From 2f40118430001994405e62ed6078dd3e705bd3e9 Mon Sep 17 00:00:00 2001 From: aditya1503 Date: Wed, 10 Jan 2024 06:26:00 +0000 Subject: [PATCH] deploy: cleanlab/cleanlab@ae085b45b538e73a059d6a9ef10d747e590ce755 --- master/.buildinfo | 2 +- .../cleanlab/benchmarking/index.doctree | Bin 3315 -> 3315 bytes .../benchmarking/noise_generation.doctree | Bin 83507 -> 83507 bytes .../.doctrees/cleanlab/classification.doctree | Bin 297397 -> 297397 bytes master/.doctrees/cleanlab/count.doctree | Bin 297132 -> 297132 bytes .../cleanlab/datalab/datalab.doctree | Bin 181018 -> 181018 bytes .../guide/custom_issue_manager.doctree | Bin 29123 -> 29123 bytes .../guide/generating_cluster_ids.doctree | Bin 6256 -> 6256 bytes .../cleanlab/datalab/guide/index.doctree | Bin 5915 -> 5915 bytes .../guide/issue_type_description.doctree | Bin 97079 -> 97079 bytes .../.doctrees/cleanlab/datalab/index.doctree | Bin 5502 -> 5502 bytes .../cleanlab/datalab/internal/data.doctree | Bin 75965 -> 75965 bytes .../datalab/internal/data_issues.doctree | Bin 76753 -> 76753 bytes .../cleanlab/datalab/internal/factory.doctree | Bin 43724 -> 43724 bytes .../cleanlab/datalab/internal/index.doctree | Bin 4562 -> 4562 bytes .../datalab/internal/issue_finder.doctree | Bin 46509 -> 46509 bytes .../_notices/not_registered.doctree | Bin 3377 -> 3377 bytes .../internal/issue_manager/duplicate.doctree | Bin 73748 -> 73748 bytes .../internal/issue_manager/imbalance.doctree | Bin 71607 -> 71607 bytes .../internal/issue_manager/index.doctree | Bin 5820 -> 5820 bytes .../issue_manager/issue_manager.doctree | Bin 82549 -> 82549 bytes .../internal/issue_manager/label.doctree | Bin 87552 -> 87552 bytes .../internal/issue_manager/noniid.doctree | Bin 90234 -> 90234 bytes .../internal/issue_manager/null.doctree | Bin 64142 -> 64142 bytes .../internal/issue_manager/outlier.doctree | Bin 75773 -> 75773 bytes .../issue_manager/regression/index.doctree | Bin 3622 -> 3622 bytes .../issue_manager/regression/label.doctree | Bin 107652 -> 107652 bytes .../underperforming_group.doctree | Bin 121020 -> 121020 bytes .../cleanlab/datalab/internal/report.doctree | Bin 32315 -> 32315 bytes .../datalab/optional_dependencies.doctree | Bin 3389 -> 3389 bytes master/.doctrees/cleanlab/dataset.doctree | Bin 102668 -> 102668 bytes .../cleanlab/experimental/cifar_cnn.doctree | Bin 346504 -> 346504 bytes .../cleanlab/experimental/coteaching.doctree | Bin 48181 -> 48181 bytes .../cleanlab/experimental/index.doctree | Bin 5382 -> 5382 bytes .../experimental/label_issues_batched.doctree | Bin 161811 -> 161811 bytes .../experimental/mnist_pytorch.doctree | Bin 492589 -> 492589 bytes master/.doctrees/cleanlab/filter.doctree | Bin 98823 -> 98823 bytes .../.doctrees/cleanlab/internal/index.doctree | Bin 4550 -> 4550 bytes .../internal/label_quality_utils.doctree | Bin 19742 -> 19742 bytes .../cleanlab/internal/latent_algebra.doctree | Bin 88865 -> 88865 bytes .../internal/multiannotator_utils.doctree | Bin 45614 -> 45614 bytes .../internal/multilabel_scorer.doctree | Bin 162776 -> 162776 bytes .../internal/multilabel_utils.doctree | Bin 33914 -> 33914 bytes .../cleanlab/internal/outlier.doctree | Bin 16196 -> 16196 bytes .../token_classification_utils.doctree | Bin 66017 -> 66017 bytes .../.doctrees/cleanlab/internal/util.doctree | Bin 208122 -> 208122 bytes .../cleanlab/internal/validation.doctree | Bin 33814 -> 33814 bytes .../cleanlab/models/fasttext.doctree | Bin 2403 -> 2403 bytes .../.doctrees/cleanlab/models/index.doctree | Bin 5060 -> 5060 bytes .../.doctrees/cleanlab/models/keras.doctree | Bin 103797 -> 103797 bytes .../.doctrees/cleanlab/multiannotator.doctree | Bin 171038 -> 171038 bytes .../multilabel_classification/dataset.doctree | Bin 67565 -> 67565 bytes .../multilabel_classification/filter.doctree | Bin 89621 -> 89621 bytes .../multilabel_classification/index.doctree | Bin 5009 -> 5009 bytes .../multilabel_classification/rank.doctree | Bin 46224 -> 46224 bytes .../cleanlab/object_detection/filter.doctree | Bin 36016 -> 36016 bytes .../cleanlab/object_detection/index.doctree | Bin 3927 -> 3927 bytes .../cleanlab/object_detection/rank.doctree | Bin 151195 -> 151195 bytes .../cleanlab/object_detection/summary.doctree | Bin 157619 -> 157619 bytes master/.doctrees/cleanlab/outlier.doctree | Bin 102110 -> 102110 bytes master/.doctrees/cleanlab/rank.doctree | Bin 117415 -> 117415 bytes .../cleanlab/regression/index.doctree | Bin 3801 -> 3801 bytes .../cleanlab/regression/learn.doctree | Bin 220237 -> 220237 bytes .../cleanlab/regression/rank.doctree | Bin 19619 -> 19619 bytes .../cleanlab/segmentation/filter.doctree | Bin 29078 -> 29078 bytes .../cleanlab/segmentation/index.doctree | Bin 3855 -> 3855 bytes .../cleanlab/segmentation/rank.doctree | Bin 53242 -> 53242 bytes .../cleanlab/segmentation/summary.doctree | Bin 67310 -> 67310 bytes .../token_classification/filter.doctree | Bin 29360 -> 29360 bytes .../token_classification/index.doctree | Bin 4017 -> 4017 bytes .../token_classification/rank.doctree | Bin 66747 -> 66747 bytes .../token_classification/summary.doctree | Bin 84588 -> 84588 bytes master/.doctrees/environment.pickle | Bin 1796133 -> 1796869 bytes master/.doctrees/index.doctree | Bin 41848 -> 41848 bytes master/.doctrees/migrating/migrate_v2.doctree | Bin 28054 -> 28054 bytes .../.doctrees/nbsphinx/tutorials/audio.ipynb | 1140 +++++----- .../tutorials/datalab/datalab_advanced.ipynb | 364 ++-- .../datalab/datalab_quickstart.ipynb | 130 +- .../nbsphinx/tutorials/datalab/tabular.ipynb | 138 +- .../nbsphinx/tutorials/datalab/text.ipynb | 1762 +++++++-------- .../nbsphinx/tutorials/dataset_health.ipynb | 34 +- master/.doctrees/nbsphinx/tutorials/faq.ipynb | 676 +++--- .../.doctrees/nbsphinx/tutorials/image.ipynb | 1734 +++++++-------- .../nbsphinx/tutorials/indepth_overview.ipynb | 210 +- .../nbsphinx/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 90 +- .../nbsphinx/tutorials/object_detection.ipynb | 146 +- .../nbsphinx/tutorials/outliers.ipynb | 350 +-- .../nbsphinx/tutorials/regression.ipynb | 162 +- .../nbsphinx/tutorials/segmentation.ipynb | 1350 ++++++------ .../nbsphinx/tutorials/tabular.ipynb | 130 +- .../.doctrees/nbsphinx/tutorials/text.ipynb | 164 +- .../tutorials/token_classification.ipynb | 173 +- master/.doctrees/tutorials/audio.doctree | Bin 327063 -> 327061 bytes .../datalab/datalab_advanced.doctree | Bin 198979 -> 198979 bytes .../datalab/datalab_quickstart.doctree | Bin 150645 -> 150645 bytes .../.doctrees/tutorials/datalab/index.doctree | Bin 3058 -> 3058 bytes .../tutorials/datalab/tabular.doctree | Bin 121277 -> 121277 bytes .../.doctrees/tutorials/datalab/text.doctree | Bin 308693 -> 308691 bytes .../tutorials/dataset_health.doctree | Bin 330828 -> 330828 bytes master/.doctrees/tutorials/faq.doctree | Bin 175067 -> 175069 bytes master/.doctrees/tutorials/image.doctree | Bin 488673 -> 488609 bytes .../tutorials/indepth_overview.doctree | Bin 212787 -> 212787 bytes master/.doctrees/tutorials/index.doctree | Bin 3170 -> 3170 bytes .../tutorials/multiannotator.doctree | Bin 137341 -> 137341 bytes .../multilabel_classification.doctree | Bin 57571 -> 57571 bytes .../tutorials/object_detection.doctree | Bin 110970 -> 110970 bytes master/.doctrees/tutorials/outliers.doctree | Bin 106815 -> 106817 bytes .../tutorials/pred_probs_cross_val.doctree | Bin 17248 -> 17248 bytes master/.doctrees/tutorials/regression.doctree | Bin 80921 -> 80921 bytes .../.doctrees/tutorials/segmentation.doctree | Bin 3066459 -> 3096133 bytes master/.doctrees/tutorials/tabular.doctree | Bin 59757 -> 59757 bytes master/.doctrees/tutorials/text.doctree | Bin 90061 -> 90061 bytes .../tutorials/token_classification.doctree | Bin 186891 -> 188820 bytes master/_modules/cleanlab/count.html | 22 +- .../experimental/label_issues_batched.html | 11 +- master/_modules/cleanlab/filter.html | 8 +- master/_sources/tutorials/audio.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 +- master/_sources/tutorials/tabular.ipynb | 2 +- master/_sources/tutorials/text.ipynb | 2 +- .../tutorials/token_classification.ipynb | 2 +- master/searchindex.js | 2 +- master/tutorials/audio.html | 2 +- master/tutorials/audio.ipynb | 1140 +++++----- .../tutorials/datalab/datalab_advanced.html | 6 +- .../tutorials/datalab/datalab_advanced.ipynb | 364 ++-- .../datalab/datalab_quickstart.ipynb | 130 +- master/tutorials/datalab/tabular.ipynb | 138 +- master/tutorials/datalab/text.html | 18 +- master/tutorials/datalab/text.ipynb | 1762 +++++++-------- master/tutorials/dataset_health.ipynb | 34 +- master/tutorials/faq.html | 6 +- master/tutorials/faq.ipynb | 676 +++--- master/tutorials/image.html | 254 +-- master/tutorials/image.ipynb | 1734 +++++++-------- master/tutorials/indepth_overview.ipynb | 210 +- master/tutorials/multiannotator.ipynb | 146 +- .../tutorials/multilabel_classification.ipynb | 90 +- master/tutorials/object_detection.ipynb | 146 +- master/tutorials/outliers.html | 4 +- master/tutorials/outliers.ipynb | 350 +-- master/tutorials/regression.ipynb | 162 +- master/tutorials/segmentation.html | 1923 +++++++++-------- master/tutorials/segmentation.ipynb | 1350 ++++++------ master/tutorials/tabular.ipynb | 130 +- master/tutorials/text.html | 2 +- master/tutorials/text.ipynb | 164 +- master/tutorials/token_classification.html | 81 +- master/tutorials/token_classification.ipynb | 173 +- versioning.js | 2 +- 162 files changed, 10260 insertions(+), 9913 deletions(-) diff --git a/master/.buildinfo b/master/.buildinfo index 0670ee0b4..fb34d3623 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: 9c5f41bf33c59b9516b6d28e90fec884 +config: 8c225318e6cf8d2ee6cecc1cde9d9d90 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 0467b65b1f8a42edfc320907acb5be3541c3d0f6..f2c285d69aab666b59969c23d147ed18e873245a 100644 GIT binary patch delta 175 zcmew?`B`#9IHO^ip^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) kszH)znt5W%k delta 175 zcmew?`B`#9IHO@&K~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; ks&QhXWwNpPR)X~APL5{ZK(5x69EId*jhmdnC$@P% zrvN(z`X}$_6rNACR3`9iV1x&bCyh#2r~ZVw>mmKail*44suXT*)^>XY>B|)#S&AuFmBB?^!qh j`q?W>t{d{rHMiSKF!FFwVgR!;Bd;Rq25biw;hz`*Yv$Dk delta 1655 zcmdno!@9YLbwfO(VOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VMR)X~APL5{ZK(5x69EId*jhmdnC$@P% zrvN(z`X}$_6rNACR3`9iV1x&bCyh#2r~ZVw>mmKail*44suXT*)^>XY>B|)#S&AuFmBB?^!qh j`q?W>t{d{rHMiSKF!FFwVgR!;Bd;Rq25biw;hz`*#&_!& diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree index 71ec1aa1c7fadfff2afd2c46d415f5ff979631d4..7efe300a365b599b8abea437dc2fea7b0e299231 100644 GIT binary patch delta 4244 zcmbuDT}YEr7{@v1ywf!I;Re&nixOj$rQ6tC%Th;mBa$CA6*O(WDv?gbuNao3*#{`G zc%c)7Wmt3*wK&;x6MbArx7`HJyeo_>MWTqju`^fRzfTvt{QZCb^YET$?>3`ZXEbZ9 zfw%@;Zoe8DR13Wx)$Q{7y`GTY*WTvx6sbO+&+B))+YdNBs>|KxZ1J}HLao{anL|*4 zNEu)4RSaUDDj4fu-XfZn+tql#MH{N^L*d6oxNe2vP%T_%`j|eoDp2*u5a4 zdmvJ)4G*PjVje;BUAsw}xbvYeRlhmGo)wQiMk#^~>?IvyZZQw>cqYAksS4xW=sdc- zfM$QMW}rJy{SavH%C9Cg`+02waVs|F3?@RR5mzD!S1@u7+Zwu4hyiUjZy~K{vuP{2 zj5Ze-$t|=wWFc|1`Ms4)q0JUMDMp(Yvq&e}yu6(pMw@4I$TZr_c9KtM^Jp&VLYP5r zpeG0^5C<_MHV?>6`xbfoYj~`j7$E8-liLtGUY@iDWWvQ_{V;^CkaL&xbBVok5>BPl zaSslz)Jj)VdF&W9@CVtok*x*QgC%MQOe5zhX+(X$r*d(|qJBQzegO z6NNk1X(sfn(L)NZO)>^#%n>6tq0z*QH;rro$v>2QntoQkF_-gi`OF4OSD| z0d{zq!x}R1HTJ>lJ?trBt0roIJV#eLO74OeMQi}Em8I9kC7TN~2iY@|emTE!ROy9` za<;7E-~qV%nEmY65C5xfgS0&53*sAaGl0tgGYmSEX6yjC9Do_F7AbMW{Q)_`mHmhT N$RG9~{}`CH{{iJay$1jQ delta 4244 zcmbuDT}YEr7{@v1ywf!I;Re&nixNZ1(k+*|EOk^jBKc8MLDTl(2z4rc6vL7<`v4^t zFLZ*i6pL=67AJddqK^ydwwu72cZJbvB2h%%*qJNu-=~XR{{FxJd3evWcbikqbE-Mg zLhOM)Z)bB1 zt=duH3O&coyHSh`$8_3_>gf0szc{2=Ma@_~MlJcOuHJs%+1$dS!C+tFqxmTnqvJDf zM0Z!HQ5_jhSH&WN=DT*24srLxK&p0gg6$NKK87iRb?qfxVqrNC@pvY^JXVMCZgd`t zFQM7rs~PCd(?0~-yYj0I&3;~+MBIvvIfH?aS;Un{!ZnOs$F_#v6rw|$ja!HxZ8mHr zSJ37{J-LlGdrTyXHorHMX|&m7CFN-Iau(@Eo8#Nb5wv+ehs>hQY$y4IHjm|!9)ua> z26}>!0?8*v#O49HY2PAme*=$H6CH$YWNI5?$IFv8fK0elt{n!?RdW8ab}q4ZPQmGP zI^n>HF{XVwMoW+j5(^uCbSxu{+6CCA^C@rPt(rIH|A>ot$Wm0u&t#!8ZAUd_h$=YCAY(19I2 zsO(k(26QqJ#UVx1)O}2GgCTm;fn(rl_M)QKde}i`77Y_`dEY?SxLG=A(a0T#CXg9abCNihE2Lz{UZ zZxmWaQQk%21d2Z=7EpXfUP5L6Mj1f)-#an|&ECAdps~0_{R8ahtWDRwHKL^K9{ryTuQT+UcA=Lg)M!qBZa~rP9AM4@w z?=}*JPbXn~SX*9fG$xlS8!`O*&*21s7n*51JAasa4l%djVjI`30G!RQUV?RHxnZ|+ zn3?cI+&Q=Qsf)APJFe0JfY;X5lZ3dKpC>OM*Rrl~bUPs;Y=#akcI<$jAE!In*Dg}q zf>2>214Q-%1gu|{<%>S%u96aAcNfCc`|!4FIz$zFPCyurCUGZUDGBxg^po9%`4Y?0q}YlW)J)k!^&7;U+~$I#SA z7a$LU)#vJMx`&O9)7^kq!MX@$DkLyl{1?jb6us2!yc=s4ZC2gN`WETw9S8y46sTi? zZ?H)0Zq~P&UZK}8`TS}5(G3?L0&Hc^!&YTJy#XPhn*w#L8si%R&Q-LWFUryxzI)$q DdG1i5 delta 3738 zcmbuB!E4iC7{Wm0u&t#!8ZAUd_h$=aA~X_&1HT*s)4AyW@3woMyl8`63ZbYMph zD!Y|{0i8@laYzw0bss8D7~*bo;23zCy{PE59(ItKMa#rn-uDmqJiD zZx-4{P~Ju11d2Z=6;Qk;FQT%4rwpL{?;W0mW^dhI(46n8QDApAe=L3%HGjVMHERA! z-*wddGsnkK{$EagK>0_{R8ahtWDRwHKhvYA{(fbnD1PQb2DSf_;qQq4+@|Z&$42=5 zyUj%5(@7W~)|VEV&56azdJI4JIh+9SLMx51^9Q--5OW(Yc5pokz}fohC0KWk8*)2` znFWu>opY<7x;U%Na+OX1yt=NDB*ex1Jb3}RmNkVVTM3b1Gj!^)qx+1@V{|+F+D&Sk z5GrhVfXIG;fc?v|H0WdQDk%|G&XLC=K*m0xw-3k`z|WRphiSq4+1Zz*4_wE@eX^Ui zzb9%lV1a*g@BY^SHKeOlej`WNWwZ3qFw5@=w7 zZ?H(}-K=djwM?&J^7+&9qZ=+h1lX!R4_lG>>^g*iVF@&_YK(6RI9Jj0z9`FTe9xZW DHkX6d diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index 2be1883de5d292d6bf871f517f7c4609126b0055..305d59825154c2cfd3d2cc36a5336c6718acc7c8 100644 GIT binary patch delta 4421 zcmbuB-%C?r7{@uUn>mkjGfSrVBMXwCnwwkOZk8DmxnoVDALPySY_ntQharnehM5|b zneiGY)UZfGOHp`Ema5O7B*^j>oJ@K zvc9HaC=F!k+3eOAXmAau{U_2{!S+AJcml1&QIL)gzNI>f=J!S{>{{SBcHiR1ZefkJ zAp(cDut=yY5&O-$Q;FDb)Vo1D^G49VS^n7|XkDB5#)b=t^jy%`pNRcnbZ;W|EwT0_ z=(+@HOLaP{Xr9A1oWjQ1K4E<;Y{%6mtY?LlUJs+z(#{xaJ$&m4YQ1~=A!>cslZ2w@ z)zc^%>)j9Z%9i_fVi!?i69biiQG47KHt^sX+UKvq*C-koD#Bg}g%ysrq1HX)!zkLy zIUY~}eCG{Neek&##RiQqrq46lRs zvybI0w4`Dv%~>$BZC~@w2dK0H@&pEf{$7ci|X#4qPixl7Y$jYATVz?p2L7 z@kJAv%ERRG0tRl~MWz!-U{JLJMt-@3EM?&_^!!H!SplzLybnEB4v}OmfkdtMEb4rW zl=A~&V!}56S`of@lK9Nf7$i0S63JB!VN%L7nn*V`3yEu0WD}IYN0%P!xX?w4u>aJ5 o{7?@m$36y0-8@24w0&get&d0{wo@do70KQJ4WNvVOp`O@FG{#hC;$Ke delta 4421 zcmbuB-%C?r7{@uUn>mkjGfSrVBMXwCnsY0+n`MSX?pTxP2YEBMvt!3rX^1h&FjJ#4 zGhX9_8Wu@tDGD#~ieLy{WN39!K^I0B1$B|3K}Hv0XZ9EL-5>CIKHukg-}Ahuw?pjh z5XUQoBBd@^8}@`e_TnPE*iI`G zbLjoSBswTOb^JF@m-|ZDJTVI(DjD`CVkngiR=QrSgXnVVjifVcO0ogQZEV!)Hexsn zWPDA*P#VZEve~UK(BL{w`%k8_yzPGq@dSE_V;~(Hd`nd$n%^6-v1@@7*nNu~yNxvj z!vqd*W07!IJocM4r{l5TsC9#O=8d3zv)pqsXkDB5#`+8K^qkkwACLV|Z=S<8oXW=9K4E>UY{%6mtY?*#Tvt$QNoN$b9=Y`dwcfq`5VgMRNkGx_ z+8Gp$_8tIwWy^g#v5Tm(iGd2hs6Xy18+h;x?eo{*YZMI(6<{xf%JN6sQ0w0DVH9m; zomk*X);(baQL3XXdukN>4e`-QS!Dj1O_+TRr~VlOTX}v3O|Uv=X5>|EJowKy!|R~^ z>|TqO@o~M# zOZ3=2)SG$wN1+D2csj3Lbdn@4eijx@;1vA71ru-nE*uBXf$IgFX_%a^CX*1@y{5Ak zzGxv+IhZ_Fz{Ksl$aEYDOqyQ6%r861QU)Hw$bXcR74Qni`Y>|!FiFG`NYsAMrp-r5 z89%5H3%2>!itxo##Ak)ZAZfXmNVaBDNC{7CBHh?5B(7HpS)c?yy7bt<`I&l?;|U3eMIuHog#6)NahA;0Hu6nnw%wn0Zeg>)&Kwi diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index 0d8d020cfaaf0baaf4105a0b2e3eb179d8da0cbe..58887c9c50dd2ff6a710855a76336708d3c40ec8 100644 GIT binary patch delta 64 zcmX^7nDOvq#ti|ChGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC ThDNCdNv3J$i7A^K8J!9Mr}7iC delta 64 zcmX^7nDOvq#ti|ChG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg T7KW+DiHVlU#^#$F8J!9M#vc@w diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index 17b7dc5917c7aba8662920b27320dca4255dd1c3..a1ee4ee06d77fe03843e8e5f9bb77f5533c0d4ee 100644 GIT binary patch delta 62 zcmexh@WEh1Hltygp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%<~fX4!~knZ6B7Uc delta 62 zcmexh@WEh1Hltx$K~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP<~fX4!~ltP6ZHT9 diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index a1e2e9df9b89f8599404223a0632ca3c380fb1a2..7c475ce892196e4ce15c3750fd968a5525e28088 100644 GIT binary patch delta 67 zcmbQOH(PH*G^0_Op^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) XszH)znt5W1{^avwx|??~CW-<8ox2m% delta 67 zcmbQOH(PH*G^0^kK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Xs&QhXWwNok{^avwx|??~CW-<8z)}>p diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index 15e84eb760369db4269ed67904dae9651b1731af..1f2144197f91b8b8faf616dbc3c57640d930df54 100644 GIT binary patch delta 68 zcmV~$u@QhU2mrvN$UFuPM8-i(M#!DDJEV65J2p%0f3A|Nd<}3QS@?)eI0(gr-LyC4 XJycbUs2`OBsnt`u`t?l63Gm)O1%(xQ delta 68 zcmdn~jdlAs)(xqQhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg Y7KW+DiHVlU#^%k_7`IPjWL#JY04F*YTmS$7 diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index ded7fad60658f4bb45a50893dd1466216da0b2fb..2a623560cc749f197dfa20eefd160250f301b81c 100644 GIT binary patch delta 175 zcmeyT^-pVqFQZ|Zp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) jszH)znt5W%4LpwQim-e4h~j+*31~ delta 175 zcmeyT^-pVqFQZ{vK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; js&QhXWwNpP4LpwQim-e4h~j&cQZ8 diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index a52f81140d9b543ebae7613a0fb17339c511ec58..5f6c35fd6cc017e13767ef87cec407100781b85f 100644 GIT binary patch delta 3706 zcmbtX-%C?*6z9(Uu(oU0*A~J>;10NK|K#f?vgrwA4ktSkyH`99b+&|#+<9t5ne7QGYGUiLh zx9yB?&?kgtX;3~c`enfz7|zw?yeznR zGu~?Eh_h=B-26*2++~FdZ^5|GM_L^%LMyb2zo-$Y23kcsE(UsuDUNU~PPEvF6;{#x zr|XINRne<$EyTSa;cW2qj0NHXhab-J!~&`;k0a}}{QrRM80w4@X5rCzl!S~zrD%i9MgtD#oWzseqHtp8M1+>wUl;o^G}&m@ zHHZ_}rV`WwD#fovbv!rsgQyCHf)z{UX=?gaxM%So&3MnK+r*fFTQT9x$DTc;=(jm) zVeHG24hrN}m^vMJb&Ljhwc4SF;^ew^4%*k-vo(=c%2pyO7iKmt({5siLp@I?4sE7r z5Ay#!%0mOwPFvJ}h?a||lou8zN_&-eh?M&*Gi}jP*J$$r#t!pkPWkR&a(c>ARHm7| S6f5elUzc&8a>vVrYJLL~+)!8m delta 3706 zcmbtX-%C?*6z9(Uu(oU0*uRgTff75v!MwlzJ=DMC|TnT92Ol2Yh~<&*z*k_ZG_9LRtGZ z%(ya_#S7_BGITWH4*Er}R}6Z+8Qz-`LY|Dzm2{<2Vjz>^gRZnk5JE{$0IoCN2q=N= zfzO&HBC=aMNkl%-4U&)oKG8qdlAsxUZ#+jx76rDTAt|PDj7ty{L15GP+I*3!`o?mz zj$$|$g&A854CNbezBxvxaNyj5tE9PeQJk^S=2px*@YBIj(t6b3P8KbPD>`a)P8RIE z9&fdB#Mu=GcK#(9ZZkraH(=b|M_L^vLL;>Meo-Tk4Yd2rxZpoaOmUcFalFk$tgwde zKiNpkuZCXfXd~|ZFlU0NrwkBxbNJyjPb{Fu@;I_a%l{A9jKQurF@Bg$n9$kRp$7i5 zUqdWFQNI^kRHMFeK1__hrVcG)BlP$V0V}?vb+E-U?~mX_@(68~7+%R1>v!$|h1>uw z`~mE|CK1mLZ{N6kZ0BY{HVco&q9kM#swER-*PC!CZzZ1WCWRHd#v`=U{<_$U!HH(Q zszI!{I+>srP%V8Ws^gj2A4F9s7L8c0Oi|OX!5s_xX~ug;>^epV+_Da*Klbh-MZd*S z52IfeRZt+e%rvOL%VRXbtCdbQ6vx+;b5OqCo~`<5rEDf5b76M<6743oIpp)S;^0P_ z_8|YyBR$kGVcMepL$p*nA-ym#QQE7#O(flCnJI&cx<*>}F=m*naME`RlUGxgBr{F# SrdW}G{kpY#q&rR~Soa$oUW1GP diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index e8aa6e72997e00e6a483421e2433d51e0152753f..46459d89052599bbf2f962c6d8f2a03f7f5c7221 100644 GIT binary patch delta 2904 zcmbuBO=}ZT6o$D;nrQq=(xlXn6rEj*($0`E4!DrIu~Gw4T+}uV=`@Mj3W`|JAY%|D z8*AlKMkMJ%EiST%c!XIfVo4yS;9@`tNF~LUo4OEmQ4qb;;t$B{4|vXV&U^2jo`E-vDMRNC$mD!v$%Ew7wzWVu+My`1_%~bnNJ_Lzr3M;{drGa)6p=8yygC zP~yCfjBM+9N{%#;pq-=rI99=|2K7(80~U}w)^genpk(WV=oPNzE~0aOxEw{TPp+<@ z*3h*-Ktx;VF5duW&GY0)(A;D!LK`=$z{+pwhx1h=uY#20i9I{1WF-Cr=Ov?)&qq7x z*}cQa0NY*oDN|fT7Wp5(G&kx6-T?=rf!~%TR*IsHZd;{xHs{9uC#)&61$9rmi z-l}-bC7)=cRE+Ss;uxKNv5B6Gs7I~Jm2NPRo?4iG*4NPEbnrW?uY;iddp1m8)`rp9 z9sJX}x*N1lOiNVXSVip)e&)w3KV~-<-Ry|@+{c1c|2+uqgC&ZLg8f?$ee5an42oN9 m3bxB?`PpY|gOcXmF4k>{RpeOrNlO%1549h?CH_%Qx&8wBuwvi< delta 2904 zcmbuBO=}ZT6o$D;nrQq=(xlXn6wR)pv@uhYfD5S`D>Wd+MQzhCnaM=k3KemoLB=3R zHrC3ej7ZXjT3loi@d&d}#F9Xy;9@`tNF~LUo4OEmQ4qb;;t$B{4|vXV&U^2?e~TvG=V(OgWHlS(X3_m~5~ zjEDV9rUTCVj?R@ftI{d=!VZj`;g|Pa>m239$RuwDyXmHH#fjn642E0)YALsPP50BI z{pS(0rRcO6ehrwZ7&+(x8ZJg=DS31RxELbkS^jQhFC9Pj>kwuZ_#{AXhwP))xn>uH z>y)~nAR}8!fzo45Bxw6+KTcFJt4;$`Z-E8mwzVQV0hBCd7`?*v{3UeG4_Csd_3_pB zs5LP62Z(4Z-IeR$tOcGP^BbFtg=phO4Osat{b-?vhoHl-Y^_3sAf7gQaWo;Cl z-Nrwy5A6o+Q!^22Y^h%Ko*emyl>+US2^R8XXg6%>pDZ&; zV6$Vxelj9zvtf%4FIl#hO}=0tJXxnfbhFMRHFErI*a9|UGtZ2v0%W)VQVMWw4qREy F2moYVUVH!m delta 1212 zcmX?emFdh?rVYM~hG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#&r7=_5uc97{U8QLZvWGbEPFTuSzp4Cf;bjL!q*O8^OU~``231;%N zPR^4Qp8Uaxb@O{!ZWc0(P}J<%yj{tVJgpNquTpm-Bd|9sYG#q+Uq#J{n|*Zy$*^%U zNNa-rWhSzMXyWFtMhDr+(ptGW!ManHEUkr;H>h%Ko*emyl>+US2^R8XXg6%>pDZ&; zV6$Vxelj9zvtf%4FIl#hO}=0tJXxnfbhFMRHFErI*a9|UGtZ2v0%W)VQVMWw4qREy F2mmA^a3lZ# diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index b13ccf35babce57fb3a091b95a4f301b5fa5c721..2e570b6e4e580ba1ed40fb5ff8fe0987afe76afe 100644 GIT binary patch delta 185 zcmcbld`WqOKciupp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) nszH)znt5W%<_5+&%tj<@)1NHBE;TuW^(^^X%{T90li~&dF4Hr# delta 185 zcmcbld`WqOKcit*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%<}#+;TmU)Y5&8fC delta 62 zcmdlewNYw=E0bYbK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP<}#+;TmV=O65Id) diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index f114ea6712e05750097818c4ae48f2582eea20e4..47fd1c2d010da4fbb785ebf4d83df5ba4ab57b08 100644 GIT binary patch delta 2566 zcmbPofMv=7mJNZ7wq=G!mdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7EOi`N_rl#rdU0$*GeS^^GSVWPEN&vME5FCDRS$7+EI=G4CN?JOAWE zEQMrgU$~i-?Fb85T9oho$&WOi&9h?NImt^hn-`?#bCIWY@&^;4%@2xV`N`A1`A^dY z4l=cN_pBhZke}S$BeT#k)CeehRsf1=2oki8-0o%;W{>?33j-a&Mlu zsfmn8+Z?!eD>)@g;NIz*&mFwPLwb;HzIbj88QLaaJU4N({T+Ta(rpA4gWGwT8SBVQ pTGKypF>-DX=3%TNGl5Ol7i8qxK23=6D;Y5abbZNoVAJafBLE@J=aB#a delta 2566 zcmbPofMv=7mJNZ7wrK@PS*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^(Ad`N_rl#rdU0$*GeS^^GSVWPEN&vME5FCDRS$7+EI=G4CN?JOAWE zEQMrgU$~i-?Fb85T9oho$&WOi&9h?NImt^hn-`?#bCIWY@&^;4%@2xV`N`A1`A^dY z4l=cN_pBhZke}S$BeT#k)CeehRsf1=2oki8-0o%;W{>?33j-a&Mlu zsfmn8+Z?!eD>)@g;NIz*&mFwPLwb;HzIbj88QLaaJU4N({T+Ta(rpA4gWGwT8SBVQ pTGKypF>-DX=3%TNGl5Ol7i8qxK23=6D;Y5abbZNoVAJafBLLh^4730M diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index f3ad6fa68260a5964590da3655a5d84b13d4ee45..d78ea6a85d57810651b8ec012eb272573a3f56db 100644 GIT binary patch delta 2528 zcmbuBO)ErU6vw;VnJ|N9yySH$N}9RX+_`QM3pOH*rW6a~nvu7JF=e5wq>x)r8bz!~ z7N`?98yO|a#zLCZD0$nM?A727pTOS-_?_Q#{^#^OkG{|A`}{!%R~HgQMfHVLhvZU4 zK~f}_Pmu$D!R1zES(X%04AeMXsv!EEeNsU7^*j2<)JezW*wlpTXV>0p7U5onKUXox zAZtyHU}QL!$7QoKejLJ=*^(uP%;pOo!)eK)m_T&S_M8TymBnTbLs@=#4)LX)bQtdj zNh4?$ZOkH1#T6QMtZEf)J62Udt5`w3cEvggi* z?NlhTJ&HE2V{<0zY97S;CE99Dv2%8dth*0i$u%61DYgV1rqs zW1oOs92)E0F|#kxL`TEj8PG;)Y&K}8r%-M-h`-NU>3lAV?X$*`_|QVog4t;^+=QjB zvCXiJj@I3onAu3)Mkl+|Sn4CJp{S7JhXNMc56?!8HO@SGp}C1|=ggL}kA7u1cf(H-{<#&8KI delta 2528 zcmdnKo@M)bmJNZ7wrK@PS*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^(Ad`N_rl#rdU0$*Gesgc?si$oSllWK)1TOD6j=A0$ub^t+;r{F4u{ z6q2RAe{wIk@Mbo)<1FN9-`vY>$x5cyHo>PHWNOuxW@98z>tr!?;ms_vL7Zf3k0w(m zFrX*jaN*k=q;ZzOvi(?zcucm#P&e5H)Ptr`LE>xGHu>m zYx9pxTPK739PF@+nXDjNxA}(49P;9M^X3SzO!EA@dGk`Ax8(VE^X9UEbaHJy7<7*e zTQ^?_eMqj2QzNdCVI#1p-Mra9W*&JFGkf#SL__iljtP?!Rfi_{r4%zJq7;(Y71prRNEgLE5vrOSs9iee?Y3uH=RKgvs-#3vS*v$BdUe zn}H!OxOv~IO7cQIXR?Eo*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%<}OAfQ2%7j=K$gzV$?yEcHmkE$v5>7@cyk`N z1S@&kH#-PsvXiHEvV$P&<_t-7^1`|ktldxgq9OUVPrm0HMxO4?@BGeD5a5&j6IeI* zg*1@s^>=>4U?U!ctzsh21He=RF(M|ahf-JUia9}nD>nNl^it}AbxG$aZ~-_d{nCyx zlNFR*lMnQ8Pqr%%-uyPJo4hmuHe$0~K?ix^0CR!F<|RdBh6BQgfU*=yT`{TR0C};2 zaD_pQ9;L3ZsoO!RD=szGkRPD}n{xjG%KbnxvLAG|G&0@32)?U9*c(eE7 z7kuQ|zWLpzQ1Ttlx|w-TD0!7p*W}#?I5*22`bnPlS(_i6m_UZs%?r=5@Q@i)3Ag`} zo5B)q3vSkYsKZO9%>wT)kz3vgycgWO?(-fp{XDtuAIElGM#d|Y=s&{8*rZLC&ub=M NXW`$@WX)L24gl!_FM0p~ delta 2596 zcmey`!uqv^bwebhVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VM%7j=K$gzV$?yEcHmkE$v5>7@cyk`N z1S@&kH#-PsvXiHEvV$P&<_t-7^1`|ktldxgq9OUVPrm0HMxO4?@BGeD5a5&j6IeI* zg*1@s^>=>4U?U!ctzsh21He=RF(M|ahf-JUia9}nD>nNl^it}AbxG$aZ~-_d{nCyx zlNFR*lMnQ8Pqr%%-uyPJo4hmuHe$0~K?ix^0CR!F<|RdBh6BQgfU*=yT`{TR0C};2 zaD_pQ9;L3ZsoO!RD=szGkRPD}n{xjG%KbnxvLAG|G&0@32)?U9*c(eE7 z7kuQ|zWLpzQ1Ttlx|w-TD0!7p*W}#?I5*22`bnPlS(_i6m_UZs%?r=5@Q@i)3Ag`} zo5B)q3vSkYsKZO9%>wT)kz3vgycgWO?(-fp{XDtuAIElGM#d|Y=s&{8*rZLC&ub=M NXW`$@WX)L24giswQ>*|0 diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 57a2932acb1e4ffada85ef90b2cd80929c150bd2..f01bb1a22c2209c11e24d89173c61eb774f90396 100644 GIT binary patch delta 2852 zcmZoz!`iThb%Q6PVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?+!MkzA1OTV!Q=fuqyS>ss9(mHkXS1ut| zvb5H1-X^e@mprYLmE@n0r*rdG1qW&Jv`*ftAUs(*!rtn*eo1Zt2$P&PfYCH@^FiB9 z{w(A$@QQ~)lqV<&6zNxgD%p?Phcb?5Js-AO@mDcMfKX}1DS-+WWb9W0T7nw#( z=oQ%<-mkzh}=H5G;xa(ffUiR2 z1z-B+Uq7z%kQH$to29q6urW$eqCZfW@un(S_Sa3fwP0l5{=kw^m)vy!;04?ELr#pQ fWTu_z|J)e4wr}uYJW5{Nf!e72+Z96??=u1bs~KhU delta 2852 zcmZoz!`iThb%Q6PVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VMTV!Q=fuqyS>ss9(mHkXS1ut| zvb5H1-X^e@mprYLmE@n0r*rdG1qW&Jv`*ftAUs(*!rtn*eo1Zt2$P&PfYCH@^FiB9 z{w(A$@QQ~)lqV<&6zNxgD%p?Phcb?5Js-AO@mDcMfKX}1DS-+WWb9W0T7nw#( z=oQ%<-mkzh}=H5G;xa(ffUiR2 z1z-B+Uq7z%kQH$to29q6urW$eqCZfW@un(S_Sa3fwP0l5{=kw^m)vy!;04?ELr#pQ fWTu_z|J)e4wr}uYJW5{Nf!e72+Z96??=u1bk}Qso diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree index 263203c9fdb8fa9246635c79b297f1390321c72f..80a9a0f582eb9802fbc504c0fe8d15166844f502 100644 GIT binary patch delta 2906 zcmex$fc4h_)(zf_hGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7A_F7-h-OHo5VB-ee2rqoi91);Wizfjpg?ce8$AAy4b%-K@fs8%kL= zCvrD4lVwEyqK(=;{%{ie=35KJaMC$nzMP57Fn}51P~AhRE2h<+rqmVMjVma1#oXqjl)6Hv zeHnQfx@mI%a_-F&x>u2x9>CfsPw0k3s=&l@^1=eunApi@$O{itWByM|rG{&)X6~hi zYtGKmqlRlP&f7{2*TgJpATz=@_b-3RK~~OfnmpkP+vJ2Q*3D)c#kt5cV$&X3e)4o~ zzHmB~ojk3RFPs+G{Nr*2H|g3pH{PE}UL7}i^R;K}yrkO*EZ>_pPxul@UI1rI4hWW- z{)&;2Z}X!6isTh32s5^~Ff*>^AqK(=;{%{ie=35KJaMC$nzMP57Fn}51P~AhRE2h<+rqmVMjVma1#oXqjl)6Hv zeHnQfx@mI%a_-F&x>u2x9>CfsPw0k3s=&l@^1=eunApi@$O{itWByM|rG{&)X6~hi zYtGKmqlRlP&f7{2*TgJpATz=@_b-3RK~~OfnmpkP+vJ2Q*3D)c#kt5cV$&X3e)4o~ zzHmB~ojk3RFPs+G{Nr*2H|g3pH{PE}UL7}i^R;K}yrkO*EZ>_pPxul@UI1rI4hWW- z{)&;2Z}X!6isTh32s5^~Ff*>^A@6TeUU*e+78YaYAk*e~3LKly$*C}sr+xE0g>*6XKCt$CMm?0+u4lTR zOxrilv^YSC&9c@z$+Q{l?^$-sm`M+|&4o@s$cyV)lP`p_ZnpHuCeP0h?Im7h`Wd4A zuWuqHw(A9unbIJ(&kW8X)Ar35Lbs6@*ASb-BGt)@wD!%Wac{^e+Y;~AZa$wb!9{us z1J*z_lk*FNHZLoZoo+sD8WZQiEnFqP4>cX3SyZ}}V@#p{m delta 2522 zcmeDC%G~#rd4oHnQCdM#R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VM@6TeUU*e+78YaYAk*e~3LKly$*C}sr+xE0g>*6XKCt$CMm?0+u4lTR zOxrilv^YSC&9c@z$+Q{l?^$-sm`M+|&4o@s$cyV)lP`p_ZnpHuCeP0h?Im7h`Wd4A zuWuqHw(A9unbIJ(&kW8X)Ar35Lbs6@*ASb-BGt)@wD!%Wac{^e+Y;~AZa$wb!9{us z1J*z_lk*FNHZLoZoo+sD8WZQiEnFqP4>cX3SyZ{ij^4E zGOT22@7ydc$izjK)}GC`q}j*_JBVzZ9PT{3M32irAs8%k`yZ+V(b z+c(GC?qeo1!pVvNM*RQGan!vJQ)R!?%{e=^L7c;947 zT`}ALFqyFda>Zu(U@}XN7Pu$uLU&W(0$`*{Y`zrHNJISl3S|%Tu$hUdNdm^t!hRwhR*7SdzjO^Q2ax+?UkY~W; k2?yD>&lh2ICNuh{>&P;4ZeJk9c#gbStluuD#(19*06*tAr2qf` delta 2831 zcmex+p5^a(mJPm)hG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#&r7!}CSHrbG+bg~Qc9?~rY>ij^4E zGOT22@7ydc$izjK)}GC`q}j*_JBVzZ9PT{3M32irAs8%k`yZ+V(b z+c(GC?qeo1!pVvNM*RQGan!vJQ)R!?%{e=^L7c;947 zT`}ALFqyFda>Zu(U@}XN7Pu$uLU&W(0$`*{Y`zrHNJISl3S|%Tu$hUdNdm^t!hRwhR*7SdzjO^Q2ax+?UkY~W; k2?yD>&lh2ICNuh{>&P;4ZeJk9c#gbStluuD#(19*01MDyUjP6A diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index 0985d37e080203f2624a3a96881122cdcfe60f43..2e9d36b0bf31b4a07fc8707cbe2f757dbba4d2e6 100644 GIT binary patch delta 62 zcmZ1`vrJ|~Fr#6ap^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%<`%|9JODU;5$gZ| delta 62 zcmZ1`vrJ|~Fr#5wK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP<`%|9JOEa!63qYr diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree index 2027f5d9a3ab9878366a37e44ca59aef2b92d158..8d51817d00706e46f49ce08dbc3de791d074f047 100644 GIT binary patch delta 3362 zcmZoU$<}g`ZG$JHVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?+!Mr$&(O%GIKY?>}Vi;;D5F7qDJt%d97pZuDokWBrX`Pu%kkg0bv zKb!F8i`-Sr4)F1S3Alv%_ zK|d&p_V=MZL~=>`yQKMv?sim3kD}Us(N)T>Cfc*Iyz#?%p@? zZobzN!AV~Ev0(E2V9w0~{h3_k>7PIO{&bfbzflaBzI zdMC>j@N7PMxPa`u2huNiB7~Pb`xi_;AJ4bhX+1p0CHqzTL%uv6{?=$NcU6 zW{lI>NO$)1jg5>`rYE>D3T*#v%ea7?3;;4=JJ1A8a&iF7dhY3w;fx^^xSxA_Z3N>s zA@WR^vn_|wkxakOnU=%Ix?Q-0(TALr2lW5E>3WTfL8Lo;J5cX#ankjHI~m)7os?8A E0K3c20RR91 delta 3362 zcmZoU$<}g`ZG$JHVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VM}Vi;;D5F7qDJt%d97pZuDokWBrX`Pu%kkg0bv zKb!F8i`-Sr4)F1S3Alv%_ zK|d&p_V=MZL~=>`yQKMv?sim3kD}Us(N)T>Cfc*Iyz#?%p@? zZobzN!AV~Ev0(E2V9w0~{h3_k>7PIO{&bfbzflaBzI zdMC>j@N7PMxPa`u2huNiB7~Pb`xi_;AJ4bhX+1p0CHqzTL%uv6{?=$NcU6 zW{lI>NO$)1jg5>`rYE>D3T*#v%ea7?3;;4=JJ1A8a&iF7dhY3w;fx^^xSxA_Z3N>s zA@WR^vn_|wkxakOnU=%Ix?Q-0(TALr2lW5E>3WTfL8Lo;J5cX#ankjHI~m)7os?8A E06z8o%K!iX diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree index 55e5155aeb3f1bc564a6913693f0fc3728f7d7b7..c0a31b509a6706390646c480e9d244cf3d1c799d 100644 GIT binary patch delta 3933 zcmbuC-D{Fj9LG7$eA;~3Qd1d=Mc5D~n?CBy5Z4lBvB*J#7ImT7oEf_4!adYO6*U6-0E=O;Gf9v0xX`2qcP3-GA_X{(#T<{Jy`JM_j22D^;O# zjyTc|(Ho7Vql0c&RCKt#Zdb(XiA_0NLs5^% z=crGqqZ4fsNIy}mfuv8Fg-D4eokms>EZaf6-Qa)m8Ps(8Wayhw8WCm4?ve_K`+sg&&-AoWU;=wu4+`{_z z!38%H;@w9;ng zAJ^h?GM~I*mt)W0Lhrm)eEqgbj@^96At(FT-A+07)AKJC*vuQ*)l6G1w1c?cuVT3e z%N-cbdX|58VkrH0)XY>brqvkE#^3dWMbpaqPL}u-7eI4m{gDbosco~1P5%0dewdoL z_0Lmu(=urTdtYCbf%BY#B*205w+3PYlNTGTnSbmiRz24FV%bIvyf#2=;1A-~1G|Pu z4UA7!c@3|P5*xU7EnXWUV_jGWsNXNp&y(#u7$(oL%bBG!`?~q-^CW<+P>U}%cpd*X zL+aS&Z#`1JNz}Y@nGDEygf|`;?!&stSF5Cq{fUtplTh-J$K)P(4E)&|`D^?S0(_PW delta 3933 zcmbuC-D{Fj9LG7$eA;~3Qd1d=MXVuAHr-=WLtIOk#Ucj{TGWMab7ts97Y%|$g359c zaw1_M*iCGKpgXh!wN;~w3L?7bCMbHlSg?y|1QJE|=>CK6^9Ov+=lA`+JmP9qSgi_` zbHpB-4JW4D5%+*|&@~ixIKnQ6BW80%LvAs4&>pczqhV((YIE7A#8Ai`5uJ2_YywnX zKTkbM9i3c!Eme;rGAnP(q_iB*2=Jm@g zW%<4fv(l7BOZ|gKA-c?x;s>BhG_NnyqQ}gJ4`r~(WmfbUTd<;==B2~)pt*_n`S#1P z`}~V?>|c(J%CYY~aRIcm}sYhs7Jr zgOxT6XI(45+cA{>J8ovG7t?ACXXEesz@lkoV>e5DiVL8*vhhfTq13w7!6tuwML$dp z-16rsx@qZ)2KK(bCIjbL1xbJd=Wh+f1ST&?teJo8Bo;l^`9jG`47}D)tl$sg_5*u{ zNDYinRXGi>jS?%kc1>OzAY&a^2dLL8(9e^t+#e**vCEmGGY2~P>+{5itx%IMNW6}J zn;~`V^0yx;-6U#WxlH@~ diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index eb68d7719368bd6b8aba8aaef5c5570f9585899a..c532ad4123108a90821c5902c7f339673e65941e 100644 GIT binary patch delta 1137 zcmdn}hjI5G#tq(#hGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7A_F81J!>u5I!L&hW|md3Yzk=a@jc?#&xGJ;`t@)Kc!rJ>08Fw-lu5I!L&hW|md3Yzk=a@jc?#&xGJ;`t@)Kc!rJ>08Fw-l*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%=B-Q}TmVis5?ufQ delta 62 zcmdlhwO49G3zK15K~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP=B-Q}TmWoi6F&d| diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 1a8ee9ff8510692d22e8c9ae59a32b90c642843c..350be101263c452cf51aee6665d1ea7f17cd92cf 100644 GIT binary patch delta 1279 zcmeBK#MZNjZG$hPVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?$?#=~Uj`XFYsc?pvn3z=Gd*_Serr**O~JIC~fen$4mDO`nQ8Iim> zlY0UySz0qTPZCri&*2%H{Y47M(7O49*gJBf^@CW(=5on;GHjd-(%LVxi(FeD$+?ni z>jA~* zlY0UySz0qTPZCri&*2%H{Y47M(7O49*gJBf^@CW(=5on;GHjd-(%LVxi(FeD$+?ni z>jA~*V!Z diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index 645be086069d913c6a60892de8e65ef8344da483..acf117593563105217b4bab830a84bf483d4382a 100644 GIT binary patch delta 12247 zcmbuFX-rgC7>0B1y~DsT49hTLH7zwMJ{*VryM)?WYo2TddmJkkW{CYt+=ZR*TVENv-zGMJ4UOdFKZ|-}m{p^PMv{ zRQfEHKFj_bHn`O8NOrqQ-BxF!+hKPmI}=^WNuEM`;sAG2Qj#;-;qVN!CA#g7LfZ_d zC&@L_I&+@8*jhaA?S<|_pG%4N?PCr0fL(+#Pkk&6WPwXnmy)0aE)&G*K1wOg#vwAGriA3?-LangWBb5_!wZ2nNJrsPd z>gjPS9F(4U!v;)<&+g7um)l~aF{*F5&CGvDWkXXoTT6=O`~L!;Z@YVz?^tw%+GF?_ zEDWz2o;Mat-m5;!pM#B>SA9~@52c=%G#8~dP0d27b<;-xRMqabU`NTT{^rR8h+-&ZWby{{$XgDZXbk~(v>eb zN{v0d5|s|AJBdoSeU*n+c-^rNQ0n1^Gbpw933!Hwet!|A(&-IouD>__jZ&92ZA7U@ z&s+znYSYigTiw0JMazi56GL-vT6x)Pqr~oKohIV8VP;URutPa&yb!9uzIyZJ_X()GJ z0(%qXZhw_cN4X#OVjEEI+1J@j$%P;2PnssVtA%@|m8t3}fSib67Hh?FMSr9s?H+9qGpmHd`0?*VO;p zoFB^u1(T1Fc8y~)>aa`HG&JxeNc5u;`1Z{g>}1*eEf2dDIP;}8g6B4}7^^$h`S zXzN^-hSW@pUx*{AWFh+m_=Mk9hin4SLJ}?+W}tjRm&3hA$wbKp=>S0Heg_f`%Yfu5 z-Yg-nST1xEX;ufR5$W0e3@Mk6c9SxIZNdFwF2v!qHbFAR0wvcRss>3>?OiV$D6K?; zAz|e-1|A2AICg{38PqpRx(+8l-y@l{lrqTRmaZbFS8Jv)%;*_-3A6>w zyVpoz?X9S+lH!m!{F{t~)e&SOHSt6)EkgRCiSsr|U!v=is`p5PfYaMnJ|T{!DF>vj z319=LqFHK0Hjj3B2(gjQ|00z~f_R?okVELw4e22IYSJ9Ia4=H*J?U;tFoE!Q6gh&6 z2SqyFWO5ifiET*7_b6d}de?tCGRr7feBZw(-x4=zxm+uwZ2`3kf|v`mE66l4gr zOg^8kL{P73iVnSBNjNzA23`h=Ua53NphA~Nhia4snSn#O5Rc=ZT~fl^?vCToD-Sl$cZ33Y1R#XZti-k*a$d`Y)EP B;kf_+ delta 12247 zcmbuFX-rgC7>0B1y~8@J!-&Dw0xrJI|Y(Xj~^L6erw)E| zG8Tqi_064vC2v=r&k3^}bW-LIdZL>2_>an@0097?xjo4ALtH0TD0iu{n zyL!Il8JcIgeL6~=w{Sd4om7tGsNcovPI!L|fYk;#ye=B0cCE0W)IU~_1E{K{VIM$L$E_;>WLkNWHx#4N6dr&| zJGU%DrTWGapt4%7!P}}(>d+=5D!se|rgk>N)TTY~iK*WE0Ijfo|2VWvcMrl#>G~HO zrG^}?N2OzrokpciU*)0|-f;W_lzRBYIg~o=6g+h|9qtx2AEhzQq zxmy5LZM$$0C62#586axgF!JhNlv;HSx!pACv>S~mb=}P*AXk;$GNA9~1MLto{N8Ys z*nWQklB1qNEJb!-&SQH`s5 zvQ;SeiVtf*xgLJ37UjMc$hM-|IR^G7_PNJSNqyNglpEcj9YeLN2C`jfom<0L3d-FV z!QMi-J6>gTQSQe>*=Ce`;dM3%;kH$j($^zd49Z;_$;KmG=h?4_VtJ_67RxrES{~1k z_b`cH+UR3~HjQD`z%{}xvso@T(ed)(EHc#jJ*VO;p zT$;j0yOWQenx`@ub!Zkf^>RH468)$HzJ2rZT`ZH&x3PBDnJ=||Jg1cf(}Q9*!UMFS z?F(26QZp@nAr7F*MeGyc6Mjz?t81AL&ue9Ybg+iyBU9uIgoI56@Fm?DLl&>z!t_+X ziIpN}!RbK4p$!-wCw?YfZeli|10=*5fneMD+9S-5CKEdW?0KzoBpi-`IcM^K@0gd? zc1On8QPl4QtAyhaRxM~8l+lW9VNv@`F2vrH{WF{4k9HyKCiA7l8>|SNyi5`fSCQS&9RSGO=|IAv7D%4L z4HEK-`q#QarNJ;~?1$To7pXb`#PiH3*@Ld$mJXt?Ce48h2R((~mpX#o3536+$bMWr zD8|zrCVQik*prm-n(pUTG@S<7hpfS}2iza6y27R*)Qv!l{LXFZU0O3jPDYzVB9NiT z4XGWk?o_!3J)2yJ1ITltoPxZpyCRjTJhM{vaxD2(&sT1g3n*o#+!q;)|5(w3+44-} z%)D?3)^;X6Tp(WpzJGD24T-#VgaLE$A_h;Y*(kr~PAxpSa0sH0Hp|)2b@seFnvIuZ zP=`0I+$%44JE9(|=KXRGGO{nr!?&N7L;0GG@+|5;DGzWfED~Pl-D}pfJMtpr6!qpK z90t-W9rBs}?o16I;IH`7VsB*tI=Rjh=}E$71+tG`=0m0s#q3^1go8hIhbne(P^{EF zTsaNIKq8CbMxYJaLTyY{N|EAgcfJsZ(6))nw>}`AkI7epdf$8rc?z^dv`mE6EMy3@ zOg^8h_|ecgiVnSBNjOA}aJ>u^RIdy~phA~RhZ>afbl0I=h^O+;t}4DgcgM+#N)a%5 znhgn?k;vml#E`{J_m!SEKULmU62TavAC99ia=Q4*k5xjYMLxyC9>Jh(2s+>b(S6T5~tg4K^kt;5IXc26B;QWANnpiQ=2z#_N$` zD=4To&rkfuPNvNXnc|xbvW&>Ic```*-)u8ZGHqX1%)WVF=}&Tl4W!+vF;It0+hf;o ZP2QR)yxC;k18y>mn8=>EdD*UhMgVq3`=I~; delta 1771 zcmdn`gK6szrVYW2wrK@PS*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^(Ad`N_rl#rdU0$*GekvMWvA#CX(@WK)1T!#DqDN@O8j>*k5xjYMLxyC9>Jh(2s+>b(S6T5~tg4K^kt;5IXc26B;QWANnpiQ=2z#_N$` zD=4To&rkfuPNvNXnc|xbvW&>Ic```*-)u8ZGHqX1%)WVF=}&Tl4W!+vF;It0+hf;o ZP2QR)yxC;k18y>mn8=>EdD*UhMgTo36leed diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index b48d4a0dd26b13398422081d7264cdcff12c9969..62ba3f688cca429c8779824df47b8fcb47d2fe91 100644 GIT binary patch delta 175 zcmZqEYSY>f&S+R>Xk?k3SeC4BYLaYZXliL{l4xn3mTG8XmTYcrZfa>{lxAUIl5A*{ gYLH}_W}cX`xr5Pwopf!JUvk_bOPAMX2d-mW0Qed*O8@`> delta 175 zcmZqEYSY>f&S;ockd&2bnQW9W$U!F}nywtA~iFH4Rij ziYS8}v5F$qcqlYD!t`K4g;JH)M4=ulc1AIOTxYPkYwDGF1`BW~&qL{Kq&7wV> zGLP(hg(+;~yr***QJ6N8H}j{`A%FAhb#Puv=39~IjZ7JrTC@*kfG-?bh@z4m+{Z7O z!)H+VAg#I~i*JpJcTB6%O-$V5+(R0CPwCx0jl!3;j1DI;(ahqLT7q=doXKIV?M?*K=zPs}}m z0`mH52-c55yDNeJC7gF9I6MwpT?sN3xamsp>@}=&CAd2Y?_3FXO~D`JBi7i)mG>}( zn_)}X2PuhcFFHQL3*-Q@{yB(?TU9uPdylM!VC);P7TgSulwlL|{(`maVHJ9X^#kUS zYjI3sc@esiuQE$NhX^}10bybH;n<__-*RyUe8^v=(dDg|w2Iw+^3zq9y5&oVJZ{JB zkkcj1`r@R6-LAqKk%^Ha_Ov)EVXF?TLUzfjWSh94k!wLWg7SW{1NQ}Gu{+%)B>GQ~ dN7zlN3nosJ*$`q0|NfWI(Qu2{(npp9W$U!b>RU0+)Tf=3Ol_=3R%P+8 zdvxcC%@HWaR`PHP;(`l);Q;)`j-Zu4n1x^lpHHP>IDx}yb_!BlrEni}$D4P6CD_Yn zpTH&L^<@jzjY69%f&V3(b0s)523uSSGBvpAO7Ls~*18hheGTti33g7xALJuuZ{zZN zn8eMn&dq})Bir-#kMIIHfV4CVF@CEKCvoqQZVN`g0d2v};BXZ-QtvNVLm$>5$MqjD zhg^$g;!6wAgM5`~`ZK2(o!+j)DxQgwGWN7sDsHOwuRwOmc9X6Ae3D!Xx)D_Ok?puID4pKvAt7Em eP99-5r70LcMP@>XCH(teVvGnad~+XJ3jGCyYAkmE diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index 30b0d6da774a2621b5d5c8f2177daf0dff82dcb2..f920bb8d717b5737014aceba192cb0921f6e1497 100644 GIT binary patch delta 15184 zcmbtbZBSI#8J=^$mY^(zg%q5RHmkMW7_2 z`5ZTTQf;Uih4{2Ljg9 zoR6tcuvL2UI2TsnDqW605dx`t{eO*&@wa&t{#33Sq5Ivnm<5wZZ}YSxHX52r^zYMu zjwlPL|NP8vfY=RWS@wPy>EN}wEOlyY!c)JfG5&%dWerWsjRp_X|D02CH{`>oANM|j zG5mi09Xbr5ocn+%iZ6&gp%~=1nWEW~Ut!5Dl&0RKx#`?qZ zN>tUpDic*5^1-p%>k3d+dwnJv>ri7JpsF`Cb)$-d>!1^lSa5qb6r!T?^8-MbX6FZ9 zoQBRVhSbXkqD! z%oOCFM4T__H5-dWJue7lTT$$eFg6;+E{$MAD0XBNO9snJH=@}VR5$%z_5z?wES80# zwcQuTo3Q^rx+^iba z-IKyzL3RJ*VUMA@KTKmusBZ6cR)FgErm>kQw(DW`8j8Iyi=l^PikrpI69zpzhph(I zK3&RXT>y>?+!}wkSEZT))&L~yHo@zKtO&RazfI6q%#c?H20;~dm#{uyc9~!3Y{Cd;@4YQ7!B=YE0Jb57)UUj0$zMr^2PhuaH?!)HDJo~ zNiYnA2cL4@aYoeZ)M54oFvd+V7vSNP)y3XKqBrGhkFz7FJRLs4J_3|UDEA`S7C0|W z_y;Su1KBn^CF%3y^v`N(&~7>I3^ zkTa>d;gMkIKnEy~J0#li;xZ+Io;e^n;5991){F^0?^XcLe(7^y3R&B|01v127o=Kb z$2M1_bA+tXbJwLq;BhIRn+$zSK8j4^TOJ&||1is>=4jc0bfQ5+!pM{`unbld=N^>r z!E0e;vT3}$C+1E(aA&3*PTi^UofRY?XdJ=&pu8fv51GgUS4tgTc?NQD&RZ&1^PEB1 zOE;_K2Y~vMV9=2z(6LUgpjR8^%fPc83Gg4B!RMF@vtB-`Qx4%51G2`m2IU2`a+f>` z2>eG}(a3L&cxT*a%ey?(3|MT|Y)` zf>XY6y!tBq98f+@wOjM&TCy5L{mJV0=l~$W^&XM}^PczZEVUAO4B5U|T}Zp;s*(2q zO)tQUD7Rd70*jplcvBT7T5Wvv?B=08Dl)1+Ij8!}FP`bM9<>XZbb?_m&FfWDfVjB{ z_VlSskaLm=E;y}bz{*)?Rpcv963WwI#|O`=kHg?2{pu_@+(Q@Cjj*!qqS^y1Z@8o) zFX*lQ`1Fd3yw$Y8`>v|>aLoM!>QZEunw>f9rg{!dSbK|xd`xGBJXX<=pHx}k4>b+> zxZVPf3f0~~Mt*}sTf)~lwFr7LTssY9oCHH9a*Zh}<>fEhG?i}~tA&#A@C+btL~qfM zpgD#ZNt~>ea@nQb2!0nJf$cyvP1p4^w0{P#X+JV}cm9rlyQ(jMZw0C_VSyT4(ZYe|}(wFTcnC zasKDrbMNkMjoIBA(^1UgE9**YD%@r6xRiv{`tsqiZ6>VFE350 zET5JdUy-mmWyRo2~_5Jr300Hai z&PP=!*cv@~QUX@s8eNV(5d^7ve18p(@^!cs{$!pSru$s=m<5wZ?{Ie_Hkw*W_3tu% zhA0cD@BHkqf!GaXMb3U0>EQKwENyyw+><}AGyVb}Wlb$Bj0O+U|Cn2KH{`>sANM?r zG)P-)xQE|6eiBtp`OJ^Ty8h&KR5k94ov3Q_+4HEXvd_oJ%(Yka5s=5189|56$nYxm{7sOkgPVAar}qo``@@N6_z@6B_dDg{XlEi7G; znS$Jti1Q`AZe!u7=S9J6D~jC_!p5W6WnpXt#g2_&$zXZuMkL#U>So-_o(FV^MY9mJ zw)*R=bvGrjB2@Qf7pp~e z_oT3wQQiNz*`uiL_cK@$s@p%46{5QR>1;NN?Rkj3j$-f2X6PZAV&*XPgh3C@W2=F+ zPnYso4}jwWx5n4&QK_zwH37-GP4Gq$D+VsZZxeKsFyz&NK~O`zrECD0T_%_duu5~Q z*oZCgi&=xAj850Fi3s?;7g+%xUd2NArh4{wK;0l9K~Sq<;6l38$mSuPBa{oNyM^r^ z6}U19@L!N-5x5cx;;aI=Lgz-V`1N)cLL)obN~GBh1`-UXfEOQ@e91mGnyNcl9hmY0 z5)4D3fv22zoe}jqb(nn)jByjp1$Z=N_prB+=uP?hT1_Y{h4^t&A-{2#6hyr?$&ZxFU?9Qp zemIah0WRa4bEPbL=6-1fAWwqfOC)f!W?LpnE6|#80S>0NDbnXi;Y|7A1Zg4CtOl41 za44NmmZBnnyqgSz^Q8#>ZIKej&)B478pxGeQ8y&Gl18IOHY}3DdFy0hBsx(v(|sQ4 z0$LRkTyi98WN4KzBHHnPM%F)d2SxRo`oEo^OwoBJa<_3 z(9K%;M?n2aFzCn<=vpUN(JRgJW#HM41o(H(z;n!nSq~rAEeG+7ep%z$!}20pxl0}g z1pWgqN_bm#A)E6pOOaL?D!b%Z)D4Nq767ehhC(AJWaR$W%7Xw$QQAp)5n2_RIxIt5 zk(oaMj_E8^x}(9KApclp0?>$7=8DEEFCjDBuuk5|%1@wWZ?v)i+bKU*DF&;)kZ;LU z?zRp=^=Zl&{!AycllX5dXlWq{!rvq7z}!a$ij*p(4CY=Yz!7xHqihQXy1-<3Y^jxu zEGn*6kaybFxkAvCBK=@`II~;{p}b|vD0DX@!I1tSnu3w-iptx!DaSwp5(Lw8Krlac zQ3QYFg0dEAKeK}bLo9j{p|v*@(a3L&cxT*cep??(3|MT|Yr> zfm6P5vib`A98ft!wOjM&TCy5NgURZ5=l~$W^*)jU^PcyeY_%GB4B5U!T}->?tKs(m zO)tRZlvk-bfyGV&yr~8gtu;P+cJbgo6&ckZpHsc&7ti!*pW1^=I>E4(7WAtrK-}B} zy9d;z$T`Ub7oJu#Vdd@jR67l1oCHHPa*Zh}<>4>bG?j0gs0EYo@H8NAL~qfM zpgD#ZiBHtZxSXKf2z(bHf$c&xP1lXHw0{P!X+5cw@}7hm_db}vo$XgaV`|Ib2#B^7y6xm5rg71U_Yp;>fh+H&Z{JB@c!6sc&v%VU}cKl4N0Gl4f9%l4xn1W@?yhn39rYo|a-@ zVVG*1m}r@7Y(BY$@c*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) jszH)znt5W%<_5+tX417ywqbLcT+7-=y3WluY+c*{E?+TB delta 175 zcmX@6d`x+RKcitF*1>%ZSn%Pn91*1Stn~TzaiaHsBVGDn_1SAt-FDAotqc1y<{O> t8_>CJn;m#|lc%+K^L)W%ZSn%Pn91*1Stn~TzaiaHsBVGDn_1SAt-FDAotqc1y<{O> t8_>CJn;m#|lc%+K^L)W zTzH5KTY(uddb9t^1?ptk9J9IRq8|^LTEp-0+K{JpdY>{Q`}QAFjGP?g8L%B_K#?iw Ldba~h_YaH!Q_=Y? delta 1740 zcmZ3ujdkHR)(z2&hG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#fI7(bJ)Yr0`1WB6uv<~(-t^hRyswj#r^AZrsRpW!Ja-PXw)by_xm z;+@P!me$VA_M+}gWNLMgI7Y741Jc!G1QXD&otwYP?j+aN48 zTzH5KTY(uddb9t^1?ptk9J9IRq8|^LTEp-0+K{JpdY>{Q`}QAFjGP?g8L%B_K#?iw Ldba~h_YaH!h1L?* diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index aa0e55092758cab278ea98b94d025e68be4169ac..cfab3480dba0f57502f8474c07a6659a30518b12 100644 GIT binary patch delta 1973 zcmZ4YglXLqrVZ(ghGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7A_BFtU=NZE~St++-c*`=nb4);Wu1KiN73CjVe9AWQd@&ARMD9As&o zxtUYIoQ!bVTqx+yOoojIRc3B(5}ibbjgvuI?};B~BGcAv>Cfb9)sR0%uGSq&H^}ho z=7TD}hD*uS>TK%5N=8yB6l~mVX|2FRrdDo`S{Aaj z=1i9J=iO}N>rY1H0E?EK&2s+j++^7tKRF;$ZgX(-1M-3kqWxjw8zC}nZ%~lkTvb}m zMuGNuHD%;FeqM+C3nWbddyxDR7 fAu_a0cAQ_idF@g!F0vdOH+dEd|K_P1W-Cfb9)sR0%uGSq&H^}ho z=7TD}hD*uS>TK%5N=8yB6l~mVX|2FRrdDo`S{Aaj z=1i9J=iO}N>rY1H0E?EK&2s+j++^7tKRF;$ZgX(-1M-3kqWxjw8zC}nZ%~lkTvb}m zMuGNuHD%;FeqM+C3nWbddyxDR7 fAu_a0cAQ_idF@g!F0vdOH+dEd|K_P1W-%1uHV9(}*I>WTGOLY0!o4 zwaYD5FQt`4DD8(`P}qeqh%WXJ2KG=CQCJ2(482rmMhdj2`TPTZ-}=_K*Is9H212HR zkg3N9@8^5Y89O<6;osK2t!#$oP~|V-h}C9h^!KM@f8LL4T_?qomjU) zQE1+xL|;|(Tk`=-Kbk#4rkqC6&r=t~MsJo?m+TY0X{|`Q)g=doaZWvwZgokEP}571 zUc>0}%ynp0Z`K{K>eZ`@KvHaWmZ7MBNh{jwEq7uJG!ZSL%4@_XqLTHjF^I{le&D!K zbmXUEJ%_8?cws#y@R8XxlbtO%1O*V**$$EWSZ|tH^sQ|G?xLtJi9yF?-iAjg+P>)+ zmUfGHv*j0B_3*ZP*l<>|;ebU9mCXhc*0)^*XG3K+w%%zFue5hq?_*_x(_m zc7jcb;XvOcXkaVF`o?|`W#*I9#lVp#D8ABX61nHQ(N&@2Ecd)Y@b)GX*n4+J1A6}5 z9f{(_HCJl%m4$l06RE1+uM_)wmo1LIqCWRq*_lwHDE@xeh~eT)D0g!7sH6mX8W%ms zb*@70vQiC@)F>RVQI-X)%yl@4ubfM3F#%25)5!%axUhAN>u_X0$)A}t2yB*7mCH_= z%YK(7%Zm#sfO?U-%2!}3{j*?|3v;L{8O)KN&Z9{(C!b!!9wG|Y3#m0JdJo}?!*h?5 zh41u}K@OJE7i`&$sz_bs5-`wXY#4d-0ZL$9Asatsqatajptryl`d@P+o{yDxpCY^d z_DPn!nQjAT_18{BX{L-lOtHY=k5hSkl)5Ial5v8*P2kY1QqN{SaR0_N!_HCFgj5`d z$Rw|{l6@l0@URMZ=TnNTZKE5R_{`Ylqle_gKK_yGWS6{;8mEB{9=s=2KL0=kXc`fL zmmg`$EX;un#3`?^hs##3v(x1#i*jKGR-6|*6tlEBm3XxMh=5R)gcQsHYg(Gc^=ZtI fZL1Y4=prm4cdStcoq!4bUzq+w%PjjFlz8P2Ez-I@ delta 5019 zcmbuD*-MmB7{+-&9i4HTnZ^YI@Q>uMhVfZy{z?{nVseur-)WEcq< z`dzfxztz3P=kz#jMR~=AZimBN>~Q$)4zJ6Z?=Mh2s@Ll-@_X&YsxRN=a(eQMY~G-! z+14D~zRlxf!{$tOpB|}Et7v46NQ)^Y3}snyCiXq{8ccUUWTPmJuh&6nP*f(@VciBr zg|S+RzN#2DmjRe|G+TvCJ%*z1)8@rSZE+pNisy#wKdnb}^Q&p!>Gi#7u z!|0;ym1tFG&P}jtclUgd6zlD^DC*1aLR-CFkQ4(=M5kzU>amGvVExN=V$#xYIIb5x zWmB-8!__UkVkaf=@3UwIJ5jzD3Lvbr2_oCrV7gHZt!M%6qG+m$LC0jyntLdEcHKcN z?N0Hc`X^d--^Sb6a5k{70h9PtI}1oy+jbG0eX6yv)pnhD=6QwnK3*p1-;L!kUZ%gH z7i>y=4Gbkh1KS{0w+@3SGai{HM)p5I@g**UC^^-Kt_lrjC8uWuXrAB30Fa9b(tu!UfS+)aHHzJ040BtG?XQW4JgTDwz~LDk*^;#zoI@ zjjNY#Gc^NAjlls6<(Rc_k($bRV4%m@(DRPnl)$crEc~d2Dy6QTUIJU_f6cXcK4#u`l&sp@ zCq?oOx&fTkA3G7H88UVs#R7x>kIMZ6bamn?S%>NK1P;wCwQS}B_wS!(*hy-fkcw?D z8RW$-vQDHK9#-K6Wt1wnpP?(+_{>=4y}M+`KK_yGWR-k~TBm{z?z|>dK7K>xXc`fL zr*CQUOw54{#3|3Phsy@8vD4%`lX7}GR-CW0DMo3rEAeRi5donp38|O^)?S>$wQ0VzcB5GmQfD3DDlc~A=m&4 diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index cf81c00dd68668a84d854077872c1ef9a61ef740..32dad457b003166dfcd8e3ad55ec086110be3a8f 100644 GIT binary patch delta 1312 zcmey>!St(xX+u1tVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?$Rj6ccHrDz{ES%8^bofBC0lWFG!7J!#V z*FO0joABlu-a>LhTG1Zt0MQeZNNn jWM~DY`50NQ&A|!mWcYJ3O#7SES{brz58M2qsf!T+^3Hq0 delta 1312 zcmey>!St(xX+u1tVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VM!#V z*FO0joABlu-a>LhTG1Zt0MQeZNNn jWM~DY`50NQ&A|!mWcYJ3O#7SES{brz58M2qsf!T+xMz+` diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index ec5b18e6bfb9f1b9d15d14c050d5f5b80364c2c6..60d87ea9dd6c9db57c792f2838f637bb6244bdc9 100644 GIT binary patch delta 534 zcmX?7ccgBEFQZ|Zp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) zszH)znt5W%xMK4LgG7i+x$h}QLJi?ig& zwE2uI$L88eie%b68Km85in0uuwks&}Z06mlM6T^1?bkP@X^>@m&E(h4s+;-mFp^>W SX2-j6N@UrZwbhDoIU@j0OD$Ib delta 1864 zcmaFZ%<{0AWkWHeZCXK6R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VMxMK4LgG7i+x$h}QLJi?ig& zwE2uI$L88eie%b68Km85in0uuwks&}Z06mlM6T^1?bkP@X^>@m&E(h4s+;-mFp^>W SX2-j6N@UrZwbhDoIU@iFpGNTj diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index e77dae346d23a9d5d074bfa60e56453ba0c791b8..592528ecedb4c4d921c469b00e828f1a975ab2bc 100644 GIT binary patch delta 8004 zcmbuE{Zmv`7{}+FWrc8oU3NDxAQ?tRCJWdFT-JcyF<}*0S<06h422B>CMaIwMIkYZ zX~J;qQpXV;dBcKEbBl!oW1WBN*&$*I7%JqJ&972 z4_Bd7d*1*`ef~|D+Bt9(rG9zLj@CNx?r*3xL#{=o`5(bE+XB^mB&d65Q1HsBH3(a zkc9CmA8FP3U&j9&{{DhoH=zy_KZy)ajT0Sm43sLkaJaDm#7mntnpovHNno$sB!At3 z2@n017=Rl^Y2!Lpag)Te3%AI){|UG-$VKM&9V44+35P z&xI$iqMp0_&%s5G0gzn4zj>HO0i9Eq)|##1y!lbuawq!wVp<9;9F?EEJ({)brDon( zPmcf{_}7H@HPfrX{&#*}Z+L+k7h-y*>!RUo+g@tqr*_jrU=^wh|F)Y(YOom6U+5S;)6%o!Z)r-|S+@~mTY7})yC5HO*Q;s-vUSpZyl z06xTGKNFjK^J(e^=16tm!XXa8tNFt&QWG2Uh*#9e5Z!@xL2=>VeMQYcjJYnfU86>J z#Y6P`_wH7lp(u z#tFl*OCCpb&7Vj2MWtqLio|LjQb(zOz^Q78r-eg;8vEAx@*ivFCE%l^$OOsPA zW!B3fVW$WrU`L4ShZDh z*L`^uqE^doF(VkYTAo_C5}?X8aXyqdv}heblxpIt<(N?cnk!Ihe9H-xTKjw_K$R^!5&)U{7?{|Z zg-S=-pT_E}lt*`U2Y|1%1)Zx<>eQYBlzOA`!M3_ z`SnVaYVYkwsV}?m1U&mx_~AP4^G!}_Ph(8|u4 zJ7z2f8@-R~;t|2c0 zOM(gAI)2$rz7O#qD(+^d)Ru1GNt?+{Yz$RB6M7x*EhAcN7@aJ38(GMgH<0JR6kHe# zVQ>d4+DlBlaW`qi;0`Vf-nbWPze5^z8Q6qROdmT&mH>kzzM*QuSDYYLAOyQUNix~= z014%jKGLH1zl{Gm{QU*F5so^H|0FUzGe-2tF;J@D!r|sT5HD@q5Y8&bNCJE17WwNg zOnAtz#0cCdN*mX*@>?XHUA#@k{7=AzK{mR-?&xR)^E#-34`}EhdJu$57BIuiXr_BCXgC{mi&xa}Al-p>L2=>VeMQYcjM*-7~_WcnF8;r~gV sGl=+bwmf&;-KmpX?FA;=M{sPe^b2Pu-2s~!gJzItbI|0uDZ-oeBVKcpZY!`@ u3)(z4MVdUDy(iDl6WQ#Mb(;+T0&Vu*JU=f(m@G$oP0nHE-#oFYl@S1(w85DG delta 1562 zcmbQ%!8EOdX+tohZCXK6R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VMmf&;-KmpX?FA;=M{sPe^b2Pu-2s~!gJzItbI|0uDZ-oeBVKcpZY!`@ u3)(z4MVdUDy(iDl6WQ#Mb(;+T0&Vu*JU=f(m@G$oP0nHE-#oFYl@S0V``4=g diff --git a/master/.doctrees/cleanlab/models/fasttext.doctree b/master/.doctrees/cleanlab/models/fasttext.doctree index f706e848ecdfe61c796895b56ce520e0941de162..b284da156a5e2293338bde9a31b44417eedfbdf4 100644 GIT binary patch delta 62 zcmaDX^jK(vAERNJp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%=6c2_>;O;P_C6J`Ja diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index 1180a89a810550516185024f42e06d2457bb14c4..fc54e65b127630cd7cfefcc854e89f1307efc6df 100644 GIT binary patch delta 175 zcmX@2enfqPH=|*hp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) gszH)znt5W%<{HLDY@}7}XX= zwRalG7Pr|wp+G#e-RTI~ZBCEV5%9Re9X7|#kjv$AdhGVF+v*6}>>bviGwceq3+HG8 zPz6rzX4#Uv8cZq<4w0Ts>-L&){Zb*dH3rF>1+RMryH%6I(#vua%UctRV7iITIKGr% za^`h!&_hxmE7Tn|KvF+@*SH)zub*`_=hi~h`+#aB*COPj)ncZCD%jIg14xY}50;Nc zkZw}&X7;fshSc|ud_G>UhCbcfpoTtnexn-t^$VgJ`isO(HFRx%SPgy2<#sjn$}2%N zbkjf+(q+oCF$&)YzrrnJ6jmmy*x8|UDQHoiTr7^&l0|rV;|XTL%9GDPIy;=spwf6| zz7h%?U<=u9U>38HTwNJN^s~ z?*HZD#Wmz(-kE#ARTs5_cczqG`*9Fxt$+ICKeNCV`>Pj+mr)Qqdz4Gd>;X`986M}f ziY&lLQ^c=K)DH|aE|F6(^l>#E0ia6aN9)N0lZVZ88zz6V5VEU!CW`PkeUvI0JUQI^vc`eOhEvOjq1AFK-9AUbF^un=p zJJ;DSF$gEd+juxeQ@}$~ q3M=I>@QGeJ1FSa1VO<{;m)mFbAhH2(yhM}8PM1E?T>1^qYJLOA)w0z9 delta 4036 zcmbuC-%FEG7{_@`_e=X>X)Q{sg;5!_wb$ITLL#z>=xQ>mi)62Jh=ogCW<{_xB9crv z(CZ+U!sx=Q;#7xqRTlja(lWxE(z@ow6j!E=da6rC`AA43#^Y9KoPm5enK}j%H^d;BInOuyZsC zr~)T;^JLE5TuiF;50Ht^828$6{Zba&8iC|>% z80jVrZ{r_3BS?M!$mhGN_0VU!J$mS4=Qrx1U%#N}p}&aU)I&G)hV;;vTn_4?7hRF{ z(5-#7NSA5P#=^e$e}!Ad!d4Cz^Rol-JkX+GaIrF0Nu})NjVG7|FBp6V()r^e!k^x7@7W=$JO@UtW5RLCRY97Arw4o!LLs5 zGycC^xwwXW+?%=wTy<8$T=)`FUWIIxFa!x3gYWMaqS zL1ElaKGa31F2-W?5aA9oD$y3oXA8+rG22MF;7DyyGFC#h`eo4(#>`sB8$~EW)4)U0 q3ajQYi^*;}1FSa9VO!OnD)ahKjOjTm`G#x<8nQ`-4E-0C{I?g*Vp_`x0O_XsVE_OC delta 1773 zcmbQYgKORnt_{(QhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#fI7;liF>!OnD)ahKjOjTm`G#x<8nQ`-4E-0C{I?g*Vp_`x0HHw?3IG5A diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index ee5949b9f8d700a9b135e6d042839e914f08fb5a..3dd611935e7a5bd6895f61cf87136bd60453ab40 100644 GIT binary patch delta 1250 zcmaFc&+@jPWrHuHVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?+^MnN*PO-@uw+pNa?o{4lDL0TuTVcSo(PJzin90g?Qp0wGOGl-2W zt(woc#tPvSMXwr-U9K(4Ks^2f=rb#tQ9ZdS4) zrhfAojYbYKwf;49b|hWv=EC9&yrgTJTv*(5~r}RN4G-i#ZQ@+BY9K mXv9aJ*2(AY%WYn9p^r@Mo6p_(woc#tPvSMXwr-U9K(4Ks^2f=rb#tQ9ZdS4) zrhfAojYbYKwf;49b|hWv=EC9&yrgTJTv*(5~r}RN4G-i#ZQ@+BY9K mXv9aJ*2(AY%WYn9p^r@Mo6p_ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 24df22d6cd7d2d374f19f3a180fec81805f26b4e..c2426b10fe98df5d0b71af41d739d19c00994795 100644 GIT binary patch delta 791 zcmbQbhjr>6)(zf_hGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7A_F82QN1HhCg@=4KV<7fhtv2-I3Kc_Z6;vUENnL)XPCS(^noy;#Z9 zI$3~IezJfl%Vr7w+2jNi&~~^Hg@RAX2{5P;n*~H=*~s)n!Cl_XSEX-CQlLG|jK7&I f?OmG{b~Q+mu66UpE0*n~YXhdQ;_d5V7}FU6z2oYM delta 791 zcmbQbhjr>6)(zf_hG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#%A82QN1HhCg@=4KV<7fhtv2-I3Kc_Z6;vUENnL)XPCS(^noy;#Z9 zI$3~IezJfl%Vr7w+2jNi&~~^Hg@RAX2{5P;n*~H=*~s)n!Cl_XSEX-CQlLG|jK7&I f?OmG{b~Q+mu66UpE0*n~YXhdQ;_d5V7}FU6*h2OL diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree index 745bf7842cff244a50145b6640c09d7c5537fca7..2f87e8cc84ab78e984c8dc7022e3dff553846926 100644 GIT binary patch delta 195 zcmbQJK2d!`A){@Xp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) zszH)znt5W1eoB6Fv3_xWX;E_O?8MXt{)n@t4g^U1lt=y>q delta 778 zcmbR6l4-(ArVZ|lhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#$V7&*w%wvow|nRMGGZ)8fHEWep+@-(&$6zG1&UPy*+LBXEQBAi*Q zWNDqa*@1rnIRPaoSWQkq2?|cw>?8MXt{)n@t4g^U1666hlU diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index 6a3f29e93f4647736fdad0c903c7804d4ba0a7db..4cba3b4acf0096834df5f2c957115794ac5dbe3d 100644 GIT binary patch delta 540 zcmdlmlW7AGc{3W885&t8Czd7ao0=pW8Jb#}nj~79r==R2m?fK=o10o18Kqemm?Rq- zr5YrerkN+EOs-+vNQSOBrY~ga3g3K-xq=*L#xW&Le#*L@EK5@+YqIB&r88)PW-majT0{~rsm$?7{ delta 540 zcmdlmlW7AGc{3WO6(nV)S|(fSn;Th}C7GBcS(uol8JMIbS{kRB8YUa2q$HWAr5IQk zrWz+ES|%HtPp)CyNQSOBrY~ga3g3K-xq=*L#xW&Le#*L@EK5@+YqIB&r88)PW-majT0{|R^pQ``> diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree index 22664d82262737c00f559819dd42f7ab4de0ce1f..1d1ef2959021e1651cd0d3830333f602cb2b6fae 100644 GIT binary patch delta 185 zcmcaEcU^8nJfmTmp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) nszH)znt5W%<_V0wj7B7D)1NHJDm6Ki`7HTb%{T94na2eHIx#ds delta 185 zcmcaEcU^8nJfmS+K~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; ns&QhXWwNpP<_V0wj7B7D)1NHJDm6Ki`7HTb%{T94na2eHMOQZG diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index 7078b41971d67e17453d25f0a2cff72b81a38a8c..5d582ce216c545e20542b1e6ec3dff6739e46a2c 100644 GIT binary patch delta 1759 zcmbO|mvi=9&JFI2hGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7AsS7~hhi>wvP~<|j;@fGEeae!Q{-=*`(i{ARpVsb0UwKY#skqoVy z4=77BlNB%Zo4eJM$*^(q0pXXKycE!wvP~<|j;@fGEeae!Q{-=*`(i{ARpVsb0UwKY#skqoVy z4=77BlNB%Zo4eJM$*^(q0pXXKycE!*M{9$$9KTlTR>BBv1F|d(6klaW2GSfyq{E>&dei=NM#hCyxu@~^E{64oRpa%zS&c_pFID=-66iYNAxHYW$uvJ%qIDPLU%}PUM;LZGSW3T!tirrzOT}!b$@^qI_;*QPh4J0Vh56mhOn`?|y zDK=x889T*h1X;eN*o-eW2Pifp(_s(AX54c2W~Ct8Z$9KH$wi6&ijV|9^7L zNVj~tV*+Es_5w4;3Jx+gPX}ri-#)>f@fw-d7jEZ>WaMBc-TLi7w|fbZt`8Vjq0`?L zFmi8~DQ0{rNQnW{S{WMy$TZ-AC?m`C30D|ZwjaI5xP{y%0>Tvahm1$<$#P5B^bJ}} b^3&VR7zMU3Qe*M{9$$9KTlTR>BBv1F|d(6klaW2GSfyq{E>&dei=NM#hCyxu@~^E{64oRpa%zS&c_pFID=-66iYNAxHYW$uvJ%qIDPLU%}PUM;LZGSW3T!tirrzOT}!b$@^qI_;*QPh4J0Vh56mhOn`?|y zDK=x889T*h1X;eN*o-eW2Pifp(_s(AX54c2W~Ct8Z$9KH$wi6&ijV|9^7L zNVj~tV*+Es_5w4;3Jx+gPX}ri-#)>f@fw-d7jEZ>WaMBc-TLi7w|fbZt`8Vjq0`?L zFmi8~DQ0{rNQnW{S{WMy$TZ-AC?m`C30D|ZwjaI5xP{y%0>Tvahm1$<$#P5B^bJ}} b^3&VR7zMU3Qe%}bb?$*~Zqb+R0Xz~)&jip=EM4AkC0mQI(+eZ0b( z)i^(sXR*uXK3)Y@vTXL;tRZ}ojV!G`oAaas$P4K3$!nyzHV4QkOeD+Z`K0UGT)6Hz z3+dV>7p_a39I%^p^ZU&=$?+;od;GR!CJJni+?h(I?VAI3CsJaw$G#$VvK;QW+45K> z7nxcWFP^p`UF-Bl2}a-TS$vGoI7!#M9jKYtjC6g#6ymy_#h-B>dBNnm-6EWEwJ90) NLW`^IVZDqUi~#;?nqU9` delta 1385 zcmcaNm+jtMwhg|FhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#gj7!Q-7YvEd>%}bb?$*~Zqb+R0Xz~)&jip=EM4AkC0mQI(+eZ0b( z)i^(sXR*uXK3)Y@vTXL;tRZ}ojV!G`oAaas$P4K3$!nyzHV4QkOeD+Z`K0UGT)6Hz z3+dV>7p_a39I%^p^ZU&=$?+;od;GR!CJJni+?h(I?VAI3CsJaw$G#$VvK;QW+45K> z7nxcWFP^p`UF-Bl2}a-TS$vGoI7!#M9jKYtjC6g#6ymy_#h-B>dBNnm-6EWEwJ90) NLW`^IVZDqUi~yb%uC4$8 diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 9ff9e83fbf541454ee8275b7a96018387ae51ebf..fe830871b121024c0ca5aca82b04450566a669fc 100644 GIT binary patch delta 2023 zcmZ2Jm3{eC_6_cghGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7AsS7!YR4WC01*%~P0yS;*GTvDt?`U4U%u&1C6}*eoOYo{cQ6 zv72WqWRVlbA1z}yPgGjXM5e8$RA-QD>j(8fa&0}YwT4`+oO&hXrHa_i+{P*7`t`5L zMsgi&W6?r}t(!kuN->ier!#GP$gpuTC?##S|3I!k4V@WT$#Sskx{cI!SN-|vmHY9E}BgeLO#soRiLt*>GrHqbbWS;3jeF@u>S2LQ* Ul5Qz5zk}*o{_QL$85c7G0N3?b7ytkO delta 2023 zcmZ2Jm3{eC_6_cghG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#eN7!YR4WC01*%~P0yS;*GTvDt?`U4U%u&1C6}*eoOYo{cQ6 zv72WqWRVlbA1z}yPgGjXM5e8$RA-QD>j(8fa&0}YwT4`+oO&hXrHa_i+{P*7`t`5L zMsgi&W6?r}t(!kuN->ier!#GP$gpuTC?##S|3I!k4V@WT$#Sskx{cI!SN-|vmHY9E}BgeLO#soRiLt*>GrHqbbWS;3jeF@u>S2LQ* Ul5Qz5zk}*o{_QL$85c7G0IJ$_IsgCw diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index 3b840c38ed0aae3816a858ae759a549391307d8e..5e656340fbc68e644c217074a051ad690db6546b 100644 GIT binary patch delta 183 zcmca9dsB8pFr#6ap^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) pszH)znt5W%<`%|wGPF&ez$iL7g2ixh6Y~VpEd}Y`9Kq7a1pwryy!xRpn}djgs$JOf57MadCqgrbI$wDOqgdT z%!vR|B8tr&3PwUZ9QKe+akw4!pxYU4RqWLvr_le8YN!= zO7R@v3$z3gN>uf4x2Qv#a`;fj@e0f?Bl;n-fuFMeHGpVJNg5%vneTMHw&+K>;+e#n z?B|g>Yo>l9oAOgHFKuvD>StBO<5w4Y0{|J1wev51&TI(n!6zTU2m!0JXYCHzLEP75p<^k6ORoJ%U>d>6vtyeo|-#f0NsQ>H8p4C^NaV}A-s|G9VIzp zjgM3U53`(~<$&oEgydosi25@mKMP}-WKH5n7nv-B0-D&~T4E77HBa-#%#d`s*zptQWM+nrStk1k{$?+7l}i31Muajed_jzZXaC_ ztY1j@<7)j?Ytn$&+(zF57iJn}5-t`uI%thazh30`(1*aC%Ouw%_Fbf^S-(^(+pCr~ zl(WorhC(qoKs)U#ND(8?Xn!pbAlFbMqFIJT;F;4JP{SmgC&(&8D>U-UT@bPhODk!k XxW3iUW7XdP`G1!F_ZF(pFlqb;7plh% delta 4150 zcmbuC?@Lor7{@th&L6AkG$qU^(aOR`UcI@kh>A*>t`f~zBeKmo)WFKrD9|^)P(o_$ ziids;Mn6di=@3UOB8eg}D`t>EsS*}LT3W)Ev{zQHb=R$GN7sfr>cg^)S%y05!$XaE5TsE5ek+$m6i&>-_=~<(8A;^ zKq>D1e4Z8{LWyd=tu}3FLk1s8IaY$%Wkf$j*71|}za|h(C~-4{Hu4?L*EZuQSKJd= zlYP9W#-3{2$R>T%!wc)2rN&uJar?CS-X?&IMcVk6K1Vu)cJRjoqZCB{Tsn=Smdk!* zb7GMWZV86~E00CK1DK{8w~SP$fGx_oF@Rc~(e=o%=>`9c)uPsKcMhZ0nFlw~J2TGOYxP;0@v1AvuZeV>ocse8tTJjJnymnUY=1)}y8$xg#qCRvO4(McwXpnwLpr<&Nr?h#U)wVZ%ocS()U zcmSI$pp7gXCsrm)$YqCGX{9J4v=Dd||7VF$W*P-HLr*x%PN>u-O7dtMa2|wA_`MqK zL!zAKu`4cW7P5w6xCMHQkTILEfvRU0J=DVXdFY(+g(L?;=f&7Tx(@hq)IW8*MVFVZ z1=cSl{Bfo6sT*w{+n0vBcyW)jX9*V}28#kgK%chiT!oy#QGAogCMn$@^eFWaLP z))lkVHKtrKI6ynpWu%CaXSBZ>2#{;265%w{0`Sb~4X9!g&JtvWsRbJOr7j5Bg~jEx YQC!<>>b4tifc!s8{(B3}Ynm|s1NZ?0Bme*a diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index 35ca977eac1fd46d1634e96d445eca87c6663a55..b130c40fa921bc317c317050c8bbdd1e3653f98a 100644 GIT binary patch delta 522 zcmZ2HlX3A(#tpHIhGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7Au&7;lrItANja^FyW{ax5(13)=jF#h;TbTMv=0Ym!Lb=8OFQm`T^U mNkoT*bX}7~qBrl5h$bUwKsx73sTz=O=i~yuz|C&nv5Wvx*Ob-( delta 522 zcmZ2HlX3A(#tpHIhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#gz7;lrItANja^FyW{ax5(13)=jF#h;TbTMv=0Ym!Lb=8OFQm`T^U mNkoT*bX}7~qBrl5h$bUwKsx73sTz=O=i~yuz|C&nv5Wwdj-1E< diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree index ded89722444f24ecdeaf839b7c1f4ee8ed4fb1c0..2680a634dd6622b75167cd116c632d23eccbd3ab 100644 GIT binary patch delta 524 zcmbRCm~q-;#tq(#hGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7At77*~>^Ya(0B@o+3lnL!SK2 jzuC94kgj#}Lmn%3(zQ)~$P=@9g-9hInOfH=zwiYBq86FT delta 524 zcmbRCm~q-;#tq(#hG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg z7KW+DiHVlU#^#f27*~>^Ya(0B@o+3lnL!SK2 jzuC94kgj#}Lmn%3(zQ)~$P=@9g-9hInOfH=zwiYB?1Q1a diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree index 8b3979ecbbb3c9de19762b2577bc7bcbb8d51fe7..d4675975106801b4fd9c3d2fe68922b694dfc06f 100644 GIT binary patch delta 185 zcmeB|>zCUQ&S+R>Xk?k3SeC4BYLaYZXliL{l4xn3mTG8XmTYcrZfa>{lxAUIl5A*{ mYLH}_W}cX`xr4Em(THSi`jZ7&#U}eQA0=O_@#bEZCN2Q9!ZQ5; delta 185 zcmeB|>zCUQ&S;ockd&2bnQWY-H?M3z?f$>;cmHv822 YlILiMcIEb0Ze-ZLfHidUY-H?M3z?f$>;cmHv822 YlILiMcIEb0Ze-ZLfHidUAp#An$F@3VMM{loXWh`U_ E0BC3~@c;k- delta 1042 zcmaFY%kr+5WrHuHVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VMAp#An$F@3VMM{loXWh`U_ E0Lmae^8f$< diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index 166e68ebaff51b23ae108028f447c5d06d3defc6..ec48a454524cc63afb6e55e5420b0b8bdff09d52 100644 GIT binary patch delta 542 zcmdn+lyL(Pc{3W885&t8Czd7ao0=pW8Jb#}nj~79r==R2m?fK=o10o18Kqemm?Rq- zr5YrerkN+EOs--4Plm30LQ#_)3RyREGnbKLDMY)#W*JshX0mM;n0$w$fGpiPn_qG^ rvXG~B@=H$P%~Jf6$#D1PdqVt7q}#e#PIP)S>Dnd(19`JUVF4om2=AST delta 542 zcmdn+lyL(Pc{3WO6(nV)S|(fSn;Th}C7GBcS(uol8JMIbS{kRB8YUa2q$HWAr5IQk zrWz+ES|%HtPp)D7Plm30LQ#_)3RyREGnbKLDMY)#W*JshX0mM;n0$w$fGpiPn_qG^ rvXG~B@=H$P%~Jf6$#D1PdqVt7q}#e#PIP)S>Dnd(19`JUVF4omgX*Pu diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index d15a145062073d0f43f026c4c408a345321da110..3a25a9c407e4e0184f90b600d4269e31eb7492af 100644 GIT binary patch delta 185 zcmdlezfpceI-_Bkp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) qszH)znt5W%<{6CZ8I4HRra#%ARc`Vg7VF8Em}ilxee)icuUr6MZZ(wv delta 185 zcmdlezfpceI-_A)K~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; qs&QhXWwNpP<{6CZ8I4HRra#%ARc`Vg7VF8Em}ilxee)icuUr6U3^+Rg diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 562ea4bb95d1e104fde55a2be6fe7a267b36a780..aee202c57cce686f45f9d128e8bd0256606ee61a 100644 GIT binary patch delta 783 zcmdnp$+EkXWrI7TVVR+kWpZL!vc9QFvXP;wrKw4xrFmMap@~_txw*NirIAsZg@H-3 zp;4+ql4+WGV#?$S#-C*9suK#^{DY~M91B5O|FD=clV|JXKP&>1PjD2FrMqeKYtCt` zWNB^NY#|`ZM7q|^bwcXod9{6Wte83n={8QT6UyJLB7cIsxXIt#qu5tXmaXBFeMN*e dZ<%XPp3M;Lz4LXG$+A5RlrpvlGBai|0sz-};c)-} delta 783 zcmdnp$+EkXWrI7TVOl{_R;p#PrM|h5g;|n`Ns@($Nt%I4N}{E4nyF#3VM1PjD2FrMqeKYtCt` zWNB^NY#|`ZM7q|^bwcXod9{6Wte83n={8QT6UyJLB7cIsxXIt#qu5tXmaXBFeMN*e dZ<%XPp3M;Lz4LXG$+A5RlrpvlGBai|0s!TF?CSsk diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index f5edf649d1e3960ce4649841ca2b0da4885cf9b5..c116620b8151ba00413ab080ef0878af03909b9b 100644 GIT binary patch delta 1079 zcmaDeh4sx8)(yUlhGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC zhDNCdNv3J$i7A`w7@5e?wvp*O8QLaqWJ;WD#kz}hcS3b?PoBtDK$h;9$qq97o9D9U zvy!iU@?3V|&AWMzkmqx-3&2L`2`pry&=qyUU6i_FyJ!ccu6QRQNPch%Zk{XmpO5Tt z5Zr8~_bZk>?UQ=MHec^4CNl&?UQ=MHec^4CNl&%td&s&ao89bYzcs&KJoUD7&#mY3=GhZt=A9bj zGD}`2FL8M~U}8Yqkkpiksciz=r?w3U3<+$X64D_fJ)nJ1YKIOT0z=xi%?S2ypBm6M z-G5|YMu(KNB_E}k?1^6PDlPOf`KJe`1O;b}dig`|8MX8Pe?R}Ajsa~uwzZ6C?^9q` z{nh!G7HJc5^2ev9r)7>WE$Y@LZ%i)x{~yk$OFl+h{2Y^pR@OxE!gL zR7w$V$qk%}T9dTesWr&8@5t>fPmT4Kn$YYC&P|v?C$*lXv*Beq6w%%i)v2>(Z+IQ5 z8Y(w+Dr#ie+o3LYIb_c*zA;%ZBldc$L3jb##sIL~2PXYk-V?v4`EQu0zJr010u)uDQBuAY|N zfttl5)K_Z7^)1(PY6-NJrFCvSOYFF6vglmK&K(k)j4gE|Fx*6JxwAk9G>R%nGk* zaSv`L)q{!`Mf>O~YU{i zB#Y$RsIXmGjGL#=N0g)$-hO z^7FDY)5lL>{UN~~fv%o(JwfrYxCJ+d>jmt0Y012hb-iS_jji2D!FA|L1nXxpI8c*j z%tf2=Dlr?OC(nhc^(c3c;_X@FQC@0Z%Eo!ZR4MssnK^9yVgUM~>i3N;Hg?Iclj`GS^R2%;*u-3cq;^NF%?w|S zs$KOYetcS8uQr_W2S?erkynwx;#=Kj2}Pqxy8mThj8ZidB$`H zHNSMb5$2#a9@}l?JE)hBMi^rp)GPVX#yki0=Bgqi*+G35Hp?h+PSqjb zP;;n;vA{t+I;^KL!$B>WFw>aCsRCB~!lgwcoIeMyP{2slRQEbx>cHT{C((s7r|-8=VAIfa0&bQEH5H2)`Nnfnjz~H|yLr zf*sVKPAxO~IH_RORJMjL|l#pg6(J!`ql> z7bayqcG_5Er&7iQoHmx&snqc&Y8qV~)P&GWhGY0U}odh_+K>ZVlaL zq}r+6L(j(;i|tf_HKytwW0alTp4-pcc(k0_d&wAXr*_OaImqZ~r}hMRj5Ma&sW*pT zJ8tx|Q}3P-_C)8*}Z{NA=!&$#~30Ez(=%dvrHO*vU#iF4T?AcFJRW^de)J zo$@?Z+}HBU^l&N4k~70!E8%nH;?kxxy9ZY4jJ<6s*Uh;Z_-xh$-x9Sth!!7p@v!*c zu1{sJI5nW#i=|9Uv1Zb=Ej(jd#J*;%x%ol_=q zVl(bv&M7F)DYHU$uDKb^nk-fr~wI-f1#f4MccFQh-{Wto=!dsCR_R`53E`z{qc?$;E|_6z@P z!L+WUQ-I}Hm%e|eQzReJ!tdE01vMb4o)k_K-*O7Fd}VHR-w>O>FUn#wf+hW{HWv5& zRV)Y2xc}JMPMG- zbE+~MrhnCFtlHFlhf_yOqnEugW?R!BO$x@qW!tKMaRb~Bx8_qEpVV*3p0=oac0>eS9M`8b!)el^&W`?LN00xFmJa=&X(>A~{%`atI}QI!T1u}r zbIRsBRldHtA8w^BYx0Qn2>XvxJS8v7seg3XCT(@AIqS(+$lOo^7>oL;9s3| zf6X9f*8snd8SJa%zoGFZ+gygSGloIsE6%TJJiGPy2OCdcZFlZY?P^Lf6z+E4*@Ex- z9&m*}lnv;s7S4%mbNAp>&MtiZrH0rRFZpn5&{v^SBl;oGxxU!`mG7SZ5PtDjY!X7J zVx5w(e`I%G4-Hek?mG;_t^xm8hM{#iPK_!YBB*hcB+mK2VM6`;bIRZI9di*XB zl}uQ`-6{8&e0nKl`Ri*B<=B!g45w)`UD}8kgu`}D9`a_3}{YwtZqW=%AJBauN(4U=G zzMAWtLly5p%6<1Mle2flO8|bZ`wMddds6X_o0ybDr^Xbq&^i78zZ?IH49y;^JT$a; zGmxMy3$b6IuD#;)nwXN6?M?yWe(zsoa`qzVp~-E}H2!=N`IlRpy|{X4?FX#Q!sWE) zs7rmy2*RAnFR}mSwrB6#{#o0%p~gQuwV~x3oSOc%ZOQ)SPEq{fQ2FB^g#OvK{qNre zlV`Z|Sj*{|S&oMR_XRiqeW$&DOLwpbh7Y^rZ#?X9Y-s*1-N9aXKJ1P^On0y+w*ORj ztQ+SP%=VrC>oeAM=MmJTREng3(SC`&c7E6~zbYEf4r70P?)q1E410L~uw(v!9mD~Y_BLr%|x6e`F$53$L(|_OOI`~H?oeaJqvF2=wP5T_Rq7N`%+0qCr_%S zINRQk@OMMsYz(#pz1@pF)g>OicHmuJKS z6t>sqPXzf(P8Ja-Y@Yj4^h6g=e#GOi-A{!}L-}*rKffUwbwl#DBzzk}^WVWM`tnTR^S4gjAH%a;JZuu%b-dx_k#|SHo&O<#eM=$!4q*MvzkSdN6!%l?fhc=? z;ap-A)7k*%whw!XJ#7BZ_0*|)a=pKBE`6}OM*d5?3uj0StVPb%Dh4VZjxO7utvFr; z{-x3NtNU!}pY_?q!|t@F*ts!#-SE#$iw}G6pO_neb?*)NpXt41ZCNAxm2isWROi29 zjr-O8xS+frgAO{k6Z?|?x}lii<Ke1xhwZHGVV!z=WU;g0o?;b9G zbw>yNH*|E_H|T{~*PQE$7xaJip!l%I*HQj3ybKqDMC0spn9ga;RpM%uFKw(EV%%{MdX*T$Rw@s;GaPa@;~=)Q8w z&!sv4QHuWohq3*2$sf^A%n$wK?>d}DipIryRb@W}apa=D`7 z_Ac&aWiE9cFLVF4GB7SYDjE+x_u3P0nrRO=Goet=44 zB*7nKjiPJSCl{vn=pWykTWiZ)?0-+N zgq%{vU*r9MyXg9-PY!>~9bZp2cV<&$HQ7rKN%C)$qQNGCJ$uC@W8dh7L zMBgx#yjpTD_4NYzUTt{_W!D1smX|!aQrix;(o2l=KfcdD@W-bQ-Mvc1KWP19y8B!T zZYR%C**Ct}M839zJdxT2!V%X5$@tqDT#s z-@z~62XvL|(~Nw%E_rs6Bc%wz2%RMR&@wQnVK=z}{n$y4qb>u0jSZIv(!{~QlBdh9 zsd@xdc6SCjDFP$*>3CpwBjjOpHWJv3NI8Wr^#X=uHOMmx$~%V34QXnW97ADA5L}Cr z6X_{dM(%E?6m*7)_dt-B`^ld4BO~t)0y(scJcI`JLE1{a<(hP(3j|3+AUJpcq|p^* za2JpThd_S8$Z@RP9^K@j)Fnn>-gLej1ZNXLhI}bkqlE4t=kx&idUrXV5{3fv?;*$1 zvj(th(Wub&9#DCQWl*Q5Jebb(2FBCA*b{=ES=uVS!D^|@Bu%O*)#!^j2;K?@dAhpdL#go~hmQown}3FpXCgrIcEk<@nU9v2c_~fk z6Gok37KuoJ{S!Lb)ea^=P>FR%i$SP&cDhihO1lO@kPrpZzpGM%f(C;;$32R5+a^Z# z%CajrNCf#o7)bPDEn3CMcvh9>{z_f)9Rkw7E67U$N*!9w$T`U%c||>kg6zXuH6&2+ zA`2s*9|00ItxBe0Aj`UgNs`Rm6hk<>XEDyhtCpG{^1KLJ?Z$CN?zWC{dJ*=V_&g6^%% zym8h@`3dSU99XAR%pn1+rk|!F*%4-|>1h~v(fxqkNCVH0ReW)}Y@s93z!r5>Ytpa` zWcnT(3cEApRn(gaW{r~P(-Ky0v(a(^m1F}uGFo0p2Uu3WOj6_MXeN>kVWYl{fl1BG z{3x+djUp`zf)30i*;(=;>c<8^oovivJ=nDIa<;sjf9KVhQ46yZ`^KwL#xVA!LgQ(M zu`dl8TNuW^4`}RU82gByahPH3OMAwL3}c_eGrnLL`(~YSlVR+`a>iYTu`jN(5kTHQ_ z>_a=oEQYZU(HO-HW8Y#io?saJFp2RT!`PQOj8_=OK1E^dWf=SRLV@uPL)kY5j57>l zzo2hiVi@~rc;gns*l(U2PG0zj{e-vS#xV9f)P@hk*iR@M%^1dh`PT?y82iCmqYJ~> zkE`;kh+U|4toLnkqm{ji@gmtpK@=8Po_W4~}_tYR4ZaV_IThOysu zGG1dC`ynA?AH&!$*BI|IjQv!KagJf^OR2^;3@c{XPYh#UNHr91{Iir{)x7bYS^jBM z!;dl9XHkt-3}c@}H99hkeGb*=!7%nER3n~Y2E$Sq7Q(Qx3}atEHKs9)efiWVWLPM} zmNATd@zi*VVeD(C#%6}GFP$0%I~dBoa%vo682iGhaf)H=>!!x%4C}+N8w_J#H8p-= z82h5Bq5I$;_BB(ZCd1g5OpS&NV_z{f0vN_VU}}UjjD5V+=)*Af;ZkET!@9EX{2J*D z|nPBV;s zjMTWuFt$B5zGqlE!=yToJQBgbKx$M0%D+Bpcr%QBc+_ahF!s?=BamUk7}gnBevwNa zK6qa`qqM0JWpy4W`$|;sjOsyKCShIeK3?8|9ftD+`Qw0z0c}H4QzoXi32dL*HXtx0 zuzgBMhm7=q_Ccu~I&=sOY1=j<*uQ;hK-+Zxk%1W>(`w>O^@zb}7;~@)*J{C5`Hj2luT>@_ssaSPmk;XXIeEU#Uj=6uBq4zbo4+ zHE{}5nn}u7u^HJt6;jDbX-F-mL9nc;5=2`blSA0PW+EjwQyN--o-Wsv80ulIKU2=N zp-&gcy=~~-S@Jj=I%|%cXG7(=^0zkBG@mJ0_lSCh(4_@(7R}v@3QsCTp-nyQg<{X- zLkG>M(s!OHx&33Zm&8yHN?IttFJa49^xJLeW|d9k=d7!gEa|#+OtC!3miY&Z^t9a1hMsyx&bOh% z*U3|C=%wcz=!EAT=-n6O1vUlRAkVR(EjG#HZRqCB@*Rn8eT_}8`vokBe0SRBCAp`R zO&*WQ4XjVTB-fUtZ0oBp%VPnl;j8i)DZ6Z&eDF8=vlV@i%yP-aK>_Ihm!0aD&hD$!W1Ktsz3h&_Vj7@TFI@*aXFd??)x9S7F=pH{|A&`;}aUrUpO{JM|>$*A^;$4nn2(KGYrC z^(6AzkGeY@1~2LoMhYK=9gbjA?fSLc(7Nl0+)!eu2Yq)`J}V8izI#j_@AOZ#Ah{#H zRe!auZnD07f;lwLz23X>dF1Z&o*d*PIK4iQzW}H8DLK@M>X^`m4?jY+{$xTMMtqF= z?q~xZKMp*58tpIfhrr_u1f9;IaX3^|r^HVnFr7yW_k9AvmG3-YRmIb`BJm@EZ@aWxd4@oIx_h30(!P1p`P97#z{%0 z^PkBp|0x5VI4Swjwa?{G%f3NF(88f~>>ISPzZ7;BB2@`&H;^b13!%5>mc z`BU=kh&ucCS3{`U_i`p(?Wi`SHQ%G?r+$gy`sdrKI(Ay9!>Ie_PIbs+k zhN7q3eq_8!YK}PH4*3bn-%PQ$eE(0VYaqqWVj%LTqx)dZUJ*7B4r_xh0i|+Sv~uBiPx1xx?2wkPU;XG{LJ1G98YUe zok}dbAMO5Nt^}3d%~2SRtRtvu3nYuE0$z4?B;zv-4zJaz&<)ALYe4$Fo3aJpx_#DN z`TDnwoS&)BWjFy&!b8dP3i^LtHI#RAnte3jOq@nJbL`uZku@N_x5C~CenfuJ6SZ+) zh1%4t327&@JuN?oCe>on$B^@pT96+3!shKbK<7~Dm*88x(1r!puHH)2Z}gv-GAI{u?XiNs$eeYqU#(n_&9vO`94iJ<9^@rFb<)9q0{IjvuO1o1hL! zJ5agfO&|yvV>dH*y;{v6^&g8i&20whXUjyMODTD!J-?t95V+;r=Mz5PKH3t=KF>#{ z*IS}Zp==Sq`rCZPla842H+O!rt1E>^DV6AUYm_%-0qTz%i42M>L@BTMgE!;=c(|5G zrzQu%8`qXqc#^$2yeD37hkE7Cu)BVv_D~6%YOgvjEk=?$4QbB@qSoWAOM(=y-`okV zRa|R{8hZw#m`UAmUvj>Vl1jruP>DS~?3Fke0_lfm?ET>p3hCNUP>C6#5WHR$1C!T% zUl;^)s-Y_aIzez`KU^7CCAhf?hv38q_WZsNN8Ni_pN&x3{^q*ZjZ(fP%Cb99NoSNW zVuHQL?{r4_=y8(MSHlx7k`J*>~C zD^qP~`%y}U4SjL6GSY_DHI!L4baNJ3M``!$eubNKwyo> zp{bm=f4uUxz@E%gwhCN%U47j2k}pMBWvXe<*3x@A3hqlNyC}D&4ir%@u}%Fu8nJ5 zYPCXnmll3%?-X4A4WWZ8QDEFzcxd}oN^^;!9@d=I%3OicompytHE=EK11>u9l(N@` zu6#x*wP~S~Lafu);rD>J7X7qMsb_ubIjk&#&VEg4U=4mA>${-Q?B>=tUQohp^tByI zU2EeF%DWQ%&c?mt z!jwaH(OgxrE_+ppvx!hCxGL8|eX?v?yed_V`ybcCkA`zun4Q zD(;F3-P*0RhO2i)g<80%A=WW_l_7$*e)Oi&&54Vwt@kni>$THvaNMVQP@e;6Rjpkx zxb*<@U!We=pARxW;pnEr%5EDv^Qf}VhQ`0c%+3`WpTN)%=%tg&0UNsKJ;mKga4&qI zT(@yAe27^?a8G`$d~QScol!oqp)Y-^9JirM&nYVeN+)F1#oFZpGitpb?6t=oa2QSa z9JRmlgWSk^{&S_h#8BHj6_b-FjJs0v@0I$tRKss6zt~dou3Ywm(!wS>`6C-#JnyqV zE4^%J@EyfP2{!P6G8ZsnIrcnyxOkDR@^>&)B%CoBFDad(2(^(Z)ShO?}9lP!m$ChCVaZeBC> zOB>f{rIy)Hj{xp^&Nq33sf_0@~Oep6`oWL5^+N>bNqXuhjDQ`JRewoJw_cs1u4HiGG_^#cPnSRm zah)LAGXtd#f6{h}v9_G4Vj2fsm2y`r?X4>c)B%FF{xVD5Y(sa=Q87<&t&&$2nL^g9 zoviQ8RWU0wrZr~1`nW``8^M--h3cDhyAf=un3*ksdRSpg%(@IEEDnlQEmE-;;WRhn znPL@JL|oN@qoi*@39wXZ}@yX~1G zh}MH9zkp1S?M9|wyud66)aHegH?T}O&CEwIUt4RgR34rK#bR@PoOxibPjw!Z z>*K;ha*(lj8~_W(0dx?6Md3iP5L_P-e*?vWZvaDCoCN8U1j&MK;IfDt01LMPuvi-a z3$y{SC>sEaumP~}8UTx}A#WB~1I40h04$^iz~X5DESLtsB543Dj0Vu11#AFW1Py?F zdJKSt&Hz~441fjA09eEffQ8EdSgZ_y1pLS`&d1&W2K;5u^!^%w&lCLW!FkcFoD$i^_* zvR&;G5(R2_sZoRgRSI{x;RsKC=8KqBQ@=+ZN#}mYSWhudJU75tNKbfA`RsEzSE z`f+clfvuU(AeY5WST4*FSdvRkp?#VC^9}pw8vaS=(NsG4C%I)S-i=J}Knq9@;h%IK z$z-gzT7tEY{c`~Sq#HrxwnMGim30miXSRkU3;(3^K4GkmZNLg+|Agb8blx9~U)dil zH};PQ{z*47$=IR3gcrwkcB(#DtUK;h$MdmS!ds0OM%)S|y!qu6Yd)iRtTG$bDB;Fn zEI~1=lvDc}3W|-)65~Jx>QDvh2&e*fH*|`;00;@gh^N#`hPIRDoih%PPwHx`cNwqgZE`@Ln#bSa+9Gtl!J2oy`Q*ou}K) zse-25ElP~N6@+h9p!QXu4pg8HRiKVkpqO{D;@PB8!riQ#Vr~YifcsqucQ__wlLvZD z2yIkWE0GPGLQ0G=6{wsF6mw3N3v<>I?xp1v^HN5!nFJpew^0RxWR6=dWRA-yHnEg& z4=$&eE0p@nX2U&g(vKlI4D&=-Ic`fl@M!+o!gqCi~Q00xtl#xlf%Ujin3w)%sMtUyT#^EE8Ys zs}(h(sEww^*2M?Z8NK?u+Z|zzDHMS1jzD!2Ae%nEp!UFBq*le+`ZKk+Dh;GN8<1b* zXDa?&?;BOMzVfyDgDQ=;Zn%lVq||{nf3GG=y{)K6q?3v5nUGtSHRcEPh%V(2U^v}y zHfh#0C(~=1iA|4CNU}DpWU8r23D&S0rv5q|2{2W$F84J(t4Xoe@MflBlQhM8G{8i< z$+jdGS+l}SiIt?8)_pxq$33KllrvG(%Y*!fniT7iA*MA_6}GWL{%m7~Rg2DEQ0rLB zhM7K>q%iB$5vK2JO8HhK%y5>*(Cdm;msZa)RVtfmY9h^}mR}-6(3L52mZ`dR^i)$# zlay?oHOI8MiBw|sf59}{sS2BK*?RcOfJYuFs!b=inrhQW8%zzPzE&7YlcaD_vGaRP z8s%3_X_JeTN66Wiz^P7=Z=eMqZ8Kfcr8w)}y{0@TsSEAA zBg7irc-y2>`rD@0qzr4_eWtIRrD<0AkV&f|1zO!cG+lHuu^k=S-JkA$Z0e@69UW`_ zE=8+Nr%#(IS#wUCYOB&nE1csgXKASwZEf!?4Y8(NGQBA&Y@!}sN~>IXz@3uR}of0$JZq=-D$#AQwzzUdY`GjRxm8iQiwJ7 zrfIXRvOS@#A8K3c{a`vNNwclr{b-uzj2K4F9aBGPtQF1dW|DdkywjJ8@0fHt@u8+r zTUmQS>O^RsFWr%~9nv_~VKI`~>Z)o(Bq`XMVbb1{q(0P9*Z!pR5>3c9p$Q8^s#mAc zu38&uFu|C<^rDA%ZR-hFEmo3pg+GH+gN}Q9tJV>fwT~n|bHje4c`kf|35snn0oVo; zfDLW{w!s8oucQFj6Da_;!31EBqX0$;z~+BYY=a5FHkbfxg9*Skm;h{p2{6}3K(@mK z$(}y}uni`_Fgod`4d8n#Ge7p2`KhOnA9~FE%wy(99y1@)X1?z-^AT<4n<_Iu?U?yt z$IQ<eE(wR#~U+0 z-I)2|#>~$)W`JOW`3wK^DT>+pJ>edKx5|T88bi56!O!I z8HX91dkXns#>~$$W`2}0^OKC3?@7%39AoCk7&AY`nE4^b%r_opeuOde=X_>3|T=4Tf(-*A}u$;HeME@pOaDM0Jl_QK4MEoOddG4n%FphE6H zh1`A2{CHBxU(XeC*D2(lQwYZyP*qF9Qn9(J)*Va5?W)>ffC1IC5dfR2X_;yaT4cOE zfVQ^Ms!>37Z72lA)wNuJyVbQ2tR}%Vv?ze7HME{;1k-+h0NtFfd6U<1wUNoVnefOX zO%1GYo?0B#(ml0409!q^K>%7!Ede08rWVbZHMKz^i?0WiJ%${b*U~~1p655@URw(x z|5KU^t*fO~5o*^5(1~K`ZGT^_M{D-0mFUJPO+`+wwRwiaYV%TFsjan^2GQrWwNL z6sim*hL-2!ycCoM4K`=rXqcPp^)qJ2>*-z^SFw_q-Wg>0z(|W1AF5RMe zhk1RX)}oH*RWEXDt*JuPGnzamGk0LJqI#Mi5^k)gbp!ZLfROr{Kc1Z#^|g*_Bx~95 zXd3sCR+Fw?L{57?Q!Cl66G=DfYY7ThN~M?ptp=qufa^xm`UctnfFB#6$0I4ap_U@F za%f`%IL?iRnulPFr(YUqVT_?s-$q(x!Oa($84a~2LbUlS6~Bep0N#YZ3TdQuks`|) zYyCkCXbjhiq&1Csu@@U_14IrBgyEu_XdNM5*hK50vb!T=Q8b;HfwtUeg4Jdq`87rT zxuDchq%Uo0sx=i{5?l&{#geb8p4D1sj?Ks^wGAVl@Cl*Px0%)%87^*yF*%T~G}DFy z^lGk+1lZ7AO9gOmp^X={*bq%gF<%j^61)5n+&kCoi0@3kV)HG1b73z0{dh2jozGXoeH(BHsq)ikGPPDfJym3tsx<7}` z1ZkrIx^~be0qp3YO;y`ZtHxchn{T_=IZX02YO!7CiqZLd!P{M#Xd4Fihu3bSezfc@hP7(#9xBtQLVn zYiB38B4-2(23j{%FhT{RSrDof8LU?!zZ%%|p8c7#8Zp@byS72cTIL2aiW#3F1|ZixG=TrJvj! zi%J5;bcXXM(8kW1p|G1)WB7fwIPYnyMVn)=Q@jzcRTA=KAz#x)OB9S$!GK+x5l_&h zw#kcfx}wHB!weyRtFsnGYlfk366mY07*$-D9YZ(fV4xmvg8@Fv%j8$~qgxf)-Az+O z;vCu)sfCJsQIVR0J6>cN-CY|f82NOguU+kKcQ{r8{#r&$6T)diyQl}|w*>m6hrOk< zZG6Hif_c(|*3`!_p`@o~lG5n$p4u2SjTxjcMl9Way|msCWc5OSals--Et-*g6SR3& zQqKVR{V=kTx}UxL4T9g|anyf6f31ZSOVj#mi2xt;$KZ*j@Bx@v*oC?Ax=@AJG^gZf zjI#3j#F9@mW{y}gN5dau>HBCc4InWFzRuGg6ls}TmIatR$uCx`u5!r{Y;>AqN<81v z)R3+YhKF$WJ0d~D9y~!EN=}5o$CkxmhT-z}g?xWYttlmsao6ng;}qHvhrQGk@`=~R zD(r^eIP0i)Gn&=T!{Q5xjFi(D2@beNdil@-A z1lWBFg%8pu2=RA93|F@0KBli$U5IYeqG&i2FOxge6k0ME4dB_{5$Rx&IKj9l7;tf0 zM&*N+Cvc*Zqha)_tNRx-BeJ(FzjZ|at&$rm*k;g!-4Mj$nTGCN}cwrGeGej#; zxKd@21?FkYClRJ#ZUiB67oq|~h+5s(xkcnZOnVd=y)sOj4G=V3n=2Ceh(sR@N2eE2 zX1ECwOs3*h3c2Ai}QEA$0Dme!?ECB zGwjvN68tqYFveD7X}-7<*^#AnQ(2737#~Yl8*42nc?$ma;>1vsdu7`ugG=*i=S0ni ze6Vj48qvcry{yj0u8zwm$5KRn&6kd5YpyE0OfhD}icOm~+|-y>&4nUo&$5{?=XF#< zYwY3Y*a}RioH5uovS>KIOn^bcC_E37 zWf+y@@e)7I(|UlamyZKf80F_{eaLqa=GcAtT0c>fv$2%ohnhr^qb3va7fQS-7le%l zOvE~PJ`1bymWi4wGT1Q@TiGzGJc-wC*d%R;sQgt{tw_2)26sx&FGRK6CTjylN#DiN z@TF|#8;eU5zcQ0!`U|7CCTmGT>8Dt_*%#H>o^0|i%Q0zmI0rXI{g6>13!}4B zv;?8+6n9_Mk*hm>JPuopSySOZ+>&?#Q!+&hqQT?sMmj&0cUr4yyxP;JY2AgUM;zVA zh336!nmfvHpU$m4WIE2JJSVR>I+q9ex#^lK7T&GN6bSr-6!Fa%6F+=vy#|h^JM-) ztJNFOx;F)fwoQ{^n?+0YTK8+U6I%TExpJCa>+VwBWZSLhQ+9#YM-&_)l#hReEYF}$ zxU;-bfT?O8b(p1P0IZsYUHUw_H47JF^C)^YR%I48Hu~90@kYWrE11{JGBt3Fv3cY+ z2MhW+-IBID0vQ!0dvdOkBQzGVGySoAc+!nRTqD~@aWZwCk7GF3%%|KU zZ0gR<*J`MoG1=DNZVR*slsaI6HdqK|au;k$CyKD4ytY8A4C$Q(*gW%ObLn_7@_D>a za}$gOBo%^jy%27gTxP~`i0c)Xd%8FIEr5gUHpAamn&IhOp~PkvUd$CU_JPUtvsoJ= zq-D0+p~n6lF6+I z8|Y-p0uaT#_&_Zy7i*P-Y*QT7^GAOs<-uWiEt88el#(gCm}hplSYv?;zePOH>5H^s zqU4>z(!PuBmfjObt6o4xKQF@iQ^{g-tSGSblyqiqbm*Cu(Ow*U()WDN`5J#;8QBWT;;crEjVlCk8j~qE^v~DRk^s7s? zWFb6DvlpS5VHV9(FfIt~bB~+c?MrwvowQ&?B~!~%ZLrX~Oc$nNcfrkaq*T)d_p0y( zpJmJ!sMRt|;mI_6nHDSRcl!a0dX-`N;g$R;j#lqR-7l{+wJ9%mW*KHqF284Ui0fsz zJ%QE9K1gJ$w;cPmBEl68*E9*nS<*T|(Pz2VPw=Y<{@mrb$4jPN%WJY%|MJF{Vc9S5xi+Z#cSbJT!g8I ziMFsgb=X=hSa8P+?xwYvv$AJ0M!K71P_|r|g+evt|Ai z+Bu)jKZP5R`K&&31RpbE55brx7<-<^A~~NbKZBbFuDDR}aSqh!@Y9HEo!zhd*cKkH zut+GNWlbsRfL@dQpT#+ric1z+<-P<#BPMk#nFt7 zoH`~zolUr0?m~&1Fv7dg#!VO~8FXh8j`$hO6yG>3SgmY7I(GB^^2sEFwr)nHGpNcI ztSMammQXKwNfYYFKGZAQmj4XevjxV>pt@VNV09YH{9e4+dEZ@+25dQGs$-u{xzven zZiQQJv}(2J#K*c!Ge34@HjTd7il$AYfS0sx3Ria(>L@x&FscZ~m{*Yg#u1a_LU0=S zzKq@KG#c|VmQDskbLbc*nCf;tFORv(LV+qC9_(N-`DwrQ=9)y8dFSHTY!d>lb)QRR0{)$AQS zjl!+yA)Z;d(1Dr!k@l<=9XO3zy@s1#487OT)wqaxP3tL?``wpe%MVP|sQ>F2s63x& zhp4S!#0dr}Rg1J!rYaRWCqW3|@V3^q{9LbX8 zkhwteKyl0b!k4-?-P?|12xq4XJ#0B6q(B<;20DuKGX?+58`w*5Mz&yJ<_Q#x9Kk5q zq1D149xmUZ1t=_LY)q!}`w>7rbJ|qbZpC7%vJ=ZgF(vQBy2iC;52P$l3_3)X>sD@^ zk(p;-s*CB~PAnT-X~96+)WD`h;kz{a4j}#!vd8Y1eex;B_tjlkQh2)Jfoz}eO^=g|~1Gw3MRW#>}&nw!V$q zt)*0VAExo8tQrS}GCxh9{@Ub6{%3I4GK5a<(*`L+n8tCMeLxGQ=&$i?;W93r7VSqB zx#V4zwk~b|3a7C)HIQrpo!JiupFzF{Fagb==?8E-H-lwzUKHbW5X;{rYIzVR)})#^ zv0fDXCtIT-s}5?l)F7sPWgz9&$B}!;HB${Ydsnl!pT_lpl(Y#Fw?kTx!a3iH95CZ{ z6pUMffptfxormC%TrA$JI(49^jzcmHZFGpY^E^-y3x<|Dxf>A{!#9ifN|CAi5u zTA1Ma3GUW+wAxBMt0Ml6_zXsT^Wzw6caET|;z>V_nGwGOb{yxscv^WJcLVYC)p1O9 zv#I(C1RdGCTD%tJ4Tq-%3r0s;at57#^aSP(uF*+oIGse-&Zd?pvB8^71t;O>T-;rV zd4oguduV0vYHfw2myq;*7m>hF%6wM~#QUSw@8Y&*EGs!afliLqyy&s(wd*?$Tw|&A zdzd@N(#-c@h_Q6^JuOkFCnwNW7UMnaqWP9xukB8C-bXX>qYLk2UxznN075Y{LF_Ka zp2Bt07D;oZMIWHMc$wLB@;zL1UazUuMw%NRz>CLHzf)+^SXy%m(PXYQ`TiWvvk-E> z+FBKn!!(ft=Pv$GtAT{iey9cGwaM8JQR5!e@+0gMJ5kn0*hC`AkDt2{X26-dE?%5m z`$!9t%+%;(!~xArOhSwUXpmq$Av&PBs#Px2aP@NMW3)4v%B5LUf&vgm+r&T;9c3VaE;EpZAI(7FJRqdLGjeR-H8Z3*RGKsnuM5d(c`0|Vi7n1T6p31BGSJTH&GzwpH2jtuy(a=lwY znq4mK!Yck*1?hxxX$B>KhH|FSOa=mJ1q1zQI|Ie^5x`KsYOWi2f8jGmI11;>)vpc?NuzSXDtH5p>67KsUgZBJ${9@k8OXI}f2p}R;bqXci?}qv1EGsrCZ7D%zJya{3}swGG(CpS zT|(R?hK7Ev%?J4GYXmN0DDN_Y8ZqQ_1smuHin)SgQ3MrV!4wri=dU2Z7eO7r!2u?M zmVbluVg%j!2FIxg8hjOZHxabwD()>KsL3^)6CHS8m|3%phud6N}s+ioJ$C^Zu4#*bR6D_85x)H>6Ns(Nj^j;!#fA&Ip4CoKzkdjAYF zCDQz#u@O$BPX%cICqzUi)8l{A3IJ-~L3Jn7=sQ{-z^8W*NSaKkzaXqNna=*A&1KYG zbnayOkd89|tf{(PxXKH7I8leth*?Mb8F5N!8~7e5UF-0I??hF@R4^`h1~Wi;~vi z*Qw@c`dIL9YWgC8S-M^XP}x;qA+#FPY*)PropRMDf#1K9K1c99aWKaZwqC8I$71n` ztgN>K$g8Y}0&J@$qqG?z$Yr&=iX{i8}YU{H=71!2h z1GstVB>-!@^o0N|z4Zcs4ccv|M8hih!X9UWl2oRJZ| z7*EzZ=K)kuE|)bhsqvjE{u zICY_kz5>+Druq{AwVQEjTQhxSr7Sk;oUktAA3EP(Z$s|YCB3Yr?m{b?>+Jc?hs{xg z9BSG^w*c&Dfei6W6)jO%4oR)_=K!8>#hHCu>raEa(HhmtA!@^^P=9?TsE_^iCoA!1 zISpB14P$6;v+}}X=xn@hf?Bn<`l>2itvOR;|Ik(!-ILtg>-Fe%Tip$R`=zwghq?|G zrmry)8uwJY#?$R|Z)ogpr!NK=&|W8ii|zHdv1i#3s1MfI`x*3DiXF9Gn2fLG$A5TTa=^o-P316+#avZ+xhFM+CchMy$Ry3YDCfUqu{ zI^G3iJb{LF#e|SR7rW|3uKdN$Ojf0tbaMczf4;L0*TdUzXgGDpa7m+r?mBxVbGSS5 zN~7c+dLh8qJ@it5nLXhRX;ihB{y4yfUYr@#n`?g98&yf8+&-LA`s#~a`6HXBS#eLZ z;sVGo-bta|J@p=x6QIjNzK+S);rWa{L%2g4z0waYPNQ!9^XX#nQzAdNDwLG%6H}Yk$sM9)tdmrKYjE*_H20_p!Y9QJv1ZzXd zNr(?J@j+H}0L_ZiAL~8W$XI@rjOB&0Q4NI zzYE}8_ySrokO2%Jj(G))f&=r~;;20+s>Rq+72g*^?j(6xDbH1(T-aTZ2{ zW?=OUBg+gF7)HO$(9?wKYNooHV&*_~$4tEvgokG8DF7`BU@p9cD9}>@z86&DEYv%U zUY`XI2qUlASa8E=#%z7G$Zb2zZ9DCqi`*jT=n8~A=3qGuqfK*ortXh&YSN=vGQ;Te zM|o}f&E>#4muFgQ9xr0@Jg)M^JYIay`Fdt0-nO%>2)w(vw@@EIl@{pi#X+3~Jc9)b z^lVX&i%bb`R9UO~7xGpOEyPM0M(-5zY&)8vbq{p5B(u zEz~tSvIMdHFnZ!KeI{MsfIIv;3-wVV#XXkdUM$tydS42r{QW}Rz~vj1Q;V=`z`L0u zeKbXH1|ztb8|=wq-hDq8a|jjMnzI;N zk}$fom?!I30(0flrV=~x zBp5?4TJ#Dy1wz#r&SugV>V;7n(5-x5UZR zZ3ULDWZJU=9iL3iSEAL)v|^<`$(5h5Co>BosP~dSf~K$1bqE)%!gdhP2Ufu(2%N8m zHzd=!)jVO$8hyM-_!vw07%kqak8tOYka%{*j9W~8;Hs(lX(^>fGOb;UtywZveM+AQ zV15b)Po^KA;)RTPniulv)0~QZ2AjWRI{J*gcI#Po>uJ_{)NaGG*k&Ztj%RuL7V9t( zB-7*T^eLh~uQ1hDD0zvFA5eT+kEChO>6#E(naIj=Nz+I2usk*c$@I%}`ebA=aXoCB zOxM=Y)k6%EqB~#*y*gGcEr!VUB08%$#{Z6JE8@P(G5nY)~ z7dP@elQ(hZ=}o-JF`Idw`!{3IaVuSA6}rldot-Q_OZN-K;nMS?DJLdFD81~5&{xVnD_%im}$yDnVZgTT0+)r-2f`dph4Sy8_ zI++f=s*kS3t5Fjd97fG}y1P}!dyH**4M^8*)3b!M4wKf2XVVA9`73&unv%vIG$hju zt3FCJqyf`uKwGxygZ13;>1mm%2%{uZ%xn5Mkx4VA&@7$}eO`o1tx@TDNx3QGQ^v49 zfbSYDnF?a?LgjT#Q*fEru>>U31p(@9$9W-{Xgjy9`x{&q{RX$f+i&PIL{VK?K3(J4 zyc9=kcIdT{Wcv;LRZYEx^JV<`IPfEJZV1NdRw32%A?rxSR3#p>Ja9$ zH3#%ix`Z$|G;Z&M=gp_7`{9-I=}-HS^L(0gfP?Q2pp)}x$U(ig#_qf@89bFn7mn$1 zWry^tkW@Q_RC!c-2)ouis(Bb2O@24_3@hvzig`zmho*a_)=5dJDcRXcNu@>})ja|a zMv(f5KD83p-pI5!#*@dFnE8^A>)719VKO;Eb=pzg5IMfgbY4bq2i1=}tOwGbW4bF1 zIL50n@)$ZSk4_zfYvxgpcX(0SDoVfWWw+ zj+&;~xTc=8+*&Nl)U2EqD`_t+x3tVX_kBy#H2t3EJlrRJzxVs|}erz5f6H>_Xnm3$n1F-4r`Ti%V9W;sgyoPR5inn_z#BEwj=(7AOszS z^{rBt9#yFyDI^|)%Ndn&;xSm6D&>#I{PEZ6n#a}TOOC6OKOBc6S*5IhLe)9*1e}#Z z(@v-n8l8kLf%cyC?@vv$W7F(%#}Q~6{$%W>Rmzrss(P0GsVYDJr~hymrSmB@!|P5# zUWWYVlz%G-%}&FlGT>SuET9Z&JOlHcA&V&-JOgu>A)BA|&ot!7va>Kkj@*3KKOHWc zo;(Z3$(}OuoPQU%dYXL>x^%u=bk4tzP(P(sVxyps&ii)@e0<+AUv@kXeKKFZc^*0n zezZLg`@#7#?*g=NzO14Ue-RowUrxRV8{d3+f%m&25;_RQb+rxLP$;UcL=0;8dyIfzyep^7b9r2~Lpz2EgM) z?SK2%h2imk!@1L!SKzMOQ-8x=si~}c7nWI5Ipr?&k0uY^g*}@lgYH44nykDBGq1_h z_h6gU}JaO z|HAPtQ+hvyZ8=j;dkB?f%9{_N(=z3tN3gregv)WTzsy|LSF|(qTeeg*gwL-8{wydA zVgv-uAbLUg)F84T1bB%S@KawGFOdwP#7lIA@QIh`48lp3Vl{Lfw2sdho zK@hrmi(Clrd5c!?yB~b(vPt;IFI8ES`q&u36+k2nvFdFUZm$F$8|H+ZhD?*jyTe zq0Z(qKUj>0@Jle1Y%UvzhywU6?v)Uzv%5SJB1$3HL!m8Q z@5YH)5T?e%gvQJJ@#2tiJWd~O`>il`mbL^{IOu5s=R*l%s9}K|k|>r!xRxk#jSH}7 zUDi?)&-Y*KLfUdUeeZ7%!BZ5Q|R_yG^x(bL`N9z{j%sIk2Zte z>m{?Ai$@qT#WD!0X3;`4g5j1eV8_slYlbIww1CyHmkewviXgyL;^fOMVLp1xi!EVN zy`{62=x4--7`SUMj~7Yub}LcttJ2AxWxLiQSuSra+*G2KO#jx{S~}Xmx|s(r+Y%`d zuCx($2rb)+w~Y7{1a}kp{!hlIWo$dKjG&{OvCG&=K4=H)XP$hyy*LP=q=WbwLhEN> z5$1XPJ3=o&w0DB-8TiG{;yNU!bb?YoT(5gFxh4N%~F+(hb zDZ|Oz_R(z9EKMFcYfP6b?4p#w1~FNVvLtXy7UGzd+Dut9? zU*5Q3Y#>)=iNWBo?CZ+dKB5T>_wFOcLV)$43_^Ne;e_y3UolK?Q_Ba&1X-^i>~|N+ z8U0|xT1boVem~f%N6Y;F;!_9-1H=F$K62u}MVKOY4S>ZmT3QDR59Kw5mq_?FljcEU z4fv)(;sk_+gT+z9XwTRo!fb#zWvGb4xNMjRG5D$~2lSM^hKm8R#t2wYqdj{@h49g?5q2yf&sa>Y1|X`^7vq3I5h3zL0^O22$@8ZxfrL+?<=U^%v-?-0+j z0uhU&Y%LTUu*P9yVC9BT1dDvM>^W9^1P>SUE*2U1jIm-d-7s8!RxEyjFt0=mHQ~cG z9$UnbrLgxKE@i1WZy4^GK29_@7*&yH;fSm>*u)-!T!xu_*SD~G+Qk{kzIxVV^f#7LONS7@n04CqN^gmA_38V~qGb4>w$9enr%gV<(DL z;9`>a7{Z=~up^9`46F3Ba_D4ONuOO_Db5-u%X5`t353a0;Al4){!s;XW|MIwIK`d@ zE6-#abNw{f2qw$OC8CwIx!@2yS(dt>nt|MHwR7$9CFxPvC%L~nqGjz#Vk1sc6<>Ohr-y$n0m4!BGH3_`#Pam&yFX7?w6*x~AM)TACRnfy| z*njg=^(q`ChS8#SuY$!pOdeS!Jie+()6R0(YB(0{SuJK5mBh;7AHo5qVht?mVe;}C zv6Ws~hj-*W4-cLh31>)ni%vm0q#T&yTXFyjbWquvp3=Ucv5Oe02#OiLAt+_|o}g@` zOP|J1Bo$bsEi^&HJAGiH6Le4$ousFbqS_VG^*HY~@7C}YcU@#M`X9yv9o54cx4nritMg}Xv zCWe{>TM!20d5@Mr@_`b4_4`OqX|*Za!;nny2}3=C&lnmKe9n+Y@FhbN0#_AFQTZ-%=B_Zc1# zJVL-*LA0lGRKsgPP*C8)J&g}0@!4^jk@8JCs2LM_N(-RuAckOqFa*3?M6(b?>Yyr! z)>E35vTHK9;z$x$5((-sBoU-A)FWufkV=rw(3l{Dp(#NoLkoh|E|xYV?HM`{bYkdC z(3PPZft{fzK^8+Fg8mEx2nOrm(uR->*F_saki(Em;6NzCP999_V}TB;lQli1jiK_z z3?&3*4CMslb723gwZkfhQEHP{@6bY z2wv4e_0MWOr7foHwG2xMUSp61Z!oM>;L_H!tfC@sGpr?ehv9XCjSTAvHZg1<*ut=p zU@OCBf)5Gs_`j888yESA;9~^5he-Q`;4>Z6+<&g8wC$ArCBv5lRSaJde9iDJ!MAD! zQn&to2YIkE{D?&m)HHTtO5OXri?Vm?ptASsDQyqcx1V7@!2yPY1ivAu+lme&!14DF zT~w7v^ptjtMmWZBlHdfxX@Wl)&JmnuxJYo8;R?Zd9bDRVl8d@%w+JpX+#$HiaF5_R z!vlhw437x@V!(T-pm*;el;9#Y61eW^qUQd-p3+Q||1U!T!6Sws0t4JJK~;tl)L;lF z@L`A|@MVZ`k(gNG2n0g{K_CO(p#`%N#E?u7!hrW>L5VN~yb(*Yzz{&B4r*SbG38SE zR?4rb=WB5U2@Ht@br94IQ;ldEb#+ix*3(n!ZY`K%1BMJLpUTi&flEtcX-P#IGqfRi zngQ?Vf?At0bR=lb(1oA{1K!vLqqJhM6SN_~?Y}okJ1)|fpaX)s6>A_tCmqzxcF|MX z5X$b#Fr1(}!$<-Bc1scPP1Vs!B2#Ry@_^Ya1Oj624mJ*aP zNP-H6l?3A%RuN2OSW7UO0q=2xS)0PJzIywc##hF=I? zW!RfV-#=Hg{7OaEBBp3?lOx?dOq`@sCe2zyw9smMNtFoIthA_xvL zL=*hZ5KHg}Lp;F|hD3s6YWq_yuS0Tzi_|6flc7GrX@*pSvkd73=MnIBLhS+!!Q5Te zL3PJfJ*72=@_7Dxouwt!aFd}8!CwsR3GOg-B)H4ah2S5CZUhe)>;w-HT&khHNuJ8n zTi%zzi(vqPH^X28Bf~HPKZa}qe}-Iw00u`N+WyQe1ym%MVGKbi!i(6aVK4-J7NLXc z(CID{$qBw~0PmKrC4r25RWmY&k4Q+6GO=LnJ+o+n6Qc!8ik zg1QB9K0&Gus?}+ba#8*w%5SXaYj{5<%;wV!%L$q?tRQI4@G3zI2E0)d>S)FA8bKR| zH>0K<<2gBW%Z3}M(!FpOcJi(~}L0fLbXzZ2v#93~jWfcKZe+~hNyBq(4w zO`tL0-KH=~Q68>;ssk>P6myX)1f>ku3Cb955mYeTAsEkak6^|rQ^>lEDdRdRSanaYZ#gktYc_O@H#^#!J7!`KR?&Q5X{L2 z9aJmc#nfzd{T2WB6Y@7Hfo7}xEqY4pNZDH%x)6NG(2ZalgPq`GhTa69GV~>IZD$!k z@&&_Sf*lOQ2)<&-CisRSm*6`F2f+^rBXNn~f3m{7?yLqE&Fs&dUqt!48A=HDGL#YQ zXK)f6V8DBMVH&?NR1zFwm_~3|ZGWm=Gf0kdky!-C8Rih2WWYOrVKS!}@TOmYGYpFe z&M~}1aDleo9b-;d25Xh8+aHI=Hm2NldzE-w_Cg9|-~(b`b?R0d*hdh?aDX73;dcU8B+FrvXoh11F$^aOYBHQAh-WxQkic+}pcVt(@(g`eryAh+ zhqpU}By*8l1St%62BZ71WFSxN2rqYDLM}bSrVDY0O%@_nhCWD!v zB|`{7YX%EJTL!$h8m8Nx!AkH90UrPG25TtNiHq0>x-j7F)li}{$#Y1g_^<%1GuiI0@!4OeAnKR1z#e zc-|X6Tm*}CP_1}LPiZqLdl|#*>JcQ%Tq?4H;YEU17#0w$VpvSDhG8keI)n;u_`s(x zKxe<9gKE}#Ou1D4E0q7Xp0BMYc!yyf!A1tWX&tKE#PAlu7KV2Swld(I>oCfP3|soq z_pfa%c;h-0`IuoF!KVzL5Nv1IPVfc8mjpW)z9RUF;ah@l09@2TKahOKMRpSW!0-#f zPYC?X3TW!jI;cL~t*5jDRDLhR?*#i94kN(!e}Lr}75NQ;pL_w+_(KOZjU#$WJ45A< zA>@0*#|46uI;ire^ptj)vd=JF>qqCG=UDLmdzj({hT8;}8152WVYp9l4ME*m|A^qG z4yqM@=_$<%ZX}0E+)<$RKRzUPxriUZKMaE40fU*~Ap_pd54Aq!&|6_4@M4H0@Mf?A z;Pnq9OHC@`$6zDyXQ)LGz>q{>W=J6jW@tbV%Fu|wg5Xj;)|e!Mi)0W)F*GNzGPER! zWoSbX$Iza@#?X-7L1nm)WaGD)p2)DmZx~PeE z!BkJoe-`tXYTb2E*>+6L$LxtzUvC{$b{{>Z;lE43*!>u$5e#6MfdJDV#4?MD3}Ki< zFpOay!3c)=1S1(15#%zwL@7cSbdP=)V*`F}nCisluF2Uyv_X)mq;Q6n zP?O*ggN@)YLO*PHZGvMusD_`Q6wZG#<^QSYYxM|DGc+VP%aBHJ9$^Mf1D_NHopVVC zHH|BJN^3^h*BDw5+;Fk9Cb`AXj^H-KGX#G#bSAjR(3RjmLQ8Dc0~ms4J<>rn%a9K_ z#h9HnfX+X>by4|7Jq6DRRHOSb3?%Sp7(x)hfd4uKQ#3P-BnW26BM4QX_J8>#7A{gq z5W!GH5XDeJU}Y#Hh-GjR#4$`Hurc62B;ol-BFi)?QX8QQcF;_MWF1uhr06MaHf7gG zXpQ9?z!22fNC#CuT~BFlD&GWQ9F~9DMI$uRMU9Y&sTVLGpC1PGwbDUlx6xCYr0jMK zD+xLvG{*|?5o1t6CmmD;UG$W;&PDlMS>7P%&hQq29icl`iO(g2Dtqgos_dhuv`v)V z51}iT?+-&zevl5T{18kH!Z$H$+h~O0)j~VJY(1rYLfJVC+X?azN^t@^2=aAM6DZJA z+Sin=F?>f*gy2#&{76!aQixTSz!0>#Ob1nEg`U!!ls%DQ62TOPsRYv*W)RF`n61F2 z&1HFkintl(6D(p_Ot2K80Nc5Y;AI_DJ6Gx{?G?&i&9H{xH3rw~BCij~PB8*v{}d!43#6s!Mm-s;9JZDqpK$a1u;pm_#rI0smW1n@TWU2UX)tJ*7QI+0Qe~ z!Q~GNU>?hhRAd3eLV_g>FA*$fkOV6kULjb`u!i6@hSv$!LvT^c-y(U3i@Zy)iD5Ir zR)!ABpJd*h7ycm$R@}|cn+6K9znhiYPl3(YB**WQg)FJD!W)uX(g0h2Ej!ml#`6- zA`=KEGgJ~xV{j47WO$C?d4@Rz^B7(vSirEbfWCh$L3tKi{t^{gu7hg%%X&&%LD{b| ztRh&;u#VsjhBpb`X4pWm5n;00{@x?mq6Au@Cc0HmX&+GbHU?B5v<5gcJSN^pYVB*AHhGu2D+JcY|_Uq&$-wtoW*;!dYpV5dXi z$6z7|UhSL~Lpy{BdQKYA|v6NlPFpi)CTTeAONhUH( zBACK3m0&u<41!q-D0If#8Da80D2czzLJEHvg@8XaLcrh7AmGni5b#$k2>2ri1ibPO0k6(Oz>Dw@ z@M1dzyvPgzuku2`3$YOJ;wc2YY6=0aeY!B>6-)?tSrGzWJA{B&;vnF~G6;Ba3Ibk) zf`FG=Am9}g2zX%x0$%@sfLAdf;FSjmctHUITsXjh-`yeLH+Bg4h1dlVzoSCHZ;cS} zOBn?G5(J@o`QSGPNa1-n1Uz?!fai=5@Vo~C9%don;S>TMn;=Z4fO}_1;rUj;0X*1Wv>$= zLIxfe0j8qJr=D84w60cdNB;IU1;9Qs|4Gg}E-d(^E^~ zL0(YTm?yol?xUY@+4=-LK0-Ak(d2kME7H;|J53)+dpsvXes%&HHd^jEAsU#ZUE)A!JRbDv-O;E#iOlKd@ik3s8ebmyu@C1i9Zv0>WUVI`;j)|f% zE zKU^b+J<0dZz?=_x((0cD%YNK!GW#s7nFAlU6w~m>leSP^AxA#xP4snc@Z(-HyvbwT zHQYK6fu>n6;;m61LaRWbA;>SV! zaN);d{Me5lKjTLLj(Z%3F5!m}lb_+xR{Zz^KO}xs;m67?RZ z*d@!-i||C5(Q^JpkxUPEiIdY?!NXD>mJ1_Pi2DV1ihO^fNP!9`%YaKz4tLLPc%I5k zVWnr8z^m;B8ZC0{CE;hp-83{IeSwID@$sY#p1+c?Ttve79popML<~Kuqqdy!3OwcM z;Uy90!XwI5I-)?pJvRiLJqS2U5b#g}0p|q*9!enKp#%aRLm*&VA>fV)0yYu?9y}mm zs~})=AmFhB0-jh+UFvja1Q+L)mlhNjPt2ZBI0~NqSH2XF4LB{jHi~u8Su9SNu8WQL z?71v9cZSz{Om7kp>zxG|vZ`@FZ5q1XIG~Xc&7CZ-@A6HCXZB*s=}waGUo<6o`aT_S zZlAR+76^H!$dOar!;w=~43B`M7sR!d$2(eM;Q`s>d;=4`v6#Hp!5Rs$**7qWp#3w} zM1Tjrf$j`30K&q`6O&F}PpXo`bUowC~ZsQv-h+{KjX~lPplp6Lr zv(>WMbH+HJ9jZ`CzOACb0c}kvghu5QL9@zoCjD>q4V7C`LZkfEq|1lf>bI7qH{syA zWpGTmkDB1}iGh~B)xD2xv9)ep-DRpeeKAsRQWK#u9UVoE3WsWpDpuyGaF!LTrReYP zKgyABlP3=NM$3`&0*xMXTHuT`@bJDx;pW=Dox4}TqvPSBI*Ea?Nw6ux9|ve@?p5y=wsh&uuqa3Bg|>uQ@rf^<%tM$jQ2q=d$N2KVQ%eRuZDfPY!_*6 zYI?4Qy{R{h+(5qnyKh5T8fi9~GQI6@qScZ+b_Udv$0E(XrnkNA=g=Z$$fdwYy*U@X z?eKc^D0952t&e@K59CM5D(P=E;mq1!^s#>-hekmgs(kE6<-#a)47~N`-6*py7Qgej z&5rVl!m&9Oj%=-@uoxamtdwR0_ICrNA=(^aRG2PpM*`9nW<{IR38~G#G#WJ)cAC|d zU^A%_t2y2<(34;_e_iB_U$SKUY;&Yx@bWq4a3iMOp|YLLWPummq|GtgAY{$Kd|5ch zT$c*iWYr1Zgj7wH4(OOukppX4YI)9h$I|~{MI|}#egZ7#lvy=RF&@KQb54^~{Klpg zz<<5d<1EJMLvC-ZY^hSWPY>KrK*F==GxM!hvH1PY9fk$Es%Gak_tB_pL!c8qM)z*Lil!{p)y@E&a}<8;@N zGp_j7@$9{4{w>akUn$+uGCjc*L&H%rC)H&2L5G)}pYe^A*YBAlJhted{$0Ggfy(Uc zpco?#K}}a0nUZ~Q*eUmO1Mg}cuV8rjDkeP1qv&yt4ra*X?%I5h&4Gm*FN9PB@ z##4g%NixI;`SF?XRy_sD1igIy%trNp{7#vkW~wKz*YGoW9=sBCrm=T6j40!O35qr1 z5VX5$KD4EJcw`Tl2Q?ffZ*(?=8?rswyMvNajQH)>T~qGpXo`oocD#Exs6J7gG*kuK z3-VKWCW5jRAu!8jN3M(zbm}9?w9#NX2U4Y)b7Et3Eub>S}qs^ zWA~9?6b08NXkQc@1wjSyY8S2-W?( zsIgzT$M$OQYqgE|WgHHOS#9A}oNI@{%h(mvRj~YSkl7p4vgeLq8|6exLyjrN2h}M< z8p12ZZSDL*Ja1M7C$%+9Ted5tiDBAuLr4a^%VC@$BuPHZ!erpXyX}}%S$5C2R21kX|GBvPN{ZFJE zXAOz;!2(XXc#Nr*XJ|~w*5QT&(mW~Tq7knmz)mA1DI^NkSCz8Lbrm7j=<1ZYz>!l_ zQIK8k918~~ct_9_PuAp+sNgzy;lW+wnVvo21_T@wFqjHQrU^M^un8hL;ruQq+b`bp z-n@_liAE=uijEk%`~N$)gF4D z!M+-;CR&)>*fKPLGS(XGUtmT8Wwd+F6f8GqhWeOx80`BnBc3w$WQK;Bel^%{qt%na zGvGbuy)#3N-hUhH7Mai@G}Sx8%icluZ4ug}Sw}DXTV7Dktafx=i_1&ll%%9=X+e+f zcPPhHURqRGkqyWA>f_xzUiMA$^A@4;-tT+acfsq0LhE_&_Of4+=9ZyR-dDZs4`scU zp|#;<3%y!~h8yv^Q}qne@MAMZ@YoCy{5K;XPh2WG4n?f3T z-f9*4T_!xgzIc3SLMUGBa+~@Vjx8;6^mM2P?5`Ui8r4NPt*2_xSFNY`B4d?Qt8Mk^ zV>N|fiE2`3Blv!X1ncL(kB^0b>Ax}?T`tELB5 z%`1KWKb@$0<7(;Yu{X47mT?jGKoq=y+287o!?GkHtchWf=a0m&W08g)o=;kYjccLa zPw$SAA)Ed1T{>-KPiI)XH(m^c%|5J75)Qfj^Ge2+I^o2i6gGGz;y;-PP#$`gNu*M#XjFOP2vOMu7m8$4mL5aJ;CU=gRh z@sSBOzc)-4&uC9rT&{YZI~+wqKJ~NusOyq4JHk@a8?R8xbrF`hx;W(aEicKh7>oM~ zmGo~vdUTm%bPlf1Bw_7k)zAKxXq6pocNSF?s?EJGoQvZN8Y*NPtHlOS&d;`55+Ka5 zTAI>C_PiY(8=*!E9LuAruBeb#td=xGg(oh?av{iwS4-Uy(iR*T(U7j4y8Yoqs&PrN zqo4#5Q|fo@u|(FzOT2E8S5#Pr7%XU2&>};2`B`8=SIX?Ymc&L1|2x4fb!O+MR~HS=D=E%*mcw^7 z*r!&M6~g)iPxaqxv3lc3((sEVf~H`V*OyqVHLFJpEroA86?Cfl*oX=HEYURL;$JLb zE!A}W%bmHnfey-s)mKSiwWu{fpE*&DqtrPdJmMd!qAAD95|1g4s_nhoVxghZ-InMu znm}H8S@!tyik!SL*!YnB7JScXqy3hM7?mGd>>QizEG``9bY$lh<&>AJ4wWPKTcRVV zJ#e6enWryms$&=Kx5PmmoAz5`e6ffVR;+RR{31MO_gjV}!dn5_-?7-#8{Of>RL6h! zi}2Dir0b3)HZ}v7T@cL~tQENT&Vl(xcFMJ%n&LeN?^yPvdC$k*tyw4B2G8-|nH*jV z!nNe^SO@`i!;>hxs!n)K9MrhbQt-G%Tv>^NRU%Tq)rmS4zZBqtx5f;ljsQaSZFfX+M|iQov9QQw-ytdot}$HjOYvk_ zBHJXZ7pX&yIX>_r@2jbi{;--)^{j3gIkSghf+t~a#+QMI}cV~uRMAv#*UQ5-s_>ZRyJn8FM>ZbNi^7<1Kz=nTUQ&;AY3 zH&!6A{SBtN_+|jSc;tfy z=jz}2#(KOuTbtJO#h;7H@q4g099DYyn0!2g@~ltSHnjH4pJPp^4^RKcCFO%ZAUT&G zGS&24+-far4i^ooM#osA@CPN{_2y2Ztrvc0@+`b$?PT%ot}3rB$Ngii?RoUbIy()X p^{8a2gO5~FD*b`v$t==Z;Ob{Bcc`(D~hUf$E+Pfor4+;Z-jotbfF`17ZRJLZZD zJ?A;D4jMdikd_*n9NMs1qmbZ1O_~e}Y0_kHV3U-j(8hzC1|VS z7SI+islK`8&}!x_T1~p#LaJ__npGZR*QJuX`Jq;a%7u&Nio=A8=8ePZnHz=Gc5zNl zPfZ$;o;0Xoa>nQp+2+WQT7sWBI?KzP7%Z7DH)~{0&hRx~4zDKQZ-}SaHA52W(X*oJ zZoWOT5^b$7d758uTAiYr%l_taAq|{e>=_PDOV2j%5BE3MiS#g^&ZsR^GS9nM$sE<9 zK?PU4QpV`)^t4p;zOQ-S^SJjp#a0!)%{SA0AJa=o%1#=Un!TXFostv-Ypd&a z(a+p_q!XQgSMYROP*B#MXI9dPVGC$eKUD2-a9wj$u#=4q~} zl_S?Cs<-)3R(I&H!G72ThF?Jj}6Cbq#@`+a^c7P|MXNdvtb2R$5Z}sD^37 zlZK?y&7P8nIl4uCYtbl!cNw7GuNYCG8l8R@e{!FZY8nbE(dnj6mCSq7{JAN(7C4t= znVmW=+w2z7P$)4eUaQT)mHAN8vu}w_%q>D{I+ra;G&m{K+#<53`SvJ}`NL|_r3l&I zfbn3qXWpdMW@AjN-a;pxI;fypB?gNA>|)+HQa0x`cQ==B?u{YXA1>4}w-_#&yA7)- z)TBFK8*19d6y{`py?J$OpPK78_Y|ztA~3A3+dm9*L5VR6e4xx9HETkf`$?6_uet1F zunq)e^pw2I7yN^@FDUS&qybKzZ06LLy!Z$ktR}(7kl8si6n*r|vzY_FZH)C%n;#lR zTfL+JLxFC%6!eT#+dL>O@-geRTX`i4nu9SZ8db)8ExfY1UW97yGSmY@wq=}CHDqm_ zs%@GFn;#98nYq%P1lV!z@QUW#k=4w%)4c5qf;l&<0$U|YEVX3f-WXBG>=fy1em=6Q zTghazP9n2gb9eF=oka8Y78R^(k2xnZK&Z?tro8!4ro8RB>|lqS7LHFn)h_znkY457 z3VUnecB*p!bZxAi3ibF-OS4l0-rc5U+Nq56NNtdv8u@O9Ho{J2)@-fyDWR5K(x%&~ z?62O{+T_^D(c6w_p>}G1z(y_EP94pDQ|o7^-aFGl%ePZ!CWu;wo%-PG>ROzg`Y@=g z*3M3S`sDyE)=pj9<)!s>%(1opdh1v%%P#z()~nhO8&%%;<9;o~PL+T4ea&9&@>S|o z)KcuSy8F6lQ|wedk1%bfooeSWTN`et;wF^W`Z_+9qj!G!gij;2bi1%;T`#TBP7NqX z&l&2-ysdBk_T9}=x zsr1uEGAf6eUWK~YxSqBP>xrke0VUMD=~^c{)wK9it*f1Co0F@JvQwRd+Gx$}RGf1| zt#t{tV~jS>PW4<}L7Tv-99DeaqDR_9yRe`8m)a~lmG*pu*3nLlt#eo#XQ!szb=8vW z)bcv@wK*l!>2xi@PHjC9t_`zOuMDi9W$RQ9ihp%oXRW(kxFf8M*56L;EgiGHrhs24Zm7dn{1~d;x=mYZBz}R=Qu6WM%8sIw@1shQ2}Aq3|a>p)v(M* zzFJcol{32hnkkxQBgZvb@}@S!PBpH!P8({YCbVroSes;{CV&0aSK443mGfO;q}Izu z%?mxfRBLOe!d^CL!8U5)&g<`L<89QT`f|Y?ZK{ntlKZXT~nucL;BIAS}SgU?y3)V8M(%IC3p%(?K=b)#e_A&hcfH-wlE zzVUN2n|Iim_gd0@FRWW^9s1Wjx2~+vflv6--B;f;b5{QA8`<-Jp^?n9{I4HL<}m-V zMsDBikgo5v_GR=JOw0gfq4JdQ6#HjRx|{az+AqxM&vm8EUbv&&>*ioTy8Mf&%B~0g zptdhuW9V!C^B1Py)NFPS@&`AY-rQ)2r53J+1~hQDL)o7mb^g`TOf~xmZ7FV+@Y~Ki zzoZ||csul_TgeU$&4JsUOC5vQRgkqqC^!n&mF!??JI(#{DDf{Rg3TCyu2=3)cSxif zLj;X`b?g-QZ|)iz^S_8h?7J2 z^%pEuzr@Jcjr`x-$o`6MW1)-x#%`nH(i&;R6p z#*JWG@~`IT^B=lCAMA=vDDX~M%6$_-2fp+D;pWXAQ+U$trOq1vvfF8Gyn|PXZ+?Hc zp=qhejs5=Wmz;aqlOazApws}=?BP(8{@hVu59It;jzYCyLy&pM{hzs?`z0qtj6@A( zVTAQ2nA{E+0_mu1sQq(`(H}Z1*dtL-#^c#)|8Qde3uXp;9PJm6#OHYqji_e;E^84` z|Cd*=|L=MJ&)J-R_w@Mn3)TDW3^nQZ?HTN$z&~J2Xl#mOG!L`?!`=Abeqf5797fP@ znk;;DUieIKrAvzqe!p{^*yEZ{#_91(#D7@N{zJxzJxKdxoc{C2i9O={|BTb;;~hfi z^98~ney#M+_oDx`b*~(K{=8uTe^mBooXCGTxBa0DAbSe^$$SBg4es7f}sAU2hs()nF-kgvcJbFJE0q(w`H0 z!xxV}{M@e|_%V?E;=z86#;?#=bt=>Cc80S5@NmNRDh>g}cl#1Av350Eux+i2Lo9o! zi}*u3qK6PpHE%ft@tm~_8)HZGN?vp$EJVD5Cz|KsKV;_WSrQK zaQ@{u<-Lfj*S}&a{R!iQA1=WPavaP57fmdzf201o_3z(1Xicc*(+t(oZ9_#a%$UvwCGG8y$l%E1~A&3^ky z1p_(n$v~#jf52(;cMs%*KWHFpzUo-_$+Y~}z5{oAa%1tB;h5_V@yz3}J`M6SuEakK z?wMnb*zSLwQ-P^E@yQ5Ve{%fi%-7#L!d-pfFvCLJ=u~HivXt>I$S7}kXzUmLzs!}y zsLy&b>i>hI-toz(|1qOJ#vs-wV>u!D7hDJZXH58UPlo<~b?B3x4E_J=(9iiTLl4i{ zm5TN_c$jl?N?-8P|NJrj@3-(L{D+yq)8@}FHlQ}=9R|~p5qOpy@5uTCqdtGmh3DVP z3Yya1ZxFl*^l|jX%TB^@`XBbhCjRgk#^2p0vY*(*zs)8FDGU8=SM*6I47L7g==c}- z3ZDyNZT>b09!~zbJD}fxL-5lTqx6n^0L=>5MlU8$Jy9wNINM!1Kgl{zD9==yw|o4LlJKSp65OB{7M=X36RNUui*-w;0GDl;dwZVdh)<_dLJs@wdOFBlOpp zQMV^%q`$}YXP%<`wPxh-fny_L@0``uQIi{7;(|ZkT&K zS+U*}{yiu4|8>)9P8-fU773#%@`B@hVe%70dtzu!=nud1#%FEVM~d(>MsmCFm_bhW z9kXc8M@|aWy62cqP3}65CYMVfpS}MhJ;yI8_FJfYbsvAqA{o|1 zjc%3~(<$;hM>l%wdq;(Oedl zqPrEuadfScm_vI$0hwJv{FZz^!JVisa2HjDeS(V>Fj~)U$)`@`D5bJEk|MlBqmHF} zh-2xU%HnwHR~gtFp2+k%@5&LDl1d4V)x4pb9OZ za*<^)wgu+1R}WwhTZsLrj0S9Sl$b<)qJSY;MXD4H<%c~%j*k|*Q|}aDH>1VgRFn>E zAj`&>dtD8~wrE(T-N#fbA|TRiba0paZL9 zpE&f|KGuJ=%1hMs4vG$&?AbC@AJje@dEP3-UFmf*&3f_+HJ(x5}tXAwP)uIm= zRRt8aNI?HfLv3n@6Ciji7G%AisQ2^Cte{ek_Vk1xq!q}()>1_Z?gjD<)^I*-8yUH$ zyG^-TZ;*KdL1Gj=XgMSO*%;Rilzhpr56D1fWnTnIKD2_7hgoxZMJx3MS(bH6pQe&0 znHjm1bsw*Wu^-5oE+Ba|av2%b)t2@fMtUZ*wEZ!j3DFqxOZ~-I+R1o4McM$Uyu+r} z@d4PTjzt4YOcZlzOfO(-hf7h^av)SbV#fabKr9YDn9+5fCH15wNf3Cm`T8&k!@HVI z%iKZYGxR2_LGxrRCtonz`Y;*En)L@ZDFubMV*PSE1w0d*jB`^(Gj(BhG$%}Ur+$Nx zX#_K-y@SQ&l+0`}cZm2jwPK@CXDD{xT`a3(L&e#2h_(F3v2q+8PeU>z%deq^MJbPU zSnqr}nv`J>++>YOA12NbqA94gSe1Ozv81hK!@MnBT*N;DtWEa7+QdEptQ9kief(Ej z%`o<5N^LX4*oP&x-3()&eAJFIjD3tzd!J$KGl|-#3}YWK)b22heOgd^$T0T3Kh3E! z{;==vX&ww?pS{y+RK`bm`3HTqMvTcm#HO`i82fga)|p}Kb6;9-hOuvAX@eQYJ{+Zu zVi^0nl9tOb_6Z`bkYVhDJlZo1W8bLJUSJseIE(f&!`Np?wEYZYAKb{%-eM^Gq=a^X zVeE4W+7}FCUjWeVF^s*`UNd;&4|_Yi=E5-cN^#AbVeHM<)) zz0g*RVHkU}s@9)j?CqYK#;`HGhGQAZUTmmMXBc~(o;HtR?1gRGa)z;2f@v=?jJ z*ynCF)f<10YD zK4+_CF^ql6R?A@+`;4ttz%cd+TWvAJm}jW1VHo>zt+tI}d~4swF!se-?F7RnG3-3U z*q3UxD-2^tWbH1)*cWOw!RM)`TJX=)YR*9Ur)f1WhOw{GYPA{0zDTPzWmp%6wFEZ0 zz%e@`dDMc*3uPf+L z$x#ZWDWk7^M0sQLQQn8{ zw(@Xh!kcPLsLcx=D748e(Nkck8zs&b-xc~$lY-ygoh}x*KK>4>Oalc~wG1y5ds;Jp zZ;tqZ(8scMu2|1NUX$evc4VtU!{?(SIn!mm=Pc*ui}eLYm$P^*5c^xvnG3}XD{3S$ z(Ta{=Bx+Xl-ePg670p;8rdiReOIdI5%!V%)$6C?*E5vjwn*FSJ$BK?!Eq1Y@XPy&B zThab&#PL@2ix=$Zs2A<%!*$|Js{*YT^Q>sSjbfG+-Ly%3Akc^N#0=dG8g4~<`!5l# z?On1}tSm6PoaNv))?S8E(^tf!LYw04;^E(D))tJzK%Q9zn)a%AQfNaf>r392j2&Wa z16`^K`@p%l9J%j8ZC_^(Td`!}{`^C*dzNPo;n5)0BIfUk@6zHdxqj7JFZZ=zHQ_kPXg=;RZ77MJ?Yyi^`nXi&_pihg$9tY+cX2 zg6GeR?Tdq~6NlU`Kx0lA+6(`p0`>j?{n9%ejXv-J1doQg}xF$n|aO45Nl$Li*AcxSS<+v7;R8r>n5vyz>5;#eTtqKaUFKC;4@@; z=Q?m;~TgUIPiBuu%D{+Vs5ct;%Y zTg}}(`=zpU=qvFeKEA;^#E@ z9x4WJu?jVMh!UFJN5z&uMA4zcZ54wD=}k8uK^izhs=ly~lrCFuTPbsl=)LeuI3y7h zkPRG6jpT{Qp@u;^NJAwIAG}OI8s{Ljr^YhI@KXl}evCzTVGCbQr~23$v`U1)FU;1D z+}5v2NOmI(OIJ@B(zLTE1Agg93Ok1~{1ouMx`d(SGbUb@dei>P5S&&ac)b-0hnwm_ zK4p+gVQUm_Dg!|s0Rnif14uA{*TNaRj~Akw;ks6!d>1GWTxP4)T^DJy03Xv;`trBU zo$skt1j>Um+?UEWM|o??qdd{Y*0*qrQ|Ocn1cNF<(A;F39^65GsS;}AIty)bcZbxn z&nD$=Xrc$C)AvJq%mdO+i=+&A&KXo?F|spzN}K84Vw_Rpy`-SuXd~`)!dtpeADU2I zbaF>(<%=RC%{Cj|;b#hx^79j%bzj10~#^Z5u$(ny5s_127!!S|`_n)bk*^ z>|`wn?o70GD7QC{I*?}M*_Nc~b)eF%EQXK!^+)SM>g9~O2iJq(Yf6>W_BBP};biV=0t9~Ahz~#hXx^W)$e>o7mK8L`= z4Mi%|It*TBBk-;nP+>k2I~t>2`y5c=8ciUW{UI95hXS`2gDCDIQe0|^x^}nB3zj^8 z^8h$GJ9+5!tQ3M`#jfErlDJK&sRhwR@01yqWii^E3D|q7%4|bw|AEY=on?hNhbvpwTPGY z{sxn3*h3m8m@I31NWLQAp*~WY17OeqX_o+aGf^7sKpl45tOHj~QRI+UScqFvb2FgzZ|K#0kHp6sXc>hrb%5GygNhc$Y6_nX}Lg_8@67+ZQ2T& zaT6WjGFxgYFw~8{iI$@*3k#$nI&CRCN7`vc&(39|$+Rp>=1HrqXg9N6_s{|ft_@e{ zRxH8Uc*Dom6kOH$(YZxv%4}cE);5b_-IFc%7fT-q&40DO+%N?`c*9W{qqCAzDYK!I zFV$Zvy-l?%*-FE8U>`cX45fWt2_4sDxl~tRsGB8Yg*079=|Qd>WNG>=>vAqSwo2M> zMVCD%EwE}~OhPRa*GQGD^mp5(Dwa21kfN=0`m0hkOUR2-dn=8G*R{O1PKvP7H+M?9Ybw~=_2V?=;Kq7 zXoTeujFX{mmhTTU3Dd#d?}6)I7zkpQB%#mJV6beeX!F2AzBP zJ?WN}d-;9oBP)9Pob-tmJ#Yawr0Z_|5Nn8z&i_bSs-tvDlpQUtF0*kaC!2-fA~=FZ zeS+GjmO<@5`h-mYpwYr{WVX^49@LTyHHK<|h1l)ngQLaI5 zPxot}6RsQN1_DFfEcF~^OaP9e`$7#m>S>f?tms5l##+V|yvoQItX$bee#Oc?;VOS? z<(?@o@33-LR+J46da`XEa*kD@m6yEIs&Lm^K4|40^OJj7(I!>p@2m=u)#c{a913d5 z?_0Uqb>z>iT&=!bY(?FI%GS!tei*<`tjPFq@} z$ZiI%YDpR_V}oU>EME>J5H2I-Gt}Sh*i#SXticLpb&bCC+yVIHJ0(G-I7|tw{ zqf0YnTwQY1CsU3#=xD|$Im?QEHCo0h@!q#MAt9WzgjRire%Uon4itp8mK)swqG$k>BA*0uE@;!<5{MKRh^t{&RrL}-gJl@eD-Z7dTPp&=ly z=gQHRGSA2}81dRFX{<$AD)$6o!4|QOF)tw+^`t7-e;RET67p{RO%l#m|!4TE?wMuAH`9+Q2%6i;nfgHo9XIMpYO6 zuvuPhMK^Di7hBQ3FUu7TxDt8 zLS1=&Ed2pa$k zuK{qGzyoWbSX2#wh1398I1PZs(g0W>4SBOD8YmV*17Ptp02Vw0V39Kb7B&N5F*ATv z7M%fP;W7aB4K@H4Cj($XG5{7K17P7X02UhqV1Y3J78L_vAu#|J4+CJqFaQ<_17Kk= zfMz;i@h?y;_yvH-R}ODvwwJ zuy7Sdi^Zxsx$r<$r*b?>)ybWQsE{{{PeGFfr;sO$Od&HCmIB3MQZSuhonnv6baFHD zP!xnL4%JE2y3ppGa;wl5p!zS+3J{G-;wIN0v8a#z5TL5<|I|~d-0m1_bv3ZQWPh&V zPpXzr0k6q5al0G$n%uMjvl-+vw-(EV83If4MQvzbWq+=-KR59wm4{C0@DE}@eLjp# z?@&ERkKj)#4_h+U8v$S)V1EwbPbyw->9R|9Z_NgWiPIWDG7NuG`Isk+w_TiU*E;cFW$_lf!n)S$u95@mAxpBHjx{y!j;*Yd)iR zB%BRv5$_BetBChX33Z^luFhs=k#?vQb)*z^3{(zFsH>hRB|KS*I$T4~l!eTTw4QguQE!)`-ept{iq|vstoe8miKQquxU8aVtc&>IGKvj$5g+9eiVb%O#m2pa z+FeJdSO~pH+smn(+T1LPwEd-oZ8OA)u=5{j8}33We6r*yOY&Pwq}w$6I+Q`V!$Svk6zp^=`?V=2X-vW!1v zIsKI7{8N_OPgxaoR*oKa*K6VaQ#H?@vV4BZ^8YET+D}=LR+e5^6sKT+I@z+6uyrY_ zZ7HgKDXL>Bs&gr-Ybh$G6xCg)a;(M2ml7tFqI#90`jn#jm7)feqVk&QgUYrUJeppL z%C}N3x^T8lXxZ|*oG1um$?Z*fln(sq`8VYrLIhvnzc0#D7 z%Exj~8SnIbu-@oLQ6I|{>C|;uw!HkM{EaO1v#h@ZpHaxCO?TzqLWTwPh%y-2nF(37 zv&4QQA5(=`0*s{F4o1b2Vlcj{7}@d&g)B?;GDder7-EU2XzZ#AA(q*G#;uBwV~MO| zEHu(AKclndT##{=YP4>NGcCg+jJ?YU!z~Be8&A3k2>gxF>*Yp)eT|akSRdm`!I>Sb zkUu+EVb`LImt`MIaX;fHf>2FtS4( znm&LYo-?+Q*`bd0z#d7dOy|!V%UCkb8!O8~SBvSgag~EG%7W%LaS)nWlD;s$E=X*x z?!SPRJ3&473$&>3Rbvxjo~7uj@vtCF&|9?UhS9C~hEbxq*Nv|rhJvy8B`{rS)D2@j zVHUw={48LY9fXdS?strvM4256twT}SQso=tX+cP`eEqF)q60z|84rwIgaiwk*~W$=6G^3@EFd75L(f z{_DYW;b%-x?2HM(&X@pfegm*GCP1VP*clU42OTuk0ecb(Otu98urnqAJ7WT{GbR8# zV*;=~9uxO>Ox)cu@q?0yKVN9#zK)4MS7_p%j)|XROx(>eaWBWDg)sNV#GM=y z_i;@82xHUJ#l)Q&6ZdIM z+@&#bk0zfxG$!~naDDQ*KV#zVjEQ?QChp9b__4&qT^SSiWK7(VF>yb}#Lpfk?!}n+ z<9jCV!kD-RW8w~siTf`m?!K7#nZv}L7ZdkgOw4u3LF?HG!^Axo6L(xp+;1`Q(}jt9 zEhg@?nD|y?;?`v1#+1)(DW98CKDQ(jH>7-SNBP`U$>&y-&mEO~?x*B)6EblNYMjq4 zD4!cpKDVEIZayaNq2%+Y=7#l^Di!S9fDQU$-4$o5ep0JiujZ6$V-q1B?IS`g+0M@U!|$esBdRfrBl92 zd7T?bL4HaT^6|qKkx2o5N;{p8M`T#0g?>sioe|0yHR-mW;tj>J{z?~szW!J#Gijs0 z(n02RX%oW-%<}`;gTgM!_@z_>Mb<^_W5{hBGY2LssG|5I;f5+o8-TBM5L#6U#1m#( zRV7RgWi9I;Lz!n4ce-%}IqmycE@SJRP`X`JNszcwGQ|cd6)Dfr&5H(pE-OsYiHfQz z?m{T7t)_GX__i7hAe3UND@l5y4BAi)c67VC;-)jQ=*MbG1Y;=FxrS0!=Z@C%)~YMD zbkU~IWxQgf8jLHHcGXZ?38BR`m98MV)r6&m(#o2cK%sP{rqWH%VW!@XF}0L1@MqUj z+R5xTNt+WxrzWFmw`*bZNhbf=Xbu-Fuovk^n`iT&5742mG6-OOT_qX7wVslt*I|7OCB`ZNEJIp$3n#aI zKR7G0K2ekDPCX?_*V}3xxmh=4A1b;dSFjC3GHnRJM%#}b1t{?V@%1r{`_W7Fm0?mp z*2>p)%MEOx#LAp;Acm$MR($B0FJxE6)_{E4(*V`ZAfX}pGJ|3oqS_g>v7wR<;1!4> z+R^YpWeC7~fyx+w&Oyp}sU0igQ@!Drf|OL9aYbhgY@`g;8CP}2nPy5ITKNdk{=mjc zdF0W$u`&)|cVlIuuK2aC7}o?1=Xu|cq3f-cFqWzw`8UO=x1-ukm65vo16{pAq&45- zV9cR*a|P-Q&8#!zLFuIQyJwh6=V#?sm_WfXvSxRMDlCmgll`Pb65{36h) zu{1dXOM4cbiNMmHMNOM4!=)@%i>A8P?&h#V&Ir*NXkA~O5w0`p1fyE}{9L@~;`i(( zHw3+sMM4Wj14wV7r0d$Pb?pkhZQ2d$7>zV?m?G=)cDlT7XI);CHb*H&ak2VlvZT3M(`uqaD7Gccd+&~RD$%sEABJHFecl@LhYQnV>2@Ag zw37`nk#jwb{uJ|ri!<$Qqeyz{4B8W=gzFijChj^nOV2Z=tl~v&$6tzb$hS1{n%5XV^^+q0zaujUwHO$I{2;^K?14ZZ0nw?hxA35j%efHSDB}09f7$ zCKp1kojJ(s3_A~@Yn@@wJf}5!PU&4RkCC*Ctp@9Le!azLLAS0-Jt2oCc2#-7x3N4eLw_?59ja zMlbhMrUC@_SElQUy!Axy^~Z3}q^JSPM1WlbupjW^YUtvaL`)#gsI4=wLGbeHQ%MuK zyq+%aHV`Y~Oxie5$(A`ksJlLMB9gGB52tBK$^?LiNs38gQ5r2=Pq%82lBF{ubw{>cZ@xxWfu3<_WnT3|LtnPH9 zrc#dvj>j)7PW3gqmbIQGxO6n_9;0}ZHx5U-Mod2}Ei2M-lH>Am-Kj-Y#gC4sD^4=I zL(wL8*AHq+e`8HrJ{^jjoonsFj2)Q2H8%=V}xV_AEu0y;JVhjy;8nC^^Hno;)*rJ~MQsy7(Ri91V$({%>yjO(tT zu1xoChEfiN$Rm{w0I?&L&H&3t!VxK;tDtn{jdU?vX;vnA7(jH2OGnI~d z^(@`VtumJWIg1qB10~@yty`8-LC;{PUXdwTN)Xa)&cd0dfF5P>LOYC7y6Rd7Y3_K` zsd$Ld)n*(8bbS=o$O4MY=AbB>mwGN+X$NMN(Qup!X!K}a-~FR;q~eXc*qxI6QI#mN zS7nS+NiXEG-bvlYU>CeJ3>)y~F<8qA=+!YeuN6?)vAk~m#wvYu{Tr-OQFLp#Q7);J z(>SHKUewp!ssDVe1OrFls>Hw8sFWzZF-}R;mA>mvcRHg!I|mwZRy8VgGz0fUUFTz2 zD4<#6c_S{4R}yq>L)>GvN4~CfE)!R4xf5VRym@%YrD(hoOue!m@2C$Z;J95t^(XQM zOq!^))wSK?==MlxKbokxLhd?=_j;d6@JV@Yo^kY1Hsl{oQk)>aHc9EC3;eAKS7u`u zoG=-N&GoCrQBWBCAWWk=cx3c0l9N8{nJZ5)RlnloSZcwDQouEkx?WfN^$59g~!>(M8V(sPtf zdcmQ(^2xKv@&f9FE6dwCSh8ADvs`5`!17$2%3IRCTwI2=q?oD7Ac;kd;q~(-Z1zGz zpQX%ra*fsObFC%0)Af#{xiJ{vG82te$#tsHg$Cxq9p5kw zWw)f)rzxX#y~H@?UU<>^sm3abB8|v1s(@=sqf)*z zl&ZM!j-H{!N)fEa>AEm+HH7D<8@=fEG^3X-IoHV0HRdo^{={e)((Qa)AloK#1hsw| z-Z$4AO(P3%sQc(?rJ~Fku^IYrpVf zu9EqSyI2-3V&R(IbFQ1Jy zFoL$shBpvFE(JJ1N6;_;y{#|(q?Tn1l`?u2HpWqvK#XT%Hf)90a$q55Q3RzI^30AF zDm`f5Xho#zbKq`A(4;v^KfUnXdT0C1#aV)B81}`{@^#4T`#IQ!%9xE#rAF}$Yt!cP zy55+Jt(>>v2xYRq4qRYVoktHIoSIcKu$znE+(uA^dGHcZ@;vwrJjr`;)SxK}>O{u& z)NMX?1kOHd&q<**^D)ENNYjq_whCXQsdG?LKQmTRp5U^c;G@MQt55`;He*UfP{0DE zm#%e{E>FNQgLlrc1&XS3Z|D}`y-;x_mq{omXdw>s5j1t7(p|6R{hxHLXEBx_Uh;Qw zv|=w>aCMonp>4g5pee;zLAm^q)iQ1sGfbs;iPA%_ zNo2fzE%&37&*PxG!NX?V0gubNtuZ>4H08k(cw@YRAvE$O96XlIL30PLGCET6QpH=( zJ3LOE`7HQb9gR=F30uKT*_I_slCf_ zc3@A)Xn23c3M?|`k7Jdnze0(GPWlR^v5-nDSKu5nkUn045oG~24eJq8jbDlNe+*4o z32&G^PQ#WW#*cX(*P#gB)T6>@VOv~;HHeA$y4vqqB}C_D>D-OaV%_Fyqjg5eHa&;R z)MAxl)J5ZTQQ$6!R&K#MH+PjS^NH5XSD~G~>C!6Pdh}-X$jd8`TTV8xWXJ=0WGUdiHB5o3Va^^EEmnQi`wXn1v&$( zbpWDNA3TpuxfRu4gQ?hx=B-iEq*knM&+7ICM+F<=3$U41l<|Tx5a84cSUFlzgS9w& zx1z$eut=U_v!3tWwMsMa171`H==|+^P7|5SxambDSmxS0;@OVkMH>!bFLhm~)J2-` zbxJ>gMeCFSy3*_Mv@r}y%k~;O(Y}{(3~EKgUc!9mSsd02Ogw^&U0=Z+;H7;=f18Q6 zBJcIMj%Y;%>v4tKo=&ev8`@Kq4KTU(GA^<0 z`iZP7uG@RCeDOgH?52Yy>q#PQ*@Q|blJjP4E?oVdu3ofN(bZ49ua>nQ0TOB7W^`U6 z`EF4{WcJ{e_9$LIg+E-225dfJ^sz0fTxy^@TVR$OEQ$x6I;V;><(xgUFuJ}4O$(!- ztx6k-t2^oHC^}kaIO~kzFC+c!V@CTWU>Nyr!%;qrhHt~(%G0{*su#8?4RnU*V`YaU z1D)$*&#^I*Kl?HcwqbPRW%OJaMZdzSLI7PkKv%|@s61<%Na}W6sPN(%>Z0)NN?o1N zNN411SL!3H4cnF0IzL3`!yWOUvTqy9+Xgs{A}tspo>`=>gKi2$+KUzpU>MbZ6<57s zl=&)#I*j(es)O80YD$ggzE^4GR;&cWo&1GYc zV`mFtBn^KJ1I77iI{(6JI8<;(y3WAL(^O|<=!~3QiU)pYxM-IWB(a#WHjXYGL;&@| zd84nb7dw&jZfp;oXy9(_Yg}t;54;ZzlMaz(-?9w`r)AqV>`wG(H?|F~G_wb7tY%fB z$UTaYmg0{toAtQtgAXykZ|uRA!qXM@U}t?Vn)T6R$JS2Naxad%ooMl1oIyI_H|0u< zuD-Yj4^8act5m0k0TpGMvkzyiPPA;F60d8l=%KG8d-g%4*?z?Z(sui?1a+mH{phl; ztl=-}9m3bojt44Mq1zwhSQz*^#+}R8+q+byzOTat>`H~NEB$rhR$Vyq6qXt1HxP~L zO3mNEWalYg)rA}0z)m!ZF28}>pivb4CRUzNwB=3Qag8G116aoS8%Pf8%G|R)|0S*- z124eAYDT9IC_SZStfD7r>LDeBV!p(a3s-UTH0L0y$R%&Hw7#_SbNFQqDjcKXqVtzDKtq-vRy^d1jC-vIjjRgy zyU$HyMHkypv&TQRTRkXoBP1?IlwgT-zS48RiW{ag?&%EdJ1Xry0)ymg-|AfWS%~%@ z#XB|%kHSEFZyU?m<_}lK$37 zjJgn^JC!zkWelMokD-gW$VnIZ9#>lF3}>CeYd8Fs5!bEv;7%_;4iBXx?LUsV5>Hn_ zSL1tsnR~_{o+UJLPGE`bNGDFpf|+^bpd)*ee+S^J~ zJbtv|ZQR-nteXimTSoE6#cj6ZIT8ckcU2MI?OweI*2snp}Jz zRUSqGXK_f(reSAsg5+=CDbV`?PPZ=}H@SIM2^RWOjdO_l^=JJ+x)^R~u+Df!@BO;6 zQnpx8WO{H8?JOekJVvjGx}L{3-u1*!n?_EWL+U5_DDOWqf)XJ0pMWsgH7A)O%JnA$wXlRfr zd%@HN+C5whYdaHJ*gu>}cb1Zt>1mhp$o^ZUr0q(iX|(d9!d?`(hk-Wq0Rwme1p~?C z@sZ-ww;yj}%;SZ(>1EHJJ?g6+DJ5-RBF$jZ6Q!g*OQh|n2n8T+wvmA>I?lily2?NZ zezk$Zc}!q)?Bj(m?rkre-=d!_C4IL<$}gJPEqY0>x%9E89ZtC@02jB*87QP(3>46L z2J+}zfWEU??VHCvo7Xl08lLy5`~_Ufa2ost!sp@i(H987gj3%ym8Std{t^+4aLT@l=tejhuHp1N zmSV5LCmKtI*RVv5rAyZk;~Pt1*Wta4rA61_ERLlI*WpWzrCv9bA;q)Z9qEl5N-{_4 z-sEceH*v2smTuof&}S_5y#?=aEbY3bq&o4%<0~v4+E)~GQ|Uw9`K+e#2$cbnwA9IN)Vc#J5U9)ouW97TA*dp?QfN2 zC$83#skNk2<*L3Hs)WjdoCVt@zH2M@Vsy+_!R2T|c8bOB!T`AC^1 zjes!~R9e_YbzhjL#tI`AzN<#zyP=^T9iyhQd zX*x??m7*O~yv5N$od~JwsLlkK@2I8%eCw!=1V|Ir@c^HQY9>GrNu8=EtwIm@fDC!)5TvaUqDC?vy)wOEU zR3{Z58g){~g5R|a&+>E`q-jG@W!1(2*=5ymfX!vqZg{hnv)UR&8)r2OJJPGpY6!p` zXLS%jyo;&<>~&GI0h+j?7eZ-?t2zuoc2h?KOm^eUPu(~(rW|J;ET{H?tV($<%P)_v z2*sPF)xH27E1>s6X=?>_D1cW*l#onMS5(^qoT!Lgk}0$ja!IC5mDEW90q*K3fR*lO z5cWzB^kg#4*Fl-eYA&e4%IZ`A7f-baV5O%z8z8_-%>h{N#nV;y#z6F=h2CnU!nWF> zLE4Ta>gJ;+i&^6rX#J?Q4?3V9S$r_Y{V3cQ{nn56_`(|U>5;G6%7tygNOdUbsi$Tx zCgumf1~~4IdSpx`XWGF4KBM^LtO=GQBC!EfXG^$x?D?L3TjGi^%;Q5bvU)X zj=HQ&J5*0IV3Woly3|!|Xg+mNEe=o}X=z=RJ-hRMT~r{JYS&ZE0K4iTzgS8PKv}UQ z)K^~sc(Fcbc5a}q26ejuY86Y=kW=A->M~I00@WpD_%k`xSy|O%X@4DiS+R66UNu6i zd?R(aGuNuiwAdfo;;2?4*CuKey59($){~^hYG0?GdaqU-1dT_sO=ERq)e9PX8>@2x zx;0S=;7SwqO&nj=H&uHn?0FgtM$JLAw~N}4T*KA)vUt{>^+6+6Vk5c~rp};q-PE#3 z64*>#!VW&o(A!xwJOpjdB9~Bgp%YIL%~BxHouCG?oO;mSW~#`RtSo9CrY?f+nJ{%} z8Lr!r>2}mxtt*>qs?Jbxi$MQm(WVI0HH+FcS0~_%^?Gx)J%CdrOkfDjiB#tS__u(W z4xueA)M9}4QR)hSFQT|?LNv-7Lgib+N`}yymg+))h*q3B*$PvA2=!}?bzulyX{{DG z4PgzQ!m2cd?sP--FSS%V(s4W^4h=(F%%2d-X{)lQW{$Q+ULiEFoth8uWjl2Nz?Akd zh7c;(L0t^6z5{1QcjTJycSKcio85_1QfGCp6MqzDH7jm4D=x?!b5x?e?bUXa5u}Q` zcnuS;!GkpFWZfD<=;bbGa0s>Os&>FrEpxk~PjcvNS4__w>eUU)SPot3rWOK>jzN8L zaQn}hi()bSIaIs5YI5Qy)B`N<1LV_E4K&AG6KQEYiXIrEmeIwBnfNd(I*4-P)Oe(Q zFAl454)u>$O#oNo(RDeL)kB?vr%>+nP&>1SQ4-K2DRd|S1D8UHJuxaNbiXHNRtlMW zsmq=C@%IYL`wGiDkOF(DF|HXIDGT_s;o4Ouy2{Kamm(Xeh(UJ5&E$YS=*$%Qx{tbq zGCF}_?#mgq`(ZFs=(T?6lN9RQpHm<8N4ryK)Br4zQ|ROXbs#`sBKl+|?MYPQT-YHS z_I?=C1dGpn^VCgY#Yt)}hXvY9dS#%x1$sl0)Xe}MgVgN+CkJun)MRxls3s}uD*#th zIJGQQT@R|`V09h9y}_JXHALM2DrP7q{Y?61C?@?(T9T#?cgH3^Ss zOw*A2aB4bC-3M@gn7S8Wd%Ai6VA62)7(n|G>e~Qb8C>Oq4E1$TD@Lk^0ET38&@f9q z28x*y=xQJCJtX>vBFT8vg-1NFmbbqByJV>p#LR^0`v!8o-AJc<$HusHXnP2DR>TrN7;~^`g+7s0J00|Rd42AU41gz49^iZebCc+L2Y3W3D034DZCaRGD zVUyH;05l0p6Azx7+2cWGx;aCQp)Qj#uL`K=WNe=WWS)!y3+TtmYKpG9f~l^c*gUB2 znxdA0@W>Q&R{_<_QBwela@1siyE@f77xgZn9l03n0`i=S9k+lcPgRHNx$R`R?WFzF zky})rDnZyT5Bq5WZOr4Dx=!QN*lE}^3+R(+yf$5?b6}ayGxeCkix@Y9t9&|x7vKJA zHLVP9+eKCc9=&)pTkS?=W~%II1D}~ZgPAkcbiE!|m=c~xWv%L(&s)_uA6sPsy_L_i z4Kv|TQ$XuY>S#UN`z*!%?lg6+>O)a8)JF8tY*nFS^AKAvpl4=b@)c0!+3GM|{Si}t z)Saqqc}$&Y-kpuJK>;Ne@M4Y?;J{EoO$vFdmlX0*`@WEOY~mc=Bm3v51NBOK!Xwl? zY0G@ok4EOHLDD$(BuoM2&BalqfWDZ^lXWRVFXhq3BG^hE-6}%Pd6X~@&B>#~^RSiY zQM36ti{;S^^U;xcB%3ivd8C<x1_d&rMD%0 zKsZOVq@jzj8n&cw7hzRsNl!1v>fDn2p20HG5>ZSXa#~XDC1^qf&0V6lRhVatiXBO& zqp)kai4k|>p{fuwyFbY z(sEUWaOQFx1M%3va&!p-<||+X5%kdto-lT$nx!Y4#S+e za`Nbuqy<_8J^L(MbUJ=y$C7c!`=);%P834)a zu|ebc`}JH!+kiogpeq~HaR381a_0Gsys@#Hc%BD0VV3d!xxuP(gLQWzE!}LZ;BCge zP0{mhbw9aP6|v?=P^B%rwKKNx)_%PON8|_^vXu|X`K_4e5fr(Nt8CbYSrkDYFZ0ee zz057-_RDZMBB=i>m|78ZvYc zIC>08%}yMdl$A6bCoW!208>G*U02zGbqU6@18aT+UDiRBo$wGMh<5V6b$yM?VqW9D z@aAjkWWA`?ET7i#Y$1xHmAll+NV0Pm)_DZicd7LNYV5`yFqcN`h9@+a_Uy(1WiA=_ z;GjI0dhdawGMD!6fk%|eTmWyXv{&^59=sQlfwX+D+7;l&URcXOO5KOWWDFhL2X|@= zIqgRU$Iw(Ae6t?|GlsO+G1te?=dWY+8AF}kz*;nhw!DF%=uN(FqR`%y@g^*(H{E+v zo$SQJ)iYQdX3+8@Sjko%Qp4#B#Jiz!{{SqnH%&MQtL#lb97N8&Y3v~mzCMIOZbf|# zs~r_~zlCLBRSI1`p~e*-QOiM6{s>aFq6J5AENw;ZM{$zm_h8Sl!k(kpx72uOx|V5> zn3$ZDo}QSvKx;+5$6&#TMjulrl;PSNnD&Nva{CNx-oTS;7@d60Xf!}|;&D~ebKJ&s zwjqjx>PH<_o6^1$suOiP!K*Ro1O}`XojC#1Y(?$f;`#4?3$v~jMV!QH*NTpx#0a;d zHm6|ct!Ue+|10di!=tF8hJm|bv)Ro}Hf_^;C;>tXNPy705H=7>q=yg^k|B*Gi}WRQ zaRq|Fm5#lkASx?}G(|-~nqZ+90qI3l1mQjBp3D)x_j{iApE+}X_s*R=v(t8V_6}Ue zb(U=p!3um%zJ3TkJm}-_5NyQIA@5<>rJj@f4#R2UIobS(3agL68GnYneZ)HjLfs!> zz08mceuO%+r1>bE>a%3-QCMeL^6XLg3YjHyj(IzU`q}ypw)_sRiwNl-R0E!OI_911 zU6fr~T2MTCu?BDT_zAw{Wy#||!Me#sk<7vNOc{1@*`2(Dk? z&I0@n`wKJ%exCjnww_eE>{stWLj3?505|cp0JvcXYp~BJmJrBHKMp6fRJq|eT(_i3 z&l9Sep(nftL2A|%1&A#Nc zD*4@MIESUmq%*3{>1W`I6Pk8LP0;u(tP*JNS?>YVG&?rUE_a-Orr~eH9$KnweNNT0 z_?)Wz@;UEeFiE%bYJp!n4|%2XpYz^rAhfsuvnqxAe((X6O4Dz!+@-RZ!m;0A2}@u%uAFsh)o-S)%hl{>+nROkeOP9N^!&xs~wzvT$)8+CTuz=~( zbQ3NT(q-OF*gDhY-kY#JmP4=1un^^P?k(6|%cak4I9->^Rkz_BUM`dFz}dcBesTwn z4lQJzyWU+P6yAmHzlFSX7tSRuWW^uQ+%EFKA8;)1BHP`A8CA*m@4;QEDtYZ5Y=Bi# z`x7oCs^q;t;UG9s{_6uBh1&n}t_$Pi{(|M1C{OawSWh|dF&t}p!aX-QSoU1f zPjoPhU$R&yDZLj9R?vZrVdVXG%h zl_`TvVi<(6CV@ZL9WjY4p?+=~2KSiZ?>N~OkuKL=w**1v8ZV(i@HUHb2vaHCF^f@< zdfp<&K={fciXk-hRw%ti0i+BVWpF(QvX}<6W(>B#pxEi0_6VRD>RJPY9itH^=i=LaQ+ z$x=Tt7=D}E>j&#`p)~nJoeO22zZebS2Y)EJP&N$^`S7FLiU6pympl<5N+H+-p)I}T z>Oh#Fw|o=`E2e`S5d@9sAXf#!vUiXHHYf>i_p!kW=_9Y(L?;MMf<+00wZY;A2w@>m zvX69yK-DkG{UNZHUzACqqBew#P|*&;!cZ|3!u3$`stG&PaJ5}(H?@;{!o(Ed?%`qu zg!AFT2_Yu}*2Rl*O$5wwfb@sG)kmHs1XfK8~}ed z74IAR!{16_g8uTo7|81{|BeweAxw#d8TFSBV#O~ed|QYMp7O0Qb(3-Ns<7X)0xpK) z#SlYVIXFQqhHx`Mj5OhY4n&M3_<*mER9Z90K{I!;t3mG}J z#ZsD}o-D02)tB|^2p9OKI^tUhQxnB52p#K+Q}AljxO!qAgzNRhXbb)m-<6cys3Fbg zQZJD#-Sy!^bI8afaTdbAN#Z<&vkk;f2yZkLA43?>2$swNZ($NC(!a4thK`FmH5PUV z3me12E|S(JFylq?SQFT87Rf^|ie9oJS#&Th!udtX$J5{+l9T3%1bIJMjDr%TO~nHU zh0Vn45MY(egz&hJfWGLT6-{CM>a(zYEW$a)%i`v+AuNLTtievuPR6B(c@REGfprdl zEx}oJYazP8c%N5AUwN_xth#nGqosI^p_NzyLDei$MPnFmoeD>Vc3d;`%aID(U_0sC zS`%rEsYg*yv0h?gK)hatg9Zfb$juS2|GG)HIpBIZ+ccnbr4Gky2#3Xrmpf~ z2iQ1!$X7dxV-QL@iTx1Tc81TdhugagtObbnuCOx$ztm0Kg5>1xa1esz>gU95NG|Ll zOqi5C#hVb?q(Nvd&!&k=fqg5$W!u)#a9cDkLqy8Cy+lI`>eE}qB$Kix^29|`vRq~t zr37*C?5l{AjnhRrK~ve}im91Ao-USot4X>yZ`81}?ATN^kVAUIIb&CEF^h`E$eq_s zv9hoa>{-p_rao|dZjPTy0c9u2+qX>(u0hU#r<{m>LI>-UF)ZgW}LANH!|^btPj54*G`^9G155aKgMrb&D1BdjO)Wx~gz z$%yBLoAOei^GB~1(mGJA0sm;AI0Iq9AaT;5xr+u1s{!KVAtDUplA$8N;H9d}Oqacf ziA-5zIDAl=``~a9izRMl2?0?)93i$sa&xvYB5xZh#$s$T3Jz#A-@$T0qSp{<&J!0P z<9Z&f9m*IaiyC?jb}z{nQ8>xQ0`VT!ICKnb+z<-kBiCelk@yU{5Hl8w=Gc>1v6yDa zmtPc%A0W&t5koB4f5sCZacC(V`tqeL6_*Y9?rCF1OM^)j=`0s^_v#`&%EbaN1vNX% zqHEDtBjljf7tAWw!a67XcV6VAVRveXHyIZrm6CN`O{j}TX=Tv-5r zsW()@dLAezRKlTVpp2RhhmC=9%yjVzgvZmNO#|hs8Sudkl-f*j1;T(?;)V(P6@dfy z!&#!6Tre9xv2M~Z2e!^`a>yK*rklJ!2d)RY$)R(_Izx)Iya?xt6gd-)MiAhjG{=;J zHT-wzdzqk@+%X+i;EO?9Z?Ma~ z%f;Ie%2vQdf}NHpZKdetY5#9|cCLi8gq=QW*DCmU?efGb;r3ERQedsGh7-}j)uPg* zBuWnZ6izS|Yv7Z%%WG@IMtTb!K6f*Fv6eFeE|2g*ocv@+o`=!%qZGhF=LP z8BX?w&Q@R^Gg!`0ky#Aq3Fa_dBzTeGcY>D~t`WSR7I)w8xaalEDLh<%fB!X7D6f%V0)uF4kUW5me+&1}nka3;_i1 zGS~>-X9y)&#}GmA5koY=286Ndr=@t3Pn77J-)DMCt3}yvhD3tR4D|`VU}#A2B|{T} z?F`KboI6=kNWNldMX;LzpRIs(`Yl6yf;|lQYz36q%g~izKf`kb2dV+y0+>c}h>N5X z9AU^HIEpY5){k}!#$YzT=%6-+<9bRvLD{Dn&JdhuxIl18?SHDu%OqF1$TfnS47UjG zGWqqC}Sum7?%ylUwTLBFsgC_7HI|ZP*qOSQ`!j1p2CnrpjIE2 z*YXG|xkLfM42D92SqvorPT2hBu#{1e7a1G`FENZKc$r}m!2*V<1XT=`1g|j6Bv^vr zRE?iQBDu&sf@KIfxOnpkR_dVE&uTrTRZ;d@hQ$Q0Gf09r8I~(>YHzcwq9X4ytR;A# z;SGXy3~v*B#PA-$28MM68yVIUd`f`l|BWP@xX5P&pCb&#rQb~Og$`=zztmIOR?6Pa zu$^Eh!!Cla7vxW@1&!3~D{1h*I-65L^UOz;N-J{$#W_fG`%Y?6txv zR+C8D!5kYfG^g^7 z7+NZDYE4*LQ<0_&?FgP_z=yS<))a;=1T7i56QnZWGg~l88wNW;I|AJQ`;c_tBK-(D zA*iRUo+s$4gId_`dP*Bi**zGB5u`DUAh0v!5cGDk`tc{-@ok*}wq%Oiusj%X?I08pAq* z=?LoE!VDOLN@nYzmVU0D(mtW`^B6V}xEMAQ%vYeQ+)A>Li)<%Y#ITECF#|ph2d!Aj zfDga{yvnebU^&AN1S=U1WzhG})hs_!k+lf?&kv~e4IR|NzNM$M<5d0~hEoLZF`Om% zfZ+nchXlC);p25MkM꼬RnBh9XCk(d;K4Z8`;AZ%fU^Bygf-e{zBEb3YOP0q} zWIKZgJe~v%*@=K1G;6zH3}CkoYIS^zDSSvz^(O_{-|L{V_vtCko2vVP!M86gKTL3t z#h;2CW(Xqqks*ZO7(+P0&kRunzcR!UoM1>GIHmSK)$-aTXShgRf^!T>1Q!?@5nNnubEwNAtI6m*QGvLhHA1W^p*31Sdl#?FSJ^D8w?JW2JHXH7k&O{47E z3^NE48DTV;bVg57(ONF$>1jF#emOuLd()~ z@cgG%(3d2AxX2EIz6@Ux^k?{nAd_Ja!9a$61cMn45DaBF>?9e^a+F{M!_NdG8IBW- zV!%g9VQKOh&JyG^Tp-XG@WE1;q%a4!KeYm`kQ8%~>jb3?w+PA@?h;fm{7Ep5;Xc85 zhKB?b8Jv$vCbM|Jb9d0RsSKV3(-_PI(-{Q8Oa?2#Y=!`WxePXfc}|v45*GtL#|jOZ z&k#+pkRgs>5kpOa#R${!2%JbDbx`}=GCif$??>mK6)X*Df>jJn2-Yw(BY2G=h2RZ_ zRs?S$sQ-7q4P&q*@9Cgg@d2hfsmmYyhbQEJqy(C+@;B%ytqWyuWav)tDML?!O$>H| z&l&mj};M~fRNwSS$5Wx)vW`(!%cN z{6fkEy9!6k-Q2!3aPZb{WNS6P-3TxVEGaFbyT!EJ`u3GOnyMR1P+pBaXh z{l)MB!F{#=sTJ@M$pbF(F~PqK_%tz8@|XdiAqFsv(m&iS1T`4GB=BU|LExo>Q~Qd< zqKozofneA};LEU&z>nboK>)*Hf*^*Y1i=hH6F5Uzj+2BloFa&1I7<-CaDgC};SxbS z!xe&>4EV4!th3tH0Ovn^&>19=i`*rs$M7dX62pChh71o08Z$g5NM`VWM>=6H%@{lt zIJM?1W-8KxK@hZJuoARp2q0+7U?XVHfKOG!d^<8k5OgNM^B+E14Mnx;ryommDl&kfCBgFytqBG(v?Cb8(2-ym z13rii4as8YPLPeTKpp?nNJc3!Rv-WK^puuP+4&3^1R6tsfS=N8V*yt7n>wgwy^SfS%3neG@9O#5YJ&F}UL#n?fRC#~l^-#@L$HD2 zeS(b)_{=&?@+rfH{`CE86AM1F4n;m^*hH{}VKc#2hOGqK7`7AaVAw^li(xmx*8omh zLEn*l!$tNIe8=zu!S@Ky8DVgkV80G(ogUCr+EL0r#PBo05r*Rku>T)rIYmW&LYQHM z!8w9obx`v-p{KOpDEkyb7MA~=;H(a+{CPd4U8C&Z7;g5b>(5Ip_~bo&0KYTbBe=@& zH^Fs=2Lv|}QjIWpOmIgB)rvp#l;#1Cki#tgRG^MOo+N*B5i`L*41(YxgO%VB13t(P zwLX)px57r?!4OJdWQYL3`yVEjXewf6h$HZ3s7c_%P=~;Z$p#ec4Lt_FPf>W)r zrX(R;q&YztLra1PhSmg84DASF7&;QfF?1nFVCX)8_P<&zJ*h|?2K*BOtfIOMeF*9^ z^do4%kV(*pVGuzRhM@#a0i5dXO3>|`noV;oQfjO|1GUgoS`KBmV#p(C%}_wlmZ6ZK zJwpjWM}*lh3#}83;r`cE7qy*s$JB7le-85(YiT;DY&)jXFnc`J*GC7H-B(X(_(utt zxzp$zi~hBLfOFoIzr!AOQz2u3k1CCF2t_P=E$`CMcr zfyS_gppfBpf?|YpTpiex4lM6j9n|V5*HhYilKOgci7@*zXU_V}TB89#wiuJ3!g5 zFdQaWg3tvkSPElM!7>CVimG7+CaW<2SDIk84k~-Cp3+WI_UjC12;M}Ph!wmAV=#kv zbx;+&ucx$2RDKEt3e-nJk z@PJ@@E?$4Dc|0Q7$wdtCUkzA@uMm1-E53#?fNynBt=OZdG&7ao%OD8$Ggt`@Fa%`M z@$V3ejfxy$2qiem5JB)0Lo~rJ3~>a<5yoS~YZ08%K{fmgrEvKZDgT_Fuhl2Gz|fH3 zB102`%Lv&x59|*LR?bx&)I6^1DXj%%-(*N7xb0+VOLCW?1HnCp&IErkbR)RW(1YLs z!hCGjLl}c*J=Q@r%a8{-nV6lCN!K4nT~xkFPeFHpYH~Bf^90@ug9&^X@P9)vM=Qe! z0)K`af4l#aSZrxB)tAeV3|rqY9Wll z6*Qe7Q3tht>gg$M7G)E2`$~Qyki{+nn(gZDZQ4_Sn)G*A) zUc#WhHae*6c6v&al-+@0IYB3cg;)W0BnB09)j?IzT~BGRIVryf%bNsg4DS%w5qe>j z*!vh%*+&OeWnVp|eMH&)5qe|!0Wb#T2kM~855`mneA%M5i6$6UEp+h9(o@=I%Fbrk zN|1xl2WPN@AWsK1gM2-ueMQ+C!#4zl2u@YQ9+F~|7qQ9`7=t#K>7c5t&{LX&vd1$_ zAehWBgPK6f`trK1d9;{U^|x(ysCq0=W;!zt)T4H3~LBp zXK=nj@;1vm1n)C^K=2Vlf1K%hf{i+;nSQFLw9hE}bB4_XTN%D2*a5*wRqiDDnv3iv z_zs~h&U6pKJ{{Cdf6!Cf0m?qiaD?C(!%qaiG8{*M>%UVhr>V#}hVuj$87>iAVYo_g z1Hpmqyh(6J2i49$^ptjwvj5h>sof`esEhV5!87ppC#uo_W3VwA89WKh3>E?(244by zgelmp00Ns1s#zhUasOL{`Jt2_f%yn3KT1z&(Ucv>5KmBxp*BHXhI#}I7#b2ZVMvBC zJpMk*(wvI4WN1auhM_G%2ZVOm@QwssbWjcNrl+*-l--jdjUc@mVEKEK^yMP`2r?O- zCm75ygkU&B7Qsk{9D+QC(F7X8G;H`71x~G47uE7oJ*ACR`C0{ogJ3+v1cJ#3J+R6t z1k-d-HBQ%4+6>B`%`gW)fA|3AvAje@<})lHSj6xO!BPfEu$*B9!D@yz1g|r^LGU&N zC$;2t8Ga!+!Eln`i~@E2bC%=+7x|6gGQ;l#*BGu7++w&*@CU;^ zg1;H=6Fg-27shb^dnR9BV+I%lFd}HU3OxzTI;d6Xt*10W*;WQWf z{V$RvhKs}!Brwz@sKby*P@f@*pbx_$kRc6F8P?s$Ls>iF4RF~ z7wajlgtE&ZIB9}%l5t$*1%inTlL)3VI0>dR%pjP}Fo$3s!%GD785ZQz_m4#=ld)S!CHpb2;O9Pi{M>`_XyS@?!*+sQ3||p^!|*ME^Lv)PBtIYw#C36i;IIy=SwHG2?I>mc%^ls13I1gGi{KxG6s++9!6O}1jnBYe zMll=qe*=u-L8n^aphIA0un_n#_!9Ut1Q6H|Cg4ni3Bq(xGmX$wS|nx16yW}+CWs|T z;372%>M$e{)MrQ{XvEN%peaK$f)oV&KdRP(AXNwY|5Y13skNp24h$U$x-fJlc#fe5 zK`#b7K_7&UIMWP*{yM02kx40a{Gt58dcHPAQVs-OJO91 zVhV#P%%sqjLMsY66oyimOQD29KMLI`WKeh>4<|6gG76(9w4_j-H;PhIDa@fzL}3Vp zLJBWaa8Z~};Ux;4D72xlfI==l&Iq%|A8;XzqcDg9{?G`BznwwApSK`nQNSNTAcc4S zA>iG42zV170^V$gfH#>T;9Xt_cq0}9-aLhXcTFL{y-z1byn_h=Z!1E;dxsG4P8w%P!RA|3k1A_0s(J~K*0MS5b!Pr1ibSA0dFWkfEx!G@Vh$%{KgIezYsei z;&)UC_^lBFekp^1UxFauR~rcU%>e>lheNw0>aDCLn)bBioEA2Hn)#St_+);#+*Ac1-36_El^^B5asLp0s+F!A~aR0-Nd|bLLYH z-Bx?5?R5B)939;3iS;z_e`WDTKk}(8IW3izG5cxWatS{4p-JZ5(`ygBv)wjDvGHxQc_dI5>!dDjYn6HNS^rb8%q8 z)-*E5+4&K1QXE^Y~auyt{!{om>SdN38IQSd~f8k&j z4*tM_#KGG*_!blGz!M4tTowp;LV+B%c%*< z-79PRF7tLbZ|b}DH$#*>7HX|kqf^?K^50NvqUVmZGjkS8|43`Jxi{<^Y3J?rq!wmP zHa0NWd&xCn)(9hX;Ic%18D_0xdDURwVSw(&;Z=x(d_(1kFssSa{*}Q#Mq0zIO==c< z*mrwCwqI^}ML|(^MQ&Ch42q$@SFGCporgVKW`|qbTB2&$%W6Qu`f^94r2)LA!Rl#r z)Uel;C&H~QElEbZG(v7oxnr+SP1zyB>SbJRwEqIfcWccCCye$+o{$@ql~YnYF1KuS zZk9ef=q^^xDB06K5sQS$ozgqPqHKz%eS`cm!rH|6iKqR5yd7bUgtwjeMq1;d3h=YE z>Xl?^B?Z`IrL>^2)v5ozT&hsAj630z47-d<(J~ovT%LxtVp2-WVxlrK9p+9>O*9m^ zXGB@QDm3ENxU8IG4UNDtm$gfFMK*khjmmOMOUj^8DnD+nHN>Ry!=(#ek{+(c{q?fg z17<<>t5f#AXst^{Te%N@Dp zjs9zAjx;>5M!~B&Vjfs)npDAPdG>}^jF;+Xz|}zROtQqu;v{&rnaWK>sAt4+S@MrH zmc};xV-58}bGT~D%IjXW-OV0ae~K{;#W{z|b1e?!?M+S}}| zjP%RwZp5!Ka%Z+*qzT8Mmg|iziJmy_kOv!C>bWy={1!DZ;rAQJOg5M!WRqqVUwH9G zz(T)0^U7O<3aLvH%qXg%)RrFU!8g;{5Iu^mOHvwV&Mf3z7PD8 zj7T#5AHM{{Xm{2Fzp%c>mMD$w{=r`Af3@(reB1+H!kgyrYr;G@9Vs|y_qQ5ax=-8v zqvDPD%}m}Z@{fb}BgB>X*RmkELem|E@>VxXh!-Xu@X@^1$I`&Pw8VdOBO`u`lV?}> z!#>;7+vwqnlZNJIo3!@wx5`s?f0MiKYX8HvjQI6V#_sY@GT|8XnwZfZ-sITlfuEJK zHth0`qbyhn*_KF8Eas2_4dEr!aUIM7Zm+NX>$Jyi|7!#^^Tcn7(2^ZW-@< zL*%6BfCx{_b;znQmYVJ_qXRY$gBOvMP71gJZw?qTIUp9oq{#vCq4+)3WzEmcF09DU zDt8nWW#e0AUUq*nIUvlxHhz6|)##j_4t>z$sDRN%c_n2pWS7Cdhva|{B|FO;>-Kv& z;Anygzv#kY_oFKBa3hY(tn~q5(DQuUhJdJ=D$}!~EW4l>9y7v{=G<}Fg$_9QQrh8? z0TW<_OxqC9)MMH__s5Zbhdt!Mx| zM_F-JQFi$ldFXH;^p$=h5MCg^KhRSyKN%1uL*KOd%k{xQUUL1ZK$A>66(Ho~>ps3- z1;xitGa;Jfn5u0S8zx^`fcySQ24XtUwAJk~le!K=H$J{wLavSYiz zBp-E@o@=nLL@8|-7*EIT)du@kIigKqL*q7s{h+LB6PRQ?Y_Q*zJK6-M81EVEf%2a= zfsKqd4|@mMplx8Y7M(ooZ{b~3N ze9)E->iyxco5ofJg~Ii~Agnn`j_nkT7t|^Rhw8tAqhUUSsO!UiIVEM-X+L!69&L+*-qhPi z+u|W)N83_pCL2Boj|x$f`4;hHYE^WW`=f1544vJNqHVwXneaZED@4Zm`-U{6yJ{|P zxR`2MQk zBj@siFI*M5Wm)BLMVVWi1BW)nzNKX)aJVVYswf$g3rnxi>%VT$-_!_l^)tN^yBRJrg+TZ9oOl4lRvLTC;V^57C%M0E9Jfu&`z3h8q7i3try zZQ(TG*h99U)@r`q<&Kf%a9KDo3pQURzSW|(0Da*^HI7mjbg8lss-ihZ$r86EhN^9H z*k+@#K8J1LK{SJ$^0KUP&VT@Da%8GrM5li?hj?hEGV8uADhlt|y8Ngn*edWipAE~6?2v1>SYq9q?%NJFG2%^J z`EO!y9Q5t}LH*#G5DwH2j)HKlesCSieywhBG>_Tj$&+TYm%954t=W0X90DIVR^y7t zRDy?=CRZi}*Hv#5hl4X%s9aw!*pr|Zf&QLuGgW-6UT^}Bg~LZxeXWo-C#ylQJF7v+ z_oLLSwBgHq#>C(_*}E!4KzH&(szTx+OsNW~Z^Zl1a@zcmNGb@Q)>rUOh;;YWst|Jr z^=fU{#ZFu_*Ywcfb=!wS>X5-P<&Jld+o~ z6TR@ZwgYB<=y!8HcW`KEyF~RCZKyHZ6W-$dLvpA$Z01$&sqI3i_cBa$8(s>1<7?<9 ze!*uE;Sjd>4y&bJTMfJPM>oyku*UEvI2GnAqwyi|HmKx6i_rro^T<36nBlV3g;ZYDo*YFs_ zcz6A-;jiaG5Aypz2(KHKjkAEmQSm6)yh^j^WCgeJrSH1%aP>-QSUHET_$R;|vSr4) z@FbXW%DV98hHUpI>%#9WkI2Lt{(rX*GS%$^Bj(e6gG_bbfBCoYWypzwwH4pL;GMCE5#rNGQke!bUi)^!BuPx?AQ&JX_09;VzmN z5uXH|!s939i9ankmi}Uic7Oj_L|IGd2>vxKG9nCrYT!ff$O*Lf!Y>f+k{c0SZC>~j zM7gV$%=kB=mix3v?ckftF4E`+fWIfeKYh?Vo_FApT+VYVW G>GeNHwn{kw diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index c691806ec6a4ce96412bef66d99de888ce2911e4..5bb6ae35e29f67e60e558e6a2afce2c96660a0a4 100644 GIT binary patch delta 64 zcmexyjOoWQrVTBOhGm9EmdS}_$@-=y$wr2zmZm0&mgZ@xh9+jo=H}+6mPSTt76vBC UhDNCdNv3J$i7A`6G9F$C0KxPW{{R30 delta 64 zcmexyjOoWQrVTBOhG_*!S*e!Emip#K7G_B%CP@}1CTRvHDT$WGX{LtBhAAmY=4mMg U7KW+DiHVlU#^#&1G9F$C0N^1N;Q#;t diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index b1c9c77919d7bd714d39e20b609163a0163af7e0..5266464b8673c1f4d054260911e8e4e33cc42f90 100644 GIT binary patch delta 64 zcmbPsn{nE0#tn-Z4a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN U4UJL_l1$Ug6H_){VLX%#0HWg*Y5)KL delta 64 zcmbPsn{nE0#tn-Z4buvevQjORE%nWfEX\n", " \n", " \n", - " is_low_information_issue\n", " low_information_score\n", + " is_low_information_issue\n", " \n", " \n", " \n", " \n", " 53050\n", - " True\n", " 0.067975\n", + " True\n", " \n", " \n", " 40875\n", - " True\n", " 0.089929\n", + " True\n", " \n", " \n", " 9594\n", - " True\n", " 0.092601\n", + " True\n", " \n", " \n", " 34825\n", - " True\n", " 0.107744\n", + " True\n", " \n", " \n", " 37530\n", - " True\n", " 0.108516\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 29, @@ -2810,10 +2810,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.795721Z", - "iopub.status.busy": "2024-01-09T15:10:06.795344Z", - "iopub.status.idle": "2024-01-09T15:10:06.993269Z", - "shell.execute_reply": "2024-01-09T15:10:06.992660Z" + "iopub.execute_input": "2024-01-10T06:19:04.117761Z", + "iopub.status.busy": "2024-01-10T06:19:04.117385Z", + "iopub.status.idle": "2024-01-10T06:19:04.309531Z", + "shell.execute_reply": "2024-01-10T06:19:04.308840Z" } }, "outputs": [ @@ -2853,10 +2853,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.995987Z", - "iopub.status.busy": "2024-01-09T15:10:06.995586Z", - "iopub.status.idle": "2024-01-09T15:10:07.000490Z", - "shell.execute_reply": "2024-01-09T15:10:06.999967Z" + "iopub.execute_input": "2024-01-10T06:19:04.312289Z", + "iopub.status.busy": "2024-01-10T06:19:04.311799Z", + "iopub.status.idle": "2024-01-10T06:19:04.316705Z", + "shell.execute_reply": "2024-01-10T06:19:04.316103Z" }, "nbsphinx": "hidden" }, @@ -2893,7 +2893,43 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0473bf97727e472ba8e7b962a84b0306": { + "013214df95b64291a68080a23b498d9c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bcf1b5301fd64b32aaf6b5bc496014ac", + "placeholder": "​", + "style": "IPY_MODEL_0de47076f12f425aaf23ccfbd50f919d", + "value": " 2/2 [00:00<00:00, 310.86it/s]" + } + }, + "0a79a261be2c484e94796f9d62c31f87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0cda3634af9749aa8e66efb500ef558a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2942,26 +2978,62 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null } }, - "0afac2fb2a2a43769de95731fcd3298f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "0d03cfe2e87e462c9a9b5432ae2d2024": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0d1536c7250b4b1e9705a71ffda4a3e7": { + "0de47076f12f425aaf23ccfbd50f919d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2976,28 +3048,7 @@ "description_width": "" } }, - "0d65f29f57ce4744b9fbd73a527cf547": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_273c728bfd7d4943ba708c71210e28c2", - "placeholder": "​", - "style": "IPY_MODEL_7cd16d1864804c7e9e2a98990e9493ae", - "value": " 2/2 [00:00<00:00, 357.04it/s]" - } - }, - "0fca6638327f48e8a39dcc8b63dc9fd0": { + "0de5c1f9f47b48ec9f04d1bf5d3ad773": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3046,10 +3097,10 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "10664e26a0ce4eeeaaf052febd6cfb01": { + "0e160c96ba624dc8b6cea376b8066800": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3101,76 +3152,23 @@ "width": null } }, - "16674e62660245f7898a4c11861c11ca": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0473bf97727e472ba8e7b962a84b0306", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cc0b0bf5734e405c9773771efd329716", - "value": 1.0 - } - }, - "16ac788950264bbdbf7ed494c4563a01": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "1b69e9574f9141228434ea3ce25da6fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "1e53b5cc161f4b1bb16500989600fd03": { + "10bb4e9e540347b6adf4e3fedbe1cc0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "21f4cd54b5d54250be17cf0f2c9b47a2": { + "190459aade984ef2aea0d2515ff57cda": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3222,7 +3220,7 @@ "width": null } }, - "273c728bfd7d4943ba708c71210e28c2": { + "1b3b170418ce4c9ab3f8a55b5a6eabc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3274,7 +3272,7 @@ "width": null } }, - "2799716cf975430297a582a0add7fc99": { + "1bd85ee9e5b94936a4a0ca8c939715dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3326,59 +3324,7 @@ "width": null } }, - "27bc9862bc214fb2bf85bbfbe4f8d12e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "280992726df64d0bb7f66f7979cacc38": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "28541a162be44b9f939e6cdf26ae4d14": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_9896d58aa931463a895c73358a4c3f52", - "placeholder": "​", - "style": "IPY_MODEL_16ac788950264bbdbf7ed494c4563a01", - "value": " 10000/0 [00:00<00:00, 469261.25 examples/s]" - } - }, - "2f41d597282241aeba4e3b627f170575": { + "1d21a5d038eb47648eec87e4e6006fd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3393,13 +3339,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ba5eaac37c12413483457def49e84997", + "layout": "IPY_MODEL_0d03cfe2e87e462c9a9b5432ae2d2024", "placeholder": "​", - "style": "IPY_MODEL_c09bc75cc58e4390a40a122a999ce70b", - "value": " 60000/0 [00:00<00:00, 835180.44 examples/s]" + "style": "IPY_MODEL_2ed35715096140afab738d789e6ea0ae", + "value": "Map (num_proc=4): 100%" } }, - "2f6f78d28b114880b2c2b118c18b20e5": { + "1f2895dbb9ee4dbeaa2353e1d22f7797": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3451,31 +3397,7 @@ "width": null } }, - "374b4a11ea22457abc80b9c0200deb3c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a7c417d50f384eb18d3f3ff5a4790f1a", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0afac2fb2a2a43769de95731fcd3298f", - "value": 60000.0 - } - }, - "3bbe9a96751c40179a552c9d1e5e4e44": { + "200254c8d04f40b599b27cab58c62c23": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3527,27 +3449,36 @@ "width": null } }, - "3c753bfc0e02462398194b5294142302": { + "2077df6b881c41f397694543602c3b21": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "45f3493a23b940adac33088d5567c92d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_190459aade984ef2aea0d2515ff57cda", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5ead0d8a08ab4a72a56b1bcf53e7b043", + "value": 30931277.0 + } + }, + "22110adf8fbd44dc907195aa03315f8f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, @@ -3594,7 +3525,7 @@ "width": null } }, - "4b5d35473c504a3e9ade960089fb1f04": { + "2695bb154afc4c0ca3e73976aa6b1557": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3609,86 +3540,83 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_21f4cd54b5d54250be17cf0f2c9b47a2", + "layout": "IPY_MODEL_cf1ffd0a959c4b1b98e9116cbd853f84", "placeholder": "​", - "style": "IPY_MODEL_1e53b5cc161f4b1bb16500989600fd03", - "value": "Downloading data: 100%" + "style": "IPY_MODEL_0a79a261be2c484e94796f9d62c31f87", + "value": "Computing checksums: 100%" } }, - "508261f3f6a147e7b5390c1cf679e69e": { + "26d3dec3ea8a42e78a9a1f322d8be9c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_eba67277054b469481c455d56e3f9aad", - "placeholder": "​", - "style": "IPY_MODEL_280992726df64d0bb7f66f7979cacc38", - "value": " 60000/60000 [00:34<00:00, 1696.59it/s]" + "layout": "IPY_MODEL_ea0795aea16d4e3ca4a1917ca4866577", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_10bb4e9e540347b6adf4e3fedbe1cc0e", + "value": 2.0 } }, - "510cae4e964449d782837526b30e6521": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "2cc8abc90f014a1cae1e929a856c504c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2ed35715096140afab738d789e6ea0ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "519d7e9f35a140c296e16eeddde62611": { + "312e7e414ea44c0cb196a98ae42a3820": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "38d235ba304a40398a6b2133d5d914d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3740,7 +3668,129 @@ "width": null } }, - "54b363f29f2e4818b014a9ec4ca3ae91": { + "398eb4cd325f46478a8fa7d7374a5549": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_eb5828f6121341f8b9392cbdc9416b5f", + "placeholder": "​", + "style": "IPY_MODEL_4ec21615f55d49f283bd0e6b2f5440a7", + "value": "Generating test split: " + } + }, + "3a2cf689648147339884bf21e41e0117": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8508ed2c71b941dd88b8226f584dd39f", + "placeholder": "​", + "style": "IPY_MODEL_ffc7d069ba3342649b031d7e4fc2b1e9", + "value": " 60000/60000 [00:12<00:00, 4348.01 examples/s]" + } + }, + "49e601fd38364edaac3c9d83a3b91d37": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1d21a5d038eb47648eec87e4e6006fd2", + "IPY_MODEL_7354bbbddf5c490c9aef5c8baf007d83", + "IPY_MODEL_3a2cf689648147339884bf21e41e0117" + ], + "layout": "IPY_MODEL_1f2895dbb9ee4dbeaa2353e1d22f7797" + } + }, + "4aaf228d5dec484e9d76a93af99b330d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e69939aec370403db4aa8a60e3dd20e3", + "IPY_MODEL_6ec843e6a46940dba4f1613a2e697ff3", + "IPY_MODEL_e8532a073a86434db3784fb380727e9f" + ], + "layout": "IPY_MODEL_ec15ac2ff1a445bd9ca4553a52d53b2f" + } + }, + "4be66381fb204b84afdc2415136fb407": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0e160c96ba624dc8b6cea376b8066800", + "placeholder": "​", + "style": "IPY_MODEL_55739e7c4da544cbb13ae2f33cc14e5c", + "value": "100%" + } + }, + "4ec21615f55d49f283bd0e6b2f5440a7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4f1471abf2d545c7bf0b67cd9327b5b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3792,23 +3842,43 @@ "width": null } }, - "57e6037d11954b07be9befbafb7414ac": { + "531d284991c7410f8db880db73019fac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1b3b170418ce4c9ab3f8a55b5a6eabc9", + "placeholder": "​", + "style": "IPY_MODEL_bc6ecce06e14491c86ba5776deecab56", + "value": " 60000/60000 [00:34<00:00, 1699.40it/s]" + } + }, + "55739e7c4da544cbb13ae2f33cc14e5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "5c44edf87c7e424a879f8270436d20bf": { + "56284ddcfc6f45b1ab26d5b2d7960182": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3824,15 +3894,46 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c1f9caadb48845b9b1a1647d370b933c", - "max": 60000.0, + "layout": "IPY_MODEL_0de5c1f9f47b48ec9f04d1bf5d3ad773", + "max": 1.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f4c22aa8136844b79a6e0860a2f644f1", - "value": 60000.0 + "style": "IPY_MODEL_dda99bdff9404fdfb5828d3010b9f8a1", + "value": 1.0 + } + }, + "5dd680e3231f43718c6308a8e45e1da2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "5fcc6710b5274de3adfd08b81c300f2d": { + "5ead0d8a08ab4a72a56b1bcf53e7b043": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "65858abaeaa64069a1e42170696ed7d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3847,13 +3948,52 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3bbe9a96751c40179a552c9d1e5e4e44", + "layout": "IPY_MODEL_4f1471abf2d545c7bf0b67cd9327b5b1", "placeholder": "​", - "style": "IPY_MODEL_cbfc2de67f8a426abb64ec2ba7a40357", - "value": " 30.9M/30.9M [00:00<00:00, 91.8MB/s]" + "style": "IPY_MODEL_65d9f41155d24ab983b2225422ae636e", + "value": " 30.9M/30.9M [00:00<00:00, 76.8MB/s]" + } + }, + "65d9f41155d24ab983b2225422ae636e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6ec843e6a46940dba4f1613a2e697ff3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d7e4b9ab15f94205a4b550065dc49563", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2cc8abc90f014a1cae1e929a856c504c", + "value": 1.0 } }, - "61dd250de95c4ed1842cab2de394be3a": { + "7354bbbddf5c490c9aef5c8baf007d83": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3869,37 +4009,52 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9de92bb3a01f49d89b55999b0dc455bc", - "max": 30931277.0, + "layout": "IPY_MODEL_e6d905af2f524d45905c52a4563e3b9f", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_27bc9862bc214fb2bf85bbfbe4f8d12e", - "value": 30931277.0 + "style": "IPY_MODEL_c804c3d8105c4289a60d0a1dff6d3cfe", + "value": 60000.0 } }, - "68568aa17e9f4f7b90407718becd059e": { + "78249ca6d2e148269fee22730149bfc7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4b5d35473c504a3e9ade960089fb1f04", - "IPY_MODEL_61dd250de95c4ed1842cab2de394be3a", - "IPY_MODEL_5fcc6710b5274de3adfd08b81c300f2d" - ], - "layout": "IPY_MODEL_54b363f29f2e4818b014a9ec4ca3ae91" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38d235ba304a40398a6b2133d5d914d3", + "placeholder": "​", + "style": "IPY_MODEL_5dd680e3231f43718c6308a8e45e1da2", + "value": "Downloading data: 100%" + } + }, + "7c26ccdbd7a74a0f83ab2ec3744b92d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "770385f5240f48bb8d9d8577f952dafc": { + "8508ed2c71b941dd88b8226f584dd39f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3951,22 +4106,7 @@ "width": null } }, - "7cd16d1864804c7e9e2a98990e9493ae": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "7e62cab4e97e49cd90922d7cd609cb8a": { + "899e58d8a40a465297f40aeada991389": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4018,28 +4158,31 @@ "width": null } }, - "8085cfb1cddf4df187e17fb4324b87a8": { + "89e68bddf9384821a6b84d7f2d4e482e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ed89461f57a848fababda5cca9289c33", - "placeholder": "​", - "style": "IPY_MODEL_3c753bfc0e02462398194b5294142302", - "value": "Generating test split: " + "layout": "IPY_MODEL_c603b29c641542a68e01cfc8bcc23136", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7c26ccdbd7a74a0f83ab2ec3744b92d4", + "value": 60000.0 } }, - "8128d07a17964361bc8e3db33cf37a1e": { + "9f23a3a591a44348963c1d972f09eea0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4091,23 +4234,29 @@ "width": null } }, - "84f017fa3cdc40bcbd0adea980114cef": { + "a3915e0b726a46b79cb5df0f0080b054": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e0717ef2b1f241a585bab71f798c07aa", + "IPY_MODEL_2077df6b881c41f397694543602c3b21", + "IPY_MODEL_65858abaeaa64069a1e42170696ed7d5" + ], + "layout": "IPY_MODEL_200254c8d04f40b599b27cab58c62c23" } }, - "84f486444a4e4ea59c0f4a16865b285d": { + "a6744175fe1842fe96c1f9105ae47256": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4159,7 +4308,7 @@ "width": null } }, - "855458eae8684ce0aa743d94aef749b9": { + "a6b5a69bbd21481db358d145d9702e00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4174,75 +4323,30 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b1ffa28e2937438089f45a54351e7fe1", - "IPY_MODEL_374b4a11ea22457abc80b9c0200deb3c", - "IPY_MODEL_508261f3f6a147e7b5390c1cf679e69e" + "IPY_MODEL_78249ca6d2e148269fee22730149bfc7", + "IPY_MODEL_df7c93e26eda4e248394e2244cfded4c", + "IPY_MODEL_d446110cb3c24b66987a4e37de6ccd06" ], - "layout": "IPY_MODEL_770385f5240f48bb8d9d8577f952dafc" - } - }, - "86c92749dbfe497da1566d9e737e1a82": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_510cae4e964449d782837526b30e6521", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_84f017fa3cdc40bcbd0adea980114cef", - "value": 1.0 + "layout": "IPY_MODEL_22110adf8fbd44dc907195aa03315f8f" } }, - "8c313b7b39c3431ab7e73a4a59b8e7b3": { + "afb50312cbe743948bbafaebb013b144": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "92fa8c609cf14d2d95dabb0540703411": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b1ecaf6071264b46af44f2cbe57df8c6", - "IPY_MODEL_c6e5ac83513d4bcfb6989f1ae3e1f300", - "IPY_MODEL_c06301b3fd424eceb2d88031f05a83ad" - ], - "layout": "IPY_MODEL_45f3493a23b940adac33088d5567c92d" - } - }, - "93c752a242324ecba32adb203ce71bb9": { + "b2ae5f58b0c44998937c4fd1e2f2054a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4257,58 +4361,43 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8128d07a17964361bc8e3db33cf37a1e", + "layout": "IPY_MODEL_e2e288cbd8d54b37921c3c1a587bb313", "placeholder": "​", - "style": "IPY_MODEL_0d1536c7250b4b1e9705a71ffda4a3e7", - "value": " 60000/60000 [00:12<00:00, 6886.27 examples/s]" + "style": "IPY_MODEL_312e7e414ea44c0cb196a98ae42a3820", + "value": " 10000/0 [00:00<00:00, 522062.71 examples/s]" } }, - "94fbfef6b3814b2db2f9b76d4a7b46e4": { + "bbdfd655d6b2431895de761767dbe4eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2f6f78d28b114880b2c2b118c18b20e5", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_57e6037d11954b07be9befbafb7414ac", - "value": 2.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "9656b81ab06946f591c468fc5e33cb45": { + "bc6ecce06e14491c86ba5776deecab56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_10664e26a0ce4eeeaaf052febd6cfb01", - "placeholder": "​", - "style": "IPY_MODEL_8c313b7b39c3431ab7e73a4a59b8e7b3", - "value": "Generating train split: " + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "9896d58aa931463a895c73358a4c3f52": { + "bcf1b5301fd64b32aaf6b5bc496014ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4360,7 +4449,7 @@ "width": null } }, - "9de92bb3a01f49d89b55999b0dc455bc": { + "c0f6e3e01f79409296a19f5fac62b3aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4412,29 +4501,7 @@ "width": null } }, - "a0de8652c458427bb4bec3eedab1bdc2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ccd2f40ce20d47d7812553a977216a3a", - "IPY_MODEL_94fbfef6b3814b2db2f9b76d4a7b46e4", - "IPY_MODEL_0d65f29f57ce4744b9fbd73a527cf547" - ], - "layout": "IPY_MODEL_b6f0ed9c24e846cf89eabfb8bdaf5c55" - } - }, - "a1c5d4b9cb52400284a21abdd20a1d42": { + "c603b29c641542a68e01cfc8bcc23136": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4486,22 +4553,45 @@ "width": null } }, - "a6c0fc5dd18c42a5af86df9bcb153041": { + "c804c3d8105c4289a60d0a1dff6d3cfe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "a7c417d50f384eb18d3f3ff5a4790f1a": { + "c98028da20994c76be789f56278cced6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_398eb4cd325f46478a8fa7d7374a5549", + "IPY_MODEL_56284ddcfc6f45b1ab26d5b2d7960182", + "IPY_MODEL_b2ae5f58b0c44998937c4fd1e2f2054a" + ], + "layout": "IPY_MODEL_1bd85ee9e5b94936a4a0ca8c939715dd" + } + }, + "cf1ffd0a959c4b1b98e9116cbd853f84": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4553,7 +4643,28 @@ "width": null } }, - "a8a55d054fad45c1bff1cce2985373f9": { + "d446110cb3c24b66987a4e37de6ccd06": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a6744175fe1842fe96c1f9105ae47256", + "placeholder": "​", + "style": "IPY_MODEL_f6100e7965f840b397c807782f4eacd6", + "value": " 5.18M/5.18M [00:00<00:00, 72.4MB/s]" + } + }, + "d7e4b9ab15f94205a4b550065dc49563": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4602,74 +4713,87 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "af600e5921e24b2d9dbb70a9b3b11f5f": { + "d8935bf4844d43b294f1960ef9619de7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b4848d3524e04470a3e6d65de5780e4d", - "IPY_MODEL_5c44edf87c7e424a879f8270436d20bf", - "IPY_MODEL_93c752a242324ecba32adb203ce71bb9" - ], - "layout": "IPY_MODEL_7e62cab4e97e49cd90922d7cd609cb8a" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "b1ecaf6071264b46af44f2cbe57df8c6": { + "dda99bdff9404fdfb5828d3010b9f8a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "df7c93e26eda4e248394e2244cfded4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c70a8658e54d4a499f7c4166a2bda30e", - "placeholder": "​", - "style": "IPY_MODEL_b5857eae1ffc4705b6bf9d6da12a4ede", - "value": "Downloading data: 100%" + "layout": "IPY_MODEL_c0f6e3e01f79409296a19f5fac62b3aa", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_afb50312cbe743948bbafaebb013b144", + "value": 5175617.0 } }, - "b1ffa28e2937438089f45a54351e7fe1": { + "dfe013943ca04be693b072c950762919": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a1c5d4b9cb52400284a21abdd20a1d42", - "placeholder": "​", - "style": "IPY_MODEL_e281b710f3db4e338a458837b4ea1475", - "value": "100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4be66381fb204b84afdc2415136fb407", + "IPY_MODEL_89e68bddf9384821a6b84d7f2d4e482e", + "IPY_MODEL_531d284991c7410f8db880db73019fac" + ], + "layout": "IPY_MODEL_899e58d8a40a465297f40aeada991389" } }, - "b4848d3524e04470a3e6d65de5780e4d": { + "e0717ef2b1f241a585bab71f798c07aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4684,28 +4808,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0fca6638327f48e8a39dcc8b63dc9fd0", + "layout": "IPY_MODEL_9f23a3a591a44348963c1d972f09eea0", "placeholder": "​", - "style": "IPY_MODEL_c22e5333d6824f38b56e519da230d233", - "value": "Map (num_proc=4): 100%" - } - }, - "b5857eae1ffc4705b6bf9d6da12a4ede": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_efb859ce22df4aedb5cd3bffda0adc5f", + "value": "Downloading data: 100%" } }, - "b6f0ed9c24e846cf89eabfb8bdaf5c55": { + "e2e288cbd8d54b37921c3c1a587bb313": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4757,7 +4866,50 @@ "width": null } }, - "ba5eaac37c12413483457def49e84997": { + "e36aa52b0d1c430cb96f1bdb16e44c82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2695bb154afc4c0ca3e73976aa6b1557", + "IPY_MODEL_26d3dec3ea8a42e78a9a1f322d8be9c3", + "IPY_MODEL_013214df95b64291a68080a23b498d9c" + ], + "layout": "IPY_MODEL_f20a65a9ab814f7e990cc1d89679131a" + } + }, + "e69939aec370403db4aa8a60e3dd20e3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0cda3634af9749aa8e66efb500ef558a", + "placeholder": "​", + "style": "IPY_MODEL_d8935bf4844d43b294f1960ef9619de7", + "value": "Generating train split: " + } + }, + "e6d905af2f524d45905c52a4563e3b9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4809,29 +4961,7 @@ "width": null } }, - "bef514edf16d4b89be1af25807f99bf8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_9656b81ab06946f591c468fc5e33cb45", - "IPY_MODEL_86c92749dbfe497da1566d9e737e1a82", - "IPY_MODEL_2f41d597282241aeba4e3b627f170575" - ], - "layout": "IPY_MODEL_519d7e9f35a140c296e16eeddde62611" - } - }, - "c06301b3fd424eceb2d88031f05a83ad": { + "e8532a073a86434db3784fb380727e9f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4846,28 +4976,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a8a55d054fad45c1bff1cce2985373f9", + "layout": "IPY_MODEL_eb9ca05ae5c842308439407a0dabc84b", "placeholder": "​", - "style": "IPY_MODEL_a6c0fc5dd18c42a5af86df9bcb153041", - "value": " 5.18M/5.18M [00:00<00:00, 10.1MB/s]" - } - }, - "c09bc75cc58e4390a40a122a999ce70b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_bbdfd655d6b2431895de761767dbe4eb", + "value": " 60000/0 [00:00<00:00, 804572.60 examples/s]" } }, - "c1f9caadb48845b9b1a1647d370b933c": { + "ea0795aea16d4e3ca4a1917ca4866577": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4919,22 +5034,7 @@ "width": null } }, - "c22e5333d6824f38b56e519da230d233": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "c477d69023fd48c7914a58cd0fee4f42": { + "eb5828f6121341f8b9392cbdc9416b5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4986,31 +5086,7 @@ "width": null } }, - "c6e5ac83513d4bcfb6989f1ae3e1f300": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c477d69023fd48c7914a58cd0fee4f42", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1a322dcb10d45a9b8ce7c18aa64c881", - "value": 5175617.0 - } - }, - "c70a8658e54d4a499f7c4166a2bda30e": { + "eb9ca05ae5c842308439407a0dabc84b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5062,112 +5138,7 @@ "width": null } }, - "cbfc2de67f8a426abb64ec2ba7a40357": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cc0b0bf5734e405c9773771efd329716": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ccd2f40ce20d47d7812553a977216a3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_84f486444a4e4ea59c0f4a16865b285d", - "placeholder": "​", - "style": "IPY_MODEL_1b69e9574f9141228434ea3ce25da6fd", - "value": "Computing checksums: 100%" - } - }, - "e1a322dcb10d45a9b8ce7c18aa64c881": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e281b710f3db4e338a458837b4ea1475": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "eacced1a3eeb4720aca48e31138fb049": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8085cfb1cddf4df187e17fb4324b87a8", - "IPY_MODEL_16674e62660245f7898a4c11861c11ca", - "IPY_MODEL_28541a162be44b9f939e6cdf26ae4d14" - ], - "layout": "IPY_MODEL_2799716cf975430297a582a0add7fc99" - } - }, - "eba67277054b469481c455d56e3f9aad": { + "ec15ac2ff1a445bd9ca4553a52d53b2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5219,7 +5190,22 @@ "width": null } }, - "ed89461f57a848fababda5cca9289c33": { + "efb859ce22df4aedb5cd3bffda0adc5f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f20a65a9ab814f7e990cc1d89679131a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5271,19 +5257,33 @@ "width": null } }, - "f4c22aa8136844b79a6e0860a2f644f1": { + "f6100e7965f840b397c807782f4eacd6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ffc7d069ba3342649b031d7e4fc2b1e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } } diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index 13d0f7ac8..3fb385f45 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": "2024-01-09T15:10:13.059896Z", - "iopub.status.busy": "2024-01-09T15:10:13.059406Z", - "iopub.status.idle": "2024-01-09T15:10:14.200564Z", - "shell.execute_reply": "2024-01-09T15:10:14.199917Z" + "iopub.execute_input": "2024-01-10T06:19:10.230756Z", + "iopub.status.busy": "2024-01-10T06:19:10.230131Z", + "iopub.status.idle": "2024-01-10T06:19:11.356341Z", + "shell.execute_reply": "2024-01-10T06:19:11.355670Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:14.203785Z", - "iopub.status.busy": "2024-01-09T15:10:14.203233Z", - "iopub.status.idle": "2024-01-09T15:10:14.490673Z", - "shell.execute_reply": "2024-01-09T15:10:14.490032Z" + "iopub.execute_input": "2024-01-10T06:19:11.359495Z", + "iopub.status.busy": "2024-01-10T06:19:11.358950Z", + "iopub.status.idle": "2024-01-10T06:19:11.645037Z", + "shell.execute_reply": "2024-01-10T06:19:11.644365Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.493625Z", - "iopub.status.busy": "2024-01-09T15:10:14.493410Z", - "iopub.status.idle": "2024-01-09T15:10:14.506054Z", - "shell.execute_reply": "2024-01-09T15:10:14.505542Z" + "iopub.execute_input": "2024-01-10T06:19:11.648431Z", + "iopub.status.busy": "2024-01-10T06:19:11.647953Z", + "iopub.status.idle": "2024-01-10T06:19:11.660400Z", + "shell.execute_reply": "2024-01-10T06:19:11.659834Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.508353Z", - "iopub.status.busy": "2024-01-09T15:10:14.508150Z", - "iopub.status.idle": "2024-01-09T15:10:14.745115Z", - "shell.execute_reply": "2024-01-09T15:10:14.744440Z" + "iopub.execute_input": "2024-01-10T06:19:11.662953Z", + "iopub.status.busy": "2024-01-10T06:19:11.662542Z", + "iopub.status.idle": "2024-01-10T06:19:11.900160Z", + "shell.execute_reply": "2024-01-10T06:19:11.899470Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.747903Z", - "iopub.status.busy": "2024-01-09T15:10:14.747683Z", - "iopub.status.idle": "2024-01-09T15:10:14.775228Z", - "shell.execute_reply": "2024-01-09T15:10:14.774537Z" + "iopub.execute_input": "2024-01-10T06:19:11.903126Z", + "iopub.status.busy": "2024-01-10T06:19:11.902693Z", + "iopub.status.idle": "2024-01-10T06:19:11.929149Z", + "shell.execute_reply": "2024-01-10T06:19:11.928601Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.777804Z", - "iopub.status.busy": "2024-01-09T15:10:14.777585Z", - "iopub.status.idle": "2024-01-09T15:10:16.172161Z", - "shell.execute_reply": "2024-01-09T15:10:16.171473Z" + "iopub.execute_input": "2024-01-10T06:19:11.931771Z", + "iopub.status.busy": "2024-01-10T06:19:11.931369Z", + "iopub.status.idle": "2024-01-10T06:19:13.340850Z", + "shell.execute_reply": "2024-01-10T06:19:13.340122Z" } }, "outputs": [ @@ -473,10 +473,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:16.175096Z", - "iopub.status.busy": "2024-01-09T15:10:16.174693Z", - "iopub.status.idle": "2024-01-09T15:10:16.200461Z", - "shell.execute_reply": "2024-01-09T15:10:16.199888Z" + "iopub.execute_input": "2024-01-10T06:19:13.343983Z", + "iopub.status.busy": "2024-01-10T06:19:13.343269Z", + "iopub.status.idle": "2024-01-10T06:19:13.368528Z", + "shell.execute_reply": "2024-01-10T06:19:13.367864Z" }, "scrolled": true }, @@ -641,10 +641,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:16.203050Z", - "iopub.status.busy": "2024-01-09T15:10:16.202705Z", - "iopub.status.idle": "2024-01-09T15:10:17.127463Z", - "shell.execute_reply": "2024-01-09T15:10:17.126664Z" + "iopub.execute_input": "2024-01-10T06:19:13.370923Z", + "iopub.status.busy": "2024-01-10T06:19:13.370715Z", + "iopub.status.idle": "2024-01-10T06:19:14.287348Z", + "shell.execute_reply": "2024-01-10T06:19:14.286696Z" }, "id": "AaHC5MRKjruT" }, @@ -763,10 +763,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.130430Z", - "iopub.status.busy": "2024-01-09T15:10:17.129998Z", - "iopub.status.idle": "2024-01-09T15:10:17.145043Z", - "shell.execute_reply": "2024-01-09T15:10:17.144392Z" + "iopub.execute_input": "2024-01-10T06:19:14.290089Z", + "iopub.status.busy": "2024-01-10T06:19:14.289653Z", + "iopub.status.idle": "2024-01-10T06:19:14.304321Z", + "shell.execute_reply": "2024-01-10T06:19:14.303795Z" }, "id": "Wy27rvyhjruU" }, @@ -815,10 +815,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.147663Z", - "iopub.status.busy": "2024-01-09T15:10:17.147264Z", - "iopub.status.idle": "2024-01-09T15:10:17.243881Z", - "shell.execute_reply": "2024-01-09T15:10:17.243115Z" + "iopub.execute_input": "2024-01-10T06:19:14.306556Z", + "iopub.status.busy": "2024-01-10T06:19:14.306353Z", + "iopub.status.idle": "2024-01-10T06:19:14.395464Z", + "shell.execute_reply": "2024-01-10T06:19:14.394757Z" }, "id": "Db8YHnyVjruU" }, @@ -925,10 +925,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.247030Z", - "iopub.status.busy": "2024-01-09T15:10:17.246476Z", - "iopub.status.idle": "2024-01-09T15:10:17.452269Z", - "shell.execute_reply": "2024-01-09T15:10:17.451560Z" + "iopub.execute_input": "2024-01-10T06:19:14.398081Z", + "iopub.status.busy": "2024-01-10T06:19:14.397790Z", + "iopub.status.idle": "2024-01-10T06:19:14.605263Z", + "shell.execute_reply": "2024-01-10T06:19:14.604556Z" }, "id": "iJqAHuS2jruV" }, @@ -965,10 +965,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.455279Z", - "iopub.status.busy": "2024-01-09T15:10:17.454820Z", - "iopub.status.idle": "2024-01-09T15:10:17.473076Z", - "shell.execute_reply": "2024-01-09T15:10:17.472539Z" + "iopub.execute_input": "2024-01-10T06:19:14.608187Z", + "iopub.status.busy": "2024-01-10T06:19:14.607708Z", + "iopub.status.idle": "2024-01-10T06:19:14.625986Z", + "shell.execute_reply": "2024-01-10T06:19:14.625325Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1030,10 +1030,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.475553Z", - "iopub.status.busy": "2024-01-09T15:10:17.475229Z", - "iopub.status.idle": "2024-01-09T15:10:17.486242Z", - "shell.execute_reply": "2024-01-09T15:10:17.485706Z" + "iopub.execute_input": "2024-01-10T06:19:14.628765Z", + "iopub.status.busy": "2024-01-10T06:19:14.628360Z", + "iopub.status.idle": "2024-01-10T06:19:14.639216Z", + "shell.execute_reply": "2024-01-10T06:19:14.638680Z" }, "id": "0lonvOYvjruV" }, @@ -1180,10 +1180,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.488657Z", - "iopub.status.busy": "2024-01-09T15:10:17.488444Z", - "iopub.status.idle": "2024-01-09T15:10:17.594254Z", - "shell.execute_reply": "2024-01-09T15:10:17.593499Z" + "iopub.execute_input": "2024-01-10T06:19:14.641682Z", + "iopub.status.busy": "2024-01-10T06:19:14.641288Z", + "iopub.status.idle": "2024-01-10T06:19:14.743511Z", + "shell.execute_reply": "2024-01-10T06:19:14.742754Z" }, "id": "MfqTCa3kjruV" }, @@ -1264,10 +1264,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.597095Z", - "iopub.status.busy": "2024-01-09T15:10:17.596808Z", - "iopub.status.idle": "2024-01-09T15:10:17.752936Z", - "shell.execute_reply": "2024-01-09T15:10:17.752174Z" + "iopub.execute_input": "2024-01-10T06:19:14.747035Z", + "iopub.status.busy": "2024-01-10T06:19:14.746126Z", + "iopub.status.idle": "2024-01-10T06:19:14.909743Z", + "shell.execute_reply": "2024-01-10T06:19:14.909013Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1327,10 +1327,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.755941Z", - "iopub.status.busy": "2024-01-09T15:10:17.755518Z", - "iopub.status.idle": "2024-01-09T15:10:17.760007Z", - "shell.execute_reply": "2024-01-09T15:10:17.759466Z" + "iopub.execute_input": "2024-01-10T06:19:14.912509Z", + "iopub.status.busy": "2024-01-10T06:19:14.912249Z", + "iopub.status.idle": "2024-01-10T06:19:14.916421Z", + "shell.execute_reply": "2024-01-10T06:19:14.915806Z" }, "id": "0rXP3ZPWjruW" }, @@ -1368,10 +1368,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.762508Z", - "iopub.status.busy": "2024-01-09T15:10:17.762204Z", - "iopub.status.idle": "2024-01-09T15:10:17.766685Z", - "shell.execute_reply": "2024-01-09T15:10:17.766079Z" + "iopub.execute_input": "2024-01-10T06:19:14.918795Z", + "iopub.status.busy": "2024-01-10T06:19:14.918435Z", + "iopub.status.idle": "2024-01-10T06:19:14.923066Z", + "shell.execute_reply": "2024-01-10T06:19:14.922436Z" }, "id": "-iRPe8KXjruW" }, @@ -1426,10 +1426,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.769023Z", - "iopub.status.busy": "2024-01-09T15:10:17.768715Z", - "iopub.status.idle": "2024-01-09T15:10:17.809115Z", - "shell.execute_reply": "2024-01-09T15:10:17.808497Z" + "iopub.execute_input": "2024-01-10T06:19:14.925674Z", + "iopub.status.busy": "2024-01-10T06:19:14.925282Z", + "iopub.status.idle": "2024-01-10T06:19:14.965643Z", + "shell.execute_reply": "2024-01-10T06:19:14.964962Z" }, "id": "ZpipUliyjruW" }, @@ -1480,10 +1480,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.811844Z", - "iopub.status.busy": "2024-01-09T15:10:17.811413Z", - "iopub.status.idle": "2024-01-09T15:10:17.865183Z", - "shell.execute_reply": "2024-01-09T15:10:17.864486Z" + "iopub.execute_input": "2024-01-10T06:19:14.968405Z", + "iopub.status.busy": "2024-01-10T06:19:14.968011Z", + "iopub.status.idle": "2024-01-10T06:19:15.015701Z", + "shell.execute_reply": "2024-01-10T06:19:15.015056Z" }, "id": "SLq-3q4xjruX" }, @@ -1552,10 +1552,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.867930Z", - "iopub.status.busy": "2024-01-09T15:10:17.867523Z", - "iopub.status.idle": "2024-01-09T15:10:17.983453Z", - "shell.execute_reply": "2024-01-09T15:10:17.982756Z" + "iopub.execute_input": "2024-01-10T06:19:15.018378Z", + "iopub.status.busy": "2024-01-10T06:19:15.017908Z", + "iopub.status.idle": "2024-01-10T06:19:15.125330Z", + "shell.execute_reply": "2024-01-10T06:19:15.124654Z" }, "id": "g5LHhhuqFbXK" }, @@ -1587,10 +1587,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.986797Z", - "iopub.status.busy": "2024-01-09T15:10:17.986363Z", - "iopub.status.idle": "2024-01-09T15:10:18.111625Z", - "shell.execute_reply": "2024-01-09T15:10:18.110842Z" + "iopub.execute_input": "2024-01-10T06:19:15.128752Z", + "iopub.status.busy": "2024-01-10T06:19:15.128227Z", + "iopub.status.idle": "2024-01-10T06:19:15.234835Z", + "shell.execute_reply": "2024-01-10T06:19:15.234115Z" }, "id": "p7w8F8ezBcet" }, @@ -1647,10 +1647,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.114528Z", - "iopub.status.busy": "2024-01-09T15:10:18.114237Z", - "iopub.status.idle": "2024-01-09T15:10:18.317159Z", - "shell.execute_reply": "2024-01-09T15:10:18.316434Z" + "iopub.execute_input": "2024-01-10T06:19:15.237722Z", + "iopub.status.busy": "2024-01-10T06:19:15.237193Z", + "iopub.status.idle": "2024-01-10T06:19:15.449470Z", + "shell.execute_reply": "2024-01-10T06:19:15.448775Z" }, "id": "WETRL74tE_sU" }, @@ -1685,10 +1685,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.320305Z", - "iopub.status.busy": "2024-01-09T15:10:18.319725Z", - "iopub.status.idle": "2024-01-09T15:10:18.547322Z", - "shell.execute_reply": "2024-01-09T15:10:18.546622Z" + "iopub.execute_input": "2024-01-10T06:19:15.452306Z", + "iopub.status.busy": "2024-01-10T06:19:15.451848Z", + "iopub.status.idle": "2024-01-10T06:19:15.710564Z", + "shell.execute_reply": "2024-01-10T06:19:15.709859Z" }, "id": "kCfdx2gOLmXS" }, @@ -1850,10 +1850,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.550435Z", - "iopub.status.busy": "2024-01-09T15:10:18.549972Z", - "iopub.status.idle": "2024-01-09T15:10:18.557124Z", - "shell.execute_reply": "2024-01-09T15:10:18.556489Z" + "iopub.execute_input": "2024-01-10T06:19:15.713390Z", + "iopub.status.busy": "2024-01-10T06:19:15.712976Z", + "iopub.status.idle": "2024-01-10T06:19:15.719738Z", + "shell.execute_reply": "2024-01-10T06:19:15.719219Z" }, "id": "-uogYRWFYnuu" }, @@ -1907,10 +1907,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.559587Z", - "iopub.status.busy": "2024-01-09T15:10:18.559361Z", - "iopub.status.idle": "2024-01-09T15:10:18.772700Z", - "shell.execute_reply": "2024-01-09T15:10:18.772094Z" + "iopub.execute_input": "2024-01-10T06:19:15.722264Z", + "iopub.status.busy": "2024-01-10T06:19:15.721862Z", + "iopub.status.idle": "2024-01-10T06:19:15.932373Z", + "shell.execute_reply": "2024-01-10T06:19:15.931673Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1957,10 +1957,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.775775Z", - "iopub.status.busy": "2024-01-09T15:10:18.775319Z", - "iopub.status.idle": "2024-01-09T15:10:19.857040Z", - "shell.execute_reply": "2024-01-09T15:10:19.856297Z" + "iopub.execute_input": "2024-01-10T06:19:15.935084Z", + "iopub.status.busy": "2024-01-10T06:19:15.934687Z", + "iopub.status.idle": "2024-01-10T06:19:17.019541Z", + "shell.execute_reply": "2024-01-10T06:19:17.018918Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 11b89b6ac..7cb50be92 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": "2024-01-09T15:10:25.630185Z", - "iopub.status.busy": "2024-01-09T15:10:25.629981Z", - "iopub.status.idle": "2024-01-09T15:10:26.695542Z", - "shell.execute_reply": "2024-01-09T15:10:26.694824Z" + "iopub.execute_input": "2024-01-10T06:19:22.603434Z", + "iopub.status.busy": "2024-01-10T06:19:22.602873Z", + "iopub.status.idle": "2024-01-10T06:19:23.665537Z", + "shell.execute_reply": "2024-01-10T06:19:23.664891Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:26.698882Z", - "iopub.status.busy": "2024-01-09T15:10:26.698521Z", - "iopub.status.idle": "2024-01-09T15:10:26.701969Z", - "shell.execute_reply": "2024-01-09T15:10:26.701416Z" + "iopub.execute_input": "2024-01-10T06:19:23.668685Z", + "iopub.status.busy": "2024-01-10T06:19:23.668141Z", + "iopub.status.idle": "2024-01-10T06:19:23.671557Z", + "shell.execute_reply": "2024-01-10T06:19:23.671020Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.704456Z", - "iopub.status.busy": "2024-01-09T15:10:26.704251Z", - "iopub.status.idle": "2024-01-09T15:10:26.712592Z", - "shell.execute_reply": "2024-01-09T15:10:26.712044Z" + "iopub.execute_input": "2024-01-10T06:19:23.674086Z", + "iopub.status.busy": "2024-01-10T06:19:23.673671Z", + "iopub.status.idle": "2024-01-10T06:19:23.682706Z", + "shell.execute_reply": "2024-01-10T06:19:23.682135Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.714829Z", - "iopub.status.busy": "2024-01-09T15:10:26.714624Z", - "iopub.status.idle": "2024-01-09T15:10:26.765428Z", - "shell.execute_reply": "2024-01-09T15:10:26.764678Z" + "iopub.execute_input": "2024-01-10T06:19:23.685247Z", + "iopub.status.busy": "2024-01-10T06:19:23.684914Z", + "iopub.status.idle": "2024-01-10T06:19:23.734906Z", + "shell.execute_reply": "2024-01-10T06:19:23.734183Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.768384Z", - "iopub.status.busy": "2024-01-09T15:10:26.768141Z", - "iopub.status.idle": "2024-01-09T15:10:26.789071Z", - "shell.execute_reply": "2024-01-09T15:10:26.788413Z" + "iopub.execute_input": "2024-01-10T06:19:23.737938Z", + "iopub.status.busy": "2024-01-10T06:19:23.737532Z", + "iopub.status.idle": "2024-01-10T06:19:23.757560Z", + "shell.execute_reply": "2024-01-10T06:19:23.757012Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.791710Z", - "iopub.status.busy": "2024-01-09T15:10:26.791480Z", - "iopub.status.idle": "2024-01-09T15:10:26.795875Z", - "shell.execute_reply": "2024-01-09T15:10:26.795260Z" + "iopub.execute_input": "2024-01-10T06:19:23.760066Z", + "iopub.status.busy": "2024-01-10T06:19:23.759679Z", + "iopub.status.idle": "2024-01-10T06:19:23.763929Z", + "shell.execute_reply": "2024-01-10T06:19:23.763417Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.798344Z", - "iopub.status.busy": "2024-01-09T15:10:26.797977Z", - "iopub.status.idle": "2024-01-09T15:10:26.825772Z", - "shell.execute_reply": "2024-01-09T15:10:26.825224Z" + "iopub.execute_input": "2024-01-10T06:19:23.766305Z", + "iopub.status.busy": "2024-01-10T06:19:23.766011Z", + "iopub.status.idle": "2024-01-10T06:19:23.794834Z", + "shell.execute_reply": "2024-01-10T06:19:23.794302Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.828273Z", - "iopub.status.busy": "2024-01-09T15:10:26.828044Z", - "iopub.status.idle": "2024-01-09T15:10:26.856999Z", - "shell.execute_reply": "2024-01-09T15:10:26.856309Z" + "iopub.execute_input": "2024-01-10T06:19:23.797382Z", + "iopub.status.busy": "2024-01-10T06:19:23.796952Z", + "iopub.status.idle": "2024-01-10T06:19:23.825002Z", + "shell.execute_reply": "2024-01-10T06:19:23.824392Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.860047Z", - "iopub.status.busy": "2024-01-09T15:10:26.859773Z", - "iopub.status.idle": "2024-01-09T15:10:28.259171Z", - "shell.execute_reply": "2024-01-09T15:10:28.258495Z" + "iopub.execute_input": "2024-01-10T06:19:23.827645Z", + "iopub.status.busy": "2024-01-10T06:19:23.827204Z", + "iopub.status.idle": "2024-01-10T06:19:25.187592Z", + "shell.execute_reply": "2024-01-10T06:19:25.186950Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.262622Z", - "iopub.status.busy": "2024-01-09T15:10:28.262162Z", - "iopub.status.idle": "2024-01-09T15:10:28.270093Z", - "shell.execute_reply": "2024-01-09T15:10:28.269455Z" + "iopub.execute_input": "2024-01-10T06:19:25.190794Z", + "iopub.status.busy": "2024-01-10T06:19:25.190198Z", + "iopub.status.idle": "2024-01-10T06:19:25.197978Z", + "shell.execute_reply": "2024-01-10T06:19:25.197424Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.272752Z", - "iopub.status.busy": "2024-01-09T15:10:28.272263Z", - "iopub.status.idle": "2024-01-09T15:10:28.286529Z", - "shell.execute_reply": "2024-01-09T15:10:28.285881Z" + "iopub.execute_input": "2024-01-10T06:19:25.200289Z", + "iopub.status.busy": "2024-01-10T06:19:25.200085Z", + "iopub.status.idle": "2024-01-10T06:19:25.214416Z", + "shell.execute_reply": "2024-01-10T06:19:25.213870Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.289024Z", - "iopub.status.busy": "2024-01-09T15:10:28.288659Z", - "iopub.status.idle": "2024-01-09T15:10:28.295566Z", - "shell.execute_reply": "2024-01-09T15:10:28.294949Z" + "iopub.execute_input": "2024-01-10T06:19:25.216891Z", + "iopub.status.busy": "2024-01-10T06:19:25.216535Z", + "iopub.status.idle": "2024-01-10T06:19:25.223577Z", + "shell.execute_reply": "2024-01-10T06:19:25.223059Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.298045Z", - "iopub.status.busy": "2024-01-09T15:10:28.297701Z", - "iopub.status.idle": "2024-01-09T15:10:28.300677Z", - "shell.execute_reply": "2024-01-09T15:10:28.300062Z" + "iopub.execute_input": "2024-01-10T06:19:25.225967Z", + "iopub.status.busy": "2024-01-10T06:19:25.225725Z", + "iopub.status.idle": "2024-01-10T06:19:25.228713Z", + "shell.execute_reply": "2024-01-10T06:19:25.228156Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.303032Z", - "iopub.status.busy": "2024-01-09T15:10:28.302676Z", - "iopub.status.idle": "2024-01-09T15:10:28.306931Z", - "shell.execute_reply": "2024-01-09T15:10:28.306304Z" + "iopub.execute_input": "2024-01-10T06:19:25.231173Z", + "iopub.status.busy": "2024-01-10T06:19:25.230771Z", + "iopub.status.idle": "2024-01-10T06:19:25.234695Z", + "shell.execute_reply": "2024-01-10T06:19:25.234060Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.309465Z", - "iopub.status.busy": "2024-01-09T15:10:28.309078Z", - "iopub.status.idle": "2024-01-09T15:10:28.311910Z", - "shell.execute_reply": "2024-01-09T15:10:28.311358Z" + "iopub.execute_input": "2024-01-10T06:19:25.237168Z", + "iopub.status.busy": "2024-01-10T06:19:25.236804Z", + "iopub.status.idle": "2024-01-10T06:19:25.239581Z", + "shell.execute_reply": "2024-01-10T06:19:25.239043Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.314289Z", - "iopub.status.busy": "2024-01-09T15:10:28.313917Z", - "iopub.status.idle": "2024-01-09T15:10:28.318953Z", - "shell.execute_reply": "2024-01-09T15:10:28.318374Z" + "iopub.execute_input": "2024-01-10T06:19:25.241971Z", + "iopub.status.busy": "2024-01-10T06:19:25.241612Z", + "iopub.status.idle": "2024-01-10T06:19:25.246328Z", + "shell.execute_reply": "2024-01-10T06:19:25.245820Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.321383Z", - "iopub.status.busy": "2024-01-09T15:10:28.321068Z", - "iopub.status.idle": "2024-01-09T15:10:28.355171Z", - "shell.execute_reply": "2024-01-09T15:10:28.354575Z" + "iopub.execute_input": "2024-01-10T06:19:25.248797Z", + "iopub.status.busy": "2024-01-10T06:19:25.248447Z", + "iopub.status.idle": "2024-01-10T06:19:25.282644Z", + "shell.execute_reply": "2024-01-10T06:19:25.282066Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.358456Z", - "iopub.status.busy": "2024-01-09T15:10:28.357990Z", - "iopub.status.idle": "2024-01-09T15:10:28.363393Z", - "shell.execute_reply": "2024-01-09T15:10:28.362794Z" + "iopub.execute_input": "2024-01-10T06:19:25.285678Z", + "iopub.status.busy": "2024-01-10T06:19:25.285230Z", + "iopub.status.idle": "2024-01-10T06:19:25.290437Z", + "shell.execute_reply": "2024-01-10T06:19:25.289851Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 36c1ca182..7b346383f 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:33.872482Z", - "iopub.status.busy": "2024-01-09T15:10:33.871938Z", - "iopub.status.idle": "2024-01-09T15:10:35.001122Z", - "shell.execute_reply": "2024-01-09T15:10:35.000405Z" + "iopub.execute_input": "2024-01-10T06:19:30.967895Z", + "iopub.status.busy": "2024-01-10T06:19:30.967699Z", + "iopub.status.idle": "2024-01-10T06:19:32.111090Z", + "shell.execute_reply": "2024-01-10T06:19:32.110423Z" }, "nbsphinx": "hidden" }, @@ -78,7 +78,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -104,10 +104,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.004129Z", - "iopub.status.busy": "2024-01-09T15:10:35.003784Z", - "iopub.status.idle": "2024-01-09T15:10:35.310402Z", - "shell.execute_reply": "2024-01-09T15:10:35.309674Z" + "iopub.execute_input": "2024-01-10T06:19:32.114057Z", + "iopub.status.busy": "2024-01-10T06:19:32.113591Z", + "iopub.status.idle": "2024-01-10T06:19:32.424380Z", + "shell.execute_reply": "2024-01-10T06:19:32.423712Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.313492Z", - "iopub.status.busy": "2024-01-09T15:10:35.313262Z", - "iopub.status.idle": "2024-01-09T15:10:35.327072Z", - "shell.execute_reply": "2024-01-09T15:10:35.326517Z" + "iopub.execute_input": "2024-01-10T06:19:32.427902Z", + "iopub.status.busy": "2024-01-10T06:19:32.427455Z", + "iopub.status.idle": "2024-01-10T06:19:32.442273Z", + "shell.execute_reply": "2024-01-10T06:19:32.441703Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.329557Z", - "iopub.status.busy": "2024-01-09T15:10:35.329181Z", - "iopub.status.idle": "2024-01-09T15:10:38.004607Z", - "shell.execute_reply": "2024-01-09T15:10:38.003950Z" + "iopub.execute_input": "2024-01-10T06:19:32.445046Z", + "iopub.status.busy": "2024-01-10T06:19:32.444637Z", + "iopub.status.idle": "2024-01-10T06:19:35.158086Z", + "shell.execute_reply": "2024-01-10T06:19:35.157420Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:38.007360Z", - "iopub.status.busy": "2024-01-09T15:10:38.006936Z", - "iopub.status.idle": "2024-01-09T15:10:39.598858Z", - "shell.execute_reply": "2024-01-09T15:10:39.598216Z" + "iopub.execute_input": "2024-01-10T06:19:35.160758Z", + "iopub.status.busy": "2024-01-10T06:19:35.160383Z", + "iopub.status.idle": "2024-01-10T06:19:36.735870Z", + "shell.execute_reply": "2024-01-10T06:19:36.735258Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:39.601830Z", - "iopub.status.busy": "2024-01-09T15:10:39.601471Z", - "iopub.status.idle": "2024-01-09T15:10:39.606043Z", - "shell.execute_reply": "2024-01-09T15:10:39.605534Z" + "iopub.execute_input": "2024-01-10T06:19:36.738886Z", + "iopub.status.busy": "2024-01-10T06:19:36.738441Z", + "iopub.status.idle": "2024-01-10T06:19:36.743531Z", + "shell.execute_reply": "2024-01-10T06:19:36.742994Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:39.608459Z", - "iopub.status.busy": "2024-01-09T15:10:39.608085Z", - "iopub.status.idle": "2024-01-09T15:10:41.011726Z", - "shell.execute_reply": "2024-01-09T15:10:41.010970Z" + "iopub.execute_input": "2024-01-10T06:19:36.745916Z", + "iopub.status.busy": "2024-01-10T06:19:36.745544Z", + "iopub.status.idle": "2024-01-10T06:19:38.110520Z", + "shell.execute_reply": "2024-01-10T06:19:38.109746Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:41.015211Z", - "iopub.status.busy": "2024-01-09T15:10:41.014357Z", - "iopub.status.idle": "2024-01-09T15:10:43.803390Z", - "shell.execute_reply": "2024-01-09T15:10:43.802651Z" + "iopub.execute_input": "2024-01-10T06:19:38.113635Z", + "iopub.status.busy": "2024-01-10T06:19:38.112981Z", + "iopub.status.idle": "2024-01-10T06:19:40.958055Z", + "shell.execute_reply": "2024-01-10T06:19:40.957384Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.805988Z", - "iopub.status.busy": "2024-01-09T15:10:43.805776Z", - "iopub.status.idle": "2024-01-09T15:10:43.810722Z", - "shell.execute_reply": "2024-01-09T15:10:43.810187Z" + "iopub.execute_input": "2024-01-10T06:19:40.960715Z", + "iopub.status.busy": "2024-01-10T06:19:40.960355Z", + "iopub.status.idle": "2024-01-10T06:19:40.965359Z", + "shell.execute_reply": "2024-01-10T06:19:40.964831Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.813015Z", - "iopub.status.busy": "2024-01-09T15:10:43.812816Z", - "iopub.status.idle": "2024-01-09T15:10:43.816883Z", - "shell.execute_reply": "2024-01-09T15:10:43.816346Z" + "iopub.execute_input": "2024-01-10T06:19:40.967914Z", + "iopub.status.busy": "2024-01-10T06:19:40.967542Z", + "iopub.status.idle": "2024-01-10T06:19:40.971564Z", + "shell.execute_reply": "2024-01-10T06:19:40.971006Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.819153Z", - "iopub.status.busy": "2024-01-09T15:10:43.818946Z", - "iopub.status.idle": "2024-01-09T15:10:43.822258Z", - "shell.execute_reply": "2024-01-09T15:10:43.821731Z" + "iopub.execute_input": "2024-01-10T06:19:40.973986Z", + "iopub.status.busy": "2024-01-10T06:19:40.973628Z", + "iopub.status.idle": "2024-01-10T06:19:40.976974Z", + "shell.execute_reply": "2024-01-10T06:19:40.976437Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 35a1dc761..c7c6f8441 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": "2024-01-09T15:10:48.634699Z", - "iopub.status.busy": "2024-01-09T15:10:48.634509Z", - "iopub.status.idle": "2024-01-09T15:10:49.698381Z", - "shell.execute_reply": "2024-01-09T15:10:49.697765Z" + "iopub.execute_input": "2024-01-10T06:19:45.560642Z", + "iopub.status.busy": "2024-01-10T06:19:45.560184Z", + "iopub.status.idle": "2024-01-10T06:19:46.672378Z", + "shell.execute_reply": "2024-01-10T06:19:46.671781Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:49.701282Z", - "iopub.status.busy": "2024-01-09T15:10:49.700795Z", - "iopub.status.idle": "2024-01-09T15:10:50.688584Z", - "shell.execute_reply": "2024-01-09T15:10:50.687863Z" + "iopub.execute_input": "2024-01-10T06:19:46.675636Z", + "iopub.status.busy": "2024-01-10T06:19:46.674959Z", + "iopub.status.idle": "2024-01-10T06:19:48.039619Z", + "shell.execute_reply": "2024-01-10T06:19:48.038865Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.691425Z", - "iopub.status.busy": "2024-01-09T15:10:50.691148Z", - "iopub.status.idle": "2024-01-09T15:10:50.694558Z", - "shell.execute_reply": "2024-01-09T15:10:50.693926Z" + "iopub.execute_input": "2024-01-10T06:19:48.042749Z", + "iopub.status.busy": "2024-01-10T06:19:48.042298Z", + "iopub.status.idle": "2024-01-10T06:19:48.045718Z", + "shell.execute_reply": "2024-01-10T06:19:48.045190Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.696938Z", - "iopub.status.busy": "2024-01-09T15:10:50.696563Z", - "iopub.status.idle": "2024-01-09T15:10:50.702523Z", - "shell.execute_reply": "2024-01-09T15:10:50.702027Z" + "iopub.execute_input": "2024-01-10T06:19:48.048076Z", + "iopub.status.busy": "2024-01-10T06:19:48.047707Z", + "iopub.status.idle": "2024-01-10T06:19:48.053658Z", + "shell.execute_reply": "2024-01-10T06:19:48.053131Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.704931Z", - "iopub.status.busy": "2024-01-09T15:10:50.704571Z", - "iopub.status.idle": "2024-01-09T15:10:51.298459Z", - "shell.execute_reply": "2024-01-09T15:10:51.297776Z" + "iopub.execute_input": "2024-01-10T06:19:48.056002Z", + "iopub.status.busy": "2024-01-10T06:19:48.055627Z", + "iopub.status.idle": "2024-01-10T06:19:48.667461Z", + "shell.execute_reply": "2024-01-10T06:19:48.666800Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.301336Z", - "iopub.status.busy": "2024-01-09T15:10:51.300843Z", - "iopub.status.idle": "2024-01-09T15:10:51.306904Z", - "shell.execute_reply": "2024-01-09T15:10:51.306309Z" + "iopub.execute_input": "2024-01-10T06:19:48.670652Z", + "iopub.status.busy": "2024-01-10T06:19:48.670174Z", + "iopub.status.idle": "2024-01-10T06:19:48.676467Z", + "shell.execute_reply": "2024-01-10T06:19:48.675874Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.309356Z", - "iopub.status.busy": "2024-01-09T15:10:51.308880Z", - "iopub.status.idle": "2024-01-09T15:10:51.313227Z", - "shell.execute_reply": "2024-01-09T15:10:51.312620Z" + "iopub.execute_input": "2024-01-10T06:19:48.678861Z", + "iopub.status.busy": "2024-01-10T06:19:48.678516Z", + "iopub.status.idle": "2024-01-10T06:19:48.682696Z", + "shell.execute_reply": "2024-01-10T06:19:48.682085Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.315422Z", - "iopub.status.busy": "2024-01-09T15:10:51.315224Z", - "iopub.status.idle": "2024-01-09T15:10:51.885911Z", - "shell.execute_reply": "2024-01-09T15:10:51.885156Z" + "iopub.execute_input": "2024-01-10T06:19:48.685095Z", + "iopub.status.busy": "2024-01-10T06:19:48.684751Z", + "iopub.status.idle": "2024-01-10T06:19:49.298576Z", + "shell.execute_reply": "2024-01-10T06:19:49.297851Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.888485Z", - "iopub.status.busy": "2024-01-09T15:10:51.888266Z", - "iopub.status.idle": "2024-01-09T15:10:51.976600Z", - "shell.execute_reply": "2024-01-09T15:10:51.976062Z" + "iopub.execute_input": "2024-01-10T06:19:49.301316Z", + "iopub.status.busy": "2024-01-10T06:19:49.301086Z", + "iopub.status.idle": "2024-01-10T06:19:49.403290Z", + "shell.execute_reply": "2024-01-10T06:19:49.402577Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.979003Z", - "iopub.status.busy": "2024-01-09T15:10:51.978648Z", - "iopub.status.idle": "2024-01-09T15:10:51.983228Z", - "shell.execute_reply": "2024-01-09T15:10:51.982602Z" + "iopub.execute_input": "2024-01-10T06:19:49.406087Z", + "iopub.status.busy": "2024-01-10T06:19:49.405678Z", + "iopub.status.idle": "2024-01-10T06:19:49.410408Z", + "shell.execute_reply": "2024-01-10T06:19:49.409813Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.985654Z", - "iopub.status.busy": "2024-01-09T15:10:51.985276Z", - "iopub.status.idle": "2024-01-09T15:10:52.359062Z", - "shell.execute_reply": "2024-01-09T15:10:52.358392Z" + "iopub.execute_input": "2024-01-10T06:19:49.412718Z", + "iopub.status.busy": "2024-01-10T06:19:49.412513Z", + "iopub.status.idle": "2024-01-10T06:19:49.802789Z", + "shell.execute_reply": "2024-01-10T06:19:49.802086Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:52.362687Z", - "iopub.status.busy": "2024-01-09T15:10:52.362275Z", - "iopub.status.idle": "2024-01-09T15:10:52.698286Z", - "shell.execute_reply": "2024-01-09T15:10:52.697683Z" + "iopub.execute_input": "2024-01-10T06:19:49.805807Z", + "iopub.status.busy": "2024-01-10T06:19:49.805357Z", + "iopub.status.idle": "2024-01-10T06:19:50.143112Z", + "shell.execute_reply": "2024-01-10T06:19:50.142432Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:52.701391Z", - "iopub.status.busy": "2024-01-09T15:10:52.700963Z", - "iopub.status.idle": "2024-01-09T15:10:53.082876Z", - "shell.execute_reply": "2024-01-09T15:10:53.082203Z" + "iopub.execute_input": "2024-01-10T06:19:50.146573Z", + "iopub.status.busy": "2024-01-10T06:19:50.146168Z", + "iopub.status.idle": "2024-01-10T06:19:50.502588Z", + "shell.execute_reply": "2024-01-10T06:19:50.501862Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:53.086499Z", - "iopub.status.busy": "2024-01-09T15:10:53.086115Z", - "iopub.status.idle": "2024-01-09T15:10:53.548571Z", - "shell.execute_reply": "2024-01-09T15:10:53.547932Z" + "iopub.execute_input": "2024-01-10T06:19:50.506244Z", + "iopub.status.busy": "2024-01-10T06:19:50.505804Z", + "iopub.status.idle": "2024-01-10T06:19:50.969893Z", + "shell.execute_reply": "2024-01-10T06:19:50.969270Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:53.552977Z", - "iopub.status.busy": "2024-01-09T15:10:53.552718Z", - "iopub.status.idle": "2024-01-09T15:10:54.003568Z", - "shell.execute_reply": "2024-01-09T15:10:54.002907Z" + "iopub.execute_input": "2024-01-10T06:19:50.974645Z", + "iopub.status.busy": "2024-01-10T06:19:50.974221Z", + "iopub.status.idle": "2024-01-10T06:19:51.430649Z", + "shell.execute_reply": "2024-01-10T06:19:51.429876Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.007091Z", - "iopub.status.busy": "2024-01-09T15:10:54.006877Z", - "iopub.status.idle": "2024-01-09T15:10:54.336048Z", - "shell.execute_reply": "2024-01-09T15:10:54.335426Z" + "iopub.execute_input": "2024-01-10T06:19:51.434311Z", + "iopub.status.busy": "2024-01-10T06:19:51.433913Z", + "iopub.status.idle": "2024-01-10T06:19:51.777065Z", + "shell.execute_reply": "2024-01-10T06:19:51.776398Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.338833Z", - "iopub.status.busy": "2024-01-09T15:10:54.338451Z", - "iopub.status.idle": "2024-01-09T15:10:54.538333Z", - "shell.execute_reply": "2024-01-09T15:10:54.537663Z" + "iopub.execute_input": "2024-01-10T06:19:51.779898Z", + "iopub.status.busy": "2024-01-10T06:19:51.779494Z", + "iopub.status.idle": "2024-01-10T06:19:51.980612Z", + "shell.execute_reply": "2024-01-10T06:19:51.979893Z" } }, "outputs": [ @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.541044Z", - "iopub.status.busy": "2024-01-09T15:10:54.540671Z", - "iopub.status.idle": "2024-01-09T15:10:54.544417Z", - "shell.execute_reply": "2024-01-09T15:10:54.543907Z" + "iopub.execute_input": "2024-01-10T06:19:51.983380Z", + "iopub.status.busy": "2024-01-10T06:19:51.982885Z", + "iopub.status.idle": "2024-01-10T06:19:51.986852Z", + "shell.execute_reply": "2024-01-10T06:19:51.986244Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index c3b680134..7da4b0e12 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": "2024-01-09T15:10:56.633647Z", - "iopub.status.busy": "2024-01-09T15:10:56.633455Z", - "iopub.status.idle": "2024-01-09T15:10:58.635058Z", - "shell.execute_reply": "2024-01-09T15:10:58.634391Z" + "iopub.execute_input": "2024-01-10T06:19:54.392516Z", + "iopub.status.busy": "2024-01-10T06:19:54.392326Z", + "iopub.status.idle": "2024-01-10T06:19:56.392552Z", + "shell.execute_reply": "2024-01-10T06:19:56.391830Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:58.638637Z", - "iopub.status.busy": "2024-01-09T15:10:58.638061Z", - "iopub.status.idle": "2024-01-09T15:10:58.971426Z", - "shell.execute_reply": "2024-01-09T15:10:58.970797Z" + "iopub.execute_input": "2024-01-10T06:19:56.395661Z", + "iopub.status.busy": "2024-01-10T06:19:56.395318Z", + "iopub.status.idle": "2024-01-10T06:19:56.729764Z", + "shell.execute_reply": "2024-01-10T06:19:56.729033Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:58.974504Z", - "iopub.status.busy": "2024-01-09T15:10:58.973978Z", - "iopub.status.idle": "2024-01-09T15:10:58.978659Z", - "shell.execute_reply": "2024-01-09T15:10:58.978045Z" + "iopub.execute_input": "2024-01-10T06:19:56.732778Z", + "iopub.status.busy": "2024-01-10T06:19:56.732256Z", + "iopub.status.idle": "2024-01-10T06:19:56.736298Z", + "shell.execute_reply": "2024-01-10T06:19:56.735811Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:58.981265Z", - "iopub.status.busy": "2024-01-09T15:10:58.980808Z", - "iopub.status.idle": "2024-01-09T15:11:03.830245Z", - "shell.execute_reply": "2024-01-09T15:11:03.829635Z" + "iopub.execute_input": "2024-01-10T06:19:56.738602Z", + "iopub.status.busy": "2024-01-10T06:19:56.738240Z", + "iopub.status.idle": "2024-01-10T06:20:01.246797Z", + "shell.execute_reply": "2024-01-10T06:20:01.246106Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6785377554c5413c9114e6cdda73e847", + "model_id": "d9eb539b8c1f4f97a89e8db15123410e", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:03.833201Z", - "iopub.status.busy": "2024-01-09T15:11:03.832604Z", - "iopub.status.idle": "2024-01-09T15:11:03.837915Z", - "shell.execute_reply": "2024-01-09T15:11:03.837288Z" + "iopub.execute_input": "2024-01-10T06:20:01.249671Z", + "iopub.status.busy": "2024-01-10T06:20:01.249198Z", + "iopub.status.idle": "2024-01-10T06:20:01.254431Z", + "shell.execute_reply": "2024-01-10T06:20:01.253797Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:03.840618Z", - "iopub.status.busy": "2024-01-09T15:11:03.840248Z", - "iopub.status.idle": "2024-01-09T15:11:04.386624Z", - "shell.execute_reply": "2024-01-09T15:11:04.385933Z" + "iopub.execute_input": "2024-01-10T06:20:01.256861Z", + "iopub.status.busy": "2024-01-10T06:20:01.256438Z", + "iopub.status.idle": "2024-01-10T06:20:01.804589Z", + "shell.execute_reply": "2024-01-10T06:20:01.803886Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:04.389435Z", - "iopub.status.busy": "2024-01-09T15:11:04.389046Z", - "iopub.status.idle": "2024-01-09T15:11:05.043291Z", - "shell.execute_reply": "2024-01-09T15:11:05.042564Z" + "iopub.execute_input": "2024-01-10T06:20:01.807206Z", + "iopub.status.busy": "2024-01-10T06:20:01.806947Z", + "iopub.status.idle": "2024-01-10T06:20:02.469451Z", + "shell.execute_reply": "2024-01-10T06:20:02.468760Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:05.046224Z", - "iopub.status.busy": "2024-01-09T15:11:05.045790Z", - "iopub.status.idle": "2024-01-09T15:11:05.049554Z", - "shell.execute_reply": "2024-01-09T15:11:05.048940Z" + "iopub.execute_input": "2024-01-10T06:20:02.472091Z", + "iopub.status.busy": "2024-01-10T06:20:02.471875Z", + "iopub.status.idle": "2024-01-10T06:20:02.475815Z", + "shell.execute_reply": "2024-01-10T06:20:02.475238Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:05.052109Z", - "iopub.status.busy": "2024-01-09T15:11:05.051725Z", - "iopub.status.idle": "2024-01-09T15:11:17.156933Z", - "shell.execute_reply": "2024-01-09T15:11:17.156258Z" + "iopub.execute_input": "2024-01-10T06:20:02.478426Z", + "iopub.status.busy": "2024-01-10T06:20:02.478197Z", + "iopub.status.idle": "2024-01-10T06:20:14.823323Z", + "shell.execute_reply": "2024-01-10T06:20:14.822587Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:17.159533Z", - "iopub.status.busy": "2024-01-09T15:11:17.159328Z", - "iopub.status.idle": "2024-01-09T15:11:18.699138Z", - "shell.execute_reply": "2024-01-09T15:11:18.698401Z" + "iopub.execute_input": "2024-01-10T06:20:14.826047Z", + "iopub.status.busy": "2024-01-10T06:20:14.825621Z", + "iopub.status.idle": "2024-01-10T06:20:16.419516Z", + "shell.execute_reply": "2024-01-10T06:20:16.418738Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:18.702304Z", - "iopub.status.busy": "2024-01-09T15:11:18.701770Z", - "iopub.status.idle": "2024-01-09T15:11:18.932225Z", - "shell.execute_reply": "2024-01-09T15:11:18.931457Z" + "iopub.execute_input": "2024-01-10T06:20:16.422525Z", + "iopub.status.busy": "2024-01-10T06:20:16.421993Z", + "iopub.status.idle": "2024-01-10T06:20:16.690358Z", + "shell.execute_reply": "2024-01-10T06:20:16.689659Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:18.935738Z", - "iopub.status.busy": "2024-01-09T15:11:18.935518Z", - "iopub.status.idle": "2024-01-09T15:11:19.594613Z", - "shell.execute_reply": "2024-01-09T15:11:19.593982Z" + "iopub.execute_input": "2024-01-10T06:20:16.694052Z", + "iopub.status.busy": "2024-01-10T06:20:16.693495Z", + "iopub.status.idle": "2024-01-10T06:20:17.376849Z", + "shell.execute_reply": "2024-01-10T06:20:17.376222Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:19.597729Z", - "iopub.status.busy": "2024-01-09T15:11:19.597226Z", - "iopub.status.idle": "2024-01-09T15:11:20.093165Z", - "shell.execute_reply": "2024-01-09T15:11:20.092485Z" + "iopub.execute_input": "2024-01-10T06:20:17.379909Z", + "iopub.status.busy": "2024-01-10T06:20:17.379542Z", + "iopub.status.idle": "2024-01-10T06:20:17.889849Z", + "shell.execute_reply": "2024-01-10T06:20:17.889143Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.095747Z", - "iopub.status.busy": "2024-01-09T15:11:20.095535Z", - "iopub.status.idle": "2024-01-09T15:11:20.339320Z", - "shell.execute_reply": "2024-01-09T15:11:20.338595Z" + "iopub.execute_input": "2024-01-10T06:20:17.892718Z", + "iopub.status.busy": "2024-01-10T06:20:17.892281Z", + "iopub.status.idle": "2024-01-10T06:20:18.146698Z", + "shell.execute_reply": "2024-01-10T06:20:18.145918Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.342022Z", - "iopub.status.busy": "2024-01-09T15:11:20.341661Z", - "iopub.status.idle": "2024-01-09T15:11:20.427396Z", - "shell.execute_reply": "2024-01-09T15:11:20.426809Z" + "iopub.execute_input": "2024-01-10T06:20:18.150320Z", + "iopub.status.busy": "2024-01-10T06:20:18.149921Z", + "iopub.status.idle": "2024-01-10T06:20:18.235461Z", + "shell.execute_reply": "2024-01-10T06:20:18.234877Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.430348Z", - "iopub.status.busy": "2024-01-09T15:11:20.429912Z", - "iopub.status.idle": "2024-01-09T15:11:59.632193Z", - "shell.execute_reply": "2024-01-09T15:11:59.631458Z" + "iopub.execute_input": "2024-01-10T06:20:18.238296Z", + "iopub.status.busy": "2024-01-10T06:20:18.238059Z", + "iopub.status.idle": "2024-01-10T06:20:56.606997Z", + "shell.execute_reply": "2024-01-10T06:20:56.606212Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:59.635064Z", - "iopub.status.busy": "2024-01-09T15:11:59.634592Z", - "iopub.status.idle": "2024-01-09T15:12:00.855953Z", - "shell.execute_reply": "2024-01-09T15:12:00.855313Z" + "iopub.execute_input": "2024-01-10T06:20:56.609877Z", + "iopub.status.busy": "2024-01-10T06:20:56.609373Z", + "iopub.status.idle": "2024-01-10T06:20:57.846657Z", + "shell.execute_reply": "2024-01-10T06:20:57.845936Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:00.859064Z", - "iopub.status.busy": "2024-01-09T15:12:00.858540Z", - "iopub.status.idle": "2024-01-09T15:12:01.050957Z", - "shell.execute_reply": "2024-01-09T15:12:01.050168Z" + "iopub.execute_input": "2024-01-10T06:20:57.850038Z", + "iopub.status.busy": "2024-01-10T06:20:57.849349Z", + "iopub.status.idle": "2024-01-10T06:20:58.037779Z", + "shell.execute_reply": "2024-01-10T06:20:58.037164Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:01.054389Z", - "iopub.status.busy": "2024-01-09T15:12:01.053917Z", - "iopub.status.idle": "2024-01-09T15:12:01.057374Z", - "shell.execute_reply": "2024-01-09T15:12:01.056825Z" + "iopub.execute_input": "2024-01-10T06:20:58.040792Z", + "iopub.status.busy": "2024-01-10T06:20:58.040310Z", + "iopub.status.idle": "2024-01-10T06:20:58.043677Z", + "shell.execute_reply": "2024-01-10T06:20:58.043170Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:01.060012Z", - "iopub.status.busy": "2024-01-09T15:12:01.059634Z", - "iopub.status.idle": "2024-01-09T15:12:01.068289Z", - "shell.execute_reply": "2024-01-09T15:12:01.067766Z" + "iopub.execute_input": "2024-01-10T06:20:58.045942Z", + "iopub.status.busy": "2024-01-10T06:20:58.045737Z", + "iopub.status.idle": "2024-01-10T06:20:58.054356Z", + "shell.execute_reply": "2024-01-10T06:20:58.053867Z" }, "nbsphinx": "hidden" }, @@ -1017,28 +1017,38 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1c66f9007a5f45f3a8aae1f485d48b4c": { + "327a08e1d5534ee99c966c84c3365801": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_96181e031601404e9bb6f42df63b1801", - "placeholder": "​", - "style": "IPY_MODEL_b472494f952d4dff9c1e2f43bba80f5b", - "value": " 170498071/170498071 [00:01<00:00, 91805669.92it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "409c38572c3f42069bdd6f978250bae9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "29e450d30be34be2a426c6e707ad0e22": { + "5424c6f35faa4960b53ddef1c4ec0405": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1090,60 +1100,7 @@ "width": null } }, - "3f77e26e680d4c8897310817324387f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4a1e83ed854c4e3b911b036220bf7ee5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6785377554c5413c9114e6cdda73e847": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8fce3ec758fb4d5bb5c2de7de41c9301", - "IPY_MODEL_a4e0e6e6dd8040e2bfa3cd596877c850", - "IPY_MODEL_1c66f9007a5f45f3a8aae1f485d48b4c" - ], - "layout": "IPY_MODEL_6bf4e8a49449469fa2b2e6ba3107109e" - } - }, - "6bf4e8a49449469fa2b2e6ba3107109e": { + "5ba665ca475147dd93b0e931cb42babc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1195,28 +1152,22 @@ "width": null } }, - "8fce3ec758fb4d5bb5c2de7de41c9301": { + "6c1729328c1842ab8fbe77faf7094fc4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d43af534bce44f7b99d75c8f60b8da12", - "placeholder": "​", - "style": "IPY_MODEL_4a1e83ed854c4e3b911b036220bf7ee5", - "value": "100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "96181e031601404e9bb6f42df63b1801": { + "831d732aa1314d0fb6bdef874bcd5787": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1268,7 +1219,28 @@ "width": null } }, - "a4e0e6e6dd8040e2bfa3cd596877c850": { + "9f7917b60d274c8d9aff9ccbf9989105": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5424c6f35faa4960b53ddef1c4ec0405", + "placeholder": "​", + "style": "IPY_MODEL_409c38572c3f42069bdd6f978250bae9", + "value": " 170498071/170498071 [00:01<00:00, 109601673.05it/s]" + } + }, + "b47353d08b504eda93deb2aba25e51a2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -1284,30 +1256,58 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_29e450d30be34be2a426c6e707ad0e22", + "layout": "IPY_MODEL_831d732aa1314d0fb6bdef874bcd5787", "max": 170498071.