_input": "2024-04-08T19:05:11.707806Z", + "iopub.status.busy": "2024-04-08T19:05:11.707520Z", + "iopub.status.idle": "2024-04-08T19:05:11.814276Z", + "shell.execute_reply": "2024-04-08T19:05:11.813792Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1302,10 +1302,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.875082Z", - "iopub.status.busy": "2024-04-06T04:27:35.874745Z", - "iopub.status.idle": "2024-04-06T04:27:35.975770Z", - "shell.execute_reply": "2024-04-06T04:27:35.975289Z" + "iopub.execute_input": "2024-04-08T19:05:11.816405Z", + "iopub.status.busy": "2024-04-08T19:05:11.816094Z", + "iopub.status.idle": "2024-04-08T19:05:11.920916Z", + "shell.execute_reply": "2024-04-08T19:05:11.920366Z" } }, "outputs": [ @@ -1353,10 +1353,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:35.977985Z", - "iopub.status.busy": "2024-04-06T04:27:35.977624Z", - "iopub.status.idle": "2024-04-06T04:27:35.980777Z", - "shell.execute_reply": "2024-04-06T04:27:35.980344Z" + "iopub.execute_input": "2024-04-08T19:05:11.923123Z", + "iopub.status.busy": "2024-04-08T19:05:11.922809Z", + "iopub.status.idle": "2024-04-08T19:05:11.926110Z", + "shell.execute_reply": "2024-04-08T19:05:11.925596Z" }, "nbsphinx": "hidden" }, @@ -1397,7 +1397,23 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0148687423ae4404946f694c128d1db8": { + "01548e5746cc4788b0d61577e4b012b3": { + "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": "" + } + }, + "03e98d7846af44088b8a646b91364594": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1412,15 +1428,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ae7e6d3ea8df4eb8badebf0329826518", + "layout": "IPY_MODEL_6add2113ed1541dfbdb3c42fbbce25fc", "placeholder": "​", - "style": "IPY_MODEL_2a522c81494744879ad988349f56ed6d", + "style": "IPY_MODEL_fc4736ca693444da8e5b28edfdc65bc5", "tabbable": null, "tooltip": null, - "value": " 15.9M/15.9M [00:00<00:00, 39.4MB/s]" + "value": "hyperparams.yaml: 100%" } }, - "0958ba1748ee4beca7da01e8e46a9749": { + "099362e1ce5c4a6b8d8d4c331a960d34": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1473,49 +1489,74 @@ "width": null } }, - "13d241d8b1a14cd99f8717170fdc36e9": { + "10f5629a4670476891a02ba1e61d2b9f": { "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_ca834c7b06bd4f90ba096fa6f8d7103c", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_01548e5746cc4788b0d61577e4b012b3", + "tabbable": null, + "tooltip": null, + "value": 15856877.0 } }, - "16bedf557e1f48c3b23c71d15d5ff3ae": { + "124ddd3253c4476b83146639e17d57b4": { "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_451ef1098e4b4a0cbc94307f8c54a69a", - "IPY_MODEL_ba716f0978944bb8b14f373794cf086b", - "IPY_MODEL_ffab753038714d3ca6e89b271ede7406" - ], - "layout": "IPY_MODEL_9e087829122b4cf38c2f2193a3ae9b93", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_37c6159b215f46748ce591cd48fe49f0", + "placeholder": "​", + "style": "IPY_MODEL_af4e6010ee0847468fae428d5bceb881", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "153a557fc0794ff2b1d801bc73e89764": { + "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 } }, - "202cf82f0f8a4e6ab6c44edcc4f643d4": { + "15e61f73e248421da683941c01a6ba8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1568,7 +1609,7 @@ "width": null } }, - "245795f1b8c14f0580deec4394842154": { + "170825b92f484861b53a305933672d63": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -1584,70 +1625,33 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_61797416aadf4af5bcea4adfab15ea37", - "max": 2041.0, + "layout": "IPY_MODEL_7a1b19cb87394307a53c97aa5de7ef54", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_648981cda80e4fe686afd77c841b011b", + "style": "IPY_MODEL_7c70a56bdf6c4f4e9f2f310fe97caf21", "tabbable": null, "tooltip": null, - "value": 2041.0 + "value": 3201.0 } }, - "25b2f2a091664e82b0b254933237ecda": { - "model_module": "@jupyter-widgets/base", + "1845bd7583734a468217bf0a33a4a04e": { + "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": "" } }, - "283a7fca65ce4358af3da78be675147f": { + "1d8d1f51752d4b69aa1135e17d8449e2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1662,15 +1666,41 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9b265031b424474386c724023fbceea8", + "layout": "IPY_MODEL_358c21f56ec2458089c2e7d7e5e7611d", "placeholder": "​", - "style": "IPY_MODEL_13d241d8b1a14cd99f8717170fdc36e9", + "style": "IPY_MODEL_b38f5dc0540f47bcb4f22237b8bc10b5", + "tabbable": null, + "tooltip": null, + "value": "embedding_model.ckpt: 100%" + } + }, + "1e261881c01a4c82af0950354ddbbf67": { + "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_7187ee77d34d4ee58aea3e5ef7ea0d6f", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a84cb458429f4ac9ba17b78d73eb1b9c", "tabbable": null, "tooltip": null, - "value": " 129k/129k [00:00<00:00, 2.64MB/s]" + "value": 128619.0 } }, - "2a522c81494744879ad988349f56ed6d": { + "2398940ef11744cd931d78b60fe1bf96": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1688,33 +1718,31 @@ "text_color": null } }, - "38cedb80c4884a7aa9c8360e66697385": { + "344f8176bb9c4df2b5f5c5842359b28c": { "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_87a12f7b60404561827c31d53af0eaa8", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_51ff76b0c91049ceab3634eafef03d03", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d7cff1a5c1c84b53a0f820ae0ae64f22", + "IPY_MODEL_10f5629a4670476891a02ba1e61d2b9f", + "IPY_MODEL_8227ef3e4a1d4b60bb8d49436af87b02" + ], + "layout": "IPY_MODEL_6b1d25f5e1274b7087e5be1edb44f263", "tabbable": null, - "tooltip": null, - "value": 16887676.0 + "tooltip": null } }, - "399a48d2dd4a4aeba66c8583c6cb3267": { + "358c21f56ec2458089c2e7d7e5e7611d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1767,7 +1795,7 @@ "width": null } }, - "4055580cee714e89b235223a231f6c86": { + "3656dfd1293341618ec097a837ccf824": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1820,53 +1848,7 @@ "width": null } }, - "451ef1098e4b4a0cbc94307f8c54a69a": { - "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_d64c977788594151a2a69bd522b4b4e2", - "placeholder": "​", - "style": "IPY_MODEL_b387baf48cbf40a79960c2e5e37c6dc5", - "tabbable": null, - "tooltip": null, - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "45c7d5509d354e7db551d2ee42562f4a": { - "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_25b2f2a091664e82b0b254933237ecda", - "placeholder": "​", - "style": "IPY_MODEL_bbb63db0bf1246e09f27e3fbcd6067f2", - "tabbable": null, - "tooltip": null, - "value": "label_encoder.txt: 100%" - } - }, - "4c0901a7fd664a0386f5506a5f1265c6": { + "37c6159b215f46748ce591cd48fe49f0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1919,7 +1901,25 @@ "width": null } }, - "4cb73865490e4acab5b8bd74d5733f8e": { + "3b7f0046c33247249156bee86a8ab344": { + "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 + } + }, + "3f10333ce59449f8893619a6a1200e72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1934,100 +1934,102 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_841d6e99393d4eacb2171cd893b4a9ae", + "layout": "IPY_MODEL_15e61f73e248421da683941c01a6ba8a", "placeholder": "​", - "style": "IPY_MODEL_ad0c26113b5641988b98da2e1367adc3", + "style": "IPY_MODEL_770aeb28530d4716aeceac624b99192d", "tabbable": null, "tooltip": null, - "value": "embedding_model.ckpt: 100%" + "value": " 129k/129k [00:00<00:00, 1.94MB/s]" } }, - "4edb5018fb694daab45e95ca9a2d5a86": { + "47f17e40d80540e8b9cd62de82c60b92": { "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_6b84bee63e5341e68a70a1df0644c356", + "placeholder": "​", + "style": "IPY_MODEL_702d80d4b5874038a51e0c3fe4e29c9c", + "tabbable": null, + "tooltip": null, + "value": " 16.9M/16.9M [00:00<00:00, 50.6MB/s]" } }, - "51ff76b0c91049ceab3634eafef03d03": { + "4911164b858e49c1b45c2acea166106f": { "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": "" - } - }, - "61797416aadf4af5bcea4adfab15ea37": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_c0a147631094428498d5242f0dae3a28", + "placeholder": "​", + "style": "IPY_MODEL_3b7f0046c33247249156bee86a8ab344", + "tabbable": null, + "tooltip": null, + "value": " 3.20k/3.20k [00:00<00:00, 736kB/s]" + } + }, + "49d50cf65e2243fe85ea4efa439383a4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "4a17f13f9daa4bd3a9e0f9a6bdcd54ce": { + "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_bca20dd54024424b908c519dc7c65413", + "placeholder": "​", + "style": "IPY_MODEL_6e856fb0c33a4b509b13af9f0bfa347d", + "tabbable": null, + "tooltip": null, + "value": " 2.04k/2.04k [00:00<00:00, 458kB/s]" } }, - "648981cda80e4fe686afd77c841b011b": { + "4c8c8519a91e4cf59efc5ee43887ceef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2043,7 +2045,7 @@ "description_width": "" } }, - "65cd560eff044447950b5de1dfe924a9": { + "593320329ae4474ea377016a9d84c521": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2058,91 +2060,119 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4cb73865490e4acab5b8bd74d5733f8e", - "IPY_MODEL_38cedb80c4884a7aa9c8360e66697385", - "IPY_MODEL_ba43a6d3c0eb4eae9b16f9526c18eff9" + "IPY_MODEL_03e98d7846af44088b8a646b91364594", + "IPY_MODEL_9dcd19b1766a4f0f9e62b19feae944e0", + "IPY_MODEL_4a17f13f9daa4bd3a9e0f9a6bdcd54ce" ], - "layout": "IPY_MODEL_d16f6084ce1149cd889d6a2cb15f31c5", + "layout": "IPY_MODEL_6f1084b3c29943cbae5ae582af5573d2", "tabbable": null, "tooltip": null } }, - "68b320632eea4620bbd3b735bdc34dd9": { + "605873f7b6924ba3a2b8d496de6a3f37": { "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_0958ba1748ee4beca7da01e8e46a9749", - "placeholder": "​", - "style": "IPY_MODEL_6b5d1d793a204f1ab4608428b5ba64b9", + "layout": "IPY_MODEL_099362e1ce5c4a6b8d8d4c331a960d34", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4c8c8519a91e4cf59efc5ee43887ceef", "tabbable": null, "tooltip": null, - "value": "hyperparams.yaml: 100%" - } - }, - "68b5eff6aadd4afb9e1940334ccb3d9b": { - "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": 16887676.0 } }, - "6b5d1d793a204f1ab4608428b5ba64b9": { - "model_module": "@jupyter-widgets/controls", + "6232f100dcfc4faea627bbfd5cf8d09e": { + "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 } }, - "6c5e187c01984c899378ec18b1b6f259": { + "69f610d6cd3c4ac58e8089ac7c27c111": { "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_1d8d1f51752d4b69aa1135e17d8449e2", + "IPY_MODEL_605873f7b6924ba3a2b8d496de6a3f37", + "IPY_MODEL_47f17e40d80540e8b9cd62de82c60b92" + ], + "layout": "IPY_MODEL_789cb19d03704e0b95b9af8afa82d248", + "tabbable": null, + "tooltip": null } }, - "753341b93da447daa4758e7e5a5610e7": { + "6add2113ed1541dfbdb3c42fbbce25fc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2195,7 +2225,7 @@ "width": null } }, - "77a067827f5d4c28b687f203c008e031": { + "6b1d25f5e1274b7087e5be1edb44f263": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2248,7 +2278,7 @@ "width": null } }, - "841d6e99393d4eacb2171cd893b4a9ae": { + "6b84bee63e5341e68a70a1df0644c356": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2301,7 +2331,7 @@ "width": null } }, - "87a12f7b60404561827c31d53af0eaa8": { + "6d97e6b4b14a4b088f0a5c2c2e610564": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2354,30 +2384,7 @@ "width": null } }, - "8848bb7bffb545109abca26a5ae067d7": { - "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_9f88f31de9d74eb0928335732d663561", - "placeholder": "​", - "style": "IPY_MODEL_b74e55a2c5174b88b66ecaa2389897db", - "tabbable": null, - "tooltip": null, - "value": " 2.04k/2.04k [00:00<00:00, 514kB/s]" - } - }, - "8d05285dc1f6426fba9ead2f8bc7365b": { + "6e856fb0c33a4b509b13af9f0bfa347d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2395,31 +2402,7 @@ "text_color": null } }, - "9527fc8121814f01a443e62ff927d47b": { - "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_fa3cbfeda5cd4b7fbc3c0c9e921e5864", - "IPY_MODEL_9d58780c4a6d406b98d0415c7c2a4ee5", - "IPY_MODEL_0148687423ae4404946f694c128d1db8" - ], - "layout": "IPY_MODEL_c639e0d31a954ec89eb3549596545904", - "tabbable": null, - "tooltip": null - } - }, - "9b265031b424474386c724023fbceea8": { + "6f1084b3c29943cbae5ae582af5573d2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2472,33 +2455,25 @@ "width": null } }, - "9d58780c4a6d406b98d0415c7c2a4ee5": { + "702d80d4b5874038a51e0c3fe4e29c9c": { "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_4c0901a7fd664a0386f5506a5f1265c6", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6c5e187c01984c899378ec18b1b6f259", - "tabbable": null, - "tooltip": null, - "value": 15856877.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9e087829122b4cf38c2f2193a3ae9b93": { + "7187ee77d34d4ee58aea3e5ef7ea0d6f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2551,7 +2526,25 @@ "width": null } }, - "9f88f31de9d74eb0928335732d663561": { + "770aeb28530d4716aeceac624b99192d": { + "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 + } + }, + "789cb19d03704e0b95b9af8afa82d248": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2604,7 +2597,7 @@ "width": null } }, - "a0795bde2b3a4af08cc09a2b34f5e2aa": { + "7a1b19cb87394307a53c97aa5de7ef54": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2657,25 +2650,72 @@ "width": null } }, - "ad0c26113b5641988b98da2e1367adc3": { + "7c70a56bdf6c4f4e9f2f310fe97caf21": { "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": "" + } + }, + "8227ef3e4a1d4b60bb8d49436af87b02": { + "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_fa4f7d539024400ead56c0975b429f2c", + "placeholder": "​", + "style": "IPY_MODEL_49d50cf65e2243fe85ea4efa439383a4", + "tabbable": null, + "tooltip": null, + "value": " 15.9M/15.9M [00:00<00:00, 47.3MB/s]" + } + }, + "9dcd19b1766a4f0f9e62b19feae944e0": { + "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_a2ac773a77354c65a9b27aead854f501", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1845bd7583734a468217bf0a33a4a04e", + "tabbable": null, + "tooltip": null, + "value": 2041.0 } }, - "ae7e6d3ea8df4eb8badebf0329826518": { + "a2ac773a77354c65a9b27aead854f501": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2728,85 +2768,47 @@ "width": null } }, - "ae98285a153e46948474fc4abeceddfd": { + "a84cb458429f4ac9ba17b78d73eb1b9c": { "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": "" } }, - "aece8b0eb585430bbc9c4df8054ab74b": { + "a992a72ca18a4979975cfb11417f3b00": { "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_77a067827f5d4c28b687f203c008e031", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4edb5018fb694daab45e95ca9a2d5a86", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_124ddd3253c4476b83146639e17d57b4", + "IPY_MODEL_170825b92f484861b53a305933672d63", + "IPY_MODEL_4911164b858e49c1b45c2acea166106f" + ], + "layout": "IPY_MODEL_6232f100dcfc4faea627bbfd5cf8d09e", "tabbable": null, - "tooltip": null, - "value": 128619.0 - } - }, - "b387baf48cbf40a79960c2e5e37c6dc5": { - "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 - } - }, - "b5504d0328b84005b17ed0cf71889d5b": { - "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": "" + "tooltip": null } }, - "b74e55a2c5174b88b66ecaa2389897db": { + "af4e6010ee0847468fae428d5bceb881": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2824,56 +2826,7 @@ "text_color": null } }, - "ba43a6d3c0eb4eae9b16f9526c18eff9": { - "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_753341b93da447daa4758e7e5a5610e7", - "placeholder": "​", - "style": "IPY_MODEL_ae98285a153e46948474fc4abeceddfd", - "tabbable": null, - "tooltip": null, - "value": " 16.9M/16.9M [00:00<00:00, 39.9MB/s]" - } - }, - "ba716f0978944bb8b14f373794cf086b": { - "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_a0795bde2b3a4af08cc09a2b34f5e2aa", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b5504d0328b84005b17ed0cf71889d5b", - "tabbable": null, - "tooltip": null, - "value": 3201.0 - } - }, - "bbb63db0bf1246e09f27e3fbcd6067f2": { + "b38f5dc0540f47bcb4f22237b8bc10b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2891,31 +2844,7 @@ "text_color": null } }, - "bd5cd533ee9f44c9a03ecc0071f84776": { - "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_68b320632eea4620bbd3b735bdc34dd9", - "IPY_MODEL_245795f1b8c14f0580deec4394842154", - "IPY_MODEL_8848bb7bffb545109abca26a5ae067d7" - ], - "layout": "IPY_MODEL_f6e17f0c62ca4e53a8d555529912ee89", - "tabbable": null, - "tooltip": null - } - }, - "c639e0d31a954ec89eb3549596545904": { + "bca20dd54024424b908c519dc7c65413": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2968,7 +2897,7 @@ "width": null } }, - "d16f6084ce1149cd889d6a2cb15f31c5": { + "c05dd7bca0b442bbb6c59095a6fdc7da": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3021,7 +2950,7 @@ "width": null } }, - "d64c977788594151a2a69bd522b4b4e2": { + "c0a147631094428498d5242f0dae3a28": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3074,31 +3003,7 @@ "width": null } }, - "f6af1b2285f641a2b47091704e8edf17": { - "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_45c7d5509d354e7db551d2ee42562f4a", - "IPY_MODEL_aece8b0eb585430bbc9c4df8054ab74b", - "IPY_MODEL_283a7fca65ce4358af3da78be675147f" - ], - "layout": "IPY_MODEL_4055580cee714e89b235223a231f6c86", - "tabbable": null, - "tooltip": null - } - }, - "f6e17f0c62ca4e53a8d555529912ee89": { + "ca834c7b06bd4f90ba096fa6f8d7103c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3151,7 +3056,7 @@ "width": null } }, - "fa3cbfeda5cd4b7fbc3c0c9e921e5864": { + "d7cff1a5c1c84b53a0f820ae0ae64f22": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3166,15 +3071,39 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_202cf82f0f8a4e6ab6c44edcc4f643d4", + "layout": "IPY_MODEL_c05dd7bca0b442bbb6c59095a6fdc7da", "placeholder": "​", - "style": "IPY_MODEL_68b5eff6aadd4afb9e1940334ccb3d9b", + "style": "IPY_MODEL_2398940ef11744cd931d78b60fe1bf96", "tabbable": null, "tooltip": null, "value": "classifier.ckpt: 100%" } }, - "ffab753038714d3ca6e89b271ede7406": { + "f91d49b1c62d4d839fff74899d0af511": { + "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_f98ecf4b23e04508a03cf3042be644e5", + "IPY_MODEL_1e261881c01a4c82af0950354ddbbf67", + "IPY_MODEL_3f10333ce59449f8893619a6a1200e72" + ], + "layout": "IPY_MODEL_6d97e6b4b14a4b088f0a5c2c2e610564", + "tabbable": null, + "tooltip": null + } + }, + "f98ecf4b23e04508a03cf3042be644e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3189,12 +3118,83 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_399a48d2dd4a4aeba66c8583c6cb3267", + "layout": "IPY_MODEL_3656dfd1293341618ec097a837ccf824", "placeholder": "​", - "style": "IPY_MODEL_8d05285dc1f6426fba9ead2f8bc7365b", + "style": "IPY_MODEL_153a557fc0794ff2b1d801bc73e89764", "tabbable": null, "tooltip": null, - "value": " 3.20k/3.20k [00:00<00:00, 894kB/s]" + "value": "label_encoder.txt: 100%" + } + }, + "fa4f7d539024400ead56c0975b429f2c": { + "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 + } + }, + "fc4736ca693444da8e5b28edfdc65bc5": { + "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/data_monitor.html b/master/tutorials/datalab/data_monitor.html index 4f1439c28..2d2aa7411 100644 --- a/master/tutorials/datalab/data_monitor.html +++ b/master/tutorials/datalab/data_monitor.html @@ -661,7 +661,7 @@

    1. Install and import required dependenciesdependencies = ["cleanlab", "matplotlib", "datasets"] # TODO: make sure this list is updated if "google.colab" in str(get_ipython()): # Check if it's running in Google Colab - %pip install git+https://github.com/cleanlab/cleanlab.git@e0b7615c1169c6d8fcae15be6477bd7327e82e00 + %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06 cmd = ' '.join([dep for dep in dependencies if dep != "cleanlab"]) %pip install $cmd else: @@ -1144,7 +1144,7 @@

    5. Use DataMonitor to find issues in new data

    -
    +
    @@ -1179,7 +1179,7 @@

    5. Use DataMonitor to find issues in new data

    -
    +
    @@ -1479,7 +1479,7 @@

    6. Learn more about the issues in the additional data

    diff --git a/master/tutorials/datalab/data_monitor.ipynb b/master/tutorials/datalab/data_monitor.ipynb index 49d632c47..f4ed52c9a 100644 --- a/master/tutorials/datalab/data_monitor.ipynb +++ b/master/tutorials/datalab/data_monitor.ipynb @@ -66,10 +66,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:39.124807Z", - "iopub.status.busy": "2024-04-06T04:27:39.124638Z", - "iopub.status.idle": "2024-04-06T04:27:40.230534Z", - "shell.execute_reply": "2024-04-06T04:27:40.229912Z" + "iopub.execute_input": "2024-04-08T19:05:16.056480Z", + "iopub.status.busy": "2024-04-08T19:05:16.056305Z", + "iopub.status.idle": "2024-04-08T19:05:17.226995Z", + "shell.execute_reply": "2024-04-08T19:05:17.226456Z" } }, "outputs": [], @@ -78,7 +78,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -103,10 +103,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.232938Z", - "iopub.status.busy": "2024-04-06T04:27:40.232697Z", - "iopub.status.idle": "2024-04-06T04:27:40.239199Z", - "shell.execute_reply": "2024-04-06T04:27:40.238786Z" + "iopub.execute_input": "2024-04-08T19:05:17.229650Z", + "iopub.status.busy": "2024-04-08T19:05:17.229169Z", + "iopub.status.idle": "2024-04-08T19:05:17.235852Z", + "shell.execute_reply": "2024-04-08T19:05:17.235318Z" } }, "outputs": [], @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.241352Z", - "iopub.status.busy": "2024-04-06T04:27:40.240938Z", - "iopub.status.idle": "2024-04-06T04:27:40.249415Z", - "shell.execute_reply": "2024-04-06T04:27:40.248876Z" + "iopub.execute_input": "2024-04-08T19:05:17.238153Z", + "iopub.status.busy": "2024-04-08T19:05:17.237822Z", + "iopub.status.idle": "2024-04-08T19:05:17.246365Z", + "shell.execute_reply": "2024-04-08T19:05:17.245924Z" } }, "outputs": [], @@ -334,10 +334,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.251546Z", - "iopub.status.busy": "2024-04-06T04:27:40.251172Z", - "iopub.status.idle": "2024-04-06T04:27:40.256527Z", - "shell.execute_reply": "2024-04-06T04:27:40.256119Z" + "iopub.execute_input": "2024-04-08T19:05:17.248259Z", + "iopub.status.busy": "2024-04-08T19:05:17.247938Z", + "iopub.status.idle": "2024-04-08T19:05:17.252838Z", + "shell.execute_reply": "2024-04-08T19:05:17.252440Z" } }, "outputs": [], @@ -350,10 +350,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.258644Z", - "iopub.status.busy": "2024-04-06T04:27:40.258268Z", - "iopub.status.idle": "2024-04-06T04:27:40.262114Z", - "shell.execute_reply": "2024-04-06T04:27:40.261656Z" + "iopub.execute_input": "2024-04-08T19:05:17.254810Z", + "iopub.status.busy": "2024-04-08T19:05:17.254482Z", + "iopub.status.idle": "2024-04-08T19:05:17.258040Z", + "shell.execute_reply": "2024-04-08T19:05:17.257638Z" } }, "outputs": [], @@ -431,10 +431,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.264175Z", - "iopub.status.busy": "2024-04-06T04:27:40.263808Z", - "iopub.status.idle": "2024-04-06T04:27:40.445579Z", - "shell.execute_reply": "2024-04-06T04:27:40.445093Z" + "iopub.execute_input": "2024-04-08T19:05:17.260050Z", + "iopub.status.busy": "2024-04-08T19:05:17.259858Z", + "iopub.status.idle": "2024-04-08T19:05:17.444599Z", + "shell.execute_reply": "2024-04-08T19:05:17.444055Z" } }, "outputs": [], @@ -488,10 +488,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.447977Z", - "iopub.status.busy": "2024-04-06T04:27:40.447537Z", - "iopub.status.idle": "2024-04-06T04:27:40.744638Z", - "shell.execute_reply": "2024-04-06T04:27:40.744091Z" + "iopub.execute_input": "2024-04-08T19:05:17.447058Z", + "iopub.status.busy": "2024-04-08T19:05:17.446673Z", + "iopub.status.idle": "2024-04-08T19:05:17.799282Z", + "shell.execute_reply": "2024-04-08T19:05:17.798689Z" } }, "outputs": [ @@ -534,10 +534,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.746848Z", - "iopub.status.busy": "2024-04-06T04:27:40.746525Z", - "iopub.status.idle": "2024-04-06T04:27:40.771572Z", - "shell.execute_reply": "2024-04-06T04:27:40.771006Z" + "iopub.execute_input": "2024-04-08T19:05:17.801743Z", + "iopub.status.busy": "2024-04-08T19:05:17.801312Z", + "iopub.status.idle": "2024-04-08T19:05:17.826322Z", + "shell.execute_reply": "2024-04-08T19:05:17.825758Z" } }, "outputs": [], @@ -562,10 +562,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:40.774039Z", - "iopub.status.busy": "2024-04-06T04:27:40.773664Z", - "iopub.status.idle": "2024-04-06T04:27:42.370106Z", - "shell.execute_reply": "2024-04-06T04:27:42.369478Z" + "iopub.execute_input": "2024-04-08T19:05:17.828759Z", + "iopub.status.busy": "2024-04-08T19:05:17.828309Z", + "iopub.status.idle": "2024-04-08T19:05:19.538623Z", + "shell.execute_reply": "2024-04-08T19:05:19.537994Z" } }, "outputs": [ @@ -648,10 +648,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:42.372440Z", - "iopub.status.busy": "2024-04-06T04:27:42.372129Z", - "iopub.status.idle": "2024-04-06T04:27:42.375563Z", - "shell.execute_reply": "2024-04-06T04:27:42.375038Z" + "iopub.execute_input": "2024-04-08T19:05:19.541116Z", + "iopub.status.busy": "2024-04-08T19:05:19.540792Z", + "iopub.status.idle": "2024-04-08T19:05:19.544925Z", + "shell.execute_reply": "2024-04-08T19:05:19.544368Z" } }, "outputs": [], @@ -677,10 +677,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:42.377711Z", - "iopub.status.busy": "2024-04-06T04:27:42.377396Z", - "iopub.status.idle": "2024-04-06T04:27:42.386960Z", - "shell.execute_reply": "2024-04-06T04:27:42.386393Z" + "iopub.execute_input": "2024-04-08T19:05:19.546793Z", + "iopub.status.busy": "2024-04-08T19:05:19.546613Z", + "iopub.status.idle": "2024-04-08T19:05:19.556501Z", + "shell.execute_reply": "2024-04-08T19:05:19.555938Z" } }, "outputs": [], @@ -717,17 +717,17 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:42.389082Z", - "iopub.status.busy": "2024-04-06T04:27:42.388643Z", - "iopub.status.idle": "2024-04-06T04:27:47.460848Z", - "shell.execute_reply": "2024-04-06T04:27:47.460241Z" + "iopub.execute_input": "2024-04-08T19:05:19.558684Z", + "iopub.status.busy": "2024-04-08T19:05:19.558297Z", + "iopub.status.idle": "2024-04-08T19:05:24.640462Z", + "shell.execute_reply": "2024-04-08T19:05:24.639883Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88a3f04de8e44875bbbccd2e92af964b", + "model_id": "2f47e1ed2b654c57ae612b85caf87e33", "version_major": 2, "version_minor": 0 }, @@ -787,17 +787,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:47.463019Z", - "iopub.status.busy": "2024-04-06T04:27:47.462687Z", - "iopub.status.idle": "2024-04-06T04:27:52.754644Z", - "shell.execute_reply": "2024-04-06T04:27:52.754068Z" + "iopub.execute_input": "2024-04-08T19:05:24.642651Z", + "iopub.status.busy": "2024-04-08T19:05:24.642467Z", + "iopub.status.idle": "2024-04-08T19:05:29.933144Z", + "shell.execute_reply": "2024-04-08T19:05:29.932579Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98349aa633c94f6f8de2ad9b4a7ddcc0", + "model_id": "4f0377a4700449e9a6fbad55e512beac", "version_major": 2, "version_minor": 0 }, @@ -925,10 +925,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:52.756904Z", - "iopub.status.busy": "2024-04-06T04:27:52.756572Z", - "iopub.status.idle": "2024-04-06T04:27:52.767494Z", - "shell.execute_reply": "2024-04-06T04:27:52.766969Z" + "iopub.execute_input": "2024-04-08T19:05:29.936196Z", + "iopub.status.busy": "2024-04-08T19:05:29.935831Z", + "iopub.status.idle": "2024-04-08T19:05:29.947084Z", + "shell.execute_reply": "2024-04-08T19:05:29.946554Z" } }, "outputs": [ @@ -1161,10 +1161,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:52.769625Z", - "iopub.status.busy": "2024-04-06T04:27:52.769240Z", - "iopub.status.idle": "2024-04-06T04:27:52.774775Z", - "shell.execute_reply": "2024-04-06T04:27:52.774299Z" + "iopub.execute_input": "2024-04-08T19:05:29.948874Z", + "iopub.status.busy": "2024-04-08T19:05:29.948698Z", + "iopub.status.idle": "2024-04-08T19:05:29.953956Z", + "shell.execute_reply": "2024-04-08T19:05:29.953551Z" } }, "outputs": [ @@ -1244,51 +1244,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "061ccf039e13444da59852bf8e450df8": { - "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 - } - }, - "07e9941853a644c2a8e7beb026aa8096": { - "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_83e26b89eb7147a9a08689975eaae0aa", - "max": 7.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a50c1ed63c444f5ea7ec0b51e28adfa9", - "tabbable": null, - "tooltip": null, - "value": 7.0 - } - }, - "1f1380d992af4fc9ac5092887976d69d": { + "07acb035ab04489f9387d9718ae0300d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1341,7 +1297,7 @@ "width": null } }, - "3501255b1f0e4d23b395426656d1061d": { + "128d9f5df2814c66a3994457d9355ec0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1356,15 +1312,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_1f1380d992af4fc9ac5092887976d69d", + "layout": "IPY_MODEL_1da7bf46e2e44e11bba14d6dae15180c", "placeholder": "​", - "style": "IPY_MODEL_d71ac3c63a39491d9ed58f8bbc64e388", + "style": "IPY_MODEL_b5256e93be0b4752bbb5eb28c7e6b6f3", "tabbable": null, "tooltip": null, - "value": " 7/7 [00:05<00:00,  1.33it/s]" + "value": "Streaming data, 1 sample(s) at a time: 100%" } }, - "41752ff7750c4b20be0a41d83676cda7": { + "12aaf4ab3e33466480a03090a29d35e8": { + "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 + } + }, + "16a9cd2938bc424383031abcba4a9aef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1379,33 +1353,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_4c96caaaddeb44fa8d834cd11d2bec53", + "layout": "IPY_MODEL_e08a5d72b2194307864f4fe35d00d7fd", "placeholder": "​", - "style": "IPY_MODEL_061ccf039e13444da59852bf8e450df8", + "style": "IPY_MODEL_12aaf4ab3e33466480a03090a29d35e8", "tabbable": null, "tooltip": null, "value": "Streaming data, 50 sample(s) at a time: 100%" } }, - "4c2a75ebabb6459aa6defc80f93384b4": { - "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 - } - }, - "4c96caaaddeb44fa8d834cd11d2bec53": { + "1da7bf46e2e44e11bba14d6dae15180c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1458,23 +1414,73 @@ "width": null } }, - "79ee40937e344d409eebbafc1d34af9b": { + "2f47e1ed2b654c57ae612b85caf87e33": { "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_128d9f5df2814c66a3994457d9355ec0", + "IPY_MODEL_9f28e0c0d18f40d1ac7a57021a0ec5c4", + "IPY_MODEL_ddda3b38d5674c32b89992a35a8fe972" + ], + "layout": "IPY_MODEL_b8ebaa74de964a48a3838444eb1fd68e", + "tabbable": null, + "tooltip": null + } + }, + "4ecbe2397e5f4e2fad1fea3a2a4dab1e": { + "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", - "bar_color": null, - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "80904aac151d4808b96b94cab7affb24": { + "4f0377a4700449e9a6fbad55e512beac": { + "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_16a9cd2938bc424383031abcba4a9aef", + "IPY_MODEL_f5759780d23e477cb1d346cb79ba1321", + "IPY_MODEL_cf3a3cedd097445ba4955da8f0688ad1" + ], + "layout": "IPY_MODEL_b9b22bcc90da434db4010e9e0673f277", + "tabbable": null, + "tooltip": null + } + }, + "6895df27e56b437fb02601057f040882": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1527,60 +1533,25 @@ "width": null } }, - "830d282b791f42c9a26b8731e4e4dd10": { - "model_module": "@jupyter-widgets/base", + "719c4562a8ea4c4fa3a5ad72ded78285": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "83e26b89eb7147a9a08689975eaae0aa": { + "9a88ec1f13854a04a2b9086e55a17666": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1633,54 +1604,51 @@ "width": null } }, - "88a3f04de8e44875bbbccd2e92af964b": { + "9f28e0c0d18f40d1ac7a57021a0ec5c4": { "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_91d86c05cce145ae9379b1550341fef6", - "IPY_MODEL_d26d3eb4fe95497db089feb11c392945", - "IPY_MODEL_c4a783fb9d8b484cbe929e6db7234964" - ], - "layout": "IPY_MODEL_91edbbc589e34aee8ae904e8b0a3009d", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_6895df27e56b437fb02601057f040882", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fdd6323d006e4b4aa97d49eb699a8f62", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 50.0 } }, - "91d86c05cce145ae9379b1550341fef6": { + "b5256e93be0b4752bbb5eb28c7e6b6f3": { "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_d80e7ac068684ff2922a11e8e6d52e25", - "placeholder": "​", - "style": "IPY_MODEL_99572cb4f6f14e83856554309f7cc07c", - "tabbable": null, - "tooltip": null, - "value": "Streaming data, 1 sample(s) at a time: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "91edbbc589e34aee8ae904e8b0a3009d": { + "b8ebaa74de964a48a3838444eb1fd68e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1733,49 +1701,7 @@ "width": null } }, - "98349aa633c94f6f8de2ad9b4a7ddcc0": { - "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_41752ff7750c4b20be0a41d83676cda7", - "IPY_MODEL_07e9941853a644c2a8e7beb026aa8096", - "IPY_MODEL_3501255b1f0e4d23b395426656d1061d" - ], - "layout": "IPY_MODEL_80904aac151d4808b96b94cab7affb24", - "tabbable": null, - "tooltip": null - } - }, - "99572cb4f6f14e83856554309f7cc07c": { - "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 - } - }, - "a272a4b17efd42cfbf635e41a3cf87bc": { + "b9b22bcc90da434db4010e9e0673f277": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1828,7 +1754,7 @@ "width": null } }, - "a50c1ed63c444f5ea7ec0b51e28adfa9": { + "c64971c872874384ad842ab855a0b637": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -1844,7 +1770,7 @@ "description_width": "" } }, - "c4a783fb9d8b484cbe929e6db7234964": { + "cf3a3cedd097445ba4955da8f0688ad1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1859,59 +1785,91 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_830d282b791f42c9a26b8731e4e4dd10", + "layout": "IPY_MODEL_9a88ec1f13854a04a2b9086e55a17666", "placeholder": "​", - "style": "IPY_MODEL_4c2a75ebabb6459aa6defc80f93384b4", + "style": "IPY_MODEL_719c4562a8ea4c4fa3a5ad72ded78285", "tabbable": null, "tooltip": null, - "value": " 50/50 [00:05<00:00,  9.89it/s]" + "value": " 7/7 [00:05<00:00,  1.33it/s]" } }, - "d26d3eb4fe95497db089feb11c392945": { + "ddda3b38d5674c32b89992a35a8fe972": { "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_a272a4b17efd42cfbf635e41a3cf87bc", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_79ee40937e344d409eebbafc1d34af9b", + "layout": "IPY_MODEL_07acb035ab04489f9387d9718ae0300d", + "placeholder": "​", + "style": "IPY_MODEL_4ecbe2397e5f4e2fad1fea3a2a4dab1e", "tabbable": null, "tooltip": null, - "value": 50.0 + "value": " 50/50 [00:05<00:00,  9.89it/s]" } }, - "d71ac3c63a39491d9ed58f8bbc64e388": { - "model_module": "@jupyter-widgets/controls", + "e08a5d72b2194307864f4fe35d00d7fd": { + "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 } }, - "d80e7ac068684ff2922a11e8e6d52e25": { + "e1d064cb9adb4b7fbfd292215390a0d8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1963,6 +1921,48 @@ "visibility": null, "width": null } + }, + "f5759780d23e477cb1d346cb79ba1321": { + "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_e1d064cb9adb4b7fbfd292215390a0d8", + "max": 7.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c64971c872874384ad842ab855a0b637", + "tabbable": null, + "tooltip": null, + "value": 7.0 + } + }, + "fdd6323d006e4b4aa97d49eb699a8f62": { + "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 ef080168e..aad672423 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1121,7 +1121,7 @@