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_3f77e26e680d4c8897310817324387f7", + "style": "IPY_MODEL_327a08e1d5534ee99c966c84c3365801", "value": 170498071.0 } }, - "b472494f952d4dff9c1e2f43bba80f5b": { + "ceb5d255b6e1467bbf644bfe84de536e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fcb1d9c928d4414fa8045d1040339642", + "placeholder": "​", + "style": "IPY_MODEL_6c1729328c1842ab8fbe77faf7094fc4", + "value": "100%" + } + }, + "d9eb539b8c1f4f97a89e8db15123410e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ceb5d255b6e1467bbf644bfe84de536e", + "IPY_MODEL_b47353d08b504eda93deb2aba25e51a2", + "IPY_MODEL_9f7917b60d274c8d9aff9ccbf9989105" + ], + "layout": "IPY_MODEL_5ba665ca475147dd93b0e931cb42babc" } }, - "d43af534bce44f7b99d75c8f60b8da12": { + "fcb1d9c928d4414fa8045d1040339642": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index dc1ec9a92..e4f71e4a3 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:05.840462Z", - "iopub.status.busy": "2024-01-09T15:12:05.840272Z", - "iopub.status.idle": "2024-01-09T15:12:06.977628Z", - "shell.execute_reply": "2024-01-09T15:12:06.977010Z" + "iopub.execute_input": "2024-01-10T06:21:03.429780Z", + "iopub.status.busy": "2024-01-10T06:21:03.429318Z", + "iopub.status.idle": "2024-01-10T06:21:04.536960Z", + "shell.execute_reply": "2024-01-10T06:21:04.536343Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:06.980932Z", - "iopub.status.busy": "2024-01-09T15:12:06.980441Z", - "iopub.status.idle": "2024-01-09T15:12:06.998272Z", - "shell.execute_reply": "2024-01-09T15:12:06.997696Z" + "iopub.execute_input": "2024-01-10T06:21:04.539950Z", + "iopub.status.busy": "2024-01-10T06:21:04.539416Z", + "iopub.status.idle": "2024-01-10T06:21:04.555703Z", + "shell.execute_reply": "2024-01-10T06:21:04.555171Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.001055Z", - "iopub.status.busy": "2024-01-09T15:12:07.000715Z", - "iopub.status.idle": "2024-01-09T15:12:07.003936Z", - "shell.execute_reply": "2024-01-09T15:12:07.003368Z" + "iopub.execute_input": "2024-01-10T06:21:04.558355Z", + "iopub.status.busy": "2024-01-10T06:21:04.557984Z", + "iopub.status.idle": "2024-01-10T06:21:04.561236Z", + "shell.execute_reply": "2024-01-10T06:21:04.560643Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.006503Z", - "iopub.status.busy": "2024-01-09T15:12:07.006138Z", - "iopub.status.idle": "2024-01-09T15:12:07.076448Z", - "shell.execute_reply": "2024-01-09T15:12:07.075824Z" + "iopub.execute_input": "2024-01-10T06:21:04.563519Z", + "iopub.status.busy": "2024-01-10T06:21:04.563180Z", + "iopub.status.idle": "2024-01-10T06:21:04.709969Z", + "shell.execute_reply": "2024-01-10T06:21:04.709340Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.079136Z", - "iopub.status.busy": "2024-01-09T15:12:07.078781Z", - "iopub.status.idle": "2024-01-09T15:12:07.366755Z", - "shell.execute_reply": "2024-01-09T15:12:07.366122Z" + "iopub.execute_input": "2024-01-10T06:21:04.712608Z", + "iopub.status.busy": "2024-01-10T06:21:04.712301Z", + "iopub.status.idle": "2024-01-10T06:21:04.987624Z", + "shell.execute_reply": "2024-01-10T06:21:04.986929Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.369612Z", - "iopub.status.busy": "2024-01-09T15:12:07.369257Z", - "iopub.status.idle": "2024-01-09T15:12:07.629024Z", - "shell.execute_reply": "2024-01-09T15:12:07.628355Z" + "iopub.execute_input": "2024-01-10T06:21:04.990511Z", + "iopub.status.busy": "2024-01-10T06:21:04.990062Z", + "iopub.status.idle": "2024-01-10T06:21:05.250260Z", + "shell.execute_reply": "2024-01-10T06:21:05.249551Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.631687Z", - "iopub.status.busy": "2024-01-09T15:12:07.631350Z", - "iopub.status.idle": "2024-01-09T15:12:07.636502Z", - "shell.execute_reply": "2024-01-09T15:12:07.635900Z" + "iopub.execute_input": "2024-01-10T06:21:05.252808Z", + "iopub.status.busy": "2024-01-10T06:21:05.252554Z", + "iopub.status.idle": "2024-01-10T06:21:05.257434Z", + "shell.execute_reply": "2024-01-10T06:21:05.256897Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.639085Z", - "iopub.status.busy": "2024-01-09T15:12:07.638701Z", - "iopub.status.idle": "2024-01-09T15:12:07.645535Z", - "shell.execute_reply": "2024-01-09T15:12:07.645020Z" + "iopub.execute_input": "2024-01-10T06:21:05.259727Z", + "iopub.status.busy": "2024-01-10T06:21:05.259513Z", + "iopub.status.idle": "2024-01-10T06:21:05.266109Z", + "shell.execute_reply": "2024-01-10T06:21:05.265624Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.648187Z", - "iopub.status.busy": "2024-01-09T15:12:07.647809Z", - "iopub.status.idle": "2024-01-09T15:12:07.650821Z", - "shell.execute_reply": "2024-01-09T15:12:07.650193Z" + "iopub.execute_input": "2024-01-10T06:21:05.268611Z", + "iopub.status.busy": "2024-01-10T06:21:05.268134Z", + "iopub.status.idle": "2024-01-10T06:21:05.271485Z", + "shell.execute_reply": "2024-01-10T06:21:05.270856Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.653165Z", - "iopub.status.busy": "2024-01-09T15:12:07.652806Z", - "iopub.status.idle": "2024-01-09T15:12:18.001852Z", - "shell.execute_reply": "2024-01-09T15:12:18.001187Z" + "iopub.execute_input": "2024-01-10T06:21:05.273934Z", + "iopub.status.busy": "2024-01-10T06:21:05.273375Z", + "iopub.status.idle": "2024-01-10T06:21:15.493411Z", + "shell.execute_reply": "2024-01-10T06:21:15.492743Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.005262Z", - "iopub.status.busy": "2024-01-09T15:12:18.004587Z", - "iopub.status.idle": "2024-01-09T15:12:18.012464Z", - "shell.execute_reply": "2024-01-09T15:12:18.011874Z" + "iopub.execute_input": "2024-01-10T06:21:15.496814Z", + "iopub.status.busy": "2024-01-10T06:21:15.496375Z", + "iopub.status.idle": "2024-01-10T06:21:15.504366Z", + "shell.execute_reply": "2024-01-10T06:21:15.503841Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.014599Z", - "iopub.status.busy": "2024-01-09T15:12:18.014395Z", - "iopub.status.idle": "2024-01-09T15:12:18.018358Z", - "shell.execute_reply": "2024-01-09T15:12:18.017721Z" + "iopub.execute_input": "2024-01-10T06:21:15.506693Z", + "iopub.status.busy": "2024-01-10T06:21:15.506461Z", + "iopub.status.idle": "2024-01-10T06:21:15.510803Z", + "shell.execute_reply": "2024-01-10T06:21:15.510172Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.020740Z", - "iopub.status.busy": "2024-01-09T15:12:18.020300Z", - "iopub.status.idle": "2024-01-09T15:12:18.024014Z", - "shell.execute_reply": "2024-01-09T15:12:18.023393Z" + "iopub.execute_input": "2024-01-10T06:21:15.513231Z", + "iopub.status.busy": "2024-01-10T06:21:15.512862Z", + "iopub.status.idle": "2024-01-10T06:21:15.516362Z", + "shell.execute_reply": "2024-01-10T06:21:15.515734Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.026410Z", - "iopub.status.busy": "2024-01-09T15:12:18.026069Z", - "iopub.status.idle": "2024-01-09T15:12:18.029251Z", - "shell.execute_reply": "2024-01-09T15:12:18.028677Z" + "iopub.execute_input": "2024-01-10T06:21:15.518807Z", + "iopub.status.busy": "2024-01-10T06:21:15.518425Z", + "iopub.status.idle": "2024-01-10T06:21:15.521733Z", + "shell.execute_reply": "2024-01-10T06:21:15.521168Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.031513Z", - "iopub.status.busy": "2024-01-09T15:12:18.031167Z", - "iopub.status.idle": "2024-01-09T15:12:18.039888Z", - "shell.execute_reply": "2024-01-09T15:12:18.039352Z" + "iopub.execute_input": "2024-01-10T06:21:15.524184Z", + "iopub.status.busy": "2024-01-10T06:21:15.523785Z", + "iopub.status.idle": "2024-01-10T06:21:15.533773Z", + "shell.execute_reply": "2024-01-10T06:21:15.532950Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.042168Z", - "iopub.status.busy": "2024-01-09T15:12:18.041966Z", - "iopub.status.idle": "2024-01-09T15:12:18.195751Z", - "shell.execute_reply": "2024-01-09T15:12:18.195047Z" + "iopub.execute_input": "2024-01-10T06:21:15.536364Z", + "iopub.status.busy": "2024-01-10T06:21:15.536012Z", + "iopub.status.idle": "2024-01-10T06:21:15.684972Z", + "shell.execute_reply": "2024-01-10T06:21:15.684237Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.198599Z", - "iopub.status.busy": "2024-01-09T15:12:18.198173Z", - "iopub.status.idle": "2024-01-09T15:12:18.332858Z", - "shell.execute_reply": "2024-01-09T15:12:18.332123Z" + "iopub.execute_input": "2024-01-10T06:21:15.688080Z", + "iopub.status.busy": "2024-01-10T06:21:15.687659Z", + "iopub.status.idle": "2024-01-10T06:21:15.819421Z", + "shell.execute_reply": "2024-01-10T06:21:15.818612Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.335602Z", - "iopub.status.busy": "2024-01-09T15:12:18.335247Z", - "iopub.status.idle": "2024-01-09T15:12:18.928023Z", - "shell.execute_reply": "2024-01-09T15:12:18.927316Z" + "iopub.execute_input": "2024-01-10T06:21:15.822502Z", + "iopub.status.busy": "2024-01-10T06:21:15.822274Z", + "iopub.status.idle": "2024-01-10T06:21:16.418907Z", + "shell.execute_reply": "2024-01-10T06:21:16.418233Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.930990Z", - "iopub.status.busy": "2024-01-09T15:12:18.930596Z", - "iopub.status.idle": "2024-01-09T15:12:19.021043Z", - "shell.execute_reply": "2024-01-09T15:12:19.020347Z" + "iopub.execute_input": "2024-01-10T06:21:16.422520Z", + "iopub.status.busy": "2024-01-10T06:21:16.421868Z", + "iopub.status.idle": "2024-01-10T06:21:16.505313Z", + "shell.execute_reply": "2024-01-10T06:21:16.504636Z" } }, "outputs": [ @@ -1056,10 +1056,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:19.024055Z", - "iopub.status.busy": "2024-01-09T15:12:19.023493Z", - "iopub.status.idle": "2024-01-09T15:12:19.033897Z", - "shell.execute_reply": "2024-01-09T15:12:19.033413Z" + "iopub.execute_input": "2024-01-10T06:21:16.508240Z", + "iopub.status.busy": "2024-01-10T06:21:16.507734Z", + "iopub.status.idle": "2024-01-10T06:21:16.517478Z", + "shell.execute_reply": "2024-01-10T06:21:16.517004Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 9b0ba559b..0b819b0e6 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": "2024-01-09T15:12:23.768822Z", - "iopub.status.busy": "2024-01-09T15:12:23.768628Z", - "iopub.status.idle": "2024-01-09T15:12:25.351894Z", - "shell.execute_reply": "2024-01-09T15:12:25.351148Z" + "iopub.execute_input": "2024-01-10T06:21:21.347024Z", + "iopub.status.busy": "2024-01-10T06:21:21.346437Z", + "iopub.status.idle": "2024-01-10T06:21:23.189285Z", + "shell.execute_reply": "2024-01-10T06:21:23.188541Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:25.354993Z", - "iopub.status.busy": "2024-01-09T15:12:25.354763Z", - "iopub.status.idle": "2024-01-09T15:13:21.827171Z", - "shell.execute_reply": "2024-01-09T15:13:21.826448Z" + "iopub.execute_input": "2024-01-10T06:21:23.192088Z", + "iopub.status.busy": "2024-01-10T06:21:23.191880Z", + "iopub.status.idle": "2024-01-10T06:22:22.377569Z", + "shell.execute_reply": "2024-01-10T06:22:22.376808Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:21.830029Z", - "iopub.status.busy": "2024-01-09T15:13:21.829817Z", - "iopub.status.idle": "2024-01-09T15:13:22.843182Z", - "shell.execute_reply": "2024-01-09T15:13:22.842504Z" + "iopub.execute_input": "2024-01-10T06:22:22.380517Z", + "iopub.status.busy": "2024-01-10T06:22:22.380259Z", + "iopub.status.idle": "2024-01-10T06:22:23.450564Z", + "shell.execute_reply": "2024-01-10T06:22:23.449819Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:13:22.845974Z", - "iopub.status.busy": "2024-01-09T15:13:22.845660Z", - "iopub.status.idle": "2024-01-09T15:13:22.849100Z", - "shell.execute_reply": "2024-01-09T15:13:22.848547Z" + "iopub.execute_input": "2024-01-10T06:22:23.453830Z", + "iopub.status.busy": "2024-01-10T06:22:23.453429Z", + "iopub.status.idle": "2024-01-10T06:22:23.457253Z", + "shell.execute_reply": "2024-01-10T06:22:23.456696Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.851692Z", - "iopub.status.busy": "2024-01-09T15:13:22.851255Z", - "iopub.status.idle": "2024-01-09T15:13:22.855201Z", - "shell.execute_reply": "2024-01-09T15:13:22.854623Z" + "iopub.execute_input": "2024-01-10T06:22:23.459910Z", + "iopub.status.busy": "2024-01-10T06:22:23.459513Z", + "iopub.status.idle": "2024-01-10T06:22:23.463699Z", + "shell.execute_reply": "2024-01-10T06:22:23.463158Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.857552Z", - "iopub.status.busy": "2024-01-09T15:13:22.857165Z", - "iopub.status.idle": "2024-01-09T15:13:22.860833Z", - "shell.execute_reply": "2024-01-09T15:13:22.860317Z" + "iopub.execute_input": "2024-01-10T06:22:23.466289Z", + "iopub.status.busy": "2024-01-10T06:22:23.465906Z", + "iopub.status.idle": "2024-01-10T06:22:23.469976Z", + "shell.execute_reply": "2024-01-10T06:22:23.469453Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.863269Z", - "iopub.status.busy": "2024-01-09T15:13:22.862914Z", - "iopub.status.idle": "2024-01-09T15:13:22.865888Z", - "shell.execute_reply": "2024-01-09T15:13:22.865340Z" + "iopub.execute_input": "2024-01-10T06:22:23.472455Z", + "iopub.status.busy": "2024-01-10T06:22:23.472097Z", + "iopub.status.idle": "2024-01-10T06:22:23.475073Z", + "shell.execute_reply": "2024-01-10T06:22:23.474523Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.868261Z", - "iopub.status.busy": "2024-01-09T15:13:22.867899Z", - "iopub.status.idle": "2024-01-09T15:14:48.127035Z", - "shell.execute_reply": "2024-01-09T15:14:48.126241Z" + "iopub.execute_input": "2024-01-10T06:22:23.477540Z", + "iopub.status.busy": "2024-01-10T06:22:23.477156Z", + "iopub.status.idle": "2024-01-10T06:23:49.090785Z", + "shell.execute_reply": "2024-01-10T06:23:49.089964Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7af30476e914a1aa2bc7bb22a08f3a8", + "model_id": "7834e62875da4394bdfc03b1501fa4a9", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a545d3bcc78342ada9bf902f7c4a7210", + "model_id": "2e3415f4577c42cb941753dfe0086640", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:48.129939Z", - "iopub.status.busy": "2024-01-09T15:14:48.129719Z", - "iopub.status.idle": "2024-01-09T15:14:48.878845Z", - "shell.execute_reply": "2024-01-09T15:14:48.878240Z" + "iopub.execute_input": "2024-01-10T06:23:49.094028Z", + "iopub.status.busy": "2024-01-10T06:23:49.093573Z", + "iopub.status.idle": "2024-01-10T06:23:49.874024Z", + "shell.execute_reply": "2024-01-10T06:23:49.873433Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:48.881431Z", - "iopub.status.busy": "2024-01-09T15:14:48.881092Z", - "iopub.status.idle": "2024-01-09T15:14:51.000672Z", - "shell.execute_reply": "2024-01-09T15:14:51.000006Z" + "iopub.execute_input": "2024-01-10T06:23:49.876789Z", + "iopub.status.busy": "2024-01-10T06:23:49.876371Z", + "iopub.status.idle": "2024-01-10T06:23:52.004759Z", + "shell.execute_reply": "2024-01-10T06:23:52.004056Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:51.003552Z", - "iopub.status.busy": "2024-01-09T15:14:51.003156Z", - "iopub.status.idle": "2024-01-09T15:15:20.041523Z", - "shell.execute_reply": "2024-01-09T15:15:20.040827Z" + "iopub.execute_input": "2024-01-10T06:23:52.007358Z", + "iopub.status.busy": "2024-01-10T06:23:52.007130Z", + "iopub.status.idle": "2024-01-10T06:24:21.670500Z", + "shell.execute_reply": "2024-01-10T06:24:21.669841Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 17167/4997817 [00:00<00:29, 171663.62it/s]" + " 0%| | 17062/4997817 [00:00<00:29, 170604.30it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 34563/4997817 [00:00<00:28, 173008.20it/s]" + " 1%| | 34285/4997817 [00:00<00:28, 171551.99it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 52101/4997817 [00:00<00:28, 174089.17it/s]" + " 1%| | 51456/4997817 [00:00<00:28, 171618.24it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 69510/4997817 [00:00<00:28, 174019.46it/s]" + " 1%|▏ | 68618/4997817 [00:00<00:28, 170859.63it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 86997/4997817 [00:00<00:28, 174323.75it/s]" + " 2%|▏ | 85805/4997817 [00:00<00:28, 171220.50it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 104430/4997817 [00:00<00:28, 169724.91it/s]" + " 2%|▏ | 102949/4997817 [00:00<00:28, 171291.99it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 122139/4997817 [00:00<00:28, 172086.44it/s]" + " 2%|▏ | 120095/4997817 [00:00<00:28, 171343.43it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 140011/4997817 [00:00<00:27, 174169.28it/s]" + " 3%|▎ | 137230/4997817 [00:00<00:28, 170491.36it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 157761/4997817 [00:00<00:27, 175200.10it/s]" + " 3%|▎ | 154281/4997817 [00:00<00:28, 169872.65it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 175374/4997817 [00:01<00:27, 175483.47it/s]" + " 3%|▎ | 171280/4997817 [00:01<00:28, 169905.50it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 192985/4997817 [00:01<00:27, 175671.16it/s]" + " 4%|▍ | 188272/4997817 [00:01<00:28, 169777.04it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 210558/4997817 [00:01<00:27, 175484.74it/s]" + " 4%|▍ | 205575/4997817 [00:01<00:28, 170760.61it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 228111/4997817 [00:01<00:27, 175443.62it/s]" + " 4%|▍ | 222652/4997817 [00:01<00:27, 170740.73it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 245659/4997817 [00:01<00:27, 175322.61it/s]" + " 5%|▍ | 240083/4997817 [00:01<00:27, 171811.81it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 263194/4997817 [00:01<00:27, 175304.12it/s]" + " 5%|▌ | 257360/4997817 [00:01<00:27, 172096.03it/s]" ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 280759/4997817 [00:01<00:26, 175405.86it/s]" + " 5%|▌ | 274605/4997817 [00:01<00:27, 172199.32it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 298301/4997817 [00:01<00:26, 175109.69it/s]" + " 6%|▌ | 291886/4997817 [00:01<00:27, 172379.81it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 315813/4997817 [00:01<00:26, 174670.09it/s]" + " 6%|▌ | 309125/4997817 [00:01<00:27, 172152.38it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 333315/4997817 [00:01<00:26, 174769.54it/s]" + " 7%|▋ | 326341/4997817 [00:01<00:27, 171949.71it/s]" ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 350881/4997817 [00:02<00:26, 175033.97it/s]" + " 7%|▋ | 343537/4997817 [00:02<00:27, 171568.72it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 368385/4997817 [00:02<00:26, 174887.75it/s]" + " 7%|▋ | 360817/4997817 [00:02<00:26, 171933.30it/s]" ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 385876/4997817 [00:02<00:26, 174891.71it/s]" + " 8%|▊ | 378023/4997817 [00:02<00:26, 171967.12it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 403366/4997817 [00:02<00:26, 174567.29it/s]" + " 8%|▊ | 395220/4997817 [00:02<00:26, 171770.69it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 421002/4997817 [00:02<00:26, 175100.23it/s]" + " 8%|▊ | 412518/4997817 [00:02<00:26, 172129.64it/s]" ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 438513/4997817 [00:02<00:26, 174881.61it/s]" + " 9%|▊ | 429746/4997817 [00:02<00:26, 172171.06it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 456002/4997817 [00:02<00:26, 170509.73it/s]" + " 9%|▉ | 446964/4997817 [00:02<00:26, 171908.29it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 473898/4997817 [00:02<00:26, 172994.94it/s]" + " 9%|▉ | 464155/4997817 [00:02<00:26, 171900.62it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 491599/4997817 [00:02<00:25, 174181.60it/s]" + " 10%|▉ | 481346/4997817 [00:02<00:26, 171355.90it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 509182/4997817 [00:02<00:25, 174668.81it/s]" + " 10%|▉ | 498619/4997817 [00:02<00:26, 171763.37it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 526728/4997817 [00:03<00:25, 174903.04it/s]" + " 10%|█ | 515796/4997817 [00:03<00:26, 171727.53it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 544360/4997817 [00:03<00:25, 175322.34it/s]" + " 11%|█ | 532984/4997817 [00:03<00:25, 171768.71it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 561899/4997817 [00:03<00:25, 175266.03it/s]" + " 11%|█ | 550240/4997817 [00:03<00:25, 172001.07it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 579430/4997817 [00:03<00:25, 175198.49it/s]" + " 11%|█▏ | 567441/4997817 [00:03<00:25, 171961.52it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 596953/4997817 [00:03<00:25, 175077.39it/s]" + " 12%|█▏ | 584638/4997817 [00:03<00:25, 171920.86it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 614463/4997817 [00:03<00:25, 174881.61it/s]" + " 12%|█▏ | 601962/4997817 [00:03<00:25, 172313.17it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 631953/4997817 [00:03<00:24, 174652.96it/s]" + " 12%|█▏ | 619303/4997817 [00:03<00:25, 172639.01it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 649420/4997817 [00:03<00:24, 174008.43it/s]" + " 13%|█▎ | 636567/4997817 [00:03<00:25, 172200.43it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 666822/4997817 [00:03<00:24, 173979.60it/s]" + " 13%|█▎ | 653788/4997817 [00:03<00:25, 171788.02it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 684221/4997817 [00:03<00:24, 173683.21it/s]" + " 13%|█▎ | 670968/4997817 [00:03<00:25, 171514.66it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 701638/4997817 [00:04<00:24, 173825.45it/s]" + " 14%|█▍ | 688120/4997817 [00:04<00:25, 170996.88it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 719021/4997817 [00:04<00:24, 173395.93it/s]" + " 14%|█▍ | 705275/4997817 [00:04<00:25, 171158.72it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 736362/4997817 [00:04<00:24, 173372.01it/s]" + " 14%|█▍ | 722435/4997817 [00:04<00:24, 171285.85it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 753700/4997817 [00:04<00:24, 172675.44it/s]" + " 15%|█▍ | 739806/4997817 [00:04<00:24, 172008.35it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 771046/4997817 [00:04<00:24, 172906.82it/s]" + " 15%|█▌ | 757008/4997817 [00:04<00:24, 171692.60it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 788553/4997817 [00:04<00:24, 173549.65it/s]" + " 15%|█▌ | 774255/4997817 [00:04<00:24, 171920.65it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 806052/4997817 [00:04<00:24, 173977.96it/s]" + " 16%|█▌ | 791671/4997817 [00:04<00:24, 172588.53it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 823451/4997817 [00:04<00:25, 166777.78it/s]" + " 16%|█▌ | 809012/4997817 [00:04<00:24, 172830.16it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 840819/4997817 [00:04<00:24, 168781.92it/s]" + " 17%|█▋ | 826296/4997817 [00:04<00:24, 171982.40it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 858322/4997817 [00:04<00:24, 170614.13it/s]" + " 17%|█▋ | 843496/4997817 [00:04<00:24, 171799.85it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 875707/4997817 [00:05<00:24, 171568.88it/s]" + " 17%|█▋ | 860677/4997817 [00:05<00:24, 171324.91it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 893295/4997817 [00:05<00:23, 172845.52it/s]" + " 18%|█▊ | 877842/4997817 [00:05<00:24, 171418.27it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 910813/4997817 [00:05<00:23, 173537.82it/s]" + " 18%|█▊ | 894985/4997817 [00:05<00:23, 171219.42it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 928368/4997817 [00:05<00:23, 174136.97it/s]" + " 18%|█▊ | 912146/4997817 [00:05<00:23, 171332.99it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 945901/4997817 [00:05<00:23, 174490.87it/s]" + " 19%|█▊ | 929303/4997817 [00:05<00:23, 171399.71it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 963359/4997817 [00:05<00:23, 174450.39it/s]" + " 19%|█▉ | 946444/4997817 [00:05<00:23, 171377.87it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 981064/4997817 [00:05<00:22, 175224.68it/s]" + " 19%|█▉ | 963692/4997817 [00:05<00:23, 171703.39it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 998591/4997817 [00:05<00:23, 167695.83it/s]" + " 20%|█▉ | 981048/4997817 [00:05<00:23, 172255.53it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1016101/4997817 [00:05<00:23, 169844.83it/s]" + " 20%|█▉ | 998359/4997817 [00:05<00:23, 172508.56it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1033639/4997817 [00:05<00:23, 171465.64it/s]" + " 20%|██ | 1015635/4997817 [00:05<00:23, 172579.19it/s]" ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1051050/4997817 [00:06<00:22, 172244.70it/s]" + " 21%|██ | 1032893/4997817 [00:06<00:23, 171418.64it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1068535/4997817 [00:06<00:22, 173015.00it/s]" + " 21%|██ | 1050039/4997817 [00:06<00:23, 171426.89it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1085860/4997817 [00:06<00:22, 172171.64it/s]" + " 21%|██▏ | 1067207/4997817 [00:06<00:22, 171496.74it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1103362/4997817 [00:06<00:22, 173017.40it/s]" + " 22%|██▏ | 1084358/4997817 [00:06<00:22, 171268.82it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1120915/4997817 [00:06<00:22, 173763.05it/s]" + " 22%|██▏ | 1101486/4997817 [00:06<00:22, 171044.54it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1138318/4997817 [00:06<00:22, 173840.60it/s]" + " 22%|██▏ | 1118591/4997817 [00:06<00:22, 170922.72it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1155709/4997817 [00:06<00:22, 173789.28it/s]" + " 23%|██▎ | 1135684/4997817 [00:06<00:22, 170420.58it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1173093/4997817 [00:06<00:22, 168493.74it/s]" + " 23%|██▎ | 1152727/4997817 [00:06<00:22, 170051.09it/s]" ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1190527/4997817 [00:06<00:22, 170204.85it/s]" + " 23%|██▎ | 1169733/4997817 [00:06<00:22, 169752.62it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1207870/4997817 [00:06<00:22, 171154.12it/s]" + " 24%|██▎ | 1186709/4997817 [00:06<00:22, 169681.27it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1225428/4997817 [00:07<00:21, 172462.89it/s]" + " 24%|██▍ | 1203678/4997817 [00:07<00:22, 168904.33it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1242924/4997817 [00:07<00:21, 173202.16it/s]" + " 24%|██▍ | 1220652/4997817 [00:07<00:22, 169150.94it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1260258/4997817 [00:07<00:21, 173197.75it/s]" + " 25%|██▍ | 1237634/4997817 [00:07<00:22, 169345.74it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1277885/4997817 [00:07<00:21, 174113.24it/s]" + " 25%|██▌ | 1254570/4997817 [00:07<00:22, 168349.15it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1295506/4997817 [00:07<00:21, 174737.72it/s]" + " 25%|██▌ | 1271732/4997817 [00:07<00:22, 169320.67it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1313036/4997817 [00:07<00:21, 174904.98it/s]" + " 26%|██▌ | 1288750/4997817 [00:07<00:21, 169571.98it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1330678/4997817 [00:07<00:20, 175355.99it/s]" + " 26%|██▌ | 1305766/4997817 [00:07<00:21, 169742.82it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1348217/4997817 [00:07<00:21, 168529.45it/s]" + " 26%|██▋ | 1322775/4997817 [00:07<00:21, 169844.98it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1365827/4997817 [00:07<00:21, 170733.60it/s]" + " 27%|██▋ | 1339793/4997817 [00:07<00:21, 169941.26it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1383392/4997817 [00:07<00:20, 172176.28it/s]" + " 27%|██▋ | 1356788/4997817 [00:07<00:21, 169935.91it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1401070/4997817 [00:08<00:20, 173534.17it/s]" + " 27%|██▋ | 1373885/4997817 [00:08<00:21, 170242.78it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1418730/4997817 [00:08<00:20, 174443.73it/s]" + " 28%|██▊ | 1390910/4997817 [00:08<00:21, 169776.77it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▊ | 1436236/4997817 [00:08<00:20, 174624.39it/s]" + " 28%|██▊ | 1407889/4997817 [00:08<00:21, 169776.41it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1453753/4997817 [00:08<00:20, 174785.08it/s]" + " 29%|██▊ | 1424867/4997817 [00:08<00:21, 169328.10it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1471304/4997817 [00:08<00:20, 175000.98it/s]" + " 29%|██▉ | 1441920/4997817 [00:08<00:20, 169683.76it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1488878/4997817 [00:08<00:20, 175219.95it/s]" + " 29%|██▉ | 1458889/4997817 [00:08<00:20, 169554.49it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1506507/4997817 [00:08<00:19, 175538.97it/s]" + " 30%|██▉ | 1475845/4997817 [00:08<00:20, 169207.98it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1524065/4997817 [00:08<00:19, 175328.61it/s]" + " 30%|██▉ | 1492767/4997817 [00:08<00:20, 169207.52it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1541696/4997817 [00:08<00:19, 175616.93it/s]" + " 30%|███ | 1509688/4997817 [00:08<00:20, 169069.76it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1559309/4997817 [00:08<00:19, 175767.69it/s]" + " 31%|███ | 1526596/4997817 [00:08<00:20, 168995.55it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1576933/4997817 [00:09<00:19, 175904.41it/s]" + " 31%|███ | 1543496/4997817 [00:09<00:20, 168767.01it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1594572/4997817 [00:09<00:19, 176045.26it/s]" + " 31%|███ | 1560373/4997817 [00:09<00:20, 168411.56it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1612238/4997817 [00:09<00:19, 176226.37it/s]" + " 32%|███▏ | 1577215/4997817 [00:09<00:20, 168093.60it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1629862/4997817 [00:09<00:19, 176141.34it/s]" + " 32%|███▏ | 1594025/4997817 [00:09<00:20, 167736.99it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1647477/4997817 [00:09<00:19, 175468.17it/s]" + " 32%|███▏ | 1610799/4997817 [00:09<00:20, 167579.26it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1665025/4997817 [00:09<00:19, 174739.75it/s]" + " 33%|███▎ | 1627581/4997817 [00:09<00:20, 167648.89it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1682608/4997817 [00:09<00:18, 175060.85it/s]" + " 33%|███▎ | 1644346/4997817 [00:09<00:20, 167601.44it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1700172/4997817 [00:09<00:18, 175230.78it/s]" + " 33%|███▎ | 1661107/4997817 [00:09<00:19, 167408.64it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1717696/4997817 [00:09<00:18, 172854.20it/s]" + " 34%|███▎ | 1678158/4997817 [00:09<00:19, 168332.79it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1735206/4997817 [00:09<00:18, 173517.80it/s]" + " 34%|███▍ | 1695148/4997817 [00:09<00:19, 168799.16it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1752699/4997817 [00:10<00:18, 173933.20it/s]" + " 34%|███▍ | 1712162/4997817 [00:10<00:19, 169197.65it/s]" ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1770288/4997817 [00:10<00:18, 174514.40it/s]" + " 35%|███▍ | 1729082/4997817 [00:10<00:19, 169086.37it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1788042/4997817 [00:10<00:18, 175415.06it/s]" + " 35%|███▍ | 1746091/4997817 [00:10<00:19, 169383.58it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1805801/4997817 [00:10<00:18, 176062.64it/s]" + " 35%|███▌ | 1763030/4997817 [00:10<00:19, 169294.56it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1823541/4997817 [00:10<00:17, 176461.35it/s]" + " 36%|███▌ | 1779986/4997817 [00:10<00:18, 169370.78it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1841194/4997817 [00:10<00:17, 176479.57it/s]" + " 36%|███▌ | 1796940/4997817 [00:10<00:18, 169415.99it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1858933/4997817 [00:10<00:17, 176749.46it/s]" + " 36%|███▋ | 1813941/4997817 [00:10<00:18, 169588.95it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1876670/4997817 [00:10<00:17, 176931.87it/s]" + " 37%|███▋ | 1830917/4997817 [00:10<00:18, 169635.93it/s]" ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1894364/4997817 [00:10<00:17, 173999.82it/s]" + " 37%|███▋ | 1847932/4997817 [00:10<00:18, 169786.39it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1911815/4997817 [00:10<00:17, 174148.01it/s]" + " 37%|███▋ | 1864911/4997817 [00:10<00:18, 169662.56it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 1929373/4997817 [00:11<00:17, 174571.30it/s]" + " 38%|███▊ | 1881878/4997817 [00:11<00:18, 169612.46it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1946838/4997817 [00:11<00:17, 174592.83it/s]" + " 38%|███▊ | 1898840/4997817 [00:11<00:18, 169466.51it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1964377/4997817 [00:11<00:17, 174828.30it/s]" + " 38%|███▊ | 1915967/4997817 [00:11<00:18, 170001.55it/s]" ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1981863/4997817 [00:11<00:17, 174660.08it/s]" + " 39%|███▊ | 1932968/4997817 [00:11<00:18, 169939.64it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 1999332/4997817 [00:11<00:17, 174199.29it/s]" + " 39%|███▉ | 1949963/4997817 [00:11<00:17, 169565.43it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2016768/4997817 [00:11<00:17, 174244.63it/s]" + " 39%|███▉ | 1966920/4997817 [00:11<00:17, 168922.06it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2034194/4997817 [00:11<00:17, 174179.70it/s]" + " 40%|███▉ | 1983850/4997817 [00:11<00:17, 169030.80it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2051629/4997817 [00:11<00:16, 174227.12it/s]" + " 40%|████ | 2000770/4997817 [00:11<00:17, 169076.88it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2069053/4997817 [00:11<00:17, 166828.01it/s]" + " 40%|████ | 2017733/4997817 [00:11<00:17, 169238.17it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2086587/4997817 [00:12<00:17, 169299.81it/s]" + " 41%|████ | 2034658/4997817 [00:11<00:17, 168730.09it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2104063/4997817 [00:12<00:16, 170898.22it/s]" + " 41%|████ | 2051532/4997817 [00:12<00:17, 168386.60it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2121442/4997817 [00:12<00:16, 171751.23it/s]" + " 41%|████▏ | 2068372/4997817 [00:12<00:17, 168018.51it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2138648/4997817 [00:12<00:16, 171573.44it/s]" + " 42%|████▏ | 2085260/4997817 [00:12<00:17, 168270.79it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2156067/4997817 [00:12<00:16, 172348.88it/s]" + " 42%|████▏ | 2102088/4997817 [00:12<00:17, 168030.44it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2173478/4997817 [00:12<00:16, 172872.90it/s]" + " 42%|████▏ | 2118962/4997817 [00:12<00:17, 168240.31it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2190870/4997817 [00:12<00:16, 173182.02it/s]" + " 43%|████▎ | 2135787/4997817 [00:12<00:17, 168132.74it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2208197/4997817 [00:12<00:16, 171459.04it/s]" + " 43%|████▎ | 2152601/4997817 [00:12<00:16, 167646.09it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2225613/4997817 [00:12<00:16, 172258.98it/s]" + " 43%|████▎ | 2169423/4997817 [00:12<00:16, 167813.54it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2243049/4997817 [00:12<00:15, 172882.18it/s]" + " 44%|████▎ | 2186297/4997817 [00:12<00:16, 168085.58it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2260546/4997817 [00:13<00:15, 173502.26it/s]" + " 44%|████▍ | 2203175/4997817 [00:12<00:16, 168289.27it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2278218/4997817 [00:13<00:15, 174462.04it/s]" + " 44%|████▍ | 2220005/4997817 [00:13<00:16, 168276.19it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2295807/4997817 [00:13<00:15, 174885.89it/s]" + " 45%|████▍ | 2236833/4997817 [00:13<00:16, 168121.20it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2313433/4997817 [00:13<00:15, 175295.26it/s]" + " 45%|████▌ | 2253646/4997817 [00:13<00:16, 168054.37it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2331175/4997817 [00:13<00:15, 175929.00it/s]" + " 45%|████▌ | 2270452/4997817 [00:13<00:16, 167426.00it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2349033/4997817 [00:13<00:14, 176719.93it/s]" + " 46%|████▌ | 2287196/4997817 [00:13<00:16, 167340.06it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2366710/4997817 [00:13<00:14, 176733.35it/s]" + " 46%|████▌ | 2303931/4997817 [00:13<00:16, 167280.92it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2384543/4997817 [00:13<00:14, 177210.55it/s]" + " 46%|████▋ | 2320696/4997817 [00:13<00:15, 167387.15it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2402270/4997817 [00:13<00:14, 177227.04it/s]" + " 47%|████▋ | 2337572/4997817 [00:13<00:15, 167794.33it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2420118/4997817 [00:13<00:14, 177598.07it/s]" + " 47%|████▋ | 2354510/4997817 [00:13<00:15, 168265.82it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2437879/4997817 [00:14<00:14, 176578.85it/s]" + " 47%|████▋ | 2371337/4997817 [00:13<00:15, 168146.95it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2455539/4997817 [00:14<00:14, 175833.38it/s]" + " 48%|████▊ | 2388246/4997817 [00:14<00:15, 168426.12it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2473124/4997817 [00:14<00:14, 175407.22it/s]" + " 48%|████▊ | 2405089/4997817 [00:14<00:15, 167971.38it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2490666/4997817 [00:14<00:14, 174637.69it/s]" + " 48%|████▊ | 2421887/4997817 [00:14<00:15, 167875.18it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2508131/4997817 [00:14<00:14, 174283.98it/s]" + " 49%|████▉ | 2438756/4997817 [00:14<00:15, 168116.52it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2525561/4997817 [00:14<00:14, 173899.65it/s]" + " 49%|████▉ | 2455622/4997817 [00:14<00:15, 168275.76it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2542952/4997817 [00:14<00:14, 173797.89it/s]" + " 49%|████▉ | 2472450/4997817 [00:14<00:15, 168246.55it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2560333/4997817 [00:14<00:14, 173525.09it/s]" + " 50%|████▉ | 2489275/4997817 [00:14<00:14, 168100.11it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2577686/4997817 [00:14<00:13, 173241.51it/s]" + " 50%|█████ | 2506165/4997817 [00:14<00:14, 168336.59it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2595051/4997817 [00:14<00:13, 173359.26it/s]" + " 50%|█████ | 2523041/4997817 [00:14<00:14, 168459.70it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2612388/4997817 [00:15<00:14, 169230.15it/s]" + " 51%|█████ | 2539888/4997817 [00:14<00:14, 168363.19it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2629603/4997817 [00:15<00:13, 170087.47it/s]" + " 51%|█████ | 2556725/4997817 [00:15<00:14, 168317.16it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2646951/4997817 [00:15<00:13, 171089.93it/s]" + " 51%|█████▏ | 2573557/4997817 [00:15<00:14, 168314.33it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2664305/4997817 [00:15<00:13, 171815.08it/s]" + " 52%|█████▏ | 2590389/4997817 [00:15<00:14, 168159.93it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2681708/4997817 [00:15<00:13, 172472.05it/s]" + " 52%|█████▏ | 2607206/4997817 [00:15<00:14, 168024.45it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2698963/4997817 [00:15<00:13, 172107.20it/s]" + " 53%|█████▎ | 2624021/4997817 [00:15<00:14, 168057.67it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2716210/4997817 [00:15<00:13, 172212.13it/s]" + " 53%|█████▎ | 2640827/4997817 [00:15<00:14, 167450.44it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2733440/4997817 [00:15<00:13, 172235.01it/s]" + " 53%|█████▎ | 2657654/4997817 [00:15<00:13, 167690.91it/s]" ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2750666/4997817 [00:15<00:13, 172154.34it/s]" + " 54%|█████▎ | 2674611/4997817 [00:15<00:13, 168247.89it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2767939/4997817 [00:15<00:12, 172322.92it/s]" + " 54%|█████▍ | 2691437/4997817 [00:15<00:13, 168060.02it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2785173/4997817 [00:16<00:12, 171849.28it/s]" + " 54%|█████▍ | 2708244/4997817 [00:15<00:13, 167385.05it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2802495/4997817 [00:16<00:12, 172256.75it/s]" + " 55%|█████▍ | 2725000/4997817 [00:16<00:13, 167432.83it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2819790/4997817 [00:16<00:12, 172463.36it/s]" + " 55%|█████▍ | 2741821/4997817 [00:16<00:13, 167661.64it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2837136/4997817 [00:16<00:12, 172759.37it/s]" + " 55%|█████▌ | 2758588/4997817 [00:16<00:13, 167266.26it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2854477/4997817 [00:16<00:12, 172952.56it/s]" + " 56%|█████▌ | 2775352/4997817 [00:16<00:13, 167374.50it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2871773/4997817 [00:16<00:12, 172939.88it/s]" + " 56%|█████▌ | 2792090/4997817 [00:16<00:13, 167063.45it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2889068/4997817 [00:16<00:12, 172847.45it/s]" + " 56%|█████▌ | 2808802/4997817 [00:16<00:13, 167076.13it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2906353/4997817 [00:16<00:12, 172745.55it/s]" + " 57%|█████▋ | 2825510/4997817 [00:16<00:13, 167042.04it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2923691/4997817 [00:16<00:11, 172931.79it/s]" + " 57%|█████▋ | 2842215/4997817 [00:16<00:12, 166910.37it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2940985/4997817 [00:16<00:11, 172739.60it/s]" + " 57%|█████▋ | 2858913/4997817 [00:16<00:12, 166926.75it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2958260/4997817 [00:17<00:11, 172302.98it/s]" + " 58%|█████▊ | 2875624/4997817 [00:16<00:12, 166976.93it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2975491/4997817 [00:17<00:11, 172241.73it/s]" + " 58%|█████▊ | 2892322/4997817 [00:17<00:12, 166542.29it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2992853/4997817 [00:17<00:11, 172653.15it/s]" + " 58%|█████▊ | 2909084/4997817 [00:17<00:12, 166860.46it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3010119/4997817 [00:17<00:11, 172640.95it/s]" + " 59%|█████▊ | 2925771/4997817 [00:17<00:12, 166846.81it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3027432/4997817 [00:17<00:11, 172784.96it/s]" + " 59%|█████▉ | 2942500/4997817 [00:17<00:12, 166975.59it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3044748/4997817 [00:17<00:11, 172894.22it/s]" + " 59%|█████▉ | 2959198/4997817 [00:17<00:12, 166708.79it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3062551/4997817 [00:17<00:11, 174432.89it/s]" + " 60%|█████▉ | 2975877/4997817 [00:17<00:12, 166728.16it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3080339/4997817 [00:17<00:10, 175465.53it/s]" + " 60%|█████▉ | 2992600/4997817 [00:17<00:12, 166874.28it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3098010/4997817 [00:17<00:10, 175837.69it/s]" + " 60%|██████ | 3009402/4997817 [00:17<00:11, 167212.72it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3115791/4997817 [00:17<00:10, 176425.65it/s]" + " 61%|██████ | 3026124/4997817 [00:17<00:11, 166887.50it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3133479/4997817 [00:18<00:10, 176556.43it/s]" + " 61%|██████ | 3042813/4997817 [00:17<00:11, 166464.96it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3151135/4997817 [00:18<00:10, 176251.63it/s]" + " 61%|██████ | 3059512/4997817 [00:18<00:11, 166617.29it/s]" ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3168923/4997817 [00:18<00:10, 176737.54it/s]" + " 62%|██████▏ | 3076399/4997817 [00:18<00:11, 167287.27it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3186597/4997817 [00:18<00:10, 176524.64it/s]" + " 62%|██████▏ | 3093129/4997817 [00:18<00:11, 167242.52it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3204269/4997817 [00:18<00:10, 176580.18it/s]" + " 62%|██████▏ | 3109854/4997817 [00:18<00:11, 164570.09it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3221928/4997817 [00:18<00:10, 176258.76it/s]" + " 63%|██████▎ | 3127061/4997817 [00:18<00:11, 166789.86it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3239689/4997817 [00:18<00:09, 176650.37it/s]" + " 63%|██████▎ | 3144295/4997817 [00:18<00:11, 168437.03it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3257355/4997817 [00:18<00:09, 176583.82it/s]" + " 63%|██████▎ | 3161147/4997817 [00:18<00:10, 168085.75it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3275014/4997817 [00:18<00:09, 176334.31it/s]" + " 64%|██████▎ | 3178372/4997817 [00:18<00:10, 169324.97it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3292787/4997817 [00:18<00:09, 176725.14it/s]" + " 64%|██████▍ | 3195555/4997817 [00:18<00:10, 170071.05it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3310460/4997817 [00:19<00:09, 172711.10it/s]" + " 64%|██████▍ | 3212860/4997817 [00:18<00:10, 170957.60it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3327849/4997817 [00:19<00:09, 173056.73it/s]" + " 65%|██████▍ | 3230096/4997817 [00:19<00:10, 171374.10it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3345397/4997817 [00:19<00:09, 173773.58it/s]" + " 65%|██████▍ | 3247326/4997817 [00:19<00:10, 171646.00it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3362816/4997817 [00:19<00:09, 173894.91it/s]" + " 65%|██████▌ | 3264493/4997817 [00:19<00:10, 171542.68it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3380358/4997817 [00:19<00:09, 174348.79it/s]" + " 66%|██████▌ | 3281649/4997817 [00:19<00:10, 171525.74it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3397799/4997817 [00:19<00:09, 174225.53it/s]" + " 66%|██████▌ | 3298803/4997817 [00:19<00:09, 171132.61it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3415226/4997817 [00:19<00:09, 174136.23it/s]" + " 66%|██████▋ | 3315917/4997817 [00:19<00:09, 170861.86it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3432753/4997817 [00:19<00:08, 174473.32it/s]" + " 67%|██████▋ | 3333004/4997817 [00:19<00:09, 170374.69it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3450203/4997817 [00:19<00:08, 174440.02it/s]" + " 67%|██████▋ | 3350043/4997817 [00:19<00:09, 170335.87it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3467649/4997817 [00:19<00:08, 174080.94it/s]" + " 67%|██████▋ | 3367100/4997817 [00:19<00:09, 170400.36it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3485059/4997817 [00:20<00:08, 172843.48it/s]" + " 68%|██████▊ | 3384218/4997817 [00:19<00:09, 170631.02it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3502346/4997817 [00:20<00:08, 166810.04it/s]" + " 68%|██████▊ | 3401284/4997817 [00:20<00:09, 170636.34it/s]" ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3519703/4997817 [00:20<00:08, 168777.34it/s]" + " 68%|██████▊ | 3418357/4997817 [00:20<00:09, 170658.72it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3537300/4997817 [00:20<00:08, 170887.58it/s]" + " 69%|██████▊ | 3435515/4997817 [00:20<00:09, 170931.41it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3554810/4997817 [00:20<00:08, 172129.81it/s]" + " 69%|██████▉ | 3452609/4997817 [00:20<00:09, 163949.22it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3572407/4997817 [00:20<00:08, 173268.31it/s]" + " 69%|██████▉ | 3469894/4997817 [00:20<00:09, 166538.18it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3590035/4997817 [00:20<00:08, 174161.61it/s]" + " 70%|██████▉ | 3486928/4997817 [00:20<00:09, 167650.09it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3607587/4997817 [00:20<00:07, 174563.62it/s]" + " 70%|███████ | 3503919/4997817 [00:20<00:08, 168314.40it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3625281/4997817 [00:20<00:07, 175270.03it/s]" + " 70%|███████ | 3520963/4997817 [00:20<00:08, 168940.54it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3642873/4997817 [00:20<00:07, 175463.20it/s]" + " 71%|███████ | 3538111/4997817 [00:20<00:08, 169693.53it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3660486/4997817 [00:21<00:07, 175659.55it/s]" + " 71%|███████ | 3555434/4997817 [00:20<00:08, 170745.59it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3678068/4997817 [00:21<00:07, 175704.41it/s]" + " 71%|███████▏ | 3572660/4997817 [00:21<00:08, 171192.35it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3695673/4997817 [00:21<00:07, 175805.15it/s]" + " 72%|███████▏ | 3590030/4997817 [00:21<00:08, 171938.61it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3713256/4997817 [00:21<00:07, 175721.21it/s]" + " 72%|███████▏ | 3607303/4997817 [00:21<00:08, 172172.46it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3730852/4997817 [00:21<00:07, 175791.21it/s]" + " 73%|███████▎ | 3624594/4997817 [00:21<00:07, 172387.33it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3748432/4997817 [00:21<00:07, 175657.15it/s]" + " 73%|███████▎ | 3641836/4997817 [00:21<00:07, 171859.14it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3765999/4997817 [00:21<00:07, 175309.41it/s]" + " 73%|███████▎ | 3659025/4997817 [00:21<00:07, 171633.99it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3783531/4997817 [00:21<00:06, 175154.10it/s]" + " 74%|███████▎ | 3676190/4997817 [00:21<00:07, 171400.84it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3801047/4997817 [00:21<00:06, 175154.54it/s]" + " 74%|███████▍ | 3693332/4997817 [00:21<00:07, 171243.48it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 3818563/4997817 [00:21<00:06, 174964.54it/s]" + " 74%|███████▍ | 3710458/4997817 [00:21<00:07, 171013.54it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3836060/4997817 [00:22<00:06, 174874.20it/s]" + " 75%|███████▍ | 3727633/4997817 [00:21<00:07, 171229.98it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3853548/4997817 [00:22<00:06, 167436.37it/s]" + " 75%|███████▍ | 3744757/4997817 [00:22<00:07, 171012.21it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3870994/4997817 [00:22<00:06, 169474.31it/s]" + " 75%|███████▌ | 3761899/4997817 [00:22<00:07, 171129.30it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3888398/4997817 [00:22<00:06, 170810.31it/s]" + " 76%|███████▌ | 3779084/4997817 [00:22<00:07, 171340.31it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3905737/4997817 [00:22<00:06, 171571.35it/s]" + " 76%|███████▌ | 3796219/4997817 [00:22<00:07, 167422.50it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3923054/4997817 [00:22<00:06, 172044.02it/s]" + " 76%|███████▋ | 3813525/4997817 [00:22<00:07, 169081.57it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3940335/4997817 [00:22<00:06, 172268.67it/s]" + " 77%|███████▋ | 3830826/4997817 [00:22<00:06, 170242.46it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3957626/4997817 [00:22<00:06, 172456.39it/s]" + " 77%|███████▋ | 3847923/4997817 [00:22<00:06, 170454.10it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3974897/4997817 [00:22<00:05, 172529.53it/s]" + " 77%|███████▋ | 3865103/4997817 [00:22<00:06, 170852.37it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3992172/4997817 [00:22<00:05, 172593.33it/s]" + " 78%|███████▊ | 3882263/4997817 [00:22<00:06, 171072.12it/s]" ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4009477/4997817 [00:23<00:05, 172727.41it/s]" + " 78%|███████▊ | 3899433/4997817 [00:22<00:06, 171255.46it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4026754/4997817 [00:23<00:05, 172335.34it/s]" + " 78%|███████▊ | 3916562/4997817 [00:23<00:06, 170928.63it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4044009/4997817 [00:23<00:05, 172396.06it/s]" + " 79%|███████▊ | 3933884/4997817 [00:23<00:06, 171610.47it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4061251/4997817 [00:23<00:05, 172348.89it/s]" + " 79%|███████▉ | 3951103/4997817 [00:23<00:06, 171779.99it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4078488/4997817 [00:23<00:05, 172038.75it/s]" + " 79%|███████▉ | 3968283/4997817 [00:23<00:05, 171759.78it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4095693/4997817 [00:23<00:05, 171753.78it/s]" + " 80%|███████▉ | 3985460/4997817 [00:23<00:05, 171748.29it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4112870/4997817 [00:23<00:05, 171591.26it/s]" + " 80%|████████ | 4002799/4997817 [00:23<00:05, 172236.63it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4130047/4997817 [00:23<00:05, 171641.11it/s]" + " 80%|████████ | 4020024/4997817 [00:23<00:05, 172120.48it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4147220/4997817 [00:23<00:04, 171663.86it/s]" + " 81%|████████ | 4037255/4997817 [00:23<00:05, 172171.92it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4164387/4997817 [00:23<00:04, 171623.45it/s]" + " 81%|████████ | 4054562/4997817 [00:23<00:05, 172438.26it/s]" ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▎ | 4181550/4997817 [00:24<00:04, 171620.07it/s]" + " 81%|████████▏ | 4071846/4997817 [00:23<00:05, 172553.59it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4198713/4997817 [00:24<00:04, 167684.36it/s]" + " 82%|████████▏ | 4089102/4997817 [00:24<00:05, 172236.48it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4216323/4997817 [00:24<00:04, 170162.44it/s]" + " 82%|████████▏ | 4106326/4997817 [00:24<00:05, 172177.95it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4233659/4997817 [00:24<00:04, 171107.67it/s]" + " 83%|████████▎ | 4123603/4997817 [00:24<00:05, 172350.70it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4251153/4997817 [00:24<00:04, 172246.18it/s]" + " 83%|████████▎ | 4140839/4997817 [00:24<00:04, 171954.68it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4268639/4997817 [00:24<00:04, 173024.33it/s]" + " 83%|████████▎ | 4158035/4997817 [00:24<00:04, 168862.02it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4286227/4997817 [00:24<00:04, 173876.71it/s]" + " 84%|████████▎ | 4175226/4997817 [00:24<00:04, 169758.76it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4303621/4997817 [00:24<00:03, 173857.38it/s]" + " 84%|████████▍ | 4192212/4997817 [00:24<00:04, 168849.98it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▋ | 4321090/4997817 [00:24<00:03, 174105.68it/s]" + " 84%|████████▍ | 4209250/4997817 [00:24<00:04, 169300.67it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4338553/4997817 [00:24<00:03, 174261.17it/s]" + " 85%|████████▍ | 4226465/4997817 [00:24<00:04, 170143.89it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4356036/4997817 [00:25<00:03, 174427.95it/s]" + " 85%|████████▍ | 4243806/4997817 [00:24<00:04, 171114.46it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4373481/4997817 [00:25<00:03, 174054.32it/s]" + " 85%|████████▌ | 4261227/4997817 [00:25<00:04, 172034.61it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4391185/4997817 [00:25<00:03, 174944.98it/s]" + " 86%|████████▌ | 4278528/4997817 [00:25<00:04, 172320.83it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4408767/4997817 [00:25<00:03, 175203.90it/s]" + " 86%|████████▌ | 4295937/4997817 [00:25<00:04, 172845.19it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 4426338/4997817 [00:25<00:03, 175352.99it/s]" + " 86%|████████▋ | 4313308/4997817 [00:25<00:03, 173100.25it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4443874/4997817 [00:25<00:03, 175167.97it/s]" + " 87%|████████▋ | 4330658/4997817 [00:25<00:03, 173214.54it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4461426/4997817 [00:25<00:03, 175270.87it/s]" + " 87%|████████▋ | 4347981/4997817 [00:25<00:03, 173208.95it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4478954/4997817 [00:25<00:02, 174505.85it/s]" + " 87%|████████▋ | 4365307/4997817 [00:25<00:03, 173218.73it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4496406/4997817 [00:25<00:02, 174301.33it/s]" + " 88%|████████▊ | 4382688/4997817 [00:25<00:03, 173390.64it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4513837/4997817 [00:25<00:02, 174251.43it/s]" + " 88%|████████▊ | 4400068/4997817 [00:25<00:03, 173507.38it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4531366/4997817 [00:26<00:02, 174558.50it/s]" + " 88%|████████▊ | 4417419/4997817 [00:25<00:03, 173309.43it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4548843/4997817 [00:26<00:02, 174618.77it/s]" + " 89%|████████▊ | 4434751/4997817 [00:26<00:03, 172930.69it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4566306/4997817 [00:26<00:02, 174572.68it/s]" + " 89%|████████▉ | 4452045/4997817 [00:26<00:03, 172511.48it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4583764/4997817 [00:26<00:02, 174261.05it/s]" + " 89%|████████▉ | 4469335/4997817 [00:26<00:03, 172622.79it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4601191/4997817 [00:26<00:02, 173985.59it/s]" + " 90%|████████▉ | 4486598/4997817 [00:26<00:02, 171589.70it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4618615/4997817 [00:26<00:02, 174059.41it/s]" + " 90%|█████████ | 4503759/4997817 [00:26<00:02, 171412.36it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4636022/4997817 [00:26<00:02, 173946.39it/s]" + " 90%|█████████ | 4520902/4997817 [00:26<00:02, 171343.15it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4653417/4997817 [00:26<00:01, 173914.36it/s]" + " 91%|█████████ | 4538037/4997817 [00:26<00:02, 171239.33it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4670809/4997817 [00:26<00:01, 173866.67it/s]" + " 91%|█████████ | 4555162/4997817 [00:26<00:02, 170876.40it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4688210/4997817 [00:26<00:01, 173907.06it/s]" + " 91%|█████████▏| 4572250/4997817 [00:26<00:02, 170565.73it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4705644/4997817 [00:27<00:01, 174033.16it/s]" + " 92%|█████████▏| 4589307/4997817 [00:27<00:02, 170097.89it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4723065/4997817 [00:27<00:01, 174084.11it/s]" + " 92%|█████████▏| 4606318/4997817 [00:27<00:02, 169865.84it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4740474/4997817 [00:27<00:01, 173944.40it/s]" + " 93%|█████████▎| 4623305/4997817 [00:27<00:02, 169631.05it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4757869/4997817 [00:27<00:01, 173731.59it/s]" + " 93%|█████████▎| 4640269/4997817 [00:27<00:02, 168872.65it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4775243/4997817 [00:27<00:01, 173526.32it/s]" + " 93%|█████████▎| 4657235/4997817 [00:27<00:02, 169105.83it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4792596/4997817 [00:27<00:01, 173265.11it/s]" + " 94%|█████████▎| 4674147/4997817 [00:27<00:01, 168226.74it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4809923/4997817 [00:27<00:01, 170701.37it/s]" + " 94%|█████████▍| 4690976/4997817 [00:27<00:01, 168242.05it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4827002/4997817 [00:27<00:01, 168010.73it/s]" + " 94%|█████████▍| 4707801/4997817 [00:27<00:01, 168102.89it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4844469/4997817 [00:27<00:00, 169967.72it/s]" + " 95%|█████████▍| 4724647/4997817 [00:27<00:01, 168205.59it/s]" ] }, { @@ -2762,7 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4861913/4997817 [00:28<00:00, 171289.71it/s]" + " 95%|█████████▍| 4741468/4997817 [00:27<00:01, 167808.58it/s]" ] }, { @@ -2770,7 +2770,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4879328/4997817 [00:28<00:00, 172138.29it/s]" + " 95%|█████████▌| 4758250/4997817 [00:28<00:01, 167370.08it/s]" ] }, { @@ -2778,7 +2778,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4896647/4997817 [00:28<00:00, 172449.50it/s]" + " 96%|█████████▌| 4774988/4997817 [00:28<00:01, 167262.31it/s]" ] }, { @@ -2786,7 +2786,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4913898/4997817 [00:28<00:00, 170167.36it/s]" + " 96%|█████████▌| 4791806/4997817 [00:28<00:01, 167533.12it/s]" ] }, { @@ -2794,7 +2794,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▊| 4931534/4997817 [00:28<00:00, 171999.39it/s]" + " 96%|█████████▌| 4808560/4997817 [00:28<00:01, 167179.89it/s]" ] }, { @@ -2802,7 +2802,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4949175/4997817 [00:28<00:00, 173309.56it/s]" + " 97%|█████████▋| 4825279/4997817 [00:28<00:01, 167129.34it/s]" ] }, { @@ -2810,7 +2810,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4966657/4997817 [00:28<00:00, 173755.94it/s]" + " 97%|█████████▋| 4842096/4997817 [00:28<00:00, 167436.03it/s]" ] }, { @@ -2818,7 +2818,7 @@ "output_type": "stream", "text": [ "\r", - "100%|█████████▉| 4984241/4997817 [00:28<00:00, 174376.33it/s]" + " 97%|█████████▋| 4859300/4997817 [00:28<00:00, 168811.84it/s]" ] }, { @@ -2826,7 +2826,71 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 4997817/4997817 [00:28<00:00, 173607.01it/s]" + " 98%|█████████▊| 4876497/4997817 [00:28<00:00, 169752.70it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4893694/4997817 [00:28<00:00, 170413.45it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4910769/4997817 [00:28<00:00, 170510.26it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▊| 4927821/4997817 [00:29<00:00, 170169.42it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4944989/4997817 [00:29<00:00, 170616.62it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4962051/4997817 [00:29<00:00, 169773.92it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4979093/4997817 [00:29<00:00, 169963.20it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4996091/4997817 [00:29<00:00, 169052.24it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 4997817/4997817 [00:29<00:00, 169841.26it/s]" ] }, { @@ -3065,10 +3129,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:20.044089Z", - "iopub.status.busy": "2024-01-09T15:15:20.043779Z", - "iopub.status.idle": "2024-01-09T15:15:27.150650Z", - "shell.execute_reply": "2024-01-09T15:15:27.149974Z" + "iopub.execute_input": "2024-01-10T06:24:21.672999Z", + "iopub.status.busy": "2024-01-10T06:24:21.672753Z", + "iopub.status.idle": "2024-01-10T06:24:28.676821Z", + "shell.execute_reply": "2024-01-10T06:24:28.676174Z" } }, "outputs": [], @@ -3082,10 +3146,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:27.153846Z", - "iopub.status.busy": "2024-01-09T15:15:27.153367Z", - "iopub.status.idle": "2024-01-09T15:15:30.339440Z", - "shell.execute_reply": "2024-01-09T15:15:30.338776Z" + "iopub.execute_input": "2024-01-10T06:24:28.679808Z", + "iopub.status.busy": "2024-01-10T06:24:28.679561Z", + "iopub.status.idle": "2024-01-10T06:24:31.714840Z", + "shell.execute_reply": "2024-01-10T06:24:31.714156Z" } }, "outputs": [ @@ -3154,17 +3218,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:30.342088Z", - "iopub.status.busy": "2024-01-09T15:15:30.341878Z", - "iopub.status.idle": "2024-01-09T15:15:31.649962Z", - "shell.execute_reply": "2024-01-09T15:15:31.649264Z" + "iopub.execute_input": "2024-01-10T06:24:31.717634Z", + "iopub.status.busy": "2024-01-10T06:24:31.717196Z", + "iopub.status.idle": "2024-01-10T06:24:33.061235Z", + "shell.execute_reply": "2024-01-10T06:24:33.060598Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3464fab77b1b47d58adbe02a6fa43f64", + "model_id": "df32bb08ce9040a1b285945a0db7765e", "version_major": 2, "version_minor": 0 }, @@ -3194,10 +3258,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:31.652920Z", - "iopub.status.busy": "2024-01-09T15:15:31.652499Z", - "iopub.status.idle": "2024-01-09T15:15:31.883005Z", - "shell.execute_reply": "2024-01-09T15:15:31.882426Z" + "iopub.execute_input": "2024-01-10T06:24:33.064302Z", + "iopub.status.busy": "2024-01-10T06:24:33.063926Z", + "iopub.status.idle": "2024-01-10T06:24:33.282005Z", + "shell.execute_reply": "2024-01-10T06:24:33.281307Z" } }, "outputs": [], @@ -3211,10 +3275,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:31.885754Z", - "iopub.status.busy": "2024-01-09T15:15:31.885535Z", - "iopub.status.idle": "2024-01-09T15:15:36.610960Z", - "shell.execute_reply": "2024-01-09T15:15:36.610317Z" + "iopub.execute_input": "2024-01-10T06:24:33.285032Z", + "iopub.status.busy": "2024-01-10T06:24:33.284637Z", + "iopub.status.idle": "2024-01-10T06:24:38.039867Z", + "shell.execute_reply": "2024-01-10T06:24:38.039183Z" } }, "outputs": [ @@ -3287,10 +3351,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:36.613553Z", - "iopub.status.busy": "2024-01-09T15:15:36.613140Z", - "iopub.status.idle": "2024-01-09T15:15:36.669631Z", - "shell.execute_reply": "2024-01-09T15:15:36.668993Z" + "iopub.execute_input": "2024-01-10T06:24:38.042406Z", + "iopub.status.busy": "2024-01-10T06:24:38.042157Z", + "iopub.status.idle": "2024-01-10T06:24:38.099615Z", + "shell.execute_reply": "2024-01-10T06:24:38.099002Z" }, "nbsphinx": "hidden" }, @@ -3334,7 +3398,67 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02c82a7576c0452793d4ac9e3a8eb771": { + "0193408c614a4e7ba4873c78a9a9bb6f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_56cad0e7c51f49fbb45538b279800284", + "placeholder": "​", + "style": "IPY_MODEL_bf748ca37ab847a08123be51451437cc", + "value": "number of examples processed for estimating thresholds: 100%" + } + }, + "06974f46f32c4476932bd765003f434a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0870fef1c46e4952a7824e05fc431a34": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_66b6aaa2007c4b1496649534471df1db", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_639ef487ed3b42848922bc4d9bae0928", + "value": 30.0 + } + }, + "0fa1181f8e9b40ffb9c9c728c90621b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3386,23 +3510,52 @@ "width": null } }, - "17fd32bd00f7422f82e222bd671eb1bf": { + "10f6d3dbf30f4eb9b246516eea1577b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bb9aed613f8f4289816e5ab5039366fb", + "placeholder": "​", + "style": "IPY_MODEL_ffbaaada2c4e444c92e4121cd1a4d331", + "value": "images processed using softmin: 100%" } }, - "1a8eeafa6ca74a69b2fa5817f03e46ed": { + "11aed75ad6be4af3af712b1b7648058f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2f0f6f38ef674c39b106ff3aea167574", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4e02709833da447092b32b34e9cd7eea", + "value": 30.0 + } + }, + "228f616c7eff4a4fbdd71be5df385c68": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3454,7 +3607,7 @@ "width": null } }, - "1c8d6e3a698d448d90427dc24f08fcae": { + "230fb679cc3c47188ee2b769167c7d0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3469,53 +3622,7 @@ "description_width": "" } }, - "2f006189586d4837b25208f274c1efda": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3764f1d408124e67b9b8ff059b1cd51e", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_17fd32bd00f7422f82e222bd671eb1bf", - "value": 30.0 - } - }, - "3464fab77b1b47d58adbe02a6fa43f64": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f18591d41acf461787dc84ddaf834e17", - "IPY_MODEL_2f006189586d4837b25208f274c1efda", - "IPY_MODEL_cd1c5ca7ad8b485cae2787936cba138c" - ], - "layout": "IPY_MODEL_e3c474e99b5a4dbda818585deb053e2b" - } - }, - "3764f1d408124e67b9b8ff059b1cd51e": { + "24f1f455503e4ea1905214c2491c6a8f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3567,7 +3674,50 @@ "width": null } }, - "3f205716e3c54df6b925d90841790bb6": { + "29a7642e497c4e9b96745b11371f32ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7d8733a782dc4953accc99a9a8b481c9", + "placeholder": "​", + "style": "IPY_MODEL_230fb679cc3c47188ee2b769167c7d0b", + "value": "number of examples processed for checking labels: 100%" + } + }, + "2e3415f4577c42cb941753dfe0086640": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_29a7642e497c4e9b96745b11371f32ef", + "IPY_MODEL_11aed75ad6be4af3af712b1b7648058f", + "IPY_MODEL_512a68cdbfd0462688ff6b886482cf94" + ], + "layout": "IPY_MODEL_228f616c7eff4a4fbdd71be5df385c68" + } + }, + "2f0f6f38ef674c39b106ff3aea167574": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3619,7 +3769,22 @@ "width": null } }, - "4503eade1b32403d863df7edfa8913d0": { + "43b3b5ba40fa4150ac9fe67bc77e7088": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4b2548e688284560a51e01b3a7d4b30a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3671,49 +3836,23 @@ "width": null } }, - "469a9896f434443d914337d391527ddc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3f205716e3c54df6b925d90841790bb6", - "placeholder": "​", - "style": "IPY_MODEL_4cf581fac6f1469fac415a93793188e5", - "value": "number of examples processed for checking labels: 100%" - } - }, - "46d009f0b3af4d06bbd5a73a66e716ea": { + "4e02709833da447092b32b34e9cd7eea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f8e816ee6b4d43439ab0a880aa663aca", - "placeholder": "​", - "style": "IPY_MODEL_ab6a158ac22041ce9d1a7bf92a8d0522", - "value": " 30/30 [00:00<00:00, 424.36it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "4ab9ee64b1fa48b488b7508e26edfecd": { + "512a68cdbfd0462688ff6b886482cf94": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3728,13 +3867,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_db0ae4d8d2874f1285894feb90065a04", + "layout": "IPY_MODEL_4b2548e688284560a51e01b3a7d4b30a", "placeholder": "​", - "style": "IPY_MODEL_5d8bd5d5b83848dcb8b2033f0d8acdf5", + "style": "IPY_MODEL_737617a1f83a4f60aacfbd1431505249", "value": " 30/30 [00:36<00:00, 1.22s/it]" } }, - "4c5dc1becde2491aa17ab44a75d7f414": { + "56cad0e7c51f49fbb45538b279800284": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3786,37 +3925,23 @@ "width": null } }, - "4cf581fac6f1469fac415a93793188e5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "5d8bd5d5b83848dcb8b2033f0d8acdf5": { + "639ef487ed3b42848922bc4d9bae0928": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "6c9508f676774e3eb9bbcf828fadfb5e": { + "66b6aaa2007c4b1496649534471df1db": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3868,7 +3993,22 @@ "width": null } }, - "7b9c5493fed74f5785964aed9e4c4ac4": { + "737617a1f83a4f60aacfbd1431505249": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7655e393baf14941a470099ab54010ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3920,22 +4060,7 @@ "width": null } }, - "884a547f5c5141f0ae8582f90a618526": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a545d3bcc78342ada9bf902f7c4a7210": { + "7834e62875da4394bdfc03b1501fa4a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3950,111 +4075,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_469a9896f434443d914337d391527ddc", - "IPY_MODEL_efa9c67a3f4c42138eea3dcdd09cffb9", - "IPY_MODEL_4ab9ee64b1fa48b488b7508e26edfecd" + "IPY_MODEL_0193408c614a4e7ba4873c78a9a9bb6f", + "IPY_MODEL_0870fef1c46e4952a7824e05fc431a34", + "IPY_MODEL_8e940a4d278d48d5bb2e566940734bee" ], - "layout": "IPY_MODEL_6c9508f676774e3eb9bbcf828fadfb5e" - } - }, - "ab6a158ac22041ce9d1a7bf92a8d0522": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "ae706412514f4a958d903b4acb3182b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "beb24d1191564974a50da25f62644e92": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4c5dc1becde2491aa17ab44a75d7f414", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d9482a6ebcbc431d9fa8d22857aecaf4", - "value": 30.0 - } - }, - "c9d9dcb94ddb4452bbf427afc230ea18": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_02c82a7576c0452793d4ac9e3a8eb771", - "placeholder": "​", - "style": "IPY_MODEL_884a547f5c5141f0ae8582f90a618526", - "value": "number of examples processed for estimating thresholds: 100%" + "layout": "IPY_MODEL_f9ed9bb79eea437e994e4b439e1c1812" } }, - "cd1c5ca7ad8b485cae2787936cba138c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4503eade1b32403d863df7edfa8913d0", - "placeholder": "​", - "style": "IPY_MODEL_e1512f3d87e742818dbc3ddba53349f5", - "value": " 30/30 [00:01<00:00, 23.68it/s]" - } - }, - "d3a3cfb6d5134ff98de3d1add75730c4": { + "7d8733a782dc4953accc99a9a8b481c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4106,23 +4134,52 @@ "width": null } }, - "d9482a6ebcbc431d9fa8d22857aecaf4": { + "8e940a4d278d48d5bb2e566940734bee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0fa1181f8e9b40ffb9c9c728c90621b5", + "placeholder": "​", + "style": "IPY_MODEL_43b3b5ba40fa4150ac9fe67bc77e7088", + "value": " 30/30 [00:00<00:00, 426.95it/s]" + } + }, + "97e851aacf72467a845502595d456b40": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_24f1f455503e4ea1905214c2491c6a8f", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_edcc710314394fceac64a53876ee634d", + "value": 30.0 } }, - "db0ae4d8d2874f1285894feb90065a04": { + "bb9aed613f8f4289816e5ab5039366fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4174,7 +4231,7 @@ "width": null } }, - "e1512f3d87e742818dbc3ddba53349f5": { + "bf748ca37ab847a08123be51451437cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4189,7 +4246,7 @@ "description_width": "" } }, - "e3c474e99b5a4dbda818585deb053e2b": { + "c5ba6d8db93145e486d14e332442a993": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4241,74 +4298,66 @@ "width": null } }, - "e7af30476e914a1aa2bc7bb22a08f3a8": { + "cb6f5dbf185a4cbca65ae3e7c593eede": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c9d9dcb94ddb4452bbf427afc230ea18", - "IPY_MODEL_beb24d1191564974a50da25f62644e92", - "IPY_MODEL_46d009f0b3af4d06bbd5a73a66e716ea" - ], - "layout": "IPY_MODEL_7b9c5493fed74f5785964aed9e4c4ac4" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c5ba6d8db93145e486d14e332442a993", + "placeholder": "​", + "style": "IPY_MODEL_06974f46f32c4476932bd765003f434a", + "value": " 30/30 [00:01<00:00, 22.49it/s]" } }, - "efa9c67a3f4c42138eea3dcdd09cffb9": { + "df32bb08ce9040a1b285945a0db7765e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d3a3cfb6d5134ff98de3d1add75730c4", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ae706412514f4a958d903b4acb3182b2", - "value": 30.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_10f6d3dbf30f4eb9b246516eea1577b6", + "IPY_MODEL_97e851aacf72467a845502595d456b40", + "IPY_MODEL_cb6f5dbf185a4cbca65ae3e7c593eede" + ], + "layout": "IPY_MODEL_7655e393baf14941a470099ab54010ed" } }, - "f18591d41acf461787dc84ddaf834e17": { + "edcc710314394fceac64a53876ee634d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1a8eeafa6ca74a69b2fa5817f03e46ed", - "placeholder": "​", - "style": "IPY_MODEL_1c8d6e3a698d448d90427dc24f08fcae", - "value": "images processed using softmin: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f8e816ee6b4d43439ab0a880aa663aca": { + "f9ed9bb79eea437e994e4b439e1c1812": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4359,6 +4408,21 @@ "visibility": null, "width": null } + }, + "ffbaaada2c4e444c92e4121cd1a4d331": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index 61a5c1157..14b0c1a72 100644 --- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:41.079536Z", - "iopub.status.busy": "2024-01-09T15:15:41.079079Z", - "iopub.status.idle": "2024-01-09T15:15:42.105627Z", - "shell.execute_reply": "2024-01-09T15:15:42.104973Z" + "iopub.execute_input": "2024-01-10T06:24:42.729158Z", + "iopub.status.busy": "2024-01-10T06:24:42.728964Z", + "iopub.status.idle": "2024-01-10T06:24:43.794701Z", + "shell.execute_reply": "2024-01-10T06:24:43.793968Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.108685Z", - "iopub.status.busy": "2024-01-09T15:15:42.108129Z", - "iopub.status.idle": "2024-01-09T15:15:42.125058Z", - "shell.execute_reply": "2024-01-09T15:15:42.124436Z" + "iopub.execute_input": "2024-01-10T06:24:43.797884Z", + "iopub.status.busy": "2024-01-10T06:24:43.797509Z", + "iopub.status.idle": "2024-01-10T06:24:43.814538Z", + "shell.execute_reply": "2024-01-10T06:24:43.814038Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.127634Z", - "iopub.status.busy": "2024-01-09T15:15:42.127253Z", - "iopub.status.idle": "2024-01-09T15:15:42.160362Z", - "shell.execute_reply": "2024-01-09T15:15:42.159841Z" + "iopub.execute_input": "2024-01-10T06:24:43.817105Z", + "iopub.status.busy": "2024-01-10T06:24:43.816655Z", + "iopub.status.idle": "2024-01-10T06:24:43.872030Z", + "shell.execute_reply": "2024-01-10T06:24:43.871363Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.162712Z", - "iopub.status.busy": "2024-01-09T15:15:42.162340Z", - "iopub.status.idle": "2024-01-09T15:15:42.165952Z", - "shell.execute_reply": "2024-01-09T15:15:42.165365Z" + "iopub.execute_input": "2024-01-10T06:24:43.874729Z", + "iopub.status.busy": "2024-01-10T06:24:43.874284Z", + "iopub.status.idle": "2024-01-10T06:24:43.878216Z", + "shell.execute_reply": "2024-01-10T06:24:43.877614Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.168301Z", - "iopub.status.busy": "2024-01-09T15:15:42.167932Z", - "iopub.status.idle": "2024-01-09T15:15:42.176945Z", - "shell.execute_reply": "2024-01-09T15:15:42.176451Z" + "iopub.execute_input": "2024-01-10T06:24:43.880614Z", + "iopub.status.busy": "2024-01-10T06:24:43.880280Z", + "iopub.status.idle": "2024-01-10T06:24:43.889118Z", + "shell.execute_reply": "2024-01-10T06:24:43.888491Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.179275Z", - "iopub.status.busy": "2024-01-09T15:15:42.179056Z", - "iopub.status.idle": "2024-01-09T15:15:42.181951Z", - "shell.execute_reply": "2024-01-09T15:15:42.181425Z" + "iopub.execute_input": "2024-01-10T06:24:43.891570Z", + "iopub.status.busy": "2024-01-10T06:24:43.891224Z", + "iopub.status.idle": "2024-01-10T06:24:43.894040Z", + "shell.execute_reply": "2024-01-10T06:24:43.893453Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.184343Z", - "iopub.status.busy": "2024-01-09T15:15:42.183982Z", - "iopub.status.idle": "2024-01-09T15:15:42.767864Z", - "shell.execute_reply": "2024-01-09T15:15:42.767250Z" + "iopub.execute_input": "2024-01-10T06:24:43.896323Z", + "iopub.status.busy": "2024-01-10T06:24:43.895979Z", + "iopub.status.idle": "2024-01-10T06:24:44.485147Z", + "shell.execute_reply": "2024-01-10T06:24:44.484416Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.770857Z", - "iopub.status.busy": "2024-01-09T15:15:42.770445Z", - "iopub.status.idle": "2024-01-09T15:15:44.010236Z", - "shell.execute_reply": "2024-01-09T15:15:44.009414Z" + "iopub.execute_input": "2024-01-10T06:24:44.488107Z", + "iopub.status.busy": "2024-01-10T06:24:44.487864Z", + "iopub.status.idle": "2024-01-10T06:24:45.798377Z", + "shell.execute_reply": "2024-01-10T06:24:45.797616Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.013747Z", - "iopub.status.busy": "2024-01-09T15:15:44.012740Z", - "iopub.status.idle": "2024-01-09T15:15:44.023500Z", - "shell.execute_reply": "2024-01-09T15:15:44.022995Z" + "iopub.execute_input": "2024-01-10T06:24:45.801399Z", + "iopub.status.busy": "2024-01-10T06:24:45.800854Z", + "iopub.status.idle": "2024-01-10T06:24:45.811242Z", + "shell.execute_reply": "2024-01-10T06:24:45.810638Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.026168Z", - "iopub.status.busy": "2024-01-09T15:15:44.025679Z", - "iopub.status.idle": "2024-01-09T15:15:44.030156Z", - "shell.execute_reply": "2024-01-09T15:15:44.029648Z" + "iopub.execute_input": "2024-01-10T06:24:45.813764Z", + "iopub.status.busy": "2024-01-10T06:24:45.813278Z", + "iopub.status.idle": "2024-01-10T06:24:45.817782Z", + "shell.execute_reply": "2024-01-10T06:24:45.817299Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.032520Z", - "iopub.status.busy": "2024-01-09T15:15:44.032153Z", - "iopub.status.idle": "2024-01-09T15:15:44.039951Z", - "shell.execute_reply": "2024-01-09T15:15:44.039422Z" + "iopub.execute_input": "2024-01-10T06:24:45.820268Z", + "iopub.status.busy": "2024-01-10T06:24:45.819907Z", + "iopub.status.idle": "2024-01-10T06:24:45.827154Z", + "shell.execute_reply": "2024-01-10T06:24:45.826654Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.042453Z", - "iopub.status.busy": "2024-01-09T15:15:44.042087Z", - "iopub.status.idle": "2024-01-09T15:15:44.164996Z", - "shell.execute_reply": "2024-01-09T15:15:44.164465Z" + "iopub.execute_input": "2024-01-10T06:24:45.829331Z", + "iopub.status.busy": "2024-01-10T06:24:45.829134Z", + "iopub.status.idle": "2024-01-10T06:24:45.955067Z", + "shell.execute_reply": "2024-01-10T06:24:45.954445Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.167367Z", - "iopub.status.busy": "2024-01-09T15:15:44.167160Z", - "iopub.status.idle": "2024-01-09T15:15:44.170213Z", - "shell.execute_reply": "2024-01-09T15:15:44.169665Z" + "iopub.execute_input": "2024-01-10T06:24:45.957778Z", + "iopub.status.busy": "2024-01-10T06:24:45.957338Z", + "iopub.status.idle": "2024-01-10T06:24:45.960533Z", + "shell.execute_reply": "2024-01-10T06:24:45.960010Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.172446Z", - "iopub.status.busy": "2024-01-09T15:15:44.172246Z", - "iopub.status.idle": "2024-01-09T15:15:45.608485Z", - "shell.execute_reply": "2024-01-09T15:15:45.607715Z" + "iopub.execute_input": "2024-01-10T06:24:45.962889Z", + "iopub.status.busy": "2024-01-10T06:24:45.962509Z", + "iopub.status.idle": "2024-01-10T06:24:47.449565Z", + "shell.execute_reply": "2024-01-10T06:24:47.448717Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:45.611753Z", - "iopub.status.busy": "2024-01-09T15:15:45.611313Z", - "iopub.status.idle": "2024-01-09T15:15:45.625782Z", - "shell.execute_reply": "2024-01-09T15:15:45.625114Z" + "iopub.execute_input": "2024-01-10T06:24:47.452855Z", + "iopub.status.busy": "2024-01-10T06:24:47.452615Z", + "iopub.status.idle": "2024-01-10T06:24:47.467433Z", + "shell.execute_reply": "2024-01-10T06:24:47.466813Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:45.628267Z", - "iopub.status.busy": "2024-01-09T15:15:45.627915Z", - "iopub.status.idle": "2024-01-09T15:15:45.662528Z", - "shell.execute_reply": "2024-01-09T15:15:45.661891Z" + "iopub.execute_input": "2024-01-10T06:24:47.469992Z", + "iopub.status.busy": "2024-01-10T06:24:47.469772Z", + "iopub.status.idle": "2024-01-10T06:24:47.521859Z", + "shell.execute_reply": "2024-01-10T06:24:47.521269Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index c9f378b4f..dd78280b5 100644 --- a/master/.doctrees/nbsphinx/tutorials/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:50.917822Z", - "iopub.status.busy": "2024-01-09T15:15:50.917628Z", - "iopub.status.idle": "2024-01-09T15:15:52.967278Z", - "shell.execute_reply": "2024-01-09T15:15:52.966578Z" + "iopub.execute_input": "2024-01-10T06:24:52.618988Z", + "iopub.status.busy": "2024-01-10T06:24:52.618787Z", + "iopub.status.idle": "2024-01-10T06:24:54.768251Z", + "shell.execute_reply": "2024-01-10T06:24:54.767619Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.970377Z", - "iopub.status.busy": "2024-01-09T15:15:52.970010Z", - "iopub.status.idle": "2024-01-09T15:15:52.973567Z", - "shell.execute_reply": "2024-01-09T15:15:52.973017Z" + "iopub.execute_input": "2024-01-10T06:24:54.771423Z", + "iopub.status.busy": "2024-01-10T06:24:54.770954Z", + "iopub.status.idle": "2024-01-10T06:24:54.774574Z", + "shell.execute_reply": "2024-01-10T06:24:54.774036Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.975908Z", - "iopub.status.busy": "2024-01-09T15:15:52.975564Z", - "iopub.status.idle": "2024-01-09T15:15:52.978717Z", - "shell.execute_reply": "2024-01-09T15:15:52.978197Z" + "iopub.execute_input": "2024-01-10T06:24:54.777223Z", + "iopub.status.busy": "2024-01-10T06:24:54.776831Z", + "iopub.status.idle": "2024-01-10T06:24:54.780144Z", + "shell.execute_reply": "2024-01-10T06:24:54.779593Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.981131Z", - "iopub.status.busy": "2024-01-09T15:15:52.980743Z", - "iopub.status.idle": "2024-01-09T15:15:53.013093Z", - "shell.execute_reply": "2024-01-09T15:15:53.012576Z" + "iopub.execute_input": "2024-01-10T06:24:54.782533Z", + "iopub.status.busy": "2024-01-10T06:24:54.782227Z", + "iopub.status.idle": "2024-01-10T06:24:54.838961Z", + "shell.execute_reply": "2024-01-10T06:24:54.838278Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.015454Z", - "iopub.status.busy": "2024-01-09T15:15:53.015084Z", - "iopub.status.idle": "2024-01-09T15:15:53.018726Z", - "shell.execute_reply": "2024-01-09T15:15:53.018223Z" + "iopub.execute_input": "2024-01-10T06:24:54.841750Z", + "iopub.status.busy": "2024-01-10T06:24:54.841341Z", + "iopub.status.idle": "2024-01-10T06:24:54.845275Z", + "shell.execute_reply": "2024-01-10T06:24:54.844697Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.020980Z", - "iopub.status.busy": "2024-01-09T15:15:53.020683Z", - "iopub.status.idle": "2024-01-09T15:15:53.024768Z", - "shell.execute_reply": "2024-01-09T15:15:53.024243Z" + "iopub.execute_input": "2024-01-10T06:24:54.847786Z", + "iopub.status.busy": "2024-01-10T06:24:54.847407Z", + "iopub.status.idle": "2024-01-10T06:24:54.851172Z", + "shell.execute_reply": "2024-01-10T06:24:54.850541Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin'}\n" + "Classes: {'getting_spare_card', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'change_pin', 'card_about_to_expire', 'beneficiary_not_allowed', 'cancel_transfer', 'supported_cards_and_currencies', 'card_payment_fee_charged'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.027012Z", - "iopub.status.busy": "2024-01-09T15:15:53.026712Z", - "iopub.status.idle": "2024-01-09T15:15:53.030093Z", - "shell.execute_reply": "2024-01-09T15:15:53.029425Z" + "iopub.execute_input": "2024-01-10T06:24:54.853595Z", + "iopub.status.busy": "2024-01-10T06:24:54.853213Z", + "iopub.status.idle": "2024-01-10T06:24:54.856682Z", + "shell.execute_reply": "2024-01-10T06:24:54.856054Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.032313Z", - "iopub.status.busy": "2024-01-09T15:15:53.032020Z", - "iopub.status.idle": "2024-01-09T15:15:53.035560Z", - "shell.execute_reply": "2024-01-09T15:15:53.035036Z" + "iopub.execute_input": "2024-01-10T06:24:54.858931Z", + "iopub.status.busy": "2024-01-10T06:24:54.858734Z", + "iopub.status.idle": "2024-01-10T06:24:54.862328Z", + "shell.execute_reply": "2024-01-10T06:24:54.861802Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.037893Z", - "iopub.status.busy": "2024-01-09T15:15:53.037524Z", - "iopub.status.idle": "2024-01-09T15:16:01.616250Z", - "shell.execute_reply": "2024-01-09T15:16:01.615598Z" + "iopub.execute_input": "2024-01-10T06:24:54.864650Z", + "iopub.status.busy": "2024-01-10T06:24:54.864455Z", + "iopub.status.idle": "2024-01-10T06:25:03.694998Z", + "shell.execute_reply": "2024-01-10T06:25:03.694259Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.619482Z", - "iopub.status.busy": "2024-01-09T15:16:01.619002Z", - "iopub.status.idle": "2024-01-09T15:16:01.622524Z", - "shell.execute_reply": "2024-01-09T15:16:01.622024Z" + "iopub.execute_input": "2024-01-10T06:25:03.698230Z", + "iopub.status.busy": "2024-01-10T06:25:03.697870Z", + "iopub.status.idle": "2024-01-10T06:25:03.701022Z", + "shell.execute_reply": "2024-01-10T06:25:03.700397Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.625056Z", - "iopub.status.busy": "2024-01-09T15:16:01.624503Z", - "iopub.status.idle": "2024-01-09T15:16:01.627569Z", - "shell.execute_reply": "2024-01-09T15:16:01.627083Z" + "iopub.execute_input": "2024-01-10T06:25:03.703600Z", + "iopub.status.busy": "2024-01-10T06:25:03.703134Z", + "iopub.status.idle": "2024-01-10T06:25:03.706162Z", + "shell.execute_reply": "2024-01-10T06:25:03.705544Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.629903Z", - "iopub.status.busy": "2024-01-09T15:16:01.629572Z", - "iopub.status.idle": "2024-01-09T15:16:03.815926Z", - "shell.execute_reply": "2024-01-09T15:16:03.815064Z" + "iopub.execute_input": "2024-01-10T06:25:03.708555Z", + "iopub.status.busy": "2024-01-10T06:25:03.708183Z", + "iopub.status.idle": "2024-01-10T06:25:05.979551Z", + "shell.execute_reply": "2024-01-10T06:25:05.978797Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.819795Z", - "iopub.status.busy": "2024-01-09T15:16:03.818925Z", - "iopub.status.idle": "2024-01-09T15:16:03.827666Z", - "shell.execute_reply": "2024-01-09T15:16:03.827001Z" + "iopub.execute_input": "2024-01-10T06:25:05.983253Z", + "iopub.status.busy": "2024-01-10T06:25:05.982458Z", + "iopub.status.idle": "2024-01-10T06:25:05.990517Z", + "shell.execute_reply": "2024-01-10T06:25:05.989921Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.830332Z", - "iopub.status.busy": "2024-01-09T15:16:03.829934Z", - "iopub.status.idle": "2024-01-09T15:16:03.834730Z", - "shell.execute_reply": "2024-01-09T15:16:03.834110Z" + "iopub.execute_input": "2024-01-10T06:25:05.993149Z", + "iopub.status.busy": "2024-01-10T06:25:05.992845Z", + "iopub.status.idle": "2024-01-10T06:25:05.996822Z", + "shell.execute_reply": "2024-01-10T06:25:05.996286Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.837368Z", - "iopub.status.busy": "2024-01-09T15:16:03.836891Z", - "iopub.status.idle": "2024-01-09T15:16:03.840679Z", - "shell.execute_reply": "2024-01-09T15:16:03.840059Z" + "iopub.execute_input": "2024-01-10T06:25:05.999247Z", + "iopub.status.busy": "2024-01-10T06:25:05.998879Z", + "iopub.status.idle": "2024-01-10T06:25:06.002319Z", + "shell.execute_reply": "2024-01-10T06:25:06.001677Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.843420Z", - "iopub.status.busy": "2024-01-09T15:16:03.843003Z", - "iopub.status.idle": "2024-01-09T15:16:03.847069Z", - "shell.execute_reply": "2024-01-09T15:16:03.846417Z" + "iopub.execute_input": "2024-01-10T06:25:06.004793Z", + "iopub.status.busy": "2024-01-10T06:25:06.004424Z", + "iopub.status.idle": "2024-01-10T06:25:06.007616Z", + "shell.execute_reply": "2024-01-10T06:25:06.007047Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.849550Z", - "iopub.status.busy": "2024-01-09T15:16:03.849146Z", - "iopub.status.idle": "2024-01-09T15:16:03.857396Z", - "shell.execute_reply": "2024-01-09T15:16:03.856742Z" + "iopub.execute_input": "2024-01-10T06:25:06.009917Z", + "iopub.status.busy": "2024-01-10T06:25:06.009623Z", + "iopub.status.idle": "2024-01-10T06:25:06.017029Z", + "shell.execute_reply": "2024-01-10T06:25:06.016419Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.859959Z", - "iopub.status.busy": "2024-01-09T15:16:03.859590Z", - "iopub.status.idle": "2024-01-09T15:16:04.111347Z", - "shell.execute_reply": "2024-01-09T15:16:04.110745Z" + "iopub.execute_input": "2024-01-10T06:25:06.019636Z", + "iopub.status.busy": "2024-01-10T06:25:06.019266Z", + "iopub.status.idle": "2024-01-10T06:25:06.263328Z", + "shell.execute_reply": "2024-01-10T06:25:06.262591Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:04.114316Z", - "iopub.status.busy": "2024-01-09T15:16:04.113883Z", - "iopub.status.idle": "2024-01-09T15:16:04.413212Z", - "shell.execute_reply": "2024-01-09T15:16:04.412556Z" + "iopub.execute_input": "2024-01-10T06:25:06.266421Z", + "iopub.status.busy": "2024-01-10T06:25:06.265949Z", + "iopub.status.idle": "2024-01-10T06:25:06.566167Z", + "shell.execute_reply": "2024-01-10T06:25:06.565453Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:04.416351Z", - "iopub.status.busy": "2024-01-09T15:16:04.415909Z", - "iopub.status.idle": "2024-01-09T15:16:04.420073Z", - "shell.execute_reply": "2024-01-09T15:16:04.419487Z" + "iopub.execute_input": "2024-01-10T06:25:06.569388Z", + "iopub.status.busy": "2024-01-10T06:25:06.568888Z", + "iopub.status.idle": "2024-01-10T06:25:06.573451Z", + "shell.execute_reply": "2024-01-10T06:25:06.572835Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 8e6ba877d..02e4c67c0 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": "2024-01-09T15:16:09.314233Z", - "iopub.status.busy": "2024-01-09T15:16:09.314010Z", - "iopub.status.idle": "2024-01-09T15:16:10.759493Z", - "shell.execute_reply": "2024-01-09T15:16:10.758787Z" + "iopub.execute_input": "2024-01-10T06:25:11.829123Z", + "iopub.status.busy": "2024-01-10T06:25:11.828927Z", + "iopub.status.idle": "2024-01-10T06:25:13.287001Z", + "shell.execute_reply": "2024-01-10T06:25:13.286300Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-09 15:16:09-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-01-10 06:25:11-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,23 +94,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.244, 2400:52e0:1a00::940:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n", - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "185.93.1.247, 2400:52e0:1a00::1070:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -123,9 +109,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.01s \r\n", "\r\n", - "2024-01-09 15:16:09 (7.00 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-01-10 06:25:12 (93.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +131,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-09 15:16:10-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.88.163, 54.231.139.129, 52.216.152.124, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.88.163|:443... connected.\r\n", + "--2024-01-10 06:25:12-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.203.65, 3.5.25.47, 54.231.172.185, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.203.65|:443... connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,10 +160,17 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 96%[==================> ] 15.71M 74.7MB/s \r", - "pred_probs.npz 100%[===================>] 16.26M 76.1MB/s in 0.2s \r\n", + "pred_probs.npz 64%[===========> ] 10.46M 52.3MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 100%[===================>] 16.26M 53.8MB/s in 0.3s \r\n", "\r\n", - "2024-01-09 15:16:10 (76.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-01-10 06:25:13 (53.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -188,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:10.762692Z", - "iopub.status.busy": "2024-01-09T15:16:10.762276Z", - "iopub.status.idle": "2024-01-09T15:16:11.800632Z", - "shell.execute_reply": "2024-01-09T15:16:11.800022Z" + "iopub.execute_input": "2024-01-10T06:25:13.289925Z", + "iopub.status.busy": "2024-01-10T06:25:13.289508Z", + "iopub.status.idle": "2024-01-10T06:25:14.320211Z", + "shell.execute_reply": "2024-01-10T06:25:14.319499Z" }, "nbsphinx": "hidden" }, @@ -202,7 +201,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -228,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:11.803735Z", - "iopub.status.busy": "2024-01-09T15:16:11.803280Z", - "iopub.status.idle": "2024-01-09T15:16:11.806897Z", - "shell.execute_reply": "2024-01-09T15:16:11.806344Z" + "iopub.execute_input": "2024-01-10T06:25:14.323244Z", + "iopub.status.busy": "2024-01-10T06:25:14.322862Z", + "iopub.status.idle": "2024-01-10T06:25:14.326575Z", + "shell.execute_reply": "2024-01-10T06:25:14.326063Z" } }, "outputs": [], @@ -281,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:11.809370Z", - "iopub.status.busy": "2024-01-09T15:16:11.809069Z", - "iopub.status.idle": "2024-01-09T15:16:11.812343Z", - "shell.execute_reply": "2024-01-09T15:16:11.811821Z" + "iopub.execute_input": "2024-01-10T06:25:14.329189Z", + "iopub.status.busy": "2024-01-10T06:25:14.328717Z", + "iopub.status.idle": "2024-01-10T06:25:14.332104Z", + "shell.execute_reply": "2024-01-10T06:25:14.331612Z" }, "nbsphinx": "hidden" }, @@ -302,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:11.814805Z", - "iopub.status.busy": "2024-01-09T15:16:11.814356Z", - "iopub.status.idle": "2024-01-09T15:16:19.718168Z", - "shell.execute_reply": "2024-01-09T15:16:19.717473Z" + "iopub.execute_input": "2024-01-10T06:25:14.334506Z", + "iopub.status.busy": "2024-01-10T06:25:14.334060Z", + "iopub.status.idle": "2024-01-10T06:25:22.328376Z", + "shell.execute_reply": "2024-01-10T06:25:22.327664Z" } }, "outputs": [], @@ -379,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:19.721335Z", - "iopub.status.busy": "2024-01-09T15:16:19.720928Z", - "iopub.status.idle": "2024-01-09T15:16:19.727079Z", - "shell.execute_reply": "2024-01-09T15:16:19.726464Z" + "iopub.execute_input": "2024-01-10T06:25:22.331443Z", + "iopub.status.busy": "2024-01-10T06:25:22.331210Z", + "iopub.status.idle": "2024-01-10T06:25:22.337488Z", + "shell.execute_reply": "2024-01-10T06:25:22.336872Z" }, "nbsphinx": "hidden" }, @@ -422,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:19.729442Z", - "iopub.status.busy": "2024-01-09T15:16:19.728999Z", - "iopub.status.idle": "2024-01-09T15:16:20.166215Z", - "shell.execute_reply": "2024-01-09T15:16:20.165584Z" + "iopub.execute_input": "2024-01-10T06:25:22.339951Z", + "iopub.status.busy": "2024-01-10T06:25:22.339480Z", + "iopub.status.idle": "2024-01-10T06:25:22.800732Z", + "shell.execute_reply": "2024-01-10T06:25:22.799973Z" } }, "outputs": [], @@ -462,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:20.169188Z", - "iopub.status.busy": "2024-01-09T15:16:20.168768Z", - "iopub.status.idle": "2024-01-09T15:16:20.174203Z", - "shell.execute_reply": "2024-01-09T15:16:20.173636Z" + "iopub.execute_input": "2024-01-10T06:25:22.803694Z", + "iopub.status.busy": "2024-01-10T06:25:22.803441Z", + "iopub.status.idle": "2024-01-10T06:25:22.809754Z", + "shell.execute_reply": "2024-01-10T06:25:22.809121Z" } }, "outputs": [ @@ -537,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:20.176666Z", - "iopub.status.busy": "2024-01-09T15:16:20.176301Z", - "iopub.status.idle": "2024-01-09T15:16:22.118952Z", - "shell.execute_reply": "2024-01-09T15:16:22.118133Z" + "iopub.execute_input": "2024-01-10T06:25:22.812247Z", + "iopub.status.busy": "2024-01-10T06:25:22.811889Z", + "iopub.status.idle": "2024-01-10T06:25:24.829457Z", + "shell.execute_reply": "2024-01-10T06:25:24.828653Z" } }, "outputs": [], @@ -562,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:22.122699Z", - "iopub.status.busy": "2024-01-09T15:16:22.121929Z", - "iopub.status.idle": "2024-01-09T15:16:22.128438Z", - "shell.execute_reply": "2024-01-09T15:16:22.127799Z" + "iopub.execute_input": "2024-01-10T06:25:24.833068Z", + "iopub.status.busy": "2024-01-10T06:25:24.832222Z", + "iopub.status.idle": "2024-01-10T06:25:24.839537Z", + "shell.execute_reply": "2024-01-10T06:25:24.838871Z" } }, "outputs": [ @@ -601,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:22.131058Z", - "iopub.status.busy": "2024-01-09T15:16:22.130684Z", - "iopub.status.idle": "2024-01-09T15:16:22.149299Z", - "shell.execute_reply": "2024-01-09T15:16:22.148635Z" + "iopub.execute_input": "2024-01-10T06:25:24.842146Z", + "iopub.status.busy": "2024-01-10T06:25:24.841643Z", + "iopub.status.idle": "2024-01-10T06:25:24.866755Z", + "shell.execute_reply": "2024-01-10T06:25:24.866053Z" } }, "outputs": [ @@ -782,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:22.151641Z", - "iopub.status.busy": "2024-01-09T15:16:22.151440Z", - "iopub.status.idle": "2024-01-09T15:16:22.183906Z", - "shell.execute_reply": "2024-01-09T15:16:22.183377Z" + "iopub.execute_input": "2024-01-10T06:25:24.869362Z", + "iopub.status.busy": "2024-01-10T06:25:24.869006Z", + "iopub.status.idle": "2024-01-10T06:25:24.901803Z", + "shell.execute_reply": "2024-01-10T06:25:24.901116Z" } }, "outputs": [ @@ -887,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:22.186276Z", - "iopub.status.busy": "2024-01-09T15:16:22.186071Z", - "iopub.status.idle": "2024-01-09T15:16:22.193827Z", - "shell.execute_reply": "2024-01-09T15:16:22.193328Z" + "iopub.execute_input": "2024-01-10T06:25:24.904601Z", + "iopub.status.busy": "2024-01-10T06:25:24.904369Z", + "iopub.status.idle": "2024-01-10T06:25:24.912699Z", + "shell.execute_reply": "2024-01-10T06:25:24.912132Z" } }, "outputs": [ @@ -964,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:22.195967Z", - "iopub.status.busy": "2024-01-09T15:16:22.195760Z", - "iopub.status.idle": "2024-01-09T15:16:24.046639Z", - "shell.execute_reply": "2024-01-09T15:16:24.046099Z" + "iopub.execute_input": "2024-01-10T06:25:24.915153Z", + "iopub.status.busy": "2024-01-10T06:25:24.914932Z", + "iopub.status.idle": "2024-01-10T06:25:26.819403Z", + "shell.execute_reply": "2024-01-10T06:25:26.818778Z" } }, "outputs": [ @@ -1139,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:24.049087Z", - "iopub.status.busy": "2024-01-09T15:16:24.048876Z", - "iopub.status.idle": "2024-01-09T15:16:24.053237Z", - "shell.execute_reply": "2024-01-09T15:16:24.052674Z" + "iopub.execute_input": "2024-01-10T06:25:26.821955Z", + "iopub.status.busy": "2024-01-10T06:25:26.821736Z", + "iopub.status.idle": "2024-01-10T06:25:26.826080Z", + "shell.execute_reply": "2024-01-10T06:25:26.825582Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree index f9dc2185e1d4750cce06e4f66ee9fda952d98622..4741172b944c6482161a4d6505d9d6a1d1806316 100644 GIT binary patch delta 8679 zcmeHLO^9Sy6}GGg(ykn4VlXpwJx3SD>16Ib_xFH7BZ&}jm;^-(I3(x(5pX3!vN2=^ z#V9Hv5_#FU$VODubT6bBaOE!JN)Q*BrAu8zTn$<1SM{pLF4TIsuZC`DxK-z#d(QWB z&hY;EpS*wm_?HLzMNORL#eCJ-Oxm%t!l6}deGVZwCTdaHtf{KxT9ESXy9ck{J(%J5 zgUh=I^G`fD`_b~B{rvFN**Av6pUF~x7n@9tp&4qLTrk>{sE1qk=I58Mz0v((wqTQt)79JTjZK9)drJ~1R>#@7K~Clu zme;@B{b@ECefZt+{Q6=d_;B)-?qi2Ok!q206Sj&Z=FkfMv!*1i+VbYj?ir-v+399D zD^;yloNN}aTFaRY{!z=nf2VtW){AYe#GoZ}v8H9bv38m>Nh9`6mdN%tj_r;a2f5U# z$(X~hZqFZHBo2y$t{ll*TN)c9zO9PfNQXAO_1*5m^7t?F$GVo+QT+PnuYU8! z<(FSR>MvJRTWg&iw`xgS(aKi|$rh7};o*d$l&p&+BJ_;)#a-0ZESB8^wr+m492xV?kJhyBin34>+)|!s4X}u}_Yk%&3 zI6D+?@g-FtsFtA(#2S2Mqb>lhBvr1rm2}W#$ zfce@YY6SAmA-A~v_}?I23(hOu^`_=z$%#;KwXlKW*XF%!&{c~|Y?NIKS`1NIPBG-H ztMW>u-sk<@F-P8S6EO$A)2!NYcRisctPMVh+z=>U0nN*%@-U^Na52 z5fjeHR%51sYXuEOHLN0mXyis7aTJquqS_sqzCUk8*t5*}T z!*SPA;VfZeV+}}YgQlsVBbugS4kwqoPxW3EtDs96p5BU~C;)h{jT02O##8U$NI`4S zlL4TkTA3XA6rv%MH9Xcx2aAH+8^{#v1}ck@!5+E>SMUiybz{TULNyh5OkyhTaOX~U zltxeys%SA?)Z7vhrx>Xk%5V*eVjkhL@xXc2ezhoeXji}` zMxX`#)}&$RXd_%kXc4~2W%PAJtCXx@bs^{p>X(4lx;Z;q!QD3iXdZvJd!`5H?v8%N zbq_6I8ghiNG`Mnf6yj?FbLXZYs```~vxX2raT_tbdN3)V=zv!!Mk}COBBZpt%nax z86(kJrvjWeW)GWUvH?0}QZzm(_(cqa;l56JN#1#Y4hB&BAf!nW=3$JN;%nU8e=6p9 ze|WK-F^6~l+C6;LKWg0>R5_y)oIV6riq#R^6_kmg_@)~i(PngC7$RigF#9!vlVMKi zLPhYnz!_!?OqLHU`-ATLvsb2SvW=CgHrzgqA!z4&og%BBN^AJqwT?e>@9ht)e_r^| zjW=F6_tpJ2W53P7MrOawSnode+l>FROWAKT_S=lDJ=3;6?Y9~Kdz-S~X6&~aSitYM q8T)OIhiv;FzMwi$7G@gLo{KKw63s=dzu delta 8905 zcmeHLO^9Sy6}GGg((ZJWegDqh^LO@U z`2FDW&ffeJ_s)K@{7*mMe}4AuVgEPtM;9cl)})DS%H&mwQbw{_=jxpzJKQ+comsy4 zR`=O%p`DjftK_{P=`9OyO>{A)tcnS8_};(fr2 zX~jb3)!E_jtKB`zzrQ+vZPqu^D8rwQ=9d?8dkVwR*Se1^ii;?xx{9iz)ZV$w*|@+- zICgGIv;Wdqk`h34=Bv3f)Pc-ERdCSh?&_KKVfcaY^4;7l_EO{AZgr^&*S{r@h^qUG@=vetRfc* zX3P6ZCfBIFVs<_o4kGh3`6ofw$fFFOd8YeH-%R1vXS$E|GO%JJ1UZkX(lXRoy-i*e z0_IJc5?xQLsnOez1bR-aqW6hwjWMT+`IBoVImTTM(d%ldD9VPw9?lDe`7a@YUe?pv z3_ne4EAT6~=MOHj2ZEe8S`j0jh`|J-eFDv@D8iC&%&&C&61?VZ4kA@eqNGp(Iu;>Y z6LcQMOv0*WDM3R5UI*BF}Xa|v&f+eR8nl1^EeO8H`w>%t}Jp7{zNJ3$7u8WytV)%2pG(uoc~be8wOG zNoUxPGf<_KQc&UHh8k6Hl(zj`a6igOC0!)Nm03wlqAW2)t%q|jbZ1XuteM53R*=W5%3!5I}JJ*iQV-X@Jf{ zJ|-lt(S?Rr5upM^lnGiMC6(YRNR~94HM$ysY0`pbVDZcDa6#}Vsz#F~5EVVdGenD# zbV?Km@Jae7TbTkR*;ArX$!u_16bqP*{Gv}(vXH^Znv=0kR)9=-6%@(|f*|BYOdY&l zx?jU)*5ax%2B>cc14hu38j-4@O{R&RA6?-LR+oL8> z)_vWldK0B*l@(|mP2m^=lTPrDB(u%OT!1=YhAnub1Zc8#l#BGR`87klVKq#NCuw65 zA~@ARFgz@&0L$R@p&6|fBZlc@HO&lG)xf=O4}R|3S`SxyPsVQ4JU5NwBbRvDEicIe-p|8VwPqf=!QM6hvNr}RCi zx8GYihTNrAX_La45TgoabXgkjx9STeZM7sc1c|MMotUvWAJ%!H@3GWb5i6aNE#Y7%R9#YQ~YGAN&n2zU<}tC&3e4Bp05Gbeou3{4gZUuZ@> z6CToNGv21Dz?1s5_3F#k*w#$?)`(k&S03s<-A8hkI3jPL%!A)Va$rTm3m1<{y?%kMN*v0TE{t@&3kRNVARw4W=o4@g(Iq9p)J(8ZaN#^UAUH)I6gC3WwND+Yrcfo1I~Jqy zq@ab4j6Hw^VPK5lprLRpxV1X8382=1Zk~3HD?5f;-zcpPZ~n9Mr(XK^^$)Cmp8C+$ zpFef#>$?rgZiBMhp!}~D-+Q6Bed%-z#oY#Fw?Wx$Q10$2cN>&`w?VnPy4h_|uzKBX sP<9)X-3I0E4rjMPd3W=)+o0?=D7y{H=8kLgA@+aTpe(UL`Td9g3;W6y7ytkO diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index a0d5324e506ca64e340652309b540f8bcc9fb0e5..d8c5fc1112cfd0f1c27697f515b287e974275933 100644 GIT binary patch delta 2218 zcmeHHIcQZu6lKPMsNcfiQds=w*BFFPobAmbXdx;HMt_q+Fnnj#4+(0%2`-3REiOf- z5X45T1i|8AAsEtFrcgwQXcSvF(rLhn!8OGb!7lC0y>rgF_naBORvEumxjV50k6@y# z9)CvAIVD4rh#*W=9qmd6JM^b{`- zgKiMjA#mp(x^Jg$n6=v9LFZvMXCaFO`5MHj0dVfGYCDIr;5Bu*rMdQsDH^0!#!G>i zB*e?$;=PyPLTOde_X<3Ho|)iS&_R$~GDbC3#*tth5z<&mwJlyu!bV5tw8fr!o^$6I zMMyeJ5~Sml5gm{(dZ*wG(3Kbnl?SA4z|2XUUQ?;aYfiDm-qyBcr)%-!8@M5!dI#6V zE6-u2m>7dsK`D`VejM&C;o5POeCKj0g$+)4Z)|GRxi21lnq`-asExz9Bk3YgXN*nCus7I*v<*?5oQC&7 zRsyWz*kg#RC6J@T10B0Q&{iiGuCGMr06nbt%*k+$rDvpGt>nVre1;?gv{1+o(CZz@oROK{_gCvcM_ zRP+du`4f8Z=zwo;Zui&DoT+CupBN))IFk&epgSY9wF%f0OpWwm&Uam9UY|2m;|pB- zi=iHVh7EAGY-P2?Vs~SIvv=Q)oqL+4&i$>rWSFYfUyqk(;OdQKGSe{9G)7;6jq%_l d>@Q8hLL3+3_{UeU5Xa_!iesginSr~OeFfSNv;P19 delta 2206 zcmeH`IcQcv6or{V6g3tGNuh<$-z5ma%yMV5un-jlmoyea!k^imMiLV98!(D!w6suD zIE9U!l^|F=ECfRuOAAE|ibk=GD0UifLgI$S6TvR+aA(eU&zv(o*)~1dcJIk*T8_9= z(RkCXn3fK}X@HVIu!fg{dK!XLWgwbHZmscLT|sLqvHceUxpt~L_69wlDZCC5lrtPC0|LfMrL+MexUgFASiO3Qdsb|w*692r+)&_P zg)$zZ_90rON>j2l`vPyM`e*S~v>iO>&Jq)yvH(V#pakWVI;p!gy16sDZvq884<)#&ppdFjeo`uP_1a1 zdWuP{=FK^*3on>v-ilyF1Rj*mM4U^b2!mi0Eb)Vi$_cKi4lX;dtchGO&y3=fORJXn zWvQYi!2``>R5;CjjFLn`ecr*s5`UfOB7dD`TK^31)Rc%_po)0b4vh@PR(Gqfap6K& z(QsLix}398m%t6vF36C9d63bUf-Ek!y)Na8G3xn>H~kW$M_+Icx>)yFjZAT{=Xmq* z(S7?5HEZ+w+jO0gjlQmQZ60^*s9Q6ek=?lY2JJ}wFL7UO_LqyeT*RO4UoPT*Dq>qT JKaUTr{RUc(qo)7> diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index fd28bc73362b653bebc8b0020426c95a3b10263c..6521938d2fcda1707759e0d764e77c6ffee4b2d7 100644 GIT binary patch delta 72 zcmex5gY)YQ&J9;M4a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN c4UJL_l1$Ug6H}U5x!PH|7`L->F%>ug09{fP*#H0l delta 72 zcmex5gY)YQ&J9;M4buvevQjORE%nWfEXF%>ug0D_Mdy8r+H diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index 96049754b8a9526f141405ed60d1cf6ffd8a6998..baf5b1b06c7309249a1cba5a430d7df4e62d1504 100644 GIT binary patch delta 62 zcmew){z-g8BBNoMp^;^BVp+1jsY$Ytp{b>*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%=1GhRTmW4z5>WsE delta 62 zcmew){z-g8BBNniK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP=1GhRTmXAp6Egq+ diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index fc1e427d314fbb09bfab70b3992126b2739a26b9..956200ca7c3105a2232d001d03f515d087071a2b 100644 GIT binary patch delta 68 zcmdnHnSJkO_6_?u4a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN Y4UJL_l1$Ug6H}Vsb8dgn$$0KO00aIOssI20 delta 68 zcmdnHnSJkO_6_?u4buvevQjORE%nWfEX3_d~Xo?m?0;QFU)Yvrtm6n#;a_Ji&@Zg1r zz!-OeToeV=L@24rD87LDrU@a+gV97JF%i+k#^{Sg{r1^=%FH3o|3G}3q-oFT`OjW^ z{cC+|ed}BE${VNu>W$Nn{^(?Sgfbi4k+4OIz^JrRIIDA_LIth0w2Wm#M4&7YFSB;> z*y|_%WY_53aoy;(aPH`=c+O}~TpB$cH;ld$K3d)Vq0ZXT;iyJGjlO*7vi#={guU+I zjp?&{4(&U*Y4dPX^|xK!4gHm`UZkB3qpkbS8ujSU0N;aRGFxqFLe>WBU5k>9TwT1I3voC}?Vz`w=^?TE#*y%UNzt`-l~k9+LN z(@q+aL{lV^&42Tcj8?QWzLTDB&&(v!Y z1u+5XQQduWZ|(TiSG)Ih1|nNZJU5m*f{U<7!Yow|&fP%`7ujWffN(dy{B9vPNwR_2G@3vo(Hso+BL zQ5eQN%U(r70o$03=d}Yn`%57vrVM(A!}Y3Q88?owXdLB4sqxXrdyjSQD#fC{zbkg^ z+H=*_pV)TgoXi_4hZs>n<-j)WnT3&IH1cB&sn_aLp%eBjGA1z)g*} zeyq2%KhO-AGl-M$KvuMzP@}}B|I6;CAqkl}l=6tKgMgrrWrpO-R#=U{f!~!tpuO=RBjZt{1q7c$ z7BnLrsig>IiyyR8AkSEc(QVWKpV6hs!(%rNy$+F&m z*_K;(w^^kCSkBSq@czgw7jT<`3j;4XGu4CN>#iNI1f|&N$Im_9d$e;`DIl{TDI}tp zJzcu338c0G`SaW&jq@Tg?v;)x#3<*{gJagko5d(l#od(i10@{uc%)Axi)5WoDuIlm zu2Hl&*JLC**8QEcWymT4ZcGFP)-G5Mb$t}Ys59z0jUXW)P0}anLUKVXqjmO;!z;KN zu!!&r@M|S%3PT)~f_eZA54K2*yZ?>8`@(a*r#eO3HMrkU$RV+V3GyH5FFG<0P%vToK7#6I$raGXz&ok?)_y4oI`65lH&}B8#f^tShiXs_ALMexcM2A24 z!KBv~m~o4&LP*>!R&bGXGjaAbn}1Pxf~?C+F15&#&Qp{%)Z-`=6PAW(HAo*|LtXF?$m6VG^-1}DMYgAhT2zM>{X zLVV3uqM7Po1H<7Gx-h{BK>=G3vVZ}EfH!6$tJC&PA#J|#CQ%EvMfH_+{d0$kV~Vg0 zCJbRm9sYk_FA+<|ZX*6!F99Sv}pM_5ZqJYB!gOWpwV%mXE zU{;BKj7-|uAA%1C0nX?J_`O2Or07*91`|@!M8}3A4wXzHQHQxppaNUK5A`Po$Ss&- zahomRRwl3cc71jKa{rp5-Kw*tzhS_bF-)k4ff>9VoRm76P0}3wm$J4+HG~BHm|+eC zN>Dun!RX;334~;75y52MP%JbaiW!Ad8Hp8yVe%p-F^9>GppE-!d=-q7CNE2NtNmMg z7Yt38mI;9B!q+qI;7a~vyrVj8Y^kbRq#0T63 zWG*8W26bR7IFm(fAg8qT5;6#jP-v3jj6+^w2yMh@f{7t%lw01SOH-v$Dt&~K zAp%$R6izIXMSvx8J=4xu!E*KB)%~3}lnmE*GZ8ibd!ks|(KOl@=5=1nF9K8%N>$KQ z2Bx6QfE2nfp#L<8lfndub&OeqX+eT42Z&q=W21@+lgs$S*YzIiECuZuE(d)w3Lfg| z8$YEr-td2X49VvC&eD9d)idjQY*?~a-L#{(X$guQ*g{}Z*8(w6&T;uR)`DfCtfHSo zi)!XdhZqop`MB=P)H3LWifbKs))2#>JJqDNnJZ1g@Rl;s^}TRLtOD$e^a0HUQv={z zt2D#;wzF;^SFr1uvCAS=P&OUZ0tB51)l~mR=_nbl4(;t-bzRBsG|rf9 zF@|Xp8w+VnfS@6aHK2kJn6&~mlu5CcY(>zgL?dbnTtV(a;{?`-^&*0ljX!^LZv;iu zd&Og_rA2fF6(PGqQ&1o~SxFrlj}FQH)6fv-TU|-crTIpy&m8WNfmE6YLea>;zA+Pe zAYU6`M+x|18x^*x5n!Jfl2fQDix3?2A&>~tK*1j&iMJ}~08ap1K@Tw(3n?PxV?!wj zB%rI)iUI}HUf+Kbq^Y`OHdW;hcH*Ux=neB@_?j;Cfvr> zV~S)DNriS-M#ObF?j<2u@oP%_+P zYr(Fsc0U9;#HU1g$_aizqDRGmT!Y0Y)?3~~*qx3M!(@y?7Z4i_#90ggaG%J!tq#K}}|ZVF-g6#sgH5Y|-hkOU=Y<1X`RR#NX|lJ>UGZ&~=6d!?}9>;ob$yY?)5A zHqc*^B;=$hp^spyM=c>vf|Pp-MmPyhVOiBolnwAo!CBR@=4WZ1JzcmjKLOsb*>YPY_hLgEY0591um9mi=|op;g7}AZ00kL^EZu)rCIZl;Q8CD#nNoC zG<(}hcXozbEX}5ub@knxkntBwv&GVEYN5G$4K{zFyZZEEY4)~#-C}9hex7Ae*j+7_ UX4A~Du-g59EzQPQnvGBSFXRoPmH+?% delta 14907 zcmeHOTdZ7V8O>Z`L4^u=gQi1J`!A{vb#65&A~G*Q={J*Uj6cK!$BgUK{$({s+A z{r#73eQSN|Yk&UsnXkWn=8NAwndbfe=46B2q?u7(2ycYQL_}wKmNd{9z0Q#snz(Z| zaDC*hlfS=xxH(=td^wyy{CZe5d}i=L0mh0HTvQ+m*zhX5AWvn zyW6{K&sx8sS|4t5x9(0mcJ1GDYx8A&ly=tk$A3HhGo97vG=IBm&mC^ZuG?+^&_u!)(l_Rv!;IdS2 zUsrsnc^&)88!HAe`QSOl$EDy>+8ib0JZ3LLw8Y|V_Z6RMUiRjbzg{sgF>1wRj(CaC ziTNn8_khJ_k|Y@tysY%h{^wU6dHV1fCsmSKY(r|23^vM=W5UF&V`52a9ovnT9y)U1 z`48b$CTHoL5sBd?%sQF{zGaM1nknVRXI6Zu+2-}5kDOE~oO#GJ5Mj6wQMiz;3sHIE z8MD4Qw0p}B;dS?Id1bWamCkQ}+4(?CpXgq>pztdS&Iuw2uaxJBb5CpzGO1iYbARvb z(f(g`MZf<0bG=grHmIzOI_o2=}TAE3~%M&iz zD2;2d_%M>t1r<#F$TzzyM-TUUzvxu67olDK+OyrOt}Db=>o!#D?sEG+o7Qcv$RfG> zZhY;NO2%@@qF{m;q}1SuN=MbSpRdKffbG=yo?1N1P2l%o=(I$0XXPXAAJumz47t>bDgRX-`6j$>XB>H z?AUgCXY1CDHzuV|R8pLrQ$Th0L}yJ^u-OMA>1f+Ky>0zLj4=`EC>4mn68F;Z>>^U! zaTe|9;Gy25ok8M3OMECZmqZ}Jqts_jc<>Zpb@jIGOZyoV!q)ViF0BeteQmzey=Z_6 zi%@NT-1tEM;{G5cpO9>t<|wjIPUzrdHbGm_M4G82EpGP63!cy&w z22(o{y>c|^(ZO5$Z+0qRdS=}Et3T{+Id5jz`jj7a*HoGiB9bOjQZFo%r~%1iRn5&Y z?$cxgiD;E6u9hh(xS@_gNJIvlIqAKf29*gYCR{k%R5-7=jWnR@nn+a4R@Y}dxRiv& z;A2o#h1j+}^waM83a6C}QO3K7#e;#OQG$Bl)*Hpm_n zJEZ35;4MJX0##8l+8jBaA`RFSwS-8mgrF!4J5fzvd8v1}Q;gg0p%k_ryxv{EN$5qh z_tH&w>}-<@)k}kxJ&rztZjcqAYk@LgoAgqly_=!{n4qd~FL60e3Tfz3 z(w5qddt+QiUCzx$v3cK6ruHc-A5spc-S|Sb07e3nYQX^&V=2X*Wr*7VwAEX;;f=Espk{zQ`F;F^Tv8B3}ze<7YZ~v)xO*K0%>TKut-PMDvEpX0}GZD## z)rFk73c?UUm?CXi1S5ZdZ#3eY*EOWaaGOvdzjuTvtE&m)^RHWbP?-K06$9NT^qWGq95rK$lQ_ zQX-7DCP2QHUn9>S*9Rvbu2m*V<9~}IJJTXB{!>bg$e)`n@mQNL8 zj|qKA9x5d#0#OpQBZN|IvH-v&P$D!47upYUWFip9aH}8%iO?E+rmTn>`omHgAo*ej zLFqL5%{RJ_bO!iRk`N#wDFh)pWy&Okmh4efQZKdNnhPK%o^M1nMl;f0HgcL+0YLIR@207Nfk+G0Q zXYA=++dnTiy_AAr^1`6{hbSWM9@HJiS zh29@Jl_-R*zqHuDu8Lu1T>sdn{@MYR=!T$zIFzA)=mQJ5U|BLrs!T{*E3qykn`YfO zuv^AH(SJ5n2II*GDJO`k93?0gfhni60QtH|oy*8ypLNqpPROee)3t@a6tmvDsdwQ3 z{SgT$^Ex2g(R&*OM1hrD^3ky{LN~arzYwz{6yjHLw7CF}p$tzDgENMP9ew^#Z`i3g zB%v`hSWO}Br9ugnvcVb+!6h}J-crQ+!>9GoS|}8*HW@-FN552-!@?2Hfc) zx%z!=7z~!GsgaG`C&Ms0qXL? zhHML{2;rdiL&rPyu%UkGjMJzIP02il{H2L|9a382MWi*-6{o%3KV+8$mjbOC)_ z2YMIQD96w|W3eL-VX6Hw8zaC1hZ=OGf5j)VVzOk9|4HBPIC{@1ay`<*KqEZTA4!du|A~d%?w*_KhPrs=OiM5k)gMNXxgA7 z3eVuM5QWZjT&UVzpoSTXpM%M;JPkn7rdig(dzuoI-&o~VP!PgFJ1Sf@>{Jea1obgM z?ZerRNb|O#ECikatrW99_1^wP8}zh7bU>J3aJd46yFoWctjrKU%4Vja9AFor87nyK zZHL^-!@{-b<@_`|EtOdQbm_1rFk5}u>`lcMWdA@vwQ(ngAWiwH#g% zrZ8Mdm|g@heK0r|H2xMdjVKJ}5mO+mQiQxGF*>;PphF3{?Z;M_52&ybm_sSqM)362MTODbXL z18D|ii0sA>CQTT+I7%dH1ckoQ777{mH~I(69)wM43S@BeXU1)XS1D}0<55^1=$TNi z7*5PVnG=D@On{Xs;O%k&muXV_HRBE?0Rtfh48rcVu;ekOgD@6Uv}YIA=v%S;j zld}1wY(6PF@gR3TDLcV{ZayiSPs*kyn8z86^GVr%8qv)sW%Ehdp2IvJoa_qf`C`KA50y diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree index fa5c7e945427cd74a2748e4b1ca672b45502019a..7fad303640f58e24a00e7aa75e0d3a01ef5e18e1 100644 GIT binary patch delta 84 zcmX>zL*&d1kqvt|4a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN j4UJL_l1$Ug6H}Vsa<;$aWCUU+AZ7+)mhEpjS>u@ilfxTq delta 84 zcmX>zL*&d1kqvt|4buvevQjORE%nWfEXu@i!AU0l zrUp*oW;$;%fwpWhu&+gb zJ~sSeXYq_bKKa})vyPtL{pRJJo$P^%}J^fl7@&Qw9r48Jhlf zrMtY_lfx|F43SfUuS9E0L3yK4N|13PWv3oI-+eylU9_gS;L#V484BAq zt+pO8_a%~M^LnLw0a>E~-nZ_?2j}0vHo9}? zTtBL@7;;V+Pzrr6QO?9LTZ_t3EwDn@lGRjp|JC4bi=vTqeEss(>j!UMc;$`jtphz; z4UMIZuAQ6ci{0s0TacE(3x&!Q#6*Oy=yIfpq>2S$Bp2)Fz~9;($_f(YB-fTjZjrp~ z${A%m$jYcL?sF+O0;Gf{L=`NSfSiLIYGksBGY~IIH{>>KOfdQROn0eQtpOHlQ$XH0 zfFL`Xn*lTGP^yYWEW$fnvFJIOs^uw#uv*PV%&mwXU;)SC zK{WS1FTbQ}Hg2@leWkWmb5Yy=`1{XJ?kx$*9%>NlEl$wn6oj7)3cS-f<`NbJi6QD5 zKqP}oLIF8pKu|@BBbEe_!oQgA4u)E+4~fA#@jqgT300!Fs|qnQrwvH0=1WM;-gDVm z4I*>P`&w=2E=GthI0u<4DpX{3HMx>_nHCoaE5=&!veI3PtfNY5UFy+l)7aWtcY7^J zLaR8=_>WtAlONyiPHp3Fx1B)qG+&3tHvaw_zT5a)e>1FQxQ)MU{B7g!=(ozzK>J_B N-(%Bv?se}y{trxyFG2tS delta 3733 zcmeHJzl)tk6y5i+MG_DR{uVax%fqa&#O%zSJ2Q8zBqYcZ6IVzh65`zX0a^vLG0P(A zf`uq7ulTBLBM2f$B&pG#e5AL7ZzkllP zHz#$y#qXDp!ny}?jL3vo73LUBwL0mX+1IQ{7ZYPh#!nwTdG_9;lONscHN=>pKqUoO z3z02W6@>=0BX9L|_x7jFp?hZQ&lA0JOynt73JGU3O$(NbK?YAbwZr{B$d-z)S>uBu%`vt@A7rSeH13wPqN%u@&T~#SjsRV?kWJbaQ9OUVh zj8_L_-dY}0^_B{YgbK;zsI-tZ068ZgRhm6`z1tm)D_FE(sxwxju|>3(J!ycT2^nW! zZgjtlnvlb_wNCZafE@PLx+{GtF-8s?z*lry><8jT=MhzrQD|eF3YZI2c_j(PGs$hvLZuQ+W2(M)&im2|6dx!W`=L?qv*G$*L{@ zy!YYy2RA0$+vodJO_Dh=lXYQ1suiHw8S3tgK5mF&CW5%J`S#7L zZ(h3m#?7V|k4(1~Ho0+r&{w+WE;ks>d0WoTWKR*qEQ)P36+BbsR2BJu#I#k?o`PpK z8M8#e0<)6{dGB1vY4&iX+Z&xr*~FSiJL?JpD=HE^f_O0MLdyS2RRezb;jE;p1wKzz zgos87mEtmrZlNeAC(Wo(0&qM`)#h>^y;bOyh2#sO_%-M#@h_!Ha+EC#TN;O~5)u<9 zGzP^5DO-qQ4Y?#CFKpQYJ+$SxfjQh+>(-~=etvp&u8GS*HcUoQ@+eseL7q`$&C$6? zF)zHBs}Hhnlw>I^&T^EiE>{w3t~EeiFv((TVREpLfJPn+g&m2CTGE?LT`*~Z&I8uM zoCi#gBl69=vot6?R18ru;BG|WtZ~5&H)d22Ofnf| zjC#Q~U}`s??`fhjuQwcMqt>y>94)LR=~4!v^RTr z-FE`pcSI0G%;cUK1(xkaCbuKu8W3veo{D%zd*gPFT0)1{x$h^8d66B(A@_JJBG>VS zh8d2UK>^qg=e5QpBX~%&~<{?^+aMj%=N-BjvU=xA(QIvpU!${ zVx5cFG0Y^gJj>y6&KcH})5)2Yob?_J*0`19Uhv>Kb6kM5h%YMdZPy-!9opMGgei!!yjc7IoQW9JL| z?;Rt*buB$se)~~UrH&z=IDsZ|@j7~3H)ZWQ%36(`7yRLDDnGoQPL~fpPO8L_J5Hmg z$m9k(LlaaF_uN2@R%7SQfBX%RkKRP5wJ7rCo9O&jW9JVKy4A912usUtuKv^fto4Ik zWMPG?7d!X;X(P>u(+EAedH?(KbgQ}h{RR4A=dce(W(>`VABl`~-bWo(dFOM)EE^3g zpVQ3eiIp`kex7vcr*tDB=TQ0HkIDQJ7_+%tvAgxz_T6Mr#`jE_{Fcb)e@Yrz4nCc9114@?SfMRV}*>E>!>9D~^8r45(WTjA zdfQYoDQ9@FPL^h0{k~3imXxr3Z8mWQr{g;sQwR$!37n^3%rSi(% zq}7Ioov0Yc1?2ZyzvktPP_Pr!i?tDHuga765xZ49_Ph#h%io=UE%~rQJzGBe zD{@SAOZ}F*#pR|e$OJik7rC@_@&IqfSng%l&%X}tDsx!_@leZ6H?GHCtFBi*K#!KA zCXs5Z|Gig{ao~{9-=%6G1HJDrWr-R|7oF0tkMz}>p&$Jxpak^qpsU|h@{V0{z$SnuS30X6@4)A>TORx@n_3W6^>DgC5`7-Gm zQF1+}dF`Ir@+(a;;l6uGn1U-qV(WaGM<)P=C;DbT)9G$X+4xb?;R^2mgKn> zWwU(eelnxg$|8om=&NK)i=;wEtzRcE>~BwR{715Q3?e|Eh%pR=mW|^%z5QF{77AJ) zyH-psvsO$GKTeKNOf6GZv;$8Pga#vbd0fpc#0E{wEqHFm0}~e6 zQ_6lo5FQi{?AObQo1P{Mt7Wsna<8ppgKp~Bpq|nl&y(9}#?vy}`B_uP1*pqZx7ZFW`D$ zCH)PQX3{Pl;MEx0EVNK!r^|j#&ZUR5Kh^!9_SsLyK=uh4JAOlcNBRv>TW7z|2eM9l zKKwt)zmy1<({_=|lx21)xAE+!>2l>@(zldh@&-sfhN+v{Fx68!@ogAp#7MQ9MsLFRlK{E4jW1H(A7=T3g6vI zPa8#@YRPGMSwMMo18tPi^5p9q=tBLJUUW3wKr^DAWPK8yn>ANVq9Wt@he@Se@#KZY)-s^DVTnZa(HL3Ox7dm#ZHQg70aX3)(v=YrB-mp^VO0|;q`g|q1B7Q@Qp z=?NJ_dhhXcNtLpE$Z)rCSo z8R>F|o>Z+k5Uec;;731d@S~p6uboKmpgA=|KN+t^Kbqs?E;_Nrq2Z6F1}9Sue>@GU z67A%bpB74)w8s|dsAV+s^P-nJX`Ys!7wNjwDbz_5p-#*6Q+jYYT{NO%Hw#jk(1-H%UtLYdj*)*HLFZ7G zFWRkbv=bZp4df=Er4O%DCgHZz*&wcIr&r?V#dhkdce`&;s{htebQXwXpyi5q1 zI(`!~j=SZ}`@W3-_59y0bx$rinvTbEnyyyX@%->yvlLe!|27CSV)DCh04vSgNKc~U zS21JNp~B^~J*0zjOTP0uJ#{=gJkM-?h-h$sYLFha?A4p;J~GZ6XhvG}tV6p!JhK|7 z?)A|x&<`q;zQns%v-g_bQa7c)kBqO_Zi8jOW|A13b*^UZv#teSWSyt~dM7=L9^><# zrR_HoN3J}VbRZAGNR$sd#x3HVr+E7e1)p@U^WnQs|8f_7lT3CFJJW0^a}Vjh=nFmg z^qhO3!1NAuauXRhTq*A@b;jiz?PLPYJqtZ?ptqwv(PuUTGNFs~w)?S`B^*X5P~1xd z39x`n`75=6rG*1Bj8zPMes4A5@ESm!}qG zaz!nPC0DMa9dgZEq+NdJ1nScz1HK%082U(Adz-YS*FOY{EnVj6r2sTuPbwAFIg{o) zKB;!;Sa<6aEPOdI^*54y4Es zvZf9m>P5QmF%>*Cj}Fs1NzhFlJTwpAse*?l>foV?k3B`R9C{8srA{kx6w^tOQv!f^ z_)Y)(vmAQNR@4@()(t>vcvdf+_)?BEW+@hcYFklPy-am#42WzsjLeB=s9HA_s#b|k z8{FeOa7$-*VN8gpZXQuuTtdWGggJMyWf@Hq#spOnru-oKMMfR zwp9Z#`y~9PV-L{m0LE0*7S7fUpzJQia_)c8?Cix*EP&aI74^}#F&mW_yVUK$*+Mmd zvQH&$`t$ebxjG-V6;W>kP_||V#y$lc$9_nEr!!%$IDoNF2J!Zf=`&^7^$2O-c45gn zVjKAs`IP#1`S;}B;iV3_J)%c%`)=v={z>LELdI2G)-d#F`r~l*cAS3O$0GYD%RQZ> z&(YFh^5ti5*47U}hePCw1Lkk^#r^#;d3QhQ)N1L_ta;#0`Xlq6PQEz21W@HP;AC`0 zsLzzp%+8b)J+<0PI8#zIeWs*&VGyU|)G*yPs)Q3I^-iBCY5q|irOz#@M@ej$mf%+C z8$5^|VFiZELSY-O!-OgHacm(Ij$_j$XO)ilwtW1>a@)4mWp~835&7OWiRgIh8|82P zwfWB5ZSt{^m7}(;uNX&d8`jA7UMJW)AZ7?ww*C(o|@EmcDpC#TG< zOp;vK_F>T!NT)Tzo@?WE2ctS9_@;l}6noar^KE2z*r*F(h zPxo}9`Alc4`7`wv&2rhZO($ddYiFhYNy+x-RUSEd?bSD}T7B77$<^zw^sm3{>Z?|* zzAn6aT`b!gl_~$XO94MHMP4(%;{N{`ld59KcC!+EQd{%{t8{!qvePQB+}lwzg~4s% z`%x@V+De!o`$oiU(@qlG4WqRFUUg-;X2*VHM4oSWe!zW~`L+|7L73Q9>;^d2emAX* zO^wGYcaj=2TsKOb!1K(In~5b{M{t{4j=@4JU~;by*46h{_mg^oY+qjDR?n^%$hPEO zw>r5l5{8nOZwu4s+~S^atk6Wgk0pkPF{gCed({=?dYlBFEy9?wDB*q_1$G!&L6Sti z;YXpJ$Z5A#JJL~;s~boyHeA!ik_2YLxar%T9r%H3Ca&;8!}ZhpZPo9QT7h8s?r*DR zvDovvs>j!OVsh8?gM>vkw?j8bjKIMr2#dLa9i&*gv&vIZ#Z0(q+IGaa9|#YN%9za= zw$8B}xg^2*>Iu{x3>h1SE%lKkIKnq$&P*3iP8_+Bono@PNWDO;DM$aPVxY4@<;L>B zvTA3;#Szf*lW@X5{nlIO)~7SWz|cZ3GI+vK>_x?txuGA}Ug)rprJMFwR+bA3ziLy) z%DeBb9w~ovapgk!_-`uLspbH8h$O}oT+1-I2$ImvwX;B^m1$>zNG8wot8;3O%em>=xQgI$ zmc+sd%|w`n?Z@0@wv+pa#4!ycc7iJeKg-4{M1T%gn~$=UvP__Dv_kE`&aZjhS#b z+;be;g-WR)P&17nz%`E8h6-~xaIkvdP7n=ZGYpN)h33)Bi7Z_EuwviyIm1m2A6_)E z5I(#h7GW0nGCM00F4z1`^^9{0WXEcKZuHmQ5KmuFM^dD{XqcFYP=+rMxj5pk@M2+_ zP8dXR|}Ywhb7nkb5_G2RArGT<(by%*5fytbDi}&+wTQaN$J)9cs%flAF(A zaJiBE^&Qn?8>Z3l9Gwzp>XtCWcWh`x;F&yeVBMl?o`f?TE5CAgwY`6AE83*Kmc#~ERI4v{(c-o2Zt37tH2rKLA~)s% zmH`e&;6U5K286xz5N zmW8>R6NetRVs#x1F*6E$Ko!Tde1^Q*O_E;L*I6(GJ^w5H^{^^aUVA}x$=O9i=xvB* zZiq%0`IckBY=p}Jn!VU@fG9E5II(RnkKs@N#K;6!#dySl&ICpcEsqo7m?DaMTVf3p z5ti`bVO#^=C<3~MrvbASMa+n^8FnqIK1}Kb!u?E>3x%Kf^=fB54iI@&+_SO348d7p z4PaU$!mfBc$b$wh(7{}RG!f;wWqJZv;9SRa9T%>W$3btO62liU_+QKS8~||KxpO_9 z1QtRkgJ~cRX?rY^tY=Xx5G?mTQayeF5*Nfm$Ee5a{VT6o8(-HAJLAx_{g5SzN(Srz z-oWR!jqCux8XJt}2QTHKGjXdSyvvuKi;r5O?S7$_$}pyOT~ zip1cN86+@FQnfx##0L>8D+*8JCT6bO`v%UvB5?ov>AMp zW5u|x+wx0DH+X189x{`VGfyBM0R5S;Nd);Y>5Wfa3km~o86{u^{6HwdlZT|yFx&_x zs19awd|Dt}u6V7wqFx|7me0Iaomh(uj05`PK_LM!g)age@nEqSq88JD6gZp3k!53# z4Fo)}Ol1QVZG>?M?!ZX7w zkR8jtf2qu=*@hQKEOddXxfw;C7kG?0upGEGt9w5ZWTVLC1oB>sGY9D@Qgj74k`RYO zv3&M--KZ4UBupG2KwLZLt_9shG)))|UQDF^rbgB{G%AprO`$-nkp7PV9sgtGu(nf8 zHgoOu3k_p|Va%(Ok%v(swaUtJzMV=lN-Y7r3DHiNv zKjSLU-F|VgXk~j$v0#>&DTa=rpjqf}q(KbH1GWc&%nQO0#}<~8BOo_qDv=K&*GB{} z65Oi~m_lnDfR{!2c}z{Iw5P04SRT$Ykc-3SL8ri~Rz0wBaxqqv$exMY3kd0vs+-Uxd-j$Dq*cE${F(gie(W22bE zwFF3U5qkstMMn&Dn!?%;FM<%TDvS*WheuqPp@&sKhVMjh95`Qex})I+Qnz8PKq>-k zE;q4>5l*L(PN=gcfdjZ~QuPACBD2au!7Zzt)DXC`-YZ>1F)7C1q2M6FKtf|Pq%e5G z$vOa+X?fYiY6Z2y)O@S*AlX>7PkqB!fqLpghn{27qUCB?+RTCp3G62hftz=)oGHKF zs3t?ThM`)+P^}>=gALUh1}Sn3)f$Fs4FlJN`i8ZkTEpQA9YeK-p<2UGts%=NiVpgR zY7IlRhCzxPL$wB6aT%&L4AmNjY7I|p9I7=8)f##iI)-WuL$!v%Dk?*@hM`)+P_3bN RDeQm0){u@JKkS7|{s+UEMmhih delta 21133 zcmeHOd9)oxnWyS5Sx7L+OI{X|cW)92BrmzIs=KF`Sum zUQSNZRsD5+wS3F(`|7*fUYW7`l^OTkIf}=Z_-i9KaC;m>+P)CJ6e4CK^jtHxc)-KZ zcjAyq9+{r!2Bx#)nNgQ7sN2%ArRQ-ca$^xYzRLu294`GR2$|(Zz23S_g+x2X%fm+2 zT_g5nU^6#}xd@HWwL;JLnS}wFDL7AuDkK^&C+!%wl#Z2QTadboc+`L?Nb zFSZ$rEZ5+XA$%-0^nHhUu5bFB$5xyUS4gCXn{wf4tEn`5FWGsV{_p!c|E(lVOL-5{ z8ERE8S=@X4&MCbIK6aq)?d2FQm#Ysucmjz@Rh zG+}}~|47z%dIYPV#n|DDE!6+=`Sp}ud1L7cf9UirQ|XZh43*E0kN@FC@}d1#ImonH z*ffUx*^VFVajIq6;7n~_NMrTzNu(!hzWNM3q9XL09Z$Wsk!GDU30d6ew2{3zX)Zl7doy_iSzIj{4WYKLC2Q1?j9O*1O!d}S zvLby-mLEhuMYGOy0h`Sx&9URjvW(`&@uV}SxwC^T%{u?HgDkIt4YT#^w`EVyOe4pn zPsyPNlTXvElP{rH<%CP zD37LT5v9SEN0TNGtRzF$o|OOJp*{P}Wr*67PVCuZ$WYa1KR!cKpOj7QI5N80nX23M zFWiE^O_Oz1ovV}9(~gmBe%-Pfj+4s&6gW$500yRA(4=iSfwrIQ9X>Hnh89i9?9zV2 zb47g8QBLQfWonvbw59CUlr6Ks2dU|5H*|9TsnF8N1FtZf!o2+k7K7|apD8EF>Dc9I zea7uO3%%21GVN4P^w2Vq={`?CLJWm^o@`m;&^}6jaV|NPPVc*a{ifrpCodwC6n~1Y z>~cjR;R2&|8u+BoRu4FK(&>}ml7k0dV>X5P{YI8;b|w2FxiBPSs@TOpT}V1dYGJ;r zZa8WtA)RHn!L!{hEw%?Dn5YR_HgsURbfDym7eHRYh}=OZkH{IR-E(M1#ZDP%F)bYl zM*9&=N(ovRX}TXS8E~NF<%=Mc`HET?4KU8ad|@qg*@Tj#FD0X@#8ub6Lb}S>7E^go zkGWGkEA?t^6U2JQW1188Hf_d_f9 ze)=idvq^iuv~!>LOPi_pOKDF02krgR&eZ#*o&S10Su(0*Nu&4DorkH<_L3>L+(bs} z4t3^D-o!*6V>gC{G{2lNxk|x0`QxCk%3=z*5?V(Z*!1!yK4Og zd34v<mMjbzq<7ivMB9NUU-B|$SE2jS(tsFxH|##uu?OJs#7i?PS#xYXL=MhSrYwB zjY-Q{4Ltjq*~8ciyDSb=UZ(;agxHe)wxLy?TR|UQ3(1NoB_b zro3k%T{FPTN%|#kk^kj#kTH^Do`=84EZxB)G(rFm#ZB6gv0wm<1HB!9$5PD$3S8+o zMzZZi@*8dA^dkDa9AJng`84}brPD}8{+4{Vq@Mpe9i<-r2Cb+E7t*s+c&huxtH%IT z_0wqe`#b6AAX2Hnfpg_a7viJx%&iqgeIJr zeEn5&^i*}%rF0%;ZtAtw;`OwfFm=It>Z*Hp(+SCIqj5;;jmzm_YWs7LmG}xe7X&w6 zL7j|X@FC$#etF zI#Xe(Powj*=BjDb&1n8<8XcR{e0e&RS?4dO)5EL8R`mzST7`H9^358;RS0MnXOGF7 zv*{)ve{`#D$I=z*$8#vlsSwf}nNuBcC>>ApZp1T?b3IOS(_Ff=O5M!Ws)xry%B=>P zX*{E!k{>Yok5p4+2GXRCZltkH`u;#cr=;)GNG9!CYtv=bio+UaI_|CcBVw7Ov&ZDC zN75T;-VF!y@h0&Sx-zGDYYCm&q(DHkBJcK>(;cO(ND>^Vd4K&3dc#5K5c>@Lt!L1Aea1a(%C(WMQnh&`}-*7J7-0af%jIII3&{qBPtLelE>fsgSz3T4ADb6_$jA7!t zbpz(zXue5qUr$e=YRoZY)<~ouxZ3~$?0vki1)FcbS#;)&o2?H3ek5KchL# zm`i)|cTIJ{JF!Lcs%bT`8)m+nsn(>;RBQB8a^~kWuW>r|Pv10^CvT?elM-(GqUN^8 zq9P+qwJ3e_-(S+aUY|-1*Rs#{sS+NWs>Ec{xJ`5V=nNurvZg-iFLHeX*|m3QKEGN< zot&(xRCxE&ow-uw2h~(6k>v@XA0oNwJGoNjX9ODzr4qSPiR86=bESe6(m8_kql@pO z?Q;Z1#<6XVzz{92uU(G75Xt?ILyCu(VRIVb|99mG`psp?IRZ@V>8GGHLrbygd9|#0 zs5t_#nf`OyK1WbV4l`2qeanM5%c4Sa^xS?)0-Zp}JjuWb1lF2T88CqWhjAS( z4KaZLWTixUVKdgaIxz$T!4?&wp$m^M?esh^Vn8dCz; zOgo!kb0KlmJJ*rp)86FNaV4P5lpuAC=~quoD4n^eE-gz~GV&s6OA$oO^BiG@uJEGR z5w4%z+7%Mvnk^wGmkzvp)K=1zg)!x&>Q`SZcigw8eAhdpwytVqGNP7tRHkn2sW4SL zvRonC)b_W^b9>oYo%h{UxpSs^=a9-Q_3+oqRrSk5DmAtG+;T&mIHxj6-E&xZvN~^0 zRq)kUQV>N`D^QI*kx zNk@6VD|M<{?=5wxzZsQLYNpvj*yu*zN!=}d>rC{0ww3O0^U?QEccuO=vz)o0@-6km zo8_|lWKU)ClzM&L#UD9)^_q*~i`QT1UB2exi_TtsS#a@s_0EDy=l}Zpm)%>Qp;%AF z`M)%y&h(mCJ<2NnyL>j68kLTBmu2!(vvN~M-3=qx@O{%`JQi+<5(LX~QG39`Kvlrh%$xD-a< zp>iNHUExQjTDqe$F{yp7x{DMtR$Wf@ltR90i&H(gZeZ>~XbIoKKj4ot3MYtJYy>Xq zAUq>mM2$z5>03d-ZP5GN_qlB`=1Lc34k#N@TRhC}iD}hq$P6nqE#a7f#RRG;Tn{S( zk#u99$4rs9GAX(7w90LykhOa9M-^5t;B2bSCDpk#)Azy3wH?&52*w@Pvn(tj#MhCC zgl0Q2`A}!|$Z{?4;{e-gJF)bHVWS4c7ExgMp5qB-TFLgLaucapaS+%JXTD`P0*By* zx;n;}JTS3hK*8TsCf>ig`VG=mz8NiWQ|<0?c|)GC45bjWuD*JHeHISI zw9VLXV zw>c{@3zK-?st>L{;lvdyk6&FNpk6C=!TyD_&Y7q3trmj6)#< z$Hi7y5;aJ11U(YDnATAPWEw^o_?(5pLb;6$A}8ReV6sIZCC_xNE3hNW3s5A(P1Ftr zv2R0?W9~&}#A8!kdQo*!u4+YmRsD?W$%Smy%u6fN>H!LXqz$-YiBNdLbz>(G+_pW# z4?UAb`FZ-5v>ZSh9>?I$EZ_EHH?%{K8y*Zpq|dWM>)HlV^LZ5LDk6s)w(y1R#i;5s z0za~xFqVP((<^(mt%$qUHeM^@-RV{5)g+V))n6j^u*m`U+tM= z#eo&Vlm)`@EH9RJ7`r|W$1)kBfj;X9m~Ard>%%pmj1lafl+1*|usmscnYJye-cIUX z0rSjg6!QM&nrb&{Wg5JXz6mQfg=>0->AQk?p%<7YlQOg{+X;}ggUX*&E-YgkT-^!| z%WhSm_A<&?KkD`yxKG^*78z#hX2f>v$k33eRuec0-++PSI7nC_19ffL5l@Gl+RSu9 z&RwW6b6|r}ap!xn?+CT~3zf;qt9MsEO=>RuUL3`?Z%VGK;d}#f;Gk~Lb0D-)W}P$N zR>WD=KUzKhly;0^83MgIANJN=5zRWhZZueGP9^Mww1t4`!6kT>3-J^lx1o~0A+79q zYaF%!F4z$t2JiD_Ujl_&Eza$f&EO)rDXg(sV2X>cb+e zJ$3v?m*>qg*dXmLWI}p^?IM z;8rZ#4&BHx9e7Xmr*FVxq^j6$6-+}b*@fPfP%*Cf8P%m9D40duS1Wn0m7LjtUw~oI zBN0WB=fUP1apZ>(0wA8u($r*Na?^Kw6Q#+*d}?ng_o?0F53r2IRp_!;HKqq*gV6v zBW^?QLLh0w=1nkKRB%1Geij{DtG{b6#nNb{F51W@7+q-JpgBA6B6dlRMfQ5>3v>+tNW%wGZ&IzsQO%?vnf zN4f}A0{CAWmHIr$W5EZiKPU6rPbI|^eRZ^6Fbi94e6TvH3&E^Fq-Qx;l8imygBga* zdafa%fC-{KAR+i61chb;+a!eda+J#sW3_62``f zePN#>#G=TPD6rD)BRrnWt#9BZqKmxRoBa)9&FX@_YWCvjk;}wYzRgHOQdpoHWFwbmD zA@40atA`#TY!~{~Fs%J`(r{b|4(z1sSOOdFI*5r352inK;=s*;cvt&hEhz7o$XN{( z9u}QhtBw`OY(M{6u$Q8#kw+D+G1JI^$8O+xp%1VsEZ_C@IRTTIu&6G~ejbt_)d5rM zNp2!tEPP}M?1)L5c|e^AobvFnh6D@(tynrhPKZZ5>A0~p941UVG+?4rZ<@i?BF1Xm zv(;7gBEIUHXRA}Y;=r?QQ<|{v`00uCOg9Vy>Dy))xN)e+rJ|NKnB*@dkNSRl12`r4PxDcaVi0636Iu+?C?cOj3|nFZKchDNqlY4AZf70u}C%$ANZ%imXN2C3S#x(?K@F<2>?!eBgy3 zKv3j>84UQ}H-ii%Wh&U7H6U%Og6(x=X~<3F zb_9VI_mLcoA=H3mCY*{Tfzi=+35f|+5jt1FP$X9eKa63QaG?0NY{8?p4l)Ya}gRC z$kNy%Wca~aY$FZeIxww4fJ(G|37fjbB)k%i*wzaM{IP KY-1E=Uh!_!q2Fb>ty zw~=%z;@ecpX+3tsa?`u@Y+YE9DJ^(&flyxhzQphBkUYaL04)P?W7F&xT>$EZ4V)n@ zI=og5ERxz*>Y^=dYT%rrRbnrYF7CbYq&X^RRO7? zV)$NS_+Fw}xi)++kzR!i-%GS+MThSt`re5Q-%Fs*VEA5Qzsl|ZXZI4xu1O;vJ@aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN e4UJL_l1$Ug6H}T$a<+fuWCUWS?H@UrolF7fEg2mE delta 76 zcmdn|k7x5go(%^$4buvevQjORE%nWfEX*Nus5BTB@OmS+cpgxv8a*QJRH;NwT3) RszH)znt5W%<|f7mTmVxx5|aP` delta 62 zcmaDP@knArAfsVgK~h$#WwNEdxsiogl8H%@g^5X;fk{fDrE!|6VX|RLN|Je6ih+e; Rs&QhXWwNpP<|f7mTmW%n6LkOp diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index 1633281fea9b50d6746eb4a04c42efd92e66b7d1..31e89bb8be2bda2977cce6967229a9fbf2533a50 100644 GIT binary patch delta 72 zcmeynf#dH6jtzS_4a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN c4UJL_l1$Ug6H}Vsa<;$aWZeFilW77U0Dq delta 72 zcmeynf#dH6jtzS_4buvevQjORE%nWfEXf&c&j diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index 9f0823d6207caaea6b94191e2ebc87dab9679894..45d517721ab06038786233b0ae9f979ff049cc3a 100644 GIT binary patch delta 80 zcmaESkooaJ<_(uPZOaUeERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN k4UJL_l1$Ug6I1k4@{^18i}Op1l2a!aGHY$Nk diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree index 2e4ffcc90ceaaa84f598500e49e89405e2cdb73d..8adf5a8fb09efb4b84a2786a7cd0d6437f43384b 100644 GIT binary patch delta 68 zcmezMi0#)Swhae34a*FTERz$aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN Y4UJL_l1$Ug6H}T$a&G^~$=JCZ06QHQ82|tP delta 68 zcmezMi0#)Swhae34buvevQjORE%nWfEX4-5E}&pO0*D-5W>#>k%*ueJqQ>TwGdNU z90;O~)`4KI5yX5DAyvMHZ*nQl>aFaqU51_cJ@Y)fzjmj7?@n!PO+z0Hr`S*PgvkIg zVPszmpiAC+Vq&mG1>$>``W;;e0>@6v{+ts``*Yx`F(eu)~)bv2>^SzCY z_K476a@A6e5!toou(@3nBrK_Aq%l0yoNOR(lC5a8W69aIo(YDu#KPlvTs?yqr?c^x zLgrX2rzDj*FdSVwJj81{N7#R>bko`0zf;zOZW+LNdr{Nsv-!2VtaKwZsF>7j7-B++4hR?Z!%%cmJ*KQn0r?H#l}%ooT3x j?a!*ZGKu9RmhG$4WvY?6C$Sv=QvPEs!|>Cl`gZy+x^5`! delta 1976 zcmeHGyJ}TI6lEj?f>kg{CEmjYu?c1#duH|sYNKGl5CsJzggARuOL)2MiPK|F4IOJcgG1A~>>NMZxTI)Zk|v6nt($-0%ZVr(FjI6c&u;yr7}4rPXf z7`#zcs5cr6`r=zPD~=2f-xh14iNKO8G-s4h-=Pvs$~HLkKB1L^iv1y)4tCvsxhJmn zI(e7H(Ow1)3K*F-&qkiAVuXTd1#~XXkZSMDh>3q45qJ#&O0YUZE+r(zx_}{yD2t_C)_N*A zSAwif+XA+jh%vG%fHngXwC`7I^?$uJ`|_hy?avoNHl5_SDvS*ZrFAZx40`eY)bjj} zLq`P|W7S?i7fRHm*-@+~=9LWX=&LH`*|In@)a#hSj>UI3#cT0&|MsN%6ZiC`xrLe8 zbLTHEbRF$pbVq!tKR?+Xx-2G|4r|aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN Y4UJL_l1$Ug6H}U>a&CXh$!KB(01jalkpKVy delta 68 zcmbRFgJtFqmJM4t4buvevQjORE%nWfEXruFfDKbDU?HEfRj?3# z{lr3vpKKLWj7d}!g~V7;BPhlKD5$ZZ!vC(d_u1#1Ta_;tgtJAOUz z>xth%`1QiC9>3oB^}(+JzefD};@5;{NRqeNKBin?18VdFC1U_Cz+F zI_sRX~}V#zp<=W`Ai& zp0v98tBTT?IR8=YipkEhC|5n&+*DhY%%#QZz0J+liF7(4PG8(QUHtM%FtgI@D7`J8 z7dJf#ONFWOpHC7WN=62Km65A|;~ydJehO5qbYtYTr@*2xRW>|L1PYTy+j^ZNnYK*G zS?ino)>bFl(;4==Hjzo>+LB`1N8zcW&-2YaZKzH`?c&(yo4s~&H6@FsC;EHIWzRRC zP^l2g!?KmA_8%!GZD>BohWcw;(=Q%;(P_6%tHZD7f`b#k2w(0uf0z5y?3Rjz$>gIA z%^y_QmW)@Qy=-|^pfGG>bLZOX92uaJsg38-`L>K$I3Km{&3`ucvY`PAO1Zo`ED^r^ z{Y5uB+{zeRby`85{95xlmC6t^*Ln23*>|LfywO~5Lzaq|^hR@_I9V=xqxruT#pz<* zTh0FB z@~Bu2ZeM=@ZU(YcMB8?7D@v9ZZ6|I;>0-fuz^y1*Zu$>Ot16jq7pwL*d#ce3>lXE! zUCs3-9hv62MJ?D1D{)KQN|rzDYMzQFm2Y3PuenJX;TE-FcXMwm*%B6S{|Lv)1_@iK z^5{LyU!y^}Q*S%Oqq1{}KJX=cu8zm^R)M72Q*G_4vcc_MHA#cjB^XNyIF3v$6)Rc3 zwik}8bXB7vE_%M-IzqtpdTodhOM|=*+6q>(4D5sZ#pxiRvvnGI>}pTH+L}~0nTqS? z(vI+^#O!}IPZh0oo?Z^DF3wm+45|0{9qHASE&^Z0ddYQlo?lj0!JOE(8xhSa4KX2y zE@4h5PrZRUXecA*H+TXjxrURKt&W{NA66K|R@l+k<2T7pg=t+qU23b_leq*vbUZb= zY!VHB_*RQa+Ca4orHM$uR;WU%So+_VUh>v@&qaIqej^xu!9EE z#3X1d)o{A9x2?hRp9&kYRlr3%K&G53!ksxERGYA2oj1m3Z5R#g9EAE= z29)h>o^vWSN$wn^FH7TcbFnCCNeHIy~MZr#g4*v1M~HxHxw z{Yf76Bbm&kxs;o$;@NhwdQ*QcV04}ar~?ph%chuYZPEs+2@uzOI!#(i15VL?6q=K>PVqF?)}*s3 z)J(L?=GtUDpJ|J$CtlMO#NNj06qXVvPC=xK)8$=Ls3|Id3kwS3-ov~j#muwN?bv{^ z^#9I6^D9o5pPof6uNV;3Q%%-fc-kd-^i)sBS}|ubnpL(PT&U4;LDeP``8*zg6`sgc zL4?ZSCrm-l?N+T*s!93l^Qmv$eWs<1w$W5gpxAr~p65hPsS;pixa=%EJ=h?n8e4yv zP5tP`b1X-lDpgZ0A6zPdi%~A9aJdW@QB^XP6|e4#%n%#qAY+qWhxB|K9+rd7M~w1C z>2mB`4@M-M&JBa)1=qKJS9*HefH8FWY?O{Nq=Kz7kau3`!HC2dGVu4Ajrf+B`KCf#B2n)#kLE5w5rxhxuYK-F@`&WSzQcB2RH zE6Gf%UCbNl@l+)+0A9KSUErKsQGZN&9n$k{*@U=pDZ167WclN*)RrhcAt;z^XTs;` z_+AodOEzE(z4QN2TE*$|nA@o>6$4_(|AKpQx?FxIJ*(SOY1Ff&k6@INrMLY=I+JV5 zs&+(F90FY&0!b!R4wU~|O0<;iDgTS7wNVwbPRENfle1yBWoB3gl)?LmW*Jm*Ed$DV z|4R)lo=Y&_=GsI)m9i!_`mdm;sRbd}1jQA}Oi{XgWCgV`P6s_q0YF~OX3SlUHf8{0 zh&d@kq+q4Xy{oB>70Yv>m5@LA|7gIG&BZa~-R8mTRz8yzt0!Zu@%Hnco*3O*2y)0| z+t3IPU5koWlq`S0mP&}yQ-VA~#lze7!r(|T;3bTt*pNbur3Fu;=Ghuny8Oq}R3ycK z*ts4xuQ**Mo<;vzo64v0j!IPz^DxE2-(wmp~ddZ{yg>hdE0XZ%gJZDHj@bnCdrrv>}n^MMx zyor*rb#0H25+HI0~6er84z9$-FC#~wak{83hm~&tjHfC%w zi{fzH5gsNf8=aI=sgL_ig8S!Fj-#K+j~moLDxImdY+Z3 z%+VocVD*>)^Xa3ks>-0PE@eVw)tM|`X!L$kAsV__PC7wR#k`7iTKjoB)r#Zmy$3}T z>1;kTxILfG;(6>pH6A}Wo{F4%@gev}GQ(q(Y%+_P6rLOYvff)CJ3x(;7~I<%IACe{ ztDyIUO7U`ow^t44Aclv%^_`tA@YM0hMsIJ2Ko^GqmQ5PH!E&H{Ane^9#l?5 ze9+`Y*G>`P++D_H#~7+{O)8nsVfG~8ZKmSJ=q(|(9pgJk{4?OKF9miF!ep_M%nkNV zt<(URa{1@P`V{JOGz^MLg%Fd^lrY#8r_1Y--mfc40Aj&#leg25{G^?jq~l3U3lI~$ zpvLJz!*!a8*ce_b3Cjv5Vy9PAx>$Wb-VGibga7-x_;0+2- zx*nG$$VJ4so0G^%E0Vj>%=ri{e!CEniR>fzoR3~V_w>PoAJB}xl5mdX*rzbIwV zWP92eR9H=~AlH%vc}$Dj`e!c54WSA*%9!1;5k-C@L;5A>?=ok(dSqYR0zt5 zb)%>gElif*oamj7zL?U7g;C-pF0Ot7CI04UZ-WgOJN09{q2gpY_GIt3=#4EqMtsh( zR<>l`N|t{<6$EPH2xd~(+f*XemQeL}(iz@94h%2Qgk{B&GrTP&(&a~I5C<|QCfuwG zvznB|l(WzYKYlidxKU&BtFytTG$s!{hbWZ>#VOOkt28G6G>!V?byM&}$*1C&#Y4lS zci6n}FUF%1^crlSmH@Qa#brB|9ZLiTy&6}O1&4Ss>PlMF7!4kaU0ltl{Z=_Et{_Pevh8f*f1F$ z^J^XWL%fV}^S}c+mlOV#n2m(jeQW^zaW2<}cS3mGQk*R1^;EIg4%J=^ z7)V@=wcvS!4H-+@FN8Zr!%CK2Z=||ZlrG@C&R8`M<&V7ZW~xCCpN}zB7Bh~zkz>Ue zZ#3M>_oFW<1Ik_ZV`y0p6`hvj;ayJkxaHij zrIXpTW|~N(&CC#(qS9DchpfVq#1plqC|zE=3Kg&>&d6yMzD6t{!J3*20=-71Lx^n) z%)%wo<-3of3Oa1r8X%-|cUh8$uc2Ca?w>FmN+vNoBwpN&jtt9lSpMzmpDw_rj|0Ig zdM=GJ9St&$WGpmOa#~*c6jj3=_hCq+%x%1%21{ga;g#s8L8!rjSmu_2k0aTb+qjIh1ul#NZ$Guf@DccoT2EcGPOjCaez%@_TBj@< z+PKmCP6hjAKuhD(zj#9?k*%{va+9|kElrRwR4&+$lBWe=-f#5p7s10o*hNB6Tr_Xs z=BYKTWO>pX+|5B=B$m?{0OBIh87nQhji6wqTk_)Kjo?t2Dj(UxU0h+h*zzj;FHDsO zy-Rg#;Opp<+LL&RR1|2eqSvrrc^woCqy`R|wCM5%D41k5T;WCRleT-0tP~Huh5nsk zRU4E+UdWDZL3=TfF?iM%G#8WXF!;zv-q$L`!gnkW$)M5N@Q&q%s>C#EVmA)@)Z4YT zHk(gm@H)Yyl4(3N^jp`{AE3q9Se?RB;)f5=8x^O^>>ldn*qR8ep+~ovST;ZZY^F-;v&)J=_RxKsGU5%TN~^79KBl*IxdGoib$Is6!SG{ zixsihk>Z7@uTiNHVj0^t+}DDAK5WjBE)Pwkm>iZY`5FDK%$?GI%K9)+%VF&e)dxj{ zfm#A9xo9iAKs+e{GR4^9gb!V4k;?0b`5vmMkk<_L{T>Tb36)VW};Y6j_(8*cU&jK6r&Le%DF@io6)gp<#%uJ>_o4|6@q{Xc>#Ic7_OSJ$D zg;J>`ZCqV&7&TQZT`A1|Q)|84I@*WF%jl@Dz7|WgByj9-A4V_G6TIUN#nQ(KVS}17 ziINE`lcZ5aF0jSSI3J!bO-EWD5^9uCJVB>5TW#?KUuCU$~LQb^2!TdOI>;_O2s--RRX;5SH7q_9r<7!FoO;jEjre{$!^M>@FE}~~6h6TVGXZvC%4P^x3 zwp3o;d!Y{-DXOMv1y#YwP-0mBl(_mNPcJcV8mbUkv%x2)`C3g9x?yGO>tFk@k>ZmX z+ACY!KU3#TNk;tC%cv}J32Y~uG8%nA5@RxJ(9&?BuWyA)$6Gh0V~@*)zKBlKvhwDu zd^51q3?_0a_}T`KRc8O%*HoziW8{%v`=VB&W#z+beBYsw8!JY**JZv)q3|)kr_i5y zB`VL*vwc2{jxb(NtMN(QRXP;|9ZJoJqptFWSl{&u? zPq{`VTC(D0gJ+}bm~hK?y-SHJ^Jt=aCr z9ZX801MfgXD+Tas>u>M(oq~O|$(-m>?eEjs7{$8)uiuVc;_F+fQAd{7F7dUNNM@7o zEcbna?X<;8pWp3^l~X$6Vc)kkV)X642282P(F-~`CNtvMDBgO((Fu3a-X<}fkr#vS zz@w|A?zZdjzBunu-{F;F<6^wYE1^|ueD!V%T!J86UgCx&zOX~x3JCn;Jhra!{i8yx zcnBrI9E!8}^dZzMgB9wJe!{o6MjZNQ=x38yEkF$L(odsK|G}Q=;{8ASnrxsK0d3iK z=LEdi_Gfw|S-Q1KL|(SucVeaZ+hcfYGPc|6Z^C0cBL-LdM~RVZoawd7s`%X+R2w5* z!>RI(=X@Vli2r-sa*zyJ3cDXiA6=+$_}_hjTAYl)6b3d2Fr8#3C-ygELh#dPK*s=> zT);w5E-41Ahn=EiId7wHCgx+vlBoC;nj|k)_rr0VvCsMXRaxoA&VuLA+ZLwEt($y% zF%@IXSmKNRiIOc&mZSd(0+{)3$2`9BKaM9{&s$L(d*AZ)bs%-C`lq?s1M4jNf@4#aP zqMl4{et;jR>Vw_op`9*PaSHBOlgS2?a4;l7ntg*36Jd#IOOUE!Zt$u=g``*8w^Z0IKueD6mJPT*lUZl&2qder!LR1_J3 z(|BlU1GezoIzOt?Y5zf4W%5}JsL?3;(LZUw<4Cj$j=l}y1Og!1RRIPOhmHZ{@@{@q zr(Iv8Qz1Rkvl^X>(xUnP5#oYxoKT%*5Nt3gv^1<_+2J5RYSTO4VK80<3Sw29EHA3} z_pTLlzwvdc$z?DF&a7}4Ba=(u%o@KCEbHTRZbv^z4N<*@T&;NNvX-vce+8--JV^Pix_%SHDqREf>2E4T2 z8}&DKOkymN$>nJXIt0j`bMjBmkR4BUnS2USc88_=OU`*=KOUyS+jw)SHWe?t|7 z#N^-j8yq4%#>Yko(CuqLw{g&o7|5Z=reg#Bcx~I+%iqU{xV6DLib;)vJX!aD+Nj=w zHQqhrn9SU>#G;+YK~}MOFRHGhZD3`a;5O`(RkTZk^4T<1VIqyeG|VSO&k_Fm&Kx&o zK=txh^>jWv~w+YL#6+)t**kkp3}0I#&bh8~|e^B4);r zeHl>x<8W#OiwF3-RFjBEs}jWdXL@1a^IhQIRg6CzF|wspPD(0GL1+?pyCrl@ zrun^|=SO?VPsdRs>55p0b;lW7cAe2V^5prA1JF4XNV#)FWcn={;WAU0;nBxa)A?bL zzq9a@DlcV;Fy(Z4 z&Km8V?jK*NieCvJ2y}dpjIQ5?!Pctd@P8Y!jqW-QWoZLWqbHx|f3HHs&oG{nQCoA$ z8O9wuHwkyT)`}IU`upHmLOPofo2U7kVKs~SS02xUUZVqoJwP;~2fc79Kz?xvb(?L+ zp;Kg&87g98M>ctAzv4K5Ungc81-(c~L{6AP9i`_)bd(OOc;d1(Ied4ne}F^YHaGoRs-c4}K<8wegX1tq^#=vk#4bBfTlux?{XH>K z&Sx=mfG(ZJ9=LD;;~HK}n)zGm&zD}TU?jZ)LKO55vtE$X8UIPJ~86(djwW;-$aa&-FagCigI;hja)7d`!J^hMYt zgRn=rHsq@|WKh?$Mq z7KvDibUA$~)x$%tw>%_^wicL0N@=~ej4C1X%BHY)CQ%b;=#otE7KQ&w%U4y$eNba3-MftXOv!!dn`XO^^FA zm7pw-Ux87Gu`IUzfUTqdz83|}x`evzdX`d!B%g{f`J1(V%q`e%H;~TN)Ul^ek2)Xk*^j~;C8~=jKOkrMk{EHs9vo^Sd z+-8<;KOQSq(r(*zUMy| zi_l~Yb(lIiR-n5Rh%k0)SdeWCG%+bxlr9&(@Bbc4(M5n*z1_;3@-T+v58M4%H~8^g z>SlAgs*4^E`fK1uI^B-tPM!h*r=}_w4ik*$iC44?CJ)}}$2!gxub}}$n=3gFY{;^n z7}@ulzh5a36k{dQF1k#USLMvCsA?FnR(y6J+Lar0DmU+^)(gA~rWUiR_0nL2_^5s4f(Sc6 zZ5pr3M79(lkEspdt&wReqi7y7k<(Q5&?4GSL!%T;4H&*F}e?WYpHPcv?JM zsI2c2KwsVMTlCdvI3lov+haU~wO-X8*VY7RHdJFU%UDMIP!m80U6d}5>lMJrPg$EyyNwJqIYq4KvPfJ^7#WC`hvm|df%7U`R1W2Nz?RyPdRRQ* z36VEHDNCDOuZjoyACRQ19Ub_gB60vOkl7x?0^yqX}8eOpu}<=rA3OfyW09DraOmgNz}*Z~>kIfW5t z2k-)eKsE)4%1^t|m11}RZYYkAO%3$IgB8QB1c%THLIyi?cyGgDQv&@QxD`R*w7Fs4 zlt9dZc2JKG4D#Q*F4kLqcYa_}WtfeZ_t~;U3!68eL`@mK{H*w%a zSH%H3G3=B;>w#I_cyR#JJXal$hTM*aW5yjkJc!r7igR5EUNiO#XxPLKXD%_{hS;^* zpb0#5H}7rkC3{{@wXf?*XvUnwPlN<2{(Z3#6u#!r&kN&a=2e96K+Liyms~~FuW4$a zsfD=wq$99B6hS;-+aJ!OIIcVoL!Ja0GKCRy)40+Kb3JtB16RL{fjTfmw?1&qvjMHc z8+}@@r^p>TJJ3(r+&wgap*h*Kb`L!>JAey6C_loC(M1;q`rFA)lQ%8UA;74jSWnEl z7)5E)or*u+6zEVZK07JU8Pf}RU=BimgwY~4f-ahaNrUs}!bt;QoJc!F(U+{A8^A6X zOTnbe#G=5Nm23{TfzY`h<-euIy8}L-kC6c7SW+auAmA;F%0+hszNjb*i+gVhc*>%( zW(jr5?_G-*2D}s}+La_P<9}&3wwm_2u1ThA&Pw6(iFxXlM5%t-;SO4T?RGjC{ z5A>~N0YzF%@M!@s?&rj8vtS!@B2K$c-%n*aXK?_}5OUW9Q@aGRQ77Wm9kiUPEvWXz z%^wNuBKT}&Kwa{i$tUtz>%XW~~p@ z^DV+w2D}Id=FJWPcMc%m>HrI~c>Ph$pRRoZ1c$YE@vTg9-@xf!SjN_44|sXv`hI-{eK!+A{HD0!NGd zqk-;)BJPYP-;anWU)}ng$7kyU*uLrT!x0hf>81zhdBQh*s;-DLe$^frkM~ZRlsn0b6?hI~Ok1A0Hl-=IeVXzoD0<9ce z^qldp2$NU5i@s&iyNE&-eG0bNpgz#dQwsMm>GhI*FM_-cV#b3(%(ZWTVH-f-2tci2 zY~D_7;g&77U1Ux3L|T>0{;DnL6Lq$3c>Naii4MeRI`c7_fYW2QYW!VviDgvp`;;pG z1+RlLuGqkMpV`7VU2ww}gELnfg2RC5or1ZR0OVVHsDOWb3thjqN&a9_5|jQ{{RWeX ziltK3u4?zTWe`uiPM^|Ki`~#6)9PUE`6=~(RR>YR_w0r`T>zN1sACH|s4t}Z#V73pbsL0h z3}S(IAF8trz$@DL-t$7t#P#hG1ouH-p%p6uUB=jp)ALbWg}L%8G+!IBZSMXG@w5S_ z%`3YT(}SynmaCen_Ga=o#v1~hn!9>xlGxIW?HtUJcIM^8)kAQYh6%PC1kPmXNr$60 zM9>D5%ECkY*ULm-%?~3zvZ(37VtiQtc6EpY96dH_TLs1bcDlppqGm%-WvU5kZ$PlM zq+X5Blwk&!R-5XZ$t11*m?@S3@7+f|rBR}CAf=4VM}lajT!0EDUr=wAP40rZ3~kWp z3NFJEfV{gkh*ruaSOOMYf+Yaibzl&U^wmx%`CKBG%`I9SY|u9xXK;t#1^>hv{7sF) zem1J_HwM~<7q!ie!B`ngPDl~a)4Bzll!TZ!K4@K^OvdL`1V@SQ`nh1Yb$|C@t3%fX z$~76HJi}|b&gNYr;+CG4cg3I#=MD9IF4rC>>RP=$lWD`|+=XL;)5I-}L0W}Uct3Ud zv%=Mu{o5+7Nx^422ZJ@GP00&~nOLv{`=jrBW@~Ep_`PnyW@WNhvfGxfF#EBAM2XHQ(1kzL!q7G2s z-%QsZ@nrp-W2sr+7z_5RqE(5j#|N$3p-F@n5xTbqJqC0eAKx16Z=gcc`^Qt`Mn!1E z32@7}TYvq4U~5_Z^N$bWYUy!fg5AaWt-;Q8dsN2R5c24+`0e_=p}`&nBDxSDLT}j> zDb%-8>inYp=Tm~XaQfYGE+fv0z*f|Ooyp^aJsnv&i&N*dbbQcTD4|;}Im|gWObX&c z>L-tKF)LxK=kY<$&)NFJWE~}(_P`|}V*i~%Pi;P(Nn;L!A7+S7b5$V{7>0tg66=(B zNv7Bs4PX8{Kv(b z9Bnwm21mt>H0c7#QR0{*(BrWhLwNh<{IF6tKG@$@G3hdUnWk1+4cuBwsog(^n)KD@ z1iJ~}DM4)4&7~E;JYFtvrjrlvcRwZAY>+yI#KnxMpjS#seljnJGf3TLphis^1Nu3# z$~^D{Q+D|v|1C36e+wi~iY$SB+A~mhpBZ0LUmpAWAWkRkI2~P)F>b34KhNo!D`)xC zwLu(N2IB_0cqERySh+m&xEJ5LDQD=#D9<8j!YS})+{N(6K!^ot`u2;ssM;@y9R)_nUe4F-4q4b?Hu<|%K5^UBPuzX!bz#8?H*fy%0_ zz-umNZlfU}XljQP7wrp3YF`SpUjKmJ?oghXf5_2AiiRc13*Qe(>H3Ny^^}zW)Bc4Z zY6B*8=6xCMCH*@LRh0+%P^-+VTFYc@%w!F+1GHh~x3M|@GtJiWThY}ngRbRJf!@B| z#P{k~gUyxcEH3_{R+vC9!^e_aHwC>`axEoe&h5NS!GTs6+w8d4jX#B&$98T}Wkdr~oSztn zMh43WusW_x9Bl;rf#CfNB{jfK)(U~AQ2}p1NE4*tC=C94EjnyVjV}kFA}fKR&<5Jc zPLoxAEOs_-Orr3#CzO*@?UF{K=C5w)Dg5YOa7Gi?yK^yOtq47)I`{ixbxW^89AjbN zG~3^;ro~@~V$F*jqhdZYMjH>4%Qh}M8Iof;Lccs^flh3AtG43R>QQIom4pde(36+~XBHn`L38LCh7b-we`{GKMYmzN^M1mc5 zL0~jYCuUk2_~vTRz<^p`*a3GlG3@~Rw z&N+mhy&Xq;CiiafI$(DOF9i{plDeG1!^cp|-ybp@9HG%J4hN*m~7H=FXr3>W|h+b|P?JCP;DHi^pz~#v&P*oW?xTTph**v(V zj}8S4sN3aN4Q}xo&}|)o2-wz1*@er$o2B!Vnw4fzKn=n z)YN2@r#R|{`zR;3%oP26DkF!8@`ps)aPOoQ4TpejLtZ(hck_zY&nR_R?9i4zzBclLv%a_p-^TBcIPqX z7_^cypsc@;YR1)L42fd>^<#{WMWDR)VyY7eLKDt6rt%nsI--CtJ7V#O?~+3lnw=8b zMV3fX7+TAsa`Rkg96DNp!LW7>104uyz&EFs>G% za^*Z7j-DbuwWSk!Z7h{gNNDhv%HdN(Y8esX=v63zfgDSuDIESn6k`*h>L_=|>#65N z#c0+Uz!BO7r83~fM1-NWBnMnlY}~Ld~_4_ zozu^w-WR(zxu>A*Us+siz&Ad8IvFDegTOf;blJn;DR^3&bgjV`DkK&W?;mH;6N3eG zo@A15c{us37N1hpzG{<8&cd*vP=ie_xr3N)ItK#>gWIn3m}& zjP2Q^3y}YJKUHY3!^plE7NBRj;?|2Ur)o@PM|TaTL=UuHj0tfMqO}f=!o#Abg@FWP zX=Z1qBOr8lA}@o_TV)lV!tWW=f}y>($#s_a*_dsU=-KH`n>!!XrKkNQo3 z8Y;afu0^TiH2$&PR+`f9qoy+1U1n(hW1;-A)~qvOu`o{*XJUqbh^S>Sw83XDy~)w~UNfwb=Hs z$iBSvIV$0fe?kezaSxstzp|yVGkciLXXzWs{1HOL07DK9D+d*vqW}yrkm5#=`T*hP z4OG^5-G}(H_4giY>Fto=j}U^tj|~v$whi(F4dl?{PYMosg~}YIAwycoYMxD5@)hP88m+i>$8h+{NubX}nQpT`mNZbx_Ax-{`unpYV1ImW?g|^>QLq>Xvs!#^v+x zwqX4zzLKhC7FQ$^cU9>4LMs-L)uVdLN_cN@<$=PJ!nX=du{0)oen{MRK0|K=EJ6;r z%<}{K=tt2<8{BPv!K3J>OJnl#kBR&yt3LGtrYHFHcUr!!dGa!|(} zcIy}&sH4+10@^ti$Pjn*mZd?N|3Z`ZRgrlIb!DD+N`N{#aJJxZQJV*MbmFB!`Tzc_ z$y@#x>4UxHT@fZ5z9srszlhi++pz@ZWRE^wubB7WfPy!;MV|1qxL^Z{rev{>1 zLC&l{t|o-;e);QY)mXknD+m96^qjy)Ot4Lcr}^h>ry& z#fYpx%C>Rur*0<;ZCrX#2y0ZYet=TWBrr~)C?Jm%UTPNxaCa?2;6Pv(NLxBDKpvB1 zmWD}}vTq10IEQQjul1j{bXFodQ!+X{+<$}sZJ&)8WdXvNup^^75Gpen^Mu~75R*Q& zTydcS6tyd^ba{_2gcU|m!$eIgj-7j&TRYsp>OPz`05>NDKLE95<3tV}rqMBg{5;}h zhoL;nXnJIK{{udzhtAwUMxFSfBQFNz1=x#uyyg zMe(7ErSR!GJyErx4A^Zye6f|N5J)L6qwU0T{#WRh6l_J6VYOJCuM9a+ljGdV(7;k! z^7bqdTu~je93`Vh?epr8<%=sb+5O_PcDD?z!DT2oKDTFBsG)N^56Uo42#&aU z;DnC1b+&de4UdF+38rkzcX7yPMECvFCmLfN4Ji2V!DYikSZ8JST4+M3-XX;25176U zyEC}F5@c-9q01S3a%70^W~E5jLT(GYt3$>`ckXOq@<{5r7VeLr>u3&js>XK~vAGz5 zF zmCqjXft$lWVU@EF97mmAAunWK-teg6IJn(q!?&Nn4}@^YTOCi~sGFmzVNaH|;kCJM z$XA11AX-2d91Enx;z($at=vHc8oQx;T-Z`CKR%V>*0~uiN9)^8KwoU_jj7ESTSEbD z0jS%0xBj6v8!NDW^63IuFb@8eJ)m)?6!ul_+gV2 zZ+2dgLI7+5odpY|DF6eU>bmaGM*yF_9^0P%(wO8NTlho4^QuI92W9d z8{7&6;aM@}Sj5^^wsJs2<(uAqbqU2`-4P*Q6%2^o6GFWmB7C9ToC&Z_78rF~e`W&e znZX>IZ2ZOF5dWt}guKSM6=HUN--u9~K?;?JU9P#K6=1!?(XV7_kjSSTLEKk^({&nF zz7b68=3tICU}4HUtMW4UEx*;oy_Nyf2v~?0&p{oHfK8afx6Fe&8i9osnylhfT=eIq`W1B zH9eFyhWX0kgb+>4F{*}vRMrmAwn+u;04yyTyETM0JQOh=BF3hz#vE zb~Oo6y`_m4t;}m$pUy<3E&&cc7nR1OWBf@T@m1!tmr{w|H8)gW*7AFELoEfM)56Eg zs6-FGCWMYHpJ>MyeW^&Xr3qb}ZzWD>fi%7+ZUOK(p20+YQVjepb!N5-hAWK7=tESZ zVC&OrT%Zb6Uj{to57dEaRiL)fd;UP3myH;sx38cg1yRfrurDq(a2#Z(8>Q~6sX)OE zT^(X(YAd!KiiK}shzoAs+N_PCjsR=JvL@IKbSN1>PJTRuc`C5;7)4cKSy6BY4Ss%0 znfkA1V@Su_l6prR!GEUMRZ>p3jL&n?D!&uqU$1hyfBwyBjXm)BIFOPz0`6MSZ$4@AmJRRj3v=SYTnze<%P=l6v=a3QMGp2&A%f{F0aF zfBY+T3ATjJf+gaj%j49?8+8{bhwY*kvh#WB8kCHqMyM=!@AScI(LLA#E+gWqwdf+s zfO7S2Y8$&YP&ZLVMht$5x{5NWjD1P1gQI}G>v(EAq7cU`tRBXyDA*F(N0z`nQWOOn z)PV-5hOyV}rJA7PprjmeP?>Ny$#Jj++<9;uY_Kqo?|!7}pdw*Q=?bm#XnP6IlB^9D zn0RbfSk(t?JH`%3Jkgw?;H@lt>;a+h<9v|DEhh<2Q6L65wOy!ORTozE0h70+?~DT5 z=MVoN3O>t*qfyZAbmV|A3@nSvK9{;cW_Tj3vv_y zD@xc&7Pt+!A)ogiKQ%1avW_cGaSi>>o!k+Z1m(x|M1SJ@{J_dmg} z#iYw$4Yr8ku0f3BSFH_U(;pjL^+7Z93RGEuQ6pAu#mVx;q{Yd$W-C#Fl}MMv(nM-T zZJ4_54BnQB6%`iRh(2jMwm$5|@d1O9Q9@HplP)2)(N{Z#sh73@htZ1lV(E}@P8>fhe0|5;D)h{od}mnr zAY8QD6sAKCM$a~QRZ|!}iH$fc%7urAu>d!{Htem&i45E=Iy+e-$a@eNN$5;^pKkoe1sAd3^javHmi`g2#gKuZ$e;A0DBc0@gd|HL&FWWQ6uKgdF9Y>=;wsw zpQlp5b`5t@E@Ll#xRXg~A=x@ZhY*u0^wPkQ>%brVpoV@+0O!~dI7}y5pmrKxu%Wda zgbPz52DCMo263GI`q^Qu;~_}?Du{@A%fr2GiDBXXBPlYN2(v)9Z3JYHjoFH9dhf+y ztl=RLUR%Vi4*mmetU!JCrPOVp>Y(D`8@x{1Sj5@?8@$_tHir8K)yDBcOh;M38`aX% zbYXJ!mDFUw8i!RNA}X#4_jXCBJ_FPPRi$w-Zz~PS;5E7e_(}^1&>mD8l6U+rj3uAz zM^lM9ApcQrp~$JdtYP1gAG(@{F0l)So@eb(KuJEfA2(on3c$x*FN?v z3}d+x>@f+C0}ZOGTg^I)PsMzRc>u5vR9T?rp2G_e+y{3W*ju1ObMrFHPmjDqvkz9H z)1|8JR_949utz@%8zlrlA63f23e7*gMDq`nHV^V`7E-92zlWHAcLpkCoGx3zGZMo& zT!Tr+TzESrX!jAD-asF^{>*TVErm%ImP(3UXQDM3ZIdpqx<8C1KNNa{t&i@WCZAD4 zw1W=@=$mRIM8B#0n6MyYLrk8^kBA~be!V=5r9Tw9!#Rg>+XC3Pl+53V$k`85<)3~9 zia&`97}R4@#D|2dF<8RR9qS$U-?LCeZUKx$SRgJ=yApMvK;5A7`9~=hFI^N4C<7D# zW1Qb^=g&h3lsA6As=(}U+)jgUET56xpVhSd=;`S!j^?Gz@lF)*3g82s8Z)Xy ztF1)3{KH>0D=Y>WHRVN@OT%$TqOm0_o;L-U#vN?f`M5FM(1CB(r)j5I&wFMFqJ=)& zLe&NJK(jQ`+Pa-bzY@auqfqYAq1-q>?ccbdU zZnbsMbX zdQIO$yUx>F4*i*ZGFyW1i;o+byH<6 zchyF)Kr`d-#(PKWX74j%#ovu{1iI9xb&O!ChR9d9x1;X2Z4B#A$>1BXS=4|D8^hO? zh8Deo7HO1}$4V(BpXm`nduPrhfomoWXA%)h55ubqbGBjSm(DivSL&*bs@r;|K7#hn zoNW=8`Xhfw#B9v1zPe8Y)t(C+tBqLFLxI!oGCC?rlQoOpMmJ;tS|r2T6sBspkZlbE z$U3iKt6;FDEpcR!Cu`>ZJKUly5@Hku7p$#f0C{DA81CJQcEgUi)ehT+$QP%}?^=l1 z;*T(3dT)KW11?^r%L0fU-S&sg61d8gPJrF=-*DeT3EVot5?QhE<8Y)bD#r~Vu7~Uh zH#%h$VX`1*Ka7pToxck=72PO5^37S06#&Uz0FM9U-*#2;yzFf|V>qPBZq`kPg@7zO_n8tviBkpq33P z&{7p3I@qCPrOR$ZG(~SoHLx303XsOyE^+!hfbV|@iB5<$+FIf2W(M&PJQzX;XmZ47{t@cDBKVgMGeKhR&2p(Cd z`Xh}jS%v(>!iyppu$k=eKFdSy#k~-33opp3e;^Wd8*uu|Imur}@Z@Uh6Y-ZbGO15w zu)&J-lP5>;;Nr;a|1X+*J3hVW4h;>xam;bL@b`!W9Ju8xa6042VJR@-7Lk`trbfU~ zA;D_O%M}(jWx^7)1>A-N$Hi^JEg?TYlNtxYf=7~-i(5n>E^XyNIpZ8^{RoQ>*95j> zCYmZsM_qatN-q7Z8}fa6TH$P8?pL< z2%hSyjv$xuoiGK33ev>6*dhg)ARL2j(4o(HjGV1&NW)KCK|CS_5{0JzauHRQB}YcQ zwRkJSm4&W0l*T4jzO7@b(A$dFMeH_AaIei`X) z)mgI7*H{c_i(rmMqizGD^U)|(qv{6bRaa82crYIEl~eyL9>D}sNz-!g)l?@Gd+bQY zmzPl~n(+)pEe>qMJU;Wbkvc-!FcusR%CwHB6snLco;?8@6ZMvKr9THPUMetyNbv2v?@1_FO(x+mM zmceBAJ9H^KJtNyj8>Q^@%!$bx7E>t?JROCSO2x6($%TyX&|v5B92D|fr=xP)NF5<< znY`F?99p`aj@_)#2t)$C`c9GKQ7ik{~bS``@f;EA)zlj7e3x>0Es$ynv2Hn3BBbS+T zP!I;F!#)ZONRwjkwWtp`=BX8|bouy(2vz}3y#l4fAXS!HrKC3c4V)hdYDHDSw7~&a zqR%lZ#@^spB3Su(@J7^fm77cH^Bav9&UE?UrU;hEM&AmpIL3PI;+7X94Sc&l-g0rK zX4rPDbnJD*eCavEZTNyZ_zkLUFR2_jA0^l0%?Q?bb_lduC6w5RXDr>wuX1=cfiWn! z1}r0lyR%q(JDOriWzON+wUJqV;e13q1=gU6+LD{v7qzB{8|7CW^I>Eg+2R%X7Hh!1Mx{Od4OC zz$s;>?1Ytdg6SM_(^EW@(W)*?M!H6^>@f$lwRO_~m32O)4B9Oi3&DVC|BRAx6!~C> zs@wXq9#JfSy!aO$qm@(#a~fuq!ep=)F`w}ww6QHGXSaK7X2qcceRo>l4NhF;ukgHh z@C^*xhdqTMTFzKd-|2vax?uCBrYIIZJ8c9*7O@#xMkZ{GoKp^adn5XLqgP6L$$t!@f+aS%6A_aQzU>ij6)kS|ycL5;qGG4h_w%qquxC3AxrOpzP0(<;148BB_i86Tt6*cgd(&TMRLP8XQY-(l=-B2q%-;6tL@s~6qfTqoOxMZ03| zWgnbR;S{>4Xpc6E&tJ#;3cfKxH{D^=C5|lCS`$ipx<~tnA^Rh^G0i1jr^Hj@;4jc& zmxScahf@LeuZ#w9ygq@<5Z%63KD_zz-2b8k4P2*lwJtrX5hLi0sdEOK9%{VGZP{PB7 zP&9(RY>l^?fKE`W5n#-LPrN{$tzCRPl_dBCN@;wziIendn*894yskd>cb8pS&@ zq8_Tm{klER!?2s%7H7_z6DNA2E%>}Iyk%H@8i8QS3UN(YiY`Q+J2#5gV+7G(RvDp7 zmR8H^?W=IyR5enZf0VIEh_Tr1_-LX?gDt*x9o3TEBaM$Py_-%np4d6D z-tqIP4uD&*3U4D~=D4V*Hj%?AXS_pv6rL);Eo6{7g|suXYI=QP1uI$J^G7N^qNTto z$3>egYr~&H`?vtS*cu_0njluTf|V?r7g3pkl5)+ZLWD}FEWX{8R60)|+2xW-WKbCV z9V#v<8>u6NK10PNRh%xrxyK@eeH>(uqsR96>$&G06%9EMV~@+p*09p$#mh7$JcNur z3l&^S#p&|P`-s)9QxVoo0_RwWRBeK0Q*x@1mQ0L>Y^Y8__#EeqXshUPTC_tkCdWNO z%yyg`?dOmnc9vl-FW+Q5d^u09PL|=P&x`giP_r_XFRvns_nu)n>ry;ymdhh2CPzO? zEbqD`ipl*v3T*bmsJ9YZH&(oe*_ttzl07TA4jaC3ekVr^6O~%lN|gWmq^5?w9l)Nw zma3Xoc(!}=DDmnXv=TB_0D@MsNJWO8CPL${psu4>2aFQMI?L8;E0rTzoBjBAmK-d>xjouHS|&hgaMd^{`bNHAc_B z4xLDW65HJVCU`EoUAt?G7|qXaKwnZy^USy1OxxRW3846KOst_xmPdS;!R%?0k%5i zKQ8K}4D7fenksK#?Z-L-XfL?dJs|hzk{kGN_cDVyHx`v_}k8>XE;rFmbdu z?Fc1`p1yC!`;8_(k4KEbRU{RjrAW4lP0wRoP+CBCZ`8r;$71*f0=8R5W2#Q!whGP$ z%T*)9dmAvIvtV~F2v~o&x(k;J`caT3Z^ZDfv;hP~xmg;QoxK!@)3!weRVh^Zg^3t$ zDoNs$7{8@-d7nJf>2;J&ZwUKe!+@b6QBDmL-G|M) zqTBnhXoGyWF@1Nt z1K*&r4x>`ZIRyBMW}MR@fh-kZ+yQ!3h!kkwR)58xdX;^t*p-xVf}Y z>Xz5eblE&2hQ*vNKcMC%lNj$%sjI?E;KCwx)9kt*(2@;z|>w9L0j2{%1x)(1eFZsj>Bnq zp5P=S2!^y}_Db9QH#4;5YgM`&J4rLNymcxmip5GVPbP}%`oyp)FN4FbV#+mmvZ-2$ z6$(rY@yYsg8)7XE0YxLu0(dp;6pNPwWo(+}Xz36CLrh(4o>lsSXLolsfvfU>1l$>b zaiSb3@0&@qFYp)!#rhy`IambB(D@XBa}%+CBHk%hhfNsx!YGG9*W?VA`Dghtjo+M% zH5baLP$eLPb&GD{SpPx+UE(Z|l~@0oqVZ(ZZO9IZHiT!>6sMsgfg5A70fhqYoPNBF z!s5%hX%!jjI>7C+9UtXNR$e|ghDq_6iC74GpU~-11<{egfJx0uATkbAI|7vECc1|a z{IpTk0b@|Udkux-i73imNl^9<<2*@mR~qe{^&QS><2S`nGi+6t@yUOn2zYarYsK2# z1{l`}bZLKZ9aRg^#smVs`Xb_Y#r##cQ&U|X4Z@D}YNX@PCWzXy71-Xl3{&2}yd{QJ z0HUa{9!p~$CC~5j_oQQ(7@!PnfJoVCmZ7))Cx!(8(9s#&@Hr+KE;S{ENb}fa}a^JQL1I7l8cm89R%#0KrH~8LqbWlk|Q0k z%~|VcNM8B4lb#mf9JMZ&*~-(5T_=V@w{mDsnqw5xBVm~@#d z-Lf@qChDRsZrPgtB2jv8j&V)-O|IcO{PRryj%2y>B|`__A5hOuT-S%?6n#Y#)3Kpi zhH(C$=-A0sNLKeUP5LMG6--FMlpDi*BNH>S0m~dSvXgCd+g_(0gE`r-rNx}=WLxX3 zx9DB>5AzU-Y=Z6#phChno0Q;dl(W~w!VUn7k+c&0+iPMeM`|sn=H;5L^k$O$JxW5^ zVnSq#Z%=;u_pyir(6&1KA=d6lwQS|L(|gHL*BQUamR4DCoyndHNN2z9KlE1e*-cQv zwI|pew{#btUGyD*Y!W?yYF78$3_>={H+M5kjce!4v1B<=dOss}cm9!jLpJ~H2N<~+ z^Q1p-ROAS8)4iZsPGa`|!1XeqT)KxiKXRKPKsoTG^V&l(aoJC(hrS~IPcMf$w%6hP zkN9^`4x*VneRk`)qT^i{UMEVyv(IVmC0=^4jPmAlTkGYb@9D*C#v_P;vf%J9k`RvC#oB|Owmd7shK%SX~>| zI!A2zG1g*Ty6GN4+q7ShzLjq0^LD@Z<}Zr;Fe0!l)m6uM|e7 z90|)3_oPvwW6g zbn)&Fy;}zsf=ZX~$i4JrqWNHBtJf*Ca38&7us%OeU+LT$(Rn+ezO91_wJPBh8{Rt+ ze8vB7@4UmKD!#bSULc`&1R+2YLXzFPgxze*qBIet3nEffU^m$aks?@-nuOkqltEAs zq$x;IVHGT31FWDTHbfA61$+DXerM)vnPo6}-~Zm{(Z`3IDW5avoH;Z1?!9y8KHXH+ zJdVDTKsR@9=HuTDa@=EfC@&8#I`w+3E`x#kY)R{oS9+m_Y6$J$VA8T+RXdIevCbMUypC(ZabZ*tk8kn{## z(T@}*%OVeVfjt3>l!eFMM;>S%R|EaUS0oF;;(8hOXG+1mC;~HB-8un_l!Y&>C#&N% zR%jTVd$G{)og2BSwFg;oX2E36jjZI##S|n~dK?^N#bqMpC-Z#1(4*#H45vtLMdY&7 zXR;cEKYxf1^8+_pTZuwxmOz+Txx8y6%yMx~Dn`=&7>U@9X2--8J;I0e>qAL1g_$-A z#%P#tck!_dYg+P0LfB6Tag2{9J<$@`$gd-<-h?b6>dpOROUs1P$)l{Ege(CJc-o3*O|{T1cv|Cf zhyjTY_TjxQTq24I$y%tC@y#bCny zs`d#tX7maT)ElPgp+%pPEqEm3b7Q#!A4N%+==^xdA%gGs5^GDmeiki6H}OIjw<$Lc zisXk+e`Cd+e%RECN{5cJUJmzRr-Zs^t=F)UnbwSmKwcC98M-M~R}c>VV8!iySQb%I z!O%}mE%xaJEAH!0+lEWb;>DQSU3^tiX}Ybk*iG4&i)(KA=C9)+T*sO+|4)e3Pk#Jj))|KL4o+d-0A@y49$$_}qsCB*mUj|vv&z-!lAGc(FlpWVCO>P|1a zrhGg3{l9rUwA)4Im#>9p8?CPLwy^hR8y-7M-G+|#n-D^0S=ovLxi7w>h5TiNh! zd@=bI#r&Qb{EA=iQC4geF4!D*`0->L!pOSAn%PR!xU?@xnDAwb{pGG-wBPE=D39oH zyXrQ?-<7+pt;(s&cTZV!%P0DUnl{9!@F8EasrV4zUtBc__dbLRW2NLml=A&Rg$plH z7;fFrhTG=ChoZ`*eMr(GKFrGC%9L3hE_9cxW_Vv?8*aJ_7nV;GxX@KT(e0Yr@Tj5p z^N6!NuLrjk5QbQEjD4}?4Zo4B=r}GCVr9v)%)xrUfbhFGKfF1eyY2Uv@Y_?Cj=6vt zj8pO^-}i4TB~f{8Phf6NByQW%qCvPrrtLVsboFvnM=mkSJ##XuM|^&GZ#K8A*+-xp z_)au#u?oxFSj&mqB5$SK!j7qBSlZ0XysX)2NQ^P~3JY$Ji1*!FJ8XFHaP$l;kmbBZ ztfUq)#qA?lm&(5X2v`2G3Q@r@v2|i$ob-Jz8=gaacrL23w1>2Uo%Vl$+j;qX!p-IP zB(Nf;oeeAeUAP^9cf>;YVK(wO<}$8XtUOtkdxXWYY+U`~3{`gw)~jxA7d4nm&1KSH zDOOyA#|*x>!tuk_9I0}wEKazulZ~&}=^0tCGj4uX2K((QTV3gc7!%bq=7U&aoWN%~ zb02*B8+?G<54fe#-PV}ZX<)02wZ*veDqiAv;P;D4u|n8|HLfo9?eDl~(UIa6ip-we z7x(^!&gXS}!WUAq`0V(z-&?aPMGK-rtNmbg$FgO8A#Yb7-rYO?L3hXGMX|*f_+^*c zjKIA8qDJCXY20Js9qq`&&hT!2hkhY{fm-HbF%!>qNV_h|4lf?WN2)N07gFbvDDgHZ zOv=Qgqr|eRFexrO(oVS1O?;jb7F`T2fjBQ(f&$Pgyk;&Pr< zvt`GXMdhBWX7j}5$H`nj(uSX0={0S*`5cuQ_C~A+Lw`6o4I8!)$|5DR0z6EwuW1Bav%$o8xVxtM%KWm!UGFz5O;KtF7 z**15}G8Kk{gpuneawk5U1Lxyu7=E;IFUlRU{WyO_UY3C$vnxf*<*v@M9G@m^gC2^( zN@4hy5*zMB!HHQdIesa};FWyQnE1*5Pp&Pyl@M4)b$l+*W%HD;YPfi&4R@^Odu*-B z2e6y6ZJvyBDD4g>&EccYF%Y9Jm*1H=RE#Tov4OlFyxh$1)*cvh(Nb~}O8JI<`+H!_ z>C3`n7IOy{^|obZ@EQqMLa`w9@}}@Izc`PSbc6@XQ{BT}>j)2)t7`byW!!_+JHvzJ z0@!PPZSL|_4NqGUaiRFO;-R&+%ViW|*J*5UB|o0YeHbm4E|kU6h0!AEdtSsg{M@pB z_?NYOBnuy6gKclZGWR$iQe!b6mZ!S(VS3qBr833q`EV9qEE~ze#LK-{Zs|!I`FIwg z=)t|-_)v5r(!__};KuBVkwS_@6!Oo!Ard|MqVUj%C=j#K_*3Rf4^7^VxWO!bB{P~V(6qXC%{_|v%M~m>7T|5X=$J_AGa-b~lUhQH;Ii*ux=Pn@bgj-`U4KIAnw^Ev`zN6E-Zqg>bR$-0-_>EfWfpNc1Cj z*&OdC12mI=*>{2Pd{Hw#}M zp%QK?-U>h$!&J8>Kiua9vN982%jKWa;jQ8QTR?Jhf{j`DhGVoWi3Gwxwnas+>6AXt_^dgokVpZ+ItkfcIR3^Tc7%hTXSgOOX31F5hz67q| zi+IGz@}1*+9zh4TiqsIEBvSlhg>hEdVtf%y^Tu*3MgW9BCQPz_6$yJU>Q# zc9M_rI*V}5&cz1wGDP`WOb6dHVifCL0(sE_-eVZt_|3Ma1eZI}dQFGCQJx!VLYC?7{N~ zH}1{K5r@9vyGG%zL(dXh7PfYZ6ybXYD9XVbqA27ni{<@5R~S4nOc1gjuAbA`fOo)QHayzjT4eAo?klrEarT#$pYA#oXx{GqP*D zOYB{Kz6Z8CzG5vdu%hL1A84N_&tvn~!fnwqxg$3(W9Lue27CKIc;~fUht3mqjt?*! z$WH>G4s+g&&u+q2e9$MR0+nLRq&;n(gUq3 z`z`Cg+M^V=A1n_+dWQmS>ZIIMt=_g|IxXOclPhv57GN!y}q=T&cnq!Sq!*lRko(C)9 z;_P^q%!qNN3K^!b$zQytvC%yh8X*f!{~g` z$i)?=9!BR!%MzPtlgT^(z|n{i7xQpIR7qZ%C}CeVx}SY~icgRptnXI*fHm;gjc_Du zdK~>9Esc1AzdP*4EkyV@S{4xxQf%_q+`)a&WG~PQ_ms-WaIJrOYx)6 z@`+u?3i(@I+yUFqMb*Dp!qk%y1rr(<0&=`$KNctQO{)+F*!3M>A;u2R^6mtT$8>*j z;Yb)BmygqcfL!6(?ReC~X3X{iL%aA&ABx!n?VDf2V6Rm+#tWhxgQ;FkezUWVXZmNllSlAM6DP8M-E39^g@P2BAnOju zOlJpsrzRVnCbA8KvRhm3XLWm+^;ySjn){PSaTEn-Iz}36Kaq7jX6uc8H25Mf>H0iu zNfr5-FI*oh#HE8M6hViZR8gu)q#*I_!2_@cZx6DA-OS2H7~0OxZp4HFVT*Py%;POjpUWpyQ&df`m%+;$fi7P(WayieluPVS|1)kk- zlF&N&Zl0(&i3P4Qtp@6HEhPvo$F0n^8u0U)bC^nF?Z#Y*;Sq5q*YuJKbr6lSj{IeK z-XLTi>Yt58{#{M%+C3zt2HVrsY`})!m^P8QuQf*-qbIR5*V#Nq(ImF4i)jqtwks-f4UY{l z@r_fV`#;;TlB&+%Hfyv1YZ`^>_)^y2Hg7chlB#zn)fsQT%VZIG#?Z-f^snHVLngza zX09<1M}(ZEi_|f-q*>EsK|wbWc5v|ge{8;Jn^7;`R5Rj5uKFcanMtkQ%Ugvo9&bho z!lsHHZ`X89Z^&Z{nGKkuQuYKkbugmtQ7}B}c1?~&opEBaobleAEJDumrquXYQg(`n zn>S!HpY(pFdKYlSq-RNH&cld9$C8vPNe^%(DzdagW(#)P?3UFn7o?EBFQzn=g>R75 z($q%AE0aa=v6ef{-btG{`id;wy(6V*@_L?eWHJxGJ{4Vfj2=ic`CiS~LWVwQjquBk zmSAsXUgBXr5Pi{Hyv)saRiBqyA3Xp)?X1p$lp4v8aM_)v#HKpSkT@$I;-xo8D>^Q2 zUb~9O=s$(xZw%WLHajM7=e7Dz;e*L)K$ZwQA-Me%9(HZ<%_cAQ(87elUqKgYi)#tK>hs`>y<6*P8bpLK%wmznKlE}DEX6(L@ zQr!S3^b}H{rwBnf86k*^$Y|R!op=yNv*T1c8t1RQyxAkX>l(6>{FLU#iwL1e_*r)G z0xvt(3!nRVB8{EDA$64HSFTd(@{e0xX8bfIE(V)Sl`+_8s+{(46w}@T-a><^G636B zB_EFbtlMiUb8gM_ROWTMvEy%2>=`I8ZI~@5J+QG!ug+9C>5ZByC%uE5L}uVJs$Fv` zkGft-cvr`Vz{5EgyW8VM{>bZE2)s;P;h_emw^9_rV-MWA;2*j-=1*m5jkEK_IGPQc zqG#CkQ^lN-^deVbtyErwoXA)`Rfe4L7)-5|)gk#XueMs)Vl^O0^yN{WIb$jt+Q_^@ zw$4LNw$5J%Th-XK#_}VE^lEJSV!3g;z6Ceqav=E)F7^$f>|Emu((_fGc`l}BlE^qC zGem@e5LyWwT8RjQLnk$fZx4@(xI4Y<=9;D};>wuX+-a6t?9eGpEOoAGUR5{_Liprn zsm5Ew8;5*uL-dHb?c?Uz6D%^gj4P+f+08#K7Fm{IT$o*+EpM78qYFn7T_?FwgQtn; zvJ}TvoH9*Lam%D8e2PncmsegiO`ca#K}ZmpkPwTp0n=oR?U^QH>_d(rQ(V`H2CtjO z>ZF;yv#=C5H({xdKeFGZ_~M7d=@rYVnq}U?zD_f@=_?p|%pje*p zTNld{{ueycS}ZR#MIy6UUTC7GuvB2MR15}m{MT2R&i@W{IVHv+bm{uGcLdGTx4gQm8L$Mq_IEvu^k(<8` z4&qk_FHogJIP%H)9rDpnywaP+a@e5I>7cn5U~JT^DVC$=7x<4~j{e3eWY%?3ZC1SM zO0#z>d=n=x;fYYfnDJr>faMz8migdHjF@uO&%x~|TRYR;w!F0ygQP);3=&H^w60SU z8!d*lL`KTr+@4k?a-@hvdI^n`b|rG8{KKoHm&nTs2odokB-Sk9Bjt*g8r$Q>2n^?s z-kPUg%`ir+8Re)gMh8Bdda+Lv<>n@ud^4Gx%uOCFOrC3uCu=M5%rPabpAWsq|Gu?E z4hvKiW(f*riDAKt`LOV6`}QI|7)C@Jb}YwC`hQY;HECFtYlVizuvme(jt&b%z8Dru z{W!6ti>nsnB~W}+JYNzwD!xR&@lo+vi5wL;icwLUi~a<*@ljE=R8~sDk&lX|r7|As z@JfwJ<)}cRAVns~*swTVB8SBlrE*v_;21K+$wV~tVF|`XpG@4d|F=^Ke&zVE4XAvD z9s?sgt9*bM7ao5(D<{Ut;8Hm(nsKcMz{to+#28b`j(5!Vh}bA9m80TvUh9@p8Wq#w zFn<1MDs$zifD92nLg00!d{nf}@M^S+hE2rq$gs$6Ri5f%TzGuts+wr@Ml!l3SA3l? zdaeN z7pDuZAlen<;ypxmbX*|zW2cL38W`25%Yjj4Iv*JQEg7uzZF_xIXH4_9LQ>jvR@xW8 z=bKNDN~+4f>uc7qRAK$9qvmZxQkwB!2-Vm(UCq8Nv3H+8VUK2IdiP?7jnYn8x3ZV7 zF|)JFQQPav!K1Mz-AqqLd1_;4xwHmGzv(h={oEP7r^m%@A?)S%`8>QNI9&#=NDP}U z0~hyg-~zfy;p5eYNlW=n3QKSKx40P~r1zMn!bii@4z)*B}|`d>>{hLRrUnw)rsr%T&?AM(%P}z*Jsy>Oxxd6 zbbceF)2K2-&Z50|{mL_BXc{vTL-QM5XlD7n4*$PTu@2k=!?S0;r?d#qauRM4p4djQ z9P-(Q0jc#3&kPx#1GpW|8FBI114i9Q^J>=_GCce7TAgRe@MMF}+EdxV9?jE|`^$Ve zvqFlPS%t`)8PTB3(5V#{XVPYIQ&)s#d8%U{nT;DH%#C?& zXXHi&0KG%vBu6j7Ee5?M1@ z$4=RkEHmY~dn~VjJj+$tHspx_6XNR4M1b|F?T-s2=+49zHVC5((o8MpHw_Rt-7mt# zjg}y(JAt=3RH!@Gm_VA|!830sO-16inKD37ldDxzVD)AuL`eHfDuS4h{?lu7KQ%m& zH%wtLd~9rH3ZtBd!Q(UK1?AXbDb>S+hM3js8pog+{{=LIqp!^jhi^44wT(06()(oY zzR6~BE#v%5RyxmYpS)LQ%9HlX@OSj24f(OSd3urA!+4F(+-b9<2F+(hHK@VXEHHD7 zwzH%HC%9DKES9p++-&rm#m+afb`&RdD|vePi7$ZWnH14@z&C{5@A= zEpqyrm$Tsynpwtb(PUP=qM5TS74967RkXNjg$k$yA(1PTG-Ki{9=_QuG&H4!1xQ z+*QS>JzMs|L76Li;R~r_#iF-o;gO2j$UrkO<&Kha{(_X$($q@TP*CB{;E1{l8?vP@nhk5Mn6;vGh1^Co!fZWk`iz+| z#weQ2>K!q=szO((6<#`GPUjU^+Om|+#j5%u8m|8u1OX^G8j0m2=#77{Z)` z%`599So9Secg!?-)YM^*zic)&-k2>z=T!jF&^bCg-0u~$y2bcHl-6g(znR{OpUNtx zaeTJ)|Iyjf|I<=yrd8xEZulvsb7G4>#kIKaHMH1pj%=~soVXTi&tYh>6}$U&^J61N z)Sy;PS=|3Ce9I^1Cc`);wW!is=E`AJH$i6?d|RcakG=Mp+1&Vct_<+6pjC88 zeLhzP_&uMSTa2W6yaok$#d)HdX?zdg@@WlKV*!qa#IQ{_>dZ8kJ| z&5JvGbmpGuj-0+`V|MWFlqN>kc`_QHL@XLw&x@TcsFg3q0R^8F-KXYVI>`5H%?9`pasj(P6#}JX94v6Z83{0`CrL@zC#RaR#+m z65ry~`7H2h|6;@=wBKn=W44V+vMhR;V^>isW4?hhpQTatTnLEMnLgl8VkV5h3vA2w1JimR|H zyZ$Y>_hztqHSGiO8MvS~5C~v*R$sn5=ydz}?RAd}A0}}Y6gmrn;SD+VzLiq(3D!`d zJI@y?a0T7?%yAxeGYjUs3;e-A2>WW~h3mTP>ny2wBhgV9Ds<+%y&;In4d9WXJLK>b z=DS_a0M-|lrr_#g*xmNa!XMYSKVvDgdG<_0d+#z^XQwCH8@BLx^8*ErkT2ikEOh$| zgF!bo&CCm88Dt*zkJn6-(cTjNiCys?4&*2 z?kKzQq0{ZxraB4(PG_F8FfZf}x*dguL4UqK-r7XLo^|^(PLT{lfFI0%tp7}0+5L=WM7P)FAV8|VSltP~~H|Pvv zja!}rFE8-Oj2mYy%4nR8n`rNoT2|X)n5Fs(FbG_HWEbS~sUnb{pYO+j2;>FbdCq)4 z^Jd7)4))bqpGd|!bh7(dQ({Q)SRizx$>5Q5$h4|#%4r_1NW zg;pqjoR!tO49_wf$2iMjKXkA+O?3xzJ?^{^R&HWi!_x48#~E@sgD|kb9dtPYk)U+> zyiQ*LgVGgn7ou7qzc5&UDFb2Y$;GlgKC{Wumg;fl2E2Ls1;N4shuez{f$|Cqy+Lfs z>CVf|FZ8>^sQaX)%$B7oc+cYB8@BGSds1_A^9u1mHQyI1EW}&|)e3x0Ol!DM&GY1Y z^CF!Ra$%+@bbEYobZ&moj}31;u7bjl!;y!HAMa4bhE^G!k}$N&2*`xmWqBw%w92xJ zhL*b!Q@+RJakxTmj3+<3#OcmQw>a~?0XZ7NB@68BE43)d&&T)N@EK)HEQR@@K)%-< zz*)lQ4TZcecLANp{Jv10$I1KHhu4sN0Yrue9~<<03J@ZglSOaGjjz<)f7hT;p2tFE zv@Qo&FzCnN_BvgKd4)LFV$Uyc$P);{IIMf~7Di6VI4|JDT@c8{ka4@+`F@NaXRb59 zz=?_8g_m98BGQSer@)1P#GrJCg8m?^!d?NGPn-yapqC=Dtd?a&mf7%}7wo>&e4LrQ z1$hNQe3str3AyvU7%d@}E7$GL4fy>oibw~Zc)APSm>6M)8}kEZy8`}IZ-=W8pBN6r zScX2z>K0;LZ=`2fUs@$i+|iepe_|5Pudct95BfnT=<^X3uPc&lLIN&SFr` z6GB%61BJL;a>LvFm481v$n9`CLIu*W7G?ZIl*<0F#6I6rTvqR>r=_xDQ>|Tw&!Q)> z)UwQC9p1L*u~%=i_bcM|6Mcwcg;wT75=}xX6$6R7REi`I3RCIvcqrN#~FAW)%Jw_x{5~e!tmmE?T;F< zuN8zleqhHFia`t6ogdm)8GRSBny2jDjT>gOL8t64W6WH(=#)Jpx!XcZg@ui8VIRJ2 zZ_JLJvezvhgq(Rr^Nb4k_t|9rZ(ot~Y%Tux4S1+LuV@UOip?wH?7TZiT#jeTMbF{y z5#5TO$DwGbr9y?G7x-TbA*JX={?~JmRP+-6>%Qvv>#)d!#G)hoFa8v>=qUedoMmn} z@R9wf#rSq1bB@n(7+>M|x&3P6&xNe+T{*2SU$K5&TDw`&8GCA-FM!~AOs6^(@Ytj1 z%Z2QLyK=T0jTT9=0gjw(wMdc;SX!^v9;5Ce*6GgHO(O8@x(eKp;C2M}A-J6uex;rQ zk0iK|;0Xj5YT-H!6!;#3mk|5_!ArF8fQAZufZ!(xK1%QtT6oQ63Ve>>GX!5C_>2}l z-AI9J;+-|#Pt|bb-fp^Bdb=7HLSO9djLQ|+OR$UJs|m)+M-4o(i2@HJ_$Go!6MT~v z-q}=v=Mp@N;8g@;*@mX&A5#=~55YSLewN^!S~#z{0>4l2NrJy1_@ow|+CqUVE|K1@ zfFt*I-6hi771*-j7km4;R0XyZY$doY!B#DtoUXtF2)>Tse1fmj!o6B5@HB!a6TFaM zyq2m_ds&78ZzXsW!Fvhbq=nyUrNGAsevROd2!2fqH_cMu-wFPO;N+#!+rMbx;n@nD zzEpITr3Kc+w(ep$h;6}~V=nHg%~pl&No+UC4kosnmi@}6uy+tUPO{U99j9g6*cJAE zV%JD^JF#oD>>aHY_9bGUm+afbKCfk;%u(3yiTy^he-rzSmi=EFg>Ae{`ne(7GZB8y zTqgR+Xt<17j^wx#r~A%M1@1&}dkJ4laC<#GH&=m+2);$acN2Vz9)8WGz{?3n8gbN7nuZOF4R^VcSr$~4a!Bg~bpQ{yk8^N0;ypQ0`dU$yk1wKyj z>k>Xq@auZ`-L4A!2f=u0oqM~=3hC`%^>DLp3fyvq?5b3Deg$AB!Ko{>T@~!Ez`Y3W zF5w{rch|#PdnoXo1do^S41&k&;ct2>@H&FmN_YpsYxS_Rw*ns~_yq}{AovA6d{-X@ z{(<0cCHy~vztzJBu2ta6S4wYR#+Ge_x3gBtuDWcc*4q{ODezSUcaU&jf;;HpF4rmW ztptyd@I-<~=;8VO6?g@~%Ot#!;AMLFjq4ToX@d7l_!WZp>){4BDDYwJ5 z4<4w%wO2`R*IAUz3C2QI-tuaK=jh?lH!1LA1n-vcA%b`7;YV*);13CYPr_dj{GJ~Eb*KVYX42aR zvxK2`eI~taFs-+*9In7Q1e+4Rl3-I0mj)DgAi>v5I7IOEdibTF0+$es9X+_(iwT~p zhpQJT@IwS|k#LycEqb_bNP*uZ_zekvOz<0ecx9mi|4Hy~60W*hdiys${QfNpoUvMV zRT|sy0&LDDIBm7It6Gdy;NArHknoKJ_t3)yMGAZu!M96zCc(Gs;fHQj;PnLGC*em3 zzE2N-H%ftz5d5Nq-y!%#J?t8zz~>46PQw2X{GA@Yd#nOCxmS9-5gYdkyq$fo?5alh zYQ6pRZ3^6(;EodRM{q|yY>ZdnQ3Q{a@Fapq>fvi{SKyTd-y`7%3BE@UFT6v6pCR~w zgkL52fF3@6rviUY@Hq+pOz=59eAxsAu7h8FxUplY$?CobZ#Q2fyWt~d-U+w zsS13G;P)l`HNo%e;m4;baMD`o?TYNoX{g;`t@L)qwOVigUaY`v2)0YO9l>@z+`d$S z2N66#!i5A6(8DvPD{v{n*zbm`y@cRtdicl;1%8;|trC8M;H`SN<}3w%i{N7tK11*^ zJ$&741^$cR-z8k_KI!e>^)Q>Gz^(3+U6syCzlF^%g46HQcGZV-6}S(ETMt6?hfF%O$*t;N^O_`w9hqmf)u({2IYe>EXpI75EE+Kb7z=1b?cB-&&=>b=OI6 z*TOeba=I8T*2%7_wNC5p#;X;$Ex|qscO%%Rhi|@Df%6F-F5z(m57)yR*C_Bpg6Bzi z4Z-vD@aJn4crU?^O89w#AJxP5`xW>jfJLb7|DlJk->Sfw56G@+iOmCH zvzy?S4`{pU-faqeEy2Acd^5ql^zcUyDe&C{-znkQ1mCHLTW(k24Fs=~@GgSa>ERJO z6!>L=4@>x6f)DHAM;=k&9|erBM00mpHb`Ipphqw4RM3+YN?V6ZsbKT;Kx=&&2ZIlMrX2)&NI(4J$rP>>{ z&N>lR;428ulW=!}^Yn0&Cl$DW;DCh36CBXP!}criB7*S_A6I+rMz&`LOxClT4n)|F zGjn#JlIY^cB>Doi`IL9M56e^!B=1m{S&!-K4JKJ3-APaIU(A;b=p>0s+9neW0LNhjLJL!fa3fgy*?BU)L9!hX;J-qg)0#79PE(y=s#L`yh zNn9@_?k08C>(y!X zYJ?Sm{tCfIB>W!1NAz&fYYO}m!RIAxY?d8xUJvhjU4ffzmR@Ya&h$bTn48)8b?Euc z+8+4n4TbGWY-h<{PYrk0!~Ww6Ja#iXvjIIXnw?7MC_OsuO$A*|?XHyY7J^sm;pg8{ z;6nsIBjGnTvqPINX>gSj5w`CZbTOfyOY}Et_j7%_z2AwjqKoTqVSBd0-aY-{=Cm!c zkLuv|tLB;cJtr0P$}O^w{1Wa#uwM_Kd{==(1P3L2`xdrr2kh0eDeo)nVq)=bFW34$ zYIuPj4t$`%;VrCmSH$2K34L6TZuw9_Kc;q1$#%aZ_>><0`ji4!-73A6#QFuGbfc{- z@E8ovGZeKu9w(W+qAt@{|klfC)qxd9R}tQtkt7~zEsdj5}hE? zxzy|gJ$wIG@vQOSHg^7bSSvK%L+Ar~^z7I1sO44J>`}?SPwY`WoAs^2{w&!Gg0)m+ zb?(Z^F)rxQQQyU*M)QZnP%xU}h{bCb>^aS0Bz3dSp6?a-8iKEWh@H8tbr*>up+kT9 zK|yaLbTqY#{kVC%Nb2A#E-3K51g|1^tAvr%!6iQ`@N)z|OYkuXBdLR5{7HeoCisho z*q&w)i;>Ww)qYXX1`kV5*Ts=96HI?t4hE!k^tHb#bUUKk65W$%q;>R)-xRu#=zOB@ zAR1{M{od~iy@cq653|yY)?F+FBdLR%|Ea)F5WJU~eu-$Lb#y*1r{dNPpCS4qqQ55^ zX&t@oZ-uV5UAjCONAB{*+oj8q*3sYoPoZ5zJGQfaCd?)nNgbSv4Uys$zlq?%)N~Qi zNbBec7KNTg^mL+^6OFWve#%hjokVXZ`YEE3*3p(q3Vo93x3{yj+?d%&>fo-G6}Z9< z>G8j*=~_Fa$C1|23z8JtN^~ZUyuW-zBdw#4B`frGL|;qvaH5gc(G9CA^kky%-oei2 z!EAz&)WJilDexwOH&D}$5{!P6Tk@HYg1NlpJjO(U(N4>wfkhC8Ll>*2_q z)^expFQj#JjYbOHp6Dxx?nN}xI=WwDg}#O8f}JeT8)g%Xqz+znxdJaGco8+dj%cKH z^ao88`bnbq5q+3wq;+&^ib9_y`ZUo$5RJ5s4mDHg>bs=NtKfJ&%-$tEj-(ELxVZwm z33lSh`|B#Ak=D`Qw@~Pti5^1qtwbZOqupr=J)7tmM6VzkX&pT=U7>doy#uvxjG4`o znqQR9v{c}C2|hthe@0Cst)nZpQfSL=>GA&&U3<55Inp}1Tc$$Wh|a>1yF5?QmaJ7r zaxQvVXi=8J_9wRQZgxBXqY3S+L*L9+&?$sYq-N(6J5k3rvMKClVmA`|7_l35>`kV^ zzE12b#C}NZD>`K@rc&2WT|Ejbcx#`YY& z*vDoYh3!skS7HZBwyT!C&7rX4i5*L9iDbuW*}YDMT}$lhJ#5_Ah_S1+YzLMzP zL?f-EYvn2Q2%*&jSD)bvfze@BeqLJ3oLwhOoZ$$r0bkbhwbEI|jgS{0xZLjRH z<~VYnw;>v79sNZgh3-N0HAD|08fhKfy01dtPV{X=mlBP%jvn7nq3Fj`-y#}m9sO5-h5nA{uZjMPXry&?rvVDxXrJ_X101=}TkVrQhO~~JeS<=G zB)T2ZeTYU{N53*qp+^#3Nc04vk=D_51}pSEL@y!w0iu!C(F2Ak^Z}xuAo?iLNbBe| zH!AcwqR$Y0foP<4^y!-vx@K7Vyc&+&=S{=X=Sb`547@2HcN@n`w2SDgiAGvSj~u4Z z!-&3#=+Q(Yt)q7iSLnG!&mwvi(Map)9|H=#hv=O|KT9;yIyx_3q2DL^B+*|GjkJ!Q zTA*%k>D)b#hk0W|I(Map)Hscif zexlb9y`5;Jb@UzM75XKjpC|flqLJ3oPu{N3-xK`}(SH+-w2uDY9SYrezw~)S9J$Xk z_sbqbT1R)jOQAav-Ja-ciAGvS&z+#qMMU31^xZ@wt)pMNTcMW|y_Dz;L?f-E>rGPV zr-*)%=$DB`T1O9@tk9nleU|7SiAGvS-#0~}YaNh2uZ|=4dCCFlbEI|jnW+lxBic=L z7ow5Y(V4{xJ)G#9i5^2V(mMLq5`~^e^lYLT(Map)-K7fsDABu!K1ejuoNirr5%!#^ zG-~9S(2Cye(-$V)KK9P>`Jok9=YsEE(3URSrYj;pAd&Bq$S+AG(ie#watV(;>#TnkSw%%@h LYp43lIeq^RBb^Xw delta 94772 zcma%E2eehi)z0gW&VvW9aC?80$L&QF!GemSVh0s?HUt#K7C{iu*aD(NWWlqAbMoo*1@ZKG9fi~dnZ&t?^Ugabcfon_eB%66M@Qk@ zRPOpOI^Fa|rwaTk@vFkG8owI+YVoVXuO2^=>5N|&{JP@T4ZrUA^}w$uehv8b!ml@e zjrjG!uP=U0_%-9#f?q3s{rDH1T>n1WcYdzpf>b=6%bs6Iq&pHF9m#Xf&7E^jGT9N& zUy$m^GvDZe*Zof^e*ej?&+C01nRMp7)H&y#o6D!t$qsNm=Ym2!c|q>nbVn|khzn!h z{~^VXJlplB6RHx~99y`{)zqml(3i+1ve^_{zt261Eq=C=c-P{<9XKc@PJ9|w3ool2(P2?_GFwbcnOw_P$``l8SKqJd&zZb zr2r5`ORW@Vds49@*!{174yh2L4ZZ&gm>8iJL(x}V+bT4m#oJe@c*%%Ge9kIYr`l>{ zW{n)k!GFj(^kTDGla#yy-fxQicl(%F^%_P#E`)tCtD& zSC%GSG5GSc7hPC~U&H)^62Az~^yz;yeHr1D;H>2NhId^5t}yLrkG@)Ccw`fguXXjS ztuACxQYml1Em3Gsvx-6eC$bOUbM-c%0TRmEJQ|t^&-`@pZ5EfZ+Ez6wu!WUfI0%&5f%LQYjGo#zrPP|qp+t?&ZtR{(rnLb-XqBO#u_ptF++#E511-OF!%oi z?@PnszP~i|_jBC+YHQN*ctSJ@Ol54_kSwNq3p{R?G{CBv&+xu=0-`& z<+74-CqXEaj8hL6nI!pUG%R%qZb!=kzJ5p5_32n|zNpcCdZmOZT8rfe-$zd+*h?zpH6VQ{7)+Y26aJOe&XVTSp)P+>El(Fi*Fm4$1JP)7-7n*wRP*BiPF$ zQ7@$-LJSQyB!SW(3|WKu+yp92X*ioX%%u~?FXmJ(F|9Fb~!b%xT zEDYhTpP|nOk3OZ)pHFuOOG0_W8ScFmCBbaKSR+!4b$;nsxL%V@B?__;s?K00!xAfk zlUQp9%9n}2pLiOh)PYlxbY^g!3{LXXA6RP4VpZqX#@GF zYup$S0ZT9oWueI`?#18%l>3;tO_@o?WLaw4P=5EdZj6weK_#0rqC+iphr|i+5ufQIm=1sdPO>qqKM` zg(;DktP@!aRwuEXAnFj#|F)FcNNKELHMQ~5n3%5fAuY+-={3tmbD$s{ zi6B9iq>2&F_br!gKv6go9314wJWr)BpULOg(ybUhr1O|RHY`E|_xCq3de8tOC?wi3 z;ydJJ=eK9q*@ z#AJNQFTDN*8&{TWOg74F>#Dy0hbf1%L>2AlDusvP(eOs2i(l1ysTCRm3xA%H1C z#iC?{^UU8UPo#pZ9nxB207ej!iwPQ9OmHa;=huBm*-{EH!KE~uZ{3JKg*~v(-K`qM z6dl&fqI{v=i=o_*?@_)qKm|C_j=Af7Us3B}$Q!}D+o$Li*w8(OvE{EK&!r9%Bf5D% zQDa7W1Q~j4=N@Xuii7#sf1pukt>0M9X`SP~FQK&1# z#e&ok(w<8RG7jPVA76=fOPCTlBNdp)8DgT82-C)hibFV0{!6r5+L%@#9!?4izo9yx zOXbm;|IO{GDqyU-Jnfsr4*DK_jSjCxcsn4GBgLWomG4183U_?y?pGs(Wxpb_LMRYo zm=QTq9LhWIhx4WxLs(=%iSVQf52{BYktr0YdNg&($WWB_#FnK7Dc&!eN?ljuT&=7Zg_69Cum|;UB11GXK#hYuwC)9 zR>Py>Ncqv-Jw0n#-tXyF6E7t4qB&^lB;Cqpu{u8k`ws8MenIgr2A?D?XCZHQ_frTVW& zwWB27cr}Hy zM7z5;zcAoAqLR%|c(9HMn{45k9<(LOB&|%=B~gkrq%96lBD-|3#o!BJ&-)eZqO=F| zaKoV1nm^ow1xh2>qSX@fbVaw7PG#eQ6{d=q=I;I*7V88j6T!R;^W2o;R2t6ngFTp3 z5WtoX2N@|ZYoj2eA#F;KDGBB`Cq0-$l!P5K9RFe{0_7xUQ-#Ww7WCGe+^%zg6G?<@#oM&4FOPuLxECt!*GtvC$I1E$#wc|Y= zG?v)zNhvn*T&_K3VPj$#>Jp~dl!o)Yr+Ow~y$;6Mv@5BANn2u6B&cXen-Ww?g88U1 zo^P>WXIRqWf<;L%fA&mbkw~Ypbx3)FaittUOk&4hf+W;{F&LP4i6?A^8htw7ehv{J zJCcBP=5gn+_9C879Ut;E%D?Q;-;fF4yLUr#oo<5NTa$#^9dtCWM z&Ua=!eo#CWx#)@^_#>GXeONY=#3V@U>J6RgX^I}00+zmJdO`;(%r{&@WwPIFPv0uo zr-bO!#Tva>a=B|ZQp13|THq{ue>P}3LVVEWR6;AR^Yn2RUh(aO{$Q?(vutmQQE`~ zP$NzSD0slqoZmv#h>Wwj7aFBUMK1RFMe=A|79WYnsrZCUT^q)`-AWa!j3UZTunfj8 zyNxQ@MGtxE+0ipSU8DIlwiJIhsW1!1luB&Dc7NE@S*Ya?P%U9w)H*DYX2WNCdWR24 zo2}l4RT;jR1|MHO;^`zJt|nEs^mwD7rQjXg{8@BF^{_A-RtRATa$|?&^G_`1CP5dl}~2c(_-uX z!o|y|DJVt`UPi4zQ7C5*)9_mw+OrAWi!>w`K+brM+9RkCqN7;!95qEnp?ueGX}E0~ z(&GDmui~{D;U_&NIu{%VO0X|Jz(^*QDrC?Mi|O`H&!EFkXsk|RDFzBbOL!H++0uqU zBWKH~;UEpR{=c490Zix~IEsNmlNqiJfkMa(HtpN6Ui)rWrQ#BHok!;MrHh z4t^UWjcf|XgA|io5+h_$%0GD<@oNb72ud*5J5(JF4Xoy2DBO_p_vSafLPhlDHRvV) zv(;5zuh`%*St6|44^IvpdF3v51&z4F@mcpm@S=&Ey#%*sL+uqRVJ}HM6A3i(02lXz73b*VC(( z-PPHP`jf_zSlh#%es$Sw4ld!86&-(hdN+DLyFjxN$QSi-SsWwsVr|VZR3qfr>SZW? zxt~0vDutaY;MDH*5sMU3WWvH2^<&wygas|(rezGr$RPU*yR5~Ui15}5@1_cSgs!sY z4#fck@?FPVZ0%oF-tM$=oX0shl;M8$**r$*?S&+rKD&_3Y4Y}|b^up2c|&G+H7T*B zZ^jyVw2OBP9StITC{oQ5TKPuoDE48iw^`~CU`Eu~&l@(wEw*NL_x_WP0vWbUl^Jd$ zdNU3Y{ii*NP9{(52vj9&hAJZ89NkD%2g0X$>QopWV-kv0X_x`*lw*w%d&ewD@aoFGh4L;z;{UE}mm2F7Y<8 z2SZ+3d&SC@EWhIty&2n;z!X;dP^xOyy)>g*@nELC~(Ia@1KN0|J(CsX@IJ z+dkBLcf~LGb%%Q2sHhb-Q|$QRUTWq^RrXsT)7J`WyB{AftBTYy&GHe4dAHl_X%(;v zSZ!I9o;bpbPVCOpy?wAxmu3FhUUzLek;)o-k-0OxO&Y0_NRC~32J+UBH$wTkW2sy< zp6Tt2?RTus3Qo$H*o$0vCThQi3_Z5?OfV`5=8+SqaP2!AHJ}7!sc|TeCBb~&XfK-l zThBw2pDAE;hgySmQmkrHjiH0k(etUs7!a073UPMM`QE4*Rwu=CeAO9VjF`GzXxNf; zq%A>b?uDQuY!M`Fy>y{BW`=27eb4e@lyuM(t1YSEn_@&xMzq2;<4`*)vnkeNzSj!| znYgiN)pVJ+S%gAA6K5w~=8cK}LY0IHeA)SEvPgB^LP1`riZ!ZfS9)7Ss8oIYN^hI^ zuU4&{gf_}hl|ntQhJCW6g{DmL;#kai*Ls^PFxNic+uSKBj>IIBX|`+%)@|!%ds`IL zRYUD45J%4TMifwQ0BlZcTO(g~nHR?gKbz}q@n_Tad28liS|2jR)0p3jR;uGVZ)@Ox z%I4X9*LkC!pO=#^)L!L%uY$e(D{u3Gn60|O8#y4eQ?91osF2TKM;WEDUv-ww4^;fN z|75mz9yPQoyq>~wrYOZS^U>@X!F4K_-!|Jj8Jjc04oV6-RkE}EvHnM~(YJY9gaU<| zc5b~5Eg#vTU~T6g*LlCgUW~S5%Kv+ziq|GT@&?N7!|$RP>1qLSsLK{`X9iVDbgBQe zVK`_luoR0fLABO8I-Jj$?>(uK{plW58G%xCHA0JZT!*!#Bk#2$T8~|KuQy_9=y3kQ zZQf5S*wgnL9?DcOyx4KS;e{=n5BUxCuZKK>PB4YK(l8$lUm~5&pnQo9l`W4Ta|{G) zofy))e(Q}D1+9L}+nZm%#EUWe%16<%K%qeC-C9Ko)8?U9I^;3Xv4{WiF>hN@kTA6A zUM~jf559m71zYb{OPilSvtkbt8izmN?ST;i&ShejyT*&@VG>ioY2nZ$1_DhMq>hkw zbobuhdP5H3e9CXBfJsxU{sfh|#9{Q8vt)OCP zZTSF(jvzm3`Zxi9iMw!!~qY9P}=6&({VOnyEozx&L^$)PN_6( zQ9P7U(2%>&qjq>(l@?*z+WZ~fs6#k^Yn^vrh1HtY*u5D^Djc>N;;Hw%ejGf(9JXAAKBzlJuu|Z2zVTjMS((YOVSZn~x|}kDVZ}Hj zuwDgX9#!p&RoEr@=Dpq@DhyGrzyCXLv^bED+=roeO)eELV9&AD=fXNN{-Trilmix^ zj(~Pt+Mq13gi8f+(0xgT?<_iOLg#H7Kv$T;Is2p8_-bFX320N4_-3%h(zn&VFX_CA zvScVA-6LkGMgH769~!TVqrSeiSbEJQbFAZYl&2()V&jONs2!~tU$YZ^v%t)Fa3+2Qp~4ggHwv|JLLCx`KT@$k(m97-L|A93_XX z8t3|bsOt-k_jO_O@=(j9QS>_|QQ1zWvq=op#OD2g!@w%3!FmN2*hPndmjl3;#we4z zABoaMFl!j#>%(rp#8*u>_|V)BpN0X>f+JBD?XYQj?U8V}4ETqElx;sAg(6T!f(<{_ z*D5$w*`n9>`IL4m^{&B`t;r|}oB&>5F!(#cntN9#lwUr?_fu7;JAd(u zieJz_=6}W8yK?_z<{ue2nSXky@0kjA=BX%=*?1C{e5f$MqeQ_xn>*wzlm;hs=2<8W z4gi1YFe>LyjzZiRcK1o%%EC zzdD-A!u=PbPs!!5dakNKI+38QPBE%K_A0beNfWKdZAe0KbMq>+Q%(qf?F7pC_UXPR z1E;L7Q<}7MGJ-8DLQT87ru!mRJ&kbx$&~%>nP^m1ykuA7Z1g465eOnSfX_eG;EAim z82GQg5>00=jV_DL{oFf+jk?O$7dI~q2%ZAMlJ~r;sG93=#3aI#{GGE*h6H1ZEg4EZ z(S}Ke4(QJ5zG$tmD%DK0Z4*p_rlQ6rK?i^@I)_^4Yi>a~NThIDiU@*50lfo`$kW0- z^06D6aJ^NangrPG*ZcaLN=Owbu{}Rwk^a+3z7s3inmbS@$f$HyDqV7;ucIVz&yBtT zmO!hi;TQWhSFkwzvj|92XW!!MZwa(&J$VUrqOaadL!NjRi{%T`zNSubv38QeV5H|R z*Ccl4?WjB&?W)ms94))|b`*Y%)f*U_w%6atcUfHjkO%!;E0iVqdeP;5RpP#9@rt_GRP*rr*lnoCUq6HSw5vHR9K{UW6;e7Z_R6aqk z$pK}=&LNyXd5h1FUDHgSMnR&FjpuN8LmfL2tW2;T3-AQSDf7^(YAp@$9k=^1O^}GJ zcFLX@R|`%isMVMtQJ7`~e+to;LEApXqDvB8nHXE~`e^ z?3zEy;lC1)g-8nI*@{2Al4%iK>cvvMuUu-ueu;Wtk3AH*{lc&uiv3QIW zXvIdbgwUr6C`a6*rNMmPt|x7K`@Oug!3^^D~`=3 zsx8g|IeHhvh)SE|e_ZDCVSg`?$60(Lg+>WySn16EB+68{XCP>sfzX+KaKb>lB%D{i z;KK}E*fJPN6*{6Sa{VUlh!XzC!l!j1@E^9tqFC+nyw%_h;7 zFaMGTyBLzDjb-wApP+}heLb3JQv#-i1WxIGf^p?1Wl-MvPn2_0xB9xWoo{LjGXGQ@ zVmSu+EKgO(mm+VgpamxzG(wBu{m_pxRN1qHWSAsAJz8@Y9?5p zlc1tz0yP4Voqzw$m^v$q6qmv#T;w~N306`~x?$K8tW3aa@0L$7fwtH)od73{+Jx(t zh{Oown{7T^RR6Ksk4-S#l4Jf}eisyzcuNN7P{l0zo^Med9l%ZBqB`nucmgFh?Kv!O zCU^RPvbgQ1Jqm(|`INy`l-35gzgbGR*|6Mi+kV!(PZmKOvz^0aJ5% z7eCt3;@E^cKkV_<)Uv;a{g_y1aXy+Y@2l88fpfYQr;9vkL z(E9Tt{F|Ssj>1g0sx(H_TOac^byhS}n4aVI*Cd8iz&g7D8%_eIJ77qKNI8%vYyC16 zy4H|>MzA!JK^Tyy)Q}9V7Kx%LkPgwZC|}sgFB7609x75%6d5qmp{xY|p^G0)b$72H zgDgBegQF;x0+CJ7Mzh#OdU+rUgcdNO5dtap!$1^>G9W*ww;!$b{e%52m6*gY5BU2S z=?rbw(LFGNdE)+l23TbJ?6^PX4Dls>{b-_ZP5PUi1=#AaAE)D0q;|W!y2bCu>kS3; zgnM2$vp$E%({OWs5(8f>+Z(W<44CfDgKwl9$Pe@RF<>Osf-TpDuDm@DzO5FCBI}@x z)>`Y^QNA?j$M~@vEI5}%dDn;^J2`_!_+5CNDaE!F{JPN7L~PD(Y!82RGHNC^FC}cm zPLsQIf+bEt&D5Zgk)*r+9ZcfJ4)S9&=jCJkZejoSL;Q_w)jj^2Iy}!n24tZ>bYwA( zwo64YXQaQsT^9|Xw*O+>kG-8`$0`4{eN%t;asIaQ`k|En+Zy7JGl=O7j*@OI_>JZ- z8^>W6j6CVhGNgeUhhrqJl0f2FN`n3xQz<|gTfP`6aLz$01zakIg@^cit29tChzkrz zgA$4QoDm07p{-cIMxZj5)ra`+tLSuR%vfF8dTcOM`OS4~>2d!4#d@Yyq)n_CwOY*# z^Y^N)%_fpbTnVR?G1w(>Uuw@uxaW^_X|)O%Ndtj)EN>ubqE@(*Y*4c}>DBN$Cc)E> z@}FI42a8}tFw#kY>Bh@cF}=bl2%p!-^C?}dz} zP>0vB$bdZL`@pp*80923UyJ-L0+Bp2e*XFC)FAG=94&R37*3dra#dFU)UnhTAPHKr zSdzTTBH(};-Fmt|a5Q@m zDNux>ek=1^kOJjE{?mCX0kCpmH*Xp^5lK+iz{L~&L0UYZM4f;24`NGR)VFii_&%%I)o4Dr5~`G3Tr zAd6K~4P;}^5WnFH%IuYkP~P*&EG`a7zIn0Ug)_ypb0qlAdC1?Lop_JGOL@7~5Bc3c zFT&rxhBAKCT`2xCI?DO32}^V`4c@&A#ov_ZW|hDi+!B=Eawu<~P1!&8QMpM#|u6KHW)3SCgr@aXR_hhtL?};J+baL?T4;42cr8!krZr!17q~ zo}2wx5}frcl0+IIPpwjPGMVjv771eFMUf194hdqSR&8+-`Ly|dED<6}nhjU1$)P-5 z_Io79&lvgB0va{U`MtkuWiHF&1O3e^o@5?7a^eYAN(-FKSFm!DV0wv_Qo;nSy5d3B zPK*6`WvSvFf!&E_@ zT7%L+BDQ2uF^Cn%!D~?-Oo^f>E?tZAP!8o+{gx`ouU|vSE+@mbyoMgD?@SbQQ43ibV5ofNc}N_8b5v3PE8R zRR+-SVKuz_3qP&N33XWtK)<0_!i)g)J;kUsmiwQh>hRJgB!F~-A~4m#-Tiaw-Tpuo z;nnRZM)71mFL+nwF!Z}W9GxO*=QMy;ID>=jI8GyII{piR{(n^z**0AgOv|bEd7U!(gwLpJ$j7m-O!u7+Fd522($DdR4FJMVk>M=1Q9>1Mhehq3}_TYAE^h*K$>RKw!JN~Qv zl*wZ_({qP*#s=^~>lD9c+@8tgacYtXVi*?3X1qG=1&Xa!lt$5nrio+JiqHre!$omM zYwiXr#h{6u4{4cVRuVY%bu^p)jU6-Xf@aJ?OW6H6g5}$^(i!${MF2xwjTPJfX*;##7B@=_99kW~ z7`Lo}i+4~%{!CK<%Ng|kA*F`l0Inxzd6 z-dbJ0hYGP2Z3tk9tA%X( z>{3slt-OI7_A35p0&%4yWlaEV$6}5y@Ap#zm;x#WA&`{`V1g|+ALVH{r1X$kp`xHn*mA#^fXS9iGO79+|HDZkU!E8KwB?T0uO+od87Iu z6FgNw*FKHzd&+b$ahVQ;<0g$f=1b`itah>>Zh(H*96+5wLiCd|W;#eK@=^`b0gt