    Functionality 2: Specifying nondefault arguments 
    -/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.
+/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.
   warnings.warn(
 /home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:378: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.
   warnings.warn(
@@ -1252,7 +1252,7 @@ 
    Functionality 3: Save and load Datalab objects
    -
    +
    @@ -1517,7 +1517,7 @@

    Functionality 4: Adding a custom IssueManager -{"state": {"8e9944f01dc04773b1a6f250b877bdac": {"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}}, "92a77433e43f4ff8a546985e6e38611a": {"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": ""}}, "17c3deaafb3d4e8ba7ed2d539ab37994": {"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_8e9944f01dc04773b1a6f250b877bdac", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_92a77433e43f4ff8a546985e6e38611a", "tabbable": null, "tooltip": null, "value": 132.0}}, "0df42a660b99494ba1a08fc9a8cdd1b4": {"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}}, "996d62a724d14e7f9758fa309375faff": {"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}}, "7bd4b0305523463bbf5bf70b1f791cc7": {"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_0df42a660b99494ba1a08fc9a8cdd1b4", "placeholder": "\u200b", "style": "IPY_MODEL_996d62a724d14e7f9758fa309375faff", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "375a2c62332d41db9fb6fda4e1c413b1": {"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}}, "bd9f710c62d542cf9e7b79052a8e5d74": {"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}}, "d482671c5f304dc1af9900d828daaf12": {"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_375a2c62332d41db9fb6fda4e1c413b1", "placeholder": "\u200b", "style": "IPY_MODEL_bd9f710c62d542cf9e7b79052a8e5d74", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200713627.93\u2007examples/s]"}}, "02bbc5143d3a4993a0d1f8da9de11d01": {"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}}, "d02d6b616f48472186e69f83078244aa": {"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_7bd4b0305523463bbf5bf70b1f791cc7", "IPY_MODEL_17c3deaafb3d4e8ba7ed2d539ab37994", "IPY_MODEL_d482671c5f304dc1af9900d828daaf12"], "layout": "IPY_MODEL_02bbc5143d3a4993a0d1f8da9de11d01", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"46bd17b1514d4e52ae0383c64d6489cd": {"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}}, "228185c613e04c9eac97098f61b94e68": {"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": ""}}, "1ae88ebb9093404db35f11a77eebf512": {"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_46bd17b1514d4e52ae0383c64d6489cd", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_228185c613e04c9eac97098f61b94e68", "tabbable": null, "tooltip": null, "value": 132.0}}, "0c239304d29045c1b64914dcd438f404": {"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}}, "d88ce31af6724956a7b3b62d32858b5b": {"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}}, "914ce1df641b45febd754b94855f6758": {"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_0c239304d29045c1b64914dcd438f404", "placeholder": "\u200b", "style": "IPY_MODEL_d88ce31af6724956a7b3b62d32858b5b", "tabbable": null, "tooltip": null, "value": "Saving\u2007the\u2007dataset\u2007(1/1\u2007shards):\u2007100%"}}, "ece75fa4a34244dc9e9c365738e7868a": {"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}}, "128b3b1a069d412baa8c95c1d60454a4": {"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}}, "b964e793dbdf439ab0a15c97a2a25707": {"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_ece75fa4a34244dc9e9c365738e7868a", "placeholder": "\u200b", "style": "IPY_MODEL_128b3b1a069d412baa8c95c1d60454a4", "tabbable": null, "tooltip": null, "value": "\u2007132/132\u2007[00:00<00:00,\u200712706.22\u2007examples/s]"}}, "a6fe3071a0de43bf8ba390921942df7b": {"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}}, "97cce15af35f4ce7a71bfd4e784c1928": {"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_914ce1df641b45febd754b94855f6758", "IPY_MODEL_1ae88ebb9093404db35f11a77eebf512", "IPY_MODEL_b964e793dbdf439ab0a15c97a2a25707"], "layout": "IPY_MODEL_a6fe3071a0de43bf8ba390921942df7b", "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 6f252d481..e435a28b7 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": "2024-04-06T04:27:55.167484Z", - "iopub.status.busy": "2024-04-06T04:27:55.167142Z", - "iopub.status.idle": "2024-04-06T04:27:56.268393Z", - "shell.execute_reply": "2024-04-06T04:27:56.267802Z" + "iopub.execute_input": "2024-04-08T19:05:32.399757Z", + "iopub.status.busy": "2024-04-08T19:05:32.399402Z", + "iopub.status.idle": "2024-04-08T19:05:33.528700Z", + "shell.execute_reply": "2024-04-08T19:05:33.528208Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:27:56.270921Z", - "iopub.status.busy": "2024-04-06T04:27:56.270628Z", - "iopub.status.idle": "2024-04-06T04:27:56.273710Z", - "shell.execute_reply": "2024-04-06T04:27:56.273181Z" + "iopub.execute_input": "2024-04-08T19:05:33.531324Z", + "iopub.status.busy": "2024-04-08T19:05:33.530883Z", + "iopub.status.idle": "2024-04-08T19:05:33.533905Z", + "shell.execute_reply": "2024-04-08T19:05:33.533461Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.276040Z", - "iopub.status.busy": "2024-04-06T04:27:56.275731Z", - "iopub.status.idle": "2024-04-06T04:27:56.284763Z", - "shell.execute_reply": "2024-04-06T04:27:56.284342Z" + "iopub.execute_input": "2024-04-08T19:05:33.536089Z", + "iopub.status.busy": "2024-04-08T19:05:33.535766Z", + "iopub.status.idle": "2024-04-08T19:05:33.544759Z", + "shell.execute_reply": "2024-04-08T19:05:33.544339Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.286793Z", - "iopub.status.busy": "2024-04-06T04:27:56.286479Z", - "iopub.status.idle": "2024-04-06T04:27:56.290759Z", - "shell.execute_reply": "2024-04-06T04:27:56.290351Z" + "iopub.execute_input": "2024-04-08T19:05:33.546655Z", + "iopub.status.busy": "2024-04-08T19:05:33.546328Z", + "iopub.status.idle": "2024-04-08T19:05:33.551312Z", + "shell.execute_reply": "2024-04-08T19:05:33.550798Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.292900Z", - "iopub.status.busy": "2024-04-06T04:27:56.292571Z", - "iopub.status.idle": "2024-04-06T04:27:56.470930Z", - "shell.execute_reply": "2024-04-06T04:27:56.470381Z" + "iopub.execute_input": "2024-04-08T19:05:33.553494Z", + "iopub.status.busy": "2024-04-08T19:05:33.553202Z", + "iopub.status.idle": "2024-04-08T19:05:33.734474Z", + "shell.execute_reply": "2024-04-08T19:05:33.733871Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.473159Z", - "iopub.status.busy": "2024-04-06T04:27:56.472901Z", - "iopub.status.idle": "2024-04-06T04:27:56.842173Z", - "shell.execute_reply": "2024-04-06T04:27:56.841568Z" + "iopub.execute_input": "2024-04-08T19:05:33.736929Z", + "iopub.status.busy": "2024-04-08T19:05:33.736685Z", + "iopub.status.idle": "2024-04-08T19:05:34.103381Z", + "shell.execute_reply": "2024-04-08T19:05:34.102792Z" } }, "outputs": [ @@ -569,10 +569,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.844403Z", - "iopub.status.busy": "2024-04-06T04:27:56.844070Z", - "iopub.status.idle": "2024-04-06T04:27:56.867433Z", - "shell.execute_reply": "2024-04-06T04:27:56.867005Z" + "iopub.execute_input": "2024-04-08T19:05:34.105764Z", + "iopub.status.busy": "2024-04-08T19:05:34.105419Z", + "iopub.status.idle": "2024-04-08T19:05:34.129090Z", + "shell.execute_reply": "2024-04-08T19:05:34.128647Z" } }, "outputs": [], @@ -608,10 +608,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.869442Z", - "iopub.status.busy": "2024-04-06T04:27:56.869133Z", - "iopub.status.idle": "2024-04-06T04:27:56.879921Z", - "shell.execute_reply": "2024-04-06T04:27:56.879386Z" + "iopub.execute_input": "2024-04-08T19:05:34.131072Z", + "iopub.status.busy": "2024-04-08T19:05:34.130769Z", + "iopub.status.idle": "2024-04-08T19:05:34.141791Z", + "shell.execute_reply": "2024-04-08T19:05:34.141283Z" } }, "outputs": [], @@ -642,10 +642,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:56.882178Z", - "iopub.status.busy": "2024-04-06T04:27:56.881838Z", - "iopub.status.idle": "2024-04-06T04:27:58.449402Z", - "shell.execute_reply": "2024-04-06T04:27:58.448837Z" + "iopub.execute_input": "2024-04-08T19:05:34.144081Z", + "iopub.status.busy": "2024-04-08T19:05:34.143717Z", + "iopub.status.idle": "2024-04-08T19:05:35.791854Z", + "shell.execute_reply": "2024-04-08T19:05:35.791186Z" } }, "outputs": [ @@ -709,10 +709,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.451881Z", - "iopub.status.busy": "2024-04-06T04:27:58.451481Z", - "iopub.status.idle": "2024-04-06T04:27:58.472786Z", - "shell.execute_reply": "2024-04-06T04:27:58.472247Z" + "iopub.execute_input": "2024-04-08T19:05:35.794545Z", + "iopub.status.busy": "2024-04-08T19:05:35.793952Z", + "iopub.status.idle": "2024-04-08T19:05:35.815345Z", + "shell.execute_reply": "2024-04-08T19:05:35.814802Z" } }, "outputs": [ @@ -821,10 +821,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.474943Z", - "iopub.status.busy": "2024-04-06T04:27:58.474628Z", - "iopub.status.idle": "2024-04-06T04:27:58.493726Z", - "shell.execute_reply": "2024-04-06T04:27:58.493177Z" + "iopub.execute_input": "2024-04-08T19:05:35.817441Z", + "iopub.status.busy": "2024-04-08T19:05:35.817058Z", + "iopub.status.idle": "2024-04-08T19:05:35.836184Z", + "shell.execute_reply": "2024-04-08T19:05:35.835672Z" } }, "outputs": [ @@ -910,7 +910,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n", + "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:348: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.\n", " warnings.warn(\n", "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:378: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n", " warnings.warn(\n", @@ -936,10 +936,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.495766Z", - "iopub.status.busy": "2024-04-06T04:27:58.495439Z", - "iopub.status.idle": "2024-04-06T04:27:58.509263Z", - "shell.execute_reply": "2024-04-06T04:27:58.508829Z" + "iopub.execute_input": "2024-04-08T19:05:35.838193Z", + "iopub.status.busy": "2024-04-08T19:05:35.838020Z", + "iopub.status.idle": "2024-04-08T19:05:35.852488Z", + "shell.execute_reply": "2024-04-08T19:05:35.852042Z" } }, "outputs": [ @@ -1069,17 +1069,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.511294Z", - "iopub.status.busy": "2024-04-06T04:27:58.510970Z", - "iopub.status.idle": "2024-04-06T04:27:58.529602Z", - "shell.execute_reply": "2024-04-06T04:27:58.529063Z" + "iopub.execute_input": "2024-04-08T19:05:35.854417Z", + "iopub.status.busy": "2024-04-08T19:05:35.854247Z", + "iopub.status.idle": "2024-04-08T19:05:35.873232Z", + "shell.execute_reply": "2024-04-08T19:05:35.872705Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d02d6b616f48472186e69f83078244aa", + "model_id": "97cce15af35f4ce7a71bfd4e784c1928", "version_major": 2, "version_minor": 0 }, @@ -1115,10 +1115,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.531477Z", - "iopub.status.busy": "2024-04-06T04:27:58.531304Z", - "iopub.status.idle": "2024-04-06T04:27:58.546063Z", - "shell.execute_reply": "2024-04-06T04:27:58.545580Z" + "iopub.execute_input": "2024-04-08T19:05:35.875329Z", + "iopub.status.busy": "2024-04-08T19:05:35.874965Z", + "iopub.status.idle": "2024-04-08T19:05:35.889385Z", + "shell.execute_reply": "2024-04-08T19:05:35.888962Z" } }, "outputs": [ @@ -1236,10 +1236,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.548107Z", - "iopub.status.busy": "2024-04-06T04:27:58.547734Z", - "iopub.status.idle": "2024-04-06T04:27:58.553403Z", - "shell.execute_reply": "2024-04-06T04:27:58.552986Z" + "iopub.execute_input": "2024-04-08T19:05:35.891511Z", + "iopub.status.busy": "2024-04-08T19:05:35.891109Z", + "iopub.status.idle": "2024-04-08T19:05:35.896831Z", + "shell.execute_reply": "2024-04-08T19:05:35.896325Z" } }, "outputs": [], @@ -1296,10 +1296,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:27:58.555509Z", - "iopub.status.busy": "2024-04-06T04:27:58.555093Z", - "iopub.status.idle": "2024-04-06T04:27:58.572894Z", - "shell.execute_reply": "2024-04-06T04:27:58.572431Z" + "iopub.execute_input": "2024-04-08T19:05:35.898838Z", + "iopub.status.busy": "2024-04-08T19:05:35.898541Z", + "iopub.status.idle": "2024-04-08T19:05:35.915657Z", + "shell.execute_reply": "2024-04-08T19:05:35.915214Z" } }, "outputs": [ @@ -1431,7 +1431,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02bbc5143d3a4993a0d1f8da9de11d01": { + "0c239304d29045c1b64914dcd438f404": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1484,7 +1484,67 @@ "width": null } }, - "0df42a660b99494ba1a08fc9a8cdd1b4": { + "128b3b1a069d412baa8c95c1d60454a4": { + "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 + } + }, + "1ae88ebb9093404db35f11a77eebf512": { + "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_46bd17b1514d4e52ae0383c64d6489cd", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_228185c613e04c9eac97098f61b94e68", + "tabbable": null, + "tooltip": null, + "value": 132.0 + } + }, + "228185c613e04c9eac97098f61b94e68": { + "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": "" + } + }, + "46bd17b1514d4e52ae0383c64d6489cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1537,33 +1597,54 @@ "width": null } }, - "17c3deaafb3d4e8ba7ed2d539ab37994": { + "914ce1df641b45febd754b94855f6758": { "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_8e9944f01dc04773b1a6f250b877bdac", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_92a77433e43f4ff8a546985e6e38611a", + "layout": "IPY_MODEL_0c239304d29045c1b64914dcd438f404", + "placeholder": "​", + "style": "IPY_MODEL_d88ce31af6724956a7b3b62d32858b5b", "tabbable": null, "tooltip": null, - "value": 132.0 + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "97cce15af35f4ce7a71bfd4e784c1928": { + "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_914ce1df641b45febd754b94855f6758", + "IPY_MODEL_1ae88ebb9093404db35f11a77eebf512", + "IPY_MODEL_b964e793dbdf439ab0a15c97a2a25707" + ], + "layout": "IPY_MODEL_a6fe3071a0de43bf8ba390921942df7b", + "tabbable": null, + "tooltip": null } }, - "375a2c62332d41db9fb6fda4e1c413b1": { + "a6fe3071a0de43bf8ba390921942df7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1616,7 +1697,7 @@ "width": null } }, - "7bd4b0305523463bbf5bf70b1f791cc7": { + "b964e793dbdf439ab0a15c97a2a25707": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1631,15 +1712,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0df42a660b99494ba1a08fc9a8cdd1b4", + "layout": "IPY_MODEL_ece75fa4a34244dc9e9c365738e7868a", "placeholder": "​", - "style": "IPY_MODEL_996d62a724d14e7f9758fa309375faff", + "style": "IPY_MODEL_128b3b1a069d412baa8c95c1d60454a4", "tabbable": null, "tooltip": null, - "value": "Saving the dataset (1/1 shards): 100%" + "value": " 132/132 [00:00<00:00, 12706.22 examples/s]" + } + }, + "d88ce31af6724956a7b3b62d32858b5b": { + "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 } }, - "8e9944f01dc04773b1a6f250b877bdac": { + "ece75fa4a34244dc9e9c365738e7868a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1691,105 +1790,6 @@ "visibility": null, "width": null } - }, - "92a77433e43f4ff8a546985e6e38611a": { - "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": "" - } - }, - "996d62a724d14e7f9758fa309375faff": { - "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 - } - }, - "bd9f710c62d542cf9e7b79052a8e5d74": { - "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 - } - }, - "d02d6b616f48472186e69f83078244aa": { - "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_7bd4b0305523463bbf5bf70b1f791cc7", - "IPY_MODEL_17c3deaafb3d4e8ba7ed2d539ab37994", - "IPY_MODEL_d482671c5f304dc1af9900d828daaf12" - ], - "layout": "IPY_MODEL_02bbc5143d3a4993a0d1f8da9de11d01", - "tabbable": null, - "tooltip": null - } - }, - "d482671c5f304dc1af9900d828daaf12": { - "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_375a2c62332d41db9fb6fda4e1c413b1", - "placeholder": "​", - "style": "IPY_MODEL_bd9f710c62d542cf9e7b79052a8e5d74", - "tabbable": null, - "tooltip": null, - "value": " 132/132 [00:00<00:00, 13627.93 examples/s]" - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 8fd460cb9..f898d2d07 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": "2024-04-06T04:28:01.140999Z", - "iopub.status.busy": "2024-04-06T04:28:01.140833Z", - "iopub.status.idle": "2024-04-06T04:28:02.262000Z", - "shell.execute_reply": "2024-04-06T04:28:02.261355Z" + "iopub.execute_input": "2024-04-08T19:05:38.472111Z", + "iopub.status.busy": "2024-04-08T19:05:38.471945Z", + "iopub.status.idle": "2024-04-08T19:05:39.585652Z", + "shell.execute_reply": "2024-04-08T19:05:39.585065Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:28:02.264649Z", - "iopub.status.busy": "2024-04-06T04:28:02.264228Z", - "iopub.status.idle": "2024-04-06T04:28:02.267145Z", - "shell.execute_reply": "2024-04-06T04:28:02.266735Z" + "iopub.execute_input": "2024-04-08T19:05:39.588303Z", + "iopub.status.busy": "2024-04-08T19:05:39.588056Z", + "iopub.status.idle": "2024-04-08T19:05:39.591474Z", + "shell.execute_reply": "2024-04-08T19:05:39.590968Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.269196Z", - "iopub.status.busy": "2024-04-06T04:28:02.268920Z", - "iopub.status.idle": "2024-04-06T04:28:02.278242Z", - "shell.execute_reply": "2024-04-06T04:28:02.277783Z" + "iopub.execute_input": "2024-04-08T19:05:39.593440Z", + "iopub.status.busy": "2024-04-08T19:05:39.593184Z", + "iopub.status.idle": "2024-04-08T19:05:39.602136Z", + "shell.execute_reply": "2024-04-08T19:05:39.601699Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.280171Z", - "iopub.status.busy": "2024-04-06T04:28:02.279844Z", - "iopub.status.idle": "2024-04-06T04:28:02.284049Z", - "shell.execute_reply": "2024-04-06T04:28:02.283641Z" + "iopub.execute_input": "2024-04-08T19:05:39.604078Z", + "iopub.status.busy": "2024-04-08T19:05:39.603759Z", + "iopub.status.idle": "2024-04-08T19:05:39.608027Z", + "shell.execute_reply": "2024-04-08T19:05:39.607640Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.286098Z", - "iopub.status.busy": "2024-04-06T04:28:02.285775Z", - "iopub.status.idle": "2024-04-06T04:28:02.470065Z", - "shell.execute_reply": "2024-04-06T04:28:02.469547Z" + "iopub.execute_input": "2024-04-08T19:05:39.609991Z", + "iopub.status.busy": "2024-04-08T19:05:39.609678Z", + "iopub.status.idle": "2024-04-08T19:05:39.789021Z", + "shell.execute_reply": "2024-04-08T19:05:39.788487Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.472692Z", - "iopub.status.busy": "2024-04-06T04:28:02.472227Z", - "iopub.status.idle": "2024-04-06T04:28:02.862171Z", - "shell.execute_reply": "2024-04-06T04:28:02.861570Z" + "iopub.execute_input": "2024-04-08T19:05:39.791556Z", + "iopub.status.busy": "2024-04-08T19:05:39.791137Z", + "iopub.status.idle": "2024-04-08T19:05:40.161861Z", + "shell.execute_reply": "2024-04-08T19:05:40.161278Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.864346Z", - "iopub.status.busy": "2024-04-06T04:28:02.864021Z", - "iopub.status.idle": "2024-04-06T04:28:02.868011Z", - "shell.execute_reply": "2024-04-06T04:28:02.867360Z" + "iopub.execute_input": "2024-04-08T19:05:40.164104Z", + "iopub.status.busy": "2024-04-08T19:05:40.163680Z", + "iopub.status.idle": "2024-04-08T19:05:40.166534Z", + "shell.execute_reply": "2024-04-08T19:05:40.165994Z" } }, "outputs": [], @@ -602,10 +602,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.870143Z", - "iopub.status.busy": "2024-04-06T04:28:02.869708Z", - "iopub.status.idle": "2024-04-06T04:28:02.905119Z", - "shell.execute_reply": "2024-04-06T04:28:02.904575Z" + "iopub.execute_input": "2024-04-08T19:05:40.168465Z", + "iopub.status.busy": "2024-04-08T19:05:40.168160Z", + "iopub.status.idle": "2024-04-08T19:05:40.204106Z", + "shell.execute_reply": "2024-04-08T19:05:40.203573Z" } }, "outputs": [ @@ -647,10 +647,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:02.907153Z", - "iopub.status.busy": "2024-04-06T04:28:02.906777Z", - "iopub.status.idle": "2024-04-06T04:28:04.522763Z", - "shell.execute_reply": "2024-04-06T04:28:04.522160Z" + "iopub.execute_input": "2024-04-08T19:05:40.206126Z", + "iopub.status.busy": "2024-04-08T19:05:40.205831Z", + "iopub.status.idle": "2024-04-08T19:05:41.861121Z", + "shell.execute_reply": "2024-04-08T19:05:41.860499Z" } }, "outputs": [ @@ -711,10 +711,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.525220Z", - "iopub.status.busy": "2024-04-06T04:28:04.524729Z", - "iopub.status.idle": "2024-04-06T04:28:04.543944Z", - "shell.execute_reply": "2024-04-06T04:28:04.543486Z" + "iopub.execute_input": "2024-04-08T19:05:41.863740Z", + "iopub.status.busy": "2024-04-08T19:05:41.863237Z", + "iopub.status.idle": "2024-04-08T19:05:41.882767Z", + "shell.execute_reply": "2024-04-08T19:05:41.882319Z" } }, "outputs": [ @@ -842,10 +842,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.545971Z", - "iopub.status.busy": "2024-04-06T04:28:04.545633Z", - "iopub.status.idle": "2024-04-06T04:28:04.551859Z", - "shell.execute_reply": "2024-04-06T04:28:04.551428Z" + "iopub.execute_input": "2024-04-08T19:05:41.884850Z", + "iopub.status.busy": "2024-04-08T19:05:41.884544Z", + "iopub.status.idle": "2024-04-08T19:05:41.890743Z", + "shell.execute_reply": "2024-04-08T19:05:41.890221Z" } }, "outputs": [ @@ -956,10 +956,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.553727Z", - "iopub.status.busy": "2024-04-06T04:28:04.553467Z", - "iopub.status.idle": "2024-04-06T04:28:04.559055Z", - "shell.execute_reply": "2024-04-06T04:28:04.558635Z" + "iopub.execute_input": "2024-04-08T19:05:41.892670Z", + "iopub.status.busy": "2024-04-08T19:05:41.892376Z", + "iopub.status.idle": "2024-04-08T19:05:41.897724Z", + "shell.execute_reply": "2024-04-08T19:05:41.897233Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.561010Z", - "iopub.status.busy": "2024-04-06T04:28:04.560761Z", - "iopub.status.idle": "2024-04-06T04:28:04.571254Z", - "shell.execute_reply": "2024-04-06T04:28:04.570824Z" + "iopub.execute_input": "2024-04-08T19:05:41.899726Z", + "iopub.status.busy": "2024-04-08T19:05:41.899419Z", + "iopub.status.idle": "2024-04-08T19:05:41.909317Z", + "shell.execute_reply": "2024-04-08T19:05:41.908907Z" } }, "outputs": [ @@ -1221,10 +1221,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.573178Z", - "iopub.status.busy": "2024-04-06T04:28:04.572871Z", - "iopub.status.idle": "2024-04-06T04:28:04.581776Z", - "shell.execute_reply": "2024-04-06T04:28:04.581258Z" + "iopub.execute_input": "2024-04-08T19:05:41.911355Z", + "iopub.status.busy": "2024-04-08T19:05:41.911042Z", + "iopub.status.idle": "2024-04-08T19:05:41.919704Z", + "shell.execute_reply": "2024-04-08T19:05:41.919301Z" } }, "outputs": [ @@ -1340,10 +1340,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.583843Z", - "iopub.status.busy": "2024-04-06T04:28:04.583519Z", - "iopub.status.idle": "2024-04-06T04:28:04.590173Z", - "shell.execute_reply": "2024-04-06T04:28:04.589690Z" + "iopub.execute_input": "2024-04-08T19:05:41.921572Z", + "iopub.status.busy": "2024-04-08T19:05:41.921400Z", + "iopub.status.idle": "2024-04-08T19:05:41.928223Z", + "shell.execute_reply": "2024-04-08T19:05:41.927710Z" }, "scrolled": true }, @@ -1468,10 +1468,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:04.592157Z", - "iopub.status.busy": "2024-04-06T04:28:04.591831Z", - "iopub.status.idle": "2024-04-06T04:28:04.600966Z", - "shell.execute_reply": "2024-04-06T04:28:04.600530Z" + "iopub.execute_input": "2024-04-08T19:05:41.930139Z", + "iopub.status.busy": "2024-04-08T19:05:41.929966Z", + "iopub.status.idle": "2024-04-08T19:05:41.939155Z", + "shell.execute_reply": "2024-04-08T19:05:41.938682Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/image.html b/master/tutorials/datalab/image.html index 1d5cf1c7b..d990358da 100644 --- a/master/tutorials/datalab/image.html +++ b/master/tutorials/datalab/image.html @@ -694,21 +694,21 @@

    2. Fetch and normalize the Fashion-MNIST dataset 
    -Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 50.7MB/s]
-Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 57.7MB/s]
+Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 44.8MB/s]
+Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 24.7MB/s]

    -
    +
    -
    +
    [3]:
@@ -763,7 +763,7 @@ 
    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.

    @@ -1012,8 +1012,8 @@

    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: 4.940
-epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.775
+epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.943
+epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.606
 Computing feature embeddings ...

    @@ -1021,7 +1021,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1035,7 +1035,7 @@

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

    -
    +
    @@ -1044,8 +1044,8 @@

    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: 4.963
-epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.588
+epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.715
+epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.438
 Computing feature embeddings ...

    @@ -1053,7 +1053,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1067,7 +1067,7 @@

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

    -
    +
    @@ -1076,8 +1076,8 @@

    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: 4.770
-epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.552
+epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.569
+epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.479
 Computing feature embeddings ...