1U}0q(fVI3u`^9NHpUrP9l%4&TJXdiFDq$r;N04 zVdqWZG9@IgF1u4o_^mdiuOuUHE;mF}TC^BaW@@OI#A9Jd4ZKSweJPPR5v9Tk7DsYb zn@SKgbL4D^fmJ9KKWE?{11LS>V**$KFJNt_`$Q_ESTw`w=BF?Q`1*oCPd0Q+po;plNgN+0?F=0{q%6 z)qy`>6zDGeagt$s#-k26DeuZt`JZq(wWsv*AnP$PKwFiW0-ejl()THy?sOT;Y8_`(bh_mSn0v?+J927{NPaYeReYn{3;KD#dF|3VmSs3uLcYkTqHSNE9b^!1G?z@r32!>^? zdg3DGmx-C0LnkP%&2{)qH{4@DChReHpEzjL66tsWkWLK>t#u8*U18sUS#KF&z3E>N-KXpKy@u+toqaaFDNohf87(nhT3(13V5qwOte7QAd%L@Aff&Z zCurmwfkCE%#op-;1h9cN>#cytgbjPIz7-f~hFX-~SW1kBZVKS8ejW$Za;)cTC|vl? z0h=%bujqYIgwz4S$Pm-&k1_h8f}cq@RxkkFD_gz@*RkfF!! z*%Ih)7$hA7;NzbRU|P8LQ`1E&%zAugI${O*U!D$Natvx7aSC96&mfL`;KNM#f>cr- z`2aa54Acgcs_}82rSR8-HT)@4d-({R?OTutdCO-GRKrfaYY-%Q2h+dwD{#dWSFq1 zoK#WqFSjUW%1JxS#{Xz|Vh8xR&osGQ3J2X3xjbHY!Vw925m%7Y7+eupT#_pd=a25t z*(OjCAuhc@jrqIaId73^CS+O^OJBI}ODOfPX8asxzvqNo1u}cpQ;zG2DP^GpJ zfn<*qtoAOi52C{c zFX0`brVcm>ABuH8?;OOC>y{&f{cyPni z53wCKCfHCUWO_GejLkbs?83Y5%?9@xydsMQIdtdh*=Fo&V4U?qa0)x?WEFu$Mn2C> zGVLgbIt?rm@?B{u!H@C=F%YFx2#I)X4@V@D^l(H%JQgkGnP`=C(u5C0C(m?h9Z($n z*+38@TAg|a7>Q2M1B)aa4q^oS(RdUtyn2;pm)=B$D}{3b#sMP~E^3z*hUP+GSxQr^ zA{gP=xqwe+Z;Xcr#8IEb$aY7o;60pgA5i(%IU zFpB~pTZcyrMD&$eX!?|0$<(k*4MfW{FqP^Ans(2c2Bs!pwcGm;VtV`KG+GcI*)`W) zu00~4pW&)p-n?Pa+(ma*8|y%G4h>#bb79}fS3J+A{mwO%4;>af)<}v1Hk5FNP$mU# zxuO3SQ*x0MCJ2`WSddBK0Pwd*1kne`gpfEn21;~yPF(F2$zVdJO_2-^08ft$q6-j- z-~cm`2o3=M-El$ma>(okHf%w#Gp;?|HpIJfU6WVi`p6HkCycIeGWUK60x z%s5mnj?7h@qB6Xf)i!a(u|a8D6z@s%km=Hd%sRvNlk<@2CSbLF(dm@wTW&?Bn*yd+ zz6IJVnYN}^o5xV`p1uf8cmm^GgDWN}DBNPuGV&pCO=%ET1sG?yJp`*JKnEBxRT8gV zv~>{IZg-*f3q{3JS{3{IFFr!UO%0k>e|Q8eO+Z@Zu(}<0-5v!&6El@AeN?-m2x?dA zCMts1=)$w|_#E3w!6y0AGCqrkGw*V$AXu6L@%cH-7t_j=C3(93c{^T?&%7S?r5hmD|0lD`ID$>Ycw0!UutRE!-O%aRsO(0gE#j~y0ffYEs7L}6w!0vbzyymU*mfEO9Djn`#5%g4-5!k#UIKaeY6r{{LDnq^b z(wiw2o?3$zT}5FggOFMmzy-)ot1ulA_6fB+aNMfkKocvr-*rAE!kpJoRm;)_ z5&)m5r3lCgS<{RNs8buW5j9`hu%>{F;NFd>{U%rxL3#TKyy%-O)-D-BraE90O)unfkJ`zw>3yGsbu zw`?YdnYSwW2koJT-T|Gxhgw$+-~@?K!7Qg&C8n^oPQai6vQ*FXt?TIxq{`v1|Bnozzvg3Y_q=&8pw=CIho7(U- zojpN(C0zmNHvqI_O9{*d>R=_mdM)lV4S$7-GN@s7RTz}fNj*7&2RD2#9VQ7fO@i4V z9c(rEpoSy&C0ZaR9U)^@E${18Vqg3b`IbuHfdH9r`0yh>s->pgBHu7&EQ8nm-AaSV@^HE54)03ZJ`k&K6- zhot~KghV7Uhe+b}cy*s}V;D3|5Y7lN&%6;BH31|I06G7Daa$1EJ0R+*7FH=zg^T(3 zF>N3yRh2)==KMAgGy$v4&_9FN-B~$6yK2*1JWxAhiA+ql*^VIY70x*nipdPE2FRj{ zFIyQeQD4AP!$8sry?PkzngAwX^c5TN7WLLWMACh@)})9rBNTRJGxN~l;3>7Gw??G@ zI2=4pz-som{}9jS5!zk5=9m%MAxYG9Xysl_6mPKL-B==8g-0rw@6!HLBu5j#Br}Ki zPVjv%!O;YSMq*n3KD^U@%}~M=qkz|4-v4MO*qMN7P_Q!r)8G}AA%mL!P`tUvHtuum6@y4>>V zJTByuCJ48gU?+?V4Jw22=UpLe{k(j!cD_`7|HUZnl35AJqh5o2ZKjZkgG|E;OoXI= zDXej-J7L7%Dxo|S5?M-sLAdy#v{l*xlT+Nekr1}=CM zF(3`vO{_SEooyj(>`^LMW3XGGWU!*eHpUMOVV@7h03RjCB`XZe&}QMY5`|0A)AWKAsHU7&&=<}LRcXl(DZ|08l2H5R$K_*#1bztl7r9KBA z&!MLb#W?qn>zu)3uS6bb9el$BI!t`me&aC8;r=&-@U3tm&~XQKIYMCBRk0Cl#?{DL zjp*oJy*f1547OT4@(9Y&0XJ(;$Raidg_}8c$<3jGrBMlAUKl~G)z~XAib>Natk&Hd z!tx^?$JUd2@blr8q=c|oE}#@U?n>0+5*5C8R0zY_fj5M(T~t5`m()l}&f!7LhZ9Ez zWZGMKH8R8m3?KM~qeB?X4!RlLvx63x4Kl;E(&m#x7|IeWIva$Yb(w8+x{anMv_~eg z%>?0y0P}*FMN0!bekL`;poWE0VU)NbN@-LqNDFo*sCAiO=K%1f9Q0VIkzI0Ss1ptyVdq%Ttj2q8IksmNPBJ}pFA9W-U}qqWO|K3fiWDo+ z>vHrF6+ZDo%D;Ob07 zrm+zrBF?bQ5228nfL1$UD&-}ZVtiJ}${GkJ39Ouo>qKsjb_{P08yGACqbno>A4lQP z;Yvr`AX#>02s4weFNJX2LTbvql$!E98kqW}w&JL)f@8ZGZnf&49>V0L+bbcL)vD>w z39nGwXU1pMz3UpPhhXWd&Ec>KEis9fCYelN14S;cfhn3Y4bmPCfhi(7z^bMZ{^500 zW-V1nKIM;J8$Bpv{H^2K*MW#`QI!BPqsEQMKd_T`;7 z(e9X_X|~5(Xm?D&YIflrl%KOUXm5?=kq)igpdBK>s#(97aa4?gHqlwe^) zIj(Ha{uCv+G@QTqAmu9fV!zSuo6YvC8&Js|1WYI64^uXSJ{q-L0rPYW4w~fq@k#pd zr~Dkr1(5EcSW*@Q)?!7F=*Yw-EB99}G>%tJNDN(Nh1i&Yn7 zU0+8b!~&sqymp054F0?bkNs6;XP;6*nGC{~ycvgNXmS)YqeM1`eqMwx`J2je2abr+ zw1w< z4AZixu!sne?qso|8LPvsO<^?l8iKok=+TMsO<^?l8c;gI=+;lEcCU??Zkk5BMonML zP@#6lKd1sv9)vQKrbh~7{iPKRTn`hsqXfN-2H}hVv+S&P6fhl*F&4`8`T_Xv-lID; zKcy!l70VdkkgO7zXFGetssq5+eMbeF@{ewMtM*Bm6zvn`pN33>lzXMYeE5%4 zSSja9K}OU{gZT^lsZjPiHSDU*=drYz7a51B@@Y)$)LQ<6qfs!O&~B%N2RQ2S`noWv z%{mrEQz;^Y&`=4pvY{F>CX9y4M9oO&j0q2PMELa1VNksGcoa@2#Z|{6r5ymiyK7j| z#N-LD6&fUQqeO4jfux7Nj#mnUC3-_6oazb^Mr?we*ce6y1WU1|oXSuSgF=%_vv&jj zqZk!>LLkm&U5Y$*#`v;!N`bTIAOWSiB>|*;aR}jw%h4DK6NKB%@4g%jk~7Br zNuodQdK4nlyj}gl>rt-Dfc&nsV(-Q`E|Tb0?g|_EpTglOwIcxbzA6J&)&-m=?gLHL zPO@YGliLE{4xus*_FkdxzarenA|iGMz}zPd5NbCL=2+vShzW~o#=dUsV=mDU$NKhMS(a-?zI88D0SE5=? zdtJ0{cr~ZzP^^G}lMm~X3eZXnq;QX-B%J?sjAG?8jF~#1)Ls(Gk2#C@O#VOc!DpIq z`i1zA5mEgmW&&2rtrNr8FhTb9u{vf~2jO5bYPGR&5|!1BuhNh_hw~-NkJgns zm!~Jg#gOXwXHlsQxL(#RJeT?Q%5dCNQ-xKiW%>F`DN7Ijy;7`rjOIi*Ge|bYa{BkL zAzMY9gi&vnX9vF?PMBdx1ZC{7D=143c>!$$c|i&qWOH6XT`URaFJ48N3TECayf?)b z&I&gx5Ay}Qe51CZ!3+mmoCVgdgkeXBce+-wLrV{dyipZ}X}K8K|p0cMXwuUgFdU=Npwpm~=eT>>;?1hpc-Jo!+^`Z?CCOx0fEOcxj zf`w7?(x!HQATrntwrDNcLgc=WMVi>MBO-ORxSyXX5(9A`eeuh{ z$aEeX@VNY>m|Hb9%A^0G$)`cP9yeq$m7>~e)JHNR&k%xpz{HITCnGWFTMXC)`2F9~ zEEN24oYaWICc%&dn8P|^yybhEgBtl?kE^Sg02paNH@w#G)@fj3U`bcQawr=*4NMfN zN19u=;f3JYl@Uwk8}?;s7bfJ`l~O>(Ez7Mkui0Bbs+!9Be`_K#?{T<6IBruW-j;BS z-n9A%O8ur2BfhEvR?en=6mIGq7tg|H@f@mL)V*bNB%sl5Cfbe)*E*hBK_^wQr21wL zB0ckzh+h~$rZ-y6ixuAE&Y?D%Ob}dT|JYL_aYt2QerRuE{pa(LJs74>ja zN_4#h2@ON+_JbmQx~ilUX5`2oY0)Z7X`!OH{(ebhu*OOvxzVT6qv>ajAWfvhe{!(KTx0B?cBA61fJCk5?GS zsg|D-XlDl<8u^|LJHwaa(}qQYRa9a;wW&lrjc+UYZj9h&MJ|zGzR{5>?1>xgP#r<- zi8yWPY(n$XkyH-mKZvq~Jir>8pol|BShA$g_7s+)Hkb%LY)hM@DfZ6O)c$G> zBb>)hilAp4__ft!G5g6$+YZ9L>i$UgRXc(ux-q@J^z5m4Jwv7(VPuSY8q>4d<7R zH;Ce@4(7fccVqNQN~{NcVTvf4fVM{TN`v|Ka|~*wprBP6%+EQWD7jw7m@t{gry(RG zS~tN*Yp0>^zWp-QT?4|$Z3#+9LfMjV{=!8>XvM4O*Z{KO=SLbv&Q(E?&5e4GlAK7> z1hv(9E2*YBL45F~np_!W$~g4kj@pl3Ml_%N8`U&S|mV) z3vBG$NNufS1pi?M5k2lB6nSBh&3!l0T5A}jS@o@JP(Gn25JQdi+lV4>Y8c`Cm$Qgp zmv>R*OLR_n7sa_al>ckC$*GXb;TXKAb$Ip1vS>2q1B^{kR1LU}U|KK%tM;1GaDMbn zCNF~7`q?TA@vTvM2AX| zKEfa~5!>T9NP@Ye$V4kvmljxx?f49Ns8x($e)}Rt)`ySj;w_kSjCMxq$#%xIJrb<6 zqJ}!<sX1zaSG!Z zp53DHlXw>k&Zhr_*-+1Ik^1Tsjs%qI^ADF2=}rHpDhh!;&qw;$4B-2jN3bU=qOBGy zBxLvEiEl)wD)ns;Kj9JLKA<|-UN;&IM#;AKFwey~45AWoh4J2C@MYRO)d| z1MwExjGs`U@#>q>w1ND>6^gOHBDZ)}1S3|%Fny;?yz2wPfl9oz1GJkJ#V5XdMB`Ge zlLpu&LwtSs?v)X2hajiD&YG80N823{KK8W;whGP+N1Iu^CEAG&@}u_~6?Ii&a+!Dm z&xd!4L|YuNjup{H*3u%fPN~%4=mFj|%JXG!N3a3%eM~8$SY+7J>S!PK;OJ-#Ix*~H zZ22lSf*tHa-r7LJ1p9LvN+o&ZjPvN)2)0FD@`yrbv91m5_e3!fu|{k_{GoRhXFoRN z@nQw&_N&Wg^SD%uGDrhua7Jfaf-Ol!2je2E5stgl&{1#4Y=~g13zU_fzb#4+qDhl9 zGG3F729z#gbrMSxQEViUNeag6W`@Pt8!1mm7NS0jnqhK5L3>i9wr^7eyIROlEY}ut zxJ5whunv{W<>)-XQ>9*or}?mLDmSr042x`a0S2XxD6quLL}~>h zcnvNI<$OD38<8?GWs4}8fyxX|?x4IncoclbyR32C+#VIh!4zDHP$Rt^qoP=_)ez3K z0Lrs3hrwELD8G3RQQ35&Rzc#?Z3rmIfu8$_Xi%yVLeHH^b5D*&ddpaB0N?)&5xQqY z)bFIWWrX2{U2Dc(#R}VGl;Kq2s&EoozFP-M7;PHibqE)))`3zxPEHkv^L0NdQc8)U z!hWOBl2@3(Dxfr_ABIWLip!X6;ryt|sAT1HR0A!;6T9AP)lqCFTt7A%#*=Y4GY?v| zsU$wpqh{mjGc8D+L=tTFnbD+{M{kLh7BT&fb&X;Hy=`1HQiEpoKFl}&LLG_`2p+KqF!-wIU>3h9TBF1bfKOSAO$lWfA265XEQ483n z&OkP0DB2M2?XSoN$|{3&(pmZLfkb-Vb?udqz$h40l>T3d0Uk+$rxiK1+u@i1H!Nx6$CM?P}kWb7J z^P#sG?v<*4vRJ!U0`gA_(Qm5j)U!eS{h`sm6_gUe8eCMyjF*z4PP{0Oossbm512v; zVFi^lbP^atE2IPtE*Oe3Z?PMklHX4mJ)G(=k|K=xB;JNCMEl6E8juKP3pmz=uMXTu z4W5D9%_AX_TD92vTSrhmMnd3;cV~4ZM53(v;YU$b7S!>DyL^_dxIXHt%V5((A6XRC z9YB%)W_UFz$pec^j#rFBcrc5ON8Ay`cew}_&xs(@Er5=U^1E1GoPX?jlBTH5X)VfT zN3D6MM6nRGi(~RI;fZK{b)Jr$61(b54$p#!4=UdKLey1+))7kIQ@rg7xKpGgzNEPQ z%qW&bhdhme8(`M(cC@utD#-@RyCT|87}c=0+5H*hq@g7J5GKzXAH@RbgRd%IwTM<+ zupD{lp!ivbA{jvXqTot?_`r!gi3NVB+y+pGtspFfX4s)`L`bmzP_GF-SZg+JW^@sB8;kq5@<~>37oPmnl1?x zmM*%92%Y$mcFnZ(<>xwjGf*gfc?q;P=TSz4mWcmq#8t7u(K}HE}St4icK{sO}+V0eC(f-A12FRaZ zgyOjP8*p>b{OWV%kpsw6jEW){qSDcaIAo=xkIITh1sqZ744^gx0H-Iu+9G|aH=$~R zWEV~{;9)hOw)oOFs=QfvQqH34!`9I3X zAM0ZNP#&k!6SgNJlZnVhR}8@`k#X?^cm|p19gUgQll8G+>_EiRw5#yI_tALp0HoQw z=~y4W_X$d)V}FVU0!}*gW~KHf&BWHjpsuk{_Ha#%S!PfLa33eg71XC2CSSSjvm5Q}O8Qu}B)y5wYi(AZD)V}{nk-`xJ ze7-DCtgxblW5+mDLSJ77t)K;}7Z4r3*dK>YQwN8cu<|FqiG2E2%}nC(rCHe&hb}&j z#6n8d0&N-|OIt!M2H)SNxEW4bw9YyrmU7lQdAp{Tix;qXOVsMJ1)McDmMjE06RcMt zUS=k8CV;UUwB*9YGhZ3(s!4!N`w0WdOQW%BZCXt3H%E{Yb^+7;W@KX#XpLgvzYM-b zLZ$`E(IU`l;fZgE`>P}2Lq3JK?x?Kd8wOahREsqiAB)nep*jgASkE!wo!8;wJ?u;O zM;m$F55#)KnJBFiMi$^RGGtHC-Fk$RBpaFyQpefp=+~OUG3jDyZ0X%;UhL zcok>u9AvJE)kzFrsab$*FvIc2B__cO&%s%@9wEwI*Ft0<;mqF>6BQJMNoMtN*myer zS0HRhwdJoDqH@|Xi}m?Y%Hg&}F|6*|tZV&C7NKg|F^hhe{*=dH9H8$SuMfmLwMi_r zp&p5AhM*fVfI0~j1Xq+2DQ<-F(-V}l#1luqve>9at!Xigi7SO*8OBbQf(%7Q1T88? zIDbD)nMu?fP)4*I!uiR$7#8u@-VHZ!`2*)QkcGy>?s(bQyjn2p9^|AcqF!x>2)cLg zW3m2h*iErIi$GL9(=G&3=8M?!id##(;uq>Pkhtr{za1L8uYye+8FRDU$GGd*Yr|s4 zus=T$a|yHf77>m{P&MzQt;${YpYBIV(5TI+`=5*rDAHDs(Mz9r=CBx6(&sFtmfo;$ zv*39~(K2znh3AjZiC0L{H@zwG3=;+HL^JW}6p?gg1k9u}1q_mSy28H%FI)JIi(vyx zr$#_X+6R8(W&F5G9Ow-mfW=!%oY4OkhMPLA4#c+b>An+U*aD-(JFpmZS{`r=u9ITe zEt~awl*~B#Z!CR(5_4DK@|B#;4*COi8yctuK&xZE$58!ss1O$`SyzuyX&92|@K0m- zAOz`94|pVd=0$CeAj8}PPsWm_f@$tQXA%AQ3hkU#$@`4q1BIGOSBxh{gVsSQnZ*r8 z$%xub@fys>>)?V3)B>OHJ=ht@+k@CjWc4{c=*-Fv2YEZrlVKO zsKnv?6PtZn+Z0y#1PN*a_`Df?Y3KS+FoHEhD=><_%-@I4nH$66dVFgvqClbp@X%@a zqVucvU}dTPQ;eXcB_%8@{rIaG7Sa#jroEC0BO;%_O-F83nR!zTi|0FcKm`W}@dg7C zsfnlPAlHN6#^%+=&EQ%QoMAh^j3q77Dww~uAa+va;vZsRLr26eO-&mYJ%TML!VG)U zzJ_Zu8d5Rr9r>FW_D7!Bqg}JuJ8OR|X^qCv`QswW7eNPW!)V<=2Or1D;rcGVAW2UW zcls_CsEwC`Uw#QE%;0L$VXK#6m=V7h`6GEr9DcM&e8NKPdq`R)WJGuA*T^I@*kbXi z2PmHeGZU|qUgp`^`(p{iLo+Jj%D{(IZiR3M3R@-Ea|S;gn!>4*+i;@gfiGh=qz(wy zV6&8Ei4v8CD+V<3n;(QcYeJ06i#lPbqrZ7s9n7 z_$4ionR>r^hS;sBX$xa57wblXTy-Lgw%Xq%G>KhOYeVV?X~((W6}4?ii-r--*Zlzn zlCA687E!3=CbXz2KZ1>GvSEfM8{WB%R;I}Z>AH-Gk9vuUM1jByL#vyO&{+P(#ACC8zM;@HNsS- zciTLABGC~P)XRW;&U&54QpC!gjLU0@BBMf@X}e5jw*@C&zxi0yk+n;$FvP9hH&!^syY72Cp0s-=D0SG zkf?%j79Z6{->-s%eE7aS*%r|NUiFf(jvm$4Zr8!{UCL_5r!+lSaDsAKD{Bch^{}>q z<+QhLQ-lNfPFNnhNNjz38=k+g>G#EIjJQN6B^`dEdqu* z1Gi*tQraa=1K^1t6+6RED@x=Nj%v^ECwdQ@+ZM#V(`;sOV;emxLARSlNj>qrwy*|j z1(0L+o!6GsfmH~U6ZA-38z$(-PHYPakZoyd!#8MwVrqUg`^XksJ{lC<=kil!qkHKEQVr1Gy*}q)=@T%+f}nX={2|WuRcDZD}pg zN|kE;5Tz`<N25!`*q;Yv$_T|66!R2t5YAKZo( zv)^^-Y9&fZTP^IZPT(wlkK2)3CZg?)y&bt#8qU`z+t60F-=O^yS!Hr(D2t{FHZoJyuFzeYlXW-fw z+DwkND9Wj$g(l`CwqLP0DZCRT5KPEx#( zYy~07tx%w%r6>FF?*G>o%wI7}vJ%9?T8WYl{dqjS^+n!djJu+OlEZXAU-?}MKL@?k$49o14YfVyRy?eaVx3mk2k5vcQf-Y zey!z|yCg5#a3~)w$DfSV|B@nX?;d{9RpV=h@nNI6k1xpLj^Xv~IVs8bVz8T^a&+QU z_GotV4(N_&;$$4m8MO}NI4?<@_$Z#2C5L$XYyJvo*&a*>8iZ4g#t{@xTz)XebxES8 zY0+rjzM5-sO|iPcE6mwio#Vg&?G^f1yo|98^D#_u+`i6GO+G0GCiKQotbPvEdX$cguJ)afhmld=W&R-@g-XC7Qn_<>;1PI+J79EyW4#h_jpL@#f-AC1}*SKA-Rz zuA(K4J9Q^-u7|(EFvd~&E>`?(4%Vm&3-||RwD0zf{2H;1WhINO0=DOC#P*WpX#P^( zQbW&S{?RgR&*xy9A=HTv4WpsjR^?`RdQ2DE$#F?)^pO?3nZ}>jYbGIRC;O#{a}{r< zvTEj5Y|9xwySZ>XP`)Dv=i}j1A*}wnlrgNJx;eE%ZZgl!!;0vv>SpU@@}g`#_smkm z+&ZB|WD|Ff>uxXzr;_%&n3E-p@(k<~;%eEtiqDxR{xcmn`z!+&%o}jxlzBlHX%f^qJ5T5b9%~&Dh zUsTgPi|3&S@jUYE()8%412NA;uJBA~)IDokO*1B{LJHxSn~bN{Tz9 z1D?;pbM?Ae=8P-TpI2ng{eRK_&Wo|QVjB$}uk*Op7B1Qgu*%MVBaBW6YrK>=svMuN zKq))Z!Wz#MCzSI{u?E%sjU22&@xZpx3SB<19MP7aVv)_PoZvNRXCv3I#Dca6_3~Re zScT!iy+RZDMVQ-o@48|WeNN_J&8Brbb4E3r9cOx+otV>%!pqM|!s$E|-h_M#td?(Q zt=gLl<1EFMuP$hZJw1Gi$Mxk7=1fuM=+qoKs~(4R+4xCR-P!qq#9UcBf`;M7YgU2O zv=^SG0JUbFuQw;f3(86m7OJ93$$?LJ2$px(DuIv6nFgBqS2OWSNT~@w)*c#&iPnyn zh(%5^&&k8+TGm6WS%u_i@;7{hw(o7uLT!*)ugHvhGp=plCn?=n!4OQcLL1LX6581G zA!h6ajsd!n=%VlV@ao%FuUjJX*HFyM3a0J_K4jnz-CQy4s1q#cr3QSHVQ}?2qC`of*(`<074rF z&=y$8)_zBQQQeF+tomVdHsw=vIVXNYZI$z4I?sj8&IBewXGu*nR!MTk#Ofkkpt?*Q z6RQhPPvFkgHe(r~?<{jx5 z*s^Zpt;*M2rA)zUYyGKt!u4#S{%j4i@SXo$;#=OeRiYK;=8E|-{in*&UYhzCo1lK1 zjl8Go$I8Tbw6|WrSZ0EPOb7SB4_Ek>zDCFUI{? zjL?@=UX6HA+A1$-IqZuD&7V5B($X0VrU;TN}g&VW*9nX!4C3Q9IwT--!^7?QE zdUEvlj^;fEcH4#Q4EA5xg(YuOx7w4{Gm#4#q<6s7wuNLP{ zQu?Dc#C;IX+ffW5Zc?9AYPRZ?kJ^t--#2(e=n=jpA?HPZp-c8|-YKf%|! zOe~*bsO7swzlr5h^pPi?=a0OJ&+=OL(aKD;ndyjgu3v1p)4EJ7SJtlen^;~&?Vk+c zgGK8!u`)8R^_*B&01+ydbN-FM(9v@@Ia{)v$3vb>z9C+ep##sT{PRFC2GHzl=j+@)ZbMWFa z_DAO7xFdwmp5(@%QpCh^qOOS)yG%WQ!Z9=6+1GSN9>B8z1S&`9SMfc9;KK44zC%po!Uvazm*sunQ3rYWTxIf}nMJ9JrXx(%UH$jSwsCzE=^26_O z->k@Hdp_n<-X;2?hv$=%SvZFhk34t-;$#DCjo*^lwuHgIw%Agu=iu9~{K*{N&WCpn zRYo*x3HgS{8X6~@6P)<^9h}P%)_(nZO5?ozv4O(%i-VP#Rooc!?5UA1T2=0XQ!OdZi4o%Gvg6s zyWi1zT0S|bapH@)a4wpyN-yb`|NvTQIk+ zt!6=IFT~Ff?ymO5Ufc)J5k{7VmbN%tP|wvq_RH@QrZu!U6VhW;wCN7sq~<1;EXw9K zW0-bqQRf;Kj7hGN>gg=h3@)uD81pE@>94h@>r=wFfN<-!gG3 zAe!jJ5pxOxhfgku!3f8)tLho3A2P7cVW@6##>>MiE!h@b_8589R%6B=!>=Ko(QfA8 zQ?>x5yd~Tv%|1_#>%00OxkR;-a@kV&6tfeDHb2Sx==s)&gO?u-EP7{w8;?7*S8d=@ z7CtZ*%fksJ$jiek57Qf3?1?#eHHA~!A)-y6ox`HXfI8xh_J48 zRyuc%*Cc_^bZ$Aud*z`_y{0k;_hPlFNM^mrueN8r7R*feY-}NSvX!yGwzhk#64o*o zL5`(X$NP5py?nMEIzYTEb~qr_9-aLuCP z8z*#a$#3!Yvi6NtDf)D&Iz4sR`Zl7)pI%q97=ao@>L_;z`{ctyPY^VBii zAXEjtgtfJ<^Mn5)#8(O*6y_p&myBJy2;Tf6g&8AFt`fd`*+P~7FMq;#Pc zhRiEbTo^4oqlf0o03I&Kl@*O9eL^wh`(eG<^HUJ8U|%9mpQ^uF^ang?h-D-r!^>Cc zwa8tX9v%O+9yFp>OtE0gB`Bt?(rbM}a6BibPwNzyrbmB0r@Nk@FGg`c73tC0=ebkw z-^d4t9jvCa>Blip6EyPi0+EY4s)ixmqFPh-%k$64=uM(2+G6_#Ehz_rA!T5z& zS(MAI33b|IIyjY`kfU=9Ub(7i_Jy;g?u!~03}E<_70=YGa1I_RR7Jf6swx(E#D24xrD&yCI<`>peGV^|!g?$*NlSD^Pd4Pf%$ST8((+KV76fQpC~HnZ z1I2QP;+{B?(CN8pPUzZ3G$mIu5_3u4soV4lBqE7wKe2`G|yK##xZ7Sp^i+nFTwAhkgA&=N-ACmW?3f^AFi7=sKupv54w^8*Ly zYlv_bgG|p#7FjOtCk9!?+hFv9eVn6kqg;T66* zMSz|j?PudH_{V0rb!jd;zEZCOw-SR++NljO7vgwx8eeudj{V;6FpXhwh(C7Kb8rr= z)5(tRENVN#IBy!W z^~`c4P2?3U;}uM0rK4KcOV$!*PGi1n+fKvBP1xCU*`1On^HNAG_GRU86^7#M+~lb| zan!W6`z`IM!aN$nq_%|h?IBA>VvV$5>D9fk-{QIiaiC6E#nP!*u)zUu{TDiUu~>8ovn;mZZKv}7j3?P6K!~b zgf=&%3SwkYtHxT>8)`D!0F)@bIi*#s`2(zDz;abpJV#{3*0d6a25+w=B_jzBP;9)F zd&CRXx2H8W3}9(}ELK+kP)Z#(=O#;aV_#GVZEP6DGn`3Fd3^%hd^$U_JEb=3KL~|J zP8Ur)W4hMFNy~Yuk-Sdn+Dn9nzT)pUu5VSFl@12cdperh#mWv^nz7AOFhz+viQn;P z>ua>sSWVHuH~~*Dtofu>cSF3o$&4Fi)3qjMy9QWt*@_7{wW{;f-SG-1t>y_2i3I+F zqk+ZiCUo?k&f9sOTmv+2fvN^4wYVg&<60A2_MTyA+4G{Ec*`2ju_OHv+0qdywW5(D zmf8)_XeS1t(K>f;?M=Rq^GP!vf74>9XK2AQIwwCR(xN@jTFS06n0aCj>la9An*5H) zVf#m97aFQE!*GjmsGWcr$GZGTa)gK_f8}L8&woB~-zX#EO5i zj4>P%R{H;0$7Vb@gBgCdc#Q{Uu>GB_W(ZMh)oX*wyv=RV zSa`6eHQR7P7(X*LrAEV&r)LGfiTOY1r?4hM*izYfFva}{Lrp;u9bmG&RCbtY!uyYcLv3 zTQ}QkGCt6TT{8ZPIu&Rwn5>fbZdP-V1b zbk)(>qjN@^`xv96EY|l7#`|WnqFk%TuuChtYK5s9Pui1f&8Dp8?0l}(ZFpMBADU{Hq8r9MfD%6bG4p^>fP@#&o(%FR?b=~au z0&8ou!O=-*ORW!7pCuZ=I18^KTQk`+HtXkxG*RG<;a0b?wN@aV@rJHeQfrn7Bx09# ztEJJZEjToa+l>5_(xsx!#$@vkJ8bq%u-Thz_8^-_9o93L=(I{{HiMGQ{<_T`noU;F z&)Vv;Mq8ybgCXZu<{Ph1?aOBI-hGI*^jIHhcwm;H%4FVChga2l>JC(4l(n7VegTvY zur@U;6K43Nu9;aeCgs|SzGbCr1KMyh!O&Y|$kiT(ey?EYTQc-jf}yvrz)(I6wS+$x z?y$Iw&1MT%&UeIJ$e@na-3>d+R?~d%IYJ#yzw7{fku_Qw?^)NWz%v zo*o^Dp=LB5(h50}d-CqRb2eMs&H97!wb}gk0^5pzu9IwhX*PSNyETvh`+aw72jgjx zS@T)Z#7GYGuukCF7@f`8xgOR|#tXu1%ZvuhB~ALo2(C zhL(vnSiq-R-pyf1jI9b5Z%Jnpic`uA<5=-Ry`zsQi%sLT*{uUoCYW$=vm@;DyWM`9 z-{B5AJ-(nnR8Z)1g?$ctnC~Dcw$8;a*MBb6X4W!eF97@S-q-+c=Zqk%ZbkENaKE2Q^0Nwetm2JQX`1x>8@raXqAq5qv8Y@qZ!rqWM^{jOGbtZL%2D_ql|M|%7n1V3NV&5F z%J)=IzJ-);B;_dlkID~{^5dlZ6;j^+Rw)0Wg7QyE`B_qq!vCoJC@F6=SA?bhToIPB zrBLpkE5|%%u84USrU)K$UO0BGxLn|0PG|X(tUHYZ=W0K4YrOtX9sQkj7MyGyl)fW%)Q_xF4JLocU+4&83z7{>z+Sh*=PU^N3Y}pbOyh8byoJ%U z4c0FVrb>)ESpAvSKBh{HJ6Lg>+$N@i!T_r1#<@6w0*A8@k3oYDU%(r(`#d4c=h%|E z!P+RgV!O3NH4}Ev`J4`S*couz9JX+QFXX}Z*2CTcA2tYKX-g~n_%7>ggDKz*dV>MK z*AWaNEY3o|Ka3YieBl6AVM9J_*3;DPd(FDta0OMc=i=E)`TQ=Q4HvBdUjBC20wHhE=5zbQ z-a<520lw1`cdWI;6~yry;R3(i7j^^-Tz-Gh>2uqmG=%qmnQxu7NmO$ywUP?3w1&Ah zQ>B(C+H;$;<4dhqU7vhXFbW}T9GZ=RHv4)Cjdazx|Tj2MLF-7Cg5ePdy zwy-bY3fr)@g+ClNr_XE0ptX5CA*}aetZL&g71=(o$Lk8C>*I6TLA1Wz?hayi7RGom z$kNKKjYU5-RbB%={3^AL;ny=Yh%pv&1To3sMUgPpo1FMSS2*PN7GRHsuYfwGGw8vn z2|E2AJC01jG~w~%jU9WqAc!Ft3@7+ua`^mSjA4f#ZaX}FY~c0d1Av}Tp(_*&WA+vO zSIi@o7{}fe?ntG^G2)uB(|fJ0Os;?zw!-*|joa-A+2MxY9|(I2Jz-xk?01HQacyo) z#l+|g+Wmom+hcbW`hq?$=8uBHup5JspC)a43IP}6tdh0~$Rnq+J}H;3EMEFJ>sFa> zVw~|^zF}Js+iYxwj*tt3)9(p~v9;503t;R?5wN+#L3^Pe(_`3;Z*w6|p~6DQF1Ia& zp9RdcLB7}(hS4c(ctOSE3OcaQ*pKfBAlij)N5B>e2YocyDy@Qcv8%**SX}IExTuAE zVV^7D!^PF*^%sVH7*XzUfj#6vk4k7zlP%y0VpO7(C+I*g_2PrH?qHzM5k@5gKH<8! z*oATR^CC(%Um%SA>-h9!FyO(J&lg5t2w^8?!o{xgD)3HPsqr_=x$acFSYr?Qf+3sV zUWkogL66HG47)Kfd>*W(1x23|m+p|wX)6q)h&zPF@z^{;f547wv(JfdYkCuhTxuX_ z3pf!VA4aLegTF%fbRnh~SHSPXl$bbmR8j$K;p5I!Y8xXiD<2=w@FZN`Fdt%jRmki0 zJAICzKj=f9ogQZ}f5Q4 zR!_Y(skD`Nbn>v(qZ_c&$=2Tg-go|c-}&!-=Rf{NlE>t~_nnvC_5OR``R{$F;y1y+ z_nrUVcm8|dS?Rr*b}uh}^ZkE$-^q@&%dLs!o(9{oe!>@Bl0Os=L|Ogzxf6|8E}Pdr zcR*F+!MV|QJLW!OjNVg}i`9{pbJ+{m=B_m^oXbXZ$?a)eK9lY2l502an9WXi$;~j9 z&t=uS=H`{H#3S2pg-SS%KI__#9>6n(F<2I_o z-Bbn0Dh;nRlr(Hh(6EIx>>~}3RT}CyPSCJBfq#+s_lbwB;s-WK;CC5*O3-kDG#KZL zFhW*oSl={JgR#|omUd%qfw9GW(FhizA*<-qS4(s^qPq}%BhipmbVhTXp4cmIH#WRz zrpFOHi(tqqcuWf&j-U$|?GA13;Bq9Lp3A5$dyYofm(`ZuB> zt7uPaoh~ZQ+l|v%tvSr}S?i=~O-t(EDZ-w1r)V_DDh;zzbqx{T{jZU9cu7Yu(g9he z<5`oWBP8kYla9%x1F}j-avMpr4J$Kr z4G|uVGg2*1k&f?32V|9wlbMM+j5Y5PeKF}Stvw7^vkO_)R56_(O{Jk_TS-G9X~-uH zorQ+d;d%89Y6T;+bp>aKV>A#ynD{XXH9(rG1`p)u3V0k?+dOL?D{Y@&%~(!4?n%&r zG?k98&51gUd#DyWNyDK84ML+8+C<1i&xSFk@6kHa5=H`cvd zj10&s>rdqAFpq&T`)<*WnRknE{OY{h$A*<*vHi@22^zB41AR4lY zUQnph<>TNs!CML5PcUQ^{F+^dBi!v*iGGRb4~T}Wq8mCT`e&klAiC-T5pu{XdXP(^ zTQ3kHZ@EB(+)6ZL6}{1|)5VkFHo;v99!M}`6@1pC!x8RwDbcqQJ)3CADmts3M6V-y zEzvuOhODB;wwLHGXv;aGT&I1aBf3vI?%* zMTaBY?Sn)gAo_WtA*<-VT_yS~(f=j-d!iw$=vCKAbge}q6bBNAbEZWOW zG-MThsGmd+Ai5vXBZ-ErqN^0^v~LaECio757ZMCv1z$Hnha=qW`-zSa{Rq*JRrI18 zB>FX?UncrPq9Lp3*Kd^QUx@yZ=xR$u$RVrf#y9D-Z6n-XBHAluiHLYE!H`w(kU=^e z;coXJ`Z}U-A{w%a-aJ^MClWoL=rW=qtLRULNc4K58PN|C4OvBJ57lYIeQ~#s5PX7Q z$SU~OTXZ|tb&6jIy^C|d%T;_rwN6uLLVBdqY>`$ zd&2HJ1br1fxn2Zw_F4pvWm8kmuNH5ZI_Ft@(>MKMc*+&r;84w zV-wt;U?0JdRq)fLIvn9WdnVD-h+afAWEE|kB+(BLy^ZKciH5AAyHA$r<3ztg^hZQP zR?$nQ=yZc)akqaWxcUkaamXt8#8e%QaJNk>M0>SbAtIhfG-MUs^md8vNpv@&2N4Zf zMGw70q9+kOf#^9zLsrpSr|ISO- zl$5 z{o*{GE`LAn_8EdN5DZxb*S%ARBi!ve_lStsyhlX5#XTb8kX7`6yCm94bRp4Qh=#19 z+1(O7oamuMk0TngivDPUP8Xkn+XT-icn!gjRdCuO9gc9f_Yu8^=)*)qR?*?b68%2W zCyD-oXvivh*Aj^~t`Z^to9OzhM93kl=<`c;I_(R%O>p)q(OzDHA**2LavhFvw{Il6 zm}o!IkX7{b6%sv*=;=f+CK|GeerBaaZzuXbq8}p~vWl*@N~bTJi@W_Q!KVm@tb%*4 z*5L?u`!_QEGto(_MZ_Vi=w)jpI&HORuhy$Y$n%MYtfJpqE784(?oRYzq9Lp3X7}p! z(VyTp!KDP36AW1e4_l|h5$^UzqSq0Pr zgJ{Sq+PX=nS6+nMYed8wuMrW?TqD{GvI?HKS%)Ls?aoAZAiA$c=Rv6oete4rk0Cfr z@a<$8vWmXARif`9dO6WsiH5AAJ8#ozpD{n?^+AGPA{ep?zVkjEj&QF(CDUh#{$UNX z73W^g>zD7B;JRx?z-z4)0dKih1RS!;biM5o?b2wQf#9wL+tu)mJ9OAq6Hb%mTL`|D zV8|-V>mJl$&FKY1-%0e^wahRWPOIVnJ|w~W3EoTaGh`aF%5?fJiT;4-cZvRzXvivh z^lqIt)Q8th#JdU;@ovCGydkULJ$rOm^V&*u4$+p%Dai{Mm_yEC>RhExFqQjcgZ&1BoBl^FLMM9T# zI_WV9{vW}=5S)Ckh&N=F>D~t!!4eW;7tUtCwLc`hO9C@;z@~q9;GLssR@3Y;Qy%M`w!{x z#EiJp-xB;M!H`v!zj{iCBi!j`>qNYptP}ChTE~he!)g4Z*bR|)Sb~cP?nrRIb)u;u ztLVwkNOTF&qlmtPXvix1;1QiJvcPMCR}dT_7_tif^QaC-xYth+{RGi3uVW3SU)Jku z|0BVl5&SX1Kay$4D${pAC(&1}XNFm5>h&VtDeFbZYOhzquO8Fk28D2Xy=W{4!PgNC zS!KDw3p%VhJ(B2QM2}z3&d!0;YWSuXC3qphcM;6UG-Q?O4KGRbBSb$;^bw*VtLQT? zOZ10Czo*gHp~BjhABZYA^HmA1hT~}XUKPUz5%7i^*#5iW^#&ze@|pza5^N^8{RUwg zvdZ+s$0hov4J@(38(8_W%X&Tg zO$k0h@Nt4qlWE8*)Bkx(qJJm)SE6fd6fuLWqHDY((dipSbD1`Z=CTnDSw;6bDbd#x z-IM5>H?rb2a9a)E^R5I>C3q6S^T;%0mFaiiljzMTbuSv5;N1joP{S!7Nbn0}`Z=QC zAsVvEwC_WSK2P*HqW^-t@v>fT|44$HZxR7-x=94Q?IzJ&kX5F?IVI8865WaD{zOAo z(FLa^dMweSiJnF@WEDO2j6|;{dL_}@Hn9ua;Iu|$8q ziJg4_jZN@R1b?E2yM8Lc^)`!$*V!y0-fFXmIAoRSg`Y{ZhiE6!-SF>2@LCNY|6GE7 z1P>>80-1)aGTrD)iC#qX-9+C@G-MS$_$!Hil<0j#A0-;Hir)0KM1Mr|`$T`enXTM+ zS+_s_MuMwv5fL|T5fN{+MMNC3%5>Xv5}mh&l|PEcCiohHEoyk&cM?2^Oy5X!h-kw}C#2 z#wK_^!IRbS>fa=I3z^ z6A`bwO+>u)HWqmkUT;&v-KtBlmtYsc-M5M6f~+#VI7y=YM2{f4lxWB*`i*3XUQF}? zqSp}(Sw%OgDbbG+y`Si3x3P)uU)JrLYf11afpb(P2<$;I$eaUr&O|37$>xaxx8B zW%|+j61|h?9Yh}_8nTN1t${?pMf4j)pCuZyitf-zqW>WJe}XpD!Xd7Cc}9pTcy8lF z*pSH#-STY4Hur1IWx!Xu!5Z4I&Rz1H6<^YLp^1d{6=-ilZxLv3C0gfd3B6sQQwY6N zpi`7+aWe_UY|SgYiO{_Q-K0dPcBe7o!_A_FC5iCR%dpKQU8?eX?i1St3MVwPv z`5=q}h$^;fro^@rtee;#f`zDJdt^y$K(Hf;ohVp{Dt2jGiCrStg~YBGEJPLiX12s0 z5bPtw{ztG7RqWMfiT$r&KP2{B!9rBAw^$@LnHBjE=W07doSU%X;fOOt6&tZiY=K~N ziR~y@h${B;T!|eb*qewQC0K|m)`Ek{Ytc7wO!Cp`7Fu_7pu`jtK_71^LCH5}CLR7I=c_cO>*v-T~ELeyt_6D!SzAV@m1l#1j z2XXkF-#7YdZ2wtv%aAJCf9z zv6zFozYtaIh0YRtM6gd2dt9&(Rjlh;iTz5jpA-A5U?Hm58C@i{AuFGWIM>@L;%s8R zIjA#46?>$s#I_f#huEHig{WeycazwlV12|+5-da&+pD|8E*0z|V)4|M*BPRUUEV`t z9~bPS#6Bljh${B&o)UXnupbe7POuPFZ1dg{TZ5%7LY%Ab5^-+IYm}AE zSiA+t7g!CQ1Pf8c-q%NBZx-wzVn+)WqKf^puf)z1>>OfO3KpV@&FwF-y9N6Yu}=yX zqKchVEV1th_Dy0x5iCR%8yz6Ae+l*?v31yq)rd1h75m2x65EztSb;Xm*e&9WPuX)8 zqKYjVD6#zo+lScUf`zDJ@4881rwR5pV(%6#L>2qWAc@^3*e%5F6D&j(TYpFbyIYVi z68XL$A*#rMHz$z%)od+N7l<{o$Oc3TqKaKVRAO7Pm75Xe7JEdLErNxpVo%>9vE2mQ zh1eSf3sJ>p;B9_!3&UT?7UVc0X9*IbiX1aSCnH#e#cH=!<2A%?7c4{-yLY6-9u@3i zVqX_5L>2p^Ph!6o>=(rTCRm6n))UZK?LBPUZp66$UJ>IoR`d|s2cn9c71YTH_x2j5 zv0h?(2^OM?eKsVqA;J2Ioh(?0DmHnP#4Z!;Vq&qP#On-E#a=&JXSMgNwHiN06`Ma% zVowV8En+_vEJPJMWs=VFSEdE|2a$Ez{-+URh$`}l$vPR~-e$AN5wuU{KG8ld!9rBA ze@&6tV!`$$c7$Lds@N{KN$hmN-cIZS!9rBA3vSn0{-(4bw-UKukPubmYj@~mgnRp{ zU|%Bk1HnS%Y;G?cHL|4IxH02HtNTn(Taq++!qiED(CX1S{5ukK4#}};lER;b!XHRs zRaX28LJ#p0g=;UP@S*9F!q#l!3uup)`aElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN U4UJL_l1$Ug6H_+d=j^)=0M39FZ~y=R delta 64 zcmaERiTUj%<_+sO4buvevQjORE%nWfEXaElo`lEzQ$X4Nc6F&CShCEsc!QEDTJN z4UJL_l1$Ug6H_+dtrV%sTG-=RHr(5NxckHOzmXFpE8qA)u2WzJ3D+qxN&+>qOwOuPom^V4!W2_GS@EaL z<|U`*#FrE$<`t)<76Dx~`F*4KbVF4}k;(TPWF{YNlAK)BBsY0+lhS0%CdJ9S8%@d4 KxA|Vv`#S(=kDj&w diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index ab33d9b954a28ff1cd25b9358438234d5b2f9325..e58d2cfa69b9996757b78f8004badb44d019a3fe 100644 GIT binary patch delta 10564 zcmcIqd0bOh_V*k}!X_jn1Q3*^FNoT?a=x|G%x5rv2=YNw9lI<}?7B3|^h z+B#By+G?$q#x@-{Tv}9gDr#+OtK&A>)-Gz7Py4G~=HB~~@REYserEVX-#h2rbI<*L z&pG$r2T$+vuRrWBTOwPN3yreFzNzz5jaj87^Gj3Arcz_7Im>J+$;y~gmTF2b&B(|w zXBmxCG7YBERAZT8l6gu-$zCCv$oD66^26GZzv>~Y z5@xMLIf>|(>7lsb@ji;I@2~Zg*;EAyNO~9f$KUEbYff3NUuvdVpOvOh)f-J2Njjs+ zV8}MpShG`0=u>v8A%pxfC=W77<@_Abk)cJS{^-D=B;p}oQt-e-#JF5fjt*W%d`3JV zD;A(rCsH(`F5#Ym(3t#m^oPv;Qw|L%YGJrHe_b-aCmFlY$98#y58zAQ3@-z4(XK!5FB0g75UfCDu0j|}4AznXl4!2k}qofNy^wYkjK^s+e!5F;aIqnk+3pV zfFZT2o`G7fsu?a(Mh7v)ih41DwN`tn%v%)nsz-IiD8ne1I>kL{_rY`Oum;!wpB)s zPuc)`xM061;9yJgg4u1Bk;?kV@T5$by%KU2g$G3$HSH`dNW6&k~5;jlk-zzt-UYI>;=K$oecPboTLuRC95Zg z3B$ZW2lJUjUXl~#&XPs^a`3MYTcoz__37RzY+HA9hT{K0 z1jhS}Kx{!2)$0uQ!Y%;x`KsK#vlqW4?yu4Vk&)u^@_l2{`q=mYX+-Q?Kn__VWdAeQ zAte7fxr{F6d!9RaDZcHlY&2cje~tEh=@Q$a-O2ZVb<$q~ry2eGoF z92WTSNDG;DJ}FtwBUMQv<(fVu_vscWrg0_+I&aX*EzG_TX;`i%v-Sr0IuE&tPKjD_ z(6aDQE-KtlS{rczpBhFI-Sh|HA(m25Pb1I?2S%V4>wL)V^I^iR5g1G&$0yL^#;GUN zB*>Dfu#9JuZ=b~9Er&V$eSVf9fge0b$V#f4o`-z=8H;0`M*v~kf zF4dqnrCW45vr(UBA$|WgJcQ?4JUuo`JoRUuGJc1rekraKA0M0ZJIA&i3eR!2rI|UE zL2~wPY&$aZE0OO~W`D!Uw=^@k<`W-&QbS+1yXMKunDr-4Ze47jbxO*lZ1Ye~i=jxb zH8quVXPZ2B!mK$nCspZZ%vvDgtxGqJbt5C!A^$BCU5b;%%4bZ_wC`@d@39i>`;nrx zA=z}=PWLx08`=hacVAX$k}H#CIs>PhZwH0uyfS4F!+NvA%52l<^{FXZEji~|xZJ?y zt_x!~U(XwhIMr$LYE4|*jYVhjNTUw%lVFhFUhdv?U>gGC*h%Gx4a=}Yv>jrbH@RY5Mb5gNVe#&NT@H_%i33Jr;>i;Fa;=`$@fE=GC944pxr z<|+}FQB-?IZQ;Dh!&z6SM0zXQUMIa3rRn0?SOU*ykbu>r+z+; zq8+rl2$^K;v(lTT6WvA6_J=OckQ7g{khFf}muH7Syi59&LrO0YZWY9>gZ)@#xxw`$ zgDc#TaA9QDx`4ZZRwy;R2YCIQF=f>L20Y0&F|uQPSYDo4-?bf#;Q4#!*+}R7iaIv` zaK3gKPZctLy_V>w7rPKdu?~il+grYde!r6YbZewhtHazHs ztC&hHDDxm4S5jA(li)ED{bCq1>bpckhP^1m*L_Ld+%9C+i=$wRt@*`JfI}>X)dAir zYm=Y&bwd1VXW4Fx`oAB%pp}HZoKJJVac8p6hs;82Z6(jV{0z3zsjm)#FQ~>utzOs~ z8{F#hFtohaP-Ti6TPNM@iymAX#ZMEAY08ReF!4@=-^RMorin<*cj-YUm(|FL%cqVIzi6pf< zqj8K=etl<-Q=a!)0#0|zH@wDt2u0n&B;Ua}X(Fn|T%`vzZ;%O%CU}OVzNIG0(@Bcm z+>+v9RFf|oV_}0!v)`dnQO)$%t6&GY{CY0F$)}6l;qO?tTsNhnvP>sg^bRu1R*XkE zKDfi5cAH-ELt;D!PW6WK&I#mibD^zqmjWpjETC3(08!?6lk;!(k{@*r2_-%3G4PXX z*eOxb&f3nk<(uZMNQ?;J`4$!+=37Vr&9|Q9{kKZd#59g{OATP@hFt@M)&vMeYUQ4A z*yJZ1$b$k;Ag|hrcPjuUkh*;)91}p+?%4}tNYP=l{8Jv_>SHm)wzmNn29W6JY*@P!~wQBz1FDguinDuLv}VgAzq4EeDlHsiz_RwA4%N zza!2^;;=wo4?_aQdKeUFyM4aY8_Sv4Q4D3&CoP7eK(QDG1>R>d3?WrlZeT4PO}o`! zuEqfUaD3H71w2=vmfX5J7`8GQeV+C2G^XJs7j+o@Lz3vru0>+282vvT(YFNh-6Q^b zB;se16pym(`OxHi(}_ zl|k$@8t&N9SZt~S?C;rN-nQq+01y|@pso(WWkJ0B76ggqN2Ly|v7T#SfiTPm22gVm z*<&18d#evTDnq+yViPuoLJZp2*bF@86sm&gcFcv5 z`Z=tHTS|z+UtQ)O>Jvpx6zQ1~hAJiB_iVx?6+Gfl2xEe2^XTE!Yz>A%ZW^;wQ=*0z zreSM;z$x6Wfl*Y*SExA7R=AU{g;xD`3U++BeQ^kU4mh2gV}(*^2!)B^MO>3FN;A+*~f-V>&E zfyKBTgodNoP4J6=ad=EgzqTi73aOuF8Ug!tisQiYk$r0focDqcSg2ivO;MDki{hd` z5Ppe*UQW%QbX0TOscFzbim1sHdc{IA8J_Gz-}9m&Ruqe?4pA{4{6285da6*@70M-- zh7ZIQo@Xm;R<0h`N97UU)wW_3R6QUPd#hM4fh?gR0eXs`Owv_se-+O3fY~^Tsl}cG z4>Fr%$Q4c|Q0A$X`~C{eLQ@_LP!M72|lYP}d0QSmn6h{bdD-d?;*oL7lm;vDlSb_x67M0gGHI~8jaaz8JM z-`94F9^$<~PCqR|zC2iz?M;O(h}vM@61s311HyTlY+8P}yo>@y> zsteg*Wb>$Upb&{mOmd)yLmV$$VlSIry6oPdV>;H@6>)5h?dDQ4L$j9<)CUsrL@?iy zj?qEvO3)n4QpkR(56~0xHKudyOIK+4bYFTmSlpMIsXguL9nh5_(iNl@=?YLYy6p5* zo-!8W)x4qiVbR5??dek?D;BfVVh=yzklI&5Z43@lbF#*Gb5l-McQq#~Kz%=C-LOBs z3(67a(?Ky&g2qm>f03FKHeW3g=1@J)RI9iuY`)sjnm{i(Q_}$))tfAsc2WJ#TWmBh zzXc_@i>}hqXd0oknNuxd?vBvorfl0Ie= za#WQb9xn@jiNMD^+shpXbv+_+Dej}|c2KvQ&LW}i6FWWYbOk)F;oN=3X4$P_+<6$@ z6uG1KZl!Rv8%E%#8cyIf8Xv2&D-7(O_Ub5H4*&uzK(!D=|4Ijcp(~{X-q46uc7@ur zv(s)3zW{kobB9w1iNeMan2zxwZ48F@z|nqCz&sC8fSktJH^hE88Bh7aC_Wv%d1~oj zs%U*IdWZIWCieG(GN(3FsBOj&#TZe`sml%F)WwHLse|2uvH%N|9tb=sOfI0ifk*fJ zL?)I<(g3vJ3n4C?*M+#@{EU6i05r()6*?j1-FFD)p)G{*4zCD{hF~|RX7>>4=N*T} z-p7LeKviN6tYVR`3t{YdxEJKSvN$`PhhYSKAu`Y{4pQ+mI-F5-ouL_HKRFBsBg(XG zHHKPF(N`fNMc1i4qi9`-NRh`}o7h~Hn4%S{EJ@2aa&*FhT3%pVS)c>7j!yWnR_uhC z3Q;O{!U`=fq$8q`v(b;nLBFdeX{BtiF!)Kk77NYxF)hn_{R~9MC+%*{P(cm;#*W$ZQV;h2vu7T)1y|kbrYA^X&F~r z1)5~lRyMHO`YHQ&UU@kEBQfhEp>(kWtz+na{0pdcEPG-%IqNv~#B$g=o;|T%Vx7RA zShiayvL^<#wS+xwv1gpc7Qhvu6i2<6*1Ami{6ic7L`Xe{dRQh*JclOX!Uw2~I#DC9 znmKPyX<2=F!o2c0>k>MiV)YIGyD95ZA>c#Yjys)xwu^rDiGH>-Kjx&ZbV!YNWyOrL qdP^m=@6RCqQo`ZyL#My9qQC2+zq8z5tkhpk2mXTnggxhQ_(tp(oRyj7naSz4jI=3s`;-j3eX7M?QI?r9HPu>f zt*DriKDEM6WSXt1S=O{HOC~NIJQiQh)!@0gp} zUo$|CdkoI-Vx0AQ9*dtGV)CXn>+<6P2QBpPJAdHpxs};!Yes5PW=fJZ$!51Fm~D28 zB`eidY00vd(QjFqcD!m#PL6$1mffC`l$4|%n9No+R4MLisM6K_2_0H*9XNSdnSYfg zerdzvhPWzO-AlMEFTwmr%#jOKc>kg(9t-$it?diT{BTTl9NttKl2%`3a?bVh@tHqG zVo}u}F~-*JmnSHHXqmBN*`p0C1k`}F%${sH;kFQ+8rkrh!yY0dqQQ<^9|(lvP58nC zGvIAM(quuYaKI0@vV}VI8U8hQxan!C^o+NI_py-Eg~_;h zQ7n=^MUmWeq=L5g2$`PmN`Z~-s`5C_pZpuz{qd1;e_rM*xj*PM-kP67EhkIIgC5U% zU=|d=&YiNzxKk(*>&B-kO58qu<`|=_>v$M}EyGk$D0?EWSiX;HEs){5VKx{d!`t$+ zAyFo@=1fS$xrMhu-6f1B8SvJ^#dzRDdi+_@_h=z*nv@XiTp;uDsfw$Luj*IaO4ZDl z;o#x-p$5uKmA^v?^)js6+B0TlhZv)-ppTvMV*w9e{Ubs6s$(eBNgs2#r8`&vc!J4~ zas@bMc~4a>Q_FXyPrYk8Het5R=9@U@(U11Zc$@oCuxuvvp~%;HzYMP){S2n0)T4* z*N?cc)TM)zvAm~F;u14ywhUiQGp)S<{( z+VHW7^?g@KqFcAZX0h25E!M=Y2v{SYS58d)4dPY{Zkan22Mkit0dG-W<=>mLhlmWMu^reSc;Qz$$4zH>y?dmBkt znRFR7t51x8|0n(y0Vq5~faN($6tdC5M}XQ=i3OJ$@9ug&S*gOZ3%>^IJ&rHtqcq*K5yt$ zeBQXBB-gS~C@vub(Gv_P$V|g68x3K7rp+5*zROHs?2^*04zoGko|NtwoLr-(LIzw- z&)hvEC?Y*A$;zng=$t;&Y)P`UPbz<2TXa|2I_8@}IB(t1{+-5^?QGvJ8aN$ssA0Hi zU0vt#X2PvxGx4VNlF`O$Y1fjD{kQ8n4!cgANgFh%1Yc`N)huB+j#RNc#-+m(P#L2t zmI^AHC8Y_=g^N{e#)0`NPrfQ1X3~`Gbqp@SIa|X}J+>}0`DUj5g#l#B?i!uZ8?$bO zNx6#<6YZdgJxWWfFZPinmp1*_13g+X0$C%ZfW=_di_28{Bn!RayXN;=4 z#psF6)4Kp+_NEL9k1~98x9jkSn}?un9HZsT(oF)R#K(uBT^yr2LG{Ln3$IssjaZK5 z9E>_iv2=%psFdEf!sAb<`5h>ZFjvx7-wKK`-G``YT_)bStw$H}ZCdwx>HjGYVguXx z%wtzA${rEQH*vrZ`cunP$Ay7CDBk!jVPcC z*xQeID|YN^{%+?_zGxi2@?tJ6%57T`RL)6E#N-@@r@XY1a<08pglZTEk4jY1=Q+Fj z1dnUiGNP*A2zoY6#yfY#qDQ?H_%)j8g4o7vM7V-HYM`aI8Kkm<*&w|&n4P+zdFFp(Z3dt zzL#+3zLpN-)Oc&FUEah{DZtl0%+`8^<#n9%ksTRv?(Q7LUM{7^*KB(H-bZ~kQ4bF|b`D$+9gPk!2e-EX!i?^KVXpH@H9{ysi;R_B8cGB-uvlWqXoAftp-R zM)}^syV#u2qCjwwhXFpe-<(%!y67H zfXttV^2-DLA>2bBday#!(;>k_3q53JG=}6iOfABbkMxC`8jRw?nqND#3c>9{bOc^_ zcqEJvYA+p5hYAmU>^nJvo(}hTXud~c7>%jD%G4Hl)HZ#PBQ`2%K7QnF1RI3Hd*6$M z?R;^>27fo?GIQC?3Mt}l*@4F*;T0~+R@WapB31G>XGC?agc`yaH8}BD0({c0qUiVt zrh979qdB_jo1LzOjhOR2=2CFEzGXUr9 z&Q|yURY+I6OA785WIs2VDRrEBj9%h>bZR_2%nTA$vM`{%N|am{yQ>5U*C1DOYP0E+ zSC#NA6_%7qpl88Aa0%fgLFuAS(lGiu3&(#R4yOVnwemrLsFl_L(UJf8d5#}^%Owio zN`NSqPXau}(i-pwie)6e?b2m11@e?1`OjF03S{w*Fa>s(N~S=9!s!uU;eyD7^Dn0` z1q{?NdQ| zmz&&O)lb9LZ>wQLpt!Oofn?Mnl-3;3mZ3z=T&rVPhSE{Z3~UT+UjI|0QqJ$f4^k$7 zMOCa2Jl7V4`Y40eAUSG&AVy*aK@Sh3S0EY+D2SKWF&79$5uC>oY|6oiGK1P`Y?1oeP*UK$F>s9;pkg&_%{RQfrOp2eZ4 z(4&WQ_INm{Vd$vf6v1&1@xgF37P5GhI*4BtgE<6dZ4cnd@rQiM6lXA zf+g276re4LsG`s`B;ka%bIwx^mEb9UlI?J%eFxT*`KDc-;~$e8Gg^Xnk$q;g2u5k? z4Rit@gB;pUW6)XG;uv(s7nPDZQ7Dq!8;fFLD&JmRN+s!!0lGR93G9VTBq5iE-z^tT zrvbQ+%Tdz&J}6mm%;ZoX6bj2d9DQHZM{qJ=qldVoFIottTAo~=F{d0_d|~?_t(R#R zSuZ00h(qCUKr3lREm~2(d$s)FCOhKMY&gRu3gNU?RPP~byr|y2T1hiP4)RSh8tT1O z8%ghv4uh|bUqt@Uy-hc$O&O#m0Y!mXC$T9~$8AF4u1SeBQ6Q5u3n5J>%;~A~m=mt! z&BSep&dXm6JvbN`uKd-b-9jR@4Y+5jl~9hRXwdX~Y(|V(ci+7yhdAyd=tv zf?l7Fo&ngT!%el>ibzh=iH>oPj&%$)nY3lmkcx2`99q#ZyzGc|EgXQx101DmVx!6T zgD9x)>v(@YN?qY;@tdQb3rCOz*(e6S#5PBlWb2+p+{=1!}%tgr_7RxB4 zB-T7+XRDfWk` zI%yk>qEHEG%|{0D_Ax40K;)VQsKD7I3hD z@?ZhF9t*pI`OS+WmznoMuuENyGW$SFum?=V2{&T}Myq0>HJGDW>c^!8nmdDe|EGGT z{l6PHzj004jj8~Cp^GS-m-e7gBqL}qvPb+v{plLcUxLZ#y(k@pa6!73G?~G`bg;-? zXU23L*`NGK3%w*Hi{3dYWCeX_M) z!g5m98wQXsT4=kTs+Uw=xn96E*|lUp3IQ4}F1_M8XGp4^-L-g2AK6e(HDKiC|u! zYK^v)AL2f)6G1?K+5SS$FGmhL?)Oa z20{(|7}t_UGkp!F95x7@>pq5Gl94g6z`G7^y3zVE29`p;q5Yu9Gju}hvTJuwh@@{8 z2HqpDux;`Tyl;C+UT09<0kaGpDA6MNr8i{3bOUeMuF_c8fZ%a1Q3#J3L}|^TI|{6* z9G|%I3<4{!9c~J&UN(r|Pe^b4euHRUVd_F;5!BvNr97UIAieOrLejg zjoC0n4Oa}Jv;|gnIH7shaRUouN^nYdA~b12BM(G*nthqHj4AK+Q{c&*Y>ec3{b~K3Zd30z?o02 z;^3fa^5A%|pcv9L9)^1F@{FASy#1O{lu@%$+V)Nu1vW1*D~}o7ZSTCX9h(6P!4Pe) z&B(DiDu@D`BSwx5)hlg#F-C!nm&+5#;`gA^8=KYS`g=6h&w}Vu_&8J}3qdEqs!cR? z0%;N%d;%__Y!crJPE^2T`0Y#{WSc}Y%r^0`mno^;i6&kRUOJ|c%_m_sn&h&)Pq&24 zCRfl$@RcvRm;CSvWGjZy`=q+$>G;JR$>1y^cb|q>)Q_w_4RLha-KQau_R&kHVUVIH zHLWh9hWzzChydp-QuKEiyyRX=taUOSway|s@zgr|aY{C=q*`Ybg{;=e7S=jvv47`R zh0*_rvezhvLoEa3c#ePcb+d69i%S?~M@`^IAIyu`jG zx-_2x9d`UfXqG4Wnw%=K?;H$;@DK_pyQWc)^XEZ{RAlj4$dCS4oxEn&{JG_o4OQ{; ztNJ<>A=C`^Yc)%Er;?mM3ok&LM^}o_mG7l1h3T^Qbz11+I=>myXI3^iR-&#ouUC=$ H3oz>6w$Z>W diff --git a/master/_modules/cleanlab/count.html b/master/_modules/cleanlab/count.html index 287ad8102..736fd9572 100644 --- a/master/_modules/cleanlab/count.html +++ b/master/_modules/cleanlab/count.html @@ -566,7 +566,11 @@