    @@ -1085,7 +1085,7 @@

    5. Compute out-of-sample predicted probabilities and feature embeddings
    -
    +
    @@ -1099,7 +1099,7 @@

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

    -
    +
    @@ -1171,7 +1171,7 @@

    7. Use cleanlab to find issues

    -
    +
    @@ -2003,35 +2003,35 @@

    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 @@ -2059,7 +2059,7 @@

    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 5dbc9174f..e5222c4f6 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": "2024-04-06T04:28:06.972790Z", - "iopub.status.busy": "2024-04-06T04:28:06.972606Z", - "iopub.status.idle": "2024-04-06T04:28:09.752985Z", - "shell.execute_reply": "2024-04-06T04:28:09.752449Z" + "iopub.execute_input": "2024-04-08T19:05:44.455598Z", + "iopub.status.busy": "2024-04-08T19:05:44.455183Z", + "iopub.status.idle": "2024-04-08T19:05:47.311029Z", + "shell.execute_reply": "2024-04-08T19:05:47.310392Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:09.755512Z", - "iopub.status.busy": "2024-04-06T04:28:09.755090Z", - "iopub.status.idle": "2024-04-06T04:28:09.758597Z", - "shell.execute_reply": "2024-04-06T04:28:09.758078Z" + "iopub.execute_input": "2024-04-08T19:05:47.313575Z", + "iopub.status.busy": "2024-04-08T19:05:47.313285Z", + "iopub.status.idle": "2024-04-08T19:05:47.317021Z", + "shell.execute_reply": "2024-04-08T19:05:47.316573Z" } }, "outputs": [], @@ -152,10 +152,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:09.760622Z", - "iopub.status.busy": "2024-04-06T04:28:09.760210Z", - "iopub.status.idle": "2024-04-06T04:28:16.295173Z", - "shell.execute_reply": "2024-04-06T04:28:16.294666Z" + "iopub.execute_input": "2024-04-08T19:05:47.319045Z", + "iopub.status.busy": "2024-04-08T19:05:47.318749Z", + "iopub.status.idle": "2024-04-08T19:06:52.995269Z", + "shell.execute_reply": "2024-04-08T19:06:52.994727Z" } }, "outputs": [ @@ -172,7 +172,7 @@ "output_type": "stream", "text": [ "\r", - "Downloading data: 34%|███▍ | 10.5M/30.9M [00:00<00:00, 27.2MB/s]" + "Downloading data: 34%|███▍ | 10.5M/30.9M [00:00<00:00, 25.5MB/s]" ] }, { @@ -180,7 +180,7 @@ "output_type": "stream", "text": [ "\r", - "Downloading data: 68%|██████▊ | 21.0M/30.9M [00:00<00:00, 45.5MB/s]" + "Downloading data: 68%|██████▊ | 21.0M/30.9M [00:00<00:00, 40.1MB/s]" ] }, { @@ -188,7 +188,15 @@ "output_type": "stream", "text": [ "\r", - "Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 50.7MB/s]" + "Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 52.4MB/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Downloading data: 100%|██████████| 30.9M/30.9M [00:00<00:00, 44.8MB/s]" ] }, { @@ -211,7 +219,15 @@ "output_type": "stream", "text": [ "\r", - "Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 57.7MB/s]" + "Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 25.1MB/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Downloading data: 100%|██████████| 5.18M/5.18M [00:00<00:00, 24.7MB/s]" ] }, { @@ -224,7 +240,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63f80561a9aa4de6b4236289ee6db555", + "model_id": "0eb8f33ec6a4418f82faef40016d8087", "version_major": 2, "version_minor": 0 }, @@ -238,7 +254,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "230d8fb552244430b2da0fe2e463b928", + "model_id": "ae20b62666184b608438ba88eb80b458", "version_major": 2, "version_minor": 0 }, @@ -280,10 +296,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:16.297360Z", - "iopub.status.busy": "2024-04-06T04:28:16.297063Z", - "iopub.status.idle": "2024-04-06T04:28:16.301215Z", - "shell.execute_reply": "2024-04-06T04:28:16.300641Z" + "iopub.execute_input": "2024-04-08T19:06:52.997585Z", + "iopub.status.busy": "2024-04-08T19:06:52.997287Z", + "iopub.status.idle": "2024-04-08T19:06:53.001006Z", + "shell.execute_reply": "2024-04-08T19:06:53.000502Z" } }, "outputs": [ @@ -308,17 +324,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:16.303464Z", - "iopub.status.busy": "2024-04-06T04:28:16.303065Z", - "iopub.status.idle": "2024-04-06T04:28:27.419249Z", - "shell.execute_reply": "2024-04-06T04:28:27.418699Z" + "iopub.execute_input": "2024-04-08T19:06:53.003093Z", + "iopub.status.busy": "2024-04-08T19:06:53.002719Z", + "iopub.status.idle": "2024-04-08T19:07:04.104000Z", + "shell.execute_reply": "2024-04-08T19:07:04.103381Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cb88b48c32274a769a3b6b2f97e0d5f7", + "model_id": "a30eaea1846c404db6f70144bebe00f7", "version_major": 2, "version_minor": 0 }, @@ -356,10 +372,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:27.421638Z", - "iopub.status.busy": "2024-04-06T04:28:27.421406Z", - "iopub.status.idle": "2024-04-06T04:28:46.417249Z", - "shell.execute_reply": "2024-04-06T04:28:46.416725Z" + "iopub.execute_input": "2024-04-08T19:07:04.106690Z", + "iopub.status.busy": "2024-04-08T19:07:04.106430Z", + "iopub.status.idle": "2024-04-08T19:07:23.674623Z", + "shell.execute_reply": "2024-04-08T19:07:23.674042Z" } }, "outputs": [], @@ -392,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:46.419936Z", - "iopub.status.busy": "2024-04-06T04:28:46.419547Z", - "iopub.status.idle": "2024-04-06T04:28:46.425574Z", - "shell.execute_reply": "2024-04-06T04:28:46.424934Z" + "iopub.execute_input": "2024-04-08T19:07:23.677241Z", + "iopub.status.busy": "2024-04-08T19:07:23.676879Z", + "iopub.status.idle": "2024-04-08T19:07:23.682812Z", + "shell.execute_reply": "2024-04-08T19:07:23.682328Z" } }, "outputs": [], @@ -433,10 +449,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:46.427717Z", - "iopub.status.busy": "2024-04-06T04:28:46.427319Z", - "iopub.status.idle": "2024-04-06T04:28:46.431436Z", - "shell.execute_reply": "2024-04-06T04:28:46.430914Z" + "iopub.execute_input": "2024-04-08T19:07:23.684649Z", + "iopub.status.busy": "2024-04-08T19:07:23.684464Z", + "iopub.status.idle": "2024-04-08T19:07:23.688678Z", + "shell.execute_reply": "2024-04-08T19:07:23.688276Z" }, "nbsphinx": "hidden" }, @@ -573,10 +589,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:46.433507Z", - "iopub.status.busy": "2024-04-06T04:28:46.433326Z", - "iopub.status.idle": "2024-04-06T04:28:46.442750Z", - "shell.execute_reply": "2024-04-06T04:28:46.442165Z" + "iopub.execute_input": "2024-04-08T19:07:23.690513Z", + "iopub.status.busy": "2024-04-08T19:07:23.690330Z", + "iopub.status.idle": "2024-04-08T19:07:23.699065Z", + "shell.execute_reply": "2024-04-08T19:07:23.698630Z" }, "nbsphinx": "hidden" }, @@ -701,10 +717,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:46.445207Z", - "iopub.status.busy": "2024-04-06T04:28:46.444709Z", - "iopub.status.idle": "2024-04-06T04:28:46.471457Z", - "shell.execute_reply": "2024-04-06T04:28:46.470848Z" + "iopub.execute_input": "2024-04-08T19:07:23.701049Z", + "iopub.status.busy": "2024-04-08T19:07:23.700760Z", + "iopub.status.idle": "2024-04-08T19:07:23.727875Z", + "shell.execute_reply": "2024-04-08T19:07:23.727411Z" } }, "outputs": [], @@ -741,10 +757,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:28:46.474138Z", - "iopub.status.busy": "2024-04-06T04:28:46.473706Z", - "iopub.status.idle": "2024-04-06T04:29:19.284754Z", - "shell.execute_reply": "2024-04-06T04:29:19.284157Z" + "iopub.execute_input": "2024-04-08T19:07:23.730030Z", + "iopub.status.busy": "2024-04-08T19:07:23.729684Z", + "iopub.status.idle": "2024-04-08T19:07:55.733757Z", + "shell.execute_reply": "2024-04-08T19:07:55.733140Z" } }, "outputs": [ @@ -760,21 +776,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.940\n" + "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.943\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.775\n", + "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.606\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb284719132643f39c4d672792c9676b", + "model_id": "655fc2039c2f44018f7440a3d2e07a6e", "version_major": 2, "version_minor": 0 }, @@ -795,7 +811,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37a89191786645eba770cde499fad762", + "model_id": "eb19d31ef8f4468b9f0d6aabf630dc05", "version_major": 2, "version_minor": 0 }, @@ -818,21 +834,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.963\n" + "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.715\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.588\n", + "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.438\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "494d4dec9fa34cdba034f2e400df4b4a", + "model_id": "e9e0602cde8548a3b201f1e9d8487610", "version_major": 2, "version_minor": 0 }, @@ -853,7 +869,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "062e939e5df64531b55a3a30c9808fb8", + "model_id": "d17335f81c08473e83f57bcb91f327b6", "version_major": 2, "version_minor": 0 }, @@ -876,21 +892,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.770\n" + "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.569\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.552\n", + "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.479\n", "Computing feature embeddings ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab2472f1e90442db812aa71752ac3895", + "model_id": "e3f2da6ea95d4ffea5b61ce1470cc963", "version_major": 2, "version_minor": 0 }, @@ -911,7 +927,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92a29e7b86344f558ffc62c421168612", + "model_id": "2dcb1da8fc8746cb8d2bcbb0b5fbca34", "version_major": 2, "version_minor": 0 }, @@ -990,10 +1006,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:29:19.287441Z", - "iopub.status.busy": "2024-04-06T04:29:19.287051Z", - "iopub.status.idle": "2024-04-06T04:29:19.302983Z", - "shell.execute_reply": "2024-04-06T04:29:19.302570Z" + "iopub.execute_input": "2024-04-08T19:07:55.736291Z", + "iopub.status.busy": "2024-04-08T19:07:55.736050Z", + "iopub.status.idle": "2024-04-08T19:07:55.752483Z", + "shell.execute_reply": "2024-04-08T19:07:55.752090Z" } }, "outputs": [], @@ -1018,10 +1034,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:29:19.304958Z", - "iopub.status.busy": "2024-04-06T04:29:19.304585Z", - "iopub.status.idle": "2024-04-06T04:29:19.752664Z", - "shell.execute_reply": "2024-04-06T04:29:19.752054Z" + "iopub.execute_input": "2024-04-08T19:07:55.754639Z", + "iopub.status.busy": "2024-04-08T19:07:55.754247Z", + "iopub.status.idle": "2024-04-08T19:07:56.225872Z", + "shell.execute_reply": "2024-04-08T19:07:56.225395Z" } }, "outputs": [], @@ -1041,10 +1057,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:29:19.755148Z", - "iopub.status.busy": "2024-04-06T04:29:19.754955Z", - "iopub.status.idle": "2024-04-06T04:32:56.986962Z", - "shell.execute_reply": "2024-04-06T04:32:56.986440Z" + "iopub.execute_input": "2024-04-08T19:07:56.228202Z", + "iopub.status.busy": "2024-04-08T19:07:56.227984Z", + "iopub.status.idle": "2024-04-08T19:11:33.257164Z", + "shell.execute_reply": "2024-04-08T19:11:33.256565Z" } }, "outputs": [ @@ -1092,7 +1108,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abb6597d1f0d493ebdf894332caa8c19", + "model_id": "4776167486a64076a84a01784b59af15", "version_major": 2, "version_minor": 0 }, @@ -1131,10 +1147,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:56.989470Z", - "iopub.status.busy": "2024-04-06T04:32:56.988908Z", - "iopub.status.idle": "2024-04-06T04:32:57.444100Z", - "shell.execute_reply": "2024-04-06T04:32:57.443568Z" + "iopub.execute_input": "2024-04-08T19:11:33.260388Z", + "iopub.status.busy": "2024-04-08T19:11:33.259500Z", + "iopub.status.idle": "2024-04-08T19:11:33.713025Z", + "shell.execute_reply": "2024-04-08T19:11:33.712497Z" } }, "outputs": [ @@ -1275,10 +1291,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:57.446845Z", - "iopub.status.busy": "2024-04-06T04:32:57.446433Z", - "iopub.status.idle": "2024-04-06T04:32:57.509789Z", - "shell.execute_reply": "2024-04-06T04:32:57.509348Z" + "iopub.execute_input": "2024-04-08T19:11:33.715813Z", + "iopub.status.busy": "2024-04-08T19:11:33.715344Z", + "iopub.status.idle": "2024-04-08T19:11:33.777262Z", + "shell.execute_reply": "2024-04-08T19:11:33.776681Z" } }, "outputs": [ @@ -1382,10 +1398,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:57.511949Z", - "iopub.status.busy": "2024-04-06T04:32:57.511620Z", - "iopub.status.idle": "2024-04-06T04:32:57.520211Z", - "shell.execute_reply": "2024-04-06T04:32:57.519795Z" + "iopub.execute_input": "2024-04-08T19:11:33.779830Z", + "iopub.status.busy": "2024-04-08T19:11:33.779438Z", + "iopub.status.idle": "2024-04-08T19:11:33.787969Z", + "shell.execute_reply": "2024-04-08T19:11:33.787446Z" } }, "outputs": [ @@ -1515,10 +1531,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:57.522295Z", - "iopub.status.busy": "2024-04-06T04:32:57.521974Z", - "iopub.status.idle": "2024-04-06T04:32:57.526386Z", - "shell.execute_reply": "2024-04-06T04:32:57.525967Z" + "iopub.execute_input": "2024-04-08T19:11:33.789917Z", + "iopub.status.busy": "2024-04-08T19:11:33.789625Z", + "iopub.status.idle": "2024-04-08T19:11:33.794135Z", + "shell.execute_reply": "2024-04-08T19:11:33.793681Z" }, "nbsphinx": "hidden" }, @@ -1564,10 +1580,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:57.528397Z", - "iopub.status.busy": "2024-04-06T04:32:57.528074Z", - "iopub.status.idle": "2024-04-06T04:32:58.038770Z", - "shell.execute_reply": "2024-04-06T04:32:58.038215Z" + "iopub.execute_input": "2024-04-08T19:11:33.796263Z", + "iopub.status.busy": "2024-04-08T19:11:33.795812Z", + "iopub.status.idle": "2024-04-08T19:11:34.299373Z", + "shell.execute_reply": "2024-04-08T19:11:34.298797Z" } }, "outputs": [ @@ -1602,10 +1618,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.041027Z", - "iopub.status.busy": "2024-04-06T04:32:58.040695Z", - "iopub.status.idle": "2024-04-06T04:32:58.049092Z", - "shell.execute_reply": "2024-04-06T04:32:58.048655Z" + "iopub.execute_input": "2024-04-08T19:11:34.301515Z", + "iopub.status.busy": "2024-04-08T19:11:34.301218Z", + "iopub.status.idle": "2024-04-08T19:11:34.309668Z", + "shell.execute_reply": "2024-04-08T19:11:34.309228Z" } }, "outputs": [ @@ -1772,10 +1788,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.051269Z", - "iopub.status.busy": "2024-04-06T04:32:58.050928Z", - "iopub.status.idle": "2024-04-06T04:32:58.057851Z", - "shell.execute_reply": "2024-04-06T04:32:58.057431Z" + "iopub.execute_input": "2024-04-08T19:11:34.311963Z", + "iopub.status.busy": "2024-04-08T19:11:34.311521Z", + "iopub.status.idle": "2024-04-08T19:11:34.319034Z", + "shell.execute_reply": "2024-04-08T19:11:34.318393Z" }, "nbsphinx": "hidden" }, @@ -1851,10 +1867,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.059904Z", - "iopub.status.busy": "2024-04-06T04:32:58.059580Z", - "iopub.status.idle": "2024-04-06T04:32:58.506533Z", - "shell.execute_reply": "2024-04-06T04:32:58.505933Z" + "iopub.execute_input": "2024-04-08T19:11:34.321092Z", + "iopub.status.busy": "2024-04-08T19:11:34.320907Z", + "iopub.status.idle": "2024-04-08T19:11:34.790785Z", + "shell.execute_reply": "2024-04-08T19:11:34.790182Z" } }, "outputs": [ @@ -1891,10 +1907,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.509305Z", - "iopub.status.busy": "2024-04-06T04:32:58.508958Z", - "iopub.status.idle": "2024-04-06T04:32:58.524162Z", - "shell.execute_reply": "2024-04-06T04:32:58.523724Z" + "iopub.execute_input": "2024-04-08T19:11:34.793183Z", + "iopub.status.busy": "2024-04-08T19:11:34.792809Z", + "iopub.status.idle": "2024-04-08T19:11:34.808943Z", + "shell.execute_reply": "2024-04-08T19:11:34.808399Z" } }, "outputs": [ @@ -2051,10 +2067,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.526434Z", - "iopub.status.busy": "2024-04-06T04:32:58.526030Z", - "iopub.status.idle": "2024-04-06T04:32:58.531557Z", - "shell.execute_reply": "2024-04-06T04:32:58.531133Z" + "iopub.execute_input": "2024-04-08T19:11:34.811183Z", + "iopub.status.busy": "2024-04-08T19:11:34.810852Z", + "iopub.status.idle": "2024-04-08T19:11:34.816363Z", + "shell.execute_reply": "2024-04-08T19:11:34.815916Z" }, "nbsphinx": "hidden" }, @@ -2099,10 +2115,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:58.533452Z", - "iopub.status.busy": "2024-04-06T04:32:58.533191Z", - "iopub.status.idle": "2024-04-06T04:32:59.000364Z", - "shell.execute_reply": "2024-04-06T04:32:58.999817Z" + "iopub.execute_input": "2024-04-08T19:11:34.818213Z", + "iopub.status.busy": "2024-04-08T19:11:34.817896Z", + "iopub.status.idle": "2024-04-08T19:11:35.279403Z", + "shell.execute_reply": "2024-04-08T19:11:35.278877Z" } }, "outputs": [ @@ -2184,10 +2200,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.002842Z", - "iopub.status.busy": "2024-04-06T04:32:59.002637Z", - "iopub.status.idle": "2024-04-06T04:32:59.012216Z", - "shell.execute_reply": "2024-04-06T04:32:59.011529Z" + "iopub.execute_input": "2024-04-08T19:11:35.281879Z", + "iopub.status.busy": "2024-04-08T19:11:35.281665Z", + "iopub.status.idle": "2024-04-08T19:11:35.291700Z", + "shell.execute_reply": "2024-04-08T19:11:35.291178Z" } }, "outputs": [ @@ -2315,10 +2331,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.014637Z", - "iopub.status.busy": "2024-04-06T04:32:59.014438Z", - "iopub.status.idle": "2024-04-06T04:32:59.020221Z", - "shell.execute_reply": "2024-04-06T04:32:59.019653Z" + "iopub.execute_input": "2024-04-08T19:11:35.293927Z", + "iopub.status.busy": "2024-04-08T19:11:35.293728Z", + "iopub.status.idle": "2024-04-08T19:11:35.299690Z", + "shell.execute_reply": "2024-04-08T19:11:35.299147Z" }, "nbsphinx": "hidden" }, @@ -2355,10 +2371,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.022440Z", - "iopub.status.busy": "2024-04-06T04:32:59.022245Z", - "iopub.status.idle": "2024-04-06T04:32:59.229238Z", - "shell.execute_reply": "2024-04-06T04:32:59.228716Z" + "iopub.execute_input": "2024-04-08T19:11:35.301832Z", + "iopub.status.busy": "2024-04-08T19:11:35.301636Z", + "iopub.status.idle": "2024-04-08T19:11:35.505219Z", + "shell.execute_reply": "2024-04-08T19:11:35.504754Z" } }, "outputs": [ @@ -2400,10 +2416,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.231415Z", - "iopub.status.busy": "2024-04-06T04:32:59.231132Z", - "iopub.status.idle": "2024-04-06T04:32:59.239395Z", - "shell.execute_reply": "2024-04-06T04:32:59.238963Z" + "iopub.execute_input": "2024-04-08T19:11:35.507136Z", + "iopub.status.busy": "2024-04-08T19:11:35.506967Z", + "iopub.status.idle": "2024-04-08T19:11:35.514273Z", + "shell.execute_reply": "2024-04-08T19:11:35.513838Z" } }, "outputs": [ @@ -2428,47 +2444,47 @@ " \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, @@ -2489,10 +2505,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.241500Z", - "iopub.status.busy": "2024-04-06T04:32:59.241175Z", - "iopub.status.idle": "2024-04-06T04:32:59.438012Z", - "shell.execute_reply": "2024-04-06T04:32:59.437410Z" + "iopub.execute_input": "2024-04-08T19:11:35.515941Z", + "iopub.status.busy": "2024-04-08T19:11:35.515782Z", + "iopub.status.idle": "2024-04-08T19:11:35.714780Z", + "shell.execute_reply": "2024-04-08T19:11:35.714213Z" } }, "outputs": [ @@ -2532,10 +2548,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:32:59.440559Z", - "iopub.status.busy": "2024-04-06T04:32:59.440204Z", - "iopub.status.idle": "2024-04-06T04:32:59.444637Z", - "shell.execute_reply": "2024-04-06T04:32:59.444201Z" + "iopub.execute_input": "2024-04-08T19:11:35.716901Z", + "iopub.status.busy": "2024-04-08T19:11:35.716713Z", + "iopub.status.idle": "2024-04-08T19:11:35.721189Z", + "shell.execute_reply": "2024-04-08T19:11:35.720773Z" }, "nbsphinx": "hidden" }, @@ -2572,60 +2588,25 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01a761c23187462489caed4c9ca92b4a": { - "model_module": "@jupyter-widgets/base", + "0211903c89794ee8b6917c76a058c50e": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "01d6c6d5c4544426af39a2a43bf4efd5": { + "051beaa23ca4413db29dc6c87c6eb69c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2678,60 +2659,49 @@ "width": null } }, - "052ba7dd44914624bc95867853cefd91": { - "model_module": "@jupyter-widgets/base", + "08fafba5db2c49c199f47a5f187bd97d": { + "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/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_2d19bf757fc54a04bea20bd6a7bfe3bd", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0ba9b8ac89734f8aa6f7b25158ce1c26", + "tabbable": null, + "tooltip": null, + "value": 10000.0 + } + }, + "0ba9b8ac89734f8aa6f7b25158ce1c26": { + "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": "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": "" } }, - "062e939e5df64531b55a3a30c9808fb8": { + "0eb8f33ec6a4418f82faef40016d8087": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2746,16 +2716,39 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_69040f6c457a458dad3f64ae864e2abc", - "IPY_MODEL_856c1d7d86824b298f7b89334a62b627", - "IPY_MODEL_e36a0c3800fd4b46a397695a9464376d" + "IPY_MODEL_5ce79bcbda6944e0a843326c3542493d", + "IPY_MODEL_e19c6ab20a44441b82013b108be2dfe5", + "IPY_MODEL_f070a30dde5346f9a173c14652288a61" ], - "layout": "IPY_MODEL_18c03d0fbf7e486fa980759c0f1d203c", + "layout": "IPY_MODEL_f229cd5bb5604bc2bbb59bbf7c81d777", "tabbable": null, "tooltip": null } }, - "07e1a5656606425a8fa445a2a98b32db": { + "1489dff3fa9f4025b92de6f3b08f875e": { + "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_2f250b135ef04e06b65dc22fa2cbe7ea", + "placeholder": "​", + "style": "IPY_MODEL_608e038942dc41a99c6a0a85532e4742", + "tabbable": null, + "tooltip": null, + "value": "100%" + } + }, + "1518031392e944988beab26f0ec70ea0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2771,56 +2764,84 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_01a761c23187462489caed4c9ca92b4a", + "layout": "IPY_MODEL_34b68eb2ae824e30b199e73d4a24f9d7", "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_44469fd7a84845fabbb26081bc4f71e6", + "style": "IPY_MODEL_934292ec1d1f47d7bfc3a64e97e22164", "tabbable": null, "tooltip": null, "value": 40.0 } }, - "0898a61434e8435fb6d38cda818ecf42": { + "1595a1d05b1744eeb8405298fae08359": { "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_fb2af0144f1742c1a26396a9b5fe4c73", + "placeholder": "​", + "style": "IPY_MODEL_b809fa6c8639462db220a761b405d58d", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "0c49ec751aa74bdeb0da999541e70abf": { + "1a475bdd7b6b4cffa0aa39c22e228634": { "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_716dbef584c74a10be95d49134e35c36", - "placeholder": "​", - "style": "IPY_MODEL_d53fb7bf594d407c9e842abf6174a266", + "layout": "IPY_MODEL_72ef0108d5814322b3ca06bd5220584b", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee8869c02a164612932f45b84d319cd2", "tabbable": null, "tooltip": null, - "value": "Generating test split: 100%" + "value": 40.0 + } + }, + "1bf8c6f4bc3e4633b0c368f433ba214d": { + "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 } }, - "0ffda52749f54b41b5d0955c9e1f987d": { + "1bf974982e4e4b2481c1302de4d0bb3b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2873,7 +2894,7 @@ "width": null } }, - "10d5b8ef6dd44439b5792f3b9229a7b3": { + "1d72b73af5474a01bc6184c102001f9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2926,83 +2947,69 @@ "width": null } }, - "154d43bd379742748defa9f93f6dec4c": { + "1feb2d3d69ac43679423195bacfa218a": { "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_59b99f3d2363470f95240494b60426e7", - "placeholder": "​", - "style": "IPY_MODEL_e630ccc4aafc46e1994dc57c453efa4c", - "tabbable": null, - "tooltip": null, - "value": " 10000/10000 [00:00<00:00, 250653.12 examples/s]" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "18c03d0fbf7e486fa980759c0f1d203c": { - "model_module": "@jupyter-widgets/base", + "2021e36f762d452aa27e529207b4d1fa": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "228bd7d078cf45699f3b012c267f6eb0": { + "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_eafbc6640e3840d6acb39b13445654da", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7c1f0a8a503e4e499f398654a3f934e5", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "18f4e2fb304e4207a8582f3b68e46f1b": { + "248aafd9fcd240928857d2c42d281abd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3055,57 +3062,7 @@ "width": null } }, - "19d828f4e1d84ef2ac773bc529b40946": { - "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": "" - } - }, - "19dc464653b042e1b8ba1ff4aa4f3231": { - "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": "" - } - }, - "19ea4bc4cf5345209bfc0f43ac43a390": { - "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 - } - }, - "1aaabe82fd3748eeb69937660d87bde7": { + "27d17868cbd6451ebcf8d7c7951e2a77": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3120,15 +3077,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_bcf53273c9a94359bf468708176a7fb9", + "layout": "IPY_MODEL_61971c6d7922459a9b1ed0c9c414cdca", "placeholder": "​", - "style": "IPY_MODEL_eda57d1ce07e4fb08048f276454d81c4", + "style": "IPY_MODEL_49cb378542644b368738ecd332b0a762", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 67.82it/s]" + "value": " 40/40 [00:00<00:00, 69.64it/s]" } }, - "1bc46611a5134fa6bf3a2e1d7329d9e1": { + "28e8e7e456ea491db9b2409a1bb6ee44": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -3143,38 +3100,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_0ffda52749f54b41b5d0955c9e1f987d", + "layout": "IPY_MODEL_dd462dfa7f9949738f0551ce01718b0d", "placeholder": "​", - "style": "IPY_MODEL_19ea4bc4cf5345209bfc0f43ac43a390", + "style": "IPY_MODEL_512b4394b0e84a1d9765ebc108a8dbdf", "tabbable": null, "tooltip": null, "value": "100%" } }, - "1d0f443e388d4ac3aa919649a0bd9f6d": { - "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_d6430b8f25744f0fbc6b7c7b994b06d4", - "placeholder": "​", - "style": "IPY_MODEL_868c933ad87744c3aa05cb4cd929090f", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.96it/s]" - } - }, - "1f1036a8b18842dfa18443fdf59a95dd": { + "2c4e61dacdec449ab2e9fa6b6459f6bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3227,7 +3161,7 @@ "width": null } }, - "21a93b6159174e5c99baaca057c26509": { + "2d19bf757fc54a04bea20bd6a7bfe3bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3280,25 +3214,7 @@ "width": null } }, - "224bb80ea8ed446ebd6daabd22c3e867": { - "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 - } - }, - "230d8fb552244430b2da0fe2e463b928": { + "2dcb1da8fc8746cb8d2bcbb0b5fbca34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3313,57 +3229,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0c49ec751aa74bdeb0da999541e70abf", - "IPY_MODEL_a8de67324e69481aa6f71eebd240332f", - "IPY_MODEL_154d43bd379742748defa9f93f6dec4c" + "IPY_MODEL_3b1df04f38894cdbb06719a2c258a28e", + "IPY_MODEL_bbab2e26c4fd423dacd41b5823f1e144", + "IPY_MODEL_ed9f2e8995144260afc1a776c3c728c6" ], - "layout": "IPY_MODEL_052ba7dd44914624bc95867853cefd91", + "layout": "IPY_MODEL_1bf974982e4e4b2481c1302de4d0bb3b", "tabbable": null, "tooltip": null } }, - "23a4c60ebe91433cabd5be6f58108e1f": { - "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_677944caba4f4f68bedc7737bb773d16", - "placeholder": "​", - "style": "IPY_MODEL_baa12a5e11064c04934b7f2251564311", - "tabbable": null, - "tooltip": null, - "value": " 40/40 [00:00<00:00, 63.99it/s]" - } - }, - "2a60a0c90b1b4f46a02f1662dd4c96e4": { - "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 - } - }, - "2b93072870f04a1daa0f16d389336ebc": { + "2e43d2f9905b4786bd2429abe71f9c11": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3416,7 +3291,7 @@ "width": null } }, - "318a2a3cc2dd449d87d92d835336d9d3": { + "2f250b135ef04e06b65dc22fa2cbe7ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3469,43 +3344,30 @@ "width": null } }, - "36f95cffa25d460ba500ec7de91933d4": { - "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 - } - }, - "374d6a83e7564f3a99a8e43b41595fa9": { + "2f426faf084c41288d432aad9a0cc67b": { "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_56fb1e127c0d4778aa58bf70adf9e292", + "placeholder": "​", + "style": "IPY_MODEL_6e2675fc6ccc4cd59366cae5f0c9e353", + "tabbable": null, + "tooltip": null, + "value": "Generating test split: 100%" } }, - "37a89191786645eba770cde499fad762": { + "340e7303a6ad4cd192d14fd90e0362a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3520,16 +3382,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_1bc46611a5134fa6bf3a2e1d7329d9e1", - "IPY_MODEL_c0d6bc39047b4081aa9475148f266798", - "IPY_MODEL_1d0f443e388d4ac3aa919649a0bd9f6d" + "IPY_MODEL_c09a89ed151f484cbc846804d8c80a5e", + "IPY_MODEL_f44238836eba40dd8365f2683b775574", + "IPY_MODEL_45ba61488f3e42d9b1983a76067096be" ], - "layout": "IPY_MODEL_dcee1ad305e747b396286c15af30cc5f", + "layout": "IPY_MODEL_c4823f24e46c4225a1be6685c385e2de", "tabbable": null, "tooltip": null } }, - "391330f212e94180b7c1d3f8b79364a2": { + "34b68eb2ae824e30b199e73d4a24f9d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3582,25 +3444,30 @@ "width": null } }, - "3cb626be1259490498bdc43c229957d5": { + "3b1df04f38894cdbb06719a2c258a28e": { "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_3ffe5eadabde4ba7bc1bf7d6584058f9", + "placeholder": "​", + "style": "IPY_MODEL_2021e36f762d452aa27e529207b4d1fa", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "432d3960ddab4b78b5489f18eb6be184": { + "3d509074899549a5988580659a594acd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3618,41 +3485,101 @@ "text_color": null } }, - "44469fd7a84845fabbb26081bc4f71e6": { - "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": "" - } - }, - "45f3fe0616ef4b6d9187942fe63d092e": { - "model_module": "@jupyter-widgets/controls", + "3ffe5eadabde4ba7bc1bf7d6584058f9": { + "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", + "_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 + } + }, + "4488e3c1bbcf4f18b659b04ab234f3ec": { + "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 } }, - "494d4dec9fa34cdba034f2e400df4b4a": { + "45ba61488f3e42d9b1983a76067096be": { + "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_051beaa23ca4413db29dc6c87c6eb69c", + "placeholder": "​", + "style": "IPY_MODEL_0211903c89794ee8b6917c76a058c50e", + "tabbable": null, + "tooltip": null, + "value": " 2/2 [00:00<00:00, 584.25it/s]" + } + }, + "4776167486a64076a84a01784b59af15": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -3667,16 +3594,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4ec9881e872043c7af778c7f3fe8b2b3", - "IPY_MODEL_79d4ea75e0b24546ab4c3e15dcd17fe0", - "IPY_MODEL_b37c27a865bd41238ca6e848152182c4" + "IPY_MODEL_1595a1d05b1744eeb8405298fae08359", + "IPY_MODEL_bf0f538039594aa7aba3502ccbb1866a", + "IPY_MODEL_b413bfe75faf4e83809cfbd3e9c0f441" ], - "layout": "IPY_MODEL_2b93072870f04a1daa0f16d389336ebc", + "layout": "IPY_MODEL_5b71bc4833f84223a9bfc19a48c54d73", "tabbable": null, "tooltip": null } }, - "4df2995557604f368c17b7801a8aa7a3": { + "49cb378542644b368738ecd332b0a762": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -3694,76 +3621,43 @@ "text_color": null } }, - "4ec9881e872043c7af778c7f3fe8b2b3": { - "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_61c55f2451eb4c4e864fb94997319e79", - "placeholder": "​", - "style": "IPY_MODEL_c2f27d13f7af40c4938b2ff919248bc7", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "567dbef175174a0bb698e0c117bdefd8": { + "4ce7b5e8a49e4daf8752a8cfb2c049a6": { "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_bb928bde160d418985c159d23435d896", - "placeholder": "​", - "style": "IPY_MODEL_224bb80ea8ed446ebd6daabd22c3e867", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "58016631c51a4c2bab081a3fcb222ce6": { + "512b4394b0e84a1d9765ebc108a8dbdf": { "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_8665b261d85149c79feba0ad3dc5a0fe", - "placeholder": "​", - "style": "IPY_MODEL_374d6a83e7564f3a99a8e43b41595fa9", - "tabbable": null, - "tooltip": null, - "value": "Generating train split: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "58b8b6bed5f84d05ade00f2d3773b9e5": { + "5299f330a8b6448cbae432e24bc2bbd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3816,7 +3710,7 @@ "width": null } }, - "59b99f3d2363470f95240494b60426e7": { + "56fb1e127c0d4778aa58bf70adf9e292": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3869,33 +3763,7 @@ "width": null } }, - "5cc1b33d526e4c2ea671e6aa4a807101": { - "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_f7dcc2439e854d37a27bed7fba490b03", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8cb9becccf604e7d912917ceb837d524", - "tabbable": null, - "tooltip": null, - "value": 2.0 - } - }, - "61c55f2451eb4c4e864fb94997319e79": { + "57413d25cf8b43a59d135e9b7f386c8b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -3948,57 +3816,56 @@ "width": null } }, - "61e1bb7f02a4489dbe279ece48634ba8": { + "57501a6ea66843e69eb94c16fd71dd26": { "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_b87839e9f74c433ab8102575e86bcd7b", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_fd142fc752ad437dac5e121ea0a962e7", + "layout": "IPY_MODEL_f430353dd08d430fba9b508f111e2cba", + "placeholder": "​", + "style": "IPY_MODEL_3d509074899549a5988580659a594acd", "tabbable": null, "tooltip": null, - "value": 40.0 + "value": " 60000/60000 [00:11<00:00, 7101.30 examples/s]" } }, - "63f80561a9aa4de6b4236289ee6db555": { + "5b300800d44b4998a13101625e6be790": { "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_58016631c51a4c2bab081a3fcb222ce6", - "IPY_MODEL_8dd48fae4b9146fc9977d90131836405", - "IPY_MODEL_a92e3ef11d2c4d27a1956ce5946d7509" - ], - "layout": "IPY_MODEL_b5d8fdd0cad64e94955d5536ac4dad32", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_8458fd5694574fd397fa3ed659cf9d21", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_822bcec6c7dc47839c1fd0d48be8aafa", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": 40.0 } }, - "677944caba4f4f68bedc7737bb773d16": { + "5b71bc4833f84223a9bfc19a48c54d73": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4051,7 +3918,7 @@ "width": null } }, - "69040f6c457a458dad3f64ae864e2abc": { + "5ce79bcbda6944e0a843326c3542493d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4066,15 +3933,67 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_cf59e27052914f59bf2cb3f866f7faf0", + "layout": "IPY_MODEL_713a34eee7ad43ca9e8faa69d5fedcd6", "placeholder": "​", - "style": "IPY_MODEL_b8708f41587841e587390f8e235ba687", + "style": "IPY_MODEL_4ce7b5e8a49e4daf8752a8cfb2c049a6", "tabbable": null, "tooltip": null, - "value": "100%" + "value": "Generating train split: 100%" + } + }, + "5df8c0f13e114da5a54feb67563465d2": { + "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 + } + }, + "608e038942dc41a99c6a0a85532e4742": { + "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 + } + }, + "60b1f65ee807482daeb5f7b6893651bb": { + "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": "" } }, - "698bb2e60fd546c7a86f3c097d682c43": { + "61971c6d7922459a9b1ed0c9c414cdca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4127,7 +4046,7 @@ "width": null } }, - "6aa2909e4e954f21a2e9b886e37872f9": { + "61ffaf9c2a1245668b4f2c0c3cec4cf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4180,60 +4099,47 @@ "width": null } }, - "716dbef584c74a10be95d49134e35c36": { - "model_module": "@jupyter-widgets/base", + "655fc2039c2f44018f7440a3d2e07a6e": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "_model_module": "@jupyter-widgets/base", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", + "_model_name": "HBoxModel", "_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": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_28e8e7e456ea491db9b2409a1bb6ee44", + "IPY_MODEL_1518031392e944988beab26f0ec70ea0", + "IPY_MODEL_cb4e85dad70c4bb69fa58d8fa9da1fd9" + ], + "layout": "IPY_MODEL_5299f330a8b6448cbae432e24bc2bbd2", + "tabbable": null, + "tooltip": null + } + }, + "6718ecc2d5994895aff73d3a70d4daa4": { + "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": "" } }, - "758003aac78a4e89a0dd59c12bfd4499": { + "68df804c48394616884c2cfd1f17c6d3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4248,15 +4154,33 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_75938d156efb49d681d43a2189e81adf", + "layout": "IPY_MODEL_1d72b73af5474a01bc6184c102001f9f", "placeholder": "​", - "style": "IPY_MODEL_45f3fe0616ef4b6d9187942fe63d092e", + "style": "IPY_MODEL_bb9b4b065b90414cab93989f1cf870db", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 63.71it/s]" + } + }, + "6e2675fc6ccc4cd59366cae5f0c9e353": { + "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 } }, - "75938d156efb49d681d43a2189e81adf": { + "6efaaef2d3fa4de3b05d6ab4ce2b1b80": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4309,49 +4233,7 @@ "width": null } }, - "75c5b3367f7243e08df5201fed89d767": { - "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": "" - } - }, - "79d4ea75e0b24546ab4c3e15dcd17fe0": { - "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_bc9ac33beb964c04b7e19f6a1e1a6eec", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_841bd0118c39439da9678bf5209d9393", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "7b9c395b1818449285ae7e333c19aed1": { + "7134548633584aebb32846399e998599": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4404,25 +4286,7 @@ "width": null } }, - "7edd891321db4e908b5ed0d68bdddab0": { - "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 - } - }, - "801329626f1a493bb35bed548fee56d1": { + "713a34eee7ad43ca9e8faa69d5fedcd6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4475,23 +4339,30 @@ "width": null } }, - "841bd0118c39439da9678bf5209d9393": { + "72e848187eb646c9ae2eb5c3c13a1b5b": { "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_6efaaef2d3fa4de3b05d6ab4ce2b1b80", + "placeholder": "​", + "style": "IPY_MODEL_9376be5b85774e909a6da2348e1e3ac7", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "84941543352247e1ba0307848d584119": { + "72ef0108d5814322b3ca06bd5220584b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4544,33 +4415,7 @@ "width": null } }, - "856c1d7d86824b298f7b89334a62b627": { - "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_1f1036a8b18842dfa18443fdf59a95dd", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ea07905d53eb44ab85413ddd0eb1103b", - "tabbable": null, - "tooltip": null, - "value": 40.0 - } - }, - "8665b261d85149c79feba0ad3dc5a0fe": { + "753491adc5dc4a15b482f01444260ff6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4623,25 +4468,7 @@ "width": null } }, - "868c933ad87744c3aa05cb4cd929090f": { - "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 - } - }, - "89b15967dfb6452597bba424994c0aed": { + "7692ec62386c4b5ea0e3da9a9d6aaab3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4694,7 +4521,7 @@ "width": null } }, - "8a36d05bb1a644cf890db7ec5c1979a1": { + "76fe935e94da4fe49c9fff42053d9f08": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -4747,76 +4574,41 @@ "width": null } }, - "8cb9becccf604e7d912917ceb837d524": { + "7859aae2967f4d8982962ef94210548f": { "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 } }, - "8cf1e91b3283461d9e7f008fa37fff9f": { - "model_module": "@jupyter-widgets/base", + "7c1f0a8a503e4e499f398654a3f934e5": { + "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": "" } }, - "8dd48fae4b9146fc9977d90131836405": { + "7cb2476470d344aebe942210b6aee3ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -4832,40 +4624,53 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_a3362f365ba145b7a2e7a9bab08c112e", - "max": 60000.0, + "layout": "IPY_MODEL_cab2229300b043cf885deb986c9d5f5d", + "max": 40.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_75c5b3367f7243e08df5201fed89d767", + "style": "IPY_MODEL_a79b0ad4d31c46fab5f329886f96aab7", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 40.0 } }, - "8fc744a4ac8745b8846e23dbc6bcb863": { + "7df899c21e114a00beea9fabc687baff": { "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_21a93b6159174e5c99baaca057c26509", - "placeholder": "​", - "style": "IPY_MODEL_4df2995557604f368c17b7801a8aa7a3", - "tabbable": null, - "tooltip": null, - "value": "100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "7fca1639267a4153bd7fb80d23c652ea": { + "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 } }, - "903744741edd4db481eac7c207012baf": { + "8040c639d3ba48d7baa187303a949a5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -4880,104 +4685,206 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_318a2a3cc2dd449d87d92d835336d9d3", + "layout": "IPY_MODEL_9ca9e07ed9664701a1f4fc752659aba7", "placeholder": "​", - "style": "IPY_MODEL_36f95cffa25d460ba500ec7de91933d4", + "style": "IPY_MODEL_d5de18c08b9a4f0fb3bae9ffcb6e2608", "tabbable": null, "tooltip": null, - "value": "100%" + "value": " 40/40 [00:00<00:00, 68.31it/s]" } }, - "915b6ef632714224897e7967dd8fed40": { + "822bcec6c7dc47839c1fd0d48be8aafa": { "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": "" } }, - "92a29e7b86344f558ffc62c421168612": { - "model_module": "@jupyter-widgets/controls", + "8458fd5694574fd397fa3ed659cf9d21": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_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_567dbef175174a0bb698e0c117bdefd8", - "IPY_MODEL_61e1bb7f02a4489dbe279ece48634ba8", - "IPY_MODEL_b52404a02e14446b9eb8d9c9687553ab" - ], - "layout": "IPY_MODEL_58b8b6bed5f84d05ade00f2d3773b9e5", - "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 } }, - "99b1fdbd6a654220895926ef07fb0a65": { - "model_module": "@jupyter-widgets/controls", + "853c7b5eeb0944a5903419f543447327": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", + "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", - "_model_name": "HBoxModel", + "_model_name": "LayoutModel", "_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_9ec788f231ad4c5da7b9ca1152c1d44b", - "IPY_MODEL_5cc1b33d526e4c2ea671e6aa4a807101", - "IPY_MODEL_ec8926923d6642cc965b8aea46f7cdb2" - ], - "layout": "IPY_MODEL_10d5b8ef6dd44439b5792f3b9229a7b3", - "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 + } + }, + "87d3d0fca9104cfab810183eee2ef94c": { + "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 } }, - "9c6eb45703d9488c8455e7918665d719": { + "934292ec1d1f47d7bfc3a64e97e22164": { "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_c977511679bc4e89a67b20d915a938ff", - "placeholder": "​", - "style": "IPY_MODEL_915b6ef632714224897e7967dd8fed40", - "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:48<00:00, 1364.59it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "9e2f25346ff041f0a43792feaf401889": { + "9376be5b85774e909a6da2348e1e3ac7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -4995,46 +4902,104 @@ "text_color": null } }, - "9ec788f231ad4c5da7b9ca1152c1d44b": { + "9ad282371d5e4b848fe0fbcf04f86eb4": { "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_6aa2909e4e954f21a2e9b886e37872f9", - "placeholder": "​", - "style": "IPY_MODEL_c76ed92d69d6444fb0a07c53c18b4c0d", - "tabbable": null, - "tooltip": null, - "value": "Computing checksums: 100%" + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "9ca9e07ed9664701a1f4fc752659aba7": { + "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 } }, - "a0876f2d03024b3bb5761a0df2937cfb": { + "9cb7ee51c974488f82e9774c7de42d77": { "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_cca4bec16de941d8a1f0eeab09f1309a", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6718ecc2d5994895aff73d3a70d4daa4", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "a3362f365ba145b7a2e7a9bab08c112e": { + "9d34e8136fdb41cba0282f20fe3254b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5087,49 +5052,49 @@ "width": null } }, - "a45cdcaa39ac4e7783e3300484169c30": { + "a30eaea1846c404db6f70144bebe00f7": { "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_a7573684d96b4a809e222e1e9c978b20", + "IPY_MODEL_9cb7ee51c974488f82e9774c7de42d77", + "IPY_MODEL_57501a6ea66843e69eb94c16fd71dd26" + ], + "layout": "IPY_MODEL_87d3d0fca9104cfab810183eee2ef94c", + "tabbable": null, + "tooltip": null } }, - "a8de67324e69481aa6f71eebd240332f": { + "a5fbf7ed0a8945eba908a5b839852cc3": { "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_698bb2e60fd546c7a86f3c097d682c43", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0898a61434e8435fb6d38cda818ecf42", - "tabbable": null, - "tooltip": null, - "value": 10000.0 + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "a92e3ef11d2c4d27a1956ce5946d7509": { + "a7573684d96b4a809e222e1e9c978b20": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5144,39 +5109,31 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_801329626f1a493bb35bed548fee56d1", + "layout": "IPY_MODEL_753491adc5dc4a15b482f01444260ff6", "placeholder": "​", - "style": "IPY_MODEL_432d3960ddab4b78b5489f18eb6be184", + "style": "IPY_MODEL_7df899c21e114a00beea9fabc687baff", "tabbable": null, "tooltip": null, - "value": " 60000/60000 [00:00<00:00, 287662.14 examples/s]" + "value": "Map (num_proc=4): 100%" } }, - "ab2472f1e90442db812aa71752ac3895": { + "a79b0ad4d31c46fab5f329886f96aab7": { "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_8fc744a4ac8745b8846e23dbc6bcb863", - "IPY_MODEL_fcd3307eda42417c8c3f83f6bd1ece28", - "IPY_MODEL_1aaabe82fd3748eeb69937660d87bde7" - ], - "layout": "IPY_MODEL_cf898ee7a0764e2b9f7a0b8a17fee462", - "tabbable": null, - "tooltip": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "abb6597d1f0d493ebdf894332caa8c19": { + "ae20b62666184b608438ba88eb80b458": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -5191,16 +5148,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_903744741edd4db481eac7c207012baf", - "IPY_MODEL_fa5a714c695a458288a525b0b07ff15d", - "IPY_MODEL_9c6eb45703d9488c8455e7918665d719" + "IPY_MODEL_2f426faf084c41288d432aad9a0cc67b", + "IPY_MODEL_08fafba5db2c49c199f47a5f187bd97d", + "IPY_MODEL_cbf43ef383f248dbaf25bd28be2369f3" ], - "layout": "IPY_MODEL_7b9c395b1818449285ae7e333c19aed1", + "layout": "IPY_MODEL_2c4e61dacdec449ab2e9fa6b6459f6bb", "tabbable": null, "tooltip": null } }, - "b37c27a865bd41238ca6e848152182c4": { + "b413bfe75faf4e83809cfbd3e9c0f441": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5215,15 +5172,49 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ba458d4944864736a9229851605bc597", + "layout": "IPY_MODEL_dd125613a65d44acb8f52566b6d32b7b", "placeholder": "​", - "style": "IPY_MODEL_f3aa86d829fc4ca5bb1496d9254e9cac", + "style": "IPY_MODEL_1feb2d3d69ac43679423195bacfa218a", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 62.56it/s]" + "value": " 60000/60000 [00:47<00:00, 1395.51it/s]" } }, - "b52404a02e14446b9eb8d9c9687553ab": { + "b809fa6c8639462db220a761b405d58d": { + "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 + } + }, + "b907020aafd94e9eb016e8a6f09cd29d": { + "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": "" + } + }, + "bb1d38afd1504d6fa78a843fce81e967": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -5238,68 +5229,59 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_391330f212e94180b7c1d3f8b79364a2", + "layout": "IPY_MODEL_c0e1beb3ae784339ae2d251c3b206de1", "placeholder": "​", - "style": "IPY_MODEL_2a60a0c90b1b4f46a02f1662dd4c96e4", + "style": "IPY_MODEL_7859aae2967f4d8982962ef94210548f", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 69.41it/s]" + "value": "100%" } }, - "b5d8fdd0cad64e94955d5536ac4dad32": { - "model_module": "@jupyter-widgets/base", + "bb9b4b065b90414cab93989f1cf870db": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "bbab2e26c4fd423dacd41b5823f1e144": { + "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_853c7b5eeb0944a5903419f543447327", + "max": 40.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d25bc5458cd94e56a86b43c849fcf730", + "tabbable": null, + "tooltip": null, + "value": 40.0 } }, - "b84a380de582459fa124b9a70e1b5359": { + "be3b3d55d750477a8208878dd73e24dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5352,25 +5334,56 @@ "width": null } }, - "b8708f41587841e587390f8e235ba687": { + "bf0f538039594aa7aba3502ccbb1866a": { "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": "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_e24f52db1e9d4d00904f83f3b85ef467", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_60b1f65ee807482daeb5f7b6893651bb", + "tabbable": null, + "tooltip": null, + "value": 60000.0 + } + }, + "c09a89ed151f484cbc846804d8c80a5e": { + "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": "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_f26ae4423ac24cafa84c3645e576b7d8", + "placeholder": "​", + "style": "IPY_MODEL_5df8c0f13e114da5a54feb67563465d2", + "tabbable": null, + "tooltip": null, + "value": "Computing checksums: 100%" } }, - "b87839e9f74c433ab8102575e86bcd7b": { + "c0cef619c3e34a9aa9fe3fbde95dd0f4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5423,7 +5436,7 @@ "width": null } }, - "ba458d4944864736a9229851605bc597": { + "c0e1beb3ae784339ae2d251c3b206de1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5476,25 +5489,7 @@ "width": null } }, - "baa12a5e11064c04934b7f2251564311": { - "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 - } - }, - "bb928bde160d418985c159d23435d896": { + "c14d4f58688049a48600554f3cec71e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5547,7 +5542,7 @@ "width": null } }, - "bc9ac33beb964c04b7e19f6a1e1a6eec": { + "c4823f24e46c4225a1be6685c385e2de": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5600,7 +5595,7 @@ "width": null } }, - "bcf53273c9a94359bf468708176a7fb9": { + "cab2229300b043cf885deb986c9d5f5d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5653,69 +5648,53 @@ "width": null } }, - "c0d6bc39047b4081aa9475148f266798": { + "cb4e85dad70c4bb69fa58d8fa9da1fd9": { "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_89b15967dfb6452597bba424994c0aed", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_19dc464653b042e1b8ba1ff4aa4f3231", + "layout": "IPY_MODEL_2e43d2f9905b4786bd2429abe71f9c11", + "placeholder": "​", + "style": "IPY_MODEL_7fca1639267a4153bd7fb80d23c652ea", "tabbable": null, "tooltip": null, - "value": 40.0 - } - }, - "c2f27d13f7af40c4938b2ff919248bc7": { - "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": " 40/40 [00:00<00:00, 63.09it/s]" } }, - "c76ed92d69d6444fb0a07c53c18b4c0d": { + "cbf43ef383f248dbaf25bd28be2369f3": { "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_9d34e8136fdb41cba0282f20fe3254b5", + "placeholder": "​", + "style": "IPY_MODEL_d962cb9310644741b92e8a219b2e7104", + "tabbable": null, + "tooltip": null, + "value": " 10000/10000 [00:00<00:00, 246927.12 examples/s]" } }, - "c977511679bc4e89a67b20d915a938ff": { + "cca4bec16de941d8a1f0eeab09f1309a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5768,54 +5747,106 @@ "width": null } }, - "cb078ccfa5bd45de92c15a832146aa12": { + "d17335f81c08473e83f57bcb91f327b6": { "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_8a36d05bb1a644cf890db7ec5c1979a1", - "placeholder": "​", - "style": "IPY_MODEL_7edd891321db4e908b5ed0d68bdddab0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1489dff3fa9f4025b92de6f3b08f875e", + "IPY_MODEL_1a475bdd7b6b4cffa0aa39c22e228634", + "IPY_MODEL_68df804c48394616884c2cfd1f17c6d3" + ], + "layout": "IPY_MODEL_76fe935e94da4fe49c9fff42053d9f08", "tabbable": null, - "tooltip": null, - "value": " 60000/60000 [00:11<00:00, 6806.11 examples/s]" + "tooltip": null } }, - "cb88b48c32274a769a3b6b2f97e0d5f7": { + "d25bc5458cd94e56a86b43c849fcf730": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "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": "" + } + }, + "d5de18c08b9a4f0fb3bae9ffcb6e2608": { + "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 + } + }, + "d962cb9310644741b92e8a219b2e7104": { + "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 + } + }, + "dcc296059a18404489cb96f85970a3f1": { + "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": "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_f15c402cc4324dd7b2cf7f6985c8d4ed", - "IPY_MODEL_dfeea2e3d11f410baa4ffc746955acbf", - "IPY_MODEL_cb078ccfa5bd45de92c15a832146aa12" - ], - "layout": "IPY_MODEL_ee99b4bb138247288c3fc9cb7044cdb8", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_f3adb5d571d446d89e42b148a0e2e0dc", + "placeholder": "​", + "style": "IPY_MODEL_a5fbf7ed0a8945eba908a5b839852cc3", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "100%" } }, - "cf59e27052914f59bf2cb3f866f7faf0": { + "dd125613a65d44acb8f52566b6d32b7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5868,7 +5899,7 @@ "width": null } }, - "cf898ee7a0764e2b9f7a0b8a17fee462": { + "dd462dfa7f9949738f0551ce01718b0d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5921,25 +5952,33 @@ "width": null } }, - "d53fb7bf594d407c9e842abf6174a266": { + "e19c6ab20a44441b82013b108be2dfe5": { "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_be3b3d55d750477a8208878dd73e24dc", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f4ad6c92501549ec9b25fa93f87e6348", + "tabbable": null, + "tooltip": null, + "value": 60000.0 } }, - "d6430b8f25744f0fbc6b7c7b994b06d4": { + "e24f52db1e9d4d00904f83f3b85ef467": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -5992,60 +6031,78 @@ "width": null } }, - "d8b68b042b5241cf86f858b91a7ab195": { - "model_module": "@jupyter-widgets/base", + "e284f57ce3ea4f358e133be317a427f8": { + "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_c0cef619c3e34a9aa9fe3fbde95dd0f4", + "placeholder": "​", + "style": "IPY_MODEL_4488e3c1bbcf4f18b659b04ab234f3ec", + "tabbable": null, + "tooltip": null, + "value": " 40/40 [00:00<00:00, 55.22it/s]" + } + }, + "e3f2da6ea95d4ffea5b61ce1470cc963": { + "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_bb1d38afd1504d6fa78a843fce81e967", + "IPY_MODEL_228bd7d078cf45699f3b012c267f6eb0", + "IPY_MODEL_27d17868cbd6451ebcf8d7c7951e2a77" + ], + "layout": "IPY_MODEL_61ffaf9c2a1245668b4f2c0c3cec4cf7", + "tabbable": null, + "tooltip": null + } + }, + "e9e0602cde8548a3b201f1e9d8487610": { + "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_72e848187eb646c9ae2eb5c3c13a1b5b", + "IPY_MODEL_5b300800d44b4998a13101625e6be790", + "IPY_MODEL_8040c639d3ba48d7baa187303a949a5f" + ], + "layout": "IPY_MODEL_248aafd9fcd240928857d2c42d281abd", + "tabbable": null, + "tooltip": null } }, - "dcee1ad305e747b396286c15af30cc5f": { + "eafbc6640e3840d6acb39b13445654da": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6098,51 +6155,31 @@ "width": null } }, - "dd33a2c7592548889d520f9105c23bb8": { - "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 - } - }, - "dfeea2e3d11f410baa4ffc746955acbf": { + "eb19d31ef8f4468b9f0d6aabf630dc05": { "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_18f4e2fb304e4207a8582f3b68e46f1b", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a0876f2d03024b3bb5761a0df2937cfb", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_dcc296059a18404489cb96f85970a3f1", + "IPY_MODEL_7cb2476470d344aebe942210b6aee3ab", + "IPY_MODEL_e284f57ce3ea4f358e133be317a427f8" + ], + "layout": "IPY_MODEL_c14d4f58688049a48600554f3cec71e9", "tabbable": null, - "tooltip": null, - "value": 60000.0 + "tooltip": null } }, - "e36a0c3800fd4b46a397695a9464376d": { + "ed9f2e8995144260afc1a776c3c728c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6157,33 +6194,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_f989133be39d4b8585b24a17aa6b8612", + "layout": "IPY_MODEL_7692ec62386c4b5ea0e3da9a9d6aaab3", "placeholder": "​", - "style": "IPY_MODEL_dd33a2c7592548889d520f9105c23bb8", + "style": "IPY_MODEL_1bf8c6f4bc3e4633b0c368f433ba214d", "tabbable": null, "tooltip": null, - "value": " 40/40 [00:00<00:00, 71.69it/s]" - } - }, - "e630ccc4aafc46e1994dc57c453efa4c": { - "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": " 40/40 [00:00<00:00, 67.07it/s]" } }, - "ea07905d53eb44ab85413ddd0eb1103b": { + "ee8869c02a164612932f45b84d319cd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -6199,31 +6218,7 @@ "description_width": "" } }, - "eb284719132643f39c4d672792c9676b": { - "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_758003aac78a4e89a0dd59c12bfd4499", - "IPY_MODEL_07e1a5656606425a8fa445a2a98b32db", - "IPY_MODEL_23a4c60ebe91433cabd5be6f58108e1f" - ], - "layout": "IPY_MODEL_84941543352247e1ba0307848d584119", - "tabbable": null, - "tooltip": null - } - }, - "ec8926923d6642cc965b8aea46f7cdb2": { + "f070a30dde5346f9a173c14652288a61": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -6238,33 +6233,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b84a380de582459fa124b9a70e1b5359", + "layout": "IPY_MODEL_57413d25cf8b43a59d135e9b7f386c8b", "placeholder": "​", - "style": "IPY_MODEL_3cb626be1259490498bdc43c229957d5", + "style": "IPY_MODEL_9ad282371d5e4b848fe0fbcf04f86eb4", "tabbable": null, "tooltip": null, - "value": " 2/2 [00:00<00:00, 514.95it/s]" - } - }, - "eda57d1ce07e4fb08048f276454d81c4": { - "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": " 60000/60000 [00:00<00:00, 300464.24 examples/s]" } }, - "ee99b4bb138247288c3fc9cb7044cdb8": { + "f229cd5bb5604bc2bbb59bbf7c81d777": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6317,48 +6294,60 @@ "width": null } }, - "f15c402cc4324dd7b2cf7f6985c8d4ed": { - "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_01d6c6d5c4544426af39a2a43bf4efd5", - "placeholder": "​", - "style": "IPY_MODEL_9e2f25346ff041f0a43792feaf401889", - "tabbable": null, - "tooltip": null, - "value": "Map (num_proc=4): 100%" - } - }, - "f3aa86d829fc4ca5bb1496d9254e9cac": { - "model_module": "@jupyter-widgets/controls", + "f26ae4423ac24cafa84c3645e576b7d8": { + "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 } }, - "f7dcc2439e854d37a27bed7fba490b03": { + "f3adb5d571d446d89e42b148a0e2e0dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6411,7 +6400,7 @@ "width": null } }, - "f989133be39d4b8585b24a17aa6b8612": { + "f430353dd08d430fba9b508f111e2cba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -6464,7 +6453,7 @@ "width": null } }, - "fa5a714c695a458288a525b0b07ff15d": { + "f44238836eba40dd8365f2683b775574": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -6480,56 +6469,83 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_8cf1e91b3283461d9e7f008fa37fff9f", - "max": 60000.0, + "layout": "IPY_MODEL_7134548633584aebb32846399e998599", + "max": 2.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a45cdcaa39ac4e7783e3300484169c30", + "style": "IPY_MODEL_b907020aafd94e9eb016e8a6f09cd29d", "tabbable": null, "tooltip": null, - "value": 60000.0 + "value": 2.0 } }, - "fcd3307eda42417c8c3f83f6bd1ece28": { + "f4ad6c92501549ec9b25fa93f87e6348": { "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_d8b68b042b5241cf86f858b91a7ab195", - "max": 40.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_19d828f4e1d84ef2ac773bc529b40946", - "tabbable": null, - "tooltip": null, - "value": 40.0 + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "fd142fc752ad437dac5e121ea0a962e7": { - "model_module": "@jupyter-widgets/controls", + "fb2af0144f1742c1a26396a9b5fe4c73": { + "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 } } }, diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 43decdf02..763870823 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:02.881954Z", - "iopub.status.busy": "2024-04-06T04:33:02.881761Z", - "iopub.status.idle": "2024-04-06T04:33:03.953480Z", - "shell.execute_reply": "2024-04-06T04:33:03.952937Z" + "iopub.execute_input": "2024-04-08T19:11:39.427663Z", + "iopub.status.busy": "2024-04-08T19:11:39.427246Z", + "iopub.status.idle": "2024-04-08T19:11:40.493201Z", + "shell.execute_reply": "2024-04-08T19:11:40.492649Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:03.956075Z", - "iopub.status.busy": "2024-04-06T04:33:03.955587Z", - "iopub.status.idle": "2024-04-06T04:33:03.973883Z", - "shell.execute_reply": "2024-04-06T04:33:03.973490Z" + "iopub.execute_input": "2024-04-08T19:11:40.495661Z", + "iopub.status.busy": "2024-04-08T19:11:40.495382Z", + "iopub.status.idle": "2024-04-08T19:11:40.513938Z", + "shell.execute_reply": "2024-04-08T19:11:40.513525Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:03.975942Z", - "iopub.status.busy": "2024-04-06T04:33:03.975699Z", - "iopub.status.idle": "2024-04-06T04:33:04.012978Z", - "shell.execute_reply": "2024-04-06T04:33:04.012510Z" + "iopub.execute_input": "2024-04-08T19:11:40.515940Z", + "iopub.status.busy": "2024-04-08T19:11:40.515700Z", + "iopub.status.idle": "2024-04-08T19:11:40.560958Z", + "shell.execute_reply": "2024-04-08T19:11:40.560524Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.014896Z", - "iopub.status.busy": "2024-04-06T04:33:04.014722Z", - "iopub.status.idle": "2024-04-06T04:33:04.018157Z", - "shell.execute_reply": "2024-04-06T04:33:04.017691Z" + "iopub.execute_input": "2024-04-08T19:11:40.562932Z", + "iopub.status.busy": "2024-04-08T19:11:40.562610Z", + "iopub.status.idle": "2024-04-08T19:11:40.566002Z", + "shell.execute_reply": "2024-04-08T19:11:40.565577Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.020151Z", - "iopub.status.busy": "2024-04-06T04:33:04.019837Z", - "iopub.status.idle": "2024-04-06T04:33:04.027381Z", - "shell.execute_reply": "2024-04-06T04:33:04.026969Z" + "iopub.execute_input": "2024-04-08T19:11:40.567900Z", + "iopub.status.busy": "2024-04-08T19:11:40.567583Z", + "iopub.status.idle": "2024-04-08T19:11:40.574730Z", + "shell.execute_reply": "2024-04-08T19:11:40.574279Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.029320Z", - "iopub.status.busy": "2024-04-06T04:33:04.029148Z", - "iopub.status.idle": "2024-04-06T04:33:04.031565Z", - "shell.execute_reply": "2024-04-06T04:33:04.031152Z" + "iopub.execute_input": "2024-04-08T19:11:40.576667Z", + "iopub.status.busy": "2024-04-08T19:11:40.576404Z", + "iopub.status.idle": "2024-04-08T19:11:40.578794Z", + "shell.execute_reply": "2024-04-08T19:11:40.578358Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:04.033457Z", - "iopub.status.busy": "2024-04-06T04:33:04.033286Z", - "iopub.status.idle": "2024-04-06T04:33:07.020218Z", - "shell.execute_reply": "2024-04-06T04:33:07.019691Z" + "iopub.execute_input": "2024-04-08T19:11:40.580843Z", + "iopub.status.busy": "2024-04-08T19:11:40.580536Z", + "iopub.status.idle": "2024-04-08T19:11:43.565419Z", + "shell.execute_reply": "2024-04-08T19:11:43.564907Z" } }, "outputs": [], @@ -402,10 +402,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:07.022814Z", - "iopub.status.busy": "2024-04-06T04:33:07.022610Z", - "iopub.status.idle": "2024-04-06T04:33:07.032179Z", - "shell.execute_reply": "2024-04-06T04:33:07.031775Z" + "iopub.execute_input": "2024-04-08T19:11:43.568043Z", + "iopub.status.busy": "2024-04-08T19:11:43.567842Z", + "iopub.status.idle": "2024-04-08T19:11:43.577436Z", + "shell.execute_reply": "2024-04-08T19:11:43.577042Z" } }, "outputs": [], @@ -437,10 +437,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:07.034095Z", - "iopub.status.busy": "2024-04-06T04:33:07.033903Z", - "iopub.status.idle": "2024-04-06T04:33:08.789065Z", - "shell.execute_reply": "2024-04-06T04:33:08.788481Z" + "iopub.execute_input": "2024-04-08T19:11:43.579441Z", + "iopub.status.busy": "2024-04-08T19:11:43.579134Z", + "iopub.status.idle": "2024-04-08T19:11:45.292514Z", + "shell.execute_reply": "2024-04-08T19:11:45.291914Z" } }, "outputs": [ @@ -485,10 +485,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.792211Z", - "iopub.status.busy": "2024-04-06T04:33:08.791532Z", - "iopub.status.idle": "2024-04-06T04:33:08.814502Z", - "shell.execute_reply": "2024-04-06T04:33:08.814015Z" + "iopub.execute_input": "2024-04-08T19:11:45.296728Z", + "iopub.status.busy": "2024-04-08T19:11:45.295425Z", + "iopub.status.idle": "2024-04-08T19:11:45.320295Z", + "shell.execute_reply": "2024-04-08T19:11:45.319812Z" }, "scrolled": true }, @@ -613,10 +613,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.817077Z", - "iopub.status.busy": "2024-04-06T04:33:08.816765Z", - "iopub.status.idle": "2024-04-06T04:33:08.825617Z", - "shell.execute_reply": "2024-04-06T04:33:08.825158Z" + "iopub.execute_input": "2024-04-08T19:11:45.323669Z", + "iopub.status.busy": "2024-04-08T19:11:45.322766Z", + "iopub.status.idle": "2024-04-08T19:11:45.333647Z", + "shell.execute_reply": "2024-04-08T19:11:45.333187Z" } }, "outputs": [ @@ -720,10 +720,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.828222Z", - "iopub.status.busy": "2024-04-06T04:33:08.827849Z", - "iopub.status.idle": "2024-04-06T04:33:08.838568Z", - "shell.execute_reply": "2024-04-06T04:33:08.838097Z" + "iopub.execute_input": "2024-04-08T19:11:45.336997Z", + "iopub.status.busy": "2024-04-08T19:11:45.336094Z", + "iopub.status.idle": "2024-04-08T19:11:45.348729Z", + "shell.execute_reply": "2024-04-08T19:11:45.348256Z" } }, "outputs": [ @@ -852,10 +852,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.841680Z", - "iopub.status.busy": "2024-04-06T04:33:08.840763Z", - "iopub.status.idle": "2024-04-06T04:33:08.851889Z", - "shell.execute_reply": "2024-04-06T04:33:08.851420Z" + "iopub.execute_input": "2024-04-08T19:11:45.352087Z", + "iopub.status.busy": "2024-04-08T19:11:45.351199Z", + "iopub.status.idle": "2024-04-08T19:11:45.362031Z", + "shell.execute_reply": "2024-04-08T19:11:45.361569Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.855383Z", - "iopub.status.busy": "2024-04-06T04:33:08.854470Z", - "iopub.status.idle": "2024-04-06T04:33:08.866911Z", - "shell.execute_reply": "2024-04-06T04:33:08.866438Z" + "iopub.execute_input": "2024-04-08T19:11:45.365401Z", + "iopub.status.busy": "2024-04-08T19:11:45.364508Z", + "iopub.status.idle": "2024-04-08T19:11:45.376129Z", + "shell.execute_reply": "2024-04-08T19:11:45.375592Z" } }, "outputs": [ @@ -1083,10 +1083,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.869543Z", - "iopub.status.busy": "2024-04-06T04:33:08.869360Z", - "iopub.status.idle": "2024-04-06T04:33:08.876491Z", - "shell.execute_reply": "2024-04-06T04:33:08.875865Z" + "iopub.execute_input": "2024-04-08T19:11:45.378395Z", + "iopub.status.busy": "2024-04-08T19:11:45.378081Z", + "iopub.status.idle": "2024-04-08T19:11:45.384257Z", + "shell.execute_reply": "2024-04-08T19:11:45.383732Z" } }, "outputs": [ @@ -1170,10 +1170,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.878704Z", - "iopub.status.busy": "2024-04-06T04:33:08.878368Z", - "iopub.status.idle": "2024-04-06T04:33:08.884874Z", - "shell.execute_reply": "2024-04-06T04:33:08.884343Z" + "iopub.execute_input": "2024-04-08T19:11:45.386145Z", + "iopub.status.busy": "2024-04-08T19:11:45.385969Z", + "iopub.status.idle": "2024-04-08T19:11:45.392100Z", + "shell.execute_reply": "2024-04-08T19:11:45.391633Z" } }, "outputs": [ @@ -1266,10 +1266,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:08.887114Z", - "iopub.status.busy": "2024-04-06T04:33:08.886669Z", - "iopub.status.idle": "2024-04-06T04:33:08.893228Z", - "shell.execute_reply": "2024-04-06T04:33:08.892752Z" + "iopub.execute_input": "2024-04-08T19:11:45.394135Z", + "iopub.status.busy": "2024-04-08T19:11:45.393818Z", + "iopub.status.idle": "2024-04-08T19:11:45.399861Z", + "shell.execute_reply": "2024-04-08T19:11:45.399452Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 26071ae2b..ebf450e28 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -757,7 +757,7 @@