Source code for cleanlab.count

 
 from cleanlab.typing import LabelLike
 from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes
-from cleanlab.internal.constants import TINY_VALUE, CONFIDENT_THRESHOLDS_LOWER_BOUND
+from cleanlab.internal.constants import (
+    TINY_VALUE,
+    CONFIDENT_THRESHOLDS_LOWER_BOUND,
+    FLOATING_POINT_COMPARISON,
+)
 
 from cleanlab.internal.util import (
     value_counts_fill_missing_classes,
@@ -678,9 +682,9 @@ 

Source code for cleanlab.count

         label_issues_mask = np.zeros(len(labels), dtype=bool)
         label_issues_mask[cl_error_indices] = True
 
-        # Remove label issues if given label == model prediction
-        pred = pred_probs.argmax(axis=1)
-        label_issues_mask[pred == labels] = False
+        # Remove label issues if model prediction is close to given label
+        mask = _reduce_issues(pred_probs=pred_probs, labels=labels)
+        label_issues_mask[mask] = False
         num_issues = np.sum(label_issues_mask)
     elif estimation_method == "off_diagonal_calibrated":
         calculated_confident_joint = compute_confident_joint(
@@ -752,6 +756,16 @@ 

Source code for cleanlab.count

     return sum(issues_idx)
 
 
+def _reduce_issues(pred_probs, labels):
+    """Returns a boolean mask denoting correct predictions or predictions within a margin around 0.5 for binary classification, suitable for filtering out indices in 'is_label_issue'."""
+    pred_probs_copy = np.copy(pred_probs)  # Make a copy of the original array
+    pred_probs_copy[np.arange(len(labels)), labels] += FLOATING_POINT_COMPARISON
+    pred = pred_probs_copy.argmax(axis=1)
+    mask = pred == labels
+    del pred_probs_copy  # Delete copy
+    return mask
+
+
 
[docs]def calibrate_confident_joint( confident_joint: np.ndarray, labels: LabelLike, *, multi_label: bool = False ) -> np.ndarray: diff --git a/master/_modules/cleanlab/experimental/label_issues_batched.html b/master/_modules/cleanlab/experimental/label_issues_batched.html index e38ae00d7..488967869 100644 --- a/master/_modules/cleanlab/experimental/label_issues_batched.html +++ b/master/_modules/cleanlab/experimental/label_issues_batched.html @@ -558,7 +558,7 @@

Source code for cleanlab.experimental.label_issues_batched

import numpy as np from typing import Optional, List, Tuple, Any -from cleanlab.count import get_confident_thresholds +from cleanlab.count import get_confident_thresholds, _reduce_issues from cleanlab.rank import find_top_issues, _compute_label_quality_scores from cleanlab.typing import LabelLike from cleanlab.internal.util import value_counts_fill_missing_classes @@ -762,11 +762,13 @@

Source code for cleanlab.experimental.label_issues_batched

pbar.close() label_issues_indices = lab.get_label_issues() + label_issues_mask = np.zeros(len(labels), dtype=bool) + label_issues_mask[label_issues_indices] = True + mask = _reduce_issues(pred_probs=pred_probs, labels=labels) + label_issues_mask[mask] = False if return_mask: - label_issues_mask = np.zeros(len(labels), dtype=bool) - label_issues_mask[label_issues_indices] = True return label_issues_mask - return label_issues_indices
+ return np.where(label_issues_mask)[0]
[docs]class LabelInspector: @@ -1222,6 +1224,7 @@

Source code for cleanlab.experimental.label_issues_batched

pred_prob = pred_probs_shared[ind, :] pred_class = np.argmax(pred_prob, axis=-1) batch_size = len(label) + if thorough: pred_gt_thresholds = pred_prob >= adj_confident_thresholds_shared max_ind = np.argmax(pred_prob * pred_gt_thresholds, axis=-1) diff --git a/master/_modules/cleanlab/filter.html b/master/_modules/cleanlab/filter.html index b19e6864f..082876c69 100644 --- a/master/_modules/cleanlab/filter.html +++ b/master/_modules/cleanlab/filter.html @@ -559,7 +559,7 @@

Source code for cleanlab.filter

 from functools import reduce
 import platform
 
-from cleanlab.count import calibrate_confident_joint, num_label_issues
+from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
 from cleanlab.rank import order_label_issues, get_label_quality_scores
 import cleanlab.internal.multilabel_scorer as ml_scorer
 from cleanlab.internal.validation import assert_valid_inputs
@@ -972,9 +972,9 @@ 