    2. Load and format the text dataset 
     This dataset has 10 classes.
-Classes: {'visa_or_mastercard', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'getting_spare_card', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer', 'lost_or_stolen_phone', 'supported_cards_and_currencies'}
+Classes: {'visa_or_mastercard', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', '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 a6257a523..cdfc50478 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": "2024-04-06T04:33:11.681681Z", - "iopub.status.busy": "2024-04-06T04:33:11.681132Z", - "iopub.status.idle": "2024-04-06T04:33:14.408684Z", - "shell.execute_reply": "2024-04-06T04:33:14.408170Z" + "iopub.execute_input": "2024-04-08T19:11:47.873795Z", + "iopub.status.busy": "2024-04-08T19:11:47.873616Z", + "iopub.status.idle": "2024-04-08T19:11:50.509546Z", + "shell.execute_reply": "2024-04-08T19:11:50.508929Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:33:14.411372Z", - "iopub.status.busy": "2024-04-06T04:33:14.410870Z", - "iopub.status.idle": "2024-04-06T04:33:14.414126Z", - "shell.execute_reply": "2024-04-06T04:33:14.413639Z" + "iopub.execute_input": "2024-04-08T19:11:50.512202Z", + "iopub.status.busy": "2024-04-08T19:11:50.511881Z", + "iopub.status.idle": "2024-04-08T19:11:50.515413Z", + "shell.execute_reply": "2024-04-08T19:11:50.514847Z" } }, "outputs": [], @@ -145,10 +145,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.416093Z", - "iopub.status.busy": "2024-04-06T04:33:14.415817Z", - "iopub.status.idle": "2024-04-06T04:33:14.419148Z", - "shell.execute_reply": "2024-04-06T04:33:14.418621Z" + "iopub.execute_input": "2024-04-08T19:11:50.517389Z", + "iopub.status.busy": "2024-04-08T19:11:50.517121Z", + "iopub.status.idle": "2024-04-08T19:11:50.520136Z", + "shell.execute_reply": "2024-04-08T19:11:50.519721Z" }, "nbsphinx": "hidden" }, @@ -178,10 +178,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.421094Z", - "iopub.status.busy": "2024-04-06T04:33:14.420828Z", - "iopub.status.idle": "2024-04-06T04:33:14.445821Z", - "shell.execute_reply": "2024-04-06T04:33:14.445234Z" + "iopub.execute_input": "2024-04-08T19:11:50.522130Z", + "iopub.status.busy": "2024-04-08T19:11:50.521805Z", + "iopub.status.idle": "2024-04-08T19:11:50.573099Z", + "shell.execute_reply": "2024-04-08T19:11:50.572633Z" } }, "outputs": [ @@ -271,10 +271,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.448095Z", - "iopub.status.busy": "2024-04-06T04:33:14.447753Z", - "iopub.status.idle": "2024-04-06T04:33:14.451521Z", - "shell.execute_reply": "2024-04-06T04:33:14.451033Z" + "iopub.execute_input": "2024-04-08T19:11:50.575235Z", + "iopub.status.busy": "2024-04-08T19:11:50.574826Z", + "iopub.status.idle": "2024-04-08T19:11:50.578661Z", + "shell.execute_reply": "2024-04-08T19:11:50.578198Z" } }, "outputs": [ @@ -283,7 +283,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'visa_or_mastercard', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'getting_spare_card', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'cancel_transfer', 'lost_or_stolen_phone', 'supported_cards_and_currencies'}\n" + "Classes: {'visa_or_mastercard', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'change_pin', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_about_to_expire'}\n" ] } ], @@ -307,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.453654Z", - "iopub.status.busy": "2024-04-06T04:33:14.453334Z", - "iopub.status.idle": "2024-04-06T04:33:14.456651Z", - "shell.execute_reply": "2024-04-06T04:33:14.456195Z" + "iopub.execute_input": "2024-04-08T19:11:50.580716Z", + "iopub.status.busy": "2024-04-08T19:11:50.580386Z", + "iopub.status.idle": "2024-04-08T19:11:50.583329Z", + "shell.execute_reply": "2024-04-08T19:11:50.582809Z" } }, "outputs": [ @@ -365,10 +365,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:14.458570Z", - "iopub.status.busy": "2024-04-06T04:33:14.458385Z", - "iopub.status.idle": "2024-04-06T04:33:18.310859Z", - "shell.execute_reply": "2024-04-06T04:33:18.310235Z" + "iopub.execute_input": "2024-04-08T19:11:50.585157Z", + "iopub.status.busy": "2024-04-08T19:11:50.584978Z", + "iopub.status.idle": "2024-04-08T19:11:54.999343Z", + "shell.execute_reply": "2024-04-08T19:11:54.998804Z" } }, "outputs": [ @@ -424,10 +424,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:18.313664Z", - "iopub.status.busy": "2024-04-06T04:33:18.313302Z", - "iopub.status.idle": "2024-04-06T04:33:19.193930Z", - "shell.execute_reply": "2024-04-06T04:33:19.193370Z" + "iopub.execute_input": "2024-04-08T19:11:55.002005Z", + "iopub.status.busy": "2024-04-08T19:11:55.001591Z", + "iopub.status.idle": "2024-04-08T19:11:55.890538Z", + "shell.execute_reply": "2024-04-08T19:11:55.889962Z" }, "scrolled": true }, @@ -459,10 +459,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:19.196805Z", - "iopub.status.busy": "2024-04-06T04:33:19.196442Z", - "iopub.status.idle": "2024-04-06T04:33:19.199261Z", - "shell.execute_reply": "2024-04-06T04:33:19.198798Z" + "iopub.execute_input": "2024-04-08T19:11:55.893249Z", + "iopub.status.busy": "2024-04-08T19:11:55.892862Z", + "iopub.status.idle": "2024-04-08T19:11:55.895882Z", + "shell.execute_reply": "2024-04-08T19:11:55.895415Z" } }, "outputs": [], @@ -482,10 +482,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:19.201572Z", - "iopub.status.busy": "2024-04-06T04:33:19.201213Z", - "iopub.status.idle": "2024-04-06T04:33:20.771182Z", - "shell.execute_reply": "2024-04-06T04:33:20.770550Z" + "iopub.execute_input": "2024-04-08T19:11:55.898149Z", + "iopub.status.busy": "2024-04-08T19:11:55.897768Z", + "iopub.status.idle": "2024-04-08T19:11:57.484712Z", + "shell.execute_reply": "2024-04-08T19:11:57.482845Z" }, "scrolled": true }, @@ -538,10 +538,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.774433Z", - "iopub.status.busy": "2024-04-06T04:33:20.773600Z", - "iopub.status.idle": "2024-04-06T04:33:20.799139Z", - "shell.execute_reply": "2024-04-06T04:33:20.798597Z" + "iopub.execute_input": "2024-04-08T19:11:57.489057Z", + "iopub.status.busy": "2024-04-08T19:11:57.487744Z", + "iopub.status.idle": "2024-04-08T19:11:57.513599Z", + "shell.execute_reply": "2024-04-08T19:11:57.513105Z" }, "scrolled": true }, @@ -666,10 +666,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.801783Z", - "iopub.status.busy": "2024-04-06T04:33:20.801390Z", - "iopub.status.idle": "2024-04-06T04:33:20.811382Z", - "shell.execute_reply": "2024-04-06T04:33:20.810884Z" + "iopub.execute_input": "2024-04-08T19:11:57.517153Z", + "iopub.status.busy": "2024-04-08T19:11:57.516242Z", + "iopub.status.idle": "2024-04-08T19:11:57.527834Z", + "shell.execute_reply": "2024-04-08T19:11:57.527359Z" }, "scrolled": true }, @@ -779,10 +779,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.813909Z", - "iopub.status.busy": "2024-04-06T04:33:20.813527Z", - "iopub.status.idle": "2024-04-06T04:33:20.818371Z", - "shell.execute_reply": "2024-04-06T04:33:20.817869Z" + "iopub.execute_input": "2024-04-08T19:11:57.531248Z", + "iopub.status.busy": "2024-04-08T19:11:57.530335Z", + "iopub.status.idle": "2024-04-08T19:11:57.536787Z", + "shell.execute_reply": "2024-04-08T19:11:57.536230Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.820606Z", - "iopub.status.busy": "2024-04-06T04:33:20.820299Z", - "iopub.status.idle": "2024-04-06T04:33:20.826482Z", - "shell.execute_reply": "2024-04-06T04:33:20.826090Z" + "iopub.execute_input": "2024-04-08T19:11:57.538876Z", + "iopub.status.busy": "2024-04-08T19:11:57.538699Z", + "iopub.status.idle": "2024-04-08T19:11:57.546063Z", + "shell.execute_reply": "2024-04-08T19:11:57.545305Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.828437Z", - "iopub.status.busy": "2024-04-06T04:33:20.828137Z", - "iopub.status.idle": "2024-04-06T04:33:20.834167Z", - "shell.execute_reply": "2024-04-06T04:33:20.833652Z" + "iopub.execute_input": "2024-04-08T19:11:57.548261Z", + "iopub.status.busy": "2024-04-08T19:11:57.547854Z", + "iopub.status.idle": "2024-04-08T19:11:57.554234Z", + "shell.execute_reply": "2024-04-08T19:11:57.553695Z" } }, "outputs": [ @@ -1026,10 +1026,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.836059Z", - "iopub.status.busy": "2024-04-06T04:33:20.835877Z", - "iopub.status.idle": "2024-04-06T04:33:20.841929Z", - "shell.execute_reply": "2024-04-06T04:33:20.841349Z" + "iopub.execute_input": "2024-04-08T19:11:57.556102Z", + "iopub.status.busy": "2024-04-08T19:11:57.555808Z", + "iopub.status.idle": "2024-04-08T19:11:57.561366Z", + "shell.execute_reply": "2024-04-08T19:11:57.560849Z" } }, "outputs": [ @@ -1137,10 +1137,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.843988Z", - "iopub.status.busy": "2024-04-06T04:33:20.843684Z", - "iopub.status.idle": "2024-04-06T04:33:20.852453Z", - "shell.execute_reply": "2024-04-06T04:33:20.851982Z" + "iopub.execute_input": "2024-04-08T19:11:57.563375Z", + "iopub.status.busy": "2024-04-08T19:11:57.563066Z", + "iopub.status.idle": "2024-04-08T19:11:57.571545Z", + "shell.execute_reply": "2024-04-08T19:11:57.571096Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.854597Z", - "iopub.status.busy": "2024-04-06T04:33:20.854199Z", - "iopub.status.idle": "2024-04-06T04:33:20.859815Z", - "shell.execute_reply": "2024-04-06T04:33:20.859258Z" + "iopub.execute_input": "2024-04-08T19:11:57.573469Z", + "iopub.status.busy": "2024-04-08T19:11:57.573151Z", + "iopub.status.idle": "2024-04-08T19:11:57.578415Z", + "shell.execute_reply": "2024-04-08T19:11:57.577995Z" } }, "outputs": [ @@ -1322,10 +1322,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.861773Z", - "iopub.status.busy": "2024-04-06T04:33:20.861471Z", - "iopub.status.idle": "2024-04-06T04:33:20.866885Z", - "shell.execute_reply": "2024-04-06T04:33:20.866352Z" + "iopub.execute_input": "2024-04-08T19:11:57.580309Z", + "iopub.status.busy": "2024-04-08T19:11:57.579985Z", + "iopub.status.idle": "2024-04-08T19:11:57.585107Z", + "shell.execute_reply": "2024-04-08T19:11:57.584704Z" } }, "outputs": [ @@ -1404,10 +1404,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.869013Z", - "iopub.status.busy": "2024-04-06T04:33:20.868609Z", - "iopub.status.idle": "2024-04-06T04:33:20.872412Z", - "shell.execute_reply": "2024-04-06T04:33:20.871871Z" + "iopub.execute_input": "2024-04-08T19:11:57.587089Z", + "iopub.status.busy": "2024-04-08T19:11:57.586774Z", + "iopub.status.idle": "2024-04-08T19:11:57.590241Z", + "shell.execute_reply": "2024-04-08T19:11:57.589704Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:20.874578Z", - "iopub.status.busy": "2024-04-06T04:33:20.874128Z", - "iopub.status.idle": "2024-04-06T04:33:20.879644Z", - "shell.execute_reply": "2024-04-06T04:33:20.879101Z" + "iopub.execute_input": "2024-04-08T19:11:57.592302Z", + "iopub.status.busy": "2024-04-08T19:11:57.591981Z", + "iopub.status.idle": "2024-04-08T19:11:57.597076Z", + "shell.execute_reply": "2024-04-08T19:11:57.596530Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 31a8923c7..8386c499c 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:24.564833Z", - "iopub.status.busy": "2024-04-06T04:33:24.564645Z", - "iopub.status.idle": "2024-04-06T04:33:25.678241Z", - "shell.execute_reply": "2024-04-06T04:33:25.677637Z" + "iopub.execute_input": "2024-04-08T19:12:00.906624Z", + "iopub.status.busy": "2024-04-08T19:12:00.906269Z", + "iopub.status.idle": "2024-04-08T19:12:02.013278Z", + "shell.execute_reply": "2024-04-08T19:12:02.012738Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.681005Z", - "iopub.status.busy": "2024-04-06T04:33:25.680432Z", - "iopub.status.idle": "2024-04-06T04:33:25.683479Z", - "shell.execute_reply": "2024-04-06T04:33:25.683004Z" + "iopub.execute_input": "2024-04-08T19:12:02.015823Z", + "iopub.status.busy": "2024-04-08T19:12:02.015525Z", + "iopub.status.idle": "2024-04-08T19:12:02.018326Z", + "shell.execute_reply": "2024-04-08T19:12:02.017864Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.685643Z", - "iopub.status.busy": "2024-04-06T04:33:25.685458Z", - "iopub.status.idle": "2024-04-06T04:33:25.698037Z", - "shell.execute_reply": "2024-04-06T04:33:25.697552Z" + "iopub.execute_input": "2024-04-08T19:12:02.020260Z", + "iopub.status.busy": "2024-04-08T19:12:02.020087Z", + "iopub.status.idle": "2024-04-08T19:12:02.032329Z", + "shell.execute_reply": "2024-04-08T19:12:02.031881Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:25.700120Z", - "iopub.status.busy": "2024-04-06T04:33:25.699931Z", - "iopub.status.idle": "2024-04-06T04:33:30.316432Z", - "shell.execute_reply": "2024-04-06T04:33:30.315931Z" + "iopub.execute_input": "2024-04-08T19:12:02.034317Z", + "iopub.status.busy": "2024-04-08T19:12:02.034142Z", + "iopub.status.idle": "2024-04-08T19:12:10.633860Z", + "shell.execute_reply": "2024-04-08T19:12:10.633305Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index d12576394..1383c5dd4 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -797,13 +797,13 @@

    How can I find label issues in big datasets with limited memory?
    -
    +
    -
    +
    @@ -1706,7 +1706,7 @@

    How to handle near-duplicate data identified by cleanlab? 
    -/tmp/ipykernel_7516/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+/tmp/ipykernel_7838/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values

    @@ -1748,7 +1748,7 @@

    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 71792b57a..35b51f794 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:32.453926Z", - "iopub.status.busy": "2024-04-06T04:33:32.453487Z", - "iopub.status.idle": "2024-04-06T04:33:33.577711Z", - "shell.execute_reply": "2024-04-06T04:33:33.577162Z" + "iopub.execute_input": "2024-04-08T19:12:12.681561Z", + "iopub.status.busy": "2024-04-08T19:12:12.681389Z", + "iopub.status.idle": "2024-04-08T19:12:13.734405Z", + "shell.execute_reply": "2024-04-08T19:12:13.733868Z" }, "nbsphinx": "hidden" }, @@ -137,10 +137,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:33.580468Z", - "iopub.status.busy": "2024-04-06T04:33:33.579978Z", - "iopub.status.idle": "2024-04-06T04:33:33.583331Z", - "shell.execute_reply": "2024-04-06T04:33:33.582894Z" + "iopub.execute_input": "2024-04-08T19:12:13.737231Z", + "iopub.status.busy": "2024-04-08T19:12:13.736791Z", + "iopub.status.idle": "2024-04-08T19:12:13.740148Z", + "shell.execute_reply": "2024-04-08T19:12:13.739710Z" } }, "outputs": [], @@ -176,10 +176,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:33.585545Z", - "iopub.status.busy": "2024-04-06T04:33:33.585109Z", - "iopub.status.idle": "2024-04-06T04:33:36.718652Z", - "shell.execute_reply": "2024-04-06T04:33:36.718005Z" + "iopub.execute_input": "2024-04-08T19:12:13.742187Z", + "iopub.status.busy": "2024-04-08T19:12:13.741855Z", + "iopub.status.idle": "2024-04-08T19:12:16.687217Z", + "shell.execute_reply": "2024-04-08T19:12:16.686507Z" } }, "outputs": [], @@ -202,10 +202,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.721727Z", - "iopub.status.busy": "2024-04-06T04:33:36.721060Z", - "iopub.status.idle": "2024-04-06T04:33:36.760399Z", - "shell.execute_reply": "2024-04-06T04:33:36.759784Z" + "iopub.execute_input": "2024-04-08T19:12:16.690229Z", + "iopub.status.busy": "2024-04-08T19:12:16.689558Z", + "iopub.status.idle": "2024-04-08T19:12:16.723065Z", + "shell.execute_reply": "2024-04-08T19:12:16.722493Z" } }, "outputs": [], @@ -228,10 +228,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.763173Z", - "iopub.status.busy": "2024-04-06T04:33:36.762842Z", - "iopub.status.idle": "2024-04-06T04:33:36.801368Z", - "shell.execute_reply": "2024-04-06T04:33:36.800735Z" + "iopub.execute_input": "2024-04-08T19:12:16.725574Z", + "iopub.status.busy": "2024-04-08T19:12:16.725213Z", + "iopub.status.idle": "2024-04-08T19:12:16.748633Z", + "shell.execute_reply": "2024-04-08T19:12:16.748076Z" } }, "outputs": [], @@ -253,10 +253,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.804245Z", - "iopub.status.busy": "2024-04-06T04:33:36.803821Z", - "iopub.status.idle": "2024-04-06T04:33:36.807084Z", - "shell.execute_reply": "2024-04-06T04:33:36.806596Z" + "iopub.execute_input": "2024-04-08T19:12:16.751185Z", + "iopub.status.busy": "2024-04-08T19:12:16.750822Z", + "iopub.status.idle": "2024-04-08T19:12:16.753746Z", + "shell.execute_reply": "2024-04-08T19:12:16.753306Z" } }, "outputs": [], @@ -278,10 +278,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.809090Z", - "iopub.status.busy": "2024-04-06T04:33:36.808779Z", - "iopub.status.idle": "2024-04-06T04:33:36.811544Z", - "shell.execute_reply": "2024-04-06T04:33:36.811006Z" + "iopub.execute_input": "2024-04-08T19:12:16.755833Z", + "iopub.status.busy": "2024-04-08T19:12:16.755526Z", + "iopub.status.idle": "2024-04-08T19:12:16.758525Z", + "shell.execute_reply": "2024-04-08T19:12:16.758102Z" } }, "outputs": [], @@ -363,10 +363,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.813573Z", - "iopub.status.busy": "2024-04-06T04:33:36.813305Z", - "iopub.status.idle": "2024-04-06T04:33:36.837656Z", - "shell.execute_reply": "2024-04-06T04:33:36.837105Z" + "iopub.execute_input": "2024-04-08T19:12:16.760530Z", + "iopub.status.busy": "2024-04-08T19:12:16.760254Z", + "iopub.status.idle": "2024-04-08T19:12:16.783193Z", + "shell.execute_reply": "2024-04-08T19:12:16.782688Z" } }, "outputs": [ @@ -380,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ad7ffe9f7e104f438570b96387ce328e", + "model_id": "6a6240bb0ab443d38a48eadee74f3ae2", "version_major": 2, "version_minor": 0 }, @@ -394,7 +394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a93f0182ebb47fc96441f7413ee50a4", + "model_id": "f9a5120ba56d4977aa0d368fb7c66d40", "version_major": 2, "version_minor": 0 }, @@ -452,10 +452,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.843747Z", - "iopub.status.busy": "2024-04-06T04:33:36.843506Z", - "iopub.status.idle": "2024-04-06T04:33:36.850771Z", - "shell.execute_reply": "2024-04-06T04:33:36.850304Z" + "iopub.execute_input": "2024-04-08T19:12:16.789722Z", + "iopub.status.busy": "2024-04-08T19:12:16.789232Z", + "iopub.status.idle": "2024-04-08T19:12:16.795676Z", + "shell.execute_reply": "2024-04-08T19:12:16.795154Z" }, "nbsphinx": "hidden" }, @@ -486,10 +486,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.853060Z", - "iopub.status.busy": "2024-04-06T04:33:36.852662Z", - "iopub.status.idle": "2024-04-06T04:33:36.856158Z", - "shell.execute_reply": "2024-04-06T04:33:36.855726Z" + "iopub.execute_input": "2024-04-08T19:12:16.797734Z", + "iopub.status.busy": "2024-04-08T19:12:16.797436Z", + "iopub.status.idle": "2024-04-08T19:12:16.800819Z", + "shell.execute_reply": "2024-04-08T19:12:16.800306Z" }, "nbsphinx": "hidden" }, @@ -512,10 +512,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.858276Z", - "iopub.status.busy": "2024-04-06T04:33:36.858000Z", - "iopub.status.idle": "2024-04-06T04:33:36.864594Z", - "shell.execute_reply": "2024-04-06T04:33:36.864108Z" + "iopub.execute_input": "2024-04-08T19:12:16.802799Z", + "iopub.status.busy": "2024-04-08T19:12:16.802383Z", + "iopub.status.idle": "2024-04-08T19:12:16.808583Z", + "shell.execute_reply": "2024-04-08T19:12:16.808080Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.866698Z", - "iopub.status.busy": "2024-04-06T04:33:36.866352Z", - "iopub.status.idle": "2024-04-06T04:33:36.905959Z", - "shell.execute_reply": "2024-04-06T04:33:36.905317Z" + "iopub.execute_input": "2024-04-08T19:12:16.810430Z", + "iopub.status.busy": "2024-04-08T19:12:16.810131Z", + "iopub.status.idle": "2024-04-08T19:12:16.843764Z", + "shell.execute_reply": "2024-04-08T19:12:16.843069Z" } }, "outputs": [], @@ -585,10 +585,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.908640Z", - "iopub.status.busy": "2024-04-06T04:33:36.908384Z", - "iopub.status.idle": "2024-04-06T04:33:36.948839Z", - "shell.execute_reply": "2024-04-06T04:33:36.948221Z" + "iopub.execute_input": "2024-04-08T19:12:16.846251Z", + "iopub.status.busy": "2024-04-08T19:12:16.846029Z", + "iopub.status.idle": "2024-04-08T19:12:16.876055Z", + "shell.execute_reply": "2024-04-08T19:12:16.875395Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:36.951895Z", - "iopub.status.busy": "2024-04-06T04:33:36.951511Z", - "iopub.status.idle": "2024-04-06T04:33:37.080581Z", - "shell.execute_reply": "2024-04-06T04:33:37.079922Z" + "iopub.execute_input": "2024-04-08T19:12:16.878797Z", + "iopub.status.busy": "2024-04-08T19:12:16.878362Z", + "iopub.status.idle": "2024-04-08T19:12:16.997690Z", + "shell.execute_reply": "2024-04-08T19:12:16.997074Z" } }, "outputs": [ @@ -737,10 +737,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:37.083569Z", - "iopub.status.busy": "2024-04-06T04:33:37.082731Z", - "iopub.status.idle": "2024-04-06T04:33:40.126106Z", - "shell.execute_reply": "2024-04-06T04:33:40.125422Z" + "iopub.execute_input": "2024-04-08T19:12:17.000317Z", + "iopub.status.busy": "2024-04-08T19:12:16.999797Z", + "iopub.status.idle": "2024-04-08T19:12:20.051499Z", + "shell.execute_reply": "2024-04-08T19:12:20.050857Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.128582Z", - "iopub.status.busy": "2024-04-06T04:33:40.128353Z", - "iopub.status.idle": "2024-04-06T04:33:40.189416Z", - "shell.execute_reply": "2024-04-06T04:33:40.188788Z" + "iopub.execute_input": "2024-04-08T19:12:20.054045Z", + "iopub.status.busy": "2024-04-08T19:12:20.053678Z", + "iopub.status.idle": "2024-04-08T19:12:20.108066Z", + "shell.execute_reply": "2024-04-08T19:12:20.107454Z" } }, "outputs": [ @@ -1285,10 +1285,10 @@ "id": "af3052ac", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.191652Z", - "iopub.status.busy": "2024-04-06T04:33:40.191314Z", - "iopub.status.idle": "2024-04-06T04:33:40.231110Z", - "shell.execute_reply": "2024-04-06T04:33:40.230569Z" + "iopub.execute_input": "2024-04-08T19:12:20.110315Z", + "iopub.status.busy": "2024-04-08T19:12:20.109981Z", + "iopub.status.idle": "2024-04-08T19:12:20.147390Z", + "shell.execute_reply": "2024-04-08T19:12:20.146954Z" } }, "outputs": [ @@ -1319,7 +1319,7 @@ }, { "cell_type": "markdown", - "id": "7997ced4", + "id": "9da437a7", "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": "57a8d119", + "id": "fce848ae", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -1340,13 +1340,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "9fb93000", + "id": "0fe990fa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.233390Z", - "iopub.status.busy": "2024-04-06T04:33:40.233191Z", - "iopub.status.idle": "2024-04-06T04:33:40.327660Z", - "shell.execute_reply": "2024-04-06T04:33:40.327127Z" + "iopub.execute_input": "2024-04-08T19:12:20.149376Z", + "iopub.status.busy": "2024-04-08T19:12:20.149051Z", + "iopub.status.idle": "2024-04-08T19:12:20.266660Z", + "shell.execute_reply": "2024-04-08T19:12:20.266055Z" } }, "outputs": [ @@ -1354,7 +1354,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ...\n", + "Finding underperforming_group issues ...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1387,7 +1393,7 @@ }, { "cell_type": "markdown", - "id": "27082dba", + "id": "e1f798da", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -1396,13 +1402,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "5a3f0b1c", + "id": "35842b9a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.330424Z", - "iopub.status.busy": "2024-04-06T04:33:40.330165Z", - "iopub.status.idle": "2024-04-06T04:33:40.412901Z", - "shell.execute_reply": "2024-04-06T04:33:40.412405Z" + "iopub.execute_input": "2024-04-08T19:12:20.269272Z", + "iopub.status.busy": "2024-04-08T19:12:20.269030Z", + "iopub.status.idle": "2024-04-08T19:12:20.330497Z", + "shell.execute_reply": "2024-04-08T19:12:20.329977Z" } }, "outputs": [ @@ -1410,14 +1416,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Finding underperforming_group issues ..." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Finding underperforming_group issues ...\n", "\n", "Audit complete. 0 issues found in the dataset.\n" ] @@ -1445,7 +1444,7 @@ }, { "cell_type": "markdown", - "id": "bb4c5299", + "id": "798d7822", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -1456,13 +1455,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "0a847975", + "id": "fdfd0a78", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.415545Z", - "iopub.status.busy": "2024-04-06T04:33:40.415364Z", - "iopub.status.idle": "2024-04-06T04:33:40.424747Z", - "shell.execute_reply": "2024-04-06T04:33:40.424323Z" + "iopub.execute_input": "2024-04-08T19:12:20.332905Z", + "iopub.status.busy": "2024-04-08T19:12:20.332706Z", + "iopub.status.idle": "2024-04-08T19:12:20.340139Z", + "shell.execute_reply": "2024-04-08T19:12:20.339592Z" } }, "outputs": [], @@ -1564,7 +1563,7 @@ }, { "cell_type": "markdown", - "id": "f6c74243", + "id": "623406db", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1579,13 +1578,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "665cd26e", + "id": "78a115a5", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.427036Z", - "iopub.status.busy": "2024-04-06T04:33:40.426714Z", - "iopub.status.idle": "2024-04-06T04:33:40.447448Z", - "shell.execute_reply": "2024-04-06T04:33:40.446876Z" + "iopub.execute_input": "2024-04-08T19:12:20.342036Z", + "iopub.status.busy": "2024-04-08T19:12:20.341739Z", + "iopub.status.idle": "2024-04-08T19:12:20.360239Z", + "shell.execute_reply": "2024-04-08T19:12:20.359697Z" } }, "outputs": [ @@ -1602,7 +1601,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_7516/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + "/tmp/ipykernel_7838/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n" ] } @@ -1636,13 +1635,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "1a0ba0a1", + "id": "40dae4e0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:40.449833Z", - "iopub.status.busy": "2024-04-06T04:33:40.449476Z", - "iopub.status.idle": "2024-04-06T04:33:40.452685Z", - "shell.execute_reply": "2024-04-06T04:33:40.452130Z" + "iopub.execute_input": "2024-04-08T19:12:20.362253Z", + "iopub.status.busy": "2024-04-08T19:12:20.361948Z", + "iopub.status.idle": "2024-04-08T19:12:20.365026Z", + "shell.execute_reply": "2024-04-08T19:12:20.364516Z" } }, "outputs": [ @@ -1737,7 +1736,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "14b2e46a058f49b7877f1e0a8fc3b5b6": { + "12810a0d31ae4f278489cceb3717deb2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1790,7 +1789,7 @@ "width": null } }, - "23c0ea245fbd417183656f1b6c07712f": { + "21b83733b12e4ab9a8e1fc932a77b365": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1843,30 +1842,65 @@ "width": null } }, - "245c87c4c2aa416db47130538b929d58": { + "6566ff0f4c9545e0a8d4ac6514750721": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HTMLModel", + "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": "" + } + }, + "68c12f7c9aa14eb596e0d4b9f346ded1": { + "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 + } + }, + "6a6240bb0ab443d38a48eadee74f3ae2": { + "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": "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_7480a084643d44109ad2e89da0ca1645", - "placeholder": "​", - "style": "IPY_MODEL_d3c14fbb47a44544abaeec177f87256f", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b66e8c48cfaa46939a45c8b4440cd4a6", + "IPY_MODEL_bf86d89ad3204f18be960f57e96ccf59", + "IPY_MODEL_b765b2a3ff1d4b41a66c68404700508b" + ], + "layout": "IPY_MODEL_cfc2c60ce9954a939a633840b1c6d1f7", "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1599963.38it/s]" + "tooltip": null } }, - "26607dc2026d44d18409e2097833bbb6": { + "995b23ebdb8b4621ae697068c80e4d01": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1919,7 +1953,60 @@ "width": null } }, - "538f177063ec4fd38db9281cbb4e4736": { + "9a847008c2cc4912b7568541c8506397": { + "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 + } + }, + "9cca6ed55b464af5a62dfafe2ab723a7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1934,15 +2021,51 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_23c0ea245fbd417183656f1b6c07712f", + "layout": "IPY_MODEL_9a847008c2cc4912b7568541c8506397", "placeholder": "​", - "style": "IPY_MODEL_f7f940143f124c22a39fad1b33b95e97", + "style": "IPY_MODEL_b0998f9686d34540aef7e956282bc228", "tabbable": null, "tooltip": null, - "value": "number of examples processed for checking labels: " + "value": " 10000/? [00:00<00:00, 1657172.66it/s]" + } + }, + "b0998f9686d34540aef7e956282bc228": { + "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 + } + }, + "b37c8cc149634a1fa49852024830088d": { + "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 } }, - "5d3a89a90e3b4986a627cb78ab15f855": { + "b66e8c48cfaa46939a45c8b4440cd4a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1957,39 +2080,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_c9e7c9ca5279402f833cd03f267b90b0", + "layout": "IPY_MODEL_12810a0d31ae4f278489cceb3717deb2", "placeholder": "​", - "style": "IPY_MODEL_7a5ac41ff90446bda080db1a245f6076", + "style": "IPY_MODEL_c9626b33f28a41cbb8e7ab7eaa6d20db", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: " } }, - "6a93f0182ebb47fc96441f7413ee50a4": { + "b765b2a3ff1d4b41a66c68404700508b": { "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_538f177063ec4fd38db9281cbb4e4736", - "IPY_MODEL_8147d6885df5404da13067a4b04feaff", - "IPY_MODEL_245c87c4c2aa416db47130538b929d58" - ], - "layout": "IPY_MODEL_26607dc2026d44d18409e2097833bbb6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_21b83733b12e4ab9a8e1fc932a77b365", + "placeholder": "​", + "style": "IPY_MODEL_b37c8cc149634a1fa49852024830088d", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": " 10000/? [00:00<00:00, 1108870.85it/s]" } }, - "7480a084643d44109ad2e89da0ca1645": { + "ba6196a4a8f546aabd03b97f4e409a2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2042,25 +2164,7 @@ "width": null } }, - "7a5ac41ff90446bda080db1a245f6076": { - "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 - } - }, - "8147d6885df5404da13067a4b04feaff": { + "bf86d89ad3204f18be960f57e96ccf59": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", @@ -2076,17 +2180,17 @@ "bar_style": "success", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_b88c297a26be479180743ea12a8782ee", + "layout": "IPY_MODEL_c1cb2e0cc26b4999826ac84dc19ced5c", "max": 50.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_8a8358bc01474398ae2c3b719fe94d03", + "style": "IPY_MODEL_6566ff0f4c9545e0a8d4ac6514750721", "tabbable": null, "tooltip": null, "value": 50.0 } }, - "83a387e924c445e394cde04898e3e09e": { + "c1cb2e0cc26b4999826ac84dc19ced5c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2139,47 +2243,30 @@ "width": null } }, - "8a8358bc01474398ae2c3b719fe94d03": { + "c5076875c8174d66894ad735da25ceb0": { "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": "" - } - }, - "ad7ffe9f7e104f438570b96387ce328e": { - "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_5d3a89a90e3b4986a627cb78ab15f855", - "IPY_MODEL_f6ab57c77c6546aeb601051c57a99435", - "IPY_MODEL_cda88cf88c8042acb6232ea9423f355f" - ], - "layout": "IPY_MODEL_14b2e46a058f49b7877f1e0a8fc3b5b6", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d0bb2759b4794473a6c806fa37369c3f", + "placeholder": "​", + "style": "IPY_MODEL_68c12f7c9aa14eb596e0d4b9f346ded1", "tabbable": null, - "tooltip": null + "tooltip": null, + "value": "number of examples processed for checking labels: " } }, - "b884a7bf75b9420f8028a27180dff7b2": { + "c9626b33f28a41cbb8e7ab7eaa6d20db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -2197,60 +2284,49 @@ "text_color": null } }, - "b88c297a26be479180743ea12a8782ee": { - "model_module": "@jupyter-widgets/base", + "cabf93bc271f433bbdf492408e032d42": { + "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": "" + } + }, + "cc473ba1779244319b406315a2ad79b3": { + "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_995b23ebdb8b4621ae697068c80e4d01", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_cabf93bc271f433bbdf492408e032d42", + "tabbable": null, + "tooltip": null, + "value": 50.0 } }, - "be6b5fe0f64b4e89bba0bc6b2e5c249c": { + "cfc2c60ce9954a939a633840b1c6d1f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2303,7 +2379,7 @@ "width": null } }, - "c9e7c9ca5279402f833cd03f267b90b0": { + "d0bb2759b4794473a6c806fa37369c3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2356,105 +2432,28 @@ "width": null } }, - "cc099a9799bc402bbc12d51076fd879a": { - "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": "" - } - }, - "cda88cf88c8042acb6232ea9423f355f": { - "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_83a387e924c445e394cde04898e3e09e", - "placeholder": "​", - "style": "IPY_MODEL_b884a7bf75b9420f8028a27180dff7b2", - "tabbable": null, - "tooltip": null, - "value": " 10000/? [00:00<00:00, 1022876.23it/s]" - } - }, - "d3c14fbb47a44544abaeec177f87256f": { - "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 - } - }, - "f6ab57c77c6546aeb601051c57a99435": { + "f9a5120ba56d4977aa0d368fb7c66d40": { "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_be6b5fe0f64b4e89bba0bc6b2e5c249c", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cc099a9799bc402bbc12d51076fd879a", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_c5076875c8174d66894ad735da25ceb0", + "IPY_MODEL_cc473ba1779244319b406315a2ad79b3", + "IPY_MODEL_9cca6ed55b464af5a62dfafe2ab723a7" + ], + "layout": "IPY_MODEL_ba6196a4a8f546aabd03b97f4e409a2f", "tabbable": null, - "tooltip": null, - "value": 50.0 - } - }, - "f7f940143f124c22a39fad1b33b95e97": { - "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 } } }, diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 09d453fd1..319d2d3ff 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": "2024-04-06T04:33:43.785678Z", - "iopub.status.busy": "2024-04-06T04:33:43.785475Z", - "iopub.status.idle": "2024-04-06T04:33:44.953788Z", - "shell.execute_reply": "2024-04-06T04:33:44.953182Z" + "iopub.execute_input": "2024-04-08T19:12:23.385502Z", + "iopub.status.busy": "2024-04-08T19:12:23.385324Z", + "iopub.status.idle": "2024-04-08T19:12:24.500994Z", + "shell.execute_reply": "2024-04-08T19:12:24.500451Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:33:44.956257Z", - "iopub.status.busy": "2024-04-06T04:33:44.955968Z", - "iopub.status.idle": "2024-04-06T04:33:45.136559Z", - "shell.execute_reply": "2024-04-06T04:33:45.135941Z" + "iopub.execute_input": "2024-04-08T19:12:24.503635Z", + "iopub.status.busy": "2024-04-08T19:12:24.503134Z", + "iopub.status.idle": "2024-04-08T19:12:24.674963Z", + "shell.execute_reply": "2024-04-08T19:12:24.674378Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.139194Z", - "iopub.status.busy": "2024-04-06T04:33:45.138996Z", - "iopub.status.idle": "2024-04-06T04:33:45.151534Z", - "shell.execute_reply": "2024-04-06T04:33:45.150954Z" + "iopub.execute_input": "2024-04-08T19:12:24.677405Z", + "iopub.status.busy": "2024-04-08T19:12:24.677010Z", + "iopub.status.idle": "2024-04-08T19:12:24.688933Z", + "shell.execute_reply": "2024-04-08T19:12:24.688406Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.153835Z", - "iopub.status.busy": "2024-04-06T04:33:45.153455Z", - "iopub.status.idle": "2024-04-06T04:33:45.364208Z", - "shell.execute_reply": "2024-04-06T04:33:45.363567Z" + "iopub.execute_input": "2024-04-08T19:12:24.690846Z", + "iopub.status.busy": "2024-04-08T19:12:24.690671Z", + "iopub.status.idle": "2024-04-08T19:12:24.894029Z", + "shell.execute_reply": "2024-04-08T19:12:24.893464Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.366694Z", - "iopub.status.busy": "2024-04-06T04:33:45.366209Z", - "iopub.status.idle": "2024-04-06T04:33:45.393157Z", - "shell.execute_reply": "2024-04-06T04:33:45.392663Z" + "iopub.execute_input": "2024-04-08T19:12:24.896362Z", + "iopub.status.busy": "2024-04-08T19:12:24.896017Z", + "iopub.status.idle": "2024-04-08T19:12:24.922340Z", + "shell.execute_reply": "2024-04-08T19:12:24.921893Z" } }, "outputs": [], @@ -428,10 +428,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:45.395608Z", - "iopub.status.busy": "2024-04-06T04:33:45.395250Z", - "iopub.status.idle": "2024-04-06T04:33:47.125309Z", - "shell.execute_reply": "2024-04-06T04:33:47.124671Z" + "iopub.execute_input": "2024-04-08T19:12:24.924554Z", + "iopub.status.busy": "2024-04-08T19:12:24.924219Z", + "iopub.status.idle": "2024-04-08T19:12:26.591119Z", + "shell.execute_reply": "2024-04-08T19:12:26.590421Z" } }, "outputs": [ @@ -483,10 +483,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:47.127865Z", - "iopub.status.busy": "2024-04-06T04:33:47.127360Z", - "iopub.status.idle": "2024-04-06T04:33:47.146064Z", - "shell.execute_reply": "2024-04-06T04:33:47.145478Z" + "iopub.execute_input": "2024-04-08T19:12:26.593843Z", + "iopub.status.busy": "2024-04-08T19:12:26.593202Z", + "iopub.status.idle": "2024-04-08T19:12:26.611348Z", + "shell.execute_reply": "2024-04-08T19:12:26.610866Z" }, "scrolled": true }, @@ -611,10 +611,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:47.148206Z", - "iopub.status.busy": "2024-04-06T04:33:47.148010Z", - "iopub.status.idle": "2024-04-06T04:33:48.575713Z", - "shell.execute_reply": "2024-04-06T04:33:48.575123Z" + "iopub.execute_input": "2024-04-08T19:12:26.613273Z", + "iopub.status.busy": "2024-04-08T19:12:26.613008Z", + "iopub.status.idle": "2024-04-08T19:12:27.994069Z", + "shell.execute_reply": "2024-04-08T19:12:27.993485Z" }, "id": "AaHC5MRKjruT" }, @@ -733,10 +733,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.578373Z", - "iopub.status.busy": "2024-04-06T04:33:48.577728Z", - "iopub.status.idle": "2024-04-06T04:33:48.591925Z", - "shell.execute_reply": "2024-04-06T04:33:48.591473Z" + "iopub.execute_input": "2024-04-08T19:12:27.996963Z", + "iopub.status.busy": "2024-04-08T19:12:27.996205Z", + "iopub.status.idle": "2024-04-08T19:12:28.010313Z", + "shell.execute_reply": "2024-04-08T19:12:28.009892Z" }, "id": "Wy27rvyhjruU" }, @@ -785,10 +785,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.594180Z", - "iopub.status.busy": "2024-04-06T04:33:48.593840Z", - "iopub.status.idle": "2024-04-06T04:33:48.670108Z", - "shell.execute_reply": "2024-04-06T04:33:48.669540Z" + "iopub.execute_input": "2024-04-08T19:12:28.012483Z", + "iopub.status.busy": "2024-04-08T19:12:28.012148Z", + "iopub.status.idle": "2024-04-08T19:12:28.092332Z", + "shell.execute_reply": "2024-04-08T19:12:28.091737Z" }, "id": "Db8YHnyVjruU" }, @@ -895,10 +895,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.672461Z", - "iopub.status.busy": "2024-04-06T04:33:48.672082Z", - "iopub.status.idle": "2024-04-06T04:33:48.894054Z", - "shell.execute_reply": "2024-04-06T04:33:48.893460Z" + "iopub.execute_input": "2024-04-08T19:12:28.094848Z", + "iopub.status.busy": "2024-04-08T19:12:28.094388Z", + "iopub.status.idle": "2024-04-08T19:12:28.305015Z", + "shell.execute_reply": "2024-04-08T19:12:28.304459Z" }, "id": "iJqAHuS2jruV" }, @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.896310Z", - "iopub.status.busy": "2024-04-06T04:33:48.895957Z", - "iopub.status.idle": "2024-04-06T04:33:48.912992Z", - "shell.execute_reply": "2024-04-06T04:33:48.912438Z" + "iopub.execute_input": "2024-04-08T19:12:28.307155Z", + "iopub.status.busy": "2024-04-08T19:12:28.306977Z", + "iopub.status.idle": "2024-04-08T19:12:28.324108Z", + "shell.execute_reply": "2024-04-08T19:12:28.323676Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1404,10 +1404,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.915370Z", - "iopub.status.busy": "2024-04-06T04:33:48.914978Z", - "iopub.status.idle": "2024-04-06T04:33:48.925166Z", - "shell.execute_reply": "2024-04-06T04:33:48.924650Z" + "iopub.execute_input": "2024-04-08T19:12:28.326002Z", + "iopub.status.busy": "2024-04-08T19:12:28.325829Z", + "iopub.status.idle": "2024-04-08T19:12:28.335620Z", + "shell.execute_reply": "2024-04-08T19:12:28.335205Z" }, "id": "0lonvOYvjruV" }, @@ -1554,10 +1554,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:48.927196Z", - "iopub.status.busy": "2024-04-06T04:33:48.927015Z", - "iopub.status.idle": "2024-04-06T04:33:49.014441Z", - "shell.execute_reply": "2024-04-06T04:33:49.013806Z" + "iopub.execute_input": "2024-04-08T19:12:28.337624Z", + "iopub.status.busy": "2024-04-08T19:12:28.337213Z", + "iopub.status.idle": "2024-04-08T19:12:28.422599Z", + "shell.execute_reply": "2024-04-08T19:12:28.421980Z" }, "id": "MfqTCa3kjruV" }, @@ -1638,10 +1638,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.016777Z", - "iopub.status.busy": "2024-04-06T04:33:49.016537Z", - "iopub.status.idle": "2024-04-06T04:33:49.145893Z", - "shell.execute_reply": "2024-04-06T04:33:49.145286Z" + "iopub.execute_input": "2024-04-08T19:12:28.424970Z", + "iopub.status.busy": "2024-04-08T19:12:28.424722Z", + "iopub.status.idle": "2024-04-08T19:12:28.549007Z", + "shell.execute_reply": "2024-04-08T19:12:28.548406Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1701,10 +1701,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.148236Z", - "iopub.status.busy": "2024-04-06T04:33:49.148006Z", - "iopub.status.idle": "2024-04-06T04:33:49.151659Z", - "shell.execute_reply": "2024-04-06T04:33:49.151136Z" + "iopub.execute_input": "2024-04-08T19:12:28.551383Z", + "iopub.status.busy": "2024-04-08T19:12:28.551092Z", + "iopub.status.idle": "2024-04-08T19:12:28.554976Z", + "shell.execute_reply": "2024-04-08T19:12:28.554255Z" }, "id": "0rXP3ZPWjruW" }, @@ -1742,10 +1742,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.153711Z", - "iopub.status.busy": "2024-04-06T04:33:49.153349Z", - "iopub.status.idle": "2024-04-06T04:33:49.157155Z", - "shell.execute_reply": "2024-04-06T04:33:49.156622Z" + "iopub.execute_input": "2024-04-08T19:12:28.557035Z", + "iopub.status.busy": "2024-04-08T19:12:28.556717Z", + "iopub.status.idle": "2024-04-08T19:12:28.560298Z", + "shell.execute_reply": "2024-04-08T19:12:28.559774Z" }, "id": "-iRPe8KXjruW" }, @@ -1800,10 +1800,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.159176Z", - "iopub.status.busy": "2024-04-06T04:33:49.158878Z", - "iopub.status.idle": "2024-04-06T04:33:49.196839Z", - "shell.execute_reply": "2024-04-06T04:33:49.196263Z" + "iopub.execute_input": "2024-04-08T19:12:28.562193Z", + "iopub.status.busy": "2024-04-08T19:12:28.561944Z", + "iopub.status.idle": "2024-04-08T19:12:28.599077Z", + "shell.execute_reply": "2024-04-08T19:12:28.598539Z" }, "id": "ZpipUliyjruW" }, @@ -1854,10 +1854,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.198950Z", - "iopub.status.busy": "2024-04-06T04:33:49.198645Z", - "iopub.status.idle": "2024-04-06T04:33:49.242193Z", - "shell.execute_reply": "2024-04-06T04:33:49.241610Z" + "iopub.execute_input": "2024-04-08T19:12:28.601134Z", + "iopub.status.busy": "2024-04-08T19:12:28.600813Z", + "iopub.status.idle": "2024-04-08T19:12:28.642167Z", + "shell.execute_reply": "2024-04-08T19:12:28.641727Z" }, "id": "SLq-3q4xjruX" }, @@ -1926,10 +1926,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.244487Z", - "iopub.status.busy": "2024-04-06T04:33:49.244090Z", - "iopub.status.idle": "2024-04-06T04:33:49.337248Z", - "shell.execute_reply": "2024-04-06T04:33:49.336579Z" + "iopub.execute_input": "2024-04-08T19:12:28.644151Z", + "iopub.status.busy": "2024-04-08T19:12:28.643835Z", + "iopub.status.idle": "2024-04-08T19:12:28.738961Z", + "shell.execute_reply": "2024-04-08T19:12:28.738341Z" }, "id": "g5LHhhuqFbXK" }, @@ -1961,10 +1961,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.339846Z", - "iopub.status.busy": "2024-04-06T04:33:49.339620Z", - "iopub.status.idle": "2024-04-06T04:33:49.430742Z", - "shell.execute_reply": "2024-04-06T04:33:49.430143Z" + "iopub.execute_input": "2024-04-08T19:12:28.741750Z", + "iopub.status.busy": "2024-04-08T19:12:28.741263Z", + "iopub.status.idle": "2024-04-08T19:12:28.827551Z", + "shell.execute_reply": "2024-04-08T19:12:28.826947Z" }, "id": "p7w8F8ezBcet" }, @@ -2021,10 +2021,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.432983Z", - "iopub.status.busy": "2024-04-06T04:33:49.432697Z", - "iopub.status.idle": "2024-04-06T04:33:49.645127Z", - "shell.execute_reply": "2024-04-06T04:33:49.644551Z" + "iopub.execute_input": "2024-04-08T19:12:28.829915Z", + "iopub.status.busy": "2024-04-08T19:12:28.829683Z", + "iopub.status.idle": "2024-04-08T19:12:29.038522Z", + "shell.execute_reply": "2024-04-08T19:12:29.037949Z" }, "id": "WETRL74tE_sU" }, @@ -2059,10 +2059,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.647536Z", - "iopub.status.busy": "2024-04-06T04:33:49.647110Z", - "iopub.status.idle": "2024-04-06T04:33:49.836451Z", - "shell.execute_reply": "2024-04-06T04:33:49.835806Z" + "iopub.execute_input": "2024-04-08T19:12:29.040910Z", + "iopub.status.busy": "2024-04-08T19:12:29.040732Z", + "iopub.status.idle": "2024-04-08T19:12:29.214576Z", + "shell.execute_reply": "2024-04-08T19:12:29.213963Z" }, "id": "kCfdx2gOLmXS" }, @@ -2224,10 +2224,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.838935Z", - "iopub.status.busy": "2024-04-06T04:33:49.838446Z", - "iopub.status.idle": "2024-04-06T04:33:49.845067Z", - "shell.execute_reply": "2024-04-06T04:33:49.844540Z" + "iopub.execute_input": "2024-04-08T19:12:29.217044Z", + "iopub.status.busy": "2024-04-08T19:12:29.216667Z", + "iopub.status.idle": "2024-04-08T19:12:29.222938Z", + "shell.execute_reply": "2024-04-08T19:12:29.222502Z" }, "id": "-uogYRWFYnuu" }, @@ -2281,10 +2281,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:49.847230Z", - "iopub.status.busy": "2024-04-06T04:33:49.846825Z", - "iopub.status.idle": "2024-04-06T04:33:50.065771Z", - "shell.execute_reply": "2024-04-06T04:33:50.065168Z" + "iopub.execute_input": "2024-04-08T19:12:29.224909Z", + "iopub.status.busy": "2024-04-08T19:12:29.224587Z", + "iopub.status.idle": "2024-04-08T19:12:29.437683Z", + "shell.execute_reply": "2024-04-08T19:12:29.437115Z" }, "id": "pG-ljrmcYp9Q" }, @@ -2331,10 +2331,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:50.068226Z", - "iopub.status.busy": "2024-04-06T04:33:50.067840Z", - "iopub.status.idle": "2024-04-06T04:33:51.143014Z", - "shell.execute_reply": "2024-04-06T04:33:51.142387Z" + "iopub.execute_input": "2024-04-08T19:12:29.439974Z", + "iopub.status.busy": "2024-04-08T19:12:29.439567Z", + "iopub.status.idle": "2024-04-08T19:12:30.486127Z", + "shell.execute_reply": "2024-04-08T19:12:30.485508Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index f2ec4a55c..a709c4ddc 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:54.655001Z", - "iopub.status.busy": "2024-04-06T04:33:54.654839Z", - "iopub.status.idle": "2024-04-06T04:33:55.737154Z", - "shell.execute_reply": "2024-04-06T04:33:55.736607Z" + "iopub.execute_input": "2024-04-08T19:12:33.752421Z", + "iopub.status.busy": "2024-04-08T19:12:33.752248Z", + "iopub.status.idle": "2024-04-08T19:12:34.830539Z", + "shell.execute_reply": "2024-04-08T19:12:34.829972Z" }, "nbsphinx": "hidden" }, @@ -102,7 +102,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -136,10 +136,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.739856Z", - "iopub.status.busy": "2024-04-06T04:33:55.739430Z", - "iopub.status.idle": "2024-04-06T04:33:55.742481Z", - "shell.execute_reply": "2024-04-06T04:33:55.741958Z" + "iopub.execute_input": "2024-04-08T19:12:34.833064Z", + "iopub.status.busy": "2024-04-08T19:12:34.832801Z", + "iopub.status.idle": "2024-04-08T19:12:34.835936Z", + "shell.execute_reply": "2024-04-08T19:12:34.835405Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.744755Z", - "iopub.status.busy": "2024-04-06T04:33:55.744422Z", - "iopub.status.idle": "2024-04-06T04:33:55.752051Z", - "shell.execute_reply": "2024-04-06T04:33:55.751620Z" + "iopub.execute_input": "2024-04-08T19:12:34.837888Z", + "iopub.status.busy": "2024-04-08T19:12:34.837708Z", + "iopub.status.idle": "2024-04-08T19:12:34.845722Z", + "shell.execute_reply": "2024-04-08T19:12:34.845317Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.754050Z", - "iopub.status.busy": "2024-04-06T04:33:55.753666Z", - "iopub.status.idle": "2024-04-06T04:33:55.808130Z", - "shell.execute_reply": "2024-04-06T04:33:55.807549Z" + "iopub.execute_input": "2024-04-08T19:12:34.847573Z", + "iopub.status.busy": "2024-04-08T19:12:34.847397Z", + "iopub.status.idle": "2024-04-08T19:12:34.894104Z", + "shell.execute_reply": "2024-04-08T19:12:34.893588Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.810525Z", - "iopub.status.busy": "2024-04-06T04:33:55.810206Z", - "iopub.status.idle": "2024-04-06T04:33:55.827426Z", - "shell.execute_reply": "2024-04-06T04:33:55.826967Z" + "iopub.execute_input": "2024-04-08T19:12:34.896019Z", + "iopub.status.busy": "2024-04-08T19:12:34.895834Z", + "iopub.status.idle": "2024-04-08T19:12:34.912597Z", + "shell.execute_reply": "2024-04-08T19:12:34.912094Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.829293Z", - "iopub.status.busy": "2024-04-06T04:33:55.829117Z", - "iopub.status.idle": "2024-04-06T04:33:55.833052Z", - "shell.execute_reply": "2024-04-06T04:33:55.832518Z" + "iopub.execute_input": "2024-04-08T19:12:34.914647Z", + "iopub.status.busy": "2024-04-08T19:12:34.914307Z", + "iopub.status.idle": "2024-04-08T19:12:34.917956Z", + "shell.execute_reply": "2024-04-08T19:12:34.917438Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.835165Z", - "iopub.status.busy": "2024-04-06T04:33:55.834833Z", - "iopub.status.idle": "2024-04-06T04:33:55.865218Z", - "shell.execute_reply": "2024-04-06T04:33:55.864706Z" + "iopub.execute_input": "2024-04-08T19:12:34.919974Z", + "iopub.status.busy": "2024-04-08T19:12:34.919671Z", + "iopub.status.idle": "2024-04-08T19:12:34.946169Z", + "shell.execute_reply": "2024-04-08T19:12:34.945655Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.867653Z", - "iopub.status.busy": "2024-04-06T04:33:55.867231Z", - "iopub.status.idle": "2024-04-06T04:33:55.894195Z", - "shell.execute_reply": "2024-04-06T04:33:55.893624Z" + "iopub.execute_input": "2024-04-08T19:12:34.948155Z", + "iopub.status.busy": "2024-04-08T19:12:34.947833Z", + "iopub.status.idle": "2024-04-08T19:12:34.973985Z", + "shell.execute_reply": "2024-04-08T19:12:34.973459Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:55.896247Z", - "iopub.status.busy": "2024-04-06T04:33:55.896066Z", - "iopub.status.idle": "2024-04-06T04:33:57.627098Z", - "shell.execute_reply": "2024-04-06T04:33:57.626566Z" + "iopub.execute_input": "2024-04-08T19:12:34.976074Z", + "iopub.status.busy": "2024-04-08T19:12:34.975781Z", + "iopub.status.idle": "2024-04-08T19:12:36.687655Z", + "shell.execute_reply": "2024-04-08T19:12:36.687110Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.629758Z", - "iopub.status.busy": "2024-04-06T04:33:57.629236Z", - "iopub.status.idle": "2024-04-06T04:33:57.636079Z", - "shell.execute_reply": "2024-04-06T04:33:57.635555Z" + "iopub.execute_input": "2024-04-08T19:12:36.690165Z", + "iopub.status.busy": "2024-04-08T19:12:36.689693Z", + "iopub.status.idle": "2024-04-08T19:12:36.696336Z", + "shell.execute_reply": "2024-04-08T19:12:36.695815Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.638210Z", - "iopub.status.busy": "2024-04-06T04:33:57.637876Z", - "iopub.status.idle": "2024-04-06T04:33:57.650276Z", - "shell.execute_reply": "2024-04-06T04:33:57.649820Z" + "iopub.execute_input": "2024-04-08T19:12:36.698324Z", + "iopub.status.busy": "2024-04-08T19:12:36.698034Z", + "iopub.status.idle": "2024-04-08T19:12:36.710339Z", + "shell.execute_reply": "2024-04-08T19:12:36.709902Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.652252Z", - "iopub.status.busy": "2024-04-06T04:33:57.651928Z", - "iopub.status.idle": "2024-04-06T04:33:57.658292Z", - "shell.execute_reply": "2024-04-06T04:33:57.657737Z" + "iopub.execute_input": "2024-04-08T19:12:36.712346Z", + "iopub.status.busy": "2024-04-08T19:12:36.711929Z", + "iopub.status.idle": "2024-04-08T19:12:36.718208Z", + "shell.execute_reply": "2024-04-08T19:12:36.717694Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.660348Z", - "iopub.status.busy": "2024-04-06T04:33:57.660033Z", - "iopub.status.idle": "2024-04-06T04:33:57.662546Z", - "shell.execute_reply": "2024-04-06T04:33:57.662096Z" + "iopub.execute_input": "2024-04-08T19:12:36.720257Z", + "iopub.status.busy": "2024-04-08T19:12:36.719972Z", + "iopub.status.idle": "2024-04-08T19:12:36.722551Z", + "shell.execute_reply": "2024-04-08T19:12:36.722114Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.664568Z", - "iopub.status.busy": "2024-04-06T04:33:57.664236Z", - "iopub.status.idle": "2024-04-06T04:33:57.667775Z", - "shell.execute_reply": "2024-04-06T04:33:57.667336Z" + "iopub.execute_input": "2024-04-08T19:12:36.724389Z", + "iopub.status.busy": "2024-04-08T19:12:36.724098Z", + "iopub.status.idle": "2024-04-08T19:12:36.727537Z", + "shell.execute_reply": "2024-04-08T19:12:36.727025Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.669724Z", - "iopub.status.busy": "2024-04-06T04:33:57.669426Z", - "iopub.status.idle": "2024-04-06T04:33:57.672060Z", - "shell.execute_reply": "2024-04-06T04:33:57.671546Z" + "iopub.execute_input": "2024-04-08T19:12:36.729415Z", + "iopub.status.busy": "2024-04-08T19:12:36.729242Z", + "iopub.status.idle": "2024-04-08T19:12:36.731642Z", + "shell.execute_reply": "2024-04-08T19:12:36.731237Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.673964Z", - "iopub.status.busy": "2024-04-06T04:33:57.673653Z", - "iopub.status.idle": "2024-04-06T04:33:57.677802Z", - "shell.execute_reply": "2024-04-06T04:33:57.677364Z" + "iopub.execute_input": "2024-04-08T19:12:36.733573Z", + "iopub.status.busy": "2024-04-08T19:12:36.733257Z", + "iopub.status.idle": "2024-04-08T19:12:36.737118Z", + "shell.execute_reply": "2024-04-08T19:12:36.736613Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.679746Z", - "iopub.status.busy": "2024-04-06T04:33:57.679562Z", - "iopub.status.idle": "2024-04-06T04:33:57.708692Z", - "shell.execute_reply": "2024-04-06T04:33:57.708184Z" + "iopub.execute_input": "2024-04-08T19:12:36.739150Z", + "iopub.status.busy": "2024-04-08T19:12:36.738829Z", + "iopub.status.idle": "2024-04-08T19:12:36.767384Z", + "shell.execute_reply": "2024-04-08T19:12:36.766867Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:33:57.711548Z", - "iopub.status.busy": "2024-04-06T04:33:57.711062Z", - "iopub.status.idle": "2024-04-06T04:33:57.716161Z", - "shell.execute_reply": "2024-04-06T04:33:57.715701Z" + "iopub.execute_input": "2024-04-08T19:12:36.769379Z", + "iopub.status.busy": "2024-04-08T19:12:36.769213Z", + "iopub.status.idle": "2024-04-08T19:12:36.773887Z", + "shell.execute_reply": "2024-04-08T19:12:36.773364Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index e4f3da5a6..93017979b 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": "2024-04-06T04:34:00.530023Z", - "iopub.status.busy": "2024-04-06T04:34:00.529838Z", - "iopub.status.idle": "2024-04-06T04:34:01.665208Z", - "shell.execute_reply": "2024-04-06T04:34:01.664664Z" + "iopub.execute_input": "2024-04-08T19:12:39.372434Z", + "iopub.status.busy": "2024-04-08T19:12:39.372031Z", + "iopub.status.idle": "2024-04-08T19:12:40.491618Z", + "shell.execute_reply": "2024-04-08T19:12:40.491005Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:01.667949Z", - "iopub.status.busy": "2024-04-06T04:34:01.667372Z", - "iopub.status.idle": "2024-04-06T04:34:01.860713Z", - "shell.execute_reply": "2024-04-06T04:34:01.860104Z" + "iopub.execute_input": "2024-04-08T19:12:40.494221Z", + "iopub.status.busy": "2024-04-08T19:12:40.493824Z", + "iopub.status.idle": "2024-04-08T19:12:40.685279Z", + "shell.execute_reply": "2024-04-08T19:12:40.684689Z" } }, "outputs": [], @@ -268,10 +268,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:01.863387Z", - "iopub.status.busy": "2024-04-06T04:34:01.863099Z", - "iopub.status.idle": "2024-04-06T04:34:01.876408Z", - "shell.execute_reply": "2024-04-06T04:34:01.875857Z" + "iopub.execute_input": "2024-04-08T19:12:40.688253Z", + "iopub.status.busy": "2024-04-08T19:12:40.687653Z", + "iopub.status.idle": "2024-04-08T19:12:40.701124Z", + "shell.execute_reply": "2024-04-08T19:12:40.700676Z" }, "nbsphinx": "hidden" }, @@ -407,10 +407,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:01.878382Z", - "iopub.status.busy": "2024-04-06T04:34:01.878075Z", - "iopub.status.idle": "2024-04-06T04:34:04.553375Z", - "shell.execute_reply": "2024-04-06T04:34:04.552763Z" + "iopub.execute_input": "2024-04-08T19:12:40.703173Z", + "iopub.status.busy": "2024-04-08T19:12:40.702854Z", + "iopub.status.idle": "2024-04-08T19:12:43.329249Z", + "shell.execute_reply": "2024-04-08T19:12:43.328755Z" } }, "outputs": [ @@ -454,10 +454,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:04.555866Z", - "iopub.status.busy": "2024-04-06T04:34:04.555447Z", - "iopub.status.idle": "2024-04-06T04:34:05.899176Z", - "shell.execute_reply": "2024-04-06T04:34:05.898628Z" + "iopub.execute_input": "2024-04-08T19:12:43.331514Z", + "iopub.status.busy": "2024-04-08T19:12:43.331169Z", + "iopub.status.idle": "2024-04-08T19:12:44.670891Z", + "shell.execute_reply": "2024-04-08T19:12:44.670276Z" } }, "outputs": [], @@ -499,10 +499,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:05.901446Z", - "iopub.status.busy": "2024-04-06T04:34:05.901252Z", - "iopub.status.idle": "2024-04-06T04:34:05.905303Z", - "shell.execute_reply": "2024-04-06T04:34:05.904832Z" + "iopub.execute_input": "2024-04-08T19:12:44.673413Z", + "iopub.status.busy": "2024-04-08T19:12:44.673216Z", + "iopub.status.idle": "2024-04-08T19:12:44.677262Z", + "shell.execute_reply": "2024-04-08T19:12:44.676816Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:05.907229Z", - "iopub.status.busy": "2024-04-06T04:34:05.906935Z", - "iopub.status.idle": "2024-04-06T04:34:07.727455Z", - "shell.execute_reply": "2024-04-06T04:34:07.726870Z" + "iopub.execute_input": "2024-04-08T19:12:44.679281Z", + "iopub.status.busy": "2024-04-08T19:12:44.678982Z", + "iopub.status.idle": "2024-04-08T19:12:46.437869Z", + "shell.execute_reply": "2024-04-08T19:12:46.437260Z" } }, "outputs": [ @@ -594,10 +594,10 @@ "id": "c18dd83b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:07.730219Z", - "iopub.status.busy": "2024-04-06T04:34:07.729486Z", - "iopub.status.idle": "2024-04-06T04:34:07.737826Z", - "shell.execute_reply": "2024-04-06T04:34:07.737345Z" + "iopub.execute_input": "2024-04-08T19:12:46.440643Z", + "iopub.status.busy": "2024-04-08T19:12:46.440072Z", + "iopub.status.idle": "2024-04-08T19:12:46.448250Z", + "shell.execute_reply": "2024-04-08T19:12:46.447724Z" } }, "outputs": [ @@ -633,10 +633,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:07.739895Z", - "iopub.status.busy": "2024-04-06T04:34:07.739580Z", - "iopub.status.idle": "2024-04-06T04:34:10.345477Z", - "shell.execute_reply": "2024-04-06T04:34:10.344972Z" + "iopub.execute_input": "2024-04-08T19:12:46.450615Z", + "iopub.status.busy": "2024-04-08T19:12:46.450220Z", + "iopub.status.idle": "2024-04-08T19:12:49.029942Z", + "shell.execute_reply": "2024-04-08T19:12:49.029325Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.347724Z", - "iopub.status.busy": "2024-04-06T04:34:10.347360Z", - "iopub.status.idle": "2024-04-06T04:34:10.351001Z", - "shell.execute_reply": "2024-04-06T04:34:10.350556Z" + "iopub.execute_input": "2024-04-08T19:12:49.032160Z", + "iopub.status.busy": "2024-04-08T19:12:49.031822Z", + "iopub.status.idle": "2024-04-08T19:12:49.035518Z", + "shell.execute_reply": "2024-04-08T19:12:49.035071Z" } }, "outputs": [ @@ -721,10 +721,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.352909Z", - "iopub.status.busy": "2024-04-06T04:34:10.352732Z", - "iopub.status.idle": "2024-04-06T04:34:10.357176Z", - "shell.execute_reply": "2024-04-06T04:34:10.356760Z" + "iopub.execute_input": "2024-04-08T19:12:49.037498Z", + "iopub.status.busy": "2024-04-08T19:12:49.037171Z", + "iopub.status.idle": "2024-04-08T19:12:49.041048Z", + "shell.execute_reply": "2024-04-08T19:12:49.040619Z" } }, "outputs": [], @@ -752,10 +752,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:10.359140Z", - "iopub.status.busy": "2024-04-06T04:34:10.358816Z", - "iopub.status.idle": "2024-04-06T04:34:10.361865Z", - "shell.execute_reply": "2024-04-06T04:34:10.361423Z" + "iopub.execute_input": "2024-04-08T19:12:49.042924Z", + "iopub.status.busy": "2024-04-08T19:12:49.042604Z", + "iopub.status.idle": "2024-04-08T19:12:49.045672Z", + "shell.execute_reply": "2024-04-08T19:12:49.045228Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index a41b44c5c..b290d6163 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:12.844775Z", - "iopub.status.busy": "2024-04-06T04:34:12.844311Z", - "iopub.status.idle": "2024-04-06T04:34:13.980776Z", - "shell.execute_reply": "2024-04-06T04:34:13.980176Z" + "iopub.execute_input": "2024-04-08T19:12:51.506697Z", + "iopub.status.busy": "2024-04-08T19:12:51.506534Z", + "iopub.status.idle": "2024-04-08T19:12:52.637000Z", + "shell.execute_reply": "2024-04-08T19:12:52.636397Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:13.983263Z", - "iopub.status.busy": "2024-04-06T04:34:13.983016Z", - "iopub.status.idle": "2024-04-06T04:34:15.579622Z", - "shell.execute_reply": "2024-04-06T04:34:15.579010Z" + "iopub.execute_input": "2024-04-08T19:12:52.639569Z", + "iopub.status.busy": "2024-04-08T19:12:52.639309Z", + "iopub.status.idle": "2024-04-08T19:12:55.104415Z", + "shell.execute_reply": "2024-04-08T19:12:55.103670Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.582324Z", - "iopub.status.busy": "2024-04-06T04:34:15.581949Z", - "iopub.status.idle": "2024-04-06T04:34:15.585226Z", - "shell.execute_reply": "2024-04-06T04:34:15.584699Z" + "iopub.execute_input": "2024-04-08T19:12:55.107140Z", + "iopub.status.busy": "2024-04-08T19:12:55.106931Z", + "iopub.status.idle": "2024-04-08T19:12:55.110341Z", + "shell.execute_reply": "2024-04-08T19:12:55.109801Z" } }, "outputs": [], @@ -169,10 +169,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.587256Z", - "iopub.status.busy": "2024-04-06T04:34:15.586885Z", - "iopub.status.idle": "2024-04-06T04:34:15.593670Z", - "shell.execute_reply": "2024-04-06T04:34:15.593228Z" + "iopub.execute_input": "2024-04-08T19:12:55.112430Z", + "iopub.status.busy": "2024-04-08T19:12:55.112060Z", + "iopub.status.idle": "2024-04-08T19:12:55.118161Z", + "shell.execute_reply": "2024-04-08T19:12:55.117642Z" } }, "outputs": [], @@ -198,10 +198,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:15.595551Z", - "iopub.status.busy": "2024-04-06T04:34:15.595372Z", - "iopub.status.idle": "2024-04-06T04:34:16.077823Z", - "shell.execute_reply": "2024-04-06T04:34:16.077255Z" + "iopub.execute_input": "2024-04-08T19:12:55.120250Z", + "iopub.status.busy": "2024-04-08T19:12:55.119951Z", + "iopub.status.idle": "2024-04-08T19:12:55.604414Z", + "shell.execute_reply": "2024-04-08T19:12:55.603862Z" }, "scrolled": true }, @@ -242,10 +242,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.079945Z", - "iopub.status.busy": "2024-04-06T04:34:16.079765Z", - "iopub.status.idle": "2024-04-06T04:34:16.085000Z", - "shell.execute_reply": "2024-04-06T04:34:16.084559Z" + "iopub.execute_input": "2024-04-08T19:12:55.607305Z", + "iopub.status.busy": "2024-04-08T19:12:55.606952Z", + "iopub.status.idle": "2024-04-08T19:12:55.612116Z", + "shell.execute_reply": "2024-04-08T19:12:55.611686Z" } }, "outputs": [ @@ -497,10 +497,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.086986Z", - "iopub.status.busy": "2024-04-06T04:34:16.086699Z", - "iopub.status.idle": "2024-04-06T04:34:16.090564Z", - "shell.execute_reply": "2024-04-06T04:34:16.090132Z" + "iopub.execute_input": "2024-04-08T19:12:55.614134Z", + "iopub.status.busy": "2024-04-08T19:12:55.613824Z", + "iopub.status.idle": "2024-04-08T19:12:55.617419Z", + "shell.execute_reply": "2024-04-08T19:12:55.617014Z" } }, "outputs": [ @@ -557,10 +557,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.092402Z", - "iopub.status.busy": "2024-04-06T04:34:16.092226Z", - "iopub.status.idle": "2024-04-06T04:34:16.742313Z", - "shell.execute_reply": "2024-04-06T04:34:16.741698Z" + "iopub.execute_input": "2024-04-08T19:12:55.619403Z", + "iopub.status.busy": "2024-04-08T19:12:55.619145Z", + "iopub.status.idle": "2024-04-08T19:12:56.292272Z", + "shell.execute_reply": "2024-04-08T19:12:56.291640Z" } }, "outputs": [ @@ -616,10 +616,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.744425Z", - "iopub.status.busy": "2024-04-06T04:34:16.744233Z", - "iopub.status.idle": "2024-04-06T04:34:16.915555Z", - "shell.execute_reply": "2024-04-06T04:34:16.915036Z" + "iopub.execute_input": "2024-04-08T19:12:56.294743Z", + "iopub.status.busy": "2024-04-08T19:12:56.294368Z", + "iopub.status.idle": "2024-04-08T19:12:56.451834Z", + "shell.execute_reply": "2024-04-08T19:12:56.451237Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.917406Z", - "iopub.status.busy": "2024-04-06T04:34:16.917231Z", - "iopub.status.idle": "2024-04-06T04:34:16.921449Z", - "shell.execute_reply": "2024-04-06T04:34:16.921026Z" + "iopub.execute_input": "2024-04-08T19:12:56.454163Z", + "iopub.status.busy": "2024-04-08T19:12:56.453784Z", + "iopub.status.idle": "2024-04-08T19:12:56.458257Z", + "shell.execute_reply": "2024-04-08T19:12:56.457717Z" } }, "outputs": [ @@ -700,10 +700,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:16.923486Z", - "iopub.status.busy": "2024-04-06T04:34:16.923119Z", - "iopub.status.idle": "2024-04-06T04:34:17.368354Z", - "shell.execute_reply": "2024-04-06T04:34:17.367768Z" + "iopub.execute_input": "2024-04-08T19:12:56.460284Z", + "iopub.status.busy": "2024-04-08T19:12:56.459945Z", + "iopub.status.idle": "2024-04-08T19:12:56.918547Z", + "shell.execute_reply": "2024-04-08T19:12:56.917913Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:17.371163Z", - "iopub.status.busy": "2024-04-06T04:34:17.370822Z", - "iopub.status.idle": "2024-04-06T04:34:17.674268Z", - "shell.execute_reply": "2024-04-06T04:34:17.673692Z" + "iopub.execute_input": "2024-04-08T19:12:56.921651Z", + "iopub.status.busy": "2024-04-08T19:12:56.921292Z", + "iopub.status.idle": "2024-04-08T19:12:57.253473Z", + "shell.execute_reply": "2024-04-08T19:12:57.252856Z" } }, "outputs": [ @@ -812,10 +812,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:17.676624Z", - "iopub.status.busy": "2024-04-06T04:34:17.676303Z", - "iopub.status.idle": "2024-04-06T04:34:18.037637Z", - "shell.execute_reply": "2024-04-06T04:34:18.037134Z" + "iopub.execute_input": "2024-04-08T19:12:57.255657Z", + "iopub.status.busy": "2024-04-08T19:12:57.255478Z", + "iopub.status.idle": "2024-04-08T19:12:57.619053Z", + "shell.execute_reply": "2024-04-08T19:12:57.618466Z" } }, "outputs": [ @@ -862,10 +862,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.040616Z", - "iopub.status.busy": "2024-04-06T04:34:18.040298Z", - "iopub.status.idle": "2024-04-06T04:34:18.480221Z", - "shell.execute_reply": "2024-04-06T04:34:18.479710Z" + "iopub.execute_input": "2024-04-08T19:12:57.621976Z", + "iopub.status.busy": "2024-04-08T19:12:57.621623Z", + "iopub.status.idle": "2024-04-08T19:12:58.060261Z", + "shell.execute_reply": "2024-04-08T19:12:58.059741Z" } }, "outputs": [ @@ -925,10 +925,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.484224Z", - "iopub.status.busy": "2024-04-06T04:34:18.483951Z", - "iopub.status.idle": "2024-04-06T04:34:18.910308Z", - "shell.execute_reply": "2024-04-06T04:34:18.909828Z" + "iopub.execute_input": "2024-04-08T19:12:58.064403Z", + "iopub.status.busy": "2024-04-08T19:12:58.064187Z", + "iopub.status.idle": "2024-04-08T19:12:58.481716Z", + "shell.execute_reply": "2024-04-08T19:12:58.481166Z" } }, "outputs": [ @@ -971,10 +971,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:18.912281Z", - "iopub.status.busy": "2024-04-06T04:34:18.912098Z", - "iopub.status.idle": "2024-04-06T04:34:19.127034Z", - "shell.execute_reply": "2024-04-06T04:34:19.126447Z" + "iopub.execute_input": "2024-04-08T19:12:58.484504Z", + "iopub.status.busy": "2024-04-08T19:12:58.484329Z", + "iopub.status.idle": "2024-04-08T19:12:58.698454Z", + "shell.execute_reply": "2024-04-08T19:12:58.697889Z" } }, "outputs": [ @@ -1017,10 +1017,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.129044Z", - "iopub.status.busy": "2024-04-06T04:34:19.128856Z", - "iopub.status.idle": "2024-04-06T04:34:19.327498Z", - "shell.execute_reply": "2024-04-06T04:34:19.327017Z" + "iopub.execute_input": "2024-04-08T19:12:58.700764Z", + "iopub.status.busy": "2024-04-08T19:12:58.700331Z", + "iopub.status.idle": "2024-04-08T19:12:58.897447Z", + "shell.execute_reply": "2024-04-08T19:12:58.896906Z" } }, "outputs": [ @@ -1067,10 +1067,10 @@ "id": "5cacec81-2adf-46a8-82c5-7ec0185d4356", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.329747Z", - "iopub.status.busy": "2024-04-06T04:34:19.329569Z", - "iopub.status.idle": "2024-04-06T04:34:19.332430Z", - "shell.execute_reply": "2024-04-06T04:34:19.332000Z" + "iopub.execute_input": "2024-04-08T19:12:58.899675Z", + "iopub.status.busy": "2024-04-08T19:12:58.899273Z", + "iopub.status.idle": "2024-04-08T19:12:58.902127Z", + "shell.execute_reply": "2024-04-08T19:12:58.901613Z" } }, "outputs": [], @@ -1090,10 +1090,10 @@ "id": "3335b8a3-d0b4-415a-a97d-c203088a124e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:19.334383Z", - "iopub.status.busy": "2024-04-06T04:34:19.334059Z", - "iopub.status.idle": "2024-04-06T04:34:20.209133Z", - "shell.execute_reply": "2024-04-06T04:34:20.208555Z" + "iopub.execute_input": "2024-04-08T19:12:58.904091Z", + "iopub.status.busy": "2024-04-08T19:12:58.903780Z", + "iopub.status.idle": "2024-04-08T19:12:59.779761Z", + "shell.execute_reply": "2024-04-08T19:12:59.779165Z" } }, "outputs": [ @@ -1172,10 +1172,10 @@ "id": "9d4b7677-6ebd-447d-b0a1-76e094686628", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.211448Z", - "iopub.status.busy": "2024-04-06T04:34:20.211008Z", - "iopub.status.idle": "2024-04-06T04:34:20.342519Z", - "shell.execute_reply": "2024-04-06T04:34:20.342095Z" + "iopub.execute_input": "2024-04-08T19:12:59.782264Z", + "iopub.status.busy": "2024-04-08T19:12:59.781937Z", + "iopub.status.idle": "2024-04-08T19:12:59.964112Z", + "shell.execute_reply": "2024-04-08T19:12:59.963525Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "59d7ee39-3785-434b-8680-9133014851cd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.344524Z", - "iopub.status.busy": "2024-04-06T04:34:20.344193Z", - "iopub.status.idle": "2024-04-06T04:34:20.458465Z", - "shell.execute_reply": "2024-04-06T04:34:20.457952Z" + "iopub.execute_input": "2024-04-08T19:12:59.966448Z", + "iopub.status.busy": "2024-04-08T19:12:59.965968Z", + "iopub.status.idle": "2024-04-08T19:13:00.154653Z", + "shell.execute_reply": "2024-04-08T19:13:00.154036Z" } }, "outputs": [], @@ -1266,10 +1266,10 @@ "id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:20.460533Z", - "iopub.status.busy": "2024-04-06T04:34:20.460222Z", - "iopub.status.idle": "2024-04-06T04:34:21.196312Z", - "shell.execute_reply": "2024-04-06T04:34:21.195737Z" + "iopub.execute_input": "2024-04-08T19:13:00.156712Z", + "iopub.status.busy": "2024-04-08T19:13:00.156532Z", + "iopub.status.idle": "2024-04-08T19:13:00.829599Z", + "shell.execute_reply": "2024-04-08T19:13:00.829059Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:21.198485Z", - "iopub.status.busy": "2024-04-06T04:34:21.198170Z", - "iopub.status.idle": "2024-04-06T04:34:21.201764Z", - "shell.execute_reply": "2024-04-06T04:34:21.201234Z" + "iopub.execute_input": "2024-04-08T19:13:00.832154Z", + "iopub.status.busy": "2024-04-08T19:13:00.831662Z", + "iopub.status.idle": "2024-04-08T19:13:00.835999Z", + "shell.execute_reply": "2024-04-08T19:13:00.835484Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 3d4c5d8af..5348041dc 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -746,7 +746,7 @@