Source code for cleanlab.filter

         label_issues_mask = find_predicted_neq_given(labels, pred_probs, multi_label=multi_label)
 
     if filter_by not in ["low_self_confidence", "low_normalized_margin"]:
-        # Remove label issues if given label == model prediction if issues haven't been removed yet
-        pred = pred_probs.argmax(axis=1)
-        label_issues_mask[pred == labels] = False
+        # Remove label issues if model prediction is close to given label
+        mask = _reduce_issues(pred_probs=pred_probs, labels=labels)
+        label_issues_mask[mask] = False
 
     if verbose:
         print("Number of label issues found: {}".format(sum(label_issues_mask)))
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index 419f4f3a0..78bcabc0a 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 d043838d0..873924016 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 fd2d9891a..cdd9591b7 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 a72a42b17..8b5de05e2 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 de96e1f37..0564cdc9b 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 accefbf10..c6c80e9ab 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 7a5c64225..c4902b61d 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 c199346da..87c720199 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 fb78ffa9c..47d015b46 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -72,7 +72,7 @@
     "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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 038d4a8fd..eb416a0f5 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 fff5910f6..205c8a7e4 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 058d5a251..0e51ee787 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -103,7 +103,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 2fe7cfeec..284494365 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index fc79cb479..cba87f74e 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
     "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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index 08f2adc6f..da8832d68 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
     "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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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 fb1e56c66..78b871e9a 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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
diff --git a/master/searchindex.js b/master/searchindex.js
index 73d041a18..5aae23d45 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/datalab/datalab", "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/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/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/report", "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/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/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "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/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.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/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/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/report.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/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/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.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/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "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", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "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", "Image Classification with PyTorch and Cleanlab", "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", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 72, 74, 75, 82, 84, 85], "helper": [1, 14, 33, 37, 39, 40, 41, 42, 43, 44, 56, 79, 81, 93], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 24, 25, 29, 30, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 71, 72, 74, 79, 83, 88], "benchmark": [1, 30, 71, 72, 74, 75, 82, 84, 85], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 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, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 74, 75, 79, 83, 88], "": [1, 2, 3, 8, 29, 30, 34, 37, 40, 42, 44, 49, 50, 54, 56, 57, 58, 59, 61, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "core": [1, 4, 33, 35, 63, 65, 90], "algorithm": [1, 2, 6, 8, 26, 31, 44, 49, 58, 67, 69, 71, 80, 82, 84, 93], "These": [1, 2, 3, 6, 8, 18, 32, 35, 36, 47, 49, 50, 53, 57, 58, 62, 66, 67, 69, 70, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "introduc": [1, 73, 80, 82], "synthet": [1, 84, 85, 90], "nois": [1, 2, 3, 29, 35, 38, 44, 50, 74, 75, 79, 84], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 17, 18, 19, 24, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 74, 79, 83, 87, 88], "classif": [1, 3, 4, 5, 8, 12, 14, 27, 29, 33, 35, 38, 40, 41, 44, 49, 50, 51, 52, 53, 58, 59, 67, 68, 69, 70, 71, 72, 74, 75, 83, 84, 87, 88, 89, 90], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 21, 22, 23, 25, 26, 33, 34, 35, 38, 40, 44, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 76, 77, 83, 84, 88, 91], "specif": [1, 3, 4, 7, 12, 13, 14, 22, 27, 32, 47, 51, 54, 57, 66, 70, 75, 77, 78, 81, 82, 93], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "modul": [1, 3, 11, 12, 13, 14, 18, 24, 27, 29, 30, 31, 32, 33, 34, 35, 44, 47, 49, 58, 59, 71, 80, 81, 85], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 20, 25, 29, 30, 31, 33, 34, 35, 38, 44, 48, 49, 50, 51, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 87, 88, 89, 90, 91, 92, 93], "gener": [1, 2, 3, 5, 8, 20, 27, 29, 40, 44, 45, 58, 59, 61, 66, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 90, 92, 93], "valid": [1, 2, 3, 4, 8, 10, 29, 35, 36, 38, 39, 40, 44, 49, 51, 54, 57, 59, 61, 62, 70, 72, 73, 74, 75, 77, 78, 79, 80, 82, 83, 85, 86, 89, 90, 91, 92, 93], "matric": [1, 3, 38, 80], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 19, 21, 27, 29, 30, 34, 35, 38, 40, 43, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 65, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "learn": [1, 2, 3, 4, 8, 12, 14, 19, 25, 27, 31, 32, 33, 34, 35, 37, 39, 44, 47, 49, 51, 58, 60, 62, 65, 69, 71, 73, 74, 77, 78, 79, 81, 83, 84, 89, 92], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "possibl": [1, 2, 3, 8, 29, 30, 34, 35, 37, 38, 40, 51, 52, 53, 54, 56, 57, 58, 59, 61, 67, 69, 70, 75, 80, 82, 84, 85, 86, 89, 90, 93], "noisi": [1, 2, 3, 8, 29, 31, 34, 35, 38, 44, 50, 51, 53, 59, 61, 62, 63, 65, 66, 72, 74, 75, 77, 78, 80, 83, 84], "given": [1, 2, 3, 8, 25, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 43, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "matrix": [1, 2, 3, 4, 8, 14, 26, 29, 35, 37, 38, 41, 44, 45, 51, 56, 57, 58, 59, 77, 87], "trace": [1, 74, 75, 82, 84, 85], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 19, 21, 22, 29, 30, 31, 33, 34, 35, 37, 38, 40, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 70, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "more": [1, 2, 3, 4, 5, 8, 11, 14, 21, 29, 30, 33, 34, 37, 40, 44, 49, 50, 51, 52, 53, 54, 56, 57, 59, 61, 62, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 80, 81, 84, 85, 86, 87, 90, 93], "function": [1, 2, 3, 4, 5, 11, 12, 14, 20, 21, 25, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 75, 79, 80, 82, 84, 85, 86, 90, 91, 92, 93], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 38, 44, 74, 75, 82, 84, 85], "py": [1, 3, 27, 30, 31, 35, 38, 40, 74, 75, 82, 84, 85], "verbos": [1, 2, 4, 5, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 33, 35, 49, 50, 51, 56, 58, 59, 61, 63, 65, 66, 70, 74, 82, 84], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 33, 34, 35, 39, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 73, 74, 75, 77, 78, 80, 81, 82, 84, 86, 87, 89, 90, 92], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70], "prior": [1, 2, 3, 29, 35, 38, 40], "repres": [1, 2, 3, 5, 8, 10, 14, 21, 29, 33, 35, 38, 41, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "p": [1, 2, 3, 8, 29, 35, 37, 38, 44, 49, 57, 58, 59, 63, 75, 77, 78, 81, 82, 84, 93], "true_label": [1, 2, 3, 29, 38, 44, 82, 84], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 20, 21, 23, 26, 29, 33, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 52, 53, 54, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 73, 74, 75, 80, 82, 84, 85, 86, 87, 90, 91, 93], "check": [1, 2, 4, 7, 8, 10, 14, 22, 30, 33, 34, 39, 45, 48, 54, 57, 61, 71, 73, 74, 75, 80, 81, 82, 84, 85, 89, 91, 92], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 19, 21, 31, 34, 38, 40, 56, 61, 75, 78, 80, 82, 84, 85, 87, 89, 92], "achiev": [1, 2, 30, 31, 34, 61, 80, 84, 93], "better": [1, 4, 35, 49, 51, 59, 61, 62, 71, 73, 75, 77, 78, 80, 82, 85, 86, 87, 92, 93], "than": [1, 2, 3, 5, 8, 21, 23, 26, 29, 35, 44, 48, 49, 54, 56, 58, 59, 61, 65, 69, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 93], "random": [1, 2, 3, 5, 8, 26, 33, 40, 49, 59, 61, 73, 74, 75, 77, 80, 81, 82, 84, 85, 87, 91], "perform": [1, 2, 5, 8, 21, 23, 26, 30, 34, 40, 57, 61, 71, 74, 80, 82, 84, 85, 88, 89, 91, 92], "averag": [1, 3, 8, 19, 23, 29, 30, 34, 40, 42, 49, 50, 57, 58, 59, 80, 84, 87], "amount": [1, 3, 81], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 75, 78, 81, 91, 92], "np": [1, 2, 3, 4, 5, 14, 26, 29, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 69, 70, 73, 74, 75, 77, 79, 80, 81, 82, 84, 85, 87, 89, 90, 91, 92, 93], "ndarrai": [1, 2, 3, 4, 14, 20, 21, 25, 26, 29, 31, 33, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 93], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 21, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 74, 75, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "shape": [1, 2, 3, 4, 14, 29, 31, 33, 35, 37, 38, 39, 40, 42, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 79, 80, 82, 85, 86, 87, 90, 93], "condit": [1, 2, 3, 38, 43, 44, 59, 81, 82, 93], "probabl": [1, 2, 3, 4, 6, 8, 14, 20, 23, 29, 33, 34, 35, 37, 38, 40, 41, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 72, 79, 80, 82, 83, 85, 86, 87, 90, 93], "k_": [1, 2, 3, 38, 44], "k_y": [1, 2, 3, 38, 44], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 42, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92], "fraction": [1, 2, 3, 8, 17, 31, 38, 44, 49, 61, 77, 80], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 84, 85, 86, 88, 89, 90, 91, 92, 93], "everi": [1, 2, 3, 4, 14, 30, 34, 35, 38, 43, 44, 51, 59, 61, 62, 73, 74, 75, 77, 78, 80, 81, 84, 86, 88, 90, 91, 93], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 91, 92, 93], "other": [1, 2, 3, 4, 8, 14, 19, 22, 29, 30, 32, 33, 34, 35, 38, 41, 44, 45, 47, 49, 50, 53, 57, 58, 59, 61, 66, 73, 74, 75, 77, 78, 80, 81, 82, 85, 87, 90, 93], "assum": [1, 2, 3, 10, 35, 38, 42, 43, 44, 59, 63, 66, 80, 87, 90, 93], "column": [1, 2, 3, 4, 8, 10, 11, 25, 29, 33, 35, 38, 40, 41, 43, 44, 49, 50, 51, 53, 54, 57, 58, 59, 61, 66, 67, 69, 70, 73, 74, 75, 78, 79, 80, 81, 82, 84, 86, 89, 90, 91, 92, 93], "sum": [1, 2, 3, 21, 26, 29, 38, 40, 44, 50, 51, 53, 56, 61, 74, 75, 80, 81, 82, 84, 85, 90, 93], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 79, 80, 88], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 17, 19, 20, 21, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 44, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 38, 40, 43, 44, 45, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 85, 86, 90, 91, 93], "bool": [1, 2, 3, 4, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 33, 34, 35, 40, 43, 44, 49, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 19, 20, 21, 23, 25, 26, 27, 30, 33, 34, 35, 44, 49, 50, 51, 53, 54, 70, 73, 75, 77, 78, 79, 80, 81, 82, 89, 92, 93], "generate_noisy_label": [1, 74, 75, 82, 84, 85], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 19, 20, 22, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 38, 40, 41, 42, 43, 44, 49, 51, 53, 56, 57, 58, 59, 61, 62, 67, 69, 70, 71, 73, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 90, 93], "perfect": [1, 2, 29, 61, 82, 86], "exactli": [1, 3, 8, 29, 30, 34, 35, 52, 58, 74, 75, 77, 78, 81, 82], "yield": [1, 30, 34], "between": [1, 4, 8, 13, 14, 18, 19, 21, 24, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 42, 47, 49, 50, 53, 56, 58, 59, 61, 62, 65, 69, 70, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "below": [1, 3, 4, 8, 29, 30, 33, 34, 35, 37, 40, 49, 50, 51, 56, 57, 65, 69, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "we": [1, 2, 3, 4, 5, 8, 11, 19, 30, 33, 34, 35, 40, 44, 45, 49, 56, 57, 59, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "loop": [1, 3, 38, 44, 81], "implement": [1, 2, 3, 4, 7, 12, 19, 30, 31, 33, 34, 38, 44, 61, 71, 73, 74, 77, 87, 88, 91], "what": [1, 4, 7, 8, 14, 27, 29, 31, 33, 35, 49, 50, 54, 56, 73, 74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "doe": [1, 2, 3, 8, 33, 34, 35, 40, 45, 56, 57, 61, 63, 65, 69, 73, 74, 75, 77, 78, 81, 85, 89, 90, 92], "do": [1, 2, 4, 8, 29, 33, 34, 44, 45, 58, 59, 63, 73, 74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "fast": 1, "explain": [1, 8], "python": [1, 2, 34, 48, 61, 74, 75, 79, 87], "pseudocod": [1, 88], "happen": [1, 8, 35, 51, 78, 84, 90], "n": [1, 2, 3, 4, 5, 29, 30, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 73, 78, 79, 80, 81, 84, 85, 89, 90, 91, 92, 93], "without": [1, 2, 4, 8, 10, 12, 17, 30, 34, 53, 61, 71, 73, 78, 82, 86, 87, 92], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 37, 39, 43, 44, 48, 49, 51, 53, 54, 56, 57, 59, 61, 63, 65, 66, 71, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92], "distinct": [1, 44, 93], "natur": [1, 8, 84, 87], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 90, 93], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "count_joint": 1, "len": [1, 2, 3, 5, 29, 33, 38, 43, 44, 45, 58, 59, 61, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93], "y": [1, 2, 3, 4, 6, 25, 26, 34, 38, 40, 44, 45, 48, 57, 61, 62, 73, 74, 75, 77, 80, 82, 84, 85, 87, 89, 92], "round": [1, 33, 35, 44, 61, 80, 89], "astyp": [1, 84], "int": [1, 2, 3, 4, 5, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 40, 41, 42, 43, 44, 50, 51, 53, 57, 58, 59, 61, 63, 65, 66, 67, 70, 73, 74, 81, 87], "rang": [1, 3, 4, 5, 10, 38, 40, 42, 44, 57, 61, 62, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 93], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 19, 29, 33, 35, 38, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93], "pragma": 1, "cover": [1, 3, 72, 79], "choic": [1, 6, 35, 80, 81, 85, 87], "replac": [1, 43, 48, 59, 74, 75, 78, 79, 80, 81, 84, 87, 91, 92], "generate_noise_matrix_from_trac": [1, 74, 75, 82, 84, 85], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 59, 73, 74, 75], "05": [1, 8, 21, 43, 57, 61, 67, 69, 79, 80, 82, 86, 90], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 74, 75, 82, 84, 85], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 45, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 74, 75, 80, 81, 82, 84, 85, 90], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 21, 34, 40, 61, 73, 74, 75, 77, 79, 82, 84, 85, 91], "max_it": [1, 73, 78, 87, 92], "10000": [1, 33, 79, 80], "x": [1, 2, 3, 4, 8, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 34, 35, 37, 38, 40, 43, 44, 45, 48, 49, 51, 57, 58, 59, 61, 63, 73, 74, 75, 77, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92], "diagon": [1, 3, 4, 35, 38, 44], "equal": [1, 3, 8, 10, 51, 56, 66, 88], "creat": [1, 2, 7, 14, 30, 33, 34, 35, 44, 61, 71, 73, 77, 78, 80, 81, 90, 92, 93], "impli": [1, 8, 29, 50, 57], "float": [1, 2, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 39, 40, 43, 44, 49, 50, 51, 53, 56, 57, 61, 65, 69, 73, 74, 75, 82, 84, 85], "entri": [1, 3, 4, 29, 30, 34, 35, 37, 41, 44, 49, 50, 51, 54, 77, 78, 82, 85, 86, 91, 92], "maximum": [1, 8, 58, 66, 70, 90], "minimum": [1, 6, 8, 17, 35, 37, 51, 56, 69], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 21, 30, 34, 35, 56, 61, 74, 80, 82, 84, 86, 87], "default": [1, 2, 3, 4, 5, 8, 12, 14, 23, 25, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 74, 80, 81, 90], "If": [1, 2, 3, 4, 8, 10, 11, 14, 21, 23, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 48, 49, 50, 51, 54, 56, 57, 58, 61, 62, 63, 65, 66, 69, 70, 71, 72, 73, 74, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "have": [1, 2, 3, 4, 8, 14, 18, 21, 24, 29, 30, 32, 33, 34, 35, 38, 40, 44, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 66, 70, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 19, 27, 29, 30, 33, 34, 35, 38, 40, 41, 43, 44, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "necessari": [1, 2, 3, 5, 8, 10, 43, 74], "In": [1, 2, 3, 8, 29, 30, 33, 34, 49, 50, 52, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "particular": [1, 4, 8, 11, 12, 14, 16, 17, 19, 21, 22, 23, 26, 30, 34, 44, 49, 53, 57, 61, 66, 70, 71, 73, 75, 78, 80, 84, 85, 87, 89, 91, 92], "satisfi": [1, 3, 29], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 25, 28, 30, 31, 32, 33, 34, 35, 38, 44, 47, 48, 51, 58, 59, 61, 63, 71, 72, 73, 79, 80, 82, 88], "argument": [1, 2, 3, 4, 8, 14, 20, 22, 25, 26, 30, 33, 34, 35, 40, 45, 48, 49, 50, 51, 53, 56, 57, 58, 59, 61, 65, 66, 67, 69, 75, 78, 79, 80, 81, 86, 89, 92, 93], "when": [1, 2, 3, 4, 8, 10, 12, 20, 21, 30, 34, 35, 38, 40, 44, 48, 51, 53, 54, 56, 58, 59, 61, 62, 74, 75, 77, 78, 81, 84, 88, 89, 90, 91, 92, 93], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "rate": [1, 2, 3, 8, 31, 44, 73, 93], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 39, 40, 44, 48, 49, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 74, 75, 77, 78, 80, 84, 85, 87, 88, 89, 90, 91, 92, 93], "note": [1, 2, 3, 5, 6, 8, 22, 26, 30, 33, 34, 35, 40, 44, 49, 54, 56, 57, 58, 59, 61, 62, 66, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "you": [1, 2, 3, 4, 5, 8, 12, 14, 29, 30, 32, 33, 34, 35, 40, 47, 48, 49, 51, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "high": [1, 2, 14, 33, 35, 44, 56, 59, 61, 74, 75, 79, 81, 82, 86, 89, 90, 91, 92, 93], "mai": [1, 2, 3, 4, 8, 11, 18, 19, 24, 29, 30, 32, 33, 34, 35, 38, 40, 44, 49, 50, 54, 56, 57, 58, 59, 61, 63, 66, 70, 72, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 89, 90, 92, 93], "imposs": [1, 8, 82], "also": [1, 2, 3, 4, 5, 8, 19, 29, 30, 33, 34, 35, 43, 48, 49, 58, 61, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "low": [1, 8, 44, 49, 71, 74, 75, 78, 82, 86, 90], "zero": [1, 3, 4, 30, 34, 37, 44, 45, 74, 81, 85, 86, 87], "forc": [1, 2, 3, 4, 34, 74, 93], "instead": [1, 2, 3, 8, 11, 14, 27, 29, 30, 33, 34, 35, 38, 44, 48, 49, 51, 53, 57, 58, 59, 61, 62, 65, 67, 69, 72, 73, 77, 78, 80, 81, 82, 85, 86, 87, 89, 90, 91, 92, 93], "onli": [1, 2, 3, 4, 5, 8, 14, 20, 21, 25, 29, 30, 33, 34, 35, 37, 38, 43, 44, 48, 49, 58, 59, 61, 63, 65, 69, 70, 71, 73, 74, 75, 78, 81, 84, 85, 86, 87, 88, 89, 90, 92, 93], "guarante": [1, 3, 4, 13, 18, 24, 30, 32, 34, 36, 38, 47, 72], "produc": [1, 2, 4, 8, 14, 40, 49, 59, 61, 63, 65, 71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93], "higher": [1, 4, 8, 29, 35, 37, 38, 40, 49, 50, 61, 75, 78, 80, 86], "opposit": [1, 93], "occur": [1, 3, 8, 29, 43, 56, 74, 75, 80, 81, 87], "small": [1, 3, 8, 29, 33, 40, 44, 50, 57, 78, 79, 81, 85, 87, 92], "numpi": [1, 3, 4, 5, 8, 10, 26, 33, 34, 40, 42, 43, 45, 48, 53, 56, 61, 62, 67, 69, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "max": [1, 35, 58, 59, 75, 81, 87], "tri": [1, 30, 34, 88], "befor": [1, 2, 3, 30, 34, 44, 58, 61, 66, 78, 80, 82, 84, 87, 89, 91, 92], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 20, 21, 25, 29, 30, 33, 34, 35, 38, 40, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 89, 90, 91], "left": [1, 2, 35, 37, 42, 44, 51, 54, 57, 74, 75, 85, 86, 87, 90], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 30, 34, 39, 40, 49, 54, 56, 57, 58, 74, 75, 79, 84, 85, 86, 93], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 30, 34, 48, 80, 82, 90], "length": [1, 4, 10, 21, 22, 29, 31, 35, 44, 51, 54, 58, 59, 61, 63, 66, 70, 73, 85, 87, 90, 91, 93], "must": [1, 2, 3, 4, 14, 29, 30, 31, 32, 34, 35, 38, 40, 41, 44, 47, 48, 49, 50, 51, 58, 59, 61, 63, 65, 66, 67, 69, 70, 73, 84, 88, 90, 93], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 29, 33, 35, 41, 44, 45, 49, 51, 57, 63, 65, 66, 67, 69, 70, 73, 80, 84, 85, 86, 90, 91, 92, 93], "ball": [1, 79], "bin": [1, 3, 51, 74, 75, 87], "ensur": [1, 2, 8, 30, 34, 44, 45, 56, 59, 61, 73, 74, 75, 78, 80, 81, 82, 87, 88, 89, 91, 92], "most": [1, 3, 4, 5, 8, 14, 29, 33, 35, 40, 48, 49, 50, 51, 54, 56, 57, 58, 59, 62, 65, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92], "least": [1, 8, 26, 29, 33, 49, 50, 56, 59, 69, 75, 80, 81, 84, 87, 90], "int_arrai": [1, 44], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 27, 29, 30, 31, 32, 33, 34, 35, 39, 40, 41, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 70, 71, 72, 73, 74, 77, 78, 81, 85, 86, 87, 88, 89, 90, 91, 92, 93], "model": [2, 3, 4, 8, 14, 25, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 43, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 74, 75, 79, 83, 88, 90, 93], "For": [2, 3, 4, 5, 7, 8, 9, 14, 19, 28, 29, 30, 33, 34, 35, 38, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 67, 69, 70, 71, 73, 75, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93], "regular": [2, 3, 33, 48], "multi": [2, 3, 8, 29, 30, 33, 34, 35, 39, 40, 41, 44, 45, 50, 51, 52, 53, 58, 59, 71, 80, 82, 83], "task": [2, 4, 5, 10, 12, 13, 14, 25, 27, 29, 33, 38, 40, 41, 42, 44, 49, 51, 59, 61, 71, 73, 78, 79, 80, 82, 85, 87, 90, 92, 93], "cleanlearn": [2, 3, 8, 20, 25, 30, 44, 48, 61, 62, 71, 72, 89, 91, 92], "wrap": [2, 30, 34, 48, 58, 61, 71, 74, 75, 77, 78, 82, 89, 91, 92], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 34, 40, 48, 57, 58, 61, 66, 73, 74, 75, 77, 78, 81, 82, 91], "sklearn": [2, 3, 4, 6, 8, 26, 29, 34, 40, 44, 48, 58, 61, 62, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92], "classifi": [2, 3, 34, 40, 44, 49, 52, 58, 59, 71, 72, 73, 77, 78, 80, 84, 85, 87, 88, 90, 91, 92, 93], "adher": [2, 34, 61], "estim": [2, 3, 4, 7, 11, 19, 29, 33, 34, 35, 38, 44, 49, 50, 51, 56, 58, 61, 63, 65, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 83, 85, 86, 87, 88, 89, 90, 93], "api": [2, 3, 12, 48, 58, 61, 72, 80, 89], "defin": [2, 3, 4, 5, 8, 12, 19, 29, 30, 31, 33, 34, 35, 59, 61, 63, 74, 75, 77, 80, 84, 87, 93], "four": [2, 8, 79, 82, 93], "clf": [2, 3, 4, 40, 61, 71, 77, 80, 82, 85, 91], "fit": [2, 3, 4, 6, 8, 34, 48, 58, 61, 71, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93], "sample_weight": [2, 34, 61, 82], "predict_proba": [2, 4, 29, 34, 40, 48, 73, 74, 75, 77, 78, 80, 82, 84, 85, 87, 91], "predict": [2, 3, 4, 6, 8, 14, 19, 20, 23, 25, 29, 33, 34, 35, 37, 38, 40, 41, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 79, 80, 82, 83, 87, 89, 90, 92, 93], "score": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 33, 35, 37, 40, 42, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 87, 89, 91, 92], "data": [2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 31, 32, 33, 34, 35, 40, 41, 44, 47, 48, 49, 50, 51, 52, 56, 58, 59, 60, 61, 66, 67, 68, 69, 70, 72, 76, 81, 83, 88, 92], "e": [2, 3, 4, 8, 10, 19, 29, 30, 33, 34, 35, 38, 40, 41, 44, 45, 49, 50, 51, 52, 58, 59, 61, 63, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92], "featur": [2, 3, 4, 6, 8, 14, 16, 20, 21, 22, 23, 25, 26, 40, 44, 58, 61, 71, 74, 75, 77, 78, 80, 82, 84, 89, 91], "element": [2, 3, 4, 29, 35, 37, 44, 49, 51, 59, 66, 67, 69, 73, 78, 80, 92, 93], "first": [2, 4, 8, 15, 21, 22, 29, 33, 40, 44, 49, 50, 54, 57, 59, 61, 73, 74, 77, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "index": [2, 8, 21, 29, 35, 42, 43, 44, 45, 50, 59, 61, 66, 69, 70, 73, 74, 75, 77, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "should": [2, 3, 4, 5, 8, 12, 19, 21, 26, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 48, 49, 50, 53, 54, 56, 57, 58, 59, 61, 62, 66, 67, 69, 70, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "differ": [2, 4, 5, 8, 11, 13, 18, 21, 22, 24, 29, 30, 32, 33, 34, 35, 36, 40, 44, 45, 47, 49, 54, 56, 58, 61, 73, 74, 75, 77, 78, 81, 82, 84, 87, 88, 91], "sampl": [2, 3, 4, 6, 8, 14, 17, 35, 37, 40, 51, 54, 57, 59, 61, 62, 71, 72, 79, 80, 82, 83, 85, 86, 89, 90, 92, 93], "size": [2, 8, 26, 30, 33, 34, 35, 40, 51, 56, 57, 61, 63, 65, 77, 80, 81, 82, 84, 85, 88, 90, 92], "here": [2, 4, 5, 8, 12, 33, 35, 38, 48, 49, 50, 51, 53, 54, 57, 58, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "re": [2, 4, 30, 34, 43, 49, 61, 71, 73, 74, 77, 78, 80, 89, 90, 91, 92, 93], "weight": [2, 8, 30, 31, 34, 40, 49, 56, 59, 61, 73, 74, 75, 78, 87, 92], "loss": [2, 31, 48, 59, 61, 81], "while": [2, 3, 8, 30, 33, 34, 39, 40, 44, 54, 57, 61, 71, 80, 81, 82, 84, 89], "train": [2, 3, 4, 8, 14, 30, 31, 34, 40, 44, 48, 49, 54, 57, 58, 61, 62, 72, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 88, 90, 93], "support": [2, 3, 4, 10, 33, 40, 44, 45, 58, 59, 69, 71, 72, 73, 74, 75, 80, 81], "your": [2, 3, 4, 7, 8, 14, 29, 30, 32, 33, 34, 35, 40, 44, 47, 48, 49, 50, 51, 53, 58, 59, 61, 62, 63, 65, 66, 72, 73, 77, 79, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "recommend": [2, 4, 8, 11, 14, 33, 35, 49, 74, 75, 80, 81, 88, 89], "furthermor": 2, "correctli": [2, 3, 8, 29, 30, 34, 35, 38, 45, 50, 51, 56, 57, 61, 63, 78, 80, 85, 86, 89, 90, 92], "clonabl": [2, 61], "via": [2, 4, 8, 11, 14, 19, 29, 31, 33, 34, 40, 44, 49, 54, 57, 58, 59, 61, 62, 65, 69, 73, 74, 75, 77, 78, 79, 80, 81, 85, 86, 87, 88, 89, 90, 91, 92, 93], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 35, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 53, 56, 58, 59, 61, 62, 65, 67, 69, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 93], "clone": [2, 61, 85], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 33, 37, 38, 39, 40, 41, 42, 43, 44, 45, 53, 57, 61, 67, 72, 74, 80, 82, 84, 85, 87, 93], "multipl": [2, 3, 4, 10, 11, 29, 35, 43, 49, 50, 51, 53, 56, 57, 61, 71, 74, 75, 80, 81, 83, 85, 86, 89], "g": [2, 3, 4, 8, 10, 19, 29, 30, 34, 35, 41, 44, 51, 52, 58, 59, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92], "manual": [2, 61, 73, 80, 87, 88, 89, 91, 92, 93], "pytorch": [2, 30, 31, 34, 61, 71, 73, 80, 83, 85, 90], "call": [2, 3, 4, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 40, 44, 48, 58, 61, 73, 74, 75, 78, 80, 82, 87, 88, 90, 92, 93], "__init__": [2, 31, 61, 81], "independ": [2, 3, 8, 50, 61, 78, 88, 93], "compat": [2, 30, 33, 34, 48, 61, 62, 65, 69, 71, 80, 88, 89, 91, 92], "neural": [2, 31, 48, 58, 61, 73, 80, 81, 85, 87], "network": [2, 30, 31, 34, 48, 58, 61, 73, 78, 80, 81, 85, 87, 92], "typic": [2, 30, 34, 58, 61, 73, 75, 77, 78, 81, 87, 88, 91, 92], "initi": [2, 3, 11, 30, 34, 49, 61, 78, 80, 91], "insid": [2, 34, 61, 80, 82], "There": [2, 3, 71, 82, 84, 85], "two": [2, 3, 8, 21, 29, 30, 33, 34, 41, 44, 54, 56, 57, 72, 74, 75, 77, 78, 80, 81, 82, 85, 89, 90, 92, 93], "new": [2, 5, 12, 19, 30, 33, 34, 39, 43, 44, 49, 61, 73, 74, 78, 79, 80, 87, 88, 92, 93], "notion": 2, "confid": [2, 3, 8, 19, 29, 33, 35, 38, 40, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 65, 69, 71, 77, 78, 81, 82, 84, 85, 86, 88, 90, 91, 93], "packag": [2, 4, 5, 7, 8, 9, 13, 28, 32, 35, 36, 44, 47, 54, 57, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "prune": [2, 3, 35, 51, 61, 72, 86], "everyth": [2, 57, 82], "els": [2, 57, 74, 79, 80, 81, 84, 85], "mathemat": [2, 3, 8, 38], "keep": [2, 11, 12, 44, 71, 74, 79, 80, 90], "belong": [2, 3, 8, 29, 35, 37, 38, 50, 51, 52, 53, 58, 59, 63, 67, 69, 70, 75, 77, 78, 81, 82, 85, 87, 90, 93], "2": [2, 3, 4, 5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 48, 50, 51, 53, 58, 59, 61, 62, 66, 67, 69, 70, 79, 80, 88], "error": [2, 3, 4, 8, 30, 34, 35, 37, 38, 42, 44, 50, 51, 53, 54, 56, 57, 59, 61, 63, 65, 66, 69, 72, 73, 74, 75, 77, 78, 79, 83, 91], "erron": [2, 3, 29, 35, 38, 44, 50, 51, 59, 61, 62, 63, 87, 89], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 33, 40, 42, 43, 49, 53, 56, 61, 62, 67, 69, 70, 71, 77, 78, 80, 85, 86, 87, 89, 90, 91, 92, 93], "linear_model": [2, 4, 29, 44, 61, 71, 73, 74, 75, 78, 80, 82, 84, 87, 92], "logisticregress": [2, 3, 4, 29, 44, 71, 73, 74, 75, 78, 80, 82, 84, 87, 92], "logreg": 2, "cl": [2, 12, 25, 61, 71, 80, 82, 89, 91, 92], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 20, 25, 27, 30, 33, 34, 35, 39, 40, 44, 48, 49, 51, 58, 59, 61, 67, 71, 73, 74, 75, 78, 79, 80, 82, 84, 86, 87, 89, 92], "x_train": [2, 74, 75, 82, 84, 85, 89, 91], "labels_maybe_with_error": 2, "had": [2, 3, 61, 86], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 26, 27, 29, 30, 32, 33, 34, 35, 47, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 76, 83, 84, 88, 89, 92], "pred": [2, 35, 44, 88, 89, 91, 92], "x_test": [2, 74, 75, 82, 85, 89, 91], "might": [2, 49, 61, 66, 74, 75, 80, 81, 91, 92], "case": [2, 3, 11, 29, 40, 49, 61, 73, 74, 75, 77, 79, 80, 81, 82, 87, 89, 91, 92, 93], "standard": [2, 3, 4, 25, 29, 35, 48, 50, 51, 53, 59, 61, 71, 74, 75, 77, 79, 82, 91], "adapt": [2, 30, 32, 44, 47, 61, 87], "skorch": [2, 61, 71, 80], "kera": [2, 47, 61, 71, 80], "scikera": [2, 48, 61, 80], "open": [2, 33, 79, 86, 93], "doesn": [2, 61, 71], "t": [2, 3, 8, 15, 22, 30, 31, 33, 34, 35, 40, 42, 43, 53, 58, 59, 61, 67, 69, 70, 71, 74, 75, 77, 78, 79, 81, 82, 85, 86, 93], "alreadi": [2, 4, 14, 30, 33, 34, 38, 48, 49, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 91, 92], "exist": [2, 4, 8, 10, 30, 33, 34, 43, 48, 54, 56, 58, 61, 71, 72, 74, 75, 78, 84, 85, 92, 93], "made": [2, 4, 14, 61, 78, 80, 81, 84, 86, 88, 89, 91, 92], "easi": [2, 38, 61, 74, 75, 79, 80, 82, 85], "inherit": [2, 5, 31, 61], "baseestim": [2, 34, 61], "yourmodel": [2, 61], "def": [2, 5, 12, 30, 34, 48, 61, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 92, 93], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 26, 30, 31, 33, 34, 35, 40, 58, 59, 61, 74, 79, 81, 85, 90, 91, 93], "refer": [2, 8, 14, 30, 34, 50, 51, 53, 54, 56, 57, 61, 65, 66, 74, 75, 77, 78, 80, 81, 82, 88, 89], "origin": [2, 4, 8, 34, 35, 43, 44, 48, 50, 51, 54, 57, 58, 61, 62, 65, 67, 69, 74, 77, 78, 80, 81, 82, 86, 87, 89, 91, 92, 93], "total": [2, 3, 29, 33, 44, 50, 70, 80, 81, 90], "state": [2, 3, 4, 30, 31, 34, 39, 61, 82, 85, 86, 93], "art": [2, 31, 82, 85], "northcutt": [2, 3, 29, 58, 59], "et": [2, 3, 29, 31, 58, 59], "al": [2, 3, 29, 31, 58, 59], "2021": [2, 3, 29, 58, 59], "weak": [2, 57], "supervis": [2, 8, 74, 75, 80, 84], "find": [2, 4, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 26, 29, 30, 32, 33, 34, 35, 39, 43, 44, 47, 54, 57, 58, 59, 61, 63, 67, 69, 72, 74, 83, 88], "uncertainti": [2, 8, 37, 58, 61, 80, 87, 89], "It": [2, 3, 4, 5, 8, 10, 11, 14, 19, 22, 25, 27, 30, 34, 35, 38, 40, 42, 49, 56, 57, 61, 71, 74, 75, 80, 81, 82, 85, 88], "work": [2, 3, 4, 5, 8, 10, 25, 29, 30, 33, 34, 35, 38, 43, 44, 45, 48, 49, 59, 61, 71, 72, 74, 75, 79, 87, 89, 92], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 32, 33, 34, 43, 44, 47, 49, 50, 53, 54, 58, 59, 61, 65, 66, 67, 69, 71, 72, 74, 75, 77, 78, 80, 81, 82, 85, 86, 87, 93], "deep": [2, 32, 34, 47, 48, 61, 78], "see": [2, 3, 4, 11, 29, 30, 33, 34, 35, 40, 44, 48, 50, 51, 53, 54, 57, 58, 59, 61, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "subfield": 2, "theori": [2, 82], "machin": [2, 4, 12, 14, 27, 32, 47, 61, 74, 75, 79, 84], "across": [2, 3, 4, 5, 8, 11, 19, 29, 33, 40, 50, 57, 58, 74, 75, 77, 78, 79, 80, 81, 82, 86, 88], "varieti": [2, 80, 91, 92], "like": [2, 3, 4, 5, 8, 12, 27, 29, 30, 33, 34, 35, 38, 44, 48, 49, 50, 53, 54, 56, 59, 61, 62, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "pu": [2, 44], "input": [2, 3, 4, 8, 14, 21, 29, 30, 33, 34, 38, 40, 43, 44, 45, 48, 57, 61, 71, 72, 75, 78, 79, 80, 81, 82, 84, 85, 86, 89, 90, 92, 93], "discret": [2, 35, 38, 44, 58, 59, 63, 65, 66], "vector": [2, 3, 4, 8, 14, 35, 38, 40, 41, 44, 58, 59, 71, 73, 74, 75, 77, 78, 81, 82, 85, 86, 87, 90, 92, 93], "would": [2, 3, 4, 30, 33, 34, 35, 44, 51, 61, 71, 74, 80, 81, 82, 87, 89, 92, 93], "obtain": [2, 4, 6, 8, 14, 35, 49, 51, 54, 57, 59, 62, 73, 75, 78, 80, 84, 86, 88, 90, 93], "been": [2, 29, 35, 38, 43, 44, 49, 50, 54, 56, 58, 59, 61, 73, 74, 77, 80, 82, 84, 85, 86, 87, 90, 93], "dure": [2, 14, 58, 61, 73, 77, 78, 80, 82, 85, 88, 89, 91, 92, 93], "denot": [2, 3, 38, 40, 44, 51, 58, 59, 69], "tild": 2, "paper": [2, 8, 49, 58, 67, 69, 79, 82, 84, 87, 89, 93], "cv_n_fold": [2, 3, 61, 92], "5": [2, 3, 4, 6, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 34, 35, 37, 39, 40, 44, 49, 50, 53, 54, 57, 61, 62, 69, 74, 78, 79, 80, 85, 86, 87, 88, 90, 92, 93], "converge_latent_estim": [2, 3], "pulearn": [2, 44], "find_label_issues_kwarg": [2, 8, 61, 72, 80, 82], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 51, 67, 80], "clean": [2, 56, 59, 61, 62, 71, 74, 75, 79, 89, 91, 92], "even": [2, 3, 29, 33, 37, 38, 44, 61, 73, 80, 82, 84, 85, 86], "messi": [2, 61, 82], "ridden": [2, 61], "autom": [2, 61, 71, 75, 79, 80], "robust": [2, 38, 61, 75, 80], "prone": [2, 61], "out": [2, 3, 4, 8, 14, 23, 30, 34, 35, 40, 48, 51, 52, 54, 57, 58, 59, 61, 62, 70, 71, 72, 79, 80, 82, 83, 85, 86, 87, 89, 90, 92, 93], "current": [2, 3, 5, 8, 11, 12, 19, 30, 34, 35, 40, 49, 56, 61, 74, 75, 80, 84], "intend": [2, 11, 12, 13, 14, 27, 36, 49, 65, 69, 73, 74, 75, 78, 82], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 35, 38, 39, 40, 41, 43, 44, 48, 49, 50, 53, 56, 57, 58, 59, 61, 63, 65, 66, 70, 72, 73, 74, 77, 78, 79, 80, 81, 82, 84, 86, 88, 91, 92, 93], "follow": [2, 3, 8, 12, 25, 29, 30, 33, 34, 40, 42, 49, 50, 54, 56, 57, 58, 61, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "experiment": [2, 30, 31, 33, 34, 51, 72, 80], "wrapper": [2, 4, 48, 73, 89, 91, 92], "around": [2, 4, 56, 74, 75, 86, 87, 93], "fasttext": [2, 47], "store": [2, 4, 8, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 58, 61, 77, 78, 79, 80, 90, 91, 92, 93], "along": [2, 40, 51, 69, 74, 75, 80, 81, 87], "dimens": [2, 42, 44, 63, 66, 80, 81, 87, 90], "select": [2, 7, 8, 21, 49, 59, 81, 84, 87], "split": [2, 3, 4, 8, 10, 33, 40, 43, 44, 61, 73, 74, 75, 77, 78, 79, 81, 82, 85, 88, 91, 93], "cross": [2, 3, 8, 29, 35, 38, 39, 40, 51, 54, 57, 59, 61, 62, 72, 73, 74, 75, 77, 78, 79, 80, 82, 83, 85, 86, 89, 90, 91, 92, 93], "fold": [2, 3, 29, 35, 38, 61, 73, 77, 79, 80, 86, 90, 91], "By": [2, 4, 29, 50, 51, 61, 74, 80, 90], "need": [2, 3, 8, 29, 30, 33, 34, 35, 50, 51, 53, 58, 61, 71, 73, 74, 75, 78, 80, 82, 84, 85, 86, 90, 92], "holdout": [2, 3, 61], "comput": [2, 3, 4, 5, 6, 8, 16, 17, 19, 20, 21, 22, 23, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 44, 49, 50, 51, 53, 56, 57, 58, 59, 61, 62, 63, 65, 71, 72, 74, 75, 79, 82, 83, 85, 86, 87, 89, 90, 92], "them": [2, 3, 4, 5, 7, 8, 9, 10, 22, 28, 30, 32, 33, 34, 35, 47, 49, 58, 61, 72, 74, 75, 77, 78, 80, 81, 84, 85, 87, 89, 90, 91, 92, 93], "numer": [2, 3, 4, 8, 11, 19, 25, 40, 56, 58, 61, 66, 71, 72, 73, 74, 75, 76, 78, 81, 82, 84, 87, 89, 91, 92], "consist": [2, 3, 30, 34, 44, 49, 90, 93], "latent": [2, 3, 38], "thei": [2, 3, 4, 13, 18, 21, 24, 30, 31, 32, 34, 35, 36, 44, 48, 51, 56, 59, 61, 62, 65, 69, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 87, 89, 92, 93], "relat": [2, 3, 11, 16, 17, 21, 22, 23, 26, 38, 44, 50, 61, 75, 78], "close": [2, 3, 8, 33, 38, 58, 73, 74, 75, 77, 78, 80, 81, 82, 86], "form": [2, 3, 8, 30, 31, 34, 38, 43, 44, 59, 61, 80], "equival": [2, 3, 30, 34, 38, 58, 87], "iter": [2, 3, 29, 30, 34, 35, 44, 50, 51, 61, 80, 84, 90], "enforc": [2, 30, 34, 44], "perfectli": [2, 29, 50, 82], "certain": [2, 3, 4, 30, 34, 48, 57, 61, 74, 75, 79, 87], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 39, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 69, 74, 75, 80, 81, 93], "keyword": [2, 3, 4, 8, 14, 20, 22, 25, 30, 33, 34, 35, 37, 40, 43, 48, 49, 51, 58, 59, 61, 67, 69, 74], "filter": [2, 3, 8, 33, 43, 50, 52, 53, 55, 57, 64, 65, 66, 68, 69, 70, 71, 72, 73, 75, 78, 79, 80, 81, 85, 86, 89, 90, 91, 92, 93], "find_label_issu": [2, 3, 8, 25, 33, 35, 50, 51, 53, 54, 56, 57, 61, 63, 65, 66, 67, 69, 70, 71, 72, 80, 85, 86, 89, 90, 91, 92, 93], "particularli": [2, 71, 84, 87], "filter_bi": [2, 3, 33, 35, 51, 72, 80], "frac_nois": [2, 35, 51, 67, 80], "min_examples_per_class": [2, 35, 51, 75, 80, 82], "impact": [2, 8, 74, 75, 81], "ml": [2, 4, 8, 13, 61, 71, 74, 75, 77, 78, 81, 84, 91, 92], "accuraci": [2, 31, 59, 73, 80, 81, 82, 84, 87, 89, 90, 91, 92], "n_job": [2, 33, 35, 51, 63, 65, 67, 80, 87, 90], "disabl": [2, 30, 34, 35, 87], "process": [2, 3, 5, 11, 14, 33, 35, 43, 49, 51, 57, 63, 65, 67, 73, 74, 80, 84, 88, 92], "caus": [2, 35, 40, 74, 75, 80], "rank": [2, 3, 8, 29, 33, 35, 40, 50, 51, 52, 54, 55, 57, 58, 60, 64, 66, 67, 68, 70, 71, 72, 74, 75, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93], "get_label_quality_scor": [2, 33, 35, 40, 49, 51, 53, 54, 56, 59, 62, 65, 67, 69, 72, 82, 85, 86, 89, 90, 93], "adjust_pred_prob": [2, 8, 53, 58, 59, 82], "control": [2, 4, 7, 8, 14, 33, 35, 42, 49, 57, 58, 61, 67, 69, 74, 75, 79, 80], "how": [2, 3, 4, 8, 11, 12, 14, 19, 29, 30, 31, 33, 34, 38, 44, 49, 50, 53, 54, 56, 58, 59, 61, 65, 69, 71, 74, 75, 77, 78, 79, 81, 86, 87, 88, 89, 90, 91, 92], "much": [2, 8, 29, 33, 35, 61, 80, 82, 84, 87], "output": [2, 3, 4, 8, 14, 30, 31, 34, 38, 44, 48, 49, 50, 54, 56, 57, 58, 61, 65, 66, 69, 70, 71, 72, 73, 74, 78, 79, 80, 81, 86, 87, 88, 89, 92], "print": [2, 4, 5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 44, 49, 50, 51, 56, 58, 59, 61, 63, 65, 66, 70, 72, 73, 75, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "suppress": [2, 33, 49, 56, 58, 59, 61, 63, 65, 66, 90, 93], "statement": [2, 33, 49, 56, 58, 59, 61, 63, 65, 66], "big": [2, 33, 51, 57, 61, 82], "limit": [2, 4, 14, 33, 51, 86, 90, 93], "memori": [2, 30, 33, 34, 51, 57, 63, 65, 74, 90], "label_issues_batch": [2, 32, 51, 80], "find_label_issues_batch": [2, 33, 51, 80], "pred_prob": [2, 3, 4, 6, 8, 14, 20, 21, 23, 26, 29, 33, 35, 37, 38, 39, 40, 41, 44, 45, 49, 50, 51, 53, 54, 57, 58, 59, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 91, 92], "threshold": [2, 3, 5, 8, 16, 17, 19, 23, 25, 26, 33, 56, 57, 58, 59, 65, 69, 74, 86, 87, 90, 93], "inverse_noise_matrix": [2, 3, 8, 38, 44, 72, 82], "label_issu": [2, 33, 35, 51, 54, 61, 63, 72, 73, 78, 80, 81, 82, 89, 91, 92], "clf_kwarg": [2, 3, 8, 61], "clf_final_kwarg": [2, 61], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 29, 33, 35, 37, 49, 50, 51, 53, 54, 56, 57, 59, 61, 62, 65, 69, 71, 73, 77, 78, 81, 82, 84, 86, 88, 89], "result": [2, 3, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 33, 34, 35, 37, 44, 51, 53, 54, 57, 59, 61, 62, 63, 65, 69, 73, 74, 75, 77, 78, 80, 81, 82, 84, 89, 90, 91, 92, 93], "identifi": [2, 3, 4, 5, 8, 10, 14, 22, 27, 29, 33, 35, 51, 54, 57, 59, 61, 62, 63, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 85, 87, 89, 90, 91, 92, 93], "final": [2, 8, 61, 77, 86, 88, 89, 91], "remain": [2, 61, 72, 81, 89, 91, 92, 93], "datasetlik": [2, 44, 61], "beyond": [2, 4, 5, 7, 9, 28, 71, 90], "pd": [2, 3, 4, 5, 11, 16, 17, 19, 20, 21, 23, 25, 26, 29, 39, 48, 49, 50, 61, 69, 73, 74, 75, 77, 78, 80, 82, 84, 89, 91, 92, 93], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 33, 39, 44, 45, 48, 49, 50, 61, 66, 70, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 89, 90, 92, 93], "scipi": [2, 4, 11, 44], "spars": [2, 4, 8, 11, 14, 26, 44, 45, 77], "csr_matrix": [2, 4, 11, 14, 26], "torch": [2, 30, 31, 34, 73, 78, 79, 81, 87, 92], "util": [2, 4, 14, 27, 30, 31, 34, 36, 49, 61, 71, 72, 73, 74, 75, 80, 81, 82, 87], "tensorflow": [2, 44, 48, 71, 73, 80], "object": [2, 4, 10, 11, 14, 27, 30, 31, 33, 34, 40, 44, 45, 48, 51, 54, 55, 56, 57, 58, 61, 69, 71, 73, 75, 77, 81, 82, 83, 89, 92], "list": [2, 3, 4, 10, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 41, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 65, 66, 67, 69, 70, 72, 73, 74, 75, 79, 80, 81, 82, 85, 86, 89, 92, 93], "index_list": 2, "subset": [2, 3, 4, 14, 29, 33, 35, 44, 59, 66, 70, 73, 77, 78, 80, 81, 85, 86, 87, 88, 89, 91, 92, 93], "wa": [2, 3, 10, 12, 33, 44, 49, 50, 56, 58, 70, 73, 74, 75, 77, 78, 80, 82, 85, 86, 88, 90, 91, 92, 93], "abl": [2, 3, 8, 61, 73, 80, 82, 84, 85], "format": [2, 3, 4, 8, 10, 30, 33, 34, 35, 38, 39, 40, 41, 44, 45, 48, 49, 50, 51, 54, 57, 58, 59, 61, 63, 65, 66, 69, 70, 74, 75, 77, 79, 81, 84, 89, 90, 91, 93], "make": [2, 3, 30, 33, 34, 40, 48, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "sure": [2, 33, 35, 40, 73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 91, 92], "shuffl": [2, 8, 44, 73, 78, 81, 85, 87], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 30, 34, 38, 40, 43, 44, 49, 54, 56, 61, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "batch": [2, 33, 44, 48, 49, 63, 65, 80, 81, 87], "order": [2, 4, 8, 29, 30, 34, 35, 38, 39, 40, 42, 44, 49, 50, 51, 54, 57, 58, 59, 63, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 86, 89, 90, 92, 93], "destroi": [2, 44], "oper": [2, 30, 33, 34, 44, 48, 59, 71, 78, 87, 91, 92], "eg": [2, 8, 44, 54, 57, 74, 75, 80], "repeat": [2, 44, 49, 84, 87], "appli": [2, 30, 34, 35, 40, 41, 43, 44, 53, 58, 67, 73, 74, 75, 77, 80, 81, 84, 85, 87, 88, 89, 90, 91, 92], "array_lik": [2, 3, 29, 35, 44, 51, 58, 62], "some": [2, 3, 4, 8, 12, 19, 29, 30, 32, 34, 35, 38, 43, 44, 47, 49, 50, 51, 53, 54, 57, 58, 59, 61, 63, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "seri": [2, 3, 33, 44, 45, 61, 69, 80], "row": [2, 3, 4, 8, 11, 22, 29, 33, 35, 37, 38, 42, 44, 49, 50, 51, 53, 58, 59, 61, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 87, 91, 93], "rather": [2, 3, 21, 29, 44, 48, 49, 56, 65, 69, 84, 88, 90, 92, 93], "leav": [2, 35], "per": [2, 3, 11, 29, 33, 35, 40, 43, 49, 50, 51, 53, 56, 57, 59, 62, 63, 65, 69, 75, 80, 86, 93], "determin": [2, 3, 8, 14, 19, 21, 25, 29, 33, 35, 40, 44, 49, 51, 54, 56, 59, 65, 69, 74, 80, 84, 87, 89], "cutoff": [2, 3, 87], "consid": [2, 3, 4, 8, 11, 14, 20, 21, 23, 26, 29, 30, 34, 35, 44, 49, 56, 58, 59, 62, 65, 69, 73, 75, 77, 78, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92], "section": [2, 3, 5, 8, 72, 77, 81], "3": [2, 3, 4, 5, 8, 29, 30, 34, 35, 38, 39, 40, 41, 42, 43, 44, 48, 51, 58, 59, 61, 62, 67, 69, 79, 80, 88], "equat": [2, 3, 38], "advanc": [2, 3, 4, 7, 8, 14, 56, 58, 69, 72, 75, 76, 82], "user": [2, 3, 4, 8, 12, 14, 22, 27, 30, 34, 35, 56, 58, 59, 61, 65, 69, 82], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 26, 27, 30, 33, 34, 35, 40, 43, 49, 50, 51, 54, 56, 58, 59, 61, 62, 70, 72, 73, 75, 78, 81, 84, 86, 89, 92], "automat": [2, 3, 4, 21, 29, 71, 77, 78, 79, 80, 81, 84, 86, 89, 90, 91, 92, 93], "greater": [2, 3, 4, 7, 8, 23, 33, 42, 44, 56, 75, 79, 80, 93], "count": [2, 19, 21, 29, 33, 35, 38, 44, 50, 51, 57, 72, 80, 81], "observ": [2, 3, 38, 73, 74, 75, 84, 87, 89], "mislabel": [2, 8, 29, 33, 35, 38, 49, 50, 51, 54, 56, 59, 65, 67, 69, 71, 73, 77, 78, 80, 81, 82, 85, 86, 89, 91, 92], "one": [2, 3, 4, 8, 21, 29, 30, 33, 34, 35, 40, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81, 84, 87, 88, 89, 91, 92, 93], "get_label_issu": [2, 33, 61, 82, 89, 91, 92], "either": [2, 3, 5, 8, 30, 33, 34, 35, 49, 51, 56, 58, 59, 63, 65, 75, 85, 86], "boolean": [2, 5, 8, 19, 33, 35, 43, 49, 51, 54, 59, 61, 63, 65, 66, 71, 73, 75, 78, 80, 81, 86, 89, 90, 92], "label_issues_mask": [2, 35, 59, 61, 72], "indic": [2, 3, 4, 5, 8, 11, 19, 29, 33, 34, 35, 37, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 67, 69, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "its": [2, 4, 7, 8, 14, 30, 33, 34, 35, 42, 43, 51, 54, 57, 58, 59, 61, 63, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 92, 93], "return_indices_ranked_bi": [2, 33, 35, 51, 67, 72, 80, 82, 85, 91, 92], "significantli": [2, 81, 82, 84, 88], "reduc": [2, 33, 35, 44, 73, 80], "time": [2, 8, 30, 33, 34, 44, 49, 72, 74, 79, 80, 81, 82, 86, 87, 89, 90, 91, 92, 93], "take": [2, 4, 8, 29, 30, 34, 39, 40, 44, 48, 59, 77, 81, 84, 91, 93], "run": [2, 4, 5, 7, 9, 12, 14, 21, 22, 28, 30, 33, 34, 61, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "skip": [2, 8, 30, 34, 61, 73, 80, 85, 93], "slow": [2, 3], "step": [2, 5, 21, 40, 57, 80, 81, 82, 84, 88], "caution": [2, 4, 80], "previous": [2, 4, 11, 44, 58, 61, 72, 73, 74, 77, 78, 84, 88, 91], "assign": [2, 5, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 39, 40, 44, 61, 74, 77, 80, 81, 89, 90, 91, 93], "individu": [2, 8, 11, 21, 30, 34, 49, 53, 56, 59, 61, 67, 69, 72, 75, 77, 80, 84, 85, 86, 91, 93], "still": [2, 33, 34, 44, 58, 80, 81, 87, 91], "extra": [2, 30, 34, 44, 48, 49, 50, 61, 78, 80, 81, 84, 87], "receiv": [2, 8, 30, 34, 50, 53, 54, 61, 63, 67, 75, 86], "overwritten": [2, 61], "callabl": [2, 3, 40, 43, 48, 53, 80], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 33, 34, 39, 43, 44, 57, 59, 61, 66, 73, 74, 75, 80, 81, 82, 85, 93], "appropri": [2, 8, 14, 51, 59, 74, 77, 85, 86], "earli": [2, 81], "stop": [2, 81], "x_valid": 2, "y_valid": 2, "could": [2, 19, 29, 44, 58, 74, 77, 81, 85, 89, 91, 93], "f": [2, 5, 73, 74, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92], "ignor": [2, 30, 34, 43, 48, 61, 66, 70, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "allow": [2, 29, 30, 33, 34, 37, 44, 49, 57, 58, 61, 63, 65, 73, 80, 81, 88, 90, 92], "access": [2, 8, 11, 30, 34, 61, 75, 81, 85], "hyperparamet": [2, 53, 58, 81], "purpos": [2, 74, 75, 80, 85, 89], "want": [2, 4, 8, 29, 33, 45, 49, 51, 61, 74, 78, 79, 81, 84, 86, 87, 88, 90, 92, 93], "explicitli": [2, 6, 8, 34, 61], "yourself": [2, 4, 33, 75], "altern": [2, 5, 8, 40, 44, 48, 49, 59, 72, 73, 77, 78, 80, 81, 82, 84, 85, 87, 89, 92], "same": [2, 3, 4, 5, 8, 10, 12, 14, 21, 25, 30, 33, 34, 35, 44, 48, 49, 51, 58, 59, 61, 65, 66, 69, 70, 71, 74, 75, 77, 78, 80, 81, 86, 87, 88, 89, 90, 91, 92], "effect": [2, 8, 22, 30, 34, 49, 58, 61, 77, 78, 80, 81, 87], "offer": [2, 4, 73, 74, 75, 78, 80, 82, 85, 92], "after": [2, 3, 4, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 44, 49, 61, 74, 78, 80, 81, 82, 84, 86, 87, 88, 89, 90, 92], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 40, 58, 61, 74, 91], "label_issues_df": [2, 61, 81], "similar": [2, 8, 29, 30, 34, 42, 44, 49, 53, 54, 56, 58, 61, 65, 69, 74, 75, 77, 78, 80, 81, 82, 86, 87, 90], "document": [2, 3, 4, 8, 12, 14, 29, 30, 33, 34, 35, 40, 43, 48, 50, 51, 53, 56, 57, 58, 61, 65, 66, 67, 69, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "descript": [2, 4, 5, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 44, 54, 61, 74, 75], "were": [2, 3, 4, 29, 34, 50, 56, 69, 73, 77, 80, 82, 84, 86, 88, 90, 91], "present": [2, 3, 4, 8, 10, 11, 17, 29, 44, 58, 66, 71, 77, 80, 81, 87], "actual": [2, 3, 4, 29, 49, 50, 59, 75, 80, 82, 93], "num_class": [2, 29, 33, 44, 48, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 91, 92], "uniqu": [2, 26, 44, 66, 74, 80, 85, 87], "given_label": [2, 4, 25, 29, 38, 61, 66, 70, 73, 74, 75, 77, 78, 81, 82, 89, 90, 92, 93], "normal": [2, 3, 21, 26, 35, 37, 40, 43, 44, 59, 80, 82, 87], "trick": [2, 80], "distribut": [2, 3, 4, 8, 21, 23, 29, 34, 35, 39, 49, 57, 58, 59, 71, 74, 75, 77, 78, 81, 87], "account": [2, 29, 49, 53, 58, 59, 78, 80, 82, 84, 85, 87, 89, 92], "word": [2, 3, 43, 69, 70, 80], "remov": [2, 8, 26, 29, 30, 34, 35, 61, 71, 78, 79, 80, 81, 87, 89, 91, 92], "so": [2, 3, 4, 5, 8, 12, 21, 29, 30, 33, 34, 35, 44, 49, 50, 56, 59, 61, 65, 69, 73, 74, 75, 78, 81, 82, 87, 90], "proportion": [2, 8, 35], "just": [2, 3, 4, 8, 11, 29, 31, 33, 44, 48, 59, 61, 63, 71, 72, 73, 75, 77, 78, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92], "procedur": 2, "get": [2, 3, 4, 6, 11, 26, 30, 31, 34, 35, 40, 43, 44, 49, 51, 53, 58, 59, 61, 62, 63, 71, 73, 78, 79, 80, 81, 82, 87, 88, 89, 91, 92], "detect": [2, 4, 5, 7, 11, 12, 14, 19, 23, 42, 52, 54, 55, 56, 57, 58, 59, 60, 61, 64, 68, 71, 74, 76, 81, 83, 85, 89, 90, 91, 92, 93], "arg": [2, 10, 19, 22, 26, 30, 31, 34, 40, 44, 59, 61], "kwarg": [2, 5, 8, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 40, 48, 61, 63, 65, 67, 80], "test": [2, 8, 21, 34, 40, 48, 61, 71, 74, 75, 77, 78, 81, 88, 89, 91, 92, 93], "expect": [2, 3, 30, 34, 35, 40, 49, 58, 59, 61, 80, 82, 84, 85, 86, 89, 91, 92, 93], "class_predict": 2, "evalu": [2, 8, 30, 31, 33, 34, 57, 61, 73, 74, 75, 80, 81, 82, 84, 88, 89, 90, 91, 92], "simpli": [2, 29, 59, 74, 75, 77, 78, 80, 82, 89, 90, 92, 93], "quantifi": [2, 4, 5, 8, 11, 35, 53, 58, 61, 71, 75, 77, 78, 81, 82, 86], "save_spac": [2, 8, 61], "potenti": [2, 8, 29, 35, 43, 51, 54, 57, 59, 61, 63, 65, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 90, 91, 93], "cach": [2, 78, 87, 92], "panda": [2, 4, 5, 10, 16, 17, 19, 20, 21, 23, 25, 26, 29, 44, 45, 48, 49, 50, 72, 73, 74, 75, 77, 78, 79, 80, 82, 84, 89, 90, 91, 92], "unlik": [2, 8, 35, 37, 40, 48, 50, 51, 53, 69, 74, 84, 85, 87, 89], "both": [2, 4, 8, 14, 21, 29, 30, 34, 35, 44, 49, 51, 59, 63, 65, 70, 71, 74, 80, 81, 82, 84, 93], "mask": [2, 33, 35, 43, 44, 51, 54, 59, 61, 63, 65, 66, 71, 79, 80, 84, 86, 90, 93], "prefer": [2, 59, 67], "plan": 2, "subsequ": [2, 3, 30, 34, 78, 80, 82, 86, 92], "invok": [2, 30, 34, 82, 88], "scratch": [2, 61], "To": [2, 4, 5, 7, 8, 9, 11, 14, 21, 28, 30, 33, 34, 35, 48, 49, 51, 53, 57, 58, 59, 61, 62, 63, 65, 71, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "share": [2, 59, 61], "mostli": [2, 44, 56, 61], "longer": [2, 39, 43, 61, 72, 78, 80, 86, 92], "info": [2, 4, 5, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 61, 69, 74, 75, 79, 80, 93], "about": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 31, 33, 37, 49, 50, 53, 57, 61, 66, 69, 73, 74, 77, 78, 79, 80, 81, 82, 84, 87], "docstr": [2, 29, 30, 34, 44, 61, 79, 82], "unless": [2, 30, 34, 61, 80], "our": [2, 3, 8, 48, 49, 59, 61, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "is_label_issu": [2, 25, 61, 73, 74, 75, 77, 78, 81, 82, 89, 92], "entir": [2, 8, 21, 33, 35, 38, 50, 51, 56, 59, 61, 63, 65, 66, 71, 74, 75, 78, 80, 81, 82, 86, 87, 88, 90, 93], "accur": [2, 3, 4, 8, 14, 29, 33, 35, 49, 50, 51, 54, 57, 59, 61, 62, 63, 65, 66, 72, 75, 77, 78, 80, 81, 84, 89], "label_qu": [2, 49, 61, 82, 84, 89, 92], "measur": [2, 29, 49, 50, 61, 71, 79, 80, 82, 84, 85, 90, 91, 93], "qualiti": [2, 3, 4, 5, 8, 11, 25, 26, 29, 33, 35, 37, 40, 49, 50, 51, 53, 54, 56, 59, 61, 62, 65, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 83, 89, 91, 92], "lower": [2, 4, 5, 8, 11, 23, 33, 40, 49, 50, 53, 56, 57, 59, 61, 62, 65, 69, 73, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 92, 93], "eas": 2, "comparison": [2, 30, 34, 57, 82, 84, 89], "against": [2, 30, 34, 74, 77, 80, 84, 85], "predicted_label": [2, 4, 25, 61, 66, 70, 73, 74, 75, 77, 78, 81, 82, 89, 90, 92], "ad": [2, 30, 34, 75, 84, 89], "precis": [2, 51, 54, 57, 80, 82, 90, 93], "definit": [2, 5, 61, 77, 91], "accessor": [2, 61], "describ": [2, 8, 49, 58, 59, 61, 67, 69, 82, 84, 85, 86, 88, 93], "precomput": [2, 4, 38, 61, 79], "clear": [2, 61, 78, 89, 92], "save": [2, 4, 14, 30, 33, 34, 57, 61, 80, 86, 90, 93], "space": [2, 8, 58, 61, 77, 79, 81], "place": [2, 30, 34, 44, 61, 84, 91], "larg": [2, 33, 61, 77, 78, 80, 81, 87, 90, 93], "deploi": [2, 61, 77, 78, 80, 81], "care": [2, 8, 30, 34, 61, 78, 80, 82], "avail": [2, 4, 5, 10, 12, 27, 34, 61, 80, 82, 84, 86, 89], "cannot": [2, 4, 10, 12, 44, 88, 93], "anymor": 2, "classmethod": [2, 16, 17, 19, 20, 21, 22, 23, 25, 26, 34, 40, 61], "__init_subclass__": [2, 34, 61], "set_": [2, 34, 61], "_request": [2, 34, 61], "pep": [2, 34, 61], "487": [2, 34, 61], "look": [2, 4, 5, 14, 30, 34, 44, 61, 66, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 90, 91, 93], "inform": [2, 4, 5, 8, 11, 14, 27, 30, 34, 44, 49, 50, 54, 57, 61, 66, 69, 70, 71, 73, 74, 77, 78, 82, 84, 87, 90, 93], "__metadata_request__": [2, 34, 61], "infer": [2, 34, 44, 61, 66, 70, 81, 84, 85, 89, 91, 92], "signatur": [2, 30, 34, 61], "accept": [2, 30, 34, 59, 61, 74, 75], "metadata": [2, 34, 61, 77, 78, 81, 93], "through": [2, 4, 5, 34, 61, 73, 75, 78, 79, 80, 84, 87, 89, 92], "develop": [2, 7, 34, 61, 80, 82, 93], "request": [2, 34, 61, 75, 78, 79, 85, 91, 92, 93], "those": [2, 3, 8, 33, 34, 35, 48, 49, 51, 57, 61, 65, 69, 70, 71, 73, 80, 81, 86, 90], "http": [2, 4, 5, 7, 8, 9, 28, 30, 31, 33, 34, 37, 44, 58, 61, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "www": [2, 34, 61, 87], "org": [2, 30, 31, 34, 44, 58, 61, 80, 82, 93], "dev": [2, 34, 61], "0487": [2, 34, 61], "get_metadata_rout": [2, 34, 61], "rout": [2, 34, 61], "pleas": [2, 30, 34, 48, 61, 71, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 89, 92, 93], "guid": [2, 5, 34, 61, 72, 81], "mechan": [2, 30, 34, 61], "metadatarequest": [2, 34, 61], "encapsul": [2, 14, 34, 56, 61], "get_param": [2, 34, 48, 61], "subobject": [2, 34, 61], "param": [2, 8, 30, 34, 48, 58, 61, 80], "name": [2, 4, 5, 8, 10, 11, 29, 30, 34, 39, 40, 44, 48, 49, 50, 57, 61, 66, 70, 73, 75, 78, 79, 80, 81, 82, 85, 90, 92, 93], "set_fit_request": [2, 34, 61], "union": [2, 3, 4, 10, 33, 34, 40, 44, 45, 51, 57, 61, 65, 69, 80], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 38, 40, 43, 44, 48, 49, 50, 54, 56, 57, 59, 61, 66, 70, 73, 74, 80, 84, 85, 93], "unchang": [2, 30, 34, 61, 93], "relev": [2, 14, 21, 34, 61, 81], "enable_metadata_rout": [2, 34, 61], "set_config": [2, 34, 61], "meta": [2, 34, 61], "rais": [2, 4, 10, 11, 30, 34, 37, 40, 61, 80], "alia": [2, 30, 34, 61], "metadata_rout": [2, 34, 61], "retain": [2, 34, 44, 61], "chang": [2, 30, 33, 34, 37, 61, 69, 73, 74, 78, 80, 86, 87, 92, 93], "version": [2, 4, 5, 7, 8, 9, 13, 18, 24, 28, 30, 32, 34, 36, 37, 44, 47, 48, 59, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "sub": [2, 34, 56, 61], "pipelin": [2, 34, 61], "otherwis": [2, 8, 29, 30, 33, 34, 35, 41, 43, 44, 51, 58, 61, 63, 65, 66, 70, 78, 80, 92], "updat": [2, 11, 30, 33, 34, 61, 72, 74, 81], "set_param": [2, 34, 48, 61], "simpl": [2, 30, 34, 35, 49, 59, 61, 74, 75, 77, 78, 81, 84, 87, 89, 91, 92], "well": [2, 3, 8, 30, 34, 37, 38, 49, 51, 57, 59, 61, 66, 69, 70, 72, 74, 75, 77, 78, 80, 81, 82, 84, 86, 87], "nest": [2, 30, 34, 61, 67, 69, 70, 93], "latter": [2, 30, 34, 61, 87], "compon": [2, 34, 61], "__": [2, 34, 61], "set_score_request": [2, 61], "structur": [3, 58, 77, 91], "unobserv": 3, "less": [3, 4, 8, 26, 33, 40, 49, 58, 59, 63, 65, 69, 75, 77, 79, 80, 81, 82, 86, 93], "channel": [3, 73, 82], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 29, 38, 44, 50, 75, 79, 92], "inv": 3, "confident_joint": [3, 19, 29, 35, 44, 50, 51, 72, 80, 82], "un": 3, "under": [3, 8, 30, 34, 50, 57, 58, 75, 77, 78, 81, 82, 87], "joint": [3, 29, 35, 38, 44, 50, 51, 79], "num_label_issu": [3, 33, 35, 51, 66, 70, 72], "estimation_method": [3, 33], "off_diagon": 3, "multi_label": [3, 29, 35, 44, 45, 51, 85], "don": [3, 71, 75, 77, 78, 81, 82, 86], "statis": 3, "compute_confident_joint": [3, 29, 35, 44, 51, 82], "off": [3, 35, 44, 56, 81, 82, 86, 87], "j": [3, 4, 29, 30, 34, 35, 51, 54, 57, 58, 67, 69, 70, 74, 75, 82, 90, 93], "confident_learn": [3, 35, 51, 82], "off_diagonal_calibr": 3, "calibr": [3, 35, 44, 49, 84], "cj": [3, 38, 44], "axi": [3, 26, 38, 40, 63, 66, 73, 74, 75, 80, 81, 82, 84, 85, 87, 89, 90], "bincount": [3, 74, 75, 82, 84, 85], "alwai": [3, 8, 30, 34, 44, 73, 82, 89, 91, 92], "estimate_issu": 3, "over": [3, 8, 30, 33, 34, 56, 57, 63, 65, 75, 77, 79, 80, 81, 82, 87, 89, 91], "As": [3, 5, 71, 74, 75, 78, 82, 89, 93], "add": [3, 4, 5, 11, 30, 34, 48, 57, 73, 74, 75, 78, 80, 81, 82, 85, 92], "approach": [3, 29, 33, 35, 77, 82, 85, 87, 89, 91], "custom": [3, 5, 8, 9, 25, 30, 33, 34, 40, 43, 59, 75, 78, 82, 92], "know": [3, 74, 75, 77, 78, 80, 81, 82, 84], "cut": [3, 56, 71, 82], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 87, 93], "underestim": 3, "few": [3, 57, 71, 75, 80, 84, 85, 86, 87, 93], "4": [3, 4, 16, 17, 19, 20, 21, 23, 25, 26, 39, 40, 43, 53, 54, 56, 57, 59, 62, 69, 79, 80, 85, 90, 93], "detail": [3, 4, 12, 14, 29, 30, 34, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 65, 66, 67, 71, 72, 73, 85, 87, 93], "num_issu": [3, 5, 33, 73, 74, 75, 77, 78, 81, 82], "calibrate_confident_joint": 3, "up": [3, 8, 15, 21, 22, 25, 35, 40, 49, 79, 80, 86, 89, 92, 93], "p_": [3, 29, 35], "pair": [3, 4, 8, 29, 35, 82], "v": [3, 8, 33, 50, 51, 53, 59, 74, 75, 85, 87, 88], "rest": [3, 4, 5, 7, 8, 9, 28, 50, 51, 53, 61, 74, 75, 77, 78, 80, 81, 82, 84, 89, 91, 92], "fashion": [3, 4, 63, 91], "2x2": 3, "incorrectli": [3, 29, 50, 51, 54, 77, 93], "calibrated_cj": 3, "c": [3, 8, 43, 51, 59, 71, 73, 74, 75, 77, 78, 80, 82, 85, 87, 88, 89, 91], "whose": [3, 4, 8, 23, 30, 34, 38, 43, 49, 53, 56, 62, 65, 69, 70, 73, 74, 75, 77, 78, 80, 81, 82, 85, 86, 87, 90, 93], "truli": [3, 87, 90], "estimate_joint": [3, 29, 82], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 51, 57, 82, 86, 88, 90, 93], "return_indices_of_off_diagon": 3, "frequenc": [3, 21, 49, 50, 57, 66, 87], "done": [3, 8, 61, 74, 80, 82, 85, 87, 88], "overfit": [3, 8, 54, 57, 73, 74, 75, 77, 78, 81, 88, 91], "classifict": 3, "singl": [3, 4, 21, 29, 30, 34, 40, 41, 44, 49, 50, 56, 57, 58, 59, 69, 73, 74, 80, 82, 85, 86, 91], "baselin": [3, 30, 35, 87, 89, 92], "proxi": 3, "tupl": [3, 26, 30, 34, 38, 39, 41, 43, 44, 49, 51, 57, 65, 67, 69, 70, 73, 93], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 33, 38, 49, 63, 65, 71, 80, 81, 90, 92], "practic": [3, 75, 81, 82, 87, 89, 91, 92], "complet": [3, 73, 74, 75, 77, 78, 80, 81, 82, 86], "gist": 3, "cj_ish": 3, "guess": [3, 38, 82, 84], "8": [3, 4, 5, 6, 39, 40, 41, 43, 53, 67, 69, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "parallel": [3, 35, 57, 67, 79], "again": [3, 48, 80, 87, 91], "simplifi": [3, 12], "understand": [3, 7, 29, 50, 57, 75, 82, 89, 90, 93], "100": [3, 30, 34, 59, 74, 75, 77, 79, 80, 81, 82, 85, 90, 91, 92, 93], "optim": [3, 30, 31, 34, 48, 81, 84], "speed": [3, 35, 79, 80, 89, 92], "dtype": [3, 20, 21, 26, 30, 34, 43, 44, 53, 69, 73, 86], "enumer": [3, 30, 34, 73, 74, 75, 81, 93], "s_label": 3, "confident_bin": 3, "6": [3, 4, 34, 40, 44, 69, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "num_confident_bin": 3, "argmax": [3, 35, 59, 63, 66, 73, 80, 82, 87, 90], "elif": 3, "estimate_lat": 3, "py_method": [3, 38], "cnt": [3, 38], "1d": [3, 4, 14, 33, 35, 40, 41, 44, 45, 53, 62, 73, 91], "eqn": [3, 38], "margin": [3, 35, 38, 40, 59], "marginal_p": [3, 38], "shorthand": [3, 11], "proport": [3, 8, 29, 50, 82, 88], "poorli": [3, 38, 91], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 82], "variabl": [3, 5, 12, 22, 44, 61, 62, 73, 74, 77, 82, 85, 89], "exact": [3, 38, 74, 75, 77, 81, 91], "within": [3, 4, 8, 13, 30, 31, 34, 36, 51, 56, 65, 67, 69, 74, 75, 80, 81, 86, 90], "percent": 3, "often": [3, 29, 38, 50, 80, 82, 88, 90], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 44, 45, 57, 73, 74, 77, 78, 80, 81, 86, 87, 92], "wai": [3, 4, 48, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 88, 91, 92], "pro": 3, "con": 3, "pred_proba": [3, 88], "combin": [3, 29, 74, 79, 80, 81, 82, 88, 89], "becaus": [3, 38, 44, 56, 78, 80, 82, 84, 86], "littl": [3, 33, 79, 86, 93], "uniform": [3, 59, 79, 80, 82], "20": [3, 5, 70, 73, 75, 78, 79, 80, 81, 82, 90, 93], "Such": [3, 81, 87], "bound": [3, 20, 30, 34, 54, 56, 57, 86], "reason": [3, 19, 30, 34], "comment": [3, 43, 93], "end": [3, 4, 30, 34, 57, 81, 90, 93], "file": [3, 4, 10, 32, 33, 47, 57, 73, 74, 77, 78, 79, 80, 86, 87, 90, 91, 93], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 82], "handl": [3, 4, 5, 8, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 72, 74, 75, 77, 78, 81, 82, 90, 91, 93], "five": [3, 54, 57, 82, 86], "estimate_cv_predicted_prob": [3, 82], "estimate_noise_matric": 3, "get_confident_threshold": [3, 33], "amongst": [3, 8], "confident_threshold": [3, 8, 19, 33, 58], "unifi": 4, "audit": [4, 7, 10, 11, 14, 73, 76, 77, 78, 80, 81, 82, 86], "kind": [4, 5, 73, 74, 77, 78, 79, 81, 82], "addit": [4, 5, 7, 8, 9, 11, 27, 28, 30, 34, 40, 45, 49, 57, 67, 73, 74, 77, 78, 81, 82, 84, 87, 88, 91, 92], "depend": [4, 5, 7, 8, 9, 10, 11, 28, 32, 35, 37, 44, 47, 51, 58, 61, 62, 71], "instal": [4, 5, 7, 8, 9, 28, 30, 32, 33, 34, 35, 47, 48, 63, 65], "pip": [4, 5, 7, 9, 28, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "development": [4, 5, 7, 9, 28], "git": [4, 5, 7, 9, 28, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "github": [4, 5, 7, 9, 28, 30, 31, 44, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "com": [4, 5, 7, 9, 28, 30, 31, 33, 37, 44, 58, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "egg": [4, 5, 7, 9, 28, 71, 79], "label_nam": [4, 5, 6, 8, 10, 26, 71, 73, 74, 75, 77, 78, 80, 81, 82], "image_kei": [4, 81], "interfac": [4, 71, 80, 82], "librari": [4, 8, 34, 54, 57, 58, 71, 74, 78, 79, 80, 92], "goal": 4, "track": [4, 11, 12, 71, 74, 79, 80, 82], "intermedi": [4, 7, 75], "statist": [4, 8, 11, 19, 21, 29, 49, 50, 57, 75, 77, 78, 81, 82], "convert": [4, 10, 30, 34, 41, 45, 49, 56, 65, 69, 72, 73, 78, 79, 80, 81, 84, 85, 86, 92], "hug": [4, 10, 81], "face": [4, 10, 14, 79, 81, 85], "kei": [4, 5, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 34, 40, 49, 50, 56, 58, 74, 75, 78, 80, 81, 82, 84, 86], "string": [4, 8, 10, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 34, 44, 49, 50, 62, 66, 69, 70, 77, 78, 80, 84, 85, 92, 93], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 34, 39, 44, 49, 50, 53, 54, 56, 57, 74, 75, 77, 78, 82, 84, 85, 86], "path": [4, 10, 30, 33, 34, 57, 73, 74, 80, 86], "local": [4, 10, 30, 31, 34, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "text": [4, 5, 8, 10, 16, 17, 19, 20, 21, 22, 23, 25, 26, 40, 58, 67, 69, 70, 71, 74, 75, 76, 79, 80, 82, 83, 84, 87], "txt": [4, 10, 93], "csv": [4, 10, 77, 78, 89, 91, 92], "json": [4, 10], "hub": [4, 10, 87], "regress": [4, 5, 10, 12, 14, 18, 25, 27, 74, 75, 78, 83, 84, 87, 92], "imag": [4, 7, 29, 34, 54, 56, 57, 58, 63, 65, 66, 71, 74, 75, 79, 80, 83, 84, 85, 86, 88, 90], "point": [4, 5, 8, 21, 30, 34, 74, 75, 77, 78, 80, 81, 82, 84], "field": [4, 8, 30, 34], "themselv": [4, 89, 91, 92], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 43, 67, 69, 75, 81, 83, 90], "load_dataset": [4, 10, 81], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 17, 29, 50, 57, 66, 70, 71, 79, 82, 86, 90, 93], "empti": [4, 10, 38, 49, 75, 80, 85], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 71, 73, 74, 75, 77, 78, 80, 81, 82], "knn_graph": [4, 8, 14, 16, 21, 23, 26, 77], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 19, 20, 21, 23, 25, 26, 73, 74, 75, 77, 78, 80, 81, 82], "sort": [4, 14, 33, 35, 40, 42, 49, 51, 54, 56, 57, 59, 65, 67, 69, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 89, 90, 91, 92, 93], "common": [4, 11, 14, 75, 76, 79, 80, 82, 85, 86, 90], "real": [4, 14, 71, 74, 75, 80, 82, 84, 85, 89, 90], "world": [4, 14, 71, 74, 75, 80, 82, 84, 89, 90], "interact": [4, 14, 78, 80], "embed": [4, 8, 14, 58, 71, 73, 74, 75, 77, 78, 82, 92], "thereof": [4, 14], "insight": [4, 14, 57, 84], "act": [4, 8, 56, 74], "issuefind": [4, 14, 27], "logic": [4, 12, 33, 35, 63, 65, 90], "best": [4, 14, 39, 49, 59, 74, 75, 77, 78, 80, 81, 84, 85, 87, 89, 91, 92, 93], "2d": [4, 14, 33, 40, 41, 43, 44, 49, 73, 85, 91], "num_exampl": [4, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 50, 73, 74, 75, 77, 78, 81, 82], "represent": [4, 8, 14, 30, 34, 41, 51, 71, 73, 74, 75, 78, 80, 81, 82, 87, 92], "num_featur": [4, 14, 30, 34, 48], "distanc": [4, 8, 14, 21, 23, 26, 42, 56, 58, 77, 87], "nearest": [4, 8, 14, 20, 21, 23, 42, 58, 75, 78, 87], "neighbor": [4, 8, 14, 20, 21, 23, 42, 58, 74, 75, 77, 78, 80, 81, 87], "graph": [4, 8, 11, 14, 21, 26], "squar": [4, 44, 61, 79, 89], "csr": 4, "evenli": 4, "omit": [4, 56, 57, 81, 86], "itself": [4, 30, 34, 86], "three": [4, 8, 29, 49, 50, 61, 66, 73, 74, 75, 77, 79, 82, 84, 88, 89, 90, 91, 93], "indptr": 4, "wise": 4, "start": [4, 5, 8, 30, 31, 34, 71, 77, 85, 93], "th": [4, 39, 43, 44, 49, 51, 54, 56, 57, 58, 67, 69, 70, 78, 85, 86, 93], "ascend": [4, 29, 42, 50, 81, 82], "segment": [4, 63, 65, 66, 83], "reflect": [4, 77, 78, 84, 86, 87, 89, 91, 92], "maintain": 4, "posit": [4, 30, 34, 44, 57, 79, 87], "nearestneighbor": [4, 8, 58, 77, 87], "kneighbors_graph": [4, 77], "illustr": 4, "todens": 4, "second": [4, 40, 42, 44, 57, 59, 74, 80, 82, 93], "duplic": [4, 7, 18, 19, 30, 34, 71, 74, 82], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 34, 40, 48], "neither": [4, 8, 12, 86], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 49, 80, 84, 93], "unspecifi": [4, 14, 35, 51], "interest": [4, 14, 19, 66, 70, 78, 82, 90, 91, 92, 93], "constructor": [4, 8, 14, 20, 25], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27], "respons": [4, 14, 19, 61, 62, 79, 89, 93], "random_st": [4, 73, 74, 75, 81, 82, 85, 87, 91], "lab": [4, 6, 16, 17, 19, 20, 21, 22, 23, 25, 26, 33, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85], "comprehens": [4, 71, 81], "nbr": 4, "n_neighbor": [4, 8, 58], "metric": [4, 8, 16, 21, 26, 44, 48, 57, 58, 73, 77, 78, 81, 82, 89, 91, 92], "euclidean": [4, 8, 56, 58, 77], "mode": [4, 30, 33, 34, 87], "4x4": 4, "float64": [4, 21, 30, 34, 69], "compress": [4, 8, 44, 63, 65], "toarrai": 4, "NOT": [4, 33, 78], "23606798": 4, "41421356": 4, "configur": [4, 14, 40, 75], "suppos": [4, 8, 54, 87, 89, 91, 92], "who": [4, 56, 77, 82, 91, 93], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "clean_learning_kwarg": [4, 8, 20, 25], "labelissuemanag": [4, 8, 20], "prune_method": [4, 72], "prune_by_noise_r": [4, 35, 51, 82], "report": [4, 5, 9, 13, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 70, 71, 73, 74, 75, 77, 78, 82, 93], "include_descript": [4, 16, 17, 19, 20, 21, 23, 25, 26, 27], "show_summary_scor": [4, 27], "summari": [4, 5, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 48, 50, 55, 64, 65, 67, 68, 69, 72, 73, 74, 75, 77, 78, 79, 81, 82, 86, 90, 93], "show": [4, 21, 30, 34, 39, 44, 57, 66, 70, 75, 77, 78, 79, 80, 81, 82, 84, 87, 89, 90, 91, 93], "top": [4, 29, 33, 35, 44, 51, 54, 57, 59, 66, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 86, 87, 89, 92, 93], "suffer": [4, 8, 11, 19, 51, 59, 70, 93], "onc": [4, 19, 29, 30, 34, 74, 80, 82, 85, 86, 91], "familiar": 4, "usag": [4, 33, 48], "found": [4, 5, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 44, 71, 73, 74, 75, 77, 78, 80, 81, 87, 89, 91, 92, 93], "issue_summari": [4, 8, 11, 74], "overal": [4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 40, 49, 50, 53, 56, 57, 61, 65, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 86, 93], "sever": [4, 5, 8, 10, 11, 19, 30, 33, 34, 35, 53, 56, 58, 59, 65, 69, 71, 73, 74, 75, 77, 78, 79, 80, 82, 86, 87, 91, 92, 93], "dataissu": [4, 11, 14, 27], "outlier": [4, 7, 12, 18, 19, 26, 36, 59, 71, 74, 75, 82, 83], "someth": [4, 5, 30, 34, 59], "123": [4, 74, 75], "456": [4, 73, 78, 91, 92], "nearest_neighbor": 4, "7": [4, 40, 41, 48, 67, 69, 73, 74, 75, 77, 78, 79, 80, 84, 85, 86, 87, 89, 90, 91, 92, 93], "9": [4, 16, 17, 19, 20, 21, 23, 25, 26, 40, 41, 53, 67, 69, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "distance_to_nearest_neighbor": [4, 74, 75, 77, 78, 81, 82], "789": 4, "get_issu": [4, 8, 11, 73, 75, 77, 78, 80, 81], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74, 75], "focu": [4, 11, 78, 90, 93], "full": [4, 8, 11, 33, 57, 81, 93], "summar": [4, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 66, 70, 71, 90], "valueerror": [4, 10, 11, 37, 40, 80], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 66, 75, 77, 78, 81, 82, 86], "lie": [4, 8, 42, 58, 59, 73, 74, 75, 77, 78, 81, 82, 92], "directli": [4, 12, 14, 27, 33, 48, 49, 75, 78, 85, 86, 89, 92], "compar": [4, 49, 58, 69, 74, 75, 77, 82], "get_issue_summari": [4, 11, 75], "get_info": [4, 11, 75, 78], "yet": [4, 15, 18, 22, 79, 84], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 22, 30, 34, 74], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 73, 74, 81], "load": [4, 10, 33, 57, 79, 80, 81, 82, 86, 87, 90, 93], "futur": [4, 8, 19, 30, 34, 49, 71, 74, 78], "overwrit": [4, 74], "separ": [4, 29, 40, 53, 74, 75, 80, 81, 86, 88], "static": 4, "rememb": [4, 78, 80, 82], "part": [4, 8, 30, 34, 35, 54, 56, 57, 73, 74, 79, 90, 93], "ident": [4, 8, 19, 44, 78], "walk": 5, "alongsid": [5, 30, 34, 74, 80], "pre": [5, 6, 8, 30, 34, 74, 75, 81, 90, 93], "runtim": [5, 30, 33, 34, 61, 63, 65, 73, 80, 81], "issue_manager_factori": [5, 12, 74], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 40, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "thing": [5, 34, 82, 89, 92], "next": [5, 49, 71, 73, 77, 78, 80, 84, 86, 89, 91, 92, 93], "dummi": 5, "randint": [5, 26, 40, 74, 75, 80], "mark": [5, 8, 72, 86, 87, 89], "regard": [5, 75, 82], "rand": [5, 40, 74, 75], "is_": [5, 8, 74], "_issu": [5, 8, 74], "issue_score_kei": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "whole": [5, 21, 30, 34, 75], "make_summari": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "popul": [5, 75, 78], "verbosity_level": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 33, 66, 70, 80, 85], "intermediate_arg": 5, "min": [5, 40, 56, 69, 74, 80, 87], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 20, 21, 22, 23, 25, 26, 74], "instanti": [5, 14, 33, 48, 58, 73, 75, 77, 92], "477762": 5, "286455": 5, "term": [5, 8, 38, 44, 57, 73, 74, 75, 77, 78, 81, 82], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 16, 23, 73, 74, 75, 77, 78, 79, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "003042": 5, "058117": 5, "11": [5, 48, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "121908": 5, "15": [5, 42, 61, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "169312": 5, "17": [5, 73, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 74, 75, 79, 82], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 26], "group": [6, 7, 21, 26, 79, 86, 93], "dbscan": [6, 8, 26, 80], "hdbscan": [6, 80], "etc": [6, 8, 19, 30, 34, 38, 48, 49, 67, 71, 74, 75, 77, 78, 80, 81, 82], "sensit": [6, 8], "ep": [6, 26, 57], "radiu": 6, "min_sampl": [6, 26], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 71, 73, 80, 81, 84, 91, 92], "kmean": [6, 80], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 26, 80], "cluster_id": [6, 8, 26, 80], "labels_": 6, "underperforming_group": [6, 8, 18, 80], "search": [7, 8, 17, 21, 22, 43, 61, 80, 88], "nondefault": 7, "Near": [7, 80], "iid": [7, 21, 75, 77, 81, 82], "imbal": [7, 18, 53, 58, 59, 75], "null": [7, 18, 75, 78, 81, 82], "togeth": [7, 8, 38, 74, 75, 77, 78, 81, 82, 89, 92, 93], "built": [7, 40], "own": [7, 30, 32, 34, 47, 53, 54, 57, 63, 67, 73, 75, 77, 78, 80, 81, 84, 85, 89, 90, 91, 92, 93], "prerequisit": 7, "basic": [7, 34, 48, 77, 78, 87], "page": [8, 75, 80, 82], "variou": [8, 11, 25, 32, 45, 47, 71, 74, 75, 77, 78, 79, 82, 84, 86, 91], "sai": [8, 30, 34, 85, 90], "why": [8, 78], "matter": [8, 29, 50], "_score": 8, "flag": [8, 19, 21, 35, 40, 50, 51, 54, 61, 71, 73, 74, 75, 77, 78, 79, 81, 82, 86, 87, 89, 90, 92], "badli": [8, 56, 93], "code": [8, 30, 34, 38, 44, 48, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "issue_scor": 8, "outlier_scor": [8, 23, 74, 75, 77, 78, 81, 82, 87], "atyp": [8, 58, 74, 75, 77, 78, 81, 82, 87], "datapoint": [8, 26, 35, 40, 44, 59, 62, 71, 73, 74, 75, 77, 78, 80, 88, 89, 91, 92], "is_issu": [8, 19], "is_outlier_issu": [8, 74, 75, 77, 78, 81, 82], "annot": [8, 29, 39, 49, 50, 51, 53, 54, 56, 57, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 86, 90], "transform": [8, 40, 42, 44, 58, 59, 75, 78, 81, 87, 91, 92, 93], "dissimilar": [8, 77, 78], "preced": 8, "cosin": [8, 58, 87], "incorrect": [8, 56, 59, 62, 73, 74, 75, 77, 78, 81, 82, 86, 89, 91], "due": [8, 33, 35, 59, 63, 65, 73, 74, 75, 77, 78, 81, 82], "appear": [8, 29, 39, 50, 51, 54, 62, 75, 77, 78, 81, 89, 90], "likelihood": [8, 33, 35, 51, 56, 58, 59, 63, 67], "now": [8, 33, 72, 73, 75, 84, 86, 87, 89, 91, 92, 93], "u": [8, 73, 74, 77, 80, 81, 82, 84, 85, 88, 89, 90, 91, 92, 93], "token": [8, 43, 65, 66, 67, 68, 69, 70, 80, 82, 83], "calcul": [8, 21, 33, 40, 49, 53, 54, 56, 57, 58, 61, 65, 79, 81], "hamper": [8, 79, 81], "analyt": [8, 71, 80, 84], "lead": [8, 56, 59, 81, 86], "draw": [8, 74, 75], "conclus": [8, 78], "try": [8, 33, 35, 48, 49, 63, 65, 71, 75, 77, 78, 80, 81, 82, 90], "veri": [8, 29, 50, 54, 56, 74, 75, 77, 78, 80, 81, 82, 84, 87, 89, 92], "rare": [8, 35, 57, 74, 75, 77, 78, 80, 81, 82], "anomal": [8, 59, 74, 75, 77, 78, 81, 82], "articl": [8, 33, 80], "ai": [8, 71, 73, 74, 75, 77, 78, 79, 80, 81, 83, 84, 85, 87, 89, 91, 92, 93], "blog": 8, "unexpect": [8, 30, 34, 78], "consequ": 8, "inspect": [8, 73, 75, 81, 82, 86, 89, 92], "neg": [8, 56, 57, 74, 75, 79], "affect": [8, 30, 34, 63, 69, 78, 80], "extrem": [8, 74, 75, 77, 78, 80, 81, 82], "rel": [8, 29, 49, 50, 58, 74, 75, 77, 78, 81, 82, 87], "record": [8, 30, 34, 73, 77, 89], "abbrevi": 8, "misspel": 8, "typo": [8, 70], "resolut": 8, "video": [8, 79], "audio": [8, 74, 75, 80, 83], "minor": [8, 43], "variat": 8, "translat": 8, "d": [8, 42, 77, 78, 82, 85, 91, 93], "constant": [8, 26, 61], "median": [8, 25], "question": [8, 19, 71, 82], "nearli": [8, 19, 75, 77, 78, 81], "awar": [8, 72, 82], "presenc": [8, 82], "signific": [8, 75, 77, 78, 81, 82], "violat": [8, 75, 77, 78, 81, 82], "assumpt": [8, 75, 77, 78, 81, 82], "changepoint": [8, 75, 77, 78, 81, 82], "shift": [8, 75, 77, 78, 81, 82], "drift": [8, 75, 77, 81, 82], "autocorrel": [8, 75, 77, 78, 81, 82], "almost": [8, 75, 77, 78, 81, 82], "adjac": [8, 75, 77, 78, 81, 82], "tend": [8, 29, 38, 75, 77, 78, 81, 82, 90, 93], "sequenti": [8, 30, 34, 48, 81], "gap": 8, "b": [8, 16, 17, 19, 20, 21, 23, 25, 26, 29, 43, 44, 69, 77, 78, 79, 82, 88, 91, 93], "x1": [8, 54, 57, 86], "x2": [8, 54, 57, 86], "10th": 8, "100th": 8, "90": [8, 69, 77, 81, 82, 88, 90, 91], "similarli": [8, 30, 34, 74, 77, 80, 81, 86], "math": [8, 81], "behind": [8, 58, 82], "fundament": 8, "proper": [8, 44, 49, 54, 57, 78, 81, 84, 86, 91], "closer": [8, 56, 86], "scenario": [8, 59, 74, 75], "underli": [8, 58, 67, 69, 93], "stem": [8, 58, 87], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 85, 87], "partit": [8, 88], "ahead": 8, "good": [8, 30, 34, 48, 50, 56, 59, 63, 65, 66, 71, 77, 78, 81], "fix": [8, 49, 78, 82, 89, 92], "problem": [8, 33, 40, 66, 71, 74, 75, 78, 80, 81], "deploy": [8, 82, 89, 91, 92], "overlook": [8, 56, 86], "fact": 8, "thu": [8, 29, 34, 50, 73, 77, 78, 82, 88, 91, 93], "diagnos": [8, 75, 80], "rarest": [8, 75, 77, 78, 81, 82], "q": [8, 86], "fall": [8, 56, 65, 69, 82, 87], "subpar": 8, "special": [8, 43], "techniqu": 8, "smote": 8, "asymmetr": [8, 29], "properli": [8, 33, 39, 44, 45, 63, 80, 85, 87, 89, 90], "too": [8, 35, 40, 58, 75, 80, 81, 86], "dark": [8, 90], "bright": [8, 93], "blurri": [8, 81], "abnorm": [8, 57, 81], "cluster": [8, 26], "slice": [8, 42], "poor": 8, "subpopul": 8, "lowest": [8, 49, 57, 75, 80, 81, 84, 85, 86, 90], "get_self_confidence_for_each_label": [8, 40, 59], "power": [8, 77, 78, 79, 81, 82, 93], "r": [8, 33, 61, 74, 75, 89, 90], "tabular": [8, 71, 74, 75, 76, 80, 83, 84], "categor": [8, 58, 74, 75, 76, 80, 89, 91], "encod": [8, 41, 57, 63, 66, 77, 78, 80, 89, 90, 91, 92], "miss": [8, 22, 30, 34, 44, 54, 56, 75, 77, 78, 80, 81, 82, 86, 89], "pattern": 8, "exert": [8, 75], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 16, 74, 75, 77, 78, 80, 81, 82], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 21, 75, 77, 78, 81, 82], "non_iid_kwarg": 8, "class_imbal": [8, 17, 75, 77, 78, 81, 82], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 20, 25], "health_summari": [8, 20, 29, 71, 79], "health_summary_kwarg": 8, "tandem": [8, 79], "view": [8, 30, 34, 35, 65, 67, 69, 71, 73, 74, 75, 77, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "ood_kwarg": 8, "outofdistribut": [8, 23, 58, 87], "outsid": 8, "knn": [8, 11, 21, 26, 58, 77, 87], "outlierissuemanag": [8, 12, 23, 74], "nearduplicateissuemanag": [8, 12, 16], "noniidissuemanag": [8, 12, 21], "num_permut": [8, 21], "permut": [8, 21], "significance_threshold": [8, 21], "signic": 8, "noniid": [8, 18], "classimbalanceissuemanag": [8, 17], "underperforminggroupissuemanag": [8, 26], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 26], "filter_cluster_id": [8, 26], "clustering_kwarg": [8, 26], "faq": [8, 71, 75, 77, 78, 81, 83], "nullissuemanag": [8, 22], "codeblock": 8, "demonstr": [8, 33, 74, 75, 78, 80, 81, 82, 84, 85, 86, 89, 90], "howev": [8, 30, 34, 44, 73, 77, 78, 81, 84, 88, 90, 91, 92], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 74, 79, 84, 86, 90], "fewer": [8, 35, 44, 86], "vice": [8, 50], "versa": [8, 50], "light": [8, 79, 81, 86, 90], "29": [8, 79, 81, 84, 85, 86, 90, 93], "low_inform": [8, 81], "odd_aspect_ratio": [8, 81], "35": [8, 74, 79, 81, 84, 85, 86, 90], "odd_siz": [8, 81], "10": [8, 16, 20, 21, 26, 30, 31, 57, 58, 59, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "doc": [8, 30, 34, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "data_issu": [9, 13, 14, 27, 74], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 48, 59, 74, 75, 81, 84], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 27, 81, 84, 89], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 81], "serv": [11, 14, 84], "central": [11, 93], "repositori": 11, "strategi": [11, 40, 80], "being": [11, 29, 30, 34, 35, 40, 43, 44, 59, 77, 80, 82, 89, 90, 91], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 19], "avoid": [11, 30, 33, 34, 35, 42, 44, 51, 54, 57, 61, 63, 65, 74, 75, 80], "recomput": [11, 92], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 20, 29, 50, 71], "get_data_statist": 11, "concret": 12, "subclass": [12, 30, 34, 58, 74], "my_issu": 12, "stabl": [13, 18, 24, 32, 36, 44, 47, 58, 72], "unregist": 13, "instati": 14, "public": [14, 82, 86, 90, 93], "creation": [14, 34], "execut": [14, 30, 34, 74, 80, 86], "coordin": [14, 54, 56, 57, 86, 93], "behavior": [14, 29, 30, 34, 57], "At": [14, 57, 80], "associ": [14, 30, 34, 57, 84], "get_available_issue_typ": 14, "isn": [15, 22], "direct": [15, 22, 30, 34], "_": [16, 19, 20, 21, 22, 25, 26, 40, 43, 44, 73, 74, 79, 81, 82, 85, 91], "classvar": [16, 17, 19, 20, 21, 22, 23, 25, 26], "short": [16, 17, 19, 20, 21, 22, 23, 25, 26, 43, 44], "item": [16, 17, 19, 20, 21, 22, 23, 25, 26, 44, 74, 75, 80, 81, 82, 84, 85], "some_info_kei": [16, 17, 19, 20, 21, 22, 23, 25, 26], "additional_info_kei": [16, 17, 19, 20, 21, 22, 23, 25, 26], "near_duplicate_set": [16, 74, 75, 77, 78, 80, 81, 82], "occurr": [16, 17, 19, 21, 22, 23, 26, 43], "collect_info": [16, 17, 19, 20, 21, 22, 23, 25, 26], "median_nn_dist": 16, "near_duplicate_scor": [16, 74, 75, 77, 78, 80, 81, 82], "info_to_omit": [16, 17, 19, 20, 21, 23, 25, 26], "compos": [16, 17, 19, 20, 21, 23, 25, 26, 30, 34, 78, 87, 92], "is_x_issu": [16, 17, 19, 20, 21, 23, 25, 26], "x_score": [16, 17, 19, 20, 21, 23, 25, 26], "val_a": [16, 17, 19, 20, 21, 23, 25, 26], "val_b1": [16, 17, 19, 20, 21, 23, 25, 26], "val_b2": [16, 17, 19, 20, 21, 23, 25, 26], "report_str": [16, 17, 19, 20, 21, 22, 23, 25, 26, 27], "class_imbalance_scor": [17, 75, 77, 78, 81, 82], "bleed": [18, 24, 32], "edg": [18, 24, 32, 56, 71, 82, 93], "sharp": [18, 24, 32], "abc": 19, "believ": [19, 90], "priori": [19, 82], "global": 19, "anoth": [19, 29, 33, 43, 56, 59, 77, 78, 80, 82, 84, 87, 92], "abstract": 19, "applic": [20, 49, 80, 82, 84, 85, 93], "typevar": [20, 30, 34, 56, 57], "_scalartype_co": 20, "covari": [20, 61, 89], "get_health_summari": 20, "summary_dict": 20, "label_scor": [20, 25, 73, 74, 75, 77, 78, 81, 82], "simplified_kolmogorov_smirnov_test": 21, "neighbor_histogram": 21, "non_neighbor_histogram": 21, "kolmogorov": 21, "smirnov": 21, "largest": [21, 33, 40, 59, 63, 65, 90], "empir": [21, 39, 49], "cumul": 21, "ecdf": 21, "histogram": [21, 77, 89], "absolut": [21, 25], "25": [21, 30, 40, 42, 75, 79, 81, 82, 84, 85, 86, 90, 93], "dimension": [21, 44, 73, 82, 87], "trial": 21, "non_iid_scor": [21, 75, 77, 78, 81, 82], "null_track": 22, "extend": [22, 41, 81, 87, 93], "superclass": 22, "arbitrari": [22, 29, 65, 69, 74, 87, 89], "prompt": 22, "address": [22, 74, 75, 78, 80, 92], "enabl": [22, 34], "null_scor": [22, 75, 78, 81, 82], "default_threshold": 23, "37037": 23, "q3_avg_dist": 23, "iqr_avg_dist": 23, "median_outlier_scor": 23, "ood": [23, 58, 59, 74, 75, 78, 81, 82, 87], "regressionlabelissuemanag": 25, "multipli": 25, "find_issues_with_predict": 25, "find_issues_with_featur": 25, "deleg": 25, "confus": [26, 29, 30, 34, 35, 44, 57, 92, 93], "50": [26, 34, 80, 82, 84, 86, 87, 90], "keepdim": [26, 80], "outlier_cluster_label": 26, "no_underperforming_cluster_id": 26, "signifi": 26, "absenc": 26, "set_knn_graph": 26, "find_issues_kwarg": 26, "perform_clust": 26, "npt": 26, "int_": 26, "id": [26, 49, 74, 80, 81, 84], "int64": [26, 73, 84], "unique_cluster_id": 26, "get_worst_clust": 26, "_description_": 26, "performed_clust": 26, "worst_cluster_id": 26, "underperforming_group_scor": 26, "exclud": [27, 66, 70, 74, 93], "get_report": 27, "overview": [29, 73, 75, 77, 78, 81, 84, 86, 87, 89, 91, 92, 93], "modifi": [29, 30, 33, 34, 44, 80, 82], "help": [29, 30, 34, 57, 71, 72, 73, 74, 77, 78, 79, 80, 81, 84, 85, 89, 90, 91, 92, 93], "rank_classes_by_label_qu": [29, 75], "merg": [29, 43, 71, 79, 80, 93], "find_overlapping_class": [29, 80, 82], "problemat": [29, 50, 66, 70, 73, 86, 93], "unnorm": [29, 50, 82], "abov": [29, 30, 33, 34, 44, 49, 56, 57, 59, 65, 69, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "model_select": [29, 40, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 89, 91, 92], "cross_val_predict": [29, 34, 73, 74, 75, 77, 78, 80, 82, 84, 88, 89, 91, 92], "get_data_labels_from_dataset": 29, "yourfavoritemodel": [29, 82], "cv": [29, 40, 73, 74, 75, 77, 82, 84, 91], "df": [29, 44, 70, 73, 80], "overall_label_qu": [29, 50], "col": 29, "prob": [29, 43, 82, 88], "divid": [29, 50, 59], "label_nois": [29, 50], "human": [29, 79, 90, 93], "clearli": [29, 59, 81, 86, 90], "num": [29, 50, 79, 82], "overlap": [29, 71, 79, 80, 82], "ontolog": 29, "publish": [29, 93], "therefor": [29, 59], "vehicl": [29, 79], "truck": [29, 79, 87, 90], "intuit": [29, 50], "car": [29, 79, 86, 90], "frequent": [29, 49, 77, 80, 89], "characterist": 29, "l": [29, 30, 34, 54, 56, 57], "class1": 29, "class2": 29, "relationship": 29, "match": [29, 30, 34, 35, 49, 50, 59, 74, 75, 79, 81, 86, 88, 90], "dog": [29, 44, 50, 52, 66, 79, 80, 87, 88, 93], "cat": [29, 44, 50, 52, 79, 80, 87, 88], "captur": [29, 73, 86, 87, 90], "co": [29, 30, 31], "noisy_label": [29, 74, 75, 85], "overlapping_class": 29, "descend": [29, 30, 34, 40, 50, 57], "overall_label_health_scor": [29, 50, 82], "suggest": [29, 49, 50, 56, 78, 80, 81, 89, 92], "half": [29, 30, 34, 50, 79, 93], "health_scor": [29, 50], "classes_by_label_qu": [29, 75], "cnn": [30, 34, 81], "cifar": [30, 31, 79, 87], "teach": [30, 31], "bhanml": 30, "blob": 30, "master": [30, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "call_bn": 30, "bn": 30, "input_channel": 30, "n_output": 30, "dropout_r": 30, "top_bn": 30, "architectur": [30, 34], "shown": [30, 57, 74, 80, 84, 87, 88, 90, 93], "forward": [30, 31, 34, 81, 84], "overridden": [30, 34], "although": [30, 34, 58, 77, 91], "recip": [30, 34], "afterward": [30, 34], "sinc": [30, 34, 37, 45, 50, 57, 65, 69, 80, 84, 85, 86, 88, 93], "former": [30, 34], "hook": [30, 34, 79], "silent": [30, 33, 34], "t_destin": [30, 34], "__call__": [30, 34, 40], "add_modul": [30, 34], "child": [30, 34], "fn": [30, 34, 57], "recurs": [30, 34, 40], "submodul": [30, 34], "children": [30, 34, 93], "nn": [30, 31, 34, 81], "init": [30, 34, 82], "no_grad": [30, 34, 81, 87], "init_weight": [30, 34], "linear": [30, 34, 78, 81, 92], "fill_": [30, 34], "net": [30, 34, 73, 79, 81], "in_featur": [30, 34], "out_featur": [30, 34], "bia": [30, 34, 81], "tensor": [30, 31, 34, 73, 81, 87], "requires_grad": [30, 34], "bfloat16": [30, 34], "cast": [30, 34, 73], "buffer": [30, 34], "datatyp": [30, 34], "member": [30, 34, 74, 75], "xdoctest": [30, 34], "undefin": [30, 34], "var": [30, 34], "buf": [30, 34], "20l": [30, 34], "1l": [30, 34], "5l": [30, 34], "immedi": [30, 34, 87], "cpu": [30, 34, 35, 73, 81], "move": [30, 34, 40, 72, 79], "cuda": [30, 34, 73, 81], "devic": [30, 34, 73, 81], "gpu": [30, 34, 73, 78, 92], "live": [30, 34], "copi": [30, 34, 61, 73, 74, 75, 77, 80, 85, 88, 89, 91], "doubl": [30, 34], "dump_patch": [30, 34], "eval": [30, 34, 81, 85, 87], "dropout": [30, 34], "batchnorm": [30, 34], "grad": [30, 34], "extra_repr": [30, 34], "line": [30, 34, 71, 74, 79, 84, 87, 93], "get_buff": [30, 34], "target": [30, 31, 34, 61, 62, 87, 89], "throw": [30, 34], "get_submodul": [30, 34], "explan": [30, 34], "fulli": [30, 34, 48, 80], "qualifi": [30, 34], "referenc": [30, 34], "attributeerror": [30, 34], "invalid": [30, 34, 78], "resolv": [30, 34, 93], "get_extra_st": [30, 34], "state_dict": [30, 34], "set_extra_st": [30, 34], "build": [30, 34, 81, 90], "pickleabl": [30, 34], "serial": [30, 34], "backward": [30, 34, 81], "break": [30, 34, 81], "pickl": [30, 34, 86], "get_paramet": [30, 34], "let": [30, 34, 58, 59, 73, 75, 77, 78, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "net_b": [30, 34], "net_c": [30, 34], "conv": [30, 34], "conv2d": [30, 34, 81], "16": [30, 34, 40, 65, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "33": [30, 34, 79, 81, 86, 90], "kernel_s": [30, 34], "stride": [30, 34], "200": [30, 34, 59, 79, 86, 93], "diagram": [30, 34, 88], "degre": [30, 34, 89], "queri": [30, 34, 75, 80, 81], "named_modul": [30, 34], "o": [30, 34, 42, 43, 73, 74, 75, 79, 80, 82, 85, 86, 93], "transit": [30, 34], "ipu": [30, 34], "load_state_dict": [30, 34], "strict": [30, 34, 40], "persist": [30, 34], "strictli": [30, 34], "namedtupl": [30, 34], "missing_kei": [30, 34], "unexpected_kei": [30, 34], "runtimeerror": [30, 34], "idx": [30, 34, 44, 45, 57, 74, 80, 81, 82, 84, 86, 87], "named_buff": [30, 34], "prefix": [30, 34, 73, 93], "prepend": [30, 34], "running_var": [30, 34], "named_children": [30, 34], "conv4": [30, 34], "conv5": [30, 34], "memo": [30, 34], "remove_dupl": [30, 34], "named_paramet": [30, 34], "register_backward_hook": [30, 34], "deprec": [30, 34, 37], "favor": [30, 34], "register_full_backward_hook": [30, 34], "removablehandl": [30, 34], "register_buff": [30, 34], "running_mean": [30, 34], "register_forward_hook": [30, 34], "won": [30, 34, 74, 75, 80, 85], "inplac": [30, 34, 84], "register_forward_pre_hook": [30, 34], "gradient": [30, 34, 77, 81, 89], "respect": [30, 34, 57, 82], "grad_input": [30, 34], "grad_output": [30, 34], "technic": [30, 34], "caller": [30, 34], "register_load_state_dict_post_hook": [30, 34], "post": [30, 34], "incompatible_kei": [30, 34], "modif": [30, 34], "thrown": [30, 34], "clearn": [30, 34], "register_modul": [30, 34], "register_paramet": [30, 34], "requires_grad_": [30, 34], "autograd": [30, 34], "freez": [30, 34, 73, 78, 92], "finetun": [30, 34], "gan": [30, 34], "share_memori": [30, 34], "share_memory_": [30, 34], "destin": [30, 34], "keep_var": [30, 34], "shallow": [30, 34], "releas": [30, 34, 72, 80, 87], "design": [30, 34], "ordereddict": [30, 34], "detach": [30, 34, 81], "non_block": [30, 34], "memory_format": [30, 34], "channels_last": [30, 34], "Its": [30, 34, 40, 50, 56], "complex": [30, 34], "integr": [30, 34, 71], "asynchron": [30, 34], "host": [30, 34], "pin": [30, 34, 78, 79, 92], "desir": [30, 34, 43, 57], "4d": [30, 34], "ignore_w": [30, 34], "determinist": [30, 34, 73], "1913": [30, 34], "3420": [30, 34], "5113": [30, 34], "2325": [30, 34], "env": [30, 34], "torch_doctest_cuda1": [30, 34], "gpu1": [30, 34], "1914": [30, 34], "5112": [30, 34], "2324": [30, 34], "float16": [30, 34], "cdoubl": [30, 34], "3741": [30, 34], "2382": [30, 34], "5593": [30, 34], "4443": [30, 34], "complex128": [30, 34], "6122": [30, 34], "1150": [30, 34], "to_empti": [30, 34], "storag": [30, 34], "dst_type": [30, 34], "xpu": [30, 34], "zero_grad": [30, 34, 81], "set_to_non": [30, 34], "context": [30, 34, 86], "noisili": [31, 82], "han": 31, "2018": 31, "cifar_cnn": [31, 32], "loss_coteach": 31, "y_1": 31, "y_2": 31, "forget_r": 31, "class_weight": 31, "logit": [31, 48, 81], "decim": [31, 44], "quickli": [31, 73, 77, 78, 80, 81, 85, 87, 90, 91, 93], "forget": [31, 40, 93], "rate_schedul": 31, "epoch": [31, 34, 80, 81], "initialize_lr_schedul": 31, "lr": [31, 34], "001": [31, 59, 80], "250": [31, 74, 75, 82, 86], "epoch_decay_start": 31, "80": [31, 77, 85, 89, 90, 91], "schedul": 31, "adjust": [31, 35, 53, 58, 59, 71, 82], "beta": 31, "adam": 31, "adjust_learning_r": 31, "alpha_plan": 31, "beta1_plan": 31, "forget_rate_schedul": 31, "num_gradu": 31, "expon": 31, "tell": [31, 78, 81, 82, 92], "train_load": [31, 34], "model1": [31, 82], "optimizer1": 31, "model2": [31, 82], "optimizer2": 31, "dataload": [31, 81, 87], "parser": 31, "parse_arg": 31, "num_iter_per_epoch": 31, "print_freq": 31, "topk": 31, "top1": 31, "top5": 31, "test_load": 31, "offici": [32, 47, 93], "wish": [32, 47, 87, 90, 93], "mnist_pytorch": 32, "coteach": [32, 72], "mini": [33, 63, 65, 80], "With": [33, 78, 82, 84, 89, 90, 92, 93], "approxim": [33, 58, 84], "low_self_confid": [33, 35, 51], "self_confid": [33, 35, 40, 51, 53, 59, 67, 69, 80, 82, 85, 91, 92], "conveni": [33, 73, 78, 92], "script": 33, "labelinspector": [33, 80], "adj_confident_thresholds_shar": 33, "labels_shar": 33, "pred_probs_shar": 33, "labels_fil": [33, 80], "pred_probs_fil": [33, 80], "batch_siz": [33, 34, 63, 65, 80, 81, 87, 90], "quality_score_kwarg": 33, "num_issue_kwarg": 33, "return_mask": 33, "variant": [33, 49, 90], "read": [33, 37, 75, 80, 82, 87, 93], "zarr": [33, 80], "memmap": [33, 90], "pythonspe": 33, "mmap": [33, 80], "hdf5": 33, "further": [33, 50, 51, 53, 56, 57, 65, 66, 73, 80], "yourfil": 33, "npy": [33, 79, 80, 90], "mmap_mod": [33, 90], "tip": [33, 35, 48, 80], "save_arrai": 33, "your_arrai": 33, "disk": [33, 79, 80], "npz": [33, 93], "maxim": [33, 49, 63, 65, 90], "multiprocess": [33, 35, 51, 63, 65, 80, 81, 90], "linux": [33, 63, 65], "physic": [33, 35, 63, 65, 86, 90], "psutil": [33, 35, 63, 65, 90], "labels_arrai": [33, 45], "predprob": 33, "pred_probs_arrai": 33, "back": [33, 57, 74, 80, 86, 87], "store_result": 33, "becom": [33, 87], "verifi": [33, 80, 84, 87], "long": [33, 49, 58, 84], "enough": [33, 44, 80], "chunk": [33, 88], "ram": [33, 79], "faster": [33, 58, 61, 63, 65, 80, 82], "end_index": 33, "labels_batch": 33, "pred_probs_batch": 33, "update_confident_threshold": 33, "batch_result": 33, "score_label_qu": 33, "indices_of_examples_with_issu": [33, 80], "shortcut": 33, "encount": [33, 35, 63], "1000": [33, 73, 78, 80, 81, 87], "aggreg": [33, 40, 49, 53, 56, 59, 69, 80, 82, 84], "get_num_issu": 33, "fetch": [33, 73, 75], "seen": [33, 80, 87, 93], "far": [33, 49], "get_quality_scor": 33, "label_quality_scor": [33, 53, 56, 59, 62, 82, 86, 89], "method1": 33, "method2": 33, "normalized_margin": [33, 35, 40, 51, 53, 59, 67, 69], "low_normalized_margin": [33, 35, 51], "issue_indic": [33, 56, 81], "update_num_issu": 33, "split_arr": 33, "arr": [33, 80], "chunksiz": 33, "convnet": 34, "bespok": [34, 48], "get_mnist_dataset": 34, "loader": [34, 81], "download": [34, 73, 80, 87], "mnist": [34, 71, 73, 79], "get_sklearn_digits_dataset": 34, "handwritten": 34, "digit": [34, 73, 79], "last": [34, 40, 54, 57, 74, 75, 80, 84, 93], "sklearn_digits_test_s": 34, "hard": [34, 79, 87], "simplenet": 34, "64": [34, 77, 81, 82, 86, 90, 91], "log_interv": 34, "01": [34, 59, 61, 73, 81, 82, 85, 86, 89, 90, 93], "momentum": 34, "no_cuda": 34, "test_batch_s": [34, 81], "templat": 34, "flexibli": 34, "among": [34, 49, 82], "test_set": 34, "Be": 34, "overrid": 34, "train_idx": [34, 44, 87], "train_label": [34, 87, 92], "scikit": [34, 44, 58, 71, 73, 74, 75, 77, 78, 80, 83, 89, 92], "set_predict_proba_request": 34, "set_predict_request": 34, "encourag": [35, 51, 59, 62], "multilabel_classif": [35, 50, 51, 53, 59, 80, 85], "pred_probs_by_class": 35, "prune_count_matrix_col": 35, "rank_by_kwarg": [35, 51, 59, 82], "num_to_remove_per_class": [35, 51], "bad": [35, 51, 56, 59, 78, 80, 92], "seem": [35, 82, 85], "aren": 35, "confidence_weighted_entropi": [35, 40, 51, 53, 59, 67, 69], "label_issues_idx": [35, 59], "entropi": [35, 37, 39, 40, 58, 59], "prune_by_class": [35, 51, 82], "predicted_neq_given": [35, 51, 82], "prune_counts_matrix": 35, "smallest": [35, 59], "unus": 35, "number_of_mislabeled_examples_in_class_k": 35, "delet": [35, 71, 80, 92], "thread": [35, 51], "window": [35, 79], "shorter": [35, 54], "find_predicted_neq_given": 35, "find_label_issues_using_argmax_confusion_matrix": 35, "latent_algebra": [36, 72], "label_quality_util": 36, "multilabel_util": [36, 85], "multilabel_scor": [36, 53], "token_classification_util": [36, 93], "get_normalized_entropi": 37, "min_allowed_prob": 37, "wikipedia": 37, "activ": [37, 39, 49, 71, 84], "towardsdatasci": 37, "cheatsheet": 37, "ec57bc067c0b": 37, "clip": [37, 44, 73], "behav": 37, "unnecessari": [37, 80], "slightli": [37, 91, 92], "interv": [37, 40, 87], "herein": 38, "inexact": 38, "cours": 38, "propag": 38, "throughout": [38, 44, 61, 73, 84, 90, 93], "compute_ps_py_inv_noise_matrix": 38, "compute_py_inv_noise_matrix": 38, "compute_inv_noise_matrix": 38, "easili": [38, 72, 73, 75, 77, 78, 82, 84, 85, 87, 88, 89, 90, 91, 92], "increas": [38, 56, 58, 59, 73, 74, 80, 84, 85, 93], "dot": [38, 69, 80], "compute_noise_matrix_from_invers": 38, "compute_pi": 38, "true_labels_class_count": 38, "compute_pyx": 38, "pyx": 38, "multiannot": 39, "assert_valid_inputs_multiannot": 39, "labels_multiannot": [39, 49], "ensembl": [39, 40, 49, 59, 77, 80, 85, 87, 89, 91], "allow_single_label": 39, "annotator_id": 39, "assert_valid_pred_prob": 39, "pred_probs_unlabel": [39, 49], "format_multiannotator_label": [39, 49, 84], "lexicograph": [39, 44], "formatted_label": [39, 44], "old": [39, 44, 72, 79], "check_consensus_label_class": 39, "consensus_label": [39, 49, 84], "consensus_method": [39, 49], "consensu": [39, 49, 71, 83, 93], "establish": [39, 89, 92], "compute_soft_cross_entropi": 39, "soft": [39, 79], "find_best_temp_scal": 39, "coarse_search_rang": [39, 61, 80], "fine_search_s": [39, 61, 80], "temperatur": [39, 40, 56, 65, 69], "scale": [39, 42, 79, 80, 87, 90, 91], "factor": [39, 40, 63, 65], "minim": [39, 56, 87], "temp_scale_pred_prob": 39, "temp": 39, "sharpen": [39, 79], "smoothen": 39, "classlabelscor": 40, "enum": 40, "get_normalized_margin_for_each_label": [40, 59], "get_confidence_weighted_entropy_for_each_label": [40, 59], "75": [40, 74, 75, 79, 84, 85, 86, 89, 90, 93], "from_str": 40, "scorer": 40, "exponential_moving_averag": [40, 53], "alpha": [40, 53, 56, 74, 75, 82, 85, 89], "exponenti": 40, "ema": 40, "s_1": 40, "s_k": 40, "ema_k": 40, "accord": [40, 51, 77, 78, 82, 93], "formula": [40, 42], "_t": 40, "cdot": 40, "s_t": 40, "qquad": 40, "leq": 40, "_1": 40, "give": [40, 59, 82, 84, 90], "recent": [40, 93], "success": 40, "previou": [40, 80, 81, 86], "discount": 40, "s_ema": 40, "175": [40, 82, 86], "softmin": [40, 53, 56, 65, 69], "underflow": 40, "nan": [40, 49, 77, 84, 89, 91], "possible_method": 40, "aggregated_scor": 40, "multilabelscor": 40, "base_scor": 40, "base_scorer_kwarg": 40, "aggregator_kwarg": [40, 53], "n_sampl": 40, "n_label": 40, "binari": [40, 44, 51, 53, 82, 93], "worst": [40, 84], "class_label_quality_scor": 40, "get_class_label_quality_scor": 40, "42": [40, 79, 81, 86, 90, 93], "452": [40, 78], "new_scor": 40, "575": 40, "get_label_quality_scores_per_class": [40, 53], "ml_scorer": 40, "multilabel_pi": 40, "binar": [40, 41], "get_cross_validated_multilabel_pred_prob": 40, "reformat": [40, 73], "wider": 40, "splitter": 40, "kfold": [40, 81], "multiclass": [40, 44, 49, 85], "onevsrestclassifi": [40, 85], "randomforestclassifi": [40, 82, 85], "n_split": [40, 75, 81, 85], "stack_compl": 41, "pred_prob_slic": 41, "get_onehot_num_class": 41, "onehot": 41, "multilabel": [41, 85], "int2onehot": [41, 85], "hot": [41, 51, 57, 63, 66, 77, 79, 80, 89, 90, 91], "onehot2int": [41, 85], "onehot_matrix": 41, "transform_distances_to_scor": 42, "exp": [42, 58, 59, 74], "dt": 42, "right": [42, 54, 57, 78, 85, 86, 87, 92], "num_neighbor": 42, "ood_features_scor": [42, 58, 87], "95122942": 42, "83945702": 42, "token_classif": [43, 67, 69, 70, 80], "get_sent": [43, 93], "sentenc": [43, 67, 69, 70, 78, 92], "readabl": 43, "filter_sent": [43, 93], "lambda": [43, 73, 74, 80, 84], "long_sent": 43, "headlin": 43, "process_token": 43, "charact": [43, 44], "s1": 43, "s2": 43, "processed_token": 43, "rule": [43, 79], "alecnlcb": 43, "entiti": [43, 71, 80, 93], "mapped_ent": 43, "unique_ident": 43, "loc": [43, 74, 75, 81, 93], "merge_prob": 43, "probs_merg": 43, "55": [43, 79, 81, 86, 89, 90], "0125": [43, 69], "0375": 43, "075": 43, "025": 43, "color_sent": 43, "color": [43, 66, 74, 75, 77, 82, 85, 87, 89, 90], "red": [43, 57, 74, 75, 79, 82, 85, 86, 87, 90], "colored_sent": 43, "termcolor": 43, "31msentenc": 43, "0m": 43, "ancillari": 44, "remove_noise_from_class": 44, "class_without_nois": 44, "any_other_class": 44, "choos": [44, 59, 77, 80, 82, 89, 91], "tradition": 44, "clip_noise_r": 44, "clip_valu": 44, "new_sum": 44, "preserv": 44, "value_count": [44, 80], "fill": 44, "wherea": [44, 51, 88], "come": [44, 74, 75, 80, 81, 90], "major": [44, 49, 72, 81, 87], "versu": [44, 82], "value_counts_fill_missing_class": 44, "get_missing_class": 44, "round_preserving_sum": 44, "obviou": 44, "cgdeboer": 44, "iteround": 44, "round_preserving_row_tot": 44, "reach": 44, "estimate_pu_f1": 44, "prob_s_eq_1": 44, "claesen": 44, "f1": [44, 57, 78, 82], "confusion_matrix": 44, "BE": 44, "print_square_matrix": 44, "left_nam": 44, "top_nam": 44, "titl": [44, 74, 75, 82, 85, 87], "short_titl": 44, "round_plac": 44, "pretti": [44, 82], "print_noise_matrix": [44, 82], "print_inverse_noise_matrix": 44, "print_joint_matrix": [44, 82], "joint_matrix": 44, "compress_int_arrai": 44, "num_possible_valu": 44, "train_val_split": 44, "holdout_idx": 44, "subset_x_i": 44, "extract": [44, 58, 73, 78, 84, 87, 90, 92], "subset_label": 44, "subset_data": 44, "extract_indices_tf": 44, "allow_shuffl": 44, "turn": [44, 71, 86], "unshuffle_tensorflow_dataset": 44, "shuffledataset": 44, "histori": 44, "pre_x": 44, "buffer_s": 44, "is_torch_dataset": 44, "is_tensorflow_dataset": 44, "csr_vstack": 44, "csr_matric": 44, "append": [44, 73, 79, 80, 81, 82, 84, 85, 87, 93], "bottom": [44, 54, 57, 86], "vstack": [44, 79, 80, 81, 82, 84, 85], "append_extra_datapoint": 44, "to_data": 44, "from_data": 44, "taken": 44, "One": [44, 58, 80], "get_num_class": 44, "label_matrix": 44, "canon": 44, "num_unique_class": 44, "get_unique_class": 44, "format_label": 44, "smart_display_datafram": 44, "displai": [44, 57, 66, 70, 73, 78, 82, 92, 93], "jupyt": [44, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "notebook": [44, 49, 73, 75, 79, 80, 82, 84, 85, 86, 90, 93], "consol": 44, "force_two_dimens": 44, "html": [44, 58, 77, 80, 82], "assert_valid_input": 45, "allow_missing_class": 45, "allow_one_class": 45, "assert_valid_class_label": 45, "assert_nonempty_input": 45, "assert_indexing_work": 45, "length_x": 45, "labels_to_arrai": 45, "labellik": 45, "keraswrappermodel": [48, 71], "keraswrappersequenti": 48, "tf": [48, 73], "legaci": 48, "lack": 48, "keraswrapp": 48, "huggingface_keras_imdb": 48, "unit": [48, 93], "model_kwarg": [48, 61], "compile_kwarg": 48, "sparsecategoricalcrossentropi": 48, "layer": [48, 73, 78, 87, 92], "dens": 48, "my_keras_model": 48, "from_logit": 48, "compil": 48, "declar": 48, "apply_softmax": 48, "analysi": 49, "analyz": [49, 71, 82, 84, 85], "get_label_quality_multiannot": [49, 84], "vote": 49, "crowdsourc": [49, 71, 84], "dawid": [49, 84], "skene": [49, 84], "analog": [49, 79, 84], "chosen": [49, 59, 80, 84], "crowdlab": [49, 84], "unlabel": [49, 77, 78, 81, 84, 87, 90], "decid": [49, 78, 79, 84, 89, 92, 93], "get_active_learning_scor": [49, 84], "activelab": [49, 84], "priorit": [49, 56, 86, 90, 93], "showcas": 49, "main": 49, "best_qual": 49, "quality_method": 49, "calibrate_prob": 49, "return_detailed_qu": 49, "return_annotator_stat": 49, "return_weight": 49, "label_quality_score_kwarg": 49, "necessarili": [49, 57, 78, 82], "did": [49, 50, 73, 77, 82, 84, 89, 91, 92], "majority_vot": 49, "ti": 49, "broken": [49, 57, 79], "highest": [49, 57, 74, 81, 88], "0th": 49, "consensus_quality_scor": [49, 84], "annotator_agr": [49, 84], "reman": 49, "1st": 49, "2nd": [49, 63], "3rd": 49, "consensus_label_suffix": 49, "consensus_quality_score_suffix": 49, "suffix": 49, "emsembl": 49, "weigh": [49, 79], "agreement": [49, 84], "agre": 49, "prevent": [49, 80], "overconfid": [49, 88], "wrong": [49, 54, 56, 72, 74, 75, 78, 80, 82, 86, 92], "detailed_label_qu": [49, 84], "annotator_stat": [49, 84], "model_weight": 49, "annotator_weight": 49, "warn": [49, 74, 75], "labels_info": 49, "num_annot": [49, 84], "deriv": [49, 84], "quality_annotator_1": 49, "quality_annotator_2": 49, "quality_annotator_m": 49, "annotator_qu": [49, 84], "num_examples_label": [49, 84], "agreement_with_consensu": [49, 84], "worst_class": [49, 84], "trustworthi": [49, 84, 89], "get_label_quality_multiannotator_ensembl": 49, "weigtht": 49, "budget": 49, "retrain": [49, 89, 92], "active_learning_scor": 49, "improv": [49, 75, 79, 80, 81, 82, 89, 90, 91, 92], "active_learning_scores_unlabel": 49, "get_active_learning_scores_ensembl": 49, "henc": [49, 73, 74, 84], "get_majority_vote_label": [49, 84], "event": 49, "lastli": [49, 77], "convert_long_to_wide_dataset": 49, "labels_multiannotator_long": 49, "wide": [49, 73, 91, 92], "suitabl": [49, 77, 91], "labels_multiannotator_wid": 49, "common_multilabel_issu": 50, "mutual": [50, 85], "exclus": [50, 85], "rank_classes_by_multilabel_qu": 50, "overall_multilabel_health_scor": 50, "multilabel_health_summari": 50, "classes_by_multilabel_qu": 50, "inner": [51, 65], "find_multilabel_issues_per_class": 51, "per_class_label_issu": 51, "label_issues_list": 51, "labels_list": 51, "pred_probs_list": [51, 59, 81, 82], "anim": [52, 87], "rat": 52, "predat": 52, "pet": 52, "reptil": 52, "manner": [53, 84, 89, 91, 92], "box": [54, 56, 57, 79, 86], "object_detect": [54, 56, 57, 86], "return_indices_ranked_by_scor": [54, 86], "overlapping_label_check": [54, 56], "suboptim": [54, 56], "locat": [54, 56, 86, 90, 93], "bbox": [54, 57, 86], "image_nam": [54, 57], "y1": [54, 57, 86], "y2": [54, 57, 86], "later": [54, 57, 58, 92, 93], "mmdetect": [54, 57, 86], "corner": [54, 57, 86], "swap": [54, 56, 66, 70], "penal": [54, 56], "concern": [54, 56, 71, 75], "aggregation_weight": 56, "imperfect": [56, 80], "chose": [56, 84, 86], "imperfectli": [56, 86], "dirti": [56, 59, 62, 89], "subtyp": 56, "badloc": 56, "nonneg": 56, "issues_from_scor": [56, 65, 66, 69, 70, 86, 90, 93], "compute_overlooked_box_scor": 56, "high_probability_threshold": 56, "auxiliary_input": [56, 57], "vari": [56, 75], "iou": [56, 57], "heavili": 56, "auxiliarytypesdict": 56, "pred_label": [56, 92], "pred_label_prob": 56, "pred_bbox": 56, "lab_label": 56, "lab_bbox": 56, "similarity_matrix": 56, "min_possible_similar": 56, "scores_overlook": 56, "compute_badloc_box_scor": 56, "low_probability_threshold": 56, "scores_badloc": 56, "compute_swap_box_scor": 56, "accident": [56, 77, 78, 80, 92], "scores_swap": 56, "pool_box_scores_per_imag": 56, "box_scor": 56, "image_scor": [56, 65, 90], "object_counts_per_imag": 57, "discov": [57, 75, 93], "auxiliari": [57, 87, 90], "_get_valid_inputs_for_compute_scor": 57, "object_count": 57, "bounding_box_size_distribut": 57, "down": 57, "bbox_siz": 57, "class_label_distribut": 57, "class_distribut": 57, "get_sorted_bbox_count_idx": 57, "plot": [57, 74, 75, 82, 85, 87, 89, 90], "sorted_idx": [57, 87], "plot_class_size_distribut": 57, "class_to_show": 57, "hidden": [57, 87], "max_class_to_show": 57, "plot_class_distribut": 57, "visual": [57, 74, 75, 81, 89, 91, 93], "prediction_threshold": 57, "overlai": [57, 86], "figsiz": [57, 74, 75, 81, 82, 85, 87], "save_path": [57, 86], "blue": [57, 79, 82, 86], "overlaid": 57, "side": [57, 79, 86], "figur": [57, 82, 85, 87, 89], "extens": [57, 82, 84], "png": [57, 86], "pdf": [57, 58], "svg": 57, "matplotlib": [57, 74, 75, 81, 82, 85, 86, 87, 89], "get_average_per_class_confusion_matrix": 57, "num_proc": [57, 81], "intersect": [57, 80], "tp": 57, "fp": 57, "ground": [57, 79, 82, 84, 89], "truth": [57, 82, 84, 89], "strength": 57, "bias": 57, "avg_metr": 57, "distionari": 57, "95": [57, 67, 69, 75, 77, 79, 82, 89, 90], "calculate_per_class_metr": 57, "per_class_metr": 57, "Of": 58, "li": 58, "smaller": [58, 85, 86], "find_top_issu": [58, 59, 87], "reli": [58, 73, 74, 75, 78, 86, 87, 92], "dist_metr": 58, "dim": [58, 81, 90], "subtract": [58, 59], "renorm": [58, 59, 80], "least_confid": 58, "sum_": 58, "log": [58, 59, 72], "softmax": [58, 65, 69, 81], "literatur": 58, "gen": 58, "liu": 58, "lochman": 58, "zach": 58, "openaccess": 58, "thecvf": 58, "content": [58, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "cvpr2023": 58, "liu_gen_pushing_the_limits_of_softmax": 58, "based_out": 58, "distribution_detection_cvpr_2023_pap": 58, "fit_scor": [58, 87], "ood_predictions_scor": 58, "pretrain": [58, 73, 78, 87, 92], "adjust_confident_threshold": 58, "probabilist": [58, 73, 74, 75, 77, 78, 87, 88, 91], "order_label_issu": [59, 72], "whichev": [59, 88], "argsort": [59, 78, 81, 82, 87, 89, 92], "max_": 59, "get_label_quality_ensemble_scor": [59, 80, 82], "weight_ensemble_members_bi": 59, "custom_weight": 59, "log_loss_search_t_valu": 59, "0001": [59, 79], "scheme": 59, "log_loss_search": 59, "log_loss": [59, 78], "1e0": 59, "1e1": 59, "1e2": 59, "2e2": 59, "quality_scor": [59, 87], "forth": 59, "top_issue_indic": 59, "rank_bi": [59, 72], "weird": [59, 70], "minu": 59, "prob_label": 59, "max_prob_not_label": 59, "idea": 59, "AND": [59, 78], "corrupt": [61, 89], "linearregress": [61, 80, 89], "y_with_nois": 61, "n_boot": [61, 80], "include_aleatoric_uncertainti": [61, 80], "sole": [61, 74, 84, 87, 91], "larger": [61, 63, 65, 78, 79, 80, 81], "bootstrap": [61, 80, 89], "resampl": [61, 73, 80], "epistem": [61, 80, 87, 89], "aleator": [61, 80, 89], "model_final_kwarg": 61, "coars": 61, "thorough": [61, 80], "fine": [61, 73, 78, 87, 92], "grain": 61, "grid": 61, "get_epistemic_uncertainti": 61, "varianc": [61, 82], "epistemic_uncertainti": 61, "get_aleatoric_uncertainti": 61, "residu": [61, 62, 80], "deviat": [61, 89], "ie": 61, "aleatoric_uncertainti": 61, "outr": 62, "contin": 62, "raw": [62, 71, 72, 75, 79, 81, 84, 86, 87], "aka": [62, 73, 82, 93], "00323821": 62, "33692597": 62, "00191686": 62, "semant": [63, 65, 66, 83], "pixel": [63, 65, 66, 87, 90], "h": [63, 65, 66, 90], "height": [63, 65, 66, 90], "w": [63, 65, 66, 90], "width": [63, 65, 66, 90], "labels_one_hot": [63, 66, 90], "stream": [63, 87, 93], "downsampl": [63, 65, 90], "shrink": [63, 65], "divis": [63, 65, 74], "segmant": [65, 66], "num_pixel_issu": [65, 90], "product": [65, 80, 81], "pixel_scor": [65, 90], "display_issu": [65, 66, 67, 69, 70, 90, 93], "highlight": [66, 70, 74, 75, 77, 90], "enter": 66, "legend": [66, 74, 75, 85, 86, 89, 90], "colormap": 66, "background": 66, "person": [66, 80, 86, 90, 93], "common_label_issu": [66, 70, 90, 93], "ambigu": [66, 70, 73, 78, 79, 82, 92, 93], "systemat": [66, 70, 84], "misunderstood": [66, 70], "issues_df": [66, 81], "filter_by_class": [66, 90], "class_index": 66, "issues_subset": [66, 70], "token_score_method": 69, "sentence_score_method": 69, "sentence_score_kwarg": 69, "compris": [69, 70], "token_scor": [69, 93], "converg": 69, "toward": 69, "_softmin_sentence_scor": 69, "sentence_scor": [69, 93], "token_info": 69, "70": [69, 77, 81, 89, 90], "02": [69, 74, 75, 81, 82, 86, 89, 90], "03": [69, 79, 82, 86, 90, 93], "04": [69, 81, 86, 89, 90], "08": [69, 78, 82, 86, 90, 93], "commonli": [70, 72, 74, 75, 85, 93], "filter_by_token": [70, 93], "But": [70, 78, 82, 93], "restrict": [70, 80], "reliabl": [71, 73, 80, 84, 90, 91], "thousand": 71, "imagenet": [71, 79], "popular": [71, 84, 86], "centric": [71, 77, 78, 81, 83], "capabl": 71, "minut": [71, 73, 77, 78, 79, 84, 85, 86, 89, 90, 91, 92, 93], "conda": 71, "feature_embed": [71, 87], "Then": [71, 80, 81, 89, 91, 92], "your_dataset": [71, 73, 74, 75, 77, 78, 80, 81], "column_name_of_label": [71, 73, 74, 75, 77, 78, 81], "plagu": [71, 75], "untrain": 71, "\u30c4": 71, "label_issues_info": [71, 75], "sklearn_compatible_model": 71, "framework": [71, 85, 86], "complianc": 71, "tag": [71, 85, 93], "sequenc": 71, "recognit": [71, 73, 80, 93], "train_data": [71, 87, 89, 91, 92], "gotten": 71, "test_data": [71, 82, 85, 87, 89, 91, 92], "deal": [71, 75], "tutori": [71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "feel": [71, 73, 75, 80], "free": [71, 73, 75, 77, 78, 80, 81, 82], "ask": [71, 80], "slack": [71, 80], "project": [71, 89], "welcom": 71, "commun": [71, 80], "guidelin": [71, 86], "piec": 71, "studio": [71, 75, 77, 78, 80, 81], "platform": [71, 77, 78, 80, 81], "automl": [71, 80], "foundat": 71, "smart": [71, 77, 78, 80, 81], "edit": [71, 80], "easier": [71, 82], "unreli": [71, 73, 77, 78, 91], "older": 72, "outlin": 72, "substitut": 72, "v2": [72, 77, 91], "get_noise_indic": 72, "psx": 72, "sorted_index_method": 72, "order_label_error": 72, "label_errors_bool": 72, "latent_estim": 72, "num_label_error": 72, "learningwithnoisylabel": 72, "neatli": 72, "organ": [72, 77, 79, 91, 93], "reorgan": 72, "baseline_method": 72, "incorpor": [72, 82], "research": [72, 82], "polyplex": 72, "terminologi": 72, "label_error": 72, "quickstart": [73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "spoken": 73, "500": [73, 87, 93], "english": [73, 79], "pronunci": 73, "wav": 73, "huggingfac": [73, 74, 75, 81], "voxceleb": 73, "speech": [73, 93], "your_pred_prob": [73, 74, 75, 77, 78], "tensorflow_io": 73, "26": [73, 74, 79, 81, 82, 84, 86, 90], "huggingface_hub": 73, "12": [73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "branch": [73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "reproduc": [73, 77, 82, 84], "command": 73, "wget": [73, 86, 90, 93], "navig": 73, "link": [73, 79, 86], "browser": 73, "jakobovski": 73, "archiv": [73, 93], "v1": 73, "tar": [73, 87], "gz": [73, 87], "mkdir": [73, 93], "spoken_digit": 73, "xf": 73, "6_nicolas_32": 73, "data_path": 73, "listdir": 73, "nondeterminist": 73, "file_nam": 73, "endswith": 73, "file_path": 73, "join": [73, 80, 81], "39": [73, 74, 78, 79, 80, 81, 86, 89, 90, 92, 93], "7_george_26": 73, "0_nicolas_24": 73, "0_nicolas_6": 73, "listen": 73, "display_exampl": 73, "click": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "expand": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "pulldown": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "colab": [73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "tfio": 73, "pathlib": 73, "ipython": 73, "load_wav_16k_mono": 73, "filenam": 73, "khz": 73, "file_cont": 73, "io": [73, 79], "read_fil": 73, "sample_r": 73, "decode_wav": 73, "desired_channel": 73, "squeez": 73, "rate_in": 73, "rate_out": 73, "16000": 73, "wav_file_nam": 73, "audio_r": 73, "wav_file_exampl": 73, "plai": [73, 79, 80], "button": 73, "wav_file_name_exampl": 73, "7_jackson_43": 73, "hear": 73, "extractor": 73, "encoderclassifi": 73, "spkrec": 73, "xvect": 73, "feature_extractor": 73, "from_hparam": 73, "run_opt": 73, "uncom": 73, "wav_audio_file_path": 73, "head": [73, 75, 77, 78, 79, 81, 82, 84, 89, 91, 92], "torchaudio": 73, "extract_audio_embed": 73, "emb": [73, 81], "signal": 73, "encode_batch": 73, "embeddings_list": [73, 81], "embeddings_arrai": 73, "512": [73, 81], "14": [73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "196315": 73, "3194594": 73, "478977": 73, "2890828": 73, "8170278": 73, "892647": 73, "24": [73, 79, 82, 84, 86, 90], "898054": 73, "256194": 73, "559642": 73, "559715": 73, "620667": 73, "285246": 73, "21": [73, 74, 79, 80, 82, 86, 90, 93], "709623": 73, "5033712": 73, "913803": 73, "8198366": 73, "1831512": 73, "208761": 73, "08426": 73, "3210406": 73, "005453": 73, "2161605": 73, "478239": 73, "682179": 73, "0538025": 73, "242471": 73, "0914207": 73, "7833488": 73, "039538": 73, "23": [73, 79, 81, 82, 86, 90], "56918": 73, "19": [73, 78, 79, 80, 81, 82, 87, 89, 90, 92], "761095": 73, "1258287": 73, "753235": 73, "3508894": 73, "598273": 73, "237122": 73, "2500": 73, "leverag": [73, 78, 80, 82, 84, 92], "tune": [73, 78, 79, 87, 92], "computation": [73, 78, 92], "intens": [73, 78, 92], "held": [73, 77, 78, 79, 86, 87, 88, 91], "straightforward": [73, 77, 91], "benefit": [73, 88, 90, 91], "tol": 73, "num_crossval_fold": [73, 77, 84, 91], "decreas": [73, 80], "never": [73, 82, 85, 87, 88], "accuracy_scor": [73, 78, 82, 91, 92], "cv_accuraci": 73, "9772": 73, "probabilit": [73, 92], "9980": 73, "176": [73, 79, 82, 85], "006488": 73, "2318": 73, "008269": 73, "986": 73, "010354": 73, "469": 73, "013459": 73, "516": 73, "013478": 73, "investig": 73, "100541": 73, "998729": 73, "998768": 73, "980980": 73, "998217": 73, "18": [73, 78, 79, 80, 81, 82, 86, 87, 89, 90, 92], "identified_label_issu": [73, 78], "lowest_quality_label": [73, 78, 82, 89, 92], "sort_valu": [73, 75, 77, 78, 80, 81, 82, 84], "1946": 73, "1871": 73, "1955": 73, "2132": 73, "worth": [73, 82], "iloc": [73, 77, 78, 89, 91, 92], "6_yweweler_35": 73, "6_yweweler_36": 73, "6_yweweler_14": 73, "6_theo_27": 73, "4_george_31": 73, "6_nicolas_8": 73, "sound": 73, "quit": [73, 87], "22": [73, 74, 79, 81, 82, 85, 86, 90, 93], "blindli": [73, 80, 89, 91, 92], "trust": [73, 80, 82, 84, 88, 89, 91, 92], "underneath": 74, "hood": 74, "alert": 74, "introduct": 74, "mayb": [74, 75, 78], "examin": [74, 75, 77, 91], "your_feature_matrix": [74, 75], "toi": [74, 75, 79, 81, 82, 84], "train_test_split": [74, 75, 87, 91, 92], "inf": [74, 75], "mid": [74, 75], "bins_map": [74, 75], "create_data": [74, 75], "y_bin": [74, 75], "y_i": [74, 75], "y_bin_idx": [74, 75], "y_train": [74, 75, 82, 89], "y_test": [74, 75, 82, 89], "y_train_idx": [74, 75], "y_test_idx": [74, 75], "test_siz": [74, 75, 91, 92], "slide": [74, 75, 79], "decis": [74, 75, 91], "boundari": [74, 75], "frame": [74, 75], "x_out": [74, 75], "tini": [74, 75], "concaten": [74, 75, 80, 88], "y_out": [74, 75], "y_out_bin": [74, 75], "y_out_bin_idx": [74, 75], "exact_duplicate_idx": [74, 75], "x_duplic": [74, 75], "y_duplic": [74, 75], "y_duplicate_idx": [74, 75], "noisy_labels_idx": [74, 75, 85], "scatter": [74, 75, 82, 85, 89], "black": [74, 75, 79, 89], "cyan": [74, 75], "pyplot": [74, 75, 81, 82, 85, 87, 89], "plt": [74, 75, 81, 82, 85, 87, 89], "plot_data": [74, 75, 82, 85, 89], "fig": [74, 75, 79, 81, 87, 89], "ax": [74, 75, 81, 87, 89], "subplot": [74, 75, 81, 87], "set_titl": [74, 75, 81, 87], "set_xlabel": [74, 75], "x_1": [74, 75], "fontsiz": [74, 75, 81, 82, 85], "set_ylabel": [74, 75], "x_2": [74, 75], "set_xlim": [74, 75], "set_ylim": [74, 75], "linestyl": [74, 75], "circl": [74, 75, 82, 85], "misclassifi": [74, 75], "zip": [74, 75, 81, 86, 93], "label_err": [74, 75], "180": [74, 75, 86], "marker": [74, 75], "facecolor": [74, 75], "edgecolor": [74, 75], "linewidth": [74, 75, 87], "dup": [74, 75], "first_legend": [74, 75], "align": [74, 75], "title_fontproperti": [74, 75], "semibold": [74, 75], "second_legend": [74, 75], "45": [74, 75, 79, 81, 82, 86, 90], "gca": [74, 75], "add_artist": [74, 75], "tight_layout": [74, 75], "ideal": [74, 75], "logist": [74, 75, 78, 84, 87, 92], "remaind": 74, "modal": [74, 75, 80, 84], "regardless": [74, 75], "132": [74, 75, 82, 86], "9318": 74, "77": [74, 75, 77, 86, 90, 91], "006939": 74, "007830": 74, "40": [74, 75, 78, 79, 81, 90], "014826": 74, "107": [74, 75, 82, 85], "021220": 74, "120": [74, 75, 91], "026403": 74, "notic": [74, 82, 84, 86], "5221": [74, 75], "126": [74, 75, 82, 86], "046465": [74, 75], "130": [74, 75], "068695": [74, 75], "129": [74, 75, 93], "127": [74, 75], "076251": [74, 75], "128": [74, 75, 81], "083941": [74, 75], "6160": [74, 75], "is_near_duplicate_issu": [74, 75, 77, 78, 80, 81, 82], "131": [74, 75, 90], "000000e": [74, 75], "00": [74, 75, 77, 79, 81, 90, 91, 93], "000002": [74, 75], "463180e": [74, 75], "07": [74, 75, 82, 86, 90], "51": [74, 75, 77, 79, 82, 86, 90], "161148": [74, 75], "859087e": [74, 75], "30": [74, 75, 79, 80, 81, 85, 90, 93], "3293": 74, "025076": 74, "026534": 74, "050766": 74, "051025": 74, "home": [74, 75, 78, 79, 87, 92], "runner": [74, 75, 78, 87, 92], "300": [74, 84, 93], "userwarn": [74, 75], "330": [74, 81, 86], "309": 74, "34": [74, 79, 82, 84, 86, 87, 90, 93], "54": [74, 79, 82, 86, 90, 93], "039117": 74, "53": [74, 75, 77, 79, 85, 86, 90, 91], "044594": 74, "105": 74, "105121": 74, "133588": 74, "43": [74, 79, 82, 86, 90, 92], "168035": 74, "125": 74, "090878": 74, "37": [74, 79, 90], "169462": 74, "109": [74, 79, 86], "194566": 74, "196302": 74, "206314": 74, "average_ood_scor": 74, "32933380816554325": 74, "52": [74, 79, 81, 86, 90, 93], "169820": 74, "087324e": 74, "89": [74, 77, 86, 89, 90], "92": [74, 82, 86, 90, 91], "259024": 74, "583757e": 74, "91": [74, 86, 90, 92], "346458": 74, "341292e": 74, "specfi": 74, "new_lab": 74, "scoring_funct": 74, "div": 74, "rem": 74, "inv_scal": 74, "49": [74, 79, 82, 86, 90], "superstitionissuemanag": 74, "unlucki": 74, "superstit": 74, "to_seri": 74, "issues_mask": 74, "summary_scor": 74, "9242": 74, "is_superstition_issu": 74, "superstition_scor": 74, "047581": 74, "090635": 74, "129591": 74, "65": [74, 81, 86, 90, 91], "164840": 74, "demo": [75, 77, 85, 91], "lurk": [75, 81, 82], "opt": 75, "hostedtoolcach": 75, "x64": 75, "lib": 75, "python3": 75, "site": 75, "_split": 75, "737": 75, "thoroughli": 75, "preprocess": [75, 77, 87, 89, 91, 92], "904": 75, "review": [75, 77, 78, 79, 80, 82, 86, 89, 90, 91, 92, 93], "8561": 75, "001894": 75, "58": [75, 77, 79, 82, 86, 90, 91], "003565": 75, "007326": 75, "008974": 75, "009699": 75, "0227": 75, "is_class_imbalance_issu": [75, 77, 78, 81, 82], "022727": 75, "86": [75, 77, 81, 82, 86, 89, 90, 91], "87": [75, 81, 86, 89, 90, 92], "0000": [75, 78, 79, 81, 82], "is_null_issu": [75, 78, 81, 82], "96": [75, 77, 79, 82, 85, 86, 89, 90, 93], "94": [75, 77, 79, 82, 86, 89, 90, 91], "93": [75, 79, 86, 89, 90, 91, 93], "8218": 75, "is_non_iid_issu": [75, 77, 78, 81, 82], "810274": 75, "826147": 75, "849587": 75, "855359": 75, "855485": 75, "821750488732925": 75, "auto": [75, 79, 80, 89, 91, 92], "conceptu": 75, "856061": 75, "522080": 75, "616034": 75, "821750": 75, "betweeen": 75, "859109": 75, "586131": 75, "664083": 75, "970324": 75, "816965": 75, "548979": 75, "641516": 75, "890575": 75, "530924": 75, "622256": 75, "601188": 75, "752776": 75, "499498": 75, "562539": 75, "948362": 75, "090224": 75, "632385": 75, "746763": 75, "878267": 75, "examples_w_issu": [75, 80], "013444": 75, "025173": 75, "026416": 75, "inde": [75, 78], "miscellan": [75, 93], "428571": 75, "111111": 75, "571429": 75, "407407": 75, "592593": 75, "337838": 75, "092593": 75, "662162": 75, "333333": [75, 79], "952381": 75, "666667": 75, "portion": 75, "huge": [75, 82], "worri": [75, 78], "critic": 75, "highli": [75, 81], "sql": [77, 91], "databas": [77, 91], "excel": [77, 91], "parquet": [77, 91], "student": [77, 89, 91, 93], "grade": [77, 89, 91], "900": [77, 89, 91], "exam": [77, 89, 91], "letter": [77, 91, 93], "hundr": [77, 91], "histgradientboostingclassifi": 77, "standardscal": [77, 87, 91], "possibli": [77, 91], "grades_data": [77, 91], "read_csv": [77, 78, 89, 91, 92], "stud_id": [77, 91], "exam_1": [77, 89, 91], "exam_2": [77, 89, 91], "exam_3": [77, 89, 91], "letter_grad": [77, 91], "f48f73": [77, 91], "0bd4e7": [77, 91], "81": [77, 78, 86, 89, 90, 91, 93], "great": [77, 79, 91], "particip": [77, 91], "cb9d7a": [77, 91], "61": [77, 81, 82, 86, 90, 91], "78": [77, 79, 82, 86, 89, 90, 91], "9acca4": [77, 91], "48": [77, 79, 81, 82, 86, 90, 91], "x_raw": [77, 91], "cat_featur": 77, "x_encod": [77, 91], "get_dummi": [77, 89, 91], "drop_first": [77, 91], "numeric_featur": [77, 91], "scaler": [77, 87, 91], "x_process": [77, 91], "fit_transform": [77, 91], "bring": [77, 78, 81, 84, 89, 91, 92], "byod": [77, 78, 81, 84, 89, 91, 92], "boost": [77, 80, 84, 89], "xgboost": [77, 80, 89], "think": [77, 78, 80, 85, 90, 93], "carefulli": [77, 78, 81, 91], "nonzero": 77, "suspici": [77, 91], "tabl": [77, 79, 84, 91], "358": 77, "294": [77, 86], "46": [77, 79, 82, 86, 90], "941": 77, "7109": 77, "000005": [77, 78, 81], "886": 77, "000059": 77, "709": 77, "000104": 77, "723": 77, "000169": 77, "689": 77, "000181": 77, "7154": 77, "012085": 77, "061510": 77, "115512": 77, "124391": 77, "214163": 77, "6165": 77, "582": [77, 81], "185": [77, 79, 86, 93], "187": [77, 79], "27": [77, 79, 81, 82, 86, 90, 93], "898": 77, "637": [77, 91], "0014": [77, 79], "595": 77, "702427": 77, "147": [77, 82, 86], "711186": 77, "157": [77, 82], "721394": 77, "771": 77, "731979": 77, "740335": 77, "0014153602099278074": 77, "1562": 77, "393": 77, "156217": 77, "391": 77, "806": 77, "805": 77, "156": [77, 82], "na": [77, 78, 81, 82, 84], "issue_result": 77, "000842": 77, "555944": 77, "004374": 77, "sorted_issu": 77, "73": [77, 79, 81, 85, 86, 89, 90], "deserv": 77, "outlier_result": 77, "sorted_outli": 77, "56": [77, 79, 81, 89, 90], "lt": [77, 78, 79, 81, 84, 90], "style": [77, 90], "font": 77, "18px": 77, "ff00ff": 77, "bac": 77, "unintend": [77, 78], "mistak": [77, 78, 81, 91, 92], "duplicate_result": 77, "690": 77, "246": [77, 86], "perhap": [77, 82, 84], "twice": 77, "67": [77, 79, 81, 86, 89, 90], "wari": [77, 78, 80], "super": [77, 78, 81], "system": [77, 78, 81, 90], "intent": [78, 92], "servic": [78, 80, 92], "onlin": [78, 92], "bank": [78, 79, 92], "banking77": [78, 92], "oo": [78, 92], "000": [78, 79, 81, 92, 93], "categori": [78, 81, 92], "scope": [78, 92], "dive": 78, "your_featur": 78, "sentence_transform": [78, 92], "sentencetransform": [78, 92], "payment": [78, 92], "cancel_transf": [78, 92], "transfer": [78, 92], "fund": [78, 92], "cancel": [78, 92], "transact": [78, 92], "my": [78, 92], "revert": [78, 92], "morn": [78, 92], "realis": [78, 92], "yesterdai": [78, 92], "rent": [78, 92], "realli": [78, 84, 90, 92], "tomorrow": [78, 92], "raw_text": [78, 92], "getting_spare_card": [78, 92], "card_about_to_expir": [78, 92], "card_payment_fee_charg": [78, 92], "change_pin": [78, 92], "apple_pay_or_google_pai": [78, 92], "lost_or_stolen_phon": [78, 92], "visa_or_mastercard": [78, 92], "supported_cards_and_curr": [78, 92], "beneficiary_not_allow": [78, 92], "utter": [78, 92], "continu": [78, 80, 81, 84, 89, 91, 92, 93], "suit": [78, 79, 80, 92], "electra": [78, 92], "discrimin": [78, 92], "googl": [78, 92], "text_embed": 78, "No": [78, 80, 92], "google_electra": [78, 92], "pool": [78, 80, 87, 92], "400": [78, 92], "data_dict": [78, 82, 84], "84": [78, 86, 90], "41": [78, 79, 86, 89, 90], "38": [78, 79, 86, 90], "9720": 78, "981": 78, "974": 78, "000150": 78, "982": [78, 79], "000218": 78, "971": 78, "000512": 78, "980": [78, 79], "000947": 78, "9122": 78, "994": 78, "676322": 78, "999": 78, "693868": 78, "697240": 78, "433": 78, "700874": 78, "989": 78, "713590": 78, "6070": 78, "160": [78, 82], "095724": 78, "148": 78, "006237": 78, "546": 78, "099340": 78, "514": 78, "006485": 78, "481": 78, "123416": 78, "008165": 78, "313": [78, 86], "564102": 78, "572258": 78, "28": [78, 79, 81, 82, 84, 90, 93], "574915": 78, "31": [78, 79, 82, 84, 86, 90], "575507": 78, "575874": 78, "658": 78, "659": [78, 89], "660": 78, "661": 78, "0800": 78, "454": 78, "453": 78, "455": 78, "791961": 78, "258508": 78, "699010": 78, "183136": 78, "771112": 78, "to_numpi": [78, 80, 89, 92], "data_with_suggested_label": 78, "suggested_label": 78, "charg": [78, 92], "cash": [78, 92], "holidai": [78, 92], "sent": [78, 92, 93], "card": [78, 79, 92], "mine": [78, 92], "expir": [78, 92], "me": [78, 92], "withdraw": 78, "monei": 78, "whoever": [78, 92], "outlier_issu": [78, 81], "lowest_quality_outli": 78, "OR": 78, "636c65616e6c616220697320617765736f6d6521": 78, "phone": [78, 79], "gone": 78, "gt": [78, 84, 93], "samp": 78, "br": 78, "press": [78, 93], "nonsens": 78, "sens": 78, "detriment": 78, "duplicate_issu": 78, "fee": 78, "pai": 78, "go": [78, 79, 82], "strongli": 78, "p_valu": 78, "benign": 78, "shortlist": [78, 89, 92], "curat": [78, 83], "mnist_test_set": 79, "imagenet_val_set": 79, "tench": 79, "goldfish": 79, "white": [79, 93], "shark": 79, "tiger": 79, "hammerhead": 79, "electr": 79, "rai": 79, "stingrai": 79, "cock": 79, "hen": 79, "ostrich": 79, "brambl": 79, "goldfinch": 79, "hous": 79, "finch": 79, "junco": 79, "indigo": 79, "bunt": 79, "american": [79, 93], "robin": 79, "bulbul": 79, "jai": 79, "magpi": 79, "chickade": 79, "dipper": 79, "kite": 79, "bald": 79, "eagl": 79, "vultur": 79, "grei": 79, "owl": 79, "fire": 79, "salamand": 79, "smooth": 79, "newt": 79, "spot": [79, 86], "axolotl": 79, "bullfrog": 79, "tree": 79, "frog": [79, 87], "tail": 79, "loggerhead": 79, "sea": 79, "turtl": 79, "leatherback": 79, "mud": 79, "terrapin": 79, "band": 79, "gecko": 79, "green": [79, 93], "iguana": 79, "carolina": 79, "anol": 79, "desert": 79, "grassland": 79, "whiptail": 79, "lizard": 79, "agama": 79, "frill": 79, "neck": 79, "allig": 79, "gila": 79, "monster": 79, "european": 79, "chameleon": 79, "komodo": 79, "dragon": 79, "nile": 79, "crocodil": 79, "triceratop": 79, "worm": 79, "snake": 79, "ring": 79, "eastern": 79, "hog": 79, "nose": 79, "kingsnak": 79, "garter": 79, "water": 79, "vine": 79, "night": 79, "boa": 79, "constrictor": 79, "african": 79, "rock": 79, "indian": 79, "cobra": 79, "mamba": 79, "saharan": 79, "horn": 79, "viper": 79, "diamondback": 79, "rattlesnak": 79, "sidewind": 79, "trilobit": 79, "harvestman": 79, "scorpion": 79, "yellow": 79, "garden": 79, "spider": 79, "barn": 79, "southern": 79, "widow": 79, "tarantula": 79, "wolf": 79, "tick": 79, "centiped": 79, "grous": 79, "ptarmigan": 79, "ruf": 79, "prairi": 79, "peacock": 79, "quail": 79, "partridg": 79, "parrot": 79, "macaw": 79, "sulphur": 79, "crest": 79, "cockatoo": 79, "lorikeet": 79, "coucal": 79, "bee": 79, "eater": 79, "hornbil": 79, "hummingbird": 79, "jacamar": 79, "toucan": 79, "duck": [79, 92], "breast": 79, "mergans": 79, "goos": 79, "swan": 79, "tusker": 79, "echidna": 79, "platypu": 79, "wallabi": 79, "koala": 79, "wombat": 79, "jellyfish": 79, "anemon": 79, "brain": 79, "coral": 79, "flatworm": 79, "nematod": 79, "conch": 79, "snail": 79, "slug": 79, "chiton": 79, "chamber": 79, "nautilu": 79, "dung": 79, "crab": 79, "fiddler": 79, "king": 79, "lobster": 79, "spini": 79, "crayfish": 79, "hermit": 79, "isopod": 79, "stork": 79, "spoonbil": 79, "flamingo": 79, "heron": 79, "egret": 79, "bittern": 79, "crane": 79, "bird": [79, 87], "limpkin": 79, "gallinul": 79, "coot": 79, "bustard": 79, "ruddi": 79, "turnston": 79, "dunlin": 79, "redshank": 79, "dowitch": 79, "oystercatch": 79, "pelican": 79, "penguin": 79, "albatross": 79, "whale": 79, "killer": 79, "dugong": 79, "lion": 79, "chihuahua": 79, "japanes": 79, "chin": 79, "maltes": 79, "pekinges": 79, "shih": 79, "tzu": 79, "charl": 79, "spaniel": 79, "papillon": 79, "terrier": 79, "rhodesian": 79, "ridgeback": 79, "afghan": [79, 93], "hound": 79, "basset": 79, "beagl": 79, "bloodhound": 79, "bluetick": 79, "coonhound": 79, "tan": 79, "walker": 79, "foxhound": 79, "redbon": 79, "borzoi": 79, "irish": 79, "wolfhound": 79, "italian": 79, "greyhound": 79, "whippet": 79, "ibizan": 79, "norwegian": 79, "elkhound": 79, "otterhound": 79, "saluki": 79, "scottish": 79, "deerhound": 79, "weimaran": 79, "staffordshir": 79, "bull": 79, "bedlington": 79, "border": 79, "kerri": 79, "norfolk": 79, "norwich": 79, "yorkshir": 79, "wire": 79, "fox": 79, "lakeland": 79, "sealyham": 79, "airedal": 79, "cairn": 79, "australian": 79, "dandi": 79, "dinmont": 79, "boston": 79, "miniatur": 79, "schnauzer": 79, "giant": 79, "tibetan": 79, "silki": 79, "coat": [79, 81], "wheaten": 79, "west": 79, "highland": 79, "lhasa": 79, "apso": 79, "flat": 79, "retriev": 79, "curli": 79, "golden": 79, "labrador": 79, "chesapeak": 79, "bai": 79, "german": [79, 93], "shorthair": 79, "pointer": 79, "vizsla": 79, "setter": 79, "gordon": 79, "brittani": 79, "clumber": 79, "springer": 79, "welsh": 79, "cocker": 79, "sussex": 79, "kuvasz": 79, "schipperk": 79, "groenendael": 79, "malinoi": 79, "briard": 79, "kelpi": 79, "komondor": 79, "sheepdog": 79, "shetland": 79, "colli": 79, "bouvier": 79, "de": 79, "flandr": 79, "rottweil": 79, "shepherd": 79, "dobermann": 79, "pinscher": 79, "swiss": [79, 93], "mountain": 79, "bernes": 79, "appenzel": 79, "sennenhund": 79, "entlebuch": 79, "boxer": 79, "bullmastiff": 79, "mastiff": 79, "french": 79, "bulldog": 79, "dane": 79, "st": 79, "bernard": 79, "huski": 79, "alaskan": 79, "malamut": 79, "siberian": 79, "dalmatian": 79, "affenpinsch": 79, "basenji": 79, "pug": 79, "leonberg": 79, "newfoundland": 79, "pyrenean": 79, "samoi": 79, "pomeranian": 79, "chow": 79, "keeshond": 79, "griffon": 79, "bruxelloi": 79, "pembrok": 79, "corgi": 79, "cardigan": 79, "poodl": 79, "mexican": 79, "hairless": 79, "tundra": 79, "coyot": 79, "dingo": 79, "dhole": 79, "wild": 79, "hyena": 79, "kit": 79, "arctic": 79, "tabbi": 79, "persian": 79, "siames": 79, "egyptian": 79, "mau": 79, "cougar": 79, "lynx": 79, "leopard": 79, "snow": 79, "jaguar": 79, "cheetah": 79, "brown": [79, 90], "bear": 79, "polar": 79, "sloth": 79, "mongoos": 79, "meerkat": 79, "beetl": 79, "ladybug": 79, "longhorn": 79, "leaf": 79, "rhinocero": 79, "weevil": 79, "fly": 79, "ant": 79, "grasshopp": 79, "cricket": 79, "stick": 79, "insect": 79, "cockroach": 79, "manti": 79, "cicada": 79, "leafhopp": 79, "lacew": 79, "dragonfli": 79, "damselfli": 79, "admir": 79, "ringlet": 79, "monarch": 79, "butterfli": 79, "gossam": 79, "wing": 79, "starfish": 79, "urchin": 79, "cucumb": 79, "cottontail": 79, "rabbit": 79, "hare": 79, "angora": 79, "hamster": 79, "porcupin": 79, "squirrel": 79, "marmot": 79, "beaver": 79, "guinea": 79, "pig": 79, "sorrel": 79, "zebra": 79, "boar": 79, "warthog": 79, "hippopotamu": 79, "ox": 79, "buffalo": 79, "bison": 79, "bighorn": 79, "sheep": 79, "alpin": 79, "ibex": 79, "hartebeest": 79, "impala": 79, "gazel": 79, "dromedari": 79, "llama": 79, "weasel": 79, "mink": 79, "polecat": 79, "foot": 79, "ferret": 79, "otter": 79, "skunk": 79, "badger": 79, "armadillo": 79, "toed": 79, "orangutan": 79, "gorilla": 79, "chimpanze": 79, "gibbon": 79, "siamang": 79, "guenon": 79, "pata": 79, "monkei": 79, "baboon": 79, "macaqu": 79, "langur": 79, "colobu": 79, "probosci": 79, "marmoset": 79, "capuchin": 79, "howler": 79, "titi": 79, "geoffroi": 79, "lemur": 79, "indri": 79, "asian": 79, "eleph": 79, "bush": 79, "snoek": 79, "eel": 79, "coho": 79, "salmon": 79, "beauti": 79, "clownfish": 79, "sturgeon": 79, "garfish": 79, "lionfish": 79, "pufferfish": 79, "abacu": 79, "abaya": 79, "academ": 79, "gown": 79, "accordion": 79, "acoust": 79, "guitar": 79, "aircraft": 79, "carrier": 79, "airlin": 79, "airship": 79, "altar": 79, "ambul": 79, "amphibi": 79, "clock": [79, 93], "apiari": 79, "apron": 79, "wast": 79, "assault": 79, "rifl": 79, "backpack": 79, "bakeri": 79, "balanc": 79, "beam": 79, "balloon": 79, "ballpoint": 79, "pen": 79, "aid": 79, "banjo": 79, "balust": 79, "barbel": 79, "barber": 79, "chair": [79, 86], "barbershop": 79, "baromet": 79, "barrel": 79, "wheelbarrow": 79, "basebal": 79, "basketbal": 79, "bassinet": 79, "bassoon": 79, "swim": 79, "cap": 79, "bath": 79, "towel": 79, "bathtub": 79, "station": 79, "wagon": 79, "lighthous": 79, "beaker": 79, "militari": 79, "beer": 79, "bottl": 79, "glass": 79, "bell": 79, "cot": 79, "bib": 79, "bicycl": [79, 90], "bikini": 79, "binder": 79, "binocular": 79, "birdhous": 79, "boathous": 79, "bobsleigh": 79, "bolo": 79, "tie": 79, "poke": 79, "bonnet": 79, "bookcas": 79, "bookstor": 79, "bow": 79, "brass": 79, "bra": 79, "breakwat": 79, "breastplat": 79, "broom": 79, "bucket": 79, "buckl": 79, "bulletproof": 79, "vest": 79, "butcher": 79, "shop": 79, "taxicab": 79, "cauldron": 79, "candl": 79, "cannon": 79, "cano": 79, "mirror": [79, 86], "carousel": 79, "tool": [79, 82, 84], "carton": 79, "wheel": 79, "teller": 79, "cassett": 79, "player": 79, "castl": 79, "catamaran": 79, "cd": 79, "cello": 79, "mobil": [79, 93], "chain": 79, "fenc": [79, 90], "mail": 79, "chainsaw": 79, "chest": 79, "chiffoni": 79, "chime": 79, "china": 79, "cabinet": 79, "christma": 79, "stock": 79, "church": 79, "movi": 79, "theater": 79, "cleaver": 79, "cliff": 79, "dwell": 79, "cloak": 79, "clog": 79, "cocktail": 79, "shaker": 79, "coffe": 79, "mug": 79, "coffeemak": 79, "coil": 79, "lock": 79, "keyboard": 79, "confectioneri": 79, "ship": [79, 87], "corkscrew": 79, "cornet": 79, "cowboi": 79, "boot": 79, "hat": 79, "cradl": 79, "crash": 79, "helmet": 79, "crate": 79, "infant": 79, "bed": 79, "crock": 79, "pot": 79, "croquet": 79, "crutch": 79, "cuirass": 79, "dam": 79, "desk": 79, "desktop": 79, "rotari": 79, "dial": 79, "telephon": 79, "diaper": 79, "watch": 79, "dine": 79, "dishcloth": 79, "dishwash": 79, "disc": 79, "brake": 79, "dock": 79, "sled": 79, "dome": 79, "doormat": 79, "drill": 79, "rig": 79, "drum": 79, "drumstick": 79, "dumbbel": 79, "dutch": 79, "oven": 79, "fan": 79, "locomot": 79, "entertain": 79, "center": 79, "envelop": 79, "espresso": 79, "powder": 79, "feather": 79, "fireboat": 79, "engin": [79, 90], "screen": 79, "sheet": 79, "flagpol": 79, "flute": 79, "footbal": 79, "forklift": 79, "fountain": 79, "poster": 79, "freight": 79, "fry": 79, "pan": 79, "fur": 79, "garbag": 79, "ga": 79, "pump": 79, "goblet": 79, "kart": 79, "golf": 79, "cart": 79, "gondola": 79, "gong": 79, "grand": 79, "piano": 79, "greenhous": 79, "grill": 79, "groceri": 79, "guillotin": 79, "barrett": 79, "hair": 79, "sprai": 79, "hammer": 79, "dryer": 79, "hand": [79, 82], "handkerchief": 79, "drive": 79, "harmonica": 79, "harp": 79, "harvest": 79, "hatchet": 79, "holster": 79, "honeycomb": 79, "hoop": 79, "skirt": 79, "horizont": 79, "bar": 79, "hors": [79, 87, 92], "drawn": 79, "hourglass": 79, "ipod": 79, "cloth": 79, "iron": 79, "jack": 79, "lantern": 79, "jean": 79, "jeep": 79, "shirt": [79, 81], "jigsaw": 79, "puzzl": 79, "pull": 79, "rickshaw": 79, "joystick": 79, "kimono": 79, "knee": 79, "pad": 79, "knot": 79, "ladl": 79, "lampshad": 79, "laptop": 79, "lawn": 79, "mower": 79, "knife": 79, "lifeboat": 79, "lighter": 79, "limousin": 79, "ocean": 79, "liner": 79, "lipstick": 79, "slip": 79, "shoe": 79, "lotion": 79, "speaker": 79, "loup": 79, "sawmil": 79, "magnet": 79, "compass": 79, "bag": [79, 81, 87, 88], "mailbox": 79, "tight": 79, "tank": 79, "manhol": 79, "maraca": 79, "marimba": 79, "maypol": 79, "maze": 79, "cup": [79, 86], "medicin": 79, "megalith": 79, "microphon": 79, "microwav": 79, "milk": 79, "minibu": 79, "miniskirt": 79, "minivan": 79, "missil": 79, "mitten": 79, "mix": 79, "bowl": 79, "modem": 79, "monasteri": 79, "monitor": 79, "mope": 79, "mortar": 79, "mosqu": 79, "mosquito": 79, "scooter": 79, "bike": 79, "tent": 79, "mous": [79, 80], "mousetrap": 79, "van": 79, "muzzl": 79, "nail": 79, "brace": 79, "necklac": 79, "nippl": 79, "obelisk": 79, "obo": 79, "ocarina": 79, "odomet": 79, "oil": 79, "oscilloscop": 79, "overskirt": 79, "bullock": 79, "oxygen": 79, "packet": 79, "paddl": 79, "padlock": 79, "paintbrush": 79, "pajama": 79, "palac": [79, 93], "parachut": 79, "park": 79, "bench": 79, "meter": 79, "passeng": 79, "patio": 79, "payphon": 79, "pedest": 79, "pencil": 79, "perfum": 79, "petri": 79, "dish": 79, "photocopi": 79, "plectrum": 79, "pickelhaub": 79, "picket": 79, "pickup": 79, "pier": 79, "piggi": 79, "pill": 79, "pillow": 79, "ping": 79, "pong": 79, "pinwheel": 79, "pirat": 79, "pitcher": 79, "plane": 79, "planetarium": 79, "plastic": 79, "plate": 79, "rack": 79, "plow": 79, "plunger": 79, "polaroid": 79, "camera": 79, "pole": [79, 90], "polic": 79, "poncho": 79, "billiard": 79, "soda": 79, "potter": 79, "prayer": 79, "rug": 79, "printer": 79, "prison": 79, "projectil": 79, "projector": 79, "hockei": 79, "puck": 79, "punch": 79, "purs": 79, "quill": 79, "quilt": 79, "race": 79, "racket": 79, "radiat": 79, "radio": 79, "telescop": 79, "rain": 79, "recreat": 79, "reel": 79, "reflex": 79, "refriger": 79, "remot": 79, "restaur": 79, "revolv": 79, "rotisseri": 79, "eras": 79, "rugbi": 79, "ruler": 79, "safe": 79, "safeti": 79, "salt": 79, "sandal": [79, 81], "sarong": 79, "saxophon": 79, "scabbard": 79, "school": 79, "bu": [79, 90], "schooner": 79, "scoreboard": 79, "crt": 79, "screw": 79, "screwdriv": 79, "seat": 79, "belt": 79, "sew": 79, "shield": 79, "shoji": 79, "basket": 79, "shovel": 79, "shower": 79, "curtain": 79, "ski": 79, "sleep": 79, "door": 79, "slot": 79, "snorkel": 79, "snowmobil": 79, "snowplow": 79, "soap": 79, "dispens": 79, "soccer": [79, 93], "sock": 79, "solar": 79, "thermal": 79, "collector": 79, "sombrero": 79, "soup": 79, "heater": 79, "shuttl": 79, "spatula": 79, "motorboat": 79, "web": 79, "spindl": 79, "sport": [79, 93], "spotlight": 79, "stage": 79, "steam": 79, "arch": 79, "bridg": 79, "steel": 79, "stethoscop": 79, "scarf": 79, "stone": 79, "wall": [79, 90], "stopwatch": 79, "stove": 79, "strainer": 79, "tram": 79, "stretcher": 79, "couch": 79, "stupa": 79, "submarin": 79, "sundial": 79, "sunglass": 79, "sunscreen": 79, "suspens": 79, "mop": 79, "sweatshirt": 79, "swimsuit": 79, "swing": 79, "switch": 79, "syring": 79, "lamp": 79, "tape": 79, "teapot": 79, "teddi": 79, "televis": [79, 93], "tenni": 79, "thatch": 79, "roof": 79, "front": 79, "thimbl": 79, "thresh": 79, "throne": 79, "tile": 79, "toaster": 79, "tobacco": 79, "toilet": 79, "totem": 79, "tow": 79, "tractor": 79, "semi": 79, "trailer": 79, "trai": 79, "trench": 79, "tricycl": 79, "trimaran": 79, "tripod": 79, "triumphal": 79, "trolleybu": 79, "trombon": 79, "tub": 79, "turnstil": 79, "typewrit": 79, "umbrella": 79, "unicycl": 79, "upright": 79, "vacuum": 79, "cleaner": 79, "vase": 79, "vault": 79, "velvet": 79, "vend": 79, "vestment": 79, "viaduct": 79, "violin": 79, "volleybal": 79, "waffl": 79, "wallet": 79, "wardrob": 79, "sink": 79, "wash": 79, "jug": 79, "tower": 79, "whiskei": 79, "whistl": 79, "wig": 79, "shade": [79, 90], "windsor": 79, "wine": 79, "wok": 79, "wooden": 79, "spoon": 79, "wool": 79, "rail": 79, "shipwreck": 79, "yawl": 79, "yurt": 79, "websit": 79, "comic": 79, "book": 79, "crossword": 79, "traffic": [79, 86, 90], "sign": [79, 90, 93], "dust": 79, "jacket": [79, 86], "menu": 79, "guacamol": 79, "consomm": 79, "trifl": 79, "ic": 79, "cream": 79, "pop": 79, "baguett": 79, "bagel": 79, "pretzel": 79, "cheeseburg": 79, "mash": 79, "potato": 79, "cabbag": 79, "broccoli": 79, "cauliflow": 79, "zucchini": 79, "spaghetti": 79, "squash": 79, "acorn": 79, "butternut": 79, "artichok": 79, "pepper": 79, "cardoon": 79, "mushroom": 79, "granni": 79, "smith": 79, "strawberri": 79, "orang": 79, "lemon": 79, "pineappl": 79, "banana": 79, "jackfruit": 79, "custard": 79, "appl": 79, "pomegran": 79, "hai": 79, "carbonara": 79, "chocol": 79, "syrup": 79, "dough": 79, "meatloaf": 79, "pizza": 79, "pie": 79, "burrito": 79, "eggnog": 79, "alp": 79, "bubbl": 79, "reef": 79, "geyser": 79, "lakeshor": 79, "promontori": 79, "shoal": 79, "seashor": 79, "vallei": 79, "volcano": 79, "bridegroom": 79, "scuba": 79, "diver": 79, "rapese": 79, "daisi": 79, "ladi": 79, "slipper": 79, "corn": 79, "rose": 79, "hip": 79, "chestnut": 79, "fungu": 79, "agar": 79, "gyromitra": 79, "stinkhorn": 79, "earth": 79, "star": 79, "wood": 79, "bolet": 79, "ear": 79, "cifar10_test_set": 79, "airplan": [79, 87], "automobil": [79, 87], "deer": [79, 87], "cifar100_test_set": 79, "aquarium_fish": 79, "babi": 79, "boi": 79, "camel": 79, "caterpillar": 79, "cattl": [79, 93], "cloud": 79, "dinosaur": 79, "dolphin": 79, "flatfish": 79, "forest": 79, "girl": 79, "kangaroo": 79, "lawn_mow": 79, "man": 79, "maple_tre": 79, "motorcycl": [79, 90], "oak_tre": 79, "orchid": 79, "palm_tre": 79, "pear": 79, "pickup_truck": 79, "pine_tre": 79, "plain": 79, "poppi": 79, "possum": 79, "raccoon": 79, "road": [79, 90], "rocket": 79, "seal": 79, "shrew": 79, "skyscrap": 79, "streetcar": 79, "sunflow": 79, "sweet_pepp": 79, "trout": 79, "tulip": 79, "willow_tre": 79, "woman": [79, 86], "caltech256": 79, "ak47": 79, "bat": 79, "glove": 79, "birdbath": 79, "blimp": 79, "bonsai": 79, "boom": 79, "breadmak": 79, "buddha": 79, "bulldoz": 79, "cactu": 79, "cake": 79, "tire": 79, "cartman": 79, "cereal": 79, "chandeli": 79, "chess": 79, "board": 79, "chimp": 79, "chopstick": 79, "coffin": 79, "coin": 79, "comet": 79, "cormor": 79, "globe": 79, "diamond": 79, "dice": 79, "doorknob": 79, "drink": 79, "straw": 79, "dumb": 79, "eiffel": 79, "elk": 79, "ewer": 79, "eyeglass": 79, "fern": 79, "fighter": 79, "jet": [79, 89], "extinguish": 79, "hydrant": 79, "firework": 79, "flashlight": 79, "floppi": 79, "fri": 79, "frisbe": 79, "galaxi": 79, "giraff": 79, "goat": 79, "gate": 79, "grape": 79, "pick": [79, 80], "hamburg": 79, "hammock": 79, "harpsichord": 79, "hawksbil": 79, "helicopt": 79, "hibiscu": 79, "homer": 79, "simpson": 79, "horsesho": 79, "air": 79, "skeleton": 79, "ibi": 79, "cone": 79, "iri": 79, "jesu": 79, "christ": 79, "joi": 79, "kayak": 79, "ketch": 79, "ladder": 79, "lath": 79, "licens": 79, "lightbulb": 79, "lightn": 79, "mandolin": 79, "mar": 79, "mattress": 79, "megaphon": 79, "menorah": 79, "microscop": 79, "minaret": 79, "minotaur": 79, "motorbik": 79, "mussel": 79, "neckti": 79, "octopu": 79, "palm": 79, "pilot": 79, "paperclip": 79, "shredder": 79, "pci": 79, "peopl": [79, 86], "pez": 79, "picnic": 79, "pram": 79, "prai": 79, "pyramid": 79, "rainbow": 79, "roulett": 79, "saddl": 79, "saturn": 79, "segwai": 79, "propel": 79, "sextant": 79, "music": 79, "skateboard": 79, "smokestack": 79, "sneaker": 79, "boat": 79, "stain": 79, "steer": 79, "stirrup": 79, "superman": 79, "sushi": 79, "armi": [79, 93], "sword": 79, "tambourin": 79, "teepe": 79, "court": 79, "theodolit": 79, "tomato": 79, "tombston": 79, "tour": 79, "pisa": 79, "treadmil": 79, "fork": 79, "tweezer": 79, "unicorn": 79, "vcr": 79, "waterfal": 79, "watermelon": 79, "weld": 79, "windmil": 79, "xylophon": 79, "yarmulk": 79, "yo": 79, "toad": 79, "twenty_news_test_set": 79, "alt": 79, "atheism": 79, "comp": 79, "graphic": [79, 90], "misc": [79, 93], "sy": 79, "ibm": 79, "pc": 79, "hardwar": 79, "mac": 79, "forsal": 79, "rec": 79, "sci": 79, "crypt": 79, "electron": 79, "med": 79, "soc": 79, "religion": 79, "christian": [79, 93], "talk": [79, 93], "polit": 79, "gun": 79, "mideast": 79, "amazon": 79, "neutral": 79, "imdb_test_set": 79, "all_class": 79, "20news_test_set": 79, "_load_classes_predprobs_label": 79, "dataset_nam": 79, "labelerror": 79, "url_bas": 79, "5392f6c71473055060be3044becdde1cbc18284d": 79, "url_label": 79, "original_test_label": 79, "_original_label": 79, "url_prob": 79, "cross_validated_predicted_prob": 79, "_pyx": 79, "num_part": 79, "datatset": 79, "bytesio": 79, "allow_pickl": 79, "pred_probs_part": 79, "url": 79, "_of_": 79, "nload": 79, "imdb": 79, "ve": [79, 80, 82, 84, 86], "interpret": [79, 80, 82], "capit": 79, "29780": 79, "256": [79, 80, 86], "780": 79, "medic": [79, 93], "doctor": 79, "254": [79, 86], "359223": 79, "640777": 79, "184": [79, 82], "258427": 79, "341176": 79, "263158": 79, "658824": 79, "337349": 79, "246575": 79, "662651": 79, "248": 79, "330000": 79, "355769": 79, "670000": 79, "251": [79, 86], "167": [79, 82, 86], "252": 79, "112": 79, "253": [79, 86], "022989": 79, "255": [79, 81], "049505": 79, "190": [79, 82, 86], "66": [79, 81, 90], "002216": 79, "000974": 79, "59": [79, 86, 90], "88": [79, 81, 82, 85, 86, 89, 90, 93], "000873": 79, "000739": 79, "79": [79, 86, 90, 91], "32635": 79, "32636": 79, "47": [79, 81, 86, 90], "32637": 79, "32638": 79, "32639": 79, "32640": 79, "051": 79, "002242": 79, "997758": 79, "002088": 79, "001045": 79, "997912": 79, "002053": 79, "997947": 79, "001980": 79, "000991": 79, "998020": 79, "001946": 79, "002915": 79, "998054": 79, "001938": 79, "002904": 79, "998062": 79, "001020": 79, "998980": 79, "001018": 79, "002035": 79, "998982": 79, "999009": 79, "0003": 79, "0002": 79, "36": [79, 81, 90, 93], "44": [79, 85, 86, 90], "71": [79, 82, 86, 90], "071": 79, "067269": 79, "929": 79, "046": 79, "058243": 79, "954": 79, "035": 79, "032096": 79, "965": 79, "031": 79, "012232": 79, "969": 79, "022": 79, "025896": 79, "978": 79, "020": [79, 82], "013092": 79, "018": 79, "013065": 79, "016": 79, "030542": 79, "984": 79, "013": 79, "020833": 79, "987": 79, "012": 79, "010020": 79, "988": 79, "0073": 79, "0020": 79, "0016": 79, "0015": 79, "0013": 79, "0012": 79, "0010": 79, "0008": 79, "0007": 79, "0006": 79, "0005": 79, "0004": 79, "244": [79, 86, 93], "98": [79, 80, 89, 90], "452381": 79, "459770": 79, "72": [79, 82, 85, 89, 90], "523364": 79, "460784": 79, "446602": 79, "57": [79, 82, 90], "68": [79, 81, 82, 86, 90, 91], "103774": 79, "030612": 79, "97": [79, 80, 82, 86, 89, 90, 91, 93], "110092": 79, "049020": 79, "99": [79, 82, 90, 91], "0034": 79, "0032": 79, "0026": 79, "0025": 79, "4945": 79, "4946": 79, "4947": 79, "4948": 79, "4949": 79, "4950": 79, "846": 79, "82": [79, 81, 82, 86, 90], "7532": 79, "532": 79, "034483": 79, "009646": 79, "965517": 79, "030457": 79, "020513": 79, "969543": 79, "028061": 79, "035443": 79, "971939": 79, "025316": 79, "005168": 79, "974684": 79, "049751": 79, "979487": 79, "019920": 79, "042802": 79, "980080": 79, "017677": 79, "005115": 79, "982323": 79, "012987": 79, "005236": 79, "987013": 79, "012723": 79, "025126": 79, "987277": 79, "010989": 79, "008264": 79, "989011": 79, "010283": 79, "027778": 79, "989717": 79, "009677": 79, "990323": 79, "007614": 79, "010127": 79, "992386": 79, "005051": 79, "994949": 79, "005025": 79, "994975": 79, "005013": 79, "994987": 79, "001859": 79, "001328": 79, "000929": 79, "000664": 79, "186": [79, 82], "188": [79, 82, 85], "189": [79, 82], "snippet": 80, "nlp": [80, 93], "mind": [80, 82], "number_of_class": 80, "total_number_of_data_point": 80, "drop": [80, 84, 89, 92], "feed": 80, "alphabet": 80, "labels_proper_format": 80, "your_classifi": 80, "issues_datafram": 80, "class_predicted_for_flagged_exampl": 80, "class_predicted_for_all_exampl": 80, "grant": 80, "datataset": 80, "fair": [80, 82], "game": 80, "speedup": [80, 87], "flexibl": 80, "tempfil": 80, "mkdtemp": 80, "sped": 80, "anywai": 80, "pred_probs_merg": 80, "merge_rare_class": 80, "count_threshold": 80, "class_mapping_orig2new": 80, "heath_summari": 80, "num_examples_per_class": 80, "rare_class": 80, "num_classes_merg": 80, "other_class": 80, "labels_merg": 80, "new_c": 80, "merged_prob": 80, "hstack": [80, 81, 82, 84], "new_class": 80, "original_class": 80, "num_check": 80, "ones_array_ref": 80, "isclos": 80, "though": [80, 82, 93], "successfulli": 80, "meaning": [80, 87], "virtuou": [80, 84], "cycl": [80, 84], "jointli": 80, "junk": 80, "clutter": 80, "unknown": 80, "caltech": 80, "combined_boolean_mask": 80, "mask1": 80, "mask2": 80, "gradientboostingclassifi": [80, 82], "true_error": [80, 82, 85], "101": [80, 86], "102": [80, 85, 86], "104": [80, 82, 86], "model_to_find_error": 80, "model_to_return": 80, "cl0": 80, "randomizedsearchcv": 80, "expens": 80, "param_distribut": 80, "learning_r": [80, 82], "max_depth": [80, 82], "magnitud": 80, "coeffici": [80, 89], "optin": 80, "environ": [80, 82], "rerun": [80, 82], "cell": [80, 82], "On": [80, 82, 86], "unabl": [80, 82], "render": [80, 82], "nbviewer": [80, 82], "cleanlearningcleanlearn": [80, 82], "linearregressionlinearregress": 80, "n_init": 80, "fit_predict": 80, "continuous_column": 80, "categorical_column": 80, "data_df": 80, "feature_a": 80, "feature_b": 80, "unexpectedli": 80, "emphas": 80, "especi": [80, 81, 89, 91, 92], "crucial": 80, "merge_duplicate_set": 80, "merge_kei": 80, "construct_group_kei": 80, "merged_set": 80, "consolidate_set": 80, "tolist": [80, 85], "issubset": 80, "frozenset": 80, "sets_list": 80, "mutabl": 80, "new_set": 80, "current_set": 80, "intersecting_set": 80, "lowest_score_strategi": 80, "sub_df": 80, "idxmin": 80, "filter_near_dupl": 80, "strategy_fn": 80, "strategy_kwarg": 80, "duplicate_row": 80, "group_kei": 80, "to_keep_indic": 80, "groupbi": 80, "explod": 80, "to_remov": 80, "isin": [80, 87], "kept": 80, "near_duplicate_issu": [80, 81], "ids_to_remove_seri": 80, "assist": 80, "streamlin": 80, "ux": 80, "agpl": 80, "compani": 80, "commerci": 80, "alter": 80, "email": 80, "discuss": 80, "anywher": 80, "profession": 80, "expert": 80, "60": [81, 82, 90], "excess": 81, "torchvis": [81, 87], "tensordataset": 81, "stratifiedkfold": [81, 85], "tqdm": 81, "fashion_mnist": 81, "num_row": 81, "60000": 81, "pil": 81, "transformed_dataset": 81, "with_format": 81, "unsqueez": 81, "cpu_count": 81, "torch_dataset": 81, "quick": [81, 85], "relu": 81, "batchnorm2d": 81, "maxpool2d": 81, "lazylinear": 81, "flatten": 81, "get_test_accuraci": 81, "testload": [81, 87], "energi": 81, "trainload": [81, 87], "n_epoch": 81, "patienc": 81, "criterion": 81, "crossentropyloss": 81, "adamw": 81, "best_test_accuraci": 81, "start_epoch": 81, "running_loss": 81, "best_epoch": 81, "end_epoch": 81, "3f": [81, 89], "acc": [81, 82], "time_taken": 81, "compute_embed": 81, "compute_pred_prob": 81, "train_batch_s": 81, "num_work": 81, "worker": [81, 93], "train_id_list": 81, "test_id_list": 81, "train_id": 81, "test_id": 81, "embeddings_model": 81, "ntrain": 81, "trainset": 81, "testset": 81, "pin_memori": 81, "fold_embed": 81, "fold_pred_prob": 81, "finish": 81, "483": 81, "835": 81, "331": 81, "310": 81, "490": 81, "stderr": [81, 90], "sphinxverbatim": [81, 90, 93], "17it": [81, 90], "10it": [81, 90], "82it": [81, 90], "62": [81, 82, 86, 89, 90], "43it": [81, 90], "06it": [81, 90], "23it": [81, 90], "56it": [81, 90], "77it": [81, 90], "28it": [81, 90], "69": [81, 82, 89, 90], "18it": [81, 90], "74": [81, 86, 89, 90, 91, 93], "26it": [81, 90], "03it": [81, 90], "492": 81, "085": 81, "290": [81, 86], "439": 81, "37it": [81, 90], "63": [81, 82, 86, 90], "36it": [81, 90], "30it": [81, 90], "41it": [81, 90], "74it": [81, 90], "09it": [81, 90], "81it": [81, 90], "49it": [81, 90], "98it": [81, 90], "38it": [81, 90], "90it": [81, 90], "476": 81, "305": [81, 89], "561": 81, "328": [81, 86], "335": 81, "258": 81, "92it": [81, 90], "14it": [81, 90], "13it": [81, 90], "32it": [81, 90], "08it": [81, 90], "94it": [81, 90], "24it": [81, 90], "42it": 81, "reorder": 81, "vision": 81, "grayscal": 81, "exce": 81, "max_preval": 81, "7620": 81, "3692": 81, "3521": 81, "225": [81, 85], "166": 81, "9661": 81, "40378": 81, "687452": 81, "54473": 81, "705050": 81, "29412": 81, "715470": 81, "25316": 81, "716273": 81, "52247": 81, "725283": 81, "9581": 81, "19228": 81, "dress": 81, "54078": 81, "000010": 81, "pullov": 81, "32657": 81, "21282": 81, "000011": 81, "11262": 81, "000014": 81, "6294": 81, "30659": 81, "000798": 81, "30968": 81, "000015": 81, "000907": 81, "9762": 81, "54565": 81, "47139": 81, "000017": 81, "001423": 81, "000026": 81, "39992": 81, "39993": 81, "39994": 81, "39995": 81, "7834": 81, "42819": 81, "629362": 81, "51431": 81, "654330": 81, "55548": 81, "658364": 81, "51191": 81, "668572": 81, "50081": 81, "669703": 81, "7834321613629787": 81, "13732": 81, "13733": 81, "13734": 81, "47635": 81, "110901": 81, "974390": 81, "998733": 81, "937117": 81, "998755": 81, "53564": 81, "5473": 81, "trouser": 81, "plot_label_issue_exampl": 81, "ncol": [81, 87], "nrow": [81, 87], "ceil": 81, "axes_list": 81, "label_issue_indic": 81, "gl": 81, "sl": 81, "fontdict": 81, "imshow": [81, 87], "cmap": [81, 89], "grai": 81, "subplots_adjust": 81, "hspace": 81, "outsiz": 81, "outlier_issues_df": 81, "depict": [81, 85, 86, 87, 88, 90], "plot_outlier_issues_exampl": 81, "n_comparison_imag": 81, "sample_from_class": 81, "number_of_sampl": 81, "non_outlier_indic": 81, "isnul": 81, "non_outlier_indices_excluding_curr": 81, "sampled_indic": 81, "label_scores_of_sampl": 81, "top_score_indic": 81, "top_label_indic": 81, "sampled_imag": 81, "get_image_given_label_and_sampl": 81, "image_from_dataset": 81, "corresponding_label": 81, "comparison_imag": 81, "images_to_plot": 81, "idlist": 81, "iterrow": 81, "closest": 81, "counterpart": 81, "near_duplicate_issues_df": 81, "plot_near_duplicate_issue_exampl": 81, "seen_id_pair": 81, "get_image_and_given_label_and_predicted_label": 81, "duplicate_imag": 81, "nd_set": 81, "challeng": 81, "dark_issu": 81, "reveal": [81, 90], "dark_scor": 81, "dark_issues_df": 81, "is_dark_issu": 81, "34848": 81, "203922": 81, "50270": 81, "204588": 81, "3936": 81, "213098": 81, "733": 81, "217686": 81, "8094": 81, "230118": 81, "plot_image_issue_exampl": 81, "difficult": 81, "disproportion": 81, "lowinfo_issu": 81, "low_information_scor": 81, "lowinfo_issues_df": 81, "is_low_information_issu": 81, "53050": 81, "067975": 81, "40875": 81, "089929": 81, "9594": 81, "092601": 81, "34825": 81, "107744": 81, "37530": 81, "108516": 81, "lot": 81, "depth": 82, "survei": [82, 93], "focus": [82, 84], "scienc": 82, "multivariate_norm": [82, 84, 85], "make_data": [82, 84], "cov": [82, 84, 85], "avg_trac": [82, 85], "test_label": [82, 85, 87, 92], "py_tru": 82, "noise_matrix_tru": 82, "noise_marix": 82, "s_test": 82, "noisy_test_label": 82, "purpl": 82, "val": 82, "namespac": 82, "exec": 82, "markerfacecolor": [82, 85], "markeredgecolor": [82, 85, 89], "markers": [82, 85, 89], "markeredgewidth": [82, 85, 89], "realist": 82, "7560": 82, "638483e": 82, "897052e": 82, "548986e": 82, "924634e": 82, "374580e": 82, "4643": 82, "050286": 82, "065420": 82, "249": [82, 86], "109420": 82, "111687": 82, "115403": 82, "6120": 82, "023714": 82, "007136": 82, "119": [82, 86], "107266": 82, "103": [82, 86], "033738": 82, "238": [82, 86], "119505": 82, "236": [82, 86], "037843": 82, "222": 82, "614915": 82, "122": [82, 86], "624422": 82, "625965": 82, "626079": 82, "118": 82, "627675": 82, "158": 82, "159": [82, 85, 86], "161": 82, "1960": 82, "196": [82, 86], "223": [82, 86], "221": 82, "219": [82, 86], "695174": 82, "323529": 82, "522929": 82, "013722": 82, "675606": 82, "646438": 82, "anyth": 82, "enhanc": [82, 84, 86], "magic": 82, "83": [82, 86, 89, 90, 91, 93], "liter": 82, "identif": 82, "x27": 82, "logisticregressionlogisticregress": 82, "ever": 82, "092": 82, "040": 82, "024": 82, "004": 82, "surpris": 82, "arxiv": 82, "ab": 82, "1705": 82, "01936": 82, "ton": 82, "yourfavoritemodel1": 82, "merged_label": 82, "merged_test_label": 82, "newli": [82, 84], "yourfavoritemodel2": 82, "yourfavoritemodel3": 82, "cl3": 82, "takeawai": 82, "That": [82, 85], "randomli": 82, "my_test_pred_prob": 82, "my_test_pr": 82, "issues_test": 82, "corrected_test_label": 82, "pretend": 82, "cl_test_pr": 82, "fairli": 82, "label_acc": 82, "percentag": 82, "offset": 82, "nquestion": 82, "overestim": 82, "answer": 82, "experienc": 82, "06": [82, 86, 90, 93], "76": [82, 85, 86, 89, 90, 91, 93], "knowledg": 82, "quantiti": [82, 89], "prioiri": 82, "known": 82, "versatil": 82, "label_issues_indic": 82, "213": [82, 86], "212": [82, 91], "218": [82, 86], "152": [82, 93], "197": [82, 86], "170": 82, "214": 82, "164": [82, 85], "198": [82, 86], "191": [82, 86], "121": [82, 92], "117": [82, 89], "206": [82, 86], "115": [82, 86], "193": 82, "194": 82, "201": [82, 86], "174": 82, "163": [82, 93], "150": [82, 84, 86], "169": 82, "151": [82, 86], "168": 82, "precision_scor": 82, "recall_scor": 82, "f1_score": 82, "true_label_issu": 82, "filter_by_list": 82, "718750": [82, 84], "807018": 82, "912": 82, "733333": 82, "800000": 82, "721311": 82, "792793": 82, "908": 82, "676923": 82, "765217": 82, "892": 82, "567901": 82, "702290": 82, "844": 82, "gaug": 82, "label_issues_count": 82, "155": [82, 86], "172": [82, 85], "easiest": 82, "modular": 82, "penalti": 82, "l2": 82, "model3": 82, "n_estim": 82, "cv_pred_probs_1": 82, "cv_pred_probs_2": 82, "cv_pred_probs_3": 82, "label_quality_scores_best": 82, "cv_pred_probs_ensembl": 82, "label_quality_scores_bett": 82, "superior": [82, 88], "workflow": [83, 89], "speechbrain": 83, "timm": 83, "glad": 84, "multiannotator_label": 84, "noisier": 84, "111": [84, 89], "local_data": [84, 85], "true_labels_train": [84, 85], "noise_matrix_bett": 84, "noise_matrix_wors": 84, "transpos": [84, 87], "dropna": 84, "zfill": 84, "row_na_check": 84, "notna": 84, "reset_index": 84, "a0001": 84, "a0002": 84, "a0003": 84, "a0004": 84, "a0005": 84, "a0006": 84, "a0007": 84, "a0008": 84, "a0009": 84, "a0010": 84, "a0041": 84, "a0042": 84, "a0043": 84, "a0044": 84, "a0045": 84, "a0046": 84, "a0047": 84, "a0048": 84, "a0049": 84, "a0050": 84, "60856743": 84, "41693214": 84, "40908785": 84, "87147629": 84, "64941785": 84, "10774851": 84, "0524466": 84, "71853246": 84, "37169848": 84, "66031048": 84, "multiannotator_util": 84, "crude": 84, "straight": 84, "majority_vote_label": 84, "736157": 84, "757738": 84, "782255": 84, "715585": 84, "824273": 84, "quality_annotator_a0001": 84, "quality_annotator_a0002": 84, "quality_annotator_a0003": 84, "quality_annotator_a0004": 84, "quality_annotator_a0005": 84, "quality_annotator_a0006": 84, "quality_annotator_a0007": 84, "quality_annotator_a0008": 84, "quality_annotator_a0009": 84, "quality_annotator_a0010": 84, "quality_annotator_a0041": 84, "quality_annotator_a0042": 84, "quality_annotator_a0043": 84, "quality_annotator_a0044": 84, "quality_annotator_a0045": 84, "quality_annotator_a0046": 84, "quality_annotator_a0047": 84, "quality_annotator_a0048": 84, "quality_annotator_a0049": 84, "quality_annotator_a0050": 84, "070551": 84, "216064": 84, "119178": 84, "alongisd": 84, "244982": 84, "208333": 84, "295978": 84, "294118": 84, "324194": 84, "310345": 84, "355315": 84, "346154": 84, "439728": 84, "480000": 84, "a0031": 84, "523205": 84, "580645": 84, "a0034": 84, "535313": 84, "607143": 84, "a0021": 84, "607002": 84, "a0015": 84, "609527": 84, "678571": 84, "a0011": 84, "621101": 84, "692308": 84, "wors": 84, "improved_consensus_label": 84, "majority_vote_accuraci": 84, "cleanlab_label_accuraci": 84, "8581081081081081": 84, "9797297297297297": 84, "besid": 84, "sorted_consensus_quality_scor": 84, "worst_qual": 84, "better_qu": 84, "worst_quality_accuraci": 84, "better_quality_accuraci": 84, "9893238434163701": 84, "improved_pred_prob": 84, "treat": [84, 85, 89, 93], "analzi": 84, "copyright": 85, "advertis": 85, "violenc": 85, "nsfw": 85, "ranked_label_issu": [85, 91, 92], "multioutput": 85, "multioutputclassifi": 85, "celeba": 85, "make_multilabel_data": 85, "boxes_coordin": 85, "box_multilabel": 85, "make_multi": 85, "bx1": 85, "by1": 85, "bx2": 85, "by2": 85, "label_list": 85, "ur": 85, "upper": 85, "inidx": 85, "logical_and": 85, "inv_d": 85, "labels_idx": 85, "true_labels_test": 85, "dict_unique_label": 85, "get_color_arrai": 85, "dcolor": 85, "aa4400": 85, "55227f": 85, "55a100": 85, "00ff00": 85, "007f7f": 85, "386b55": 85, "0000ff": 85, "simplic": 85, "advis": 85, "y_onehot": 85, "single_class_label": 85, "stratifi": [85, 88], "kf": 85, "train_index": 85, "test_index": 85, "clf_cv": 85, "x_train_cv": 85, "x_test_cv": 85, "y_train_cv": 85, "y_test_cv": 85, "y_pred_cv": 85, "saw": 85, "num_to_displai": 85, "09": [85, 86, 90, 93], "275": 85, "267": 85, "171": 85, "234": 85, "165": 85, "227": [85, 86], "262": [85, 86], "263": [85, 86], "266": [85, 86], "139": [85, 93], "143": [85, 86], "216": [85, 86, 93], "265": 85, "despit": [85, 93], "suspect": 85, "888": 85, "8224": 85, "9632": 85, "968": 85, "6512": 85, "0444": 85, "774": 85, "labels_binary_format": 85, "labels_list_format": 85, "surround": 86, "scene": 86, "coco": 86, "everydai": 86, "has_label_issu": 86, "insal": 86, "nc": [86, 90, 93], "s3": [86, 90, 93], "amazonaw": [86, 90, 93], "objectdetectionbenchmark": 86, "tutorial_obj": 86, "pkl": 86, "example_imag": 86, "unzip": [86, 93], "begin": 86, "detectron2": 86, "image_path": 86, "rb": 86, "image_to_visu": 86, "seg_map": 86, "334": 86, "float32": 86, "bboxes_ignor": 86, "286": 86, "285": 86, "224": 86, "231": [86, 93], "293": 86, "235": 86, "289": [86, 89], "282": 86, "281": 86, "271": 86, "280": 86, "277": 86, "279": 86, "287": 86, "299": 86, "276": 86, "307": 86, "321": 86, "326": 86, "333": 86, "261": 86, "319": 86, "257": 86, "295": 86, "283": 86, "243": 86, "303": 86, "316": 86, "247": 86, "323": 86, "327": 86, "226": 86, "228": 86, "232": 86, "239": 86, "240": 86, "209": 86, "242": 86, "202": 86, "230": 86, "215": 86, "220": 86, "229": 86, "85": [86, 89, 90], "217": 86, "237": 86, "207": 86, "204": 86, "205": 86, "153": 86, "149": 86, "140": 86, "124": [86, 93], "268": 86, "273": 86, "108": 86, "284": 86, "110": 86, "136": 86, "145": 86, "173": 86, "297": 86, "317": 86, "192": 86, "329": 86, "332": 86, "324": 86, "203": 86, "320": 86, "314": 86, "199": 86, "291": 86, "000000481413": 86, "jpg": 86, "42398": 86, "44503": 86, "337": [86, 92], "29968": 86, "336": 86, "21005": 86, "9978472": 86, "forgot": 86, "drew": 86, "label_issue_idx": 86, "num_examples_to_show": 86, "113": [86, 89], "candid": 86, "97489622": 86, "70610878": 86, "98764951": 86, "88899237": 86, "99085805": 86, "issue_idx": 86, "95569726e": 86, "03354841e": 86, "57510169e": 86, "58447666e": 86, "39755858e": 86, "suppli": 86, "issue_to_visu": 86, "000000009483": 86, "95569726168054e": 86, "addition": [86, 90], "visibl": 86, "missmatch": 86, "likelei": 86, "agnost": 86, "vaidat": 86, "inconsist": 86, "000000395701": 86, "033548411774308e": 86, "armchair": 86, "tv": 86, "000000154004": 86, "38300759625496356": 86, "foreground": 86, "000000448410": 86, "0008575101690203273": 86, "crowd": 86, "alon": 86, "explor": [86, 87], "resembl": [86, 87], "contribut": 86, "000000499768": 86, "9748962231208227": 86, "000000521141": 86, "8889923658893665": 86, "000000143931": 86, "9876495074395956": 86, "train_feature_embed": 87, "ood_train_feature_scor": 87, "test_feature_embed": 87, "ood_test_feature_scor": 87, "ood_train_predictions_scor": 87, "train_pred_prob": 87, "ood_test_predictions_scor": 87, "test_pred_prob": 87, "pylab": 87, "rcparam": 87, "baggingclassifi": 87, "therebi": 87, "rescal": 87, "transform_norm": 87, "totensor": 87, "root": 87, "animal_class": 87, "non_animal_class": 87, "animal_idx": 87, "test_idx": 87, "toronto": 87, "edu": 87, "kriz": 87, "5000": 87, "plot_imag": 87, "visualize_outli": 87, "txt_class": 87, "img": [87, 89], "npimg": 87, "show_label": 87, "data_subset": 87, "resnet50": 87, "corpu": 87, "2048": 87, "embed_imag": 87, "create_model": 87, "rwightman": 87, "v0": 87, "rsb": 87, "resnet50_a1_0": 87, "14fe96d1": 87, "pth": 87, "checkpoint": 87, "strang": 87, "odd": 87, "train_ood_features_scor": 87, "top_train_ood_features_idx": 87, "fun": 87, "negat": 87, "homogen": 87, "bottom_train_ood_features_idx": 87, "test_ood_features_scor": 87, "top_ood_features_idx": 87, "inevit": 87, "trade": 87, "5th": 87, "percentil": 87, "fifth_percentil": 87, "plt_rang": 87, "hist": 87, "train_outlier_scor": 87, "ylabel": 87, "axvlin": 87, "test_outlier_scor": 87, "ood_features_indic": 87, "revisit": 87, "unusu": 87, "return_invers": 87, "train_feature_embeddings_sc": 87, "test_feature_embeddings_sc": 87, "train_pred_label": 87, "9702": 87, "train_ood_predictions_scor": 87, "test_ood_predictions_scor": 87, "mainli": [87, 93], "lost": 87, "unsuit": 88, "ok": [88, 93], "convention": 88, "aforement": 88, "hypothet": 88, "contrast": 88, "tradit": 88, "disjoint": 88, "out_of_sample_pred_probs_for_a": 88, "out_of_sample_pred_probs_for_b": 88, "out_of_sample_pred_probs_for_c": 88, "out_of_sample_pred_prob": 88, "price": 89, "incom": 89, "ag": 89, "histgradientboostingregressor": 89, "r2_score": 89, "student_grades_r": 89, "final_scor": 89, "true_final_scor": 89, "homework": 89, "3d": 89, "hue": 89, "mpl_toolkit": 89, "mplot3d": 89, "axes3d": 89, "errors_idx": 89, "add_subplot": 89, "z": 89, "colorbar": 89, "errors_mask": 89, "feature_column": 89, "predicted_column": 89, "x_train_raw": 89, "x_test_raw": 89, "categorical_featur": [89, 91], "randomforestregressor": 89, "629763": 89, "521450": 89, "954607": 89, "547234": 89, "338296": 89, "754531": 89, "619090": 89, "312295": 89, "806626": 89, "784048": 89, "identified_issu": [89, 92], "367": 89, "560": 89, "318": 89, "688": 89, "657": 89, "view_datapoint": 89, "concat": 89, "consum": [89, 92], "baseline_model": [89, 92], "preds_og": 89, "r2_og": 89, "838": 89, "robustli": [89, 91, 92], "acceler": [89, 92], "found_label_issu": 89, "preds_cl": 89, "r2_cl": 89, "925": 89, "effort": [89, 91, 92], "favorit": 89, "64404888e": 89, "06755306e": 89, "05302732e": 89, "66635743e": 89, "53166364e": 89, "synthia": 90, "imagesegment": 90, "given_mask": 90, "predicted_mask": 90, "set_printopt": [90, 93], "sky": 90, "sidewalk": 90, "veget": 90, "terrain": 90, "rider": 90, "pred_probs_filepath": 90, "1088": 90, "1920": 90, "label_filepath": 90, "synthia_class": 90, "maunal": 90, "100000": 90, "244800": 90, "leftmost": 90, "area": 90, "middl": [90, 93], "infact": 90, "rightmost": 90, "discrep": 90, "4997817": 90, "17167": 90, "171663": 90, "62it": 90, "34563": 90, "173008": 90, "20it": 90, "52101": 90, "174089": 90, "69510": 90, "174019": 90, "46it": 90, "86997": 90, "174323": 90, "75it": 90, "104430": 90, "169724": 90, "91it": 90, "122139": 90, "172086": 90, "44it": 90, "140011": 90, "174169": 90, "157761": 90, "175200": 90, "175374": 90, "175483": 90, "47it": 90, "192985": 90, "175671": 90, "16it": 90, "210558": 90, "175484": 90, "228111": 90, "175443": 90, "245659": 90, "175322": 90, "61it": 90, "263194": 90, "175304": 90, "12it": 90, "280759": 90, "175405": 90, "86it": 90, "298301": 90, "175109": 90, "69it": 90, "315813": 90, "174670": 90, "333315": 90, "174769": 90, "54it": 90, "350881": 90, "175033": 90, "97it": 90, "368385": 90, "174887": 90, "385876": 90, "174891": 90, "71it": 90, "403366": 90, "174567": 90, "29it": 90, "421002": 90, "175100": 90, "438513": 90, "174881": 90, "456002": 90, "170509": 90, "73it": 90, "473898": 90, "172994": 90, "491599": 90, "174181": 90, "60it": 90, "509182": 90, "174668": 90, "526728": 90, "174903": 90, "04it": 90, "544360": 90, "34it": 90, "561899": 90, "175266": 90, "579430": 90, "175198": 90, "596953": 90, "175077": 90, "39it": 90, "614463": 90, "631953": 90, "174652": 90, "96it": 90, "649420": 90, "174008": 90, "666822": 90, "173979": 90, "684221": 90, "173683": 90, "21it": 90, "701638": 90, "173825": 90, "45it": 90, "719021": 90, "173395": 90, "93it": 90, "736362": 90, "173372": 90, "01it": 90, "753700": 90, "172675": 90, "771046": 90, "172906": 90, "788553": 90, "173549": 90, "65it": 90, "806052": 90, "173977": 90, "823451": 90, "166777": 90, "78it": 90, "840819": 90, "168781": 90, "858322": 90, "170614": 90, "875707": 90, "171568": 90, "88it": 90, "893295": 90, "172845": 90, "52it": 90, "910813": 90, "173537": 90, "928368": 90, "174136": 90, "945901": 90, "174490": 90, "87it": 90, "963359": 90, "174450": 90, "981064": 90, "175224": 90, "68it": 90, "998591": 90, "167695": 90, "83it": 90, "1016101": 90, "169844": 90, "1033639": 90, "171465": 90, "64it": 90, "1051050": 90, "172244": 90, "70it": 90, "1068535": 90, "173015": 90, "00it": 90, "1085860": 90, "172171": 90, "1103362": 90, "173017": 90, "40it": 90, "1120915": 90, "173763": 90, "05it": 90, "1138318": 90, "173840": 90, "1155709": 90, "173789": 90, "1173093": 90, "168493": 90, "1190527": 90, "170204": 90, "85it": 90, "1207870": 90, "171154": 90, "1225428": 90, "172462": 90, "89it": 90, "1242924": 90, "173202": 90, "1260258": 90, "173197": 90, "1277885": 90, "174113": 90, "1295506": 90, "174737": 90, "72it": 90, "1313036": 90, "174904": 90, "1330678": 90, "175355": 90, "99it": 90, "1348217": 90, "168529": 90, "1365827": 90, "170733": 90, "1383392": 90, "172176": 90, "1401070": 90, "173534": 90, "1418730": 90, "174443": 90, "1436236": 90, "174624": 90, "1453753": 90, "174785": 90, "1471304": 90, "175000": 90, "1488878": 90, "175219": 90, "95it": 90, "1506507": 90, "175538": 90, "1524065": 90, "175328": 90, "1541696": 90, "175616": 90, "1559309": 90, "175767": 90, "1576933": 90, "175904": 90, "1594572": 90, "176045": 90, "1612238": 90, "176226": 90, "1629862": 90, "176141": 90, "1647477": 90, "175468": 90, "1665025": 90, "174739": 90, "1682608": 90, "175060": 90, "1700172": 90, "175230": 90, "1717696": 90, "172854": 90, "1735206": 90, "173517": 90, "80it": 90, "1752699": 90, "173933": 90, "1770288": 90, "174514": 90, "1788042": 90, "175415": 90, "1805801": 90, "176062": 90, "1823541": 90, "176461": 90, "35it": 90, "1841194": 90, "176479": 90, "57it": 90, "1858933": 90, "176749": 90, "1876670": 90, "176931": 90, "1894364": 90, "173999": 90, "1911815": 90, "174148": 90, "1929373": 90, "174571": 90, "1946838": 90, "174592": 90, "1964377": 90, "174828": 90, "1981863": 90, "174660": 90, "1999332": 90, "174199": 90, "2016768": 90, "174244": 90, "63it": 90, "2034194": 90, "174179": 90, "2051629": 90, "174227": 90, "2069053": 90, "166828": 90, "2086587": 90, "169299": 90, "2104063": 90, "170898": 90, "22it": 90, "2121442": 90, "171751": 90, "2138648": 90, "171573": 90, "2156067": 90, "172348": 90, "2173478": 90, "172872": 90, "2190870": 90, "173182": 90, "02it": 90, "2208197": 90, "171459": 90, "2225613": 90, "172258": 90, "2243049": 90, "172882": 90, "2260546": 90, "173502": 90, "2278218": 90, "174462": 90, "2295807": 90, "174885": 90, "2313433": 90, "175295": 90, "2331175": 90, "175929": 90, "2349033": 90, "176719": 90, "2366710": 90, "176733": 90, "2384543": 90, "177210": 90, "55it": 90, "2402270": 90, "177227": 90, "2420118": 90, "177598": 90, "07it": 90, "2437879": 90, "176578": 90, "2455539": 90, "175833": 90, "2473124": 90, "175407": 90, "2490666": 90, "174637": 90, "2508131": 90, "174283": 90, "2525561": 90, "173899": 90, "2542952": 90, "173797": 90, "2560333": 90, "173525": 90, "2577686": 90, "173241": 90, "51it": 90, "2595051": 90, "173359": 90, "2612388": 90, "169230": 90, "15it": 90, "2629603": 90, "170087": 90, "2646951": 90, "171089": 90, "2664305": 90, "171815": 90, "2681708": 90, "172472": 90, "2698963": 90, "172107": 90, "2716210": 90, "172212": 90, "2733440": 90, "172235": 90, "2750666": 90, "172154": 90, "2767939": 90, "172322": 90, "2785173": 90, "171849": 90, "2802495": 90, "172256": 90, "2819790": 90, "172463": 90, "2837136": 90, "172759": 90, "2854477": 90, "172952": 90, "2871773": 90, "172939": 90, "2889068": 90, "172847": 90, "2906353": 90, "172745": 90, "2923691": 90, "172931": 90, "79it": 90, "2940985": 90, "172739": 90, "2958260": 90, "172302": 90, "2975491": 90, "172241": 90, "2992853": 90, "172653": 90, "3010119": 90, "172640": 90, "3027432": 90, "172784": 90, "3044748": 90, "172894": 90, "3062551": 90, "174432": 90, "3080339": 90, "175465": 90, "53it": 90, "3098010": 90, "175837": 90, "3115791": 90, "176425": 90, "3133479": 90, "176556": 90, "3151135": 90, "176251": 90, "3168923": 90, "176737": 90, "3186597": 90, "176524": 90, "3204269": 90, "176580": 90, "3221928": 90, "176258": 90, "76it": 90, "3239689": 90, "176650": 90, "3257355": 90, "176583": 90, "3275014": 90, "176334": 90, "31it": 90, "3292787": 90, "176725": 90, "3310460": 90, "172711": 90, "3327849": 90, "173056": 90, "3345397": 90, "173773": 90, "58it": 90, "3362816": 90, "173894": 90, "3380358": 90, "174348": 90, "3397799": 90, "174225": 90, "3415226": 90, "3432753": 90, "174473": 90, "3450203": 90, "174440": 90, "3467649": 90, "174080": 90, "3485059": 90, "172843": 90, "48it": 90, "3502346": 90, "166810": 90, "3519703": 90, "168777": 90, "3537300": 90, "170887": 90, "3554810": 90, "172129": 90, "3572407": 90, "173268": 90, "3590035": 90, "174161": 90, "3607587": 90, "174563": 90, "3625281": 90, "175270": 90, "3642873": 90, "175463": 90, "3660486": 90, "175659": 90, "3678068": 90, "175704": 90, "3695673": 90, "175805": 90, "3713256": 90, "175721": 90, "3730852": 90, "175791": 90, "3748432": 90, "175657": 90, "3765999": 90, "175309": 90, "3783531": 90, "175154": 90, "3801047": 90, "3818563": 90, "174964": 90, "3836060": 90, "174874": 90, "3853548": 90, "167436": 90, "3870994": 90, "169474": 90, "3888398": 90, "170810": 90, "3905737": 90, "171571": 90, "3923054": 90, "172044": 90, "3940335": 90, "172268": 90, "67it": 90, "3957626": 90, "172456": 90, "3974897": 90, "172529": 90, "3992172": 90, "172593": 90, "33it": 90, "4009477": 90, "172727": 90, "4026754": 90, "172335": 90, "4044009": 90, "172396": 90, "4061251": 90, "4078488": 90, "172038": 90, "4095693": 90, "171753": 90, "4112870": 90, "171591": 90, "4130047": 90, "171641": 90, "11it": 90, "4147220": 90, "4164387": 90, "171623": 90, "4181550": 90, "171620": 90, "4198713": 90, "167684": 90, "4216323": 90, "170162": 90, "4233659": 90, "171107": 90, "4251153": 90, "172246": 90, "4268639": 90, "173024": 90, "4286227": 90, "173876": 90, "4303621": 90, "173857": 90, "4321090": 90, "174105": 90, "4338553": 90, "174261": 90, "4356036": 90, "174427": 90, "4373481": 90, "174054": 90, "4391185": 90, "174944": 90, "4408767": 90, "175203": 90, "4426338": 90, "175352": 90, "4443874": 90, "175167": 90, "4461426": 90, "4478954": 90, "174505": 90, "4496406": 90, "174301": 90, "4513837": 90, "174251": 90, "4531366": 90, "174558": 90, "50it": 90, "4548843": 90, "174618": 90, "4566306": 90, "174572": 90, "4583764": 90, "4601191": 90, "173985": 90, "59it": 90, "4618615": 90, "174059": 90, "4636022": 90, "173946": 90, "4653417": 90, "173914": 90, "4670809": 90, "173866": 90, "4688210": 90, "173907": 90, "4705644": 90, "174033": 90, "4723065": 90, "174084": 90, "4740474": 90, "173944": 90, "4757869": 90, "173731": 90, "4775243": 90, "173526": 90, "4792596": 90, "173265": 90, "4809923": 90, "170701": 90, "4827002": 90, "168010": 90, "4844469": 90, "169967": 90, "4861913": 90, "171289": 90, "4879328": 90, "172138": 90, "4896647": 90, "172449": 90, "4913898": 90, "170167": 90, "4931534": 90, "171999": 90, "4949175": 90, "173309": 90, "4966657": 90, "173755": 90, "4984241": 90, "174376": 90, "173607": 90, "3263230": 90, "783379": 90, "275110": 90, "255792": 90, "78225": 90, "55990": 90, "54427": 90, "33591": 90, "24645": 90, "21308": 90, "15045": 90, "14171": 90, "13832": 90, "13498": 90, "11490": 90, "9164": 90, "8769": 90, "6999": 90, "6031": 90, "5011": 90, "mistakenli": 90, "class_issu": 90, "aim": [90, 93], "domin": 90, "extratreesclassifi": 91, "extratre": 91, "labelencod": [91, 92], "labels_raw": 91, "interg": [91, 92], "tress": 91, "827": 91, "cheat": 91, "0pt": 91, "233": 91, "labels_train": 91, "labels_test": 91, "acc_og": [91, 92], "783068783068783": 91, "acc_cl": [91, 92], "8095238095238095": 91, "earlier": [92, 93], "raw_label": 92, "raw_train_text": 92, "raw_test_text": 92, "raw_train_label": 92, "raw_test_label": 92, "encond": 92, "train_text": 92, "test_text": 92, "858050": 92, "545854": 92, "826194": 92, "965814": 92, "791923": 92, "646": 92, "390": 92, "628": 92, "702": 92, "863": 92, "135": 92, "735": 92, "print_as_df": 92, "inverse_transform": 92, "fight": 92, "bunch": 93, "conll": 93, "2003": 93, "love": 93, "n_i": 93, "optional_list_of_ordered_class_nam": 93, "deepai": 93, "conll2003": 93, "rm": 93, "tokenclassif": 93, "2024": 93, "2400": 93, "52e0": 93, "1a00": 93, "940": 93, "connect": 93, "443": 93, "await": 93, "982975": 93, "960k": 93, "kb": 93, "959": 93, "94k": 93, "mb": 93, "directori": 93, "inflat": 93, "17045998": 93, "16m": 93, "octet": 93, "71m": 93, "7mb": 93, "26m": 93, "1mb": 93, "bert": 93, "read_npz": 93, "filepath": 93, "corrsespond": 93, "iob2": 93, "given_ent": 93, "entity_map": 93, "readfil": 93, "sep": 93, "startswith": 93, "docstart": 93, "isalpha": 93, "isupp": 93, "indices_to_preview": 93, "nsentenc": 93, "eu": 93, "reject": 93, "boycott": 93, "british": 93, "lamb": 93, "00030412": 93, "00023826": 93, "99936208": 93, "00007009": 93, "00002545": 93, "99998795": 93, "00000401": 93, "00000218": 93, "00000455": 93, "00000131": 93, "00000749": 93, "99996115": 93, "00001371": 93, "0000087": 93, "00000895": 93, "99998936": 93, "00000382": 93, "00000178": 93, "00000366": 93, "00000137": 93, "99999101": 93, "00000266": 93, "00000174": 93, "0000035": 93, "00000109": 93, "99998768": 93, "00000482": 93, "00000202": 93, "00000438": 93, "0000011": 93, "00000465": 93, "99996392": 93, "00001105": 93, "0000116": 93, "00000878": 93, "99998671": 93, "00000364": 93, "00000213": 93, "00000472": 93, "00000281": 93, "99999073": 93, "00000211": 93, "00000159": 93, "00000442": 93, "00000115": 93, "peter": 93, "blackburn": 93, "00000358": 93, "00000529": 93, "99995623": 93, "000022": 93, "0000129": 93, "0000024": 93, "00001812": 93, "99994141": 93, "00001645": 93, "00002162": 93, "brussel": 93, "1996": 93, "00001172": 93, "00000821": 93, "00004661": 93, "0000618": 93, "99987167": 93, "99999061": 93, "00000201": 93, "00000195": 93, "00000408": 93, "00000135": 93, "2254": 93, "2907": 93, "19392": 93, "9962": 93, "8904": 93, "19303": 93, "12918": 93, "9256": 93, "11855": 93, "18392": 93, "20426": 93, "19402": 93, "14744": 93, "19371": 93, "4645": 93, "10331": 93, "9430": 93, "6143": 93, "18367": 93, "12914": 93, "todai": 93, "weather": 93, "march": 93, "scalfaro": 93, "northern": 93, "himself": 93, "said": 93, "germani": 93, "nastja": 93, "rysich": 93, "north": 93, "spla": 93, "fought": 93, "khartoum": 93, "govern": 93, "south": 93, "1983": 93, "autonomi": 93, "animist": 93, "region": 93, "moslem": 93, "arabis": 93, "mayor": 93, "antonio": 93, "gonzalez": 93, "garcia": 93, "revolutionari": 93, "parti": 93, "wednesdai": 93, "troop": 93, "raid": 93, "farm": 93, "stole": 93, "rape": 93, "women": 93, "spring": 93, "chg": 93, "hrw": 93, "12pct": 93, "princ": 93, "photo": 93, "moment": 93, "spokeswoman": 93, "rainier": 93, "told": 93, "reuter": 93, "danila": 93, "carib": 93, "w224": 93, "equip": 93, "radiomet": 93, "earn": 93, "19996": 93, "london": 93, "denom": 93, "sale": 93, "uk": 93, "jp": 93, "fr": 93, "maccabi": 93, "hapoel": 93, "haifa": 93, "tel": 93, "aviv": 93, "hospit": 93, "rever": 93, "roman": 93, "cathol": 93, "nun": 93, "admit": 93, "calcutta": 93, "week": 93, "ago": 93, "fever": 93, "vomit": 93, "allianc": 93, "embattl": 93, "kabul": 93, "salang": 93, "highwai": 93, "mondai": 93, "tuesdai": 93, "suprem": 93, "council": 93, "led": 93, "jumbish": 93, "milli": 93, "movement": 93, "warlord": 93, "abdul": 93, "rashid": 93, "dostum": 93, "dollar": 93, "exchang": 93, "3570": 93, "12049": 93, "born": 93, "1937": 93, "provinc": 93, "anhui": 93, "dai": 93, "came": 93, "shanghai": 93, "citi": 93, "prolif": 93, "author": 93, "teacher": 93, "chines": 93, "16764": 93, "1990": 93, "historian": 93, "alan": 93, "john": 93, "percival": 93, "taylor": 93, "di": 93, "20446": 93, "pace": 93, "bowler": 93, "ian": 93, "harvei": 93, "claim": 93, "victoria": 93, "15514": 93, "cotti": 93, "osc": 93, "foreign": 93, "minist": 93, "7525": 93, "sultan": 93, "specter": 93, "met": 93, "crown": 93, "abdullah": 93, "defenc": 93, "aviat": 93, "jeddah": 93, "saudi": 93, "agenc": 93, "2288": 93, "hi": 93, "customari": 93, "outfit": 93, "champion": 93, "damp": 93, "scalp": 93, "canada": 93, "reign": 93, "olymp": 93, "donovan": 93, "bailei": 93, "1992": 93, "linford": 93, "christi": 93, "britain": 93, "1984": 93, "1988": 93, "carl": 93, "lewi": 93, "ambigi": 93, "punctuat": 93, "chicago": 93, "digest": 93, "philadelphia": 93, "usda": 93, "york": 93, "token_issu": 93, "471": 93, "kean": 93, "year": 93, "contract": 93, "manchest": 93, "19072": 93, "societi": 93, "million": 93, "bite": 93, "deliv": 93, "19910": 93, "father": 93, "clarenc": 93, "woolmer": 93, "renam": 93, "uttar": 93, "pradesh": 93, "india": 93, "ranji": 93, "trophi": 93, "nation": 93, "championship": 93, "captain": 93, "1949": 93, "15658": 93, "19879": 93, "iii": 93, "brian": 93, "shimer": 93, "randi": 93, "jone": 93, "19104": 93}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [29, 0, 0, "-", "dataset"], [32, 0, 0, "-", "experimental"], [35, 0, 0, "-", "filter"], [36, 0, 0, "-", "internal"], [47, 0, 0, "-", "models"], [49, 0, 0, "-", "multiannotator"], [52, 0, 0, "-", "multilabel_classification"], [55, 0, 0, "-", "object_detection"], [58, 0, 0, "-", "outlier"], [59, 0, 0, "-", "rank"], [60, 0, 0, "-", "regression"], [64, 0, 0, "-", "segmentation"], [68, 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.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [27, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "duplicate"], [17, 0, 0, "-", "imbalance"], [19, 0, 0, "-", "issue_manager"], [20, 0, 0, "-", "label"], [21, 0, 0, "-", "noniid"], [22, 0, 0, "-", "null"], [23, 0, 0, "-", "outlier"], [26, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[16, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 6, 1, "", "near_duplicate_sets"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[17, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[19, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[20, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 3, 1, "", "get_health_summary"], [20, 6, 1, "", "health_summary_parameters"], [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.noniid": [[21, 2, 1, "", "NonIIDIssueManager"], [21, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[22, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[23, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[23, 6, 1, "", "DEFAULT_THRESHOLDS"], [23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 6, 1, "", "ood"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[25, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[25, 2, 1, "", "RegressionLabelIssueManager"], [25, 1, 1, "", "find_issues_with_features"], [25, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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.underperforming_group": [[26, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[26, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [26, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "filter_cluster_ids"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_worst_cluster"], [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, "", "perform_clustering"], [26, 3, 1, "", "report"], [26, 3, 1, "", "set_knn_graph"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[27, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[27, 3, 1, "", "get_report"], [27, 3, 1, "", "report"]], "cleanlab.dataset": [[29, 1, 1, "", "find_overlapping_classes"], [29, 1, 1, "", "health_summary"], [29, 1, 1, "", "overall_label_health_score"], [29, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[30, 0, 0, "-", "cifar_cnn"], [31, 0, 0, "-", "coteaching"], [33, 0, 0, "-", "label_issues_batched"], [34, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[30, 2, 1, "", "CNN"], [30, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[30, 6, 1, "", "T_destination"], [30, 3, 1, "", "__call__"], [30, 3, 1, "", "add_module"], [30, 3, 1, "", "apply"], [30, 3, 1, "", "bfloat16"], [30, 3, 1, "", "buffers"], [30, 3, 1, "", "children"], [30, 3, 1, "", "cpu"], [30, 3, 1, "", "cuda"], [30, 3, 1, "", "double"], [30, 6, 1, "", "dump_patches"], [30, 3, 1, "", "eval"], [30, 3, 1, "", "extra_repr"], [30, 3, 1, "", "float"], [30, 3, 1, "id0", "forward"], [30, 3, 1, "", "get_buffer"], [30, 3, 1, "", "get_extra_state"], [30, 3, 1, "", "get_parameter"], [30, 3, 1, "", "get_submodule"], [30, 3, 1, "", "half"], [30, 3, 1, "", "ipu"], [30, 3, 1, "", "load_state_dict"], [30, 3, 1, "", "modules"], [30, 3, 1, "", "named_buffers"], [30, 3, 1, "", "named_children"], [30, 3, 1, "", "named_modules"], [30, 3, 1, "", "named_parameters"], [30, 3, 1, "", "parameters"], [30, 3, 1, "", "register_backward_hook"], [30, 3, 1, "", "register_buffer"], [30, 3, 1, "", "register_forward_hook"], [30, 3, 1, "", "register_forward_pre_hook"], [30, 3, 1, "", "register_full_backward_hook"], [30, 3, 1, "", "register_load_state_dict_post_hook"], [30, 3, 1, "", "register_module"], [30, 3, 1, "", "register_parameter"], [30, 3, 1, "", "requires_grad_"], [30, 3, 1, "", "set_extra_state"], [30, 3, 1, "", "share_memory"], [30, 3, 1, "", "state_dict"], [30, 3, 1, "", "to"], [30, 3, 1, "", "to_empty"], [30, 3, 1, "", "train"], [30, 6, 1, "", "training"], [30, 3, 1, "", "type"], [30, 3, 1, "", "xpu"], [30, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[31, 1, 1, "", "adjust_learning_rate"], [31, 1, 1, "", "evaluate"], [31, 1, 1, "", "forget_rate_scheduler"], [31, 1, 1, "", "initialize_lr_scheduler"], [31, 1, 1, "", "loss_coteaching"], [31, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[33, 2, 1, "", "LabelInspector"], [33, 7, 1, "", "adj_confident_thresholds_shared"], [33, 1, 1, "", "find_label_issues_batched"], [33, 7, 1, "", "labels_shared"], [33, 7, 1, "", "pred_probs_shared"], [33, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[33, 3, 1, "", "get_confident_thresholds"], [33, 3, 1, "", "get_label_issues"], [33, 3, 1, "", "get_num_issues"], [33, 3, 1, "", "get_quality_scores"], [33, 3, 1, "", "score_label_quality"], [33, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[34, 2, 1, "", "CNN"], [34, 2, 1, "", "SimpleNet"], [34, 1, 1, "", "get_mnist_dataset"], [34, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[34, 3, 1, "", "__init_subclass__"], [34, 6, 1, "", "batch_size"], [34, 6, 1, "", "dataset"], [34, 6, 1, "", "epochs"], [34, 3, 1, "id0", "fit"], [34, 3, 1, "", "get_metadata_routing"], [34, 3, 1, "", "get_params"], [34, 6, 1, "", "loader"], [34, 6, 1, "", "log_interval"], [34, 6, 1, "", "lr"], [34, 6, 1, "", "momentum"], [34, 6, 1, "", "no_cuda"], [34, 3, 1, "id1", "predict"], [34, 3, 1, "id4", "predict_proba"], [34, 6, 1, "", "seed"], [34, 3, 1, "", "set_fit_request"], [34, 3, 1, "", "set_params"], [34, 3, 1, "", "set_predict_proba_request"], [34, 3, 1, "", "set_predict_request"], [34, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[34, 6, 1, "", "T_destination"], [34, 3, 1, "", "__call__"], [34, 3, 1, "", "add_module"], [34, 3, 1, "", "apply"], [34, 3, 1, "", "bfloat16"], [34, 3, 1, "", "buffers"], [34, 3, 1, "", "children"], [34, 3, 1, "", "cpu"], [34, 3, 1, "", "cuda"], [34, 3, 1, "", "double"], [34, 6, 1, "", "dump_patches"], [34, 3, 1, "", "eval"], [34, 3, 1, "", "extra_repr"], [34, 3, 1, "", "float"], [34, 3, 1, "", "forward"], [34, 3, 1, "", "get_buffer"], [34, 3, 1, "", "get_extra_state"], [34, 3, 1, "", "get_parameter"], [34, 3, 1, "", "get_submodule"], [34, 3, 1, "", "half"], [34, 3, 1, "", "ipu"], [34, 3, 1, "", "load_state_dict"], [34, 3, 1, "", "modules"], [34, 3, 1, "", "named_buffers"], [34, 3, 1, "", "named_children"], [34, 3, 1, "", "named_modules"], [34, 3, 1, "", "named_parameters"], [34, 3, 1, "", "parameters"], [34, 3, 1, "", "register_backward_hook"], [34, 3, 1, "", "register_buffer"], [34, 3, 1, "", "register_forward_hook"], [34, 3, 1, "", "register_forward_pre_hook"], [34, 3, 1, "", "register_full_backward_hook"], [34, 3, 1, "", "register_load_state_dict_post_hook"], [34, 3, 1, "", "register_module"], [34, 3, 1, "", "register_parameter"], [34, 3, 1, "", "requires_grad_"], [34, 3, 1, "", "set_extra_state"], [34, 3, 1, "", "share_memory"], [34, 3, 1, "", "state_dict"], [34, 3, 1, "", "to"], [34, 3, 1, "", "to_empty"], [34, 3, 1, "", "train"], [34, 6, 1, "", "training"], [34, 3, 1, "", "type"], [34, 3, 1, "", "xpu"], [34, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[35, 1, 1, "", "find_label_issues"], [35, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [35, 1, 1, "", "find_predicted_neq_given"], [35, 7, 1, "", "pred_probs_by_class"], [35, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[37, 0, 0, "-", "label_quality_utils"], [38, 0, 0, "-", "latent_algebra"], [39, 0, 0, "-", "multiannotator_utils"], [40, 0, 0, "-", "multilabel_scorer"], [41, 0, 0, "-", "multilabel_utils"], [42, 0, 0, "-", "outlier"], [43, 0, 0, "-", "token_classification_utils"], [44, 0, 0, "-", "util"], [45, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[37, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[38, 1, 1, "", "compute_inv_noise_matrix"], [38, 1, 1, "", "compute_noise_matrix_from_inverse"], [38, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [38, 1, 1, "", "compute_py"], [38, 1, 1, "", "compute_py_inv_noise_matrix"], [38, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[39, 1, 1, "", "assert_valid_inputs_multiannotator"], [39, 1, 1, "", "assert_valid_pred_probs"], [39, 1, 1, "", "check_consensus_label_classes"], [39, 1, 1, "", "compute_soft_cross_entropy"], [39, 1, 1, "", "find_best_temp_scaler"], [39, 1, 1, "", "format_multiannotator_labels"], [39, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[40, 2, 1, "", "Aggregator"], [40, 2, 1, "", "ClassLabelScorer"], [40, 2, 1, "", "MultilabelScorer"], [40, 1, 1, "", "exponential_moving_average"], [40, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [40, 1, 1, "", "get_label_quality_scores"], [40, 1, 1, "", "multilabel_py"], [40, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[40, 3, 1, "", "__call__"], [40, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[40, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [40, 6, 1, "", "NORMALIZED_MARGIN"], [40, 6, 1, "", "SELF_CONFIDENCE"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[40, 3, 1, "", "__call__"], [40, 3, 1, "", "aggregate"], [40, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[41, 1, 1, "", "get_onehot_num_classes"], [41, 1, 1, "", "int2onehot"], [41, 1, 1, "", "onehot2int"], [41, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[42, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[43, 1, 1, "", "color_sentence"], [43, 1, 1, "", "filter_sentence"], [43, 1, 1, "", "get_sentence"], [43, 1, 1, "", "mapping"], [43, 1, 1, "", "merge_probs"], [43, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[44, 1, 1, "", "append_extra_datapoint"], [44, 1, 1, "", "clip_noise_rates"], [44, 1, 1, "", "clip_values"], [44, 1, 1, "", "compress_int_array"], [44, 1, 1, "", "confusion_matrix"], [44, 1, 1, "", "csr_vstack"], [44, 1, 1, "", "estimate_pu_f1"], [44, 1, 1, "", "extract_indices_tf"], [44, 1, 1, "", "force_two_dimensions"], [44, 1, 1, "", "format_labels"], [44, 1, 1, "", "get_missing_classes"], [44, 1, 1, "", "get_num_classes"], [44, 1, 1, "", "get_unique_classes"], [44, 1, 1, "", "is_tensorflow_dataset"], [44, 1, 1, "", "is_torch_dataset"], [44, 1, 1, "", "num_unique_classes"], [44, 1, 1, "", "print_inverse_noise_matrix"], [44, 1, 1, "", "print_joint_matrix"], [44, 1, 1, "", "print_noise_matrix"], [44, 1, 1, "", "print_square_matrix"], [44, 1, 1, "", "remove_noise_from_class"], [44, 1, 1, "", "round_preserving_row_totals"], [44, 1, 1, "", "round_preserving_sum"], [44, 1, 1, "", "smart_display_dataframe"], [44, 1, 1, "", "subset_X_y"], [44, 1, 1, "", "subset_data"], [44, 1, 1, "", "subset_labels"], [44, 1, 1, "", "train_val_split"], [44, 1, 1, "", "unshuffle_tensorflow_dataset"], [44, 1, 1, "", "value_counts"], [44, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[45, 1, 1, "", "assert_indexing_works"], [45, 1, 1, "", "assert_nonempty_input"], [45, 1, 1, "", "assert_valid_class_labels"], [45, 1, 1, "", "assert_valid_inputs"], [45, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[48, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[48, 2, 1, "", "KerasWrapperModel"], [48, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[48, 3, 1, "", "fit"], [48, 3, 1, "", "get_params"], [48, 3, 1, "", "predict"], [48, 3, 1, "", "predict_proba"], [48, 3, 1, "", "set_params"], [48, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[48, 3, 1, "", "fit"], [48, 3, 1, "", "get_params"], [48, 3, 1, "", "predict"], [48, 3, 1, "", "predict_proba"], [48, 3, 1, "", "set_params"], [48, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[49, 1, 1, "", "convert_long_to_wide_dataset"], [49, 1, 1, "", "get_active_learning_scores"], [49, 1, 1, "", "get_active_learning_scores_ensemble"], [49, 1, 1, "", "get_label_quality_multiannotator"], [49, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [49, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[50, 0, 0, "-", "dataset"], [51, 0, 0, "-", "filter"], [53, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[50, 1, 1, "", "common_multilabel_issues"], [50, 1, 1, "", "multilabel_health_summary"], [50, 1, 1, "", "overall_multilabel_health_score"], [50, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[51, 1, 1, "", "find_label_issues"], [51, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[53, 1, 1, "", "get_label_quality_scores"], [53, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[54, 0, 0, "-", "filter"], [56, 0, 0, "-", "rank"], [57, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[54, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[56, 1, 1, "", "compute_badloc_box_scores"], [56, 1, 1, "", "compute_overlooked_box_scores"], [56, 1, 1, "", "compute_swap_box_scores"], [56, 1, 1, "", "get_label_quality_scores"], [56, 1, 1, "", "issues_from_scores"], [56, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[57, 1, 1, "", "bounding_box_size_distribution"], [57, 1, 1, "", "calculate_per_class_metrics"], [57, 1, 1, "", "class_label_distribution"], [57, 1, 1, "", "get_average_per_class_confusion_matrix"], [57, 1, 1, "", "get_sorted_bbox_count_idxs"], [57, 1, 1, "", "object_counts_per_image"], [57, 1, 1, "", "plot_class_distribution"], [57, 1, 1, "", "plot_class_size_distributions"], [57, 1, 1, "", "visualize"]], "cleanlab.outlier": [[58, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[58, 3, 1, "", "fit"], [58, 3, 1, "", "fit_score"], [58, 3, 1, "", "score"]], "cleanlab.rank": [[59, 1, 1, "", "find_top_issues"], [59, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [59, 1, 1, "", "get_label_quality_ensemble_scores"], [59, 1, 1, "", "get_label_quality_scores"], [59, 1, 1, "", "get_normalized_margin_for_each_label"], [59, 1, 1, "", "get_self_confidence_for_each_label"], [59, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[61, 0, 0, "-", "learn"], [62, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[61, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[61, 3, 1, "", "__init_subclass__"], [61, 3, 1, "", "find_label_issues"], [61, 3, 1, "", "fit"], [61, 3, 1, "", "get_aleatoric_uncertainty"], [61, 3, 1, "", "get_epistemic_uncertainty"], [61, 3, 1, "", "get_label_issues"], [61, 3, 1, "", "get_metadata_routing"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "save_space"], [61, 3, 1, "", "score"], [61, 3, 1, "", "set_fit_request"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[62, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[63, 0, 0, "-", "filter"], [65, 0, 0, "-", "rank"], [66, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[63, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[65, 1, 1, "", "get_label_quality_scores"], [65, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[66, 1, 1, "", "common_label_issues"], [66, 1, 1, "", "display_issues"], [66, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[70, 1, 1, "", "common_label_issues"], [70, 1, 1, "", "display_issues"], [70, 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, 73, 77, 78, 80, 81, 82, 85, 91, 92, 93], "count": [3, 82], "datalab": [4, 5, 7, 8, 9, 74, 75, 76, 77, 78, 82], "creat": [5, 74, 75, 82, 84], "your": [5, 71, 74, 75, 78, 80, 82], "own": 5, "issu": [5, 7, 8, 18, 25, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 90, 91, 93], "manag": [5, 18], "prerequisit": 5, "implement": 5, "issuemanag": [5, 74], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 74], "us": [5, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "gener": 6, "cluster": [6, 80], "id": 6, "guid": [7, 9], "type": [7, 8, 82], "custom": [7, 74], "can": [8, 75, 79, 80, 82, 84], "detect": [8, 75, 77, 78, 80, 82, 86, 87], "estim": [8, 82, 84], "each": 8, "label": [8, 20, 25, 71, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 89, 90, 91, 92, 93], "outlier": [8, 23, 42, 58, 77, 78, 81, 87], "Near": [8, 75, 77, 78, 81], "duplic": [8, 16, 75, 77, 78, 80, 81], "non": [8, 78], "iid": [8, 78], "class": [8, 72, 82, 90], "imbal": [8, 17], "imag": [8, 81, 87], "specif": [8, 18, 90], "underperform": [8, 80], "group": [8, 80], "null": [8, 22], "option": 8, "paramet": [8, 82], "get": [9, 74, 75, 84, 85, 86, 90, 93], "start": [9, 79], "api": 9, "refer": 9, "data": [10, 71, 73, 74, 75, 77, 78, 79, 80, 82, 84, 85, 86, 87, 89, 90, 91, 93], "data_issu": 11, "factori": 12, "intern": [13, 36], "issue_find": 14, "issue_manag": [18, 19], "regist": 18, "unregist": 18, "ml": [18, 80, 82], "task": 18, "noniid": 21, "regress": [24, 60, 61, 62, 80, 89], "prioriti": 25, "order": 25, "find": [25, 71, 73, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "underperforming_group": 26, "report": [27, 81], "dataset": [29, 50, 71, 75, 78, 79, 80, 81, 82, 85, 86, 87, 89, 90, 92, 93], "cifar_cnn": 30, "coteach": 31, "experiment": 32, "label_issues_batch": 33, "mnist_pytorch": 34, "filter": [35, 51, 54, 63, 67, 82], "label_quality_util": 37, "latent_algebra": 38, "multiannotator_util": 39, "multilabel_scor": 40, "multilabel_util": 41, "token_classification_util": 43, "util": 44, "valid": [45, 81, 88], "fasttext": 46, "model": [47, 71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "kera": 48, "multiannot": [49, 84], "multilabel_classif": 52, "rank": [53, 56, 59, 62, 65, 69, 82], "object_detect": 55, "summari": [57, 66, 70], "learn": [61, 75, 80, 82, 91], "segment": [64, 90], "token_classif": [68, 93], "cleanlab": [71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "open": [71, 80], "sourc": [71, 80], "document": 71, "quickstart": 71, "1": [71, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "instal": [71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "2": [71, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "common": [71, 72, 93], "3": [71, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "handl": [71, 80], "error": [71, 80, 81, 82, 84, 85, 86, 89, 90, 92, 93], "train": [71, 73, 80, 87, 89, 91, 92], "robust": [71, 82, 89, 91, 92], "noisi": [71, 82, 89, 91, 92], "4": [71, 73, 74, 75, 77, 78, 81, 82, 84, 86, 87, 89, 91, 92], "curat": [71, 79], "fix": [71, 80], "level": [71, 79, 82, 93], "5": [71, 73, 75, 77, 81, 82, 84, 89, 91], "improv": [71, 84], "via": [71, 82, 84], "mani": [71, 82], "other": [71, 84, 86, 89], "techniqu": 71, "contribut": 71, "easi": [71, 77, 78, 81], "mode": [71, 77, 78, 81], "how": [72, 80, 82, 84, 85, 93], "migrat": 72, "version": 72, "0": 72, "from": [72, 74, 75, 82, 89, 91, 92], "pre": [72, 73, 80, 87], "function": [72, 74], "name": 72, "chang": 72, "modul": [72, 82], "new": 72, "remov": 72, "argument": [72, 74], "variabl": 72, "audio": 73, "speechbrain": 73, "depend": [73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "import": [73, 74, 75, 79, 81, 82, 84], "them": [73, 79, 82], "load": [73, 74, 75, 77, 78, 89, 91, 92], "featur": [73, 81, 87], "fit": 73, "linear": 73, "comput": [73, 77, 78, 80, 81, 84, 88, 91], "out": [73, 74, 75, 77, 78, 81, 84, 88, 91], "sampl": [73, 74, 75, 77, 78, 81, 84, 88, 91], "predict": [73, 74, 75, 77, 78, 81, 84, 85, 86, 88, 91], "probabl": [73, 74, 75, 77, 78, 81, 84, 88, 91], "workflow": [74, 82], "audit": [74, 75], "requir": [74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "classifi": [74, 75], "instanti": 74, "object": [74, 86], "increment": 74, "search": 74, "specifi": [74, 80], "nondefault": 74, "save": 74, "ad": 74, "A": 75, "unifi": 75, "all": [75, 82], "kind": [75, 86], "skip": [75, 79, 82, 84], "detail": [75, 79, 82, 84], "more": [75, 82, 89, 91, 92], "about": 75, "addit": 75, "inform": [75, 81], "tutori": [76, 79, 83], "tabular": [77, 91], "numer": 77, "categor": 77, "column": 77, "process": [77, 87, 89, 91], "select": [77, 91], "construct": 77, "k": [77, 81, 88], "nearest": 77, "neighbour": 77, "graph": 77, "text": [78, 92, 93], "format": [78, 80, 85, 86, 92], "defin": [78, 81, 89, 92], "drift": 78, "fetch": [79, 81], "evalu": 79, "health": [79, 82], "8": [79, 82], "popular": 79, "faq": 80, "what": [80, 82, 88], "do": [80, 82], "i": [80, 82, 88], "infer": 80, "correct": 80, "exampl": [80, 81, 82, 87], "ha": 80, "flag": 80, "should": 80, "v": 80, "test": [80, 82, 87], "big": 80, "limit": 80, "memori": 80, "why": 80, "isn": 80, "t": 80, "cleanlearn": [80, 82], "work": [80, 82, 84, 93], "me": 80, "differ": [80, 86], "clean": [80, 82], "final": 80, "hyperparamet": 80, "tune": 80, "onli": 80, "one": [80, 82, 85, 90], "doe": [80, 84, 93], "take": 80, "so": 80, "long": 80, "slice": 80, "when": [80, 82], "identifi": [80, 86], "run": 80, "licens": 80, "under": 80, "an": 80, "answer": 80, "question": 80, "pytorch": [81, 87], "normal": 81, "fashion": 81, "mnist": 81, "prepar": 81, "fold": [81, 88], "cross": [81, 88], "embed": [81, 87], "7": [81, 82], "view": 81, "most": [81, 93], "like": 81, "sever": 81, "set": [81, 82], "dark": 81, "top": [81, 90], "low": 81, "The": 82, "centric": 82, "ai": 82, "machin": 82, "find_label_issu": 82, "line": 82, "code": 82, "visual": [82, 86, 87, 90], "twenti": 82, "lowest": 82, "qualiti": [82, 84, 85, 86, 90, 93], "see": 82, "now": 82, "let": 82, "": 82, "happen": 82, "we": 82, "merg": 82, "seafoam": 82, "green": 82, "yellow": 82, "too": 82, "you": 82, "re": 82, "6": 82, "One": 82, "score": [82, 84, 85, 86, 90, 93], "rule": 82, "overal": [82, 90], "accur": 82, "thi": 82, "directli": 82, "fulli": 82, "character": 82, "nois": 82, "matrix": [82, 85], "joint": 82, "prior": 82, "true": 82, "distribut": 82, "flip": 82, "rate": 82, "ani": 82, "again": 82, "support": 82, "lot": 82, "method": 82, "filter_bi": 82, "automat": 82, "everi": 82, "uniqu": 82, "num_label_issu": 82, "threshold": 82, "found": 82, "Not": 82, "sure": 82, "ensembl": 82, "multipl": [82, 84], "predictor": 82, "consensu": 84, "annot": 84, "initi": 84, "major": 84, "vote": 84, "better": 84, "statist": 84, "compar": 84, "inspect": 84, "potenti": [84, 89, 92], "retrain": 84, "further": 84, "multi": 85, "given": 85, "hot": 85, "binari": 85, "download": [86, 90, 93], "objectlab": 86, "timm": 87, "cifar10": 87, "some": 87, "pred_prob": [87, 90, 93], "wai": 89, "semant": 90, "which": 90, "ar": 90, "commonli": 90, "mislabel": [90, 93], "focus": 90, "scikit": 91, "token": 93, "word": 93, "sentenc": 93, "contain": 93, "particular": 93}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}})
\ No newline at end of file
+Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "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/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/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/report", "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/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/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "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/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.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/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/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/report.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/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/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.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/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "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", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "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", "Image Classification with PyTorch and Cleanlab", "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", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 72, 74, 75, 82, 84, 85], "helper": [1, 14, 33, 37, 39, 40, 41, 42, 43, 44, 56, 79, 81, 93], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 24, 25, 29, 30, 32, 33, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 71, 72, 74, 79, 83, 88], "benchmark": [1, 30, 71, 72, 74, 75, 82, 84, 85], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 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, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 74, 75, 79, 83, 88], "": [1, 2, 3, 8, 29, 30, 34, 37, 40, 42, 44, 49, 50, 54, 56, 57, 58, 59, 61, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "core": [1, 4, 33, 35, 63, 65, 90], "algorithm": [1, 2, 6, 8, 26, 31, 44, 49, 58, 67, 69, 71, 80, 82, 84, 93], "These": [1, 2, 3, 6, 8, 18, 32, 35, 36, 47, 49, 50, 53, 57, 58, 62, 66, 67, 69, 70, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "introduc": [1, 73, 80, 82], "synthet": [1, 84, 85, 90], "nois": [1, 2, 3, 29, 35, 38, 44, 50, 74, 75, 79, 84], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 17, 18, 19, 24, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 74, 79, 83, 87, 88], "classif": [1, 3, 4, 5, 8, 12, 14, 27, 29, 33, 35, 38, 40, 41, 44, 49, 50, 51, 52, 53, 58, 59, 67, 68, 69, 70, 71, 72, 74, 75, 83, 84, 87, 88, 89, 90], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 21, 22, 23, 25, 26, 33, 34, 35, 38, 40, 44, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 76, 77, 83, 84, 88, 91], "specif": [1, 3, 4, 7, 12, 13, 14, 22, 27, 32, 47, 51, 54, 57, 66, 70, 75, 77, 78, 81, 82, 93], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "modul": [1, 3, 11, 12, 13, 14, 18, 24, 27, 29, 30, 31, 32, 33, 34, 35, 44, 47, 49, 58, 59, 71, 80, 81, 85], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 20, 25, 29, 30, 31, 33, 34, 35, 38, 44, 48, 49, 50, 51, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 87, 88, 89, 90, 91, 92, 93], "gener": [1, 2, 3, 5, 8, 20, 27, 29, 40, 44, 45, 58, 59, 61, 66, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 88, 89, 90, 92, 93], "valid": [1, 2, 3, 4, 8, 10, 29, 35, 36, 38, 39, 40, 44, 49, 51, 54, 57, 59, 61, 62, 70, 72, 73, 74, 75, 77, 78, 79, 80, 82, 83, 85, 86, 89, 90, 91, 92, 93], "matric": [1, 3, 38, 80], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 19, 21, 27, 29, 30, 34, 35, 38, 40, 43, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 65, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "learn": [1, 2, 3, 4, 8, 12, 14, 19, 25, 27, 31, 32, 33, 34, 35, 37, 39, 44, 47, 49, 51, 58, 60, 62, 65, 69, 71, 73, 74, 77, 78, 79, 81, 83, 84, 89, 92], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "possibl": [1, 2, 3, 8, 29, 30, 34, 35, 37, 38, 40, 51, 52, 53, 54, 56, 57, 58, 59, 61, 67, 69, 70, 75, 80, 82, 84, 85, 86, 89, 90, 93], "noisi": [1, 2, 3, 8, 29, 31, 34, 35, 38, 44, 50, 51, 53, 59, 61, 62, 63, 65, 66, 72, 74, 75, 77, 78, 80, 83, 84], "given": [1, 2, 3, 8, 25, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 43, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "matrix": [1, 2, 3, 4, 8, 14, 26, 29, 35, 37, 38, 41, 44, 45, 51, 56, 57, 58, 59, 77, 87], "trace": [1, 74, 75, 82, 84, 85], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 19, 21, 22, 29, 30, 31, 33, 34, 35, 37, 38, 40, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 70, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "more": [1, 2, 3, 4, 5, 8, 11, 14, 21, 29, 30, 33, 34, 37, 40, 44, 49, 50, 51, 52, 53, 54, 56, 57, 59, 61, 62, 65, 66, 67, 69, 71, 73, 74, 77, 78, 79, 80, 81, 84, 85, 86, 87, 90, 93], "function": [1, 2, 3, 4, 5, 11, 12, 14, 20, 21, 25, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 75, 79, 80, 82, 84, 85, 86, 90, 91, 92, 93], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 38, 44, 74, 75, 82, 84, 85], "py": [1, 3, 27, 30, 31, 35, 38, 40, 74, 75, 82, 84, 85], "verbos": [1, 2, 4, 5, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 33, 35, 49, 50, 51, 56, 58, 59, 61, 63, 65, 66, 70, 74, 82, 84], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 33, 34, 35, 39, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 73, 74, 75, 77, 78, 80, 81, 82, 84, 86, 87, 89, 90, 92], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70], "prior": [1, 2, 3, 29, 35, 38, 40], "repres": [1, 2, 3, 5, 8, 10, 14, 21, 29, 33, 35, 38, 41, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "p": [1, 2, 3, 8, 29, 35, 37, 38, 44, 49, 57, 58, 59, 63, 75, 77, 78, 81, 82, 84, 93], "true_label": [1, 2, 3, 29, 38, 44, 82, 84], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 20, 21, 23, 26, 29, 33, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 52, 53, 54, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 73, 74, 75, 80, 82, 84, 85, 86, 87, 90, 91, 93], "check": [1, 2, 4, 7, 8, 10, 14, 22, 30, 33, 34, 39, 45, 48, 54, 57, 61, 71, 73, 74, 75, 80, 81, 82, 84, 85, 89, 91, 92], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 19, 21, 31, 34, 38, 40, 56, 61, 75, 78, 80, 82, 84, 85, 87, 89, 92], "achiev": [1, 2, 30, 31, 34, 61, 80, 84, 93], "better": [1, 4, 35, 49, 51, 59, 61, 62, 71, 73, 75, 77, 78, 80, 82, 85, 86, 87, 92, 93], "than": [1, 2, 3, 5, 8, 21, 23, 26, 29, 35, 44, 48, 49, 54, 56, 58, 59, 61, 65, 69, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 93], "random": [1, 2, 3, 5, 8, 26, 33, 40, 49, 59, 61, 73, 74, 75, 77, 80, 81, 82, 84, 85, 87, 91], "perform": [1, 2, 5, 8, 21, 23, 26, 30, 34, 40, 57, 61, 71, 74, 80, 82, 84, 85, 88, 89, 91, 92], "averag": [1, 3, 8, 19, 23, 29, 30, 34, 40, 42, 49, 50, 57, 58, 59, 80, 84, 87], "amount": [1, 3, 81], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 75, 78, 81, 91, 92], "np": [1, 2, 3, 4, 5, 14, 26, 29, 31, 33, 35, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 69, 70, 73, 74, 75, 77, 79, 80, 81, 82, 84, 85, 87, 89, 90, 91, 92, 93], "ndarrai": [1, 2, 3, 4, 14, 20, 21, 25, 26, 29, 31, 33, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 93], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 21, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 74, 75, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "shape": [1, 2, 3, 4, 14, 29, 31, 33, 35, 37, 38, 39, 40, 42, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 79, 80, 82, 85, 86, 87, 90, 93], "condit": [1, 2, 3, 38, 43, 44, 59, 81, 82, 93], "probabl": [1, 2, 3, 4, 6, 8, 14, 20, 23, 29, 33, 34, 35, 37, 38, 40, 41, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 72, 79, 80, 82, 83, 85, 86, 87, 90, 93], "k_": [1, 2, 3, 38, 44], "k_y": [1, 2, 3, 38, 44], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 42, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92], "fraction": [1, 2, 3, 8, 17, 31, 38, 44, 49, 61, 77, 80], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 43, 44, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 84, 85, 86, 88, 89, 90, 91, 92, 93], "everi": [1, 2, 3, 4, 14, 30, 34, 35, 38, 43, 44, 51, 59, 61, 62, 73, 74, 75, 77, 78, 80, 81, 84, 86, 88, 90, 91, 93], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 91, 92, 93], "other": [1, 2, 3, 4, 8, 14, 19, 22, 29, 30, 32, 33, 34, 35, 38, 41, 44, 45, 47, 49, 50, 53, 57, 58, 59, 61, 66, 73, 74, 75, 77, 78, 80, 81, 82, 85, 87, 90, 93], "assum": [1, 2, 3, 10, 35, 38, 42, 43, 44, 59, 63, 66, 80, 87, 90, 93], "column": [1, 2, 3, 4, 8, 10, 11, 25, 29, 33, 35, 38, 40, 41, 43, 44, 49, 50, 51, 53, 54, 57, 58, 59, 61, 66, 67, 69, 70, 73, 74, 75, 78, 79, 80, 81, 82, 84, 86, 89, 90, 91, 92, 93], "sum": [1, 2, 3, 21, 26, 29, 38, 40, 44, 50, 51, 53, 56, 61, 74, 75, 80, 81, 82, 84, 85, 90, 93], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 79, 80, 88], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 17, 19, 20, 21, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 44, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 38, 40, 43, 44, 45, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 47, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 85, 86, 90, 91, 93], "bool": [1, 2, 3, 4, 10, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 33, 34, 35, 40, 43, 44, 49, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 70], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 19, 20, 21, 23, 25, 26, 27, 30, 33, 34, 35, 44, 49, 50, 51, 53, 54, 70, 73, 75, 77, 78, 79, 80, 81, 82, 89, 92, 93], "generate_noisy_label": [1, 74, 75, 82, 84, 85], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 19, 20, 22, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 38, 40, 41, 42, 43, 44, 49, 51, 53, 56, 57, 58, 59, 61, 62, 67, 69, 70, 71, 73, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 90, 93], "perfect": [1, 2, 29, 61, 82, 86], "exactli": [1, 3, 8, 29, 30, 34, 35, 52, 58, 74, 75, 77, 78, 81, 82], "yield": [1, 30, 34], "between": [1, 4, 8, 13, 14, 18, 19, 21, 24, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 42, 47, 49, 50, 53, 56, 58, 59, 61, 62, 65, 69, 70, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93], "below": [1, 3, 4, 8, 29, 30, 33, 34, 35, 37, 40, 49, 50, 51, 56, 57, 65, 69, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "we": [1, 2, 3, 4, 5, 8, 11, 19, 30, 33, 34, 35, 40, 44, 45, 49, 56, 57, 59, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "loop": [1, 3, 38, 44, 81], "implement": [1, 2, 3, 4, 7, 12, 19, 30, 31, 33, 34, 38, 44, 61, 71, 73, 74, 77, 87, 88, 91], "what": [1, 4, 7, 8, 14, 27, 29, 31, 33, 35, 49, 50, 54, 56, 73, 74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "doe": [1, 2, 3, 8, 33, 34, 35, 40, 45, 56, 57, 61, 63, 65, 69, 73, 74, 75, 77, 78, 81, 85, 89, 90, 92], "do": [1, 2, 4, 8, 29, 33, 34, 44, 45, 58, 59, 63, 73, 74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "fast": 1, "explain": [1, 8], "python": [1, 2, 34, 48, 61, 74, 75, 79, 87], "pseudocod": [1, 88], "happen": [1, 8, 35, 51, 78, 84, 90], "n": [1, 2, 3, 4, 5, 29, 30, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 73, 78, 79, 80, 81, 84, 85, 89, 90, 91, 92, 93], "without": [1, 2, 4, 8, 10, 12, 17, 30, 34, 53, 61, 71, 73, 78, 82, 86, 87, 92], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 37, 39, 43, 44, 48, 49, 51, 53, 54, 56, 57, 59, 61, 63, 65, 66, 71, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92], "distinct": [1, 44, 93], "natur": [1, 8, 84, 87], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 90, 93], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "count_joint": 1, "len": [1, 2, 3, 5, 29, 33, 38, 43, 44, 45, 58, 59, 61, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92, 93], "y": [1, 2, 3, 4, 6, 25, 26, 34, 38, 40, 44, 45, 48, 57, 61, 62, 73, 74, 75, 77, 80, 82, 84, 85, 87, 89, 92], "round": [1, 33, 35, 44, 61, 80, 89], "astyp": [1, 84], "int": [1, 2, 3, 4, 5, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 40, 41, 42, 43, 44, 50, 51, 53, 57, 58, 59, 61, 63, 65, 66, 67, 70, 73, 74, 81, 87], "rang": [1, 3, 4, 5, 10, 38, 40, 42, 44, 57, 61, 62, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 93], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 19, 29, 33, 35, 38, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 92, 93], "pragma": 1, "cover": [1, 3, 72, 79], "choic": [1, 6, 35, 80, 81, 85, 87], "replac": [1, 43, 48, 59, 74, 75, 78, 79, 80, 81, 84, 87, 91, 92], "generate_noise_matrix_from_trac": [1, 74, 75, 82, 84, 85], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 59, 73, 74, 75], "05": [1, 8, 21, 43, 57, 61, 67, 69, 79, 80, 82, 86, 90], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 74, 75, 82, 84, 85], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 45, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 74, 75, 80, 81, 82, 84, 85, 90], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 21, 34, 40, 61, 73, 74, 75, 77, 79, 82, 84, 85, 91], "max_it": [1, 73, 78, 87, 92], "10000": [1, 33, 79, 80], "x": [1, 2, 3, 4, 8, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 34, 35, 37, 38, 40, 43, 44, 45, 48, 49, 51, 57, 58, 59, 61, 63, 73, 74, 75, 77, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92], "diagon": [1, 3, 4, 35, 38, 44], "equal": [1, 3, 8, 10, 51, 56, 66, 88], "creat": [1, 2, 7, 14, 30, 33, 34, 35, 44, 61, 71, 73, 77, 78, 80, 81, 90, 92, 93], "impli": [1, 8, 29, 50, 57], "float": [1, 2, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 39, 40, 43, 44, 49, 50, 51, 53, 56, 57, 61, 65, 69, 73, 74, 75, 82, 84, 85], "entri": [1, 3, 4, 29, 30, 34, 35, 37, 41, 44, 49, 50, 51, 54, 77, 78, 82, 85, 86, 91, 92], "maximum": [1, 8, 58, 66, 70, 90], "minimum": [1, 6, 8, 17, 35, 37, 51, 56, 69], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 21, 30, 34, 35, 56, 61, 74, 80, 82, 84, 86, 87], "default": [1, 2, 3, 4, 5, 8, 12, 14, 23, 25, 27, 29, 30, 31, 33, 34, 35, 37, 38, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 74, 80, 81, 90], "If": [1, 2, 3, 4, 8, 10, 11, 14, 21, 23, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 48, 49, 50, 51, 54, 56, 57, 58, 61, 62, 63, 65, 66, 69, 70, 71, 72, 73, 74, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "have": [1, 2, 3, 4, 8, 14, 18, 21, 24, 29, 30, 32, 33, 34, 35, 38, 40, 44, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 62, 66, 70, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 19, 27, 29, 30, 33, 34, 35, 38, 40, 41, 43, 44, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 61, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "necessari": [1, 2, 3, 5, 8, 10, 43, 74], "In": [1, 2, 3, 8, 29, 30, 33, 34, 49, 50, 52, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "particular": [1, 4, 8, 11, 12, 14, 16, 17, 19, 21, 22, 23, 26, 30, 34, 44, 49, 53, 57, 61, 66, 70, 71, 73, 75, 78, 80, 84, 85, 87, 89, 91, 92], "satisfi": [1, 3, 29], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 25, 28, 30, 31, 32, 33, 34, 35, 38, 44, 47, 48, 51, 58, 59, 61, 63, 71, 72, 73, 79, 80, 82, 88], "argument": [1, 2, 3, 4, 8, 14, 20, 22, 25, 26, 30, 33, 34, 35, 40, 45, 48, 49, 50, 51, 53, 56, 57, 58, 59, 61, 65, 66, 67, 69, 75, 78, 79, 80, 81, 86, 89, 92, 93], "when": [1, 2, 3, 4, 8, 10, 12, 20, 21, 30, 34, 35, 38, 40, 44, 48, 51, 53, 54, 56, 58, 59, 61, 62, 74, 75, 77, 78, 81, 84, 88, 89, 90, 91, 92, 93], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 48, 49, 50, 51, 54, 56, 57, 58, 59, 61, 63, 66, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "rate": [1, 2, 3, 8, 31, 44, 73, 93], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 39, 40, 44, 48, 49, 51, 54, 56, 57, 58, 59, 61, 63, 65, 66, 74, 75, 77, 78, 80, 84, 85, 87, 88, 89, 90, 91, 92, 93], "note": [1, 2, 3, 5, 6, 8, 22, 26, 30, 33, 34, 35, 40, 44, 49, 54, 56, 57, 58, 59, 61, 62, 66, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "you": [1, 2, 3, 4, 5, 8, 12, 14, 29, 30, 32, 33, 34, 35, 40, 47, 48, 49, 51, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "high": [1, 2, 14, 33, 35, 44, 56, 59, 61, 74, 75, 79, 81, 82, 86, 89, 90, 91, 92, 93], "mai": [1, 2, 3, 4, 8, 11, 18, 19, 24, 29, 30, 32, 33, 34, 35, 38, 40, 44, 49, 50, 54, 56, 57, 58, 59, 61, 63, 66, 70, 72, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 89, 90, 92, 93], "imposs": [1, 8, 82], "also": [1, 2, 3, 4, 5, 8, 19, 29, 30, 33, 34, 35, 43, 48, 49, 58, 61, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "low": [1, 8, 44, 49, 71, 74, 75, 78, 82, 86, 90], "zero": [1, 3, 4, 30, 34, 37, 44, 45, 74, 81, 85, 86, 87], "forc": [1, 2, 3, 4, 34, 74, 93], "instead": [1, 2, 3, 8, 11, 14, 27, 29, 30, 33, 34, 35, 38, 44, 48, 49, 51, 53, 57, 58, 59, 61, 62, 65, 67, 69, 72, 73, 77, 78, 80, 81, 82, 85, 86, 87, 89, 90, 91, 92, 93], "onli": [1, 2, 3, 4, 5, 8, 14, 20, 21, 25, 29, 30, 33, 34, 35, 37, 38, 43, 44, 48, 49, 58, 59, 61, 63, 65, 69, 70, 71, 73, 74, 75, 78, 81, 84, 85, 86, 87, 88, 89, 90, 92, 93], "guarante": [1, 3, 4, 13, 18, 24, 30, 32, 34, 36, 38, 47, 72], "produc": [1, 2, 4, 8, 14, 40, 49, 59, 61, 63, 65, 71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93], "higher": [1, 4, 8, 29, 35, 37, 38, 40, 49, 50, 61, 75, 78, 80, 86], "opposit": [1, 93], "occur": [1, 3, 8, 29, 43, 56, 74, 75, 80, 81, 87], "small": [1, 3, 8, 29, 33, 40, 44, 50, 57, 78, 79, 81, 85, 87, 92], "numpi": [1, 3, 4, 5, 8, 10, 26, 33, 34, 40, 42, 43, 45, 48, 53, 56, 61, 62, 67, 69, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "max": [1, 35, 58, 59, 75, 81, 87], "tri": [1, 30, 34, 88], "befor": [1, 2, 3, 30, 34, 44, 58, 61, 66, 78, 80, 82, 84, 87, 89, 91, 92], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 20, 21, 25, 29, 30, 33, 34, 35, 38, 40, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 66, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 89, 90, 91], "left": [1, 2, 35, 37, 42, 44, 51, 54, 57, 74, 75, 85, 86, 87, 90], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 30, 34, 39, 40, 49, 54, 56, 57, 58, 74, 75, 79, 84, 85, 86, 93], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 30, 34, 48, 80, 82, 90], "length": [1, 4, 10, 21, 22, 29, 31, 35, 44, 51, 54, 58, 59, 61, 63, 66, 70, 73, 85, 87, 90, 91, 93], "must": [1, 2, 3, 4, 14, 29, 30, 31, 32, 34, 35, 38, 40, 41, 44, 47, 48, 49, 50, 51, 58, 59, 61, 63, 65, 66, 67, 69, 70, 73, 84, 88, 90, 93], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 29, 33, 35, 41, 44, 45, 49, 51, 57, 63, 65, 66, 67, 69, 70, 73, 80, 84, 85, 86, 90, 91, 92, 93], "ball": [1, 79], "bin": [1, 3, 51, 74, 75, 87], "ensur": [1, 2, 8, 30, 34, 44, 45, 56, 59, 61, 73, 74, 75, 78, 80, 81, 82, 87, 88, 89, 91, 92], "most": [1, 3, 4, 5, 8, 14, 29, 33, 35, 40, 48, 49, 50, 51, 54, 56, 57, 58, 59, 62, 65, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92], "least": [1, 8, 26, 29, 33, 49, 50, 56, 59, 69, 75, 80, 81, 84, 87, 90], "int_arrai": [1, 44], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 27, 29, 30, 31, 32, 33, 34, 35, 39, 40, 41, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 66, 67, 70, 71, 72, 73, 74, 77, 78, 81, 85, 86, 87, 88, 89, 90, 91, 92, 93], "model": [2, 3, 4, 8, 14, 25, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 43, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 72, 74, 75, 79, 83, 88, 90, 93], "For": [2, 3, 4, 5, 7, 8, 9, 14, 19, 28, 29, 30, 33, 34, 35, 38, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 65, 67, 69, 70, 71, 73, 75, 77, 79, 80, 81, 82, 84, 85, 86, 87, 88, 90, 91, 92, 93], "regular": [2, 3, 33, 48], "multi": [2, 3, 8, 29, 30, 33, 34, 35, 39, 40, 41, 44, 45, 50, 51, 52, 53, 58, 59, 71, 80, 82, 83], "task": [2, 4, 5, 10, 12, 13, 14, 25, 27, 29, 33, 38, 40, 41, 42, 44, 49, 51, 59, 61, 71, 73, 78, 79, 80, 82, 85, 87, 90, 92, 93], "cleanlearn": [2, 3, 8, 20, 25, 30, 44, 48, 61, 62, 71, 72, 89, 91, 92], "wrap": [2, 30, 34, 48, 58, 61, 71, 74, 75, 77, 78, 82, 89, 91, 92], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 34, 40, 48, 57, 58, 61, 66, 73, 74, 75, 77, 78, 81, 82, 91], "sklearn": [2, 3, 4, 6, 8, 26, 29, 34, 40, 44, 48, 58, 61, 62, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92], "classifi": [2, 3, 34, 40, 44, 49, 52, 58, 59, 71, 72, 73, 77, 78, 80, 84, 85, 87, 88, 90, 91, 92, 93], "adher": [2, 34, 61], "estim": [2, 3, 4, 7, 11, 19, 29, 33, 34, 35, 38, 44, 49, 50, 51, 56, 58, 61, 63, 65, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 83, 85, 86, 87, 88, 89, 90, 93], "api": [2, 3, 12, 48, 58, 61, 72, 80, 89], "defin": [2, 3, 4, 5, 8, 12, 19, 29, 30, 31, 33, 34, 35, 59, 61, 63, 74, 75, 77, 80, 84, 87, 93], "four": [2, 8, 79, 82, 93], "clf": [2, 3, 4, 40, 61, 71, 77, 80, 82, 85, 91], "fit": [2, 3, 4, 6, 8, 34, 48, 58, 61, 71, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 88, 89, 91, 92, 93], "sample_weight": [2, 34, 61, 82], "predict_proba": [2, 4, 29, 34, 40, 48, 73, 74, 75, 77, 78, 80, 82, 84, 85, 87, 91], "predict": [2, 3, 4, 6, 8, 14, 19, 20, 23, 25, 29, 33, 34, 35, 37, 38, 40, 41, 43, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 72, 79, 80, 82, 83, 87, 89, 90, 92, 93], "score": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 33, 35, 37, 40, 42, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 87, 89, 91, 92], "data": [2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 31, 32, 33, 34, 35, 40, 41, 44, 47, 48, 49, 50, 51, 52, 56, 58, 59, 60, 61, 66, 67, 68, 69, 70, 72, 76, 81, 83, 88, 92], "e": [2, 3, 4, 8, 10, 19, 29, 30, 33, 34, 35, 38, 40, 41, 44, 45, 49, 50, 51, 52, 58, 59, 61, 63, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92], "featur": [2, 3, 4, 6, 8, 14, 16, 20, 21, 22, 23, 25, 26, 40, 44, 58, 61, 71, 74, 75, 77, 78, 80, 82, 84, 89, 91], "element": [2, 3, 4, 29, 35, 37, 44, 49, 51, 59, 66, 67, 69, 73, 78, 80, 92, 93], "first": [2, 4, 8, 15, 21, 22, 29, 33, 40, 44, 49, 50, 54, 57, 59, 61, 73, 74, 77, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "index": [2, 8, 21, 29, 35, 42, 43, 44, 45, 50, 59, 61, 66, 69, 70, 73, 74, 75, 77, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "should": [2, 3, 4, 5, 8, 12, 19, 21, 26, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 48, 49, 50, 53, 54, 56, 57, 58, 59, 61, 62, 66, 67, 69, 70, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 37, 38, 40, 43, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 63, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "differ": [2, 4, 5, 8, 11, 13, 18, 21, 22, 24, 29, 30, 32, 33, 34, 35, 36, 40, 44, 45, 47, 49, 54, 56, 58, 61, 73, 74, 75, 77, 78, 81, 82, 84, 87, 88, 91], "sampl": [2, 3, 4, 6, 8, 14, 17, 35, 37, 40, 51, 54, 57, 59, 61, 62, 71, 72, 79, 80, 82, 83, 85, 86, 89, 90, 92, 93], "size": [2, 8, 26, 30, 33, 34, 35, 40, 51, 56, 57, 61, 63, 65, 77, 80, 81, 82, 84, 85, 88, 90, 92], "here": [2, 4, 5, 8, 12, 33, 35, 38, 48, 49, 50, 51, 53, 54, 57, 58, 69, 71, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "re": [2, 4, 30, 34, 43, 49, 61, 71, 73, 74, 77, 78, 80, 89, 90, 91, 92, 93], "weight": [2, 8, 30, 31, 34, 40, 49, 56, 59, 61, 73, 74, 75, 78, 87, 92], "loss": [2, 31, 48, 59, 61, 81], "while": [2, 3, 8, 30, 33, 34, 39, 40, 44, 54, 57, 61, 71, 80, 81, 82, 84, 89], "train": [2, 3, 4, 8, 14, 30, 31, 34, 40, 44, 48, 49, 54, 57, 58, 61, 62, 72, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 88, 90, 93], "support": [2, 3, 4, 10, 33, 40, 44, 45, 58, 59, 69, 71, 72, 73, 74, 75, 80, 81], "your": [2, 3, 4, 7, 8, 14, 29, 30, 32, 33, 34, 35, 40, 44, 47, 48, 49, 50, 51, 53, 58, 59, 61, 62, 63, 65, 66, 72, 73, 77, 79, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "recommend": [2, 4, 8, 11, 14, 33, 35, 49, 74, 75, 80, 81, 88, 89], "furthermor": 2, "correctli": [2, 3, 8, 29, 30, 34, 35, 38, 45, 50, 51, 56, 57, 61, 63, 78, 80, 85, 86, 89, 90, 92], "clonabl": [2, 61], "via": [2, 4, 8, 11, 14, 19, 29, 31, 33, 34, 40, 44, 49, 54, 57, 58, 59, 61, 62, 65, 69, 73, 74, 75, 77, 78, 79, 80, 81, 85, 86, 87, 88, 89, 90, 91, 92, 93], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 35, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 53, 56, 58, 59, 61, 62, 65, 67, 69, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 93], "clone": [2, 61, 85], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 33, 37, 38, 39, 40, 41, 42, 43, 44, 45, 53, 57, 61, 67, 72, 74, 80, 82, 84, 85, 87, 93], "multipl": [2, 3, 4, 10, 11, 29, 35, 43, 49, 50, 51, 53, 56, 57, 61, 71, 74, 75, 80, 81, 83, 85, 86, 89], "g": [2, 3, 4, 8, 10, 19, 29, 30, 34, 35, 41, 44, 51, 52, 58, 59, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92], "manual": [2, 61, 73, 80, 87, 88, 89, 91, 92, 93], "pytorch": [2, 30, 31, 34, 61, 71, 73, 80, 83, 85, 90], "call": [2, 3, 4, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 40, 44, 48, 58, 61, 73, 74, 75, 78, 80, 82, 87, 88, 90, 92, 93], "__init__": [2, 31, 61, 81], "independ": [2, 3, 8, 50, 61, 78, 88, 93], "compat": [2, 30, 33, 34, 48, 61, 62, 65, 69, 71, 80, 88, 89, 91, 92], "neural": [2, 31, 48, 58, 61, 73, 80, 81, 85, 87], "network": [2, 30, 31, 34, 48, 58, 61, 73, 78, 80, 81, 85, 87, 92], "typic": [2, 30, 34, 58, 61, 73, 75, 77, 78, 81, 87, 88, 91, 92], "initi": [2, 3, 11, 30, 34, 49, 61, 78, 80, 91], "insid": [2, 34, 61, 80, 82], "There": [2, 3, 71, 82, 84, 85], "two": [2, 3, 8, 21, 29, 30, 33, 34, 41, 44, 54, 56, 57, 72, 74, 75, 77, 78, 80, 81, 82, 85, 89, 90, 92, 93], "new": [2, 5, 12, 19, 30, 33, 34, 39, 43, 44, 49, 61, 73, 74, 78, 79, 80, 87, 88, 92, 93], "notion": 2, "confid": [2, 3, 8, 19, 29, 33, 35, 38, 40, 44, 49, 50, 51, 54, 56, 57, 58, 59, 61, 65, 69, 71, 77, 78, 81, 82, 84, 85, 86, 88, 90, 91, 93], "packag": [2, 4, 5, 7, 8, 9, 13, 28, 32, 35, 36, 44, 47, 54, 57, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "prune": [2, 3, 35, 51, 61, 72, 86], "everyth": [2, 57, 82], "els": [2, 57, 74, 79, 80, 81, 84, 85], "mathemat": [2, 3, 8, 38], "keep": [2, 11, 12, 44, 71, 74, 79, 80, 90], "belong": [2, 3, 8, 29, 35, 37, 38, 50, 51, 52, 53, 58, 59, 63, 67, 69, 70, 75, 77, 78, 81, 82, 85, 87, 90, 93], "2": [2, 3, 4, 5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 48, 50, 51, 53, 58, 59, 61, 62, 66, 67, 69, 70, 79, 80, 88], "error": [2, 3, 4, 8, 30, 34, 35, 37, 38, 42, 44, 50, 51, 53, 54, 56, 57, 59, 61, 63, 65, 66, 69, 72, 73, 74, 75, 77, 78, 79, 83, 91], "erron": [2, 3, 29, 35, 38, 44, 50, 51, 59, 61, 62, 63, 87, 89], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 33, 40, 42, 43, 49, 53, 56, 61, 62, 67, 69, 70, 71, 77, 78, 80, 85, 86, 87, 89, 90, 91, 92, 93], "linear_model": [2, 4, 29, 44, 61, 71, 73, 74, 75, 78, 80, 82, 84, 87, 92], "logisticregress": [2, 3, 4, 29, 44, 71, 73, 74, 75, 78, 80, 82, 84, 87, 92], "logreg": 2, "cl": [2, 12, 25, 61, 71, 80, 82, 89, 91, 92], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 20, 25, 27, 30, 33, 34, 35, 39, 40, 44, 48, 49, 51, 58, 59, 61, 67, 71, 73, 74, 75, 78, 79, 80, 82, 84, 86, 87, 89, 92], "x_train": [2, 74, 75, 82, 84, 85, 89, 91], "labels_maybe_with_error": 2, "had": [2, 3, 61, 86], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 26, 27, 29, 30, 32, 33, 34, 35, 47, 50, 51, 52, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 76, 83, 84, 88, 89, 92], "pred": [2, 35, 44, 88, 89, 91, 92], "x_test": [2, 74, 75, 82, 85, 89, 91], "might": [2, 49, 61, 66, 74, 75, 80, 81, 91, 92], "case": [2, 3, 11, 29, 40, 49, 61, 73, 74, 75, 77, 79, 80, 81, 82, 87, 89, 91, 92, 93], "standard": [2, 3, 4, 25, 29, 35, 48, 50, 51, 53, 59, 61, 71, 74, 75, 77, 79, 82, 91], "adapt": [2, 30, 32, 44, 47, 61, 87], "skorch": [2, 61, 71, 80], "kera": [2, 47, 61, 71, 80], "scikera": [2, 48, 61, 80], "open": [2, 33, 79, 86, 93], "doesn": [2, 61, 71], "t": [2, 3, 8, 15, 22, 30, 31, 33, 34, 35, 40, 42, 43, 53, 58, 59, 61, 67, 69, 70, 71, 74, 75, 77, 78, 79, 81, 82, 85, 86, 93], "alreadi": [2, 4, 14, 30, 33, 34, 38, 48, 49, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 91, 92], "exist": [2, 4, 8, 10, 30, 33, 34, 43, 48, 54, 56, 58, 61, 71, 72, 74, 75, 78, 84, 85, 92, 93], "made": [2, 4, 14, 61, 78, 80, 81, 84, 86, 88, 89, 91, 92], "easi": [2, 38, 61, 74, 75, 79, 80, 82, 85], "inherit": [2, 5, 31, 61], "baseestim": [2, 34, 61], "yourmodel": [2, 61], "def": [2, 5, 12, 30, 34, 48, 61, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 92, 93], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 26, 30, 31, 33, 34, 35, 40, 58, 59, 61, 74, 79, 81, 85, 90, 91, 93], "refer": [2, 8, 14, 30, 34, 50, 51, 53, 54, 56, 57, 61, 65, 66, 74, 75, 77, 78, 80, 81, 82, 88, 89], "origin": [2, 4, 8, 34, 35, 43, 44, 48, 50, 51, 54, 57, 58, 61, 62, 65, 67, 69, 74, 77, 78, 80, 81, 82, 86, 87, 89, 91, 92, 93], "total": [2, 3, 29, 33, 44, 50, 70, 80, 81, 90], "state": [2, 3, 4, 30, 31, 34, 39, 61, 82, 85, 86, 93], "art": [2, 31, 82, 85], "northcutt": [2, 3, 29, 58, 59], "et": [2, 3, 29, 31, 58, 59], "al": [2, 3, 29, 31, 58, 59], "2021": [2, 3, 29, 58, 59], "weak": [2, 57], "supervis": [2, 8, 74, 75, 80, 84], "find": [2, 4, 8, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 26, 29, 30, 32, 33, 34, 35, 39, 43, 44, 47, 54, 57, 58, 59, 61, 63, 67, 69, 72, 74, 83, 88], "uncertainti": [2, 8, 37, 58, 61, 80, 87, 89], "It": [2, 3, 4, 5, 8, 10, 11, 14, 19, 22, 25, 27, 30, 34, 35, 38, 40, 42, 49, 56, 57, 61, 71, 74, 75, 80, 81, 82, 85, 88], "work": [2, 3, 4, 5, 8, 10, 25, 29, 30, 33, 34, 35, 38, 43, 44, 45, 48, 49, 59, 61, 71, 72, 74, 75, 79, 87, 89, 92], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 30, 32, 33, 34, 43, 44, 47, 49, 50, 53, 54, 58, 59, 61, 65, 66, 67, 69, 71, 72, 74, 75, 77, 78, 80, 81, 82, 85, 86, 87, 93], "deep": [2, 32, 34, 47, 48, 61, 78], "see": [2, 3, 4, 11, 29, 30, 33, 34, 35, 40, 44, 48, 50, 51, 53, 54, 57, 58, 59, 61, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "subfield": 2, "theori": [2, 82], "machin": [2, 4, 12, 14, 27, 32, 47, 61, 74, 75, 79, 84], "across": [2, 3, 4, 5, 8, 11, 19, 29, 33, 40, 50, 57, 58, 74, 75, 77, 78, 79, 80, 81, 82, 86, 88], "varieti": [2, 80, 91, 92], "like": [2, 3, 4, 5, 8, 12, 27, 29, 30, 33, 34, 35, 38, 44, 48, 49, 50, 53, 54, 56, 59, 61, 62, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "pu": [2, 44], "input": [2, 3, 4, 8, 14, 21, 29, 30, 33, 34, 38, 40, 43, 44, 45, 48, 57, 61, 71, 72, 75, 78, 79, 80, 81, 82, 84, 85, 86, 89, 90, 92, 93], "discret": [2, 35, 38, 44, 58, 59, 63, 65, 66], "vector": [2, 3, 4, 8, 14, 35, 38, 40, 41, 44, 58, 59, 71, 73, 74, 75, 77, 78, 81, 82, 85, 86, 87, 90, 92, 93], "would": [2, 3, 4, 30, 33, 34, 35, 44, 51, 61, 71, 74, 80, 81, 82, 87, 89, 92, 93], "obtain": [2, 4, 6, 8, 14, 35, 49, 51, 54, 57, 59, 62, 73, 75, 78, 80, 84, 86, 88, 90, 93], "been": [2, 29, 35, 38, 43, 44, 49, 50, 54, 56, 58, 59, 61, 73, 74, 77, 80, 82, 84, 85, 86, 87, 90, 93], "dure": [2, 14, 58, 61, 73, 77, 78, 80, 82, 85, 88, 89, 91, 92, 93], "denot": [2, 3, 38, 40, 44, 51, 58, 59, 69], "tild": 2, "paper": [2, 8, 49, 58, 67, 69, 79, 82, 84, 87, 89, 93], "cv_n_fold": [2, 3, 61, 92], "5": [2, 3, 4, 6, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 34, 35, 37, 39, 40, 44, 49, 50, 53, 54, 57, 61, 62, 69, 74, 78, 79, 80, 85, 86, 87, 88, 90, 92, 93], "converge_latent_estim": [2, 3], "pulearn": [2, 44], "find_label_issues_kwarg": [2, 8, 61, 72, 80, 82], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 51, 67, 80], "clean": [2, 56, 59, 61, 62, 71, 74, 75, 79, 89, 91, 92], "even": [2, 3, 29, 33, 37, 38, 44, 61, 73, 80, 82, 84, 85, 86], "messi": [2, 61, 82], "ridden": [2, 61], "autom": [2, 61, 71, 75, 79, 80], "robust": [2, 38, 61, 75, 80], "prone": [2, 61], "out": [2, 3, 4, 8, 14, 23, 30, 34, 35, 40, 48, 51, 52, 54, 57, 58, 59, 61, 62, 70, 71, 72, 79, 80, 82, 83, 85, 86, 87, 89, 90, 92, 93], "current": [2, 3, 5, 8, 11, 12, 19, 30, 34, 35, 40, 49, 56, 61, 74, 75, 80, 84], "intend": [2, 11, 12, 13, 14, 27, 36, 49, 65, 69, 73, 74, 75, 78, 82], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 35, 38, 39, 40, 41, 43, 44, 48, 49, 50, 53, 56, 57, 58, 59, 61, 63, 65, 66, 70, 72, 73, 74, 77, 78, 79, 80, 81, 82, 84, 86, 88, 91, 92, 93], "follow": [2, 3, 8, 12, 25, 29, 30, 33, 34, 40, 42, 49, 50, 54, 56, 57, 58, 61, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "experiment": [2, 30, 31, 33, 34, 51, 72, 80], "wrapper": [2, 4, 48, 73, 89, 91, 92], "around": [2, 4, 56, 74, 75, 86, 87, 93], "fasttext": [2, 47], "store": [2, 4, 8, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 58, 61, 77, 78, 79, 80, 90, 91, 92, 93], "along": [2, 40, 51, 69, 74, 75, 80, 81, 87], "dimens": [2, 42, 44, 63, 66, 80, 81, 87, 90], "select": [2, 7, 8, 21, 49, 59, 81, 84, 87], "split": [2, 3, 4, 8, 10, 33, 40, 43, 44, 61, 73, 74, 75, 77, 78, 79, 81, 82, 85, 88, 91, 93], "cross": [2, 3, 8, 29, 35, 38, 39, 40, 51, 54, 57, 59, 61, 62, 72, 73, 74, 75, 77, 78, 79, 80, 82, 83, 85, 86, 89, 90, 91, 92, 93], "fold": [2, 3, 29, 35, 38, 61, 73, 77, 79, 80, 86, 90, 91], "By": [2, 4, 29, 50, 51, 61, 74, 80, 90], "need": [2, 3, 8, 29, 30, 33, 34, 35, 50, 51, 53, 58, 61, 71, 73, 74, 75, 78, 80, 82, 84, 85, 86, 90, 92], "holdout": [2, 3, 61], "comput": [2, 3, 4, 5, 6, 8, 16, 17, 19, 20, 21, 22, 23, 26, 29, 30, 31, 33, 34, 35, 37, 38, 39, 40, 42, 44, 49, 50, 51, 53, 56, 57, 58, 59, 61, 62, 63, 65, 71, 72, 74, 75, 79, 82, 83, 85, 86, 87, 89, 90, 92], "them": [2, 3, 4, 5, 7, 8, 9, 10, 22, 28, 30, 32, 33, 34, 35, 47, 49, 58, 61, 72, 74, 75, 77, 78, 80, 81, 84, 85, 87, 89, 90, 91, 92, 93], "numer": [2, 3, 4, 8, 11, 19, 25, 40, 56, 58, 61, 66, 71, 72, 73, 74, 75, 76, 78, 81, 82, 84, 87, 89, 91, 92], "consist": [2, 3, 30, 34, 44, 49, 90, 93], "latent": [2, 3, 38], "thei": [2, 3, 4, 13, 18, 21, 24, 30, 31, 32, 34, 35, 36, 44, 48, 51, 56, 59, 61, 62, 65, 69, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 87, 89, 92, 93], "relat": [2, 3, 11, 16, 17, 21, 22, 23, 26, 38, 44, 50, 61, 75, 78], "close": [2, 3, 8, 33, 38, 58, 73, 74, 75, 77, 78, 80, 81, 82, 86], "form": [2, 3, 8, 30, 31, 34, 38, 43, 44, 59, 61, 80], "equival": [2, 3, 30, 34, 38, 58, 87], "iter": [2, 3, 29, 30, 34, 35, 44, 50, 51, 61, 80, 84, 90], "enforc": [2, 30, 34, 44], "perfectli": [2, 29, 50, 82], "certain": [2, 3, 4, 30, 34, 48, 57, 61, 74, 75, 79, 87], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 33, 34, 35, 39, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 69, 74, 75, 80, 81, 93], "keyword": [2, 3, 4, 8, 14, 20, 22, 25, 30, 33, 34, 35, 37, 40, 43, 48, 49, 51, 58, 59, 61, 67, 69, 74], "filter": [2, 3, 8, 33, 43, 50, 52, 53, 55, 57, 64, 65, 66, 68, 69, 70, 71, 72, 73, 75, 78, 79, 80, 81, 85, 86, 89, 90, 91, 92, 93], "find_label_issu": [2, 3, 8, 25, 33, 35, 50, 51, 53, 54, 56, 57, 61, 63, 65, 66, 67, 69, 70, 71, 72, 80, 85, 86, 89, 90, 91, 92, 93], "particularli": [2, 71, 84, 87], "filter_bi": [2, 3, 33, 35, 51, 72, 80], "frac_nois": [2, 35, 51, 67, 80], "min_examples_per_class": [2, 35, 51, 75, 80, 82], "impact": [2, 8, 74, 75, 81], "ml": [2, 4, 8, 13, 61, 71, 74, 75, 77, 78, 81, 84, 91, 92], "accuraci": [2, 31, 59, 73, 80, 81, 82, 84, 87, 89, 90, 91, 92], "n_job": [2, 33, 35, 51, 63, 65, 67, 80, 87, 90], "disabl": [2, 30, 34, 35, 87], "process": [2, 3, 5, 11, 14, 33, 35, 43, 49, 51, 57, 63, 65, 67, 73, 74, 80, 84, 88, 92], "caus": [2, 35, 40, 74, 75, 80], "rank": [2, 3, 8, 29, 33, 35, 40, 50, 51, 52, 54, 55, 57, 58, 60, 64, 66, 67, 68, 70, 71, 72, 74, 75, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93], "get_label_quality_scor": [2, 33, 35, 40, 49, 51, 53, 54, 56, 59, 62, 65, 67, 69, 72, 82, 85, 86, 89, 90, 93], "adjust_pred_prob": [2, 8, 53, 58, 59, 82], "control": [2, 4, 7, 8, 14, 33, 35, 42, 49, 57, 58, 61, 67, 69, 74, 75, 79, 80], "how": [2, 3, 4, 8, 11, 12, 14, 19, 29, 30, 31, 33, 34, 38, 44, 49, 50, 53, 54, 56, 58, 59, 61, 65, 69, 71, 74, 75, 77, 78, 79, 81, 86, 87, 88, 89, 90, 91, 92], "much": [2, 8, 29, 33, 35, 61, 80, 82, 84, 87], "output": [2, 3, 4, 8, 14, 30, 31, 34, 38, 44, 48, 49, 50, 54, 56, 57, 58, 61, 65, 66, 69, 70, 71, 72, 73, 74, 78, 79, 80, 81, 86, 87, 88, 89, 92], "print": [2, 4, 5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 44, 49, 50, 51, 56, 58, 59, 61, 63, 65, 66, 70, 72, 73, 75, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "suppress": [2, 33, 49, 56, 58, 59, 61, 63, 65, 66, 90, 93], "statement": [2, 33, 49, 56, 58, 59, 61, 63, 65, 66], "big": [2, 33, 51, 57, 61, 82], "limit": [2, 4, 14, 33, 51, 86, 90, 93], "memori": [2, 30, 33, 34, 51, 57, 63, 65, 74, 90], "label_issues_batch": [2, 32, 51, 80], "find_label_issues_batch": [2, 33, 51, 80], "pred_prob": [2, 3, 4, 6, 8, 14, 20, 21, 23, 26, 29, 33, 35, 37, 38, 39, 40, 41, 44, 45, 49, 50, 51, 53, 54, 57, 58, 59, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 91, 92], "threshold": [2, 3, 5, 8, 16, 17, 19, 23, 25, 26, 33, 56, 57, 58, 59, 65, 69, 74, 86, 87, 90, 93], "inverse_noise_matrix": [2, 3, 8, 38, 44, 72, 82], "label_issu": [2, 33, 35, 51, 54, 61, 63, 72, 73, 78, 80, 81, 82, 89, 91, 92], "clf_kwarg": [2, 3, 8, 61], "clf_final_kwarg": [2, 61], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 29, 33, 35, 37, 49, 50, 51, 53, 54, 56, 57, 59, 61, 62, 65, 69, 71, 73, 77, 78, 81, 82, 84, 86, 88, 89], "result": [2, 3, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 33, 34, 35, 37, 44, 51, 53, 54, 57, 59, 61, 62, 63, 65, 69, 73, 74, 75, 77, 78, 80, 81, 82, 84, 89, 90, 91, 92, 93], "identifi": [2, 3, 4, 5, 8, 10, 14, 22, 27, 29, 33, 35, 51, 54, 57, 59, 61, 62, 63, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 79, 81, 82, 85, 87, 89, 90, 91, 92, 93], "final": [2, 8, 61, 77, 86, 88, 89, 91], "remain": [2, 61, 72, 81, 89, 91, 92, 93], "datasetlik": [2, 44, 61], "beyond": [2, 4, 5, 7, 9, 28, 71, 90], "pd": [2, 3, 4, 5, 11, 16, 17, 19, 20, 21, 23, 25, 26, 29, 39, 48, 49, 50, 61, 69, 73, 74, 75, 77, 78, 80, 82, 84, 89, 91, 92, 93], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 33, 39, 44, 45, 48, 49, 50, 61, 66, 70, 72, 73, 74, 75, 77, 78, 80, 81, 82, 84, 89, 90, 92, 93], "scipi": [2, 4, 11, 44], "spars": [2, 4, 8, 11, 14, 26, 44, 45, 77], "csr_matrix": [2, 4, 11, 14, 26], "torch": [2, 30, 31, 34, 73, 78, 79, 81, 87, 92], "util": [2, 4, 14, 27, 30, 31, 34, 36, 49, 61, 71, 72, 73, 74, 75, 80, 81, 82, 87], "tensorflow": [2, 44, 48, 71, 73, 80], "object": [2, 4, 10, 11, 14, 27, 30, 31, 33, 34, 40, 44, 45, 48, 51, 54, 55, 56, 57, 58, 61, 69, 71, 73, 75, 77, 81, 82, 83, 89, 92], "list": [2, 3, 4, 10, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 41, 43, 44, 45, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 65, 66, 67, 69, 70, 72, 73, 74, 75, 79, 80, 81, 82, 85, 86, 89, 92, 93], "index_list": 2, "subset": [2, 3, 4, 14, 29, 33, 35, 44, 59, 66, 70, 73, 77, 78, 80, 81, 85, 86, 87, 88, 89, 91, 92, 93], "wa": [2, 3, 10, 12, 33, 44, 49, 50, 56, 58, 70, 73, 74, 75, 77, 78, 80, 82, 85, 86, 88, 90, 91, 92, 93], "abl": [2, 3, 8, 61, 73, 80, 82, 84, 85], "format": [2, 3, 4, 8, 10, 30, 33, 34, 35, 38, 39, 40, 41, 44, 45, 48, 49, 50, 51, 54, 57, 58, 59, 61, 63, 65, 66, 69, 70, 74, 75, 77, 79, 81, 84, 89, 90, 91, 93], "make": [2, 3, 30, 33, 34, 40, 48, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "sure": [2, 33, 35, 40, 73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 91, 92], "shuffl": [2, 8, 44, 73, 78, 81, 85, 87], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 30, 34, 38, 40, 43, 44, 49, 54, 56, 61, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "batch": [2, 33, 44, 48, 49, 63, 65, 80, 81, 87], "order": [2, 4, 8, 29, 30, 34, 35, 38, 39, 40, 42, 44, 49, 50, 51, 54, 57, 58, 59, 63, 66, 67, 69, 70, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 86, 89, 90, 92, 93], "destroi": [2, 44], "oper": [2, 30, 33, 34, 44, 48, 59, 71, 78, 87, 91, 92], "eg": [2, 8, 44, 54, 57, 74, 75, 80], "repeat": [2, 44, 49, 84, 87], "appli": [2, 30, 34, 35, 40, 41, 43, 44, 53, 58, 67, 73, 74, 75, 77, 80, 81, 84, 85, 87, 88, 89, 90, 91, 92], "array_lik": [2, 3, 29, 35, 44, 51, 58, 62], "some": [2, 3, 4, 8, 12, 19, 29, 30, 32, 34, 35, 38, 43, 44, 47, 49, 50, 51, 53, 54, 57, 58, 59, 61, 63, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "seri": [2, 3, 33, 44, 45, 61, 69, 80], "row": [2, 3, 4, 8, 11, 22, 29, 33, 35, 37, 38, 42, 44, 49, 50, 51, 53, 58, 59, 61, 66, 67, 69, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 87, 91, 93], "rather": [2, 3, 21, 29, 44, 48, 49, 56, 65, 69, 84, 88, 90, 92, 93], "leav": [2, 35], "per": [2, 3, 11, 29, 33, 35, 40, 43, 49, 50, 51, 53, 56, 57, 59, 62, 63, 65, 69, 75, 80, 86, 93], "determin": [2, 3, 8, 14, 19, 21, 25, 29, 33, 35, 40, 44, 49, 51, 54, 56, 59, 65, 69, 74, 80, 84, 87, 89], "cutoff": [2, 3, 87], "consid": [2, 3, 4, 8, 11, 14, 20, 21, 23, 26, 29, 30, 34, 35, 44, 49, 56, 58, 59, 62, 65, 69, 73, 75, 77, 78, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92], "section": [2, 3, 5, 8, 72, 77, 81], "3": [2, 3, 4, 5, 8, 29, 30, 34, 35, 38, 39, 40, 41, 42, 43, 44, 48, 51, 58, 59, 61, 62, 67, 69, 79, 80, 88], "equat": [2, 3, 38], "advanc": [2, 3, 4, 7, 8, 14, 56, 58, 69, 72, 75, 76, 82], "user": [2, 3, 4, 8, 12, 14, 22, 27, 30, 34, 35, 56, 58, 59, 61, 65, 69, 82], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 26, 27, 30, 33, 34, 35, 40, 43, 49, 50, 51, 54, 56, 58, 59, 61, 62, 70, 72, 73, 75, 78, 81, 84, 86, 89, 92], "automat": [2, 3, 4, 21, 29, 71, 77, 78, 79, 80, 81, 84, 86, 89, 90, 91, 92, 93], "greater": [2, 3, 4, 7, 8, 23, 33, 42, 44, 56, 75, 79, 80, 93], "count": [2, 19, 21, 29, 33, 35, 38, 44, 50, 51, 57, 72, 80, 81], "observ": [2, 3, 38, 73, 74, 75, 84, 87, 89], "mislabel": [2, 8, 29, 33, 35, 38, 49, 50, 51, 54, 56, 59, 65, 67, 69, 71, 73, 77, 78, 80, 81, 82, 85, 86, 89, 91, 92], "one": [2, 3, 4, 8, 21, 29, 30, 33, 34, 35, 40, 44, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81, 84, 87, 88, 89, 91, 92, 93], "get_label_issu": [2, 33, 61, 82, 89, 91, 92], "either": [2, 3, 5, 8, 30, 33, 34, 35, 49, 51, 56, 58, 59, 63, 65, 75, 85, 86], "boolean": [2, 5, 8, 19, 33, 35, 43, 49, 51, 54, 59, 61, 63, 65, 66, 71, 73, 75, 78, 80, 81, 86, 89, 90, 92], "label_issues_mask": [2, 35, 59, 61, 72], "indic": [2, 3, 4, 5, 8, 11, 19, 29, 33, 34, 35, 37, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 61, 62, 65, 67, 69, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "its": [2, 4, 7, 8, 14, 30, 33, 34, 35, 42, 43, 51, 54, 57, 58, 59, 61, 63, 67, 69, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 92, 93], "return_indices_ranked_bi": [2, 33, 35, 51, 67, 72, 80, 82, 85, 91, 92], "significantli": [2, 81, 82, 84, 88], "reduc": [2, 33, 35, 44, 73, 80], "time": [2, 8, 30, 33, 34, 44, 49, 72, 74, 79, 80, 81, 82, 86, 87, 89, 90, 91, 92, 93], "take": [2, 4, 8, 29, 30, 34, 39, 40, 44, 48, 59, 77, 81, 84, 91, 93], "run": [2, 4, 5, 7, 9, 12, 14, 21, 22, 28, 30, 33, 34, 61, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "skip": [2, 8, 30, 34, 61, 73, 80, 85, 93], "slow": [2, 3], "step": [2, 5, 21, 40, 57, 80, 81, 82, 84, 88], "caution": [2, 4, 80], "previous": [2, 4, 11, 44, 58, 61, 72, 73, 74, 77, 78, 84, 88, 91], "assign": [2, 5, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 39, 40, 44, 61, 74, 77, 80, 81, 89, 90, 91, 93], "individu": [2, 8, 11, 21, 30, 34, 49, 53, 56, 59, 61, 67, 69, 72, 75, 77, 80, 84, 85, 86, 91, 93], "still": [2, 33, 34, 44, 58, 80, 81, 87, 91], "extra": [2, 30, 34, 44, 48, 49, 50, 61, 78, 80, 81, 84, 87], "receiv": [2, 8, 30, 34, 50, 53, 54, 61, 63, 67, 75, 86], "overwritten": [2, 61], "callabl": [2, 3, 40, 43, 48, 53, 80], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 33, 34, 39, 43, 44, 57, 59, 61, 66, 73, 74, 75, 80, 81, 82, 85, 93], "appropri": [2, 8, 14, 51, 59, 74, 77, 85, 86], "earli": [2, 81], "stop": [2, 81], "x_valid": 2, "y_valid": 2, "could": [2, 19, 29, 44, 58, 74, 77, 81, 85, 89, 91, 93], "f": [2, 5, 73, 74, 77, 78, 79, 80, 81, 82, 84, 85, 87, 89, 91, 92], "ignor": [2, 30, 34, 43, 48, 61, 66, 70, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "allow": [2, 29, 30, 33, 34, 37, 44, 49, 57, 58, 61, 63, 65, 73, 80, 81, 88, 90, 92], "access": [2, 8, 11, 30, 34, 61, 75, 81, 85], "hyperparamet": [2, 53, 58, 81], "purpos": [2, 74, 75, 80, 85, 89], "want": [2, 4, 8, 29, 33, 45, 49, 51, 61, 74, 78, 79, 81, 84, 86, 87, 88, 90, 92, 93], "explicitli": [2, 6, 8, 34, 61], "yourself": [2, 4, 33, 75], "altern": [2, 5, 8, 40, 44, 48, 49, 59, 72, 73, 77, 78, 80, 81, 82, 84, 85, 87, 89, 92], "same": [2, 3, 4, 5, 8, 10, 12, 14, 21, 25, 30, 33, 34, 35, 44, 48, 49, 51, 58, 59, 61, 65, 66, 69, 70, 71, 74, 75, 77, 78, 80, 81, 86, 87, 88, 89, 90, 91, 92], "effect": [2, 8, 22, 30, 34, 49, 58, 61, 77, 78, 80, 81, 87], "offer": [2, 4, 73, 74, 75, 78, 80, 82, 85, 92], "after": [2, 3, 4, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 44, 49, 61, 74, 78, 80, 81, 82, 84, 86, 87, 88, 89, 90, 92], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 40, 58, 61, 74, 91], "label_issues_df": [2, 61, 81], "similar": [2, 8, 29, 30, 34, 42, 44, 49, 53, 54, 56, 58, 61, 65, 69, 74, 75, 77, 78, 80, 81, 82, 86, 87, 90], "document": [2, 3, 4, 8, 12, 14, 29, 30, 33, 34, 35, 40, 43, 48, 50, 51, 53, 56, 57, 58, 61, 65, 66, 67, 69, 72, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "descript": [2, 4, 5, 8, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 44, 54, 61, 74, 75], "were": [2, 3, 4, 29, 34, 50, 56, 69, 73, 77, 80, 82, 84, 86, 88, 90, 91], "present": [2, 3, 4, 8, 10, 11, 17, 29, 44, 58, 66, 71, 77, 80, 81, 87], "actual": [2, 3, 4, 29, 49, 50, 59, 75, 80, 82, 93], "num_class": [2, 29, 33, 44, 48, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 91, 92], "uniqu": [2, 26, 44, 66, 74, 80, 85, 87], "given_label": [2, 4, 25, 29, 38, 61, 66, 70, 73, 74, 75, 77, 78, 81, 82, 89, 90, 92, 93], "normal": [2, 3, 21, 26, 35, 37, 40, 43, 44, 59, 80, 82, 87], "trick": [2, 80], "distribut": [2, 3, 4, 8, 21, 23, 29, 34, 35, 39, 49, 57, 58, 59, 71, 74, 75, 77, 78, 81, 87], "account": [2, 29, 49, 53, 58, 59, 78, 80, 82, 84, 85, 87, 89, 92], "word": [2, 3, 43, 69, 70, 80], "remov": [2, 8, 26, 29, 30, 34, 35, 61, 71, 78, 79, 80, 81, 87, 89, 91, 92], "so": [2, 3, 4, 5, 8, 12, 21, 29, 30, 33, 34, 35, 44, 49, 50, 56, 59, 61, 65, 69, 73, 74, 75, 78, 81, 82, 87, 90], "proportion": [2, 8, 35], "just": [2, 3, 4, 8, 11, 29, 31, 33, 44, 48, 59, 61, 63, 71, 72, 73, 75, 77, 78, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92], "procedur": 2, "get": [2, 3, 4, 6, 11, 26, 30, 31, 34, 35, 40, 43, 44, 49, 51, 53, 58, 59, 61, 62, 63, 71, 73, 78, 79, 80, 81, 82, 87, 88, 89, 91, 92], "detect": [2, 4, 5, 7, 11, 12, 14, 19, 23, 42, 52, 54, 55, 56, 57, 58, 59, 60, 61, 64, 68, 71, 74, 76, 81, 83, 85, 89, 90, 91, 92, 93], "arg": [2, 10, 19, 22, 26, 30, 31, 34, 40, 44, 59, 61], "kwarg": [2, 5, 8, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 40, 48, 61, 63, 65, 67, 80], "test": [2, 8, 21, 34, 40, 48, 61, 71, 74, 75, 77, 78, 81, 88, 89, 91, 92, 93], "expect": [2, 3, 30, 34, 35, 40, 49, 58, 59, 61, 80, 82, 84, 85, 86, 89, 91, 92, 93], "class_predict": 2, "evalu": [2, 8, 30, 31, 33, 34, 57, 61, 73, 74, 75, 80, 81, 82, 84, 88, 89, 90, 91, 92], "simpli": [2, 29, 59, 74, 75, 77, 78, 80, 82, 89, 90, 92, 93], "quantifi": [2, 4, 5, 8, 11, 35, 53, 58, 61, 71, 75, 77, 78, 81, 82, 86], "save_spac": [2, 8, 61], "potenti": [2, 8, 29, 35, 43, 51, 54, 57, 59, 61, 63, 65, 72, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 90, 91, 93], "cach": [2, 78, 87, 92], "panda": [2, 4, 5, 10, 16, 17, 19, 20, 21, 23, 25, 26, 29, 44, 45, 48, 49, 50, 72, 73, 74, 75, 77, 78, 79, 80, 82, 84, 89, 90, 91, 92], "unlik": [2, 8, 35, 37, 40, 48, 50, 51, 53, 69, 74, 84, 85, 87, 89], "both": [2, 4, 8, 14, 21, 29, 30, 34, 35, 44, 49, 51, 59, 63, 65, 70, 71, 74, 80, 81, 82, 84, 93], "mask": [2, 33, 35, 43, 44, 51, 54, 59, 61, 63, 65, 66, 71, 79, 80, 84, 86, 90, 93], "prefer": [2, 59, 67], "plan": 2, "subsequ": [2, 3, 30, 34, 78, 80, 82, 86, 92], "invok": [2, 30, 34, 82, 88], "scratch": [2, 61], "To": [2, 4, 5, 7, 8, 9, 11, 14, 21, 28, 30, 33, 34, 35, 48, 49, 51, 53, 57, 58, 59, 61, 62, 63, 65, 71, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "share": [2, 59, 61], "mostli": [2, 44, 56, 61], "longer": [2, 39, 43, 61, 72, 78, 80, 86, 92], "info": [2, 4, 5, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 61, 69, 74, 75, 79, 80, 93], "about": [2, 3, 4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 31, 33, 37, 49, 50, 53, 57, 61, 66, 69, 73, 74, 77, 78, 79, 80, 81, 82, 84, 87], "docstr": [2, 29, 30, 34, 44, 61, 79, 82], "unless": [2, 30, 34, 61, 80], "our": [2, 3, 8, 48, 49, 59, 61, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "is_label_issu": [2, 25, 61, 73, 74, 75, 77, 78, 81, 82, 89, 92], "entir": [2, 8, 21, 33, 35, 38, 50, 51, 56, 59, 61, 63, 65, 66, 71, 74, 75, 78, 80, 81, 82, 86, 87, 88, 90, 93], "accur": [2, 3, 4, 8, 14, 29, 33, 35, 49, 50, 51, 54, 57, 59, 61, 62, 63, 65, 66, 72, 75, 77, 78, 80, 81, 84, 89], "label_qu": [2, 49, 61, 82, 84, 89, 92], "measur": [2, 29, 49, 50, 61, 71, 79, 80, 82, 84, 85, 90, 91, 93], "qualiti": [2, 3, 4, 5, 8, 11, 25, 26, 29, 33, 35, 37, 40, 49, 50, 51, 53, 54, 56, 59, 61, 62, 65, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 83, 89, 91, 92], "lower": [2, 4, 5, 8, 11, 23, 33, 40, 49, 50, 53, 56, 57, 59, 61, 62, 65, 69, 73, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 92, 93], "eas": 2, "comparison": [2, 30, 34, 57, 82, 84, 89], "against": [2, 30, 34, 74, 77, 80, 84, 85], "predicted_label": [2, 4, 25, 61, 66, 70, 73, 74, 75, 77, 78, 81, 82, 89, 90, 92], "ad": [2, 30, 34, 75, 84, 89], "precis": [2, 51, 54, 57, 80, 82, 90, 93], "definit": [2, 5, 61, 77, 91], "accessor": [2, 61], "describ": [2, 8, 49, 58, 59, 61, 67, 69, 82, 84, 85, 86, 88, 93], "precomput": [2, 4, 38, 61, 79], "clear": [2, 61, 78, 89, 92], "save": [2, 4, 14, 30, 33, 34, 57, 61, 80, 86, 90, 93], "space": [2, 8, 58, 61, 77, 79, 81], "place": [2, 30, 34, 44, 61, 84, 91], "larg": [2, 33, 61, 77, 78, 80, 81, 87, 90, 93], "deploi": [2, 61, 77, 78, 80, 81], "care": [2, 8, 30, 34, 61, 78, 80, 82], "avail": [2, 4, 5, 10, 12, 27, 34, 61, 80, 82, 84, 86, 89], "cannot": [2, 4, 10, 12, 44, 88, 93], "anymor": 2, "classmethod": [2, 16, 17, 19, 20, 21, 22, 23, 25, 26, 34, 40, 61], "__init_subclass__": [2, 34, 61], "set_": [2, 34, 61], "_request": [2, 34, 61], "pep": [2, 34, 61], "487": [2, 34, 61], "look": [2, 4, 5, 14, 30, 34, 44, 61, 66, 74, 75, 77, 78, 80, 82, 84, 85, 86, 87, 90, 91, 93], "inform": [2, 4, 5, 8, 11, 14, 27, 30, 34, 44, 49, 50, 54, 57, 61, 66, 69, 70, 71, 73, 74, 77, 78, 82, 84, 87, 90, 93], "__metadata_request__": [2, 34, 61], "infer": [2, 34, 44, 61, 66, 70, 81, 84, 85, 89, 91, 92], "signatur": [2, 30, 34, 61], "accept": [2, 30, 34, 59, 61, 74, 75], "metadata": [2, 34, 61, 77, 78, 81, 93], "through": [2, 4, 5, 34, 61, 73, 75, 78, 79, 80, 84, 87, 89, 92], "develop": [2, 7, 34, 61, 80, 82, 93], "request": [2, 34, 61, 75, 78, 79, 85, 91, 92, 93], "those": [2, 3, 8, 33, 34, 35, 48, 49, 51, 57, 61, 65, 69, 70, 71, 73, 80, 81, 86, 90], "http": [2, 4, 5, 7, 8, 9, 28, 30, 31, 33, 34, 37, 44, 58, 61, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "www": [2, 34, 61, 87], "org": [2, 30, 31, 34, 44, 58, 61, 80, 82, 93], "dev": [2, 34, 61], "0487": [2, 34, 61], "get_metadata_rout": [2, 34, 61], "rout": [2, 34, 61], "pleas": [2, 30, 34, 48, 61, 71, 73, 74, 75, 78, 79, 80, 81, 82, 84, 85, 87, 89, 92, 93], "guid": [2, 5, 34, 61, 72, 81], "mechan": [2, 30, 34, 61], "metadatarequest": [2, 34, 61], "encapsul": [2, 14, 34, 56, 61], "get_param": [2, 34, 48, 61], "subobject": [2, 34, 61], "param": [2, 8, 30, 34, 48, 58, 61, 80], "name": [2, 4, 5, 8, 10, 11, 29, 30, 34, 39, 40, 44, 48, 49, 50, 57, 61, 66, 70, 73, 75, 78, 79, 80, 81, 82, 85, 90, 92, 93], "set_fit_request": [2, 34, 61], "union": [2, 3, 4, 10, 33, 34, 40, 44, 45, 51, 57, 61, 65, 69, 80], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 33, 34, 35, 38, 40, 43, 44, 48, 49, 50, 54, 56, 57, 59, 61, 66, 70, 73, 74, 80, 84, 85, 93], "unchang": [2, 30, 34, 61, 93], "relev": [2, 14, 21, 34, 61, 81], "enable_metadata_rout": [2, 34, 61], "set_config": [2, 34, 61], "meta": [2, 34, 61], "rais": [2, 4, 10, 11, 30, 34, 37, 40, 61, 80], "alia": [2, 30, 34, 61], "metadata_rout": [2, 34, 61], "retain": [2, 34, 44, 61], "chang": [2, 30, 33, 34, 37, 61, 69, 73, 74, 78, 80, 86, 87, 92, 93], "version": [2, 4, 5, 7, 8, 9, 13, 18, 24, 28, 30, 32, 34, 36, 37, 44, 47, 48, 59, 61, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92, 93], "sub": [2, 34, 56, 61], "pipelin": [2, 34, 61], "otherwis": [2, 8, 29, 30, 33, 34, 35, 41, 43, 44, 51, 58, 61, 63, 65, 66, 70, 78, 80, 92], "updat": [2, 11, 30, 33, 34, 61, 72, 74, 81], "set_param": [2, 34, 48, 61], "simpl": [2, 30, 34, 35, 49, 59, 61, 74, 75, 77, 78, 81, 84, 87, 89, 91, 92], "well": [2, 3, 8, 30, 34, 37, 38, 49, 51, 57, 59, 61, 66, 69, 70, 72, 74, 75, 77, 78, 80, 81, 82, 84, 86, 87], "nest": [2, 30, 34, 61, 67, 69, 70, 93], "latter": [2, 30, 34, 61, 87], "compon": [2, 34, 61], "__": [2, 34, 61], "set_score_request": [2, 61], "structur": [3, 58, 77, 91], "unobserv": 3, "less": [3, 4, 8, 26, 33, 40, 49, 58, 59, 63, 65, 69, 75, 77, 79, 80, 81, 82, 86, 93], "channel": [3, 73, 82], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 29, 38, 44, 50, 75, 79, 92], "inv": 3, "confident_joint": [3, 19, 29, 35, 44, 50, 51, 72, 80, 82], "un": 3, "under": [3, 8, 30, 34, 50, 57, 58, 75, 77, 78, 81, 82, 87], "joint": [3, 29, 35, 38, 44, 50, 51, 79], "num_label_issu": [3, 33, 35, 51, 66, 70, 72], "estimation_method": [3, 33], "off_diagon": 3, "multi_label": [3, 29, 35, 44, 45, 51, 85], "don": [3, 71, 75, 77, 78, 81, 82, 86], "statis": 3, "compute_confident_joint": [3, 29, 35, 44, 51, 82], "off": [3, 35, 44, 56, 81, 82, 86, 87], "j": [3, 4, 29, 30, 34, 35, 51, 54, 57, 58, 67, 69, 70, 74, 75, 82, 90, 93], "confident_learn": [3, 35, 51, 82], "off_diagonal_calibr": 3, "calibr": [3, 35, 44, 49, 84], "cj": [3, 38, 44], "axi": [3, 26, 38, 40, 63, 66, 73, 74, 75, 80, 81, 82, 84, 85, 87, 89, 90], "bincount": [3, 74, 75, 82, 84, 85], "alwai": [3, 8, 30, 34, 44, 73, 82, 89, 91, 92], "estimate_issu": 3, "over": [3, 8, 30, 33, 34, 56, 57, 63, 65, 75, 77, 79, 80, 81, 82, 87, 89, 91], "As": [3, 5, 71, 74, 75, 78, 82, 89, 93], "add": [3, 4, 5, 11, 30, 34, 48, 57, 73, 74, 75, 78, 80, 81, 82, 85, 92], "approach": [3, 29, 33, 35, 77, 82, 85, 87, 89, 91], "custom": [3, 5, 8, 9, 25, 30, 33, 34, 40, 43, 59, 75, 78, 82, 92], "know": [3, 74, 75, 77, 78, 80, 81, 82, 84], "cut": [3, 56, 71, 82], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 87, 93], "underestim": 3, "few": [3, 57, 71, 75, 80, 84, 85, 86, 87, 93], "4": [3, 4, 16, 17, 19, 20, 21, 23, 25, 26, 39, 40, 43, 53, 54, 56, 57, 59, 62, 69, 79, 80, 85, 90, 93], "detail": [3, 4, 12, 14, 29, 30, 34, 40, 44, 48, 49, 50, 51, 53, 54, 56, 57, 58, 65, 66, 67, 71, 72, 73, 85, 87, 93], "num_issu": [3, 5, 33, 73, 74, 75, 77, 78, 81, 82], "calibrate_confident_joint": 3, "up": [3, 8, 15, 21, 22, 25, 35, 40, 49, 79, 80, 86, 89, 92, 93], "p_": [3, 29, 35], "pair": [3, 4, 8, 29, 35, 82], "v": [3, 8, 33, 50, 51, 53, 59, 74, 75, 85, 87, 88], "rest": [3, 4, 5, 7, 8, 9, 28, 50, 51, 53, 61, 74, 75, 77, 78, 80, 81, 82, 84, 89, 91, 92], "fashion": [3, 4, 63, 91], "2x2": 3, "incorrectli": [3, 29, 50, 51, 54, 77, 93], "calibrated_cj": 3, "c": [3, 8, 43, 51, 59, 71, 73, 74, 75, 77, 78, 80, 82, 85, 87, 88, 89, 91], "whose": [3, 4, 8, 23, 30, 34, 38, 43, 49, 53, 56, 62, 65, 69, 70, 73, 74, 75, 77, 78, 80, 81, 82, 85, 86, 87, 90, 93], "truli": [3, 87, 90], "estimate_joint": [3, 29, 82], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 51, 57, 82, 86, 88, 90, 93], "return_indices_of_off_diagon": 3, "frequenc": [3, 21, 49, 50, 57, 66, 87], "done": [3, 8, 61, 74, 80, 82, 85, 87, 88], "overfit": [3, 8, 54, 57, 73, 74, 75, 77, 78, 81, 88, 91], "classifict": 3, "singl": [3, 4, 21, 29, 30, 34, 40, 41, 44, 49, 50, 56, 57, 58, 59, 69, 73, 74, 80, 82, 85, 86, 91], "baselin": [3, 30, 35, 87, 89, 92], "proxi": 3, "tupl": [3, 26, 30, 34, 38, 39, 41, 43, 44, 49, 51, 57, 65, 67, 69, 70, 73, 93], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 33, 38, 49, 63, 65, 71, 80, 81, 90, 92], "practic": [3, 75, 81, 82, 87, 89, 91, 92], "complet": [3, 73, 74, 75, 77, 78, 80, 81, 82, 86], "gist": 3, "cj_ish": 3, "guess": [3, 38, 82, 84], "8": [3, 4, 5, 6, 39, 40, 41, 43, 53, 67, 69, 73, 74, 75, 77, 78, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "parallel": [3, 35, 57, 67, 79], "again": [3, 48, 80, 87, 91], "simplifi": [3, 12], "understand": [3, 7, 29, 50, 57, 75, 82, 89, 90, 93], "100": [3, 30, 34, 59, 74, 75, 77, 79, 80, 81, 82, 85, 90, 91, 92, 93], "optim": [3, 30, 31, 34, 48, 81, 84], "speed": [3, 35, 79, 80, 89, 92], "dtype": [3, 20, 21, 26, 30, 34, 43, 44, 53, 69, 73, 86], "enumer": [3, 30, 34, 73, 74, 75, 81, 93], "s_label": 3, "confident_bin": 3, "6": [3, 4, 34, 40, 44, 69, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "num_confident_bin": 3, "argmax": [3, 35, 59, 63, 66, 73, 80, 82, 87, 90], "elif": 3, "estimate_lat": 3, "py_method": [3, 38], "cnt": [3, 38], "1d": [3, 4, 14, 33, 35, 40, 41, 44, 45, 53, 62, 73, 91], "eqn": [3, 38], "margin": [3, 35, 38, 40, 59], "marginal_p": [3, 38], "shorthand": [3, 11], "proport": [3, 8, 29, 50, 82, 88], "poorli": [3, 38, 91], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 82], "variabl": [3, 5, 12, 22, 44, 61, 62, 73, 74, 77, 82, 85, 89], "exact": [3, 38, 74, 75, 77, 81, 91], "within": [3, 4, 8, 13, 30, 31, 34, 36, 51, 56, 65, 67, 69, 74, 75, 80, 81, 86, 90], "percent": 3, "often": [3, 29, 38, 50, 80, 82, 88, 90], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 44, 45, 57, 73, 74, 77, 78, 80, 81, 86, 87, 92], "wai": [3, 4, 48, 71, 72, 73, 74, 75, 77, 78, 80, 82, 84, 85, 86, 88, 91, 92], "pro": 3, "con": 3, "pred_proba": [3, 88], "combin": [3, 29, 74, 79, 80, 81, 82, 88, 89], "becaus": [3, 38, 44, 56, 78, 80, 82, 84, 86], "littl": [3, 33, 79, 86, 93], "uniform": [3, 59, 79, 80, 82], "20": [3, 5, 70, 73, 75, 78, 79, 80, 81, 82, 90, 93], "Such": [3, 81, 87], "bound": [3, 20, 30, 34, 54, 56, 57, 86], "reason": [3, 19, 30, 34], "comment": [3, 43, 93], "end": [3, 4, 30, 34, 57, 81, 90, 93], "file": [3, 4, 10, 32, 33, 47, 57, 73, 74, 77, 78, 79, 80, 86, 87, 90, 91, 93], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 82], "handl": [3, 4, 5, 8, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 33, 34, 72, 74, 75, 77, 78, 81, 82, 90, 91, 93], "five": [3, 54, 57, 82, 86], "estimate_cv_predicted_prob": [3, 82], "estimate_noise_matric": 3, "get_confident_threshold": [3, 33], "amongst": [3, 8], "confident_threshold": [3, 8, 19, 33, 58], "unifi": 4, "audit": [4, 7, 10, 11, 14, 73, 76, 77, 78, 80, 81, 82, 86], "kind": [4, 5, 73, 74, 77, 78, 79, 81, 82], "addit": [4, 5, 7, 8, 9, 11, 27, 28, 30, 34, 40, 45, 49, 57, 67, 73, 74, 77, 78, 81, 82, 84, 87, 88, 91, 92], "depend": [4, 5, 7, 8, 9, 10, 11, 28, 32, 35, 37, 44, 47, 51, 58, 61, 62, 71], "instal": [4, 5, 7, 8, 9, 28, 30, 32, 33, 34, 35, 47, 48, 63, 65], "pip": [4, 5, 7, 9, 28, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "development": [4, 5, 7, 9, 28], "git": [4, 5, 7, 9, 28, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "github": [4, 5, 7, 9, 28, 30, 31, 44, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "com": [4, 5, 7, 9, 28, 30, 31, 33, 37, 44, 58, 71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "egg": [4, 5, 7, 9, 28, 71, 79], "label_nam": [4, 5, 6, 8, 10, 26, 71, 73, 74, 75, 77, 78, 80, 81, 82], "image_kei": [4, 81], "interfac": [4, 71, 80, 82], "librari": [4, 8, 34, 54, 57, 58, 71, 74, 78, 79, 80, 92], "goal": 4, "track": [4, 11, 12, 71, 74, 79, 80, 82], "intermedi": [4, 7, 75], "statist": [4, 8, 11, 19, 21, 29, 49, 50, 57, 75, 77, 78, 81, 82], "convert": [4, 10, 30, 34, 41, 45, 49, 56, 65, 69, 72, 73, 78, 79, 80, 81, 84, 85, 86, 92], "hug": [4, 10, 81], "face": [4, 10, 14, 79, 81, 85], "kei": [4, 5, 8, 10, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 34, 40, 49, 50, 56, 58, 74, 75, 78, 80, 81, 82, 84, 86], "string": [4, 8, 10, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 34, 44, 49, 50, 62, 66, 69, 70, 77, 78, 80, 84, 85, 92, 93], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 30, 34, 39, 44, 49, 50, 53, 54, 56, 57, 74, 75, 77, 78, 82, 84, 85, 86], "path": [4, 10, 30, 33, 34, 57, 73, 74, 80, 86], "local": [4, 10, 30, 31, 34, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "text": [4, 5, 8, 10, 16, 17, 19, 20, 21, 22, 23, 25, 26, 40, 58, 67, 69, 70, 71, 74, 75, 76, 79, 80, 82, 83, 84, 87], "txt": [4, 10, 93], "csv": [4, 10, 77, 78, 89, 91, 92], "json": [4, 10], "hub": [4, 10, 87], "regress": [4, 5, 10, 12, 14, 18, 25, 27, 74, 75, 78, 83, 84, 87, 92], "imag": [4, 7, 29, 34, 54, 56, 57, 58, 63, 65, 66, 71, 74, 75, 79, 80, 83, 84, 85, 86, 88, 90], "point": [4, 5, 8, 21, 30, 34, 74, 75, 77, 78, 80, 81, 82, 84], "field": [4, 8, 30, 34], "themselv": [4, 89, 91, 92], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 29, 43, 67, 69, 75, 81, 83, 90], "load_dataset": [4, 10, 81], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 17, 29, 50, 57, 66, 70, 71, 79, 82, 86, 90, 93], "empti": [4, 10, 38, 49, 75, 80, 85], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 71, 73, 74, 75, 77, 78, 80, 81, 82], "knn_graph": [4, 8, 14, 16, 21, 23, 26, 77], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 19, 20, 21, 23, 25, 26, 73, 74, 75, 77, 78, 80, 81, 82], "sort": [4, 14, 33, 35, 40, 42, 49, 51, 54, 56, 57, 59, 65, 67, 69, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 89, 90, 91, 92, 93], "common": [4, 11, 14, 75, 76, 79, 80, 82, 85, 86, 90], "real": [4, 14, 71, 74, 75, 80, 82, 84, 85, 89, 90], "world": [4, 14, 71, 74, 75, 80, 82, 84, 89, 90], "interact": [4, 14, 78, 80], "embed": [4, 8, 14, 58, 71, 73, 74, 75, 77, 78, 82, 92], "thereof": [4, 14], "insight": [4, 14, 57, 84], "act": [4, 8, 56, 74], "issuefind": [4, 14, 27], "logic": [4, 12, 33, 35, 63, 65, 90], "best": [4, 14, 39, 49, 59, 74, 75, 77, 78, 80, 81, 84, 85, 87, 89, 91, 92, 93], "2d": [4, 14, 33, 40, 41, 43, 44, 49, 73, 85, 91], "num_exampl": [4, 14, 16, 17, 19, 20, 21, 23, 25, 26, 27, 29, 50, 73, 74, 75, 77, 78, 81, 82], "represent": [4, 8, 14, 30, 34, 41, 51, 71, 73, 74, 75, 78, 80, 81, 82, 87, 92], "num_featur": [4, 14, 30, 34, 48], "distanc": [4, 8, 14, 21, 23, 26, 42, 56, 58, 77, 87], "nearest": [4, 8, 14, 20, 21, 23, 42, 58, 75, 78, 87], "neighbor": [4, 8, 14, 20, 21, 23, 42, 58, 74, 75, 77, 78, 80, 81, 87], "graph": [4, 8, 11, 14, 21, 26], "squar": [4, 44, 61, 79, 89], "csr": 4, "evenli": 4, "omit": [4, 56, 57, 81, 86], "itself": [4, 30, 34, 86], "three": [4, 8, 29, 49, 50, 61, 66, 73, 74, 75, 77, 79, 82, 84, 88, 89, 90, 91, 93], "indptr": 4, "wise": 4, "start": [4, 5, 8, 30, 31, 34, 71, 77, 85, 93], "th": [4, 39, 43, 44, 49, 51, 54, 56, 57, 58, 67, 69, 70, 78, 85, 86, 93], "ascend": [4, 29, 42, 50, 81, 82], "segment": [4, 63, 65, 66, 83], "reflect": [4, 77, 78, 84, 86, 87, 89, 91, 92], "maintain": 4, "posit": [4, 30, 34, 44, 57, 79, 87], "nearestneighbor": [4, 8, 58, 77, 87], "kneighbors_graph": [4, 77], "illustr": 4, "todens": 4, "second": [4, 40, 42, 44, 57, 59, 74, 80, 82, 93], "duplic": [4, 7, 18, 19, 30, 34, 71, 74, 82], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27, 30, 34, 40, 48], "neither": [4, 8, 12, 86], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 49, 80, 84, 93], "unspecifi": [4, 14, 35, 51], "interest": [4, 14, 19, 66, 70, 78, 82, 90, 91, 92, 93], "constructor": [4, 8, 14, 20, 25], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 25, 26, 27], "respons": [4, 14, 19, 61, 62, 79, 89, 93], "random_st": [4, 73, 74, 75, 81, 82, 85, 87, 91], "lab": [4, 6, 16, 17, 19, 20, 21, 22, 23, 25, 26, 33, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85], "comprehens": [4, 71, 81], "nbr": 4, "n_neighbor": [4, 8, 58], "metric": [4, 8, 16, 21, 26, 44, 48, 57, 58, 73, 77, 78, 81, 82, 89, 91, 92], "euclidean": [4, 8, 56, 58, 77], "mode": [4, 30, 33, 34, 87], "4x4": 4, "float64": [4, 21, 30, 34, 69], "compress": [4, 8, 44, 63, 65], "toarrai": 4, "NOT": [4, 33, 78], "23606798": 4, "41421356": 4, "configur": [4, 14, 40, 75], "suppos": [4, 8, 54, 87, 89, 91, 92], "who": [4, 56, 77, 82, 91, 93], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "clean_learning_kwarg": [4, 8, 20, 25], "labelissuemanag": [4, 8, 20], "prune_method": [4, 72], "prune_by_noise_r": [4, 35, 51, 82], "report": [4, 5, 9, 13, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 70, 71, 73, 74, 75, 77, 78, 82, 93], "include_descript": [4, 16, 17, 19, 20, 21, 23, 25, 26, 27], "show_summary_scor": [4, 27], "summari": [4, 5, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 48, 50, 55, 64, 65, 67, 68, 69, 72, 73, 74, 75, 77, 78, 79, 81, 82, 86, 90, 93], "show": [4, 21, 30, 34, 39, 44, 57, 66, 70, 75, 77, 78, 79, 80, 81, 82, 84, 87, 89, 90, 91, 93], "top": [4, 29, 33, 35, 44, 51, 54, 57, 59, 66, 70, 71, 73, 74, 75, 77, 78, 79, 80, 82, 86, 87, 89, 92, 93], "suffer": [4, 8, 11, 19, 51, 59, 70, 93], "onc": [4, 19, 29, 30, 34, 74, 80, 82, 85, 86, 91], "familiar": 4, "usag": [4, 33, 48], "found": [4, 5, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 30, 34, 44, 71, 73, 74, 75, 77, 78, 80, 81, 87, 89, 91, 92, 93], "issue_summari": [4, 8, 11, 74], "overal": [4, 5, 8, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 40, 49, 50, 53, 56, 57, 61, 65, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 86, 93], "sever": [4, 5, 8, 10, 11, 19, 30, 33, 34, 35, 53, 56, 58, 59, 65, 69, 71, 73, 74, 75, 77, 78, 79, 80, 82, 86, 87, 91, 92, 93], "dataissu": [4, 11, 14, 27], "outlier": [4, 7, 12, 18, 19, 26, 36, 59, 71, 74, 75, 82, 83], "someth": [4, 5, 30, 34, 59], "123": [4, 74, 75], "456": [4, 73, 78, 91, 92], "nearest_neighbor": 4, "7": [4, 40, 41, 48, 67, 69, 73, 74, 75, 77, 78, 79, 80, 84, 85, 86, 87, 89, 90, 91, 92, 93], "9": [4, 16, 17, 19, 20, 21, 23, 25, 26, 40, 41, 53, 67, 69, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "distance_to_nearest_neighbor": [4, 74, 75, 77, 78, 81, 82], "789": 4, "get_issu": [4, 8, 11, 73, 75, 77, 78, 80, 81], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74, 75], "focu": [4, 11, 78, 90, 93], "full": [4, 8, 11, 33, 57, 81, 93], "summar": [4, 11, 16, 17, 19, 20, 21, 22, 23, 25, 26, 29, 50, 66, 70, 71, 90], "valueerror": [4, 10, 11, 37, 40, 80], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 66, 75, 77, 78, 81, 82, 86], "lie": [4, 8, 42, 58, 59, 73, 74, 75, 77, 78, 81, 82, 92], "directli": [4, 12, 14, 27, 33, 48, 49, 75, 78, 85, 86, 89, 92], "compar": [4, 49, 58, 69, 74, 75, 77, 82], "get_issue_summari": [4, 11, 75], "get_info": [4, 11, 75, 78], "yet": [4, 15, 18, 22, 79, 84], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 22, 30, 34, 74], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 73, 74, 81], "load": [4, 10, 33, 57, 79, 80, 81, 82, 86, 87, 90, 93], "futur": [4, 8, 19, 30, 34, 49, 71, 74, 78], "overwrit": [4, 74], "separ": [4, 29, 40, 53, 74, 75, 80, 81, 86, 88], "static": 4, "rememb": [4, 78, 80, 82], "part": [4, 8, 30, 34, 35, 54, 56, 57, 73, 74, 79, 90, 93], "ident": [4, 8, 19, 44, 78], "walk": 5, "alongsid": [5, 30, 34, 74, 80], "pre": [5, 6, 8, 30, 34, 74, 75, 81, 90, 93], "runtim": [5, 30, 33, 34, 61, 63, 65, 73, 80, 81], "issue_manager_factori": [5, 12, 74], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 40, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "thing": [5, 34, 82, 89, 92], "next": [5, 49, 71, 73, 77, 78, 80, 84, 86, 89, 91, 92, 93], "dummi": 5, "randint": [5, 26, 40, 74, 75, 80], "mark": [5, 8, 72, 86, 87, 89], "regard": [5, 75, 82], "rand": [5, 40, 74, 75], "is_": [5, 8, 74], "_issu": [5, 8, 74], "issue_score_kei": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "whole": [5, 21, 30, 34, 75], "make_summari": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26, 74], "popul": [5, 75, 78], "verbosity_level": [5, 16, 17, 19, 20, 21, 22, 23, 25, 26], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 33, 66, 70, 80, 85], "intermediate_arg": 5, "min": [5, 40, 56, 69, 74, 80, 87], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 20, 21, 22, 23, 25, 26, 74], "instanti": [5, 14, 33, 48, 58, 73, 75, 77, 92], "477762": 5, "286455": 5, "term": [5, 8, 38, 44, 57, 73, 74, 75, 77, 78, 81, 82], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 16, 23, 73, 74, 75, 77, 78, 79, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "003042": 5, "058117": 5, "11": [5, 48, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "121908": 5, "15": [5, 42, 61, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "169312": 5, "17": [5, 73, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 74, 75, 79, 82], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 26], "group": [6, 7, 21, 26, 79, 86, 93], "dbscan": [6, 8, 26, 80], "hdbscan": [6, 80], "etc": [6, 8, 19, 30, 34, 38, 48, 49, 67, 71, 74, 75, 77, 78, 80, 81, 82], "sensit": [6, 8], "ep": [6, 26, 57], "radiu": 6, "min_sampl": [6, 26], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 71, 73, 80, 81, 84, 91, 92], "kmean": [6, 80], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 26, 80], "cluster_id": [6, 8, 26, 80], "labels_": 6, "underperforming_group": [6, 8, 18, 80], "search": [7, 8, 17, 21, 22, 43, 61, 80, 88], "nondefault": 7, "Near": [7, 80], "iid": [7, 21, 75, 77, 81, 82], "imbal": [7, 18, 53, 58, 59, 75], "null": [7, 18, 75, 78, 81, 82], "togeth": [7, 8, 38, 74, 75, 77, 78, 81, 82, 89, 92, 93], "built": [7, 40], "own": [7, 30, 32, 34, 47, 53, 54, 57, 63, 67, 73, 75, 77, 78, 80, 81, 84, 85, 89, 90, 91, 92, 93], "prerequisit": 7, "basic": [7, 34, 48, 77, 78, 87], "page": [8, 75, 80, 82], "variou": [8, 11, 25, 32, 45, 47, 71, 74, 75, 77, 78, 79, 82, 84, 86, 91], "sai": [8, 30, 34, 85, 90], "why": [8, 78], "matter": [8, 29, 50], "_score": 8, "flag": [8, 19, 21, 35, 40, 50, 51, 54, 61, 71, 73, 74, 75, 77, 78, 79, 81, 82, 86, 87, 89, 90, 92], "badli": [8, 56, 93], "code": [8, 30, 34, 38, 44, 48, 71, 72, 73, 74, 75, 77, 78, 79, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "issue_scor": 8, "outlier_scor": [8, 23, 74, 75, 77, 78, 81, 82, 87], "atyp": [8, 58, 74, 75, 77, 78, 81, 82, 87], "datapoint": [8, 26, 35, 40, 44, 59, 62, 71, 73, 74, 75, 77, 78, 80, 88, 89, 91, 92], "is_issu": [8, 19], "is_outlier_issu": [8, 74, 75, 77, 78, 81, 82], "annot": [8, 29, 39, 49, 50, 51, 53, 54, 56, 57, 66, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 86, 90], "transform": [8, 40, 42, 44, 58, 59, 75, 78, 81, 87, 91, 92, 93], "dissimilar": [8, 77, 78], "preced": 8, "cosin": [8, 58, 87], "incorrect": [8, 56, 59, 62, 73, 74, 75, 77, 78, 81, 82, 86, 89, 91], "due": [8, 33, 35, 59, 63, 65, 73, 74, 75, 77, 78, 81, 82], "appear": [8, 29, 39, 50, 51, 54, 62, 75, 77, 78, 81, 89, 90], "likelihood": [8, 33, 35, 51, 56, 58, 59, 63, 67], "now": [8, 33, 72, 73, 75, 84, 86, 87, 89, 91, 92, 93], "u": [8, 73, 74, 77, 80, 81, 82, 84, 85, 88, 89, 90, 91, 92, 93], "token": [8, 43, 65, 66, 67, 68, 69, 70, 80, 82, 83], "calcul": [8, 21, 33, 40, 49, 53, 54, 56, 57, 58, 61, 65, 79, 81], "hamper": [8, 79, 81], "analyt": [8, 71, 80, 84], "lead": [8, 56, 59, 81, 86], "draw": [8, 74, 75], "conclus": [8, 78], "try": [8, 33, 35, 48, 49, 63, 65, 71, 75, 77, 78, 80, 81, 82, 90], "veri": [8, 29, 50, 54, 56, 74, 75, 77, 78, 80, 81, 82, 84, 87, 89, 92], "rare": [8, 35, 57, 74, 75, 77, 78, 80, 81, 82], "anomal": [8, 59, 74, 75, 77, 78, 81, 82], "articl": [8, 33, 80], "ai": [8, 71, 73, 74, 75, 77, 78, 79, 80, 81, 83, 84, 85, 87, 89, 91, 92, 93], "blog": 8, "unexpect": [8, 30, 34, 78], "consequ": 8, "inspect": [8, 73, 75, 81, 82, 86, 89, 92], "neg": [8, 56, 57, 74, 75, 79], "affect": [8, 30, 34, 63, 69, 78, 80], "extrem": [8, 74, 75, 77, 78, 80, 81, 82], "rel": [8, 29, 49, 50, 58, 74, 75, 77, 78, 81, 82, 87], "record": [8, 30, 34, 73, 77, 89], "abbrevi": 8, "misspel": 8, "typo": [8, 70], "resolut": 8, "video": [8, 79], "audio": [8, 74, 75, 80, 83], "minor": [8, 43], "variat": 8, "translat": 8, "d": [8, 42, 77, 78, 82, 85, 91, 93], "constant": [8, 26, 61], "median": [8, 25], "question": [8, 19, 71, 82], "nearli": [8, 19, 75, 77, 78, 81], "awar": [8, 72, 82], "presenc": [8, 82], "signific": [8, 75, 77, 78, 81, 82], "violat": [8, 75, 77, 78, 81, 82], "assumpt": [8, 75, 77, 78, 81, 82], "changepoint": [8, 75, 77, 78, 81, 82], "shift": [8, 75, 77, 78, 81, 82], "drift": [8, 75, 77, 81, 82], "autocorrel": [8, 75, 77, 78, 81, 82], "almost": [8, 75, 77, 78, 81, 82], "adjac": [8, 75, 77, 78, 81, 82], "tend": [8, 29, 38, 75, 77, 78, 81, 82, 90, 93], "sequenti": [8, 30, 34, 48, 81], "gap": 8, "b": [8, 16, 17, 19, 20, 21, 23, 25, 26, 29, 43, 44, 69, 77, 78, 79, 82, 88, 91, 93], "x1": [8, 54, 57, 86], "x2": [8, 54, 57, 86], "10th": 8, "100th": 8, "90": [8, 69, 77, 82, 88, 90, 91], "similarli": [8, 30, 34, 74, 77, 80, 81, 86], "math": [8, 81], "behind": [8, 58, 82], "fundament": 8, "proper": [8, 44, 49, 54, 57, 78, 81, 84, 86, 91], "closer": [8, 56, 86], "scenario": [8, 59, 74, 75], "underli": [8, 58, 67, 69, 93], "stem": [8, 58, 87], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 85, 87], "partit": [8, 88], "ahead": 8, "good": [8, 30, 34, 48, 50, 56, 59, 63, 65, 66, 71, 77, 78, 81], "fix": [8, 49, 78, 82, 89, 92], "problem": [8, 33, 40, 66, 71, 74, 75, 78, 80, 81], "deploy": [8, 82, 89, 91, 92], "overlook": [8, 56, 86], "fact": 8, "thu": [8, 29, 34, 50, 73, 77, 78, 82, 88, 91, 93], "diagnos": [8, 75, 80], "rarest": [8, 75, 77, 78, 81, 82], "q": [8, 86], "fall": [8, 56, 65, 69, 82, 87], "subpar": 8, "special": [8, 43], "techniqu": 8, "smote": 8, "asymmetr": [8, 29], "properli": [8, 33, 39, 44, 45, 63, 80, 85, 87, 89, 90], "too": [8, 35, 40, 58, 75, 80, 81, 86], "dark": [8, 90], "bright": [8, 93], "blurri": [8, 81], "abnorm": [8, 57, 81], "cluster": [8, 26], "slice": [8, 42], "poor": 8, "subpopul": 8, "lowest": [8, 49, 57, 75, 80, 81, 84, 85, 86, 90], "get_self_confidence_for_each_label": [8, 40, 59], "power": [8, 77, 78, 79, 81, 82, 93], "r": [8, 33, 61, 74, 75, 89, 90], "tabular": [8, 71, 74, 75, 76, 80, 83, 84], "categor": [8, 58, 74, 75, 76, 80, 89, 91], "encod": [8, 41, 57, 63, 66, 77, 78, 80, 89, 90, 91, 92], "miss": [8, 22, 30, 34, 44, 54, 56, 75, 77, 78, 80, 81, 82, 86, 89], "pattern": 8, "exert": [8, 75], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 16, 74, 75, 77, 78, 80, 81, 82], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 21, 75, 77, 78, 81, 82], "non_iid_kwarg": 8, "class_imbal": [8, 17, 75, 77, 78, 81, 82], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 20, 25], "health_summari": [8, 20, 29, 71, 79], "health_summary_kwarg": 8, "tandem": [8, 79], "view": [8, 30, 34, 35, 65, 67, 69, 71, 73, 74, 75, 77, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93], "ood_kwarg": 8, "outofdistribut": [8, 23, 58, 87], "outsid": 8, "knn": [8, 11, 21, 26, 58, 77, 87], "outlierissuemanag": [8, 12, 23, 74], "nearduplicateissuemanag": [8, 12, 16], "noniidissuemanag": [8, 12, 21], "num_permut": [8, 21], "permut": [8, 21], "significance_threshold": [8, 21], "signic": 8, "noniid": [8, 18], "classimbalanceissuemanag": [8, 17], "underperforminggroupissuemanag": [8, 26], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 26], "filter_cluster_id": [8, 26], "clustering_kwarg": [8, 26], "faq": [8, 71, 75, 77, 78, 81, 83], "nullissuemanag": [8, 22], "codeblock": 8, "demonstr": [8, 33, 74, 75, 78, 80, 81, 82, 84, 85, 86, 89, 90], "howev": [8, 30, 34, 44, 73, 77, 78, 81, 84, 88, 90, 91, 92], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 74, 79, 81, 84, 86, 90], "fewer": [8, 35, 44, 86], "vice": [8, 50], "versa": [8, 50], "light": [8, 79, 81, 86, 90], "29": [8, 79, 81, 84, 85, 86, 90, 93], "low_inform": [8, 81], "odd_aspect_ratio": [8, 81], "35": [8, 74, 79, 84, 85, 86, 90], "odd_siz": [8, 81], "10": [8, 16, 20, 21, 26, 30, 31, 57, 58, 59, 70, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93], "doc": [8, 30, 34, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "data_issu": [9, 13, 14, 27, 74], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 48, 59, 74, 75, 81, 84], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 27, 81, 84, 89], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 81], "serv": [11, 14, 84], "central": [11, 93], "repositori": 11, "strategi": [11, 40, 80], "being": [11, 29, 30, 34, 35, 40, 43, 44, 59, 77, 80, 82, 89, 90, 91], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 19], "avoid": [11, 30, 33, 34, 35, 42, 44, 51, 54, 57, 61, 63, 65, 74, 75, 80], "recomput": [11, 92], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 20, 29, 50, 71], "get_data_statist": 11, "concret": 12, "subclass": [12, 30, 34, 58, 74], "my_issu": 12, "stabl": [13, 18, 24, 32, 36, 44, 47, 58, 72], "unregist": 13, "instati": 14, "public": [14, 82, 86, 90, 93], "creation": [14, 34], "execut": [14, 30, 34, 74, 80, 86], "coordin": [14, 54, 56, 57, 86, 93], "behavior": [14, 29, 30, 34, 57], "At": [14, 57, 80], "associ": [14, 30, 34, 57, 84], "get_available_issue_typ": 14, "isn": [15, 22], "direct": [15, 22, 30, 34], "_": [16, 19, 20, 21, 22, 25, 26, 40, 43, 44, 73, 74, 79, 81, 82, 85, 91], "classvar": [16, 17, 19, 20, 21, 22, 23, 25, 26], "short": [16, 17, 19, 20, 21, 22, 23, 25, 26, 43, 44], "item": [16, 17, 19, 20, 21, 22, 23, 25, 26, 44, 74, 75, 80, 81, 82, 84, 85], "some_info_kei": [16, 17, 19, 20, 21, 22, 23, 25, 26], "additional_info_kei": [16, 17, 19, 20, 21, 22, 23, 25, 26], "near_duplicate_set": [16, 74, 75, 77, 78, 80, 81, 82], "occurr": [16, 17, 19, 21, 22, 23, 26, 43], "collect_info": [16, 17, 19, 20, 21, 22, 23, 25, 26], "median_nn_dist": 16, "near_duplicate_scor": [16, 74, 75, 77, 78, 80, 81, 82], "info_to_omit": [16, 17, 19, 20, 21, 23, 25, 26], "compos": [16, 17, 19, 20, 21, 23, 25, 26, 30, 34, 78, 87, 92], "is_x_issu": [16, 17, 19, 20, 21, 23, 25, 26], "x_score": [16, 17, 19, 20, 21, 23, 25, 26], "val_a": [16, 17, 19, 20, 21, 23, 25, 26], "val_b1": [16, 17, 19, 20, 21, 23, 25, 26], "val_b2": [16, 17, 19, 20, 21, 23, 25, 26], "report_str": [16, 17, 19, 20, 21, 22, 23, 25, 26, 27], "class_imbalance_scor": [17, 75, 77, 78, 81, 82], "bleed": [18, 24, 32], "edg": [18, 24, 32, 56, 71, 82, 93], "sharp": [18, 24, 32], "abc": 19, "believ": [19, 90], "priori": [19, 82], "global": 19, "anoth": [19, 29, 33, 43, 56, 59, 77, 78, 80, 82, 84, 87, 92], "abstract": 19, "applic": [20, 49, 80, 82, 84, 85, 93], "typevar": [20, 30, 34, 56, 57], "_scalartype_co": 20, "covari": [20, 61, 89], "get_health_summari": 20, "summary_dict": 20, "label_scor": [20, 25, 73, 74, 75, 77, 78, 81, 82], "simplified_kolmogorov_smirnov_test": 21, "neighbor_histogram": 21, "non_neighbor_histogram": 21, "kolmogorov": 21, "smirnov": 21, "largest": [21, 33, 40, 59, 63, 65, 90], "empir": [21, 39, 49], "cumul": 21, "ecdf": 21, "histogram": [21, 77, 89], "absolut": [21, 25], "25": [21, 30, 40, 42, 75, 79, 81, 82, 84, 85, 86, 90, 93], "dimension": [21, 44, 73, 82, 87], "trial": 21, "non_iid_scor": [21, 75, 77, 78, 81, 82], "null_track": 22, "extend": [22, 41, 81, 87, 93], "superclass": 22, "arbitrari": [22, 29, 65, 69, 74, 87, 89], "prompt": 22, "address": [22, 74, 75, 78, 80, 92], "enabl": [22, 34], "null_scor": [22, 75, 78, 81, 82], "default_threshold": 23, "37037": 23, "q3_avg_dist": 23, "iqr_avg_dist": 23, "median_outlier_scor": 23, "ood": [23, 58, 59, 74, 75, 78, 81, 82, 87], "regressionlabelissuemanag": 25, "multipli": 25, "find_issues_with_predict": 25, "find_issues_with_featur": 25, "deleg": 25, "confus": [26, 29, 30, 34, 35, 44, 57, 92, 93], "50": [26, 34, 80, 81, 82, 84, 86, 87, 90], "keepdim": [26, 80], "outlier_cluster_label": 26, "no_underperforming_cluster_id": 26, "signifi": 26, "absenc": 26, "set_knn_graph": 26, "find_issues_kwarg": 26, "perform_clust": 26, "npt": 26, "int_": 26, "id": [26, 49, 74, 80, 81, 84], "int64": [26, 73, 84], "unique_cluster_id": 26, "get_worst_clust": 26, "_description_": 26, "performed_clust": 26, "worst_cluster_id": 26, "underperforming_group_scor": 26, "exclud": [27, 66, 70, 74, 93], "get_report": 27, "overview": [29, 73, 75, 77, 78, 81, 84, 86, 87, 89, 91, 92, 93], "modifi": [29, 30, 33, 34, 44, 80, 82], "help": [29, 30, 34, 57, 71, 72, 73, 74, 77, 78, 79, 80, 81, 84, 85, 89, 90, 91, 92, 93], "rank_classes_by_label_qu": [29, 75], "merg": [29, 43, 71, 79, 80, 93], "find_overlapping_class": [29, 80, 82], "problemat": [29, 50, 66, 70, 73, 86, 93], "unnorm": [29, 50, 82], "abov": [29, 30, 33, 34, 44, 49, 56, 57, 59, 65, 69, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89, 90, 91, 92, 93], "model_select": [29, 40, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 87, 89, 91, 92], "cross_val_predict": [29, 34, 73, 74, 75, 77, 78, 80, 82, 84, 88, 89, 91, 92], "get_data_labels_from_dataset": 29, "yourfavoritemodel": [29, 82], "cv": [29, 40, 73, 74, 75, 77, 82, 84, 91], "df": [29, 44, 70, 73, 80], "overall_label_qu": [29, 50], "col": 29, "prob": [29, 43, 82, 88], "divid": [29, 50, 59], "label_nois": [29, 50], "human": [29, 79, 90, 93], "clearli": [29, 59, 81, 86, 90], "num": [29, 50, 79, 82], "overlap": [29, 71, 79, 80, 82], "ontolog": 29, "publish": [29, 93], "therefor": [29, 59], "vehicl": [29, 79], "truck": [29, 79, 87, 90], "intuit": [29, 50], "car": [29, 79, 86, 90], "frequent": [29, 49, 77, 80, 89], "characterist": 29, "l": [29, 30, 34, 54, 56, 57], "class1": 29, "class2": 29, "relationship": 29, "match": [29, 30, 34, 35, 49, 50, 59, 74, 75, 79, 81, 86, 88, 90], "dog": [29, 44, 50, 52, 66, 79, 80, 87, 88, 93], "cat": [29, 44, 50, 52, 79, 80, 87, 88], "captur": [29, 73, 86, 87, 90], "co": [29, 30, 31], "noisy_label": [29, 74, 75, 85], "overlapping_class": 29, "descend": [29, 30, 34, 40, 50, 57], "overall_label_health_scor": [29, 50, 82], "suggest": [29, 49, 50, 56, 78, 80, 81, 89, 92], "half": [29, 30, 34, 50, 79, 93], "health_scor": [29, 50], "classes_by_label_qu": [29, 75], "cnn": [30, 34, 81], "cifar": [30, 31, 79, 87], "teach": [30, 31], "bhanml": 30, "blob": 30, "master": [30, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "call_bn": 30, "bn": 30, "input_channel": 30, "n_output": 30, "dropout_r": 30, "top_bn": 30, "architectur": [30, 34], "shown": [30, 57, 74, 80, 84, 87, 88, 90, 93], "forward": [30, 31, 34, 81, 84], "overridden": [30, 34], "although": [30, 34, 58, 77, 91], "recip": [30, 34], "afterward": [30, 34], "sinc": [30, 34, 37, 45, 50, 57, 65, 69, 80, 84, 85, 86, 88, 93], "former": [30, 34], "hook": [30, 34, 79], "silent": [30, 33, 34], "t_destin": [30, 34], "__call__": [30, 34, 40], "add_modul": [30, 34], "child": [30, 34], "fn": [30, 34, 57], "recurs": [30, 34, 40], "submodul": [30, 34], "children": [30, 34, 93], "nn": [30, 31, 34, 81], "init": [30, 34, 82], "no_grad": [30, 34, 81, 87], "init_weight": [30, 34], "linear": [30, 34, 78, 81, 92], "fill_": [30, 34], "net": [30, 34, 73, 79, 81], "in_featur": [30, 34], "out_featur": [30, 34], "bia": [30, 34, 81], "tensor": [30, 31, 34, 73, 81, 87], "requires_grad": [30, 34], "bfloat16": [30, 34], "cast": [30, 34, 73], "buffer": [30, 34], "datatyp": [30, 34], "member": [30, 34, 74, 75], "xdoctest": [30, 34], "undefin": [30, 34], "var": [30, 34], "buf": [30, 34], "20l": [30, 34], "1l": [30, 34], "5l": [30, 34], "immedi": [30, 34, 87], "cpu": [30, 34, 35, 73, 81], "move": [30, 34, 40, 72, 79], "cuda": [30, 34, 73, 81], "devic": [30, 34, 73, 81], "gpu": [30, 34, 73, 78, 92], "live": [30, 34], "copi": [30, 34, 61, 73, 74, 75, 77, 80, 85, 88, 89, 91], "doubl": [30, 34], "dump_patch": [30, 34], "eval": [30, 34, 81, 85, 87], "dropout": [30, 34], "batchnorm": [30, 34], "grad": [30, 34], "extra_repr": [30, 34], "line": [30, 34, 71, 74, 79, 84, 87, 93], "get_buff": [30, 34], "target": [30, 31, 34, 61, 62, 87, 89], "throw": [30, 34], "get_submodul": [30, 34], "explan": [30, 34], "fulli": [30, 34, 48, 80], "qualifi": [30, 34], "referenc": [30, 34], "attributeerror": [30, 34], "invalid": [30, 34, 78], "resolv": [30, 34, 93], "get_extra_st": [30, 34], "state_dict": [30, 34], "set_extra_st": [30, 34], "build": [30, 34, 81, 90], "pickleabl": [30, 34], "serial": [30, 34], "backward": [30, 34, 81], "break": [30, 34, 81], "pickl": [30, 34, 86], "get_paramet": [30, 34], "let": [30, 34, 58, 59, 73, 75, 77, 78, 80, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "net_b": [30, 34], "net_c": [30, 34], "conv": [30, 34], "conv2d": [30, 34, 81], "16": [30, 34, 40, 65, 73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 92, 93], "33": [30, 34, 79, 81, 86, 90], "kernel_s": [30, 34], "stride": [30, 34], "200": [30, 34, 59, 79, 86, 93], "diagram": [30, 34, 88], "degre": [30, 34, 89], "queri": [30, 34, 75, 80, 81], "named_modul": [30, 34], "o": [30, 34, 42, 43, 73, 74, 75, 79, 80, 82, 85, 86, 93], "transit": [30, 34], "ipu": [30, 34], "load_state_dict": [30, 34], "strict": [30, 34, 40], "persist": [30, 34], "strictli": [30, 34], "namedtupl": [30, 34], "missing_kei": [30, 34], "unexpected_kei": [30, 34], "runtimeerror": [30, 34], "idx": [30, 34, 44, 45, 57, 74, 80, 81, 82, 84, 86, 87], "named_buff": [30, 34], "prefix": [30, 34, 73, 93], "prepend": [30, 34], "running_var": [30, 34], "named_children": [30, 34], "conv4": [30, 34], "conv5": [30, 34], "memo": [30, 34], "remove_dupl": [30, 34], "named_paramet": [30, 34], "register_backward_hook": [30, 34], "deprec": [30, 34, 37], "favor": [30, 34], "register_full_backward_hook": [30, 34], "removablehandl": [30, 34], "register_buff": [30, 34], "running_mean": [30, 34], "register_forward_hook": [30, 34], "won": [30, 34, 74, 75, 80, 85], "inplac": [30, 34, 84], "register_forward_pre_hook": [30, 34], "gradient": [30, 34, 77, 81, 89], "respect": [30, 34, 57, 82], "grad_input": [30, 34], "grad_output": [30, 34], "technic": [30, 34], "caller": [30, 34], "register_load_state_dict_post_hook": [30, 34], "post": [30, 34], "incompatible_kei": [30, 34], "modif": [30, 34], "thrown": [30, 34], "clearn": [30, 34], "register_modul": [30, 34], "register_paramet": [30, 34], "requires_grad_": [30, 34], "autograd": [30, 34], "freez": [30, 34, 73, 78, 92], "finetun": [30, 34], "gan": [30, 34], "share_memori": [30, 34], "share_memory_": [30, 34], "destin": [30, 34], "keep_var": [30, 34], "shallow": [30, 34], "releas": [30, 34, 72, 80, 87], "design": [30, 34], "ordereddict": [30, 34], "detach": [30, 34, 81], "non_block": [30, 34], "memory_format": [30, 34], "channels_last": [30, 34], "Its": [30, 34, 40, 50, 56], "complex": [30, 34], "integr": [30, 34, 71], "asynchron": [30, 34], "host": [30, 34], "pin": [30, 34, 78, 79, 92], "desir": [30, 34, 43, 57], "4d": [30, 34], "ignore_w": [30, 34], "determinist": [30, 34, 73], "1913": [30, 34], "3420": [30, 34], "5113": [30, 34], "2325": [30, 34], "env": [30, 34], "torch_doctest_cuda1": [30, 34], "gpu1": [30, 34], "1914": [30, 34], "5112": [30, 34], "2324": [30, 34], "float16": [30, 34], "cdoubl": [30, 34], "3741": [30, 34], "2382": [30, 34], "5593": [30, 34], "4443": [30, 34], "complex128": [30, 34], "6122": [30, 34], "1150": [30, 34], "to_empti": [30, 34], "storag": [30, 34], "dst_type": [30, 34], "xpu": [30, 34], "zero_grad": [30, 34, 81], "set_to_non": [30, 34], "context": [30, 34, 86], "noisili": [31, 82], "han": 31, "2018": 31, "cifar_cnn": [31, 32], "loss_coteach": 31, "y_1": 31, "y_2": 31, "forget_r": 31, "class_weight": 31, "logit": [31, 48, 81], "decim": [31, 44], "quickli": [31, 73, 77, 78, 80, 81, 85, 87, 90, 91, 93], "forget": [31, 40, 93], "rate_schedul": 31, "epoch": [31, 34, 80, 81], "initialize_lr_schedul": 31, "lr": [31, 34], "001": [31, 59, 80], "250": [31, 74, 75, 82, 86], "epoch_decay_start": 31, "80": [31, 77, 81, 85, 89, 90, 91], "schedul": 31, "adjust": [31, 35, 53, 58, 59, 71, 82], "beta": 31, "adam": 31, "adjust_learning_r": 31, "alpha_plan": 31, "beta1_plan": 31, "forget_rate_schedul": 31, "num_gradu": 31, "expon": 31, "tell": [31, 78, 81, 82, 92], "train_load": [31, 34], "model1": [31, 82], "optimizer1": 31, "model2": [31, 82], "optimizer2": 31, "dataload": [31, 81, 87], "parser": 31, "parse_arg": 31, "num_iter_per_epoch": 31, "print_freq": 31, "topk": 31, "top1": 31, "top5": 31, "test_load": 31, "offici": [32, 47, 93], "wish": [32, 47, 87, 90, 93], "mnist_pytorch": 32, "coteach": [32, 72], "mini": [33, 63, 65, 80], "With": [33, 78, 82, 84, 89, 90, 92, 93], "approxim": [33, 58, 84], "low_self_confid": [33, 35, 51], "self_confid": [33, 35, 40, 51, 53, 59, 67, 69, 80, 82, 85, 91, 92], "conveni": [33, 73, 78, 92], "script": 33, "labelinspector": [33, 80], "adj_confident_thresholds_shar": 33, "labels_shar": 33, "pred_probs_shar": 33, "labels_fil": [33, 80], "pred_probs_fil": [33, 80], "batch_siz": [33, 34, 63, 65, 80, 81, 87, 90], "quality_score_kwarg": 33, "num_issue_kwarg": 33, "return_mask": 33, "variant": [33, 49, 90], "read": [33, 37, 75, 80, 82, 87, 93], "zarr": [33, 80], "memmap": [33, 90], "pythonspe": 33, "mmap": [33, 80], "hdf5": 33, "further": [33, 50, 51, 53, 56, 57, 65, 66, 73, 80], "yourfil": 33, "npy": [33, 79, 80, 90], "mmap_mod": [33, 90], "tip": [33, 35, 48, 80], "save_arrai": 33, "your_arrai": 33, "disk": [33, 79, 80], "npz": [33, 93], "maxim": [33, 49, 63, 65, 90], "multiprocess": [33, 35, 51, 63, 65, 80, 81, 90], "linux": [33, 63, 65], "physic": [33, 35, 63, 65, 86, 90], "psutil": [33, 35, 63, 65, 90], "labels_arrai": [33, 45], "predprob": 33, "pred_probs_arrai": 33, "back": [33, 57, 74, 80, 86, 87], "store_result": 33, "becom": [33, 87], "verifi": [33, 80, 84, 87], "long": [33, 49, 58, 84], "enough": [33, 44, 80], "chunk": [33, 88], "ram": [33, 79], "faster": [33, 58, 61, 63, 65, 80, 82], "end_index": 33, "labels_batch": 33, "pred_probs_batch": 33, "update_confident_threshold": 33, "batch_result": 33, "score_label_qu": 33, "indices_of_examples_with_issu": [33, 80], "shortcut": 33, "encount": [33, 35, 63], "1000": [33, 73, 78, 80, 81, 87], "aggreg": [33, 40, 49, 53, 56, 59, 69, 80, 82, 84], "get_num_issu": 33, "fetch": [33, 73, 75], "seen": [33, 80, 87, 93], "far": [33, 49], "get_quality_scor": 33, "label_quality_scor": [33, 53, 56, 59, 62, 82, 86, 89], "method1": 33, "method2": 33, "normalized_margin": [33, 35, 40, 51, 53, 59, 67, 69], "low_normalized_margin": [33, 35, 51], "issue_indic": [33, 56, 81], "update_num_issu": 33, "split_arr": 33, "arr": [33, 80], "chunksiz": 33, "convnet": 34, "bespok": [34, 48], "get_mnist_dataset": 34, "loader": [34, 81], "download": [34, 73, 80, 87], "mnist": [34, 71, 73, 79], "get_sklearn_digits_dataset": 34, "handwritten": 34, "digit": [34, 73, 79], "last": [34, 40, 54, 57, 74, 75, 80, 84, 93], "sklearn_digits_test_s": 34, "hard": [34, 79, 87], "simplenet": 34, "64": [34, 77, 81, 82, 86, 90, 91, 93], "log_interv": 34, "01": [34, 59, 61, 73, 81, 82, 85, 86, 89, 90, 93], "momentum": 34, "no_cuda": 34, "test_batch_s": [34, 81], "templat": 34, "flexibli": 34, "among": [34, 49, 82], "test_set": 34, "Be": 34, "overrid": 34, "train_idx": [34, 44, 87], "train_label": [34, 87, 92], "scikit": [34, 44, 58, 71, 73, 74, 75, 77, 78, 80, 83, 89, 92], "set_predict_proba_request": 34, "set_predict_request": 34, "encourag": [35, 51, 59, 62], "multilabel_classif": [35, 50, 51, 53, 59, 80, 85], "pred_probs_by_class": 35, "prune_count_matrix_col": 35, "rank_by_kwarg": [35, 51, 59, 82], "num_to_remove_per_class": [35, 51], "bad": [35, 51, 56, 59, 78, 80, 92], "seem": [35, 82, 85], "aren": 35, "confidence_weighted_entropi": [35, 40, 51, 53, 59, 67, 69], "label_issues_idx": [35, 59], "entropi": [35, 37, 39, 40, 58, 59], "prune_by_class": [35, 51, 82], "predicted_neq_given": [35, 51, 82], "prune_counts_matrix": 35, "smallest": [35, 59], "unus": 35, "number_of_mislabeled_examples_in_class_k": 35, "delet": [35, 71, 80, 92], "thread": [35, 51], "window": [35, 79], "shorter": [35, 54], "find_predicted_neq_given": 35, "find_label_issues_using_argmax_confusion_matrix": 35, "latent_algebra": [36, 72], "label_quality_util": 36, "multilabel_util": [36, 85], "multilabel_scor": [36, 53], "token_classification_util": [36, 93], "get_normalized_entropi": 37, "min_allowed_prob": 37, "wikipedia": 37, "activ": [37, 39, 49, 71, 84], "towardsdatasci": 37, "cheatsheet": 37, "ec57bc067c0b": 37, "clip": [37, 44, 73], "behav": 37, "unnecessari": [37, 80], "slightli": [37, 91, 92], "interv": [37, 40, 87], "herein": 38, "inexact": 38, "cours": 38, "propag": 38, "throughout": [38, 44, 61, 73, 84, 90, 93], "compute_ps_py_inv_noise_matrix": 38, "compute_py_inv_noise_matrix": 38, "compute_inv_noise_matrix": 38, "easili": [38, 72, 73, 75, 77, 78, 82, 84, 85, 87, 88, 89, 90, 91, 92], "increas": [38, 56, 58, 59, 73, 74, 80, 84, 85, 93], "dot": [38, 69, 80], "compute_noise_matrix_from_invers": 38, "compute_pi": 38, "true_labels_class_count": 38, "compute_pyx": 38, "pyx": 38, "multiannot": 39, "assert_valid_inputs_multiannot": 39, "labels_multiannot": [39, 49], "ensembl": [39, 40, 49, 59, 77, 80, 85, 87, 89, 91], "allow_single_label": 39, "annotator_id": 39, "assert_valid_pred_prob": 39, "pred_probs_unlabel": [39, 49], "format_multiannotator_label": [39, 49, 84], "lexicograph": [39, 44], "formatted_label": [39, 44], "old": [39, 44, 72, 79], "check_consensus_label_class": 39, "consensus_label": [39, 49, 84], "consensus_method": [39, 49], "consensu": [39, 49, 71, 83, 93], "establish": [39, 89, 92], "compute_soft_cross_entropi": 39, "soft": [39, 79], "find_best_temp_scal": 39, "coarse_search_rang": [39, 61, 80], "fine_search_s": [39, 61, 80], "temperatur": [39, 40, 56, 65, 69], "scale": [39, 42, 79, 80, 87, 90, 91], "factor": [39, 40, 63, 65], "minim": [39, 56, 87], "temp_scale_pred_prob": 39, "temp": 39, "sharpen": [39, 79], "smoothen": 39, "classlabelscor": 40, "enum": 40, "get_normalized_margin_for_each_label": [40, 59], "get_confidence_weighted_entropy_for_each_label": [40, 59], "75": [40, 74, 75, 79, 84, 85, 86, 89, 90, 93], "from_str": 40, "scorer": 40, "exponential_moving_averag": [40, 53], "alpha": [40, 53, 56, 74, 75, 82, 85, 89], "exponenti": 40, "ema": 40, "s_1": 40, "s_k": 40, "ema_k": 40, "accord": [40, 51, 77, 78, 82, 93], "formula": [40, 42], "_t": 40, "cdot": 40, "s_t": 40, "qquad": 40, "leq": 40, "_1": 40, "give": [40, 59, 82, 84, 90], "recent": [40, 93], "success": 40, "previou": [40, 80, 81, 86], "discount": 40, "s_ema": 40, "175": [40, 82, 86], "softmin": [40, 53, 56, 65, 69], "underflow": 40, "nan": [40, 49, 77, 84, 89, 91], "possible_method": 40, "aggregated_scor": 40, "multilabelscor": 40, "base_scor": 40, "base_scorer_kwarg": 40, "aggregator_kwarg": [40, 53], "n_sampl": 40, "n_label": 40, "binari": [40, 44, 51, 53, 82, 93], "worst": [40, 84], "class_label_quality_scor": 40, "get_class_label_quality_scor": 40, "42": [40, 79, 81, 86, 90, 93], "452": [40, 78], "new_scor": 40, "575": 40, "get_label_quality_scores_per_class": [40, 53], "ml_scorer": 40, "multilabel_pi": 40, "binar": [40, 41], "get_cross_validated_multilabel_pred_prob": 40, "reformat": [40, 73], "wider": 40, "splitter": 40, "kfold": [40, 81], "multiclass": [40, 44, 49, 85], "onevsrestclassifi": [40, 85], "randomforestclassifi": [40, 82, 85], "n_split": [40, 75, 81, 85], "stack_compl": 41, "pred_prob_slic": 41, "get_onehot_num_class": 41, "onehot": 41, "multilabel": [41, 85], "int2onehot": [41, 85], "hot": [41, 51, 57, 63, 66, 77, 79, 80, 89, 90, 91], "onehot2int": [41, 85], "onehot_matrix": 41, "transform_distances_to_scor": 42, "exp": [42, 58, 59, 74], "dt": 42, "right": [42, 54, 57, 78, 85, 86, 87, 92], "num_neighbor": 42, "ood_features_scor": [42, 58, 87], "95122942": 42, "83945702": 42, "token_classif": [43, 67, 69, 70, 80], "get_sent": [43, 93], "sentenc": [43, 67, 69, 70, 78, 92], "readabl": 43, "filter_sent": [43, 93], "lambda": [43, 73, 74, 80, 84], "long_sent": 43, "headlin": 43, "process_token": 43, "charact": [43, 44], "s1": 43, "s2": 43, "processed_token": 43, "rule": [43, 79], "alecnlcb": 43, "entiti": [43, 71, 80, 93], "mapped_ent": 43, "unique_ident": 43, "loc": [43, 74, 75, 81, 93], "merge_prob": 43, "probs_merg": 43, "55": [43, 79, 86, 89, 90], "0125": [43, 69], "0375": 43, "075": 43, "025": 43, "color_sent": 43, "color": [43, 66, 74, 75, 77, 82, 85, 87, 89, 90], "red": [43, 57, 74, 75, 79, 82, 85, 86, 87, 90], "colored_sent": 43, "termcolor": 43, "31msentenc": 43, "0m": 43, "ancillari": 44, "remove_noise_from_class": 44, "class_without_nois": 44, "any_other_class": 44, "choos": [44, 59, 77, 80, 82, 89, 91], "tradition": 44, "clip_noise_r": 44, "clip_valu": 44, "new_sum": 44, "preserv": 44, "value_count": [44, 80], "fill": 44, "wherea": [44, 51, 88], "come": [44, 74, 75, 80, 81, 90], "major": [44, 49, 72, 81, 87], "versu": [44, 82], "value_counts_fill_missing_class": 44, "get_missing_class": 44, "round_preserving_sum": 44, "obviou": 44, "cgdeboer": 44, "iteround": 44, "round_preserving_row_tot": 44, "reach": 44, "estimate_pu_f1": 44, "prob_s_eq_1": 44, "claesen": 44, "f1": [44, 57, 78, 82], "confusion_matrix": 44, "BE": 44, "print_square_matrix": 44, "left_nam": 44, "top_nam": 44, "titl": [44, 74, 75, 82, 85, 87], "short_titl": 44, "round_plac": 44, "pretti": [44, 82], "print_noise_matrix": [44, 82], "print_inverse_noise_matrix": 44, "print_joint_matrix": [44, 82], "joint_matrix": 44, "compress_int_arrai": 44, "num_possible_valu": 44, "train_val_split": 44, "holdout_idx": 44, "subset_x_i": 44, "extract": [44, 58, 73, 78, 84, 87, 90, 92], "subset_label": 44, "subset_data": 44, "extract_indices_tf": 44, "allow_shuffl": 44, "turn": [44, 71, 86], "unshuffle_tensorflow_dataset": 44, "shuffledataset": 44, "histori": 44, "pre_x": 44, "buffer_s": 44, "is_torch_dataset": 44, "is_tensorflow_dataset": 44, "csr_vstack": 44, "csr_matric": 44, "append": [44, 73, 79, 80, 81, 82, 84, 85, 87, 93], "bottom": [44, 54, 57, 86], "vstack": [44, 79, 80, 81, 82, 84, 85], "append_extra_datapoint": 44, "to_data": 44, "from_data": 44, "taken": 44, "One": [44, 58, 80], "get_num_class": 44, "label_matrix": 44, "canon": 44, "num_unique_class": 44, "get_unique_class": 44, "format_label": 44, "smart_display_datafram": 44, "displai": [44, 57, 66, 70, 73, 78, 82, 92, 93], "jupyt": [44, 73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "notebook": [44, 49, 73, 75, 79, 80, 82, 84, 85, 86, 90, 93], "consol": 44, "force_two_dimens": 44, "html": [44, 58, 77, 80, 82], "assert_valid_input": 45, "allow_missing_class": 45, "allow_one_class": 45, "assert_valid_class_label": 45, "assert_nonempty_input": 45, "assert_indexing_work": 45, "length_x": 45, "labels_to_arrai": 45, "labellik": 45, "keraswrappermodel": [48, 71], "keraswrappersequenti": 48, "tf": [48, 73], "legaci": 48, "lack": 48, "keraswrapp": 48, "huggingface_keras_imdb": 48, "unit": [48, 93], "model_kwarg": [48, 61], "compile_kwarg": 48, "sparsecategoricalcrossentropi": 48, "layer": [48, 73, 78, 87, 92], "dens": 48, "my_keras_model": 48, "from_logit": 48, "compil": 48, "declar": 48, "apply_softmax": 48, "analysi": 49, "analyz": [49, 71, 82, 84, 85], "get_label_quality_multiannot": [49, 84], "vote": 49, "crowdsourc": [49, 71, 84], "dawid": [49, 84], "skene": [49, 84], "analog": [49, 79, 84], "chosen": [49, 59, 80, 84], "crowdlab": [49, 84], "unlabel": [49, 77, 78, 81, 84, 87, 90], "decid": [49, 78, 79, 84, 89, 92, 93], "get_active_learning_scor": [49, 84], "activelab": [49, 84], "priorit": [49, 56, 86, 90, 93], "showcas": 49, "main": 49, "best_qual": 49, "quality_method": 49, "calibrate_prob": 49, "return_detailed_qu": 49, "return_annotator_stat": 49, "return_weight": 49, "label_quality_score_kwarg": 49, "necessarili": [49, 57, 78, 82], "did": [49, 50, 73, 77, 82, 84, 89, 91, 92], "majority_vot": 49, "ti": 49, "broken": [49, 57, 79], "highest": [49, 57, 74, 81, 88], "0th": 49, "consensus_quality_scor": [49, 84], "annotator_agr": [49, 84], "reman": 49, "1st": 49, "2nd": [49, 63], "3rd": 49, "consensus_label_suffix": 49, "consensus_quality_score_suffix": 49, "suffix": 49, "emsembl": 49, "weigh": [49, 79], "agreement": [49, 84], "agre": 49, "prevent": [49, 80], "overconfid": [49, 88], "wrong": [49, 54, 56, 72, 74, 75, 78, 80, 82, 86, 92], "detailed_label_qu": [49, 84], "annotator_stat": [49, 84], "model_weight": 49, "annotator_weight": 49, "warn": [49, 74, 75], "labels_info": 49, "num_annot": [49, 84], "deriv": [49, 84], "quality_annotator_1": 49, "quality_annotator_2": 49, "quality_annotator_m": 49, "annotator_qu": [49, 84], "num_examples_label": [49, 84], "agreement_with_consensu": [49, 84], "worst_class": [49, 84], "trustworthi": [49, 84, 89], "get_label_quality_multiannotator_ensembl": 49, "weigtht": 49, "budget": 49, "retrain": [49, 89, 92], "active_learning_scor": 49, "improv": [49, 75, 79, 80, 81, 82, 89, 90, 91, 92], "active_learning_scores_unlabel": 49, "get_active_learning_scores_ensembl": 49, "henc": [49, 73, 74, 84], "get_majority_vote_label": [49, 84], "event": 49, "lastli": [49, 77], "convert_long_to_wide_dataset": 49, "labels_multiannotator_long": 49, "wide": [49, 73, 91, 92], "suitabl": [49, 77, 91], "labels_multiannotator_wid": 49, "common_multilabel_issu": 50, "mutual": [50, 85], "exclus": [50, 85], "rank_classes_by_multilabel_qu": 50, "overall_multilabel_health_scor": 50, "multilabel_health_summari": 50, "classes_by_multilabel_qu": 50, "inner": [51, 65], "find_multilabel_issues_per_class": 51, "per_class_label_issu": 51, "label_issues_list": 51, "labels_list": 51, "pred_probs_list": [51, 59, 81, 82], "anim": [52, 87], "rat": 52, "predat": 52, "pet": 52, "reptil": 52, "manner": [53, 84, 89, 91, 92], "box": [54, 56, 57, 79, 86], "object_detect": [54, 56, 57, 86], "return_indices_ranked_by_scor": [54, 86], "overlapping_label_check": [54, 56], "suboptim": [54, 56], "locat": [54, 56, 86, 90, 93], "bbox": [54, 57, 86], "image_nam": [54, 57], "y1": [54, 57, 86], "y2": [54, 57, 86], "later": [54, 57, 58, 92, 93], "mmdetect": [54, 57, 86], "corner": [54, 57, 86], "swap": [54, 56, 66, 70], "penal": [54, 56], "concern": [54, 56, 71, 75], "aggregation_weight": 56, "imperfect": [56, 80], "chose": [56, 84, 86], "imperfectli": [56, 86], "dirti": [56, 59, 62, 89], "subtyp": 56, "badloc": 56, "nonneg": 56, "issues_from_scor": [56, 65, 66, 69, 70, 86, 90, 93], "compute_overlooked_box_scor": 56, "high_probability_threshold": 56, "auxiliary_input": [56, 57], "vari": [56, 75], "iou": [56, 57], "heavili": 56, "auxiliarytypesdict": 56, "pred_label": [56, 92], "pred_label_prob": 56, "pred_bbox": 56, "lab_label": 56, "lab_bbox": 56, "similarity_matrix": 56, "min_possible_similar": 56, "scores_overlook": 56, "compute_badloc_box_scor": 56, "low_probability_threshold": 56, "scores_badloc": 56, "compute_swap_box_scor": 56, "accident": [56, 77, 78, 80, 92], "scores_swap": 56, "pool_box_scores_per_imag": 56, "box_scor": 56, "image_scor": [56, 65, 90], "object_counts_per_imag": 57, "discov": [57, 75, 93], "auxiliari": [57, 87, 90], "_get_valid_inputs_for_compute_scor": 57, "object_count": 57, "bounding_box_size_distribut": 57, "down": 57, "bbox_siz": 57, "class_label_distribut": 57, "class_distribut": 57, "get_sorted_bbox_count_idx": 57, "plot": [57, 74, 75, 82, 85, 87, 89, 90], "sorted_idx": [57, 87], "plot_class_size_distribut": 57, "class_to_show": 57, "hidden": [57, 87], "max_class_to_show": 57, "plot_class_distribut": 57, "visual": [57, 74, 75, 81, 89, 91, 93], "prediction_threshold": 57, "overlai": [57, 86], "figsiz": [57, 74, 75, 81, 82, 85, 87], "save_path": [57, 86], "blue": [57, 79, 82, 86], "overlaid": 57, "side": [57, 79, 86], "figur": [57, 82, 85, 87, 89], "extens": [57, 82, 84], "png": [57, 86], "pdf": [57, 58], "svg": 57, "matplotlib": [57, 74, 75, 81, 82, 85, 86, 87, 89], "get_average_per_class_confusion_matrix": 57, "num_proc": [57, 81], "intersect": [57, 80], "tp": 57, "fp": 57, "ground": [57, 79, 82, 84, 89], "truth": [57, 82, 84, 89], "strength": 57, "bias": 57, "avg_metr": 57, "distionari": 57, "95": [57, 67, 69, 75, 77, 79, 82, 89, 90], "calculate_per_class_metr": 57, "per_class_metr": 57, "Of": 58, "li": 58, "smaller": [58, 85, 86], "find_top_issu": [58, 59, 87], "reli": [58, 73, 74, 75, 78, 86, 87, 92], "dist_metr": 58, "dim": [58, 81, 90], "subtract": [58, 59], "renorm": [58, 59, 80], "least_confid": 58, "sum_": 58, "log": [58, 59, 72], "softmax": [58, 65, 69, 81], "literatur": 58, "gen": 58, "liu": 58, "lochman": 58, "zach": 58, "openaccess": 58, "thecvf": 58, "content": [58, 73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "cvpr2023": 58, "liu_gen_pushing_the_limits_of_softmax": 58, "based_out": 58, "distribution_detection_cvpr_2023_pap": 58, "fit_scor": [58, 87], "ood_predictions_scor": 58, "pretrain": [58, 73, 78, 87, 92], "adjust_confident_threshold": 58, "probabilist": [58, 73, 74, 75, 77, 78, 87, 88, 91], "order_label_issu": [59, 72], "whichev": [59, 88], "argsort": [59, 78, 81, 82, 87, 89, 92], "max_": 59, "get_label_quality_ensemble_scor": [59, 80, 82], "weight_ensemble_members_bi": 59, "custom_weight": 59, "log_loss_search_t_valu": 59, "0001": [59, 79], "scheme": 59, "log_loss_search": 59, "log_loss": [59, 78], "1e0": 59, "1e1": 59, "1e2": 59, "2e2": 59, "quality_scor": [59, 87], "forth": 59, "top_issue_indic": 59, "rank_bi": [59, 72], "weird": [59, 70], "minu": 59, "prob_label": 59, "max_prob_not_label": 59, "idea": 59, "AND": [59, 78], "corrupt": [61, 89], "linearregress": [61, 80, 89], "y_with_nois": 61, "n_boot": [61, 80], "include_aleatoric_uncertainti": [61, 80], "sole": [61, 74, 84, 87, 91], "larger": [61, 63, 65, 78, 79, 80, 81], "bootstrap": [61, 80, 89], "resampl": [61, 73, 80], "epistem": [61, 80, 87, 89], "aleator": [61, 80, 89], "model_final_kwarg": 61, "coars": 61, "thorough": [61, 80], "fine": [61, 73, 78, 87, 92], "grain": 61, "grid": 61, "get_epistemic_uncertainti": 61, "varianc": [61, 82], "epistemic_uncertainti": 61, "get_aleatoric_uncertainti": 61, "residu": [61, 62, 80], "deviat": [61, 89], "ie": 61, "aleatoric_uncertainti": 61, "outr": 62, "contin": 62, "raw": [62, 71, 72, 75, 79, 81, 84, 86, 87], "aka": [62, 73, 82, 93], "00323821": 62, "33692597": 62, "00191686": 62, "semant": [63, 65, 66, 83], "pixel": [63, 65, 66, 87, 90], "h": [63, 65, 66, 90], "height": [63, 65, 66, 90], "w": [63, 65, 66, 90], "width": [63, 65, 66, 90], "labels_one_hot": [63, 66, 90], "stream": [63, 87, 93], "downsampl": [63, 65, 90], "shrink": [63, 65], "divis": [63, 65, 74], "segmant": [65, 66], "num_pixel_issu": [65, 90], "product": [65, 80, 81], "pixel_scor": [65, 90], "display_issu": [65, 66, 67, 69, 70, 90, 93], "highlight": [66, 70, 74, 75, 77, 90], "enter": 66, "legend": [66, 74, 75, 85, 86, 89, 90], "colormap": 66, "background": 66, "person": [66, 80, 86, 90, 93], "common_label_issu": [66, 70, 90, 93], "ambigu": [66, 70, 73, 78, 79, 82, 92, 93], "systemat": [66, 70, 84], "misunderstood": [66, 70], "issues_df": [66, 81], "filter_by_class": [66, 90], "class_index": 66, "issues_subset": [66, 70], "token_score_method": 69, "sentence_score_method": 69, "sentence_score_kwarg": 69, "compris": [69, 70], "token_scor": [69, 93], "converg": 69, "toward": 69, "_softmin_sentence_scor": 69, "sentence_scor": [69, 93], "token_info": 69, "70": [69, 77, 89, 90], "02": [69, 74, 75, 81, 82, 86, 89, 90], "03": [69, 79, 82, 86, 90, 93], "04": [69, 81, 86, 89, 90], "08": [69, 78, 82, 86, 90, 93], "commonli": [70, 72, 74, 75, 85, 93], "filter_by_token": [70, 93], "But": [70, 78, 82, 93], "restrict": [70, 80], "reliabl": [71, 73, 80, 84, 90, 91], "thousand": 71, "imagenet": [71, 79], "popular": [71, 84, 86], "centric": [71, 77, 78, 81, 83], "capabl": 71, "minut": [71, 73, 77, 78, 79, 84, 85, 86, 89, 90, 91, 92, 93], "conda": 71, "feature_embed": [71, 87], "Then": [71, 80, 81, 89, 91, 92], "your_dataset": [71, 73, 74, 75, 77, 78, 80, 81], "column_name_of_label": [71, 73, 74, 75, 77, 78, 81], "plagu": [71, 75], "untrain": 71, "\u30c4": 71, "label_issues_info": [71, 75], "sklearn_compatible_model": 71, "framework": [71, 85, 86], "complianc": 71, "tag": [71, 85, 93], "sequenc": 71, "recognit": [71, 73, 80, 93], "train_data": [71, 87, 89, 91, 92], "gotten": 71, "test_data": [71, 82, 85, 87, 89, 91, 92], "deal": [71, 75], "tutori": [71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "feel": [71, 73, 75, 80], "free": [71, 73, 75, 77, 78, 80, 81, 82], "ask": [71, 80], "slack": [71, 80], "project": [71, 89], "welcom": 71, "commun": [71, 80], "guidelin": [71, 86], "piec": 71, "studio": [71, 75, 77, 78, 80, 81], "platform": [71, 77, 78, 80, 81], "automl": [71, 80], "foundat": 71, "smart": [71, 77, 78, 80, 81], "edit": [71, 80], "easier": [71, 82], "unreli": [71, 73, 77, 78, 91], "older": 72, "outlin": 72, "substitut": 72, "v2": [72, 77, 91], "get_noise_indic": 72, "psx": 72, "sorted_index_method": 72, "order_label_error": 72, "label_errors_bool": 72, "latent_estim": 72, "num_label_error": 72, "learningwithnoisylabel": 72, "neatli": 72, "organ": [72, 77, 79, 91, 93], "reorgan": 72, "baseline_method": 72, "incorpor": [72, 82], "research": [72, 82], "polyplex": 72, "terminologi": 72, "label_error": 72, "quickstart": [73, 74, 75, 77, 78, 79, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "spoken": 73, "500": [73, 87, 93], "english": [73, 79], "pronunci": 73, "wav": 73, "huggingfac": [73, 74, 75, 81], "voxceleb": 73, "speech": [73, 93], "your_pred_prob": [73, 74, 75, 77, 78], "tensorflow_io": 73, "26": [73, 74, 79, 81, 82, 84, 86, 90], "huggingface_hub": 73, "12": [73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 86, 87, 89, 90, 91, 92, 93], "branch": [73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 91, 92], "reproduc": [73, 77, 82, 84], "command": 73, "wget": [73, 86, 90, 93], "navig": 73, "link": [73, 79, 86], "browser": 73, "jakobovski": 73, "archiv": [73, 93], "v1": 73, "tar": [73, 87], "gz": [73, 87], "mkdir": [73, 93], "spoken_digit": 73, "xf": 73, "6_nicolas_32": 73, "data_path": 73, "listdir": 73, "nondeterminist": 73, "file_nam": 73, "endswith": 73, "file_path": 73, "join": [73, 80, 81], "39": [73, 74, 78, 79, 80, 81, 86, 89, 90, 92, 93], "7_george_26": 73, "0_nicolas_24": 73, "0_nicolas_6": 73, "listen": 73, "display_exampl": 73, "click": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "expand": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "pulldown": [73, 74, 75, 79, 81, 82, 84, 85, 87, 89, 93], "colab": [73, 74, 75, 79, 80, 81, 82, 84, 85, 87, 89, 93], "tfio": 73, "pathlib": 73, "ipython": 73, "load_wav_16k_mono": 73, "filenam": 73, "khz": 73, "file_cont": 73, "io": [73, 79], "read_fil": 73, "sample_r": 73, "decode_wav": 73, "desired_channel": 73, "squeez": 73, "rate_in": 73, "rate_out": 73, "16000": 73, "wav_file_nam": 73, "audio_r": 73, "wav_file_exampl": 73, "plai": [73, 79, 80], "button": 73, "wav_file_name_exampl": 73, "7_jackson_43": 73, "hear": 73, "extractor": 73, "encoderclassifi": 73, "spkrec": 73, "xvect": 73, "feature_extractor": 73, "from_hparam": 73, "run_opt": 73, "uncom": 73, "wav_audio_file_path": 73, "head": [73, 75, 77, 78, 79, 81, 82, 84, 89, 91, 92], "torchaudio": 73, "extract_audio_embed": 73, "emb": [73, 81], "signal": 73, "encode_batch": 73, "embeddings_list": [73, 81], "embeddings_arrai": 73, "512": [73, 81], "14": [73, 74, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "196315": 73, "3194594": 73, "478977": 73, "2890828": 73, "8170278": 73, "892647": 73, "24": [73, 79, 81, 82, 84, 86, 90], "898054": 73, "256194": 73, "559642": 73, "559715": 73, "620667": 73, "285246": 73, "21": [73, 74, 79, 80, 82, 86, 90, 93], "709623": 73, "5033712": 73, "913803": 73, "8198366": 73, "1831512": 73, "208761": 73, "08426": 73, "3210406": 73, "005453": 73, "2161605": 73, "478239": 73, "682179": 73, "0538025": 73, "242471": 73, "0914207": 73, "7833488": 73, "039538": 73, "23": [73, 79, 81, 82, 86, 90], "56918": 73, "19": [73, 78, 79, 80, 81, 82, 87, 89, 90, 92], "761095": 73, "1258287": 73, "753235": 73, "3508894": 73, "598273": 73, "237122": 73, "2500": 73, "leverag": [73, 78, 80, 82, 84, 92], "tune": [73, 78, 79, 87, 92], "computation": [73, 78, 92], "intens": [73, 78, 92], "held": [73, 77, 78, 79, 86, 87, 88, 91], "straightforward": [73, 77, 91], "benefit": [73, 88, 90, 91], "tol": 73, "num_crossval_fold": [73, 77, 84, 91], "decreas": [73, 80], "never": [73, 82, 85, 87, 88], "accuracy_scor": [73, 78, 82, 91, 92], "cv_accuraci": 73, "9772": 73, "probabilit": [73, 92], "9980": 73, "176": [73, 79, 82, 85], "006488": 73, "2318": 73, "008269": 73, "986": 73, "010354": 73, "469": 73, "013459": 73, "516": 73, "013478": 73, "investig": 73, "100541": 73, "998729": 73, "998768": 73, "980980": 73, "998217": 73, "18": [73, 78, 79, 80, 81, 82, 86, 87, 89, 90, 92], "identified_label_issu": [73, 78], "lowest_quality_label": [73, 78, 82, 89, 92], "sort_valu": [73, 75, 77, 78, 80, 81, 82, 84], "1946": 73, "1871": 73, "1955": 73, "2132": 73, "worth": [73, 82], "iloc": [73, 77, 78, 89, 91, 92], "6_yweweler_35": 73, "6_yweweler_36": 73, "6_yweweler_14": 73, "6_theo_27": 73, "4_george_31": 73, "6_nicolas_8": 73, "sound": 73, "quit": [73, 87], "22": [73, 74, 79, 81, 82, 85, 86, 90, 93], "blindli": [73, 80, 89, 91, 92], "trust": [73, 80, 82, 84, 88, 89, 91, 92], "underneath": 74, "hood": 74, "alert": 74, "introduct": 74, "mayb": [74, 75, 78], "examin": [74, 75, 77, 91], "your_feature_matrix": [74, 75], "toi": [74, 75, 79, 81, 82, 84], "train_test_split": [74, 75, 87, 91, 92], "inf": [74, 75], "mid": [74, 75], "bins_map": [74, 75], "create_data": [74, 75], "y_bin": [74, 75], "y_i": [74, 75], "y_bin_idx": [74, 75], "y_train": [74, 75, 82, 89], "y_test": [74, 75, 82, 89], "y_train_idx": [74, 75], "y_test_idx": [74, 75], "test_siz": [74, 75, 91, 92], "slide": [74, 75, 79], "decis": [74, 75, 91], "boundari": [74, 75], "frame": [74, 75], "x_out": [74, 75], "tini": [74, 75], "concaten": [74, 75, 80, 88], "y_out": [74, 75], "y_out_bin": [74, 75], "y_out_bin_idx": [74, 75], "exact_duplicate_idx": [74, 75], "x_duplic": [74, 75], "y_duplic": [74, 75], "y_duplicate_idx": [74, 75], "noisy_labels_idx": [74, 75, 85], "scatter": [74, 75, 82, 85, 89], "black": [74, 75, 79, 89], "cyan": [74, 75], "pyplot": [74, 75, 81, 82, 85, 87, 89], "plt": [74, 75, 81, 82, 85, 87, 89], "plot_data": [74, 75, 82, 85, 89], "fig": [74, 75, 79, 81, 87, 89], "ax": [74, 75, 81, 87, 89], "subplot": [74, 75, 81, 87], "set_titl": [74, 75, 81, 87], "set_xlabel": [74, 75], "x_1": [74, 75], "fontsiz": [74, 75, 81, 82, 85], "set_ylabel": [74, 75], "x_2": [74, 75], "set_xlim": [74, 75], "set_ylim": [74, 75], "linestyl": [74, 75], "circl": [74, 75, 82, 85], "misclassifi": [74, 75], "zip": [74, 75, 81, 86, 93], "label_err": [74, 75], "180": [74, 75, 86], "marker": [74, 75], "facecolor": [74, 75], "edgecolor": [74, 75], "linewidth": [74, 75, 87], "dup": [74, 75], "first_legend": [74, 75], "align": [74, 75], "title_fontproperti": [74, 75], "semibold": [74, 75], "second_legend": [74, 75], "45": [74, 75, 79, 81, 82, 86, 90], "gca": [74, 75], "add_artist": [74, 75], "tight_layout": [74, 75], "ideal": [74, 75], "logist": [74, 75, 78, 84, 87, 92], "remaind": 74, "modal": [74, 75, 80, 84], "regardless": [74, 75], "132": [74, 75, 82, 86], "9318": 74, "77": [74, 75, 77, 86, 90, 91], "006939": 74, "007830": 74, "40": [74, 75, 78, 79, 81, 90], "014826": 74, "107": [74, 75, 82, 85], "021220": 74, "120": [74, 75, 91], "026403": 74, "notic": [74, 82, 84, 86], "5221": [74, 75], "126": [74, 75, 82, 86], "046465": [74, 75], "130": [74, 75], "068695": [74, 75], "129": [74, 75], "127": [74, 75], "076251": [74, 75], "128": [74, 75, 81], "083941": [74, 75], "6160": [74, 75], "is_near_duplicate_issu": [74, 75, 77, 78, 80, 81, 82], "131": [74, 75, 90], "000000e": [74, 75], "00": [74, 75, 77, 79, 81, 90, 91], "000002": [74, 75], "463180e": [74, 75], "07": [74, 75, 82, 86, 90], "51": [74, 75, 77, 79, 82, 86, 90], "161148": [74, 75], "859087e": [74, 75], "30": [74, 75, 79, 80, 81, 85, 90, 93], "3293": 74, "025076": 74, "026534": 74, "050766": 74, "051025": 74, "home": [74, 75, 78, 79, 87, 92], "runner": [74, 75, 78, 87, 92], "300": [74, 84, 93], "userwarn": [74, 75], "330": [74, 81, 86], "309": 74, "34": [74, 79, 81, 82, 84, 86, 87, 90, 93], "54": [74, 79, 82, 86, 90, 93], "039117": 74, "53": [74, 75, 77, 79, 81, 85, 86, 90, 91, 93], "044594": 74, "105": 74, "105121": 74, "133588": 74, "43": [74, 79, 81, 82, 86, 90, 92], "168035": 74, "125": 74, "090878": 74, "37": [74, 79, 90], "169462": 74, "109": [74, 79, 86], "194566": 74, "196302": 74, "206314": 74, "average_ood_scor": 74, "32933380816554325": 74, "52": [74, 79, 86, 90, 93], "169820": 74, "087324e": 74, "89": [74, 77, 86, 89, 90], "92": [74, 82, 86, 90, 91], "259024": 74, "583757e": 74, "91": [74, 86, 90, 92], "346458": 74, "341292e": 74, "specfi": 74, "new_lab": 74, "scoring_funct": 74, "div": 74, "rem": 74, "inv_scal": 74, "49": [74, 79, 81, 82, 86, 90], "superstitionissuemanag": 74, "unlucki": 74, "superstit": 74, "to_seri": 74, "issues_mask": 74, "summary_scor": 74, "9242": 74, "is_superstition_issu": 74, "superstition_scor": 74, "047581": 74, "090635": 74, "129591": 74, "65": [74, 81, 86, 90, 91, 93], "164840": 74, "demo": [75, 77, 85, 91], "lurk": [75, 81, 82], "opt": 75, "hostedtoolcach": 75, "x64": 75, "lib": 75, "python3": 75, "site": 75, "_split": 75, "737": 75, "thoroughli": 75, "preprocess": [75, 77, 87, 89, 91, 92], "904": 75, "review": [75, 77, 78, 79, 80, 82, 86, 89, 90, 91, 92, 93], "8561": 75, "001894": 75, "58": [75, 77, 79, 82, 86, 90, 91], "003565": 75, "007326": 75, "008974": 75, "009699": 75, "0227": 75, "is_class_imbalance_issu": [75, 77, 78, 81, 82], "022727": 75, "86": [75, 77, 81, 82, 86, 89, 90, 91], "87": [75, 81, 86, 89, 90, 92], "0000": [75, 78, 79, 81, 82], "is_null_issu": [75, 78, 81, 82], "96": [75, 77, 79, 82, 85, 86, 89, 90], "94": [75, 77, 79, 82, 86, 89, 90, 91], "93": [75, 79, 86, 89, 90, 91, 93], "8218": 75, "is_non_iid_issu": [75, 77, 78, 81, 82], "810274": 75, "826147": 75, "849587": 75, "855359": 75, "855485": 75, "821750488732925": 75, "auto": [75, 79, 80, 89, 91, 92], "conceptu": 75, "856061": 75, "522080": 75, "616034": 75, "821750": 75, "betweeen": 75, "859109": 75, "586131": 75, "664083": 75, "970324": 75, "816965": 75, "548979": 75, "641516": 75, "890575": 75, "530924": 75, "622256": 75, "601188": 75, "752776": 75, "499498": 75, "562539": 75, "948362": 75, "090224": 75, "632385": 75, "746763": 75, "878267": 75, "examples_w_issu": [75, 80], "013444": 75, "025173": 75, "026416": 75, "inde": [75, 78], "miscellan": [75, 93], "428571": 75, "111111": 75, "571429": 75, "407407": 75, "592593": 75, "337838": 75, "092593": 75, "662162": 75, "333333": [75, 79], "952381": 75, "666667": 75, "portion": 75, "huge": [75, 82], "worri": [75, 78], "critic": 75, "highli": [75, 81], "sql": [77, 91], "databas": [77, 91], "excel": [77, 91], "parquet": [77, 91], "student": [77, 89, 91, 93], "grade": [77, 89, 91], "900": [77, 89, 91], "exam": [77, 89, 91], "letter": [77, 91, 93], "hundr": [77, 91], "histgradientboostingclassifi": 77, "standardscal": [77, 87, 91], "possibli": [77, 91], "grades_data": [77, 91], "read_csv": [77, 78, 89, 91, 92], "stud_id": [77, 91], "exam_1": [77, 89, 91], "exam_2": [77, 89, 91], "exam_3": [77, 89, 91], "letter_grad": [77, 91], "f48f73": [77, 91], "0bd4e7": [77, 91], "81": [77, 78, 86, 89, 90, 91, 93], "great": [77, 79, 91], "particip": [77, 91], "cb9d7a": [77, 91], "61": [77, 81, 82, 86, 90, 91], "78": [77, 79, 82, 86, 89, 90, 91], "9acca4": [77, 91], "48": [77, 79, 82, 86, 90, 91], "x_raw": [77, 91], "cat_featur": 77, "x_encod": [77, 91], "get_dummi": [77, 89, 91], "drop_first": [77, 91], "numeric_featur": [77, 91], "scaler": [77, 87, 91], "x_process": [77, 91], "fit_transform": [77, 91], "bring": [77, 78, 81, 84, 89, 91, 92], "byod": [77, 78, 81, 84, 89, 91, 92], "boost": [77, 80, 84, 89], "xgboost": [77, 80, 89], "think": [77, 78, 80, 85, 90, 93], "carefulli": [77, 78, 81, 91], "nonzero": 77, "suspici": [77, 91], "tabl": [77, 79, 84, 91], "358": 77, "294": [77, 86], "46": [77, 79, 81, 82, 86, 90], "941": 77, "7109": 77, "000005": [77, 78, 81], "886": 77, "000059": 77, "709": 77, "000104": 77, "723": 77, "000169": 77, "689": 77, "000181": 77, "7154": 77, "012085": 77, "061510": 77, "115512": 77, "124391": 77, "214163": 77, "6165": 77, "582": 77, "185": [77, 79, 86, 93], "187": [77, 79], "27": [77, 79, 82, 86, 90, 93], "898": 77, "637": [77, 91], "0014": [77, 79], "595": 77, "702427": 77, "147": [77, 82, 86], "711186": 77, "157": [77, 82], "721394": 77, "771": 77, "731979": 77, "740335": 77, "0014153602099278074": 77, "1562": 77, "393": 77, "156217": 77, "391": 77, "806": 77, "805": 77, "156": [77, 82], "na": [77, 78, 81, 82, 84], "issue_result": 77, "000842": 77, "555944": 77, "004374": 77, "sorted_issu": 77, "73": [77, 79, 85, 86, 89, 90], "deserv": 77, "outlier_result": 77, "sorted_outli": 77, "56": [77, 79, 89, 90], "lt": [77, 78, 79, 81, 84, 90], "style": [77, 90], "font": 77, "18px": 77, "ff00ff": 77, "bac": 77, "unintend": [77, 78], "mistak": [77, 78, 81, 91, 92], "duplicate_result": 77, "690": 77, "246": [77, 86], "perhap": [77, 82, 84], "twice": 77, "67": [77, 79, 81, 86, 89, 90], "wari": [77, 78, 80], "super": [77, 78, 81], "system": [77, 78, 81, 90], "intent": [78, 92], "servic": [78, 80, 92], "onlin": [78, 92], "bank": [78, 79, 92], "banking77": [78, 92], "oo": [78, 92], "000": [78, 79, 81, 92, 93], "categori": [78, 81, 92], "scope": [78, 92], "dive": 78, "your_featur": 78, "sentence_transform": [78, 92], "sentencetransform": [78, 92], "payment": [78, 92], "cancel_transf": [78, 92], "transfer": [78, 92], "fund": [78, 92], "cancel": [78, 92], "transact": [78, 92], "my": [78, 92], "revert": [78, 92], "morn": [78, 92], "realis": [78, 92], "yesterdai": [78, 92], "rent": [78, 92], "realli": [78, 84, 90, 92], "tomorrow": [78, 92], "raw_text": [78, 92], "change_pin": [78, 92], "card_payment_fee_charg": [78, 92], "apple_pay_or_google_pai": [78, 92], "visa_or_mastercard": [78, 92], "getting_spare_card": [78, 92], "card_about_to_expir": [78, 92], "lost_or_stolen_phon": [78, 92], "supported_cards_and_curr": [78, 92], "beneficiary_not_allow": [78, 92], "utter": [78, 92], "continu": [78, 80, 81, 84, 89, 91, 92, 93], "suit": [78, 79, 80, 92], "electra": [78, 92], "discrimin": [78, 92], "googl": [78, 92], "text_embed": 78, "No": [78, 80, 92], "google_electra": [78, 92], "pool": [78, 80, 87, 92], "400": [78, 92], "data_dict": [78, 82, 84], "84": [78, 86, 90], "41": [78, 79, 86, 89, 90], "38": [78, 79, 86, 90], "9720": 78, "981": 78, "974": 78, "000150": 78, "982": [78, 79], "000218": 78, "971": 78, "000512": 78, "980": [78, 79], "000947": 78, "9122": 78, "994": 78, "676322": 78, "999": 78, "693868": 78, "697240": 78, "433": 78, "700874": 78, "989": 78, "713590": 78, "6070": 78, "160": [78, 82], "095724": 78, "148": 78, "006237": 78, "546": 78, "099340": 78, "514": 78, "006485": 78, "481": 78, "123416": 78, "008165": 78, "313": [78, 86], "564102": 78, "572258": 78, "28": [78, 79, 81, 82, 84, 90, 93], "574915": 78, "31": [78, 79, 82, 84, 86, 90], "575507": 78, "575874": 78, "658": 78, "659": [78, 89], "660": 78, "661": 78, "0800": 78, "454": 78, "453": 78, "455": 78, "791961": 78, "258508": 78, "699010": 78, "183136": 78, "771112": 78, "to_numpi": [78, 80, 89, 92], "data_with_suggested_label": 78, "suggested_label": 78, "charg": [78, 92], "cash": [78, 92], "holidai": [78, 92], "sent": [78, 92, 93], "card": [78, 79, 92], "mine": [78, 92], "expir": [78, 92], "me": [78, 92], "withdraw": 78, "monei": 78, "whoever": [78, 92], "outlier_issu": [78, 81], "lowest_quality_outli": 78, "OR": 78, "636c65616e6c616220697320617765736f6d6521": 78, "phone": [78, 79], "gone": 78, "gt": [78, 84, 93], "samp": 78, "br": 78, "press": [78, 93], "nonsens": 78, "sens": 78, "detriment": 78, "duplicate_issu": 78, "fee": 78, "pai": 78, "go": [78, 79, 82], "strongli": 78, "p_valu": 78, "benign": 78, "shortlist": [78, 89, 92], "curat": [78, 83], "mnist_test_set": 79, "imagenet_val_set": 79, "tench": 79, "goldfish": 79, "white": [79, 93], "shark": 79, "tiger": 79, "hammerhead": 79, "electr": 79, "rai": 79, "stingrai": 79, "cock": 79, "hen": 79, "ostrich": 79, "brambl": 79, "goldfinch": 79, "hous": 79, "finch": 79, "junco": 79, "indigo": 79, "bunt": 79, "american": [79, 93], "robin": 79, "bulbul": 79, "jai": 79, "magpi": 79, "chickade": 79, "dipper": 79, "kite": 79, "bald": 79, "eagl": 79, "vultur": 79, "grei": 79, "owl": 79, "fire": 79, "salamand": 79, "smooth": 79, "newt": 79, "spot": [79, 86], "axolotl": 79, "bullfrog": 79, "tree": 79, "frog": [79, 87], "tail": 79, "loggerhead": 79, "sea": 79, "turtl": 79, "leatherback": 79, "mud": 79, "terrapin": 79, "band": 79, "gecko": 79, "green": [79, 93], "iguana": 79, "carolina": 79, "anol": 79, "desert": 79, "grassland": 79, "whiptail": 79, "lizard": 79, "agama": 79, "frill": 79, "neck": 79, "allig": 79, "gila": 79, "monster": 79, "european": 79, "chameleon": 79, "komodo": 79, "dragon": 79, "nile": 79, "crocodil": 79, "triceratop": 79, "worm": 79, "snake": 79, "ring": 79, "eastern": 79, "hog": 79, "nose": 79, "kingsnak": 79, "garter": 79, "water": 79, "vine": 79, "night": 79, "boa": 79, "constrictor": 79, "african": 79, "rock": 79, "indian": 79, "cobra": 79, "mamba": 79, "saharan": 79, "horn": 79, "viper": 79, "diamondback": 79, "rattlesnak": 79, "sidewind": 79, "trilobit": 79, "harvestman": 79, "scorpion": 79, "yellow": 79, "garden": 79, "spider": 79, "barn": 79, "southern": 79, "widow": 79, "tarantula": 79, "wolf": 79, "tick": 79, "centiped": 79, "grous": 79, "ptarmigan": 79, "ruf": 79, "prairi": 79, "peacock": 79, "quail": 79, "partridg": 79, "parrot": 79, "macaw": 79, "sulphur": 79, "crest": 79, "cockatoo": 79, "lorikeet": 79, "coucal": 79, "bee": 79, "eater": 79, "hornbil": 79, "hummingbird": 79, "jacamar": 79, "toucan": 79, "duck": [79, 92], "breast": 79, "mergans": 79, "goos": 79, "swan": 79, "tusker": 79, "echidna": 79, "platypu": 79, "wallabi": 79, "koala": 79, "wombat": 79, "jellyfish": 79, "anemon": 79, "brain": 79, "coral": 79, "flatworm": 79, "nematod": 79, "conch": 79, "snail": 79, "slug": 79, "chiton": 79, "chamber": 79, "nautilu": 79, "dung": 79, "crab": 79, "fiddler": 79, "king": 79, "lobster": 79, "spini": 79, "crayfish": 79, "hermit": 79, "isopod": 79, "stork": 79, "spoonbil": 79, "flamingo": 79, "heron": 79, "egret": 79, "bittern": 79, "crane": 79, "bird": [79, 87], "limpkin": 79, "gallinul": 79, "coot": 79, "bustard": 79, "ruddi": 79, "turnston": 79, "dunlin": 79, "redshank": 79, "dowitch": 79, "oystercatch": 79, "pelican": 79, "penguin": 79, "albatross": 79, "whale": 79, "killer": 79, "dugong": 79, "lion": 79, "chihuahua": 79, "japanes": 79, "chin": 79, "maltes": 79, "pekinges": 79, "shih": 79, "tzu": 79, "charl": 79, "spaniel": 79, "papillon": 79, "terrier": 79, "rhodesian": 79, "ridgeback": 79, "afghan": [79, 93], "hound": 79, "basset": 79, "beagl": 79, "bloodhound": 79, "bluetick": 79, "coonhound": 79, "tan": 79, "walker": 79, "foxhound": 79, "redbon": 79, "borzoi": 79, "irish": 79, "wolfhound": 79, "italian": 79, "greyhound": 79, "whippet": 79, "ibizan": 79, "norwegian": 79, "elkhound": 79, "otterhound": 79, "saluki": 79, "scottish": 79, "deerhound": 79, "weimaran": 79, "staffordshir": 79, "bull": 79, "bedlington": 79, "border": 79, "kerri": 79, "norfolk": 79, "norwich": 79, "yorkshir": 79, "wire": 79, "fox": 79, "lakeland": 79, "sealyham": 79, "airedal": 79, "cairn": 79, "australian": 79, "dandi": 79, "dinmont": 79, "boston": 79, "miniatur": 79, "schnauzer": 79, "giant": 79, "tibetan": 79, "silki": 79, "coat": [79, 81], "wheaten": 79, "west": 79, "highland": 79, "lhasa": 79, "apso": 79, "flat": 79, "retriev": 79, "curli": 79, "golden": 79, "labrador": 79, "chesapeak": 79, "bai": 79, "german": [79, 93], "shorthair": 79, "pointer": 79, "vizsla": 79, "setter": 79, "gordon": 79, "brittani": 79, "clumber": 79, "springer": 79, "welsh": 79, "cocker": 79, "sussex": 79, "kuvasz": 79, "schipperk": 79, "groenendael": 79, "malinoi": 79, "briard": 79, "kelpi": 79, "komondor": 79, "sheepdog": 79, "shetland": 79, "colli": 79, "bouvier": 79, "de": 79, "flandr": 79, "rottweil": 79, "shepherd": 79, "dobermann": 79, "pinscher": 79, "swiss": [79, 93], "mountain": 79, "bernes": 79, "appenzel": 79, "sennenhund": 79, "entlebuch": 79, "boxer": 79, "bullmastiff": 79, "mastiff": 79, "french": 79, "bulldog": 79, "dane": 79, "st": 79, "bernard": 79, "huski": 79, "alaskan": 79, "malamut": 79, "siberian": 79, "dalmatian": 79, "affenpinsch": 79, "basenji": 79, "pug": 79, "leonberg": 79, "newfoundland": 79, "pyrenean": 79, "samoi": 79, "pomeranian": 79, "chow": 79, "keeshond": 79, "griffon": 79, "bruxelloi": 79, "pembrok": 79, "corgi": 79, "cardigan": 79, "poodl": 79, "mexican": 79, "hairless": 79, "tundra": 79, "coyot": 79, "dingo": 79, "dhole": 79, "wild": 79, "hyena": 79, "kit": 79, "arctic": 79, "tabbi": 79, "persian": 79, "siames": 79, "egyptian": 79, "mau": 79, "cougar": 79, "lynx": 79, "leopard": 79, "snow": 79, "jaguar": 79, "cheetah": 79, "brown": [79, 90], "bear": 79, "polar": 79, "sloth": 79, "mongoos": 79, "meerkat": 79, "beetl": 79, "ladybug": 79, "longhorn": 79, "leaf": 79, "rhinocero": 79, "weevil": 79, "fly": 79, "ant": 79, "grasshopp": 79, "cricket": 79, "stick": 79, "insect": 79, "cockroach": 79, "manti": 79, "cicada": 79, "leafhopp": 79, "lacew": 79, "dragonfli": 79, "damselfli": 79, "admir": 79, "ringlet": 79, "monarch": 79, "butterfli": 79, "gossam": 79, "wing": 79, "starfish": 79, "urchin": 79, "cucumb": 79, "cottontail": 79, "rabbit": 79, "hare": 79, "angora": 79, "hamster": 79, "porcupin": 79, "squirrel": 79, "marmot": 79, "beaver": 79, "guinea": 79, "pig": 79, "sorrel": 79, "zebra": 79, "boar": 79, "warthog": 79, "hippopotamu": 79, "ox": 79, "buffalo": 79, "bison": 79, "bighorn": 79, "sheep": 79, "alpin": 79, "ibex": 79, "hartebeest": 79, "impala": 79, "gazel": 79, "dromedari": 79, "llama": 79, "weasel": 79, "mink": 79, "polecat": 79, "foot": 79, "ferret": 79, "otter": 79, "skunk": 79, "badger": 79, "armadillo": 79, "toed": 79, "orangutan": 79, "gorilla": 79, "chimpanze": 79, "gibbon": 79, "siamang": 79, "guenon": 79, "pata": 79, "monkei": 79, "baboon": 79, "macaqu": 79, "langur": 79, "colobu": 79, "probosci": 79, "marmoset": 79, "capuchin": 79, "howler": 79, "titi": 79, "geoffroi": 79, "lemur": 79, "indri": 79, "asian": 79, "eleph": 79, "bush": 79, "snoek": 79, "eel": 79, "coho": 79, "salmon": 79, "beauti": 79, "clownfish": 79, "sturgeon": 79, "garfish": 79, "lionfish": 79, "pufferfish": 79, "abacu": 79, "abaya": 79, "academ": 79, "gown": 79, "accordion": 79, "acoust": 79, "guitar": 79, "aircraft": 79, "carrier": 79, "airlin": 79, "airship": 79, "altar": 79, "ambul": 79, "amphibi": 79, "clock": [79, 93], "apiari": 79, "apron": 79, "wast": 79, "assault": 79, "rifl": 79, "backpack": 79, "bakeri": 79, "balanc": 79, "beam": 79, "balloon": 79, "ballpoint": 79, "pen": 79, "aid": 79, "banjo": 79, "balust": 79, "barbel": 79, "barber": 79, "chair": [79, 86], "barbershop": 79, "baromet": 79, "barrel": 79, "wheelbarrow": 79, "basebal": 79, "basketbal": 79, "bassinet": 79, "bassoon": 79, "swim": 79, "cap": 79, "bath": 79, "towel": 79, "bathtub": 79, "station": 79, "wagon": 79, "lighthous": 79, "beaker": 79, "militari": 79, "beer": 79, "bottl": 79, "glass": 79, "bell": 79, "cot": 79, "bib": 79, "bicycl": [79, 90], "bikini": 79, "binder": 79, "binocular": 79, "birdhous": 79, "boathous": 79, "bobsleigh": 79, "bolo": 79, "tie": 79, "poke": 79, "bonnet": 79, "bookcas": 79, "bookstor": 79, "bow": 79, "brass": 79, "bra": 79, "breakwat": 79, "breastplat": 79, "broom": 79, "bucket": 79, "buckl": 79, "bulletproof": 79, "vest": 79, "butcher": 79, "shop": 79, "taxicab": 79, "cauldron": 79, "candl": 79, "cannon": 79, "cano": 79, "mirror": [79, 86], "carousel": 79, "tool": [79, 82, 84], "carton": 79, "wheel": 79, "teller": 79, "cassett": 79, "player": 79, "castl": 79, "catamaran": 79, "cd": 79, "cello": 79, "mobil": [79, 93], "chain": 79, "fenc": [79, 90], "mail": 79, "chainsaw": 79, "chest": 79, "chiffoni": 79, "chime": 79, "china": 79, "cabinet": 79, "christma": 79, "stock": 79, "church": 79, "movi": 79, "theater": 79, "cleaver": 79, "cliff": 79, "dwell": 79, "cloak": 79, "clog": 79, "cocktail": 79, "shaker": 79, "coffe": 79, "mug": 79, "coffeemak": 79, "coil": 79, "lock": 79, "keyboard": 79, "confectioneri": 79, "ship": [79, 87], "corkscrew": 79, "cornet": 79, "cowboi": 79, "boot": 79, "hat": 79, "cradl": 79, "crash": 79, "helmet": 79, "crate": 79, "infant": 79, "bed": 79, "crock": 79, "pot": 79, "croquet": 79, "crutch": 79, "cuirass": 79, "dam": 79, "desk": 79, "desktop": 79, "rotari": 79, "dial": 79, "telephon": 79, "diaper": 79, "watch": 79, "dine": 79, "dishcloth": 79, "dishwash": 79, "disc": 79, "brake": 79, "dock": 79, "sled": 79, "dome": 79, "doormat": 79, "drill": 79, "rig": 79, "drum": 79, "drumstick": 79, "dumbbel": 79, "dutch": 79, "oven": 79, "fan": 79, "locomot": 79, "entertain": 79, "center": 79, "envelop": 79, "espresso": 79, "powder": 79, "feather": 79, "fireboat": 79, "engin": [79, 90], "screen": 79, "sheet": 79, "flagpol": 79, "flute": 79, "footbal": 79, "forklift": 79, "fountain": 79, "poster": 79, "freight": 79, "fry": 79, "pan": 79, "fur": 79, "garbag": 79, "ga": 79, "pump": 79, "goblet": 79, "kart": 79, "golf": 79, "cart": 79, "gondola": 79, "gong": 79, "grand": 79, "piano": 79, "greenhous": 79, "grill": 79, "groceri": 79, "guillotin": 79, "barrett": 79, "hair": 79, "sprai": 79, "hammer": 79, "dryer": 79, "hand": [79, 82], "handkerchief": 79, "drive": 79, "harmonica": 79, "harp": 79, "harvest": 79, "hatchet": 79, "holster": 79, "honeycomb": 79, "hoop": 79, "skirt": 79, "horizont": 79, "bar": 79, "hors": [79, 87, 92], "drawn": 79, "hourglass": 79, "ipod": 79, "cloth": 79, "iron": 79, "jack": 79, "lantern": 79, "jean": 79, "jeep": 79, "shirt": [79, 81], "jigsaw": 79, "puzzl": 79, "pull": 79, "rickshaw": 79, "joystick": 79, "kimono": 79, "knee": 79, "pad": 79, "knot": 79, "ladl": 79, "lampshad": 79, "laptop": 79, "lawn": 79, "mower": 79, "knife": 79, "lifeboat": 79, "lighter": 79, "limousin": 79, "ocean": 79, "liner": 79, "lipstick": 79, "slip": 79, "shoe": 79, "lotion": 79, "speaker": 79, "loup": 79, "sawmil": 79, "magnet": 79, "compass": 79, "bag": [79, 81, 87, 88], "mailbox": 79, "tight": 79, "tank": 79, "manhol": 79, "maraca": 79, "marimba": 79, "maypol": 79, "maze": 79, "cup": [79, 86], "medicin": 79, "megalith": 79, "microphon": 79, "microwav": 79, "milk": 79, "minibu": 79, "miniskirt": 79, "minivan": 79, "missil": 79, "mitten": 79, "mix": 79, "bowl": 79, "modem": 79, "monasteri": 79, "monitor": 79, "mope": 79, "mortar": 79, "mosqu": 79, "mosquito": 79, "scooter": 79, "bike": 79, "tent": 79, "mous": [79, 80], "mousetrap": 79, "van": 79, "muzzl": 79, "nail": 79, "brace": 79, "necklac": 79, "nippl": 79, "obelisk": 79, "obo": 79, "ocarina": 79, "odomet": 79, "oil": 79, "oscilloscop": 79, "overskirt": 79, "bullock": 79, "oxygen": 79, "packet": 79, "paddl": 79, "padlock": 79, "paintbrush": 79, "pajama": 79, "palac": [79, 93], "parachut": 79, "park": 79, "bench": 79, "meter": 79, "passeng": 79, "patio": 79, "payphon": 79, "pedest": 79, "pencil": 79, "perfum": 79, "petri": 79, "dish": 79, "photocopi": 79, "plectrum": 79, "pickelhaub": 79, "picket": 79, "pickup": 79, "pier": 79, "piggi": 79, "pill": 79, "pillow": 79, "ping": 79, "pong": 79, "pinwheel": 79, "pirat": 79, "pitcher": 79, "plane": 79, "planetarium": 79, "plastic": 79, "plate": 79, "rack": 79, "plow": 79, "plunger": 79, "polaroid": 79, "camera": 79, "pole": [79, 90], "polic": 79, "poncho": 79, "billiard": 79, "soda": 79, "potter": 79, "prayer": 79, "rug": 79, "printer": 79, "prison": 79, "projectil": 79, "projector": 79, "hockei": 79, "puck": 79, "punch": 79, "purs": 79, "quill": 79, "quilt": 79, "race": 79, "racket": 79, "radiat": 79, "radio": 79, "telescop": 79, "rain": 79, "recreat": 79, "reel": 79, "reflex": 79, "refriger": 79, "remot": 79, "restaur": 79, "revolv": 79, "rotisseri": 79, "eras": 79, "rugbi": 79, "ruler": 79, "safe": 79, "safeti": 79, "salt": 79, "sandal": [79, 81], "sarong": 79, "saxophon": 79, "scabbard": 79, "school": 79, "bu": [79, 90], "schooner": 79, "scoreboard": 79, "crt": 79, "screw": 79, "screwdriv": 79, "seat": 79, "belt": 79, "sew": 79, "shield": 79, "shoji": 79, "basket": 79, "shovel": 79, "shower": 79, "curtain": 79, "ski": 79, "sleep": 79, "door": 79, "slot": 79, "snorkel": 79, "snowmobil": 79, "snowplow": 79, "soap": 79, "dispens": 79, "soccer": [79, 93], "sock": 79, "solar": 79, "thermal": 79, "collector": 79, "sombrero": 79, "soup": 79, "heater": 79, "shuttl": 79, "spatula": 79, "motorboat": 79, "web": 79, "spindl": 79, "sport": [79, 93], "spotlight": 79, "stage": 79, "steam": 79, "arch": 79, "bridg": 79, "steel": 79, "stethoscop": 79, "scarf": 79, "stone": 79, "wall": [79, 90], "stopwatch": 79, "stove": 79, "strainer": 79, "tram": 79, "stretcher": 79, "couch": 79, "stupa": 79, "submarin": 79, "sundial": 79, "sunglass": 79, "sunscreen": 79, "suspens": 79, "mop": 79, "sweatshirt": 79, "swimsuit": 79, "swing": 79, "switch": 79, "syring": 79, "lamp": 79, "tape": 79, "teapot": 79, "teddi": 79, "televis": [79, 93], "tenni": 79, "thatch": 79, "roof": 79, "front": 79, "thimbl": 79, "thresh": 79, "throne": 79, "tile": 79, "toaster": 79, "tobacco": 79, "toilet": 79, "totem": 79, "tow": 79, "tractor": 79, "semi": 79, "trailer": 79, "trai": 79, "trench": 79, "tricycl": 79, "trimaran": 79, "tripod": 79, "triumphal": 79, "trolleybu": 79, "trombon": 79, "tub": 79, "turnstil": 79, "typewrit": 79, "umbrella": 79, "unicycl": 79, "upright": 79, "vacuum": 79, "cleaner": 79, "vase": 79, "vault": 79, "velvet": 79, "vend": 79, "vestment": 79, "viaduct": 79, "violin": 79, "volleybal": 79, "waffl": 79, "wallet": 79, "wardrob": 79, "sink": 79, "wash": 79, "jug": 79, "tower": 79, "whiskei": 79, "whistl": 79, "wig": 79, "shade": [79, 90], "windsor": 79, "wine": 79, "wok": 79, "wooden": 79, "spoon": 79, "wool": 79, "rail": 79, "shipwreck": 79, "yawl": 79, "yurt": 79, "websit": 79, "comic": 79, "book": 79, "crossword": 79, "traffic": [79, 86, 90], "sign": [79, 90, 93], "dust": 79, "jacket": [79, 86], "menu": 79, "guacamol": 79, "consomm": 79, "trifl": 79, "ic": 79, "cream": 79, "pop": 79, "baguett": 79, "bagel": 79, "pretzel": 79, "cheeseburg": 79, "mash": 79, "potato": 79, "cabbag": 79, "broccoli": 79, "cauliflow": 79, "zucchini": 79, "spaghetti": 79, "squash": 79, "acorn": 79, "butternut": 79, "artichok": 79, "pepper": 79, "cardoon": 79, "mushroom": 79, "granni": 79, "smith": 79, "strawberri": 79, "orang": 79, "lemon": 79, "pineappl": 79, "banana": 79, "jackfruit": 79, "custard": 79, "appl": 79, "pomegran": 79, "hai": 79, "carbonara": 79, "chocol": 79, "syrup": 79, "dough": 79, "meatloaf": 79, "pizza": 79, "pie": 79, "burrito": 79, "eggnog": 79, "alp": 79, "bubbl": 79, "reef": 79, "geyser": 79, "lakeshor": 79, "promontori": 79, "shoal": 79, "seashor": 79, "vallei": 79, "volcano": 79, "bridegroom": 79, "scuba": 79, "diver": 79, "rapese": 79, "daisi": 79, "ladi": 79, "slipper": 79, "corn": 79, "rose": 79, "hip": 79, "chestnut": 79, "fungu": 79, "agar": 79, "gyromitra": 79, "stinkhorn": 79, "earth": 79, "star": 79, "wood": 79, "bolet": 79, "ear": 79, "cifar10_test_set": 79, "airplan": [79, 87], "automobil": [79, 87], "deer": [79, 87], "cifar100_test_set": 79, "aquarium_fish": 79, "babi": 79, "boi": 79, "camel": 79, "caterpillar": 79, "cattl": [79, 93], "cloud": 79, "dinosaur": 79, "dolphin": 79, "flatfish": 79, "forest": 79, "girl": 79, "kangaroo": 79, "lawn_mow": 79, "man": 79, "maple_tre": 79, "motorcycl": [79, 90], "oak_tre": 79, "orchid": 79, "palm_tre": 79, "pear": 79, "pickup_truck": 79, "pine_tre": 79, "plain": 79, "poppi": 79, "possum": 79, "raccoon": 79, "road": [79, 90], "rocket": 79, "seal": 79, "shrew": 79, "skyscrap": 79, "streetcar": 79, "sunflow": 79, "sweet_pepp": 79, "trout": 79, "tulip": 79, "willow_tre": 79, "woman": [79, 86], "caltech256": 79, "ak47": 79, "bat": 79, "glove": 79, "birdbath": 79, "blimp": 79, "bonsai": 79, "boom": 79, "breadmak": 79, "buddha": 79, "bulldoz": 79, "cactu": 79, "cake": 79, "tire": 79, "cartman": 79, "cereal": 79, "chandeli": 79, "chess": 79, "board": 79, "chimp": 79, "chopstick": 79, "coffin": 79, "coin": 79, "comet": 79, "cormor": 79, "globe": 79, "diamond": 79, "dice": 79, "doorknob": 79, "drink": 79, "straw": 79, "dumb": 79, "eiffel": 79, "elk": 79, "ewer": 79, "eyeglass": 79, "fern": 79, "fighter": 79, "jet": [79, 89], "extinguish": 79, "hydrant": 79, "firework": 79, "flashlight": 79, "floppi": 79, "fri": 79, "frisbe": 79, "galaxi": 79, "giraff": 79, "goat": 79, "gate": 79, "grape": 79, "pick": [79, 80], "hamburg": 79, "hammock": 79, "harpsichord": 79, "hawksbil": 79, "helicopt": 79, "hibiscu": 79, "homer": 79, "simpson": 79, "horsesho": 79, "air": 79, "skeleton": 79, "ibi": 79, "cone": 79, "iri": 79, "jesu": 79, "christ": 79, "joi": 79, "kayak": 79, "ketch": 79, "ladder": 79, "lath": 79, "licens": 79, "lightbulb": 79, "lightn": 79, "mandolin": 79, "mar": 79, "mattress": 79, "megaphon": 79, "menorah": 79, "microscop": 79, "minaret": 79, "minotaur": 79, "motorbik": 79, "mussel": 79, "neckti": 79, "octopu": 79, "palm": 79, "pilot": 79, "paperclip": 79, "shredder": 79, "pci": 79, "peopl": [79, 86], "pez": 79, "picnic": 79, "pram": 79, "prai": 79, "pyramid": 79, "rainbow": 79, "roulett": 79, "saddl": 79, "saturn": 79, "segwai": 79, "propel": 79, "sextant": 79, "music": 79, "skateboard": 79, "smokestack": 79, "sneaker": 79, "boat": 79, "stain": 79, "steer": 79, "stirrup": 79, "superman": 79, "sushi": 79, "armi": [79, 93], "sword": 79, "tambourin": 79, "teepe": 79, "court": 79, "theodolit": 79, "tomato": 79, "tombston": 79, "tour": 79, "pisa": 79, "treadmil": 79, "fork": 79, "tweezer": 79, "unicorn": 79, "vcr": 79, "waterfal": 79, "watermelon": 79, "weld": 79, "windmil": 79, "xylophon": 79, "yarmulk": 79, "yo": 79, "toad": 79, "twenty_news_test_set": 79, "alt": 79, "atheism": 79, "comp": 79, "graphic": [79, 90], "misc": [79, 93], "sy": 79, "ibm": 79, "pc": 79, "hardwar": 79, "mac": 79, "forsal": 79, "rec": 79, "sci": 79, "crypt": 79, "electron": 79, "med": 79, "soc": 79, "religion": 79, "christian": [79, 93], "talk": [79, 93], "polit": 79, "gun": 79, "mideast": 79, "amazon": 79, "neutral": 79, "imdb_test_set": 79, "all_class": 79, "20news_test_set": 79, "_load_classes_predprobs_label": 79, "dataset_nam": 79, "labelerror": 79, "url_bas": 79, "5392f6c71473055060be3044becdde1cbc18284d": 79, "url_label": 79, "original_test_label": 79, "_original_label": 79, "url_prob": 79, "cross_validated_predicted_prob": 79, "_pyx": 79, "num_part": 79, "datatset": 79, "bytesio": 79, "allow_pickl": 79, "pred_probs_part": 79, "url": 79, "_of_": 79, "nload": 79, "imdb": 79, "ve": [79, 80, 82, 84, 86], "interpret": [79, 80, 82], "capit": 79, "29780": 79, "256": [79, 80, 86], "780": 79, "medic": [79, 93], "doctor": 79, "254": [79, 86], "359223": 79, "640777": 79, "184": [79, 82], "258427": 79, "341176": 79, "263158": 79, "658824": 79, "337349": 79, "246575": 79, "662651": 79, "248": 79, "330000": 79, "355769": 79, "670000": 79, "251": [79, 86], "167": [79, 82, 86], "252": 79, "112": 79, "253": [79, 86], "022989": 79, "255": [79, 81], "049505": 79, "190": [79, 82, 86], "66": [79, 81, 90], "002216": 79, "000974": 79, "59": [79, 81, 86, 90], "88": [79, 81, 82, 85, 86, 89, 90], "000873": 79, "000739": 79, "79": [79, 86, 90, 91], "32635": 79, "32636": 79, "47": [79, 86, 90, 93], "32637": 79, "32638": 79, "32639": 79, "32640": 79, "051": 79, "002242": 79, "997758": 79, "002088": 79, "001045": 79, "997912": 79, "002053": 79, "997947": 79, "001980": 79, "000991": 79, "998020": 79, "001946": 79, "002915": 79, "998054": 79, "001938": 79, "002904": 79, "998062": 79, "001020": 79, "998980": 79, "001018": 79, "002035": 79, "998982": 79, "999009": 79, "0003": 79, "0002": 79, "36": [79, 90, 93], "44": [79, 85, 86, 90], "71": [79, 82, 86, 90], "071": 79, "067269": 79, "929": 79, "046": 79, "058243": 79, "954": 79, "035": 79, "032096": 79, "965": 79, "031": 79, "012232": 79, "969": 79, "022": 79, "025896": 79, "978": 79, "020": [79, 81, 82], "013092": 79, "018": 79, "013065": 79, "016": 79, "030542": 79, "984": 79, "013": 79, "020833": 79, "987": 79, "012": 79, "010020": 79, "988": 79, "0073": 79, "0020": 79, "0016": 79, "0015": 79, "0013": 79, "0012": 79, "0010": 79, "0008": 79, "0007": 79, "0006": 79, "0005": 79, "0004": 79, "244": [79, 86], "98": [79, 80, 89, 90], "452381": 79, "459770": 79, "72": [79, 81, 82, 85, 89, 90], "523364": 79, "460784": 79, "446602": 79, "57": [79, 81, 82, 90], "68": [79, 81, 82, 86, 90, 91], "103774": 79, "030612": 79, "97": [79, 80, 82, 86, 89, 90, 91, 93], "110092": 79, "049020": 79, "99": [79, 82, 90, 91], "0034": 79, "0032": 79, "0026": 79, "0025": 79, "4945": 79, "4946": 79, "4947": 79, "4948": 79, "4949": 79, "4950": 79, "846": 79, "82": [79, 81, 82, 86, 90], "7532": 79, "532": 79, "034483": 79, "009646": 79, "965517": 79, "030457": 79, "020513": 79, "969543": 79, "028061": 79, "035443": 79, "971939": 79, "025316": 79, "005168": 79, "974684": 79, "049751": 79, "979487": 79, "019920": 79, "042802": 79, "980080": 79, "017677": 79, "005115": 79, "982323": 79, "012987": 79, "005236": 79, "987013": 79, "012723": 79, "025126": 79, "987277": 79, "010989": 79, "008264": 79, "989011": 79, "010283": 79, "027778": 79, "989717": 79, "009677": 79, "990323": 79, "007614": 79, "010127": 79, "992386": 79, "005051": 79, "994949": 79, "005025": 79, "994975": 79, "005013": 79, "994987": 79, "001859": 79, "001328": 79, "000929": 79, "000664": 79, "186": [79, 82], "188": [79, 82, 85], "189": [79, 82], "snippet": 80, "nlp": [80, 93], "mind": [80, 82], "number_of_class": 80, "total_number_of_data_point": 80, "drop": [80, 84, 89, 92], "feed": 80, "alphabet": 80, "labels_proper_format": 80, "your_classifi": 80, "issues_datafram": 80, "class_predicted_for_flagged_exampl": 80, "class_predicted_for_all_exampl": 80, "grant": 80, "datataset": 80, "fair": [80, 82], "game": 80, "speedup": [80, 87], "flexibl": 80, "tempfil": 80, "mkdtemp": 80, "sped": 80, "anywai": 80, "pred_probs_merg": 80, "merge_rare_class": 80, "count_threshold": 80, "class_mapping_orig2new": 80, "heath_summari": 80, "num_examples_per_class": 80, "rare_class": 80, "num_classes_merg": 80, "other_class": 80, "labels_merg": 80, "new_c": 80, "merged_prob": 80, "hstack": [80, 81, 82, 84], "new_class": 80, "original_class": 80, "num_check": 80, "ones_array_ref": 80, "isclos": 80, "though": [80, 82, 93], "successfulli": 80, "meaning": [80, 87], "virtuou": [80, 84], "cycl": [80, 84], "jointli": 80, "junk": 80, "clutter": 80, "unknown": 80, "caltech": 80, "combined_boolean_mask": 80, "mask1": 80, "mask2": 80, "gradientboostingclassifi": [80, 82], "true_error": [80, 82, 85], "101": [80, 86], "102": [80, 85, 86], "104": [80, 82, 86], "model_to_find_error": 80, "model_to_return": 80, "cl0": 80, "randomizedsearchcv": 80, "expens": 80, "param_distribut": 80, "learning_r": [80, 82], "max_depth": [80, 82], "magnitud": 80, "coeffici": [80, 89], "optin": 80, "environ": [80, 82], "rerun": [80, 82], "cell": [80, 82], "On": [80, 82, 86], "unabl": [80, 82], "render": [80, 82], "nbviewer": [80, 82], "cleanlearningcleanlearn": [80, 82], "linearregressionlinearregress": 80, "n_init": 80, "fit_predict": 80, "continuous_column": 80, "categorical_column": 80, "data_df": 80, "feature_a": 80, "feature_b": 80, "unexpectedli": 80, "emphas": 80, "especi": [80, 81, 89, 91, 92], "crucial": 80, "merge_duplicate_set": 80, "merge_kei": 80, "construct_group_kei": 80, "merged_set": 80, "consolidate_set": 80, "tolist": [80, 85], "issubset": 80, "frozenset": 80, "sets_list": 80, "mutabl": 80, "new_set": 80, "current_set": 80, "intersecting_set": 80, "lowest_score_strategi": 80, "sub_df": 80, "idxmin": 80, "filter_near_dupl": 80, "strategy_fn": 80, "strategy_kwarg": 80, "duplicate_row": 80, "group_kei": 80, "to_keep_indic": 80, "groupbi": 80, "explod": 80, "to_remov": 80, "isin": [80, 87], "kept": 80, "near_duplicate_issu": [80, 81], "ids_to_remove_seri": 80, "assist": 80, "streamlin": 80, "ux": 80, "agpl": 80, "compani": 80, "commerci": 80, "alter": 80, "email": 80, "discuss": 80, "anywher": 80, "profession": 80, "expert": 80, "60": [81, 82, 90], "excess": 81, "torchvis": [81, 87], "tensordataset": 81, "stratifiedkfold": [81, 85], "tqdm": 81, "fashion_mnist": 81, "num_row": 81, "60000": 81, "pil": 81, "transformed_dataset": 81, "with_format": 81, "unsqueez": 81, "cpu_count": 81, "torch_dataset": 81, "quick": [81, 85], "relu": 81, "batchnorm2d": 81, "maxpool2d": 81, "lazylinear": 81, "flatten": 81, "get_test_accuraci": 81, "testload": [81, 87], "energi": 81, "trainload": [81, 87], "n_epoch": 81, "patienc": 81, "criterion": 81, "crossentropyloss": 81, "adamw": 81, "best_test_accuraci": 81, "start_epoch": 81, "running_loss": 81, "best_epoch": 81, "end_epoch": 81, "3f": [81, 89], "acc": [81, 82], "time_taken": 81, "compute_embed": 81, "compute_pred_prob": 81, "train_batch_s": 81, "num_work": 81, "worker": [81, 93], "train_id_list": 81, "test_id_list": 81, "train_id": 81, "test_id": 81, "embeddings_model": 81, "ntrain": 81, "trainset": 81, "testset": 81, "pin_memori": 81, "fold_embed": 81, "fold_pred_prob": 81, "finish": 81, "483": 81, "835": 81, "995": 81, "331": 81, "310": 81, "856": 81, "stderr": [81, 90], "sphinxverbatim": [81, 90, 93], "60it": [81, 90], "93it": [81, 90], "30it": [81, 90], "62": [81, 82, 86, 89, 90], "63": [81, 82, 86, 90], "41it": [81, 90], "49it": [81, 90], "40it": [81, 90], "04it": [81, 90], "32it": [81, 90], "28it": [81, 90], "13it": [81, 90], "85": [81, 86, 89, 90], "69": [81, 82, 89, 90], "37it": [81, 90], "82it": [81, 90], "492": 81, "085": 81, "786": 81, "290": [81, 86], "727": 81, "26it": [81, 90], "21it": [81, 90], "51it": [81, 90], "70it": [81, 90], "88it": [81, 90], "35it": [81, 90], "20it": [81, 90], "31it": [81, 90], "89it": [81, 90], "476": 81, "305": [81, 89], "328": [81, 86], "335": 81, "627": 81, "29it": [81, 90], "17it": [81, 90], "22it": [81, 90], "74it": [81, 90], "09it": [81, 90], "84it": [81, 90], "71it": [81, 90], "01it": [81, 90], "reorder": 81, "vision": 81, "grayscal": 81, "exce": 81, "max_preval": 81, "7620": 81, "3692": 81, "3521": 81, "225": [81, 85], "166": 81, "9661": 81, "40378": 81, "687452": 81, "54473": 81, "705050": 81, "29412": 81, "715470": 81, "25316": 81, "716273": 81, "52247": 81, "725283": 81, "9581": 81, "19228": 81, "dress": 81, "54078": 81, "000010": 81, "pullov": 81, "32657": 81, "21282": 81, "000011": 81, "11262": 81, "000014": 81, "6294": 81, "30659": 81, "000798": 81, "30968": 81, "000015": 81, "258": 81, "000907": 81, "9762": 81, "54565": 81, "47139": 81, "000017": 81, "001423": 81, "000026": 81, "39992": 81, "39993": 81, "39994": 81, "39995": 81, "7834": 81, "42819": 81, "629362": 81, "51431": 81, "654330": 81, "55548": 81, "658364": 81, "51191": 81, "668572": 81, "50081": 81, "669703": 81, "7834321613629787": 81, "13732": 81, "13733": 81, "13734": 81, "47635": 81, "110901": 81, "974390": 81, "998733": 81, "937117": 81, "998755": 81, "53564": 81, "5473": 81, "trouser": 81, "plot_label_issue_exampl": 81, "ncol": [81, 87], "nrow": [81, 87], "ceil": 81, "axes_list": 81, "label_issue_indic": 81, "gl": 81, "sl": 81, "fontdict": 81, "imshow": [81, 87], "cmap": [81, 89], "grai": 81, "subplots_adjust": 81, "hspace": 81, "outsiz": 81, "outlier_issues_df": 81, "depict": [81, 85, 86, 87, 88, 90], "plot_outlier_issues_exampl": 81, "n_comparison_imag": 81, "sample_from_class": 81, "number_of_sampl": 81, "non_outlier_indic": 81, "isnul": 81, "non_outlier_indices_excluding_curr": 81, "sampled_indic": 81, "label_scores_of_sampl": 81, "top_score_indic": 81, "top_label_indic": 81, "sampled_imag": 81, "get_image_given_label_and_sampl": 81, "image_from_dataset": 81, "corresponding_label": 81, "comparison_imag": 81, "images_to_plot": 81, "idlist": 81, "iterrow": 81, "closest": 81, "counterpart": 81, "near_duplicate_issues_df": 81, "plot_near_duplicate_issue_exampl": 81, "seen_id_pair": 81, "get_image_and_given_label_and_predicted_label": 81, "duplicate_imag": 81, "nd_set": 81, "challeng": 81, "dark_issu": 81, "reveal": [81, 90], "dark_scor": 81, "dark_issues_df": 81, "is_dark_issu": 81, "34848": 81, "203922": 81, "50270": 81, "204588": 81, "3936": 81, "213098": 81, "733": 81, "217686": 81, "8094": 81, "230118": 81, "plot_image_issue_exampl": 81, "difficult": 81, "disproportion": 81, "lowinfo_issu": 81, "low_information_scor": 81, "lowinfo_issues_df": 81, "is_low_information_issu": 81, "53050": 81, "067975": 81, "40875": 81, "089929": 81, "9594": 81, "092601": 81, "34825": 81, "107744": 81, "37530": 81, "108516": 81, "lot": 81, "depth": 82, "survei": [82, 93], "focus": [82, 84], "scienc": 82, "multivariate_norm": [82, 84, 85], "make_data": [82, 84], "cov": [82, 84, 85], "avg_trac": [82, 85], "test_label": [82, 85, 87, 92], "py_tru": 82, "noise_matrix_tru": 82, "noise_marix": 82, "s_test": 82, "noisy_test_label": 82, "purpl": 82, "val": 82, "namespac": 82, "exec": 82, "markerfacecolor": [82, 85], "markeredgecolor": [82, 85, 89], "markers": [82, 85, 89], "markeredgewidth": [82, 85, 89], "realist": 82, "7560": 82, "638483e": 82, "897052e": 82, "548986e": 82, "924634e": 82, "374580e": 82, "4643": 82, "050286": 82, "065420": 82, "249": [82, 86], "109420": 82, "111687": 82, "115403": 82, "6120": 82, "023714": 82, "007136": 82, "119": [82, 86], "107266": 82, "103": [82, 86], "033738": 82, "238": [82, 86], "119505": 82, "236": [82, 86], "037843": 82, "222": 82, "614915": 82, "122": [82, 86], "624422": 82, "625965": 82, "626079": 82, "118": 82, "627675": 82, "158": 82, "159": [82, 85, 86], "161": 82, "1960": 82, "196": [82, 86], "223": [82, 86], "221": 82, "219": [82, 86], "695174": 82, "323529": 82, "522929": 82, "013722": 82, "675606": 82, "646438": 82, "anyth": 82, "enhanc": [82, 84, 86], "magic": 82, "83": [82, 86, 89, 90, 91, 93], "liter": 82, "identif": 82, "x27": 82, "logisticregressionlogisticregress": 82, "ever": 82, "092": 82, "040": 82, "024": 82, "004": 82, "surpris": 82, "arxiv": 82, "ab": 82, "1705": 82, "01936": 82, "ton": 82, "yourfavoritemodel1": 82, "merged_label": 82, "merged_test_label": 82, "newli": [82, 84], "yourfavoritemodel2": 82, "yourfavoritemodel3": 82, "cl3": 82, "takeawai": 82, "That": [82, 85], "randomli": 82, "my_test_pred_prob": 82, "my_test_pr": 82, "issues_test": 82, "corrected_test_label": 82, "pretend": 82, "cl_test_pr": 82, "fairli": 82, "label_acc": 82, "percentag": 82, "offset": 82, "nquestion": 82, "overestim": 82, "answer": 82, "experienc": 82, "06": [82, 86, 90, 93], "76": [82, 85, 86, 89, 90, 91], "knowledg": 82, "quantiti": [82, 89], "prioiri": 82, "known": 82, "versatil": 82, "label_issues_indic": 82, "213": [82, 86], "212": [82, 91], "218": [82, 86], "152": 82, "197": [82, 86], "170": 82, "214": 82, "164": [82, 85], "198": [82, 86], "191": [82, 86], "121": [82, 92], "117": [82, 89], "206": [82, 86], "115": [82, 86], "193": 82, "194": 82, "201": [82, 86], "174": 82, "163": 82, "150": [82, 84, 86], "169": 82, "151": [82, 86], "168": 82, "precision_scor": 82, "recall_scor": 82, "f1_score": 82, "true_label_issu": 82, "filter_by_list": 82, "718750": [82, 84], "807018": 82, "912": 82, "733333": 82, "800000": 82, "721311": 82, "792793": 82, "908": 82, "676923": 82, "765217": 82, "892": 82, "567901": 82, "702290": 82, "844": 82, "gaug": 82, "label_issues_count": 82, "155": [82, 86], "172": [82, 85, 93], "easiest": 82, "modular": 82, "penalti": 82, "l2": 82, "model3": 82, "n_estim": 82, "cv_pred_probs_1": 82, "cv_pred_probs_2": 82, "cv_pred_probs_3": 82, "label_quality_scores_best": 82, "cv_pred_probs_ensembl": 82, "label_quality_scores_bett": 82, "superior": [82, 88], "workflow": [83, 89], "speechbrain": 83, "timm": 83, "glad": 84, "multiannotator_label": 84, "noisier": 84, "111": [84, 89], "local_data": [84, 85], "true_labels_train": [84, 85], "noise_matrix_bett": 84, "noise_matrix_wors": 84, "transpos": [84, 87], "dropna": 84, "zfill": 84, "row_na_check": 84, "notna": 84, "reset_index": 84, "a0001": 84, "a0002": 84, "a0003": 84, "a0004": 84, "a0005": 84, "a0006": 84, "a0007": 84, "a0008": 84, "a0009": 84, "a0010": 84, "a0041": 84, "a0042": 84, "a0043": 84, "a0044": 84, "a0045": 84, "a0046": 84, "a0047": 84, "a0048": 84, "a0049": 84, "a0050": 84, "60856743": 84, "41693214": 84, "40908785": 84, "87147629": 84, "64941785": 84, "10774851": 84, "0524466": 84, "71853246": 84, "37169848": 84, "66031048": 84, "multiannotator_util": 84, "crude": 84, "straight": 84, "majority_vote_label": 84, "736157": 84, "757738": 84, "782255": 84, "715585": 84, "824273": 84, "quality_annotator_a0001": 84, "quality_annotator_a0002": 84, "quality_annotator_a0003": 84, "quality_annotator_a0004": 84, "quality_annotator_a0005": 84, "quality_annotator_a0006": 84, "quality_annotator_a0007": 84, "quality_annotator_a0008": 84, "quality_annotator_a0009": 84, "quality_annotator_a0010": 84, "quality_annotator_a0041": 84, "quality_annotator_a0042": 84, "quality_annotator_a0043": 84, "quality_annotator_a0044": 84, "quality_annotator_a0045": 84, "quality_annotator_a0046": 84, "quality_annotator_a0047": 84, "quality_annotator_a0048": 84, "quality_annotator_a0049": 84, "quality_annotator_a0050": 84, "070551": 84, "216064": 84, "119178": 84, "alongisd": 84, "244982": 84, "208333": 84, "295978": 84, "294118": 84, "324194": 84, "310345": 84, "355315": 84, "346154": 84, "439728": 84, "480000": 84, "a0031": 84, "523205": 84, "580645": 84, "a0034": 84, "535313": 84, "607143": 84, "a0021": 84, "607002": 84, "a0015": 84, "609527": 84, "678571": 84, "a0011": 84, "621101": 84, "692308": 84, "wors": 84, "improved_consensus_label": 84, "majority_vote_accuraci": 84, "cleanlab_label_accuraci": 84, "8581081081081081": 84, "9797297297297297": 84, "besid": 84, "sorted_consensus_quality_scor": 84, "worst_qual": 84, "better_qu": 84, "worst_quality_accuraci": 84, "better_quality_accuraci": 84, "9893238434163701": 84, "improved_pred_prob": 84, "treat": [84, 85, 89, 93], "analzi": 84, "copyright": 85, "advertis": 85, "violenc": 85, "nsfw": 85, "ranked_label_issu": [85, 91, 92], "multioutput": 85, "multioutputclassifi": 85, "celeba": 85, "make_multilabel_data": 85, "boxes_coordin": 85, "box_multilabel": 85, "make_multi": 85, "bx1": 85, "by1": 85, "bx2": 85, "by2": 85, "label_list": 85, "ur": 85, "upper": 85, "inidx": 85, "logical_and": 85, "inv_d": 85, "labels_idx": 85, "true_labels_test": 85, "dict_unique_label": 85, "get_color_arrai": 85, "dcolor": 85, "aa4400": 85, "55227f": 85, "55a100": 85, "00ff00": 85, "007f7f": 85, "386b55": 85, "0000ff": 85, "simplic": 85, "advis": 85, "y_onehot": 85, "single_class_label": 85, "stratifi": [85, 88], "kf": 85, "train_index": 85, "test_index": 85, "clf_cv": 85, "x_train_cv": 85, "x_test_cv": 85, "y_train_cv": 85, "y_test_cv": 85, "y_pred_cv": 85, "saw": 85, "num_to_displai": 85, "09": [85, 86, 90], "275": 85, "267": 85, "171": 85, "234": 85, "165": 85, "227": [85, 86], "262": [85, 86], "263": [85, 86], "266": [85, 86], "139": 85, "143": [85, 86], "216": [85, 86], "265": 85, "despit": [85, 93], "suspect": 85, "888": 85, "8224": 85, "9632": 85, "968": 85, "6512": 85, "0444": 85, "774": 85, "labels_binary_format": 85, "labels_list_format": 85, "surround": 86, "scene": 86, "coco": 86, "everydai": 86, "has_label_issu": 86, "insal": 86, "nc": [86, 90, 93], "s3": [86, 90, 93], "amazonaw": [86, 90, 93], "objectdetectionbenchmark": 86, "tutorial_obj": 86, "pkl": 86, "example_imag": 86, "unzip": [86, 93], "begin": 86, "detectron2": 86, "image_path": 86, "rb": 86, "image_to_visu": 86, "seg_map": 86, "334": 86, "float32": 86, "bboxes_ignor": 86, "286": 86, "285": 86, "224": 86, "231": [86, 93], "293": 86, "235": 86, "289": [86, 89], "282": 86, "74": [86, 89, 90, 91], "281": 86, "271": 86, "280": 86, "277": 86, "279": 86, "287": 86, "299": 86, "276": 86, "307": 86, "321": 86, "326": 86, "333": 86, "261": 86, "319": 86, "257": 86, "295": 86, "283": 86, "243": 86, "303": 86, "316": 86, "247": [86, 93], "323": 86, "327": 86, "226": 86, "228": 86, "232": 86, "239": 86, "240": 86, "209": 86, "242": 86, "202": 86, "230": 86, "215": 86, "220": 86, "229": 86, "217": 86, "237": 86, "207": 86, "204": 86, "205": 86, "153": 86, "149": 86, "140": 86, "124": 86, "268": 86, "273": 86, "108": 86, "284": 86, "110": 86, "136": 86, "145": 86, "173": 86, "297": 86, "317": 86, "192": 86, "329": 86, "332": 86, "324": 86, "203": [86, 93], "320": 86, "314": 86, "199": 86, "291": 86, "000000481413": 86, "jpg": 86, "42398": 86, "44503": 86, "337": [86, 92], "29968": 86, "336": 86, "21005": 86, "9978472": 86, "forgot": 86, "drew": 86, "label_issue_idx": 86, "num_examples_to_show": 86, "113": [86, 89], "candid": 86, "97489622": 86, "70610878": 86, "98764951": 86, "88899237": 86, "99085805": 86, "issue_idx": 86, "95569726e": 86, "03354841e": 86, "57510169e": 86, "58447666e": 86, "39755858e": 86, "suppli": 86, "issue_to_visu": 86, "000000009483": 86, "95569726168054e": 86, "addition": [86, 90], "visibl": 86, "missmatch": 86, "likelei": 86, "agnost": 86, "vaidat": 86, "inconsist": 86, "000000395701": 86, "033548411774308e": 86, "armchair": 86, "tv": 86, "000000154004": 86, "38300759625496356": 86, "foreground": 86, "000000448410": 86, "0008575101690203273": 86, "crowd": 86, "alon": 86, "explor": [86, 87], "resembl": [86, 87], "contribut": 86, "000000499768": 86, "9748962231208227": 86, "000000521141": 86, "8889923658893665": 86, "000000143931": 86, "9876495074395956": 86, "train_feature_embed": 87, "ood_train_feature_scor": 87, "test_feature_embed": 87, "ood_test_feature_scor": 87, "ood_train_predictions_scor": 87, "train_pred_prob": 87, "ood_test_predictions_scor": 87, "test_pred_prob": 87, "pylab": 87, "rcparam": 87, "baggingclassifi": 87, "therebi": 87, "rescal": 87, "transform_norm": 87, "totensor": 87, "root": 87, "animal_class": 87, "non_animal_class": 87, "animal_idx": 87, "test_idx": 87, "toronto": 87, "edu": 87, "kriz": 87, "5000": 87, "plot_imag": 87, "visualize_outli": 87, "txt_class": 87, "img": [87, 89], "npimg": 87, "show_label": 87, "data_subset": 87, "resnet50": 87, "corpu": 87, "2048": 87, "embed_imag": 87, "create_model": 87, "rwightman": 87, "v0": 87, "rsb": 87, "resnet50_a1_0": 87, "14fe96d1": 87, "pth": 87, "checkpoint": 87, "strang": 87, "odd": 87, "train_ood_features_scor": 87, "top_train_ood_features_idx": 87, "fun": 87, "negat": 87, "homogen": 87, "bottom_train_ood_features_idx": 87, "test_ood_features_scor": 87, "top_ood_features_idx": 87, "inevit": 87, "trade": 87, "5th": 87, "percentil": 87, "fifth_percentil": 87, "plt_rang": 87, "hist": 87, "train_outlier_scor": 87, "ylabel": 87, "axvlin": 87, "test_outlier_scor": 87, "ood_features_indic": 87, "revisit": 87, "unusu": 87, "return_invers": 87, "train_feature_embeddings_sc": 87, "test_feature_embeddings_sc": 87, "train_pred_label": 87, "9702": 87, "train_ood_predictions_scor": 87, "test_ood_predictions_scor": 87, "mainli": [87, 93], "lost": 87, "unsuit": 88, "ok": [88, 93], "convention": 88, "aforement": 88, "hypothet": 88, "contrast": 88, "tradit": 88, "disjoint": 88, "out_of_sample_pred_probs_for_a": 88, "out_of_sample_pred_probs_for_b": 88, "out_of_sample_pred_probs_for_c": 88, "out_of_sample_pred_prob": 88, "price": 89, "incom": 89, "ag": 89, "histgradientboostingregressor": 89, "r2_score": 89, "student_grades_r": 89, "final_scor": 89, "true_final_scor": 89, "homework": 89, "3d": 89, "hue": 89, "mpl_toolkit": 89, "mplot3d": 89, "axes3d": 89, "errors_idx": 89, "add_subplot": 89, "z": 89, "colorbar": 89, "errors_mask": 89, "feature_column": 89, "predicted_column": 89, "x_train_raw": 89, "x_test_raw": 89, "categorical_featur": [89, 91], "randomforestregressor": 89, "629763": 89, "521450": 89, "954607": 89, "547234": 89, "338296": 89, "754531": 89, "619090": 89, "312295": 89, "806626": 89, "784048": 89, "identified_issu": [89, 92], "367": 89, "560": 89, "318": 89, "688": 89, "657": 89, "view_datapoint": 89, "concat": 89, "consum": [89, 92], "baseline_model": [89, 92], "preds_og": 89, "r2_og": 89, "838": 89, "robustli": [89, 91, 92], "acceler": [89, 92], "found_label_issu": 89, "preds_cl": 89, "r2_cl": 89, "925": 89, "effort": [89, 91, 92], "favorit": 89, "64404888e": 89, "06755306e": 89, "05302732e": 89, "66635743e": 89, "53166364e": 89, "synthia": 90, "imagesegment": 90, "given_mask": 90, "predicted_mask": 90, "set_printopt": [90, 93], "sky": 90, "sidewalk": 90, "veget": 90, "terrain": 90, "rider": 90, "pred_probs_filepath": 90, "1088": 90, "1920": 90, "label_filepath": 90, "synthia_class": 90, "maunal": 90, "100000": 90, "244800": 90, "leftmost": 90, "area": 90, "middl": [90, 93], "infact": 90, "rightmost": 90, "discrep": 90, "4997817": 90, "17062": 90, "170604": 90, "34285": 90, "171551": 90, "99it": 90, "51456": 90, "171618": 90, "24it": 90, "68618": 90, "170859": 90, "63it": 90, "85805": 90, "171220": 90, "50it": 90, "102949": 90, "171291": 90, "120095": 90, "171343": 90, "43it": 90, "137230": 90, "170491": 90, "36it": 90, "154281": 90, "169872": 90, "65it": 90, "171280": 90, "169905": 90, "188272": 90, "169777": 90, "205575": 90, "170760": 90, "61it": 90, "222652": 90, "170740": 90, "73it": 90, "240083": 90, "171811": 90, "81it": 90, "257360": 90, "172096": 90, "03it": 90, "274605": 90, "172199": 90, "291886": 90, "172379": 90, "309125": 90, "172152": 90, "38it": 90, "326341": 90, "171949": 90, "343537": 90, "171568": 90, "72it": 90, "360817": 90, "171933": 90, "378023": 90, "171967": 90, "12it": 90, "395220": 90, "171770": 90, "69it": 90, "412518": 90, "172129": 90, "64it": 90, "429746": 90, "172171": 90, "06it": 90, "446964": 90, "171908": 90, "464155": 90, "171900": 90, "62it": 90, "481346": 90, "171355": 90, "90it": 90, "498619": 90, "171763": 90, "515796": 90, "171727": 90, "53it": 90, "532984": 90, "171768": 90, "550240": 90, "172001": 90, "07it": 90, "567441": 90, "171961": 90, "52it": 90, "584638": 90, "171920": 90, "86it": 90, "601962": 90, "172313": 90, "619303": 90, "172639": 90, "636567": 90, "172200": 90, "653788": 90, "171788": 90, "02it": 90, "670968": 90, "171514": 90, "66it": 90, "688120": 90, "170996": 90, "705275": 90, "171158": 90, "722435": 90, "171285": 90, "85it": 90, "739806": 90, "172008": 90, "757008": 90, "171692": 90, "774255": 90, "791671": 90, "172588": 90, "809012": 90, "172830": 90, "16it": 90, "826296": 90, "171982": 90, "843496": 90, "171799": 90, "860677": 90, "171324": 90, "91it": 90, "877842": 90, "171418": 90, "27it": 90, "894985": 90, "171219": 90, "42it": 90, "912146": 90, "171332": 90, "929303": 90, "171399": 90, "946444": 90, "171377": 90, "87it": 90, "963692": 90, "171703": 90, "39it": 90, "981048": 90, "172255": 90, "998359": 90, "172508": 90, "56it": 90, "1015635": 90, "172579": 90, "19it": 90, "1032893": 90, "1050039": 90, "171426": 90, "1067207": 90, "171496": 90, "1084358": 90, "171268": 90, "1101486": 90, "171044": 90, "54it": 90, "1118591": 90, "170922": 90, "1135684": 90, "170420": 90, "58it": 90, "1152727": 90, "170051": 90, "1169733": 90, "169752": 90, "1186709": 90, "169681": 90, "1203678": 90, "168904": 90, "33it": 90, "1220652": 90, "169150": 90, "94it": 90, "1237634": 90, "169345": 90, "1254570": 90, "168349": 90, "15it": 90, "1271732": 90, "169320": 90, "67it": 90, "1288750": 90, "169571": 90, "98it": 90, "1305766": 90, "169742": 90, "1322775": 90, "169844": 90, "1339793": 90, "169941": 90, "1356788": 90, "169935": 90, "1373885": 90, "170242": 90, "78it": 90, "1390910": 90, "169776": 90, "77it": 90, "1407889": 90, "1424867": 90, "169328": 90, "10it": 90, "1441920": 90, "169683": 90, "76it": 90, "1458889": 90, "169554": 90, "1475845": 90, "169207": 90, "1492767": 90, "1509688": 90, "169069": 90, "1526596": 90, "168995": 90, "55it": 90, "1543496": 90, "168767": 90, "1560373": 90, "168411": 90, "1577215": 90, "168093": 90, "1594025": 90, "167736": 90, "1610799": 90, "167579": 90, "1627581": 90, "167648": 90, "1644346": 90, "167601": 90, "44it": 90, "1661107": 90, "167408": 90, "1678158": 90, "168332": 90, "79it": 90, "1695148": 90, "168799": 90, "1712162": 90, "169197": 90, "1729082": 90, "169086": 90, "1746091": 90, "169383": 90, "1763030": 90, "169294": 90, "1779986": 90, "169370": 90, "1796940": 90, "169415": 90, "1813941": 90, "169588": 90, "95it": 90, "1830917": 90, "169635": 90, "1847932": 90, "169786": 90, "1864911": 90, "169662": 90, "1881878": 90, "169612": 90, "46it": 90, "1898840": 90, "169466": 90, "1915967": 90, "170001": 90, "1932968": 90, "169939": 90, "1949963": 90, "169565": 90, "1966920": 90, "168922": 90, "1983850": 90, "169030": 90, "80it": 90, "2000770": 90, "169076": 90, "2017733": 90, "169238": 90, "2034658": 90, "168730": 90, "2051532": 90, "168386": 90, "2068372": 90, "168018": 90, "2085260": 90, "168270": 90, "2102088": 90, "168030": 90, "2118962": 90, "168240": 90, "2135787": 90, "168132": 90, "2152601": 90, "167646": 90, "2169423": 90, "167813": 90, "2186297": 90, "168085": 90, "2203175": 90, "168289": 90, "2220005": 90, "168276": 90, "2236833": 90, "168121": 90, "2253646": 90, "168054": 90, "2270452": 90, "167426": 90, "00it": 90, "2287196": 90, "167340": 90, "2303931": 90, "167280": 90, "92it": 90, "2320696": 90, "167387": 90, "2337572": 90, "167794": 90, "2354510": 90, "168265": 90, "2371337": 90, "168146": 90, "2388246": 90, "168426": 90, "2405089": 90, "167971": 90, "2421887": 90, "167875": 90, "18it": 90, "2438756": 90, "168116": 90, "2455622": 90, "168275": 90, "2472450": 90, "168246": 90, "2489275": 90, "168100": 90, "11it": 90, "2506165": 90, "168336": 90, "59it": 90, "2523041": 90, "168459": 90, "2539888": 90, "168363": 90, "2556725": 90, "168317": 90, "2573557": 90, "168314": 90, "2590389": 90, "168159": 90, "2607206": 90, "168024": 90, "45it": 90, "2624021": 90, "168057": 90, "2640827": 90, "167450": 90, "2657654": 90, "167690": 90, "2674611": 90, "168247": 90, "2691437": 90, "168060": 90, "2708244": 90, "167385": 90, "05it": 90, "2725000": 90, "167432": 90, "83it": 90, "2741821": 90, "167661": 90, "2758588": 90, "167266": 90, "2775352": 90, "167374": 90, "2792090": 90, "167063": 90, "2808802": 90, "167076": 90, "2825510": 90, "167042": 90, "2842215": 90, "166910": 90, "2858913": 90, "166926": 90, "75it": 90, "2875624": 90, "166976": 90, "2892322": 90, "166542": 90, "2909084": 90, "166860": 90, "2925771": 90, "166846": 90, "2942500": 90, "166975": 90, "2959198": 90, "166708": 90, "2975877": 90, "166728": 90, "2992600": 90, "166874": 90, "3009402": 90, "167212": 90, "3026124": 90, "166887": 90, "3042813": 90, "166464": 90, "96it": 90, "3059512": 90, "166617": 90, "3076399": 90, "167287": 90, "3093129": 90, "167242": 90, "3109854": 90, "164570": 90, "3127061": 90, "166789": 90, "3144295": 90, "168437": 90, "3161147": 90, "3178372": 90, "169324": 90, "97it": 90, "3195555": 90, "170071": 90, "3212860": 90, "170957": 90, "3230096": 90, "171374": 90, "3247326": 90, "171646": 90, "3264493": 90, "171542": 90, "68it": 90, "3281649": 90, "171525": 90, "3298803": 90, "171132": 90, "3315917": 90, "170861": 90, "3333004": 90, "170374": 90, "3350043": 90, "170335": 90, "3367100": 90, "170400": 90, "3384218": 90, "170631": 90, "3401284": 90, "170636": 90, "34it": 90, "3418357": 90, "170658": 90, "3435515": 90, "170931": 90, "3452609": 90, "163949": 90, "3469894": 90, "166538": 90, "3486928": 90, "167650": 90, "3503919": 90, "3520963": 90, "168940": 90, "3538111": 90, "169693": 90, "3555434": 90, "170745": 90, "3572660": 90, "171192": 90, "3590030": 90, "171938": 90, "3607303": 90, "172172": 90, "3624594": 90, "172387": 90, "3641836": 90, "171859": 90, "14it": 90, "3659025": 90, "171633": 90, "3676190": 90, "171400": 90, "3693332": 90, "171243": 90, "48it": 90, "3710458": 90, "171013": 90, "3727633": 90, "171229": 90, "3744757": 90, "171012": 90, "3761899": 90, "171129": 90, "3779084": 90, "171340": 90, "3796219": 90, "167422": 90, "3813525": 90, "169081": 90, "57it": 90, "3830826": 90, "3847923": 90, "170454": 90, "3865103": 90, "170852": 90, "3882263": 90, "171072": 90, "3899433": 90, "171255": 90, "3916562": 90, "170928": 90, "3933884": 90, "171610": 90, "47it": 90, "3951103": 90, "171779": 90, "3968283": 90, "171759": 90, "3985460": 90, "171748": 90, "4002799": 90, "172236": 90, "4020024": 90, "172120": 90, "4037255": 90, "4054562": 90, "172438": 90, "4071846": 90, "172553": 90, "4089102": 90, "4106326": 90, "172177": 90, "4123603": 90, "172350": 90, "4140839": 90, "171954": 90, "4158035": 90, "168862": 90, "4175226": 90, "169758": 90, "4192212": 90, "168849": 90, "4209250": 90, "169300": 90, "4226465": 90, "170143": 90, "4243806": 90, "171114": 90, "4261227": 90, "172034": 90, "4278528": 90, "172320": 90, "4295937": 90, "172845": 90, "4313308": 90, "173100": 90, "25it": 90, "4330658": 90, "173214": 90, "4347981": 90, "173208": 90, "4365307": 90, "173218": 90, "4382688": 90, "173390": 90, "4400068": 90, "173507": 90, "4417419": 90, "173309": 90, "4434751": 90, "172930": 90, "4452045": 90, "172511": 90, "4469335": 90, "172622": 90, "4486598": 90, "171589": 90, "4503759": 90, "171412": 90, "4520902": 90, "4538037": 90, "171239": 90, "4555162": 90, "170876": 90, "4572250": 90, "170565": 90, "4589307": 90, "170097": 90, "4606318": 90, "169865": 90, "4623305": 90, "169631": 90, "4640269": 90, "168872": 90, "4657235": 90, "169105": 90, "4674147": 90, "168226": 90, "4690976": 90, "168242": 90, "4707801": 90, "168102": 90, "4724647": 90, "168205": 90, "4741468": 90, "167808": 90, "4758250": 90, "167370": 90, "08it": 90, "4774988": 90, "167262": 90, "4791806": 90, "167533": 90, "4808560": 90, "167179": 90, "4825279": 90, "167129": 90, "4842096": 90, "167436": 90, "4859300": 90, "168811": 90, "4876497": 90, "4893694": 90, "170413": 90, "4910769": 90, "170510": 90, "4927821": 90, "170169": 90, "4944989": 90, "170616": 90, "4962051": 90, "169773": 90, "4979093": 90, "169963": 90, "4996091": 90, "169052": 90, "169841": 90, "3263230": 90, "783379": 90, "275110": 90, "255792": 90, "78225": 90, "55990": 90, "54427": 90, "33591": 90, "24645": 90, "21308": 90, "15045": 90, "14171": 90, "13832": 90, "13498": 90, "11490": 90, "9164": 90, "8769": 90, "6999": 90, "6031": 90, "5011": 90, "mistakenli": 90, "class_issu": 90, "aim": [90, 93], "domin": 90, "extratreesclassifi": 91, "extratre": 91, "labelencod": [91, 92], "labels_raw": 91, "interg": [91, 92], "tress": 91, "827": 91, "cheat": 91, "0pt": 91, "233": 91, "labels_train": 91, "labels_test": 91, "acc_og": [91, 92], "783068783068783": 91, "acc_cl": [91, 92], "8095238095238095": 91, "earlier": [92, 93], "raw_label": 92, "raw_train_text": 92, "raw_test_text": 92, "raw_train_label": 92, "raw_test_label": 92, "encond": 92, "train_text": 92, "test_text": 92, "858050": 92, "545854": 92, "826194": 92, "965814": 92, "791923": 92, "646": 92, "390": 92, "628": 92, "702": 92, "863": 92, "135": 92, "735": 92, "print_as_df": 92, "inverse_transform": 92, "fight": 92, "bunch": 93, "conll": 93, "2003": 93, "love": 93, "n_i": 93, "optional_list_of_ordered_class_nam": 93, "deepai": 93, "conll2003": 93, "rm": 93, "tokenclassif": 93, "2024": 93, "2400": 93, "52e0": 93, "1a00": 93, "1070": 93, "connect": 93, "443": 93, "await": 93, "982975": 93, "960k": 93, "kb": 93, "959": 93, "94k": 93, "mb": 93, "directori": 93, "inflat": 93, "17045998": 93, "16m": 93, "octet": 93, "46m": 93, "3mb": 93, "26m": 93, "8mb": 93, "bert": 93, "read_npz": 93, "filepath": 93, "corrsespond": 93, "iob2": 93, "given_ent": 93, "entity_map": 93, "readfil": 93, "sep": 93, "startswith": 93, "docstart": 93, "isalpha": 93, "isupp": 93, "indices_to_preview": 93, "nsentenc": 93, "eu": 93, "reject": 93, "boycott": 93, "british": 93, "lamb": 93, "00030412": 93, "00023826": 93, "99936208": 93, "00007009": 93, "00002545": 93, "99998795": 93, "00000401": 93, "00000218": 93, "00000455": 93, "00000131": 93, "00000749": 93, "99996115": 93, "00001371": 93, "0000087": 93, "00000895": 93, "99998936": 93, "00000382": 93, "00000178": 93, "00000366": 93, "00000137": 93, "99999101": 93, "00000266": 93, "00000174": 93, "0000035": 93, "00000109": 93, "99998768": 93, "00000482": 93, "00000202": 93, "00000438": 93, "0000011": 93, "00000465": 93, "99996392": 93, "00001105": 93, "0000116": 93, "00000878": 93, "99998671": 93, "00000364": 93, "00000213": 93, "00000472": 93, "00000281": 93, "99999073": 93, "00000211": 93, "00000159": 93, "00000442": 93, "00000115": 93, "peter": 93, "blackburn": 93, "00000358": 93, "00000529": 93, "99995623": 93, "000022": 93, "0000129": 93, "0000024": 93, "00001812": 93, "99994141": 93, "00001645": 93, "00002162": 93, "brussel": 93, "1996": 93, "00001172": 93, "00000821": 93, "00004661": 93, "0000618": 93, "99987167": 93, "99999061": 93, "00000201": 93, "00000195": 93, "00000408": 93, "00000135": 93, "2254": 93, "2907": 93, "19392": 93, "9962": 93, "8904": 93, "19303": 93, "12918": 93, "9256": 93, "11855": 93, "18392": 93, "20426": 93, "19402": 93, "14744": 93, "19371": 93, "4645": 93, "10331": 93, "9430": 93, "6143": 93, "18367": 93, "12914": 93, "todai": 93, "weather": 93, "march": 93, "scalfaro": 93, "northern": 93, "himself": 93, "said": 93, "germani": 93, "nastja": 93, "rysich": 93, "north": 93, "spla": 93, "fought": 93, "khartoum": 93, "govern": 93, "south": 93, "1983": 93, "autonomi": 93, "animist": 93, "region": 93, "moslem": 93, "arabis": 93, "mayor": 93, "antonio": 93, "gonzalez": 93, "garcia": 93, "revolutionari": 93, "parti": 93, "wednesdai": 93, "troop": 93, "raid": 93, "farm": 93, "stole": 93, "rape": 93, "women": 93, "spring": 93, "chg": 93, "hrw": 93, "12pct": 93, "princ": 93, "photo": 93, "moment": 93, "spokeswoman": 93, "rainier": 93, "told": 93, "reuter": 93, "danila": 93, "carib": 93, "w224": 93, "equip": 93, "radiomet": 93, "earn": 93, "19996": 93, "london": 93, "denom": 93, "sale": 93, "uk": 93, "jp": 93, "fr": 93, "maccabi": 93, "hapoel": 93, "haifa": 93, "tel": 93, "aviv": 93, "hospit": 93, "rever": 93, "roman": 93, "cathol": 93, "nun": 93, "admit": 93, "calcutta": 93, "week": 93, "ago": 93, "fever": 93, "vomit": 93, "allianc": 93, "embattl": 93, "kabul": 93, "salang": 93, "highwai": 93, "mondai": 93, "tuesdai": 93, "suprem": 93, "council": 93, "led": 93, "jumbish": 93, "milli": 93, "movement": 93, "warlord": 93, "abdul": 93, "rashid": 93, "dostum": 93, "dollar": 93, "exchang": 93, "3570": 93, "12049": 93, "born": 93, "1937": 93, "provinc": 93, "anhui": 93, "dai": 93, "came": 93, "shanghai": 93, "citi": 93, "prolif": 93, "author": 93, "teacher": 93, "chines": 93, "16764": 93, "1990": 93, "historian": 93, "alan": 93, "john": 93, "percival": 93, "taylor": 93, "di": 93, "20446": 93, "pace": 93, "bowler": 93, "ian": 93, "harvei": 93, "claim": 93, "victoria": 93, "15514": 93, "cotti": 93, "osc": 93, "foreign": 93, "minist": 93, "7525": 93, "sultan": 93, "specter": 93, "met": 93, "crown": 93, "abdullah": 93, "defenc": 93, "aviat": 93, "jeddah": 93, "saudi": 93, "agenc": 93, "2288": 93, "hi": 93, "customari": 93, "outfit": 93, "champion": 93, "damp": 93, "scalp": 93, "canada": 93, "reign": 93, "olymp": 93, "donovan": 93, "bailei": 93, "1992": 93, "linford": 93, "christi": 93, "britain": 93, "1984": 93, "1988": 93, "carl": 93, "lewi": 93, "ambigi": 93, "punctuat": 93, "chicago": 93, "digest": 93, "philadelphia": 93, "usda": 93, "york": 93, "token_issu": 93, "471": 93, "kean": 93, "year": 93, "contract": 93, "manchest": 93, "19072": 93, "societi": 93, "million": 93, "bite": 93, "deliv": 93, "19910": 93, "father": 93, "clarenc": 93, "woolmer": 93, "renam": 93, "uttar": 93, "pradesh": 93, "india": 93, "ranji": 93, "trophi": 93, "nation": 93, "championship": 93, "captain": 93, "1949": 93, "15658": 93, "19879": 93, "iii": 93, "brian": 93, "shimer": 93, "randi": 93, "jone": 93, "19104": 93}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [29, 0, 0, "-", "dataset"], [32, 0, 0, "-", "experimental"], [35, 0, 0, "-", "filter"], [36, 0, 0, "-", "internal"], [47, 0, 0, "-", "models"], [49, 0, 0, "-", "multiannotator"], [52, 0, 0, "-", "multilabel_classification"], [55, 0, 0, "-", "object_detection"], [58, 0, 0, "-", "outlier"], [59, 0, 0, "-", "rank"], [60, 0, 0, "-", "regression"], [64, 0, 0, "-", "segmentation"], [68, 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.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [27, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "duplicate"], [17, 0, 0, "-", "imbalance"], [19, 0, 0, "-", "issue_manager"], [20, 0, 0, "-", "label"], [21, 0, 0, "-", "noniid"], [22, 0, 0, "-", "null"], [23, 0, 0, "-", "outlier"], [26, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[16, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 6, 1, "", "near_duplicate_sets"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[17, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[19, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[20, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 3, 1, "", "get_health_summary"], [20, 6, 1, "", "health_summary_parameters"], [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.noniid": [[21, 2, 1, "", "NonIIDIssueManager"], [21, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[22, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[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.outlier": [[23, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[23, 6, 1, "", "DEFAULT_THRESHOLDS"], [23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 6, 1, "", "ood"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[25, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[25, 2, 1, "", "RegressionLabelIssueManager"], [25, 1, 1, "", "find_issues_with_features"], [25, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[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.underperforming_group": [[26, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[26, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [26, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "filter_cluster_ids"], [26, 3, 1, "", "find_issues"], [26, 3, 1, "", "get_worst_cluster"], [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, "", "perform_clustering"], [26, 3, 1, "", "report"], [26, 3, 1, "", "set_knn_graph"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[27, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[27, 3, 1, "", "get_report"], [27, 3, 1, "", "report"]], "cleanlab.dataset": [[29, 1, 1, "", "find_overlapping_classes"], [29, 1, 1, "", "health_summary"], [29, 1, 1, "", "overall_label_health_score"], [29, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[30, 0, 0, "-", "cifar_cnn"], [31, 0, 0, "-", "coteaching"], [33, 0, 0, "-", "label_issues_batched"], [34, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[30, 2, 1, "", "CNN"], [30, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[30, 6, 1, "", "T_destination"], [30, 3, 1, "", "__call__"], [30, 3, 1, "", "add_module"], [30, 3, 1, "", "apply"], [30, 3, 1, "", "bfloat16"], [30, 3, 1, "", "buffers"], [30, 3, 1, "", "children"], [30, 3, 1, "", "cpu"], [30, 3, 1, "", "cuda"], [30, 3, 1, "", "double"], [30, 6, 1, "", "dump_patches"], [30, 3, 1, "", "eval"], [30, 3, 1, "", "extra_repr"], [30, 3, 1, "", "float"], [30, 3, 1, "id0", "forward"], [30, 3, 1, "", "get_buffer"], [30, 3, 1, "", "get_extra_state"], [30, 3, 1, "", "get_parameter"], [30, 3, 1, "", "get_submodule"], [30, 3, 1, "", "half"], [30, 3, 1, "", "ipu"], [30, 3, 1, "", "load_state_dict"], [30, 3, 1, "", "modules"], [30, 3, 1, "", "named_buffers"], [30, 3, 1, "", "named_children"], [30, 3, 1, "", "named_modules"], [30, 3, 1, "", "named_parameters"], [30, 3, 1, "", "parameters"], [30, 3, 1, "", "register_backward_hook"], [30, 3, 1, "", "register_buffer"], [30, 3, 1, "", "register_forward_hook"], [30, 3, 1, "", "register_forward_pre_hook"], [30, 3, 1, "", "register_full_backward_hook"], [30, 3, 1, "", "register_load_state_dict_post_hook"], [30, 3, 1, "", "register_module"], [30, 3, 1, "", "register_parameter"], [30, 3, 1, "", "requires_grad_"], [30, 3, 1, "", "set_extra_state"], [30, 3, 1, "", "share_memory"], [30, 3, 1, "", "state_dict"], [30, 3, 1, "", "to"], [30, 3, 1, "", "to_empty"], [30, 3, 1, "", "train"], [30, 6, 1, "", "training"], [30, 3, 1, "", "type"], [30, 3, 1, "", "xpu"], [30, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[31, 1, 1, "", "adjust_learning_rate"], [31, 1, 1, "", "evaluate"], [31, 1, 1, "", "forget_rate_scheduler"], [31, 1, 1, "", "initialize_lr_scheduler"], [31, 1, 1, "", "loss_coteaching"], [31, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[33, 2, 1, "", "LabelInspector"], [33, 7, 1, "", "adj_confident_thresholds_shared"], [33, 1, 1, "", "find_label_issues_batched"], [33, 7, 1, "", "labels_shared"], [33, 7, 1, "", "pred_probs_shared"], [33, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[33, 3, 1, "", "get_confident_thresholds"], [33, 3, 1, "", "get_label_issues"], [33, 3, 1, "", "get_num_issues"], [33, 3, 1, "", "get_quality_scores"], [33, 3, 1, "", "score_label_quality"], [33, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[34, 2, 1, "", "CNN"], [34, 2, 1, "", "SimpleNet"], [34, 1, 1, "", "get_mnist_dataset"], [34, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[34, 3, 1, "", "__init_subclass__"], [34, 6, 1, "", "batch_size"], [34, 6, 1, "", "dataset"], [34, 6, 1, "", "epochs"], [34, 3, 1, "id0", "fit"], [34, 3, 1, "", "get_metadata_routing"], [34, 3, 1, "", "get_params"], [34, 6, 1, "", "loader"], [34, 6, 1, "", "log_interval"], [34, 6, 1, "", "lr"], [34, 6, 1, "", "momentum"], [34, 6, 1, "", "no_cuda"], [34, 3, 1, "id1", "predict"], [34, 3, 1, "id4", "predict_proba"], [34, 6, 1, "", "seed"], [34, 3, 1, "", "set_fit_request"], [34, 3, 1, "", "set_params"], [34, 3, 1, "", "set_predict_proba_request"], [34, 3, 1, "", "set_predict_request"], [34, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[34, 6, 1, "", "T_destination"], [34, 3, 1, "", "__call__"], [34, 3, 1, "", "add_module"], [34, 3, 1, "", "apply"], [34, 3, 1, "", "bfloat16"], [34, 3, 1, "", "buffers"], [34, 3, 1, "", "children"], [34, 3, 1, "", "cpu"], [34, 3, 1, "", "cuda"], [34, 3, 1, "", "double"], [34, 6, 1, "", "dump_patches"], [34, 3, 1, "", "eval"], [34, 3, 1, "", "extra_repr"], [34, 3, 1, "", "float"], [34, 3, 1, "", "forward"], [34, 3, 1, "", "get_buffer"], [34, 3, 1, "", "get_extra_state"], [34, 3, 1, "", "get_parameter"], [34, 3, 1, "", "get_submodule"], [34, 3, 1, "", "half"], [34, 3, 1, "", "ipu"], [34, 3, 1, "", "load_state_dict"], [34, 3, 1, "", "modules"], [34, 3, 1, "", "named_buffers"], [34, 3, 1, "", "named_children"], [34, 3, 1, "", "named_modules"], [34, 3, 1, "", "named_parameters"], [34, 3, 1, "", "parameters"], [34, 3, 1, "", "register_backward_hook"], [34, 3, 1, "", "register_buffer"], [34, 3, 1, "", "register_forward_hook"], [34, 3, 1, "", "register_forward_pre_hook"], [34, 3, 1, "", "register_full_backward_hook"], [34, 3, 1, "", "register_load_state_dict_post_hook"], [34, 3, 1, "", "register_module"], [34, 3, 1, "", "register_parameter"], [34, 3, 1, "", "requires_grad_"], [34, 3, 1, "", "set_extra_state"], [34, 3, 1, "", "share_memory"], [34, 3, 1, "", "state_dict"], [34, 3, 1, "", "to"], [34, 3, 1, "", "to_empty"], [34, 3, 1, "", "train"], [34, 6, 1, "", "training"], [34, 3, 1, "", "type"], [34, 3, 1, "", "xpu"], [34, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[35, 1, 1, "", "find_label_issues"], [35, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [35, 1, 1, "", "find_predicted_neq_given"], [35, 7, 1, "", "pred_probs_by_class"], [35, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[37, 0, 0, "-", "label_quality_utils"], [38, 0, 0, "-", "latent_algebra"], [39, 0, 0, "-", "multiannotator_utils"], [40, 0, 0, "-", "multilabel_scorer"], [41, 0, 0, "-", "multilabel_utils"], [42, 0, 0, "-", "outlier"], [43, 0, 0, "-", "token_classification_utils"], [44, 0, 0, "-", "util"], [45, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[37, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[38, 1, 1, "", "compute_inv_noise_matrix"], [38, 1, 1, "", "compute_noise_matrix_from_inverse"], [38, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [38, 1, 1, "", "compute_py"], [38, 1, 1, "", "compute_py_inv_noise_matrix"], [38, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[39, 1, 1, "", "assert_valid_inputs_multiannotator"], [39, 1, 1, "", "assert_valid_pred_probs"], [39, 1, 1, "", "check_consensus_label_classes"], [39, 1, 1, "", "compute_soft_cross_entropy"], [39, 1, 1, "", "find_best_temp_scaler"], [39, 1, 1, "", "format_multiannotator_labels"], [39, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[40, 2, 1, "", "Aggregator"], [40, 2, 1, "", "ClassLabelScorer"], [40, 2, 1, "", "MultilabelScorer"], [40, 1, 1, "", "exponential_moving_average"], [40, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [40, 1, 1, "", "get_label_quality_scores"], [40, 1, 1, "", "multilabel_py"], [40, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[40, 3, 1, "", "__call__"], [40, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[40, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [40, 6, 1, "", "NORMALIZED_MARGIN"], [40, 6, 1, "", "SELF_CONFIDENCE"], [40, 3, 1, "", "__call__"], [40, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[40, 3, 1, "", "__call__"], [40, 3, 1, "", "aggregate"], [40, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[41, 1, 1, "", "get_onehot_num_classes"], [41, 1, 1, "", "int2onehot"], [41, 1, 1, "", "onehot2int"], [41, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[42, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[43, 1, 1, "", "color_sentence"], [43, 1, 1, "", "filter_sentence"], [43, 1, 1, "", "get_sentence"], [43, 1, 1, "", "mapping"], [43, 1, 1, "", "merge_probs"], [43, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[44, 1, 1, "", "append_extra_datapoint"], [44, 1, 1, "", "clip_noise_rates"], [44, 1, 1, "", "clip_values"], [44, 1, 1, "", "compress_int_array"], [44, 1, 1, "", "confusion_matrix"], [44, 1, 1, "", "csr_vstack"], [44, 1, 1, "", "estimate_pu_f1"], [44, 1, 1, "", "extract_indices_tf"], [44, 1, 1, "", "force_two_dimensions"], [44, 1, 1, "", "format_labels"], [44, 1, 1, "", "get_missing_classes"], [44, 1, 1, "", "get_num_classes"], [44, 1, 1, "", "get_unique_classes"], [44, 1, 1, "", "is_tensorflow_dataset"], [44, 1, 1, "", "is_torch_dataset"], [44, 1, 1, "", "num_unique_classes"], [44, 1, 1, "", "print_inverse_noise_matrix"], [44, 1, 1, "", "print_joint_matrix"], [44, 1, 1, "", "print_noise_matrix"], [44, 1, 1, "", "print_square_matrix"], [44, 1, 1, "", "remove_noise_from_class"], [44, 1, 1, "", "round_preserving_row_totals"], [44, 1, 1, "", "round_preserving_sum"], [44, 1, 1, "", "smart_display_dataframe"], [44, 1, 1, "", "subset_X_y"], [44, 1, 1, "", "subset_data"], [44, 1, 1, "", "subset_labels"], [44, 1, 1, "", "train_val_split"], [44, 1, 1, "", "unshuffle_tensorflow_dataset"], [44, 1, 1, "", "value_counts"], [44, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[45, 1, 1, "", "assert_indexing_works"], [45, 1, 1, "", "assert_nonempty_input"], [45, 1, 1, "", "assert_valid_class_labels"], [45, 1, 1, "", "assert_valid_inputs"], [45, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[48, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[48, 2, 1, "", "KerasWrapperModel"], [48, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[48, 3, 1, "", "fit"], [48, 3, 1, "", "get_params"], [48, 3, 1, "", "predict"], [48, 3, 1, "", "predict_proba"], [48, 3, 1, "", "set_params"], [48, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[48, 3, 1, "", "fit"], [48, 3, 1, "", "get_params"], [48, 3, 1, "", "predict"], [48, 3, 1, "", "predict_proba"], [48, 3, 1, "", "set_params"], [48, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[49, 1, 1, "", "convert_long_to_wide_dataset"], [49, 1, 1, "", "get_active_learning_scores"], [49, 1, 1, "", "get_active_learning_scores_ensemble"], [49, 1, 1, "", "get_label_quality_multiannotator"], [49, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [49, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[50, 0, 0, "-", "dataset"], [51, 0, 0, "-", "filter"], [53, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[50, 1, 1, "", "common_multilabel_issues"], [50, 1, 1, "", "multilabel_health_summary"], [50, 1, 1, "", "overall_multilabel_health_score"], [50, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[51, 1, 1, "", "find_label_issues"], [51, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[53, 1, 1, "", "get_label_quality_scores"], [53, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[54, 0, 0, "-", "filter"], [56, 0, 0, "-", "rank"], [57, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[54, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[56, 1, 1, "", "compute_badloc_box_scores"], [56, 1, 1, "", "compute_overlooked_box_scores"], [56, 1, 1, "", "compute_swap_box_scores"], [56, 1, 1, "", "get_label_quality_scores"], [56, 1, 1, "", "issues_from_scores"], [56, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[57, 1, 1, "", "bounding_box_size_distribution"], [57, 1, 1, "", "calculate_per_class_metrics"], [57, 1, 1, "", "class_label_distribution"], [57, 1, 1, "", "get_average_per_class_confusion_matrix"], [57, 1, 1, "", "get_sorted_bbox_count_idxs"], [57, 1, 1, "", "object_counts_per_image"], [57, 1, 1, "", "plot_class_distribution"], [57, 1, 1, "", "plot_class_size_distributions"], [57, 1, 1, "", "visualize"]], "cleanlab.outlier": [[58, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[58, 3, 1, "", "fit"], [58, 3, 1, "", "fit_score"], [58, 3, 1, "", "score"]], "cleanlab.rank": [[59, 1, 1, "", "find_top_issues"], [59, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [59, 1, 1, "", "get_label_quality_ensemble_scores"], [59, 1, 1, "", "get_label_quality_scores"], [59, 1, 1, "", "get_normalized_margin_for_each_label"], [59, 1, 1, "", "get_self_confidence_for_each_label"], [59, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[61, 0, 0, "-", "learn"], [62, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[61, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[61, 3, 1, "", "__init_subclass__"], [61, 3, 1, "", "find_label_issues"], [61, 3, 1, "", "fit"], [61, 3, 1, "", "get_aleatoric_uncertainty"], [61, 3, 1, "", "get_epistemic_uncertainty"], [61, 3, 1, "", "get_label_issues"], [61, 3, 1, "", "get_metadata_routing"], [61, 3, 1, "", "get_params"], [61, 3, 1, "", "predict"], [61, 3, 1, "", "save_space"], [61, 3, 1, "", "score"], [61, 3, 1, "", "set_fit_request"], [61, 3, 1, "", "set_params"], [61, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[62, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[63, 0, 0, "-", "filter"], [65, 0, 0, "-", "rank"], [66, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[63, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[65, 1, 1, "", "get_label_quality_scores"], [65, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[66, 1, 1, "", "common_label_issues"], [66, 1, 1, "", "display_issues"], [66, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[67, 0, 0, "-", "filter"], [69, 0, 0, "-", "rank"], [70, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[67, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[69, 1, 1, "", "get_label_quality_scores"], [69, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[70, 1, 1, "", "common_label_issues"], [70, 1, 1, "", "display_issues"], [70, 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, 73, 77, 78, 80, 81, 82, 85, 91, 92, 93], "count": [3, 82], "datalab": [4, 5, 7, 8, 9, 74, 75, 76, 77, 78, 82], "creat": [5, 74, 75, 82, 84], "your": [5, 71, 74, 75, 78, 80, 82], "own": 5, "issu": [5, 7, 8, 18, 25, 71, 73, 74, 75, 77, 78, 79, 80, 81, 82, 85, 86, 90, 91, 93], "manag": [5, 18], "prerequisit": 5, "implement": 5, "issuemanag": [5, 74], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 74], "us": [5, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "gener": 6, "cluster": [6, 80], "id": 6, "guid": [7, 9], "type": [7, 8, 82], "custom": [7, 74], "can": [8, 75, 79, 80, 82, 84], "detect": [8, 75, 77, 78, 80, 82, 86, 87], "estim": [8, 82, 84], "each": 8, "label": [8, 20, 25, 71, 73, 75, 77, 78, 80, 81, 82, 84, 85, 86, 89, 90, 91, 92, 93], "outlier": [8, 23, 42, 58, 77, 78, 81, 87], "Near": [8, 75, 77, 78, 81], "duplic": [8, 16, 75, 77, 78, 80, 81], "non": [8, 78], "iid": [8, 78], "class": [8, 72, 82, 90], "imbal": [8, 17], "imag": [8, 81, 87], "specif": [8, 18, 90], "underperform": [8, 80], "group": [8, 80], "null": [8, 22], "option": 8, "paramet": [8, 82], "get": [9, 74, 75, 84, 85, 86, 90, 93], "start": [9, 79], "api": 9, "refer": 9, "data": [10, 71, 73, 74, 75, 77, 78, 79, 80, 82, 84, 85, 86, 87, 89, 90, 91, 93], "data_issu": 11, "factori": 12, "intern": [13, 36], "issue_find": 14, "issue_manag": [18, 19], "regist": 18, "unregist": 18, "ml": [18, 80, 82], "task": 18, "noniid": 21, "regress": [24, 60, 61, 62, 80, 89], "prioriti": 25, "order": 25, "find": [25, 71, 73, 75, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "underperforming_group": 26, "report": [27, 81], "dataset": [29, 50, 71, 75, 78, 79, 80, 81, 82, 85, 86, 87, 89, 90, 92, 93], "cifar_cnn": 30, "coteach": 31, "experiment": 32, "label_issues_batch": 33, "mnist_pytorch": 34, "filter": [35, 51, 54, 63, 67, 82], "label_quality_util": 37, "latent_algebra": 38, "multiannotator_util": 39, "multilabel_scor": 40, "multilabel_util": 41, "token_classification_util": 43, "util": 44, "valid": [45, 81, 88], "fasttext": 46, "model": [47, 71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 91, 92], "kera": 48, "multiannot": [49, 84], "multilabel_classif": 52, "rank": [53, 56, 59, 62, 65, 69, 82], "object_detect": 55, "summari": [57, 66, 70], "learn": [61, 75, 80, 82, 91], "segment": [64, 90], "token_classif": [68, 93], "cleanlab": [71, 73, 77, 78, 80, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "open": [71, 80], "sourc": [71, 80], "document": 71, "quickstart": 71, "1": [71, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "instal": [71, 73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "2": [71, 72, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "common": [71, 72, 93], "3": [71, 73, 74, 75, 77, 78, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "handl": [71, 80], "error": [71, 80, 81, 82, 84, 85, 86, 89, 90, 92, 93], "train": [71, 73, 80, 87, 89, 91, 92], "robust": [71, 82, 89, 91, 92], "noisi": [71, 82, 89, 91, 92], "4": [71, 73, 74, 75, 77, 78, 81, 82, 84, 86, 87, 89, 91, 92], "curat": [71, 79], "fix": [71, 80], "level": [71, 79, 82, 93], "5": [71, 73, 75, 77, 81, 82, 84, 89, 91], "improv": [71, 84], "via": [71, 82, 84], "mani": [71, 82], "other": [71, 84, 86, 89], "techniqu": 71, "contribut": 71, "easi": [71, 77, 78, 81], "mode": [71, 77, 78, 81], "how": [72, 80, 82, 84, 85, 93], "migrat": 72, "version": 72, "0": 72, "from": [72, 74, 75, 82, 89, 91, 92], "pre": [72, 73, 80, 87], "function": [72, 74], "name": 72, "chang": 72, "modul": [72, 82], "new": 72, "remov": 72, "argument": [72, 74], "variabl": 72, "audio": 73, "speechbrain": 73, "depend": [73, 74, 75, 77, 78, 79, 81, 82, 84, 85, 86, 87, 89, 90, 91, 92, 93], "import": [73, 74, 75, 79, 81, 82, 84], "them": [73, 79, 82], "load": [73, 74, 75, 77, 78, 89, 91, 92], "featur": [73, 81, 87], "fit": 73, "linear": 73, "comput": [73, 77, 78, 80, 81, 84, 88, 91], "out": [73, 74, 75, 77, 78, 81, 84, 88, 91], "sampl": [73, 74, 75, 77, 78, 81, 84, 88, 91], "predict": [73, 74, 75, 77, 78, 81, 84, 85, 86, 88, 91], "probabl": [73, 74, 75, 77, 78, 81, 84, 88, 91], "workflow": [74, 82], "audit": [74, 75], "requir": [74, 75, 77, 78, 81, 84, 85, 86, 87, 89, 90, 91, 92, 93], "classifi": [74, 75], "instanti": 74, "object": [74, 86], "increment": 74, "search": 74, "specifi": [74, 80], "nondefault": 74, "save": 74, "ad": 74, "A": 75, "unifi": 75, "all": [75, 82], "kind": [75, 86], "skip": [75, 79, 82, 84], "detail": [75, 79, 82, 84], "more": [75, 82, 89, 91, 92], "about": 75, "addit": 75, "inform": [75, 81], "tutori": [76, 79, 83], "tabular": [77, 91], "numer": 77, "categor": 77, "column": 77, "process": [77, 87, 89, 91], "select": [77, 91], "construct": 77, "k": [77, 81, 88], "nearest": 77, "neighbour": 77, "graph": 77, "text": [78, 92, 93], "format": [78, 80, 85, 86, 92], "defin": [78, 81, 89, 92], "drift": 78, "fetch": [79, 81], "evalu": 79, "health": [79, 82], "8": [79, 82], "popular": 79, "faq": 80, "what": [80, 82, 88], "do": [80, 82], "i": [80, 82, 88], "infer": 80, "correct": 80, "exampl": [80, 81, 82, 87], "ha": 80, "flag": 80, "should": 80, "v": 80, "test": [80, 82, 87], "big": 80, "limit": 80, "memori": 80, "why": 80, "isn": 80, "t": 80, "cleanlearn": [80, 82], "work": [80, 82, 84, 93], "me": 80, "differ": [80, 86], "clean": [80, 82], "final": 80, "hyperparamet": 80, "tune": 80, "onli": 80, "one": [80, 82, 85, 90], "doe": [80, 84, 93], "take": 80, "so": 80, "long": 80, "slice": 80, "when": [80, 82], "identifi": [80, 86], "run": 80, "licens": 80, "under": 80, "an": 80, "answer": 80, "question": 80, "pytorch": [81, 87], "normal": 81, "fashion": 81, "mnist": 81, "prepar": 81, "fold": [81, 88], "cross": [81, 88], "embed": [81, 87], "7": [81, 82], "view": 81, "most": [81, 93], "like": 81, "sever": 81, "set": [81, 82], "dark": 81, "top": [81, 90], "low": 81, "The": 82, "centric": 82, "ai": 82, "machin": 82, "find_label_issu": 82, "line": 82, "code": 82, "visual": [82, 86, 87, 90], "twenti": 82, "lowest": 82, "qualiti": [82, 84, 85, 86, 90, 93], "see": 82, "now": 82, "let": 82, "": 82, "happen": 82, "we": 82, "merg": 82, "seafoam": 82, "green": 82, "yellow": 82, "too": 82, "you": 82, "re": 82, "6": 82, "One": 82, "score": [82, 84, 85, 86, 90, 93], "rule": 82, "overal": [82, 90], "accur": 82, "thi": 82, "directli": 82, "fulli": 82, "character": 82, "nois": 82, "matrix": [82, 85], "joint": 82, "prior": 82, "true": 82, "distribut": 82, "flip": 82, "rate": 82, "ani": 82, "again": 82, "support": 82, "lot": 82, "method": 82, "filter_bi": 82, "automat": 82, "everi": 82, "uniqu": 82, "num_label_issu": 82, "threshold": 82, "found": 82, "Not": 82, "sure": 82, "ensembl": 82, "multipl": [82, 84], "predictor": 82, "consensu": 84, "annot": 84, "initi": 84, "major": 84, "vote": 84, "better": 84, "statist": 84, "compar": 84, "inspect": 84, "potenti": [84, 89, 92], "retrain": 84, "further": 84, "multi": 85, "given": 85, "hot": 85, "binari": 85, "download": [86, 90, 93], "objectlab": 86, "timm": 87, "cifar10": 87, "some": 87, "pred_prob": [87, 90, 93], "wai": 89, "semant": 90, "which": 90, "ar": 90, "commonli": 90, "mislabel": [90, 93], "focus": 90, "scikit": 91, "token": 93, "word": 93, "sentenc": 93, "contain": 93, "particular": 93}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}})
\ No newline at end of file
diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 8bfa3174d..239f5b844 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1495,7 +1495,7 @@ 