    2. Pre-process the Cifar10 dataset 
    -100%|██████████| 170498071/170498071 [00:02<00:00, 72776359.59it/s]
+100%|██████████| 170498071/170498071 [00:04<00:00, 37601665.95it/s]
    @@ -876,7 +876,7 @@

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

    -
    +
    @@ -1090,7 +1090,7 @@

    4. Use cleanlab and here.

    diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index dff88146d..c8a250110 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:23.301443Z", - "iopub.status.busy": "2024-04-06T04:34:23.301280Z", - "iopub.status.idle": "2024-04-06T04:34:25.945799Z", - "shell.execute_reply": "2024-04-06T04:34:25.945183Z" + "iopub.execute_input": "2024-04-08T19:13:03.168340Z", + "iopub.status.busy": "2024-04-08T19:13:03.168171Z", + "iopub.status.idle": "2024-04-08T19:13:05.872246Z", + "shell.execute_reply": "2024-04-08T19:13:05.871721Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:34:25.948528Z", - "iopub.status.busy": "2024-04-06T04:34:25.948218Z", - "iopub.status.idle": "2024-04-06T04:34:26.266936Z", - "shell.execute_reply": "2024-04-06T04:34:26.266392Z" + "iopub.execute_input": "2024-04-08T19:13:05.874860Z", + "iopub.status.busy": "2024-04-08T19:13:05.874355Z", + "iopub.status.idle": "2024-04-08T19:13:06.204418Z", + "shell.execute_reply": "2024-04-08T19:13:06.203821Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:26.269348Z", - "iopub.status.busy": "2024-04-06T04:34:26.269036Z", - "iopub.status.idle": "2024-04-06T04:34:26.272997Z", - "shell.execute_reply": "2024-04-06T04:34:26.272583Z" + "iopub.execute_input": "2024-04-08T19:13:06.206962Z", + "iopub.status.busy": "2024-04-08T19:13:06.206657Z", + "iopub.status.idle": "2024-04-08T19:13:06.210651Z", + "shell.execute_reply": "2024-04-08T19:13:06.210217Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:26.275074Z", - "iopub.status.busy": "2024-04-06T04:34:26.274739Z", - "iopub.status.idle": "2024-04-06T04:34:31.314624Z", - "shell.execute_reply": "2024-04-06T04:34:31.314110Z" + "iopub.execute_input": "2024-04-08T19:13:06.212643Z", + "iopub.status.busy": "2024-04-08T19:13:06.212236Z", + "iopub.status.idle": "2024-04-08T19:13:14.211316Z", + "shell.execute_reply": "2024-04-08T19:13:14.210735Z" } }, "outputs": [ @@ -252,7 +252,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 1769472/170498071 [00:00<00:09, 17538639.93it/s]" + " 0%| | 32768/170498071 [00:00<11:46, 241421.69it/s]" ] }, { @@ -260,7 +260,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 8192000/170498071 [00:00<00:03, 44831466.83it/s]" + " 0%| | 229376/170498071 [00:00<03:01, 939950.18it/s]" ] }, { @@ -268,7 +268,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 13041664/170498071 [00:00<00:03, 46433907.51it/s]" + " 1%| | 884736/170498071 [00:00<01:03, 2688209.96it/s]" ] }, { @@ -276,7 +276,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 19791872/170498071 [00:00<00:02, 54704480.34it/s]" + " 2%|▏ | 3538944/170498071 [00:00<00:18, 9239543.74it/s]" ] }, { @@ -284,7 +284,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 25788416/170498071 [00:00<00:02, 56333002.76it/s]" + " 6%|▌ | 9633792/170498071 [00:00<00:07, 21711409.29it/s]" ] }, { @@ -292,7 +292,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 31424512/170498071 [00:00<00:02, 55036228.43it/s]" + " 9%|▉ | 15695872/170498071 [00:00<00:04, 32236184.47it/s]" ] }, { @@ -300,7 +300,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 37978112/170498071 [00:00<00:02, 58347764.86it/s]" + " 11%|█▏ | 19202048/170498071 [00:00<00:04, 31213118.98it/s]" ] }, { @@ -308,7 +308,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 43843584/170498071 [00:00<00:02, 56723331.51it/s]" + " 15%|█▍ | 25165824/170498071 [00:01<00:03, 36748958.36it/s]" ] }, { @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 49676288/170498071 [00:00<00:02, 57186066.18it/s]" + " 17%|█▋ | 29196288/170498071 [00:01<00:03, 37692490.20it/s]" ] }, { @@ -324,7 +324,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 56197120/170498071 [00:01<00:01, 59520956.47it/s]" + " 20%|██ | 34471936/170498071 [00:01<00:03, 41145514.19it/s]" ] }, { @@ -332,7 +332,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 62193664/170498071 [00:01<00:01, 55914267.41it/s]" + " 23%|██▎ | 38699008/170498071 [00:01<00:03, 40166399.16it/s]" ] }, { @@ -340,7 +340,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 68943872/170498071 [00:01<00:01, 59219350.19it/s]" + " 26%|██▌ | 43909120/170498071 [00:01<00:02, 43337775.10it/s]" ] }, { @@ -348,7 +348,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 77758464/170498071 [00:01<00:01, 67611548.92it/s]" + " 28%|██▊ | 48332800/170498071 [00:01<00:02, 41475797.12it/s]" ] }, { @@ -356,7 +356,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 87556096/170498071 [00:01<00:01, 76485308.02it/s]" + " 31%|███ | 52822016/170498071 [00:01<00:02, 42394898.27it/s]" ] }, { @@ -364,7 +364,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 98697216/170498071 [00:01<00:00, 86783379.22it/s]" + " 33%|███▎ | 57114624/170498071 [00:01<00:02, 40475978.10it/s]" ] }, { @@ -372,7 +372,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 107642880/170498071 [00:01<00:00, 87414535.88it/s]" + " 36%|███▌ | 61571072/170498071 [00:01<00:02, 41609620.80it/s]" ] }, { @@ -380,7 +380,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 117768192/170498071 [00:01<00:00, 91407747.36it/s]" + " 39%|███▊ | 65863680/170498071 [00:02<00:02, 39729349.49it/s]" ] }, { @@ -388,7 +388,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 127565824/170498071 [00:01<00:00, 93045985.07it/s]" + " 41%|████▏ | 70418432/170498071 [00:02<00:02, 41333024.63it/s]" ] }, { @@ -396,7 +396,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 136904704/170498071 [00:01<00:00, 88471190.33it/s]" + " 44%|████▍ | 74809344/170498071 [00:02<00:02, 42035778.22it/s]" ] }, { @@ -404,7 +404,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 146505728/170498071 [00:02<00:00, 90636821.57it/s]" + " 46%|████▋ | 79069184/170498071 [00:02<00:02, 40305702.80it/s]" ] }, { @@ -412,7 +412,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 155648000/170498071 [00:02<00:00, 86904824.23it/s]" + " 49%|████▉ | 83427328/170498071 [00:02<00:02, 41196757.42it/s]" ] }, { @@ -420,7 +420,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 165478400/170498071 [00:02<00:00, 90092503.62it/s]" + " 51%|█████▏ | 87588864/170498071 [00:02<00:02, 39637138.62it/s]" ] }, { @@ -428,7 +428,151 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 170498071/170498071 [00:02<00:00, 72776359.59it/s]" + " 54%|█████▍ | 91881472/170498071 [00:02<00:01, 40536471.23it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 56%|█████▋ | 95977472/170498071 [00:02<00:01, 39252273.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 59%|█████▉ | 100466688/170498071 [00:02<00:01, 40454041.31it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 62%|██████▏ | 104955904/170498071 [00:02<00:01, 39445045.53it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 64%|██████▍ | 109510656/170498071 [00:03<00:01, 41083281.40it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 67%|██████▋ | 113803264/170498071 [00:03<00:01, 41583370.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 69%|██████▉ | 117997568/170498071 [00:03<00:01, 40073702.55it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 72%|███████▏ | 122290176/170498071 [00:03<00:01, 40828624.13it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 74%|███████▍ | 126418944/170498071 [00:03<00:01, 39461562.96it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 77%|███████▋ | 130744320/170498071 [00:03<00:00, 40509967.30it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 79%|███████▉ | 135266304/170498071 [00:03<00:00, 41845498.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 82%|████████▏ | 139493376/170498071 [00:03<00:00, 40939532.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 85%|████████▍ | 144211968/170498071 [00:03<00:00, 42696106.22it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 87%|████████▋ | 148504576/170498071 [00:04<00:00, 41635041.16it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 90%|████████▉ | 152961024/170498071 [00:04<00:00, 42468713.05it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 92%|█████████▏| 157253632/170498071 [00:04<00:00, 42241843.01it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 95%|█████████▍| 161939456/170498071 [00:04<00:00, 43462412.29it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 166297600/170498071 [00:04<00:00, 43386976.71it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 170498071/170498071 [00:04<00:00, 37601665.95it/s]" ] }, { @@ -546,10 +690,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.316844Z", - "iopub.status.busy": "2024-04-06T04:34:31.316485Z", - "iopub.status.idle": "2024-04-06T04:34:31.321190Z", - "shell.execute_reply": "2024-04-06T04:34:31.320736Z" + "iopub.execute_input": "2024-04-08T19:13:14.213408Z", + "iopub.status.busy": "2024-04-08T19:13:14.213222Z", + "iopub.status.idle": "2024-04-08T19:13:14.217828Z", + "shell.execute_reply": "2024-04-08T19:13:14.217410Z" }, "nbsphinx": "hidden" }, @@ -600,10 +744,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.323414Z", - "iopub.status.busy": "2024-04-06T04:34:31.323024Z", - "iopub.status.idle": "2024-04-06T04:34:31.843073Z", - "shell.execute_reply": "2024-04-06T04:34:31.842461Z" + "iopub.execute_input": "2024-04-08T19:13:14.219646Z", + "iopub.status.busy": "2024-04-08T19:13:14.219474Z", + "iopub.status.idle": "2024-04-08T19:13:14.735288Z", + "shell.execute_reply": "2024-04-08T19:13:14.734716Z" } }, "outputs": [ @@ -636,10 +780,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:31.845476Z", - "iopub.status.busy": "2024-04-06T04:34:31.845122Z", - "iopub.status.idle": "2024-04-06T04:34:32.343468Z", - "shell.execute_reply": "2024-04-06T04:34:32.342863Z" + "iopub.execute_input": "2024-04-08T19:13:14.737482Z", + "iopub.status.busy": "2024-04-08T19:13:14.737170Z", + "iopub.status.idle": "2024-04-08T19:13:15.227922Z", + "shell.execute_reply": "2024-04-08T19:13:15.227323Z" } }, "outputs": [ @@ -677,10 +821,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:32.345737Z", - "iopub.status.busy": "2024-04-06T04:34:32.345520Z", - "iopub.status.idle": "2024-04-06T04:34:32.349079Z", - "shell.execute_reply": "2024-04-06T04:34:32.348636Z" + "iopub.execute_input": "2024-04-08T19:13:15.229967Z", + "iopub.status.busy": "2024-04-08T19:13:15.229777Z", + "iopub.status.idle": "2024-04-08T19:13:15.233685Z", + "shell.execute_reply": "2024-04-08T19:13:15.233276Z" } }, "outputs": [], @@ -703,17 +847,17 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:32.351161Z", - "iopub.status.busy": "2024-04-06T04:34:32.350840Z", - "iopub.status.idle": "2024-04-06T04:34:45.259522Z", - "shell.execute_reply": "2024-04-06T04:34:45.258934Z" + "iopub.execute_input": "2024-04-08T19:13:15.235578Z", + "iopub.status.busy": "2024-04-08T19:13:15.235253Z", + "iopub.status.idle": "2024-04-08T19:13:27.791114Z", + "shell.execute_reply": "2024-04-08T19:13:27.790500Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "991b461cb5f14fa38412734f4f788575", + "model_id": "2bb5503dd8b443508a98689b99426ed1", "version_major": 2, "version_minor": 0 }, @@ -772,10 +916,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:45.261911Z", - "iopub.status.busy": "2024-04-06T04:34:45.261529Z", - "iopub.status.idle": "2024-04-06T04:34:46.966878Z", - "shell.execute_reply": "2024-04-06T04:34:46.966282Z" + "iopub.execute_input": "2024-04-08T19:13:27.793604Z", + "iopub.status.busy": "2024-04-08T19:13:27.793211Z", + "iopub.status.idle": "2024-04-08T19:13:29.587802Z", + "shell.execute_reply": "2024-04-08T19:13:29.587253Z" } }, "outputs": [ @@ -819,10 +963,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:46.969590Z", - "iopub.status.busy": "2024-04-06T04:34:46.969163Z", - "iopub.status.idle": "2024-04-06T04:34:47.194956Z", - "shell.execute_reply": "2024-04-06T04:34:47.194388Z" + "iopub.execute_input": "2024-04-08T19:13:29.590598Z", + "iopub.status.busy": "2024-04-08T19:13:29.590127Z", + "iopub.status.idle": "2024-04-08T19:13:29.858111Z", + "shell.execute_reply": "2024-04-08T19:13:29.857584Z" } }, "outputs": [ @@ -858,10 +1002,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:47.197286Z", - "iopub.status.busy": "2024-04-06T04:34:47.197100Z", - "iopub.status.idle": "2024-04-06T04:34:47.844542Z", - "shell.execute_reply": "2024-04-06T04:34:47.843965Z" + "iopub.execute_input": "2024-04-08T19:13:29.861034Z", + "iopub.status.busy": "2024-04-08T19:13:29.860632Z", + "iopub.status.idle": "2024-04-08T19:13:30.577484Z", + "shell.execute_reply": "2024-04-08T19:13:30.576958Z" } }, "outputs": [ @@ -911,10 +1055,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:47.847025Z", - "iopub.status.busy": "2024-04-06T04:34:47.846663Z", - "iopub.status.idle": "2024-04-06T04:34:48.133586Z", - "shell.execute_reply": "2024-04-06T04:34:48.133164Z" + "iopub.execute_input": "2024-04-08T19:13:30.580265Z", + "iopub.status.busy": "2024-04-08T19:13:30.579691Z", + "iopub.status.idle": "2024-04-08T19:13:30.924605Z", + "shell.execute_reply": "2024-04-08T19:13:30.924026Z" } }, "outputs": [ @@ -962,10 +1106,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.135743Z", - "iopub.status.busy": "2024-04-06T04:34:48.135451Z", - "iopub.status.idle": "2024-04-06T04:34:48.362823Z", - "shell.execute_reply": "2024-04-06T04:34:48.362258Z" + "iopub.execute_input": "2024-04-08T19:13:30.926999Z", + "iopub.status.busy": "2024-04-08T19:13:30.926574Z", + "iopub.status.idle": "2024-04-08T19:13:31.175317Z", + "shell.execute_reply": "2024-04-08T19:13:31.174782Z" } }, "outputs": [ @@ -1021,10 +1165,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.365290Z", - "iopub.status.busy": "2024-04-06T04:34:48.364817Z", - "iopub.status.idle": "2024-04-06T04:34:48.441430Z", - "shell.execute_reply": "2024-04-06T04:34:48.440837Z" + "iopub.execute_input": "2024-04-08T19:13:31.177937Z", + "iopub.status.busy": "2024-04-08T19:13:31.177576Z", + "iopub.status.idle": "2024-04-08T19:13:31.272978Z", + "shell.execute_reply": "2024-04-08T19:13:31.272473Z" } }, "outputs": [], @@ -1045,10 +1189,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:48.444056Z", - "iopub.status.busy": "2024-04-06T04:34:48.443776Z", - "iopub.status.idle": "2024-04-06T04:34:58.624130Z", - "shell.execute_reply": "2024-04-06T04:34:58.623554Z" + "iopub.execute_input": "2024-04-08T19:13:31.275519Z", + "iopub.status.busy": "2024-04-08T19:13:31.275167Z", + "iopub.status.idle": "2024-04-08T19:13:41.679014Z", + "shell.execute_reply": "2024-04-08T19:13:41.678397Z" } }, "outputs": [ @@ -1085,10 +1229,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:34:58.626457Z", - "iopub.status.busy": "2024-04-06T04:34:58.626142Z", - "iopub.status.idle": "2024-04-06T04:35:00.411515Z", - "shell.execute_reply": "2024-04-06T04:35:00.411019Z" + "iopub.execute_input": "2024-04-08T19:13:41.681464Z", + "iopub.status.busy": "2024-04-08T19:13:41.681014Z", + "iopub.status.idle": "2024-04-08T19:13:43.393278Z", + "shell.execute_reply": "2024-04-08T19:13:43.392676Z" } }, "outputs": [ @@ -1119,10 +1263,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.414250Z", - "iopub.status.busy": "2024-04-06T04:35:00.413656Z", - "iopub.status.idle": "2024-04-06T04:35:00.626834Z", - "shell.execute_reply": "2024-04-06T04:35:00.626355Z" + "iopub.execute_input": "2024-04-08T19:13:43.395878Z", + "iopub.status.busy": "2024-04-08T19:13:43.395510Z", + "iopub.status.idle": "2024-04-08T19:13:43.601564Z", + "shell.execute_reply": "2024-04-08T19:13:43.600964Z" } }, "outputs": [], @@ -1136,10 +1280,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.629326Z", - "iopub.status.busy": "2024-04-06T04:35:00.628902Z", - "iopub.status.idle": "2024-04-06T04:35:00.632039Z", - "shell.execute_reply": "2024-04-06T04:35:00.631618Z" + "iopub.execute_input": "2024-04-08T19:13:43.604043Z", + "iopub.status.busy": "2024-04-08T19:13:43.603730Z", + "iopub.status.idle": "2024-04-08T19:13:43.606880Z", + "shell.execute_reply": "2024-04-08T19:13:43.606367Z" } }, "outputs": [], @@ -1161,10 +1305,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:00.634061Z", - "iopub.status.busy": "2024-04-06T04:35:00.633729Z", - "iopub.status.idle": "2024-04-06T04:35:00.641703Z", - "shell.execute_reply": "2024-04-06T04:35:00.641295Z" + "iopub.execute_input": "2024-04-08T19:13:43.609039Z", + "iopub.status.busy": "2024-04-08T19:13:43.608752Z", + "iopub.status.idle": "2024-04-08T19:13:43.617066Z", + "shell.execute_reply": "2024-04-08T19:13:43.616668Z" }, "nbsphinx": "hidden" }, @@ -1209,7 +1353,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "15c8db426b2d442dafc5fec0ada46d26": { + "007c6ddc44eb433e853f88ed09044f49": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1262,7 +1406,7 @@ "width": null } }, - "18e0c03543334359bae24bc35d678719": { + "086fdb340ddc44499e840c6359ce1479": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1280,7 +1424,30 @@ "text_color": null } }, - "5888f59c5c7747d284d2a1179b08220a": { + "0c6902059f6d43049f050a70f2c4d5ed": { + "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_007c6ddc44eb433e853f88ed09044f49", + "placeholder": "​", + "style": "IPY_MODEL_8e6e75da45e94500ac3664d6571c19a5", + "tabbable": null, + "tooltip": null, + "value": " 102M/102M [00:00<00:00, 261MB/s]" + } + }, + "0c87bf09ff7545318077176d0bc67dc5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1333,53 +1500,31 @@ "width": null } }, - "62503695057042fe9e46cf6d976cf0ec": { + "2bb5503dd8b443508a98689b99426ed1": { "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_dc69440eba354ce18f5a8f226872b05a", - "placeholder": "​", - "style": "IPY_MODEL_18e0c03543334359bae24bc35d678719", - "tabbable": null, - "tooltip": null, - "value": " 102M/102M [00:00<00:00, 180MB/s]" - } - }, - "66a60ffdadfe43c49835a3149977dd23": { - "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_5888f59c5c7747d284d2a1179b08220a", - "placeholder": "​", - "style": "IPY_MODEL_a901c8deef634fefa5bf50b380005288", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b99b680885934cdfa31bc3a843e20724", + "IPY_MODEL_f15ac1823a7f4e549da71d08245aa9b2", + "IPY_MODEL_0c6902059f6d43049f050a70f2c4d5ed" + ], + "layout": "IPY_MODEL_0c87bf09ff7545318077176d0bc67dc5", "tabbable": null, - "tooltip": null, - "value": "model.safetensors: 100%" + "tooltip": null } }, - "710b50fb237d4bfa800dd8ccca2aa500": { + "3897024bcca245b1bc58655ded2b9bc5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1432,57 +1577,7 @@ "width": null } }, - "721bf251193348b0a2bc03a41fa88621": { - "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_710b50fb237d4bfa800dd8ccca2aa500", - "max": 102469840.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ad6af0ebf6a84194902f8859297785ed", - "tabbable": null, - "tooltip": null, - "value": 102469840.0 - } - }, - "991b461cb5f14fa38412734f4f788575": { - "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_66a60ffdadfe43c49835a3149977dd23", - "IPY_MODEL_721bf251193348b0a2bc03a41fa88621", - "IPY_MODEL_62503695057042fe9e46cf6d976cf0ec" - ], - "layout": "IPY_MODEL_15c8db426b2d442dafc5fec0ada46d26", - "tabbable": null, - "tooltip": null - } - }, - "a901c8deef634fefa5bf50b380005288": { + "8e6e75da45e94500ac3664d6571c19a5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1500,23 +1595,30 @@ "text_color": null } }, - "ad6af0ebf6a84194902f8859297785ed": { + "b99b680885934cdfa31bc3a843e20724": { "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_3897024bcca245b1bc58655ded2b9bc5", + "placeholder": "​", + "style": "IPY_MODEL_086fdb340ddc44499e840c6359ce1479", + "tabbable": null, + "tooltip": null, + "value": "model.safetensors: 100%" } }, - "dc69440eba354ce18f5a8f226872b05a": { + "c8047222b06d47abb1cddbdcb8b6aaff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1568,6 +1670,48 @@ "visibility": null, "width": null } + }, + "f15ac1823a7f4e549da71d08245aa9b2": { + "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_c8047222b06d47abb1cddbdcb8b6aaff", + "max": 102469840.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fb1f241a35b74a80a9334872055927bc", + "tabbable": null, + "tooltip": null, + "value": 102469840.0 + } + }, + "fb1f241a35b74a80a9334872055927bc": { + "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/regression.ipynb b/master/tutorials/regression.ipynb index bac3e263c..673215d3c 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -102,10 +102,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:04.945916Z", - "iopub.status.busy": "2024-04-06T04:35:04.945744Z", - "iopub.status.idle": "2024-04-06T04:35:06.052331Z", - "shell.execute_reply": "2024-04-06T04:35:06.051744Z" + "iopub.execute_input": "2024-04-08T19:13:47.803397Z", + "iopub.status.busy": "2024-04-08T19:13:47.802938Z", + "iopub.status.idle": "2024-04-08T19:13:48.925278Z", + "shell.execute_reply": "2024-04-08T19:13:48.924752Z" }, "nbsphinx": "hidden" }, @@ -117,7 +117,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.054800Z", - "iopub.status.busy": "2024-04-06T04:35:06.054557Z", - "iopub.status.idle": "2024-04-06T04:35:06.072120Z", - "shell.execute_reply": "2024-04-06T04:35:06.071716Z" + "iopub.execute_input": "2024-04-08T19:13:48.927915Z", + "iopub.status.busy": "2024-04-08T19:13:48.927470Z", + "iopub.status.idle": "2024-04-08T19:13:48.945021Z", + "shell.execute_reply": "2024-04-08T19:13:48.944602Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.074202Z", - "iopub.status.busy": "2024-04-06T04:35:06.073811Z", - "iopub.status.idle": "2024-04-06T04:35:06.076794Z", - "shell.execute_reply": "2024-04-06T04:35:06.076351Z" + "iopub.execute_input": "2024-04-08T19:13:48.947242Z", + "iopub.status.busy": "2024-04-08T19:13:48.946736Z", + "iopub.status.idle": "2024-04-08T19:13:48.949732Z", + "shell.execute_reply": "2024-04-08T19:13:48.949294Z" }, "nbsphinx": "hidden" }, @@ -199,10 +199,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.078868Z", - "iopub.status.busy": "2024-04-06T04:35:06.078492Z", - "iopub.status.idle": "2024-04-06T04:35:06.208916Z", - "shell.execute_reply": "2024-04-06T04:35:06.208494Z" + "iopub.execute_input": "2024-04-08T19:13:48.951557Z", + "iopub.status.busy": "2024-04-08T19:13:48.951388Z", + "iopub.status.idle": "2024-04-08T19:13:49.150115Z", + "shell.execute_reply": "2024-04-08T19:13:49.149617Z" } }, "outputs": [ @@ -375,10 +375,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.211100Z", - "iopub.status.busy": "2024-04-06T04:35:06.210666Z", - "iopub.status.idle": "2024-04-06T04:35:06.392965Z", - "shell.execute_reply": "2024-04-06T04:35:06.392412Z" + "iopub.execute_input": "2024-04-08T19:13:49.152258Z", + "iopub.status.busy": "2024-04-08T19:13:49.151925Z", + "iopub.status.idle": "2024-04-08T19:13:49.328521Z", + "shell.execute_reply": "2024-04-08T19:13:49.328018Z" }, "nbsphinx": "hidden" }, @@ -418,10 +418,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.395403Z", - "iopub.status.busy": "2024-04-06T04:35:06.395013Z", - "iopub.status.idle": "2024-04-06T04:35:06.638949Z", - "shell.execute_reply": "2024-04-06T04:35:06.638348Z" + "iopub.execute_input": "2024-04-08T19:13:49.330913Z", + "iopub.status.busy": "2024-04-08T19:13:49.330551Z", + "iopub.status.idle": "2024-04-08T19:13:49.538644Z", + "shell.execute_reply": "2024-04-08T19:13:49.538041Z" } }, "outputs": [ @@ -457,10 +457,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.641297Z", - "iopub.status.busy": "2024-04-06T04:35:06.640953Z", - "iopub.status.idle": "2024-04-06T04:35:06.645580Z", - "shell.execute_reply": "2024-04-06T04:35:06.645032Z" + "iopub.execute_input": "2024-04-08T19:13:49.540725Z", + "iopub.status.busy": "2024-04-08T19:13:49.540437Z", + "iopub.status.idle": "2024-04-08T19:13:49.544691Z", + "shell.execute_reply": "2024-04-08T19:13:49.544278Z" } }, "outputs": [], @@ -478,10 +478,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.647781Z", - "iopub.status.busy": "2024-04-06T04:35:06.647427Z", - "iopub.status.idle": "2024-04-06T04:35:06.654351Z", - "shell.execute_reply": "2024-04-06T04:35:06.653847Z" + "iopub.execute_input": "2024-04-08T19:13:49.546581Z", + "iopub.status.busy": "2024-04-08T19:13:49.546300Z", + "iopub.status.idle": "2024-04-08T19:13:49.552461Z", + "shell.execute_reply": "2024-04-08T19:13:49.552021Z" } }, "outputs": [], @@ -528,10 +528,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.656526Z", - "iopub.status.busy": "2024-04-06T04:35:06.656127Z", - "iopub.status.idle": "2024-04-06T04:35:06.658766Z", - "shell.execute_reply": "2024-04-06T04:35:06.658318Z" + "iopub.execute_input": "2024-04-08T19:13:49.554456Z", + "iopub.status.busy": "2024-04-08T19:13:49.554124Z", + "iopub.status.idle": "2024-04-08T19:13:49.556701Z", + "shell.execute_reply": "2024-04-08T19:13:49.556278Z" } }, "outputs": [], @@ -546,10 +546,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:06.660791Z", - "iopub.status.busy": "2024-04-06T04:35:06.660469Z", - "iopub.status.idle": "2024-04-06T04:35:14.877273Z", - "shell.execute_reply": "2024-04-06T04:35:14.876740Z" + "iopub.execute_input": "2024-04-08T19:13:49.558519Z", + "iopub.status.busy": "2024-04-08T19:13:49.558220Z", + "iopub.status.idle": "2024-04-08T19:13:57.783852Z", + "shell.execute_reply": "2024-04-08T19:13:57.783248Z" } }, "outputs": [], @@ -573,10 +573,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.880136Z", - "iopub.status.busy": "2024-04-06T04:35:14.879546Z", - "iopub.status.idle": "2024-04-06T04:35:14.886452Z", - "shell.execute_reply": "2024-04-06T04:35:14.885981Z" + "iopub.execute_input": "2024-04-08T19:13:57.787203Z", + "iopub.status.busy": "2024-04-08T19:13:57.786649Z", + "iopub.status.idle": "2024-04-08T19:13:57.794488Z", + "shell.execute_reply": "2024-04-08T19:13:57.794042Z" } }, "outputs": [ @@ -679,10 +679,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.888384Z", - "iopub.status.busy": "2024-04-06T04:35:14.888208Z", - "iopub.status.idle": "2024-04-06T04:35:14.891854Z", - "shell.execute_reply": "2024-04-06T04:35:14.891406Z" + "iopub.execute_input": "2024-04-08T19:13:57.796488Z", + "iopub.status.busy": "2024-04-08T19:13:57.796215Z", + "iopub.status.idle": "2024-04-08T19:13:57.799641Z", + "shell.execute_reply": "2024-04-08T19:13:57.799235Z" } }, "outputs": [], @@ -697,10 +697,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.893970Z", - "iopub.status.busy": "2024-04-06T04:35:14.893580Z", - "iopub.status.idle": "2024-04-06T04:35:14.896696Z", - "shell.execute_reply": "2024-04-06T04:35:14.896194Z" + "iopub.execute_input": "2024-04-08T19:13:57.801520Z", + "iopub.status.busy": "2024-04-08T19:13:57.801263Z", + "iopub.status.idle": "2024-04-08T19:13:57.804571Z", + "shell.execute_reply": "2024-04-08T19:13:57.804133Z" } }, "outputs": [ @@ -735,10 +735,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.898553Z", - "iopub.status.busy": "2024-04-06T04:35:14.898382Z", - "iopub.status.idle": "2024-04-06T04:35:14.901388Z", - "shell.execute_reply": "2024-04-06T04:35:14.900951Z" + "iopub.execute_input": "2024-04-08T19:13:57.806527Z", + "iopub.status.busy": "2024-04-08T19:13:57.806223Z", + "iopub.status.idle": "2024-04-08T19:13:57.809258Z", + "shell.execute_reply": "2024-04-08T19:13:57.808725Z" } }, "outputs": [], @@ -757,10 +757,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.903150Z", - "iopub.status.busy": "2024-04-06T04:35:14.902982Z", - "iopub.status.idle": "2024-04-06T04:35:14.910845Z", - "shell.execute_reply": "2024-04-06T04:35:14.910300Z" + "iopub.execute_input": "2024-04-08T19:13:57.811263Z", + "iopub.status.busy": "2024-04-08T19:13:57.810959Z", + "iopub.status.idle": "2024-04-08T19:13:57.818786Z", + "shell.execute_reply": "2024-04-08T19:13:57.818238Z" } }, "outputs": [ @@ -884,10 +884,10 @@ "id": "9131d82d", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.912783Z", - "iopub.status.busy": "2024-04-06T04:35:14.912607Z", - "iopub.status.idle": "2024-04-06T04:35:14.915272Z", - "shell.execute_reply": "2024-04-06T04:35:14.914817Z" + "iopub.execute_input": "2024-04-08T19:13:57.820889Z", + "iopub.status.busy": "2024-04-08T19:13:57.820509Z", + "iopub.status.idle": "2024-04-08T19:13:57.823238Z", + "shell.execute_reply": "2024-04-08T19:13:57.822711Z" }, "nbsphinx": "hidden" }, @@ -922,10 +922,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:14.917066Z", - "iopub.status.busy": "2024-04-06T04:35:14.916896Z", - "iopub.status.idle": "2024-04-06T04:35:15.039512Z", - "shell.execute_reply": "2024-04-06T04:35:15.038973Z" + "iopub.execute_input": "2024-04-08T19:13:57.825125Z", + "iopub.status.busy": "2024-04-08T19:13:57.824848Z", + "iopub.status.idle": "2024-04-08T19:13:57.944411Z", + "shell.execute_reply": "2024-04-08T19:13:57.943830Z" } }, "outputs": [ @@ -964,10 +964,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.041688Z", - "iopub.status.busy": "2024-04-06T04:35:15.041372Z", - "iopub.status.idle": "2024-04-06T04:35:15.143758Z", - "shell.execute_reply": "2024-04-06T04:35:15.143187Z" + "iopub.execute_input": "2024-04-08T19:13:57.946652Z", + "iopub.status.busy": "2024-04-08T19:13:57.946416Z", + "iopub.status.idle": "2024-04-08T19:13:58.050381Z", + "shell.execute_reply": "2024-04-08T19:13:58.049796Z" } }, "outputs": [ @@ -1023,10 +1023,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.146009Z", - "iopub.status.busy": "2024-04-06T04:35:15.145689Z", - "iopub.status.idle": "2024-04-06T04:35:15.632674Z", - "shell.execute_reply": "2024-04-06T04:35:15.632055Z" + "iopub.execute_input": "2024-04-08T19:13:58.052902Z", + "iopub.status.busy": "2024-04-08T19:13:58.052525Z", + "iopub.status.idle": "2024-04-08T19:13:58.541282Z", + "shell.execute_reply": "2024-04-08T19:13:58.540644Z" } }, "outputs": [], @@ -1042,10 +1042,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.635337Z", - "iopub.status.busy": "2024-04-06T04:35:15.634992Z", - "iopub.status.idle": "2024-04-06T04:35:15.743506Z", - "shell.execute_reply": "2024-04-06T04:35:15.742910Z" + "iopub.execute_input": "2024-04-08T19:13:58.544024Z", + "iopub.status.busy": "2024-04-08T19:13:58.543626Z", + "iopub.status.idle": "2024-04-08T19:13:58.648994Z", + "shell.execute_reply": "2024-04-08T19:13:58.648352Z" } }, "outputs": [ @@ -1080,10 +1080,10 @@ "id": "dbab6fb3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.745851Z", - "iopub.status.busy": "2024-04-06T04:35:15.745490Z", - "iopub.status.idle": "2024-04-06T04:35:15.753696Z", - "shell.execute_reply": "2024-04-06T04:35:15.753263Z" + "iopub.execute_input": "2024-04-08T19:13:58.651502Z", + "iopub.status.busy": "2024-04-08T19:13:58.651135Z", + "iopub.status.idle": "2024-04-08T19:13:58.659988Z", + "shell.execute_reply": "2024-04-08T19:13:58.659533Z" } }, "outputs": [ @@ -1190,10 +1190,10 @@ "id": "5b39b8b5", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.755695Z", - "iopub.status.busy": "2024-04-06T04:35:15.755367Z", - "iopub.status.idle": "2024-04-06T04:35:15.758042Z", - "shell.execute_reply": "2024-04-06T04:35:15.757595Z" + "iopub.execute_input": "2024-04-08T19:13:58.661959Z", + "iopub.status.busy": "2024-04-08T19:13:58.661702Z", + "iopub.status.idle": "2024-04-08T19:13:58.664433Z", + "shell.execute_reply": "2024-04-08T19:13:58.663997Z" }, "nbsphinx": "hidden" }, @@ -1218,10 +1218,10 @@ "id": "df06525b", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:15.760007Z", - "iopub.status.busy": "2024-04-06T04:35:15.759679Z", - "iopub.status.idle": "2024-04-06T04:35:21.169503Z", - "shell.execute_reply": "2024-04-06T04:35:21.168860Z" + "iopub.execute_input": "2024-04-08T19:13:58.666308Z", + "iopub.status.busy": "2024-04-08T19:13:58.666060Z", + "iopub.status.idle": "2024-04-08T19:14:04.108369Z", + "shell.execute_reply": "2024-04-08T19:14:04.107772Z" } }, "outputs": [ @@ -1265,10 +1265,10 @@ "id": "05282559", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:21.172001Z", - "iopub.status.busy": "2024-04-06T04:35:21.171800Z", - "iopub.status.idle": "2024-04-06T04:35:21.180339Z", - "shell.execute_reply": "2024-04-06T04:35:21.179868Z" + "iopub.execute_input": "2024-04-08T19:14:04.110851Z", + "iopub.status.busy": "2024-04-08T19:14:04.110406Z", + "iopub.status.idle": "2024-04-08T19:14:04.119270Z", + "shell.execute_reply": "2024-04-08T19:14:04.118848Z" } }, "outputs": [ @@ -1377,10 +1377,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:21.182362Z", - "iopub.status.busy": "2024-04-06T04:35:21.182047Z", - "iopub.status.idle": "2024-04-06T04:35:21.256060Z", - "shell.execute_reply": "2024-04-06T04:35:21.255579Z" + "iopub.execute_input": "2024-04-08T19:14:04.121410Z", + "iopub.status.busy": "2024-04-08T19:14:04.120984Z", + "iopub.status.idle": "2024-04-08T19:14:04.185861Z", + "shell.execute_reply": "2024-04-08T19:14:04.185384Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index 60b73eda9..ed3c1520a 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -766,13 +766,13 @@

    3. Use cleanlab to find label issues

    -
    +
    -
    +
    @@ -857,7 +857,7 @@

    Classes which are commonly mislabeled overall

    -
    +
    @@ -1106,7 +1106,7 @@

    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().