5. Use cleanlab to find label issues -{"state": {"4063343446cd482cb11b46d3df87246e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "13781132c8804186a0865b7bbcd2e591": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "8b87b7e492794fcf91e1c7f79232a01d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4063343446cd482cb11b46d3df87246e", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_13781132c8804186a0865b7bbcd2e591", "value": 2041.0}}, "ae7b344f7f434b13877dad579ac07a77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "823b7955cac0424899a7c5b6b685c486": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "cdbdfe089cab4bf9a5ea0a244b0aafda": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ae7b344f7f434b13877dad579ac07a77", "placeholder": "\u200b", "style": "IPY_MODEL_823b7955cac0424899a7c5b6b685c486", "value": "hyperparams.yaml: 100%"}}, "4dfe7d8c071648c2b934e7cdb2ec2a90": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "615637211dc04af2aed1b3bba5ea8596": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "729c2ae86a5645ca9cd108c2b6f9c2cc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4dfe7d8c071648c2b934e7cdb2ec2a90", "placeholder": "\u200b", "style": "IPY_MODEL_615637211dc04af2aed1b3bba5ea8596", "value": " 2.04k/2.04k [00:00<00:00, 317kB/s]"}}, "3cc29bf9b55a46758dc9ddd821a02f30": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0a7de036ee0c4320ae8b98137ead8037": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_cdbdfe089cab4bf9a5ea0a244b0aafda", "IPY_MODEL_8b87b7e492794fcf91e1c7f79232a01d", "IPY_MODEL_729c2ae86a5645ca9cd108c2b6f9c2cc"], "layout": "IPY_MODEL_3cc29bf9b55a46758dc9ddd821a02f30"}}, "eaa89de072e34b04a6781c39bdaa558e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f4cbe610d7194f3d828db4bf60d71e45": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "62479e82388f45dfb382aa78d5a90da7": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_eaa89de072e34b04a6781c39bdaa558e", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f4cbe610d7194f3d828db4bf60d71e45", "value": 16887676.0}}, "19a2a500478645319fbf86c80e42325c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "65fb869c08214a2b88caffbbdcf2cc11": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "cdd9b83f64e24d54a0178022a9e9b927": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_19a2a500478645319fbf86c80e42325c", "placeholder": "\u200b", "style": "IPY_MODEL_65fb869c08214a2b88caffbbdcf2cc11", "value": "embedding_model.ckpt: 100%"}}, "d8af2785324a4a00b959538c4da2b098": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "14fc86d90cf340ec9faeda09d6593d79": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "7059dd5715754f35b7931bc46150cd09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d8af2785324a4a00b959538c4da2b098", "placeholder": "\u200b", "style": "IPY_MODEL_14fc86d90cf340ec9faeda09d6593d79", "value": " 16.9M/16.9M [00:00<00:00, 151MB/s]"}}, "914a74ee16a1451b96723782dbca47d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d9f0f34e69a9470eb88dba5a5c3f860e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_cdd9b83f64e24d54a0178022a9e9b927", "IPY_MODEL_62479e82388f45dfb382aa78d5a90da7", "IPY_MODEL_7059dd5715754f35b7931bc46150cd09"], "layout": "IPY_MODEL_914a74ee16a1451b96723782dbca47d8"}}, "d637eb1d21cd4a16a38be4eeb02f45a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6f715c1db9094175a88af2f161104eaf": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "dea94251217b42afaa219b91eb9b33aa": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d637eb1d21cd4a16a38be4eeb02f45a1", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6f715c1db9094175a88af2f161104eaf", "value": 3201.0}}, "7b90d26b8f8b4f919760b86c4d1742c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "33825b7787434a56a94733873fb887cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "c34ef0b5ca444d4bbcb14e97f81343a5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7b90d26b8f8b4f919760b86c4d1742c4", "placeholder": "\u200b", "style": "IPY_MODEL_33825b7787434a56a94733873fb887cd", "value": "mean_var_norm_emb.ckpt: 100%"}}, "4fd02fb5d18c4b8aaf9b6967720e99a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "46fa497c584c45ea869730e6da019fb3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "1951fdc5671f4f328292bbc904486e0f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4fd02fb5d18c4b8aaf9b6967720e99a0", "placeholder": "\u200b", "style": "IPY_MODEL_46fa497c584c45ea869730e6da019fb3", "value": " 3.20k/3.20k [00:00<00:00, 545kB/s]"}}, "1964ff0f9094477b85ad121bcb3b5d58": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0a920abd4d484ac2867c08681d58f8d2": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_c34ef0b5ca444d4bbcb14e97f81343a5", "IPY_MODEL_dea94251217b42afaa219b91eb9b33aa", "IPY_MODEL_1951fdc5671f4f328292bbc904486e0f"], "layout": "IPY_MODEL_1964ff0f9094477b85ad121bcb3b5d58"}}, "bb102f3ed7e0431cad14807f7cf3f644": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f06ad0145a504d3fbf1bbb6780d02567": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "9de56d04c8ce438994a9eb69098fb30d": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bb102f3ed7e0431cad14807f7cf3f644", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f06ad0145a504d3fbf1bbb6780d02567", "value": 15856877.0}}, "cbdd07a776d04d7a957bd663d30abf45": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "45d77f78794b4aa689653d5263d7cbca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "2fd04e524990495ba9947389aedbdf3e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_cbdd07a776d04d7a957bd663d30abf45", "placeholder": "\u200b", "style": "IPY_MODEL_45d77f78794b4aa689653d5263d7cbca", "value": "classifier.ckpt: 100%"}}, "5b18a2d24e6043abb9a43c2ec41d6dcd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "56382e9082674de18880fd42360d5ebd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "9b1cd59f08934034a3f53af7c00e994e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5b18a2d24e6043abb9a43c2ec41d6dcd", "placeholder": "\u200b", "style": "IPY_MODEL_56382e9082674de18880fd42360d5ebd", "value": " 15.9M/15.9M [00:00<00:00, 56.4MB/s]"}}, "23726b106a2d4fafa0acc4b81d357027": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "16a4d93dcc9145408265488c376252df": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2fd04e524990495ba9947389aedbdf3e", "IPY_MODEL_9de56d04c8ce438994a9eb69098fb30d", "IPY_MODEL_9b1cd59f08934034a3f53af7c00e994e"], "layout": "IPY_MODEL_23726b106a2d4fafa0acc4b81d357027"}}, "59bb2956c80346cf8e4c363144a39de8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a76703ce1af54b969e9cb48a377ea7cb": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "fcb4a6be6cd3453398ed1b8f29fa8c87": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_59bb2956c80346cf8e4c363144a39de8", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_a76703ce1af54b969e9cb48a377ea7cb", "value": 128619.0}}, "edc490a506b842919ee53a26ae6db865": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a7e6bac0d9514509b2e562a48bca9c9f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "9ab330c8425343a088256d87bce25c89": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_edc490a506b842919ee53a26ae6db865", "placeholder": "\u200b", "style": "IPY_MODEL_a7e6bac0d9514509b2e562a48bca9c9f", "value": "label_encoder.txt: 100%"}}, "b4764ab6aeec48f1b0c73ed44065c436": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d75128dea06c4a5b82cdc93a3bedd8ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "72cc7cb38e37421aacccca523b696a9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b4764ab6aeec48f1b0c73ed44065c436", "placeholder": "\u200b", "style": "IPY_MODEL_d75128dea06c4a5b82cdc93a3bedd8ea", "value": " 129k/129k [00:00<00:00, 14.3MB/s]"}}, "0b7c2180da4e4e178a17b470e892804f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f5e4f48e25074ee49303bafcf1893878": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_9ab330c8425343a088256d87bce25c89", "IPY_MODEL_fcb4a6be6cd3453398ed1b8f29fa8c87", "IPY_MODEL_72cc7cb38e37421aacccca523b696a9c"], "layout": "IPY_MODEL_0b7c2180da4e4e178a17b470e892804f"}}}, "version_major": 2, "version_minor": 0} +{"state": {"f6fd30592fee4f22ba0d241c0e2862e2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "850228d498cd4b3e87f32f1257313d81": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "93b351e64c634bd99c642a728e59c557": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f6fd30592fee4f22ba0d241c0e2862e2", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_850228d498cd4b3e87f32f1257313d81", "value": 2041.0}}, "a6b20f82cfa9402a8c7fd2cf43db10f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "86951f39c00e4109b4ca60ffcc939b40": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "a4ffd871ac9d43fc97f1519c2af296fa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a6b20f82cfa9402a8c7fd2cf43db10f8", "placeholder": "\u200b", "style": "IPY_MODEL_86951f39c00e4109b4ca60ffcc939b40", "value": "hyperparams.yaml: 100%"}}, "b970971e982644df933a6fc215f217f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "998226c5bbfd44e3a06638c497e2daa1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "72aa3b5eca7d46d3a61ae6039af64c8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b970971e982644df933a6fc215f217f9", "placeholder": "\u200b", "style": "IPY_MODEL_998226c5bbfd44e3a06638c497e2daa1", "value": " 2.04k/2.04k [00:00<00:00, 328kB/s]"}}, "ee59111c59fe4214a106ea7b4d3bd396": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f544c0ca919a47c6a4bfa1f492857422": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_a4ffd871ac9d43fc97f1519c2af296fa", "IPY_MODEL_93b351e64c634bd99c642a728e59c557", "IPY_MODEL_72aa3b5eca7d46d3a61ae6039af64c8c"], "layout": "IPY_MODEL_ee59111c59fe4214a106ea7b4d3bd396"}}, "d003c7d9dffe4306a34d02339a448a43": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6960472d0e964654a68f2ad54c93a2b4": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "5d33a6963baa4b95bba45ab0b9e33c8a": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d003c7d9dffe4306a34d02339a448a43", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6960472d0e964654a68f2ad54c93a2b4", "value": 16887676.0}}, "ebe77964da2d45548943f4419f211879": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fdeba1c93e6747be9c5211333d0768bc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "8f7fda0a4e234263a3618a65ccc48db1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ebe77964da2d45548943f4419f211879", "placeholder": "\u200b", "style": "IPY_MODEL_fdeba1c93e6747be9c5211333d0768bc", "value": "embedding_model.ckpt: 100%"}}, "228b6665d63741019e33f90d71f5f3f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cd45f60e3ff442c6a9dd8562909119d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "0834d56d62714c28b3aeb79b3ddc680d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_228b6665d63741019e33f90d71f5f3f9", "placeholder": "\u200b", "style": "IPY_MODEL_cd45f60e3ff442c6a9dd8562909119d3", "value": " 16.9M/16.9M [00:00<00:00, 254MB/s]"}}, "2ca02285df6a43f6900cce3b23ad30aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5133c28de8264b41a420172eca319163": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_8f7fda0a4e234263a3618a65ccc48db1", "IPY_MODEL_5d33a6963baa4b95bba45ab0b9e33c8a", "IPY_MODEL_0834d56d62714c28b3aeb79b3ddc680d"], "layout": "IPY_MODEL_2ca02285df6a43f6900cce3b23ad30aa"}}, "557b8d9ac1ca4cf68a90a2259b2a61dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "34bc9fad69fc44a9ab325fff185f11f9": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "fa64a22ea146494ca2914d3484db6765": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_557b8d9ac1ca4cf68a90a2259b2a61dd", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_34bc9fad69fc44a9ab325fff185f11f9", "value": 3201.0}}, "b990bf93a73a48fa96ef81a45ecc7cf8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f9c3e8763ecb425aa275ed93f80ef66a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "ed5b9304939b4878afcb87cc1e060aae": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b990bf93a73a48fa96ef81a45ecc7cf8", "placeholder": "\u200b", "style": "IPY_MODEL_f9c3e8763ecb425aa275ed93f80ef66a", "value": "mean_var_norm_emb.ckpt: 100%"}}, "202693369eef41eeb079e15b2dca6fa5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "111a40f2a0704b5cb8269c337a9d4501": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "0fce71ef080443ad95ba0ab7ab1fde8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_202693369eef41eeb079e15b2dca6fa5", "placeholder": "\u200b", "style": "IPY_MODEL_111a40f2a0704b5cb8269c337a9d4501", "value": " 3.20k/3.20k [00:00<00:00, 537kB/s]"}}, "f99279c3718c4390825bb04cca778044": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4162f5fe77e74a6482908480569b3dd0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ed5b9304939b4878afcb87cc1e060aae", "IPY_MODEL_fa64a22ea146494ca2914d3484db6765", "IPY_MODEL_0fce71ef080443ad95ba0ab7ab1fde8c"], "layout": "IPY_MODEL_f99279c3718c4390825bb04cca778044"}}, "c6e0bbd765f348708cf3b1437c7cb9a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6d27aad7fad246ef8d47399f2e4310da": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "a4d57cf88a0e4fb6a5afe457b792d93e": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c6e0bbd765f348708cf3b1437c7cb9a8", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6d27aad7fad246ef8d47399f2e4310da", "value": 15856877.0}}, "2498f913b927410bb9ae4dcefb918991": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bb8670831c2e4347bf8ee34b069cb5ac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "eeb9be4c886941159edec7fc558e7a5a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2498f913b927410bb9ae4dcefb918991", "placeholder": "\u200b", "style": "IPY_MODEL_bb8670831c2e4347bf8ee34b069cb5ac", "value": "classifier.ckpt: 100%"}}, "60d401f5c6c74a328a80e74f9ba2de83": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "520a1c7807ef42ee92526b78323c1d3b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "dbe436d691f14d889ecb1e75dee91ee6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_60d401f5c6c74a328a80e74f9ba2de83", "placeholder": "\u200b", "style": "IPY_MODEL_520a1c7807ef42ee92526b78323c1d3b", "value": " 15.9M/15.9M [00:00<00:00, 347MB/s]"}}, "e5513548b6dc4815a177c264a29d47eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "990db9ddb0344d739ad0a89adcc4197e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_eeb9be4c886941159edec7fc558e7a5a", "IPY_MODEL_a4d57cf88a0e4fb6a5afe457b792d93e", "IPY_MODEL_dbe436d691f14d889ecb1e75dee91ee6"], "layout": "IPY_MODEL_e5513548b6dc4815a177c264a29d47eb"}}, "25c64fccecfb4812882e32cd62028cda": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fcee34ed095e4db6a88e4559efaff29f": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "28125c9aef0d4dc093ba42b225b7c7ef": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_25c64fccecfb4812882e32cd62028cda", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_fcee34ed095e4db6a88e4559efaff29f", "value": 128619.0}}, "e420c8493f574d69aef12450cf0a9a14": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c169e87cf101414c8b083a4b20e420ca": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "ffa606c5f25748859eb966e69110de72": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e420c8493f574d69aef12450cf0a9a14", "placeholder": "\u200b", "style": "IPY_MODEL_c169e87cf101414c8b083a4b20e420ca", "value": "label_encoder.txt: 100%"}}, "c41a3321793d4d608d8a59946471fbcc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "172b44924b8b475cb3b836b0f901ac71": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "06ab656526714167a212ab573f39a6e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c41a3321793d4d608d8a59946471fbcc", "placeholder": "\u200b", "style": "IPY_MODEL_172b44924b8b475cb3b836b0f901ac71", "value": " 129k/129k [00:00<00:00, 6.45MB/s]"}}, "531bd00fa5ad4184b070387d6f1fcf62": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3223b85e60f248499b88202f60b03b49": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ffa606c5f25748859eb966e69110de72", "IPY_MODEL_28125c9aef0d4dc093ba42b225b7c7ef", "IPY_MODEL_06ab656526714167a212ab573f39a6e8"], "layout": "IPY_MODEL_531bd00fa5ad4184b070387d6f1fcf62"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/audio.ipynb b/master/tutorials/audio.ipynb index 426953eb5..22d86d2d5 100644 --- a/master/tutorials/audio.ipynb +++ b/master/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:48.447914Z", - "iopub.status.busy": "2024-01-09T15:03:48.447720Z", - "iopub.status.idle": "2024-01-09T15:03:51.687491Z", - "shell.execute_reply": "2024-01-09T15:03:51.686865Z" + "iopub.execute_input": "2024-01-10T06:12:41.259375Z", + "iopub.status.busy": "2024-01-10T06:12:41.259181Z", + "iopub.status.idle": "2024-01-10T06:12:44.627929Z", + "shell.execute_reply": "2024-01-10T06:12:44.627193Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:03:51.690471Z", - "iopub.status.busy": "2024-01-09T15:03:51.689913Z", - "iopub.status.idle": "2024-01-09T15:03:51.693400Z", - "shell.execute_reply": "2024-01-09T15:03:51.692765Z" + "iopub.execute_input": "2024-01-10T06:12:44.631157Z", + "iopub.status.busy": "2024-01-10T06:12:44.630753Z", + "iopub.status.idle": "2024-01-10T06:12:44.634288Z", + "shell.execute_reply": "2024-01-10T06:12:44.633678Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:51.695678Z", - "iopub.status.busy": "2024-01-09T15:03:51.695331Z", - "iopub.status.idle": "2024-01-09T15:03:51.700275Z", - "shell.execute_reply": "2024-01-09T15:03:51.699673Z" + "iopub.execute_input": "2024-01-10T06:12:44.636787Z", + "iopub.status.busy": "2024-01-10T06:12:44.636307Z", + "iopub.status.idle": "2024-01-10T06:12:44.641333Z", + "shell.execute_reply": "2024-01-10T06:12:44.640736Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-09T15:03:51.702642Z", - "iopub.status.busy": "2024-01-09T15:03:51.702301Z", - "iopub.status.idle": "2024-01-09T15:03:53.280698Z", - "shell.execute_reply": "2024-01-09T15:03:53.279988Z" + "iopub.execute_input": "2024-01-10T06:12:44.643861Z", + "iopub.status.busy": "2024-01-10T06:12:44.643521Z", + "iopub.status.idle": "2024-01-10T06:12:46.354519Z", + "shell.execute_reply": "2024-01-10T06:12:46.353792Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-09T15:03:53.283799Z", - "iopub.status.busy": "2024-01-09T15:03:53.283558Z", - "iopub.status.idle": "2024-01-09T15:03:53.296069Z", - "shell.execute_reply": "2024-01-09T15:03:53.295469Z" + "iopub.execute_input": "2024-01-10T06:12:46.357667Z", + "iopub.status.busy": "2024-01-10T06:12:46.357425Z", + "iopub.status.idle": "2024-01-10T06:12:46.370136Z", + "shell.execute_reply": "2024-01-10T06:12:46.369486Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:53.328003Z", - "iopub.status.busy": "2024-01-09T15:03:53.327793Z", - "iopub.status.idle": "2024-01-09T15:03:53.333424Z", - "shell.execute_reply": "2024-01-09T15:03:53.332876Z" + "iopub.execute_input": "2024-01-10T06:12:46.403886Z", + "iopub.status.busy": "2024-01-10T06:12:46.403279Z", + "iopub.status.idle": "2024-01-10T06:12:46.409469Z", + "shell.execute_reply": "2024-01-10T06:12:46.408796Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-09T15:03:53.335847Z", - "iopub.status.busy": "2024-01-09T15:03:53.335398Z", - "iopub.status.idle": "2024-01-09T15:03:54.130364Z", - "shell.execute_reply": "2024-01-09T15:03:54.129657Z" + "iopub.execute_input": "2024-01-10T06:12:46.412199Z", + "iopub.status.busy": "2024-01-10T06:12:46.411701Z", + "iopub.status.idle": "2024-01-10T06:12:47.129236Z", + "shell.execute_reply": "2024-01-10T06:12:47.128626Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:54.133134Z", - "iopub.status.busy": "2024-01-09T15:03:54.132738Z", - "iopub.status.idle": "2024-01-09T15:03:54.992719Z", - "shell.execute_reply": "2024-01-09T15:03:54.992134Z" + "iopub.execute_input": "2024-01-10T06:12:47.131854Z", + "iopub.status.busy": "2024-01-10T06:12:47.131458Z", + "iopub.status.idle": "2024-01-10T06:12:48.015833Z", + "shell.execute_reply": "2024-01-10T06:12:48.015271Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-01-09T15:03:54.995664Z", - "iopub.status.busy": "2024-01-09T15:03:54.995251Z", - "iopub.status.idle": "2024-01-09T15:03:55.017302Z", - "shell.execute_reply": "2024-01-09T15:03:55.016630Z" + "iopub.execute_input": "2024-01-10T06:12:48.018530Z", + "iopub.status.busy": "2024-01-10T06:12:48.018300Z", + "iopub.status.idle": "2024-01-10T06:12:48.041148Z", + "shell.execute_reply": "2024-01-10T06:12:48.040614Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:55.019589Z", - "iopub.status.busy": "2024-01-09T15:03:55.019380Z", - "iopub.status.idle": "2024-01-09T15:03:55.022748Z", - "shell.execute_reply": "2024-01-09T15:03:55.022152Z" + "iopub.execute_input": "2024-01-10T06:12:48.043328Z", + "iopub.status.busy": "2024-01-10T06:12:48.043129Z", + "iopub.status.idle": "2024-01-10T06:12:48.046599Z", + "shell.execute_reply": "2024-01-10T06:12:48.046087Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:03:55.025030Z", - "iopub.status.busy": "2024-01-09T15:03:55.024730Z", - "iopub.status.idle": "2024-01-09T15:04:13.469516Z", - "shell.execute_reply": "2024-01-09T15:04:13.468791Z" + "iopub.execute_input": "2024-01-10T06:12:48.048804Z", + "iopub.status.busy": "2024-01-10T06:12:48.048609Z", + "iopub.status.idle": "2024-01-10T06:13:07.565893Z", + "shell.execute_reply": "2024-01-10T06:13:07.565174Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:13.473026Z", - "iopub.status.busy": "2024-01-09T15:04:13.472558Z", - "iopub.status.idle": "2024-01-09T15:04:13.477253Z", - "shell.execute_reply": "2024-01-09T15:04:13.476701Z" + "iopub.execute_input": "2024-01-10T06:13:07.569479Z", + "iopub.status.busy": "2024-01-10T06:13:07.568916Z", + "iopub.status.idle": "2024-01-10T06:13:07.573445Z", + "shell.execute_reply": "2024-01-10T06:13:07.572829Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:13.479912Z", - "iopub.status.busy": "2024-01-09T15:04:13.479440Z", - "iopub.status.idle": "2024-01-09T15:04:18.936622Z", - "shell.execute_reply": "2024-01-09T15:04:18.935939Z" + "iopub.execute_input": "2024-01-10T06:13:07.576084Z", + "iopub.status.busy": "2024-01-10T06:13:07.575635Z", + "iopub.status.idle": "2024-01-10T06:13:13.160325Z", + "shell.execute_reply": "2024-01-10T06:13:13.159580Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:18.940118Z", - "iopub.status.busy": "2024-01-09T15:04:18.939692Z", - "iopub.status.idle": "2024-01-09T15:04:18.944957Z", - "shell.execute_reply": "2024-01-09T15:04:18.944384Z" + "iopub.execute_input": "2024-01-10T06:13:13.163818Z", + "iopub.status.busy": "2024-01-10T06:13:13.163341Z", + "iopub.status.idle": "2024-01-10T06:13:13.169098Z", + "shell.execute_reply": "2024-01-10T06:13:13.168477Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:18.947938Z", - "iopub.status.busy": "2024-01-09T15:04:18.947507Z", - "iopub.status.idle": "2024-01-09T15:04:19.038569Z", - "shell.execute_reply": "2024-01-09T15:04:19.037912Z" + "iopub.execute_input": "2024-01-10T06:13:13.173106Z", + "iopub.status.busy": "2024-01-10T06:13:13.171931Z", + "iopub.status.idle": "2024-01-10T06:13:13.286084Z", + "shell.execute_reply": "2024-01-10T06:13:13.285356Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.041636Z", - "iopub.status.busy": "2024-01-09T15:04:19.041027Z", - "iopub.status.idle": "2024-01-09T15:04:19.051373Z", - "shell.execute_reply": "2024-01-09T15:04:19.050744Z" + "iopub.execute_input": "2024-01-10T06:13:13.289090Z", + "iopub.status.busy": "2024-01-10T06:13:13.288656Z", + "iopub.status.idle": "2024-01-10T06:13:13.298785Z", + "shell.execute_reply": "2024-01-10T06:13:13.298212Z" }, "scrolled": true }, @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.053786Z", - "iopub.status.busy": "2024-01-09T15:04:19.053423Z", - "iopub.status.idle": "2024-01-09T15:04:19.061772Z", - "shell.execute_reply": "2024-01-09T15:04:19.061136Z" + "iopub.execute_input": "2024-01-10T06:13:13.301337Z", + "iopub.status.busy": "2024-01-10T06:13:13.300949Z", + "iopub.status.idle": "2024-01-10T06:13:13.309539Z", + "shell.execute_reply": "2024-01-10T06:13:13.308933Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.064332Z", - "iopub.status.busy": "2024-01-09T15:04:19.063839Z", - "iopub.status.idle": "2024-01-09T15:04:19.068734Z", - "shell.execute_reply": "2024-01-09T15:04:19.068124Z" + "iopub.execute_input": "2024-01-10T06:13:13.312077Z", + "iopub.status.busy": "2024-01-10T06:13:13.311693Z", + "iopub.status.idle": "2024-01-10T06:13:13.316598Z", + "shell.execute_reply": "2024-01-10T06:13:13.316047Z" } }, "outputs": [ @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.071079Z", - "iopub.status.busy": "2024-01-09T15:04:19.070732Z", - "iopub.status.idle": "2024-01-09T15:04:19.076638Z", - "shell.execute_reply": "2024-01-09T15:04:19.075999Z" + "iopub.execute_input": "2024-01-10T06:13:13.319161Z", + "iopub.status.busy": "2024-01-10T06:13:13.318787Z", + "iopub.status.idle": "2024-01-10T06:13:13.325198Z", + "shell.execute_reply": "2024-01-10T06:13:13.324553Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.079068Z", - "iopub.status.busy": "2024-01-09T15:04:19.078683Z", - "iopub.status.idle": "2024-01-09T15:04:19.193762Z", - "shell.execute_reply": "2024-01-09T15:04:19.193074Z" + "iopub.execute_input": "2024-01-10T06:13:13.327754Z", + "iopub.status.busy": "2024-01-10T06:13:13.327373Z", + "iopub.status.idle": "2024-01-10T06:13:13.443959Z", + "shell.execute_reply": "2024-01-10T06:13:13.443366Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.196127Z", - "iopub.status.busy": "2024-01-09T15:04:19.195918Z", - "iopub.status.idle": "2024-01-09T15:04:19.307571Z", - "shell.execute_reply": "2024-01-09T15:04:19.306946Z" + "iopub.execute_input": "2024-01-10T06:13:13.446679Z", + "iopub.status.busy": "2024-01-10T06:13:13.446417Z", + "iopub.status.idle": "2024-01-10T06:13:13.557900Z", + "shell.execute_reply": "2024-01-10T06:13:13.557249Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.310132Z", - "iopub.status.busy": "2024-01-09T15:04:19.309819Z", - "iopub.status.idle": "2024-01-09T15:04:19.417125Z", - "shell.execute_reply": "2024-01-09T15:04:19.416481Z" + "iopub.execute_input": "2024-01-10T06:13:13.560589Z", + "iopub.status.busy": "2024-01-10T06:13:13.560140Z", + "iopub.status.idle": "2024-01-10T06:13:13.670534Z", + "shell.execute_reply": "2024-01-10T06:13:13.669855Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1282,10 +1282,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.419594Z", - "iopub.status.busy": "2024-01-09T15:04:19.419205Z", - "iopub.status.idle": "2024-01-09T15:04:19.527064Z", - "shell.execute_reply": "2024-01-09T15:04:19.526420Z" + "iopub.execute_input": "2024-01-10T06:13:13.673234Z", + "iopub.status.busy": "2024-01-10T06:13:13.672760Z", + "iopub.status.idle": "2024-01-10T06:13:13.783675Z", + "shell.execute_reply": "2024-01-10T06:13:13.783105Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:19.529678Z", - "iopub.status.busy": "2024-01-09T15:04:19.529354Z", - "iopub.status.idle": "2024-01-09T15:04:19.532764Z", - "shell.execute_reply": "2024-01-09T15:04:19.532186Z" + "iopub.execute_input": "2024-01-10T06:13:13.786289Z", + "iopub.status.busy": "2024-01-10T06:13:13.785901Z", + "iopub.status.idle": "2024-01-10T06:13:13.789366Z", + "shell.execute_reply": "2024-01-10T06:13:13.788812Z" }, "nbsphinx": "hidden" }, @@ -1377,119 +1377,70 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0a7de036ee0c4320ae8b98137ead8037": { + "06ab656526714167a212ab573f39a6e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cdbdfe089cab4bf9a5ea0a244b0aafda", - "IPY_MODEL_8b87b7e492794fcf91e1c7f79232a01d", - "IPY_MODEL_729c2ae86a5645ca9cd108c2b6f9c2cc" - ], - "layout": "IPY_MODEL_3cc29bf9b55a46758dc9ddd821a02f30" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c41a3321793d4d608d8a59946471fbcc", + "placeholder": "​", + "style": "IPY_MODEL_172b44924b8b475cb3b836b0f901ac71", + "value": " 129k/129k [00:00<00:00, 6.45MB/s]" } }, - "0a920abd4d484ac2867c08681d58f8d2": { + "0834d56d62714c28b3aeb79b3ddc680d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c34ef0b5ca444d4bbcb14e97f81343a5", - "IPY_MODEL_dea94251217b42afaa219b91eb9b33aa", - "IPY_MODEL_1951fdc5671f4f328292bbc904486e0f" - ], - "layout": "IPY_MODEL_1964ff0f9094477b85ad121bcb3b5d58" - } - }, - "0b7c2180da4e4e178a17b470e892804f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_228b6665d63741019e33f90d71f5f3f9", + "placeholder": "​", + "style": "IPY_MODEL_cd45f60e3ff442c6a9dd8562909119d3", + "value": " 16.9M/16.9M [00:00<00:00, 254MB/s]" } }, - "13781132c8804186a0865b7bbcd2e591": { + "0fce71ef080443ad95ba0ab7ab1fde8c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_202693369eef41eeb079e15b2dca6fa5", + "placeholder": "​", + "style": "IPY_MODEL_111a40f2a0704b5cb8269c337a9d4501", + "value": " 3.20k/3.20k [00:00<00:00, 537kB/s]" } }, - "14fc86d90cf340ec9faeda09d6593d79": { + "111a40f2a0704b5cb8269c337a9d4501": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1504,50 +1455,22 @@ "description_width": "" } }, - "16a4d93dcc9145408265488c376252df": { + "172b44924b8b475cb3b836b0f901ac71": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2fd04e524990495ba9947389aedbdf3e", - "IPY_MODEL_9de56d04c8ce438994a9eb69098fb30d", - "IPY_MODEL_9b1cd59f08934034a3f53af7c00e994e" - ], - "layout": "IPY_MODEL_23726b106a2d4fafa0acc4b81d357027" - } - }, - "1951fdc5671f4f328292bbc904486e0f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4fd02fb5d18c4b8aaf9b6967720e99a0", - "placeholder": "​", - "style": "IPY_MODEL_46fa497c584c45ea869730e6da019fb3", - "value": " 3.20k/3.20k [00:00<00:00, 545kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "1964ff0f9094477b85ad121bcb3b5d58": { + "202693369eef41eeb079e15b2dca6fa5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1599,7 +1522,7 @@ "width": null } }, - "19a2a500478645319fbf86c80e42325c": { + "228b6665d63741019e33f90d71f5f3f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1651,7 +1574,7 @@ "width": null } }, - "23726b106a2d4fafa0acc4b81d357027": { + "2498f913b927410bb9ae4dcefb918991": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1703,43 +1626,7 @@ "width": null } }, - "2fd04e524990495ba9947389aedbdf3e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cbdd07a776d04d7a957bd663d30abf45", - "placeholder": "​", - "style": "IPY_MODEL_45d77f78794b4aa689653d5263d7cbca", - "value": "classifier.ckpt: 100%" - } - }, - "33825b7787434a56a94733873fb887cd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "3cc29bf9b55a46758dc9ddd821a02f30": { + "25c64fccecfb4812882e32cd62028cda": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1791,7 +1678,31 @@ "width": null } }, - "4063343446cd482cb11b46d3df87246e": { + "28125c9aef0d4dc093ba42b225b7c7ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_25c64fccecfb4812882e32cd62028cda", + "max": 128619.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_fcee34ed095e4db6a88e4559efaff29f", + "value": 128619.0 + } + }, + "2ca02285df6a43f6900cce3b23ad30aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1843,22 +1754,89 @@ "width": null } }, - "45d77f78794b4aa689653d5263d7cbca": { + "3223b85e60f248499b88202f60b03b49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ffa606c5f25748859eb966e69110de72", + "IPY_MODEL_28125c9aef0d4dc093ba42b225b7c7ef", + "IPY_MODEL_06ab656526714167a212ab573f39a6e8" + ], + "layout": "IPY_MODEL_531bd00fa5ad4184b070387d6f1fcf62" + } + }, + "34bc9fad69fc44a9ab325fff185f11f9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "46fa497c584c45ea869730e6da019fb3": { + "4162f5fe77e74a6482908480569b3dd0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed5b9304939b4878afcb87cc1e060aae", + "IPY_MODEL_fa64a22ea146494ca2914d3484db6765", + "IPY_MODEL_0fce71ef080443ad95ba0ab7ab1fde8c" + ], + "layout": "IPY_MODEL_f99279c3718c4390825bb04cca778044" + } + }, + "5133c28de8264b41a420172eca319163": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8f7fda0a4e234263a3618a65ccc48db1", + "IPY_MODEL_5d33a6963baa4b95bba45ab0b9e33c8a", + "IPY_MODEL_0834d56d62714c28b3aeb79b3ddc680d" + ], + "layout": "IPY_MODEL_2ca02285df6a43f6900cce3b23ad30aa" + } + }, + "520a1c7807ef42ee92526b78323c1d3b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1873,7 +1851,7 @@ "description_width": "" } }, - "4dfe7d8c071648c2b934e7cdb2ec2a90": { + "531bd00fa5ad4184b070387d6f1fcf62": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1925,7 +1903,7 @@ "width": null } }, - "4fd02fb5d18c4b8aaf9b6967720e99a0": { + "557b8d9ac1ca4cf68a90a2259b2a61dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1977,22 +1955,31 @@ "width": null } }, - "56382e9082674de18880fd42360d5ebd": { + "5d33a6963baa4b95bba45ab0b9e33c8a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d003c7d9dffe4306a34d02339a448a43", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6960472d0e964654a68f2ad54c93a2b4", + "value": 16887676.0 } }, - "59bb2956c80346cf8e4c363144a39de8": { + "60d401f5c6c74a328a80e74f9ba2de83": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2044,129 +2031,91 @@ "width": null } }, - "5b18a2d24e6043abb9a43c2ec41d6dcd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "6960472d0e964654a68f2ad54c93a2b4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "615637211dc04af2aed1b3bba5ea8596": { + "6d27aad7fad246ef8d47399f2e4310da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "62479e82388f45dfb382aa78d5a90da7": { + "72aa3b5eca7d46d3a61ae6039af64c8c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_eaa89de072e34b04a6781c39bdaa558e", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f4cbe610d7194f3d828db4bf60d71e45", - "value": 16887676.0 + "layout": "IPY_MODEL_b970971e982644df933a6fc215f217f9", + "placeholder": "​", + "style": "IPY_MODEL_998226c5bbfd44e3a06638c497e2daa1", + "value": " 2.04k/2.04k [00:00<00:00, 328kB/s]" } }, - "65fb869c08214a2b88caffbbdcf2cc11": { + "850228d498cd4b3e87f32f1257313d81": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "6f715c1db9094175a88af2f161104eaf": { + "86951f39c00e4109b4ca60ffcc939b40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "7059dd5715754f35b7931bc46150cd09": { + "8f7fda0a4e234263a3618a65ccc48db1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2181,34 +2130,98 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d8af2785324a4a00b959538c4da2b098", + "layout": "IPY_MODEL_ebe77964da2d45548943f4419f211879", "placeholder": "​", - "style": "IPY_MODEL_14fc86d90cf340ec9faeda09d6593d79", - "value": " 16.9M/16.9M [00:00<00:00, 151MB/s]" + "style": "IPY_MODEL_fdeba1c93e6747be9c5211333d0768bc", + "value": "embedding_model.ckpt: 100%" } }, - "729c2ae86a5645ca9cd108c2b6f9c2cc": { + "93b351e64c634bd99c642a728e59c557": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4dfe7d8c071648c2b934e7cdb2ec2a90", - "placeholder": "​", - "style": "IPY_MODEL_615637211dc04af2aed1b3bba5ea8596", - "value": " 2.04k/2.04k [00:00<00:00, 317kB/s]" + "layout": "IPY_MODEL_f6fd30592fee4f22ba0d241c0e2862e2", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_850228d498cd4b3e87f32f1257313d81", + "value": 2041.0 } }, - "72cc7cb38e37421aacccca523b696a9c": { + "990db9ddb0344d739ad0a89adcc4197e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_eeb9be4c886941159edec7fc558e7a5a", + "IPY_MODEL_a4d57cf88a0e4fb6a5afe457b792d93e", + "IPY_MODEL_dbe436d691f14d889ecb1e75dee91ee6" + ], + "layout": "IPY_MODEL_e5513548b6dc4815a177c264a29d47eb" + } + }, + "998226c5bbfd44e3a06638c497e2daa1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a4d57cf88a0e4fb6a5afe457b792d93e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c6e0bbd765f348708cf3b1437c7cb9a8", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6d27aad7fad246ef8d47399f2e4310da", + "value": 15856877.0 + } + }, + "a4ffd871ac9d43fc97f1519c2af296fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2223,13 +2236,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_b4764ab6aeec48f1b0c73ed44065c436", + "layout": "IPY_MODEL_a6b20f82cfa9402a8c7fd2cf43db10f8", "placeholder": "​", - "style": "IPY_MODEL_d75128dea06c4a5b82cdc93a3bedd8ea", - "value": " 129k/129k [00:00<00:00, 14.3MB/s]" + "style": "IPY_MODEL_86951f39c00e4109b4ca60ffcc939b40", + "value": "hyperparams.yaml: 100%" } }, - "7b90d26b8f8b4f919760b86c4d1742c4": { + "a6b20f82cfa9402a8c7fd2cf43db10f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2281,46 +2294,59 @@ "width": null } }, - "823b7955cac0424899a7c5b6b685c486": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "b970971e982644df933a6fc215f217f9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "8b87b7e492794fcf91e1c7f79232a01d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4063343446cd482cb11b46d3df87246e", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_13781132c8804186a0865b7bbcd2e591", - "value": 2041.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "914a74ee16a1451b96723782dbca47d8": { + "b990bf93a73a48fa96ef81a45ecc7cf8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2372,89 +2398,22 @@ "width": null } }, - "9ab330c8425343a088256d87bce25c89": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_edc490a506b842919ee53a26ae6db865", - "placeholder": "​", - "style": "IPY_MODEL_a7e6bac0d9514509b2e562a48bca9c9f", - "value": "label_encoder.txt: 100%" - } - }, - "9b1cd59f08934034a3f53af7c00e994e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5b18a2d24e6043abb9a43c2ec41d6dcd", - "placeholder": "​", - "style": "IPY_MODEL_56382e9082674de18880fd42360d5ebd", - "value": " 15.9M/15.9M [00:00<00:00, 56.4MB/s]" - } - }, - "9de56d04c8ce438994a9eb69098fb30d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_bb102f3ed7e0431cad14807f7cf3f644", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f06ad0145a504d3fbf1bbb6780d02567", - "value": 15856877.0 - } - }, - "a76703ce1af54b969e9cb48a377ea7cb": { + "bb8670831c2e4347bf8ee34b069cb5ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "a7e6bac0d9514509b2e562a48bca9c9f": { + "c169e87cf101414c8b083a4b20e420ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2469,7 +2428,7 @@ "description_width": "" } }, - "ae7b344f7f434b13877dad579ac07a77": { + "c41a3321793d4d608d8a59946471fbcc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2521,7 +2480,7 @@ "width": null } }, - "b4764ab6aeec48f1b0c73ed44065c436": { + "c6e0bbd765f348708cf3b1437c7cb9a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2573,7 +2532,22 @@ "width": null } }, - "bb102f3ed7e0431cad14807f7cf3f644": { + "cd45f60e3ff442c6a9dd8562909119d3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d003c7d9dffe4306a34d02339a448a43": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2625,7 +2599,7 @@ "width": null } }, - "c34ef0b5ca444d4bbcb14e97f81343a5": { + "dbe436d691f14d889ecb1e75dee91ee6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2640,13 +2614,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_7b90d26b8f8b4f919760b86c4d1742c4", + "layout": "IPY_MODEL_60d401f5c6c74a328a80e74f9ba2de83", "placeholder": "​", - "style": "IPY_MODEL_33825b7787434a56a94733873fb887cd", - "value": "mean_var_norm_emb.ckpt: 100%" + "style": "IPY_MODEL_520a1c7807ef42ee92526b78323c1d3b", + "value": " 15.9M/15.9M [00:00<00:00, 347MB/s]" } }, - "cbdd07a776d04d7a957bd663d30abf45": { + "e420c8493f574d69aef12450cf0a9a14": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2698,49 +2672,59 @@ "width": null } }, - "cdbdfe089cab4bf9a5ea0a244b0aafda": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ae7b344f7f434b13877dad579ac07a77", - "placeholder": "​", - "style": "IPY_MODEL_823b7955cac0424899a7c5b6b685c486", - "value": "hyperparams.yaml: 100%" - } - }, - "cdd9b83f64e24d54a0178022a9e9b927": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "e5513548b6dc4815a177c264a29d47eb": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_19a2a500478645319fbf86c80e42325c", - "placeholder": "​", - "style": "IPY_MODEL_65fb869c08214a2b88caffbbdcf2cc11", - "value": "embedding_model.ckpt: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d637eb1d21cd4a16a38be4eeb02f45a1": { + "ebe77964da2d45548943f4419f211879": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2792,22 +2776,28 @@ "width": null } }, - "d75128dea06c4a5b82cdc93a3bedd8ea": { + "ed5b9304939b4878afcb87cc1e060aae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_b990bf93a73a48fa96ef81a45ecc7cf8", + "placeholder": "​", + "style": "IPY_MODEL_f9c3e8763ecb425aa275ed93f80ef66a", + "value": "mean_var_norm_emb.ckpt: 100%" } }, - "d8af2785324a4a00b959538c4da2b098": { + "ee59111c59fe4214a106ea7b4d3bd396": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2859,53 +2849,50 @@ "width": null } }, - "d9f0f34e69a9470eb88dba5a5c3f860e": { + "eeb9be4c886941159edec7fc558e7a5a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cdd9b83f64e24d54a0178022a9e9b927", - "IPY_MODEL_62479e82388f45dfb382aa78d5a90da7", - "IPY_MODEL_7059dd5715754f35b7931bc46150cd09" - ], - "layout": "IPY_MODEL_914a74ee16a1451b96723782dbca47d8" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2498f913b927410bb9ae4dcefb918991", + "placeholder": "​", + "style": "IPY_MODEL_bb8670831c2e4347bf8ee34b069cb5ac", + "value": "classifier.ckpt: 100%" } }, - "dea94251217b42afaa219b91eb9b33aa": { + "f544c0ca919a47c6a4bfa1f492857422": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d637eb1d21cd4a16a38be4eeb02f45a1", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6f715c1db9094175a88af2f161104eaf", - "value": 3201.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a4ffd871ac9d43fc97f1519c2af296fa", + "IPY_MODEL_93b351e64c634bd99c642a728e59c557", + "IPY_MODEL_72aa3b5eca7d46d3a61ae6039af64c8c" + ], + "layout": "IPY_MODEL_ee59111c59fe4214a106ea7b4d3bd396" } }, - "eaa89de072e34b04a6781c39bdaa558e": { + "f6fd30592fee4f22ba0d241c0e2862e2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2957,7 +2944,7 @@ "width": null } }, - "edc490a506b842919ee53a26ae6db865": { + "f99279c3718c4390825bb04cca778044": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3009,23 +2996,46 @@ "width": null } }, - "f06ad0145a504d3fbf1bbb6780d02567": { + "f9c3e8763ecb425aa275ed93f80ef66a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "f4cbe610d7194f3d828db4bf60d71e45": { + "fa64a22ea146494ca2914d3484db6765": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_557b8d9ac1ca4cf68a90a2259b2a61dd", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_34bc9fad69fc44a9ab325fff185f11f9", + "value": 3201.0 + } + }, + "fcee34ed095e4db6a88e4559efaff29f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -3041,50 +3051,40 @@ "description_width": "" } }, - "f5e4f48e25074ee49303bafcf1893878": { + "fdeba1c93e6747be9c5211333d0768bc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_9ab330c8425343a088256d87bce25c89", - "IPY_MODEL_fcb4a6be6cd3453398ed1b8f29fa8c87", - "IPY_MODEL_72cc7cb38e37421aacccca523b696a9c" - ], - "layout": "IPY_MODEL_0b7c2180da4e4e178a17b470e892804f" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "fcb4a6be6cd3453398ed1b8f29fa8c87": { + "ffa606c5f25748859eb966e69110de72": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_59bb2956c80346cf8e4c363144a39de8", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a76703ce1af54b969e9cb48a377ea7cb", - "value": 128619.0 + "layout": "IPY_MODEL_e420c8493f574d69aef12450cf0a9a14", + "placeholder": "​", + "style": "IPY_MODEL_c169e87cf101414c8b083a4b20e420ca", + "value": "label_encoder.txt: 100%" } } }, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index bcd44d777..8aa12fb61 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1313,7 +1313,7 @@

Functionality 2: Specifying nondefault arguments
-
+

@@ -1709,7 +1709,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"c8de47b934124049a6778940535a853e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1a984da391434a579c4e8cdea77f9f2d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4c33ba09eb7a44989d751311b4a6a997": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c8de47b934124049a6778940535a853e", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1a984da391434a579c4e8cdea77f9f2d", "value": 132.0}}, "376c1aba8f2b448c9d57e86953cac6d8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "adff6eac22854dd2bb797123ac7621c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "e2b4a19ddce24664b05a205091c3c23e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_376c1aba8f2b448c9d57e86953cac6d8", "placeholder": "\u200b", "style": "IPY_MODEL_adff6eac22854dd2bb797123ac7621c8", "value": "Saving the dataset (1/1 shards): 100%"}}, "c5282ca5da5e43d78ac733697024d974": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "47e7b53818db4c37a9e352c2973136a7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "6f7ea0dc41db415b83cee60e01c091a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c5282ca5da5e43d78ac733697024d974", "placeholder": "\u200b", "style": "IPY_MODEL_47e7b53818db4c37a9e352c2973136a7", "value": " 132/132 [00:00<00:00, 10105.47 examples/s]"}}, "b0911ba67f8f43928bd6dc293d6ecf50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4effa48406144cbeaa8fcd45054ccc19": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_e2b4a19ddce24664b05a205091c3c23e", "IPY_MODEL_4c33ba09eb7a44989d751311b4a6a997", "IPY_MODEL_6f7ea0dc41db415b83cee60e01c091a4"], "layout": "IPY_MODEL_b0911ba67f8f43928bd6dc293d6ecf50"}}}, "version_major": 2, "version_minor": 0} +{"state": {"bfd4faf7258443a39bd2fcd278bc83ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dac1e3e656dd43008dc2870d6342af0e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "822795f0acf14d819bd90d6ea63181ec": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bfd4faf7258443a39bd2fcd278bc83ac", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_dac1e3e656dd43008dc2870d6342af0e", "value": 132.0}}, "13d483977eca497389f4722102d836b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4666b6ad60954d5887cfd7eebc497a5e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "777abd15d2bc4f3dbbcd901eb1bfa3d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_13d483977eca497389f4722102d836b9", "placeholder": "\u200b", "style": "IPY_MODEL_4666b6ad60954d5887cfd7eebc497a5e", "value": "Saving the dataset (1/1 shards): 100%"}}, "0fff0021442e4b26a3ef037cd77b53b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1c6e36c143374b619efebd1763224d51": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "07ce684158734aa2ad7d27dfdc5022e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0fff0021442e4b26a3ef037cd77b53b9", "placeholder": "\u200b", "style": "IPY_MODEL_1c6e36c143374b619efebd1763224d51", "value": " 132/132 [00:00<00:00, 10237.77 examples/s]"}}, "e6c108b6484046cdb7acc0ffe213b8ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fea3d5c35ae9493f8a9be8704822e8fd": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_777abd15d2bc4f3dbbcd901eb1bfa3d7", "IPY_MODEL_822795f0acf14d819bd90d6ea63181ec", "IPY_MODEL_07ce684158734aa2ad7d27dfdc5022e9"], "layout": "IPY_MODEL_e6c108b6484046cdb7acc0ffe213b8ef"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 3dedbf39c..80ed64de9 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": "2024-01-09T15:04:24.784445Z", - "iopub.status.busy": "2024-01-09T15:04:24.783804Z", - "iopub.status.idle": "2024-01-09T15:04:25.883992Z", - "shell.execute_reply": "2024-01-09T15:04:25.883377Z" + "iopub.execute_input": "2024-01-10T06:13:18.391900Z", + "iopub.status.busy": "2024-01-10T06:13:18.391450Z", + "iopub.status.idle": "2024-01-10T06:13:19.482980Z", + "shell.execute_reply": "2024-01-10T06:13:19.482325Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:04:25.886794Z", - "iopub.status.busy": "2024-01-09T15:04:25.886497Z", - "iopub.status.idle": "2024-01-09T15:04:25.889701Z", - "shell.execute_reply": "2024-01-09T15:04:25.889178Z" + "iopub.execute_input": "2024-01-10T06:13:19.486136Z", + "iopub.status.busy": "2024-01-10T06:13:19.485670Z", + "iopub.status.idle": "2024-01-10T06:13:19.488975Z", + "shell.execute_reply": "2024-01-10T06:13:19.488407Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:25.892100Z", - "iopub.status.busy": "2024-01-09T15:04:25.891865Z", - "iopub.status.idle": "2024-01-09T15:04:25.901407Z", - "shell.execute_reply": "2024-01-09T15:04:25.900875Z" + "iopub.execute_input": "2024-01-10T06:13:19.491624Z", + "iopub.status.busy": "2024-01-10T06:13:19.491166Z", + "iopub.status.idle": "2024-01-10T06:13:19.500559Z", + "shell.execute_reply": "2024-01-10T06:13:19.499968Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:25.903536Z", - "iopub.status.busy": "2024-01-09T15:04:25.903342Z", - "iopub.status.idle": "2024-01-09T15:04:25.908446Z", - "shell.execute_reply": "2024-01-09T15:04:25.907811Z" + "iopub.execute_input": "2024-01-10T06:13:19.503166Z", + "iopub.status.busy": "2024-01-10T06:13:19.502771Z", + "iopub.status.idle": "2024-01-10T06:13:19.507585Z", + "shell.execute_reply": "2024-01-10T06:13:19.507087Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:25.910823Z", - "iopub.status.busy": "2024-01-09T15:04:25.910625Z", - "iopub.status.idle": "2024-01-09T15:04:26.195889Z", - "shell.execute_reply": "2024-01-09T15:04:26.195169Z" + "iopub.execute_input": "2024-01-10T06:13:19.510043Z", + "iopub.status.busy": "2024-01-10T06:13:19.509670Z", + "iopub.status.idle": "2024-01-10T06:13:19.797634Z", + "shell.execute_reply": "2024-01-10T06:13:19.797007Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:26.199074Z", - "iopub.status.busy": "2024-01-09T15:04:26.198528Z", - "iopub.status.idle": "2024-01-09T15:04:26.569756Z", - "shell.execute_reply": "2024-01-09T15:04:26.569060Z" + "iopub.execute_input": "2024-01-10T06:13:19.800537Z", + "iopub.status.busy": "2024-01-10T06:13:19.800126Z", + "iopub.status.idle": "2024-01-10T06:13:20.113225Z", + "shell.execute_reply": "2024-01-10T06:13:20.112562Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:26.572827Z", - "iopub.status.busy": "2024-01-09T15:04:26.572236Z", - "iopub.status.idle": "2024-01-09T15:04:26.596506Z", - "shell.execute_reply": "2024-01-09T15:04:26.595875Z" + "iopub.execute_input": "2024-01-10T06:13:20.116093Z", + "iopub.status.busy": "2024-01-10T06:13:20.115570Z", + "iopub.status.idle": "2024-01-10T06:13:20.140762Z", + "shell.execute_reply": "2024-01-10T06:13:20.140077Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:26.599094Z", - "iopub.status.busy": "2024-01-09T15:04:26.598884Z", - "iopub.status.idle": "2024-01-09T15:04:26.610674Z", - "shell.execute_reply": "2024-01-09T15:04:26.610171Z" + "iopub.execute_input": "2024-01-10T06:13:20.143786Z", + "iopub.status.busy": "2024-01-10T06:13:20.143411Z", + "iopub.status.idle": "2024-01-10T06:13:20.155764Z", + "shell.execute_reply": "2024-01-10T06:13:20.155189Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:26.613249Z", - "iopub.status.busy": "2024-01-09T15:04:26.612719Z", - "iopub.status.idle": "2024-01-09T15:04:28.022863Z", - "shell.execute_reply": "2024-01-09T15:04:28.022038Z" + "iopub.execute_input": "2024-01-10T06:13:20.158588Z", + "iopub.status.busy": "2024-01-10T06:13:20.158210Z", + "iopub.status.idle": "2024-01-10T06:13:21.490758Z", + "shell.execute_reply": "2024-01-10T06:13:21.490064Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.026390Z", - "iopub.status.busy": "2024-01-09T15:04:28.025731Z", - "iopub.status.idle": "2024-01-09T15:04:28.051399Z", - "shell.execute_reply": "2024-01-09T15:04:28.050662Z" + "iopub.execute_input": "2024-01-10T06:13:21.493933Z", + "iopub.status.busy": "2024-01-10T06:13:21.493289Z", + "iopub.status.idle": "2024-01-10T06:13:21.516427Z", + "shell.execute_reply": "2024-01-10T06:13:21.515797Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.054744Z", - "iopub.status.busy": "2024-01-09T15:04:28.054165Z", - "iopub.status.idle": "2024-01-09T15:04:28.079450Z", - "shell.execute_reply": "2024-01-09T15:04:28.078680Z" + "iopub.execute_input": "2024-01-10T06:13:21.519110Z", + "iopub.status.busy": "2024-01-10T06:13:21.518726Z", + "iopub.status.idle": "2024-01-10T06:13:21.542421Z", + "shell.execute_reply": "2024-01-10T06:13:21.541682Z" } }, "outputs": [ @@ -909,7 +909,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:300: 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:300: 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:330: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n", " warnings.warn(\n", @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.082720Z", - "iopub.status.busy": "2024-01-09T15:04:28.082253Z", - "iopub.status.idle": "2024-01-09T15:04:28.099446Z", - "shell.execute_reply": "2024-01-09T15:04:28.098771Z" + "iopub.execute_input": "2024-01-10T06:13:21.545363Z", + "iopub.status.busy": "2024-01-10T06:13:21.544862Z", + "iopub.status.idle": "2024-01-10T06:13:21.560354Z", + "shell.execute_reply": "2024-01-10T06:13:21.559667Z" } }, "outputs": [ @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.102326Z", - "iopub.status.busy": "2024-01-09T15:04:28.101888Z", - "iopub.status.idle": "2024-01-09T15:04:28.126685Z", - "shell.execute_reply": "2024-01-09T15:04:28.125930Z" + "iopub.execute_input": "2024-01-10T06:13:21.563179Z", + "iopub.status.busy": "2024-01-10T06:13:21.562695Z", + "iopub.status.idle": "2024-01-10T06:13:21.585983Z", + "shell.execute_reply": "2024-01-10T06:13:21.585340Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4effa48406144cbeaa8fcd45054ccc19", + "model_id": "fea3d5c35ae9493f8a9be8704822e8fd", "version_major": 2, "version_minor": 0 }, @@ -1114,10 +1114,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.129379Z", - "iopub.status.busy": "2024-01-09T15:04:28.128962Z", - "iopub.status.idle": "2024-01-09T15:04:28.145570Z", - "shell.execute_reply": "2024-01-09T15:04:28.144832Z" + "iopub.execute_input": "2024-01-10T06:13:21.588659Z", + "iopub.status.busy": "2024-01-10T06:13:21.588249Z", + "iopub.status.idle": "2024-01-10T06:13:21.604290Z", + "shell.execute_reply": "2024-01-10T06:13:21.603671Z" } }, "outputs": [ @@ -1235,10 +1235,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.148566Z", - "iopub.status.busy": "2024-01-09T15:04:28.148154Z", - "iopub.status.idle": "2024-01-09T15:04:28.155027Z", - "shell.execute_reply": "2024-01-09T15:04:28.154389Z" + "iopub.execute_input": "2024-01-10T06:13:21.606904Z", + "iopub.status.busy": "2024-01-10T06:13:21.606491Z", + "iopub.status.idle": "2024-01-10T06:13:21.612922Z", + "shell.execute_reply": "2024-01-10T06:13:21.612309Z" } }, "outputs": [], @@ -1295,10 +1295,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:28.157782Z", - "iopub.status.busy": "2024-01-09T15:04:28.157263Z", - "iopub.status.idle": "2024-01-09T15:04:28.178322Z", - "shell.execute_reply": "2024-01-09T15:04:28.177695Z" + "iopub.execute_input": "2024-01-10T06:13:21.615569Z", + "iopub.status.busy": "2024-01-10T06:13:21.615193Z", + "iopub.status.idle": "2024-01-10T06:13:21.634604Z", + "shell.execute_reply": "2024-01-10T06:13:21.634038Z" } }, "outputs": [ @@ -1430,23 +1430,80 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1a984da391434a579c4e8cdea77f9f2d": { + "07ce684158734aa2ad7d27dfdc5022e9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0fff0021442e4b26a3ef037cd77b53b9", + "placeholder": "​", + "style": "IPY_MODEL_1c6e36c143374b619efebd1763224d51", + "value": " 132/132 [00:00<00:00, 10237.77 examples/s]" + } + }, + "0fff0021442e4b26a3ef037cd77b53b9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "376c1aba8f2b448c9d57e86953cac6d8": { + "13d483977eca497389f4722102d836b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1498,7 +1555,7 @@ "width": null } }, - "47e7b53818db4c37a9e352c2973136a7": { + "1c6e36c143374b619efebd1763224d51": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1513,53 +1570,22 @@ "description_width": "" } }, - "4c33ba09eb7a44989d751311b4a6a997": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c8de47b934124049a6778940535a853e", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1a984da391434a579c4e8cdea77f9f2d", - "value": 132.0 - } - }, - "4effa48406144cbeaa8fcd45054ccc19": { + "4666b6ad60954d5887cfd7eebc497a5e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e2b4a19ddce24664b05a205091c3c23e", - "IPY_MODEL_4c33ba09eb7a44989d751311b4a6a997", - "IPY_MODEL_6f7ea0dc41db415b83cee60e01c091a4" - ], - "layout": "IPY_MODEL_b0911ba67f8f43928bd6dc293d6ecf50" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "6f7ea0dc41db415b83cee60e01c091a4": { + "777abd15d2bc4f3dbbcd901eb1bfa3d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1574,28 +1600,37 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c5282ca5da5e43d78ac733697024d974", + "layout": "IPY_MODEL_13d483977eca497389f4722102d836b9", "placeholder": "​", - "style": "IPY_MODEL_47e7b53818db4c37a9e352c2973136a7", - "value": " 132/132 [00:00<00:00, 10105.47 examples/s]" + "style": "IPY_MODEL_4666b6ad60954d5887cfd7eebc497a5e", + "value": "Saving the dataset (1/1 shards): 100%" } }, - "adff6eac22854dd2bb797123ac7621c8": { + "822795f0acf14d819bd90d6ea63181ec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bfd4faf7258443a39bd2fcd278bc83ac", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dac1e3e656dd43008dc2870d6342af0e", + "value": 132.0 } }, - "b0911ba67f8f43928bd6dc293d6ecf50": { + "bfd4faf7258443a39bd2fcd278bc83ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1647,59 +1682,23 @@ "width": null } }, - "c5282ca5da5e43d78ac733697024d974": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "dac1e3e656dd43008dc2870d6342af0e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c8de47b934124049a6778940535a853e": { + "e6c108b6484046cdb7acc0ffe213b8ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1751,25 +1750,26 @@ "width": null } }, - "e2b4a19ddce24664b05a205091c3c23e": { + "fea3d5c35ae9493f8a9be8704822e8fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_376c1aba8f2b448c9d57e86953cac6d8", - "placeholder": "​", - "style": "IPY_MODEL_adff6eac22854dd2bb797123ac7621c8", - "value": "Saving the dataset (1/1 shards): 100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_777abd15d2bc4f3dbbcd901eb1bfa3d7", + "IPY_MODEL_822795f0acf14d819bd90d6ea63181ec", + "IPY_MODEL_07ce684158734aa2ad7d27dfdc5022e9" + ], + "layout": "IPY_MODEL_e6c108b6484046cdb7acc0ffe213b8ef" } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 71af5efdc..286e31e35 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": "2024-01-09T15:04:33.697845Z", - "iopub.status.busy": "2024-01-09T15:04:33.697390Z", - "iopub.status.idle": "2024-01-09T15:04:34.798951Z", - "shell.execute_reply": "2024-01-09T15:04:34.798323Z" + "iopub.execute_input": "2024-01-10T06:13:26.619683Z", + "iopub.status.busy": "2024-01-10T06:13:26.619488Z", + "iopub.status.idle": "2024-01-10T06:13:27.745801Z", + "shell.execute_reply": "2024-01-10T06:13:27.745151Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:04:34.801914Z", - "iopub.status.busy": "2024-01-09T15:04:34.801405Z", - "iopub.status.idle": "2024-01-09T15:04:34.804659Z", - "shell.execute_reply": "2024-01-09T15:04:34.804145Z" + "iopub.execute_input": "2024-01-10T06:13:27.748654Z", + "iopub.status.busy": "2024-01-10T06:13:27.748325Z", + "iopub.status.idle": "2024-01-10T06:13:27.751770Z", + "shell.execute_reply": "2024-01-10T06:13:27.751229Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:34.807278Z", - "iopub.status.busy": "2024-01-09T15:04:34.806875Z", - "iopub.status.idle": "2024-01-09T15:04:34.816865Z", - "shell.execute_reply": "2024-01-09T15:04:34.816343Z" + "iopub.execute_input": "2024-01-10T06:13:27.754354Z", + "iopub.status.busy": "2024-01-10T06:13:27.753974Z", + "iopub.status.idle": "2024-01-10T06:13:27.763975Z", + "shell.execute_reply": "2024-01-10T06:13:27.763377Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:34.819176Z", - "iopub.status.busy": "2024-01-09T15:04:34.818792Z", - "iopub.status.idle": "2024-01-09T15:04:34.823791Z", - "shell.execute_reply": "2024-01-09T15:04:34.823310Z" + "iopub.execute_input": "2024-01-10T06:13:27.766552Z", + "iopub.status.busy": "2024-01-10T06:13:27.766172Z", + "iopub.status.idle": "2024-01-10T06:13:27.771201Z", + "shell.execute_reply": "2024-01-10T06:13:27.770666Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:34.826281Z", - "iopub.status.busy": "2024-01-09T15:04:34.825960Z", - "iopub.status.idle": "2024-01-09T15:04:35.119209Z", - "shell.execute_reply": "2024-01-09T15:04:35.118557Z" + "iopub.execute_input": "2024-01-10T06:13:27.773953Z", + "iopub.status.busy": "2024-01-10T06:13:27.773555Z", + "iopub.status.idle": "2024-01-10T06:13:28.061463Z", + "shell.execute_reply": "2024-01-10T06:13:28.060825Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:35.122082Z", - "iopub.status.busy": "2024-01-09T15:04:35.121691Z", - "iopub.status.idle": "2024-01-09T15:04:35.492738Z", - "shell.execute_reply": "2024-01-09T15:04:35.492070Z" + "iopub.execute_input": "2024-01-10T06:13:28.064511Z", + "iopub.status.busy": "2024-01-10T06:13:28.064106Z", + "iopub.status.idle": "2024-01-10T06:13:28.444087Z", + "shell.execute_reply": "2024-01-10T06:13:28.443404Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:35.495607Z", - "iopub.status.busy": "2024-01-09T15:04:35.495107Z", - "iopub.status.idle": "2024-01-09T15:04:35.498298Z", - "shell.execute_reply": "2024-01-09T15:04:35.497682Z" + "iopub.execute_input": "2024-01-10T06:13:28.446987Z", + "iopub.status.busy": "2024-01-10T06:13:28.446714Z", + "iopub.status.idle": "2024-01-10T06:13:28.449701Z", + "shell.execute_reply": "2024-01-10T06:13:28.449201Z" } }, "outputs": [], @@ -601,10 +601,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:35.500793Z", - "iopub.status.busy": "2024-01-09T15:04:35.500447Z", - "iopub.status.idle": "2024-01-09T15:04:35.538764Z", - "shell.execute_reply": "2024-01-09T15:04:35.538136Z" + "iopub.execute_input": "2024-01-10T06:13:28.452290Z", + "iopub.status.busy": "2024-01-10T06:13:28.451933Z", + "iopub.status.idle": "2024-01-10T06:13:28.490839Z", + "shell.execute_reply": "2024-01-10T06:13:28.490127Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:35.541321Z", - "iopub.status.busy": "2024-01-09T15:04:35.540847Z", - "iopub.status.idle": "2024-01-09T15:04:36.831979Z", - "shell.execute_reply": "2024-01-09T15:04:36.831335Z" + "iopub.execute_input": "2024-01-10T06:13:28.493623Z", + "iopub.status.busy": "2024-01-10T06:13:28.493214Z", + "iopub.status.idle": "2024-01-10T06:13:29.876381Z", + "shell.execute_reply": "2024-01-10T06:13:29.875700Z" } }, "outputs": [ @@ -701,10 +701,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.834949Z", - "iopub.status.busy": "2024-01-09T15:04:36.834312Z", - "iopub.status.idle": "2024-01-09T15:04:36.859652Z", - "shell.execute_reply": "2024-01-09T15:04:36.859101Z" + "iopub.execute_input": "2024-01-10T06:13:29.879396Z", + "iopub.status.busy": "2024-01-10T06:13:29.878867Z", + "iopub.status.idle": "2024-01-10T06:13:29.905840Z", + "shell.execute_reply": "2024-01-10T06:13:29.905222Z" } }, "outputs": [ @@ -878,10 +878,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.862183Z", - "iopub.status.busy": "2024-01-09T15:04:36.861796Z", - "iopub.status.idle": "2024-01-09T15:04:36.869482Z", - "shell.execute_reply": "2024-01-09T15:04:36.868837Z" + "iopub.execute_input": "2024-01-10T06:13:29.908674Z", + "iopub.status.busy": "2024-01-10T06:13:29.908246Z", + "iopub.status.idle": "2024-01-10T06:13:29.915341Z", + "shell.execute_reply": "2024-01-10T06:13:29.914800Z" } }, "outputs": [ @@ -985,10 +985,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.871883Z", - "iopub.status.busy": "2024-01-09T15:04:36.871509Z", - "iopub.status.idle": "2024-01-09T15:04:36.878507Z", - "shell.execute_reply": "2024-01-09T15:04:36.877296Z" + "iopub.execute_input": "2024-01-10T06:13:29.917753Z", + "iopub.status.busy": "2024-01-10T06:13:29.917379Z", + "iopub.status.idle": "2024-01-10T06:13:29.923844Z", + "shell.execute_reply": "2024-01-10T06:13:29.923288Z" } }, "outputs": [ @@ -1055,10 +1055,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.881129Z", - "iopub.status.busy": "2024-01-09T15:04:36.880697Z", - "iopub.status.idle": "2024-01-09T15:04:36.892958Z", - "shell.execute_reply": "2024-01-09T15:04:36.892329Z" + "iopub.execute_input": "2024-01-10T06:13:29.926169Z", + "iopub.status.busy": "2024-01-10T06:13:29.925829Z", + "iopub.status.idle": "2024-01-10T06:13:29.936668Z", + "shell.execute_reply": "2024-01-10T06:13:29.936027Z" } }, "outputs": [ @@ -1231,10 +1231,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.895435Z", - "iopub.status.busy": "2024-01-09T15:04:36.895079Z", - "iopub.status.idle": "2024-01-09T15:04:36.904466Z", - "shell.execute_reply": "2024-01-09T15:04:36.903849Z" + "iopub.execute_input": "2024-01-10T06:13:29.939259Z", + "iopub.status.busy": "2024-01-10T06:13:29.938858Z", + "iopub.status.idle": "2024-01-10T06:13:29.949031Z", + "shell.execute_reply": "2024-01-10T06:13:29.948457Z" } }, "outputs": [ @@ -1350,10 +1350,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.906937Z", - "iopub.status.busy": "2024-01-09T15:04:36.906500Z", - "iopub.status.idle": "2024-01-09T15:04:36.914191Z", - "shell.execute_reply": "2024-01-09T15:04:36.913561Z" + "iopub.execute_input": "2024-01-10T06:13:29.951601Z", + "iopub.status.busy": "2024-01-10T06:13:29.951195Z", + "iopub.status.idle": "2024-01-10T06:13:29.959192Z", + "shell.execute_reply": "2024-01-10T06:13:29.958517Z" }, "scrolled": true }, @@ -1478,10 +1478,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:36.916629Z", - "iopub.status.busy": "2024-01-09T15:04:36.916233Z", - "iopub.status.idle": "2024-01-09T15:04:36.926014Z", - "shell.execute_reply": "2024-01-09T15:04:36.925397Z" + "iopub.execute_input": "2024-01-10T06:13:29.961669Z", + "iopub.status.busy": "2024-01-10T06:13:29.961294Z", + "iopub.status.idle": "2024-01-10T06:13:29.971591Z", + "shell.execute_reply": "2024-01-10T06:13:29.970943Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 8268012ed..05c0fc6d0 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": "2024-01-09T15:04:41.697730Z", - "iopub.status.busy": "2024-01-09T15:04:41.697526Z", - "iopub.status.idle": "2024-01-09T15:04:42.743420Z", - "shell.execute_reply": "2024-01-09T15:04:42.742797Z" + "iopub.execute_input": "2024-01-10T06:13:34.642782Z", + "iopub.status.busy": "2024-01-10T06:13:34.642338Z", + "iopub.status.idle": "2024-01-10T06:13:35.711028Z", + "shell.execute_reply": "2024-01-10T06:13:35.710358Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:04:42.746370Z", - "iopub.status.busy": "2024-01-09T15:04:42.745993Z", - "iopub.status.idle": "2024-01-09T15:04:42.762570Z", - "shell.execute_reply": "2024-01-09T15:04:42.761922Z" + "iopub.execute_input": "2024-01-10T06:13:35.714280Z", + "iopub.status.busy": "2024-01-10T06:13:35.713776Z", + "iopub.status.idle": "2024-01-10T06:13:35.731031Z", + "shell.execute_reply": "2024-01-10T06:13:35.730464Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:42.764840Z", - "iopub.status.busy": "2024-01-09T15:04:42.764640Z", - "iopub.status.idle": "2024-01-09T15:04:42.879506Z", - "shell.execute_reply": "2024-01-09T15:04:42.878882Z" + "iopub.execute_input": "2024-01-10T06:13:35.733700Z", + "iopub.status.busy": "2024-01-10T06:13:35.733480Z", + "iopub.status.idle": "2024-01-10T06:13:35.919903Z", + "shell.execute_reply": "2024-01-10T06:13:35.919256Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:42.881789Z", - "iopub.status.busy": "2024-01-09T15:04:42.881579Z", - "iopub.status.idle": "2024-01-09T15:04:42.885515Z", - "shell.execute_reply": "2024-01-09T15:04:42.884866Z" + "iopub.execute_input": "2024-01-10T06:13:35.922543Z", + "iopub.status.busy": "2024-01-10T06:13:35.922146Z", + "iopub.status.idle": "2024-01-10T06:13:35.926047Z", + "shell.execute_reply": "2024-01-10T06:13:35.925541Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:42.887986Z", - "iopub.status.busy": "2024-01-09T15:04:42.887640Z", - "iopub.status.idle": "2024-01-09T15:04:42.895578Z", - "shell.execute_reply": "2024-01-09T15:04:42.895089Z" + "iopub.execute_input": "2024-01-10T06:13:35.928442Z", + "iopub.status.busy": "2024-01-10T06:13:35.928230Z", + "iopub.status.idle": "2024-01-10T06:13:35.936664Z", + "shell.execute_reply": "2024-01-10T06:13:35.935983Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:42.897983Z", - "iopub.status.busy": "2024-01-09T15:04:42.897622Z", - "iopub.status.idle": "2024-01-09T15:04:42.900402Z", - "shell.execute_reply": "2024-01-09T15:04:42.899898Z" + "iopub.execute_input": "2024-01-10T06:13:35.939625Z", + "iopub.status.busy": "2024-01-10T06:13:35.939226Z", + "iopub.status.idle": "2024-01-10T06:13:35.942101Z", + "shell.execute_reply": "2024-01-10T06:13:35.941546Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:42.902687Z", - "iopub.status.busy": "2024-01-09T15:04:42.902319Z", - "iopub.status.idle": "2024-01-09T15:04:46.565424Z", - "shell.execute_reply": "2024-01-09T15:04:46.564633Z" + "iopub.execute_input": "2024-01-10T06:13:35.944554Z", + "iopub.status.busy": "2024-01-10T06:13:35.944192Z", + "iopub.status.idle": "2024-01-10T06:13:39.637594Z", + "shell.execute_reply": "2024-01-10T06:13:39.636942Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:46.569071Z", - "iopub.status.busy": "2024-01-09T15:04:46.568475Z", - "iopub.status.idle": "2024-01-09T15:04:46.578678Z", - "shell.execute_reply": "2024-01-09T15:04:46.578034Z" + "iopub.execute_input": "2024-01-10T06:13:39.640668Z", + "iopub.status.busy": "2024-01-10T06:13:39.640285Z", + "iopub.status.idle": "2024-01-10T06:13:39.650224Z", + "shell.execute_reply": "2024-01-10T06:13:39.649592Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:46.581294Z", - "iopub.status.busy": "2024-01-09T15:04:46.581025Z", - "iopub.status.idle": "2024-01-09T15:04:48.007671Z", - "shell.execute_reply": "2024-01-09T15:04:48.006949Z" + "iopub.execute_input": "2024-01-10T06:13:39.653012Z", + "iopub.status.busy": "2024-01-10T06:13:39.652527Z", + "iopub.status.idle": "2024-01-10T06:13:41.067365Z", + "shell.execute_reply": "2024-01-10T06:13:41.066639Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.011177Z", - "iopub.status.busy": "2024-01-09T15:04:48.010504Z", - "iopub.status.idle": "2024-01-09T15:04:48.036446Z", - "shell.execute_reply": "2024-01-09T15:04:48.035851Z" + "iopub.execute_input": "2024-01-10T06:13:41.070894Z", + "iopub.status.busy": "2024-01-10T06:13:41.070181Z", + "iopub.status.idle": "2024-01-10T06:13:41.096554Z", + "shell.execute_reply": "2024-01-10T06:13:41.095923Z" }, "scrolled": true }, @@ -624,10 +624,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.039498Z", - "iopub.status.busy": "2024-01-09T15:04:48.039055Z", - "iopub.status.idle": "2024-01-09T15:04:48.049188Z", - "shell.execute_reply": "2024-01-09T15:04:48.048598Z" + "iopub.execute_input": "2024-01-10T06:13:41.099706Z", + "iopub.status.busy": "2024-01-10T06:13:41.099250Z", + "iopub.status.idle": "2024-01-10T06:13:41.109639Z", + "shell.execute_reply": "2024-01-10T06:13:41.109017Z" } }, "outputs": [ @@ -731,10 +731,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.052114Z", - "iopub.status.busy": "2024-01-09T15:04:48.051670Z", - "iopub.status.idle": "2024-01-09T15:04:48.063723Z", - "shell.execute_reply": "2024-01-09T15:04:48.063151Z" + "iopub.execute_input": "2024-01-10T06:13:41.113653Z", + "iopub.status.busy": "2024-01-10T06:13:41.112495Z", + "iopub.status.idle": "2024-01-10T06:13:41.128314Z", + "shell.execute_reply": "2024-01-10T06:13:41.127671Z" } }, "outputs": [ @@ -863,10 +863,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.067961Z", - "iopub.status.busy": "2024-01-09T15:04:48.066547Z", - "iopub.status.idle": "2024-01-09T15:04:48.079954Z", - "shell.execute_reply": "2024-01-09T15:04:48.079350Z" + "iopub.execute_input": "2024-01-10T06:13:41.132950Z", + "iopub.status.busy": "2024-01-10T06:13:41.131794Z", + "iopub.status.idle": "2024-01-10T06:13:41.145507Z", + "shell.execute_reply": "2024-01-10T06:13:41.144878Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.084377Z", - "iopub.status.busy": "2024-01-09T15:04:48.083263Z", - "iopub.status.idle": "2024-01-09T15:04:48.098808Z", - "shell.execute_reply": "2024-01-09T15:04:48.098306Z" + "iopub.execute_input": "2024-01-10T06:13:41.150079Z", + "iopub.status.busy": "2024-01-10T06:13:41.148942Z", + "iopub.status.idle": "2024-01-10T06:13:41.163497Z", + "shell.execute_reply": "2024-01-10T06:13:41.162983Z" } }, "outputs": [ @@ -1094,10 +1094,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.101600Z", - "iopub.status.busy": "2024-01-09T15:04:48.101272Z", - "iopub.status.idle": "2024-01-09T15:04:48.109277Z", - "shell.execute_reply": "2024-01-09T15:04:48.108615Z" + "iopub.execute_input": "2024-01-10T06:13:41.166435Z", + "iopub.status.busy": "2024-01-10T06:13:41.166050Z", + "iopub.status.idle": "2024-01-10T06:13:41.173457Z", + "shell.execute_reply": "2024-01-10T06:13:41.172926Z" } }, "outputs": [ @@ -1181,10 +1181,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.111576Z", - "iopub.status.busy": "2024-01-09T15:04:48.111394Z", - "iopub.status.idle": "2024-01-09T15:04:48.118116Z", - "shell.execute_reply": "2024-01-09T15:04:48.117576Z" + "iopub.execute_input": "2024-01-10T06:13:41.175909Z", + "iopub.status.busy": "2024-01-10T06:13:41.175694Z", + "iopub.status.idle": "2024-01-10T06:13:41.183057Z", + "shell.execute_reply": "2024-01-10T06:13:41.182383Z" } }, "outputs": [ @@ -1277,10 +1277,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:48.121120Z", - "iopub.status.busy": "2024-01-09T15:04:48.120895Z", - "iopub.status.idle": "2024-01-09T15:04:48.127958Z", - "shell.execute_reply": "2024-01-09T15:04:48.127427Z" + "iopub.execute_input": "2024-01-10T06:13:41.185507Z", + "iopub.status.busy": "2024-01-10T06:13:41.185293Z", + "iopub.status.idle": "2024-01-10T06:13:41.192997Z", + "shell.execute_reply": "2024-01-10T06:13:41.192302Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 1f62f73e0..3f01fc8be 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -943,7 +943,7 @@

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

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

@@ -990,43 +990,43 @@

2. Load and format the text dataset

-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1789,7 +1789,7 @@

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/text.ipynb b/master/tutorials/datalab/text.ipynb index e6e977288..316bee9cd 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": "2024-01-09T15:04:52.936825Z", - "iopub.status.busy": "2024-01-09T15:04:52.936630Z", - "iopub.status.idle": "2024-01-09T15:04:55.330472Z", - "shell.execute_reply": "2024-01-09T15:04:55.329853Z" + "iopub.execute_input": "2024-01-10T06:13:45.711059Z", + "iopub.status.busy": "2024-01-10T06:13:45.710475Z", + "iopub.status.idle": "2024-01-10T06:13:48.261537Z", + "shell.execute_reply": "2024-01-10T06:13:48.260841Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1cebe96871fd4f46a47ca5f8c79927e8", + "model_id": "ecde268159ad4b6c9238a02d8a130669", "version_major": 2, "version_minor": 0 }, @@ -118,7 +118,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.333789Z", - "iopub.status.busy": "2024-01-09T15:04:55.333073Z", - "iopub.status.idle": "2024-01-09T15:04:55.336841Z", - "shell.execute_reply": "2024-01-09T15:04:55.336349Z" + "iopub.execute_input": "2024-01-10T06:13:48.264527Z", + "iopub.status.busy": "2024-01-10T06:13:48.264176Z", + "iopub.status.idle": "2024-01-10T06:13:48.267728Z", + "shell.execute_reply": "2024-01-10T06:13:48.267130Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.339102Z", - "iopub.status.busy": "2024-01-09T15:04:55.338896Z", - "iopub.status.idle": "2024-01-09T15:04:55.342199Z", - "shell.execute_reply": "2024-01-09T15:04:55.341641Z" + "iopub.execute_input": "2024-01-10T06:13:48.270213Z", + "iopub.status.busy": "2024-01-10T06:13:48.269855Z", + "iopub.status.idle": "2024-01-10T06:13:48.273267Z", + "shell.execute_reply": "2024-01-10T06:13:48.272653Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.344371Z", - "iopub.status.busy": "2024-01-09T15:04:55.344174Z", - "iopub.status.idle": "2024-01-09T15:04:55.366710Z", - "shell.execute_reply": "2024-01-09T15:04:55.366167Z" + "iopub.execute_input": "2024-01-10T06:13:48.275463Z", + "iopub.status.busy": "2024-01-10T06:13:48.275268Z", + "iopub.status.idle": "2024-01-10T06:13:48.347672Z", + "shell.execute_reply": "2024-01-10T06:13:48.346991Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.368826Z", - "iopub.status.busy": "2024-01-09T15:04:55.368628Z", - "iopub.status.idle": "2024-01-09T15:04:55.372617Z", - "shell.execute_reply": "2024-01-09T15:04:55.371997Z" + "iopub.execute_input": "2024-01-10T06:13:48.350279Z", + "iopub.status.busy": "2024-01-10T06:13:48.350052Z", + "iopub.status.idle": "2024-01-10T06:13:48.354861Z", + "shell.execute_reply": "2024-01-10T06:13:48.354307Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'getting_spare_card', 'card_about_to_expire', 'card_payment_fee_charged', 'change_pin', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed'}\n" + "Classes: {'change_pin', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'beneficiary_not_allowed'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.374970Z", - "iopub.status.busy": "2024-01-09T15:04:55.374594Z", - "iopub.status.idle": "2024-01-09T15:04:55.378425Z", - "shell.execute_reply": "2024-01-09T15:04:55.377880Z" + "iopub.execute_input": "2024-01-10T06:13:48.357102Z", + "iopub.status.busy": "2024-01-10T06:13:48.356899Z", + "iopub.status.idle": "2024-01-10T06:13:48.360744Z", + "shell.execute_reply": "2024-01-10T06:13:48.360210Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:04:55.380898Z", - "iopub.status.busy": "2024-01-09T15:04:55.380530Z", - "iopub.status.idle": "2024-01-09T15:05:05.370166Z", - "shell.execute_reply": "2024-01-09T15:05:05.369424Z" + "iopub.execute_input": "2024-01-10T06:13:48.363321Z", + "iopub.status.busy": "2024-01-10T06:13:48.363090Z", + "iopub.status.idle": "2024-01-10T06:13:57.704452Z", + "shell.execute_reply": "2024-01-10T06:13:57.703719Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a3f96dc93f14541a05c0d5b3542f3cc", + "model_id": "a8ffa81464b747aabcd524f5b6004746", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2a3161f65602451ead05660832c12904", + "model_id": "5216ca84d3a1452cbddcd5994453d513", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edebcb31832543559fd5233dfb5cd909", + "model_id": "e3fe28eb102c4226a6521a5c52251366", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8dd7625fdd5247e2bd53f9cf65f0e506", + "model_id": "9205d83347e448338bc7a0902cba4636", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5192a3570e82452ea248df7de2e577d4", + "model_id": "0e1ed050fd3e40b1a25a47dc3dc51056", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58ff5ba84e224829a1f48ff88467266a", + "model_id": "a2f4fdacb911421b921cb9244d7615bb", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ad0cf1c048344d4acf9acd6b0b229b9", + "model_id": "a83cf26330f24f4ba5ef6fd1ad5505fe", "version_major": 2, "version_minor": 0 }, @@ -535,10 +535,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:05.373583Z", - "iopub.status.busy": "2024-01-09T15:05:05.373100Z", - "iopub.status.idle": "2024-01-09T15:05:06.536452Z", - "shell.execute_reply": "2024-01-09T15:05:06.535785Z" + "iopub.execute_input": "2024-01-10T06:13:57.707843Z", + "iopub.status.busy": "2024-01-10T06:13:57.707437Z", + "iopub.status.idle": "2024-01-10T06:13:58.886455Z", + "shell.execute_reply": "2024-01-10T06:13:58.885773Z" }, "scrolled": true }, @@ -570,10 +570,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:06.539974Z", - "iopub.status.busy": "2024-01-09T15:05:06.539532Z", - "iopub.status.idle": "2024-01-09T15:05:06.542628Z", - "shell.execute_reply": "2024-01-09T15:05:06.542080Z" + "iopub.execute_input": "2024-01-10T06:13:58.890144Z", + "iopub.status.busy": "2024-01-10T06:13:58.889698Z", + "iopub.status.idle": "2024-01-10T06:13:58.892840Z", + "shell.execute_reply": "2024-01-10T06:13:58.892280Z" } }, "outputs": [], @@ -593,10 +593,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:06.545486Z", - "iopub.status.busy": "2024-01-09T15:05:06.545038Z", - "iopub.status.idle": "2024-01-09T15:05:07.892586Z", - "shell.execute_reply": "2024-01-09T15:05:07.891841Z" + "iopub.execute_input": "2024-01-10T06:13:58.895717Z", + "iopub.status.busy": "2024-01-10T06:13:58.895295Z", + "iopub.status.idle": "2024-01-10T06:14:00.257372Z", + "shell.execute_reply": "2024-01-10T06:14:00.256547Z" }, "scrolled": true }, @@ -640,10 +640,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.896137Z", - "iopub.status.busy": "2024-01-09T15:05:07.895447Z", - "iopub.status.idle": "2024-01-09T15:05:07.929798Z", - "shell.execute_reply": "2024-01-09T15:05:07.929210Z" + "iopub.execute_input": "2024-01-10T06:14:00.261200Z", + "iopub.status.busy": "2024-01-10T06:14:00.260285Z", + "iopub.status.idle": "2024-01-10T06:14:00.295159Z", + "shell.execute_reply": "2024-01-10T06:14:00.294488Z" }, "scrolled": true }, @@ -808,10 +808,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.932711Z", - "iopub.status.busy": "2024-01-09T15:05:07.932276Z", - "iopub.status.idle": "2024-01-09T15:05:07.942835Z", - "shell.execute_reply": "2024-01-09T15:05:07.942254Z" + "iopub.execute_input": "2024-01-10T06:14:00.299424Z", + "iopub.status.busy": "2024-01-10T06:14:00.298094Z", + "iopub.status.idle": "2024-01-10T06:14:00.311396Z", + "shell.execute_reply": "2024-01-10T06:14:00.310797Z" }, "scrolled": true }, @@ -921,10 +921,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.945777Z", - "iopub.status.busy": "2024-01-09T15:05:07.945339Z", - "iopub.status.idle": "2024-01-09T15:05:07.950490Z", - "shell.execute_reply": "2024-01-09T15:05:07.950003Z" + "iopub.execute_input": "2024-01-10T06:14:00.315625Z", + "iopub.status.busy": "2024-01-10T06:14:00.314540Z", + "iopub.status.idle": "2024-01-10T06:14:00.320884Z", + "shell.execute_reply": "2024-01-10T06:14:00.320096Z" } }, "outputs": [ @@ -962,10 +962,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.952678Z", - "iopub.status.busy": "2024-01-09T15:05:07.952329Z", - "iopub.status.idle": "2024-01-09T15:05:07.958593Z", - "shell.execute_reply": "2024-01-09T15:05:07.958140Z" + "iopub.execute_input": "2024-01-10T06:14:00.323848Z", + "iopub.status.busy": "2024-01-10T06:14:00.323481Z", + "iopub.status.idle": "2024-01-10T06:14:00.332079Z", + "shell.execute_reply": "2024-01-10T06:14:00.331155Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.960793Z", - "iopub.status.busy": "2024-01-09T15:05:07.960454Z", - "iopub.status.idle": "2024-01-09T15:05:07.966865Z", - "shell.execute_reply": "2024-01-09T15:05:07.966321Z" + "iopub.execute_input": "2024-01-10T06:14:00.334887Z", + "iopub.status.busy": "2024-01-10T06:14:00.334512Z", + "iopub.status.idle": "2024-01-10T06:14:00.341819Z", + "shell.execute_reply": "2024-01-10T06:14:00.341196Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.968930Z", - "iopub.status.busy": "2024-01-09T15:05:07.968729Z", - "iopub.status.idle": "2024-01-09T15:05:07.975136Z", - "shell.execute_reply": "2024-01-09T15:05:07.974600Z" + "iopub.execute_input": "2024-01-10T06:14:00.344333Z", + "iopub.status.busy": "2024-01-10T06:14:00.343964Z", + "iopub.status.idle": "2024-01-10T06:14:00.350787Z", + "shell.execute_reply": "2024-01-10T06:14:00.350197Z" } }, "outputs": [ @@ -1279,10 +1279,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.977526Z", - "iopub.status.busy": "2024-01-09T15:05:07.977136Z", - "iopub.status.idle": "2024-01-09T15:05:07.986486Z", - "shell.execute_reply": "2024-01-09T15:05:07.985955Z" + "iopub.execute_input": "2024-01-10T06:14:00.353100Z", + "iopub.status.busy": "2024-01-10T06:14:00.352897Z", + "iopub.status.idle": "2024-01-10T06:14:00.362475Z", + "shell.execute_reply": "2024-01-10T06:14:00.361847Z" } }, "outputs": [ @@ -1393,10 +1393,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.988874Z", - "iopub.status.busy": "2024-01-09T15:05:07.988503Z", - "iopub.status.idle": "2024-01-09T15:05:07.994425Z", - "shell.execute_reply": "2024-01-09T15:05:07.993779Z" + "iopub.execute_input": "2024-01-10T06:14:00.364896Z", + "iopub.status.busy": "2024-01-10T06:14:00.364520Z", + "iopub.status.idle": "2024-01-10T06:14:00.370683Z", + "shell.execute_reply": "2024-01-10T06:14:00.370027Z" } }, "outputs": [ @@ -1464,10 +1464,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:07.996915Z", - "iopub.status.busy": "2024-01-09T15:05:07.996508Z", - "iopub.status.idle": "2024-01-09T15:05:08.165833Z", - "shell.execute_reply": "2024-01-09T15:05:08.165148Z" + "iopub.execute_input": "2024-01-10T06:14:00.373237Z", + "iopub.status.busy": "2024-01-10T06:14:00.372863Z", + "iopub.status.idle": "2024-01-10T06:14:00.555773Z", + "shell.execute_reply": "2024-01-10T06:14:00.555089Z" } }, "outputs": [ @@ -1546,10 +1546,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:08.168577Z", - "iopub.status.busy": "2024-01-09T15:05:08.168165Z", - "iopub.status.idle": "2024-01-09T15:05:08.172214Z", - "shell.execute_reply": "2024-01-09T15:05:08.171601Z" + "iopub.execute_input": "2024-01-10T06:14:00.558702Z", + "iopub.status.busy": "2024-01-10T06:14:00.558311Z", + "iopub.status.idle": "2024-01-10T06:14:00.562319Z", + "shell.execute_reply": "2024-01-10T06:14:00.561767Z" } }, "outputs": [ @@ -1597,10 +1597,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:08.174778Z", - "iopub.status.busy": "2024-01-09T15:05:08.174406Z", - "iopub.status.idle": "2024-01-09T15:05:08.179958Z", - "shell.execute_reply": "2024-01-09T15:05:08.179319Z" + "iopub.execute_input": "2024-01-10T06:14:00.564637Z", + "iopub.status.busy": "2024-01-10T06:14:00.564438Z", + "iopub.status.idle": "2024-01-10T06:14:00.570104Z", + "shell.execute_reply": "2024-01-10T06:14:00.569531Z" }, "nbsphinx": "hidden" }, @@ -1650,7 +1650,75 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "026559e7decf43c89f24a39a24d489a1": { + "00aeb2f30e6344248d19643ed798bbc4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "07295e89cb4b472d930107e8be8c3829": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0bcbb8b6b2d4472c93f1b70d408cd3ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1666,7 +1734,22 @@ "description_width": "" } }, - "039278c2dc98424e8d26ebc4d7b5c8bb": { + "0bdd3c5d23d740bcbecf41f59a191232": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0cf1ac3dd226438bb8f2183f94973f95": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1718,7 +1801,29 @@ "width": null } }, - "06a8eb81d63a4c88a955be7f160c04bc": { + "0e1ed050fd3e40b1a25a47dc3dc51056": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cc7b20aac74d43c4b6cd6b12d0aac738", + "IPY_MODEL_716710cbdfca4c36ad130987a4499373", + "IPY_MODEL_8ec5def0730d432096e2d85bcfac23b7" + ], + "layout": "IPY_MODEL_548c8271d3b54e1b9f45689a0af4ccba" + } + }, + "107c4fe3b4a3466da18c28dc8cee707d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1734,7 +1839,7 @@ "description_width": "" } }, - "0a30c7647cea4d0c86e1bac4aebda2c9": { + "1470e51bfd5741a397860a52aab1d71d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1786,28 +1891,23 @@ "width": null } }, - "0df50ccc401a42da900c59f92a9af9cd": { + "1712941961e44a459dbba5626cad267a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_50ebc01178f44025875479e884e4c60b", - "placeholder": "​", - "style": "IPY_MODEL_5270477f12694d7f8c915c290dff977e", - "value": "config.json: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "0f3b11abfa434ce282432fbca4bbe463": { + "1aa2f8588c6444898d007a076c585e62": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1859,7 +1959,7 @@ "width": null } }, - "11545c9500c64eb18aa6e36ce787f59f": { + "1d7fc981d1054225be5aceac76877fb0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1874,7 +1974,7 @@ "description_width": "" } }, - "15aa4a13359442b8bfaac92e8e56f00a": { + "22324387e5b743e681be79d036fe31d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1926,45 +2026,59 @@ "width": null } }, - "1cebe96871fd4f46a47ca5f8c79927e8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_94a2e0882c3c4360854b3e0aa42907e0", - "IPY_MODEL_591f0c6d4e2548678dd96f6354ac6c85", - "IPY_MODEL_e3c75851f2ae4c0cb4f0f703cb1debaa" - ], - "layout": "IPY_MODEL_96db13ac4be740028a4c6e9033947665" - } - }, - "1d90f14634fd4c81a50d3b5ed6541022": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "2288246d5a4d4b4bb1a1f8e73a2b12b0": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "1eb875c0ce8d4a3a8585d0a975c097f2": { + "26520c10c4674e25ad89fb718b9068ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1979,13 +2093,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f797883057d342189cff36c480402605", + "layout": "IPY_MODEL_63924927f0884276833e034800f11bb7", "placeholder": "​", - "style": "IPY_MODEL_72e591c3fd4e471588b4ce928a7c2e5a", - "value": "README.md: 100%" + "style": "IPY_MODEL_96700a76a0d44e99acf09ab0d477e8d4", + "value": " 665/665 [00:00<00:00, 79.8kB/s]" } }, - "2005d11d26934f8f9985a9165588a260": { + "2689e33ba4f945b7ac5af43480cf3280": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2037,69 +2151,58 @@ "width": null } }, - "2676e2b551614c4fbdc4f393d8248c66": { + "29250c8e90c943acb40ea406ff6da5fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6995a36f823c4cd8bc71de9a979a34e3", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3d2508c00ed34672955fb3effcc82025", - "value": 665.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "2a3161f65602451ead05660832c12904": { + "299ed4dc9db74c09a4b3e505aac77bff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1eb875c0ce8d4a3a8585d0a975c097f2", - "IPY_MODEL_f0fbdbb23cec4ea580fa21562e2202d6", - "IPY_MODEL_4d353cb9ec074c9d84209ea6a5ef2a24" - ], - "layout": "IPY_MODEL_64a20fee3f764d61b35d2bf23e507fea" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "3d2508c00ed34672955fb3effcc82025": { + "2b86d2d875af44f2b3a146159c86ec2f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5cf0f1b890b0431f9d2784e18231784a", + "placeholder": "​", + "style": "IPY_MODEL_29250c8e90c943acb40ea406ff6da5fa", + "value": " 2.21k/2.21k [00:00<00:00, 271kB/s]" } }, - "3d62881c97144e56823557f591ccae62": { + "2d32842e064e40b0a1541bfad497928a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2114,13 +2217,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0a30c7647cea4d0c86e1bac4aebda2c9", + "layout": "IPY_MODEL_34c788bf54c24aa5949787bb0eff94b7", "placeholder": "​", - "style": "IPY_MODEL_dfa01259636d459f8124ae0841c44bbb", - "value": " 54.2M/54.2M [00:01<00:00, 41.0MB/s]" + "style": "IPY_MODEL_1d7fc981d1054225be5aceac76877fb0", + "value": "README.md: 100%" } }, - "40e7904b76fd4671b2566405a929446c": { + "2e093e53552c4e41afeabb5b2a3769ad": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2172,7 +2275,7 @@ "width": null } }, - "413dea2dee22458694fdc56a47cd22ce": { + "2fdc481b5a9c4ec0ad0aaa7fa7cb3307": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2224,28 +2327,7 @@ "width": null } }, - "416d621b6a134b0583f61900576441e6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f14e8068badf47cd9d8641bbe6b8638f", - "placeholder": "​", - "style": "IPY_MODEL_4a19a63034234bdb820f05d54e8fa968", - "value": " 391/391 [00:00<00:00, 47.8kB/s]" - } - }, - "45e0c247edd54184ae169e7c1e3b817e": { + "34ac7447b3444f598b53edbda4e5eb49": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2297,7 +2379,7 @@ "width": null } }, - "48a740279ca54a049b9cb634a5934112": { + "34c788bf54c24aa5949787bb0eff94b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2349,7 +2431,7 @@ "width": null } }, - "4a19a63034234bdb820f05d54e8fa968": { + "3f584acf4fc44c58936eed00bb2fbd84": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2364,28 +2446,22 @@ "description_width": "" } }, - "4d353cb9ec074c9d84209ea6a5ef2a24": { + "3f5be0ecc2ee4433a4c0d5fd1d44050d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0f3b11abfa434ce282432fbca4bbe463", - "placeholder": "​", - "style": "IPY_MODEL_8795bb2e600a4c758fa16ac4e83f2960", - "value": " 2.21k/2.21k [00:00<00:00, 264kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "50ebc01178f44025875479e884e4c60b": { + "448413c257e146d0938329daabce12f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2437,68 +2513,7 @@ "width": null } }, - "5192a3570e82452ea248df7de2e577d4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b87bfbd3a0614ed18ce87f2ec21f4f29", - "IPY_MODEL_53e8f3202f5848d1beb767a6bf08e850", - "IPY_MODEL_c52439e6445548bcb25298e8ae2f3200" - ], - "layout": "IPY_MODEL_cec245bc179c43e6971e3b796b3cd02c" - } - }, - "5270477f12694d7f8c915c290dff977e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "53e8f3202f5848d1beb767a6bf08e850": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d2fb7e18f3154c12aa89cc9e9cfc4b54", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_06a8eb81d63a4c88a955be7f160c04bc", - "value": 466062.0 - } - }, - "55be151666a642ffb07f716c9fbc8517": { + "45ab12896d8d462491cd97cbbe6e0de9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2550,68 +2565,28 @@ "width": null } }, - "5723ab4b48f24b23949ad541af004147": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "58ff5ba84e224829a1f48ff88467266a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b4059ca3a0f64c16b88ff9c54e2e27f7", - "IPY_MODEL_b74031bd745340b68120aacedebd5564", - "IPY_MODEL_a7e4aaa667ed4653a190c0cfb3183d2c" - ], - "layout": "IPY_MODEL_abc142a87c8f48879095fe4969f275ed" - } - }, - "591f0c6d4e2548678dd96f6354ac6c85": { + "4bb63de4770d456d913a1961bcf657cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6666d4330ad04b66b3e33b09fc5e6f7a", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9c6f685339c449688fd394fcee7451f6", - "value": 0.0 + "layout": "IPY_MODEL_1aa2f8588c6444898d007a076c585e62", + "placeholder": "​", + "style": "IPY_MODEL_dc10e1dfa7e84e1295522909b4df112e", + "value": "tokenizer_config.json: 100%" } }, - "5e6d99dfb9cb417285ca592b2944ee2f": { + "4bba66e3d211422d85ee43a076d810c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2663,23 +2638,28 @@ "width": null } }, - "63b21fc9e82042f7884ae1ce3b6f2829": { + "4d45f78561ad4e2dab7952457334e52f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5ea866887ad340808b148446c2959147", + "placeholder": "​", + "style": "IPY_MODEL_611e0881e57549d683be88bce77f5e93", + "value": " 54.2M/54.2M [00:00<00:00, 300MB/s]" } }, - "64a20fee3f764d61b35d2bf23e507fea": { + "4e23b67da06044dba971afb2dd4d1aa0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2731,7 +2711,89 @@ "width": null } }, - "6666d4330ad04b66b3e33b09fc5e6f7a": { + "4f56a289968f46b29b61725fc61088a3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0cf1ac3dd226438bb8f2183f94973f95", + "placeholder": "​", + "style": "IPY_MODEL_0bdd3c5d23d740bcbecf41f59a191232", + "value": " 391/391 [00:00<00:00, 50.7kB/s]" + } + }, + "4f910b3893a04fd9bcdf56fca9550818": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8091370fb4e0431db0c9c95eb345ad39", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0bcbb8b6b2d4472c93f1b70d408cd3ae", + "value": 0.0 + } + }, + "5216ca84d3a1452cbddcd5994453d513": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2d32842e064e40b0a1541bfad497928a", + "IPY_MODEL_f0f5cf6ee8c24c67aa14f8cf08fe4995", + "IPY_MODEL_2b86d2d875af44f2b3a146159c86ec2f" + ], + "layout": "IPY_MODEL_9727413214ed4e8f9466aeffdafda413" + } + }, + "548c53d229894551afc50feabf6d6b61": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "548c8271d3b54e1b9f45689a0af4ccba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2780,10 +2842,31 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null + } + }, + "5490d628713d40f9a89e63379c65fad1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ed1fe01152fa4642ad138486a2634cc1", + "placeholder": "​", + "style": "IPY_MODEL_5b42a34f8c9842c387f1f19cb3ff7e41", + "value": " 0/0 [00:00<?, ?it/s]" } }, - "6995a36f823c4cd8bc71de9a979a34e3": { + "5673c01340384e06bafc786376498d98": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2835,29 +2918,7 @@ "width": null } }, - "6ad0cf1c048344d4acf9acd6b0b229b9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_754105ebd1324793b442ec4d4ac49ee7", - "IPY_MODEL_edb9e0dda6784b558d2bcd9d8bc3479c", - "IPY_MODEL_bc9a7b1ddd804306bdb61c1256832b88" - ], - "layout": "IPY_MODEL_2005d11d26934f8f9985a9165588a260" - } - }, - "6ccd21fd4efd4515b964998880a4a21f": { + "5b42a34f8c9842c387f1f19cb3ff7e41": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2872,7 +2933,7 @@ "description_width": "" } }, - "70004e70551b492594febb6f476623be": { + "5b5556e01ebd4822b2fb6d01833df8b3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2924,50 +2985,14 @@ "width": null } }, - "72e591c3fd4e471588b4ce928a7c2e5a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "5cf0f1b890b0431f9d2784e18231784a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "754105ebd1324793b442ec4d4ac49ee7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_94401e735d514322adbdf29fd0f21dd9", - "placeholder": "​", - "style": "IPY_MODEL_6ccd21fd4efd4515b964998880a4a21f", - "value": "vocab.txt: 100%" - } - }, - "7b7ca1cc3b0444bb99946a99aab74b10": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", @@ -3012,7 +3037,7 @@ "width": null } }, - "81616443535847e4aeb1de0477681ea9": { + "5ea866887ad340808b148446c2959147": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3064,22 +3089,7 @@ "width": null } }, - "8795bb2e600a4c758fa16ac4e83f2960": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "8c88f34a98344b639d1c5778d06df97d": { + "5f836f718e46477f9c5d4c1f0b77124b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3094,35 +3104,28 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_70004e70551b492594febb6f476623be", + "layout": "IPY_MODEL_bee13efee5384807a5acf80366bb301c", "placeholder": "​", - "style": "IPY_MODEL_e076f128a2614381a2b84d276da5bb99", - "value": ".gitattributes: 100%" + "style": "IPY_MODEL_299ed4dc9db74c09a4b3e505aac77bff", + "value": " 232k/232k [00:00<00:00, 22.1MB/s]" } }, - "8dd7625fdd5247e2bd53f9cf65f0e506": { + "611e0881e57549d683be88bce77f5e93": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_a10676c26a7f4220ad1aafc64bf7c6c8", - "IPY_MODEL_c46615d7ecb541c0aa7e616d6cfc77c3", - "IPY_MODEL_3d62881c97144e56823557f591ccae62" - ], - "layout": "IPY_MODEL_c836d7185adf437d90324e77dbc79679" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "94401e735d514322adbdf29fd0f21dd9": { + "63924927f0884276833e034800f11bb7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3174,7 +3177,47 @@ "width": null } }, - "94a2e0882c3c4360854b3e0aa42907e0": { + "64a2b9e3be0f4e9abe6e46ced115eba4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "65ece1a93a484e9aac2d3dcb5fdc3536": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8a08d94d414a4bcdae4627a725465dbb", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_107c4fe3b4a3466da18c28dc8cee707d", + "value": 54245363.0 + } + }, + "6df5afad53374d6d8ab0eaa9b99fae57": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3189,13 +3232,92 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_be6e89352d424088b96eb4465989b20b", + "layout": "IPY_MODEL_00aeb2f30e6344248d19643ed798bbc4", "placeholder": "​", - "style": "IPY_MODEL_e26476d0fd9c40e4b3f84ee527bff859", - "value": "" + "style": "IPY_MODEL_75440754a86442628eeaec421c0a38f4", + "value": ".gitattributes: 100%" + } + }, + "713082df2a4347929fd826c21024138e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "716710cbdfca4c36ad130987a4499373": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2fdc481b5a9c4ec0ad0aaa7fa7cb3307", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_802d1cbe83234ba281ed3371ace74923", + "value": 466062.0 + } + }, + "75440754a86442628eeaec421c0a38f4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "79f717794c4a4d7782524bbabf970864": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_22324387e5b743e681be79d036fe31d6", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_07295e89cb4b472d930107e8be8c3829", + "value": 391.0 } }, - "96db13ac4be740028a4c6e9033947665": { + "7edb79c30d4044b7a47c715c2672cfbc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3247,7 +3369,23 @@ "width": null } }, - "98e6b4c5130e40e1bbfe1d74b3bdf5bc": { + "802d1cbe83234ba281ed3371ace74923": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "8091370fb4e0431db0c9c95eb345ad39": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3296,63 +3434,10 @@ "right": null, "top": null, "visibility": null, - "width": null - } - }, - "9a3f96dc93f14541a05c0d5b3542f3cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8c88f34a98344b639d1c5778d06df97d", - "IPY_MODEL_cee31b293df14917a873f5a0db6a1e7b", - "IPY_MODEL_416d621b6a134b0583f61900576441e6" - ], - "layout": "IPY_MODEL_40e7904b76fd4671b2566405a929446c" - } - }, - "9c6f685339c449688fd394fcee7451f6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "9ca6cbc476d546499156a75875b4bc2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "width": "20px" } }, - "a10676c26a7f4220ad1aafc64bf7c6c8": { + "83e38a4d06ee41978d13c8bc0fc7f05b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3367,49 +3452,37 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_15aa4a13359442b8bfaac92e8e56f00a", + "layout": "IPY_MODEL_34ac7447b3444f598b53edbda4e5eb49", "placeholder": "​", - "style": "IPY_MODEL_c4859cf68a804f86966b4e479c9bc55e", - "value": "pytorch_model.bin: 100%" - } - }, - "a57cc65827794637833bbacf88cc6fb9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_3f584acf4fc44c58936eed00bb2fbd84", + "value": "config.json: 100%" } }, - "a7e4aaa667ed4653a190c0cfb3183d2c": { + "83f4027e79e14315afd20df28b9abfdf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_aa5c16a4ab554f218bcbd4b2c5706ba0", - "placeholder": "​", - "style": "IPY_MODEL_beb898bc24a64c7a991ae487cbb1ee4f", - "value": " 29.0/29.0 [00:00<00:00, 3.74kB/s]" + "layout": "IPY_MODEL_84c39cc7a1444d2a9a639d6f61385d0c", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_1712941961e44a459dbba5626cad267a", + "value": 231508.0 } }, - "aa5c16a4ab554f218bcbd4b2c5706ba0": { + "84c39cc7a1444d2a9a639d6f61385d0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3461,7 +3534,7 @@ "width": null } }, - "abc142a87c8f48879095fe4969f275ed": { + "8a08d94d414a4bcdae4627a725465dbb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3513,52 +3586,23 @@ "width": null } }, - "b4059ca3a0f64c16b88ff9c54e2e27f7": { + "8a93b8a27e874ab58e3e844b2a4af451": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e6739aed0696484d86fb8482f69a781b", - "placeholder": "​", - "style": "IPY_MODEL_9ca6cbc476d546499156a75875b4bc2e", - "value": "tokenizer_config.json: 100%" - } - }, - "b74031bd745340b68120aacedebd5564": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_45e0c247edd54184ae169e7c1e3b817e", - "max": 29.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_1d90f14634fd4c81a50d3b5ed6541022", - "value": 29.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "b87bfbd3a0614ed18ce87f2ec21f4f29": { + "8bf51f93ea06457590d4b3262c7f7f95": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3573,65 +3617,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_fe0c7728800c4788952c0b915d5cb347", + "layout": "IPY_MODEL_a8e84cb8a6a14048bd15044652da2df5", "placeholder": "​", - "style": "IPY_MODEL_a57cc65827794637833bbacf88cc6fb9", - "value": "tokenizer.json: 100%" - } - }, - "b88af088f92946fdbcf1c7bef6bcefc8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "style": "IPY_MODEL_d292cfd1afb542c782847708fc78ffed", + "value": "" } }, - "bc9a7b1ddd804306bdb61c1256832b88": { + "8ec5def0730d432096e2d85bcfac23b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3646,119 +3638,35 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_48a740279ca54a049b9cb634a5934112", + "layout": "IPY_MODEL_448413c257e146d0938329daabce12f7", "placeholder": "​", - "style": "IPY_MODEL_5723ab4b48f24b23949ad541af004147", - "value": " 232k/232k [00:00<00:00, 26.4MB/s]" - } - }, - "be6e89352d424088b96eb4465989b20b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "style": "IPY_MODEL_ae74085e8acf4192a5c6377c3f74f4aa", + "value": " 466k/466k [00:00<00:00, 12.5MB/s]" } }, - "beb898bc24a64c7a991ae487cbb1ee4f": { + "9205d83347e448338bc7a0902cba4636": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "c26d634462be46c99aa9378b4347bd9b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "c46615d7ecb541c0aa7e616d6cfc77c3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_413dea2dee22458694fdc56a47cd22ce", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_dcf354a6983b48158cd4d0e490457a0b", - "value": 54245363.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d7190d315d09402f9308e2aa2d0abebb", + "IPY_MODEL_65ece1a93a484e9aac2d3dcb5fdc3536", + "IPY_MODEL_4d45f78561ad4e2dab7952457334e52f" + ], + "layout": "IPY_MODEL_a1355e0db37f4778b5ec8f1a709f24fa" } }, - "c4859cf68a804f86966b4e479c9bc55e": { + "96700a76a0d44e99acf09ab0d477e8d4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3773,28 +3681,7 @@ "description_width": "" } }, - "c52439e6445548bcb25298e8ae2f3200": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7b7ca1cc3b0444bb99946a99aab74b10", - "placeholder": "​", - "style": "IPY_MODEL_c26d634462be46c99aa9378b4347bd9b", - "value": " 466k/466k [00:00<00:00, 33.3MB/s]" - } - }, - "c836d7185adf437d90324e77dbc79679": { + "9727413214ed4e8f9466aeffdafda413": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3846,7 +3733,7 @@ "width": null } }, - "cec245bc179c43e6971e3b796b3cd02c": { + "a1355e0db37f4778b5ec8f1a709f24fa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3898,31 +3785,51 @@ "width": null } }, - "cee31b293df14917a873f5a0db6a1e7b": { + "a2f4fdacb911421b921cb9244d7615bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5e6d99dfb9cb417285ca592b2944ee2f", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_63b21fc9e82042f7884ae1ce3b6f2829", - "value": 391.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4bb63de4770d456d913a1961bcf657cb", + "IPY_MODEL_bb9d4da501f6429484245a533c3f046b", + "IPY_MODEL_baf7e1b71fe4454d80981120c3297e64" + ], + "layout": "IPY_MODEL_d13975def1d44bfa9f8441616c936a6d" + } + }, + "a83cf26330f24f4ba5ef6fd1ad5505fe": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d6a918d748714014855bbe6352ff3a56", + "IPY_MODEL_83f4027e79e14315afd20df28b9abfdf", + "IPY_MODEL_5f836f718e46477f9c5d4c1f0b77124b" + ], + "layout": "IPY_MODEL_1470e51bfd5741a397860a52aab1d71d" } }, - "d2fb7e18f3154c12aa89cc9e9cfc4b54": { + "a8e84cb8a6a14048bd15044652da2df5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3974,23 +3881,29 @@ "width": null } }, - "dcf354a6983b48158cd4d0e490457a0b": { + "a8ffa81464b747aabcd524f5b6004746": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6df5afad53374d6d8ab0eaa9b99fae57", + "IPY_MODEL_79f717794c4a4d7782524bbabf970864", + "IPY_MODEL_4f56a289968f46b29b61725fc61088a3" + ], + "layout": "IPY_MODEL_2689e33ba4f945b7ac5af43480cf3280" } }, - "ddde05a1142642e3b583fad570a32d9b": { + "ae74085e8acf4192a5c6377c3f74f4aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4005,7 +3918,7 @@ "description_width": "" } }, - "dfa01259636d459f8124ae0841c44bbb": { + "b9e2ca9c7f43440a89be28ce852168f3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4020,7 +3933,7 @@ "description_width": "" } }, - "e076f128a2614381a2b84d276da5bb99": { + "ba1e2472b2594b9ea818d15f3c3d11bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4035,22 +3948,31 @@ "description_width": "" } }, - "e26476d0fd9c40e4b3f84ee527bff859": { + "baac3a399e3843b58fa1e6dd697a4ef3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_45ab12896d8d462491cd97cbbe6e0de9", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_713082df2a4347929fd826c21024138e", + "value": 665.0 } }, - "e3c75851f2ae4c0cb4f0f703cb1debaa": { + "baf7e1b71fe4454d80981120c3297e64": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4065,13 +3987,37 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_55be151666a642ffb07f716c9fbc8517", + "layout": "IPY_MODEL_5673c01340384e06bafc786376498d98", "placeholder": "​", - "style": "IPY_MODEL_ddde05a1142642e3b583fad570a32d9b", - "value": " 0/0 [00:00<?, ?it/s]" + "style": "IPY_MODEL_3f5be0ecc2ee4433a4c0d5fd1d44050d", + "value": " 29.0/29.0 [00:00<00:00, 3.62kB/s]" + } + }, + "bb9d4da501f6429484245a533c3f046b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4bba66e3d211422d85ee43a076d810c6", + "max": 29.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8a93b8a27e874ab58e3e844b2a4af451", + "value": 29.0 } }, - "e6739aed0696484d86fb8482f69a781b": { + "bee13efee5384807a5acf80366bb301c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4123,77 +4069,7 @@ "width": null } }, - "edb9e0dda6784b558d2bcd9d8bc3479c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_81616443535847e4aeb1de0477681ea9", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_026559e7decf43c89f24a39a24d489a1", - "value": 231508.0 - } - }, - "edebcb31832543559fd5233dfb5cd909": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0df50ccc401a42da900c59f92a9af9cd", - "IPY_MODEL_2676e2b551614c4fbdc4f393d8248c66", - "IPY_MODEL_fa48a511a7064435bb9ec35a82adea4f" - ], - "layout": "IPY_MODEL_039278c2dc98424e8d26ebc4d7b5c8bb" - } - }, - "f0fbdbb23cec4ea580fa21562e2202d6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b88af088f92946fdbcf1c7bef6bcefc8", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f9f5ae483743491891121bf4dac21fba", - "value": 2211.0 - } - }, - "f14e8068badf47cd9d8641bbe6b8638f": { + "c2ee2fac0caf41bcb4e8a14a0536f3bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4245,7 +4121,28 @@ "width": null } }, - "f797883057d342189cff36c480402605": { + "cc7b20aac74d43c4b6cd6b12d0aac738": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7edb79c30d4044b7a47c715c2672cfbc", + "placeholder": "​", + "style": "IPY_MODEL_548c53d229894551afc50feabf6d6b61", + "value": "tokenizer.json: 100%" + } + }, + "d13975def1d44bfa9f8441616c936a6d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4297,23 +4194,22 @@ "width": null } }, - "f9f5ae483743491891121bf4dac21fba": { + "d292cfd1afb542c782847708fc78ffed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "fa48a511a7064435bb9ec35a82adea4f": { + "d6a918d748714014855bbe6352ff3a56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4328,13 +4224,93 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_98e6b4c5130e40e1bbfe1d74b3bdf5bc", + "layout": "IPY_MODEL_2e093e53552c4e41afeabb5b2a3769ad", "placeholder": "​", - "style": "IPY_MODEL_11545c9500c64eb18aa6e36ce787f59f", - "value": " 665/665 [00:00<00:00, 81.4kB/s]" + "style": "IPY_MODEL_ba1e2472b2594b9ea818d15f3c3d11bb", + "value": "vocab.txt: 100%" + } + }, + "d7190d315d09402f9308e2aa2d0abebb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2288246d5a4d4b4bb1a1f8e73a2b12b0", + "placeholder": "​", + "style": "IPY_MODEL_b9e2ca9c7f43440a89be28ce852168f3", + "value": "pytorch_model.bin: 100%" + } + }, + "dc10e1dfa7e84e1295522909b4df112e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "fe0c7728800c4788952c0b915d5cb347": { + "e3fe28eb102c4226a6521a5c52251366": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_83e38a4d06ee41978d13c8bc0fc7f05b", + "IPY_MODEL_baac3a399e3843b58fa1e6dd697a4ef3", + "IPY_MODEL_26520c10c4674e25ad89fb718b9068ce" + ], + "layout": "IPY_MODEL_c2ee2fac0caf41bcb4e8a14a0536f3bf" + } + }, + "ecde268159ad4b6c9238a02d8a130669": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8bf51f93ea06457590d4b3262c7f7f95", + "IPY_MODEL_4f910b3893a04fd9bcdf56fca9550818", + "IPY_MODEL_5490d628713d40f9a89e63379c65fad1" + ], + "layout": "IPY_MODEL_5b5556e01ebd4822b2fb6d01833df8b3" + } + }, + "ed1fe01152fa4642ad138486a2634cc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4385,6 +4361,30 @@ "visibility": null, "width": null } + }, + "f0f5cf6ee8c24c67aa14f8cf08fe4995": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_4e23b67da06044dba971afb2dd4d1aa0", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_64a2b9e3be0f4e9abe6e46ced115eba4", + "value": 2211.0 + } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 9cb39a367..7b01efb69 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": "2024-01-09T15:05:13.070173Z", - "iopub.status.busy": "2024-01-09T15:05:13.069973Z", - "iopub.status.idle": "2024-01-09T15:05:14.099327Z", - "shell.execute_reply": "2024-01-09T15:05:14.098622Z" + "iopub.execute_input": "2024-01-10T06:14:06.083796Z", + "iopub.status.busy": "2024-01-10T06:14:06.083596Z", + "iopub.status.idle": "2024-01-10T06:14:07.138995Z", + "shell.execute_reply": "2024-01-10T06:14:07.138339Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:05:14.102376Z", - "iopub.status.busy": "2024-01-09T15:05:14.102042Z", - "iopub.status.idle": "2024-01-09T15:05:14.105062Z", - "shell.execute_reply": "2024-01-09T15:05:14.104558Z" + "iopub.execute_input": "2024-01-10T06:14:07.141966Z", + "iopub.status.busy": "2024-01-10T06:14:07.141624Z", + "iopub.status.idle": "2024-01-10T06:14:07.144683Z", + "shell.execute_reply": "2024-01-10T06:14:07.144141Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:14.107516Z", - "iopub.status.busy": "2024-01-09T15:05:14.107335Z", - "iopub.status.idle": "2024-01-09T15:05:14.119777Z", - "shell.execute_reply": "2024-01-09T15:05:14.119253Z" + "iopub.execute_input": "2024-01-10T06:14:07.147109Z", + "iopub.status.busy": "2024-01-10T06:14:07.146900Z", + "iopub.status.idle": "2024-01-10T06:14:07.160356Z", + "shell.execute_reply": "2024-01-10T06:14:07.159857Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:14.122331Z", - "iopub.status.busy": "2024-01-09T15:05:14.121868Z", - "iopub.status.idle": "2024-01-09T15:05:17.633818Z", - "shell.execute_reply": "2024-01-09T15:05:17.633233Z" + "iopub.execute_input": "2024-01-10T06:14:07.162918Z", + "iopub.status.busy": "2024-01-10T06:14:07.162487Z", + "iopub.status.idle": "2024-01-10T06:14:12.103424Z", + "shell.execute_reply": "2024-01-10T06:14:12.102793Z" }, "id": "dhTHOg8Pyv5G" }, diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index a6e0da796..e9d9615a2 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -937,13 +937,13 @@

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

-
+
-
+
@@ -1444,7 +1444,7 @@

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: info@cleanlab.ai

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 784090bc9..b59c84e7b 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:21.838774Z", - "iopub.status.busy": "2024-01-09T15:05:21.838584Z", - "iopub.status.idle": "2024-01-09T15:05:22.837641Z", - "shell.execute_reply": "2024-01-09T15:05:22.837004Z" + "iopub.execute_input": "2024-01-10T06:14:16.607280Z", + "iopub.status.busy": "2024-01-10T06:14:16.607078Z", + "iopub.status.idle": "2024-01-10T06:14:17.657250Z", + "shell.execute_reply": "2024-01-10T06:14:17.656618Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:22.840599Z", - "iopub.status.busy": "2024-01-09T15:05:22.840294Z", - "iopub.status.idle": "2024-01-09T15:05:22.843902Z", - "shell.execute_reply": "2024-01-09T15:05:22.843399Z" + "iopub.execute_input": "2024-01-10T06:14:17.660564Z", + "iopub.status.busy": "2024-01-10T06:14:17.659972Z", + "iopub.status.idle": "2024-01-10T06:14:17.663681Z", + "shell.execute_reply": "2024-01-10T06:14:17.663061Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:22.846286Z", - "iopub.status.busy": "2024-01-09T15:05:22.845937Z", - "iopub.status.idle": "2024-01-09T15:05:24.829149Z", - "shell.execute_reply": "2024-01-09T15:05:24.828357Z" + "iopub.execute_input": "2024-01-10T06:14:17.666105Z", + "iopub.status.busy": "2024-01-10T06:14:17.665680Z", + "iopub.status.idle": "2024-01-10T06:14:19.736489Z", + "shell.execute_reply": "2024-01-10T06:14:19.735788Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.832730Z", - "iopub.status.busy": "2024-01-09T15:05:24.831964Z", - "iopub.status.idle": "2024-01-09T15:05:24.868905Z", - "shell.execute_reply": "2024-01-09T15:05:24.868248Z" + "iopub.execute_input": "2024-01-10T06:14:19.740030Z", + "iopub.status.busy": "2024-01-10T06:14:19.739173Z", + "iopub.status.idle": "2024-01-10T06:14:19.787352Z", + "shell.execute_reply": "2024-01-10T06:14:19.786521Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.872065Z", - "iopub.status.busy": "2024-01-09T15:05:24.871690Z", - "iopub.status.idle": "2024-01-09T15:05:24.914043Z", - "shell.execute_reply": "2024-01-09T15:05:24.913329Z" + "iopub.execute_input": "2024-01-10T06:14:19.790338Z", + "iopub.status.busy": "2024-01-10T06:14:19.790052Z", + "iopub.status.idle": "2024-01-10T06:14:19.829158Z", + "shell.execute_reply": "2024-01-10T06:14:19.828462Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.917184Z", - "iopub.status.busy": "2024-01-09T15:05:24.916806Z", - "iopub.status.idle": "2024-01-09T15:05:24.920045Z", - "shell.execute_reply": "2024-01-09T15:05:24.919503Z" + "iopub.execute_input": "2024-01-10T06:14:19.832420Z", + "iopub.status.busy": "2024-01-10T06:14:19.831982Z", + "iopub.status.idle": "2024-01-10T06:14:19.835284Z", + "shell.execute_reply": "2024-01-10T06:14:19.834658Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.922358Z", - "iopub.status.busy": "2024-01-09T15:05:24.922060Z", - "iopub.status.idle": "2024-01-09T15:05:24.924789Z", - "shell.execute_reply": "2024-01-09T15:05:24.924232Z" + "iopub.execute_input": "2024-01-10T06:14:19.837757Z", + "iopub.status.busy": "2024-01-10T06:14:19.837304Z", + "iopub.status.idle": "2024-01-10T06:14:19.840314Z", + "shell.execute_reply": "2024-01-10T06:14:19.839702Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.927300Z", - "iopub.status.busy": "2024-01-09T15:05:24.926940Z", - "iopub.status.idle": "2024-01-09T15:05:24.959017Z", - "shell.execute_reply": "2024-01-09T15:05:24.958347Z" + "iopub.execute_input": "2024-01-10T06:14:19.843019Z", + "iopub.status.busy": "2024-01-10T06:14:19.842419Z", + "iopub.status.idle": "2024-01-10T06:14:19.870315Z", + "shell.execute_reply": "2024-01-10T06:14:19.869647Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "12aac2e2f8024fe885e50e0432168751", + "model_id": "8cfd59fe33214a6cacbf9a0ac8550cc0", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06b8878df89c419fbf4cec0d2a3dd902", + "model_id": "9e9c54ccd4dc4b5f967bcea2a3fdb025", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.965325Z", - "iopub.status.busy": "2024-01-09T15:05:24.964748Z", - "iopub.status.idle": "2024-01-09T15:05:24.972127Z", - "shell.execute_reply": "2024-01-09T15:05:24.971625Z" + "iopub.execute_input": "2024-01-10T06:14:19.878259Z", + "iopub.status.busy": "2024-01-10T06:14:19.877780Z", + "iopub.status.idle": "2024-01-10T06:14:19.885221Z", + "shell.execute_reply": "2024-01-10T06:14:19.884563Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.974469Z", - "iopub.status.busy": "2024-01-09T15:05:24.974266Z", - "iopub.status.idle": "2024-01-09T15:05:24.977981Z", - "shell.execute_reply": "2024-01-09T15:05:24.977459Z" + "iopub.execute_input": "2024-01-10T06:14:19.888245Z", + "iopub.status.busy": "2024-01-10T06:14:19.887801Z", + "iopub.status.idle": "2024-01-10T06:14:19.891951Z", + "shell.execute_reply": "2024-01-10T06:14:19.891387Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.980149Z", - "iopub.status.busy": "2024-01-09T15:05:24.979943Z", - "iopub.status.idle": "2024-01-09T15:05:24.986958Z", - "shell.execute_reply": "2024-01-09T15:05:24.986423Z" + "iopub.execute_input": "2024-01-10T06:14:19.894543Z", + "iopub.status.busy": "2024-01-10T06:14:19.894157Z", + "iopub.status.idle": "2024-01-10T06:14:19.901425Z", + "shell.execute_reply": "2024-01-10T06:14:19.900807Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:24.989357Z", - "iopub.status.busy": "2024-01-09T15:05:24.988842Z", - "iopub.status.idle": "2024-01-09T15:05:25.028483Z", - "shell.execute_reply": "2024-01-09T15:05:25.027813Z" + "iopub.execute_input": "2024-01-10T06:14:19.904233Z", + "iopub.status.busy": "2024-01-10T06:14:19.903763Z", + "iopub.status.idle": "2024-01-10T06:14:19.946997Z", + "shell.execute_reply": "2024-01-10T06:14:19.946238Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:25.031627Z", - "iopub.status.busy": "2024-01-09T15:05:25.031098Z", - "iopub.status.idle": "2024-01-09T15:05:25.068437Z", - "shell.execute_reply": "2024-01-09T15:05:25.067771Z" + "iopub.execute_input": "2024-01-10T06:14:19.950396Z", + "iopub.status.busy": "2024-01-10T06:14:19.949775Z", + "iopub.status.idle": "2024-01-10T06:14:19.997923Z", + "shell.execute_reply": "2024-01-10T06:14:19.997218Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:25.071695Z", - "iopub.status.busy": "2024-01-09T15:05:25.071432Z", - "iopub.status.idle": "2024-01-09T15:05:25.185910Z", - "shell.execute_reply": "2024-01-09T15:05:25.185260Z" + "iopub.execute_input": "2024-01-10T06:14:20.001469Z", + "iopub.status.busy": "2024-01-10T06:14:20.000877Z", + "iopub.status.idle": "2024-01-10T06:14:20.125778Z", + "shell.execute_reply": "2024-01-10T06:14:20.125022Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:25.188691Z", - "iopub.status.busy": "2024-01-09T15:05:25.188307Z", - "iopub.status.idle": "2024-01-09T15:05:27.696310Z", - "shell.execute_reply": "2024-01-09T15:05:27.695586Z" + "iopub.execute_input": "2024-01-10T06:14:20.128815Z", + "iopub.status.busy": "2024-01-10T06:14:20.128382Z", + "iopub.status.idle": "2024-01-10T06:14:22.666460Z", + "shell.execute_reply": "2024-01-10T06:14:22.665723Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.699193Z", - "iopub.status.busy": "2024-01-09T15:05:27.698761Z", - "iopub.status.idle": "2024-01-09T15:05:27.761897Z", - "shell.execute_reply": "2024-01-09T15:05:27.761341Z" + "iopub.execute_input": "2024-01-10T06:14:22.669156Z", + "iopub.status.busy": "2024-01-10T06:14:22.668916Z", + "iopub.status.idle": "2024-01-10T06:14:22.729466Z", + "shell.execute_reply": "2024-01-10T06:14:22.728793Z" } }, "outputs": [ @@ -802,7 +802,7 @@ }, { "cell_type": "markdown", - "id": "91607190", + "id": "55d634cf", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -810,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "95b8614f", + "id": "94584059", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -823,13 +823,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "fcd6d4b7", + "id": "b9628b75", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.764759Z", - "iopub.status.busy": "2024-01-09T15:05:27.764103Z", - "iopub.status.idle": "2024-01-09T15:05:27.872976Z", - "shell.execute_reply": "2024-01-09T15:05:27.872313Z" + "iopub.execute_input": "2024-01-10T06:14:22.732231Z", + "iopub.status.busy": "2024-01-10T06:14:22.731955Z", + "iopub.status.idle": "2024-01-10T06:14:22.846588Z", + "shell.execute_reply": "2024-01-10T06:14:22.845700Z" } }, "outputs": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "f6086634", + "id": "c5fd2f4f", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -879,13 +879,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "7f132efa", + "id": "cfb1a98d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.876279Z", - "iopub.status.busy": "2024-01-09T15:05:27.876024Z", - "iopub.status.idle": "2024-01-09T15:05:27.955161Z", - "shell.execute_reply": "2024-01-09T15:05:27.954468Z" + "iopub.execute_input": "2024-01-10T06:14:22.850451Z", + "iopub.status.busy": "2024-01-10T06:14:22.849579Z", + "iopub.status.idle": "2024-01-10T06:14:22.926903Z", + "shell.execute_reply": "2024-01-10T06:14:22.926034Z" } }, "outputs": [ @@ -921,7 +921,7 @@ }, { "cell_type": "markdown", - "id": "04a32bde", + "id": "c797b51e", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -932,13 +932,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "9d0001f6", + "id": "8e4b5314", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.957933Z", - "iopub.status.busy": "2024-01-09T15:05:27.957729Z", - "iopub.status.idle": "2024-01-09T15:05:27.966173Z", - "shell.execute_reply": "2024-01-09T15:05:27.965653Z" + "iopub.execute_input": "2024-01-10T06:14:22.930016Z", + "iopub.status.busy": "2024-01-10T06:14:22.929774Z", + "iopub.status.idle": "2024-01-10T06:14:22.938891Z", + "shell.execute_reply": "2024-01-10T06:14:22.938159Z" } }, "outputs": [], @@ -1040,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "78808af8", + "id": "f2ecdef4", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1055,13 +1055,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "7a4c5bd8", + "id": "6fd6d9e2", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.968259Z", - "iopub.status.busy": "2024-01-09T15:05:27.968061Z", - "iopub.status.idle": "2024-01-09T15:05:27.987156Z", - "shell.execute_reply": "2024-01-09T15:05:27.986617Z" + "iopub.execute_input": "2024-01-10T06:14:22.941671Z", + "iopub.status.busy": "2024-01-10T06:14:22.941170Z", + "iopub.status.idle": "2024-01-10T06:14:22.960867Z", + "shell.execute_reply": "2024-01-10T06:14:22.960198Z" } }, "outputs": [ @@ -1104,13 +1104,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "c77dce6a", + "id": "5f4f4b91", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:27.989237Z", - "iopub.status.busy": "2024-01-09T15:05:27.989025Z", - "iopub.status.idle": "2024-01-09T15:05:27.993364Z", - "shell.execute_reply": "2024-01-09T15:05:27.992830Z" + "iopub.execute_input": "2024-01-10T06:14:22.963415Z", + "iopub.status.busy": "2024-01-10T06:14:22.963144Z", + "iopub.status.idle": "2024-01-10T06:14:22.967965Z", + "shell.execute_reply": "2024-01-10T06:14:22.967414Z" } }, "outputs": [ @@ -1205,29 +1205,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "06b8878df89c419fbf4cec0d2a3dd902": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_364b0c48b8bc4334b6fcd52ae302359d", - "IPY_MODEL_cd3afc81656e42b08e0c282ab6b269cd", - "IPY_MODEL_d5860c4439e943a2a2eb68d4d1f46c3f" - ], - "layout": "IPY_MODEL_143dcb9804114f19ba296904261e2213" - } - }, - "08d058631fee4e30aab4d3c143b6f569": { + "02873622a71341419941aef0bea6e764": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1279,29 +1257,140 @@ "width": null } }, - "12aac2e2f8024fe885e50e0432168751": { + "0fe2c8dcc89d4a6b876606303720da8f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "1fa28da4022f49c4a420ea8bb4ec2b8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "2713f998e4784419837d175595e938da": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f254523f6351435eb7f8661229184b43", - "IPY_MODEL_fcb2b80f091245719c41d89ccddb5746", - "IPY_MODEL_8517e251a04d46cea54f15d9e78393eb" - ], - "layout": "IPY_MODEL_08d058631fee4e30aab4d3c143b6f569" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a763fb86bc604616a2bccb416889f17e", + "placeholder": "​", + "style": "IPY_MODEL_3bf08f79e11044c8ae9e3d01b165f5f4", + "value": " 10000/? [00:00<00:00, 914329.56it/s]" + } + }, + "29567219cce744c894901fc5fc7153d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_78a360b742ee4bfdbe43f36a0804f07c", + "placeholder": "​", + "style": "IPY_MODEL_72e10f0daba942d680242edaf7b639de", + "value": "number of examples processed for estimating thresholds: " + } + }, + "3185b489256446899fe0eb2000abd99c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d72fa4dce6994dca8b4d202761dbdea7", + "placeholder": "​", + "style": "IPY_MODEL_1fa28da4022f49c4a420ea8bb4ec2b8a", + "value": " 10000/? [00:00<00:00, 1247859.10it/s]" + } + }, + "3bf08f79e11044c8ae9e3d01b165f5f4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6b53e6a747e74d2e95f8918385b84461": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_71e12b2915714163a8efb7d8d2520038", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_864de33388604a6eb55ee4141e8cc2d4", + "value": 50.0 } }, - "143dcb9804114f19ba296904261e2213": { + "71e12b2915714163a8efb7d8d2520038": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1353,28 +1442,22 @@ "width": null } }, - "364b0c48b8bc4334b6fcd52ae302359d": { + "72e10f0daba942d680242edaf7b639de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a2a430d78e6d41439e06751309e92ba0", - "placeholder": "​", - "style": "IPY_MODEL_a75047052c7e4474a91a719eefb7f05e", - "value": "number of examples processed for checking labels: " + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "5bd63d78a24b40b5a3f27b8dbcf087a8": { + "78a360b742ee4bfdbe43f36a0804f07c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1426,7 +1509,46 @@ "width": null } }, - "5fb88c97bb3d4d6cb52bb6b2b77669dc": { + "79c3f1111ee84f5193cd4e4f09142b4d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8248c43ab0a54ea49dace7b985c9d289": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_e8af98ac12044b6db8b480e52844de57", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_0fe2c8dcc89d4a6b876606303720da8f", + "value": 50.0 + } + }, + "864de33388604a6eb55ee4141e8cc2d4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1442,7 +1564,72 @@ "description_width": "" } }, - "6d96cb8308c74b19998b8e69da66a8ad": { + "8cfd59fe33214a6cacbf9a0ac8550cc0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_29567219cce744c894901fc5fc7153d6", + "IPY_MODEL_6b53e6a747e74d2e95f8918385b84461", + "IPY_MODEL_2713f998e4784419837d175595e938da" + ], + "layout": "IPY_MODEL_fbe38660da0047debe95399554068fe2" + } + }, + "9e9c54ccd4dc4b5f967bcea2a3fdb025": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a6a5c473500a4e26903882a3f7500cf3", + "IPY_MODEL_8248c43ab0a54ea49dace7b985c9d289", + "IPY_MODEL_3185b489256446899fe0eb2000abd99c" + ], + "layout": "IPY_MODEL_bb2b6a662d9f4040851f3df8c7115e9a" + } + }, + "a6a5c473500a4e26903882a3f7500cf3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_02873622a71341419941aef0bea6e764", + "placeholder": "​", + "style": "IPY_MODEL_79c3f1111ee84f5193cd4e4f09142b4d", + "value": "number of examples processed for checking labels: " + } + }, + "a763fb86bc604616a2bccb416889f17e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1494,7 +1681,7 @@ "width": null } }, - "76cb7866b67945bb824de5fde9bcb9f7": { + "bb2b6a662d9f4040851f3df8c7115e9a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1546,59 +1733,7 @@ "width": null } }, - "7fe9c76b6fb44e5288b7a4ca20c235da": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "8517e251a04d46cea54f15d9e78393eb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_eeaaab9b927f461b8429c966083a74a8", - "placeholder": "​", - "style": "IPY_MODEL_b35f527db3a14eb98f20897e0acb196b", - "value": " 10000/? [00:00<00:00, 910143.22it/s]" - } - }, - "88f758eba9c0483da019bc322ddc7a0c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a2a430d78e6d41439e06751309e92ba0": { + "d72fa4dce6994dca8b4d202761dbdea7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1650,97 +1785,7 @@ "width": null } }, - "a75047052c7e4474a91a719eefb7f05e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "b35f527db3a14eb98f20897e0acb196b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cd3afc81656e42b08e0c282ab6b269cd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5bd63d78a24b40b5a3f27b8dbcf087a8", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5fb88c97bb3d4d6cb52bb6b2b77669dc", - "value": 50.0 - } - }, - "d5860c4439e943a2a2eb68d4d1f46c3f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6d96cb8308c74b19998b8e69da66a8ad", - "placeholder": "​", - "style": "IPY_MODEL_df793af498f04e65ba802b784fdff92f", - "value": " 10000/? [00:00<00:00, 843313.50it/s]" - } - }, - "df793af498f04e65ba802b784fdff92f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "eeaaab9b927f461b8429c966083a74a8": { + "e8af98ac12044b6db8b480e52844de57": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1792,28 +1837,7 @@ "width": null } }, - "f254523f6351435eb7f8661229184b43": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f6f0cc8ef82843edb484e2b99c7f8370", - "placeholder": "​", - "style": "IPY_MODEL_88f758eba9c0483da019bc322ddc7a0c", - "value": "number of examples processed for estimating thresholds: " - } - }, - "f6f0cc8ef82843edb484e2b99c7f8370": { + "fbe38660da0047debe95399554068fe2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1864,30 +1888,6 @@ "visibility": null, "width": null } - }, - "fcb2b80f091245719c41d89ccddb5746": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_76cb7866b67945bb824de5fde9bcb9f7", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7fe9c76b6fb44e5288b7a4ca20c235da", - "value": 50.0 - } } }, "version_major": 2, diff --git a/master/tutorials/image.html b/master/tutorials/image.html index b67dc977a..e60a72b7a 100644 --- a/master/tutorials/image.html +++ b/master/tutorials/image.html @@ -879,25 +879,25 @@

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.

@@ -1260,16 +1260,16 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
8%|▊ | 3/40 [00:00&lt;00:01, 26.17it/s]
+
5%|▌ | 2/40 [00:00&lt;00:02, 18.60it/s]

</pre>

-
8%|▊ | 3/40 [00:00<00:01, 26.17it/s]
+
5%|▌ | 2/40 [00:00<00:02, 18.60it/s]

end{sphinxVerbatim}

-

8%|▊ | 3/40 [00:00<00:01, 26.17it/s]

+

5%|▌ | 2/40 [00:00<00:02, 18.60it/s]

-
25%|██▌ | 10/40 [00:00&lt;00:00, 48.10it/s]
+
25%|██▌ | 10/40 [00:00&lt;00:00, 49.93it/s]

</pre>

-
25%|██▌ | 10/40 [00:00<00:00, 48.10it/s]
+
25%|██▌ | 10/40 [00:00<00:00, 49.93it/s]

end{sphinxVerbatim}

-

25%|██▌ | 10/40 [00:00<00:00, 48.10it/s]

+

25%|██▌ | 10/40 [00:00<00:00, 49.93it/s]

-
42%|████▎ | 17/40 [00:00&lt;00:00, 56.82it/s]
+
45%|████▌ | 18/40 [00:00&lt;00:00, 60.30it/s]

</pre>

-
42%|████▎ | 17/40 [00:00<00:00, 56.82it/s]
+
45%|████▌ | 18/40 [00:00<00:00, 60.30it/s]

end{sphinxVerbatim}

-

42%|████▎ | 17/40 [00:00<00:00, 56.82it/s]

+

45%|████▌ | 18/40 [00:00<00:00, 60.30it/s]

-
62%|██████▎ | 25/40 [00:00&lt;00:00, 62.43it/s]
+
62%|██████▎ | 25/40 [00:00&lt;00:00, 63.41it/s]

</pre>

-
62%|██████▎ | 25/40 [00:00<00:00, 62.43it/s]
+
62%|██████▎ | 25/40 [00:00<00:00, 63.41it/s]

end{sphinxVerbatim}

-

62%|██████▎ | 25/40 [00:00<00:00, 62.43it/s]

+

62%|██████▎ | 25/40 [00:00<00:00, 63.41it/s]

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 66.06it/s]
+
82%|████████▎ | 33/40 [00:00&lt;00:00, 67.49it/s]

</pre>

-
82%|████████▎ | 33/40 [00:00<00:00, 66.06it/s]
+
82%|████████▎ | 33/40 [00:00<00:00, 67.49it/s]

end{sphinxVerbatim}

-

82%|████████▎ | 33/40 [00:00<00:00, 66.06it/s]

+

82%|████████▎ | 33/40 [00:00<00:00, 67.49it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 61.23it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 63.40it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 61.23it/s]
+
100%|██████████| 40/40 [00:00<00:00, 63.40it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 61.23it/s]

+

100%|██████████| 40/40 [00:00<00:00, 63.40it/s]

-
8%|▊ | 3/40 [00:00&lt;00:01, 27.56it/s]
+
5%|▌ | 2/40 [00:00&lt;00:02, 18.04it/s]

</pre>

-
8%|▊ | 3/40 [00:00<00:01, 27.56it/s]
+
5%|▌ | 2/40 [00:00<00:02, 18.04it/s]

end{sphinxVerbatim}

-

8%|▊ | 3/40 [00:00<00:01, 27.56it/s]

+

5%|▌ | 2/40 [00:00<00:02, 18.04it/s]

-
28%|██▊ | 11/40 [00:00&lt;00:00, 55.77it/s]
+
25%|██▌ | 10/40 [00:00&lt;00:00, 49.32it/s]

</pre>

-
28%|██▊ | 11/40 [00:00<00:00, 55.77it/s]
+
25%|██▌ | 10/40 [00:00<00:00, 49.32it/s]

end{sphinxVerbatim}

-

28%|██▊ | 11/40 [00:00<00:00, 55.77it/s]

+

25%|██▌ | 10/40 [00:00<00:00, 49.32it/s]

-
48%|████▊ | 19/40 [00:00&lt;00:00, 65.28it/s]
+
45%|████▌ | 18/40 [00:00&lt;00:00, 59.28it/s]

</pre>

-
48%|████▊ | 19/40 [00:00<00:00, 65.28it/s]
+
45%|████▌ | 18/40 [00:00<00:00, 59.28it/s]

end{sphinxVerbatim}

-

48%|████▊ | 19/40 [00:00<00:00, 65.28it/s]

+

45%|████▌ | 18/40 [00:00<00:00, 59.28it/s]

-
68%|██████▊ | 27/40 [00:00&lt;00:00, 69.18it/s]
+
65%|██████▌ | 26/40 [00:00&lt;00:00, 65.13it/s]

</pre>

-
68%|██████▊ | 27/40 [00:00<00:00, 69.18it/s]
+
65%|██████▌ | 26/40 [00:00<00:00, 65.13it/s]

end{sphinxVerbatim}

-

68%|██████▊ | 27/40 [00:00<00:00, 69.18it/s]

+

65%|██████▌ | 26/40 [00:00<00:00, 65.13it/s]

-
90%|█████████ | 36/40 [00:00&lt;00:00, 74.26it/s]
+
85%|████████▌ | 34/40 [00:00&lt;00:00, 69.37it/s]

</pre>

-
90%|█████████ | 36/40 [00:00<00:00, 74.26it/s]
+
85%|████████▌ | 34/40 [00:00<00:00, 69.37it/s]

end{sphinxVerbatim}

-

90%|█████████ | 36/40 [00:00<00:00, 74.26it/s]

+

85%|████████▌ | 34/40 [00:00<00:00, 69.37it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 68.03it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 63.82it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 68.03it/s]
+
100%|██████████| 40/40 [00:00<00:00, 63.82it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 68.03it/s]

+

100%|██████████| 40/40 [00:00<00:00, 63.82it/s]

@@ -1656,16 +1656,16 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
5%|▌ | 2/40 [00:00&lt;00:02, 18.23it/s]
+
5%|▌ | 2/40 [00:00&lt;00:02, 18.26it/s]

</pre>

-
5%|▌ | 2/40 [00:00<00:02, 18.23it/s]
+
5%|▌ | 2/40 [00:00<00:02, 18.26it/s]

end{sphinxVerbatim}

-

5%|▌ | 2/40 [00:00<00:02, 18.23it/s]

+

5%|▌ | 2/40 [00:00<00:02, 18.26it/s]

-
25%|██▌ | 10/40 [00:00&lt;00:00, 52.37it/s]
+
25%|██▌ | 10/40 [00:00&lt;00:00, 50.49it/s]

</pre>

-
25%|██▌ | 10/40 [00:00<00:00, 52.37it/s]
+
25%|██▌ | 10/40 [00:00<00:00, 50.49it/s]

end{sphinxVerbatim}

-

25%|██▌ | 10/40 [00:00<00:00, 52.37it/s]

+

25%|██▌ | 10/40 [00:00<00:00, 50.49it/s]

-
45%|████▌ | 18/40 [00:00&lt;00:00, 63.36it/s]
+
45%|████▌ | 18/40 [00:00&lt;00:00, 61.21it/s]

</pre>

-
45%|████▌ | 18/40 [00:00<00:00, 63.36it/s]
+
45%|████▌ | 18/40 [00:00<00:00, 61.21it/s]

end{sphinxVerbatim}

-

45%|████▌ | 18/40 [00:00<00:00, 63.36it/s]

+

45%|████▌ | 18/40 [00:00<00:00, 61.21it/s]

-
65%|██████▌ | 26/40 [00:00&lt;00:00, 68.30it/s]
+
65%|██████▌ | 26/40 [00:00&lt;00:00, 65.51it/s]

</pre>

-
65%|██████▌ | 26/40 [00:00<00:00, 68.30it/s]
+
65%|██████▌ | 26/40 [00:00<00:00, 65.51it/s]

end{sphinxVerbatim}

-

65%|██████▌ | 26/40 [00:00<00:00, 68.30it/s]

+

65%|██████▌ | 26/40 [00:00<00:00, 65.51it/s]

-
88%|████████▊ | 35/40 [00:00&lt;00:00, 73.41it/s]
+
85%|████████▌ | 34/40 [00:00&lt;00:00, 69.51it/s]

</pre>

-
88%|████████▊ | 35/40 [00:00<00:00, 73.41it/s]
+
85%|████████▌ | 34/40 [00:00<00:00, 69.51it/s]

end{sphinxVerbatim}

-

88%|████████▊ | 35/40 [00:00<00:00, 73.41it/s]

+

85%|████████▌ | 34/40 [00:00<00:00, 69.51it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 66.74it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 64.37it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 66.74it/s]
+
100%|██████████| 40/40 [00:00<00:00, 64.37it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 66.74it/s]

+

100%|██████████| 40/40 [00:00<00:00, 64.37it/s]

-
2%|▎ | 1/40 [00:00&lt;00:04, 9.09it/s]
+
5%|▌ | 2/40 [00:00&lt;00:02, 17.70it/s]

</pre>

-
2%|▎ | 1/40 [00:00<00:04, 9.09it/s]
+
5%|▌ | 2/40 [00:00<00:02, 17.70it/s]

end{sphinxVerbatim}

-

2%|▎ | 1/40 [00:00<00:04, 9.09it/s]

+

5%|▌ | 2/40 [00:00<00:02, 17.70it/s]

-
22%|██▎ | 9/40 [00:00&lt;00:00, 47.81it/s]
+
22%|██▎ | 9/40 [00:00&lt;00:00, 46.88it/s]

</pre>

-
22%|██▎ | 9/40 [00:00<00:00, 47.81it/s]
+
22%|██▎ | 9/40 [00:00<00:00, 46.88it/s]

end{sphinxVerbatim}

-

22%|██▎ | 9/40 [00:00<00:00, 47.81it/s]

+

22%|██▎ | 9/40 [00:00<00:00, 46.88it/s]

-
42%|████▎ | 17/40 [00:00&lt;00:00, 60.49it/s]
+
42%|████▎ | 17/40 [00:00&lt;00:00, 59.35it/s]

</pre>

-
42%|████▎ | 17/40 [00:00<00:00, 60.49it/s]
+
42%|████▎ | 17/40 [00:00<00:00, 59.35it/s]

end{sphinxVerbatim}

-

42%|████▎ | 17/40 [00:00<00:00, 60.49it/s]

+

42%|████▎ | 17/40 [00:00<00:00, 59.35it/s]

-
62%|██████▎ | 25/40 [00:00&lt;00:00, 65.98it/s]
+
62%|██████▎ | 25/40 [00:00&lt;00:00, 65.20it/s]

</pre>

-
62%|██████▎ | 25/40 [00:00<00:00, 65.98it/s]
+
62%|██████▎ | 25/40 [00:00<00:00, 65.20it/s]

end{sphinxVerbatim}

-

62%|██████▎ | 25/40 [00:00<00:00, 65.98it/s]

+

62%|██████▎ | 25/40 [00:00<00:00, 65.20it/s]

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 70.38it/s]
+
82%|████████▎ | 33/40 [00:00&lt;00:00, 69.31it/s]

</pre>

-
82%|████████▎ | 33/40 [00:00<00:00, 70.38it/s]
+
82%|████████▎ | 33/40 [00:00<00:00, 69.31it/s]

end{sphinxVerbatim}

-

82%|████████▎ | 33/40 [00:00<00:00, 70.38it/s]

+

82%|████████▎ | 33/40 [00:00<00:00, 69.31it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 64.90it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 63.89it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 64.90it/s]
+
100%|██████████| 40/40 [00:00<00:00, 63.89it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 64.90it/s]

+

100%|██████████| 40/40 [00:00<00:00, 63.89it/s]

@@ -2052,16 +2052,16 @@

5. Compute out-of-sample predicted probabilities and feature embeddings
-
5%|▌ | 2/40 [00:00&lt;00:01, 19.92it/s]
+
2%|▎ | 1/40 [00:00&lt;00:04, 9.29it/s]

</pre>

-
5%|▌ | 2/40 [00:00<00:01, 19.92it/s]
+
2%|▎ | 1/40 [00:00<00:04, 9.29it/s]

end{sphinxVerbatim}

-

5%|▌ | 2/40 [00:00<00:01, 19.92it/s]

+

2%|▎ | 1/40 [00:00<00:04, 9.29it/s]

-
22%|██▎ | 9/40 [00:00&lt;00:00, 47.14it/s]
+
20%|██ | 8/40 [00:00&lt;00:00, 43.17it/s]

</pre>

-
22%|██▎ | 9/40 [00:00<00:00, 47.14it/s]
+
20%|██ | 8/40 [00:00<00:00, 43.17it/s]

end{sphinxVerbatim}

-

22%|██▎ | 9/40 [00:00<00:00, 47.14it/s]

+

20%|██ | 8/40 [00:00<00:00, 43.17it/s]

-
42%|████▎ | 17/40 [00:00&lt;00:00, 60.13it/s]
+
40%|████ | 16/40 [00:00&lt;00:00, 57.22it/s]

</pre>

-
42%|████▎ | 17/40 [00:00<00:00, 60.13it/s]
+
40%|████ | 16/40 [00:00<00:00, 57.22it/s]

end{sphinxVerbatim}

-

42%|████▎ | 17/40 [00:00<00:00, 60.13it/s]

+

40%|████ | 16/40 [00:00<00:00, 57.22it/s]

-
62%|██████▎ | 25/40 [00:00&lt;00:00, 66.32it/s]
+
60%|██████ | 24/40 [00:00&lt;00:00, 62.89it/s]

</pre>

-
62%|██████▎ | 25/40 [00:00<00:00, 66.32it/s]
+
60%|██████ | 24/40 [00:00<00:00, 62.89it/s]

end{sphinxVerbatim}

-

62%|██████▎ | 25/40 [00:00<00:00, 66.32it/s]

+

60%|██████ | 24/40 [00:00<00:00, 62.89it/s]

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 70.08it/s]
+
80%|████████ | 32/40 [00:00&lt;00:00, 66.74it/s]

</pre>

-
82%|████████▎ | 33/40 [00:00<00:00, 70.08it/s]
+
80%|████████ | 32/40 [00:00<00:00, 66.74it/s]

end{sphinxVerbatim}

-

82%|████████▎ | 33/40 [00:00<00:00, 70.08it/s]

+

80%|████████ | 32/40 [00:00<00:00, 66.74it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 64.94it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 62.35it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 64.94it/s]
+
100%|██████████| 40/40 [00:00<00:00, 62.35it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 64.94it/s]

+

100%|██████████| 40/40 [00:00<00:00, 62.35it/s]

-
8%|▊ | 3/40 [00:00&lt;00:01, 27.24it/s]
+
5%|▌ | 2/40 [00:00&lt;00:01, 19.28it/s]

</pre>

-
8%|▊ | 3/40 [00:00<00:01, 27.24it/s]
+
5%|▌ | 2/40 [00:00<00:01, 19.28it/s]

end{sphinxVerbatim}

-

8%|▊ | 3/40 [00:00<00:01, 27.24it/s]

+

5%|▌ | 2/40 [00:00<00:01, 19.28it/s]

-
28%|██▊ | 11/40 [00:00&lt;00:00, 55.17it/s]
+
25%|██▌ | 10/40 [00:00&lt;00:00, 53.09it/s]

</pre>

-
28%|██▊ | 11/40 [00:00<00:00, 55.17it/s]
+
25%|██▌ | 10/40 [00:00<00:00, 53.09it/s]

end{sphinxVerbatim}

-

28%|██▊ | 11/40 [00:00<00:00, 55.17it/s]

+

25%|██▌ | 10/40 [00:00<00:00, 53.09it/s]

-
48%|████▊ | 19/40 [00:00&lt;00:00, 64.42it/s]
+
45%|████▌ | 18/40 [00:00&lt;00:00, 63.84it/s]

</pre>

-
48%|████▊ | 19/40 [00:00<00:00, 64.42it/s]
+
45%|████▌ | 18/40 [00:00<00:00, 63.84it/s]

end{sphinxVerbatim}

-

48%|████▊ | 19/40 [00:00<00:00, 64.42it/s]

+

45%|████▌ | 18/40 [00:00<00:00, 63.84it/s]

-
68%|██████▊ | 27/40 [00:00&lt;00:00, 69.41it/s]
+
65%|██████▌ | 26/40 [00:00&lt;00:00, 68.71it/s]

</pre>

-
68%|██████▊ | 27/40 [00:00<00:00, 69.41it/s]
+
65%|██████▌ | 26/40 [00:00<00:00, 68.71it/s]

end{sphinxVerbatim}

-

68%|██████▊ | 27/40 [00:00<00:00, 69.41it/s]

+

65%|██████▌ | 26/40 [00:00<00:00, 68.71it/s]

-
90%|█████████ | 36/40 [00:00&lt;00:00, 74.08it/s]
+
85%|████████▌ | 34/40 [00:00&lt;00:00, 72.01it/s]

</pre>

-
90%|█████████ | 36/40 [00:00<00:00, 74.08it/s]
+
85%|████████▌ | 34/40 [00:00<00:00, 72.01it/s]

end{sphinxVerbatim}

-

90%|█████████ | 36/40 [00:00<00:00, 74.08it/s]

+

85%|████████▌ | 34/40 [00:00<00:00, 72.01it/s]

-
100%|██████████| 40/40 [00:00&lt;00:00, 67.82it/s]
+
100%|██████████| 40/40 [00:00&lt;00:00, 66.60it/s]

</pre>

-
100%|██████████| 40/40 [00:00<00:00, 67.82it/s]
+
100%|██████████| 40/40 [00:00<00:00, 66.60it/s]

end{sphinxVerbatim}

-

100%|██████████| 40/40 [00:00<00:00, 67.82it/s]

+

100%|██████████| 40/40 [00:00<00:00, 66.60it/s]

-
+
@@ -3366,35 +3366,35 @@

Low information images - is_low_information_issue low_information_score + is_low_information_issue 53050 - True 0.067975 + True 40875 - True 0.089929 + True 9594 - True 0.092601 + True 34825 - True 0.107744 + True 37530 - True 0.108516 + True @@ -3422,7 +3422,7 @@

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/image.ipynb b/master/tutorials/image.ipynb index 136680759..ac230171a 100644 --- a/master/tutorials/image.ipynb +++ b/master/tutorials/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:33.148177Z", - "iopub.status.busy": "2024-01-09T15:05:33.147984Z", - "iopub.status.idle": "2024-01-09T15:05:35.275933Z", - "shell.execute_reply": "2024-01-09T15:05:35.275316Z" + "iopub.execute_input": "2024-01-10T06:14:27.983735Z", + "iopub.status.busy": "2024-01-10T06:14:27.983198Z", + "iopub.status.idle": "2024-01-10T06:14:30.187037Z", + "shell.execute_reply": "2024-01-10T06:14:30.186416Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:35.278609Z", - "iopub.status.busy": "2024-01-09T15:05:35.278299Z", - "iopub.status.idle": "2024-01-09T15:05:35.281940Z", - "shell.execute_reply": "2024-01-09T15:05:35.281389Z" + "iopub.execute_input": "2024-01-10T06:14:30.189697Z", + "iopub.status.busy": "2024-01-10T06:14:30.189376Z", + "iopub.status.idle": "2024-01-10T06:14:30.193168Z", + "shell.execute_reply": "2024-01-10T06:14:30.192626Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:35.284024Z", - "iopub.status.busy": "2024-01-09T15:05:35.283813Z", - "iopub.status.idle": "2024-01-09T15:05:37.104596Z", - "shell.execute_reply": "2024-01-09T15:05:37.103925Z" + "iopub.execute_input": "2024-01-10T06:14:30.195644Z", + "iopub.status.busy": "2024-01-10T06:14:30.195302Z", + "iopub.status.idle": "2024-01-10T06:14:32.296992Z", + "shell.execute_reply": "2024-01-10T06:14:32.296276Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68568aa17e9f4f7b90407718becd059e", + "model_id": "a3915e0b726a46b79cb5df0f0080b054", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92fa8c609cf14d2d95dabb0540703411", + "model_id": "a6b5a69bbd21481db358d145d9702e00", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bef514edf16d4b89be1af25807f99bf8", + "model_id": "4aaf228d5dec484e9d76a93af99b330d", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eacced1a3eeb4720aca48e31138fb049", + "model_id": "c98028da20994c76be789f56278cced6", "version_major": 2, "version_minor": 0 }, @@ -246,10 +246,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:37.107341Z", - "iopub.status.busy": "2024-01-09T15:05:37.107013Z", - "iopub.status.idle": "2024-01-09T15:05:37.111168Z", - "shell.execute_reply": "2024-01-09T15:05:37.110579Z" + "iopub.execute_input": "2024-01-10T06:14:32.299905Z", + "iopub.status.busy": "2024-01-10T06:14:32.299545Z", + "iopub.status.idle": "2024-01-10T06:14:32.303682Z", + "shell.execute_reply": "2024-01-10T06:14:32.303114Z" } }, "outputs": [ @@ -274,17 +274,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:37.113293Z", - "iopub.status.busy": "2024-01-09T15:05:37.113088Z", - "iopub.status.idle": "2024-01-09T15:05:49.320037Z", - "shell.execute_reply": "2024-01-09T15:05:49.319313Z" + "iopub.execute_input": "2024-01-10T06:14:32.306297Z", + "iopub.status.busy": "2024-01-10T06:14:32.305739Z", + "iopub.status.idle": "2024-01-10T06:14:45.017686Z", + "shell.execute_reply": "2024-01-10T06:14:45.017075Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af600e5921e24b2d9dbb70a9b3b11f5f", + "model_id": "49e601fd38364edaac3c9d83a3b91d37", "version_major": 2, "version_minor": 0 }, @@ -322,10 +322,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:05:49.323112Z", - "iopub.status.busy": "2024-01-09T15:05:49.322551Z", - "iopub.status.idle": "2024-01-09T15:06:11.528677Z", - "shell.execute_reply": "2024-01-09T15:06:11.528057Z" + "iopub.execute_input": "2024-01-10T06:14:45.020665Z", + "iopub.status.busy": "2024-01-10T06:14:45.020391Z", + "iopub.status.idle": "2024-01-10T06:15:07.261960Z", + "shell.execute_reply": "2024-01-10T06:15:07.261322Z" } }, "outputs": [], @@ -358,10 +358,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:11.531754Z", - "iopub.status.busy": "2024-01-09T15:06:11.531321Z", - "iopub.status.idle": "2024-01-09T15:06:11.537352Z", - "shell.execute_reply": "2024-01-09T15:06:11.536817Z" + "iopub.execute_input": "2024-01-10T06:15:07.265138Z", + "iopub.status.busy": "2024-01-10T06:15:07.264694Z", + "iopub.status.idle": "2024-01-10T06:15:07.270905Z", + "shell.execute_reply": "2024-01-10T06:15:07.270259Z" } }, "outputs": [], @@ -399,10 +399,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:11.539721Z", - "iopub.status.busy": "2024-01-09T15:06:11.539353Z", - "iopub.status.idle": "2024-01-09T15:06:11.543476Z", - "shell.execute_reply": "2024-01-09T15:06:11.542991Z" + "iopub.execute_input": "2024-01-10T06:15:07.273540Z", + "iopub.status.busy": "2024-01-10T06:15:07.273150Z", + "iopub.status.idle": "2024-01-10T06:15:07.277723Z", + "shell.execute_reply": "2024-01-10T06:15:07.277189Z" }, "nbsphinx": "hidden" }, @@ -539,10 +539,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:11.545801Z", - "iopub.status.busy": "2024-01-09T15:06:11.545509Z", - "iopub.status.idle": "2024-01-09T15:06:11.555188Z", - "shell.execute_reply": "2024-01-09T15:06:11.554664Z" + "iopub.execute_input": "2024-01-10T06:15:07.280348Z", + "iopub.status.busy": "2024-01-10T06:15:07.279953Z", + "iopub.status.idle": "2024-01-10T06:15:07.292535Z", + "shell.execute_reply": "2024-01-10T06:15:07.291681Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:11.557545Z", - "iopub.status.busy": "2024-01-09T15:06:11.557147Z", - "iopub.status.idle": "2024-01-09T15:06:11.587114Z", - "shell.execute_reply": "2024-01-09T15:06:11.586505Z" + "iopub.execute_input": "2024-01-10T06:15:07.295504Z", + "iopub.status.busy": "2024-01-10T06:15:07.294946Z", + "iopub.status.idle": "2024-01-10T06:15:07.327125Z", + "shell.execute_reply": "2024-01-10T06:15:07.326463Z" } }, "outputs": [], @@ -707,10 +707,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:11.589724Z", - "iopub.status.busy": "2024-01-09T15:06:11.589357Z", - "iopub.status.idle": "2024-01-09T15:06:42.460475Z", - "shell.execute_reply": "2024-01-09T15:06:42.458780Z" + "iopub.execute_input": "2024-01-10T06:15:07.330122Z", + "iopub.status.busy": "2024-01-10T06:15:07.329678Z", + "iopub.status.idle": "2024-01-10T06:15:40.131617Z", + "shell.execute_reply": "2024-01-10T06:15:40.130564Z" } }, "outputs": [ @@ -726,14 +726,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.835\n" + "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.995\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.490\n", + "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.856\n", "Computing feature embeddings ...\n" ] }, @@ -750,7 +750,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 3/40 [00:00<00:01, 26.17it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.60it/s]" ] }, { @@ -758,7 +758,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 48.10it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 49.93it/s]" ] }, { @@ -766,7 +766,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 56.82it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 60.30it/s]" ] }, { @@ -774,7 +774,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 62.43it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 63.41it/s]" ] }, { @@ -782,7 +782,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 66.06it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 67.49it/s]" ] }, { @@ -790,7 +790,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 61.23it/s]" + "100%|██████████| 40/40 [00:00<00:00, 63.40it/s]" ] }, { @@ -820,7 +820,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 3/40 [00:00<00:01, 27.56it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.04it/s]" ] }, { @@ -828,7 +828,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 11/40 [00:00<00:00, 55.77it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 49.32it/s]" ] }, { @@ -836,7 +836,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 19/40 [00:00<00:00, 65.28it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 59.28it/s]" ] }, { @@ -844,7 +844,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 27/40 [00:00<00:00, 69.18it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 65.13it/s]" ] }, { @@ -852,7 +852,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 36/40 [00:00<00:00, 74.26it/s]" + " 85%|████████▌ | 34/40 [00:00<00:00, 69.37it/s]" ] }, { @@ -860,7 +860,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 68.03it/s]" + "100%|██████████| 40/40 [00:00<00:00, 63.82it/s]" ] }, { @@ -882,14 +882,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.582\n" + "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.786\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.439\n", + "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.727\n", "Computing feature embeddings ...\n" ] }, @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:02, 18.23it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.26it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 52.37it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 50.49it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 18/40 [00:00<00:00, 63.36it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 61.21it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 26/40 [00:00<00:00, 68.30it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 65.51it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 35/40 [00:00<00:00, 73.41it/s]" + " 85%|████████▌ | 34/40 [00:00<00:00, 69.51it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 66.74it/s]" + "100%|██████████| 40/40 [00:00<00:00, 64.37it/s]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 9.09it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 17.70it/s]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 47.81it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 46.88it/s]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 60.49it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 59.35it/s]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 65.98it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 65.20it/s]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 70.38it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 69.31it/s]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 64.90it/s]" + "100%|██████████| 40/40 [00:00<00:00, 63.89it/s]" ] }, { @@ -1038,14 +1038,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.561\n" + "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 5.020\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.258\n", + "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.627\n", "Computing feature embeddings ...\n" ] }, @@ -1062,7 +1062,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:01, 19.92it/s]" + " 2%|▎ | 1/40 [00:00<00:04, 9.29it/s]" ] }, { @@ -1070,7 +1070,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 47.14it/s]" + " 20%|██ | 8/40 [00:00<00:00, 43.17it/s]" ] }, { @@ -1078,7 +1078,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 60.13it/s]" + " 40%|████ | 16/40 [00:00<00:00, 57.22it/s]" ] }, { @@ -1086,7 +1086,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 66.32it/s]" + " 60%|██████ | 24/40 [00:00<00:00, 62.89it/s]" ] }, { @@ -1094,7 +1094,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 70.08it/s]" + " 80%|████████ | 32/40 [00:00<00:00, 66.74it/s]" ] }, { @@ -1102,7 +1102,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 64.94it/s]" + "100%|██████████| 40/40 [00:00<00:00, 62.35it/s]" ] }, { @@ -1132,7 +1132,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 3/40 [00:00<00:01, 27.24it/s]" + " 5%|▌ | 2/40 [00:00<00:01, 19.28it/s]" ] }, { @@ -1140,7 +1140,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 11/40 [00:00<00:00, 55.17it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 53.09it/s]" ] }, { @@ -1148,7 +1148,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 19/40 [00:00<00:00, 64.42it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 63.84it/s]" ] }, { @@ -1156,7 +1156,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 27/40 [00:00<00:00, 69.41it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 68.71it/s]" ] }, { @@ -1164,7 +1164,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 36/40 [00:00<00:00, 74.08it/s]" + " 85%|████████▌ | 34/40 [00:00<00:00, 72.01it/s]" ] }, { @@ -1172,7 +1172,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 67.82it/s]" + "100%|██████████| 40/40 [00:00<00:00, 66.60it/s]" ] }, { @@ -1249,10 +1249,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:42.463508Z", - "iopub.status.busy": "2024-01-09T15:06:42.463036Z", - "iopub.status.idle": "2024-01-09T15:06:42.479597Z", - "shell.execute_reply": "2024-01-09T15:06:42.479085Z" + "iopub.execute_input": "2024-01-10T06:15:40.134484Z", + "iopub.status.busy": "2024-01-10T06:15:40.134046Z", + "iopub.status.idle": "2024-01-10T06:15:40.150308Z", + "shell.execute_reply": "2024-01-10T06:15:40.149745Z" } }, "outputs": [], @@ -1277,10 +1277,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:42.482082Z", - "iopub.status.busy": "2024-01-09T15:06:42.481591Z", - "iopub.status.idle": "2024-01-09T15:06:42.917661Z", - "shell.execute_reply": "2024-01-09T15:06:42.916927Z" + "iopub.execute_input": "2024-01-10T06:15:40.153062Z", + "iopub.status.busy": "2024-01-10T06:15:40.152743Z", + "iopub.status.idle": "2024-01-10T06:15:40.614160Z", + "shell.execute_reply": "2024-01-10T06:15:40.613451Z" } }, "outputs": [], @@ -1300,10 +1300,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:06:42.920522Z", - "iopub.status.busy": "2024-01-09T15:06:42.920309Z", - "iopub.status.idle": "2024-01-09T15:10:04.199554Z", - "shell.execute_reply": "2024-01-09T15:10:04.198800Z" + "iopub.execute_input": "2024-01-10T06:15:40.617172Z", + "iopub.status.busy": "2024-01-10T06:15:40.616955Z", + "iopub.status.idle": "2024-01-10T06:19:01.674604Z", + "shell.execute_reply": "2024-01-10T06:19:01.673956Z" } }, "outputs": [ @@ -1342,7 +1342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "855458eae8684ce0aa743d94aef749b9", + "model_id": "dfe013943ca04be693b072c950762919", "version_major": 2, "version_minor": 0 }, @@ -1381,10 +1381,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:04.202656Z", - "iopub.status.busy": "2024-01-09T15:10:04.202006Z", - "iopub.status.idle": "2024-01-09T15:10:04.734951Z", - "shell.execute_reply": "2024-01-09T15:10:04.734168Z" + "iopub.execute_input": "2024-01-10T06:19:01.677701Z", + "iopub.status.busy": "2024-01-10T06:19:01.676946Z", + "iopub.status.idle": "2024-01-10T06:19:02.197889Z", + "shell.execute_reply": "2024-01-10T06:19:02.197157Z" } }, "outputs": [ @@ -1596,10 +1596,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:04.737803Z", - "iopub.status.busy": "2024-01-09T15:10:04.737424Z", - "iopub.status.idle": "2024-01-09T15:10:04.801113Z", - "shell.execute_reply": "2024-01-09T15:10:04.800500Z" + "iopub.execute_input": "2024-01-10T06:19:02.201016Z", + "iopub.status.busy": "2024-01-10T06:19:02.200424Z", + "iopub.status.idle": "2024-01-10T06:19:02.240523Z", + "shell.execute_reply": "2024-01-10T06:19:02.239757Z" } }, "outputs": [ @@ -1703,10 +1703,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:04.803963Z", - "iopub.status.busy": "2024-01-09T15:10:04.803445Z", - "iopub.status.idle": "2024-01-09T15:10:04.813194Z", - "shell.execute_reply": "2024-01-09T15:10:04.812614Z" + "iopub.execute_input": "2024-01-10T06:19:02.243308Z", + "iopub.status.busy": "2024-01-10T06:19:02.243099Z", + "iopub.status.idle": "2024-01-10T06:19:02.253144Z", + "shell.execute_reply": "2024-01-10T06:19:02.252390Z" } }, "outputs": [ @@ -1836,10 +1836,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:04.815955Z", - "iopub.status.busy": "2024-01-09T15:10:04.815443Z", - "iopub.status.idle": "2024-01-09T15:10:04.820835Z", - "shell.execute_reply": "2024-01-09T15:10:04.820317Z" + "iopub.execute_input": "2024-01-10T06:19:02.255838Z", + "iopub.status.busy": "2024-01-10T06:19:02.255628Z", + "iopub.status.idle": "2024-01-10T06:19:02.261037Z", + "shell.execute_reply": "2024-01-10T06:19:02.260283Z" }, "nbsphinx": "hidden" }, @@ -1885,10 +1885,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:04.823342Z", - "iopub.status.busy": "2024-01-09T15:10:04.822984Z", - "iopub.status.idle": "2024-01-09T15:10:05.338421Z", - "shell.execute_reply": "2024-01-09T15:10:05.337695Z" + "iopub.execute_input": "2024-01-10T06:19:02.263582Z", + "iopub.status.busy": "2024-01-10T06:19:02.263372Z", + "iopub.status.idle": "2024-01-10T06:19:02.747505Z", + "shell.execute_reply": "2024-01-10T06:19:02.746806Z" } }, "outputs": [ @@ -1923,10 +1923,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.341162Z", - "iopub.status.busy": "2024-01-09T15:10:05.340656Z", - "iopub.status.idle": "2024-01-09T15:10:05.349976Z", - "shell.execute_reply": "2024-01-09T15:10:05.349332Z" + "iopub.execute_input": "2024-01-10T06:19:02.749994Z", + "iopub.status.busy": "2024-01-10T06:19:02.749785Z", + "iopub.status.idle": "2024-01-10T06:19:02.760052Z", + "shell.execute_reply": "2024-01-10T06:19:02.759451Z" } }, "outputs": [ @@ -2093,10 +2093,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.352457Z", - "iopub.status.busy": "2024-01-09T15:10:05.352102Z", - "iopub.status.idle": "2024-01-09T15:10:05.360090Z", - "shell.execute_reply": "2024-01-09T15:10:05.359466Z" + "iopub.execute_input": "2024-01-10T06:19:02.762581Z", + "iopub.status.busy": "2024-01-10T06:19:02.762116Z", + "iopub.status.idle": "2024-01-10T06:19:02.771043Z", + "shell.execute_reply": "2024-01-10T06:19:02.770414Z" }, "nbsphinx": "hidden" }, @@ -2172,10 +2172,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.362566Z", - "iopub.status.busy": "2024-01-09T15:10:05.362186Z", - "iopub.status.idle": "2024-01-09T15:10:05.836762Z", - "shell.execute_reply": "2024-01-09T15:10:05.836191Z" + "iopub.execute_input": "2024-01-10T06:19:02.773686Z", + "iopub.status.busy": "2024-01-10T06:19:02.773202Z", + "iopub.status.idle": "2024-01-10T06:19:03.493718Z", + "shell.execute_reply": "2024-01-10T06:19:03.493061Z" } }, "outputs": [ @@ -2212,10 +2212,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.839586Z", - "iopub.status.busy": "2024-01-09T15:10:05.839100Z", - "iopub.status.idle": "2024-01-09T15:10:05.856168Z", - "shell.execute_reply": "2024-01-09T15:10:05.855566Z" + "iopub.execute_input": "2024-01-10T06:19:03.496501Z", + "iopub.status.busy": "2024-01-10T06:19:03.496107Z", + "iopub.status.idle": "2024-01-10T06:19:03.513465Z", + "shell.execute_reply": "2024-01-10T06:19:03.512805Z" } }, "outputs": [ @@ -2372,10 +2372,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.858821Z", - "iopub.status.busy": "2024-01-09T15:10:05.858436Z", - "iopub.status.idle": "2024-01-09T15:10:05.864556Z", - "shell.execute_reply": "2024-01-09T15:10:05.863932Z" + "iopub.execute_input": "2024-01-10T06:19:03.516337Z", + "iopub.status.busy": "2024-01-10T06:19:03.515917Z", + "iopub.status.idle": "2024-01-10T06:19:03.521933Z", + "shell.execute_reply": "2024-01-10T06:19:03.521410Z" }, "nbsphinx": "hidden" }, @@ -2420,10 +2420,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:05.866844Z", - "iopub.status.busy": "2024-01-09T15:10:05.866491Z", - "iopub.status.idle": "2024-01-09T15:10:06.557011Z", - "shell.execute_reply": "2024-01-09T15:10:06.555900Z" + "iopub.execute_input": "2024-01-10T06:19:03.524251Z", + "iopub.status.busy": "2024-01-10T06:19:03.523892Z", + "iopub.status.idle": "2024-01-10T06:19:03.912280Z", + "shell.execute_reply": "2024-01-10T06:19:03.911576Z" } }, "outputs": [ @@ -2505,10 +2505,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.560103Z", - "iopub.status.busy": "2024-01-09T15:10:06.559851Z", - "iopub.status.idle": "2024-01-09T15:10:06.570384Z", - "shell.execute_reply": "2024-01-09T15:10:06.569700Z" + "iopub.execute_input": "2024-01-10T06:19:03.915329Z", + "iopub.status.busy": "2024-01-10T06:19:03.915107Z", + "iopub.status.idle": "2024-01-10T06:19:03.925635Z", + "shell.execute_reply": "2024-01-10T06:19:03.925000Z" } }, "outputs": [ @@ -2636,10 +2636,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.573607Z", - "iopub.status.busy": "2024-01-09T15:10:06.573189Z", - "iopub.status.idle": "2024-01-09T15:10:06.579796Z", - "shell.execute_reply": "2024-01-09T15:10:06.579138Z" + "iopub.execute_input": "2024-01-10T06:19:03.928193Z", + "iopub.status.busy": "2024-01-10T06:19:03.927986Z", + "iopub.status.idle": "2024-01-10T06:19:03.933195Z", + "shell.execute_reply": "2024-01-10T06:19:03.932669Z" }, "nbsphinx": "hidden" }, @@ -2676,10 +2676,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.582744Z", - "iopub.status.busy": "2024-01-09T15:10:06.582507Z", - "iopub.status.idle": "2024-01-09T15:10:06.782529Z", - "shell.execute_reply": "2024-01-09T15:10:06.781834Z" + "iopub.execute_input": "2024-01-10T06:19:03.935880Z", + "iopub.status.busy": "2024-01-10T06:19:03.935309Z", + "iopub.status.idle": "2024-01-10T06:19:04.104020Z", + "shell.execute_reply": "2024-01-10T06:19:04.103402Z" } }, "outputs": [ @@ -2721,10 +2721,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.784983Z", - "iopub.status.busy": "2024-01-09T15:10:06.784769Z", - "iopub.status.idle": "2024-01-09T15:10:06.793384Z", - "shell.execute_reply": "2024-01-09T15:10:06.792845Z" + "iopub.execute_input": "2024-01-10T06:19:04.106941Z", + "iopub.status.busy": "2024-01-10T06:19:04.106499Z", + "iopub.status.idle": "2024-01-10T06:19:04.115435Z", + "shell.execute_reply": "2024-01-10T06:19:04.114817Z" } }, "outputs": [ @@ -2749,47 +2749,47 @@ " \n", " \n", " \n", - " is_low_information_issue\n", " low_information_score\n", + " is_low_information_issue\n", " \n", " \n", " \n", " \n", " 53050\n", - " True\n", " 0.067975\n", + " True\n", " \n", " \n", " 40875\n", - " True\n", " 0.089929\n", + " True\n", " \n", " \n", " 9594\n", - " True\n", " 0.092601\n", + " True\n", " \n", " \n", " 34825\n", - " True\n", " 0.107744\n", + " True\n", " \n", " \n", " 37530\n", - " True\n", " 0.108516\n", + " True\n", " \n", " \n", "\n", "

" ], "text/plain": [ - " 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" + " 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" ] }, "execution_count": 29, @@ -2810,10 +2810,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.795721Z", - "iopub.status.busy": "2024-01-09T15:10:06.795344Z", - "iopub.status.idle": "2024-01-09T15:10:06.993269Z", - "shell.execute_reply": "2024-01-09T15:10:06.992660Z" + "iopub.execute_input": "2024-01-10T06:19:04.117761Z", + "iopub.status.busy": "2024-01-10T06:19:04.117385Z", + "iopub.status.idle": "2024-01-10T06:19:04.309531Z", + "shell.execute_reply": "2024-01-10T06:19:04.308840Z" } }, "outputs": [ @@ -2853,10 +2853,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:06.995987Z", - "iopub.status.busy": "2024-01-09T15:10:06.995586Z", - "iopub.status.idle": "2024-01-09T15:10:07.000490Z", - "shell.execute_reply": "2024-01-09T15:10:06.999967Z" + "iopub.execute_input": "2024-01-10T06:19:04.312289Z", + "iopub.status.busy": "2024-01-10T06:19:04.311799Z", + "iopub.status.idle": "2024-01-10T06:19:04.316705Z", + "shell.execute_reply": "2024-01-10T06:19:04.316103Z" }, "nbsphinx": "hidden" }, @@ -2893,7 +2893,43 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0473bf97727e472ba8e7b962a84b0306": { + "013214df95b64291a68080a23b498d9c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bcf1b5301fd64b32aaf6b5bc496014ac", + "placeholder": "​", + "style": "IPY_MODEL_0de47076f12f425aaf23ccfbd50f919d", + "value": " 2/2 [00:00<00:00, 310.86it/s]" + } + }, + "0a79a261be2c484e94796f9d62c31f87": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0cda3634af9749aa8e66efb500ef558a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2942,26 +2978,62 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null } }, - "0afac2fb2a2a43769de95731fcd3298f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "0d03cfe2e87e462c9a9b5432ae2d2024": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "0d1536c7250b4b1e9705a71ffda4a3e7": { + "0de47076f12f425aaf23ccfbd50f919d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2976,28 +3048,7 @@ "description_width": "" } }, - "0d65f29f57ce4744b9fbd73a527cf547": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_273c728bfd7d4943ba708c71210e28c2", - "placeholder": "​", - "style": "IPY_MODEL_7cd16d1864804c7e9e2a98990e9493ae", - "value": " 2/2 [00:00<00:00, 357.04it/s]" - } - }, - "0fca6638327f48e8a39dcc8b63dc9fd0": { + "0de5c1f9f47b48ec9f04d1bf5d3ad773": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3046,10 +3097,10 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "10664e26a0ce4eeeaaf052febd6cfb01": { + "0e160c96ba624dc8b6cea376b8066800": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3101,76 +3152,23 @@ "width": null } }, - "16674e62660245f7898a4c11861c11ca": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0473bf97727e472ba8e7b962a84b0306", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_cc0b0bf5734e405c9773771efd329716", - "value": 1.0 - } - }, - "16ac788950264bbdbf7ed494c4563a01": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "1b69e9574f9141228434ea3ce25da6fd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "1e53b5cc161f4b1bb16500989600fd03": { + "10bb4e9e540347b6adf4e3fedbe1cc0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "21f4cd54b5d54250be17cf0f2c9b47a2": { + "190459aade984ef2aea0d2515ff57cda": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3222,7 +3220,7 @@ "width": null } }, - "273c728bfd7d4943ba708c71210e28c2": { + "1b3b170418ce4c9ab3f8a55b5a6eabc9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3274,7 +3272,7 @@ "width": null } }, - "2799716cf975430297a582a0add7fc99": { + "1bd85ee9e5b94936a4a0ca8c939715dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3326,59 +3324,7 @@ "width": null } }, - "27bc9862bc214fb2bf85bbfbe4f8d12e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "280992726df64d0bb7f66f7979cacc38": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "28541a162be44b9f939e6cdf26ae4d14": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_9896d58aa931463a895c73358a4c3f52", - "placeholder": "​", - "style": "IPY_MODEL_16ac788950264bbdbf7ed494c4563a01", - "value": " 10000/0 [00:00<00:00, 469261.25 examples/s]" - } - }, - "2f41d597282241aeba4e3b627f170575": { + "1d21a5d038eb47648eec87e4e6006fd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3393,13 +3339,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ba5eaac37c12413483457def49e84997", + "layout": "IPY_MODEL_0d03cfe2e87e462c9a9b5432ae2d2024", "placeholder": "​", - "style": "IPY_MODEL_c09bc75cc58e4390a40a122a999ce70b", - "value": " 60000/0 [00:00<00:00, 835180.44 examples/s]" + "style": "IPY_MODEL_2ed35715096140afab738d789e6ea0ae", + "value": "Map (num_proc=4): 100%" } }, - "2f6f78d28b114880b2c2b118c18b20e5": { + "1f2895dbb9ee4dbeaa2353e1d22f7797": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3451,31 +3397,7 @@ "width": null } }, - "374b4a11ea22457abc80b9c0200deb3c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a7c417d50f384eb18d3f3ff5a4790f1a", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0afac2fb2a2a43769de95731fcd3298f", - "value": 60000.0 - } - }, - "3bbe9a96751c40179a552c9d1e5e4e44": { + "200254c8d04f40b599b27cab58c62c23": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3527,27 +3449,36 @@ "width": null } }, - "3c753bfc0e02462398194b5294142302": { + "2077df6b881c41f397694543602c3b21": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "45f3493a23b940adac33088d5567c92d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_190459aade984ef2aea0d2515ff57cda", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5ead0d8a08ab4a72a56b1bcf53e7b043", + "value": 30931277.0 + } + }, + "22110adf8fbd44dc907195aa03315f8f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, @@ -3594,7 +3525,7 @@ "width": null } }, - "4b5d35473c504a3e9ade960089fb1f04": { + "2695bb154afc4c0ca3e73976aa6b1557": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3609,86 +3540,83 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_21f4cd54b5d54250be17cf0f2c9b47a2", + "layout": "IPY_MODEL_cf1ffd0a959c4b1b98e9116cbd853f84", "placeholder": "​", - "style": "IPY_MODEL_1e53b5cc161f4b1bb16500989600fd03", - "value": "Downloading data: 100%" + "style": "IPY_MODEL_0a79a261be2c484e94796f9d62c31f87", + "value": "Computing checksums: 100%" } }, - "508261f3f6a147e7b5390c1cf679e69e": { + "26d3dec3ea8a42e78a9a1f322d8be9c3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_eba67277054b469481c455d56e3f9aad", - "placeholder": "​", - "style": "IPY_MODEL_280992726df64d0bb7f66f7979cacc38", - "value": " 60000/60000 [00:34<00:00, 1696.59it/s]" + "layout": "IPY_MODEL_ea0795aea16d4e3ca4a1917ca4866577", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_10bb4e9e540347b6adf4e3fedbe1cc0e", + "value": 2.0 } }, - "510cae4e964449d782837526b30e6521": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "2cc8abc90f014a1cae1e929a856c504c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "2ed35715096140afab738d789e6ea0ae": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "519d7e9f35a140c296e16eeddde62611": { + "312e7e414ea44c0cb196a98ae42a3820": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "38d235ba304a40398a6b2133d5d914d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3740,7 +3668,129 @@ "width": null } }, - "54b363f29f2e4818b014a9ec4ca3ae91": { + "398eb4cd325f46478a8fa7d7374a5549": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_eb5828f6121341f8b9392cbdc9416b5f", + "placeholder": "​", + "style": "IPY_MODEL_4ec21615f55d49f283bd0e6b2f5440a7", + "value": "Generating test split: " + } + }, + "3a2cf689648147339884bf21e41e0117": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8508ed2c71b941dd88b8226f584dd39f", + "placeholder": "​", + "style": "IPY_MODEL_ffc7d069ba3342649b031d7e4fc2b1e9", + "value": " 60000/60000 [00:12<00:00, 4348.01 examples/s]" + } + }, + "49e601fd38364edaac3c9d83a3b91d37": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1d21a5d038eb47648eec87e4e6006fd2", + "IPY_MODEL_7354bbbddf5c490c9aef5c8baf007d83", + "IPY_MODEL_3a2cf689648147339884bf21e41e0117" + ], + "layout": "IPY_MODEL_1f2895dbb9ee4dbeaa2353e1d22f7797" + } + }, + "4aaf228d5dec484e9d76a93af99b330d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e69939aec370403db4aa8a60e3dd20e3", + "IPY_MODEL_6ec843e6a46940dba4f1613a2e697ff3", + "IPY_MODEL_e8532a073a86434db3784fb380727e9f" + ], + "layout": "IPY_MODEL_ec15ac2ff1a445bd9ca4553a52d53b2f" + } + }, + "4be66381fb204b84afdc2415136fb407": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0e160c96ba624dc8b6cea376b8066800", + "placeholder": "​", + "style": "IPY_MODEL_55739e7c4da544cbb13ae2f33cc14e5c", + "value": "100%" + } + }, + "4ec21615f55d49f283bd0e6b2f5440a7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4f1471abf2d545c7bf0b67cd9327b5b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3792,23 +3842,43 @@ "width": null } }, - "57e6037d11954b07be9befbafb7414ac": { + "531d284991c7410f8db880db73019fac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1b3b170418ce4c9ab3f8a55b5a6eabc9", + "placeholder": "​", + "style": "IPY_MODEL_bc6ecce06e14491c86ba5776deecab56", + "value": " 60000/60000 [00:34<00:00, 1699.40it/s]" + } + }, + "55739e7c4da544cbb13ae2f33cc14e5c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "5c44edf87c7e424a879f8270436d20bf": { + "56284ddcfc6f45b1ab26d5b2d7960182": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3824,15 +3894,46 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c1f9caadb48845b9b1a1647d370b933c", - "max": 60000.0, + "layout": "IPY_MODEL_0de5c1f9f47b48ec9f04d1bf5d3ad773", + "max": 1.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f4c22aa8136844b79a6e0860a2f644f1", - "value": 60000.0 + "style": "IPY_MODEL_dda99bdff9404fdfb5828d3010b9f8a1", + "value": 1.0 + } + }, + "5dd680e3231f43718c6308a8e45e1da2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "5fcc6710b5274de3adfd08b81c300f2d": { + "5ead0d8a08ab4a72a56b1bcf53e7b043": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "65858abaeaa64069a1e42170696ed7d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3847,13 +3948,52 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3bbe9a96751c40179a552c9d1e5e4e44", + "layout": "IPY_MODEL_4f1471abf2d545c7bf0b67cd9327b5b1", "placeholder": "​", - "style": "IPY_MODEL_cbfc2de67f8a426abb64ec2ba7a40357", - "value": " 30.9M/30.9M [00:00<00:00, 91.8MB/s]" + "style": "IPY_MODEL_65d9f41155d24ab983b2225422ae636e", + "value": " 30.9M/30.9M [00:00<00:00, 76.8MB/s]" + } + }, + "65d9f41155d24ab983b2225422ae636e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6ec843e6a46940dba4f1613a2e697ff3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d7e4b9ab15f94205a4b550065dc49563", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2cc8abc90f014a1cae1e929a856c504c", + "value": 1.0 } }, - "61dd250de95c4ed1842cab2de394be3a": { + "7354bbbddf5c490c9aef5c8baf007d83": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3869,37 +4009,52 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9de92bb3a01f49d89b55999b0dc455bc", - "max": 30931277.0, + "layout": "IPY_MODEL_e6d905af2f524d45905c52a4563e3b9f", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_27bc9862bc214fb2bf85bbfbe4f8d12e", - "value": 30931277.0 + "style": "IPY_MODEL_c804c3d8105c4289a60d0a1dff6d3cfe", + "value": 60000.0 } }, - "68568aa17e9f4f7b90407718becd059e": { + "78249ca6d2e148269fee22730149bfc7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4b5d35473c504a3e9ade960089fb1f04", - "IPY_MODEL_61dd250de95c4ed1842cab2de394be3a", - "IPY_MODEL_5fcc6710b5274de3adfd08b81c300f2d" - ], - "layout": "IPY_MODEL_54b363f29f2e4818b014a9ec4ca3ae91" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38d235ba304a40398a6b2133d5d914d3", + "placeholder": "​", + "style": "IPY_MODEL_5dd680e3231f43718c6308a8e45e1da2", + "value": "Downloading data: 100%" + } + }, + "7c26ccdbd7a74a0f83ab2ec3744b92d4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "770385f5240f48bb8d9d8577f952dafc": { + "8508ed2c71b941dd88b8226f584dd39f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3951,22 +4106,7 @@ "width": null } }, - "7cd16d1864804c7e9e2a98990e9493ae": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "7e62cab4e97e49cd90922d7cd609cb8a": { + "899e58d8a40a465297f40aeada991389": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4018,28 +4158,31 @@ "width": null } }, - "8085cfb1cddf4df187e17fb4324b87a8": { + "89e68bddf9384821a6b84d7f2d4e482e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ed89461f57a848fababda5cca9289c33", - "placeholder": "​", - "style": "IPY_MODEL_3c753bfc0e02462398194b5294142302", - "value": "Generating test split: " + "layout": "IPY_MODEL_c603b29c641542a68e01cfc8bcc23136", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_7c26ccdbd7a74a0f83ab2ec3744b92d4", + "value": 60000.0 } }, - "8128d07a17964361bc8e3db33cf37a1e": { + "9f23a3a591a44348963c1d972f09eea0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4091,23 +4234,29 @@ "width": null } }, - "84f017fa3cdc40bcbd0adea980114cef": { + "a3915e0b726a46b79cb5df0f0080b054": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e0717ef2b1f241a585bab71f798c07aa", + "IPY_MODEL_2077df6b881c41f397694543602c3b21", + "IPY_MODEL_65858abaeaa64069a1e42170696ed7d5" + ], + "layout": "IPY_MODEL_200254c8d04f40b599b27cab58c62c23" } }, - "84f486444a4e4ea59c0f4a16865b285d": { + "a6744175fe1842fe96c1f9105ae47256": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4159,7 +4308,7 @@ "width": null } }, - "855458eae8684ce0aa743d94aef749b9": { + "a6b5a69bbd21481db358d145d9702e00": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4174,75 +4323,30 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_b1ffa28e2937438089f45a54351e7fe1", - "IPY_MODEL_374b4a11ea22457abc80b9c0200deb3c", - "IPY_MODEL_508261f3f6a147e7b5390c1cf679e69e" + "IPY_MODEL_78249ca6d2e148269fee22730149bfc7", + "IPY_MODEL_df7c93e26eda4e248394e2244cfded4c", + "IPY_MODEL_d446110cb3c24b66987a4e37de6ccd06" ], - "layout": "IPY_MODEL_770385f5240f48bb8d9d8577f952dafc" - } - }, - "86c92749dbfe497da1566d9e737e1a82": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_510cae4e964449d782837526b30e6521", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_84f017fa3cdc40bcbd0adea980114cef", - "value": 1.0 + "layout": "IPY_MODEL_22110adf8fbd44dc907195aa03315f8f" } }, - "8c313b7b39c3431ab7e73a4a59b8e7b3": { + "afb50312cbe743948bbafaebb013b144": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "92fa8c609cf14d2d95dabb0540703411": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b1ecaf6071264b46af44f2cbe57df8c6", - "IPY_MODEL_c6e5ac83513d4bcfb6989f1ae3e1f300", - "IPY_MODEL_c06301b3fd424eceb2d88031f05a83ad" - ], - "layout": "IPY_MODEL_45f3493a23b940adac33088d5567c92d" - } - }, - "93c752a242324ecba32adb203ce71bb9": { + "b2ae5f58b0c44998937c4fd1e2f2054a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4257,58 +4361,43 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8128d07a17964361bc8e3db33cf37a1e", + "layout": "IPY_MODEL_e2e288cbd8d54b37921c3c1a587bb313", "placeholder": "​", - "style": "IPY_MODEL_0d1536c7250b4b1e9705a71ffda4a3e7", - "value": " 60000/60000 [00:12<00:00, 6886.27 examples/s]" + "style": "IPY_MODEL_312e7e414ea44c0cb196a98ae42a3820", + "value": " 10000/0 [00:00<00:00, 522062.71 examples/s]" } }, - "94fbfef6b3814b2db2f9b76d4a7b46e4": { + "bbdfd655d6b2431895de761767dbe4eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2f6f78d28b114880b2c2b118c18b20e5", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_57e6037d11954b07be9befbafb7414ac", - "value": 2.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "9656b81ab06946f591c468fc5e33cb45": { + "bc6ecce06e14491c86ba5776deecab56": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_10664e26a0ce4eeeaaf052febd6cfb01", - "placeholder": "​", - "style": "IPY_MODEL_8c313b7b39c3431ab7e73a4a59b8e7b3", - "value": "Generating train split: " + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "9896d58aa931463a895c73358a4c3f52": { + "bcf1b5301fd64b32aaf6b5bc496014ac": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4360,7 +4449,7 @@ "width": null } }, - "9de92bb3a01f49d89b55999b0dc455bc": { + "c0f6e3e01f79409296a19f5fac62b3aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4412,29 +4501,7 @@ "width": null } }, - "a0de8652c458427bb4bec3eedab1bdc2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_ccd2f40ce20d47d7812553a977216a3a", - "IPY_MODEL_94fbfef6b3814b2db2f9b76d4a7b46e4", - "IPY_MODEL_0d65f29f57ce4744b9fbd73a527cf547" - ], - "layout": "IPY_MODEL_b6f0ed9c24e846cf89eabfb8bdaf5c55" - } - }, - "a1c5d4b9cb52400284a21abdd20a1d42": { + "c603b29c641542a68e01cfc8bcc23136": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4486,22 +4553,45 @@ "width": null } }, - "a6c0fc5dd18c42a5af86df9bcb153041": { + "c804c3d8105c4289a60d0a1dff6d3cfe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "a7c417d50f384eb18d3f3ff5a4790f1a": { + "c98028da20994c76be789f56278cced6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_398eb4cd325f46478a8fa7d7374a5549", + "IPY_MODEL_56284ddcfc6f45b1ab26d5b2d7960182", + "IPY_MODEL_b2ae5f58b0c44998937c4fd1e2f2054a" + ], + "layout": "IPY_MODEL_1bd85ee9e5b94936a4a0ca8c939715dd" + } + }, + "cf1ffd0a959c4b1b98e9116cbd853f84": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4553,7 +4643,28 @@ "width": null } }, - "a8a55d054fad45c1bff1cce2985373f9": { + "d446110cb3c24b66987a4e37de6ccd06": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a6744175fe1842fe96c1f9105ae47256", + "placeholder": "​", + "style": "IPY_MODEL_f6100e7965f840b397c807782f4eacd6", + "value": " 5.18M/5.18M [00:00<00:00, 72.4MB/s]" + } + }, + "d7e4b9ab15f94205a4b550065dc49563": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4602,74 +4713,87 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "af600e5921e24b2d9dbb70a9b3b11f5f": { + "d8935bf4844d43b294f1960ef9619de7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b4848d3524e04470a3e6d65de5780e4d", - "IPY_MODEL_5c44edf87c7e424a879f8270436d20bf", - "IPY_MODEL_93c752a242324ecba32adb203ce71bb9" - ], - "layout": "IPY_MODEL_7e62cab4e97e49cd90922d7cd609cb8a" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "b1ecaf6071264b46af44f2cbe57df8c6": { + "dda99bdff9404fdfb5828d3010b9f8a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "df7c93e26eda4e248394e2244cfded4c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c70a8658e54d4a499f7c4166a2bda30e", - "placeholder": "​", - "style": "IPY_MODEL_b5857eae1ffc4705b6bf9d6da12a4ede", - "value": "Downloading data: 100%" + "layout": "IPY_MODEL_c0f6e3e01f79409296a19f5fac62b3aa", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_afb50312cbe743948bbafaebb013b144", + "value": 5175617.0 } }, - "b1ffa28e2937438089f45a54351e7fe1": { + "dfe013943ca04be693b072c950762919": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a1c5d4b9cb52400284a21abdd20a1d42", - "placeholder": "​", - "style": "IPY_MODEL_e281b710f3db4e338a458837b4ea1475", - "value": "100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4be66381fb204b84afdc2415136fb407", + "IPY_MODEL_89e68bddf9384821a6b84d7f2d4e482e", + "IPY_MODEL_531d284991c7410f8db880db73019fac" + ], + "layout": "IPY_MODEL_899e58d8a40a465297f40aeada991389" } }, - "b4848d3524e04470a3e6d65de5780e4d": { + "e0717ef2b1f241a585bab71f798c07aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4684,28 +4808,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0fca6638327f48e8a39dcc8b63dc9fd0", + "layout": "IPY_MODEL_9f23a3a591a44348963c1d972f09eea0", "placeholder": "​", - "style": "IPY_MODEL_c22e5333d6824f38b56e519da230d233", - "value": "Map (num_proc=4): 100%" - } - }, - "b5857eae1ffc4705b6bf9d6da12a4ede": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_efb859ce22df4aedb5cd3bffda0adc5f", + "value": "Downloading data: 100%" } }, - "b6f0ed9c24e846cf89eabfb8bdaf5c55": { + "e2e288cbd8d54b37921c3c1a587bb313": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4757,7 +4866,50 @@ "width": null } }, - "ba5eaac37c12413483457def49e84997": { + "e36aa52b0d1c430cb96f1bdb16e44c82": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_2695bb154afc4c0ca3e73976aa6b1557", + "IPY_MODEL_26d3dec3ea8a42e78a9a1f322d8be9c3", + "IPY_MODEL_013214df95b64291a68080a23b498d9c" + ], + "layout": "IPY_MODEL_f20a65a9ab814f7e990cc1d89679131a" + } + }, + "e69939aec370403db4aa8a60e3dd20e3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0cda3634af9749aa8e66efb500ef558a", + "placeholder": "​", + "style": "IPY_MODEL_d8935bf4844d43b294f1960ef9619de7", + "value": "Generating train split: " + } + }, + "e6d905af2f524d45905c52a4563e3b9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4809,29 +4961,7 @@ "width": null } }, - "bef514edf16d4b89be1af25807f99bf8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_9656b81ab06946f591c468fc5e33cb45", - "IPY_MODEL_86c92749dbfe497da1566d9e737e1a82", - "IPY_MODEL_2f41d597282241aeba4e3b627f170575" - ], - "layout": "IPY_MODEL_519d7e9f35a140c296e16eeddde62611" - } - }, - "c06301b3fd424eceb2d88031f05a83ad": { + "e8532a073a86434db3784fb380727e9f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4846,28 +4976,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a8a55d054fad45c1bff1cce2985373f9", + "layout": "IPY_MODEL_eb9ca05ae5c842308439407a0dabc84b", "placeholder": "​", - "style": "IPY_MODEL_a6c0fc5dd18c42a5af86df9bcb153041", - "value": " 5.18M/5.18M [00:00<00:00, 10.1MB/s]" - } - }, - "c09bc75cc58e4390a40a122a999ce70b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_bbdfd655d6b2431895de761767dbe4eb", + "value": " 60000/0 [00:00<00:00, 804572.60 examples/s]" } }, - "c1f9caadb48845b9b1a1647d370b933c": { + "ea0795aea16d4e3ca4a1917ca4866577": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4919,22 +5034,7 @@ "width": null } }, - "c22e5333d6824f38b56e519da230d233": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "c477d69023fd48c7914a58cd0fee4f42": { + "eb5828f6121341f8b9392cbdc9416b5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4986,31 +5086,7 @@ "width": null } }, - "c6e5ac83513d4bcfb6989f1ae3e1f300": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c477d69023fd48c7914a58cd0fee4f42", - "max": 5175617.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e1a322dcb10d45a9b8ce7c18aa64c881", - "value": 5175617.0 - } - }, - "c70a8658e54d4a499f7c4166a2bda30e": { + "eb9ca05ae5c842308439407a0dabc84b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5062,112 +5138,7 @@ "width": null } }, - "cbfc2de67f8a426abb64ec2ba7a40357": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cc0b0bf5734e405c9773771efd329716": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ccd2f40ce20d47d7812553a977216a3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_84f486444a4e4ea59c0f4a16865b285d", - "placeholder": "​", - "style": "IPY_MODEL_1b69e9574f9141228434ea3ce25da6fd", - "value": "Computing checksums: 100%" - } - }, - "e1a322dcb10d45a9b8ce7c18aa64c881": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "e281b710f3db4e338a458837b4ea1475": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "eacced1a3eeb4720aca48e31138fb049": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8085cfb1cddf4df187e17fb4324b87a8", - "IPY_MODEL_16674e62660245f7898a4c11861c11ca", - "IPY_MODEL_28541a162be44b9f939e6cdf26ae4d14" - ], - "layout": "IPY_MODEL_2799716cf975430297a582a0add7fc99" - } - }, - "eba67277054b469481c455d56e3f9aad": { + "ec15ac2ff1a445bd9ca4553a52d53b2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5219,7 +5190,22 @@ "width": null } }, - "ed89461f57a848fababda5cca9289c33": { + "efb859ce22df4aedb5cd3bffda0adc5f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f20a65a9ab814f7e990cc1d89679131a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5271,19 +5257,33 @@ "width": null } }, - "f4c22aa8136844b79a6e0860a2f644f1": { + "f6100e7965f840b397c807782f4eacd6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ffc7d069ba3342649b031d7e4fc2b1e9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } } diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index 13d0f7ac8..3fb385f45 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": "2024-01-09T15:10:13.059896Z", - "iopub.status.busy": "2024-01-09T15:10:13.059406Z", - "iopub.status.idle": "2024-01-09T15:10:14.200564Z", - "shell.execute_reply": "2024-01-09T15:10:14.199917Z" + "iopub.execute_input": "2024-01-10T06:19:10.230756Z", + "iopub.status.busy": "2024-01-10T06:19:10.230131Z", + "iopub.status.idle": "2024-01-10T06:19:11.356341Z", + "shell.execute_reply": "2024-01-10T06:19:11.355670Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:14.203785Z", - "iopub.status.busy": "2024-01-09T15:10:14.203233Z", - "iopub.status.idle": "2024-01-09T15:10:14.490673Z", - "shell.execute_reply": "2024-01-09T15:10:14.490032Z" + "iopub.execute_input": "2024-01-10T06:19:11.359495Z", + "iopub.status.busy": "2024-01-10T06:19:11.358950Z", + "iopub.status.idle": "2024-01-10T06:19:11.645037Z", + "shell.execute_reply": "2024-01-10T06:19:11.644365Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.493625Z", - "iopub.status.busy": "2024-01-09T15:10:14.493410Z", - "iopub.status.idle": "2024-01-09T15:10:14.506054Z", - "shell.execute_reply": "2024-01-09T15:10:14.505542Z" + "iopub.execute_input": "2024-01-10T06:19:11.648431Z", + "iopub.status.busy": "2024-01-10T06:19:11.647953Z", + "iopub.status.idle": "2024-01-10T06:19:11.660400Z", + "shell.execute_reply": "2024-01-10T06:19:11.659834Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.508353Z", - "iopub.status.busy": "2024-01-09T15:10:14.508150Z", - "iopub.status.idle": "2024-01-09T15:10:14.745115Z", - "shell.execute_reply": "2024-01-09T15:10:14.744440Z" + "iopub.execute_input": "2024-01-10T06:19:11.662953Z", + "iopub.status.busy": "2024-01-10T06:19:11.662542Z", + "iopub.status.idle": "2024-01-10T06:19:11.900160Z", + "shell.execute_reply": "2024-01-10T06:19:11.899470Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.747903Z", - "iopub.status.busy": "2024-01-09T15:10:14.747683Z", - "iopub.status.idle": "2024-01-09T15:10:14.775228Z", - "shell.execute_reply": "2024-01-09T15:10:14.774537Z" + "iopub.execute_input": "2024-01-10T06:19:11.903126Z", + "iopub.status.busy": "2024-01-10T06:19:11.902693Z", + "iopub.status.idle": "2024-01-10T06:19:11.929149Z", + "shell.execute_reply": "2024-01-10T06:19:11.928601Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:14.777804Z", - "iopub.status.busy": "2024-01-09T15:10:14.777585Z", - "iopub.status.idle": "2024-01-09T15:10:16.172161Z", - "shell.execute_reply": "2024-01-09T15:10:16.171473Z" + "iopub.execute_input": "2024-01-10T06:19:11.931771Z", + "iopub.status.busy": "2024-01-10T06:19:11.931369Z", + "iopub.status.idle": "2024-01-10T06:19:13.340850Z", + "shell.execute_reply": "2024-01-10T06:19:13.340122Z" } }, "outputs": [ @@ -473,10 +473,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:16.175096Z", - "iopub.status.busy": "2024-01-09T15:10:16.174693Z", - "iopub.status.idle": "2024-01-09T15:10:16.200461Z", - "shell.execute_reply": "2024-01-09T15:10:16.199888Z" + "iopub.execute_input": "2024-01-10T06:19:13.343983Z", + "iopub.status.busy": "2024-01-10T06:19:13.343269Z", + "iopub.status.idle": "2024-01-10T06:19:13.368528Z", + "shell.execute_reply": "2024-01-10T06:19:13.367864Z" }, "scrolled": true }, @@ -641,10 +641,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:16.203050Z", - "iopub.status.busy": "2024-01-09T15:10:16.202705Z", - "iopub.status.idle": "2024-01-09T15:10:17.127463Z", - "shell.execute_reply": "2024-01-09T15:10:17.126664Z" + "iopub.execute_input": "2024-01-10T06:19:13.370923Z", + "iopub.status.busy": "2024-01-10T06:19:13.370715Z", + "iopub.status.idle": "2024-01-10T06:19:14.287348Z", + "shell.execute_reply": "2024-01-10T06:19:14.286696Z" }, "id": "AaHC5MRKjruT" }, @@ -763,10 +763,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.130430Z", - "iopub.status.busy": "2024-01-09T15:10:17.129998Z", - "iopub.status.idle": "2024-01-09T15:10:17.145043Z", - "shell.execute_reply": "2024-01-09T15:10:17.144392Z" + "iopub.execute_input": "2024-01-10T06:19:14.290089Z", + "iopub.status.busy": "2024-01-10T06:19:14.289653Z", + "iopub.status.idle": "2024-01-10T06:19:14.304321Z", + "shell.execute_reply": "2024-01-10T06:19:14.303795Z" }, "id": "Wy27rvyhjruU" }, @@ -815,10 +815,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.147663Z", - "iopub.status.busy": "2024-01-09T15:10:17.147264Z", - "iopub.status.idle": "2024-01-09T15:10:17.243881Z", - "shell.execute_reply": "2024-01-09T15:10:17.243115Z" + "iopub.execute_input": "2024-01-10T06:19:14.306556Z", + "iopub.status.busy": "2024-01-10T06:19:14.306353Z", + "iopub.status.idle": "2024-01-10T06:19:14.395464Z", + "shell.execute_reply": "2024-01-10T06:19:14.394757Z" }, "id": "Db8YHnyVjruU" }, @@ -925,10 +925,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.247030Z", - "iopub.status.busy": "2024-01-09T15:10:17.246476Z", - "iopub.status.idle": "2024-01-09T15:10:17.452269Z", - "shell.execute_reply": "2024-01-09T15:10:17.451560Z" + "iopub.execute_input": "2024-01-10T06:19:14.398081Z", + "iopub.status.busy": "2024-01-10T06:19:14.397790Z", + "iopub.status.idle": "2024-01-10T06:19:14.605263Z", + "shell.execute_reply": "2024-01-10T06:19:14.604556Z" }, "id": "iJqAHuS2jruV" }, @@ -965,10 +965,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.455279Z", - "iopub.status.busy": "2024-01-09T15:10:17.454820Z", - "iopub.status.idle": "2024-01-09T15:10:17.473076Z", - "shell.execute_reply": "2024-01-09T15:10:17.472539Z" + "iopub.execute_input": "2024-01-10T06:19:14.608187Z", + "iopub.status.busy": "2024-01-10T06:19:14.607708Z", + "iopub.status.idle": "2024-01-10T06:19:14.625986Z", + "shell.execute_reply": "2024-01-10T06:19:14.625325Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1030,10 +1030,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.475553Z", - "iopub.status.busy": "2024-01-09T15:10:17.475229Z", - "iopub.status.idle": "2024-01-09T15:10:17.486242Z", - "shell.execute_reply": "2024-01-09T15:10:17.485706Z" + "iopub.execute_input": "2024-01-10T06:19:14.628765Z", + "iopub.status.busy": "2024-01-10T06:19:14.628360Z", + "iopub.status.idle": "2024-01-10T06:19:14.639216Z", + "shell.execute_reply": "2024-01-10T06:19:14.638680Z" }, "id": "0lonvOYvjruV" }, @@ -1180,10 +1180,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.488657Z", - "iopub.status.busy": "2024-01-09T15:10:17.488444Z", - "iopub.status.idle": "2024-01-09T15:10:17.594254Z", - "shell.execute_reply": "2024-01-09T15:10:17.593499Z" + "iopub.execute_input": "2024-01-10T06:19:14.641682Z", + "iopub.status.busy": "2024-01-10T06:19:14.641288Z", + "iopub.status.idle": "2024-01-10T06:19:14.743511Z", + "shell.execute_reply": "2024-01-10T06:19:14.742754Z" }, "id": "MfqTCa3kjruV" }, @@ -1264,10 +1264,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.597095Z", - "iopub.status.busy": "2024-01-09T15:10:17.596808Z", - "iopub.status.idle": "2024-01-09T15:10:17.752936Z", - "shell.execute_reply": "2024-01-09T15:10:17.752174Z" + "iopub.execute_input": "2024-01-10T06:19:14.747035Z", + "iopub.status.busy": "2024-01-10T06:19:14.746126Z", + "iopub.status.idle": "2024-01-10T06:19:14.909743Z", + "shell.execute_reply": "2024-01-10T06:19:14.909013Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1327,10 +1327,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.755941Z", - "iopub.status.busy": "2024-01-09T15:10:17.755518Z", - "iopub.status.idle": "2024-01-09T15:10:17.760007Z", - "shell.execute_reply": "2024-01-09T15:10:17.759466Z" + "iopub.execute_input": "2024-01-10T06:19:14.912509Z", + "iopub.status.busy": "2024-01-10T06:19:14.912249Z", + "iopub.status.idle": "2024-01-10T06:19:14.916421Z", + "shell.execute_reply": "2024-01-10T06:19:14.915806Z" }, "id": "0rXP3ZPWjruW" }, @@ -1368,10 +1368,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.762508Z", - "iopub.status.busy": "2024-01-09T15:10:17.762204Z", - "iopub.status.idle": "2024-01-09T15:10:17.766685Z", - "shell.execute_reply": "2024-01-09T15:10:17.766079Z" + "iopub.execute_input": "2024-01-10T06:19:14.918795Z", + "iopub.status.busy": "2024-01-10T06:19:14.918435Z", + "iopub.status.idle": "2024-01-10T06:19:14.923066Z", + "shell.execute_reply": "2024-01-10T06:19:14.922436Z" }, "id": "-iRPe8KXjruW" }, @@ -1426,10 +1426,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.769023Z", - "iopub.status.busy": "2024-01-09T15:10:17.768715Z", - "iopub.status.idle": "2024-01-09T15:10:17.809115Z", - "shell.execute_reply": "2024-01-09T15:10:17.808497Z" + "iopub.execute_input": "2024-01-10T06:19:14.925674Z", + "iopub.status.busy": "2024-01-10T06:19:14.925282Z", + "iopub.status.idle": "2024-01-10T06:19:14.965643Z", + "shell.execute_reply": "2024-01-10T06:19:14.964962Z" }, "id": "ZpipUliyjruW" }, @@ -1480,10 +1480,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.811844Z", - "iopub.status.busy": "2024-01-09T15:10:17.811413Z", - "iopub.status.idle": "2024-01-09T15:10:17.865183Z", - "shell.execute_reply": "2024-01-09T15:10:17.864486Z" + "iopub.execute_input": "2024-01-10T06:19:14.968405Z", + "iopub.status.busy": "2024-01-10T06:19:14.968011Z", + "iopub.status.idle": "2024-01-10T06:19:15.015701Z", + "shell.execute_reply": "2024-01-10T06:19:15.015056Z" }, "id": "SLq-3q4xjruX" }, @@ -1552,10 +1552,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.867930Z", - "iopub.status.busy": "2024-01-09T15:10:17.867523Z", - "iopub.status.idle": "2024-01-09T15:10:17.983453Z", - "shell.execute_reply": "2024-01-09T15:10:17.982756Z" + "iopub.execute_input": "2024-01-10T06:19:15.018378Z", + "iopub.status.busy": "2024-01-10T06:19:15.017908Z", + "iopub.status.idle": "2024-01-10T06:19:15.125330Z", + "shell.execute_reply": "2024-01-10T06:19:15.124654Z" }, "id": "g5LHhhuqFbXK" }, @@ -1587,10 +1587,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:17.986797Z", - "iopub.status.busy": "2024-01-09T15:10:17.986363Z", - "iopub.status.idle": "2024-01-09T15:10:18.111625Z", - "shell.execute_reply": "2024-01-09T15:10:18.110842Z" + "iopub.execute_input": "2024-01-10T06:19:15.128752Z", + "iopub.status.busy": "2024-01-10T06:19:15.128227Z", + "iopub.status.idle": "2024-01-10T06:19:15.234835Z", + "shell.execute_reply": "2024-01-10T06:19:15.234115Z" }, "id": "p7w8F8ezBcet" }, @@ -1647,10 +1647,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.114528Z", - "iopub.status.busy": "2024-01-09T15:10:18.114237Z", - "iopub.status.idle": "2024-01-09T15:10:18.317159Z", - "shell.execute_reply": "2024-01-09T15:10:18.316434Z" + "iopub.execute_input": "2024-01-10T06:19:15.237722Z", + "iopub.status.busy": "2024-01-10T06:19:15.237193Z", + "iopub.status.idle": "2024-01-10T06:19:15.449470Z", + "shell.execute_reply": "2024-01-10T06:19:15.448775Z" }, "id": "WETRL74tE_sU" }, @@ -1685,10 +1685,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.320305Z", - "iopub.status.busy": "2024-01-09T15:10:18.319725Z", - "iopub.status.idle": "2024-01-09T15:10:18.547322Z", - "shell.execute_reply": "2024-01-09T15:10:18.546622Z" + "iopub.execute_input": "2024-01-10T06:19:15.452306Z", + "iopub.status.busy": "2024-01-10T06:19:15.451848Z", + "iopub.status.idle": "2024-01-10T06:19:15.710564Z", + "shell.execute_reply": "2024-01-10T06:19:15.709859Z" }, "id": "kCfdx2gOLmXS" }, @@ -1850,10 +1850,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.550435Z", - "iopub.status.busy": "2024-01-09T15:10:18.549972Z", - "iopub.status.idle": "2024-01-09T15:10:18.557124Z", - "shell.execute_reply": "2024-01-09T15:10:18.556489Z" + "iopub.execute_input": "2024-01-10T06:19:15.713390Z", + "iopub.status.busy": "2024-01-10T06:19:15.712976Z", + "iopub.status.idle": "2024-01-10T06:19:15.719738Z", + "shell.execute_reply": "2024-01-10T06:19:15.719219Z" }, "id": "-uogYRWFYnuu" }, @@ -1907,10 +1907,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.559587Z", - "iopub.status.busy": "2024-01-09T15:10:18.559361Z", - "iopub.status.idle": "2024-01-09T15:10:18.772700Z", - "shell.execute_reply": "2024-01-09T15:10:18.772094Z" + "iopub.execute_input": "2024-01-10T06:19:15.722264Z", + "iopub.status.busy": "2024-01-10T06:19:15.721862Z", + "iopub.status.idle": "2024-01-10T06:19:15.932373Z", + "shell.execute_reply": "2024-01-10T06:19:15.931673Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1957,10 +1957,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:18.775775Z", - "iopub.status.busy": "2024-01-09T15:10:18.775319Z", - "iopub.status.idle": "2024-01-09T15:10:19.857040Z", - "shell.execute_reply": "2024-01-09T15:10:19.856297Z" + "iopub.execute_input": "2024-01-10T06:19:15.935084Z", + "iopub.status.busy": "2024-01-10T06:19:15.934687Z", + "iopub.status.idle": "2024-01-10T06:19:17.019541Z", + "shell.execute_reply": "2024-01-10T06:19:17.018918Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 11b89b6ac..7cb50be92 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:25.630185Z", - "iopub.status.busy": "2024-01-09T15:10:25.629981Z", - "iopub.status.idle": "2024-01-09T15:10:26.695542Z", - "shell.execute_reply": "2024-01-09T15:10:26.694824Z" + "iopub.execute_input": "2024-01-10T06:19:22.603434Z", + "iopub.status.busy": "2024-01-10T06:19:22.602873Z", + "iopub.status.idle": "2024-01-10T06:19:23.665537Z", + "shell.execute_reply": "2024-01-10T06:19:23.664891Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:26.698882Z", - "iopub.status.busy": "2024-01-09T15:10:26.698521Z", - "iopub.status.idle": "2024-01-09T15:10:26.701969Z", - "shell.execute_reply": "2024-01-09T15:10:26.701416Z" + "iopub.execute_input": "2024-01-10T06:19:23.668685Z", + "iopub.status.busy": "2024-01-10T06:19:23.668141Z", + "iopub.status.idle": "2024-01-10T06:19:23.671557Z", + "shell.execute_reply": "2024-01-10T06:19:23.671020Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.704456Z", - "iopub.status.busy": "2024-01-09T15:10:26.704251Z", - "iopub.status.idle": "2024-01-09T15:10:26.712592Z", - "shell.execute_reply": "2024-01-09T15:10:26.712044Z" + "iopub.execute_input": "2024-01-10T06:19:23.674086Z", + "iopub.status.busy": "2024-01-10T06:19:23.673671Z", + "iopub.status.idle": "2024-01-10T06:19:23.682706Z", + "shell.execute_reply": "2024-01-10T06:19:23.682135Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.714829Z", - "iopub.status.busy": "2024-01-09T15:10:26.714624Z", - "iopub.status.idle": "2024-01-09T15:10:26.765428Z", - "shell.execute_reply": "2024-01-09T15:10:26.764678Z" + "iopub.execute_input": "2024-01-10T06:19:23.685247Z", + "iopub.status.busy": "2024-01-10T06:19:23.684914Z", + "iopub.status.idle": "2024-01-10T06:19:23.734906Z", + "shell.execute_reply": "2024-01-10T06:19:23.734183Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.768384Z", - "iopub.status.busy": "2024-01-09T15:10:26.768141Z", - "iopub.status.idle": "2024-01-09T15:10:26.789071Z", - "shell.execute_reply": "2024-01-09T15:10:26.788413Z" + "iopub.execute_input": "2024-01-10T06:19:23.737938Z", + "iopub.status.busy": "2024-01-10T06:19:23.737532Z", + "iopub.status.idle": "2024-01-10T06:19:23.757560Z", + "shell.execute_reply": "2024-01-10T06:19:23.757012Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.791710Z", - "iopub.status.busy": "2024-01-09T15:10:26.791480Z", - "iopub.status.idle": "2024-01-09T15:10:26.795875Z", - "shell.execute_reply": "2024-01-09T15:10:26.795260Z" + "iopub.execute_input": "2024-01-10T06:19:23.760066Z", + "iopub.status.busy": "2024-01-10T06:19:23.759679Z", + "iopub.status.idle": "2024-01-10T06:19:23.763929Z", + "shell.execute_reply": "2024-01-10T06:19:23.763417Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.798344Z", - "iopub.status.busy": "2024-01-09T15:10:26.797977Z", - "iopub.status.idle": "2024-01-09T15:10:26.825772Z", - "shell.execute_reply": "2024-01-09T15:10:26.825224Z" + "iopub.execute_input": "2024-01-10T06:19:23.766305Z", + "iopub.status.busy": "2024-01-10T06:19:23.766011Z", + "iopub.status.idle": "2024-01-10T06:19:23.794834Z", + "shell.execute_reply": "2024-01-10T06:19:23.794302Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.828273Z", - "iopub.status.busy": "2024-01-09T15:10:26.828044Z", - "iopub.status.idle": "2024-01-09T15:10:26.856999Z", - "shell.execute_reply": "2024-01-09T15:10:26.856309Z" + "iopub.execute_input": "2024-01-10T06:19:23.797382Z", + "iopub.status.busy": "2024-01-10T06:19:23.796952Z", + "iopub.status.idle": "2024-01-10T06:19:23.825002Z", + "shell.execute_reply": "2024-01-10T06:19:23.824392Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:26.860047Z", - "iopub.status.busy": "2024-01-09T15:10:26.859773Z", - "iopub.status.idle": "2024-01-09T15:10:28.259171Z", - "shell.execute_reply": "2024-01-09T15:10:28.258495Z" + "iopub.execute_input": "2024-01-10T06:19:23.827645Z", + "iopub.status.busy": "2024-01-10T06:19:23.827204Z", + "iopub.status.idle": "2024-01-10T06:19:25.187592Z", + "shell.execute_reply": "2024-01-10T06:19:25.186950Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.262622Z", - "iopub.status.busy": "2024-01-09T15:10:28.262162Z", - "iopub.status.idle": "2024-01-09T15:10:28.270093Z", - "shell.execute_reply": "2024-01-09T15:10:28.269455Z" + "iopub.execute_input": "2024-01-10T06:19:25.190794Z", + "iopub.status.busy": "2024-01-10T06:19:25.190198Z", + "iopub.status.idle": "2024-01-10T06:19:25.197978Z", + "shell.execute_reply": "2024-01-10T06:19:25.197424Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.272752Z", - "iopub.status.busy": "2024-01-09T15:10:28.272263Z", - "iopub.status.idle": "2024-01-09T15:10:28.286529Z", - "shell.execute_reply": "2024-01-09T15:10:28.285881Z" + "iopub.execute_input": "2024-01-10T06:19:25.200289Z", + "iopub.status.busy": "2024-01-10T06:19:25.200085Z", + "iopub.status.idle": "2024-01-10T06:19:25.214416Z", + "shell.execute_reply": "2024-01-10T06:19:25.213870Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.289024Z", - "iopub.status.busy": "2024-01-09T15:10:28.288659Z", - "iopub.status.idle": "2024-01-09T15:10:28.295566Z", - "shell.execute_reply": "2024-01-09T15:10:28.294949Z" + "iopub.execute_input": "2024-01-10T06:19:25.216891Z", + "iopub.status.busy": "2024-01-10T06:19:25.216535Z", + "iopub.status.idle": "2024-01-10T06:19:25.223577Z", + "shell.execute_reply": "2024-01-10T06:19:25.223059Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.298045Z", - "iopub.status.busy": "2024-01-09T15:10:28.297701Z", - "iopub.status.idle": "2024-01-09T15:10:28.300677Z", - "shell.execute_reply": "2024-01-09T15:10:28.300062Z" + "iopub.execute_input": "2024-01-10T06:19:25.225967Z", + "iopub.status.busy": "2024-01-10T06:19:25.225725Z", + "iopub.status.idle": "2024-01-10T06:19:25.228713Z", + "shell.execute_reply": "2024-01-10T06:19:25.228156Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.303032Z", - "iopub.status.busy": "2024-01-09T15:10:28.302676Z", - "iopub.status.idle": "2024-01-09T15:10:28.306931Z", - "shell.execute_reply": "2024-01-09T15:10:28.306304Z" + "iopub.execute_input": "2024-01-10T06:19:25.231173Z", + "iopub.status.busy": "2024-01-10T06:19:25.230771Z", + "iopub.status.idle": "2024-01-10T06:19:25.234695Z", + "shell.execute_reply": "2024-01-10T06:19:25.234060Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.309465Z", - "iopub.status.busy": "2024-01-09T15:10:28.309078Z", - "iopub.status.idle": "2024-01-09T15:10:28.311910Z", - "shell.execute_reply": "2024-01-09T15:10:28.311358Z" + "iopub.execute_input": "2024-01-10T06:19:25.237168Z", + "iopub.status.busy": "2024-01-10T06:19:25.236804Z", + "iopub.status.idle": "2024-01-10T06:19:25.239581Z", + "shell.execute_reply": "2024-01-10T06:19:25.239043Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.314289Z", - "iopub.status.busy": "2024-01-09T15:10:28.313917Z", - "iopub.status.idle": "2024-01-09T15:10:28.318953Z", - "shell.execute_reply": "2024-01-09T15:10:28.318374Z" + "iopub.execute_input": "2024-01-10T06:19:25.241971Z", + "iopub.status.busy": "2024-01-10T06:19:25.241612Z", + "iopub.status.idle": "2024-01-10T06:19:25.246328Z", + "shell.execute_reply": "2024-01-10T06:19:25.245820Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.321383Z", - "iopub.status.busy": "2024-01-09T15:10:28.321068Z", - "iopub.status.idle": "2024-01-09T15:10:28.355171Z", - "shell.execute_reply": "2024-01-09T15:10:28.354575Z" + "iopub.execute_input": "2024-01-10T06:19:25.248797Z", + "iopub.status.busy": "2024-01-10T06:19:25.248447Z", + "iopub.status.idle": "2024-01-10T06:19:25.282644Z", + "shell.execute_reply": "2024-01-10T06:19:25.282066Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:28.358456Z", - "iopub.status.busy": "2024-01-09T15:10:28.357990Z", - "iopub.status.idle": "2024-01-09T15:10:28.363393Z", - "shell.execute_reply": "2024-01-09T15:10:28.362794Z" + "iopub.execute_input": "2024-01-10T06:19:25.285678Z", + "iopub.status.busy": "2024-01-10T06:19:25.285230Z", + "iopub.status.idle": "2024-01-10T06:19:25.290437Z", + "shell.execute_reply": "2024-01-10T06:19:25.289851Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index 36c1ca182..7b346383f 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:33.872482Z", - "iopub.status.busy": "2024-01-09T15:10:33.871938Z", - "iopub.status.idle": "2024-01-09T15:10:35.001122Z", - "shell.execute_reply": "2024-01-09T15:10:35.000405Z" + "iopub.execute_input": "2024-01-10T06:19:30.967895Z", + "iopub.status.busy": "2024-01-10T06:19:30.967699Z", + "iopub.status.idle": "2024-01-10T06:19:32.111090Z", + "shell.execute_reply": "2024-01-10T06:19:32.110423Z" }, "nbsphinx": "hidden" }, @@ -78,7 +78,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -104,10 +104,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.004129Z", - "iopub.status.busy": "2024-01-09T15:10:35.003784Z", - "iopub.status.idle": "2024-01-09T15:10:35.310402Z", - "shell.execute_reply": "2024-01-09T15:10:35.309674Z" + "iopub.execute_input": "2024-01-10T06:19:32.114057Z", + "iopub.status.busy": "2024-01-10T06:19:32.113591Z", + "iopub.status.idle": "2024-01-10T06:19:32.424380Z", + "shell.execute_reply": "2024-01-10T06:19:32.423712Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.313492Z", - "iopub.status.busy": "2024-01-09T15:10:35.313262Z", - "iopub.status.idle": "2024-01-09T15:10:35.327072Z", - "shell.execute_reply": "2024-01-09T15:10:35.326517Z" + "iopub.execute_input": "2024-01-10T06:19:32.427902Z", + "iopub.status.busy": "2024-01-10T06:19:32.427455Z", + "iopub.status.idle": "2024-01-10T06:19:32.442273Z", + "shell.execute_reply": "2024-01-10T06:19:32.441703Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:35.329557Z", - "iopub.status.busy": "2024-01-09T15:10:35.329181Z", - "iopub.status.idle": "2024-01-09T15:10:38.004607Z", - "shell.execute_reply": "2024-01-09T15:10:38.003950Z" + "iopub.execute_input": "2024-01-10T06:19:32.445046Z", + "iopub.status.busy": "2024-01-10T06:19:32.444637Z", + "iopub.status.idle": "2024-01-10T06:19:35.158086Z", + "shell.execute_reply": "2024-01-10T06:19:35.157420Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:38.007360Z", - "iopub.status.busy": "2024-01-09T15:10:38.006936Z", - "iopub.status.idle": "2024-01-09T15:10:39.598858Z", - "shell.execute_reply": "2024-01-09T15:10:39.598216Z" + "iopub.execute_input": "2024-01-10T06:19:35.160758Z", + "iopub.status.busy": "2024-01-10T06:19:35.160383Z", + "iopub.status.idle": "2024-01-10T06:19:36.735870Z", + "shell.execute_reply": "2024-01-10T06:19:36.735258Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:39.601830Z", - "iopub.status.busy": "2024-01-09T15:10:39.601471Z", - "iopub.status.idle": "2024-01-09T15:10:39.606043Z", - "shell.execute_reply": "2024-01-09T15:10:39.605534Z" + "iopub.execute_input": "2024-01-10T06:19:36.738886Z", + "iopub.status.busy": "2024-01-10T06:19:36.738441Z", + "iopub.status.idle": "2024-01-10T06:19:36.743531Z", + "shell.execute_reply": "2024-01-10T06:19:36.742994Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:39.608459Z", - "iopub.status.busy": "2024-01-09T15:10:39.608085Z", - "iopub.status.idle": "2024-01-09T15:10:41.011726Z", - "shell.execute_reply": "2024-01-09T15:10:41.010970Z" + "iopub.execute_input": "2024-01-10T06:19:36.745916Z", + "iopub.status.busy": "2024-01-10T06:19:36.745544Z", + "iopub.status.idle": "2024-01-10T06:19:38.110520Z", + "shell.execute_reply": "2024-01-10T06:19:38.109746Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:41.015211Z", - "iopub.status.busy": "2024-01-09T15:10:41.014357Z", - "iopub.status.idle": "2024-01-09T15:10:43.803390Z", - "shell.execute_reply": "2024-01-09T15:10:43.802651Z" + "iopub.execute_input": "2024-01-10T06:19:38.113635Z", + "iopub.status.busy": "2024-01-10T06:19:38.112981Z", + "iopub.status.idle": "2024-01-10T06:19:40.958055Z", + "shell.execute_reply": "2024-01-10T06:19:40.957384Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.805988Z", - "iopub.status.busy": "2024-01-09T15:10:43.805776Z", - "iopub.status.idle": "2024-01-09T15:10:43.810722Z", - "shell.execute_reply": "2024-01-09T15:10:43.810187Z" + "iopub.execute_input": "2024-01-10T06:19:40.960715Z", + "iopub.status.busy": "2024-01-10T06:19:40.960355Z", + "iopub.status.idle": "2024-01-10T06:19:40.965359Z", + "shell.execute_reply": "2024-01-10T06:19:40.964831Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.813015Z", - "iopub.status.busy": "2024-01-09T15:10:43.812816Z", - "iopub.status.idle": "2024-01-09T15:10:43.816883Z", - "shell.execute_reply": "2024-01-09T15:10:43.816346Z" + "iopub.execute_input": "2024-01-10T06:19:40.967914Z", + "iopub.status.busy": "2024-01-10T06:19:40.967542Z", + "iopub.status.idle": "2024-01-10T06:19:40.971564Z", + "shell.execute_reply": "2024-01-10T06:19:40.971006Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:43.819153Z", - "iopub.status.busy": "2024-01-09T15:10:43.818946Z", - "iopub.status.idle": "2024-01-09T15:10:43.822258Z", - "shell.execute_reply": "2024-01-09T15:10:43.821731Z" + "iopub.execute_input": "2024-01-10T06:19:40.973986Z", + "iopub.status.busy": "2024-01-10T06:19:40.973628Z", + "iopub.status.idle": "2024-01-10T06:19:40.976974Z", + "shell.execute_reply": "2024-01-10T06:19:40.976437Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 35a1dc761..c7c6f8441 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:48.634699Z", - "iopub.status.busy": "2024-01-09T15:10:48.634509Z", - "iopub.status.idle": "2024-01-09T15:10:49.698381Z", - "shell.execute_reply": "2024-01-09T15:10:49.697765Z" + "iopub.execute_input": "2024-01-10T06:19:45.560642Z", + "iopub.status.busy": "2024-01-10T06:19:45.560184Z", + "iopub.status.idle": "2024-01-10T06:19:46.672378Z", + "shell.execute_reply": "2024-01-10T06:19:46.671781Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:49.701282Z", - "iopub.status.busy": "2024-01-09T15:10:49.700795Z", - "iopub.status.idle": "2024-01-09T15:10:50.688584Z", - "shell.execute_reply": "2024-01-09T15:10:50.687863Z" + "iopub.execute_input": "2024-01-10T06:19:46.675636Z", + "iopub.status.busy": "2024-01-10T06:19:46.674959Z", + "iopub.status.idle": "2024-01-10T06:19:48.039619Z", + "shell.execute_reply": "2024-01-10T06:19:48.038865Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.691425Z", - "iopub.status.busy": "2024-01-09T15:10:50.691148Z", - "iopub.status.idle": "2024-01-09T15:10:50.694558Z", - "shell.execute_reply": "2024-01-09T15:10:50.693926Z" + "iopub.execute_input": "2024-01-10T06:19:48.042749Z", + "iopub.status.busy": "2024-01-10T06:19:48.042298Z", + "iopub.status.idle": "2024-01-10T06:19:48.045718Z", + "shell.execute_reply": "2024-01-10T06:19:48.045190Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.696938Z", - "iopub.status.busy": "2024-01-09T15:10:50.696563Z", - "iopub.status.idle": "2024-01-09T15:10:50.702523Z", - "shell.execute_reply": "2024-01-09T15:10:50.702027Z" + "iopub.execute_input": "2024-01-10T06:19:48.048076Z", + "iopub.status.busy": "2024-01-10T06:19:48.047707Z", + "iopub.status.idle": "2024-01-10T06:19:48.053658Z", + "shell.execute_reply": "2024-01-10T06:19:48.053131Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:50.704931Z", - "iopub.status.busy": "2024-01-09T15:10:50.704571Z", - "iopub.status.idle": "2024-01-09T15:10:51.298459Z", - "shell.execute_reply": "2024-01-09T15:10:51.297776Z" + "iopub.execute_input": "2024-01-10T06:19:48.056002Z", + "iopub.status.busy": "2024-01-10T06:19:48.055627Z", + "iopub.status.idle": "2024-01-10T06:19:48.667461Z", + "shell.execute_reply": "2024-01-10T06:19:48.666800Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.301336Z", - "iopub.status.busy": "2024-01-09T15:10:51.300843Z", - "iopub.status.idle": "2024-01-09T15:10:51.306904Z", - "shell.execute_reply": "2024-01-09T15:10:51.306309Z" + "iopub.execute_input": "2024-01-10T06:19:48.670652Z", + "iopub.status.busy": "2024-01-10T06:19:48.670174Z", + "iopub.status.idle": "2024-01-10T06:19:48.676467Z", + "shell.execute_reply": "2024-01-10T06:19:48.675874Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.309356Z", - "iopub.status.busy": "2024-01-09T15:10:51.308880Z", - "iopub.status.idle": "2024-01-09T15:10:51.313227Z", - "shell.execute_reply": "2024-01-09T15:10:51.312620Z" + "iopub.execute_input": "2024-01-10T06:19:48.678861Z", + "iopub.status.busy": "2024-01-10T06:19:48.678516Z", + "iopub.status.idle": "2024-01-10T06:19:48.682696Z", + "shell.execute_reply": "2024-01-10T06:19:48.682085Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.315422Z", - "iopub.status.busy": "2024-01-09T15:10:51.315224Z", - "iopub.status.idle": "2024-01-09T15:10:51.885911Z", - "shell.execute_reply": "2024-01-09T15:10:51.885156Z" + "iopub.execute_input": "2024-01-10T06:19:48.685095Z", + "iopub.status.busy": "2024-01-10T06:19:48.684751Z", + "iopub.status.idle": "2024-01-10T06:19:49.298576Z", + "shell.execute_reply": "2024-01-10T06:19:49.297851Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.888485Z", - "iopub.status.busy": "2024-01-09T15:10:51.888266Z", - "iopub.status.idle": "2024-01-09T15:10:51.976600Z", - "shell.execute_reply": "2024-01-09T15:10:51.976062Z" + "iopub.execute_input": "2024-01-10T06:19:49.301316Z", + "iopub.status.busy": "2024-01-10T06:19:49.301086Z", + "iopub.status.idle": "2024-01-10T06:19:49.403290Z", + "shell.execute_reply": "2024-01-10T06:19:49.402577Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.979003Z", - "iopub.status.busy": "2024-01-09T15:10:51.978648Z", - "iopub.status.idle": "2024-01-09T15:10:51.983228Z", - "shell.execute_reply": "2024-01-09T15:10:51.982602Z" + "iopub.execute_input": "2024-01-10T06:19:49.406087Z", + "iopub.status.busy": "2024-01-10T06:19:49.405678Z", + "iopub.status.idle": "2024-01-10T06:19:49.410408Z", + "shell.execute_reply": "2024-01-10T06:19:49.409813Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:51.985654Z", - "iopub.status.busy": "2024-01-09T15:10:51.985276Z", - "iopub.status.idle": "2024-01-09T15:10:52.359062Z", - "shell.execute_reply": "2024-01-09T15:10:52.358392Z" + "iopub.execute_input": "2024-01-10T06:19:49.412718Z", + "iopub.status.busy": "2024-01-10T06:19:49.412513Z", + "iopub.status.idle": "2024-01-10T06:19:49.802789Z", + "shell.execute_reply": "2024-01-10T06:19:49.802086Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:52.362687Z", - "iopub.status.busy": "2024-01-09T15:10:52.362275Z", - "iopub.status.idle": "2024-01-09T15:10:52.698286Z", - "shell.execute_reply": "2024-01-09T15:10:52.697683Z" + "iopub.execute_input": "2024-01-10T06:19:49.805807Z", + "iopub.status.busy": "2024-01-10T06:19:49.805357Z", + "iopub.status.idle": "2024-01-10T06:19:50.143112Z", + "shell.execute_reply": "2024-01-10T06:19:50.142432Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:52.701391Z", - "iopub.status.busy": "2024-01-09T15:10:52.700963Z", - "iopub.status.idle": "2024-01-09T15:10:53.082876Z", - "shell.execute_reply": "2024-01-09T15:10:53.082203Z" + "iopub.execute_input": "2024-01-10T06:19:50.146573Z", + "iopub.status.busy": "2024-01-10T06:19:50.146168Z", + "iopub.status.idle": "2024-01-10T06:19:50.502588Z", + "shell.execute_reply": "2024-01-10T06:19:50.501862Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:53.086499Z", - "iopub.status.busy": "2024-01-09T15:10:53.086115Z", - "iopub.status.idle": "2024-01-09T15:10:53.548571Z", - "shell.execute_reply": "2024-01-09T15:10:53.547932Z" + "iopub.execute_input": "2024-01-10T06:19:50.506244Z", + "iopub.status.busy": "2024-01-10T06:19:50.505804Z", + "iopub.status.idle": "2024-01-10T06:19:50.969893Z", + "shell.execute_reply": "2024-01-10T06:19:50.969270Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:53.552977Z", - "iopub.status.busy": "2024-01-09T15:10:53.552718Z", - "iopub.status.idle": "2024-01-09T15:10:54.003568Z", - "shell.execute_reply": "2024-01-09T15:10:54.002907Z" + "iopub.execute_input": "2024-01-10T06:19:50.974645Z", + "iopub.status.busy": "2024-01-10T06:19:50.974221Z", + "iopub.status.idle": "2024-01-10T06:19:51.430649Z", + "shell.execute_reply": "2024-01-10T06:19:51.429876Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.007091Z", - "iopub.status.busy": "2024-01-09T15:10:54.006877Z", - "iopub.status.idle": "2024-01-09T15:10:54.336048Z", - "shell.execute_reply": "2024-01-09T15:10:54.335426Z" + "iopub.execute_input": "2024-01-10T06:19:51.434311Z", + "iopub.status.busy": "2024-01-10T06:19:51.433913Z", + "iopub.status.idle": "2024-01-10T06:19:51.777065Z", + "shell.execute_reply": "2024-01-10T06:19:51.776398Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.338833Z", - "iopub.status.busy": "2024-01-09T15:10:54.338451Z", - "iopub.status.idle": "2024-01-09T15:10:54.538333Z", - "shell.execute_reply": "2024-01-09T15:10:54.537663Z" + "iopub.execute_input": "2024-01-10T06:19:51.779898Z", + "iopub.status.busy": "2024-01-10T06:19:51.779494Z", + "iopub.status.idle": "2024-01-10T06:19:51.980612Z", + "shell.execute_reply": "2024-01-10T06:19:51.979893Z" } }, "outputs": [ @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:54.541044Z", - "iopub.status.busy": "2024-01-09T15:10:54.540671Z", - "iopub.status.idle": "2024-01-09T15:10:54.544417Z", - "shell.execute_reply": "2024-01-09T15:10:54.543907Z" + "iopub.execute_input": "2024-01-10T06:19:51.983380Z", + "iopub.status.busy": "2024-01-10T06:19:51.982885Z", + "iopub.status.idle": "2024-01-10T06:19:51.986852Z", + "shell.execute_reply": "2024-01-10T06:19:51.986244Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index 777ef8764..26fc2cc0a 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -931,7 +931,7 @@

2. Pre-process the Cifar10 dataset

-
+
@@ -1297,7 +1297,7 @@

4. Use cleanlab and here.

diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb index c3b680134..7da4b0e12 100644 --- a/master/tutorials/outliers.ipynb +++ b/master/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:56.633647Z", - "iopub.status.busy": "2024-01-09T15:10:56.633455Z", - "iopub.status.idle": "2024-01-09T15:10:58.635058Z", - "shell.execute_reply": "2024-01-09T15:10:58.634391Z" + "iopub.execute_input": "2024-01-10T06:19:54.392516Z", + "iopub.status.busy": "2024-01-10T06:19:54.392326Z", + "iopub.status.idle": "2024-01-10T06:19:56.392552Z", + "shell.execute_reply": "2024-01-10T06:19:56.391830Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:10:58.638637Z", - "iopub.status.busy": "2024-01-09T15:10:58.638061Z", - "iopub.status.idle": "2024-01-09T15:10:58.971426Z", - "shell.execute_reply": "2024-01-09T15:10:58.970797Z" + "iopub.execute_input": "2024-01-10T06:19:56.395661Z", + "iopub.status.busy": "2024-01-10T06:19:56.395318Z", + "iopub.status.idle": "2024-01-10T06:19:56.729764Z", + "shell.execute_reply": "2024-01-10T06:19:56.729033Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:58.974504Z", - "iopub.status.busy": "2024-01-09T15:10:58.973978Z", - "iopub.status.idle": "2024-01-09T15:10:58.978659Z", - "shell.execute_reply": "2024-01-09T15:10:58.978045Z" + "iopub.execute_input": "2024-01-10T06:19:56.732778Z", + "iopub.status.busy": "2024-01-10T06:19:56.732256Z", + "iopub.status.idle": "2024-01-10T06:19:56.736298Z", + "shell.execute_reply": "2024-01-10T06:19:56.735811Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:10:58.981265Z", - "iopub.status.busy": "2024-01-09T15:10:58.980808Z", - "iopub.status.idle": "2024-01-09T15:11:03.830245Z", - "shell.execute_reply": "2024-01-09T15:11:03.829635Z" + "iopub.execute_input": "2024-01-10T06:19:56.738602Z", + "iopub.status.busy": "2024-01-10T06:19:56.738240Z", + "iopub.status.idle": "2024-01-10T06:20:01.246797Z", + "shell.execute_reply": "2024-01-10T06:20:01.246106Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6785377554c5413c9114e6cdda73e847", + "model_id": "d9eb539b8c1f4f97a89e8db15123410e", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:03.833201Z", - "iopub.status.busy": "2024-01-09T15:11:03.832604Z", - "iopub.status.idle": "2024-01-09T15:11:03.837915Z", - "shell.execute_reply": "2024-01-09T15:11:03.837288Z" + "iopub.execute_input": "2024-01-10T06:20:01.249671Z", + "iopub.status.busy": "2024-01-10T06:20:01.249198Z", + "iopub.status.idle": "2024-01-10T06:20:01.254431Z", + "shell.execute_reply": "2024-01-10T06:20:01.253797Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:03.840618Z", - "iopub.status.busy": "2024-01-09T15:11:03.840248Z", - "iopub.status.idle": "2024-01-09T15:11:04.386624Z", - "shell.execute_reply": "2024-01-09T15:11:04.385933Z" + "iopub.execute_input": "2024-01-10T06:20:01.256861Z", + "iopub.status.busy": "2024-01-10T06:20:01.256438Z", + "iopub.status.idle": "2024-01-10T06:20:01.804589Z", + "shell.execute_reply": "2024-01-10T06:20:01.803886Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:04.389435Z", - "iopub.status.busy": "2024-01-09T15:11:04.389046Z", - "iopub.status.idle": "2024-01-09T15:11:05.043291Z", - "shell.execute_reply": "2024-01-09T15:11:05.042564Z" + "iopub.execute_input": "2024-01-10T06:20:01.807206Z", + "iopub.status.busy": "2024-01-10T06:20:01.806947Z", + "iopub.status.idle": "2024-01-10T06:20:02.469451Z", + "shell.execute_reply": "2024-01-10T06:20:02.468760Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:05.046224Z", - "iopub.status.busy": "2024-01-09T15:11:05.045790Z", - "iopub.status.idle": "2024-01-09T15:11:05.049554Z", - "shell.execute_reply": "2024-01-09T15:11:05.048940Z" + "iopub.execute_input": "2024-01-10T06:20:02.472091Z", + "iopub.status.busy": "2024-01-10T06:20:02.471875Z", + "iopub.status.idle": "2024-01-10T06:20:02.475815Z", + "shell.execute_reply": "2024-01-10T06:20:02.475238Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:05.052109Z", - "iopub.status.busy": "2024-01-09T15:11:05.051725Z", - "iopub.status.idle": "2024-01-09T15:11:17.156933Z", - "shell.execute_reply": "2024-01-09T15:11:17.156258Z" + "iopub.execute_input": "2024-01-10T06:20:02.478426Z", + "iopub.status.busy": "2024-01-10T06:20:02.478197Z", + "iopub.status.idle": "2024-01-10T06:20:14.823323Z", + "shell.execute_reply": "2024-01-10T06:20:14.822587Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:17.159533Z", - "iopub.status.busy": "2024-01-09T15:11:17.159328Z", - "iopub.status.idle": "2024-01-09T15:11:18.699138Z", - "shell.execute_reply": "2024-01-09T15:11:18.698401Z" + "iopub.execute_input": "2024-01-10T06:20:14.826047Z", + "iopub.status.busy": "2024-01-10T06:20:14.825621Z", + "iopub.status.idle": "2024-01-10T06:20:16.419516Z", + "shell.execute_reply": "2024-01-10T06:20:16.418738Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:18.702304Z", - "iopub.status.busy": "2024-01-09T15:11:18.701770Z", - "iopub.status.idle": "2024-01-09T15:11:18.932225Z", - "shell.execute_reply": "2024-01-09T15:11:18.931457Z" + "iopub.execute_input": "2024-01-10T06:20:16.422525Z", + "iopub.status.busy": "2024-01-10T06:20:16.421993Z", + "iopub.status.idle": "2024-01-10T06:20:16.690358Z", + "shell.execute_reply": "2024-01-10T06:20:16.689659Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:18.935738Z", - "iopub.status.busy": "2024-01-09T15:11:18.935518Z", - "iopub.status.idle": "2024-01-09T15:11:19.594613Z", - "shell.execute_reply": "2024-01-09T15:11:19.593982Z" + "iopub.execute_input": "2024-01-10T06:20:16.694052Z", + "iopub.status.busy": "2024-01-10T06:20:16.693495Z", + "iopub.status.idle": "2024-01-10T06:20:17.376849Z", + "shell.execute_reply": "2024-01-10T06:20:17.376222Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:19.597729Z", - "iopub.status.busy": "2024-01-09T15:11:19.597226Z", - "iopub.status.idle": "2024-01-09T15:11:20.093165Z", - "shell.execute_reply": "2024-01-09T15:11:20.092485Z" + "iopub.execute_input": "2024-01-10T06:20:17.379909Z", + "iopub.status.busy": "2024-01-10T06:20:17.379542Z", + "iopub.status.idle": "2024-01-10T06:20:17.889849Z", + "shell.execute_reply": "2024-01-10T06:20:17.889143Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.095747Z", - "iopub.status.busy": "2024-01-09T15:11:20.095535Z", - "iopub.status.idle": "2024-01-09T15:11:20.339320Z", - "shell.execute_reply": "2024-01-09T15:11:20.338595Z" + "iopub.execute_input": "2024-01-10T06:20:17.892718Z", + "iopub.status.busy": "2024-01-10T06:20:17.892281Z", + "iopub.status.idle": "2024-01-10T06:20:18.146698Z", + "shell.execute_reply": "2024-01-10T06:20:18.145918Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.342022Z", - "iopub.status.busy": "2024-01-09T15:11:20.341661Z", - "iopub.status.idle": "2024-01-09T15:11:20.427396Z", - "shell.execute_reply": "2024-01-09T15:11:20.426809Z" + "iopub.execute_input": "2024-01-10T06:20:18.150320Z", + "iopub.status.busy": "2024-01-10T06:20:18.149921Z", + "iopub.status.idle": "2024-01-10T06:20:18.235461Z", + "shell.execute_reply": "2024-01-10T06:20:18.234877Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:20.430348Z", - "iopub.status.busy": "2024-01-09T15:11:20.429912Z", - "iopub.status.idle": "2024-01-09T15:11:59.632193Z", - "shell.execute_reply": "2024-01-09T15:11:59.631458Z" + "iopub.execute_input": "2024-01-10T06:20:18.238296Z", + "iopub.status.busy": "2024-01-10T06:20:18.238059Z", + "iopub.status.idle": "2024-01-10T06:20:56.606997Z", + "shell.execute_reply": "2024-01-10T06:20:56.606212Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:11:59.635064Z", - "iopub.status.busy": "2024-01-09T15:11:59.634592Z", - "iopub.status.idle": "2024-01-09T15:12:00.855953Z", - "shell.execute_reply": "2024-01-09T15:12:00.855313Z" + "iopub.execute_input": "2024-01-10T06:20:56.609877Z", + "iopub.status.busy": "2024-01-10T06:20:56.609373Z", + "iopub.status.idle": "2024-01-10T06:20:57.846657Z", + "shell.execute_reply": "2024-01-10T06:20:57.845936Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:00.859064Z", - "iopub.status.busy": "2024-01-09T15:12:00.858540Z", - "iopub.status.idle": "2024-01-09T15:12:01.050957Z", - "shell.execute_reply": "2024-01-09T15:12:01.050168Z" + "iopub.execute_input": "2024-01-10T06:20:57.850038Z", + "iopub.status.busy": "2024-01-10T06:20:57.849349Z", + "iopub.status.idle": "2024-01-10T06:20:58.037779Z", + "shell.execute_reply": "2024-01-10T06:20:58.037164Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:01.054389Z", - "iopub.status.busy": "2024-01-09T15:12:01.053917Z", - "iopub.status.idle": "2024-01-09T15:12:01.057374Z", - "shell.execute_reply": "2024-01-09T15:12:01.056825Z" + "iopub.execute_input": "2024-01-10T06:20:58.040792Z", + "iopub.status.busy": "2024-01-10T06:20:58.040310Z", + "iopub.status.idle": "2024-01-10T06:20:58.043677Z", + "shell.execute_reply": "2024-01-10T06:20:58.043170Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:01.060012Z", - "iopub.status.busy": "2024-01-09T15:12:01.059634Z", - "iopub.status.idle": "2024-01-09T15:12:01.068289Z", - "shell.execute_reply": "2024-01-09T15:12:01.067766Z" + "iopub.execute_input": "2024-01-10T06:20:58.045942Z", + "iopub.status.busy": "2024-01-10T06:20:58.045737Z", + "iopub.status.idle": "2024-01-10T06:20:58.054356Z", + "shell.execute_reply": "2024-01-10T06:20:58.053867Z" }, "nbsphinx": "hidden" }, @@ -1017,28 +1017,38 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1c66f9007a5f45f3a8aae1f485d48b4c": { + "327a08e1d5534ee99c966c84c3365801": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_96181e031601404e9bb6f42df63b1801", - "placeholder": "​", - "style": "IPY_MODEL_b472494f952d4dff9c1e2f43bba80f5b", - "value": " 170498071/170498071 [00:01<00:00, 91805669.92it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "409c38572c3f42069bdd6f978250bae9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "29e450d30be34be2a426c6e707ad0e22": { + "5424c6f35faa4960b53ddef1c4ec0405": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1090,60 +1100,7 @@ "width": null } }, - "3f77e26e680d4c8897310817324387f7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4a1e83ed854c4e3b911b036220bf7ee5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6785377554c5413c9114e6cdda73e847": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8fce3ec758fb4d5bb5c2de7de41c9301", - "IPY_MODEL_a4e0e6e6dd8040e2bfa3cd596877c850", - "IPY_MODEL_1c66f9007a5f45f3a8aae1f485d48b4c" - ], - "layout": "IPY_MODEL_6bf4e8a49449469fa2b2e6ba3107109e" - } - }, - "6bf4e8a49449469fa2b2e6ba3107109e": { + "5ba665ca475147dd93b0e931cb42babc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1195,28 +1152,22 @@ "width": null } }, - "8fce3ec758fb4d5bb5c2de7de41c9301": { + "6c1729328c1842ab8fbe77faf7094fc4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d43af534bce44f7b99d75c8f60b8da12", - "placeholder": "​", - "style": "IPY_MODEL_4a1e83ed854c4e3b911b036220bf7ee5", - "value": "100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "96181e031601404e9bb6f42df63b1801": { + "831d732aa1314d0fb6bdef874bcd5787": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1268,7 +1219,28 @@ "width": null } }, - "a4e0e6e6dd8040e2bfa3cd596877c850": { + "9f7917b60d274c8d9aff9ccbf9989105": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_5424c6f35faa4960b53ddef1c4ec0405", + "placeholder": "​", + "style": "IPY_MODEL_409c38572c3f42069bdd6f978250bae9", + "value": " 170498071/170498071 [00:01<00:00, 109601673.05it/s]" + } + }, + "b47353d08b504eda93deb2aba25e51a2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -1284,30 +1256,58 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_29e450d30be34be2a426c6e707ad0e22", + "layout": "IPY_MODEL_831d732aa1314d0fb6bdef874bcd5787", "max": 170498071.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_3f77e26e680d4c8897310817324387f7", + "style": "IPY_MODEL_327a08e1d5534ee99c966c84c3365801", "value": 170498071.0 } }, - "b472494f952d4dff9c1e2f43bba80f5b": { + "ceb5d255b6e1467bbf644bfe84de536e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fcb1d9c928d4414fa8045d1040339642", + "placeholder": "​", + "style": "IPY_MODEL_6c1729328c1842ab8fbe77faf7094fc4", + "value": "100%" + } + }, + "d9eb539b8c1f4f97a89e8db15123410e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ceb5d255b6e1467bbf644bfe84de536e", + "IPY_MODEL_b47353d08b504eda93deb2aba25e51a2", + "IPY_MODEL_9f7917b60d274c8d9aff9ccbf9989105" + ], + "layout": "IPY_MODEL_5ba665ca475147dd93b0e931cb42babc" } }, - "d43af534bce44f7b99d75c8f60b8da12": { + "fcb1d9c928d4414fa8045d1040339642": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index dc1ec9a92..e4f71e4a3 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:05.840462Z", - "iopub.status.busy": "2024-01-09T15:12:05.840272Z", - "iopub.status.idle": "2024-01-09T15:12:06.977628Z", - "shell.execute_reply": "2024-01-09T15:12:06.977010Z" + "iopub.execute_input": "2024-01-10T06:21:03.429780Z", + "iopub.status.busy": "2024-01-10T06:21:03.429318Z", + "iopub.status.idle": "2024-01-10T06:21:04.536960Z", + "shell.execute_reply": "2024-01-10T06:21:04.536343Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:06.980932Z", - "iopub.status.busy": "2024-01-09T15:12:06.980441Z", - "iopub.status.idle": "2024-01-09T15:12:06.998272Z", - "shell.execute_reply": "2024-01-09T15:12:06.997696Z" + "iopub.execute_input": "2024-01-10T06:21:04.539950Z", + "iopub.status.busy": "2024-01-10T06:21:04.539416Z", + "iopub.status.idle": "2024-01-10T06:21:04.555703Z", + "shell.execute_reply": "2024-01-10T06:21:04.555171Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.001055Z", - "iopub.status.busy": "2024-01-09T15:12:07.000715Z", - "iopub.status.idle": "2024-01-09T15:12:07.003936Z", - "shell.execute_reply": "2024-01-09T15:12:07.003368Z" + "iopub.execute_input": "2024-01-10T06:21:04.558355Z", + "iopub.status.busy": "2024-01-10T06:21:04.557984Z", + "iopub.status.idle": "2024-01-10T06:21:04.561236Z", + "shell.execute_reply": "2024-01-10T06:21:04.560643Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.006503Z", - "iopub.status.busy": "2024-01-09T15:12:07.006138Z", - "iopub.status.idle": "2024-01-09T15:12:07.076448Z", - "shell.execute_reply": "2024-01-09T15:12:07.075824Z" + "iopub.execute_input": "2024-01-10T06:21:04.563519Z", + "iopub.status.busy": "2024-01-10T06:21:04.563180Z", + "iopub.status.idle": "2024-01-10T06:21:04.709969Z", + "shell.execute_reply": "2024-01-10T06:21:04.709340Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.079136Z", - "iopub.status.busy": "2024-01-09T15:12:07.078781Z", - "iopub.status.idle": "2024-01-09T15:12:07.366755Z", - "shell.execute_reply": "2024-01-09T15:12:07.366122Z" + "iopub.execute_input": "2024-01-10T06:21:04.712608Z", + "iopub.status.busy": "2024-01-10T06:21:04.712301Z", + "iopub.status.idle": "2024-01-10T06:21:04.987624Z", + "shell.execute_reply": "2024-01-10T06:21:04.986929Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.369612Z", - "iopub.status.busy": "2024-01-09T15:12:07.369257Z", - "iopub.status.idle": "2024-01-09T15:12:07.629024Z", - "shell.execute_reply": "2024-01-09T15:12:07.628355Z" + "iopub.execute_input": "2024-01-10T06:21:04.990511Z", + "iopub.status.busy": "2024-01-10T06:21:04.990062Z", + "iopub.status.idle": "2024-01-10T06:21:05.250260Z", + "shell.execute_reply": "2024-01-10T06:21:05.249551Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.631687Z", - "iopub.status.busy": "2024-01-09T15:12:07.631350Z", - "iopub.status.idle": "2024-01-09T15:12:07.636502Z", - "shell.execute_reply": "2024-01-09T15:12:07.635900Z" + "iopub.execute_input": "2024-01-10T06:21:05.252808Z", + "iopub.status.busy": "2024-01-10T06:21:05.252554Z", + "iopub.status.idle": "2024-01-10T06:21:05.257434Z", + "shell.execute_reply": "2024-01-10T06:21:05.256897Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.639085Z", - "iopub.status.busy": "2024-01-09T15:12:07.638701Z", - "iopub.status.idle": "2024-01-09T15:12:07.645535Z", - "shell.execute_reply": "2024-01-09T15:12:07.645020Z" + "iopub.execute_input": "2024-01-10T06:21:05.259727Z", + "iopub.status.busy": "2024-01-10T06:21:05.259513Z", + "iopub.status.idle": "2024-01-10T06:21:05.266109Z", + "shell.execute_reply": "2024-01-10T06:21:05.265624Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.648187Z", - "iopub.status.busy": "2024-01-09T15:12:07.647809Z", - "iopub.status.idle": "2024-01-09T15:12:07.650821Z", - "shell.execute_reply": "2024-01-09T15:12:07.650193Z" + "iopub.execute_input": "2024-01-10T06:21:05.268611Z", + "iopub.status.busy": "2024-01-10T06:21:05.268134Z", + "iopub.status.idle": "2024-01-10T06:21:05.271485Z", + "shell.execute_reply": "2024-01-10T06:21:05.270856Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:07.653165Z", - "iopub.status.busy": "2024-01-09T15:12:07.652806Z", - "iopub.status.idle": "2024-01-09T15:12:18.001852Z", - "shell.execute_reply": "2024-01-09T15:12:18.001187Z" + "iopub.execute_input": "2024-01-10T06:21:05.273934Z", + "iopub.status.busy": "2024-01-10T06:21:05.273375Z", + "iopub.status.idle": "2024-01-10T06:21:15.493411Z", + "shell.execute_reply": "2024-01-10T06:21:15.492743Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.005262Z", - "iopub.status.busy": "2024-01-09T15:12:18.004587Z", - "iopub.status.idle": "2024-01-09T15:12:18.012464Z", - "shell.execute_reply": "2024-01-09T15:12:18.011874Z" + "iopub.execute_input": "2024-01-10T06:21:15.496814Z", + "iopub.status.busy": "2024-01-10T06:21:15.496375Z", + "iopub.status.idle": "2024-01-10T06:21:15.504366Z", + "shell.execute_reply": "2024-01-10T06:21:15.503841Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.014599Z", - "iopub.status.busy": "2024-01-09T15:12:18.014395Z", - "iopub.status.idle": "2024-01-09T15:12:18.018358Z", - "shell.execute_reply": "2024-01-09T15:12:18.017721Z" + "iopub.execute_input": "2024-01-10T06:21:15.506693Z", + "iopub.status.busy": "2024-01-10T06:21:15.506461Z", + "iopub.status.idle": "2024-01-10T06:21:15.510803Z", + "shell.execute_reply": "2024-01-10T06:21:15.510172Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.020740Z", - "iopub.status.busy": "2024-01-09T15:12:18.020300Z", - "iopub.status.idle": "2024-01-09T15:12:18.024014Z", - "shell.execute_reply": "2024-01-09T15:12:18.023393Z" + "iopub.execute_input": "2024-01-10T06:21:15.513231Z", + "iopub.status.busy": "2024-01-10T06:21:15.512862Z", + "iopub.status.idle": "2024-01-10T06:21:15.516362Z", + "shell.execute_reply": "2024-01-10T06:21:15.515734Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.026410Z", - "iopub.status.busy": "2024-01-09T15:12:18.026069Z", - "iopub.status.idle": "2024-01-09T15:12:18.029251Z", - "shell.execute_reply": "2024-01-09T15:12:18.028677Z" + "iopub.execute_input": "2024-01-10T06:21:15.518807Z", + "iopub.status.busy": "2024-01-10T06:21:15.518425Z", + "iopub.status.idle": "2024-01-10T06:21:15.521733Z", + "shell.execute_reply": "2024-01-10T06:21:15.521168Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.031513Z", - "iopub.status.busy": "2024-01-09T15:12:18.031167Z", - "iopub.status.idle": "2024-01-09T15:12:18.039888Z", - "shell.execute_reply": "2024-01-09T15:12:18.039352Z" + "iopub.execute_input": "2024-01-10T06:21:15.524184Z", + "iopub.status.busy": "2024-01-10T06:21:15.523785Z", + "iopub.status.idle": "2024-01-10T06:21:15.533773Z", + "shell.execute_reply": "2024-01-10T06:21:15.532950Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.042168Z", - "iopub.status.busy": "2024-01-09T15:12:18.041966Z", - "iopub.status.idle": "2024-01-09T15:12:18.195751Z", - "shell.execute_reply": "2024-01-09T15:12:18.195047Z" + "iopub.execute_input": "2024-01-10T06:21:15.536364Z", + "iopub.status.busy": "2024-01-10T06:21:15.536012Z", + "iopub.status.idle": "2024-01-10T06:21:15.684972Z", + "shell.execute_reply": "2024-01-10T06:21:15.684237Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.198599Z", - "iopub.status.busy": "2024-01-09T15:12:18.198173Z", - "iopub.status.idle": "2024-01-09T15:12:18.332858Z", - "shell.execute_reply": "2024-01-09T15:12:18.332123Z" + "iopub.execute_input": "2024-01-10T06:21:15.688080Z", + "iopub.status.busy": "2024-01-10T06:21:15.687659Z", + "iopub.status.idle": "2024-01-10T06:21:15.819421Z", + "shell.execute_reply": "2024-01-10T06:21:15.818612Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.335602Z", - "iopub.status.busy": "2024-01-09T15:12:18.335247Z", - "iopub.status.idle": "2024-01-09T15:12:18.928023Z", - "shell.execute_reply": "2024-01-09T15:12:18.927316Z" + "iopub.execute_input": "2024-01-10T06:21:15.822502Z", + "iopub.status.busy": "2024-01-10T06:21:15.822274Z", + "iopub.status.idle": "2024-01-10T06:21:16.418907Z", + "shell.execute_reply": "2024-01-10T06:21:16.418233Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:18.930990Z", - "iopub.status.busy": "2024-01-09T15:12:18.930596Z", - "iopub.status.idle": "2024-01-09T15:12:19.021043Z", - "shell.execute_reply": "2024-01-09T15:12:19.020347Z" + "iopub.execute_input": "2024-01-10T06:21:16.422520Z", + "iopub.status.busy": "2024-01-10T06:21:16.421868Z", + "iopub.status.idle": "2024-01-10T06:21:16.505313Z", + "shell.execute_reply": "2024-01-10T06:21:16.504636Z" } }, "outputs": [ @@ -1056,10 +1056,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:19.024055Z", - "iopub.status.busy": "2024-01-09T15:12:19.023493Z", - "iopub.status.idle": "2024-01-09T15:12:19.033897Z", - "shell.execute_reply": "2024-01-09T15:12:19.033413Z" + "iopub.execute_input": "2024-01-10T06:21:16.508240Z", + "iopub.status.busy": "2024-01-10T06:21:16.507734Z", + "iopub.status.idle": "2024-01-10T06:21:16.517478Z", + "shell.execute_reply": "2024-01-10T06:21:16.517004Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html index 0c8aa15de..8261ff5c4 100644 --- a/master/tutorials/segmentation.html +++ b/master/tutorials/segmentation.html @@ -960,13 +960,13 @@

3. Use cleanlab to find label issues

-
+
-
+
-
0%| | 17167/4997817 [00:00&lt;00:29, 171663.62it/s]
+
0%| | 17062/4997817 [00:00&lt;00:29, 170604.30it/s]

</pre>

-
0%| | 17167/4997817 [00:00<00:29, 171663.62it/s]
+
0%| | 17062/4997817 [00:00<00:29, 170604.30it/s]

end{sphinxVerbatim}

-

0%| | 17167/4997817 [00:00<00:29, 171663.62it/s]

+

0%| | 17062/4997817 [00:00<00:29, 170604.30it/s]

-
1%| | 34563/4997817 [00:00&lt;00:28, 173008.20it/s]
+
1%| | 34285/4997817 [00:00&lt;00:28, 171551.99it/s]

</pre>

-
1%| | 34563/4997817 [00:00<00:28, 173008.20it/s]
+
1%| | 34285/4997817 [00:00<00:28, 171551.99it/s]

end{sphinxVerbatim}

-

1%| | 34563/4997817 [00:00<00:28, 173008.20it/s]

+

1%| | 34285/4997817 [00:00<00:28, 171551.99it/s]

-
1%| | 52101/4997817 [00:00&lt;00:28, 174089.17it/s]
+
1%| | 51456/4997817 [00:00&lt;00:28, 171618.24it/s]

</pre>

-
1%| | 52101/4997817 [00:00<00:28, 174089.17it/s]
+
1%| | 51456/4997817 [00:00<00:28, 171618.24it/s]

end{sphinxVerbatim}

-

1%| | 52101/4997817 [00:00<00:28, 174089.17it/s]

+

1%| | 51456/4997817 [00:00<00:28, 171618.24it/s]

-
1%|▏ | 69510/4997817 [00:00&lt;00:28, 174019.46it/s]
+
1%|▏ | 68618/4997817 [00:00&lt;00:28, 170859.63it/s]

</pre>

-
1%|▏ | 69510/4997817 [00:00<00:28, 174019.46it/s]
+
1%|▏ | 68618/4997817 [00:00<00:28, 170859.63it/s]

end{sphinxVerbatim}

-

1%|▏ | 69510/4997817 [00:00<00:28, 174019.46it/s]

+

1%|▏ | 68618/4997817 [00:00<00:28, 170859.63it/s]

-
2%|▏ | 86997/4997817 [00:00&lt;00:28, 174323.75it/s]
+
2%|▏ | 85805/4997817 [00:00&lt;00:28, 171220.50it/s]

</pre>

-
2%|▏ | 86997/4997817 [00:00<00:28, 174323.75it/s]
+
2%|▏ | 85805/4997817 [00:00<00:28, 171220.50it/s]

end{sphinxVerbatim}

-

2%|▏ | 86997/4997817 [00:00<00:28, 174323.75it/s]

+

2%|▏ | 85805/4997817 [00:00<00:28, 171220.50it/s]

-
2%|▏ | 104430/4997817 [00:00&lt;00:28, 169724.91it/s]
+
2%|▏ | 102949/4997817 [00:00&lt;00:28, 171291.99it/s]

</pre>

-
2%|▏ | 104430/4997817 [00:00<00:28, 169724.91it/s]
+
2%|▏ | 102949/4997817 [00:00<00:28, 171291.99it/s]

end{sphinxVerbatim}

-

2%|▏ | 104430/4997817 [00:00<00:28, 169724.91it/s]

+

2%|▏ | 102949/4997817 [00:00<00:28, 171291.99it/s]

-
2%|▏ | 122139/4997817 [00:00&lt;00:28, 172086.44it/s]
+
2%|▏ | 120095/4997817 [00:00&lt;00:28, 171343.43it/s]

</pre>

-
2%|▏ | 122139/4997817 [00:00<00:28, 172086.44it/s]
+
2%|▏ | 120095/4997817 [00:00<00:28, 171343.43it/s]

end{sphinxVerbatim}

-

2%|▏ | 122139/4997817 [00:00<00:28, 172086.44it/s]

+

2%|▏ | 120095/4997817 [00:00<00:28, 171343.43it/s]

-
3%|▎ | 140011/4997817 [00:00&lt;00:27, 174169.28it/s]
+
3%|▎ | 137230/4997817 [00:00&lt;00:28, 170491.36it/s]

</pre>

-
3%|▎ | 140011/4997817 [00:00<00:27, 174169.28it/s]
+
3%|▎ | 137230/4997817 [00:00<00:28, 170491.36it/s]

end{sphinxVerbatim}

-

3%|▎ | 140011/4997817 [00:00<00:27, 174169.28it/s]

+

3%|▎ | 137230/4997817 [00:00<00:28, 170491.36it/s]

-
3%|▎ | 157761/4997817 [00:00&lt;00:27, 175200.10it/s]
+
3%|▎ | 154281/4997817 [00:00&lt;00:28, 169872.65it/s]

</pre>

-
3%|▎ | 157761/4997817 [00:00<00:27, 175200.10it/s]
+
3%|▎ | 154281/4997817 [00:00<00:28, 169872.65it/s]

end{sphinxVerbatim}

-

3%|▎ | 157761/4997817 [00:00<00:27, 175200.10it/s]

+

3%|▎ | 154281/4997817 [00:00<00:28, 169872.65it/s]

-
4%|▎ | 175374/4997817 [00:01&lt;00:27, 175483.47it/s]
+
3%|▎ | 171280/4997817 [00:01&lt;00:28, 169905.50it/s]

</pre>

-
4%|▎ | 175374/4997817 [00:01<00:27, 175483.47it/s]
+
3%|▎ | 171280/4997817 [00:01<00:28, 169905.50it/s]

end{sphinxVerbatim}

-

4%|▎ | 175374/4997817 [00:01<00:27, 175483.47it/s]

+

3%|▎ | 171280/4997817 [00:01<00:28, 169905.50it/s]

-
4%|▍ | 192985/4997817 [00:01&lt;00:27, 175671.16it/s]
+
4%|▍ | 188272/4997817 [00:01&lt;00:28, 169777.04it/s]

</pre>

-
4%|▍ | 192985/4997817 [00:01<00:27, 175671.16it/s]
+
4%|▍ | 188272/4997817 [00:01<00:28, 169777.04it/s]

end{sphinxVerbatim}

-

4%|▍ | 192985/4997817 [00:01<00:27, 175671.16it/s]

+

4%|▍ | 188272/4997817 [00:01<00:28, 169777.04it/s]

-
4%|▍ | 210558/4997817 [00:01&lt;00:27, 175484.74it/s]
+
4%|▍ | 205575/4997817 [00:01&lt;00:28, 170760.61it/s]

</pre>

-
4%|▍ | 210558/4997817 [00:01<00:27, 175484.74it/s]
+
4%|▍ | 205575/4997817 [00:01<00:28, 170760.61it/s]

end{sphinxVerbatim}

-

4%|▍ | 210558/4997817 [00:01<00:27, 175484.74it/s]

+

4%|▍ | 205575/4997817 [00:01<00:28, 170760.61it/s]

-
5%|▍ | 228111/4997817 [00:01&lt;00:27, 175443.62it/s]
+
4%|▍ | 222652/4997817 [00:01&lt;00:27, 170740.73it/s]

</pre>

-
5%|▍ | 228111/4997817 [00:01<00:27, 175443.62it/s]
+
4%|▍ | 222652/4997817 [00:01<00:27, 170740.73it/s]

end{sphinxVerbatim}

-

5%|▍ | 228111/4997817 [00:01<00:27, 175443.62it/s]

+

4%|▍ | 222652/4997817 [00:01<00:27, 170740.73it/s]

-
5%|▍ | 245659/4997817 [00:01&lt;00:27, 175322.61it/s]
+
5%|▍ | 240083/4997817 [00:01&lt;00:27, 171811.81it/s]

</pre>

-
5%|▍ | 245659/4997817 [00:01<00:27, 175322.61it/s]
+
5%|▍ | 240083/4997817 [00:01<00:27, 171811.81it/s]

end{sphinxVerbatim}

-

5%|▍ | 245659/4997817 [00:01<00:27, 175322.61it/s]

+

5%|▍ | 240083/4997817 [00:01<00:27, 171811.81it/s]

-
5%|▌ | 263194/4997817 [00:01&lt;00:27, 175304.12it/s]
+
5%|▌ | 257360/4997817 [00:01&lt;00:27, 172096.03it/s]

</pre>

-
5%|▌ | 263194/4997817 [00:01<00:27, 175304.12it/s]
+
5%|▌ | 257360/4997817 [00:01<00:27, 172096.03it/s]

end{sphinxVerbatim}

-

5%|▌ | 263194/4997817 [00:01<00:27, 175304.12it/s]

+

5%|▌ | 257360/4997817 [00:01<00:27, 172096.03it/s]

-
6%|▌ | 280759/4997817 [00:01&lt;00:26, 175405.86it/s]
+
5%|▌ | 274605/4997817 [00:01&lt;00:27, 172199.32it/s]

</pre>

-
6%|▌ | 280759/4997817 [00:01<00:26, 175405.86it/s]
+
5%|▌ | 274605/4997817 [00:01<00:27, 172199.32it/s]

end{sphinxVerbatim}

-

6%|▌ | 280759/4997817 [00:01<00:26, 175405.86it/s]

+

5%|▌ | 274605/4997817 [00:01<00:27, 172199.32it/s]

-
6%|▌ | 298301/4997817 [00:01&lt;00:26, 175109.69it/s]
+
6%|▌ | 291886/4997817 [00:01&lt;00:27, 172379.81it/s]

</pre>

-
6%|▌ | 298301/4997817 [00:01<00:26, 175109.69it/s]
+
6%|▌ | 291886/4997817 [00:01<00:27, 172379.81it/s]

end{sphinxVerbatim}

-

6%|▌ | 298301/4997817 [00:01<00:26, 175109.69it/s]

+

6%|▌ | 291886/4997817 [00:01<00:27, 172379.81it/s]

-
6%|▋ | 315813/4997817 [00:01&lt;00:26, 174670.09it/s]
+
6%|▌ | 309125/4997817 [00:01&lt;00:27, 172152.38it/s]

</pre>

-
6%|▋ | 315813/4997817 [00:01<00:26, 174670.09it/s]
+
6%|▌ | 309125/4997817 [00:01<00:27, 172152.38it/s]

end{sphinxVerbatim}

-

6%|▋ | 315813/4997817 [00:01<00:26, 174670.09it/s]

+

6%|▌ | 309125/4997817 [00:01<00:27, 172152.38it/s]

-
7%|▋ | 333315/4997817 [00:01&lt;00:26, 174769.54it/s]
+
7%|▋ | 326341/4997817 [00:01&lt;00:27, 171949.71it/s]

</pre>

-
7%|▋ | 333315/4997817 [00:01<00:26, 174769.54it/s]
+
7%|▋ | 326341/4997817 [00:01<00:27, 171949.71it/s]

end{sphinxVerbatim}

-

7%|▋ | 333315/4997817 [00:01<00:26, 174769.54it/s]

+

7%|▋ | 326341/4997817 [00:01<00:27, 171949.71it/s]

-
7%|▋ | 350881/4997817 [00:02&lt;00:26, 175033.97it/s]
+
7%|▋ | 343537/4997817 [00:02&lt;00:27, 171568.72it/s]

</pre>

-
7%|▋ | 350881/4997817 [00:02<00:26, 175033.97it/s]
+
7%|▋ | 343537/4997817 [00:02<00:27, 171568.72it/s]

end{sphinxVerbatim}

-

7%|▋ | 350881/4997817 [00:02<00:26, 175033.97it/s]

+

7%|▋ | 343537/4997817 [00:02<00:27, 171568.72it/s]

-
7%|▋ | 368385/4997817 [00:02&lt;00:26, 174887.75it/s]
+
7%|▋ | 360817/4997817 [00:02&lt;00:26, 171933.30it/s]

</pre>

-
7%|▋ | 368385/4997817 [00:02<00:26, 174887.75it/s]
+
7%|▋ | 360817/4997817 [00:02<00:26, 171933.30it/s]

end{sphinxVerbatim}

-

7%|▋ | 368385/4997817 [00:02<00:26, 174887.75it/s]

+

7%|▋ | 360817/4997817 [00:02<00:26, 171933.30it/s]

-
8%|▊ | 385876/4997817 [00:02&lt;00:26, 174891.71it/s]
+
8%|▊ | 378023/4997817 [00:02&lt;00:26, 171967.12it/s]

</pre>

-
8%|▊ | 385876/4997817 [00:02<00:26, 174891.71it/s]
+
8%|▊ | 378023/4997817 [00:02<00:26, 171967.12it/s]

end{sphinxVerbatim}

-

8%|▊ | 385876/4997817 [00:02<00:26, 174891.71it/s]

+

8%|▊ | 378023/4997817 [00:02<00:26, 171967.12it/s]

-
8%|▊ | 403366/4997817 [00:02&lt;00:26, 174567.29it/s]
+
8%|▊ | 395220/4997817 [00:02&lt;00:26, 171770.69it/s]

</pre>

-
8%|▊ | 403366/4997817 [00:02<00:26, 174567.29it/s]
+
8%|▊ | 395220/4997817 [00:02<00:26, 171770.69it/s]

end{sphinxVerbatim}

-

8%|▊ | 403366/4997817 [00:02<00:26, 174567.29it/s]

+

8%|▊ | 395220/4997817 [00:02<00:26, 171770.69it/s]

-
8%|▊ | 421002/4997817 [00:02&lt;00:26, 175100.23it/s]
+
8%|▊ | 412518/4997817 [00:02&lt;00:26, 172129.64it/s]

</pre>

-
8%|▊ | 421002/4997817 [00:02<00:26, 175100.23it/s]
+
8%|▊ | 412518/4997817 [00:02<00:26, 172129.64it/s]

end{sphinxVerbatim}

-

8%|▊ | 421002/4997817 [00:02<00:26, 175100.23it/s]

+

8%|▊ | 412518/4997817 [00:02<00:26, 172129.64it/s]

-
9%|▉ | 438513/4997817 [00:02&lt;00:26, 174881.61it/s]
+
9%|▊ | 429746/4997817 [00:02&lt;00:26, 172171.06it/s]

</pre>

-
9%|▉ | 438513/4997817 [00:02<00:26, 174881.61it/s]
+
9%|▊ | 429746/4997817 [00:02<00:26, 172171.06it/s]

end{sphinxVerbatim}

-

9%|▉ | 438513/4997817 [00:02<00:26, 174881.61it/s]

+

9%|▊ | 429746/4997817 [00:02<00:26, 172171.06it/s]

-
9%|▉ | 456002/4997817 [00:02&lt;00:26, 170509.73it/s]
+
9%|▉ | 446964/4997817 [00:02&lt;00:26, 171908.29it/s]

</pre>

-
9%|▉ | 456002/4997817 [00:02<00:26, 170509.73it/s]
+
9%|▉ | 446964/4997817 [00:02<00:26, 171908.29it/s]

end{sphinxVerbatim}

-

9%|▉ | 456002/4997817 [00:02<00:26, 170509.73it/s]

+

9%|▉ | 446964/4997817 [00:02<00:26, 171908.29it/s]

-
9%|▉ | 473898/4997817 [00:02&lt;00:26, 172994.94it/s]
+
9%|▉ | 464155/4997817 [00:02&lt;00:26, 171900.62it/s]

</pre>

-
9%|▉ | 473898/4997817 [00:02<00:26, 172994.94it/s]
+
9%|▉ | 464155/4997817 [00:02<00:26, 171900.62it/s]

end{sphinxVerbatim}

-

9%|▉ | 473898/4997817 [00:02<00:26, 172994.94it/s]

+

9%|▉ | 464155/4997817 [00:02<00:26, 171900.62it/s]

-
10%|▉ | 491599/4997817 [00:02&lt;00:25, 174181.60it/s]
+
10%|▉ | 481346/4997817 [00:02&lt;00:26, 171355.90it/s]

</pre>

-
10%|▉ | 491599/4997817 [00:02<00:25, 174181.60it/s]
+
10%|▉ | 481346/4997817 [00:02<00:26, 171355.90it/s]

end{sphinxVerbatim}

-

10%|▉ | 491599/4997817 [00:02<00:25, 174181.60it/s]

+

10%|▉ | 481346/4997817 [00:02<00:26, 171355.90it/s]

-
10%|█ | 509182/4997817 [00:02&lt;00:25, 174668.81it/s]
+
10%|▉ | 498619/4997817 [00:02&lt;00:26, 171763.37it/s]

</pre>

-
10%|█ | 509182/4997817 [00:02<00:25, 174668.81it/s]
+
10%|▉ | 498619/4997817 [00:02<00:26, 171763.37it/s]

end{sphinxVerbatim}

-

10%|█ | 509182/4997817 [00:02<00:25, 174668.81it/s]

+

10%|▉ | 498619/4997817 [00:02<00:26, 171763.37it/s]

-
11%|█ | 526728/4997817 [00:03&lt;00:25, 174903.04it/s]
+
10%|█ | 515796/4997817 [00:03&lt;00:26, 171727.53it/s]

</pre>

-
11%|█ | 526728/4997817 [00:03<00:25, 174903.04it/s]
+
10%|█ | 515796/4997817 [00:03<00:26, 171727.53it/s]

end{sphinxVerbatim}

-

11%|█ | 526728/4997817 [00:03<00:25, 174903.04it/s]

+

10%|█ | 515796/4997817 [00:03<00:26, 171727.53it/s]

-
11%|█ | 544360/4997817 [00:03&lt;00:25, 175322.34it/s]
+
11%|█ | 532984/4997817 [00:03&lt;00:25, 171768.71it/s]

</pre>

-
11%|█ | 544360/4997817 [00:03<00:25, 175322.34it/s]
+
11%|█ | 532984/4997817 [00:03<00:25, 171768.71it/s]

end{sphinxVerbatim}

-

11%|█ | 544360/4997817 [00:03<00:25, 175322.34it/s]

+

11%|█ | 532984/4997817 [00:03<00:25, 171768.71it/s]

-
11%|█ | 561899/4997817 [00:03&lt;00:25, 175266.03it/s]
+
11%|█ | 550240/4997817 [00:03&lt;00:25, 172001.07it/s]

</pre>

-
11%|█ | 561899/4997817 [00:03<00:25, 175266.03it/s]
+
11%|█ | 550240/4997817 [00:03<00:25, 172001.07it/s]

end{sphinxVerbatim}

-

11%|█ | 561899/4997817 [00:03<00:25, 175266.03it/s]

+

11%|█ | 550240/4997817 [00:03<00:25, 172001.07it/s]

-
12%|█▏ | 579430/4997817 [00:03&lt;00:25, 175198.49it/s]
+
11%|█▏ | 567441/4997817 [00:03&lt;00:25, 171961.52it/s]

</pre>

-
12%|█▏ | 579430/4997817 [00:03<00:25, 175198.49it/s]
+
11%|█▏ | 567441/4997817 [00:03<00:25, 171961.52it/s]

end{sphinxVerbatim}

-

12%|█▏ | 579430/4997817 [00:03<00:25, 175198.49it/s]

+

11%|█▏ | 567441/4997817 [00:03<00:25, 171961.52it/s]

-
12%|█▏ | 596953/4997817 [00:03&lt;00:25, 175077.39it/s]
+
12%|█▏ | 584638/4997817 [00:03&lt;00:25, 171920.86it/s]

</pre>

-
12%|█▏ | 596953/4997817 [00:03<00:25, 175077.39it/s]
+
12%|█▏ | 584638/4997817 [00:03<00:25, 171920.86it/s]

end{sphinxVerbatim}

-

12%|█▏ | 596953/4997817 [00:03<00:25, 175077.39it/s]

+

12%|█▏ | 584638/4997817 [00:03<00:25, 171920.86it/s]

-
12%|█▏ | 614463/4997817 [00:03&lt;00:25, 174881.61it/s]
+
12%|█▏ | 601962/4997817 [00:03&lt;00:25, 172313.17it/s]

</pre>

-
12%|█▏ | 614463/4997817 [00:03<00:25, 174881.61it/s]
+
12%|█▏ | 601962/4997817 [00:03<00:25, 172313.17it/s]

end{sphinxVerbatim}

-

12%|█▏ | 614463/4997817 [00:03<00:25, 174881.61it/s]

+

12%|█▏ | 601962/4997817 [00:03<00:25, 172313.17it/s]

-
13%|█▎ | 631953/4997817 [00:03&lt;00:24, 174652.96it/s]
+
12%|█▏ | 619303/4997817 [00:03&lt;00:25, 172639.01it/s]

</pre>

-
13%|█▎ | 631953/4997817 [00:03<00:24, 174652.96it/s]
+
12%|█▏ | 619303/4997817 [00:03<00:25, 172639.01it/s]

end{sphinxVerbatim}

-

13%|█▎ | 631953/4997817 [00:03<00:24, 174652.96it/s]

+

12%|█▏ | 619303/4997817 [00:03<00:25, 172639.01it/s]

-
13%|█▎ | 649420/4997817 [00:03&lt;00:24, 174008.43it/s]
+
13%|█▎ | 636567/4997817 [00:03&lt;00:25, 172200.43it/s]

</pre>

-
13%|█▎ | 649420/4997817 [00:03<00:24, 174008.43it/s]
+
13%|█▎ | 636567/4997817 [00:03<00:25, 172200.43it/s]

end{sphinxVerbatim}

-

13%|█▎ | 649420/4997817 [00:03<00:24, 174008.43it/s]

+

13%|█▎ | 636567/4997817 [00:03<00:25, 172200.43it/s]

-
13%|█▎ | 666822/4997817 [00:03&lt;00:24, 173979.60it/s]
+
13%|█▎ | 653788/4997817 [00:03&lt;00:25, 171788.02it/s]

</pre>

-
13%|█▎ | 666822/4997817 [00:03<00:24, 173979.60it/s]
+
13%|█▎ | 653788/4997817 [00:03<00:25, 171788.02it/s]

end{sphinxVerbatim}

-

13%|█▎ | 666822/4997817 [00:03<00:24, 173979.60it/s]

+

13%|█▎ | 653788/4997817 [00:03<00:25, 171788.02it/s]

-
14%|█▎ | 684221/4997817 [00:03&lt;00:24, 173683.21it/s]
+
13%|█▎ | 670968/4997817 [00:03&lt;00:25, 171514.66it/s]

</pre>

-
14%|█▎ | 684221/4997817 [00:03<00:24, 173683.21it/s]
+
13%|█▎ | 670968/4997817 [00:03<00:25, 171514.66it/s]

end{sphinxVerbatim}

-

14%|█▎ | 684221/4997817 [00:03<00:24, 173683.21it/s]

+

13%|█▎ | 670968/4997817 [00:03<00:25, 171514.66it/s]

-
14%|█▍ | 701638/4997817 [00:04&lt;00:24, 173825.45it/s]
+
14%|█▍ | 688120/4997817 [00:04&lt;00:25, 170996.88it/s]

</pre>

-
14%|█▍ | 701638/4997817 [00:04<00:24, 173825.45it/s]
+
14%|█▍ | 688120/4997817 [00:04<00:25, 170996.88it/s]

end{sphinxVerbatim}

-

14%|█▍ | 701638/4997817 [00:04<00:24, 173825.45it/s]

+

14%|█▍ | 688120/4997817 [00:04<00:25, 170996.88it/s]

-
14%|█▍ | 719021/4997817 [00:04&lt;00:24, 173395.93it/s]
+
14%|█▍ | 705275/4997817 [00:04&lt;00:25, 171158.72it/s]

</pre>

-
14%|█▍ | 719021/4997817 [00:04<00:24, 173395.93it/s]
+
14%|█▍ | 705275/4997817 [00:04<00:25, 171158.72it/s]

end{sphinxVerbatim}

-

14%|█▍ | 719021/4997817 [00:04<00:24, 173395.93it/s]

+

14%|█▍ | 705275/4997817 [00:04<00:25, 171158.72it/s]

-
15%|█▍ | 736362/4997817 [00:04&lt;00:24, 173372.01it/s]
+
14%|█▍ | 722435/4997817 [00:04&lt;00:24, 171285.85it/s]

</pre>

-
15%|█▍ | 736362/4997817 [00:04<00:24, 173372.01it/s]
+
14%|█▍ | 722435/4997817 [00:04<00:24, 171285.85it/s]

end{sphinxVerbatim}

-

15%|█▍ | 736362/4997817 [00:04<00:24, 173372.01it/s]

+

14%|█▍ | 722435/4997817 [00:04<00:24, 171285.85it/s]

-
15%|█▌ | 753700/4997817 [00:04&lt;00:24, 172675.44it/s]
+
15%|█▍ | 739806/4997817 [00:04&lt;00:24, 172008.35it/s]

</pre>

-
15%|█▌ | 753700/4997817 [00:04<00:24, 172675.44it/s]
+
15%|█▍ | 739806/4997817 [00:04<00:24, 172008.35it/s]

end{sphinxVerbatim}

-

15%|█▌ | 753700/4997817 [00:04<00:24, 172675.44it/s]

+

15%|█▍ | 739806/4997817 [00:04<00:24, 172008.35it/s]

-
15%|█▌ | 771046/4997817 [00:04&lt;00:24, 172906.82it/s]
+
15%|█▌ | 757008/4997817 [00:04&lt;00:24, 171692.60it/s]

</pre>

-
15%|█▌ | 771046/4997817 [00:04<00:24, 172906.82it/s]
+
15%|█▌ | 757008/4997817 [00:04<00:24, 171692.60it/s]

end{sphinxVerbatim}

-

15%|█▌ | 771046/4997817 [00:04<00:24, 172906.82it/s]

+

15%|█▌ | 757008/4997817 [00:04<00:24, 171692.60it/s]

-
16%|█▌ | 788553/4997817 [00:04&lt;00:24, 173549.65it/s]
+
15%|█▌ | 774255/4997817 [00:04&lt;00:24, 171920.65it/s]

</pre>

-
16%|█▌ | 788553/4997817 [00:04<00:24, 173549.65it/s]
+
15%|█▌ | 774255/4997817 [00:04<00:24, 171920.65it/s]

end{sphinxVerbatim}

-

16%|█▌ | 788553/4997817 [00:04<00:24, 173549.65it/s]

+

15%|█▌ | 774255/4997817 [00:04<00:24, 171920.65it/s]

-
16%|█▌ | 806052/4997817 [00:04&lt;00:24, 173977.96it/s]
+
16%|█▌ | 791671/4997817 [00:04&lt;00:24, 172588.53it/s]

</pre>

-
16%|█▌ | 806052/4997817 [00:04<00:24, 173977.96it/s]
+
16%|█▌ | 791671/4997817 [00:04<00:24, 172588.53it/s]

end{sphinxVerbatim}

-

16%|█▌ | 806052/4997817 [00:04<00:24, 173977.96it/s]

+

16%|█▌ | 791671/4997817 [00:04<00:24, 172588.53it/s]

-
16%|█▋ | 823451/4997817 [00:04&lt;00:25, 166777.78it/s]
+
16%|█▌ | 809012/4997817 [00:04&lt;00:24, 172830.16it/s]

</pre>

-
16%|█▋ | 823451/4997817 [00:04<00:25, 166777.78it/s]
+
16%|█▌ | 809012/4997817 [00:04<00:24, 172830.16it/s]

end{sphinxVerbatim}

-

16%|█▋ | 823451/4997817 [00:04<00:25, 166777.78it/s]

+

16%|█▌ | 809012/4997817 [00:04<00:24, 172830.16it/s]

-
17%|█▋ | 840819/4997817 [00:04&lt;00:24, 168781.92it/s]
+
17%|█▋ | 826296/4997817 [00:04&lt;00:24, 171982.40it/s]

</pre>

-
17%|█▋ | 840819/4997817 [00:04<00:24, 168781.92it/s]
+
17%|█▋ | 826296/4997817 [00:04<00:24, 171982.40it/s]

end{sphinxVerbatim}

-

17%|█▋ | 840819/4997817 [00:04<00:24, 168781.92it/s]

+

17%|█▋ | 826296/4997817 [00:04<00:24, 171982.40it/s]

-
17%|█▋ | 858322/4997817 [00:04&lt;00:24, 170614.13it/s]
+
17%|█▋ | 843496/4997817 [00:04&lt;00:24, 171799.85it/s]

</pre>

-
17%|█▋ | 858322/4997817 [00:04<00:24, 170614.13it/s]
+
17%|█▋ | 843496/4997817 [00:04<00:24, 171799.85it/s]

end{sphinxVerbatim}

-

17%|█▋ | 858322/4997817 [00:04<00:24, 170614.13it/s]

+

17%|█▋ | 843496/4997817 [00:04<00:24, 171799.85it/s]

-
18%|█▊ | 875707/4997817 [00:05&lt;00:24, 171568.88it/s]
+
17%|█▋ | 860677/4997817 [00:05&lt;00:24, 171324.91it/s]

</pre>

-
18%|█▊ | 875707/4997817 [00:05<00:24, 171568.88it/s]
+
17%|█▋ | 860677/4997817 [00:05<00:24, 171324.91it/s]

end{sphinxVerbatim}

-

18%|█▊ | 875707/4997817 [00:05<00:24, 171568.88it/s]

+

17%|█▋ | 860677/4997817 [00:05<00:24, 171324.91it/s]

-
18%|█▊ | 893295/4997817 [00:05&lt;00:23, 172845.52it/s]
+
18%|█▊ | 877842/4997817 [00:05&lt;00:24, 171418.27it/s]

</pre>

-
18%|█▊ | 893295/4997817 [00:05<00:23, 172845.52it/s]
+
18%|█▊ | 877842/4997817 [00:05<00:24, 171418.27it/s]

end{sphinxVerbatim}

-

18%|█▊ | 893295/4997817 [00:05<00:23, 172845.52it/s]

+

18%|█▊ | 877842/4997817 [00:05<00:24, 171418.27it/s]

-
18%|█▊ | 910813/4997817 [00:05&lt;00:23, 173537.82it/s]
+
18%|█▊ | 894985/4997817 [00:05&lt;00:23, 171219.42it/s]

</pre>

-
18%|█▊ | 910813/4997817 [00:05<00:23, 173537.82it/s]
+
18%|█▊ | 894985/4997817 [00:05<00:23, 171219.42it/s]

end{sphinxVerbatim}

-

18%|█▊ | 910813/4997817 [00:05<00:23, 173537.82it/s]

+

18%|█▊ | 894985/4997817 [00:05<00:23, 171219.42it/s]

-
19%|█▊ | 928368/4997817 [00:05&lt;00:23, 174136.97it/s]
+
18%|█▊ | 912146/4997817 [00:05&lt;00:23, 171332.99it/s]

</pre>

-
19%|█▊ | 928368/4997817 [00:05<00:23, 174136.97it/s]
+
18%|█▊ | 912146/4997817 [00:05<00:23, 171332.99it/s]

end{sphinxVerbatim}

-

19%|█▊ | 928368/4997817 [00:05<00:23, 174136.97it/s]

+

18%|█▊ | 912146/4997817 [00:05<00:23, 171332.99it/s]

-
19%|█▉ | 945901/4997817 [00:05&lt;00:23, 174490.87it/s]
+
19%|█▊ | 929303/4997817 [00:05&lt;00:23, 171399.71it/s]

</pre>

-
19%|█▉ | 945901/4997817 [00:05<00:23, 174490.87it/s]
+
19%|█▊ | 929303/4997817 [00:05<00:23, 171399.71it/s]

end{sphinxVerbatim}

-

19%|█▉ | 945901/4997817 [00:05<00:23, 174490.87it/s]

+

19%|█▊ | 929303/4997817 [00:05<00:23, 171399.71it/s]

-
19%|█▉ | 963359/4997817 [00:05&lt;00:23, 174450.39it/s]
+
19%|█▉ | 946444/4997817 [00:05&lt;00:23, 171377.87it/s]

</pre>

-
19%|█▉ | 963359/4997817 [00:05<00:23, 174450.39it/s]
+
19%|█▉ | 946444/4997817 [00:05<00:23, 171377.87it/s]

end{sphinxVerbatim}

-

19%|█▉ | 963359/4997817 [00:05<00:23, 174450.39it/s]

+

19%|█▉ | 946444/4997817 [00:05<00:23, 171377.87it/s]

-
20%|█▉ | 981064/4997817 [00:05&lt;00:22, 175224.68it/s]
+
19%|█▉ | 963692/4997817 [00:05&lt;00:23, 171703.39it/s]

</pre>

-
20%|█▉ | 981064/4997817 [00:05<00:22, 175224.68it/s]
+
19%|█▉ | 963692/4997817 [00:05<00:23, 171703.39it/s]

end{sphinxVerbatim}

-

20%|█▉ | 981064/4997817 [00:05<00:22, 175224.68it/s]

+

19%|█▉ | 963692/4997817 [00:05<00:23, 171703.39it/s]

-
20%|█▉ | 998591/4997817 [00:05&lt;00:23, 167695.83it/s]
+
20%|█▉ | 981048/4997817 [00:05&lt;00:23, 172255.53it/s]

</pre>

-
20%|█▉ | 998591/4997817 [00:05<00:23, 167695.83it/s]
+
20%|█▉ | 981048/4997817 [00:05<00:23, 172255.53it/s]

end{sphinxVerbatim}

-

20%|█▉ | 998591/4997817 [00:05<00:23, 167695.83it/s]

+

20%|█▉ | 981048/4997817 [00:05<00:23, 172255.53it/s]

-
20%|██ | 1016101/4997817 [00:05&lt;00:23, 169844.83it/s]
+
20%|█▉ | 998359/4997817 [00:05&lt;00:23, 172508.56it/s]

</pre>

-
20%|██ | 1016101/4997817 [00:05<00:23, 169844.83it/s]
+
20%|█▉ | 998359/4997817 [00:05<00:23, 172508.56it/s]

end{sphinxVerbatim}

-

20%|██ | 1016101/4997817 [00:05<00:23, 169844.83it/s]

+

20%|█▉ | 998359/4997817 [00:05<00:23, 172508.56it/s]

-
21%|██ | 1033639/4997817 [00:05&lt;00:23, 171465.64it/s]
+
20%|██ | 1015635/4997817 [00:05&lt;00:23, 172579.19it/s]

</pre>

-
21%|██ | 1033639/4997817 [00:05<00:23, 171465.64it/s]
+
20%|██ | 1015635/4997817 [00:05<00:23, 172579.19it/s]

end{sphinxVerbatim}

-

21%|██ | 1033639/4997817 [00:05<00:23, 171465.64it/s]

+

20%|██ | 1015635/4997817 [00:05<00:23, 172579.19it/s]

-
21%|██ | 1051050/4997817 [00:06&lt;00:22, 172244.70it/s]
+
21%|██ | 1032893/4997817 [00:06&lt;00:23, 171418.64it/s]

</pre>

-
21%|██ | 1051050/4997817 [00:06<00:22, 172244.70it/s]
+
21%|██ | 1032893/4997817 [00:06<00:23, 171418.64it/s]

end{sphinxVerbatim}

-

21%|██ | 1051050/4997817 [00:06<00:22, 172244.70it/s]

+

21%|██ | 1032893/4997817 [00:06<00:23, 171418.64it/s]

-
21%|██▏ | 1068535/4997817 [00:06&lt;00:22, 173015.00it/s]
+
21%|██ | 1050039/4997817 [00:06&lt;00:23, 171426.89it/s]

</pre>

-
21%|██▏ | 1068535/4997817 [00:06<00:22, 173015.00it/s]
+
21%|██ | 1050039/4997817 [00:06<00:23, 171426.89it/s]

end{sphinxVerbatim}

-

21%|██▏ | 1068535/4997817 [00:06<00:22, 173015.00it/s]

+

21%|██ | 1050039/4997817 [00:06<00:23, 171426.89it/s]

-
22%|██▏ | 1085860/4997817 [00:06&lt;00:22, 172171.64it/s]
+
21%|██▏ | 1067207/4997817 [00:06&lt;00:22, 171496.74it/s]

</pre>

-
22%|██▏ | 1085860/4997817 [00:06<00:22, 172171.64it/s]
+
21%|██▏ | 1067207/4997817 [00:06<00:22, 171496.74it/s]

end{sphinxVerbatim}

-

22%|██▏ | 1085860/4997817 [00:06<00:22, 172171.64it/s]

+

21%|██▏ | 1067207/4997817 [00:06<00:22, 171496.74it/s]

-
22%|██▏ | 1103362/4997817 [00:06&lt;00:22, 173017.40it/s]
+
22%|██▏ | 1084358/4997817 [00:06&lt;00:22, 171268.82it/s]

</pre>

-
22%|██▏ | 1103362/4997817 [00:06<00:22, 173017.40it/s]
+
22%|██▏ | 1084358/4997817 [00:06<00:22, 171268.82it/s]

end{sphinxVerbatim}

-

22%|██▏ | 1103362/4997817 [00:06<00:22, 173017.40it/s]

+

22%|██▏ | 1084358/4997817 [00:06<00:22, 171268.82it/s]

-
22%|██▏ | 1120915/4997817 [00:06&lt;00:22, 173763.05it/s]
+
22%|██▏ | 1101486/4997817 [00:06&lt;00:22, 171044.54it/s]

</pre>

-
22%|██▏ | 1120915/4997817 [00:06<00:22, 173763.05it/s]
+
22%|██▏ | 1101486/4997817 [00:06<00:22, 171044.54it/s]

end{sphinxVerbatim}

-

22%|██▏ | 1120915/4997817 [00:06<00:22, 173763.05it/s]

+

22%|██▏ | 1101486/4997817 [00:06<00:22, 171044.54it/s]

-
23%|██▎ | 1138318/4997817 [00:06&lt;00:22, 173840.60it/s]
+
22%|██▏ | 1118591/4997817 [00:06&lt;00:22, 170922.72it/s]

</pre>

-
23%|██▎ | 1138318/4997817 [00:06<00:22, 173840.60it/s]
+
22%|██▏ | 1118591/4997817 [00:06<00:22, 170922.72it/s]

end{sphinxVerbatim}

-

23%|██▎ | 1138318/4997817 [00:06<00:22, 173840.60it/s]

+

22%|██▏ | 1118591/4997817 [00:06<00:22, 170922.72it/s]

-
23%|██▎ | 1155709/4997817 [00:06&lt;00:22, 173789.28it/s]
+
23%|██▎ | 1135684/4997817 [00:06&lt;00:22, 170420.58it/s]

</pre>

-
23%|██▎ | 1155709/4997817 [00:06<00:22, 173789.28it/s]
+
23%|██▎ | 1135684/4997817 [00:06<00:22, 170420.58it/s]

end{sphinxVerbatim}

-

23%|██▎ | 1155709/4997817 [00:06<00:22, 173789.28it/s]

+

23%|██▎ | 1135684/4997817 [00:06<00:22, 170420.58it/s]

-
23%|██▎ | 1173093/4997817 [00:06&lt;00:22, 168493.74it/s]
+
23%|██▎ | 1152727/4997817 [00:06&lt;00:22, 170051.09it/s]

</pre>

-
23%|██▎ | 1173093/4997817 [00:06<00:22, 168493.74it/s]
+
23%|██▎ | 1152727/4997817 [00:06<00:22, 170051.09it/s]

end{sphinxVerbatim}

-

23%|██▎ | 1173093/4997817 [00:06<00:22, 168493.74it/s]

+

23%|██▎ | 1152727/4997817 [00:06<00:22, 170051.09it/s]

-
24%|██▍ | 1190527/4997817 [00:06&lt;00:22, 170204.85it/s]
+
23%|██▎ | 1169733/4997817 [00:06&lt;00:22, 169752.62it/s]

</pre>

-
24%|██▍ | 1190527/4997817 [00:06<00:22, 170204.85it/s]
+
23%|██▎ | 1169733/4997817 [00:06<00:22, 169752.62it/s]

end{sphinxVerbatim}

-

24%|██▍ | 1190527/4997817 [00:06<00:22, 170204.85it/s]

+

23%|██▎ | 1169733/4997817 [00:06<00:22, 169752.62it/s]

-
24%|██▍ | 1207870/4997817 [00:06&lt;00:22, 171154.12it/s]
+
24%|██▎ | 1186709/4997817 [00:06&lt;00:22, 169681.27it/s]

</pre>

-
24%|██▍ | 1207870/4997817 [00:06<00:22, 171154.12it/s]
+
24%|██▎ | 1186709/4997817 [00:06<00:22, 169681.27it/s]

end{sphinxVerbatim}

-

24%|██▍ | 1207870/4997817 [00:06<00:22, 171154.12it/s]

+

24%|██▎ | 1186709/4997817 [00:06<00:22, 169681.27it/s]

-
25%|██▍ | 1225428/4997817 [00:07&lt;00:21, 172462.89it/s]
+
24%|██▍ | 1203678/4997817 [00:07&lt;00:22, 168904.33it/s]

</pre>

-
25%|██▍ | 1225428/4997817 [00:07<00:21, 172462.89it/s]
+
24%|██▍ | 1203678/4997817 [00:07<00:22, 168904.33it/s]

end{sphinxVerbatim}

-

25%|██▍ | 1225428/4997817 [00:07<00:21, 172462.89it/s]

+

24%|██▍ | 1203678/4997817 [00:07<00:22, 168904.33it/s]

-
25%|██▍ | 1242924/4997817 [00:07&lt;00:21, 173202.16it/s]
+
24%|██▍ | 1220652/4997817 [00:07&lt;00:22, 169150.94it/s]

</pre>

-
25%|██▍ | 1242924/4997817 [00:07<00:21, 173202.16it/s]
+
24%|██▍ | 1220652/4997817 [00:07<00:22, 169150.94it/s]

end{sphinxVerbatim}

-

25%|██▍ | 1242924/4997817 [00:07<00:21, 173202.16it/s]

+

24%|██▍ | 1220652/4997817 [00:07<00:22, 169150.94it/s]

-
25%|██▌ | 1260258/4997817 [00:07&lt;00:21, 173197.75it/s]
+
25%|██▍ | 1237634/4997817 [00:07&lt;00:22, 169345.74it/s]

</pre>

-
25%|██▌ | 1260258/4997817 [00:07<00:21, 173197.75it/s]
+
25%|██▍ | 1237634/4997817 [00:07<00:22, 169345.74it/s]

end{sphinxVerbatim}

-

25%|██▌ | 1260258/4997817 [00:07<00:21, 173197.75it/s]

+

25%|██▍ | 1237634/4997817 [00:07<00:22, 169345.74it/s]

-
26%|██▌ | 1277885/4997817 [00:07&lt;00:21, 174113.24it/s]
+
25%|██▌ | 1254570/4997817 [00:07&lt;00:22, 168349.15it/s]

</pre>

-
26%|██▌ | 1277885/4997817 [00:07<00:21, 174113.24it/s]
+
25%|██▌ | 1254570/4997817 [00:07<00:22, 168349.15it/s]

end{sphinxVerbatim}

-

26%|██▌ | 1277885/4997817 [00:07<00:21, 174113.24it/s]

+

25%|██▌ | 1254570/4997817 [00:07<00:22, 168349.15it/s]

-
26%|██▌ | 1295506/4997817 [00:07&lt;00:21, 174737.72it/s]
+
25%|██▌ | 1271732/4997817 [00:07&lt;00:22, 169320.67it/s]

</pre>

-
26%|██▌ | 1295506/4997817 [00:07<00:21, 174737.72it/s]
+
25%|██▌ | 1271732/4997817 [00:07<00:22, 169320.67it/s]

end{sphinxVerbatim}

-

26%|██▌ | 1295506/4997817 [00:07<00:21, 174737.72it/s]

+

25%|██▌ | 1271732/4997817 [00:07<00:22, 169320.67it/s]

-
26%|██▋ | 1313036/4997817 [00:07&lt;00:21, 174904.98it/s]
+
26%|██▌ | 1288750/4997817 [00:07&lt;00:21, 169571.98it/s]

</pre>

-
26%|██▋ | 1313036/4997817 [00:07<00:21, 174904.98it/s]
+
26%|██▌ | 1288750/4997817 [00:07<00:21, 169571.98it/s]

end{sphinxVerbatim}

-

26%|██▋ | 1313036/4997817 [00:07<00:21, 174904.98it/s]

+

26%|██▌ | 1288750/4997817 [00:07<00:21, 169571.98it/s]

-
27%|██▋ | 1330678/4997817 [00:07&lt;00:20, 175355.99it/s]
+
26%|██▌ | 1305766/4997817 [00:07&lt;00:21, 169742.82it/s]

</pre>

-
27%|██▋ | 1330678/4997817 [00:07<00:20, 175355.99it/s]
+
26%|██▌ | 1305766/4997817 [00:07<00:21, 169742.82it/s]

end{sphinxVerbatim}

-

27%|██▋ | 1330678/4997817 [00:07<00:20, 175355.99it/s]

+

26%|██▌ | 1305766/4997817 [00:07<00:21, 169742.82it/s]

-
27%|██▋ | 1348217/4997817 [00:07&lt;00:21, 168529.45it/s]
+
26%|██▋ | 1322775/4997817 [00:07&lt;00:21, 169844.98it/s]

</pre>

-
27%|██▋ | 1348217/4997817 [00:07<00:21, 168529.45it/s]
+
26%|██▋ | 1322775/4997817 [00:07<00:21, 169844.98it/s]

end{sphinxVerbatim}

-

27%|██▋ | 1348217/4997817 [00:07<00:21, 168529.45it/s]

+

26%|██▋ | 1322775/4997817 [00:07<00:21, 169844.98it/s]

-
27%|██▋ | 1365827/4997817 [00:07&lt;00:21, 170733.60it/s]
+
27%|██▋ | 1339793/4997817 [00:07&lt;00:21, 169941.26it/s]

</pre>

-
27%|██▋ | 1365827/4997817 [00:07<00:21, 170733.60it/s]
+
27%|██▋ | 1339793/4997817 [00:07<00:21, 169941.26it/s]

end{sphinxVerbatim}

-

27%|██▋ | 1365827/4997817 [00:07<00:21, 170733.60it/s]

+

27%|██▋ | 1339793/4997817 [00:07<00:21, 169941.26it/s]

-
28%|██▊ | 1383392/4997817 [00:07&lt;00:20, 172176.28it/s]
+
27%|██▋ | 1356788/4997817 [00:07&lt;00:21, 169935.91it/s]

</pre>

-
28%|██▊ | 1383392/4997817 [00:07<00:20, 172176.28it/s]
+
27%|██▋ | 1356788/4997817 [00:07<00:21, 169935.91it/s]

end{sphinxVerbatim}

-

28%|██▊ | 1383392/4997817 [00:07<00:20, 172176.28it/s]

+

27%|██▋ | 1356788/4997817 [00:07<00:21, 169935.91it/s]

-
28%|██▊ | 1401070/4997817 [00:08&lt;00:20, 173534.17it/s]
+
27%|██▋ | 1373885/4997817 [00:08&lt;00:21, 170242.78it/s]

</pre>

-
28%|██▊ | 1401070/4997817 [00:08<00:20, 173534.17it/s]
+
27%|██▋ | 1373885/4997817 [00:08<00:21, 170242.78it/s]

end{sphinxVerbatim}

-

28%|██▊ | 1401070/4997817 [00:08<00:20, 173534.17it/s]

+

27%|██▋ | 1373885/4997817 [00:08<00:21, 170242.78it/s]

-
28%|██▊ | 1418730/4997817 [00:08&lt;00:20, 174443.73it/s]
+
28%|██▊ | 1390910/4997817 [00:08&lt;00:21, 169776.77it/s]

</pre>

-
28%|██▊ | 1418730/4997817 [00:08<00:20, 174443.73it/s]
+
28%|██▊ | 1390910/4997817 [00:08<00:21, 169776.77it/s]

end{sphinxVerbatim}

-

28%|██▊ | 1418730/4997817 [00:08<00:20, 174443.73it/s]

+

28%|██▊ | 1390910/4997817 [00:08<00:21, 169776.77it/s]

-
29%|██▊ | 1436236/4997817 [00:08&lt;00:20, 174624.39it/s]
+
28%|██▊ | 1407889/4997817 [00:08&lt;00:21, 169776.41it/s]

</pre>

-
29%|██▊ | 1436236/4997817 [00:08<00:20, 174624.39it/s]
+
28%|██▊ | 1407889/4997817 [00:08<00:21, 169776.41it/s]

end{sphinxVerbatim}

-

29%|██▊ | 1436236/4997817 [00:08<00:20, 174624.39it/s]

+

28%|██▊ | 1407889/4997817 [00:08<00:21, 169776.41it/s]

-
29%|██▉ | 1453753/4997817 [00:08&lt;00:20, 174785.08it/s]
+
29%|██▊ | 1424867/4997817 [00:08&lt;00:21, 169328.10it/s]

</pre>

-
29%|██▉ | 1453753/4997817 [00:08<00:20, 174785.08it/s]
+
29%|██▊ | 1424867/4997817 [00:08<00:21, 169328.10it/s]

end{sphinxVerbatim}

-

29%|██▉ | 1453753/4997817 [00:08<00:20, 174785.08it/s]

+

29%|██▊ | 1424867/4997817 [00:08<00:21, 169328.10it/s]

-
29%|██▉ | 1471304/4997817 [00:08&lt;00:20, 175000.98it/s]
+
29%|██▉ | 1441920/4997817 [00:08&lt;00:20, 169683.76it/s]

</pre>

-
29%|██▉ | 1471304/4997817 [00:08<00:20, 175000.98it/s]
+
29%|██▉ | 1441920/4997817 [00:08<00:20, 169683.76it/s]

end{sphinxVerbatim}

-

29%|██▉ | 1471304/4997817 [00:08<00:20, 175000.98it/s]

+

29%|██▉ | 1441920/4997817 [00:08<00:20, 169683.76it/s]

-
30%|██▉ | 1488878/4997817 [00:08&lt;00:20, 175219.95it/s]
+
29%|██▉ | 1458889/4997817 [00:08&lt;00:20, 169554.49it/s]

</pre>

-
30%|██▉ | 1488878/4997817 [00:08<00:20, 175219.95it/s]
+
29%|██▉ | 1458889/4997817 [00:08<00:20, 169554.49it/s]

end{sphinxVerbatim}

-

30%|██▉ | 1488878/4997817 [00:08<00:20, 175219.95it/s]

+

29%|██▉ | 1458889/4997817 [00:08<00:20, 169554.49it/s]

-
30%|███ | 1506507/4997817 [00:08&lt;00:19, 175538.97it/s]
+
30%|██▉ | 1475845/4997817 [00:08&lt;00:20, 169207.98it/s]

</pre>

-
30%|███ | 1506507/4997817 [00:08<00:19, 175538.97it/s]
+
30%|██▉ | 1475845/4997817 [00:08<00:20, 169207.98it/s]

end{sphinxVerbatim}

-

30%|███ | 1506507/4997817 [00:08<00:19, 175538.97it/s]

+

30%|██▉ | 1475845/4997817 [00:08<00:20, 169207.98it/s]

-
30%|███ | 1524065/4997817 [00:08&lt;00:19, 175328.61it/s]
+
30%|██▉ | 1492767/4997817 [00:08&lt;00:20, 169207.52it/s]

</pre>

-
30%|███ | 1524065/4997817 [00:08<00:19, 175328.61it/s]
+
30%|██▉ | 1492767/4997817 [00:08<00:20, 169207.52it/s]

end{sphinxVerbatim}

-

30%|███ | 1524065/4997817 [00:08<00:19, 175328.61it/s]

+

30%|██▉ | 1492767/4997817 [00:08<00:20, 169207.52it/s]

-
31%|███ | 1541696/4997817 [00:08&lt;00:19, 175616.93it/s]
+
30%|███ | 1509688/4997817 [00:08&lt;00:20, 169069.76it/s]

</pre>

-
31%|███ | 1541696/4997817 [00:08<00:19, 175616.93it/s]
+
30%|███ | 1509688/4997817 [00:08<00:20, 169069.76it/s]

end{sphinxVerbatim}

-

31%|███ | 1541696/4997817 [00:08<00:19, 175616.93it/s]

+

30%|███ | 1509688/4997817 [00:08<00:20, 169069.76it/s]

-
31%|███ | 1559309/4997817 [00:08&lt;00:19, 175767.69it/s]
+
31%|███ | 1526596/4997817 [00:08&lt;00:20, 168995.55it/s]

</pre>

-
31%|███ | 1559309/4997817 [00:08<00:19, 175767.69it/s]
+
31%|███ | 1526596/4997817 [00:08<00:20, 168995.55it/s]

end{sphinxVerbatim}

-

31%|███ | 1559309/4997817 [00:08<00:19, 175767.69it/s]

+

31%|███ | 1526596/4997817 [00:08<00:20, 168995.55it/s]

-
32%|███▏ | 1576933/4997817 [00:09&lt;00:19, 175904.41it/s]
+
31%|███ | 1543496/4997817 [00:09&lt;00:20, 168767.01it/s]

</pre>

-
32%|███▏ | 1576933/4997817 [00:09<00:19, 175904.41it/s]
+
31%|███ | 1543496/4997817 [00:09<00:20, 168767.01it/s]

end{sphinxVerbatim}

-

32%|███▏ | 1576933/4997817 [00:09<00:19, 175904.41it/s]

+

31%|███ | 1543496/4997817 [00:09<00:20, 168767.01it/s]

-
32%|███▏ | 1594572/4997817 [00:09&lt;00:19, 176045.26it/s]
+
31%|███ | 1560373/4997817 [00:09&lt;00:20, 168411.56it/s]

</pre>

-
32%|███▏ | 1594572/4997817 [00:09<00:19, 176045.26it/s]
+
31%|███ | 1560373/4997817 [00:09<00:20, 168411.56it/s]

end{sphinxVerbatim}

-

32%|███▏ | 1594572/4997817 [00:09<00:19, 176045.26it/s]

+

31%|███ | 1560373/4997817 [00:09<00:20, 168411.56it/s]

-
32%|███▏ | 1612238/4997817 [00:09&lt;00:19, 176226.37it/s]
+
32%|███▏ | 1577215/4997817 [00:09&lt;00:20, 168093.60it/s]

</pre>

-
32%|███▏ | 1612238/4997817 [00:09<00:19, 176226.37it/s]
+
32%|███▏ | 1577215/4997817 [00:09<00:20, 168093.60it/s]

end{sphinxVerbatim}

-

32%|███▏ | 1612238/4997817 [00:09<00:19, 176226.37it/s]

+

32%|███▏ | 1577215/4997817 [00:09<00:20, 168093.60it/s]

-
33%|███▎ | 1629862/4997817 [00:09&lt;00:19, 176141.34it/s]
+
32%|███▏ | 1594025/4997817 [00:09&lt;00:20, 167736.99it/s]

</pre>

-
33%|███▎ | 1629862/4997817 [00:09<00:19, 176141.34it/s]
+
32%|███▏ | 1594025/4997817 [00:09<00:20, 167736.99it/s]

end{sphinxVerbatim}

-

33%|███▎ | 1629862/4997817 [00:09<00:19, 176141.34it/s]

+

32%|███▏ | 1594025/4997817 [00:09<00:20, 167736.99it/s]

-
33%|███▎ | 1647477/4997817 [00:09&lt;00:19, 175468.17it/s]
+
32%|███▏ | 1610799/4997817 [00:09&lt;00:20, 167579.26it/s]

</pre>

-
33%|███▎ | 1647477/4997817 [00:09<00:19, 175468.17it/s]
+
32%|███▏ | 1610799/4997817 [00:09<00:20, 167579.26it/s]

end{sphinxVerbatim}

-

33%|███▎ | 1647477/4997817 [00:09<00:19, 175468.17it/s]

+

32%|███▏ | 1610799/4997817 [00:09<00:20, 167579.26it/s]

-
33%|███▎ | 1665025/4997817 [00:09&lt;00:19, 174739.75it/s]
+
33%|███▎ | 1627581/4997817 [00:09&lt;00:20, 167648.89it/s]

</pre>

-
33%|███▎ | 1665025/4997817 [00:09<00:19, 174739.75it/s]
+
33%|███▎ | 1627581/4997817 [00:09<00:20, 167648.89it/s]

end{sphinxVerbatim}

-

33%|███▎ | 1665025/4997817 [00:09<00:19, 174739.75it/s]

+

33%|███▎ | 1627581/4997817 [00:09<00:20, 167648.89it/s]

-
34%|███▎ | 1682608/4997817 [00:09&lt;00:18, 175060.85it/s]
+
33%|███▎ | 1644346/4997817 [00:09&lt;00:20, 167601.44it/s]

</pre>

-
34%|███▎ | 1682608/4997817 [00:09<00:18, 175060.85it/s]
+
33%|███▎ | 1644346/4997817 [00:09<00:20, 167601.44it/s]

end{sphinxVerbatim}

-

34%|███▎ | 1682608/4997817 [00:09<00:18, 175060.85it/s]

+

33%|███▎ | 1644346/4997817 [00:09<00:20, 167601.44it/s]

-
34%|███▍ | 1700172/4997817 [00:09&lt;00:18, 175230.78it/s]
+
33%|███▎ | 1661107/4997817 [00:09&lt;00:19, 167408.64it/s]

</pre>

-
34%|███▍ | 1700172/4997817 [00:09<00:18, 175230.78it/s]
+
33%|███▎ | 1661107/4997817 [00:09<00:19, 167408.64it/s]

end{sphinxVerbatim}

-

34%|███▍ | 1700172/4997817 [00:09<00:18, 175230.78it/s]

+

33%|███▎ | 1661107/4997817 [00:09<00:19, 167408.64it/s]

-
34%|███▍ | 1717696/4997817 [00:09&lt;00:18, 172854.20it/s]
+
34%|███▎ | 1678158/4997817 [00:09&lt;00:19, 168332.79it/s]

</pre>

-
34%|███▍ | 1717696/4997817 [00:09<00:18, 172854.20it/s]
+
34%|███▎ | 1678158/4997817 [00:09<00:19, 168332.79it/s]

end{sphinxVerbatim}

-

34%|███▍ | 1717696/4997817 [00:09<00:18, 172854.20it/s]

+

34%|███▎ | 1678158/4997817 [00:09<00:19, 168332.79it/s]

-
35%|███▍ | 1735206/4997817 [00:09&lt;00:18, 173517.80it/s]
+
34%|███▍ | 1695148/4997817 [00:09&lt;00:19, 168799.16it/s]

</pre>

-
35%|███▍ | 1735206/4997817 [00:09<00:18, 173517.80it/s]
+
34%|███▍ | 1695148/4997817 [00:09<00:19, 168799.16it/s]

end{sphinxVerbatim}

-

35%|███▍ | 1735206/4997817 [00:09<00:18, 173517.80it/s]

+

34%|███▍ | 1695148/4997817 [00:09<00:19, 168799.16it/s]

-
35%|███▌ | 1752699/4997817 [00:10&lt;00:18, 173933.20it/s]
+
34%|███▍ | 1712162/4997817 [00:10&lt;00:19, 169197.65it/s]

</pre>

-
35%|███▌ | 1752699/4997817 [00:10<00:18, 173933.20it/s]
+
34%|███▍ | 1712162/4997817 [00:10<00:19, 169197.65it/s]

end{sphinxVerbatim}

-

35%|███▌ | 1752699/4997817 [00:10<00:18, 173933.20it/s]

+

34%|███▍ | 1712162/4997817 [00:10<00:19, 169197.65it/s]

-
35%|███▌ | 1770288/4997817 [00:10&lt;00:18, 174514.40it/s]
+
35%|███▍ | 1729082/4997817 [00:10&lt;00:19, 169086.37it/s]

</pre>

-
35%|███▌ | 1770288/4997817 [00:10<00:18, 174514.40it/s]
+
35%|███▍ | 1729082/4997817 [00:10<00:19, 169086.37it/s]

end{sphinxVerbatim}

-

35%|███▌ | 1770288/4997817 [00:10<00:18, 174514.40it/s]

+

35%|███▍ | 1729082/4997817 [00:10<00:19, 169086.37it/s]

-
36%|███▌ | 1788042/4997817 [00:10&lt;00:18, 175415.06it/s]
+
35%|███▍ | 1746091/4997817 [00:10&lt;00:19, 169383.58it/s]

</pre>

-
36%|███▌ | 1788042/4997817 [00:10<00:18, 175415.06it/s]
+
35%|███▍ | 1746091/4997817 [00:10<00:19, 169383.58it/s]

end{sphinxVerbatim}

-

36%|███▌ | 1788042/4997817 [00:10<00:18, 175415.06it/s]

+

35%|███▍ | 1746091/4997817 [00:10<00:19, 169383.58it/s]

-
36%|███▌ | 1805801/4997817 [00:10&lt;00:18, 176062.64it/s]
+
35%|███▌ | 1763030/4997817 [00:10&lt;00:19, 169294.56it/s]

</pre>

-
36%|███▌ | 1805801/4997817 [00:10<00:18, 176062.64it/s]
+
35%|███▌ | 1763030/4997817 [00:10<00:19, 169294.56it/s]

end{sphinxVerbatim}

-

36%|███▌ | 1805801/4997817 [00:10<00:18, 176062.64it/s]

+

35%|███▌ | 1763030/4997817 [00:10<00:19, 169294.56it/s]

-
36%|███▋ | 1823541/4997817 [00:10&lt;00:17, 176461.35it/s]
+
36%|███▌ | 1779986/4997817 [00:10&lt;00:18, 169370.78it/s]

</pre>

-
36%|███▋ | 1823541/4997817 [00:10<00:17, 176461.35it/s]
+
36%|███▌ | 1779986/4997817 [00:10<00:18, 169370.78it/s]

end{sphinxVerbatim}

-

36%|███▋ | 1823541/4997817 [00:10<00:17, 176461.35it/s]

+

36%|███▌ | 1779986/4997817 [00:10<00:18, 169370.78it/s]

-
37%|███▋ | 1841194/4997817 [00:10&lt;00:17, 176479.57it/s]
+
36%|███▌ | 1796940/4997817 [00:10&lt;00:18, 169415.99it/s]

</pre>

-
37%|███▋ | 1841194/4997817 [00:10<00:17, 176479.57it/s]
+
36%|███▌ | 1796940/4997817 [00:10<00:18, 169415.99it/s]

end{sphinxVerbatim}

-

37%|███▋ | 1841194/4997817 [00:10<00:17, 176479.57it/s]

+

36%|███▌ | 1796940/4997817 [00:10<00:18, 169415.99it/s]

-
37%|███▋ | 1858933/4997817 [00:10&lt;00:17, 176749.46it/s]
+
36%|███▋ | 1813941/4997817 [00:10&lt;00:18, 169588.95it/s]

</pre>

-
37%|███▋ | 1858933/4997817 [00:10<00:17, 176749.46it/s]
+
36%|███▋ | 1813941/4997817 [00:10<00:18, 169588.95it/s]

end{sphinxVerbatim}

-

37%|███▋ | 1858933/4997817 [00:10<00:17, 176749.46it/s]

+

36%|███▋ | 1813941/4997817 [00:10<00:18, 169588.95it/s]

-
38%|███▊ | 1876670/4997817 [00:10&lt;00:17, 176931.87it/s]
+
37%|███▋ | 1830917/4997817 [00:10&lt;00:18, 169635.93it/s]

</pre>

-
38%|███▊ | 1876670/4997817 [00:10<00:17, 176931.87it/s]
+
37%|███▋ | 1830917/4997817 [00:10<00:18, 169635.93it/s]

end{sphinxVerbatim}

-

38%|███▊ | 1876670/4997817 [00:10<00:17, 176931.87it/s]

+

37%|███▋ | 1830917/4997817 [00:10<00:18, 169635.93it/s]

-
38%|███▊ | 1894364/4997817 [00:10&lt;00:17, 173999.82it/s]
+
37%|███▋ | 1847932/4997817 [00:10&lt;00:18, 169786.39it/s]

</pre>

-
38%|███▊ | 1894364/4997817 [00:10<00:17, 173999.82it/s]
+
37%|███▋ | 1847932/4997817 [00:10<00:18, 169786.39it/s]

end{sphinxVerbatim}

-

38%|███▊ | 1894364/4997817 [00:10<00:17, 173999.82it/s]

+

37%|███▋ | 1847932/4997817 [00:10<00:18, 169786.39it/s]

-
38%|███▊ | 1911815/4997817 [00:10&lt;00:17, 174148.01it/s]
+
37%|███▋ | 1864911/4997817 [00:10&lt;00:18, 169662.56it/s]

</pre>

-
38%|███▊ | 1911815/4997817 [00:10<00:17, 174148.01it/s]
+
37%|███▋ | 1864911/4997817 [00:10<00:18, 169662.56it/s]

end{sphinxVerbatim}

-

38%|███▊ | 1911815/4997817 [00:10<00:17, 174148.01it/s]

+

37%|███▋ | 1864911/4997817 [00:10<00:18, 169662.56it/s]

-
39%|███▊ | 1929373/4997817 [00:11&lt;00:17, 174571.30it/s]
+
38%|███▊ | 1881878/4997817 [00:11&lt;00:18, 169612.46it/s]

</pre>

-
39%|███▊ | 1929373/4997817 [00:11<00:17, 174571.30it/s]
+
38%|███▊ | 1881878/4997817 [00:11<00:18, 169612.46it/s]

end{sphinxVerbatim}

-

39%|███▊ | 1929373/4997817 [00:11<00:17, 174571.30it/s]

+

38%|███▊ | 1881878/4997817 [00:11<00:18, 169612.46it/s]

-
39%|███▉ | 1946838/4997817 [00:11&lt;00:17, 174592.83it/s]
+
38%|███▊ | 1898840/4997817 [00:11&lt;00:18, 169466.51it/s]

</pre>

-
39%|███▉ | 1946838/4997817 [00:11<00:17, 174592.83it/s]
+
38%|███▊ | 1898840/4997817 [00:11<00:18, 169466.51it/s]

end{sphinxVerbatim}

-

39%|███▉ | 1946838/4997817 [00:11<00:17, 174592.83it/s]

+

38%|███▊ | 1898840/4997817 [00:11<00:18, 169466.51it/s]

-
39%|███▉ | 1964377/4997817 [00:11&lt;00:17, 174828.30it/s]
+
38%|███▊ | 1915967/4997817 [00:11&lt;00:18, 170001.55it/s]

</pre>

-
39%|███▉ | 1964377/4997817 [00:11<00:17, 174828.30it/s]
+
38%|███▊ | 1915967/4997817 [00:11<00:18, 170001.55it/s]

end{sphinxVerbatim}

-

39%|███▉ | 1964377/4997817 [00:11<00:17, 174828.30it/s]

+

38%|███▊ | 1915967/4997817 [00:11<00:18, 170001.55it/s]

-
40%|███▉ | 1981863/4997817 [00:11&lt;00:17, 174660.08it/s]
+
39%|███▊ | 1932968/4997817 [00:11&lt;00:18, 169939.64it/s]

</pre>

-
40%|███▉ | 1981863/4997817 [00:11<00:17, 174660.08it/s]
+
39%|███▊ | 1932968/4997817 [00:11<00:18, 169939.64it/s]

end{sphinxVerbatim}

-

40%|███▉ | 1981863/4997817 [00:11<00:17, 174660.08it/s]

+

39%|███▊ | 1932968/4997817 [00:11<00:18, 169939.64it/s]

-
40%|████ | 1999332/4997817 [00:11&lt;00:17, 174199.29it/s]
+
39%|███▉ | 1949963/4997817 [00:11&lt;00:17, 169565.43it/s]

</pre>

-
40%|████ | 1999332/4997817 [00:11<00:17, 174199.29it/s]
+
39%|███▉ | 1949963/4997817 [00:11<00:17, 169565.43it/s]

end{sphinxVerbatim}

-

40%|████ | 1999332/4997817 [00:11<00:17, 174199.29it/s]

+

39%|███▉ | 1949963/4997817 [00:11<00:17, 169565.43it/s]

-
40%|████ | 2016768/4997817 [00:11&lt;00:17, 174244.63it/s]
+
39%|███▉ | 1966920/4997817 [00:11&lt;00:17, 168922.06it/s]

</pre>

-
40%|████ | 2016768/4997817 [00:11<00:17, 174244.63it/s]
+
39%|███▉ | 1966920/4997817 [00:11<00:17, 168922.06it/s]

end{sphinxVerbatim}

-

40%|████ | 2016768/4997817 [00:11<00:17, 174244.63it/s]

+

39%|███▉ | 1966920/4997817 [00:11<00:17, 168922.06it/s]

-
41%|████ | 2034194/4997817 [00:11&lt;00:17, 174179.70it/s]
+
40%|███▉ | 1983850/4997817 [00:11&lt;00:17, 169030.80it/s]

</pre>

-
41%|████ | 2034194/4997817 [00:11<00:17, 174179.70it/s]
+
40%|███▉ | 1983850/4997817 [00:11<00:17, 169030.80it/s]

end{sphinxVerbatim}

-

41%|████ | 2034194/4997817 [00:11<00:17, 174179.70it/s]

+

40%|███▉ | 1983850/4997817 [00:11<00:17, 169030.80it/s]

-
41%|████ | 2051629/4997817 [00:11&lt;00:16, 174227.12it/s]
+
40%|████ | 2000770/4997817 [00:11&lt;00:17, 169076.88it/s]

</pre>

-
41%|████ | 2051629/4997817 [00:11<00:16, 174227.12it/s]
+
40%|████ | 2000770/4997817 [00:11<00:17, 169076.88it/s]

end{sphinxVerbatim}

-

41%|████ | 2051629/4997817 [00:11<00:16, 174227.12it/s]

+

40%|████ | 2000770/4997817 [00:11<00:17, 169076.88it/s]

-
41%|████▏ | 2069053/4997817 [00:11&lt;00:17, 166828.01it/s]
+
40%|████ | 2017733/4997817 [00:11&lt;00:17, 169238.17it/s]

</pre>

-
41%|████▏ | 2069053/4997817 [00:11<00:17, 166828.01it/s]
+
40%|████ | 2017733/4997817 [00:11<00:17, 169238.17it/s]

end{sphinxVerbatim}

-

41%|████▏ | 2069053/4997817 [00:11<00:17, 166828.01it/s]

+

40%|████ | 2017733/4997817 [00:11<00:17, 169238.17it/s]

-
42%|████▏ | 2086587/4997817 [00:12&lt;00:17, 169299.81it/s]
+
41%|████ | 2034658/4997817 [00:11&lt;00:17, 168730.09it/s]

</pre>

-
42%|████▏ | 2086587/4997817 [00:12<00:17, 169299.81it/s]
+
41%|████ | 2034658/4997817 [00:11<00:17, 168730.09it/s]

end{sphinxVerbatim}

-

42%|████▏ | 2086587/4997817 [00:12<00:17, 169299.81it/s]

+

41%|████ | 2034658/4997817 [00:11<00:17, 168730.09it/s]

-
42%|████▏ | 2104063/4997817 [00:12&lt;00:16, 170898.22it/s]
+
41%|████ | 2051532/4997817 [00:12&lt;00:17, 168386.60it/s]

</pre>

-
42%|████▏ | 2104063/4997817 [00:12<00:16, 170898.22it/s]
+
41%|████ | 2051532/4997817 [00:12<00:17, 168386.60it/s]

end{sphinxVerbatim}

-

42%|████▏ | 2104063/4997817 [00:12<00:16, 170898.22it/s]

+

41%|████ | 2051532/4997817 [00:12<00:17, 168386.60it/s]

-
42%|████▏ | 2121442/4997817 [00:12&lt;00:16, 171751.23it/s]
+
41%|████▏ | 2068372/4997817 [00:12&lt;00:17, 168018.51it/s]

</pre>

-
42%|████▏ | 2121442/4997817 [00:12<00:16, 171751.23it/s]
+
41%|████▏ | 2068372/4997817 [00:12<00:17, 168018.51it/s]

end{sphinxVerbatim}

-

42%|████▏ | 2121442/4997817 [00:12<00:16, 171751.23it/s]

+

41%|████▏ | 2068372/4997817 [00:12<00:17, 168018.51it/s]

-
43%|████▎ | 2138648/4997817 [00:12&lt;00:16, 171573.44it/s]
+
42%|████▏ | 2085260/4997817 [00:12&lt;00:17, 168270.79it/s]

</pre>

-
43%|████▎ | 2138648/4997817 [00:12<00:16, 171573.44it/s]
+
42%|████▏ | 2085260/4997817 [00:12<00:17, 168270.79it/s]

end{sphinxVerbatim}

-

43%|████▎ | 2138648/4997817 [00:12<00:16, 171573.44it/s]

+

42%|████▏ | 2085260/4997817 [00:12<00:17, 168270.79it/s]

-
43%|████▎ | 2156067/4997817 [00:12&lt;00:16, 172348.88it/s]
+
42%|████▏ | 2102088/4997817 [00:12&lt;00:17, 168030.44it/s]

</pre>

-
43%|████▎ | 2156067/4997817 [00:12<00:16, 172348.88it/s]
+
42%|████▏ | 2102088/4997817 [00:12<00:17, 168030.44it/s]

end{sphinxVerbatim}

-

43%|████▎ | 2156067/4997817 [00:12<00:16, 172348.88it/s]

+

42%|████▏ | 2102088/4997817 [00:12<00:17, 168030.44it/s]

-
43%|████▎ | 2173478/4997817 [00:12&lt;00:16, 172872.90it/s]
+
42%|████▏ | 2118962/4997817 [00:12&lt;00:17, 168240.31it/s]