    @@ -1162,7 +1162,7 @@

    Get label quality scores -{"state": {"a6b9abd68857477cbdc3898a45b7c10f": {"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}}, "40eb33493e30499e97354717869f83be": {"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": ""}}, "28dfa37fd60e430591dba8d65770190b": {"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_a6b9abd68857477cbdc3898a45b7c10f", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_40eb33493e30499e97354717869f83be", "tabbable": null, "tooltip": null, "value": 30.0}}, "7f6b7ffd00bb4a2caab3b50683296936": {"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}}, "cbbaae80c54e40cabbdb2edd591551d4": {"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}}, "621c7249a2054bf59226e64a4b8f7081": {"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_7f6b7ffd00bb4a2caab3b50683296936", "placeholder": "\u200b", "style": "IPY_MODEL_cbbaae80c54e40cabbdb2edd591551d4", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "06ef42058bc7433ca11271d89630261f": {"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}}, "60b778a0ef8e44b39608af38b47ca860": {"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}}, "7e7920af14e54024b17aa6179ca38f35": {"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_06ef42058bc7433ca11271d89630261f", "placeholder": "\u200b", "style": "IPY_MODEL_60b778a0ef8e44b39608af38b47ca860", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007407.39it/s]"}}, "4ad7bb286eca4c09948bb75a92a76c29": {"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}}, "430f85b602e34595b215cff777f2e22c": {"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_621c7249a2054bf59226e64a4b8f7081", "IPY_MODEL_28dfa37fd60e430591dba8d65770190b", "IPY_MODEL_7e7920af14e54024b17aa6179ca38f35"], "layout": "IPY_MODEL_4ad7bb286eca4c09948bb75a92a76c29", "tabbable": null, "tooltip": null}}, "686826beb1be457bab28c73bcd2ffefb": {"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}}, "8db16dbcca284d40921261550c3c9241": {"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": ""}}, "0e3edfaa67be4256bf241604d1fdfd54": {"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_686826beb1be457bab28c73bcd2ffefb", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_8db16dbcca284d40921261550c3c9241", "tabbable": null, "tooltip": null, "value": 30.0}}, "02c8e49579ab4c2cb5010e5d696f7f75": {"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}}, "646252281ae14f8886cc21d9e56afec5": {"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}}, "8b3e196faee64d279374daae7049f9a7": {"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_02c8e49579ab4c2cb5010e5d696f7f75", "placeholder": "\u200b", "style": "IPY_MODEL_646252281ae14f8886cc21d9e56afec5", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "957a7ec883f34696b2479a90a87bdbcb": {"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}}, "562e407d8d8745fc83b37ab7c94d1b60": {"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}}, "2d5ba316556d415b8f9eceaac5bda45b": {"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_957a7ec883f34696b2479a90a87bdbcb", "placeholder": "\u200b", "style": "IPY_MODEL_562e407d8d8745fc83b37ab7c94d1b60", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.41it/s]"}}, "7c0fbc0da1b843e89059929faa931860": {"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}}, "72840f69ea214918a754b98c138bcd01": {"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_8b3e196faee64d279374daae7049f9a7", "IPY_MODEL_0e3edfaa67be4256bf241604d1fdfd54", "IPY_MODEL_2d5ba316556d415b8f9eceaac5bda45b"], "layout": "IPY_MODEL_7c0fbc0da1b843e89059929faa931860", "tabbable": null, "tooltip": null}}, "60ce120b6f0d4b35939edd06a49b48fa": {"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}}, "5bb6200f11d74e33b58d64accf19085f": {"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": ""}}, "f5b6a3b6f2474f7c8d2b531dbc37f186": {"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_60ce120b6f0d4b35939edd06a49b48fa", "max": 4997817.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5bb6200f11d74e33b58d64accf19085f", "tabbable": null, "tooltip": null, "value": 4997817.0}}, "42508acbff6b4c069678d14fb3bb650d": {"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}}, "d2dd7b61c9974c56af1e4ae1413f64cf": {"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}}, "20149c21eb1a4d47b708a9f402b4f051": {"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_42508acbff6b4c069678d14fb3bb650d", "placeholder": "\u200b", "style": "IPY_MODEL_d2dd7b61c9974c56af1e4ae1413f64cf", "tabbable": null, "tooltip": null, "value": "100%"}}, "ea8cdf6af87544368e97f412bed093e3": {"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}}, "ff45dd3e934e4e0c9f7fa5da7043dbd7": {"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}}, "182b3bcc537c404793b98eb84a4a9fef": {"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_ea8cdf6af87544368e97f412bed093e3", "placeholder": "\u200b", "style": "IPY_MODEL_ff45dd3e934e4e0c9f7fa5da7043dbd7", "tabbable": null, "tooltip": null, "value": "\u20074997817/4997817\u2007[00:32<00:00,\u2007154776.29it/s]"}}, "d2ba7ead109f40f88a0f2fdcd7f79091": {"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}}, "d3ec0bdaf05d45038d515229edd1fce4": {"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_20149c21eb1a4d47b708a9f402b4f051", "IPY_MODEL_f5b6a3b6f2474f7c8d2b531dbc37f186", "IPY_MODEL_182b3bcc537c404793b98eb84a4a9fef"], "layout": "IPY_MODEL_d2ba7ead109f40f88a0f2fdcd7f79091", "tabbable": null, "tooltip": null}}, "2a73fcdba5f1491486544ab0e5fd82f6": {"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}}, "83ed95add600447dbcd4112a82b755ac": {"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": ""}}, "232cb3f4436c4a29aa37462ded23ed0a": {"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_2a73fcdba5f1491486544ab0e5fd82f6", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_83ed95add600447dbcd4112a82b755ac", "tabbable": null, "tooltip": null, "value": 30.0}}, "70deac36097f491e8666a288db33cf71": {"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}}, "f73f43e9ac32458989fecfd81a100778": {"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}}, "07b675adb016454a9854b4ea300e4cfe": {"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_70deac36097f491e8666a288db33cf71", "placeholder": "\u200b", "style": "IPY_MODEL_f73f43e9ac32458989fecfd81a100778", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "9efde9d20e674f91930d36a8fffde7e0": {"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}}, "0ffcaa24253e452f8a885fef873bdcfb": {"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}}, "2bc90b79c1364f709f2cc5d8d3ac1a01": {"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_9efde9d20e674f91930d36a8fffde7e0", "placeholder": "\u200b", "style": "IPY_MODEL_0ffcaa24253e452f8a885fef873bdcfb", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.88it/s]"}}, "5b27262a9cd94dcca7fa54df4cceb26a": {"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}}, "a41dd8fa1c914418a704a6e5e8be1e2e": {"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_07b675adb016454a9854b4ea300e4cfe", "IPY_MODEL_232cb3f4436c4a29aa37462ded23ed0a", "IPY_MODEL_2bc90b79c1364f709f2cc5d8d3ac1a01"], "layout": "IPY_MODEL_5b27262a9cd94dcca7fa54df4cceb26a", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} +{"state": {"b85f1eb5f0e64530870f6fbf949cda91": {"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}}, "d7bd991e20f14b0c98dd8d679d84939c": {"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": ""}}, "365979231dd54217a8aac4a279204067": {"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_b85f1eb5f0e64530870f6fbf949cda91", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d7bd991e20f14b0c98dd8d679d84939c", "tabbable": null, "tooltip": null, "value": 30.0}}, "68c0ed1d2b074054bc0c083b8ccd8a71": {"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}}, "6249a51dbc1145c985fed7ad172f3f6f": {"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}}, "6b76fce815c1461d9f75cd9b932f9d87": {"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_68c0ed1d2b074054bc0c083b8ccd8a71", "placeholder": "\u200b", "style": "IPY_MODEL_6249a51dbc1145c985fed7ad172f3f6f", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007estimating\u2007thresholds:\u2007100%"}}, "99e35d66f5634eb28a74eb5bfa102bde": {"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}}, "773d8ea77b7443f2ade5b4c99a303fcb": {"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}}, "ce77d0d3de7c4b089021754738d5ceb4": {"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_99e35d66f5634eb28a74eb5bfa102bde", "placeholder": "\u200b", "style": "IPY_MODEL_773d8ea77b7443f2ade5b4c99a303fcb", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:00<00:00,\u2007405.54it/s]"}}, "5abd50f2ff82441194222375826e37c9": {"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}}, "f80951daaff1439bae07b22f26431578": {"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_6b76fce815c1461d9f75cd9b932f9d87", "IPY_MODEL_365979231dd54217a8aac4a279204067", "IPY_MODEL_ce77d0d3de7c4b089021754738d5ceb4"], "layout": "IPY_MODEL_5abd50f2ff82441194222375826e37c9", "tabbable": null, "tooltip": null}}, "010891f081ec41c1aabdc9984ea3e880": {"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}}, "beb83a56568c469189a5939e700e69a4": {"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": ""}}, "83945befc2ec4271af67b93797cadd10": {"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_010891f081ec41c1aabdc9984ea3e880", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_beb83a56568c469189a5939e700e69a4", "tabbable": null, "tooltip": null, "value": 30.0}}, "b2e6852040e34be68af10fb4a3c2b9a7": {"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}}, "c4adc9fb2d8c49ab9d863ff53317708d": {"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}}, "d2ea6fbb7475458087e30a3aee7c2421": {"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_b2e6852040e34be68af10fb4a3c2b9a7", "placeholder": "\u200b", "style": "IPY_MODEL_c4adc9fb2d8c49ab9d863ff53317708d", "tabbable": null, "tooltip": null, "value": "number\u2007of\u2007examples\u2007processed\u2007for\u2007checking\u2007labels:\u2007100%"}}, "ffe9b9351b8a4b129d0904a8757b9a46": {"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}}, "fc53622cc52741d8bde9b20fe8c9a3c0": {"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}}, "93bd352e7edc4aac8660f540a733e80f": {"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_ffe9b9351b8a4b129d0904a8757b9a46", "placeholder": "\u200b", "style": "IPY_MODEL_fc53622cc52741d8bde9b20fe8c9a3c0", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:21<00:00,\u2007\u20071.44it/s]"}}, "e5304a7b2a15495792b3302840dc6164": {"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}}, "4414546b77b44486a511d3a262f3937f": {"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_d2ea6fbb7475458087e30a3aee7c2421", "IPY_MODEL_83945befc2ec4271af67b93797cadd10", "IPY_MODEL_93bd352e7edc4aac8660f540a733e80f"], "layout": "IPY_MODEL_e5304a7b2a15495792b3302840dc6164", "tabbable": null, "tooltip": null}}, "380290952081433398d72252f55f3512": {"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}}, "c158f6f873284cbaa7be7bbaebbd7763": {"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": ""}}, "e05c95cfe29749928b6771d970a5fc81": {"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_380290952081433398d72252f55f3512", "max": 4997817.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_c158f6f873284cbaa7be7bbaebbd7763", "tabbable": null, "tooltip": null, "value": 4997817.0}}, "f618acb94c794f1ba33254b732f3da99": {"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}}, "bdd0d707ff2c40a783c34c22c644bff2": {"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}}, "5ac2298939434623ab7427ecd71bb845": {"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_f618acb94c794f1ba33254b732f3da99", "placeholder": "\u200b", "style": "IPY_MODEL_bdd0d707ff2c40a783c34c22c644bff2", "tabbable": null, "tooltip": null, "value": "100%"}}, "7388d890f96b440d9df5bb2bcf630681": {"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}}, "99f6441d531f4710baa405c0d7109b5f": {"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}}, "26332d6a8cc44c2e97dbf9c486f38f25": {"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_7388d890f96b440d9df5bb2bcf630681", "placeholder": "\u200b", "style": "IPY_MODEL_99f6441d531f4710baa405c0d7109b5f", "tabbable": null, "tooltip": null, "value": "\u20074997817/4997817\u2007[00:32<00:00,\u2007154803.51it/s]"}}, "40897686e955404f91514151f240c0d0": {"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}}, "71098e13b4334a47bbac4b75032ea150": {"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_5ac2298939434623ab7427ecd71bb845", "IPY_MODEL_e05c95cfe29749928b6771d970a5fc81", "IPY_MODEL_26332d6a8cc44c2e97dbf9c486f38f25"], "layout": "IPY_MODEL_40897686e955404f91514151f240c0d0", "tabbable": null, "tooltip": null}}, "301a3fcd99094c40a1fb9f975619c430": {"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}}, "332840cfdb9a451ebd2a374dae54e637": {"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": ""}}, "c68ef40a35e04fc39427a9f284523b28": {"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_301a3fcd99094c40a1fb9f975619c430", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_332840cfdb9a451ebd2a374dae54e637", "tabbable": null, "tooltip": null, "value": 30.0}}, "2c287de703a14c5cb0941da5922a6c41": {"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}}, "1983a07fcf9348578a07d2aee3240441": {"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}}, "2644b4fc5fda48acb706485e94aabc6a": {"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_2c287de703a14c5cb0941da5922a6c41", "placeholder": "\u200b", "style": "IPY_MODEL_1983a07fcf9348578a07d2aee3240441", "tabbable": null, "tooltip": null, "value": "images\u2007processed\u2007using\u2007softmin:\u2007100%"}}, "001fb5816a074024996ee285412c72e2": {"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}}, "dcb3aba2d5ee498da09596d6b1189652": {"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}}, "328b4d1e22fb41b9be4bc667200a34d2": {"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_001fb5816a074024996ee285412c72e2", "placeholder": "\u200b", "style": "IPY_MODEL_dcb3aba2d5ee498da09596d6b1189652", "tabbable": null, "tooltip": null, "value": "\u200730/30\u2007[00:01<00:00,\u200721.04it/s]"}}, "ef987a4e9c5445f2a1965215131e2de0": {"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}}, "abbf1d8d4dc4498dbba438256d734fad": {"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_2644b4fc5fda48acb706485e94aabc6a", "IPY_MODEL_c68ef40a35e04fc39427a9f284523b28", "IPY_MODEL_328b4d1e22fb41b9be4bc667200a34d2"], "layout": "IPY_MODEL_ef987a4e9c5445f2a1965215131e2de0", "tabbable": null, "tooltip": null}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 89e2cb219..7512e088c 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:24.243928Z", - "iopub.status.busy": "2024-04-06T04:35:24.243468Z", - "iopub.status.idle": "2024-04-06T04:35:26.047952Z", - "shell.execute_reply": "2024-04-06T04:35:26.047292Z" + "iopub.execute_input": "2024-04-08T19:14:07.395028Z", + "iopub.status.busy": "2024-04-08T19:14:07.394566Z", + "iopub.status.idle": "2024-04-08T19:14:11.485319Z", + "shell.execute_reply": "2024-04-08T19:14:11.484630Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:35:26.050495Z", - "iopub.status.busy": "2024-04-06T04:35:26.050118Z", - "iopub.status.idle": "2024-04-06T04:36:08.935704Z", - "shell.execute_reply": "2024-04-06T04:36:08.935125Z" + "iopub.execute_input": "2024-04-08T19:14:11.488001Z", + "iopub.status.busy": "2024-04-08T19:14:11.487586Z", + "iopub.status.idle": "2024-04-08T19:15:03.035425Z", + "shell.execute_reply": "2024-04-08T19:15:03.034793Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:08.938332Z", - "iopub.status.busy": "2024-04-06T04:36:08.937887Z", - "iopub.status.idle": "2024-04-06T04:36:09.999880Z", - "shell.execute_reply": "2024-04-06T04:36:09.999323Z" + "iopub.execute_input": "2024-04-08T19:15:03.037988Z", + "iopub.status.busy": "2024-04-08T19:15:03.037617Z", + "iopub.status.idle": "2024-04-08T19:15:04.144423Z", + "shell.execute_reply": "2024-04-08T19:15:04.143898Z" }, "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\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": "2024-04-06T04:36:10.002451Z", - "iopub.status.busy": "2024-04-06T04:36:10.002049Z", - "iopub.status.idle": "2024-04-06T04:36:10.005300Z", - "shell.execute_reply": "2024-04-06T04:36:10.004764Z" + "iopub.execute_input": "2024-04-08T19:15:04.146910Z", + "iopub.status.busy": "2024-04-08T19:15:04.146510Z", + "iopub.status.idle": "2024-04-08T19:15:04.149732Z", + "shell.execute_reply": "2024-04-08T19:15:04.149284Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.007484Z", - "iopub.status.busy": "2024-04-06T04:36:10.007053Z", - "iopub.status.idle": "2024-04-06T04:36:10.010737Z", - "shell.execute_reply": "2024-04-06T04:36:10.010232Z" + "iopub.execute_input": "2024-04-08T19:15:04.151905Z", + "iopub.status.busy": "2024-04-08T19:15:04.151503Z", + "iopub.status.idle": "2024-04-08T19:15:04.155404Z", + "shell.execute_reply": "2024-04-08T19:15:04.154966Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.012726Z", - "iopub.status.busy": "2024-04-06T04:36:10.012460Z", - "iopub.status.idle": "2024-04-06T04:36:10.016097Z", - "shell.execute_reply": "2024-04-06T04:36:10.015646Z" + "iopub.execute_input": "2024-04-08T19:15:04.157319Z", + "iopub.status.busy": "2024-04-08T19:15:04.157012Z", + "iopub.status.idle": "2024-04-08T19:15:04.160392Z", + "shell.execute_reply": "2024-04-08T19:15:04.159984Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.018111Z", - "iopub.status.busy": "2024-04-06T04:36:10.017712Z", - "iopub.status.idle": "2024-04-06T04:36:10.020470Z", - "shell.execute_reply": "2024-04-06T04:36:10.020044Z" + "iopub.execute_input": "2024-04-08T19:15:04.162271Z", + "iopub.status.busy": "2024-04-08T19:15:04.161951Z", + "iopub.status.idle": "2024-04-08T19:15:04.164604Z", + "shell.execute_reply": "2024-04-08T19:15:04.164202Z" } }, "outputs": [], @@ -333,17 +333,17 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:36:10.022477Z", - "iopub.status.busy": "2024-04-06T04:36:10.022151Z", - "iopub.status.idle": "2024-04-06T04:37:25.589281Z", - "shell.execute_reply": "2024-04-06T04:37:25.588682Z" + "iopub.execute_input": "2024-04-08T19:15:04.166521Z", + "iopub.status.busy": "2024-04-08T19:15:04.166174Z", + "iopub.status.idle": "2024-04-08T19:16:20.073084Z", + "shell.execute_reply": "2024-04-08T19:16:20.072471Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "430f85b602e34595b215cff777f2e22c", + "model_id": "f80951daaff1439bae07b22f26431578", "version_major": 2, "version_minor": 0 }, @@ -357,7 +357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72840f69ea214918a754b98c138bcd01", + "model_id": "4414546b77b44486a511d3a262f3937f", "version_major": 2, "version_minor": 0 }, @@ -400,10 +400,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:25.591755Z", - "iopub.status.busy": "2024-04-06T04:37:25.591547Z", - "iopub.status.idle": "2024-04-06T04:37:26.256442Z", - "shell.execute_reply": "2024-04-06T04:37:26.255866Z" + "iopub.execute_input": "2024-04-08T19:16:20.075744Z", + "iopub.status.busy": "2024-04-08T19:16:20.075337Z", + "iopub.status.idle": "2024-04-08T19:16:20.750666Z", + "shell.execute_reply": "2024-04-08T19:16:20.750121Z" } }, "outputs": [ @@ -446,10 +446,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:26.258907Z", - "iopub.status.busy": "2024-04-06T04:37:26.258403Z", - "iopub.status.idle": "2024-04-06T04:37:28.986847Z", - "shell.execute_reply": "2024-04-06T04:37:28.986249Z" + "iopub.execute_input": "2024-04-08T19:16:20.753014Z", + "iopub.status.busy": "2024-04-08T19:16:20.752584Z", + "iopub.status.idle": "2024-04-08T19:16:23.452100Z", + "shell.execute_reply": "2024-04-08T19:16:23.451576Z" } }, "outputs": [ @@ -519,17 +519,17 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:37:28.989011Z", - "iopub.status.busy": "2024-04-06T04:37:28.988663Z", - "iopub.status.idle": "2024-04-06T04:38:01.556933Z", - "shell.execute_reply": "2024-04-06T04:38:01.556488Z" + "iopub.execute_input": "2024-04-08T19:16:23.454306Z", + "iopub.status.busy": "2024-04-08T19:16:23.454030Z", + "iopub.status.idle": "2024-04-08T19:16:56.036315Z", + "shell.execute_reply": "2024-04-08T19:16:56.035779Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3ec0bdaf05d45038d515229edd1fce4", + "model_id": "71098e13b4334a47bbac4b75032ea150", "version_major": 2, "version_minor": 0 }, @@ -769,10 +769,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:01.559018Z", - "iopub.status.busy": "2024-04-06T04:38:01.558830Z", - "iopub.status.idle": "2024-04-06T04:38:15.949783Z", - "shell.execute_reply": "2024-04-06T04:38:15.949244Z" + "iopub.execute_input": "2024-04-08T19:16:56.038307Z", + "iopub.status.busy": "2024-04-08T19:16:56.038128Z", + "iopub.status.idle": "2024-04-08T19:17:10.772843Z", + "shell.execute_reply": "2024-04-08T19:17:10.772275Z" } }, "outputs": [], @@ -786,10 +786,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:15.952245Z", - "iopub.status.busy": "2024-04-06T04:38:15.951882Z", - "iopub.status.idle": "2024-04-06T04:38:19.737339Z", - "shell.execute_reply": "2024-04-06T04:38:19.736758Z" + "iopub.execute_input": "2024-04-08T19:17:10.775162Z", + "iopub.status.busy": "2024-04-08T19:17:10.774934Z", + "iopub.status.idle": "2024-04-08T19:17:14.602622Z", + "shell.execute_reply": "2024-04-08T19:17:14.602118Z" } }, "outputs": [ @@ -858,17 +858,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:19.739665Z", - "iopub.status.busy": "2024-04-06T04:38:19.739350Z", - "iopub.status.idle": "2024-04-06T04:38:21.124441Z", - "shell.execute_reply": "2024-04-06T04:38:21.123826Z" + "iopub.execute_input": "2024-04-08T19:17:14.604704Z", + "iopub.status.busy": "2024-04-08T19:17:14.604405Z", + "iopub.status.idle": "2024-04-08T19:17:16.041428Z", + "shell.execute_reply": "2024-04-08T19:17:16.040893Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a41dd8fa1c914418a704a6e5e8be1e2e", + "model_id": "abbf1d8d4dc4498dbba438256d734fad", "version_major": 2, "version_minor": 0 }, @@ -898,10 +898,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:21.126758Z", - "iopub.status.busy": "2024-04-06T04:38:21.126415Z", - "iopub.status.idle": "2024-04-06T04:38:21.155079Z", - "shell.execute_reply": "2024-04-06T04:38:21.154584Z" + "iopub.execute_input": "2024-04-08T19:17:16.043794Z", + "iopub.status.busy": "2024-04-08T19:17:16.043524Z", + "iopub.status.idle": "2024-04-08T19:17:16.075824Z", + "shell.execute_reply": "2024-04-08T19:17:16.075325Z" } }, "outputs": [], @@ -915,10 +915,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:21.157615Z", - "iopub.status.busy": "2024-04-06T04:38:21.157258Z", - "iopub.status.idle": "2024-04-06T04:38:27.224964Z", - "shell.execute_reply": "2024-04-06T04:38:27.224445Z" + "iopub.execute_input": "2024-04-08T19:17:16.078301Z", + "iopub.status.busy": "2024-04-08T19:17:16.077923Z", + "iopub.status.idle": "2024-04-08T19:17:22.245581Z", + "shell.execute_reply": "2024-04-08T19:17:22.245090Z" } }, "outputs": [ @@ -991,10 +991,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:27.227144Z", - "iopub.status.busy": "2024-04-06T04:38:27.226803Z", - "iopub.status.idle": "2024-04-06T04:38:27.282607Z", - "shell.execute_reply": "2024-04-06T04:38:27.282076Z" + "iopub.execute_input": "2024-04-08T19:17:22.247739Z", + "iopub.status.busy": "2024-04-08T19:17:22.247405Z", + "iopub.status.idle": "2024-04-08T19:17:22.303831Z", + "shell.execute_reply": "2024-04-08T19:17:22.303283Z" }, "nbsphinx": "hidden" }, @@ -1038,7 +1038,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02c8e49579ab4c2cb5010e5d696f7f75": { + "001fb5816a074024996ee285412c72e2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1091,7 +1091,7 @@ "width": null } }, - "06ef42058bc7433ca11271d89630261f": { + "010891f081ec41c1aabdc9984ea3e880": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1144,56 +1144,7 @@ "width": null } }, - "07b675adb016454a9854b4ea300e4cfe": { - "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_70deac36097f491e8666a288db33cf71", - "placeholder": "​", - "style": "IPY_MODEL_f73f43e9ac32458989fecfd81a100778", - "tabbable": null, - "tooltip": null, - "value": "images processed using softmin: 100%" - } - }, - "0e3edfaa67be4256bf241604d1fdfd54": { - "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_686826beb1be457bab28c73bcd2ffefb", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_8db16dbcca284d40921261550c3c9241", - "tabbable": null, - "tooltip": null, - "value": 30.0 - } - }, - "0ffcaa24253e452f8a885fef873bdcfb": { + "1983a07fcf9348578a07d2aee3240441": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1211,7 +1162,7 @@ "text_color": null } }, - "182b3bcc537c404793b98eb84a4a9fef": { + "26332d6a8cc44c2e97dbf9c486f38f25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1226,15 +1177,15 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_ea8cdf6af87544368e97f412bed093e3", + "layout": "IPY_MODEL_7388d890f96b440d9df5bb2bcf630681", "placeholder": "​", - "style": "IPY_MODEL_ff45dd3e934e4e0c9f7fa5da7043dbd7", + "style": "IPY_MODEL_99f6441d531f4710baa405c0d7109b5f", "tabbable": null, "tooltip": null, - "value": " 4997817/4997817 [00:32<00:00, 154776.29it/s]" + "value": " 4997817/4997817 [00:32<00:00, 154803.51it/s]" } }, - "20149c21eb1a4d47b708a9f402b4f051": { + "2644b4fc5fda48acb706485e94aabc6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1249,67 +1200,68 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_42508acbff6b4c069678d14fb3bb650d", + "layout": "IPY_MODEL_2c287de703a14c5cb0941da5922a6c41", "placeholder": "​", - "style": "IPY_MODEL_d2dd7b61c9974c56af1e4ae1413f64cf", - "tabbable": null, - "tooltip": null, - "value": "100%" - } - }, - "232cb3f4436c4a29aa37462ded23ed0a": { - "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_2a73fcdba5f1491486544ab0e5fd82f6", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_83ed95add600447dbcd4112a82b755ac", + "style": "IPY_MODEL_1983a07fcf9348578a07d2aee3240441", "tabbable": null, "tooltip": null, - "value": 30.0 + "value": "images processed using softmin: 100%" } }, - "28dfa37fd60e430591dba8d65770190b": { - "model_module": "@jupyter-widgets/controls", + "2c287de703a14c5cb0941da5922a6c41": { + "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_a6b9abd68857477cbdc3898a45b7c10f", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_40eb33493e30499e97354717869f83be", - "tabbable": null, - "tooltip": null, - "value": 30.0 + "_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 } }, - "2a73fcdba5f1491486544ab0e5fd82f6": { + "301a3fcd99094c40a1fb9f975619c430": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1362,7 +1314,7 @@ "width": null } }, - "2bc90b79c1364f709f2cc5d8d3ac1a01": { + "328b4d1e22fb41b9be4bc667200a34d2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1377,54 +1329,57 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_9efde9d20e674f91930d36a8fffde7e0", + "layout": "IPY_MODEL_001fb5816a074024996ee285412c72e2", "placeholder": "​", - "style": "IPY_MODEL_0ffcaa24253e452f8a885fef873bdcfb", + "style": "IPY_MODEL_dcb3aba2d5ee498da09596d6b1189652", "tabbable": null, "tooltip": null, - "value": " 30/30 [00:01<00:00, 21.88it/s]" + "value": " 30/30 [00:01<00:00, 21.04it/s]" } }, - "2d5ba316556d415b8f9eceaac5bda45b": { + "332840cfdb9a451ebd2a374dae54e637": { "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_957a7ec883f34696b2479a90a87bdbcb", - "placeholder": "​", - "style": "IPY_MODEL_562e407d8d8745fc83b37ab7c94d1b60", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:21<00:00,  1.41it/s]" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "40eb33493e30499e97354717869f83be": { + "365979231dd54217a8aac4a279204067": { "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_b85f1eb5f0e64530870f6fbf949cda91", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d7bd991e20f14b0c98dd8d679d84939c", + "tabbable": null, + "tooltip": null, + "value": 30.0 } }, - "42508acbff6b4c069678d14fb3bb650d": { + "380290952081433398d72252f55f3512": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1477,31 +1432,7 @@ "width": null } }, - "430f85b602e34595b215cff777f2e22c": { - "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_621c7249a2054bf59226e64a4b8f7081", - "IPY_MODEL_28dfa37fd60e430591dba8d65770190b", - "IPY_MODEL_7e7920af14e54024b17aa6179ca38f35" - ], - "layout": "IPY_MODEL_4ad7bb286eca4c09948bb75a92a76c29", - "tabbable": null, - "tooltip": null - } - }, - "4ad7bb286eca4c09948bb75a92a76c29": { + "40897686e955404f91514151f240c0d0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1554,25 +1485,31 @@ "width": null } }, - "562e407d8d8745fc83b37ab7c94d1b60": { + "4414546b77b44486a511d3a262f3937f": { "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_d2ea6fbb7475458087e30a3aee7c2421", + "IPY_MODEL_83945befc2ec4271af67b93797cadd10", + "IPY_MODEL_93bd352e7edc4aac8660f540a733e80f" + ], + "layout": "IPY_MODEL_e5304a7b2a15495792b3302840dc6164", + "tabbable": null, + "tooltip": null } }, - "5b27262a9cd94dcca7fa54df4cceb26a": { + "5abd50f2ff82441194222375826e37c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1625,23 +1562,30 @@ "width": null } }, - "5bb6200f11d74e33b58d64accf19085f": { + "5ac2298939434623ab7427ecd71bb845": { "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_f618acb94c794f1ba33254b732f3da99", + "placeholder": "​", + "style": "IPY_MODEL_bdd0d707ff2c40a783c34c22c644bff2", + "tabbable": null, + "tooltip": null, + "value": "100%" } }, - "60b778a0ef8e44b39608af38b47ca860": { + "6249a51dbc1145c985fed7ad172f3f6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", @@ -1659,7 +1603,7 @@ "text_color": null } }, - "60ce120b6f0d4b35939edd06a49b48fa": { + "68c0ed1d2b074054bc0c083b8ccd8a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1712,7 +1656,7 @@ "width": null } }, - "621c7249a2054bf59226e64a4b8f7081": { + "6b76fce815c1461d9f75cd9b932f9d87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -1727,33 +1671,39 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_7f6b7ffd00bb4a2caab3b50683296936", + "layout": "IPY_MODEL_68c0ed1d2b074054bc0c083b8ccd8a71", "placeholder": "​", - "style": "IPY_MODEL_cbbaae80c54e40cabbdb2edd591551d4", + "style": "IPY_MODEL_6249a51dbc1145c985fed7ad172f3f6f", "tabbable": null, "tooltip": null, "value": "number of examples processed for estimating thresholds: 100%" } }, - "646252281ae14f8886cc21d9e56afec5": { + "71098e13b4334a47bbac4b75032ea150": { "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_5ac2298939434623ab7427ecd71bb845", + "IPY_MODEL_e05c95cfe29749928b6771d970a5fc81", + "IPY_MODEL_26332d6a8cc44c2e97dbf9c486f38f25" + ], + "layout": "IPY_MODEL_40897686e955404f91514151f240c0d0", + "tabbable": null, + "tooltip": null } }, - "686826beb1be457bab28c73bcd2ffefb": { + "7388d890f96b440d9df5bb2bcf630681": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1806,28 +1756,95 @@ "width": null } }, - "70deac36097f491e8666a288db33cf71": { - "model_module": "@jupyter-widgets/base", + "773d8ea77b7443f2ade5b4c99a303fcb": { + "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, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "83945befc2ec4271af67b93797cadd10": { + "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_010891f081ec41c1aabdc9984ea3e880", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_beb83a56568c469189a5939e700e69a4", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "93bd352e7edc4aac8660f540a733e80f": { + "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_ffe9b9351b8a4b129d0904a8757b9a46", + "placeholder": "​", + "style": "IPY_MODEL_fc53622cc52741d8bde9b20fe8c9a3c0", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:21<00:00,  1.44it/s]" + } + }, + "99e35d66f5634eb28a74eb5bfa102bde": { + "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, @@ -1859,7 +1876,25 @@ "width": null } }, - "72840f69ea214918a754b98c138bcd01": { + "99f6441d531f4710baa405c0d7109b5f": { + "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 + } + }, + "abbf1d8d4dc4498dbba438256d734fad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -1874,16 +1909,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8b3e196faee64d279374daae7049f9a7", - "IPY_MODEL_0e3edfaa67be4256bf241604d1fdfd54", - "IPY_MODEL_2d5ba316556d415b8f9eceaac5bda45b" + "IPY_MODEL_2644b4fc5fda48acb706485e94aabc6a", + "IPY_MODEL_c68ef40a35e04fc39427a9f284523b28", + "IPY_MODEL_328b4d1e22fb41b9be4bc667200a34d2" ], - "layout": "IPY_MODEL_7c0fbc0da1b843e89059929faa931860", + "layout": "IPY_MODEL_ef987a4e9c5445f2a1965215131e2de0", "tabbable": null, "tooltip": null } }, - "7c0fbc0da1b843e89059929faa931860": { + "b2e6852040e34be68af10fb4a3c2b9a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -1936,30 +1971,7 @@ "width": null } }, - "7e7920af14e54024b17aa6179ca38f35": { - "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_06ef42058bc7433ca11271d89630261f", - "placeholder": "​", - "style": "IPY_MODEL_60b778a0ef8e44b39608af38b47ca860", - "tabbable": null, - "tooltip": null, - "value": " 30/30 [00:00<00:00, 407.39it/s]" - } - }, - "7f6b7ffd00bb4a2caab3b50683296936": { + "b85f1eb5f0e64530870f6fbf949cda91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2012,7 +2024,41 @@ "width": null } }, - "83ed95add600447dbcd4112a82b755ac": { + "bdd0d707ff2c40a783c34c22c644bff2": { + "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 + } + }, + "beb83a56568c469189a5939e700e69a4": { + "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": "" + } + }, + "c158f6f873284cbaa7be7bbaebbd7763": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2028,7 +2074,51 @@ "description_width": "" } }, - "8b3e196faee64d279374daae7049f9a7": { + "c4adc9fb2d8c49ab9d863ff53317708d": { + "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 + } + }, + "c68ef40a35e04fc39427a9f284523b28": { + "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_301a3fcd99094c40a1fb9f975619c430", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_332840cfdb9a451ebd2a374dae54e637", + "tabbable": null, + "tooltip": null, + "value": 30.0 + } + }, + "ce77d0d3de7c4b089021754738d5ceb4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", @@ -2043,15 +2133,38 @@ "_view_name": "HTMLView", "description": "", "description_allow_html": false, - "layout": "IPY_MODEL_02c8e49579ab4c2cb5010e5d696f7f75", + "layout": "IPY_MODEL_99e35d66f5634eb28a74eb5bfa102bde", "placeholder": "​", - "style": "IPY_MODEL_646252281ae14f8886cc21d9e56afec5", + "style": "IPY_MODEL_773d8ea77b7443f2ade5b4c99a303fcb", + "tabbable": null, + "tooltip": null, + "value": " 30/30 [00:00<00:00, 405.54it/s]" + } + }, + "d2ea6fbb7475458087e30a3aee7c2421": { + "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_b2e6852040e34be68af10fb4a3c2b9a7", + "placeholder": "​", + "style": "IPY_MODEL_c4adc9fb2d8c49ab9d863ff53317708d", "tabbable": null, "tooltip": null, "value": "number of examples processed for checking labels: 100%" } }, - "8db16dbcca284d40921261550c3c9241": { + "d7bd991e20f14b0c98dd8d679d84939c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", @@ -2067,60 +2180,51 @@ "description_width": "" } }, - "957a7ec883f34696b2479a90a87bdbcb": { - "model_module": "@jupyter-widgets/base", + "dcb3aba2d5ee498da09596d6b1189652": { + "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, - "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", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } }, - "9efde9d20e674f91930d36a8fffde7e0": { + "e05c95cfe29749928b6771d970a5fc81": { + "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_380290952081433398d72252f55f3512", + "max": 4997817.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c158f6f873284cbaa7be7bbaebbd7763", + "tabbable": null, + "tooltip": null, + "value": 4997817.0 + } + }, + "e5304a7b2a15495792b3302840dc6164": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2173,31 +2277,7 @@ "width": null } }, - "a41dd8fa1c914418a704a6e5e8be1e2e": { - "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_07b675adb016454a9854b4ea300e4cfe", - "IPY_MODEL_232cb3f4436c4a29aa37462ded23ed0a", - "IPY_MODEL_2bc90b79c1364f709f2cc5d8d3ac1a01" - ], - "layout": "IPY_MODEL_5b27262a9cd94dcca7fa54df4cceb26a", - "tabbable": null, - "tooltip": null - } - }, - "a6b9abd68857477cbdc3898a45b7c10f": { + "ef987a4e9c5445f2a1965215131e2de0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2250,25 +2330,7 @@ "width": null } }, - "cbbaae80c54e40cabbdb2edd591551d4": { - "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 - } - }, - "d2ba7ead109f40f88a0f2fdcd7f79091": { + "f618acb94c794f1ba33254b732f3da99": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2321,25 +2383,7 @@ "width": null } }, - "d2dd7b61c9974c56af1e4ae1413f64cf": { - "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 - } - }, - "d3ec0bdaf05d45038d515229edd1fce4": { + "f80951daaff1439bae07b22f26431578": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", @@ -2354,16 +2398,34 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_20149c21eb1a4d47b708a9f402b4f051", - "IPY_MODEL_f5b6a3b6f2474f7c8d2b531dbc37f186", - "IPY_MODEL_182b3bcc537c404793b98eb84a4a9fef" + "IPY_MODEL_6b76fce815c1461d9f75cd9b932f9d87", + "IPY_MODEL_365979231dd54217a8aac4a279204067", + "IPY_MODEL_ce77d0d3de7c4b089021754738d5ceb4" ], - "layout": "IPY_MODEL_d2ba7ead109f40f88a0f2fdcd7f79091", + "layout": "IPY_MODEL_5abd50f2ff82441194222375826e37c9", "tabbable": null, "tooltip": null } }, - "ea8cdf6af87544368e97f412bed093e3": { + "fc53622cc52741d8bde9b20fe8c9a3c0": { + "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 + } + }, + "ffe9b9351b8a4b129d0904a8757b9a46": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", @@ -2415,68 +2477,6 @@ "visibility": null, "width": null } - }, - "f5b6a3b6f2474f7c8d2b531dbc37f186": { - "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_60ce120b6f0d4b35939edd06a49b48fa", - "max": 4997817.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5bb6200f11d74e33b58d64accf19085f", - "tabbable": null, - "tooltip": null, - "value": 4997817.0 - } - }, - "f73f43e9ac32458989fecfd81a100778": { - "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 - } - }, - "ff45dd3e934e4e0c9f7fa5da7043dbd7": { - "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 - } } }, "version_major": 2, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 1c7d43cec..0cc09dfcf 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -676,16 +676,16 @@

    1. Install required dependencies and download data 
    ---2024-04-06 04:38:29--  https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 169.150.236.98, 2400:52e0:1a00::718:1
-Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.
+--2024-04-08 19:17:24--  https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 143.244.49.177, 2400:52e0:1a01::994:1
+Connecting to data.deepai.org (data.deepai.org)|143.244.49.177|:443... connected.
 HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
 
-conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.04s
+conll2003.zip       100%[===================>] 959.94K  5.49MB/s    in 0.2s
 
-2024-04-06 04:38:29 (22.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-04-08 19:17:24 (5.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]
 
 mkdir: cannot create directory ‘data’: File exists
 Archive:  conll2003.zip
@@ -693,16 +693,16 @@ 
    1. Install required dependencies and download data

    diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index e868c19b5..1733ede8c 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:29.398070Z", - "iopub.status.busy": "2024-04-06T04:38:29.397578Z", - "iopub.status.idle": "2024-04-06T04:38:30.762030Z", - "shell.execute_reply": "2024-04-06T04:38:30.761463Z" + "iopub.execute_input": "2024-04-08T19:17:24.524829Z", + "iopub.status.busy": "2024-04-08T19:17:24.524651Z", + "iopub.status.idle": "2024-04-08T19:17:26.451617Z", + "shell.execute_reply": "2024-04-08T19:17:26.450937Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-04-06 04:38:29-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-04-08 19:17:24-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.98, 2400:52e0:1a00::718:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.98|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "143.244.49.177, 2400:52e0:1a01::994:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.49.177|:443... 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 +116,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.04s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.49MB/s in 0.2s \r\n", "\r\n", - "2024-04-06 04:38:29 (22.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-04-08 19:17:24 (5.49 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,9 +138,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-04-06 04:38:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.84.148, 52.216.129.163, 52.217.231.17, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.84.148|:443... " + "--2024-04-08 19:17:25-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.130.187, 54.231.165.233, 52.216.62.161, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.130.187|:443... " ] }, { @@ -167,7 +174,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 14%[=> ] 2.33M 11.7MB/s " + "pred_probs.npz 1%[ ] 211.53K 926KB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 22%[===> ] 3.71M 8.12MB/s " ] }, { @@ -175,9 +190,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 46.9MB/s in 0.3s \r\n", + "pred_probs.npz 94%[=================> ] 15.37M 22.6MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 23.5MB/s in 0.7s \r\n", "\r\n", - "2024-04-06 04:38:30 (46.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-04-08 19:17:26 (23.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -194,10 +210,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:30.764412Z", - "iopub.status.busy": "2024-04-06T04:38:30.764032Z", - "iopub.status.idle": "2024-04-06T04:38:31.972111Z", - "shell.execute_reply": "2024-04-06T04:38:31.971535Z" + "iopub.execute_input": "2024-04-08T19:17:26.454458Z", + "iopub.status.busy": "2024-04-08T19:17:26.454223Z", + "iopub.status.idle": "2024-04-08T19:17:27.676181Z", + "shell.execute_reply": "2024-04-08T19:17:27.675698Z" }, "nbsphinx": "hidden" }, @@ -208,7 +224,7 @@ "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@e0b7615c1169c6d8fcae15be6477bd7327e82e00\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@cc319efea07da004d1544c0577402d71f309fa06\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -234,10 +250,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.974580Z", - "iopub.status.busy": "2024-04-06T04:38:31.974308Z", - "iopub.status.idle": "2024-04-06T04:38:31.977556Z", - "shell.execute_reply": "2024-04-06T04:38:31.977128Z" + "iopub.execute_input": "2024-04-08T19:17:27.678806Z", + "iopub.status.busy": "2024-04-08T19:17:27.678375Z", + "iopub.status.idle": "2024-04-08T19:17:27.681955Z", + "shell.execute_reply": "2024-04-08T19:17:27.681515Z" } }, "outputs": [], @@ -287,10 +303,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.979700Z", - "iopub.status.busy": "2024-04-06T04:38:31.979317Z", - "iopub.status.idle": "2024-04-06T04:38:31.982377Z", - "shell.execute_reply": "2024-04-06T04:38:31.981830Z" + "iopub.execute_input": "2024-04-08T19:17:27.683962Z", + "iopub.status.busy": "2024-04-08T19:17:27.683699Z", + "iopub.status.idle": "2024-04-08T19:17:27.686524Z", + "shell.execute_reply": "2024-04-08T19:17:27.686095Z" }, "nbsphinx": "hidden" }, @@ -308,10 +324,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:31.984377Z", - "iopub.status.busy": "2024-04-06T04:38:31.984017Z", - "iopub.status.idle": "2024-04-06T04:38:41.053110Z", - "shell.execute_reply": "2024-04-06T04:38:41.052521Z" + "iopub.execute_input": "2024-04-08T19:17:27.688377Z", + "iopub.status.busy": "2024-04-08T19:17:27.688200Z", + "iopub.status.idle": "2024-04-08T19:17:36.852616Z", + "shell.execute_reply": "2024-04-08T19:17:36.852071Z" } }, "outputs": [], @@ -385,10 +401,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.055687Z", - "iopub.status.busy": "2024-04-06T04:38:41.055498Z", - "iopub.status.idle": "2024-04-06T04:38:41.061081Z", - "shell.execute_reply": "2024-04-06T04:38:41.060531Z" + "iopub.execute_input": "2024-04-08T19:17:36.855120Z", + "iopub.status.busy": "2024-04-08T19:17:36.854821Z", + "iopub.status.idle": "2024-04-08T19:17:36.860286Z", + "shell.execute_reply": "2024-04-08T19:17:36.859865Z" }, "nbsphinx": "hidden" }, @@ -428,10 +444,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.063230Z", - "iopub.status.busy": "2024-04-06T04:38:41.062809Z", - "iopub.status.idle": "2024-04-06T04:38:41.426124Z", - "shell.execute_reply": "2024-04-06T04:38:41.425590Z" + "iopub.execute_input": "2024-04-08T19:17:36.862236Z", + "iopub.status.busy": "2024-04-08T19:17:36.861904Z", + "iopub.status.idle": "2024-04-08T19:17:37.207147Z", + "shell.execute_reply": "2024-04-08T19:17:37.206565Z" } }, "outputs": [], @@ -468,10 +484,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.428511Z", - "iopub.status.busy": "2024-04-06T04:38:41.428316Z", - "iopub.status.idle": "2024-04-06T04:38:41.432566Z", - "shell.execute_reply": "2024-04-06T04:38:41.432029Z" + "iopub.execute_input": "2024-04-08T19:17:37.209618Z", + "iopub.status.busy": "2024-04-08T19:17:37.209283Z", + "iopub.status.idle": "2024-04-08T19:17:37.213376Z", + "shell.execute_reply": "2024-04-08T19:17:37.212864Z" } }, "outputs": [ @@ -543,10 +559,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:41.434828Z", - "iopub.status.busy": "2024-04-06T04:38:41.434438Z", - "iopub.status.idle": "2024-04-06T04:38:43.797032Z", - "shell.execute_reply": "2024-04-06T04:38:43.796336Z" + "iopub.execute_input": "2024-04-08T19:17:37.215394Z", + "iopub.status.busy": "2024-04-08T19:17:37.215083Z", + "iopub.status.idle": "2024-04-08T19:17:39.552115Z", + "shell.execute_reply": "2024-04-08T19:17:39.551400Z" } }, "outputs": [], @@ -568,10 +584,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.800002Z", - "iopub.status.busy": "2024-04-06T04:38:43.799357Z", - "iopub.status.idle": "2024-04-06T04:38:43.803395Z", - "shell.execute_reply": "2024-04-06T04:38:43.802849Z" + "iopub.execute_input": "2024-04-08T19:17:39.555424Z", + "iopub.status.busy": "2024-04-08T19:17:39.554614Z", + "iopub.status.idle": "2024-04-08T19:17:39.558938Z", + "shell.execute_reply": "2024-04-08T19:17:39.558474Z" } }, "outputs": [ @@ -607,10 +623,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.805438Z", - "iopub.status.busy": "2024-04-06T04:38:43.805041Z", - "iopub.status.idle": "2024-04-06T04:38:43.810204Z", - "shell.execute_reply": "2024-04-06T04:38:43.809632Z" + "iopub.execute_input": "2024-04-08T19:17:39.560894Z", + "iopub.status.busy": "2024-04-08T19:17:39.560573Z", + "iopub.status.idle": "2024-04-08T19:17:39.565814Z", + "shell.execute_reply": "2024-04-08T19:17:39.565368Z" } }, "outputs": [ @@ -788,10 +804,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.812097Z", - "iopub.status.busy": "2024-04-06T04:38:43.811923Z", - "iopub.status.idle": "2024-04-06T04:38:43.837570Z", - "shell.execute_reply": "2024-04-06T04:38:43.837054Z" + "iopub.execute_input": "2024-04-08T19:17:39.567759Z", + "iopub.status.busy": "2024-04-08T19:17:39.567433Z", + "iopub.status.idle": "2024-04-08T19:17:39.593200Z", + "shell.execute_reply": "2024-04-08T19:17:39.592668Z" } }, "outputs": [ @@ -893,10 +909,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.839685Z", - "iopub.status.busy": "2024-04-06T04:38:43.839262Z", - "iopub.status.idle": "2024-04-06T04:38:43.843573Z", - "shell.execute_reply": "2024-04-06T04:38:43.843046Z" + "iopub.execute_input": "2024-04-08T19:17:39.595168Z", + "iopub.status.busy": "2024-04-08T19:17:39.594990Z", + "iopub.status.idle": "2024-04-08T19:17:39.599302Z", + "shell.execute_reply": "2024-04-08T19:17:39.598861Z" } }, "outputs": [ @@ -970,10 +986,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:43.845456Z", - "iopub.status.busy": "2024-04-06T04:38:43.845286Z", - "iopub.status.idle": "2024-04-06T04:38:45.262927Z", - "shell.execute_reply": "2024-04-06T04:38:45.262416Z" + "iopub.execute_input": "2024-04-08T19:17:39.601340Z", + "iopub.status.busy": "2024-04-08T19:17:39.600975Z", + "iopub.status.idle": "2024-04-08T19:17:41.028748Z", + "shell.execute_reply": "2024-04-08T19:17:41.028272Z" } }, "outputs": [ @@ -1145,10 +1161,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-04-06T04:38:45.265138Z", - "iopub.status.busy": "2024-04-06T04:38:45.264818Z", - "iopub.status.idle": "2024-04-06T04:38:45.268799Z", - "shell.execute_reply": "2024-04-06T04:38:45.268374Z" + "iopub.execute_input": "2024-04-08T19:17:41.030867Z", + "iopub.status.busy": "2024-04-08T19:17:41.030672Z", + "iopub.status.idle": "2024-04-08T19:17:41.034715Z", + "shell.execute_reply": "2024-04-08T19:17:41.034283Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 753559168..4db28125d 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.6.3", - commit_hash: "e0b7615c1169c6d8fcae15be6477bd7327e82e00", + commit_hash: "cc319efea07da004d1544c0577402d71f309fa06", }; \ No newline at end of file