</pre>

-
43%|████▎ | 2173478/4997817 [00:12<00:16, 172872.90it/s]
+
42%|████▏ | 2118962/4997817 [00:12<00:17, 168240.31it/s]

end{sphinxVerbatim}

-

43%|████▎ | 2173478/4997817 [00:12<00:16, 172872.90it/s]

+

42%|████▏ | 2118962/4997817 [00:12<00:17, 168240.31it/s]

-
44%|████▍ | 2190870/4997817 [00:12&lt;00:16, 173182.02it/s]
+
43%|████▎ | 2135787/4997817 [00:12&lt;00:17, 168132.74it/s]

</pre>

-
44%|████▍ | 2190870/4997817 [00:12<00:16, 173182.02it/s]
+
43%|████▎ | 2135787/4997817 [00:12<00:17, 168132.74it/s]

end{sphinxVerbatim}

-

44%|████▍ | 2190870/4997817 [00:12<00:16, 173182.02it/s]

+

43%|████▎ | 2135787/4997817 [00:12<00:17, 168132.74it/s]

-
44%|████▍ | 2208197/4997817 [00:12&lt;00:16, 171459.04it/s]
+
43%|████▎ | 2152601/4997817 [00:12&lt;00:16, 167646.09it/s]

</pre>

-
44%|████▍ | 2208197/4997817 [00:12<00:16, 171459.04it/s]
+
43%|████▎ | 2152601/4997817 [00:12<00:16, 167646.09it/s]

end{sphinxVerbatim}

-

44%|████▍ | 2208197/4997817 [00:12<00:16, 171459.04it/s]

+

43%|████▎ | 2152601/4997817 [00:12<00:16, 167646.09it/s]

-
45%|████▍ | 2225613/4997817 [00:12&lt;00:16, 172258.98it/s]
+
43%|████▎ | 2169423/4997817 [00:12&lt;00:16, 167813.54it/s]

</pre>

-
45%|████▍ | 2225613/4997817 [00:12<00:16, 172258.98it/s]
+
43%|████▎ | 2169423/4997817 [00:12<00:16, 167813.54it/s]

end{sphinxVerbatim}

-

45%|████▍ | 2225613/4997817 [00:12<00:16, 172258.98it/s]

+

43%|████▎ | 2169423/4997817 [00:12<00:16, 167813.54it/s]

-
45%|████▍ | 2243049/4997817 [00:12&lt;00:15, 172882.18it/s]
+
44%|████▎ | 2186297/4997817 [00:12&lt;00:16, 168085.58it/s]

</pre>

-
45%|████▍ | 2243049/4997817 [00:12<00:15, 172882.18it/s]
+
44%|████▎ | 2186297/4997817 [00:12<00:16, 168085.58it/s]

end{sphinxVerbatim}

-

45%|████▍ | 2243049/4997817 [00:12<00:15, 172882.18it/s]

+

44%|████▎ | 2186297/4997817 [00:12<00:16, 168085.58it/s]

-
45%|████▌ | 2260546/4997817 [00:13&lt;00:15, 173502.26it/s]
+
44%|████▍ | 2203175/4997817 [00:12&lt;00:16, 168289.27it/s]

</pre>

-
45%|████▌ | 2260546/4997817 [00:13<00:15, 173502.26it/s]
+
44%|████▍ | 2203175/4997817 [00:12<00:16, 168289.27it/s]

end{sphinxVerbatim}

-

45%|████▌ | 2260546/4997817 [00:13<00:15, 173502.26it/s]

+

44%|████▍ | 2203175/4997817 [00:12<00:16, 168289.27it/s]

-
46%|████▌ | 2278218/4997817 [00:13&lt;00:15, 174462.04it/s]
+
44%|████▍ | 2220005/4997817 [00:13&lt;00:16, 168276.19it/s]

</pre>

-
46%|████▌ | 2278218/4997817 [00:13<00:15, 174462.04it/s]
+
44%|████▍ | 2220005/4997817 [00:13<00:16, 168276.19it/s]

end{sphinxVerbatim}

-

46%|████▌ | 2278218/4997817 [00:13<00:15, 174462.04it/s]

+

44%|████▍ | 2220005/4997817 [00:13<00:16, 168276.19it/s]

-
46%|████▌ | 2295807/4997817 [00:13&lt;00:15, 174885.89it/s]
+
45%|████▍ | 2236833/4997817 [00:13&lt;00:16, 168121.20it/s]

</pre>

-
46%|████▌ | 2295807/4997817 [00:13<00:15, 174885.89it/s]
+
45%|████▍ | 2236833/4997817 [00:13<00:16, 168121.20it/s]

end{sphinxVerbatim}

-

46%|████▌ | 2295807/4997817 [00:13<00:15, 174885.89it/s]

+

45%|████▍ | 2236833/4997817 [00:13<00:16, 168121.20it/s]

-
46%|████▋ | 2313433/4997817 [00:13&lt;00:15, 175295.26it/s]
+
45%|████▌ | 2253646/4997817 [00:13&lt;00:16, 168054.37it/s]

</pre>

-
46%|████▋ | 2313433/4997817 [00:13<00:15, 175295.26it/s]
+
45%|████▌ | 2253646/4997817 [00:13<00:16, 168054.37it/s]

end{sphinxVerbatim}

-

46%|████▋ | 2313433/4997817 [00:13<00:15, 175295.26it/s]

+

45%|████▌ | 2253646/4997817 [00:13<00:16, 168054.37it/s]

-
47%|████▋ | 2331175/4997817 [00:13&lt;00:15, 175929.00it/s]
+
45%|████▌ | 2270452/4997817 [00:13&lt;00:16, 167426.00it/s]

</pre>

-
47%|████▋ | 2331175/4997817 [00:13<00:15, 175929.00it/s]
+
45%|████▌ | 2270452/4997817 [00:13<00:16, 167426.00it/s]

end{sphinxVerbatim}

-

47%|████▋ | 2331175/4997817 [00:13<00:15, 175929.00it/s]

+

45%|████▌ | 2270452/4997817 [00:13<00:16, 167426.00it/s]

-
47%|████▋ | 2349033/4997817 [00:13&lt;00:14, 176719.93it/s]
+
46%|████▌ | 2287196/4997817 [00:13&lt;00:16, 167340.06it/s]

</pre>

-
47%|████▋ | 2349033/4997817 [00:13<00:14, 176719.93it/s]
+
46%|████▌ | 2287196/4997817 [00:13<00:16, 167340.06it/s]

end{sphinxVerbatim}

-

47%|████▋ | 2349033/4997817 [00:13<00:14, 176719.93it/s]

+

46%|████▌ | 2287196/4997817 [00:13<00:16, 167340.06it/s]

-
47%|████▋ | 2366710/4997817 [00:13&lt;00:14, 176733.35it/s]
+
46%|████▌ | 2303931/4997817 [00:13&lt;00:16, 167280.92it/s]

</pre>

-
47%|████▋ | 2366710/4997817 [00:13<00:14, 176733.35it/s]
+
46%|████▌ | 2303931/4997817 [00:13<00:16, 167280.92it/s]

end{sphinxVerbatim}

-

47%|████▋ | 2366710/4997817 [00:13<00:14, 176733.35it/s]

+

46%|████▌ | 2303931/4997817 [00:13<00:16, 167280.92it/s]

-
48%|████▊ | 2384543/4997817 [00:13&lt;00:14, 177210.55it/s]
+
46%|████▋ | 2320696/4997817 [00:13&lt;00:15, 167387.15it/s]

</pre>

-
48%|████▊ | 2384543/4997817 [00:13<00:14, 177210.55it/s]
+
46%|████▋ | 2320696/4997817 [00:13<00:15, 167387.15it/s]

end{sphinxVerbatim}

-

48%|████▊ | 2384543/4997817 [00:13<00:14, 177210.55it/s]

+

46%|████▋ | 2320696/4997817 [00:13<00:15, 167387.15it/s]

-
48%|████▊ | 2402270/4997817 [00:13&lt;00:14, 177227.04it/s]
+
47%|████▋ | 2337572/4997817 [00:13&lt;00:15, 167794.33it/s]

</pre>

-
48%|████▊ | 2402270/4997817 [00:13<00:14, 177227.04it/s]
+
47%|████▋ | 2337572/4997817 [00:13<00:15, 167794.33it/s]

end{sphinxVerbatim}

-

48%|████▊ | 2402270/4997817 [00:13<00:14, 177227.04it/s]

+

47%|████▋ | 2337572/4997817 [00:13<00:15, 167794.33it/s]

-
48%|████▊ | 2420118/4997817 [00:13&lt;00:14, 177598.07it/s]
+
47%|████▋ | 2354510/4997817 [00:13&lt;00:15, 168265.82it/s]

</pre>

-
48%|████▊ | 2420118/4997817 [00:13<00:14, 177598.07it/s]
+
47%|████▋ | 2354510/4997817 [00:13<00:15, 168265.82it/s]

end{sphinxVerbatim}

-

48%|████▊ | 2420118/4997817 [00:13<00:14, 177598.07it/s]

+

47%|████▋ | 2354510/4997817 [00:13<00:15, 168265.82it/s]

-
49%|████▉ | 2437879/4997817 [00:14&lt;00:14, 176578.85it/s]
+
47%|████▋ | 2371337/4997817 [00:13&lt;00:15, 168146.95it/s]

</pre>

-
49%|████▉ | 2437879/4997817 [00:14<00:14, 176578.85it/s]
+
47%|████▋ | 2371337/4997817 [00:13<00:15, 168146.95it/s]

end{sphinxVerbatim}

-

49%|████▉ | 2437879/4997817 [00:14<00:14, 176578.85it/s]

+

47%|████▋ | 2371337/4997817 [00:13<00:15, 168146.95it/s]

-
49%|████▉ | 2455539/4997817 [00:14&lt;00:14, 175833.38it/s]
+
48%|████▊ | 2388246/4997817 [00:14&lt;00:15, 168426.12it/s]

</pre>

-
49%|████▉ | 2455539/4997817 [00:14<00:14, 175833.38it/s]
+
48%|████▊ | 2388246/4997817 [00:14<00:15, 168426.12it/s]

end{sphinxVerbatim}

-

49%|████▉ | 2455539/4997817 [00:14<00:14, 175833.38it/s]

+

48%|████▊ | 2388246/4997817 [00:14<00:15, 168426.12it/s]

-
49%|████▉ | 2473124/4997817 [00:14&lt;00:14, 175407.22it/s]
+
48%|████▊ | 2405089/4997817 [00:14&lt;00:15, 167971.38it/s]

</pre>

-
49%|████▉ | 2473124/4997817 [00:14<00:14, 175407.22it/s]
+
48%|████▊ | 2405089/4997817 [00:14<00:15, 167971.38it/s]

end{sphinxVerbatim}

-

49%|████▉ | 2473124/4997817 [00:14<00:14, 175407.22it/s]

+

48%|████▊ | 2405089/4997817 [00:14<00:15, 167971.38it/s]

-
50%|████▉ | 2490666/4997817 [00:14&lt;00:14, 174637.69it/s]
+
48%|████▊ | 2421887/4997817 [00:14&lt;00:15, 167875.18it/s]

</pre>

-
50%|████▉ | 2490666/4997817 [00:14<00:14, 174637.69it/s]
+
48%|████▊ | 2421887/4997817 [00:14<00:15, 167875.18it/s]

end{sphinxVerbatim}

-

50%|████▉ | 2490666/4997817 [00:14<00:14, 174637.69it/s]

+

48%|████▊ | 2421887/4997817 [00:14<00:15, 167875.18it/s]

-
50%|█████ | 2508131/4997817 [00:14&lt;00:14, 174283.98it/s]
+
49%|████▉ | 2438756/4997817 [00:14&lt;00:15, 168116.52it/s]

</pre>

-
50%|█████ | 2508131/4997817 [00:14<00:14, 174283.98it/s]
+
49%|████▉ | 2438756/4997817 [00:14<00:15, 168116.52it/s]

end{sphinxVerbatim}

-

50%|█████ | 2508131/4997817 [00:14<00:14, 174283.98it/s]

+

49%|████▉ | 2438756/4997817 [00:14<00:15, 168116.52it/s]

-
51%|█████ | 2525561/4997817 [00:14&lt;00:14, 173899.65it/s]
+
49%|████▉ | 2455622/4997817 [00:14&lt;00:15, 168275.76it/s]

</pre>

-
51%|█████ | 2525561/4997817 [00:14<00:14, 173899.65it/s]
+
49%|████▉ | 2455622/4997817 [00:14<00:15, 168275.76it/s]

end{sphinxVerbatim}

-

51%|█████ | 2525561/4997817 [00:14<00:14, 173899.65it/s]

+

49%|████▉ | 2455622/4997817 [00:14<00:15, 168275.76it/s]

-
51%|█████ | 2542952/4997817 [00:14&lt;00:14, 173797.89it/s]
+
49%|████▉ | 2472450/4997817 [00:14&lt;00:15, 168246.55it/s]

</pre>

-
51%|█████ | 2542952/4997817 [00:14<00:14, 173797.89it/s]
+
49%|████▉ | 2472450/4997817 [00:14<00:15, 168246.55it/s]

end{sphinxVerbatim}

-

51%|█████ | 2542952/4997817 [00:14<00:14, 173797.89it/s]

+

49%|████▉ | 2472450/4997817 [00:14<00:15, 168246.55it/s]

-
51%|█████ | 2560333/4997817 [00:14&lt;00:14, 173525.09it/s]
+
50%|████▉ | 2489275/4997817 [00:14&lt;00:14, 168100.11it/s]

</pre>

-
51%|█████ | 2560333/4997817 [00:14<00:14, 173525.09it/s]
+
50%|████▉ | 2489275/4997817 [00:14<00:14, 168100.11it/s]

end{sphinxVerbatim}

-

51%|█████ | 2560333/4997817 [00:14<00:14, 173525.09it/s]

+

50%|████▉ | 2489275/4997817 [00:14<00:14, 168100.11it/s]

-
52%|█████▏ | 2577686/4997817 [00:14&lt;00:13, 173241.51it/s]
+
50%|█████ | 2506165/4997817 [00:14&lt;00:14, 168336.59it/s]

</pre>

-
52%|█████▏ | 2577686/4997817 [00:14<00:13, 173241.51it/s]
+
50%|█████ | 2506165/4997817 [00:14<00:14, 168336.59it/s]

end{sphinxVerbatim}

-

52%|█████▏ | 2577686/4997817 [00:14<00:13, 173241.51it/s]

+

50%|█████ | 2506165/4997817 [00:14<00:14, 168336.59it/s]

-
52%|█████▏ | 2595051/4997817 [00:14&lt;00:13, 173359.26it/s]
+
50%|█████ | 2523041/4997817 [00:14&lt;00:14, 168459.70it/s]

</pre>

-
52%|█████▏ | 2595051/4997817 [00:14<00:13, 173359.26it/s]
+
50%|█████ | 2523041/4997817 [00:14<00:14, 168459.70it/s]

end{sphinxVerbatim}

-

52%|█████▏ | 2595051/4997817 [00:14<00:13, 173359.26it/s]

+

50%|█████ | 2523041/4997817 [00:14<00:14, 168459.70it/s]

-
52%|█████▏ | 2612388/4997817 [00:15&lt;00:14, 169230.15it/s]
+
51%|█████ | 2539888/4997817 [00:14&lt;00:14, 168363.19it/s]

</pre>

-
52%|█████▏ | 2612388/4997817 [00:15<00:14, 169230.15it/s]
+
51%|█████ | 2539888/4997817 [00:14<00:14, 168363.19it/s]

end{sphinxVerbatim}

-

52%|█████▏ | 2612388/4997817 [00:15<00:14, 169230.15it/s]

+

51%|█████ | 2539888/4997817 [00:14<00:14, 168363.19it/s]

-
53%|█████▎ | 2629603/4997817 [00:15&lt;00:13, 170087.47it/s]
+
51%|█████ | 2556725/4997817 [00:15&lt;00:14, 168317.16it/s]

</pre>

-
53%|█████▎ | 2629603/4997817 [00:15<00:13, 170087.47it/s]
+
51%|█████ | 2556725/4997817 [00:15<00:14, 168317.16it/s]

end{sphinxVerbatim}

-

53%|█████▎ | 2629603/4997817 [00:15<00:13, 170087.47it/s]

+

51%|█████ | 2556725/4997817 [00:15<00:14, 168317.16it/s]

-
53%|█████▎ | 2646951/4997817 [00:15&lt;00:13, 171089.93it/s]
+
51%|█████▏ | 2573557/4997817 [00:15&lt;00:14, 168314.33it/s]

</pre>

-
53%|█████▎ | 2646951/4997817 [00:15<00:13, 171089.93it/s]
+
51%|█████▏ | 2573557/4997817 [00:15<00:14, 168314.33it/s]

end{sphinxVerbatim}

-

53%|█████▎ | 2646951/4997817 [00:15<00:13, 171089.93it/s]

+

51%|█████▏ | 2573557/4997817 [00:15<00:14, 168314.33it/s]

-
53%|█████▎ | 2664305/4997817 [00:15&lt;00:13, 171815.08it/s]
+
52%|█████▏ | 2590389/4997817 [00:15&lt;00:14, 168159.93it/s]

</pre>

-
53%|█████▎ | 2664305/4997817 [00:15<00:13, 171815.08it/s]
+
52%|█████▏ | 2590389/4997817 [00:15<00:14, 168159.93it/s]

end{sphinxVerbatim}

-

53%|█████▎ | 2664305/4997817 [00:15<00:13, 171815.08it/s]

+

52%|█████▏ | 2590389/4997817 [00:15<00:14, 168159.93it/s]

-
54%|█████▎ | 2681708/4997817 [00:15&lt;00:13, 172472.05it/s]
+
52%|█████▏ | 2607206/4997817 [00:15&lt;00:14, 168024.45it/s]

</pre>

-
54%|█████▎ | 2681708/4997817 [00:15<00:13, 172472.05it/s]
+
52%|█████▏ | 2607206/4997817 [00:15<00:14, 168024.45it/s]

end{sphinxVerbatim}

-

54%|█████▎ | 2681708/4997817 [00:15<00:13, 172472.05it/s]

+

52%|█████▏ | 2607206/4997817 [00:15<00:14, 168024.45it/s]

-
54%|█████▍ | 2698963/4997817 [00:15&lt;00:13, 172107.20it/s]
+
53%|█████▎ | 2624021/4997817 [00:15&lt;00:14, 168057.67it/s]

</pre>

-
54%|█████▍ | 2698963/4997817 [00:15<00:13, 172107.20it/s]
+
53%|█████▎ | 2624021/4997817 [00:15<00:14, 168057.67it/s]

end{sphinxVerbatim}

-

54%|█████▍ | 2698963/4997817 [00:15<00:13, 172107.20it/s]

+

53%|█████▎ | 2624021/4997817 [00:15<00:14, 168057.67it/s]

-
54%|█████▍ | 2716210/4997817 [00:15&lt;00:13, 172212.13it/s]
+
53%|█████▎ | 2640827/4997817 [00:15&lt;00:14, 167450.44it/s]

</pre>

-
54%|█████▍ | 2716210/4997817 [00:15<00:13, 172212.13it/s]
+
53%|█████▎ | 2640827/4997817 [00:15<00:14, 167450.44it/s]

end{sphinxVerbatim}

-

54%|█████▍ | 2716210/4997817 [00:15<00:13, 172212.13it/s]

+

53%|█████▎ | 2640827/4997817 [00:15<00:14, 167450.44it/s]

-
55%|█████▍ | 2733440/4997817 [00:15&lt;00:13, 172235.01it/s]
+
53%|█████▎ | 2657654/4997817 [00:15&lt;00:13, 167690.91it/s]

</pre>

-
55%|█████▍ | 2733440/4997817 [00:15<00:13, 172235.01it/s]
+
53%|█████▎ | 2657654/4997817 [00:15<00:13, 167690.91it/s]

end{sphinxVerbatim}

-

55%|█████▍ | 2733440/4997817 [00:15<00:13, 172235.01it/s]

+

53%|█████▎ | 2657654/4997817 [00:15<00:13, 167690.91it/s]

-
55%|█████▌ | 2750666/4997817 [00:15&lt;00:13, 172154.34it/s]
+
54%|█████▎ | 2674611/4997817 [00:15&lt;00:13, 168247.89it/s]

</pre>

-
55%|█████▌ | 2750666/4997817 [00:15<00:13, 172154.34it/s]
+
54%|█████▎ | 2674611/4997817 [00:15<00:13, 168247.89it/s]

end{sphinxVerbatim}

-

55%|█████▌ | 2750666/4997817 [00:15<00:13, 172154.34it/s]

+

54%|█████▎ | 2674611/4997817 [00:15<00:13, 168247.89it/s]

-
55%|█████▌ | 2767939/4997817 [00:15&lt;00:12, 172322.92it/s]
+
54%|█████▍ | 2691437/4997817 [00:15&lt;00:13, 168060.02it/s]

</pre>

-
55%|█████▌ | 2767939/4997817 [00:15<00:12, 172322.92it/s]
+
54%|█████▍ | 2691437/4997817 [00:15<00:13, 168060.02it/s]

end{sphinxVerbatim}

-

55%|█████▌ | 2767939/4997817 [00:15<00:12, 172322.92it/s]

+

54%|█████▍ | 2691437/4997817 [00:15<00:13, 168060.02it/s]

-
56%|█████▌ | 2785173/4997817 [00:16&lt;00:12, 171849.28it/s]
+
54%|█████▍ | 2708244/4997817 [00:15&lt;00:13, 167385.05it/s]

</pre>

-
56%|█████▌ | 2785173/4997817 [00:16<00:12, 171849.28it/s]
+
54%|█████▍ | 2708244/4997817 [00:15<00:13, 167385.05it/s]

end{sphinxVerbatim}

-

56%|█████▌ | 2785173/4997817 [00:16<00:12, 171849.28it/s]

+

54%|█████▍ | 2708244/4997817 [00:15<00:13, 167385.05it/s]

-
56%|█████▌ | 2802495/4997817 [00:16&lt;00:12, 172256.75it/s]
+
55%|█████▍ | 2725000/4997817 [00:16&lt;00:13, 167432.83it/s]

</pre>

-
56%|█████▌ | 2802495/4997817 [00:16<00:12, 172256.75it/s]
+
55%|█████▍ | 2725000/4997817 [00:16<00:13, 167432.83it/s]

end{sphinxVerbatim}

-

56%|█████▌ | 2802495/4997817 [00:16<00:12, 172256.75it/s]

+

55%|█████▍ | 2725000/4997817 [00:16<00:13, 167432.83it/s]

-
56%|█████▋ | 2819790/4997817 [00:16&lt;00:12, 172463.36it/s]
+
55%|█████▍ | 2741821/4997817 [00:16&lt;00:13, 167661.64it/s]

</pre>

-
56%|█████▋ | 2819790/4997817 [00:16<00:12, 172463.36it/s]
+
55%|█████▍ | 2741821/4997817 [00:16<00:13, 167661.64it/s]

end{sphinxVerbatim}

-

56%|█████▋ | 2819790/4997817 [00:16<00:12, 172463.36it/s]

+

55%|█████▍ | 2741821/4997817 [00:16<00:13, 167661.64it/s]

-
57%|█████▋ | 2837136/4997817 [00:16&lt;00:12, 172759.37it/s]
+
55%|█████▌ | 2758588/4997817 [00:16&lt;00:13, 167266.26it/s]

</pre>

-
57%|█████▋ | 2837136/4997817 [00:16<00:12, 172759.37it/s]
+
55%|█████▌ | 2758588/4997817 [00:16<00:13, 167266.26it/s]

end{sphinxVerbatim}

-

57%|█████▋ | 2837136/4997817 [00:16<00:12, 172759.37it/s]

+

55%|█████▌ | 2758588/4997817 [00:16<00:13, 167266.26it/s]

-
57%|█████▋ | 2854477/4997817 [00:16&lt;00:12, 172952.56it/s]
+
56%|█████▌ | 2775352/4997817 [00:16&lt;00:13, 167374.50it/s]

</pre>

-
57%|█████▋ | 2854477/4997817 [00:16<00:12, 172952.56it/s]
+
56%|█████▌ | 2775352/4997817 [00:16<00:13, 167374.50it/s]

end{sphinxVerbatim}

-

57%|█████▋ | 2854477/4997817 [00:16<00:12, 172952.56it/s]

+

56%|█████▌ | 2775352/4997817 [00:16<00:13, 167374.50it/s]

-
57%|█████▋ | 2871773/4997817 [00:16&lt;00:12, 172939.88it/s]
+
56%|█████▌ | 2792090/4997817 [00:16&lt;00:13, 167063.45it/s]

</pre>

-
57%|█████▋ | 2871773/4997817 [00:16<00:12, 172939.88it/s]
+
56%|█████▌ | 2792090/4997817 [00:16<00:13, 167063.45it/s]

end{sphinxVerbatim}

-

57%|█████▋ | 2871773/4997817 [00:16<00:12, 172939.88it/s]

+

56%|█████▌ | 2792090/4997817 [00:16<00:13, 167063.45it/s]

-
58%|█████▊ | 2889068/4997817 [00:16&lt;00:12, 172847.45it/s]
+
56%|█████▌ | 2808802/4997817 [00:16&lt;00:13, 167076.13it/s]

</pre>

-
58%|█████▊ | 2889068/4997817 [00:16<00:12, 172847.45it/s]
+
56%|█████▌ | 2808802/4997817 [00:16<00:13, 167076.13it/s]

end{sphinxVerbatim}

-

58%|█████▊ | 2889068/4997817 [00:16<00:12, 172847.45it/s]

+

56%|█████▌ | 2808802/4997817 [00:16<00:13, 167076.13it/s]

-
58%|█████▊ | 2906353/4997817 [00:16&lt;00:12, 172745.55it/s]
+
57%|█████▋ | 2825510/4997817 [00:16&lt;00:13, 167042.04it/s]

</pre>

-
58%|█████▊ | 2906353/4997817 [00:16<00:12, 172745.55it/s]
+
57%|█████▋ | 2825510/4997817 [00:16<00:13, 167042.04it/s]

end{sphinxVerbatim}

-

58%|█████▊ | 2906353/4997817 [00:16<00:12, 172745.55it/s]

+

57%|█████▋ | 2825510/4997817 [00:16<00:13, 167042.04it/s]

-
58%|█████▊ | 2923691/4997817 [00:16&lt;00:11, 172931.79it/s]
+
57%|█████▋ | 2842215/4997817 [00:16&lt;00:12, 166910.37it/s]

</pre>

-
58%|█████▊ | 2923691/4997817 [00:16<00:11, 172931.79it/s]
+
57%|█████▋ | 2842215/4997817 [00:16<00:12, 166910.37it/s]

end{sphinxVerbatim}

-

58%|█████▊ | 2923691/4997817 [00:16<00:11, 172931.79it/s]

+

57%|█████▋ | 2842215/4997817 [00:16<00:12, 166910.37it/s]

-
59%|█████▉ | 2940985/4997817 [00:16&lt;00:11, 172739.60it/s]
+
57%|█████▋ | 2858913/4997817 [00:16&lt;00:12, 166926.75it/s]

</pre>

-
59%|█████▉ | 2940985/4997817 [00:16<00:11, 172739.60it/s]
+
57%|█████▋ | 2858913/4997817 [00:16<00:12, 166926.75it/s]

end{sphinxVerbatim}

-

59%|█████▉ | 2940985/4997817 [00:16<00:11, 172739.60it/s]

+

57%|█████▋ | 2858913/4997817 [00:16<00:12, 166926.75it/s]

-
59%|█████▉ | 2958260/4997817 [00:17&lt;00:11, 172302.98it/s]
+
58%|█████▊ | 2875624/4997817 [00:16&lt;00:12, 166976.93it/s]

</pre>

-
59%|█████▉ | 2958260/4997817 [00:17<00:11, 172302.98it/s]
+
58%|█████▊ | 2875624/4997817 [00:16<00:12, 166976.93it/s]

end{sphinxVerbatim}

-

59%|█████▉ | 2958260/4997817 [00:17<00:11, 172302.98it/s]

+

58%|█████▊ | 2875624/4997817 [00:16<00:12, 166976.93it/s]

-
60%|█████▉ | 2975491/4997817 [00:17&lt;00:11, 172241.73it/s]
+
58%|█████▊ | 2892322/4997817 [00:17&lt;00:12, 166542.29it/s]

</pre>

-
60%|█████▉ | 2975491/4997817 [00:17<00:11, 172241.73it/s]
+
58%|█████▊ | 2892322/4997817 [00:17<00:12, 166542.29it/s]

end{sphinxVerbatim}

-

60%|█████▉ | 2975491/4997817 [00:17<00:11, 172241.73it/s]

+

58%|█████▊ | 2892322/4997817 [00:17<00:12, 166542.29it/s]

-
60%|█████▉ | 2992853/4997817 [00:17&lt;00:11, 172653.15it/s]
+
58%|█████▊ | 2909084/4997817 [00:17&lt;00:12, 166860.46it/s]

</pre>

-
60%|█████▉ | 2992853/4997817 [00:17<00:11, 172653.15it/s]
+
58%|█████▊ | 2909084/4997817 [00:17<00:12, 166860.46it/s]

end{sphinxVerbatim}

-

60%|█████▉ | 2992853/4997817 [00:17<00:11, 172653.15it/s]

+

58%|█████▊ | 2909084/4997817 [00:17<00:12, 166860.46it/s]

-
60%|██████ | 3010119/4997817 [00:17&lt;00:11, 172640.95it/s]
+
59%|█████▊ | 2925771/4997817 [00:17&lt;00:12, 166846.81it/s]

</pre>

-
60%|██████ | 3010119/4997817 [00:17<00:11, 172640.95it/s]
+
59%|█████▊ | 2925771/4997817 [00:17<00:12, 166846.81it/s]

end{sphinxVerbatim}

-

60%|██████ | 3010119/4997817 [00:17<00:11, 172640.95it/s]

+

59%|█████▊ | 2925771/4997817 [00:17<00:12, 166846.81it/s]

-
61%|██████ | 3027432/4997817 [00:17&lt;00:11, 172784.96it/s]
+
59%|█████▉ | 2942500/4997817 [00:17&lt;00:12, 166975.59it/s]

</pre>

-
61%|██████ | 3027432/4997817 [00:17<00:11, 172784.96it/s]
+
59%|█████▉ | 2942500/4997817 [00:17<00:12, 166975.59it/s]

end{sphinxVerbatim}

-

61%|██████ | 3027432/4997817 [00:17<00:11, 172784.96it/s]

+

59%|█████▉ | 2942500/4997817 [00:17<00:12, 166975.59it/s]

-
61%|██████ | 3044748/4997817 [00:17&lt;00:11, 172894.22it/s]
+
59%|█████▉ | 2959198/4997817 [00:17&lt;00:12, 166708.79it/s]

</pre>

-
61%|██████ | 3044748/4997817 [00:17<00:11, 172894.22it/s]
+
59%|█████▉ | 2959198/4997817 [00:17<00:12, 166708.79it/s]

end{sphinxVerbatim}

-

61%|██████ | 3044748/4997817 [00:17<00:11, 172894.22it/s]

+

59%|█████▉ | 2959198/4997817 [00:17<00:12, 166708.79it/s]

-
61%|██████▏ | 3062551/4997817 [00:17&lt;00:11, 174432.89it/s]
+
60%|█████▉ | 2975877/4997817 [00:17&lt;00:12, 166728.16it/s]

</pre>

-
61%|██████▏ | 3062551/4997817 [00:17<00:11, 174432.89it/s]
+
60%|█████▉ | 2975877/4997817 [00:17<00:12, 166728.16it/s]

end{sphinxVerbatim}

-

61%|██████▏ | 3062551/4997817 [00:17<00:11, 174432.89it/s]

+

60%|█████▉ | 2975877/4997817 [00:17<00:12, 166728.16it/s]

-
62%|██████▏ | 3080339/4997817 [00:17&lt;00:10, 175465.53it/s]
+
60%|█████▉ | 2992600/4997817 [00:17&lt;00:12, 166874.28it/s]

</pre>

-
62%|██████▏ | 3080339/4997817 [00:17<00:10, 175465.53it/s]
+
60%|█████▉ | 2992600/4997817 [00:17<00:12, 166874.28it/s]

end{sphinxVerbatim}

-

62%|██████▏ | 3080339/4997817 [00:17<00:10, 175465.53it/s]

+

60%|█████▉ | 2992600/4997817 [00:17<00:12, 166874.28it/s]

-
62%|██████▏ | 3098010/4997817 [00:17&lt;00:10, 175837.69it/s]
+
60%|██████ | 3009402/4997817 [00:17&lt;00:11, 167212.72it/s]

</pre>

-
62%|██████▏ | 3098010/4997817 [00:17<00:10, 175837.69it/s]
+
60%|██████ | 3009402/4997817 [00:17<00:11, 167212.72it/s]

end{sphinxVerbatim}

-

62%|██████▏ | 3098010/4997817 [00:17<00:10, 175837.69it/s]

+

60%|██████ | 3009402/4997817 [00:17<00:11, 167212.72it/s]

-
62%|██████▏ | 3115791/4997817 [00:17&lt;00:10, 176425.65it/s]
+
61%|██████ | 3026124/4997817 [00:17&lt;00:11, 166887.50it/s]

</pre>

-
62%|██████▏ | 3115791/4997817 [00:17<00:10, 176425.65it/s]
+
61%|██████ | 3026124/4997817 [00:17<00:11, 166887.50it/s]

end{sphinxVerbatim}

-

62%|██████▏ | 3115791/4997817 [00:17<00:10, 176425.65it/s]

+

61%|██████ | 3026124/4997817 [00:17<00:11, 166887.50it/s]

-
63%|██████▎ | 3133479/4997817 [00:18&lt;00:10, 176556.43it/s]
+
61%|██████ | 3042813/4997817 [00:17&lt;00:11, 166464.96it/s]

</pre>

-
63%|██████▎ | 3133479/4997817 [00:18<00:10, 176556.43it/s]
+
61%|██████ | 3042813/4997817 [00:17<00:11, 166464.96it/s]

end{sphinxVerbatim}

-

63%|██████▎ | 3133479/4997817 [00:18<00:10, 176556.43it/s]

+

61%|██████ | 3042813/4997817 [00:17<00:11, 166464.96it/s]

-
63%|██████▎ | 3151135/4997817 [00:18&lt;00:10, 176251.63it/s]
+
61%|██████ | 3059512/4997817 [00:18&lt;00:11, 166617.29it/s]

</pre>

-
63%|██████▎ | 3151135/4997817 [00:18<00:10, 176251.63it/s]
+
61%|██████ | 3059512/4997817 [00:18<00:11, 166617.29it/s]

end{sphinxVerbatim}

-

63%|██████▎ | 3151135/4997817 [00:18<00:10, 176251.63it/s]

+

61%|██████ | 3059512/4997817 [00:18<00:11, 166617.29it/s]

-
63%|██████▎ | 3168923/4997817 [00:18&lt;00:10, 176737.54it/s]
+
62%|██████▏ | 3076399/4997817 [00:18&lt;00:11, 167287.27it/s]

</pre>

-
63%|██████▎ | 3168923/4997817 [00:18<00:10, 176737.54it/s]
+
62%|██████▏ | 3076399/4997817 [00:18<00:11, 167287.27it/s]

end{sphinxVerbatim}

-

63%|██████▎ | 3168923/4997817 [00:18<00:10, 176737.54it/s]

+

62%|██████▏ | 3076399/4997817 [00:18<00:11, 167287.27it/s]

-
64%|██████▍ | 3186597/4997817 [00:18&lt;00:10, 176524.64it/s]
+
62%|██████▏ | 3093129/4997817 [00:18&lt;00:11, 167242.52it/s]

</pre>

-
64%|██████▍ | 3186597/4997817 [00:18<00:10, 176524.64it/s]
+
62%|██████▏ | 3093129/4997817 [00:18<00:11, 167242.52it/s]

end{sphinxVerbatim}

-

64%|██████▍ | 3186597/4997817 [00:18<00:10, 176524.64it/s]

+

62%|██████▏ | 3093129/4997817 [00:18<00:11, 167242.52it/s]

-
64%|██████▍ | 3204269/4997817 [00:18&lt;00:10, 176580.18it/s]
+
62%|██████▏ | 3109854/4997817 [00:18&lt;00:11, 164570.09it/s]

</pre>

-
64%|██████▍ | 3204269/4997817 [00:18<00:10, 176580.18it/s]
+
62%|██████▏ | 3109854/4997817 [00:18<00:11, 164570.09it/s]

end{sphinxVerbatim}

-

64%|██████▍ | 3204269/4997817 [00:18<00:10, 176580.18it/s]

+

62%|██████▏ | 3109854/4997817 [00:18<00:11, 164570.09it/s]

-
64%|██████▍ | 3221928/4997817 [00:18&lt;00:10, 176258.76it/s]
+
63%|██████▎ | 3127061/4997817 [00:18&lt;00:11, 166789.86it/s]

</pre>

-
64%|██████▍ | 3221928/4997817 [00:18<00:10, 176258.76it/s]
+
63%|██████▎ | 3127061/4997817 [00:18<00:11, 166789.86it/s]

end{sphinxVerbatim}

-

64%|██████▍ | 3221928/4997817 [00:18<00:10, 176258.76it/s]

+

63%|██████▎ | 3127061/4997817 [00:18<00:11, 166789.86it/s]

-
65%|██████▍ | 3239689/4997817 [00:18&lt;00:09, 176650.37it/s]
+
63%|██████▎ | 3144295/4997817 [00:18&lt;00:11, 168437.03it/s]

</pre>

-
65%|██████▍ | 3239689/4997817 [00:18<00:09, 176650.37it/s]
+
63%|██████▎ | 3144295/4997817 [00:18<00:11, 168437.03it/s]

end{sphinxVerbatim}

-

65%|██████▍ | 3239689/4997817 [00:18<00:09, 176650.37it/s]

+

63%|██████▎ | 3144295/4997817 [00:18<00:11, 168437.03it/s]

-
65%|██████▌ | 3257355/4997817 [00:18&lt;00:09, 176583.82it/s]
+
63%|██████▎ | 3161147/4997817 [00:18&lt;00:10, 168085.75it/s]

</pre>

-
65%|██████▌ | 3257355/4997817 [00:18<00:09, 176583.82it/s]
+
63%|██████▎ | 3161147/4997817 [00:18<00:10, 168085.75it/s]

end{sphinxVerbatim}

-

65%|██████▌ | 3257355/4997817 [00:18<00:09, 176583.82it/s]

+

63%|██████▎ | 3161147/4997817 [00:18<00:10, 168085.75it/s]

-
66%|██████▌ | 3275014/4997817 [00:18&lt;00:09, 176334.31it/s]
+
64%|██████▎ | 3178372/4997817 [00:18&lt;00:10, 169324.97it/s]

</pre>

-
66%|██████▌ | 3275014/4997817 [00:18<00:09, 176334.31it/s]
+
64%|██████▎ | 3178372/4997817 [00:18<00:10, 169324.97it/s]

end{sphinxVerbatim}

-

66%|██████▌ | 3275014/4997817 [00:18<00:09, 176334.31it/s]

+

64%|██████▎ | 3178372/4997817 [00:18<00:10, 169324.97it/s]

-
66%|██████▌ | 3292787/4997817 [00:18&lt;00:09, 176725.14it/s]
+
64%|██████▍ | 3195555/4997817 [00:18&lt;00:10, 170071.05it/s]

</pre>

-
66%|██████▌ | 3292787/4997817 [00:18<00:09, 176725.14it/s]
+
64%|██████▍ | 3195555/4997817 [00:18<00:10, 170071.05it/s]

end{sphinxVerbatim}

-

66%|██████▌ | 3292787/4997817 [00:18<00:09, 176725.14it/s]

+

64%|██████▍ | 3195555/4997817 [00:18<00:10, 170071.05it/s]

-
66%|██████▌ | 3310460/4997817 [00:19&lt;00:09, 172711.10it/s]
+
64%|██████▍ | 3212860/4997817 [00:18&lt;00:10, 170957.60it/s]

</pre>

-
66%|██████▌ | 3310460/4997817 [00:19<00:09, 172711.10it/s]
+
64%|██████▍ | 3212860/4997817 [00:18<00:10, 170957.60it/s]

end{sphinxVerbatim}

-

66%|██████▌ | 3310460/4997817 [00:19<00:09, 172711.10it/s]

+

64%|██████▍ | 3212860/4997817 [00:18<00:10, 170957.60it/s]

-
67%|██████▋ | 3327849/4997817 [00:19&lt;00:09, 173056.73it/s]
+
65%|██████▍ | 3230096/4997817 [00:19&lt;00:10, 171374.10it/s]

</pre>

-
67%|██████▋ | 3327849/4997817 [00:19<00:09, 173056.73it/s]
+
65%|██████▍ | 3230096/4997817 [00:19<00:10, 171374.10it/s]

end{sphinxVerbatim}

-

67%|██████▋ | 3327849/4997817 [00:19<00:09, 173056.73it/s]

+

65%|██████▍ | 3230096/4997817 [00:19<00:10, 171374.10it/s]

-
67%|██████▋ | 3345397/4997817 [00:19&lt;00:09, 173773.58it/s]
+
65%|██████▍ | 3247326/4997817 [00:19&lt;00:10, 171646.00it/s]

</pre>

-
67%|██████▋ | 3345397/4997817 [00:19<00:09, 173773.58it/s]
+
65%|██████▍ | 3247326/4997817 [00:19<00:10, 171646.00it/s]

end{sphinxVerbatim}

-

67%|██████▋ | 3345397/4997817 [00:19<00:09, 173773.58it/s]

+

65%|██████▍ | 3247326/4997817 [00:19<00:10, 171646.00it/s]

-
67%|██████▋ | 3362816/4997817 [00:19&lt;00:09, 173894.91it/s]
+
65%|██████▌ | 3264493/4997817 [00:19&lt;00:10, 171542.68it/s]

</pre>

-
67%|██████▋ | 3362816/4997817 [00:19<00:09, 173894.91it/s]
+
65%|██████▌ | 3264493/4997817 [00:19<00:10, 171542.68it/s]

end{sphinxVerbatim}

-

67%|██████▋ | 3362816/4997817 [00:19<00:09, 173894.91it/s]

+

65%|██████▌ | 3264493/4997817 [00:19<00:10, 171542.68it/s]

-
68%|██████▊ | 3380358/4997817 [00:19&lt;00:09, 174348.79it/s]
+
66%|██████▌ | 3281649/4997817 [00:19&lt;00:10, 171525.74it/s]

</pre>

-
68%|██████▊ | 3380358/4997817 [00:19<00:09, 174348.79it/s]
+
66%|██████▌ | 3281649/4997817 [00:19<00:10, 171525.74it/s]

end{sphinxVerbatim}

-

68%|██████▊ | 3380358/4997817 [00:19<00:09, 174348.79it/s]

+

66%|██████▌ | 3281649/4997817 [00:19<00:10, 171525.74it/s]

-
68%|██████▊ | 3397799/4997817 [00:19&lt;00:09, 174225.53it/s]
+
66%|██████▌ | 3298803/4997817 [00:19&lt;00:09, 171132.61it/s]

</pre>

-
68%|██████▊ | 3397799/4997817 [00:19<00:09, 174225.53it/s]
+
66%|██████▌ | 3298803/4997817 [00:19<00:09, 171132.61it/s]

end{sphinxVerbatim}

-

68%|██████▊ | 3397799/4997817 [00:19<00:09, 174225.53it/s]

+

66%|██████▌ | 3298803/4997817 [00:19<00:09, 171132.61it/s]

-
68%|██████▊ | 3415226/4997817 [00:19&lt;00:09, 174136.23it/s]
+
66%|██████▋ | 3315917/4997817 [00:19&lt;00:09, 170861.86it/s]

</pre>

-
68%|██████▊ | 3415226/4997817 [00:19<00:09, 174136.23it/s]
+
66%|██████▋ | 3315917/4997817 [00:19<00:09, 170861.86it/s]

end{sphinxVerbatim}

-

68%|██████▊ | 3415226/4997817 [00:19<00:09, 174136.23it/s]

+

66%|██████▋ | 3315917/4997817 [00:19<00:09, 170861.86it/s]

-
69%|██████▊ | 3432753/4997817 [00:19&lt;00:08, 174473.32it/s]
+
67%|██████▋ | 3333004/4997817 [00:19&lt;00:09, 170374.69it/s]

</pre>

-
69%|██████▊ | 3432753/4997817 [00:19<00:08, 174473.32it/s]
+
67%|██████▋ | 3333004/4997817 [00:19<00:09, 170374.69it/s]

end{sphinxVerbatim}

-

69%|██████▊ | 3432753/4997817 [00:19<00:08, 174473.32it/s]

+

67%|██████▋ | 3333004/4997817 [00:19<00:09, 170374.69it/s]

-
69%|██████▉ | 3450203/4997817 [00:19&lt;00:08, 174440.02it/s]
+
67%|██████▋ | 3350043/4997817 [00:19&lt;00:09, 170335.87it/s]

</pre>

-
69%|██████▉ | 3450203/4997817 [00:19<00:08, 174440.02it/s]
+
67%|██████▋ | 3350043/4997817 [00:19<00:09, 170335.87it/s]

end{sphinxVerbatim}

-

69%|██████▉ | 3450203/4997817 [00:19<00:08, 174440.02it/s]

+

67%|██████▋ | 3350043/4997817 [00:19<00:09, 170335.87it/s]

-
69%|██████▉ | 3467649/4997817 [00:19&lt;00:08, 174080.94it/s]
+
67%|██████▋ | 3367100/4997817 [00:19&lt;00:09, 170400.36it/s]

</pre>

-
69%|██████▉ | 3467649/4997817 [00:19<00:08, 174080.94it/s]
+
67%|██████▋ | 3367100/4997817 [00:19<00:09, 170400.36it/s]

end{sphinxVerbatim}

-

69%|██████▉ | 3467649/4997817 [00:19<00:08, 174080.94it/s]

+

67%|██████▋ | 3367100/4997817 [00:19<00:09, 170400.36it/s]

-
70%|██████▉ | 3485059/4997817 [00:20&lt;00:08, 172843.48it/s]
+
68%|██████▊ | 3384218/4997817 [00:19&lt;00:09, 170631.02it/s]

</pre>

-
70%|██████▉ | 3485059/4997817 [00:20<00:08, 172843.48it/s]
+
68%|██████▊ | 3384218/4997817 [00:19<00:09, 170631.02it/s]

end{sphinxVerbatim}

-

70%|██████▉ | 3485059/4997817 [00:20<00:08, 172843.48it/s]

+

68%|██████▊ | 3384218/4997817 [00:19<00:09, 170631.02it/s]

-
70%|███████ | 3502346/4997817 [00:20&lt;00:08, 166810.04it/s]
+
68%|██████▊ | 3401284/4997817 [00:20&lt;00:09, 170636.34it/s]

</pre>

-
70%|███████ | 3502346/4997817 [00:20<00:08, 166810.04it/s]
+
68%|██████▊ | 3401284/4997817 [00:20<00:09, 170636.34it/s]

end{sphinxVerbatim}

-

70%|███████ | 3502346/4997817 [00:20<00:08, 166810.04it/s]

+

68%|██████▊ | 3401284/4997817 [00:20<00:09, 170636.34it/s]

-
70%|███████ | 3519703/4997817 [00:20&lt;00:08, 168777.34it/s]
+
68%|██████▊ | 3418357/4997817 [00:20&lt;00:09, 170658.72it/s]

</pre>

-
70%|███████ | 3519703/4997817 [00:20<00:08, 168777.34it/s]
+
68%|██████▊ | 3418357/4997817 [00:20<00:09, 170658.72it/s]

end{sphinxVerbatim}

-

70%|███████ | 3519703/4997817 [00:20<00:08, 168777.34it/s]

+

68%|██████▊ | 3418357/4997817 [00:20<00:09, 170658.72it/s]

-
71%|███████ | 3537300/4997817 [00:20&lt;00:08, 170887.58it/s]
+
69%|██████▊ | 3435515/4997817 [00:20&lt;00:09, 170931.41it/s]

</pre>

-
71%|███████ | 3537300/4997817 [00:20<00:08, 170887.58it/s]
+
69%|██████▊ | 3435515/4997817 [00:20<00:09, 170931.41it/s]

end{sphinxVerbatim}

-

71%|███████ | 3537300/4997817 [00:20<00:08, 170887.58it/s]

+

69%|██████▊ | 3435515/4997817 [00:20<00:09, 170931.41it/s]

-
71%|███████ | 3554810/4997817 [00:20&lt;00:08, 172129.81it/s]
+
69%|██████▉ | 3452609/4997817 [00:20&lt;00:09, 163949.22it/s]

</pre>

-
71%|███████ | 3554810/4997817 [00:20<00:08, 172129.81it/s]
+
69%|██████▉ | 3452609/4997817 [00:20<00:09, 163949.22it/s]

end{sphinxVerbatim}

-

71%|███████ | 3554810/4997817 [00:20<00:08, 172129.81it/s]

+

69%|██████▉ | 3452609/4997817 [00:20<00:09, 163949.22it/s]

-
71%|███████▏ | 3572407/4997817 [00:20&lt;00:08, 173268.31it/s]
+
69%|██████▉ | 3469894/4997817 [00:20&lt;00:09, 166538.18it/s]

</pre>

-
71%|███████▏ | 3572407/4997817 [00:20<00:08, 173268.31it/s]
+
69%|██████▉ | 3469894/4997817 [00:20<00:09, 166538.18it/s]

end{sphinxVerbatim}

-

71%|███████▏ | 3572407/4997817 [00:20<00:08, 173268.31it/s]

+

69%|██████▉ | 3469894/4997817 [00:20<00:09, 166538.18it/s]

-
72%|███████▏ | 3590035/4997817 [00:20&lt;00:08, 174161.61it/s]
+
70%|██████▉ | 3486928/4997817 [00:20&lt;00:09, 167650.09it/s]

</pre>

-
72%|███████▏ | 3590035/4997817 [00:20<00:08, 174161.61it/s]
+
70%|██████▉ | 3486928/4997817 [00:20<00:09, 167650.09it/s]

end{sphinxVerbatim}

-

72%|███████▏ | 3590035/4997817 [00:20<00:08, 174161.61it/s]

+

70%|██████▉ | 3486928/4997817 [00:20<00:09, 167650.09it/s]

-
72%|███████▏ | 3607587/4997817 [00:20&lt;00:07, 174563.62it/s]
+
70%|███████ | 3503919/4997817 [00:20&lt;00:08, 168314.40it/s]

</pre>

-
72%|███████▏ | 3607587/4997817 [00:20<00:07, 174563.62it/s]
+
70%|███████ | 3503919/4997817 [00:20<00:08, 168314.40it/s]

end{sphinxVerbatim}

-

72%|███████▏ | 3607587/4997817 [00:20<00:07, 174563.62it/s]

+

70%|███████ | 3503919/4997817 [00:20<00:08, 168314.40it/s]

-
73%|███████▎ | 3625281/4997817 [00:20&lt;00:07, 175270.03it/s]
+
70%|███████ | 3520963/4997817 [00:20&lt;00:08, 168940.54it/s]

</pre>

-
73%|███████▎ | 3625281/4997817 [00:20<00:07, 175270.03it/s]
+
70%|███████ | 3520963/4997817 [00:20<00:08, 168940.54it/s]

end{sphinxVerbatim}

-

73%|███████▎ | 3625281/4997817 [00:20<00:07, 175270.03it/s]

+

70%|███████ | 3520963/4997817 [00:20<00:08, 168940.54it/s]

-
73%|███████▎ | 3642873/4997817 [00:20&lt;00:07, 175463.20it/s]
+
71%|███████ | 3538111/4997817 [00:20&lt;00:08, 169693.53it/s]

</pre>

-
73%|███████▎ | 3642873/4997817 [00:20<00:07, 175463.20it/s]
+
71%|███████ | 3538111/4997817 [00:20<00:08, 169693.53it/s]

end{sphinxVerbatim}

-

73%|███████▎ | 3642873/4997817 [00:20<00:07, 175463.20it/s]

+

71%|███████ | 3538111/4997817 [00:20<00:08, 169693.53it/s]

-
73%|███████▎ | 3660486/4997817 [00:21&lt;00:07, 175659.55it/s]
+
71%|███████ | 3555434/4997817 [00:20&lt;00:08, 170745.59it/s]

</pre>

-
73%|███████▎ | 3660486/4997817 [00:21<00:07, 175659.55it/s]
+
71%|███████ | 3555434/4997817 [00:20<00:08, 170745.59it/s]

end{sphinxVerbatim}

-

73%|███████▎ | 3660486/4997817 [00:21<00:07, 175659.55it/s]

+

71%|███████ | 3555434/4997817 [00:20<00:08, 170745.59it/s]

-
74%|███████▎ | 3678068/4997817 [00:21&lt;00:07, 175704.41it/s]
+
71%|███████▏ | 3572660/4997817 [00:21&lt;00:08, 171192.35it/s]

</pre>

-
74%|███████▎ | 3678068/4997817 [00:21<00:07, 175704.41it/s]
+
71%|███████▏ | 3572660/4997817 [00:21<00:08, 171192.35it/s]

end{sphinxVerbatim}

-

74%|███████▎ | 3678068/4997817 [00:21<00:07, 175704.41it/s]

+

71%|███████▏ | 3572660/4997817 [00:21<00:08, 171192.35it/s]

-
74%|███████▍ | 3695673/4997817 [00:21&lt;00:07, 175805.15it/s]
+
72%|███████▏ | 3590030/4997817 [00:21&lt;00:08, 171938.61it/s]

</pre>

-
74%|███████▍ | 3695673/4997817 [00:21<00:07, 175805.15it/s]
+
72%|███████▏ | 3590030/4997817 [00:21<00:08, 171938.61it/s]

end{sphinxVerbatim}

-

74%|███████▍ | 3695673/4997817 [00:21<00:07, 175805.15it/s]

+

72%|███████▏ | 3590030/4997817 [00:21<00:08, 171938.61it/s]

-
74%|███████▍ | 3713256/4997817 [00:21&lt;00:07, 175721.21it/s]
+
72%|███████▏ | 3607303/4997817 [00:21&lt;00:08, 172172.46it/s]

</pre>

-
74%|███████▍ | 3713256/4997817 [00:21<00:07, 175721.21it/s]
+
72%|███████▏ | 3607303/4997817 [00:21<00:08, 172172.46it/s]

end{sphinxVerbatim}

-

74%|███████▍ | 3713256/4997817 [00:21<00:07, 175721.21it/s]

+

72%|███████▏ | 3607303/4997817 [00:21<00:08, 172172.46it/s]

-
75%|███████▍ | 3730852/4997817 [00:21&lt;00:07, 175791.21it/s]
+
73%|███████▎ | 3624594/4997817 [00:21&lt;00:07, 172387.33it/s]

</pre>

-
75%|███████▍ | 3730852/4997817 [00:21<00:07, 175791.21it/s]
+
73%|███████▎ | 3624594/4997817 [00:21<00:07, 172387.33it/s]

end{sphinxVerbatim}

-

75%|███████▍ | 3730852/4997817 [00:21<00:07, 175791.21it/s]

+

73%|███████▎ | 3624594/4997817 [00:21<00:07, 172387.33it/s]

-
75%|███████▌ | 3748432/4997817 [00:21&lt;00:07, 175657.15it/s]
+
73%|███████▎ | 3641836/4997817 [00:21&lt;00:07, 171859.14it/s]

</pre>

-
75%|███████▌ | 3748432/4997817 [00:21<00:07, 175657.15it/s]
+
73%|███████▎ | 3641836/4997817 [00:21<00:07, 171859.14it/s]

end{sphinxVerbatim}

-

75%|███████▌ | 3748432/4997817 [00:21<00:07, 175657.15it/s]

+

73%|███████▎ | 3641836/4997817 [00:21<00:07, 171859.14it/s]

-
75%|███████▌ | 3765999/4997817 [00:21&lt;00:07, 175309.41it/s]
+
73%|███████▎ | 3659025/4997817 [00:21&lt;00:07, 171633.99it/s]

</pre>

-
75%|███████▌ | 3765999/4997817 [00:21<00:07, 175309.41it/s]
+
73%|███████▎ | 3659025/4997817 [00:21<00:07, 171633.99it/s]

end{sphinxVerbatim}

-

75%|███████▌ | 3765999/4997817 [00:21<00:07, 175309.41it/s]

+

73%|███████▎ | 3659025/4997817 [00:21<00:07, 171633.99it/s]

-
76%|███████▌ | 3783531/4997817 [00:21&lt;00:06, 175154.10it/s]
+
74%|███████▎ | 3676190/4997817 [00:21&lt;00:07, 171400.84it/s]

</pre>

-
76%|███████▌ | 3783531/4997817 [00:21<00:06, 175154.10it/s]
+
74%|███████▎ | 3676190/4997817 [00:21<00:07, 171400.84it/s]

end{sphinxVerbatim}

-

76%|███████▌ | 3783531/4997817 [00:21<00:06, 175154.10it/s]

+

74%|███████▎ | 3676190/4997817 [00:21<00:07, 171400.84it/s]

-
76%|███████▌ | 3801047/4997817 [00:21&lt;00:06, 175154.54it/s]
+
74%|███████▍ | 3693332/4997817 [00:21&lt;00:07, 171243.48it/s]

</pre>

-
76%|███████▌ | 3801047/4997817 [00:21<00:06, 175154.54it/s]
+
74%|███████▍ | 3693332/4997817 [00:21<00:07, 171243.48it/s]

end{sphinxVerbatim}

-

76%|███████▌ | 3801047/4997817 [00:21<00:06, 175154.54it/s]

+

74%|███████▍ | 3693332/4997817 [00:21<00:07, 171243.48it/s]

-
76%|███████▋ | 3818563/4997817 [00:21&lt;00:06, 174964.54it/s]
+
74%|███████▍ | 3710458/4997817 [00:21&lt;00:07, 171013.54it/s]

</pre>

-
76%|███████▋ | 3818563/4997817 [00:21<00:06, 174964.54it/s]
+
74%|███████▍ | 3710458/4997817 [00:21<00:07, 171013.54it/s]

end{sphinxVerbatim}

-

76%|███████▋ | 3818563/4997817 [00:21<00:06, 174964.54it/s]

+

74%|███████▍ | 3710458/4997817 [00:21<00:07, 171013.54it/s]

-
77%|███████▋ | 3836060/4997817 [00:22&lt;00:06, 174874.20it/s]
+
75%|███████▍ | 3727633/4997817 [00:21&lt;00:07, 171229.98it/s]

</pre>

-
77%|███████▋ | 3836060/4997817 [00:22<00:06, 174874.20it/s]
+
75%|███████▍ | 3727633/4997817 [00:21<00:07, 171229.98it/s]

end{sphinxVerbatim}

-

77%|███████▋ | 3836060/4997817 [00:22<00:06, 174874.20it/s]

+

75%|███████▍ | 3727633/4997817 [00:21<00:07, 171229.98it/s]

-
77%|███████▋ | 3853548/4997817 [00:22&lt;00:06, 167436.37it/s]
+
75%|███████▍ | 3744757/4997817 [00:22&lt;00:07, 171012.21it/s]

</pre>

-
77%|███████▋ | 3853548/4997817 [00:22<00:06, 167436.37it/s]
+
75%|███████▍ | 3744757/4997817 [00:22<00:07, 171012.21it/s]

end{sphinxVerbatim}

-

77%|███████▋ | 3853548/4997817 [00:22<00:06, 167436.37it/s]

+

75%|███████▍ | 3744757/4997817 [00:22<00:07, 171012.21it/s]

-
77%|███████▋ | 3870994/4997817 [00:22&lt;00:06, 169474.31it/s]
+
75%|███████▌ | 3761899/4997817 [00:22&lt;00:07, 171129.30it/s]

</pre>

-
77%|███████▋ | 3870994/4997817 [00:22<00:06, 169474.31it/s]
+
75%|███████▌ | 3761899/4997817 [00:22<00:07, 171129.30it/s]

end{sphinxVerbatim}

-

77%|███████▋ | 3870994/4997817 [00:22<00:06, 169474.31it/s]

+

75%|███████▌ | 3761899/4997817 [00:22<00:07, 171129.30it/s]

-
78%|███████▊ | 3888398/4997817 [00:22&lt;00:06, 170810.31it/s]
+
76%|███████▌ | 3779084/4997817 [00:22&lt;00:07, 171340.31it/s]

</pre>

-
78%|███████▊ | 3888398/4997817 [00:22<00:06, 170810.31it/s]
+
76%|███████▌ | 3779084/4997817 [00:22<00:07, 171340.31it/s]

end{sphinxVerbatim}

-

78%|███████▊ | 3888398/4997817 [00:22<00:06, 170810.31it/s]

+

76%|███████▌ | 3779084/4997817 [00:22<00:07, 171340.31it/s]

-
78%|███████▊ | 3905737/4997817 [00:22&lt;00:06, 171571.35it/s]
+
76%|███████▌ | 3796219/4997817 [00:22&lt;00:07, 167422.50it/s]

</pre>

-
78%|███████▊ | 3905737/4997817 [00:22<00:06, 171571.35it/s]
+
76%|███████▌ | 3796219/4997817 [00:22<00:07, 167422.50it/s]

end{sphinxVerbatim}

-

78%|███████▊ | 3905737/4997817 [00:22<00:06, 171571.35it/s]

+

76%|███████▌ | 3796219/4997817 [00:22<00:07, 167422.50it/s]

-
78%|███████▊ | 3923054/4997817 [00:22&lt;00:06, 172044.02it/s]
+
76%|███████▋ | 3813525/4997817 [00:22&lt;00:07, 169081.57it/s]

</pre>

-
78%|███████▊ | 3923054/4997817 [00:22<00:06, 172044.02it/s]
+
76%|███████▋ | 3813525/4997817 [00:22<00:07, 169081.57it/s]

end{sphinxVerbatim}

-

78%|███████▊ | 3923054/4997817 [00:22<00:06, 172044.02it/s]

+

76%|███████▋ | 3813525/4997817 [00:22<00:07, 169081.57it/s]

-
79%|███████▉ | 3940335/4997817 [00:22&lt;00:06, 172268.67it/s]
+
77%|███████▋ | 3830826/4997817 [00:22&lt;00:06, 170242.46it/s]

</pre>

-
79%|███████▉ | 3940335/4997817 [00:22<00:06, 172268.67it/s]
+
77%|███████▋ | 3830826/4997817 [00:22<00:06, 170242.46it/s]

end{sphinxVerbatim}

-

79%|███████▉ | 3940335/4997817 [00:22<00:06, 172268.67it/s]

+

77%|███████▋ | 3830826/4997817 [00:22<00:06, 170242.46it/s]

-
79%|███████▉ | 3957626/4997817 [00:22&lt;00:06, 172456.39it/s]
+
77%|███████▋ | 3847923/4997817 [00:22&lt;00:06, 170454.10it/s]

</pre>

-
79%|███████▉ | 3957626/4997817 [00:22<00:06, 172456.39it/s]
+
77%|███████▋ | 3847923/4997817 [00:22<00:06, 170454.10it/s]

end{sphinxVerbatim}

-

79%|███████▉ | 3957626/4997817 [00:22<00:06, 172456.39it/s]

+

77%|███████▋ | 3847923/4997817 [00:22<00:06, 170454.10it/s]

-
80%|███████▉ | 3974897/4997817 [00:22&lt;00:05, 172529.53it/s]
+
77%|███████▋ | 3865103/4997817 [00:22&lt;00:06, 170852.37it/s]

</pre>

-
80%|███████▉ | 3974897/4997817 [00:22<00:05, 172529.53it/s]
+
77%|███████▋ | 3865103/4997817 [00:22<00:06, 170852.37it/s]

end{sphinxVerbatim}

-

80%|███████▉ | 3974897/4997817 [00:22<00:05, 172529.53it/s]

+

77%|███████▋ | 3865103/4997817 [00:22<00:06, 170852.37it/s]

-
80%|███████▉ | 3992172/4997817 [00:22&lt;00:05, 172593.33it/s]
+
78%|███████▊ | 3882263/4997817 [00:22&lt;00:06, 171072.12it/s]

</pre>

-
80%|███████▉ | 3992172/4997817 [00:22<00:05, 172593.33it/s]
+
78%|███████▊ | 3882263/4997817 [00:22<00:06, 171072.12it/s]

end{sphinxVerbatim}

-

80%|███████▉ | 3992172/4997817 [00:22<00:05, 172593.33it/s]

+

78%|███████▊ | 3882263/4997817 [00:22<00:06, 171072.12it/s]

-
80%|████████ | 4009477/4997817 [00:23&lt;00:05, 172727.41it/s]
+
78%|███████▊ | 3899433/4997817 [00:22&lt;00:06, 171255.46it/s]

</pre>

-
80%|████████ | 4009477/4997817 [00:23<00:05, 172727.41it/s]
+
78%|███████▊ | 3899433/4997817 [00:22<00:06, 171255.46it/s]

end{sphinxVerbatim}

-

80%|████████ | 4009477/4997817 [00:23<00:05, 172727.41it/s]

+

78%|███████▊ | 3899433/4997817 [00:22<00:06, 171255.46it/s]

-
81%|████████ | 4026754/4997817 [00:23&lt;00:05, 172335.34it/s]
+
78%|███████▊ | 3916562/4997817 [00:23&lt;00:06, 170928.63it/s]

</pre>

-
81%|████████ | 4026754/4997817 [00:23<00:05, 172335.34it/s]
+
78%|███████▊ | 3916562/4997817 [00:23<00:06, 170928.63it/s]

end{sphinxVerbatim}

-

81%|████████ | 4026754/4997817 [00:23<00:05, 172335.34it/s]

+

78%|███████▊ | 3916562/4997817 [00:23<00:06, 170928.63it/s]

-
81%|████████ | 4044009/4997817 [00:23&lt;00:05, 172396.06it/s]
+
79%|███████▊ | 3933884/4997817 [00:23&lt;00:06, 171610.47it/s]

</pre>

-
81%|████████ | 4044009/4997817 [00:23<00:05, 172396.06it/s]
+
79%|███████▊ | 3933884/4997817 [00:23<00:06, 171610.47it/s]

end{sphinxVerbatim}

-

81%|████████ | 4044009/4997817 [00:23<00:05, 172396.06it/s]

+

79%|███████▊ | 3933884/4997817 [00:23<00:06, 171610.47it/s]

-
81%|████████▏ | 4061251/4997817 [00:23&lt;00:05, 172348.89it/s]
+
79%|███████▉ | 3951103/4997817 [00:23&lt;00:06, 171779.99it/s]

</pre>

-
81%|████████▏ | 4061251/4997817 [00:23<00:05, 172348.89it/s]
+
79%|███████▉ | 3951103/4997817 [00:23<00:06, 171779.99it/s]

end{sphinxVerbatim}

-

81%|████████▏ | 4061251/4997817 [00:23<00:05, 172348.89it/s]

+

79%|███████▉ | 3951103/4997817 [00:23<00:06, 171779.99it/s]

-
82%|████████▏ | 4078488/4997817 [00:23&lt;00:05, 172038.75it/s]
+
79%|███████▉ | 3968283/4997817 [00:23&lt;00:05, 171759.78it/s]

</pre>

-
82%|████████▏ | 4078488/4997817 [00:23<00:05, 172038.75it/s]
+
79%|███████▉ | 3968283/4997817 [00:23<00:05, 171759.78it/s]

end{sphinxVerbatim}

-

82%|████████▏ | 4078488/4997817 [00:23<00:05, 172038.75it/s]

+

79%|███████▉ | 3968283/4997817 [00:23<00:05, 171759.78it/s]

-
82%|████████▏ | 4095693/4997817 [00:23&lt;00:05, 171753.78it/s]
+
80%|███████▉ | 3985460/4997817 [00:23&lt;00:05, 171748.29it/s]

</pre>

-
82%|████████▏ | 4095693/4997817 [00:23<00:05, 171753.78it/s]
+
80%|███████▉ | 3985460/4997817 [00:23<00:05, 171748.29it/s]

end{sphinxVerbatim}

-

82%|████████▏ | 4095693/4997817 [00:23<00:05, 171753.78it/s]

+

80%|███████▉ | 3985460/4997817 [00:23<00:05, 171748.29it/s]

-
82%|████████▏ | 4112870/4997817 [00:23&lt;00:05, 171591.26it/s]
+
80%|████████ | 4002799/4997817 [00:23&lt;00:05, 172236.63it/s]

</pre>

-
82%|████████▏ | 4112870/4997817 [00:23<00:05, 171591.26it/s]
+
80%|████████ | 4002799/4997817 [00:23<00:05, 172236.63it/s]

end{sphinxVerbatim}

-

82%|████████▏ | 4112870/4997817 [00:23<00:05, 171591.26it/s]

+

80%|████████ | 4002799/4997817 [00:23<00:05, 172236.63it/s]

-
83%|████████▎ | 4130047/4997817 [00:23&lt;00:05, 171641.11it/s]
+
80%|████████ | 4020024/4997817 [00:23&lt;00:05, 172120.48it/s]

</pre>

-
83%|████████▎ | 4130047/4997817 [00:23<00:05, 171641.11it/s]
+
80%|████████ | 4020024/4997817 [00:23<00:05, 172120.48it/s]

end{sphinxVerbatim}

-

83%|████████▎ | 4130047/4997817 [00:23<00:05, 171641.11it/s]

+

80%|████████ | 4020024/4997817 [00:23<00:05, 172120.48it/s]

-
83%|████████▎ | 4147220/4997817 [00:23&lt;00:04, 171663.86it/s]
+
81%|████████ | 4037255/4997817 [00:23&lt;00:05, 172171.92it/s]

</pre>

-
83%|████████▎ | 4147220/4997817 [00:23<00:04, 171663.86it/s]
+
81%|████████ | 4037255/4997817 [00:23<00:05, 172171.92it/s]

end{sphinxVerbatim}

-

83%|████████▎ | 4147220/4997817 [00:23<00:04, 171663.86it/s]

+

81%|████████ | 4037255/4997817 [00:23<00:05, 172171.92it/s]

-
83%|████████▎ | 4164387/4997817 [00:23&lt;00:04, 171623.45it/s]
+
81%|████████ | 4054562/4997817 [00:23&lt;00:05, 172438.26it/s]

</pre>

-
83%|████████▎ | 4164387/4997817 [00:23<00:04, 171623.45it/s]
+
81%|████████ | 4054562/4997817 [00:23<00:05, 172438.26it/s]

end{sphinxVerbatim}

-

83%|████████▎ | 4164387/4997817 [00:23<00:04, 171623.45it/s]

+

81%|████████ | 4054562/4997817 [00:23<00:05, 172438.26it/s]

-
84%|████████▎ | 4181550/4997817 [00:24&lt;00:04, 171620.07it/s]
+
81%|████████▏ | 4071846/4997817 [00:23&lt;00:05, 172553.59it/s]

</pre>

-
84%|████████▎ | 4181550/4997817 [00:24<00:04, 171620.07it/s]
+
81%|████████▏ | 4071846/4997817 [00:23<00:05, 172553.59it/s]

end{sphinxVerbatim}

-

84%|████████▎ | 4181550/4997817 [00:24<00:04, 171620.07it/s]

+

81%|████████▏ | 4071846/4997817 [00:23<00:05, 172553.59it/s]

-
84%|████████▍ | 4198713/4997817 [00:24&lt;00:04, 167684.36it/s]
+
82%|████████▏ | 4089102/4997817 [00:24&lt;00:05, 172236.48it/s]

</pre>

-
84%|████████▍ | 4198713/4997817 [00:24<00:04, 167684.36it/s]
+
82%|████████▏ | 4089102/4997817 [00:24<00:05, 172236.48it/s]

end{sphinxVerbatim}

-

84%|████████▍ | 4198713/4997817 [00:24<00:04, 167684.36it/s]

+

82%|████████▏ | 4089102/4997817 [00:24<00:05, 172236.48it/s]

-
84%|████████▍ | 4216323/4997817 [00:24&lt;00:04, 170162.44it/s]
+
82%|████████▏ | 4106326/4997817 [00:24&lt;00:05, 172177.95it/s]

</pre>

-
84%|████████▍ | 4216323/4997817 [00:24<00:04, 170162.44it/s]
+
82%|████████▏ | 4106326/4997817 [00:24<00:05, 172177.95it/s]

end{sphinxVerbatim}

-

84%|████████▍ | 4216323/4997817 [00:24<00:04, 170162.44it/s]

+

82%|████████▏ | 4106326/4997817 [00:24<00:05, 172177.95it/s]

-
85%|████████▍ | 4233659/4997817 [00:24&lt;00:04, 171107.67it/s]
+
83%|████████▎ | 4123603/4997817 [00:24&lt;00:05, 172350.70it/s]

</pre>

-
85%|████████▍ | 4233659/4997817 [00:24<00:04, 171107.67it/s]
+
83%|████████▎ | 4123603/4997817 [00:24<00:05, 172350.70it/s]

end{sphinxVerbatim}

-

85%|████████▍ | 4233659/4997817 [00:24<00:04, 171107.67it/s]

+

83%|████████▎ | 4123603/4997817 [00:24<00:05, 172350.70it/s]

-
85%|████████▌ | 4251153/4997817 [00:24&lt;00:04, 172246.18it/s]
+
83%|████████▎ | 4140839/4997817 [00:24&lt;00:04, 171954.68it/s]

</pre>

-
85%|████████▌ | 4251153/4997817 [00:24<00:04, 172246.18it/s]
+
83%|████████▎ | 4140839/4997817 [00:24<00:04, 171954.68it/s]

end{sphinxVerbatim}

-

85%|████████▌ | 4251153/4997817 [00:24<00:04, 172246.18it/s]

+

83%|████████▎ | 4140839/4997817 [00:24<00:04, 171954.68it/s]

-
85%|████████▌ | 4268639/4997817 [00:24&lt;00:04, 173024.33it/s]
+
83%|████████▎ | 4158035/4997817 [00:24&lt;00:04, 168862.02it/s]

</pre>

-
85%|████████▌ | 4268639/4997817 [00:24<00:04, 173024.33it/s]
+
83%|████████▎ | 4158035/4997817 [00:24<00:04, 168862.02it/s]

end{sphinxVerbatim}

-

85%|████████▌ | 4268639/4997817 [00:24<00:04, 173024.33it/s]

+

83%|████████▎ | 4158035/4997817 [00:24<00:04, 168862.02it/s]

-
86%|████████▌ | 4286227/4997817 [00:24&lt;00:04, 173876.71it/s]
+
84%|████████▎ | 4175226/4997817 [00:24&lt;00:04, 169758.76it/s]

</pre>

-
86%|████████▌ | 4286227/4997817 [00:24<00:04, 173876.71it/s]
+
84%|████████▎ | 4175226/4997817 [00:24<00:04, 169758.76it/s]

end{sphinxVerbatim}

-

86%|████████▌ | 4286227/4997817 [00:24<00:04, 173876.71it/s]

+

84%|████████▎ | 4175226/4997817 [00:24<00:04, 169758.76it/s]

-
86%|████████▌ | 4303621/4997817 [00:24&lt;00:03, 173857.38it/s]
+
84%|████████▍ | 4192212/4997817 [00:24&lt;00:04, 168849.98it/s]

</pre>

-
86%|████████▌ | 4303621/4997817 [00:24<00:03, 173857.38it/s]
+
84%|████████▍ | 4192212/4997817 [00:24<00:04, 168849.98it/s]

end{sphinxVerbatim}

-

86%|████████▌ | 4303621/4997817 [00:24<00:03, 173857.38it/s]

+

84%|████████▍ | 4192212/4997817 [00:24<00:04, 168849.98it/s]

-
86%|████████▋ | 4321090/4997817 [00:24&lt;00:03, 174105.68it/s]
+
84%|████████▍ | 4209250/4997817 [00:24&lt;00:04, 169300.67it/s]

</pre>

-
86%|████████▋ | 4321090/4997817 [00:24<00:03, 174105.68it/s]
+
84%|████████▍ | 4209250/4997817 [00:24<00:04, 169300.67it/s]

end{sphinxVerbatim}

-

86%|████████▋ | 4321090/4997817 [00:24<00:03, 174105.68it/s]

+

84%|████████▍ | 4209250/4997817 [00:24<00:04, 169300.67it/s]

-
87%|████████▋ | 4338553/4997817 [00:24&lt;00:03, 174261.17it/s]
+
85%|████████▍ | 4226465/4997817 [00:24&lt;00:04, 170143.89it/s]

</pre>

-
87%|████████▋ | 4338553/4997817 [00:24<00:03, 174261.17it/s]
+
85%|████████▍ | 4226465/4997817 [00:24<00:04, 170143.89it/s]

end{sphinxVerbatim}

-

87%|████████▋ | 4338553/4997817 [00:24<00:03, 174261.17it/s]

+

85%|████████▍ | 4226465/4997817 [00:24<00:04, 170143.89it/s]

-
87%|████████▋ | 4356036/4997817 [00:25&lt;00:03, 174427.95it/s]
+
85%|████████▍ | 4243806/4997817 [00:24&lt;00:04, 171114.46it/s]

</pre>

-
87%|████████▋ | 4356036/4997817 [00:25<00:03, 174427.95it/s]
+
85%|████████▍ | 4243806/4997817 [00:24<00:04, 171114.46it/s]

end{sphinxVerbatim}

-

87%|████████▋ | 4356036/4997817 [00:25<00:03, 174427.95it/s]

+

85%|████████▍ | 4243806/4997817 [00:24<00:04, 171114.46it/s]

-
88%|████████▊ | 4373481/4997817 [00:25&lt;00:03, 174054.32it/s]
+
85%|████████▌ | 4261227/4997817 [00:25&lt;00:04, 172034.61it/s]

</pre>

-
88%|████████▊ | 4373481/4997817 [00:25<00:03, 174054.32it/s]
+
85%|████████▌ | 4261227/4997817 [00:25<00:04, 172034.61it/s]

end{sphinxVerbatim}

-

88%|████████▊ | 4373481/4997817 [00:25<00:03, 174054.32it/s]

+

85%|████████▌ | 4261227/4997817 [00:25<00:04, 172034.61it/s]

-
88%|████████▊ | 4391185/4997817 [00:25&lt;00:03, 174944.98it/s]
+
86%|████████▌ | 4278528/4997817 [00:25&lt;00:04, 172320.83it/s]

</pre>

-
88%|████████▊ | 4391185/4997817 [00:25<00:03, 174944.98it/s]
+
86%|████████▌ | 4278528/4997817 [00:25<00:04, 172320.83it/s]

end{sphinxVerbatim}

-

88%|████████▊ | 4391185/4997817 [00:25<00:03, 174944.98it/s]

+

86%|████████▌ | 4278528/4997817 [00:25<00:04, 172320.83it/s]

-
88%|████████▊ | 4408767/4997817 [00:25&lt;00:03, 175203.90it/s]
+
86%|████████▌ | 4295937/4997817 [00:25&lt;00:04, 172845.19it/s]

</pre>

-
88%|████████▊ | 4408767/4997817 [00:25<00:03, 175203.90it/s]
+
86%|████████▌ | 4295937/4997817 [00:25<00:04, 172845.19it/s]

end{sphinxVerbatim}

-

88%|████████▊ | 4408767/4997817 [00:25<00:03, 175203.90it/s]

+

86%|████████▌ | 4295937/4997817 [00:25<00:04, 172845.19it/s]

-
89%|████████▊ | 4426338/4997817 [00:25&lt;00:03, 175352.99it/s]
+
86%|████████▋ | 4313308/4997817 [00:25&lt;00:03, 173100.25it/s]

</pre>

-
89%|████████▊ | 4426338/4997817 [00:25<00:03, 175352.99it/s]
+
86%|████████▋ | 4313308/4997817 [00:25<00:03, 173100.25it/s]

end{sphinxVerbatim}

-

89%|████████▊ | 4426338/4997817 [00:25<00:03, 175352.99it/s]

+

86%|████████▋ | 4313308/4997817 [00:25<00:03, 173100.25it/s]

-
89%|████████▉ | 4443874/4997817 [00:25&lt;00:03, 175167.97it/s]
+
87%|████████▋ | 4330658/4997817 [00:25&lt;00:03, 173214.54it/s]

</pre>

-
89%|████████▉ | 4443874/4997817 [00:25<00:03, 175167.97it/s]
+
87%|████████▋ | 4330658/4997817 [00:25<00:03, 173214.54it/s]

end{sphinxVerbatim}

-

89%|████████▉ | 4443874/4997817 [00:25<00:03, 175167.97it/s]

+

87%|████████▋ | 4330658/4997817 [00:25<00:03, 173214.54it/s]

-
89%|████████▉ | 4461426/4997817 [00:25&lt;00:03, 175270.87it/s]
+
87%|████████▋ | 4347981/4997817 [00:25&lt;00:03, 173208.95it/s]

</pre>

-
89%|████████▉ | 4461426/4997817 [00:25<00:03, 175270.87it/s]
+
87%|████████▋ | 4347981/4997817 [00:25<00:03, 173208.95it/s]

end{sphinxVerbatim}

-

89%|████████▉ | 4461426/4997817 [00:25<00:03, 175270.87it/s]

+

87%|████████▋ | 4347981/4997817 [00:25<00:03, 173208.95it/s]

-
90%|████████▉ | 4478954/4997817 [00:25&lt;00:02, 174505.85it/s]
+
87%|████████▋ | 4365307/4997817 [00:25&lt;00:03, 173218.73it/s]

</pre>

-
90%|████████▉ | 4478954/4997817 [00:25<00:02, 174505.85it/s]
+
87%|████████▋ | 4365307/4997817 [00:25<00:03, 173218.73it/s]

end{sphinxVerbatim}

-

90%|████████▉ | 4478954/4997817 [00:25<00:02, 174505.85it/s]

+

87%|████████▋ | 4365307/4997817 [00:25<00:03, 173218.73it/s]

-
90%|████████▉ | 4496406/4997817 [00:25&lt;00:02, 174301.33it/s]
+
88%|████████▊ | 4382688/4997817 [00:25&lt;00:03, 173390.64it/s]

</pre>

-
90%|████████▉ | 4496406/4997817 [00:25<00:02, 174301.33it/s]
+
88%|████████▊ | 4382688/4997817 [00:25<00:03, 173390.64it/s]

end{sphinxVerbatim}

-

90%|████████▉ | 4496406/4997817 [00:25<00:02, 174301.33it/s]

+

88%|████████▊ | 4382688/4997817 [00:25<00:03, 173390.64it/s]

-
90%|█████████ | 4513837/4997817 [00:25&lt;00:02, 174251.43it/s]
+
88%|████████▊ | 4400068/4997817 [00:25&lt;00:03, 173507.38it/s]

</pre>

-
90%|█████████ | 4513837/4997817 [00:25<00:02, 174251.43it/s]
+
88%|████████▊ | 4400068/4997817 [00:25<00:03, 173507.38it/s]

end{sphinxVerbatim}

-

90%|█████████ | 4513837/4997817 [00:25<00:02, 174251.43it/s]

+

88%|████████▊ | 4400068/4997817 [00:25<00:03, 173507.38it/s]

-
91%|█████████ | 4531366/4997817 [00:26&lt;00:02, 174558.50it/s]
+
88%|████████▊ | 4417419/4997817 [00:25&lt;00:03, 173309.43it/s]

</pre>

-
91%|█████████ | 4531366/4997817 [00:26<00:02, 174558.50it/s]
+
88%|████████▊ | 4417419/4997817 [00:25<00:03, 173309.43it/s]

end{sphinxVerbatim}

-

91%|█████████ | 4531366/4997817 [00:26<00:02, 174558.50it/s]

+

88%|████████▊ | 4417419/4997817 [00:25<00:03, 173309.43it/s]

-
91%|█████████ | 4548843/4997817 [00:26&lt;00:02, 174618.77it/s]
+
89%|████████▊ | 4434751/4997817 [00:26&lt;00:03, 172930.69it/s]

</pre>

-
91%|█████████ | 4548843/4997817 [00:26<00:02, 174618.77it/s]
+
89%|████████▊ | 4434751/4997817 [00:26<00:03, 172930.69it/s]

end{sphinxVerbatim}

-

91%|█████████ | 4548843/4997817 [00:26<00:02, 174618.77it/s]

+

89%|████████▊ | 4434751/4997817 [00:26<00:03, 172930.69it/s]

-
91%|█████████▏| 4566306/4997817 [00:26&lt;00:02, 174572.68it/s]
+
89%|████████▉ | 4452045/4997817 [00:26&lt;00:03, 172511.48it/s]

</pre>

-
91%|█████████▏| 4566306/4997817 [00:26<00:02, 174572.68it/s]
+
89%|████████▉ | 4452045/4997817 [00:26<00:03, 172511.48it/s]

end{sphinxVerbatim}

-

91%|█████████▏| 4566306/4997817 [00:26<00:02, 174572.68it/s]

+

89%|████████▉ | 4452045/4997817 [00:26<00:03, 172511.48it/s]

-
92%|█████████▏| 4583764/4997817 [00:26&lt;00:02, 174261.05it/s]
+
89%|████████▉ | 4469335/4997817 [00:26&lt;00:03, 172622.79it/s]

</pre>

-
92%|█████████▏| 4583764/4997817 [00:26<00:02, 174261.05it/s]
+
89%|████████▉ | 4469335/4997817 [00:26<00:03, 172622.79it/s]

end{sphinxVerbatim}

-

92%|█████████▏| 4583764/4997817 [00:26<00:02, 174261.05it/s]

+

89%|████████▉ | 4469335/4997817 [00:26<00:03, 172622.79it/s]

-
92%|█████████▏| 4601191/4997817 [00:26&lt;00:02, 173985.59it/s]
+
90%|████████▉ | 4486598/4997817 [00:26&lt;00:02, 171589.70it/s]

</pre>

-
92%|█████████▏| 4601191/4997817 [00:26<00:02, 173985.59it/s]
+
90%|████████▉ | 4486598/4997817 [00:26<00:02, 171589.70it/s]

end{sphinxVerbatim}

-

92%|█████████▏| 4601191/4997817 [00:26<00:02, 173985.59it/s]

+

90%|████████▉ | 4486598/4997817 [00:26<00:02, 171589.70it/s]

-
92%|█████████▏| 4618615/4997817 [00:26&lt;00:02, 174059.41it/s]
+
90%|█████████ | 4503759/4997817 [00:26&lt;00:02, 171412.36it/s]

</pre>

-
92%|█████████▏| 4618615/4997817 [00:26<00:02, 174059.41it/s]
+
90%|█████████ | 4503759/4997817 [00:26<00:02, 171412.36it/s]

end{sphinxVerbatim}

-

92%|█████████▏| 4618615/4997817 [00:26<00:02, 174059.41it/s]

+

90%|█████████ | 4503759/4997817 [00:26<00:02, 171412.36it/s]

-
93%|█████████▎| 4636022/4997817 [00:26&lt;00:02, 173946.39it/s]
+
90%|█████████ | 4520902/4997817 [00:26&lt;00:02, 171343.15it/s]

</pre>

-
93%|█████████▎| 4636022/4997817 [00:26<00:02, 173946.39it/s]
+
90%|█████████ | 4520902/4997817 [00:26<00:02, 171343.15it/s]

end{sphinxVerbatim}

-

93%|█████████▎| 4636022/4997817 [00:26<00:02, 173946.39it/s]

+

90%|█████████ | 4520902/4997817 [00:26<00:02, 171343.15it/s]

-
93%|█████████▎| 4653417/4997817 [00:26&lt;00:01, 173914.36it/s]
+
91%|█████████ | 4538037/4997817 [00:26&lt;00:02, 171239.33it/s]

</pre>

-
93%|█████████▎| 4653417/4997817 [00:26<00:01, 173914.36it/s]
+
91%|█████████ | 4538037/4997817 [00:26<00:02, 171239.33it/s]

end{sphinxVerbatim}

-

93%|█████████▎| 4653417/4997817 [00:26<00:01, 173914.36it/s]

+

91%|█████████ | 4538037/4997817 [00:26<00:02, 171239.33it/s]

-
93%|█████████▎| 4670809/4997817 [00:26&lt;00:01, 173866.67it/s]
+
91%|█████████ | 4555162/4997817 [00:26&lt;00:02, 170876.40it/s]

</pre>

-
93%|█████████▎| 4670809/4997817 [00:26<00:01, 173866.67it/s]
+
91%|█████████ | 4555162/4997817 [00:26<00:02, 170876.40it/s]

end{sphinxVerbatim}

-

93%|█████████▎| 4670809/4997817 [00:26<00:01, 173866.67it/s]

+

91%|█████████ | 4555162/4997817 [00:26<00:02, 170876.40it/s]

-
94%|█████████▍| 4688210/4997817 [00:26&lt;00:01, 173907.06it/s]
+
91%|█████████▏| 4572250/4997817 [00:26&lt;00:02, 170565.73it/s]

</pre>

-
94%|█████████▍| 4688210/4997817 [00:26<00:01, 173907.06it/s]
+
91%|█████████▏| 4572250/4997817 [00:26<00:02, 170565.73it/s]

end{sphinxVerbatim}

-

94%|█████████▍| 4688210/4997817 [00:26<00:01, 173907.06it/s]

+

91%|█████████▏| 4572250/4997817 [00:26<00:02, 170565.73it/s]

-
94%|█████████▍| 4705644/4997817 [00:27&lt;00:01, 174033.16it/s]
+
92%|█████████▏| 4589307/4997817 [00:27&lt;00:02, 170097.89it/s]

</pre>

-
94%|█████████▍| 4705644/4997817 [00:27<00:01, 174033.16it/s]
+
92%|█████████▏| 4589307/4997817 [00:27<00:02, 170097.89it/s]

end{sphinxVerbatim}

-

94%|█████████▍| 4705644/4997817 [00:27<00:01, 174033.16it/s]

+

92%|█████████▏| 4589307/4997817 [00:27<00:02, 170097.89it/s]

-
95%|█████████▍| 4723065/4997817 [00:27&lt;00:01, 174084.11it/s]
+
92%|█████████▏| 4606318/4997817 [00:27&lt;00:02, 169865.84it/s]

</pre>

-
95%|█████████▍| 4723065/4997817 [00:27<00:01, 174084.11it/s]
+
92%|█████████▏| 4606318/4997817 [00:27<00:02, 169865.84it/s]

end{sphinxVerbatim}

-

95%|█████████▍| 4723065/4997817 [00:27<00:01, 174084.11it/s]

+

92%|█████████▏| 4606318/4997817 [00:27<00:02, 169865.84it/s]

-
95%|█████████▍| 4740474/4997817 [00:27&lt;00:01, 173944.40it/s]
+
93%|█████████▎| 4623305/4997817 [00:27&lt;00:02, 169631.05it/s]

</pre>

-
95%|█████████▍| 4740474/4997817 [00:27<00:01, 173944.40it/s]
+
93%|█████████▎| 4623305/4997817 [00:27<00:02, 169631.05it/s]

end{sphinxVerbatim}

-

95%|█████████▍| 4740474/4997817 [00:27<00:01, 173944.40it/s]

+

93%|█████████▎| 4623305/4997817 [00:27<00:02, 169631.05it/s]

-
95%|█████████▌| 4757869/4997817 [00:27&lt;00:01, 173731.59it/s]
+
93%|█████████▎| 4640269/4997817 [00:27&lt;00:02, 168872.65it/s]

</pre>

-
95%|█████████▌| 4757869/4997817 [00:27<00:01, 173731.59it/s]
+
93%|█████████▎| 4640269/4997817 [00:27<00:02, 168872.65it/s]

end{sphinxVerbatim}

-

95%|█████████▌| 4757869/4997817 [00:27<00:01, 173731.59it/s]

+

93%|█████████▎| 4640269/4997817 [00:27<00:02, 168872.65it/s]

-
96%|█████████▌| 4775243/4997817 [00:27&lt;00:01, 173526.32it/s]
+
93%|█████████▎| 4657235/4997817 [00:27&lt;00:02, 169105.83it/s]

</pre>

-
96%|█████████▌| 4775243/4997817 [00:27<00:01, 173526.32it/s]
+
93%|█████████▎| 4657235/4997817 [00:27<00:02, 169105.83it/s]

end{sphinxVerbatim}

-

96%|█████████▌| 4775243/4997817 [00:27<00:01, 173526.32it/s]

+

93%|█████████▎| 4657235/4997817 [00:27<00:02, 169105.83it/s]

-
96%|█████████▌| 4792596/4997817 [00:27&lt;00:01, 173265.11it/s]
+
94%|█████████▎| 4674147/4997817 [00:27&lt;00:01, 168226.74it/s]

</pre>

-
96%|█████████▌| 4792596/4997817 [00:27<00:01, 173265.11it/s]
+
94%|█████████▎| 4674147/4997817 [00:27<00:01, 168226.74it/s]

end{sphinxVerbatim}

-

96%|█████████▌| 4792596/4997817 [00:27<00:01, 173265.11it/s]

+

94%|█████████▎| 4674147/4997817 [00:27<00:01, 168226.74it/s]

-
96%|█████████▌| 4809923/4997817 [00:27&lt;00:01, 170701.37it/s]
+
94%|█████████▍| 4690976/4997817 [00:27&lt;00:01, 168242.05it/s]

</pre>

-
96%|█████████▌| 4809923/4997817 [00:27<00:01, 170701.37it/s]
+
94%|█████████▍| 4690976/4997817 [00:27<00:01, 168242.05it/s]

end{sphinxVerbatim}

-

96%|█████████▌| 4809923/4997817 [00:27<00:01, 170701.37it/s]

+

94%|█████████▍| 4690976/4997817 [00:27<00:01, 168242.05it/s]

-
97%|█████████▋| 4827002/4997817 [00:27&lt;00:01, 168010.73it/s]
+
94%|█████████▍| 4707801/4997817 [00:27&lt;00:01, 168102.89it/s]

</pre>

-
97%|█████████▋| 4827002/4997817 [00:27<00:01, 168010.73it/s]
+
94%|█████████▍| 4707801/4997817 [00:27<00:01, 168102.89it/s]

end{sphinxVerbatim}

-

97%|█████████▋| 4827002/4997817 [00:27<00:01, 168010.73it/s]

+

94%|█████████▍| 4707801/4997817 [00:27<00:01, 168102.89it/s]

-
97%|█████████▋| 4844469/4997817 [00:27&lt;00:00, 169967.72it/s]
+
95%|█████████▍| 4724647/4997817 [00:27&lt;00:01, 168205.59it/s]

</pre>

-
97%|█████████▋| 4844469/4997817 [00:27<00:00, 169967.72it/s]
+
95%|█████████▍| 4724647/4997817 [00:27<00:01, 168205.59it/s]

end{sphinxVerbatim}

-

97%|█████████▋| 4844469/4997817 [00:27<00:00, 169967.72it/s]

+

95%|█████████▍| 4724647/4997817 [00:27<00:01, 168205.59it/s]

-
97%|█████████▋| 4861913/4997817 [00:28&lt;00:00, 171289.71it/s]
+
95%|█████████▍| 4741468/4997817 [00:27&lt;00:01, 167808.58it/s]

</pre>

-
97%|█████████▋| 4861913/4997817 [00:28<00:00, 171289.71it/s]
+
95%|█████████▍| 4741468/4997817 [00:27<00:01, 167808.58it/s]

end{sphinxVerbatim}

-

97%|█████████▋| 4861913/4997817 [00:28<00:00, 171289.71it/s]

+

95%|█████████▍| 4741468/4997817 [00:27<00:01, 167808.58it/s]

-
98%|█████████▊| 4879328/4997817 [00:28&lt;00:00, 172138.29it/s]
+
95%|█████████▌| 4758250/4997817 [00:28&lt;00:01, 167370.08it/s]

</pre>

-
98%|█████████▊| 4879328/4997817 [00:28<00:00, 172138.29it/s]
+
95%|█████████▌| 4758250/4997817 [00:28<00:01, 167370.08it/s]

end{sphinxVerbatim}

-

98%|█████████▊| 4879328/4997817 [00:28<00:00, 172138.29it/s]

+

95%|█████████▌| 4758250/4997817 [00:28<00:01, 167370.08it/s]

-
98%|█████████▊| 4896647/4997817 [00:28&lt;00:00, 172449.50it/s]
+
96%|█████████▌| 4774988/4997817 [00:28&lt;00:01, 167262.31it/s]

</pre>

-
98%|█████████▊| 4896647/4997817 [00:28<00:00, 172449.50it/s]
+
96%|█████████▌| 4774988/4997817 [00:28<00:01, 167262.31it/s]

end{sphinxVerbatim}

-

98%|█████████▊| 4896647/4997817 [00:28<00:00, 172449.50it/s]

+

96%|█████████▌| 4774988/4997817 [00:28<00:01, 167262.31it/s]

-
98%|█████████▊| 4913898/4997817 [00:28&lt;00:00, 170167.36it/s]
+
96%|█████████▌| 4791806/4997817 [00:28&lt;00:01, 167533.12it/s]

</pre>

-
98%|█████████▊| 4913898/4997817 [00:28<00:00, 170167.36it/s]
+
96%|█████████▌| 4791806/4997817 [00:28<00:01, 167533.12it/s]

end{sphinxVerbatim}

-

98%|█████████▊| 4913898/4997817 [00:28<00:00, 170167.36it/s]

+

96%|█████████▌| 4791806/4997817 [00:28<00:01, 167533.12it/s]

-
99%|█████████▊| 4931534/4997817 [00:28&lt;00:00, 171999.39it/s]
+
96%|█████████▌| 4808560/4997817 [00:28&lt;00:01, 167179.89it/s]

</pre>

-
99%|█████████▊| 4931534/4997817 [00:28<00:00, 171999.39it/s]
+
96%|█████████▌| 4808560/4997817 [00:28<00:01, 167179.89it/s]

end{sphinxVerbatim}

-

99%|█████████▊| 4931534/4997817 [00:28<00:00, 171999.39it/s]

+

96%|█████████▌| 4808560/4997817 [00:28<00:01, 167179.89it/s]

-
99%|█████████▉| 4949175/4997817 [00:28&lt;00:00, 173309.56it/s]
+
97%|█████████▋| 4825279/4997817 [00:28&lt;00:01, 167129.34it/s]

</pre>

-
99%|█████████▉| 4949175/4997817 [00:28<00:00, 173309.56it/s]
+
97%|█████████▋| 4825279/4997817 [00:28<00:01, 167129.34it/s]

end{sphinxVerbatim}

-

99%|█████████▉| 4949175/4997817 [00:28<00:00, 173309.56it/s]

+

97%|█████████▋| 4825279/4997817 [00:28<00:01, 167129.34it/s]

-
99%|█████████▉| 4966657/4997817 [00:28&lt;00:00, 173755.94it/s]
+
97%|█████████▋| 4842096/4997817 [00:28&lt;00:00, 167436.03it/s]

</pre>

-
99%|█████████▉| 4966657/4997817 [00:28<00:00, 173755.94it/s]
+
97%|█████████▋| 4842096/4997817 [00:28<00:00, 167436.03it/s]

end{sphinxVerbatim}

-

99%|█████████▉| 4966657/4997817 [00:28<00:00, 173755.94it/s]

+

97%|█████████▋| 4842096/4997817 [00:28<00:00, 167436.03it/s]

+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
97%|█████████▋| 4859300/4997817 [00:28&lt;00:00, 168811.84it/s]
+

</pre>

+
+
+
97%|█████████▋| 4859300/4997817 [00:28<00:00, 168811.84it/s]
+

end{sphinxVerbatim}

+
+
+
+

97%|█████████▋| 4859300/4997817 [00:28<00:00, 168811.84it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
98%|█████████▊| 4876497/4997817 [00:28&lt;00:00, 169752.70it/s]
+

</pre>

+
+
+
98%|█████████▊| 4876497/4997817 [00:28<00:00, 169752.70it/s]
+

end{sphinxVerbatim}

+
+
+
+

98%|█████████▊| 4876497/4997817 [00:28<00:00, 169752.70it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
98%|█████████▊| 4893694/4997817 [00:28&lt;00:00, 170413.45it/s]
+

</pre>

+
+
+
98%|█████████▊| 4893694/4997817 [00:28<00:00, 170413.45it/s]
+

end{sphinxVerbatim}

+
+
+
+

98%|█████████▊| 4893694/4997817 [00:28<00:00, 170413.45it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
98%|█████████▊| 4910769/4997817 [00:28&lt;00:00, 170510.26it/s]
+

</pre>

+
+
+
98%|█████████▊| 4910769/4997817 [00:28<00:00, 170510.26it/s]
+

end{sphinxVerbatim}

+
+
+
+

98%|█████████▊| 4910769/4997817 [00:28<00:00, 170510.26it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
99%|█████████▊| 4927821/4997817 [00:29&lt;00:00, 170169.42it/s]
+

</pre>

+
+
+
99%|█████████▊| 4927821/4997817 [00:29<00:00, 170169.42it/s]
+

end{sphinxVerbatim}

+
+
+
+

99%|█████████▊| 4927821/4997817 [00:29<00:00, 170169.42it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
99%|█████████▉| 4944989/4997817 [00:29&lt;00:00, 170616.62it/s]
+

</pre>

+
+
+
99%|█████████▉| 4944989/4997817 [00:29<00:00, 170616.62it/s]
+

end{sphinxVerbatim}

+
+
+
+

99%|█████████▉| 4944989/4997817 [00:29<00:00, 170616.62it/s]

+
+
+
+
+
+
+
+
+
more-to-come:
+

+
class:
+

stderr

+
+
+
+
+
99%|█████████▉| 4962051/4997817 [00:29&lt;00:00, 169773.92it/s]
+

</pre>

+
+
+
99%|█████████▉| 4962051/4997817 [00:29<00:00, 169773.92it/s]
+

end{sphinxVerbatim}

+
+
+
+

99%|█████████▉| 4962051/4997817 [00:29<00:00, 169773.92it/s]

+
+
+
+
+
+
+
+
+
+
100%|█████████▉| 4979093/4997817 [00:29&lt;00:00, 169963.20it/s]
+

</pre>

+
+
+
100%|█████████▉| 4979093/4997817 [00:29<00:00, 169963.20it/s]
+

end{sphinxVerbatim}

+
+
+
+

100%|█████████▉| 4979093/4997817 [00:29<00:00, 169963.20it/s]

-
100%|█████████▉| 4984241/4997817 [00:28&lt;00:00, 174376.33it/s]
+
100%|█████████▉| 4996091/4997817 [00:29&lt;00:00, 169052.24it/s]

</pre>

-
100%|█████████▉| 4984241/4997817 [00:28<00:00, 174376.33it/s]
+
100%|█████████▉| 4996091/4997817 [00:29<00:00, 169052.24it/s]

end{sphinxVerbatim}

-

100%|█████████▉| 4984241/4997817 [00:28<00:00, 174376.33it/s]

+

100%|█████████▉| 4996091/4997817 [00:29<00:00, 169052.24it/s]

-
100%|██████████| 4997817/4997817 [00:28&lt;00:00, 173607.01it/s]
+
100%|██████████| 4997817/4997817 [00:29&lt;00:00, 169841.26it/s]

</pre>

-
100%|██████████| 4997817/4997817 [00:28<00:00, 173607.01it/s]
+
100%|██████████| 4997817/4997817 [00:29<00:00, 169841.26it/s]

end{sphinxVerbatim}

-

100%|██████████| 4997817/4997817 [00:28<00:00, 173607.01it/s]

+

100%|██████████| 4997817/4997817 [00:29<00:00, 169841.26it/s]

-
+

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().

@@ -8802,7 +9001,7 @@

Get label quality scores -{"state": {"4c5dc1becde2491aa17ab44a75d7f414": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d9482a6ebcbc431d9fa8d22857aecaf4": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "beb24d1191564974a50da25f62644e92": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4c5dc1becde2491aa17ab44a75d7f414", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d9482a6ebcbc431d9fa8d22857aecaf4", "value": 30.0}}, "02c82a7576c0452793d4ac9e3a8eb771": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "884a547f5c5141f0ae8582f90a618526": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "c9d9dcb94ddb4452bbf427afc230ea18": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_02c82a7576c0452793d4ac9e3a8eb771", "placeholder": "\u200b", "style": "IPY_MODEL_884a547f5c5141f0ae8582f90a618526", "value": "number of examples processed for estimating thresholds: 100%"}}, "f8e816ee6b4d43439ab0a880aa663aca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ab6a158ac22041ce9d1a7bf92a8d0522": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "46d009f0b3af4d06bbd5a73a66e716ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f8e816ee6b4d43439ab0a880aa663aca", "placeholder": "\u200b", "style": "IPY_MODEL_ab6a158ac22041ce9d1a7bf92a8d0522", "value": " 30/30 [00:00<00:00, 424.36it/s]"}}, "7b9c5493fed74f5785964aed9e4c4ac4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e7af30476e914a1aa2bc7bb22a08f3a8": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_c9d9dcb94ddb4452bbf427afc230ea18", "IPY_MODEL_beb24d1191564974a50da25f62644e92", "IPY_MODEL_46d009f0b3af4d06bbd5a73a66e716ea"], "layout": "IPY_MODEL_7b9c5493fed74f5785964aed9e4c4ac4"}}, "d3a3cfb6d5134ff98de3d1add75730c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ae706412514f4a958d903b4acb3182b2": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "efa9c67a3f4c42138eea3dcdd09cffb9": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d3a3cfb6d5134ff98de3d1add75730c4", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_ae706412514f4a958d903b4acb3182b2", "value": 30.0}}, "3f205716e3c54df6b925d90841790bb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4cf581fac6f1469fac415a93793188e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "469a9896f434443d914337d391527ddc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3f205716e3c54df6b925d90841790bb6", "placeholder": "\u200b", "style": "IPY_MODEL_4cf581fac6f1469fac415a93793188e5", "value": "number of examples processed for checking labels: 100%"}}, "db0ae4d8d2874f1285894feb90065a04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5d8bd5d5b83848dcb8b2033f0d8acdf5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "4ab9ee64b1fa48b488b7508e26edfecd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_db0ae4d8d2874f1285894feb90065a04", "placeholder": "\u200b", "style": "IPY_MODEL_5d8bd5d5b83848dcb8b2033f0d8acdf5", "value": " 30/30 [00:36<00:00, 1.22s/it]"}}, "6c9508f676774e3eb9bbcf828fadfb5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a545d3bcc78342ada9bf902f7c4a7210": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_469a9896f434443d914337d391527ddc", "IPY_MODEL_efa9c67a3f4c42138eea3dcdd09cffb9", "IPY_MODEL_4ab9ee64b1fa48b488b7508e26edfecd"], "layout": "IPY_MODEL_6c9508f676774e3eb9bbcf828fadfb5e"}}, "3764f1d408124e67b9b8ff059b1cd51e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "17fd32bd00f7422f82e222bd671eb1bf": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "2f006189586d4837b25208f274c1efda": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3764f1d408124e67b9b8ff059b1cd51e", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_17fd32bd00f7422f82e222bd671eb1bf", "value": 30.0}}, "1a8eeafa6ca74a69b2fa5817f03e46ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1c8d6e3a698d448d90427dc24f08fcae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "f18591d41acf461787dc84ddaf834e17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1a8eeafa6ca74a69b2fa5817f03e46ed", "placeholder": "\u200b", "style": "IPY_MODEL_1c8d6e3a698d448d90427dc24f08fcae", "value": "images processed using softmin: 100%"}}, "4503eade1b32403d863df7edfa8913d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e1512f3d87e742818dbc3ddba53349f5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "cd1c5ca7ad8b485cae2787936cba138c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4503eade1b32403d863df7edfa8913d0", "placeholder": "\u200b", "style": "IPY_MODEL_e1512f3d87e742818dbc3ddba53349f5", "value": " 30/30 [00:01<00:00, 23.68it/s]"}}, "e3c474e99b5a4dbda818585deb053e2b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3464fab77b1b47d58adbe02a6fa43f64": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_f18591d41acf461787dc84ddaf834e17", "IPY_MODEL_2f006189586d4837b25208f274c1efda", "IPY_MODEL_cd1c5ca7ad8b485cae2787936cba138c"], "layout": "IPY_MODEL_e3c474e99b5a4dbda818585deb053e2b"}}}, "version_major": 2, "version_minor": 0} +{"state": {"66b6aaa2007c4b1496649534471df1db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "639ef487ed3b42848922bc4d9bae0928": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "0870fef1c46e4952a7824e05fc431a34": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_66b6aaa2007c4b1496649534471df1db", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_639ef487ed3b42848922bc4d9bae0928", "value": 30.0}}, "56cad0e7c51f49fbb45538b279800284": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bf748ca37ab847a08123be51451437cc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "0193408c614a4e7ba4873c78a9a9bb6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_56cad0e7c51f49fbb45538b279800284", "placeholder": "\u200b", "style": "IPY_MODEL_bf748ca37ab847a08123be51451437cc", "value": "number of examples processed for estimating thresholds: 100%"}}, "0fa1181f8e9b40ffb9c9c728c90621b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "43b3b5ba40fa4150ac9fe67bc77e7088": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "8e940a4d278d48d5bb2e566940734bee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0fa1181f8e9b40ffb9c9c728c90621b5", "placeholder": "\u200b", "style": "IPY_MODEL_43b3b5ba40fa4150ac9fe67bc77e7088", "value": " 30/30 [00:00<00:00, 426.95it/s]"}}, "f9ed9bb79eea437e994e4b439e1c1812": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7834e62875da4394bdfc03b1501fa4a9": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_0193408c614a4e7ba4873c78a9a9bb6f", "IPY_MODEL_0870fef1c46e4952a7824e05fc431a34", "IPY_MODEL_8e940a4d278d48d5bb2e566940734bee"], "layout": "IPY_MODEL_f9ed9bb79eea437e994e4b439e1c1812"}}, "2f0f6f38ef674c39b106ff3aea167574": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4e02709833da447092b32b34e9cd7eea": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "11aed75ad6be4af3af712b1b7648058f": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2f0f6f38ef674c39b106ff3aea167574", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_4e02709833da447092b32b34e9cd7eea", "value": 30.0}}, "7d8733a782dc4953accc99a9a8b481c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "230fb679cc3c47188ee2b769167c7d0b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "29a7642e497c4e9b96745b11371f32ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7d8733a782dc4953accc99a9a8b481c9", "placeholder": "\u200b", "style": "IPY_MODEL_230fb679cc3c47188ee2b769167c7d0b", "value": "number of examples processed for checking labels: 100%"}}, "4b2548e688284560a51e01b3a7d4b30a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "737617a1f83a4f60aacfbd1431505249": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "512a68cdbfd0462688ff6b886482cf94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4b2548e688284560a51e01b3a7d4b30a", "placeholder": "\u200b", "style": "IPY_MODEL_737617a1f83a4f60aacfbd1431505249", "value": " 30/30 [00:36<00:00, 1.22s/it]"}}, "228f616c7eff4a4fbdd71be5df385c68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2e3415f4577c42cb941753dfe0086640": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_29a7642e497c4e9b96745b11371f32ef", "IPY_MODEL_11aed75ad6be4af3af712b1b7648058f", "IPY_MODEL_512a68cdbfd0462688ff6b886482cf94"], "layout": "IPY_MODEL_228f616c7eff4a4fbdd71be5df385c68"}}, "24f1f455503e4ea1905214c2491c6a8f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "edcc710314394fceac64a53876ee634d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "97e851aacf72467a845502595d456b40": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_24f1f455503e4ea1905214c2491c6a8f", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_edcc710314394fceac64a53876ee634d", "value": 30.0}}, "bb9aed613f8f4289816e5ab5039366fb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ffbaaada2c4e444c92e4121cd1a4d331": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "10f6d3dbf30f4eb9b246516eea1577b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bb9aed613f8f4289816e5ab5039366fb", "placeholder": "\u200b", "style": "IPY_MODEL_ffbaaada2c4e444c92e4121cd1a4d331", "value": "images processed using softmin: 100%"}}, "c5ba6d8db93145e486d14e332442a993": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "06974f46f32c4476932bd765003f434a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "cb6f5dbf185a4cbca65ae3e7c593eede": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c5ba6d8db93145e486d14e332442a993", "placeholder": "\u200b", "style": "IPY_MODEL_06974f46f32c4476932bd765003f434a", "value": " 30/30 [00:01<00:00, 22.49it/s]"}}, "7655e393baf14941a470099ab54010ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "df32bb08ce9040a1b285945a0db7765e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_10f6d3dbf30f4eb9b246516eea1577b6", "IPY_MODEL_97e851aacf72467a845502595d456b40", "IPY_MODEL_cb6f5dbf185a4cbca65ae3e7c593eede"], "layout": "IPY_MODEL_7655e393baf14941a470099ab54010ed"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb index 9b0ba559b..0b819b0e6 100644 --- a/master/tutorials/segmentation.ipynb +++ b/master/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:23.768822Z", - "iopub.status.busy": "2024-01-09T15:12:23.768628Z", - "iopub.status.idle": "2024-01-09T15:12:25.351894Z", - "shell.execute_reply": "2024-01-09T15:12:25.351148Z" + "iopub.execute_input": "2024-01-10T06:21:21.347024Z", + "iopub.status.busy": "2024-01-10T06:21:21.346437Z", + "iopub.status.idle": "2024-01-10T06:21:23.189285Z", + "shell.execute_reply": "2024-01-10T06:21:23.188541Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:12:25.354993Z", - "iopub.status.busy": "2024-01-09T15:12:25.354763Z", - "iopub.status.idle": "2024-01-09T15:13:21.827171Z", - "shell.execute_reply": "2024-01-09T15:13:21.826448Z" + "iopub.execute_input": "2024-01-10T06:21:23.192088Z", + "iopub.status.busy": "2024-01-10T06:21:23.191880Z", + "iopub.status.idle": "2024-01-10T06:22:22.377569Z", + "shell.execute_reply": "2024-01-10T06:22:22.376808Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:21.830029Z", - "iopub.status.busy": "2024-01-09T15:13:21.829817Z", - "iopub.status.idle": "2024-01-09T15:13:22.843182Z", - "shell.execute_reply": "2024-01-09T15:13:22.842504Z" + "iopub.execute_input": "2024-01-10T06:22:22.380517Z", + "iopub.status.busy": "2024-01-10T06:22:22.380259Z", + "iopub.status.idle": "2024-01-10T06:22:23.450564Z", + "shell.execute_reply": "2024-01-10T06:22:23.449819Z" }, "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\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": "2024-01-09T15:13:22.845974Z", - "iopub.status.busy": "2024-01-09T15:13:22.845660Z", - "iopub.status.idle": "2024-01-09T15:13:22.849100Z", - "shell.execute_reply": "2024-01-09T15:13:22.848547Z" + "iopub.execute_input": "2024-01-10T06:22:23.453830Z", + "iopub.status.busy": "2024-01-10T06:22:23.453429Z", + "iopub.status.idle": "2024-01-10T06:22:23.457253Z", + "shell.execute_reply": "2024-01-10T06:22:23.456696Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.851692Z", - "iopub.status.busy": "2024-01-09T15:13:22.851255Z", - "iopub.status.idle": "2024-01-09T15:13:22.855201Z", - "shell.execute_reply": "2024-01-09T15:13:22.854623Z" + "iopub.execute_input": "2024-01-10T06:22:23.459910Z", + "iopub.status.busy": "2024-01-10T06:22:23.459513Z", + "iopub.status.idle": "2024-01-10T06:22:23.463699Z", + "shell.execute_reply": "2024-01-10T06:22:23.463158Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.857552Z", - "iopub.status.busy": "2024-01-09T15:13:22.857165Z", - "iopub.status.idle": "2024-01-09T15:13:22.860833Z", - "shell.execute_reply": "2024-01-09T15:13:22.860317Z" + "iopub.execute_input": "2024-01-10T06:22:23.466289Z", + "iopub.status.busy": "2024-01-10T06:22:23.465906Z", + "iopub.status.idle": "2024-01-10T06:22:23.469976Z", + "shell.execute_reply": "2024-01-10T06:22:23.469453Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.863269Z", - "iopub.status.busy": "2024-01-09T15:13:22.862914Z", - "iopub.status.idle": "2024-01-09T15:13:22.865888Z", - "shell.execute_reply": "2024-01-09T15:13:22.865340Z" + "iopub.execute_input": "2024-01-10T06:22:23.472455Z", + "iopub.status.busy": "2024-01-10T06:22:23.472097Z", + "iopub.status.idle": "2024-01-10T06:22:23.475073Z", + "shell.execute_reply": "2024-01-10T06:22:23.474523Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:13:22.868261Z", - "iopub.status.busy": "2024-01-09T15:13:22.867899Z", - "iopub.status.idle": "2024-01-09T15:14:48.127035Z", - "shell.execute_reply": "2024-01-09T15:14:48.126241Z" + "iopub.execute_input": "2024-01-10T06:22:23.477540Z", + "iopub.status.busy": "2024-01-10T06:22:23.477156Z", + "iopub.status.idle": "2024-01-10T06:23:49.090785Z", + "shell.execute_reply": "2024-01-10T06:23:49.089964Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7af30476e914a1aa2bc7bb22a08f3a8", + "model_id": "7834e62875da4394bdfc03b1501fa4a9", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a545d3bcc78342ada9bf902f7c4a7210", + "model_id": "2e3415f4577c42cb941753dfe0086640", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:48.129939Z", - "iopub.status.busy": "2024-01-09T15:14:48.129719Z", - "iopub.status.idle": "2024-01-09T15:14:48.878845Z", - "shell.execute_reply": "2024-01-09T15:14:48.878240Z" + "iopub.execute_input": "2024-01-10T06:23:49.094028Z", + "iopub.status.busy": "2024-01-10T06:23:49.093573Z", + "iopub.status.idle": "2024-01-10T06:23:49.874024Z", + "shell.execute_reply": "2024-01-10T06:23:49.873433Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:48.881431Z", - "iopub.status.busy": "2024-01-09T15:14:48.881092Z", - "iopub.status.idle": "2024-01-09T15:14:51.000672Z", - "shell.execute_reply": "2024-01-09T15:14:51.000006Z" + "iopub.execute_input": "2024-01-10T06:23:49.876789Z", + "iopub.status.busy": "2024-01-10T06:23:49.876371Z", + "iopub.status.idle": "2024-01-10T06:23:52.004759Z", + "shell.execute_reply": "2024-01-10T06:23:52.004056Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:14:51.003552Z", - "iopub.status.busy": "2024-01-09T15:14:51.003156Z", - "iopub.status.idle": "2024-01-09T15:15:20.041523Z", - "shell.execute_reply": "2024-01-09T15:15:20.040827Z" + "iopub.execute_input": "2024-01-10T06:23:52.007358Z", + "iopub.status.busy": "2024-01-10T06:23:52.007130Z", + "iopub.status.idle": "2024-01-10T06:24:21.670500Z", + "shell.execute_reply": "2024-01-10T06:24:21.669841Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 17167/4997817 [00:00<00:29, 171663.62it/s]" + " 0%| | 17062/4997817 [00:00<00:29, 170604.30it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 34563/4997817 [00:00<00:28, 173008.20it/s]" + " 1%| | 34285/4997817 [00:00<00:28, 171551.99it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 52101/4997817 [00:00<00:28, 174089.17it/s]" + " 1%| | 51456/4997817 [00:00<00:28, 171618.24it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 69510/4997817 [00:00<00:28, 174019.46it/s]" + " 1%|▏ | 68618/4997817 [00:00<00:28, 170859.63it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 86997/4997817 [00:00<00:28, 174323.75it/s]" + " 2%|▏ | 85805/4997817 [00:00<00:28, 171220.50it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 104430/4997817 [00:00<00:28, 169724.91it/s]" + " 2%|▏ | 102949/4997817 [00:00<00:28, 171291.99it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 122139/4997817 [00:00<00:28, 172086.44it/s]" + " 2%|▏ | 120095/4997817 [00:00<00:28, 171343.43it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 140011/4997817 [00:00<00:27, 174169.28it/s]" + " 3%|▎ | 137230/4997817 [00:00<00:28, 170491.36it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 157761/4997817 [00:00<00:27, 175200.10it/s]" + " 3%|▎ | 154281/4997817 [00:00<00:28, 169872.65it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 175374/4997817 [00:01<00:27, 175483.47it/s]" + " 3%|▎ | 171280/4997817 [00:01<00:28, 169905.50it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 192985/4997817 [00:01<00:27, 175671.16it/s]" + " 4%|▍ | 188272/4997817 [00:01<00:28, 169777.04it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 210558/4997817 [00:01<00:27, 175484.74it/s]" + " 4%|▍ | 205575/4997817 [00:01<00:28, 170760.61it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 228111/4997817 [00:01<00:27, 175443.62it/s]" + " 4%|▍ | 222652/4997817 [00:01<00:27, 170740.73it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 245659/4997817 [00:01<00:27, 175322.61it/s]" + " 5%|▍ | 240083/4997817 [00:01<00:27, 171811.81it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 263194/4997817 [00:01<00:27, 175304.12it/s]" + " 5%|▌ | 257360/4997817 [00:01<00:27, 172096.03it/s]" ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 280759/4997817 [00:01<00:26, 175405.86it/s]" + " 5%|▌ | 274605/4997817 [00:01<00:27, 172199.32it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 298301/4997817 [00:01<00:26, 175109.69it/s]" + " 6%|▌ | 291886/4997817 [00:01<00:27, 172379.81it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 315813/4997817 [00:01<00:26, 174670.09it/s]" + " 6%|▌ | 309125/4997817 [00:01<00:27, 172152.38it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 333315/4997817 [00:01<00:26, 174769.54it/s]" + " 7%|▋ | 326341/4997817 [00:01<00:27, 171949.71it/s]" ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 350881/4997817 [00:02<00:26, 175033.97it/s]" + " 7%|▋ | 343537/4997817 [00:02<00:27, 171568.72it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 368385/4997817 [00:02<00:26, 174887.75it/s]" + " 7%|▋ | 360817/4997817 [00:02<00:26, 171933.30it/s]" ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 385876/4997817 [00:02<00:26, 174891.71it/s]" + " 8%|▊ | 378023/4997817 [00:02<00:26, 171967.12it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 403366/4997817 [00:02<00:26, 174567.29it/s]" + " 8%|▊ | 395220/4997817 [00:02<00:26, 171770.69it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 421002/4997817 [00:02<00:26, 175100.23it/s]" + " 8%|▊ | 412518/4997817 [00:02<00:26, 172129.64it/s]" ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 438513/4997817 [00:02<00:26, 174881.61it/s]" + " 9%|▊ | 429746/4997817 [00:02<00:26, 172171.06it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 456002/4997817 [00:02<00:26, 170509.73it/s]" + " 9%|▉ | 446964/4997817 [00:02<00:26, 171908.29it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 473898/4997817 [00:02<00:26, 172994.94it/s]" + " 9%|▉ | 464155/4997817 [00:02<00:26, 171900.62it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 491599/4997817 [00:02<00:25, 174181.60it/s]" + " 10%|▉ | 481346/4997817 [00:02<00:26, 171355.90it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 509182/4997817 [00:02<00:25, 174668.81it/s]" + " 10%|▉ | 498619/4997817 [00:02<00:26, 171763.37it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 526728/4997817 [00:03<00:25, 174903.04it/s]" + " 10%|█ | 515796/4997817 [00:03<00:26, 171727.53it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 544360/4997817 [00:03<00:25, 175322.34it/s]" + " 11%|█ | 532984/4997817 [00:03<00:25, 171768.71it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 561899/4997817 [00:03<00:25, 175266.03it/s]" + " 11%|█ | 550240/4997817 [00:03<00:25, 172001.07it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 579430/4997817 [00:03<00:25, 175198.49it/s]" + " 11%|█▏ | 567441/4997817 [00:03<00:25, 171961.52it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 596953/4997817 [00:03<00:25, 175077.39it/s]" + " 12%|█▏ | 584638/4997817 [00:03<00:25, 171920.86it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 614463/4997817 [00:03<00:25, 174881.61it/s]" + " 12%|█▏ | 601962/4997817 [00:03<00:25, 172313.17it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 631953/4997817 [00:03<00:24, 174652.96it/s]" + " 12%|█▏ | 619303/4997817 [00:03<00:25, 172639.01it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 649420/4997817 [00:03<00:24, 174008.43it/s]" + " 13%|█▎ | 636567/4997817 [00:03<00:25, 172200.43it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 666822/4997817 [00:03<00:24, 173979.60it/s]" + " 13%|█▎ | 653788/4997817 [00:03<00:25, 171788.02it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 684221/4997817 [00:03<00:24, 173683.21it/s]" + " 13%|█▎ | 670968/4997817 [00:03<00:25, 171514.66it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 701638/4997817 [00:04<00:24, 173825.45it/s]" + " 14%|█▍ | 688120/4997817 [00:04<00:25, 170996.88it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 719021/4997817 [00:04<00:24, 173395.93it/s]" + " 14%|█▍ | 705275/4997817 [00:04<00:25, 171158.72it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 736362/4997817 [00:04<00:24, 173372.01it/s]" + " 14%|█▍ | 722435/4997817 [00:04<00:24, 171285.85it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 753700/4997817 [00:04<00:24, 172675.44it/s]" + " 15%|█▍ | 739806/4997817 [00:04<00:24, 172008.35it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 771046/4997817 [00:04<00:24, 172906.82it/s]" + " 15%|█▌ | 757008/4997817 [00:04<00:24, 171692.60it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 788553/4997817 [00:04<00:24, 173549.65it/s]" + " 15%|█▌ | 774255/4997817 [00:04<00:24, 171920.65it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 806052/4997817 [00:04<00:24, 173977.96it/s]" + " 16%|█▌ | 791671/4997817 [00:04<00:24, 172588.53it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 823451/4997817 [00:04<00:25, 166777.78it/s]" + " 16%|█▌ | 809012/4997817 [00:04<00:24, 172830.16it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 840819/4997817 [00:04<00:24, 168781.92it/s]" + " 17%|█▋ | 826296/4997817 [00:04<00:24, 171982.40it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 858322/4997817 [00:04<00:24, 170614.13it/s]" + " 17%|█▋ | 843496/4997817 [00:04<00:24, 171799.85it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 875707/4997817 [00:05<00:24, 171568.88it/s]" + " 17%|█▋ | 860677/4997817 [00:05<00:24, 171324.91it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 893295/4997817 [00:05<00:23, 172845.52it/s]" + " 18%|█▊ | 877842/4997817 [00:05<00:24, 171418.27it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 910813/4997817 [00:05<00:23, 173537.82it/s]" + " 18%|█▊ | 894985/4997817 [00:05<00:23, 171219.42it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 928368/4997817 [00:05<00:23, 174136.97it/s]" + " 18%|█▊ | 912146/4997817 [00:05<00:23, 171332.99it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 945901/4997817 [00:05<00:23, 174490.87it/s]" + " 19%|█▊ | 929303/4997817 [00:05<00:23, 171399.71it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 963359/4997817 [00:05<00:23, 174450.39it/s]" + " 19%|█▉ | 946444/4997817 [00:05<00:23, 171377.87it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 981064/4997817 [00:05<00:22, 175224.68it/s]" + " 19%|█▉ | 963692/4997817 [00:05<00:23, 171703.39it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 998591/4997817 [00:05<00:23, 167695.83it/s]" + " 20%|█▉ | 981048/4997817 [00:05<00:23, 172255.53it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1016101/4997817 [00:05<00:23, 169844.83it/s]" + " 20%|█▉ | 998359/4997817 [00:05<00:23, 172508.56it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1033639/4997817 [00:05<00:23, 171465.64it/s]" + " 20%|██ | 1015635/4997817 [00:05<00:23, 172579.19it/s]" ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1051050/4997817 [00:06<00:22, 172244.70it/s]" + " 21%|██ | 1032893/4997817 [00:06<00:23, 171418.64it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1068535/4997817 [00:06<00:22, 173015.00it/s]" + " 21%|██ | 1050039/4997817 [00:06<00:23, 171426.89it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1085860/4997817 [00:06<00:22, 172171.64it/s]" + " 21%|██▏ | 1067207/4997817 [00:06<00:22, 171496.74it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1103362/4997817 [00:06<00:22, 173017.40it/s]" + " 22%|██▏ | 1084358/4997817 [00:06<00:22, 171268.82it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1120915/4997817 [00:06<00:22, 173763.05it/s]" + " 22%|██▏ | 1101486/4997817 [00:06<00:22, 171044.54it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1138318/4997817 [00:06<00:22, 173840.60it/s]" + " 22%|██▏ | 1118591/4997817 [00:06<00:22, 170922.72it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1155709/4997817 [00:06<00:22, 173789.28it/s]" + " 23%|██▎ | 1135684/4997817 [00:06<00:22, 170420.58it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1173093/4997817 [00:06<00:22, 168493.74it/s]" + " 23%|██▎ | 1152727/4997817 [00:06<00:22, 170051.09it/s]" ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1190527/4997817 [00:06<00:22, 170204.85it/s]" + " 23%|██▎ | 1169733/4997817 [00:06<00:22, 169752.62it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1207870/4997817 [00:06<00:22, 171154.12it/s]" + " 24%|██▎ | 1186709/4997817 [00:06<00:22, 169681.27it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1225428/4997817 [00:07<00:21, 172462.89it/s]" + " 24%|██▍ | 1203678/4997817 [00:07<00:22, 168904.33it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1242924/4997817 [00:07<00:21, 173202.16it/s]" + " 24%|██▍ | 1220652/4997817 [00:07<00:22, 169150.94it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1260258/4997817 [00:07<00:21, 173197.75it/s]" + " 25%|██▍ | 1237634/4997817 [00:07<00:22, 169345.74it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1277885/4997817 [00:07<00:21, 174113.24it/s]" + " 25%|██▌ | 1254570/4997817 [00:07<00:22, 168349.15it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1295506/4997817 [00:07<00:21, 174737.72it/s]" + " 25%|██▌ | 1271732/4997817 [00:07<00:22, 169320.67it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1313036/4997817 [00:07<00:21, 174904.98it/s]" + " 26%|██▌ | 1288750/4997817 [00:07<00:21, 169571.98it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1330678/4997817 [00:07<00:20, 175355.99it/s]" + " 26%|██▌ | 1305766/4997817 [00:07<00:21, 169742.82it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1348217/4997817 [00:07<00:21, 168529.45it/s]" + " 26%|██▋ | 1322775/4997817 [00:07<00:21, 169844.98it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1365827/4997817 [00:07<00:21, 170733.60it/s]" + " 27%|██▋ | 1339793/4997817 [00:07<00:21, 169941.26it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1383392/4997817 [00:07<00:20, 172176.28it/s]" + " 27%|██▋ | 1356788/4997817 [00:07<00:21, 169935.91it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1401070/4997817 [00:08<00:20, 173534.17it/s]" + " 27%|██▋ | 1373885/4997817 [00:08<00:21, 170242.78it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1418730/4997817 [00:08<00:20, 174443.73it/s]" + " 28%|██▊ | 1390910/4997817 [00:08<00:21, 169776.77it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▊ | 1436236/4997817 [00:08<00:20, 174624.39it/s]" + " 28%|██▊ | 1407889/4997817 [00:08<00:21, 169776.41it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1453753/4997817 [00:08<00:20, 174785.08it/s]" + " 29%|██▊ | 1424867/4997817 [00:08<00:21, 169328.10it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1471304/4997817 [00:08<00:20, 175000.98it/s]" + " 29%|██▉ | 1441920/4997817 [00:08<00:20, 169683.76it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1488878/4997817 [00:08<00:20, 175219.95it/s]" + " 29%|██▉ | 1458889/4997817 [00:08<00:20, 169554.49it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1506507/4997817 [00:08<00:19, 175538.97it/s]" + " 30%|██▉ | 1475845/4997817 [00:08<00:20, 169207.98it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1524065/4997817 [00:08<00:19, 175328.61it/s]" + " 30%|██▉ | 1492767/4997817 [00:08<00:20, 169207.52it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1541696/4997817 [00:08<00:19, 175616.93it/s]" + " 30%|███ | 1509688/4997817 [00:08<00:20, 169069.76it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1559309/4997817 [00:08<00:19, 175767.69it/s]" + " 31%|███ | 1526596/4997817 [00:08<00:20, 168995.55it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1576933/4997817 [00:09<00:19, 175904.41it/s]" + " 31%|███ | 1543496/4997817 [00:09<00:20, 168767.01it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1594572/4997817 [00:09<00:19, 176045.26it/s]" + " 31%|███ | 1560373/4997817 [00:09<00:20, 168411.56it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1612238/4997817 [00:09<00:19, 176226.37it/s]" + " 32%|███▏ | 1577215/4997817 [00:09<00:20, 168093.60it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1629862/4997817 [00:09<00:19, 176141.34it/s]" + " 32%|███▏ | 1594025/4997817 [00:09<00:20, 167736.99it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1647477/4997817 [00:09<00:19, 175468.17it/s]" + " 32%|███▏ | 1610799/4997817 [00:09<00:20, 167579.26it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1665025/4997817 [00:09<00:19, 174739.75it/s]" + " 33%|███▎ | 1627581/4997817 [00:09<00:20, 167648.89it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1682608/4997817 [00:09<00:18, 175060.85it/s]" + " 33%|███▎ | 1644346/4997817 [00:09<00:20, 167601.44it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1700172/4997817 [00:09<00:18, 175230.78it/s]" + " 33%|███▎ | 1661107/4997817 [00:09<00:19, 167408.64it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1717696/4997817 [00:09<00:18, 172854.20it/s]" + " 34%|███▎ | 1678158/4997817 [00:09<00:19, 168332.79it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1735206/4997817 [00:09<00:18, 173517.80it/s]" + " 34%|███▍ | 1695148/4997817 [00:09<00:19, 168799.16it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1752699/4997817 [00:10<00:18, 173933.20it/s]" + " 34%|███▍ | 1712162/4997817 [00:10<00:19, 169197.65it/s]" ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1770288/4997817 [00:10<00:18, 174514.40it/s]" + " 35%|███▍ | 1729082/4997817 [00:10<00:19, 169086.37it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1788042/4997817 [00:10<00:18, 175415.06it/s]" + " 35%|███▍ | 1746091/4997817 [00:10<00:19, 169383.58it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1805801/4997817 [00:10<00:18, 176062.64it/s]" + " 35%|███▌ | 1763030/4997817 [00:10<00:19, 169294.56it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1823541/4997817 [00:10<00:17, 176461.35it/s]" + " 36%|███▌ | 1779986/4997817 [00:10<00:18, 169370.78it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1841194/4997817 [00:10<00:17, 176479.57it/s]" + " 36%|███▌ | 1796940/4997817 [00:10<00:18, 169415.99it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1858933/4997817 [00:10<00:17, 176749.46it/s]" + " 36%|███▋ | 1813941/4997817 [00:10<00:18, 169588.95it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1876670/4997817 [00:10<00:17, 176931.87it/s]" + " 37%|███▋ | 1830917/4997817 [00:10<00:18, 169635.93it/s]" ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1894364/4997817 [00:10<00:17, 173999.82it/s]" + " 37%|███▋ | 1847932/4997817 [00:10<00:18, 169786.39it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1911815/4997817 [00:10<00:17, 174148.01it/s]" + " 37%|███▋ | 1864911/4997817 [00:10<00:18, 169662.56it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 1929373/4997817 [00:11<00:17, 174571.30it/s]" + " 38%|███▊ | 1881878/4997817 [00:11<00:18, 169612.46it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1946838/4997817 [00:11<00:17, 174592.83it/s]" + " 38%|███▊ | 1898840/4997817 [00:11<00:18, 169466.51it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1964377/4997817 [00:11<00:17, 174828.30it/s]" + " 38%|███▊ | 1915967/4997817 [00:11<00:18, 170001.55it/s]" ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1981863/4997817 [00:11<00:17, 174660.08it/s]" + " 39%|███▊ | 1932968/4997817 [00:11<00:18, 169939.64it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 1999332/4997817 [00:11<00:17, 174199.29it/s]" + " 39%|███▉ | 1949963/4997817 [00:11<00:17, 169565.43it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2016768/4997817 [00:11<00:17, 174244.63it/s]" + " 39%|███▉ | 1966920/4997817 [00:11<00:17, 168922.06it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2034194/4997817 [00:11<00:17, 174179.70it/s]" + " 40%|███▉ | 1983850/4997817 [00:11<00:17, 169030.80it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2051629/4997817 [00:11<00:16, 174227.12it/s]" + " 40%|████ | 2000770/4997817 [00:11<00:17, 169076.88it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2069053/4997817 [00:11<00:17, 166828.01it/s]" + " 40%|████ | 2017733/4997817 [00:11<00:17, 169238.17it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2086587/4997817 [00:12<00:17, 169299.81it/s]" + " 41%|████ | 2034658/4997817 [00:11<00:17, 168730.09it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2104063/4997817 [00:12<00:16, 170898.22it/s]" + " 41%|████ | 2051532/4997817 [00:12<00:17, 168386.60it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2121442/4997817 [00:12<00:16, 171751.23it/s]" + " 41%|████▏ | 2068372/4997817 [00:12<00:17, 168018.51it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2138648/4997817 [00:12<00:16, 171573.44it/s]" + " 42%|████▏ | 2085260/4997817 [00:12<00:17, 168270.79it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2156067/4997817 [00:12<00:16, 172348.88it/s]" + " 42%|████▏ | 2102088/4997817 [00:12<00:17, 168030.44it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2173478/4997817 [00:12<00:16, 172872.90it/s]" + " 42%|████▏ | 2118962/4997817 [00:12<00:17, 168240.31it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2190870/4997817 [00:12<00:16, 173182.02it/s]" + " 43%|████▎ | 2135787/4997817 [00:12<00:17, 168132.74it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2208197/4997817 [00:12<00:16, 171459.04it/s]" + " 43%|████▎ | 2152601/4997817 [00:12<00:16, 167646.09it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2225613/4997817 [00:12<00:16, 172258.98it/s]" + " 43%|████▎ | 2169423/4997817 [00:12<00:16, 167813.54it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2243049/4997817 [00:12<00:15, 172882.18it/s]" + " 44%|████▎ | 2186297/4997817 [00:12<00:16, 168085.58it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2260546/4997817 [00:13<00:15, 173502.26it/s]" + " 44%|████▍ | 2203175/4997817 [00:12<00:16, 168289.27it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2278218/4997817 [00:13<00:15, 174462.04it/s]" + " 44%|████▍ | 2220005/4997817 [00:13<00:16, 168276.19it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2295807/4997817 [00:13<00:15, 174885.89it/s]" + " 45%|████▍ | 2236833/4997817 [00:13<00:16, 168121.20it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2313433/4997817 [00:13<00:15, 175295.26it/s]" + " 45%|████▌ | 2253646/4997817 [00:13<00:16, 168054.37it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2331175/4997817 [00:13<00:15, 175929.00it/s]" + " 45%|████▌ | 2270452/4997817 [00:13<00:16, 167426.00it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2349033/4997817 [00:13<00:14, 176719.93it/s]" + " 46%|████▌ | 2287196/4997817 [00:13<00:16, 167340.06it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2366710/4997817 [00:13<00:14, 176733.35it/s]" + " 46%|████▌ | 2303931/4997817 [00:13<00:16, 167280.92it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2384543/4997817 [00:13<00:14, 177210.55it/s]" + " 46%|████▋ | 2320696/4997817 [00:13<00:15, 167387.15it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2402270/4997817 [00:13<00:14, 177227.04it/s]" + " 47%|████▋ | 2337572/4997817 [00:13<00:15, 167794.33it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2420118/4997817 [00:13<00:14, 177598.07it/s]" + " 47%|████▋ | 2354510/4997817 [00:13<00:15, 168265.82it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2437879/4997817 [00:14<00:14, 176578.85it/s]" + " 47%|████▋ | 2371337/4997817 [00:13<00:15, 168146.95it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2455539/4997817 [00:14<00:14, 175833.38it/s]" + " 48%|████▊ | 2388246/4997817 [00:14<00:15, 168426.12it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2473124/4997817 [00:14<00:14, 175407.22it/s]" + " 48%|████▊ | 2405089/4997817 [00:14<00:15, 167971.38it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2490666/4997817 [00:14<00:14, 174637.69it/s]" + " 48%|████▊ | 2421887/4997817 [00:14<00:15, 167875.18it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2508131/4997817 [00:14<00:14, 174283.98it/s]" + " 49%|████▉ | 2438756/4997817 [00:14<00:15, 168116.52it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2525561/4997817 [00:14<00:14, 173899.65it/s]" + " 49%|████▉ | 2455622/4997817 [00:14<00:15, 168275.76it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2542952/4997817 [00:14<00:14, 173797.89it/s]" + " 49%|████▉ | 2472450/4997817 [00:14<00:15, 168246.55it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2560333/4997817 [00:14<00:14, 173525.09it/s]" + " 50%|████▉ | 2489275/4997817 [00:14<00:14, 168100.11it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2577686/4997817 [00:14<00:13, 173241.51it/s]" + " 50%|█████ | 2506165/4997817 [00:14<00:14, 168336.59it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2595051/4997817 [00:14<00:13, 173359.26it/s]" + " 50%|█████ | 2523041/4997817 [00:14<00:14, 168459.70it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2612388/4997817 [00:15<00:14, 169230.15it/s]" + " 51%|█████ | 2539888/4997817 [00:14<00:14, 168363.19it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2629603/4997817 [00:15<00:13, 170087.47it/s]" + " 51%|█████ | 2556725/4997817 [00:15<00:14, 168317.16it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2646951/4997817 [00:15<00:13, 171089.93it/s]" + " 51%|█████▏ | 2573557/4997817 [00:15<00:14, 168314.33it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2664305/4997817 [00:15<00:13, 171815.08it/s]" + " 52%|█████▏ | 2590389/4997817 [00:15<00:14, 168159.93it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2681708/4997817 [00:15<00:13, 172472.05it/s]" + " 52%|█████▏ | 2607206/4997817 [00:15<00:14, 168024.45it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2698963/4997817 [00:15<00:13, 172107.20it/s]" + " 53%|█████▎ | 2624021/4997817 [00:15<00:14, 168057.67it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2716210/4997817 [00:15<00:13, 172212.13it/s]" + " 53%|█████▎ | 2640827/4997817 [00:15<00:14, 167450.44it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2733440/4997817 [00:15<00:13, 172235.01it/s]" + " 53%|█████▎ | 2657654/4997817 [00:15<00:13, 167690.91it/s]" ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2750666/4997817 [00:15<00:13, 172154.34it/s]" + " 54%|█████▎ | 2674611/4997817 [00:15<00:13, 168247.89it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2767939/4997817 [00:15<00:12, 172322.92it/s]" + " 54%|█████▍ | 2691437/4997817 [00:15<00:13, 168060.02it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2785173/4997817 [00:16<00:12, 171849.28it/s]" + " 54%|█████▍ | 2708244/4997817 [00:15<00:13, 167385.05it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2802495/4997817 [00:16<00:12, 172256.75it/s]" + " 55%|█████▍ | 2725000/4997817 [00:16<00:13, 167432.83it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2819790/4997817 [00:16<00:12, 172463.36it/s]" + " 55%|█████▍ | 2741821/4997817 [00:16<00:13, 167661.64it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2837136/4997817 [00:16<00:12, 172759.37it/s]" + " 55%|█████▌ | 2758588/4997817 [00:16<00:13, 167266.26it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2854477/4997817 [00:16<00:12, 172952.56it/s]" + " 56%|█████▌ | 2775352/4997817 [00:16<00:13, 167374.50it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2871773/4997817 [00:16<00:12, 172939.88it/s]" + " 56%|█████▌ | 2792090/4997817 [00:16<00:13, 167063.45it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2889068/4997817 [00:16<00:12, 172847.45it/s]" + " 56%|█████▌ | 2808802/4997817 [00:16<00:13, 167076.13it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2906353/4997817 [00:16<00:12, 172745.55it/s]" + " 57%|█████▋ | 2825510/4997817 [00:16<00:13, 167042.04it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2923691/4997817 [00:16<00:11, 172931.79it/s]" + " 57%|█████▋ | 2842215/4997817 [00:16<00:12, 166910.37it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2940985/4997817 [00:16<00:11, 172739.60it/s]" + " 57%|█████▋ | 2858913/4997817 [00:16<00:12, 166926.75it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2958260/4997817 [00:17<00:11, 172302.98it/s]" + " 58%|█████▊ | 2875624/4997817 [00:16<00:12, 166976.93it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2975491/4997817 [00:17<00:11, 172241.73it/s]" + " 58%|█████▊ | 2892322/4997817 [00:17<00:12, 166542.29it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2992853/4997817 [00:17<00:11, 172653.15it/s]" + " 58%|█████▊ | 2909084/4997817 [00:17<00:12, 166860.46it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3010119/4997817 [00:17<00:11, 172640.95it/s]" + " 59%|█████▊ | 2925771/4997817 [00:17<00:12, 166846.81it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3027432/4997817 [00:17<00:11, 172784.96it/s]" + " 59%|█████▉ | 2942500/4997817 [00:17<00:12, 166975.59it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3044748/4997817 [00:17<00:11, 172894.22it/s]" + " 59%|█████▉ | 2959198/4997817 [00:17<00:12, 166708.79it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3062551/4997817 [00:17<00:11, 174432.89it/s]" + " 60%|█████▉ | 2975877/4997817 [00:17<00:12, 166728.16it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3080339/4997817 [00:17<00:10, 175465.53it/s]" + " 60%|█████▉ | 2992600/4997817 [00:17<00:12, 166874.28it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3098010/4997817 [00:17<00:10, 175837.69it/s]" + " 60%|██████ | 3009402/4997817 [00:17<00:11, 167212.72it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3115791/4997817 [00:17<00:10, 176425.65it/s]" + " 61%|██████ | 3026124/4997817 [00:17<00:11, 166887.50it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3133479/4997817 [00:18<00:10, 176556.43it/s]" + " 61%|██████ | 3042813/4997817 [00:17<00:11, 166464.96it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3151135/4997817 [00:18<00:10, 176251.63it/s]" + " 61%|██████ | 3059512/4997817 [00:18<00:11, 166617.29it/s]" ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3168923/4997817 [00:18<00:10, 176737.54it/s]" + " 62%|██████▏ | 3076399/4997817 [00:18<00:11, 167287.27it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3186597/4997817 [00:18<00:10, 176524.64it/s]" + " 62%|██████▏ | 3093129/4997817 [00:18<00:11, 167242.52it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3204269/4997817 [00:18<00:10, 176580.18it/s]" + " 62%|██████▏ | 3109854/4997817 [00:18<00:11, 164570.09it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3221928/4997817 [00:18<00:10, 176258.76it/s]" + " 63%|██████▎ | 3127061/4997817 [00:18<00:11, 166789.86it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3239689/4997817 [00:18<00:09, 176650.37it/s]" + " 63%|██████▎ | 3144295/4997817 [00:18<00:11, 168437.03it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3257355/4997817 [00:18<00:09, 176583.82it/s]" + " 63%|██████▎ | 3161147/4997817 [00:18<00:10, 168085.75it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3275014/4997817 [00:18<00:09, 176334.31it/s]" + " 64%|██████▎ | 3178372/4997817 [00:18<00:10, 169324.97it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3292787/4997817 [00:18<00:09, 176725.14it/s]" + " 64%|██████▍ | 3195555/4997817 [00:18<00:10, 170071.05it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3310460/4997817 [00:19<00:09, 172711.10it/s]" + " 64%|██████▍ | 3212860/4997817 [00:18<00:10, 170957.60it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3327849/4997817 [00:19<00:09, 173056.73it/s]" + " 65%|██████▍ | 3230096/4997817 [00:19<00:10, 171374.10it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3345397/4997817 [00:19<00:09, 173773.58it/s]" + " 65%|██████▍ | 3247326/4997817 [00:19<00:10, 171646.00it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3362816/4997817 [00:19<00:09, 173894.91it/s]" + " 65%|██████▌ | 3264493/4997817 [00:19<00:10, 171542.68it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3380358/4997817 [00:19<00:09, 174348.79it/s]" + " 66%|██████▌ | 3281649/4997817 [00:19<00:10, 171525.74it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3397799/4997817 [00:19<00:09, 174225.53it/s]" + " 66%|██████▌ | 3298803/4997817 [00:19<00:09, 171132.61it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3415226/4997817 [00:19<00:09, 174136.23it/s]" + " 66%|██████▋ | 3315917/4997817 [00:19<00:09, 170861.86it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3432753/4997817 [00:19<00:08, 174473.32it/s]" + " 67%|██████▋ | 3333004/4997817 [00:19<00:09, 170374.69it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3450203/4997817 [00:19<00:08, 174440.02it/s]" + " 67%|██████▋ | 3350043/4997817 [00:19<00:09, 170335.87it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3467649/4997817 [00:19<00:08, 174080.94it/s]" + " 67%|██████▋ | 3367100/4997817 [00:19<00:09, 170400.36it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3485059/4997817 [00:20<00:08, 172843.48it/s]" + " 68%|██████▊ | 3384218/4997817 [00:19<00:09, 170631.02it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3502346/4997817 [00:20<00:08, 166810.04it/s]" + " 68%|██████▊ | 3401284/4997817 [00:20<00:09, 170636.34it/s]" ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3519703/4997817 [00:20<00:08, 168777.34it/s]" + " 68%|██████▊ | 3418357/4997817 [00:20<00:09, 170658.72it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3537300/4997817 [00:20<00:08, 170887.58it/s]" + " 69%|██████▊ | 3435515/4997817 [00:20<00:09, 170931.41it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3554810/4997817 [00:20<00:08, 172129.81it/s]" + " 69%|██████▉ | 3452609/4997817 [00:20<00:09, 163949.22it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3572407/4997817 [00:20<00:08, 173268.31it/s]" + " 69%|██████▉ | 3469894/4997817 [00:20<00:09, 166538.18it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3590035/4997817 [00:20<00:08, 174161.61it/s]" + " 70%|██████▉ | 3486928/4997817 [00:20<00:09, 167650.09it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3607587/4997817 [00:20<00:07, 174563.62it/s]" + " 70%|███████ | 3503919/4997817 [00:20<00:08, 168314.40it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3625281/4997817 [00:20<00:07, 175270.03it/s]" + " 70%|███████ | 3520963/4997817 [00:20<00:08, 168940.54it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3642873/4997817 [00:20<00:07, 175463.20it/s]" + " 71%|███████ | 3538111/4997817 [00:20<00:08, 169693.53it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3660486/4997817 [00:21<00:07, 175659.55it/s]" + " 71%|███████ | 3555434/4997817 [00:20<00:08, 170745.59it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3678068/4997817 [00:21<00:07, 175704.41it/s]" + " 71%|███████▏ | 3572660/4997817 [00:21<00:08, 171192.35it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3695673/4997817 [00:21<00:07, 175805.15it/s]" + " 72%|███████▏ | 3590030/4997817 [00:21<00:08, 171938.61it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3713256/4997817 [00:21<00:07, 175721.21it/s]" + " 72%|███████▏ | 3607303/4997817 [00:21<00:08, 172172.46it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3730852/4997817 [00:21<00:07, 175791.21it/s]" + " 73%|███████▎ | 3624594/4997817 [00:21<00:07, 172387.33it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3748432/4997817 [00:21<00:07, 175657.15it/s]" + " 73%|███████▎ | 3641836/4997817 [00:21<00:07, 171859.14it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3765999/4997817 [00:21<00:07, 175309.41it/s]" + " 73%|███████▎ | 3659025/4997817 [00:21<00:07, 171633.99it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3783531/4997817 [00:21<00:06, 175154.10it/s]" + " 74%|███████▎ | 3676190/4997817 [00:21<00:07, 171400.84it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3801047/4997817 [00:21<00:06, 175154.54it/s]" + " 74%|███████▍ | 3693332/4997817 [00:21<00:07, 171243.48it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 3818563/4997817 [00:21<00:06, 174964.54it/s]" + " 74%|███████▍ | 3710458/4997817 [00:21<00:07, 171013.54it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3836060/4997817 [00:22<00:06, 174874.20it/s]" + " 75%|███████▍ | 3727633/4997817 [00:21<00:07, 171229.98it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3853548/4997817 [00:22<00:06, 167436.37it/s]" + " 75%|███████▍ | 3744757/4997817 [00:22<00:07, 171012.21it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3870994/4997817 [00:22<00:06, 169474.31it/s]" + " 75%|███████▌ | 3761899/4997817 [00:22<00:07, 171129.30it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3888398/4997817 [00:22<00:06, 170810.31it/s]" + " 76%|███████▌ | 3779084/4997817 [00:22<00:07, 171340.31it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3905737/4997817 [00:22<00:06, 171571.35it/s]" + " 76%|███████▌ | 3796219/4997817 [00:22<00:07, 167422.50it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3923054/4997817 [00:22<00:06, 172044.02it/s]" + " 76%|███████▋ | 3813525/4997817 [00:22<00:07, 169081.57it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3940335/4997817 [00:22<00:06, 172268.67it/s]" + " 77%|███████▋ | 3830826/4997817 [00:22<00:06, 170242.46it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3957626/4997817 [00:22<00:06, 172456.39it/s]" + " 77%|███████▋ | 3847923/4997817 [00:22<00:06, 170454.10it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3974897/4997817 [00:22<00:05, 172529.53it/s]" + " 77%|███████▋ | 3865103/4997817 [00:22<00:06, 170852.37it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3992172/4997817 [00:22<00:05, 172593.33it/s]" + " 78%|███████▊ | 3882263/4997817 [00:22<00:06, 171072.12it/s]" ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4009477/4997817 [00:23<00:05, 172727.41it/s]" + " 78%|███████▊ | 3899433/4997817 [00:22<00:06, 171255.46it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4026754/4997817 [00:23<00:05, 172335.34it/s]" + " 78%|███████▊ | 3916562/4997817 [00:23<00:06, 170928.63it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4044009/4997817 [00:23<00:05, 172396.06it/s]" + " 79%|███████▊ | 3933884/4997817 [00:23<00:06, 171610.47it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4061251/4997817 [00:23<00:05, 172348.89it/s]" + " 79%|███████▉ | 3951103/4997817 [00:23<00:06, 171779.99it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4078488/4997817 [00:23<00:05, 172038.75it/s]" + " 79%|███████▉ | 3968283/4997817 [00:23<00:05, 171759.78it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4095693/4997817 [00:23<00:05, 171753.78it/s]" + " 80%|███████▉ | 3985460/4997817 [00:23<00:05, 171748.29it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4112870/4997817 [00:23<00:05, 171591.26it/s]" + " 80%|████████ | 4002799/4997817 [00:23<00:05, 172236.63it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4130047/4997817 [00:23<00:05, 171641.11it/s]" + " 80%|████████ | 4020024/4997817 [00:23<00:05, 172120.48it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4147220/4997817 [00:23<00:04, 171663.86it/s]" + " 81%|████████ | 4037255/4997817 [00:23<00:05, 172171.92it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4164387/4997817 [00:23<00:04, 171623.45it/s]" + " 81%|████████ | 4054562/4997817 [00:23<00:05, 172438.26it/s]" ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▎ | 4181550/4997817 [00:24<00:04, 171620.07it/s]" + " 81%|████████▏ | 4071846/4997817 [00:23<00:05, 172553.59it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4198713/4997817 [00:24<00:04, 167684.36it/s]" + " 82%|████████▏ | 4089102/4997817 [00:24<00:05, 172236.48it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4216323/4997817 [00:24<00:04, 170162.44it/s]" + " 82%|████████▏ | 4106326/4997817 [00:24<00:05, 172177.95it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4233659/4997817 [00:24<00:04, 171107.67it/s]" + " 83%|████████▎ | 4123603/4997817 [00:24<00:05, 172350.70it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4251153/4997817 [00:24<00:04, 172246.18it/s]" + " 83%|████████▎ | 4140839/4997817 [00:24<00:04, 171954.68it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4268639/4997817 [00:24<00:04, 173024.33it/s]" + " 83%|████████▎ | 4158035/4997817 [00:24<00:04, 168862.02it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4286227/4997817 [00:24<00:04, 173876.71it/s]" + " 84%|████████▎ | 4175226/4997817 [00:24<00:04, 169758.76it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4303621/4997817 [00:24<00:03, 173857.38it/s]" + " 84%|████████▍ | 4192212/4997817 [00:24<00:04, 168849.98it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▋ | 4321090/4997817 [00:24<00:03, 174105.68it/s]" + " 84%|████████▍ | 4209250/4997817 [00:24<00:04, 169300.67it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4338553/4997817 [00:24<00:03, 174261.17it/s]" + " 85%|████████▍ | 4226465/4997817 [00:24<00:04, 170143.89it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4356036/4997817 [00:25<00:03, 174427.95it/s]" + " 85%|████████▍ | 4243806/4997817 [00:24<00:04, 171114.46it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4373481/4997817 [00:25<00:03, 174054.32it/s]" + " 85%|████████▌ | 4261227/4997817 [00:25<00:04, 172034.61it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4391185/4997817 [00:25<00:03, 174944.98it/s]" + " 86%|████████▌ | 4278528/4997817 [00:25<00:04, 172320.83it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4408767/4997817 [00:25<00:03, 175203.90it/s]" + " 86%|████████▌ | 4295937/4997817 [00:25<00:04, 172845.19it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 4426338/4997817 [00:25<00:03, 175352.99it/s]" + " 86%|████████▋ | 4313308/4997817 [00:25<00:03, 173100.25it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4443874/4997817 [00:25<00:03, 175167.97it/s]" + " 87%|████████▋ | 4330658/4997817 [00:25<00:03, 173214.54it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4461426/4997817 [00:25<00:03, 175270.87it/s]" + " 87%|████████▋ | 4347981/4997817 [00:25<00:03, 173208.95it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4478954/4997817 [00:25<00:02, 174505.85it/s]" + " 87%|████████▋ | 4365307/4997817 [00:25<00:03, 173218.73it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4496406/4997817 [00:25<00:02, 174301.33it/s]" + " 88%|████████▊ | 4382688/4997817 [00:25<00:03, 173390.64it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4513837/4997817 [00:25<00:02, 174251.43it/s]" + " 88%|████████▊ | 4400068/4997817 [00:25<00:03, 173507.38it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4531366/4997817 [00:26<00:02, 174558.50it/s]" + " 88%|████████▊ | 4417419/4997817 [00:25<00:03, 173309.43it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4548843/4997817 [00:26<00:02, 174618.77it/s]" + " 89%|████████▊ | 4434751/4997817 [00:26<00:03, 172930.69it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4566306/4997817 [00:26<00:02, 174572.68it/s]" + " 89%|████████▉ | 4452045/4997817 [00:26<00:03, 172511.48it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4583764/4997817 [00:26<00:02, 174261.05it/s]" + " 89%|████████▉ | 4469335/4997817 [00:26<00:03, 172622.79it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4601191/4997817 [00:26<00:02, 173985.59it/s]" + " 90%|████████▉ | 4486598/4997817 [00:26<00:02, 171589.70it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4618615/4997817 [00:26<00:02, 174059.41it/s]" + " 90%|█████████ | 4503759/4997817 [00:26<00:02, 171412.36it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4636022/4997817 [00:26<00:02, 173946.39it/s]" + " 90%|█████████ | 4520902/4997817 [00:26<00:02, 171343.15it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4653417/4997817 [00:26<00:01, 173914.36it/s]" + " 91%|█████████ | 4538037/4997817 [00:26<00:02, 171239.33it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4670809/4997817 [00:26<00:01, 173866.67it/s]" + " 91%|█████████ | 4555162/4997817 [00:26<00:02, 170876.40it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4688210/4997817 [00:26<00:01, 173907.06it/s]" + " 91%|█████████▏| 4572250/4997817 [00:26<00:02, 170565.73it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4705644/4997817 [00:27<00:01, 174033.16it/s]" + " 92%|█████████▏| 4589307/4997817 [00:27<00:02, 170097.89it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4723065/4997817 [00:27<00:01, 174084.11it/s]" + " 92%|█████████▏| 4606318/4997817 [00:27<00:02, 169865.84it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4740474/4997817 [00:27<00:01, 173944.40it/s]" + " 93%|█████████▎| 4623305/4997817 [00:27<00:02, 169631.05it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4757869/4997817 [00:27<00:01, 173731.59it/s]" + " 93%|█████████▎| 4640269/4997817 [00:27<00:02, 168872.65it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4775243/4997817 [00:27<00:01, 173526.32it/s]" + " 93%|█████████▎| 4657235/4997817 [00:27<00:02, 169105.83it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4792596/4997817 [00:27<00:01, 173265.11it/s]" + " 94%|█████████▎| 4674147/4997817 [00:27<00:01, 168226.74it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4809923/4997817 [00:27<00:01, 170701.37it/s]" + " 94%|█████████▍| 4690976/4997817 [00:27<00:01, 168242.05it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4827002/4997817 [00:27<00:01, 168010.73it/s]" + " 94%|█████████▍| 4707801/4997817 [00:27<00:01, 168102.89it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4844469/4997817 [00:27<00:00, 169967.72it/s]" + " 95%|█████████▍| 4724647/4997817 [00:27<00:01, 168205.59it/s]" ] }, { @@ -2762,7 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4861913/4997817 [00:28<00:00, 171289.71it/s]" + " 95%|█████████▍| 4741468/4997817 [00:27<00:01, 167808.58it/s]" ] }, { @@ -2770,7 +2770,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4879328/4997817 [00:28<00:00, 172138.29it/s]" + " 95%|█████████▌| 4758250/4997817 [00:28<00:01, 167370.08it/s]" ] }, { @@ -2778,7 +2778,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4896647/4997817 [00:28<00:00, 172449.50it/s]" + " 96%|█████████▌| 4774988/4997817 [00:28<00:01, 167262.31it/s]" ] }, { @@ -2786,7 +2786,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4913898/4997817 [00:28<00:00, 170167.36it/s]" + " 96%|█████████▌| 4791806/4997817 [00:28<00:01, 167533.12it/s]" ] }, { @@ -2794,7 +2794,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▊| 4931534/4997817 [00:28<00:00, 171999.39it/s]" + " 96%|█████████▌| 4808560/4997817 [00:28<00:01, 167179.89it/s]" ] }, { @@ -2802,7 +2802,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4949175/4997817 [00:28<00:00, 173309.56it/s]" + " 97%|█████████▋| 4825279/4997817 [00:28<00:01, 167129.34it/s]" ] }, { @@ -2810,7 +2810,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4966657/4997817 [00:28<00:00, 173755.94it/s]" + " 97%|█████████▋| 4842096/4997817 [00:28<00:00, 167436.03it/s]" ] }, { @@ -2818,7 +2818,7 @@ "output_type": "stream", "text": [ "\r", - "100%|█████████▉| 4984241/4997817 [00:28<00:00, 174376.33it/s]" + " 97%|█████████▋| 4859300/4997817 [00:28<00:00, 168811.84it/s]" ] }, { @@ -2826,7 +2826,71 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 4997817/4997817 [00:28<00:00, 173607.01it/s]" + " 98%|█████████▊| 4876497/4997817 [00:28<00:00, 169752.70it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4893694/4997817 [00:28<00:00, 170413.45it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4910769/4997817 [00:28<00:00, 170510.26it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▊| 4927821/4997817 [00:29<00:00, 170169.42it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4944989/4997817 [00:29<00:00, 170616.62it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4962051/4997817 [00:29<00:00, 169773.92it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4979093/4997817 [00:29<00:00, 169963.20it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4996091/4997817 [00:29<00:00, 169052.24it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 4997817/4997817 [00:29<00:00, 169841.26it/s]" ] }, { @@ -3065,10 +3129,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:20.044089Z", - "iopub.status.busy": "2024-01-09T15:15:20.043779Z", - "iopub.status.idle": "2024-01-09T15:15:27.150650Z", - "shell.execute_reply": "2024-01-09T15:15:27.149974Z" + "iopub.execute_input": "2024-01-10T06:24:21.672999Z", + "iopub.status.busy": "2024-01-10T06:24:21.672753Z", + "iopub.status.idle": "2024-01-10T06:24:28.676821Z", + "shell.execute_reply": "2024-01-10T06:24:28.676174Z" } }, "outputs": [], @@ -3082,10 +3146,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:27.153846Z", - "iopub.status.busy": "2024-01-09T15:15:27.153367Z", - "iopub.status.idle": "2024-01-09T15:15:30.339440Z", - "shell.execute_reply": "2024-01-09T15:15:30.338776Z" + "iopub.execute_input": "2024-01-10T06:24:28.679808Z", + "iopub.status.busy": "2024-01-10T06:24:28.679561Z", + "iopub.status.idle": "2024-01-10T06:24:31.714840Z", + "shell.execute_reply": "2024-01-10T06:24:31.714156Z" } }, "outputs": [ @@ -3154,17 +3218,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:30.342088Z", - "iopub.status.busy": "2024-01-09T15:15:30.341878Z", - "iopub.status.idle": "2024-01-09T15:15:31.649962Z", - "shell.execute_reply": "2024-01-09T15:15:31.649264Z" + "iopub.execute_input": "2024-01-10T06:24:31.717634Z", + "iopub.status.busy": "2024-01-10T06:24:31.717196Z", + "iopub.status.idle": "2024-01-10T06:24:33.061235Z", + "shell.execute_reply": "2024-01-10T06:24:33.060598Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3464fab77b1b47d58adbe02a6fa43f64", + "model_id": "df32bb08ce9040a1b285945a0db7765e", "version_major": 2, "version_minor": 0 }, @@ -3194,10 +3258,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:31.652920Z", - "iopub.status.busy": "2024-01-09T15:15:31.652499Z", - "iopub.status.idle": "2024-01-09T15:15:31.883005Z", - "shell.execute_reply": "2024-01-09T15:15:31.882426Z" + "iopub.execute_input": "2024-01-10T06:24:33.064302Z", + "iopub.status.busy": "2024-01-10T06:24:33.063926Z", + "iopub.status.idle": "2024-01-10T06:24:33.282005Z", + "shell.execute_reply": "2024-01-10T06:24:33.281307Z" } }, "outputs": [], @@ -3211,10 +3275,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:31.885754Z", - "iopub.status.busy": "2024-01-09T15:15:31.885535Z", - "iopub.status.idle": "2024-01-09T15:15:36.610960Z", - "shell.execute_reply": "2024-01-09T15:15:36.610317Z" + "iopub.execute_input": "2024-01-10T06:24:33.285032Z", + "iopub.status.busy": "2024-01-10T06:24:33.284637Z", + "iopub.status.idle": "2024-01-10T06:24:38.039867Z", + "shell.execute_reply": "2024-01-10T06:24:38.039183Z" } }, "outputs": [ @@ -3287,10 +3351,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:36.613553Z", - "iopub.status.busy": "2024-01-09T15:15:36.613140Z", - "iopub.status.idle": "2024-01-09T15:15:36.669631Z", - "shell.execute_reply": "2024-01-09T15:15:36.668993Z" + "iopub.execute_input": "2024-01-10T06:24:38.042406Z", + "iopub.status.busy": "2024-01-10T06:24:38.042157Z", + "iopub.status.idle": "2024-01-10T06:24:38.099615Z", + "shell.execute_reply": "2024-01-10T06:24:38.099002Z" }, "nbsphinx": "hidden" }, @@ -3334,7 +3398,67 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "02c82a7576c0452793d4ac9e3a8eb771": { + "0193408c614a4e7ba4873c78a9a9bb6f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_56cad0e7c51f49fbb45538b279800284", + "placeholder": "​", + "style": "IPY_MODEL_bf748ca37ab847a08123be51451437cc", + "value": "number of examples processed for estimating thresholds: 100%" + } + }, + "06974f46f32c4476932bd765003f434a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0870fef1c46e4952a7824e05fc431a34": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_66b6aaa2007c4b1496649534471df1db", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_639ef487ed3b42848922bc4d9bae0928", + "value": 30.0 + } + }, + "0fa1181f8e9b40ffb9c9c728c90621b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3386,23 +3510,52 @@ "width": null } }, - "17fd32bd00f7422f82e222bd671eb1bf": { + "10f6d3dbf30f4eb9b246516eea1577b6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bb9aed613f8f4289816e5ab5039366fb", + "placeholder": "​", + "style": "IPY_MODEL_ffbaaada2c4e444c92e4121cd1a4d331", + "value": "images processed using softmin: 100%" } }, - "1a8eeafa6ca74a69b2fa5817f03e46ed": { + "11aed75ad6be4af3af712b1b7648058f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2f0f6f38ef674c39b106ff3aea167574", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_4e02709833da447092b32b34e9cd7eea", + "value": 30.0 + } + }, + "228f616c7eff4a4fbdd71be5df385c68": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3454,7 +3607,7 @@ "width": null } }, - "1c8d6e3a698d448d90427dc24f08fcae": { + "230fb679cc3c47188ee2b769167c7d0b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3469,53 +3622,7 @@ "description_width": "" } }, - "2f006189586d4837b25208f274c1efda": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3764f1d408124e67b9b8ff059b1cd51e", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_17fd32bd00f7422f82e222bd671eb1bf", - "value": 30.0 - } - }, - "3464fab77b1b47d58adbe02a6fa43f64": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f18591d41acf461787dc84ddaf834e17", - "IPY_MODEL_2f006189586d4837b25208f274c1efda", - "IPY_MODEL_cd1c5ca7ad8b485cae2787936cba138c" - ], - "layout": "IPY_MODEL_e3c474e99b5a4dbda818585deb053e2b" - } - }, - "3764f1d408124e67b9b8ff059b1cd51e": { + "24f1f455503e4ea1905214c2491c6a8f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3567,7 +3674,50 @@ "width": null } }, - "3f205716e3c54df6b925d90841790bb6": { + "29a7642e497c4e9b96745b11371f32ef": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7d8733a782dc4953accc99a9a8b481c9", + "placeholder": "​", + "style": "IPY_MODEL_230fb679cc3c47188ee2b769167c7d0b", + "value": "number of examples processed for checking labels: 100%" + } + }, + "2e3415f4577c42cb941753dfe0086640": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_29a7642e497c4e9b96745b11371f32ef", + "IPY_MODEL_11aed75ad6be4af3af712b1b7648058f", + "IPY_MODEL_512a68cdbfd0462688ff6b886482cf94" + ], + "layout": "IPY_MODEL_228f616c7eff4a4fbdd71be5df385c68" + } + }, + "2f0f6f38ef674c39b106ff3aea167574": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3619,7 +3769,22 @@ "width": null } }, - "4503eade1b32403d863df7edfa8913d0": { + "43b3b5ba40fa4150ac9fe67bc77e7088": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4b2548e688284560a51e01b3a7d4b30a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3671,49 +3836,23 @@ "width": null } }, - "469a9896f434443d914337d391527ddc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3f205716e3c54df6b925d90841790bb6", - "placeholder": "​", - "style": "IPY_MODEL_4cf581fac6f1469fac415a93793188e5", - "value": "number of examples processed for checking labels: 100%" - } - }, - "46d009f0b3af4d06bbd5a73a66e716ea": { + "4e02709833da447092b32b34e9cd7eea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f8e816ee6b4d43439ab0a880aa663aca", - "placeholder": "​", - "style": "IPY_MODEL_ab6a158ac22041ce9d1a7bf92a8d0522", - "value": " 30/30 [00:00<00:00, 424.36it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "4ab9ee64b1fa48b488b7508e26edfecd": { + "512a68cdbfd0462688ff6b886482cf94": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3728,13 +3867,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_db0ae4d8d2874f1285894feb90065a04", + "layout": "IPY_MODEL_4b2548e688284560a51e01b3a7d4b30a", "placeholder": "​", - "style": "IPY_MODEL_5d8bd5d5b83848dcb8b2033f0d8acdf5", + "style": "IPY_MODEL_737617a1f83a4f60aacfbd1431505249", "value": " 30/30 [00:36<00:00, 1.22s/it]" } }, - "4c5dc1becde2491aa17ab44a75d7f414": { + "56cad0e7c51f49fbb45538b279800284": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3786,37 +3925,23 @@ "width": null } }, - "4cf581fac6f1469fac415a93793188e5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "5d8bd5d5b83848dcb8b2033f0d8acdf5": { + "639ef487ed3b42848922bc4d9bae0928": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "6c9508f676774e3eb9bbcf828fadfb5e": { + "66b6aaa2007c4b1496649534471df1db": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3868,7 +3993,22 @@ "width": null } }, - "7b9c5493fed74f5785964aed9e4c4ac4": { + "737617a1f83a4f60aacfbd1431505249": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "7655e393baf14941a470099ab54010ed": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3920,22 +4060,7 @@ "width": null } }, - "884a547f5c5141f0ae8582f90a618526": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a545d3bcc78342ada9bf902f7c4a7210": { + "7834e62875da4394bdfc03b1501fa4a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3950,111 +4075,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_469a9896f434443d914337d391527ddc", - "IPY_MODEL_efa9c67a3f4c42138eea3dcdd09cffb9", - "IPY_MODEL_4ab9ee64b1fa48b488b7508e26edfecd" + "IPY_MODEL_0193408c614a4e7ba4873c78a9a9bb6f", + "IPY_MODEL_0870fef1c46e4952a7824e05fc431a34", + "IPY_MODEL_8e940a4d278d48d5bb2e566940734bee" ], - "layout": "IPY_MODEL_6c9508f676774e3eb9bbcf828fadfb5e" - } - }, - "ab6a158ac22041ce9d1a7bf92a8d0522": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "ae706412514f4a958d903b4acb3182b2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "beb24d1191564974a50da25f62644e92": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4c5dc1becde2491aa17ab44a75d7f414", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d9482a6ebcbc431d9fa8d22857aecaf4", - "value": 30.0 - } - }, - "c9d9dcb94ddb4452bbf427afc230ea18": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_02c82a7576c0452793d4ac9e3a8eb771", - "placeholder": "​", - "style": "IPY_MODEL_884a547f5c5141f0ae8582f90a618526", - "value": "number of examples processed for estimating thresholds: 100%" + "layout": "IPY_MODEL_f9ed9bb79eea437e994e4b439e1c1812" } }, - "cd1c5ca7ad8b485cae2787936cba138c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4503eade1b32403d863df7edfa8913d0", - "placeholder": "​", - "style": "IPY_MODEL_e1512f3d87e742818dbc3ddba53349f5", - "value": " 30/30 [00:01<00:00, 23.68it/s]" - } - }, - "d3a3cfb6d5134ff98de3d1add75730c4": { + "7d8733a782dc4953accc99a9a8b481c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4106,23 +4134,52 @@ "width": null } }, - "d9482a6ebcbc431d9fa8d22857aecaf4": { + "8e940a4d278d48d5bb2e566940734bee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0fa1181f8e9b40ffb9c9c728c90621b5", + "placeholder": "​", + "style": "IPY_MODEL_43b3b5ba40fa4150ac9fe67bc77e7088", + "value": " 30/30 [00:00<00:00, 426.95it/s]" + } + }, + "97e851aacf72467a845502595d456b40": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_24f1f455503e4ea1905214c2491c6a8f", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_edcc710314394fceac64a53876ee634d", + "value": 30.0 } }, - "db0ae4d8d2874f1285894feb90065a04": { + "bb9aed613f8f4289816e5ab5039366fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4174,7 +4231,7 @@ "width": null } }, - "e1512f3d87e742818dbc3ddba53349f5": { + "bf748ca37ab847a08123be51451437cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4189,7 +4246,7 @@ "description_width": "" } }, - "e3c474e99b5a4dbda818585deb053e2b": { + "c5ba6d8db93145e486d14e332442a993": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4241,74 +4298,66 @@ "width": null } }, - "e7af30476e914a1aa2bc7bb22a08f3a8": { + "cb6f5dbf185a4cbca65ae3e7c593eede": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c9d9dcb94ddb4452bbf427afc230ea18", - "IPY_MODEL_beb24d1191564974a50da25f62644e92", - "IPY_MODEL_46d009f0b3af4d06bbd5a73a66e716ea" - ], - "layout": "IPY_MODEL_7b9c5493fed74f5785964aed9e4c4ac4" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c5ba6d8db93145e486d14e332442a993", + "placeholder": "​", + "style": "IPY_MODEL_06974f46f32c4476932bd765003f434a", + "value": " 30/30 [00:01<00:00, 22.49it/s]" } }, - "efa9c67a3f4c42138eea3dcdd09cffb9": { + "df32bb08ce9040a1b285945a0db7765e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d3a3cfb6d5134ff98de3d1add75730c4", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_ae706412514f4a958d903b4acb3182b2", - "value": 30.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_10f6d3dbf30f4eb9b246516eea1577b6", + "IPY_MODEL_97e851aacf72467a845502595d456b40", + "IPY_MODEL_cb6f5dbf185a4cbca65ae3e7c593eede" + ], + "layout": "IPY_MODEL_7655e393baf14941a470099ab54010ed" } }, - "f18591d41acf461787dc84ddaf834e17": { + "edcc710314394fceac64a53876ee634d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1a8eeafa6ca74a69b2fa5817f03e46ed", - "placeholder": "​", - "style": "IPY_MODEL_1c8d6e3a698d448d90427dc24f08fcae", - "value": "images processed using softmin: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f8e816ee6b4d43439ab0a880aa663aca": { + "f9ed9bb79eea437e994e4b439e1c1812": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4359,6 +4408,21 @@ "visibility": null, "width": null } + }, + "ffbaaada2c4e444c92e4121cd1a4d331": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } } }, "version_major": 2, diff --git a/master/tutorials/tabular.ipynb b/master/tutorials/tabular.ipynb index 61a5c1157..14b0c1a72 100644 --- a/master/tutorials/tabular.ipynb +++ b/master/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:41.079536Z", - "iopub.status.busy": "2024-01-09T15:15:41.079079Z", - "iopub.status.idle": "2024-01-09T15:15:42.105627Z", - "shell.execute_reply": "2024-01-09T15:15:42.104973Z" + "iopub.execute_input": "2024-01-10T06:24:42.729158Z", + "iopub.status.busy": "2024-01-10T06:24:42.728964Z", + "iopub.status.idle": "2024-01-10T06:24:43.794701Z", + "shell.execute_reply": "2024-01-10T06:24:43.793968Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.108685Z", - "iopub.status.busy": "2024-01-09T15:15:42.108129Z", - "iopub.status.idle": "2024-01-09T15:15:42.125058Z", - "shell.execute_reply": "2024-01-09T15:15:42.124436Z" + "iopub.execute_input": "2024-01-10T06:24:43.797884Z", + "iopub.status.busy": "2024-01-10T06:24:43.797509Z", + "iopub.status.idle": "2024-01-10T06:24:43.814538Z", + "shell.execute_reply": "2024-01-10T06:24:43.814038Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.127634Z", - "iopub.status.busy": "2024-01-09T15:15:42.127253Z", - "iopub.status.idle": "2024-01-09T15:15:42.160362Z", - "shell.execute_reply": "2024-01-09T15:15:42.159841Z" + "iopub.execute_input": "2024-01-10T06:24:43.817105Z", + "iopub.status.busy": "2024-01-10T06:24:43.816655Z", + "iopub.status.idle": "2024-01-10T06:24:43.872030Z", + "shell.execute_reply": "2024-01-10T06:24:43.871363Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.162712Z", - "iopub.status.busy": "2024-01-09T15:15:42.162340Z", - "iopub.status.idle": "2024-01-09T15:15:42.165952Z", - "shell.execute_reply": "2024-01-09T15:15:42.165365Z" + "iopub.execute_input": "2024-01-10T06:24:43.874729Z", + "iopub.status.busy": "2024-01-10T06:24:43.874284Z", + "iopub.status.idle": "2024-01-10T06:24:43.878216Z", + "shell.execute_reply": "2024-01-10T06:24:43.877614Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.168301Z", - "iopub.status.busy": "2024-01-09T15:15:42.167932Z", - "iopub.status.idle": "2024-01-09T15:15:42.176945Z", - "shell.execute_reply": "2024-01-09T15:15:42.176451Z" + "iopub.execute_input": "2024-01-10T06:24:43.880614Z", + "iopub.status.busy": "2024-01-10T06:24:43.880280Z", + "iopub.status.idle": "2024-01-10T06:24:43.889118Z", + "shell.execute_reply": "2024-01-10T06:24:43.888491Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.179275Z", - "iopub.status.busy": "2024-01-09T15:15:42.179056Z", - "iopub.status.idle": "2024-01-09T15:15:42.181951Z", - "shell.execute_reply": "2024-01-09T15:15:42.181425Z" + "iopub.execute_input": "2024-01-10T06:24:43.891570Z", + "iopub.status.busy": "2024-01-10T06:24:43.891224Z", + "iopub.status.idle": "2024-01-10T06:24:43.894040Z", + "shell.execute_reply": "2024-01-10T06:24:43.893453Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.184343Z", - "iopub.status.busy": "2024-01-09T15:15:42.183982Z", - "iopub.status.idle": "2024-01-09T15:15:42.767864Z", - "shell.execute_reply": "2024-01-09T15:15:42.767250Z" + "iopub.execute_input": "2024-01-10T06:24:43.896323Z", + "iopub.status.busy": "2024-01-10T06:24:43.895979Z", + "iopub.status.idle": "2024-01-10T06:24:44.485147Z", + "shell.execute_reply": "2024-01-10T06:24:44.484416Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:42.770857Z", - "iopub.status.busy": "2024-01-09T15:15:42.770445Z", - "iopub.status.idle": "2024-01-09T15:15:44.010236Z", - "shell.execute_reply": "2024-01-09T15:15:44.009414Z" + "iopub.execute_input": "2024-01-10T06:24:44.488107Z", + "iopub.status.busy": "2024-01-10T06:24:44.487864Z", + "iopub.status.idle": "2024-01-10T06:24:45.798377Z", + "shell.execute_reply": "2024-01-10T06:24:45.797616Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.013747Z", - "iopub.status.busy": "2024-01-09T15:15:44.012740Z", - "iopub.status.idle": "2024-01-09T15:15:44.023500Z", - "shell.execute_reply": "2024-01-09T15:15:44.022995Z" + "iopub.execute_input": "2024-01-10T06:24:45.801399Z", + "iopub.status.busy": "2024-01-10T06:24:45.800854Z", + "iopub.status.idle": "2024-01-10T06:24:45.811242Z", + "shell.execute_reply": "2024-01-10T06:24:45.810638Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.026168Z", - "iopub.status.busy": "2024-01-09T15:15:44.025679Z", - "iopub.status.idle": "2024-01-09T15:15:44.030156Z", - "shell.execute_reply": "2024-01-09T15:15:44.029648Z" + "iopub.execute_input": "2024-01-10T06:24:45.813764Z", + "iopub.status.busy": "2024-01-10T06:24:45.813278Z", + "iopub.status.idle": "2024-01-10T06:24:45.817782Z", + "shell.execute_reply": "2024-01-10T06:24:45.817299Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.032520Z", - "iopub.status.busy": "2024-01-09T15:15:44.032153Z", - "iopub.status.idle": "2024-01-09T15:15:44.039951Z", - "shell.execute_reply": "2024-01-09T15:15:44.039422Z" + "iopub.execute_input": "2024-01-10T06:24:45.820268Z", + "iopub.status.busy": "2024-01-10T06:24:45.819907Z", + "iopub.status.idle": "2024-01-10T06:24:45.827154Z", + "shell.execute_reply": "2024-01-10T06:24:45.826654Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.042453Z", - "iopub.status.busy": "2024-01-09T15:15:44.042087Z", - "iopub.status.idle": "2024-01-09T15:15:44.164996Z", - "shell.execute_reply": "2024-01-09T15:15:44.164465Z" + "iopub.execute_input": "2024-01-10T06:24:45.829331Z", + "iopub.status.busy": "2024-01-10T06:24:45.829134Z", + "iopub.status.idle": "2024-01-10T06:24:45.955067Z", + "shell.execute_reply": "2024-01-10T06:24:45.954445Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.167367Z", - "iopub.status.busy": "2024-01-09T15:15:44.167160Z", - "iopub.status.idle": "2024-01-09T15:15:44.170213Z", - "shell.execute_reply": "2024-01-09T15:15:44.169665Z" + "iopub.execute_input": "2024-01-10T06:24:45.957778Z", + "iopub.status.busy": "2024-01-10T06:24:45.957338Z", + "iopub.status.idle": "2024-01-10T06:24:45.960533Z", + "shell.execute_reply": "2024-01-10T06:24:45.960010Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:44.172446Z", - "iopub.status.busy": "2024-01-09T15:15:44.172246Z", - "iopub.status.idle": "2024-01-09T15:15:45.608485Z", - "shell.execute_reply": "2024-01-09T15:15:45.607715Z" + "iopub.execute_input": "2024-01-10T06:24:45.962889Z", + "iopub.status.busy": "2024-01-10T06:24:45.962509Z", + "iopub.status.idle": "2024-01-10T06:24:47.449565Z", + "shell.execute_reply": "2024-01-10T06:24:47.448717Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:45.611753Z", - "iopub.status.busy": "2024-01-09T15:15:45.611313Z", - "iopub.status.idle": "2024-01-09T15:15:45.625782Z", - "shell.execute_reply": "2024-01-09T15:15:45.625114Z" + "iopub.execute_input": "2024-01-10T06:24:47.452855Z", + "iopub.status.busy": "2024-01-10T06:24:47.452615Z", + "iopub.status.idle": "2024-01-10T06:24:47.467433Z", + "shell.execute_reply": "2024-01-10T06:24:47.466813Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:45.628267Z", - "iopub.status.busy": "2024-01-09T15:15:45.627915Z", - "iopub.status.idle": "2024-01-09T15:15:45.662528Z", - "shell.execute_reply": "2024-01-09T15:15:45.661891Z" + "iopub.execute_input": "2024-01-10T06:24:47.469992Z", + "iopub.status.busy": "2024-01-10T06:24:47.469772Z", + "iopub.status.idle": "2024-01-10T06:24:47.521859Z", + "shell.execute_reply": "2024-01-10T06:24:47.521269Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/text.html b/master/tutorials/text.html index 27a9e4852..418182f5c 100644 --- a/master/tutorials/text.html +++ b/master/tutorials/text.html @@ -969,7 +969,7 @@

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

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

diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index c9f378b4f..dd78280b5 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:50.917822Z", - "iopub.status.busy": "2024-01-09T15:15:50.917628Z", - "iopub.status.idle": "2024-01-09T15:15:52.967278Z", - "shell.execute_reply": "2024-01-09T15:15:52.966578Z" + "iopub.execute_input": "2024-01-10T06:24:52.618988Z", + "iopub.status.busy": "2024-01-10T06:24:52.618787Z", + "iopub.status.idle": "2024-01-10T06:24:54.768251Z", + "shell.execute_reply": "2024-01-10T06:24:54.767619Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "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@a4b161252872240d82d8cc343d69ac060faf20e9\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.970377Z", - "iopub.status.busy": "2024-01-09T15:15:52.970010Z", - "iopub.status.idle": "2024-01-09T15:15:52.973567Z", - "shell.execute_reply": "2024-01-09T15:15:52.973017Z" + "iopub.execute_input": "2024-01-10T06:24:54.771423Z", + "iopub.status.busy": "2024-01-10T06:24:54.770954Z", + "iopub.status.idle": "2024-01-10T06:24:54.774574Z", + "shell.execute_reply": "2024-01-10T06:24:54.774036Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.975908Z", - "iopub.status.busy": "2024-01-09T15:15:52.975564Z", - "iopub.status.idle": "2024-01-09T15:15:52.978717Z", - "shell.execute_reply": "2024-01-09T15:15:52.978197Z" + "iopub.execute_input": "2024-01-10T06:24:54.777223Z", + "iopub.status.busy": "2024-01-10T06:24:54.776831Z", + "iopub.status.idle": "2024-01-10T06:24:54.780144Z", + "shell.execute_reply": "2024-01-10T06:24:54.779593Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:52.981131Z", - "iopub.status.busy": "2024-01-09T15:15:52.980743Z", - "iopub.status.idle": "2024-01-09T15:15:53.013093Z", - "shell.execute_reply": "2024-01-09T15:15:53.012576Z" + "iopub.execute_input": "2024-01-10T06:24:54.782533Z", + "iopub.status.busy": "2024-01-10T06:24:54.782227Z", + "iopub.status.idle": "2024-01-10T06:24:54.838961Z", + "shell.execute_reply": "2024-01-10T06:24:54.838278Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.015454Z", - "iopub.status.busy": "2024-01-09T15:15:53.015084Z", - "iopub.status.idle": "2024-01-09T15:15:53.018726Z", - "shell.execute_reply": "2024-01-09T15:15:53.018223Z" + "iopub.execute_input": "2024-01-10T06:24:54.841750Z", + "iopub.status.busy": "2024-01-10T06:24:54.841341Z", + "iopub.status.idle": "2024-01-10T06:24:54.845275Z", + "shell.execute_reply": "2024-01-10T06:24:54.844697Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.020980Z", - "iopub.status.busy": "2024-01-09T15:15:53.020683Z", - "iopub.status.idle": "2024-01-09T15:15:53.024768Z", - "shell.execute_reply": "2024-01-09T15:15:53.024243Z" + "iopub.execute_input": "2024-01-10T06:24:54.847786Z", + "iopub.status.busy": "2024-01-10T06:24:54.847407Z", + "iopub.status.idle": "2024-01-10T06:24:54.851172Z", + "shell.execute_reply": "2024-01-10T06:24:54.850541Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'apple_pay_or_google_pay', 'cancel_transfer', 'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin'}\n" + "Classes: {'getting_spare_card', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'change_pin', 'card_about_to_expire', 'beneficiary_not_allowed', 'cancel_transfer', 'supported_cards_and_currencies', 'card_payment_fee_charged'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.027012Z", - "iopub.status.busy": "2024-01-09T15:15:53.026712Z", - "iopub.status.idle": "2024-01-09T15:15:53.030093Z", - "shell.execute_reply": "2024-01-09T15:15:53.029425Z" + "iopub.execute_input": "2024-01-10T06:24:54.853595Z", + "iopub.status.busy": "2024-01-10T06:24:54.853213Z", + "iopub.status.idle": "2024-01-10T06:24:54.856682Z", + "shell.execute_reply": "2024-01-10T06:24:54.856054Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.032313Z", - "iopub.status.busy": "2024-01-09T15:15:53.032020Z", - "iopub.status.idle": "2024-01-09T15:15:53.035560Z", - "shell.execute_reply": "2024-01-09T15:15:53.035036Z" + "iopub.execute_input": "2024-01-10T06:24:54.858931Z", + "iopub.status.busy": "2024-01-10T06:24:54.858734Z", + "iopub.status.idle": "2024-01-10T06:24:54.862328Z", + "shell.execute_reply": "2024-01-10T06:24:54.861802Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:15:53.037893Z", - "iopub.status.busy": "2024-01-09T15:15:53.037524Z", - "iopub.status.idle": "2024-01-09T15:16:01.616250Z", - "shell.execute_reply": "2024-01-09T15:16:01.615598Z" + "iopub.execute_input": "2024-01-10T06:24:54.864650Z", + "iopub.status.busy": "2024-01-10T06:24:54.864455Z", + "iopub.status.idle": "2024-01-10T06:25:03.694998Z", + "shell.execute_reply": "2024-01-10T06:25:03.694259Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.619482Z", - "iopub.status.busy": "2024-01-09T15:16:01.619002Z", - "iopub.status.idle": "2024-01-09T15:16:01.622524Z", - "shell.execute_reply": "2024-01-09T15:16:01.622024Z" + "iopub.execute_input": "2024-01-10T06:25:03.698230Z", + "iopub.status.busy": "2024-01-10T06:25:03.697870Z", + "iopub.status.idle": "2024-01-10T06:25:03.701022Z", + "shell.execute_reply": "2024-01-10T06:25:03.700397Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.625056Z", - "iopub.status.busy": "2024-01-09T15:16:01.624503Z", - "iopub.status.idle": "2024-01-09T15:16:01.627569Z", - "shell.execute_reply": "2024-01-09T15:16:01.627083Z" + "iopub.execute_input": "2024-01-10T06:25:03.703600Z", + "iopub.status.busy": "2024-01-10T06:25:03.703134Z", + "iopub.status.idle": "2024-01-10T06:25:03.706162Z", + "shell.execute_reply": "2024-01-10T06:25:03.705544Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:01.629903Z", - "iopub.status.busy": "2024-01-09T15:16:01.629572Z", - "iopub.status.idle": "2024-01-09T15:16:03.815926Z", - "shell.execute_reply": "2024-01-09T15:16:03.815064Z" + "iopub.execute_input": "2024-01-10T06:25:03.708555Z", + "iopub.status.busy": "2024-01-10T06:25:03.708183Z", + "iopub.status.idle": "2024-01-10T06:25:05.979551Z", + "shell.execute_reply": "2024-01-10T06:25:05.978797Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.819795Z", - "iopub.status.busy": "2024-01-09T15:16:03.818925Z", - "iopub.status.idle": "2024-01-09T15:16:03.827666Z", - "shell.execute_reply": "2024-01-09T15:16:03.827001Z" + "iopub.execute_input": "2024-01-10T06:25:05.983253Z", + "iopub.status.busy": "2024-01-10T06:25:05.982458Z", + "iopub.status.idle": "2024-01-10T06:25:05.990517Z", + "shell.execute_reply": "2024-01-10T06:25:05.989921Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.830332Z", - "iopub.status.busy": "2024-01-09T15:16:03.829934Z", - "iopub.status.idle": "2024-01-09T15:16:03.834730Z", - "shell.execute_reply": "2024-01-09T15:16:03.834110Z" + "iopub.execute_input": "2024-01-10T06:25:05.993149Z", + "iopub.status.busy": "2024-01-10T06:25:05.992845Z", + "iopub.status.idle": "2024-01-10T06:25:05.996822Z", + "shell.execute_reply": "2024-01-10T06:25:05.996286Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.837368Z", - "iopub.status.busy": "2024-01-09T15:16:03.836891Z", - "iopub.status.idle": "2024-01-09T15:16:03.840679Z", - "shell.execute_reply": "2024-01-09T15:16:03.840059Z" + "iopub.execute_input": "2024-01-10T06:25:05.999247Z", + "iopub.status.busy": "2024-01-10T06:25:05.998879Z", + "iopub.status.idle": "2024-01-10T06:25:06.002319Z", + "shell.execute_reply": "2024-01-10T06:25:06.001677Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.843420Z", - "iopub.status.busy": "2024-01-09T15:16:03.843003Z", - "iopub.status.idle": "2024-01-09T15:16:03.847069Z", - "shell.execute_reply": "2024-01-09T15:16:03.846417Z" + "iopub.execute_input": "2024-01-10T06:25:06.004793Z", + "iopub.status.busy": "2024-01-10T06:25:06.004424Z", + "iopub.status.idle": "2024-01-10T06:25:06.007616Z", + "shell.execute_reply": "2024-01-10T06:25:06.007047Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.849550Z", - "iopub.status.busy": "2024-01-09T15:16:03.849146Z", - "iopub.status.idle": "2024-01-09T15:16:03.857396Z", - "shell.execute_reply": "2024-01-09T15:16:03.856742Z" + "iopub.execute_input": "2024-01-10T06:25:06.009917Z", + "iopub.status.busy": "2024-01-10T06:25:06.009623Z", + "iopub.status.idle": "2024-01-10T06:25:06.017029Z", + "shell.execute_reply": "2024-01-10T06:25:06.016419Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:03.859959Z", - "iopub.status.busy": "2024-01-09T15:16:03.859590Z", - "iopub.status.idle": "2024-01-09T15:16:04.111347Z", - "shell.execute_reply": "2024-01-09T15:16:04.110745Z" + "iopub.execute_input": "2024-01-10T06:25:06.019636Z", + "iopub.status.busy": "2024-01-10T06:25:06.019266Z", + "iopub.status.idle": "2024-01-10T06:25:06.263328Z", + "shell.execute_reply": "2024-01-10T06:25:06.262591Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:04.114316Z", - "iopub.status.busy": "2024-01-09T15:16:04.113883Z", - "iopub.status.idle": "2024-01-09T15:16:04.413212Z", - "shell.execute_reply": "2024-01-09T15:16:04.412556Z" + "iopub.execute_input": "2024-01-10T06:25:06.266421Z", + "iopub.status.busy": "2024-01-10T06:25:06.265949Z", + "iopub.status.idle": "2024-01-10T06:25:06.566167Z", + "shell.execute_reply": "2024-01-10T06:25:06.565453Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-09T15:16:04.416351Z", - "iopub.status.busy": "2024-01-09T15:16:04.415909Z", - "iopub.status.idle": "2024-01-09T15:16:04.420073Z", - "shell.execute_reply": "2024-01-09T15:16:04.419487Z" + "iopub.execute_input": "2024-01-10T06:25:06.569388Z", + "iopub.status.busy": "2024-01-10T06:25:06.568888Z", + "iopub.status.idle": "2024-01-10T06:25:06.573451Z", + "shell.execute_reply": "2024-01-10T06:25:06.572835Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 6fa0401e2..d39373b09 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -862,7 +862,7 @@

1. Install required dependencies and download data
---2024-01-09 15:16:09--  https://data.deepai.org/conll2003.zip
+--2024-01-10 06:25:11--  https://data.deepai.org/conll2003.zip
 Resolving data.deepai.org (data.deepai.org)...
 
@@ -871,25 +871,9 @@

1. Install required dependencies and download data
-185.93.1.244, 2400:52e0:1a00::940:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443...
-
- -
-
-
-
-
-connected.
-HTTP request sent, awaiting response...
-
-
-
-
-
-
-
-200 OK
+185.93.1.247, 2400:52e0:1a00::1070:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.
+HTTP request sent, awaiting response... 200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’
@@ -910,25 +894,25 @@

1. Install required dependencies and download data
-

conll2003.zip 100%[===================&gt;] 959.94K –.-KB/s in 0.1s

+

conll2003.zip 100%[===================&gt;] 959.94K –.-KB/s in 0.01s

-

2024-01-09 15:16:09 (7.00 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+

2024-01-10 06:25:12 (93.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]

mkdir: cannot create directory ‘data’: File exists </pre>

-

conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.1s

+

conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.01s

-

2024-01-09 15:16:09 (7.00 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+

2024-01-10 06:25:12 (93.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]

mkdir: cannot create directory ‘data’: File exists end{sphinxVerbatim}

-

conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.1s

+

conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.01s

-

2024-01-09 15:16:09 (7.00 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+

2024-01-10 06:25:12 (93.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]

mkdir: cannot create directory ‘data’: File exists

+
+
+
+
+
 HTTP request sent, awaiting response...
 
@@ -974,32 +965,46 @@

1. Install required dependencies and download data

pred_probs.npz 0%[ ] 0 –.-KB/s

+
+
+
+
+
+
+
+
pred_probs.npz 64%[===========&gt; ] 10.46M 52.3MB/s
+

</pre>

+
+
+
pred_probs.npz 64%[===========> ] 10.46M 52.3MB/s
+

end{sphinxVerbatim}

+
+
+
+

pred_probs.npz 64%[===========> ] 10.46M 52.3MB/s

-

pred_probs.npz 96%[==================&gt; ] 15.71M 74.7MB/s -pred_probs.npz 100%[===================&gt;] 16.26M 76.1MB/s in 0.2s

+

pred_probs.npz 100%[===================&gt;] 16.26M 53.8MB/s in 0.3s

-

2024-01-09 15:16:10 (76.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+

2024-01-10 06:25:13 (53.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

</pre>

-

pred_probs.npz 96%[==================> ] 15.71M 74.7MB/s -pred_probs.npz 100%[===================>] 16.26M 76.1MB/s in 0.2s

+

pred_probs.npz 100%[===================>] 16.26M 53.8MB/s in 0.3s

-

2024-01-09 15:16:10 (76.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+

2024-01-10 06:25:13 (53.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

end{sphinxVerbatim}

-

pred_probs.npz 96%[==================> ] 15.71M 74.7MB/s -pred_probs.npz 100%[===================>] 16.26M 76.1MB/s in 0.2s

+

pred_probs.npz 100%[===================>] 16.26M 53.8MB/s in 0.3s

-

2024-01-09 15:16:10 (76.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+

2024-01-10 06:25:13 (53.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

[3]:
diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb
index 8e6ba877d..02e4c67c0 100644
--- a/master/tutorials/token_classification.ipynb
+++ b/master/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
    "id": "ae8a08e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:09.314233Z",
-     "iopub.status.busy": "2024-01-09T15:16:09.314010Z",
-     "iopub.status.idle": "2024-01-09T15:16:10.759493Z",
-     "shell.execute_reply": "2024-01-09T15:16:10.758787Z"
+     "iopub.execute_input": "2024-01-10T06:25:11.829123Z",
+     "iopub.status.busy": "2024-01-10T06:25:11.828927Z",
+     "iopub.status.idle": "2024-01-10T06:25:13.287001Z",
+     "shell.execute_reply": "2024-01-10T06:25:13.286300Z"
     }
    },
    "outputs": [
@@ -86,7 +86,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-01-09 15:16:09--  https://data.deepai.org/conll2003.zip\r\n",
+      "--2024-01-10 06:25:11--  https://data.deepai.org/conll2003.zip\r\n",
       "Resolving data.deepai.org (data.deepai.org)... "
      ]
     },
@@ -94,23 +94,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "185.93.1.244, 2400:52e0:1a00::940:1\r\n",
-      "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "connected.\r\n",
-      "HTTP request sent, awaiting response... "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "200 OK\r\n",
+      "185.93.1.247, 2400:52e0:1a00::1070:1\r\n",
+      "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.\r\n",
+      "HTTP request sent, awaiting response... 200 OK\r\n",
       "Length: 982975 (960K) [application/zip]\r\n",
       "Saving to: ‘conll2003.zip’\r\n",
       "\r\n",
@@ -123,9 +109,9 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s    \r\n",
+      "conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.01s   \r\n",
       "\r\n",
-      "2024-01-09 15:16:09 (7.00 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+      "2024-01-10 06:25:12 (93.5 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
       "\r\n",
       "mkdir: cannot create directory ‘data’: File exists\r\n"
      ]
@@ -145,9 +131,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-01-09 15:16:10--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
-      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.88.163, 54.231.139.129, 52.216.152.124, ...\r\n",
-      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.88.163|:443... connected.\r\n",
+      "--2024-01-10 06:25:12--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.203.65, 3.5.25.47, 54.231.172.185, ...\r\n",
+      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.203.65|:443... connected.\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "HTTP request sent, awaiting response... "
      ]
     },
@@ -168,10 +160,17 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "pred_probs.npz       96%[==================> ]  15.71M  74.7MB/s               \r",
-      "pred_probs.npz      100%[===================>]  16.26M  76.1MB/s    in 0.2s    \r\n",
+      "pred_probs.npz       64%[===========>        ]  10.46M  52.3MB/s               "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "pred_probs.npz      100%[===================>]  16.26M  53.8MB/s    in 0.3s    \r\n",
       "\r\n",
-      "2024-01-09 15:16:10 (76.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+      "2024-01-10 06:25:13 (53.8 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
       "\r\n"
      ]
     }
@@ -188,10 +187,10 @@
    "id": "439b0305",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:10.762692Z",
-     "iopub.status.busy": "2024-01-09T15:16:10.762276Z",
-     "iopub.status.idle": "2024-01-09T15:16:11.800632Z",
-     "shell.execute_reply": "2024-01-09T15:16:11.800022Z"
+     "iopub.execute_input": "2024-01-10T06:25:13.289925Z",
+     "iopub.status.busy": "2024-01-10T06:25:13.289508Z",
+     "iopub.status.idle": "2024-01-10T06:25:14.320211Z",
+     "shell.execute_reply": "2024-01-10T06:25:14.319499Z"
     },
     "nbsphinx": "hidden"
    },
@@ -202,7 +201,7 @@
     "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@a4b161252872240d82d8cc343d69ac060faf20e9\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@ae085b45b538e73a059d6a9ef10d747e590ce755\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -228,10 +227,10 @@
    "id": "a1349304",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:11.803735Z",
-     "iopub.status.busy": "2024-01-09T15:16:11.803280Z",
-     "iopub.status.idle": "2024-01-09T15:16:11.806897Z",
-     "shell.execute_reply": "2024-01-09T15:16:11.806344Z"
+     "iopub.execute_input": "2024-01-10T06:25:14.323244Z",
+     "iopub.status.busy": "2024-01-10T06:25:14.322862Z",
+     "iopub.status.idle": "2024-01-10T06:25:14.326575Z",
+     "shell.execute_reply": "2024-01-10T06:25:14.326063Z"
     }
    },
    "outputs": [],
@@ -281,10 +280,10 @@
    "id": "ab9d59a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:11.809370Z",
-     "iopub.status.busy": "2024-01-09T15:16:11.809069Z",
-     "iopub.status.idle": "2024-01-09T15:16:11.812343Z",
-     "shell.execute_reply": "2024-01-09T15:16:11.811821Z"
+     "iopub.execute_input": "2024-01-10T06:25:14.329189Z",
+     "iopub.status.busy": "2024-01-10T06:25:14.328717Z",
+     "iopub.status.idle": "2024-01-10T06:25:14.332104Z",
+     "shell.execute_reply": "2024-01-10T06:25:14.331612Z"
     },
     "nbsphinx": "hidden"
    },
@@ -302,10 +301,10 @@
    "id": "519cb80c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:11.814805Z",
-     "iopub.status.busy": "2024-01-09T15:16:11.814356Z",
-     "iopub.status.idle": "2024-01-09T15:16:19.718168Z",
-     "shell.execute_reply": "2024-01-09T15:16:19.717473Z"
+     "iopub.execute_input": "2024-01-10T06:25:14.334506Z",
+     "iopub.status.busy": "2024-01-10T06:25:14.334060Z",
+     "iopub.status.idle": "2024-01-10T06:25:22.328376Z",
+     "shell.execute_reply": "2024-01-10T06:25:22.327664Z"
     }
    },
    "outputs": [],
@@ -379,10 +378,10 @@
    "id": "202f1526",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:19.721335Z",
-     "iopub.status.busy": "2024-01-09T15:16:19.720928Z",
-     "iopub.status.idle": "2024-01-09T15:16:19.727079Z",
-     "shell.execute_reply": "2024-01-09T15:16:19.726464Z"
+     "iopub.execute_input": "2024-01-10T06:25:22.331443Z",
+     "iopub.status.busy": "2024-01-10T06:25:22.331210Z",
+     "iopub.status.idle": "2024-01-10T06:25:22.337488Z",
+     "shell.execute_reply": "2024-01-10T06:25:22.336872Z"
     },
     "nbsphinx": "hidden"
    },
@@ -422,10 +421,10 @@
    "id": "a4381f03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:19.729442Z",
-     "iopub.status.busy": "2024-01-09T15:16:19.728999Z",
-     "iopub.status.idle": "2024-01-09T15:16:20.166215Z",
-     "shell.execute_reply": "2024-01-09T15:16:20.165584Z"
+     "iopub.execute_input": "2024-01-10T06:25:22.339951Z",
+     "iopub.status.busy": "2024-01-10T06:25:22.339480Z",
+     "iopub.status.idle": "2024-01-10T06:25:22.800732Z",
+     "shell.execute_reply": "2024-01-10T06:25:22.799973Z"
     }
    },
    "outputs": [],
@@ -462,10 +461,10 @@
    "id": "7842e4a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:20.169188Z",
-     "iopub.status.busy": "2024-01-09T15:16:20.168768Z",
-     "iopub.status.idle": "2024-01-09T15:16:20.174203Z",
-     "shell.execute_reply": "2024-01-09T15:16:20.173636Z"
+     "iopub.execute_input": "2024-01-10T06:25:22.803694Z",
+     "iopub.status.busy": "2024-01-10T06:25:22.803441Z",
+     "iopub.status.idle": "2024-01-10T06:25:22.809754Z",
+     "shell.execute_reply": "2024-01-10T06:25:22.809121Z"
     }
    },
    "outputs": [
@@ -537,10 +536,10 @@
    "id": "2c2ad9ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:20.176666Z",
-     "iopub.status.busy": "2024-01-09T15:16:20.176301Z",
-     "iopub.status.idle": "2024-01-09T15:16:22.118952Z",
-     "shell.execute_reply": "2024-01-09T15:16:22.118133Z"
+     "iopub.execute_input": "2024-01-10T06:25:22.812247Z",
+     "iopub.status.busy": "2024-01-10T06:25:22.811889Z",
+     "iopub.status.idle": "2024-01-10T06:25:24.829457Z",
+     "shell.execute_reply": "2024-01-10T06:25:24.828653Z"
     }
    },
    "outputs": [],
@@ -562,10 +561,10 @@
    "id": "95dc7268",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:22.122699Z",
-     "iopub.status.busy": "2024-01-09T15:16:22.121929Z",
-     "iopub.status.idle": "2024-01-09T15:16:22.128438Z",
-     "shell.execute_reply": "2024-01-09T15:16:22.127799Z"
+     "iopub.execute_input": "2024-01-10T06:25:24.833068Z",
+     "iopub.status.busy": "2024-01-10T06:25:24.832222Z",
+     "iopub.status.idle": "2024-01-10T06:25:24.839537Z",
+     "shell.execute_reply": "2024-01-10T06:25:24.838871Z"
     }
    },
    "outputs": [
@@ -601,10 +600,10 @@
    "id": "e13de188",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:22.131058Z",
-     "iopub.status.busy": "2024-01-09T15:16:22.130684Z",
-     "iopub.status.idle": "2024-01-09T15:16:22.149299Z",
-     "shell.execute_reply": "2024-01-09T15:16:22.148635Z"
+     "iopub.execute_input": "2024-01-10T06:25:24.842146Z",
+     "iopub.status.busy": "2024-01-10T06:25:24.841643Z",
+     "iopub.status.idle": "2024-01-10T06:25:24.866755Z",
+     "shell.execute_reply": "2024-01-10T06:25:24.866053Z"
     }
    },
    "outputs": [
@@ -782,10 +781,10 @@
    "id": "e4a006bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:22.151641Z",
-     "iopub.status.busy": "2024-01-09T15:16:22.151440Z",
-     "iopub.status.idle": "2024-01-09T15:16:22.183906Z",
-     "shell.execute_reply": "2024-01-09T15:16:22.183377Z"
+     "iopub.execute_input": "2024-01-10T06:25:24.869362Z",
+     "iopub.status.busy": "2024-01-10T06:25:24.869006Z",
+     "iopub.status.idle": "2024-01-10T06:25:24.901803Z",
+     "shell.execute_reply": "2024-01-10T06:25:24.901116Z"
     }
    },
    "outputs": [
@@ -887,10 +886,10 @@
    "id": "c8f4e163",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:22.186276Z",
-     "iopub.status.busy": "2024-01-09T15:16:22.186071Z",
-     "iopub.status.idle": "2024-01-09T15:16:22.193827Z",
-     "shell.execute_reply": "2024-01-09T15:16:22.193328Z"
+     "iopub.execute_input": "2024-01-10T06:25:24.904601Z",
+     "iopub.status.busy": "2024-01-10T06:25:24.904369Z",
+     "iopub.status.idle": "2024-01-10T06:25:24.912699Z",
+     "shell.execute_reply": "2024-01-10T06:25:24.912132Z"
     }
    },
    "outputs": [
@@ -964,10 +963,10 @@
    "id": "db0b5179",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:22.195967Z",
-     "iopub.status.busy": "2024-01-09T15:16:22.195760Z",
-     "iopub.status.idle": "2024-01-09T15:16:24.046639Z",
-     "shell.execute_reply": "2024-01-09T15:16:24.046099Z"
+     "iopub.execute_input": "2024-01-10T06:25:24.915153Z",
+     "iopub.status.busy": "2024-01-10T06:25:24.914932Z",
+     "iopub.status.idle": "2024-01-10T06:25:26.819403Z",
+     "shell.execute_reply": "2024-01-10T06:25:26.818778Z"
     }
    },
    "outputs": [
@@ -1139,10 +1138,10 @@
    "id": "a18795eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-09T15:16:24.049087Z",
-     "iopub.status.busy": "2024-01-09T15:16:24.048876Z",
-     "iopub.status.idle": "2024-01-09T15:16:24.053237Z",
-     "shell.execute_reply": "2024-01-09T15:16:24.052674Z"
+     "iopub.execute_input": "2024-01-10T06:25:26.821955Z",
+     "iopub.status.busy": "2024-01-10T06:25:26.821736Z",
+     "iopub.status.idle": "2024-01-10T06:25:26.826080Z",
+     "shell.execute_reply": "2024-01-10T06:25:26.825582Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/versioning.js b/versioning.js
index 6a3886517..c7b323a30 100644
--- a/versioning.js
+++ b/versioning.js
@@ -1,4 +1,4 @@
 var Version = {
   version_number: "v2.5.0",
-  commit_hash: "a4b161252872240d82d8cc343d69ac060faf20e9",
+  commit_hash: "ae085b45b538e73a059d6a9ef10d747e590ce755",
 };
\ No newline at end of file